Extended Abstracts for the 50th Commemorative
International Symposium of
the Princess Takamatsu Cancer Research Fund, 2019

NEW HORIZONS FOR CANCER RESEARCH
AND PRECISION MEDICINE
November 12-14, 2019
Tokyo, Japan

Edited by :Hiroyuki Mano, Elaine R. Mardis, Kohzoh Imai,
Masanobu Oshima and Ryuzo Ueda

Princess Takamatsu Cancer Research Fund

CONTENTS

KeYNoTe LeCTUre
Pediatric Cancer Precision Medicine
Elaine R. Mardis ..............................................................................................................................1

PreCisioN CaNCer MediCiNedevelopment of Japanese Cancer Genomic Medicine Platform
Hiroyuki Mano ................................................................................................................................4
inactivation of dNa repair to improve immune surveillance
Alberto Bardelli ...............................................................................................................................8

CaNCer GeNoMiCs aNd ePiGeNoMiCs
Quantitative Medicine for Breast Cancer PatientsCharles M. Perou ..........................................................................................................................11
epigenetic Heterogeneity of Hepatic Lineage Tumors
Hiroyuki Aburatani ....................................................................................................................18
The “Peter Pan” syndrome: stalled development Through redistributionof Key epigenetic Marks at the Core of oncohistone Pathogenesis
Nada Jabado ....................................................................................................................................21i

iMMUNoGeNoMiCs
Modeling Tumor immunity from rNa-seq
X. Shirley Liu ...................................................................................................................................30
immune suppressive Mechanisms in the Tumor Microenvironment
Hiroyoshi Nishikawa ................................................................................................................34
Proteogenomics and immunopeptidomics for the development of
Personalized Cancer immunotherapy
Michal Bassani-Sternberg .......................................................................................................39

CaNCer eVoLUTioN
interpreting the Cancer Genomes Through Physical and functional
Models of Cancer Cells
Trey Ideker ........................................................................................................................................44
origin of Cancer in inflammatory Tissues
Seishi Ogawa ...................................................................................................................................48
Pathology from the Molecular scale on up
Garry P. Nolan ................................................................................................................................52

NoNCodiNG rNa
Long Non-Coding rNas and Metastatic Progression of Colorectal Cancer
Christopher A. Maher ...............................................................................................................54ii

advances in diagnostic and Therapeutic application of extracellular
Vesicles in Cancer
Takahiro Ochiya ............................................................................................................................57
LncrNas: Lost in Translation
Eleonora Leucci .............................................................................................................................62

MeTaBoLiC TarGeTs iN CaNCer
Pi 3-Kinase and Cancer Metabolism
Lewis C. Cantley ...........................................................................................................................67
The Mechanisms of obesity-associated Liver Cancer Progression
Naoko Ohtani .................................................................................................................................72
Metabolic Phenotypes and Cancer Progression
Ralph J. DeBerardinis ................................................................................................................74

CaNCer ModeLs (orGaNoids, PdX, GeM)
Mechanisms of drug resistance in Lung Cancer
Katerina Politi .................................................................................................................................78
Biological Mechanism of Polyclonal Metastasis of Colorectal Cancer
Masanobu Oshima ......................................................................................................................82
single Cell analysis of differentiation and drug response in
Glioblastoma
Peter A. Sims....................................................................................................................................86iii

TUMor MiCroeNViroNMeNT
Neutralizing Protumor inflammation: Lessons Learned form Preclinical
Mouse Models
Lisa M. Coussens ..........................................................................................................................89role of intraepithelial Lymphocytes in Colon Cancer
Daniel Mucida ................................................................................................................................91

NaKaHara MeMoriaL LeCTUre
TGf-β family signaling in Progression of Cancer
Kohei Miyazono ............................................................................................................................96

NoVeL iMMUNoTHeraPeUTiCs
Car T Cells: The emergence of synthetic immunity
Michel Sadelain ...........................................................................................................................100
advances of Car-T Cell Therapy for solid Tumors
Koji Tamada ...................................................................................................................................102
The synthetic future of Car T Cell Cancer immunotherapy
Michael C. Jensen .......................................................................................................................107iv

BasiC MeCHaNisMs of CarCiNoGeNesis
deciphering and Targeting Clonal evolution in Myeloid Malignancies
Ross L. Levine ...............................................................................................................................109
Cell Competition during Cancer development
Yasuyuki Fujita ............................................................................................................................118
New Cancer Vulnerabilities and Therapeutics in the rNa world
Trey Westbrook ............................................................................................................................122

CoNCLUdiNG reMarKs
Hiroyuki Mano ............................................................................................................................124
List of Participants ................................................................................................................................126
List of Previous international symposia series.............................................................148

v

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1

PEDIATRIC CANCER PRECISION MEDICINEElaine R. Mardis
The Steve and Cindy Rasmussen Institute for Genomic Medicine
Nationwide Children’s Hospital
575 Children’s Crossroad, Columbus, OH 43215, USA(elaine.mardis@nationwidechildrens.org)

Pediatric cancer has largely not benefitted from the clinical use of targeted or
immunotherapies, due to a lack of clinical trials and other factors including significant
differences in the somatic driver alterations when compared to adult cancers. The result of
this lack of progress is that cancer remains the leading cause of death by disease in children
in first world countries, and those children who do survive their disease face significant
adverse health impacts from current therapies for the remainder of their lives, including anelevated risk for secondary cancers.
Our efforts of the past two years have sought to develop and integrate genomic assaysof pediatric cancers into the rubric of interdisciplinary molecular tumor boards conductedin collaboration with our oncology providers. These efforts aim to incorporate genomicsbased molecular diagnosis, prognosis and therapeutic implications in the context ofconventional diagnostics. Of equal importance is the availability of clinical trials that
include pediatric cancer patients and provide access to targeted therapies in the setting ofrecurrent or treatment-refractory cancers. We have developed DNA- and RNA-based
assays that provide unbiased evaluation of cancer and germline, and corresponding
analytical pipelines that provide a broad range of diagnostically relevant information foreach patient studied. To-date, we have studied over 150 patients on a research protocolthat identifies DNA level germline and somatic alterations using high depth exome andevaluates all chromosomes for copy number alterations. In addition, we analyze cancerspecific RNAseq data through multiple analytical pipelines to provide information on
fusion gene drivers, over-expressed genes, activated and repressed pathways, and thetumor microenvironment. Recently, we have integrated methylation arrays into the2

E. R. Mardis

evaluation of these patients, which provide subtyping and copy number alteration data.
The diagnostic yield of this set of assays and analytics is over 75%, which is reasonably
high compared to other reports in pediatric cancer applied genomics (Figure 1). We also
are beginning to see our diagnoses applied to patient care, which is an important route toultimately decreasing the impact of treatment-associated sequelae for pediatric cancer
survivors. Importantly, the ability to study longitudinal samples from previously banked
surgical specimens and our multiplex analyses have provided a virtuous cycle of new
research questions that we anticipate will yield new insights into tumor biology. New
funding will permit an exploration of our observations of increased immune cell infiltrationin recurrent CNS cancers and how this infiltrate might be indicative of an immunesuppressive microenvironment that could be targeted with novel therapeutics. By sharing
our data and outcomes, along with our analytical methods, we hope to extend theapplication of genomics to pediatric precision medicine, thereby introducing assays andinformation that will guide providers to a more nuanced view of each patient and their
possible treatment avenues.

figure 1

Composite report of medically relevant findings from the Institute for Genomic Medicine
Pediatric Cancer Protocol. Medically relevant findings include prognostic, diagnostic andtherapeutic indications from genomic testing using DNA and RNA assays of pediatric cancers. The tumor type is broadly indicated by color coding as indicated, with different findings indicated according to the number of findings by different blue shades.

Keynote Lecture

3

References
1. Schieffer KM, Varga E, Miller KE, Agarwal V, Koboldt DC, Brennan P, Kelly B, DaveWalla A, Pierson CR, Finlay JL, AbdelBaki MS, White P, Magrini V, Wilson RK, Mardis
ER, Cottrell CE. Expanding the clinical history associated with syndromic Klippel-Feil:
A unique case of comorbidity with medulloblastoma. Eur J Med Genet. 2019
Aug;62(8):103701.
2. Miller KE, Kelly B, Fitch J, Ross N, Avenarius MR, Varga E, Koboldt DC, Boué DR,
Magrini V, Coven SL, Finlay JL, Cottrell CE, White P, Gastier-Foster JM, Wilson RK,
Leonard J, Mardis ER. Genome sequencing identifies somatic BRAF duplication c17941796dupTAC;p.Thr599dup in pediatric patient with low-grade ganglioglioma. Cold
Spring Harb Mol Case Stud. 2018 Apr 2;4(2).

Elaine R. Mardis, PhD
1984-1989
1989-1993
1993-20162016-present

Graduated with PhD from University of Oklahoma,
Department of Chemistry and Biochemistry
Bio-Rad Laboratories, Senior Scientist, R&D
Faculty member and Co-Director, McDonnell Genome
Institute, Washington University School of Medicine
Co-Executive Director, The Steve and Cindy
Rasmussen Institute for Genomic Medicine at
Nationwide Children’s Hospital
Professor of Pediatrics, The Ohio State University
College of Medicine

4

DEVELOPMENT OF JAPANESE CANCER GENOMIC
MEDICINE PLATFORM

Hiroyuki Mano
Center for Cancer Genomics and Advanced Therapeutics/Research InstituteNational Cancer Center
5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan(hmano@ncc.go.jp)

Our discovery of EML4-ALK oncogene in lung cancer brought highly effective ALK
inhibitors to the patients carrying this fusion gene. Identification of other ALK fusions invarious cancer subtypes further led to realize the importance of beyond-organ, gene-based
cancer classification scheme [1], ushering in the “cancer genomic medicine” to the clinics.
To assist clinical sequencing of cancer specimens, we examined a possible caner genepanel test that sensitively and accurately detects gene fusions as well as single nucleotide
variations [2]. For detecting gene fusions from formalin–fixed paraffin–embedded (FFPE)
specimens, there are principally two approaches; one is to extract genomic DNAs and to
capture intronic regions, and the other is to extract RNAs from FFPE and to convert them
to cDNA for sequencing. While the latter approach is supposed to be inferior to the former
due to RNA degradation in FFPE, we unexpectedly observed that the opposite is the case.
Comparison of these two approaches by using fusion–positive FFPE samples revealed thatRNA sequencing even from FFPE materials provides much higher sensitivity andspecificity for fusion detection.
We thus developed a twin panel system, Todai OncoPanel (TOP), that is comprised of
TOP DNA panel interrogating 464 genes and TOP RNA panel examining the fusions of 365
genes and the expression of 109 genes (Figure 1). It can also assess allele–specificchromosome copy number aberrations, microsatellite status and exon skipping. The TOP
panel system allows reliable genome profiling even from small lung biopsy specimens [3],and is under Advanced Medical Care as of November 2019 to prove its clinical utility.
Japan planned to adapt gene-panel testing to optimize cancer treatments under thenational health insurance system. To discuss a necessary platform to perform such cancer

Precision Cancer Medicine

5

genomic medicine in a nation–wide manner, The Expert Meeting for Cancer Genomic
Medicine Promotion Consortium was held in the Spring of 2017 in The Ministry of Health,Labour and Welfare (MHLW). The Expert Meeting recommends a step-wise adaptation of
genomic medicine, i.e. such medicine should be first conducted only in designatedhospitals, and the number of these hospitals shall be increased gradually. Anotherimportant proposal from the Expert Meeting is to set a central datacenter to aggregate
genomic as well as clinical information of gene-panel tests. MHLW accordingly designated,in the Spring of 2018, eleven Core Hospitals for Cancer Genomic Medicine and 156 of
Cooperative Hospitals for Cancer Genomic Medicine. The Ministry also established, in June
2018, The Center for Cancer Genomics and Advanced Therapeutics (C-CAT) to store andutilize genomic/clinical information (Figure 2).

figure 1

The TOP panel systemfigure 2

The cancer genomic medicine platform in Japan6

H. ManoThe genomic medicine platform in Japan, consisting of these hospitals and C-CAT,
officially started on June 1st 2019 with two approved cancer gene panels, “OncoGuide NCC
Oncopanel” and “FoundationOne CDx”. When patients are admitted to the designatedhospitals and give consent to genomic medicine, their specimens will be sent to certified
laboratories for sequencing. While the resultant data will be sent back to hospitals, thegenomic data will also be sent to C-CAT. Clinical information of patients will be transferred
to C-CAT from corresponding hospitals. These clinical and genomic information for every
patient will be referred to C-CAT cancer knowledge database (CKDB) to generate “CKDB
findings” reports containing the information of matched clinical trials and clinicalannotation for each mutation. The patients’ data are further utilized, upon their consent, byacademia and industries for drug/biomarker development. We are preparing the “C-CAT
Cloud” system so that such genomic and clinical information shall become available toacademia and third parties.
References
1. Mano H. ALKoma: a cancer subtype with a shared target. Cancer Discov. 2012:2;495502.
2. Kohsaka S, Tatsuno K, Ueno T, Nagano M, Shinozaki-Ushiku A, Ushiku T, Takai D,
Ikegami M, Kobayashi H, Kage H, Ando M, Hata K, Ueda H, Yamamoto S, Kojima S,
Oseto K, Akaike K, Suehara Y, Hayashi T, Saito T, Takahashi F, Takahashi K,
Takamochi K, Suzuki K, Nagayama S, Oda Y, Mimori K, Ishihara S, Yatomi Y, Nagase
T, Nakajima J, Tanaka S, Fukayama M, Oda K, Nangaku M, Miyazono K, Miyagawa K,
Aburatani H, Mano H. Comprehensive assay for the molecular profiling of cancer bytarget enrichment from formalin-fixed paraffin-embedded specimens. Cancer Sci.
2019;110:1464-1479.
3. Kage H, Kohsaka S, Shinozaki-Ushiku A, Hiraishi Y, Sato J, Nagayama K, Ushiku T,
Takai D, Nakajima J, Miyagawa K, Aburatani H, Mano H, Nagase T. Small lung tumor
biopsy samples are feasible for high quality targeted next generation sequencing.
Cancer Sci. 2019;110:2652-2657.

Precision Cancer Medicine

7

Hiroyuki Mano, MD, PhD
1984
1986-1989

1989-1991
1991-1993

1993-2001
2001-20132013-20182016-present
2018-present

Graduation from School of Medicine, Faculty of
Medicine, The University of Tokyo
Clinical Fellow, The Third Department of Internal
Medicine, Faculty of Medicine, The University ofTokyo
Postdoctoral Researcher, Department of Biochemistry,
St. Jude Children’s Research Hospital
Assistant Professor, The Third Department of Internal
Medicine, Faculty of Medicine, The University of
Tokyo
Associate Professor, Department of Molecular Biology,
Jichi Medical University
Professor, Division of Functional Genomics, Jichi
Medical University
Professor, Department of Cellular Signaling, Graduate
School of Medicine, The University of Tokyo
Director, Research Institute, National Cancer Center
Director, Center for Cancer Genomics and Advanced
Therapeutics, National Cancer Center

8

INACTIVATION OF DNA REPAIR TO IMPROVE IMMUNESURVEILLANCE

Alberto Bardelli
Department of Oncology, University of Torino
Laboratory Molecular Oncology, Candiolo Cancer Institute FPO - IRCCS, Candiolo (TO)EACR (European Association for Cancer Research)
SP 142 km 3,95 - 10060 Candiolo (TO) - Italy(alberto.bardelli@unito.it)

The mismatch repair (MMR) system, which detects and corrects base mismatches,
insertions, and deletions that occur during DNA synthesis, is deregulated in approximately
20% of human cancers. MMR deficient tumors have peculiar properties, including early
onset, metastatic potential but generally favorable prognosis, and remarkable response to
immune therapy. The functional bases of these atypical clinical features are poorly
understood.
We studied how the genomic landscape of MMR deficient tumors affects their biologicaland clinical behaviors. MutL homolog 1 (MLH1) was genetically inactivated in colorectal,
breast and pancreatic mouse cancer cells. The growth of MMR deficient cells wascomparable to their proficient counterparts in vitro and upon transplantation in
immunocompromised mice. In contrast MMR deficient cancer cells grew poorly whentransplanted in syngeneic mice. MMR inactivation increased the mutational burden andled to dynamic mutational profiles, resulting in persistent renewal of neoantigens in vitro
and in vivo, while MMR proficient cells exhibited stable mutational loads and neoantigen
pro-files over time. Immune surveillance improved when cancer cells in which MLH1 hadbeen inactivated accumulated neoantigens for several generations.
We next assessed whether the mutational and neoantigen profiles were also modulated
in human CRC carrying DNA specific repair defects. To this end Whole Exome Sequencing(WES) and RNA sequencing (RNAseq) were performed in CRC cell lines, in vitro and invivo, and in CRC patient-derived xenografts (PDXs) to track longitudinally genomic
profiles, clonal evolution, mutational signatures and predicted neoantigens. The majorityof CRC models showed remarkably stable mutational and neoantigen profiles, howeverPrecision Cancer Medicine

9those carrying defects in DNA repair genes continuously diversified. Rapidly evolving andevolutionary stable CRCs displayed characteristic genomic signatures, and transcriptional
profiles. We further found that molecules implicated in antigen presentation occurred
selectively in highly mutated and rapidly evolving CRC.
These results indicate that human CRC cells carrying alterations in DNA repair
pathways display dynamic neoantigen patterns that fluctuate over time. Longitudinalmonitoring of the neoantigen landscape could be relevant in the context of precision
medicine.
Overall these data suggest that targeting DNA repair processes can increase the burdenof neoantigens in tumor cells and could be exploited for therapeutic approaches.
References
1. Germano G, Lamba S, Rospo G, Barault L, Magrì A, Maione F, Russo M, Crisafulli G,
Bartolini A, Lerda G, Siravegna G, Mussolin B, Frapolli R, Montone M, Morano F, deBraud F, Amirouchene-Angelozzi N, Marsoni S, D'Incalci M, Orlandi A, Giraudo E,
Sartore-Bianchi A, Siena S, Pietrantonio F, Di Nicolantonio F, Bardelli A. Inactivation of
DNA repair triggers neoantigen generation and impairs tumour growth. Nature. 2017
Dec 7;552(7683):116-120. doi: 10.1038/nature24673. Epub 2017 Nov 29. PubMed PMID:
29186113.
2. Rospo G, Lorenzato A, Amirouchene-Angelozzi N, Magrì A, Cancelliere C, Corti G,
Negrino C, Amodio V, Montone M, Bartolini A, Barault L, Novara L, Isella C, Medico E,
Bertotti A, Trusolino L, Germano G, Di Nicolantonio F, Bardelli A. Evolving neoantigen
profiles in colorectal cancers with DNA repair defects. Genome Med. 2019 Jun
28;11(1):42. doi: 10.1186/s13073-019-0654-6. PubMed PMID: 31253177; PubMed Central
PMCID: PMC6599263.
3. Truini A, Germano G, Bardelli A. Inactivation of DNA repair-prospects for boosting
cancer immune surveillance. Genome Med. 2018 Nov 28;10(1):91. doi: 10.1186/s13073018-0603-9. PubMed PMID: 30486892; PubMed Central PMCID: PMC6260742.
4. Germano G, Amirouchene-Angelozzi N, Rospo G, Bardelli A. The Clinical Impact of
the Genomic Landscape of Mismatch Repair-Deficient Cancers. Cancer Discov. 2018
Dec;8(12):1518-1528. doi: 10.1158/2159-8290.CD-18-0150. Epub 2018 Nov 15. Review.
PubMed PMID: 30442708.

10

A. Bardelli

Alberto Bardelli, PhD
1991-1996

1996-1999
1999-2004

2004-present
2016-present
2018-present

PhD in Biochemistry and Molecular Biology,
University College London (UCL). Ludwig Institute forCancer Research. London (UK). Supervisor: Prof. M.
Waterfield.
Postdoctoral Fellow, IRCC and the University ofTorino, School of Medicine
Postdoctoral Fellow, The Johns Hopkins University;
School of Medicine and the Howard Hughes Medical
Institute, Supervisor: Prof. Bert Vogelstein.
Director, Laboratory of Molecular Oncology
Full Professor, University of Torino, Dept. of Oncology
President, EACR - European Association for Cancer
Research

11

QUANTITATIVE MEDICINE FOR BREAST CANCER PATIENTSCharles M. Perou
Department of Genetics/Computational Medicine Program
Lineberger Comprehensive Cancer Center
Marsico Hall, 5th Floor, CB#7599, 125 Mason Farm Road
The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA(cperou@med.unc.edu)

Gene-expression profiling has had a considerable impact on our understanding of breast
cancer biology and clinical care. During the past 20 years, five intrinsic molecular subtypesof breast cancer (Luminal A, Luminal B, HER2-enriched, Basal-like and Normal-like)[1],and a rare subtype with features of stem cells and EMT (Claudin-low)[2], have been
identified and intensively studied. Over the years the breast cancer intrinsic subtype
designation has been shown to predict multiple therapeutic strategies[3-6], including thoseoutlined in Figure 1; in addition, immune cell types can also be quantitatively identified

figure 1

12

C. M. Perouusing gene expression profiling[7] and predict prognosis, the benefit of chemotherapy in
TNBCs, and may also be linked to an ability to predict response to immune checkpoint
blockade[8]. My presentation will briefly touch upon each of these topics with an emphasison the TNBC/basal-like subtype and it’s interactions with the immune system.
The “intrinsic subtype” gene expression patterns were originally identified using DNA
microarrays, and have been further developed into a biomarker often referred to as the“PAM50” assay[3], which is currently approved for use in >25 countries around the world.
The PAM50 subtyping assay provides important information within Hormone Receptor(HR)-positive breast cancer patients, where the Luminal A and B subtypes represent the
majority of cases. Compared to Luminal A, Luminal B tumors are characterized by higher
expression of proliferation/cell cycle-related genes and lower expression of several ERregulated genes such as the progesterone receptor[9]. Clinically, Luminal B tumors show
higher pCR rates to neoadjuvant chemotherapy, but worse survival at 5-years and 10-years
regardless of adjuvant systemic therapy. The Luminal A vs B distinction, together withtumor size and nodal status, is encompassed within the “PAM50 ROR Score”[3], whichquantitatively predicts recurrence and overall survival[10]. In particular, the ROR-PT scoreis able to identify a low risk group of patients whose 10 year disease free survival is >95%
when these patients receive 5 years of endocrine therapy only[11]. Importantly, in the USA
the current endocrine therapy recommendation is for an ER/PR+ patient to receive 10
years of therapy, however, the ROR-PT low score patients have an outstanding 10 year
survival when receiving only 5 years of endocrine therapy, suggesting that 5 years offigure 2

Annual hazard rate curve for all patients according to risk of recurrencegroups.

Cancer Genomics and Epigenomics

13

treatment is a good option for these patients. Therefore this gene expression basedclassification can inform clinical decision making concerning the length of endocrine
therapy treatment (i.e. 5 years vs 10 years)[12].
Triple Negative Breast Cancers (TNBC) are amongst the most clinically challenging
because of their poor prognosis and paucity of treatment options, which is predominately
chemotherapeutics. Through analyses of population-based studies, we and others havedetermined that TNBC are more common in younger women, and African American
women, which contributes to racial disparities in mortality due to breast cancer seen in theUSA[13, 14]. Within TNBC, the Basal-like subtype predominates (70-80%)[15], but all the
intrinsic subtypes are present. Pan-Cancer analyses of 33 tumor types using 10,000 TCGA
tumors revealed that the breast cancer Basal-like subtype is a unique disease entity[16],which is also supported by epidemiological studies[13]. In addition, genomic predictors ofchemotherapy benefit for TNBC identify immune cell features as positive predictors of
response, thus highlighting the importance of the immune microenvironment in responseto chemotherapy (Figure 3 and [17]).

figure 3

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C. M. Perou

Within clinically HER2+ disease, all the intrinsic subtypes can again be identified, albeit
with different proportions when compared to HER2-negative tumors[6]. Among them, theHER2-enriched subtype represents the majority of HER2+ tumors. Recent data suggeststhat patients with HER2-enriched subtype tumors benefit the most from trastuzumab, andthe most from dual HER2 blockade composed of trastuzumab and lapatinib[6]. We again
noted that genomic signatures of immune cells predicted a higher pathological complete
response rate in the HER2+ neoadjuvant setting, suggesting that tumor subtype plus
measures of the immune microenvironment each provide clinically relevant
information[18].
Lastly, as was mentioned above, many distinct immune cell types can be identified
using gene expression profiling of bulk tumors including B cells, T cells, andMacrophages[7]. In addition, we have shown that these immune cell genomic signatures
are prognostic of patient outcomes across most solid epithelial cancers[19]. For TNBC
patients, an Immune Checkpoint Inhibitor (ICI) was recently approved using PDL1+ by
IHC as the biomarker[20]. To develop improved biomarkers for TNBC patients receiving
ICI, we first turned to our murine mammary TNBC models, which showed a low tumor
mutation burden (TMB) and low ICI responsiveness. Therefore we developed cell lines
from each of two distinct murine TNBC tumors, “mutagenized” these in vitro using either
ectopic expression of APOBEC3 or UV irradiation, and determined by exome sequencingthat we increased the TMB levels by 2-4X; these high TMB mammary tumors were thengrown in vivo in immune competent mice and we determined that they were highly
sensitive to ICIs (Figure 4 and [8]). The results from these “mutagenized” lines have two
important implications: first that high TMB/neoantigens is likely a key determinant of ICI
responsiveness, and second, that we now have multiple ICI responsive mammary TNBC
models for further investigations of the molecular mechanisms of this responsiveness.
Building upon this, one of the mechanistic insights coming from these mammary TNBC
studies was that genomic signatures of B-cells, in particular B cell signatures containing
mature antibody production (so called “IgG signature”), was seen in all responsive models.
Interestingly, a human counterpart of this murine “IgG” signature has also been previously
identified by ourselves, and many others, and is highly prognostic across multiple humantumor types including breast and lung cancers[19]. Finally, we performed in vivo cell
depletion studies in these ICI responsive TNBC “mutagenized” models, and noted that
depletion of B cells (as well as depletion of CD4+ or CD8+ T Cells) abrogated the response
to ICI suggesting that both B cells and T cells are needed for full responsiveness[8].
In closing, much of our current and future research is focused on the development ofintegrated computational predictors that link together somatic genetics, gene expression
features, and pathology features using Machine Learning approaches[18, 21]. Much of ourCancer Genomics and Epigenomics

figure 415High TMB Mouse Models are Sensitivity to Immune Checkpoint Inhibitors

research has shown that predicting complex phenotypes like drug responsiveness is not
simple, and that no single gene/protein can robustly predict response; instead our results
suggest that it is genetic features of the tumor (somatic mutations, TMB, Chromosome
Copy Number changes), gene expression subtype (cell type of origin, epigenetics), andfeatures of the microenvironment (immune cells, fibroblasts) that each make a predictive
contribution. Thus, integrated predictors that can link these features together can provide
objective and quantitative assays to help guide treatment escalation, or de-escalation,
therefore offering more personalized approaches in the Era of Precision Medicine.
References
1. Perou, C.M., et al., Molecular portraits of human breast tumours. Nature, 2000. 406(6797): p.
747-52.
2. Prat, A., et al., Phenotypic and molecular characterization of the claudin-low intrinsic subtypeof breast cancer. Breast Cancer Res, 2010. 12(5): p. R68.
3. Parker, J.S., et al., Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes. J
Clin Oncol, 2009.

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C. M. Perou

4. Nielsen, T.O., et al., A comparison of PAM50 intrinsic subtyping with immunohistochemistry
and clinical prognostic factors in tamoxifen-treated estrogen receptor-positive breast cancer.
Clin Cancer Res, 2010. 16(21): p. 5222-32.
5. Usary, J., et al., Predicting drug responsiveness in human cancers using genetically engineered
mice. Clin Cancer Res, 2013. 19(17): p. 4889-99.
6. Carey, L.A., et al., Molecular Heterogeneity and Response to Neoadjuvant Human Epidermal
Growth Factor Receptor 2 Targeting in CALGB 40601, a Randomized Phase III Trial of
Paclitaxel Plus Trastuzumab With or Without Lapatinib. J Clin Oncol, 2016. 34(6): p. 542-9.
7. Iglesia, M.D., et al., Prognostic B-Cell Signatures using mRNA-Seq in Patients with SubtypeSpecific Breast and Ovarian Cancer. Clin Cancer Res, 2014. 20(14): p. 3818-29.
8. Hollern, D.P., et al., B Cells and T Follicular Helper Cells Mediate Response to Checkpoint
Inhibitors in High Mutation Burden Mouse Models of Breast Cancer. Cell, 2019. 179(5): p.
1191-1206 e21.
9. _CGAN, C.G.A.R. Network, and C.M. Perou, Comprehensive molecular portraits of humanbreast tumours. Nature, 2012. 490(7418): p. 61-70.
10. Wallden, B., et al., Development and verification of the PAM50-based Prosigna breast cancer
gene signature assay. BMC Med Genomics, 2015. 8: p. 54.
11. Dowsett, M., et al., Comparison of PAM50 risk of recurrence score with oncotype DX and
IHC4 for predicting risk of distant recurrence after endocrine therapy. J Clin Oncol, 2013.
31(22): p. 2783-90.
12. Sestak, I., et al., Prediction of Late Distant Recurrence After 5 Years of Endocrine Treatment:
A Combined Analysis of Patients From the Austrian Breast and Colorectal Cancer Study Group
8 and Arimidex, Tamoxifen Alone or in Combination Randomized Trials Using the PAM50
Risk of Recurrence Score. J Clin Oncol, 2014.
13. Millikan, R.C., et al., Epidemiology of basal-like breast cancer. Breast Cancer Res Treat,
2008. 109(1): p. 123-39.
14. Carey, L.A., et al., Race, breast cancer subtypes, and survival in the Carolina Breast Cancer
Study. Jama, 2006. 295(21): p. 2492-502.
15. Prat, A., et al., Molecular characterization of basal-like and non-basal-like triple-negative breast
cancer. Oncologist, 2013. 18(2): p. 123-33.
16. Hoadley, K.A., et al., Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000
Tumors from 33 Types of Cancer. Cell, 2018. 173(2): p. 291-304 e6.
17. Hoadley, K., et al. Multivariate Analysis of Subtype and Gene Expression Signatures
Predictive of Pathologic Complete Response (pCR) in Triple-Negative Breast Cancer (TNBC):CALGB 40603 (Alliance). in San Antonio Breast Cancer Symposium. 2016. San Antonio, TX.
18. Tanioka, M., et al., Integrated Analysis of RNA and DNA from the Phase III Trial CALGB
40601 Identifies Predictors of Response to Trastuzumab-Based Neoadjuvant Chemotherapy in
HER2-Positive Breast Cancer. Clin Cancer Res, 2018.

Cancer Genomics and Epigenomics

17

19. Iglesia, M.D., et al., Genomic Analysis of Immune Cell Infiltrates Across 11 Tumor Types. J
Natl Cancer Inst, 2016. 108(11).
20. Schmid, P., et al., Atezolizumab and Nab-Paclitaxel in Advanced Triple-Negative Breast
Cancer. N Engl J Med, 2018. 379(22): p. 2108-2121.
21. Wong, K.Y., et al., I-Boost: an integrative boosting approach for predicting survival time withmultiple genomics platforms. Genome Biol, 2019. 20(1): p. 52.

Charles M. Perou, PhD
1984-1987
1989-1997
1997-2000
2000-present
2011-present
2018-present

BS from Bates College
PhD in Experimental Pathology, University of Utah
Postdoc in David Botstein Lab at Stanford University
Professor, Department of Genetics, University of North
Carolina
The May Goldman Shaw Professor of Molecular
Oncology
Co-Director of the UNC Computational Medicine
Program

18

EPIGENETIC HETEROGENEITY OF HEPATIC LINEAGE
TUMORS

Hiroyuki Aburatani
Genome Science Laboratory
Research Center for Advanced Science and Technology
The University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan(haburata-tky@umin.ac.jp)

Genomic intratumoral heterogeneity has been extensively studied in many cancers, but
the extent of phenotypic heterogeneity is uncertain and could be caused by epigenetic
heterogeneity.
First, we have studied the molecular landscape of liver tumors in adult and children,
namely hepatocellular carcinoma and hepatoblastoma (HBL), which is the most common
primary liver tumor in children. To uncover HBL, we performed comprehensive analysesof genomic, epigenomic and transcriptomic profiles for the 168 cases in Japan. Consistent
with the prior studies, the number of somatic mutational events was very few, while
activation of canonical Wnt pathway was observed in >75% cases. DNA methylome
profiles focusing on hepatic enhancers identified the distinct subgroups, which is
associated with onset age, TERT promoter mutations, loss of imprinting of IGF2/H19 andalpha fetoprotein (AFP) production. Motif analysis of subgroup-specific hypomethylated
enhancers indicated activation of the transcription factors that may implicate “the cell oforigin” of HBLs.
In development, liver arises from foregut, although the precise mechanisms remain tobe known. A subgroup of gastric cancer produces AFP and shows aggressive phenotype[1].
Whole exome analysis showed TP53 as the most frequently mutated gene, while DNA
methylation profiling demonstrated that AFP-producing gastric tumor has a uniquemethylation pattern (Figure 1). Single cell transcriptome analysis of AFP-producing tumor
organoid cells revealed heterogeneity in cell fate, showing distinct trajectories for AFPpositive and negative cell clusters. RNA velocity (La Manno, Nature 2018) was used toshow the branching lineages, hepatic and intestinal, respectively (Figure 2). IntegrativeCancer Genomics and Epigenomics

19

analysis with transcription factor network and chromatin accessibility would elucidate theorigin of hepatic lineage cell population from cells of intestinal origin.

demethylated in AFP_GC

T

Cluster
T or N
AFPGC
Normal like
Hypermutator
Metaplasia
Diffuse stroma
Intestinal
No RNA‐seq data
Tumor content

figure 1afigure 2

N

AFPGC

Normal like

Hypermutator

Metaplasia

Diffuse stroma

Intestinal

Methylation profiling of gastric cancers
DNA methylation was measured by using Infinium EPIC array (illumina).

b

APOB

AFP

MKI67

GPC3

KRT18

KRT8

Single cell transcriptome of AFP-producing gastric cancer organoid cells.
a. Diffusion map
b. RNA velocity map of representative genes.

20

H. Aburatani

References
1. Yamazawa S, Ushiku T, Shinozaki-Ushiku A, Hayashi A, Iwasaki A, Abe H, TagashiraA, Yamashita H, Seto Y, Aburatani H, Fukayama M. Gastric Cancer With Primitive
Enterocyte Phenotype: An Aggressive Subgroup of Intestinal-type Adenocarcinoma.
Am J Surg Pathol. 41(7):989-997. 2017

Hiroyuki Aburatani, MD, PhD
1980-1981
1983-1988
1988-1991
1988-1994
1995-1999
1999-2001

2001-present

Internship in Internal Medicine, University of Tokyo
Clinical Research Fellow, Third Department of Internal
Medicine, University of Tokyo
Assistant Professor, Third Department of Internal
Medicine, University of Tokyo
Visiting Scientist, Center for Cancer Research, MIT,
Cambridge, MA, USA
Assistant Professor, Third Department of Internal
Medicine, University of Tokyo
Associate Professor, Genome Science Division,
Research Center for Advanced Science and Technology,
University of Tokyo
Professor, Genome Science Division, Research Center
for Advanced Science and Technology, University of
Tokyo

21

THE “PETER PAN” SYNDROME: STALLED DEVELOPMENT
THROUGH REDISTRIBUTION OF KEY EPIGENETIC MARKSAT THE CORE OF ONCOHISTONE PATHOGENESIS

Nada Jabado
Departments of Pediatrics and Human Genetics, McGill University
Division of Pediatric Hematology-Oncology and CHDD Program
McGill University Health Care Center and MUHC Research Institute1001 Decarie Boulevard, Montreal, QC H3J1P4 Canada(Nada.jabado@mcgill.ca)

A growing number of cancers, especially in children and young adults, are due toepigenetic dysfunction, which promotes their genesis, progression and metastatic
processes. Our group [1, 2] was one of two[3] to first identify a histone mutation in humandisease, irrefutably linking epigenetic deregulation to cancer. High-frequency recurrent
somatic mutations at specific residues in histone H3 variants occur in a particularly lethal
form of brain tumor, high-grade gliomas (HGG) affecting children and young adults[4-8].
Since this initial discovery, we and others uncovered these “oncohistones” as we label themin bone cancers[9], in posterior-fossa ependymomas group A (PFA-EP) [10], another deadly
brain tumor which affects infants and young children, in subsets of head and necksquamous cell carcinomas[11], in undifferentiated sarcomas[12] and in acute myeloid
leukemia or myelodysplastic syndromes[13, 14]. Interestingly we helped show how
expression of EZHIP (Enhancer of Zeste Homologs Inhibitory Protein, also termed
CXORF67) facilitates the pathogenesis of PFA-EP[15, 16]. EZHIP has a highly conserved
sequence within its C-terminus which acts as an H3K27M oncohistone-mimic and isnecessary and sufficient to inhibit the catalytic activity of EZH2/PRC2 in vitro and invivo[16]. Thus, the ‘K-to-M’ paradigm, first established for oncohistones, has now been
extended to non-histone substrates (Figure 1). Lastly, recent work from the lab of David
Allis uncovered previously unappreciated mutations on core histones in several humancancers[17]. These findings further widen the spectrum of cancer-associated oncohistones
and underscore the disease relevance of studying this new mechanism of oncogenesis.
Seminal findings from the Allis lab showed that K-to-M mutations in H3 variants inhibit
the methyltransferase activity of the enzyme depositing the respective methyl marks on

22

N. Jabado

figure 1

Cancers with Oncohistones and the Oncohistone-mimic EZHIP. Upper panel describes theH3 tail with the three residues where mutations in H3 variants were identified in humandisease: H3K27, H3.3G34 and H3K36.

lysine residues in the histone tail[18]. These heterozygote somatic mutations act as
dominant negative variants, even if they occur in only one of the 32 genes encoding for an
H3. How H3K27M affects tumorigenesis, and its requirement for tumor maintenance, hasremained controversial. Mutant H3K27M, which can occur in both the canonical (H3.1 orH3.2) and non-canonical (H3.3) histone variants, contributes to only a fraction of the total
H3 pool (3-17%)[18]. However, it has a dominant effect as it drastically reduces overalllevels of the repressive H3K27me3 mark in cells[18-20]. In vitro, H3K27M has been shownto severely affect the catalytic activity of the Polycomb Repressive Complex (PRC2), which
normally catalyzes H3K27 methylation (reviewed in [21]). Crystal structure data suggested
that the strong binding of PRC2 to H3K27M-containing nucleosomes potentially sequesters
and inactivates PRC2, thereby leading to global reduction of H3K27me3 levels[22, 23].
Beside this model, several others have been proposed to explain how the loss of H3K27me3
results in tumorigenesis. Chan et al.[24] compared H3K27M-DIPG lines to neuralprogenitor cells (NPCs) to argue that H3K27me3 loss in large genomic areas leads toincreased gene expression at bivalent promoters (marked, in the normal state, by both
H3K27me3 and H3K4me3)[25], while at hundreds of gene loci exhibiting retention of EZH2
(the catalytic subunit of PRC2), H3K27me3 deposition actually increases. Mohammad etal.[26] compared joint H3K27M and PDGFB overexpression in mouse NPCs to H3K27M-

Cancer Genomics and Epigenomics

23

DIPG cell lines. They proposed that increased H3K27me3 occurs at several genomic locithat “strongly” bind PRC2, in contrast to “weaker” binding sites that lose the mark.
Accordingly, differential effects of H3K27M on distinct genomic loci could be explained byvarying levels of PRC2 activity across sites in H3K27M cells [26]. In contrast, Piunti etal.[27] suggested specific enrichment of H3.3K27M-carrying nucleosomes in activelytranscribed genomic regions where H3.3 is preferentially deposited, which then precludes
PRC2 recruitment and leads to heterotypic H3K27ac-K27M nucleosome formation,
H3K27ac increase, and bromodomain protein recruitment in tumors. Thus, despite many
enticing hypotheses, a unified view on downstream effects of H3K27M was lacking. Since
H3K27M is tumorigenic only when introduced in specific neurodevelopmental windows[26-29], while H3K27me3 deposition varies with cellular context and differentiation stage[30], the absence of the appropriate isogenic background likely represents a major
confounder. We present herein our findings on K-to-M mutagenesis and show how thesemutations act on the chromatin to stall development and differentiation and promotetumor formation. We show that K-to-M mutations (H3K27M and H3K36M) block terminal
differentiation of the progenitor cells in which they arise, leading to stalled development the “Peter Pan Syndrome” - as we name it.
H3K36M mutations are the main driver in chondroblastomas and do not require
additional oncogenic partnership to mediate oncogenicity. We thus started with thesemutants to investigate how they affect tumor formation. Introducing H3K36M in mousemesenchymal stem cells led to a block in the differentiation of these progenitors into
adipocytes, osteocytes or chondroblast, and induced aggressive sarcomas in vivo in NSG
mice. We showed that this is achieved through genome-wide rewiring of opposing
chromatin marks which leads to increased intergenic H3K27me3 deposition andredistribution and titration of the PRC1 complex. Indeed, H3K36M mutants block the
methyltransferase activity of NSD1 and 2, which mediates H3K36 mono and dimethylation, as well as SETD2, which mediates H3K36 trimethylation. We show that the
global decrease of the deposition of H3K36me2, a mark of active chromatin which is highly
abundant in the intergenome and opposes H3K27me3 deposition is at the core of H3K36M
oncogenicity. Indeed, decreased deposition of H3K36me2 leads to increased intergenic
deposition of the repressive H3K27me3 mark, which spreads from its boundaries as the
PRC2 complex is then unhindered in depositing this repressive mark[12]. This in turn leads
to redistribution of PRC1 which is titrated from its normal sites. The net effect is loss of
PRC1 suppression as the phenotype we observe in H3K36M is reproduced when weknocked down in mesenchymal stem cells RING1A/B, which are core component of thiscomplex[12]. This led us to investigate other cancers where demethylation of H3K36 may
be reduced. We thus identified H3K36M in aggressive undifferentiated sarcomas[12] and in24

N. Jabado

a subset of squamous cell Head and Neck cancers, where this oncohistone was mutually
exclusive with loss-of-function mutations in NSD1[11] (Figure 1). Thus, in H3K36M
tumors, defective spread of the abundant H3K36me2 mark leads to pervasive invasion of
the repressive H3K27me3 mark, dilution of PRC1 repression and subsequent differentiation
blockade[12] (Figure 2).
High-grade gliomas (HGG) defined by histone 3 K27M driver mutations exhibit global
loss of H3K27 trimethylation and reciprocal gain of H3K27 acetylation, respectively
shaping repressive and active chromatin landscapes. To assess the effects of H3K27M
mutagenesis we used an isogenic tumor model system where we CRISPR/Cas edited themutation in primary human glioma cell lines. Building on seminal findings from the Allislab (16), we showed that cells are stuck in a progenitor state of unlimited self-renewal
capacity, as they are unable to spread the repressive H3K27me3 and me2 marks to achieve
further differentiation (17) (Figure 2). Indeed, PRC2 is recruited normally to its nucleationsites at CpG islands (CGI) in the cell/lineage of origin and starts depositing and spreading
the H3K27me2 and me3 marks. H3K27me2 can spread outside of the CGI and reach thelevel of spread of H3K27me3 in isogenic controls, wild type for the K27M mutation, while
H3K27me3 deposition is not enough to go beyond these CGIs. This shows that the PRC2

figure 2

Potential effects of oncohistones and and oncohistone mimics on the chromatin and theirrole in mediating tumorigenesis.

Cancer Genomics and Epigenomics

25

complex is not blocked on chromatin by mutant nucleosomes as previously suspected, butthat the main effect of the mutation is to drastically slow the deposition of the H3K27me3
mark and to a lesser effect that of H3K27me2 in each cell cycle. We presume that this may
account why these mutations are mainly found in specific cancers as they can mainly affectprogenitors with specific metabolic contingencies, limited H3K27me3 baseline levels andshort cell cycles. Possibly, the relative abundance of the H3K327me3 mark with a longer
cell cycle in more mature cells can strongly mitigate K27M oncogenicity, making it close toimpossible for these mutations to impact more differentiated cells. Furthermore, in keepingwith specific progenitors amenable to transformation, we could only generate high-grade
gliomas in mice when introducing the H3K27M mutation during a narrow developmental
window, between E9.5 and E13.5 of neurogenesis and only when in association with Tp53
loss[29]. Our work also shows that these mutations are important for initiating andmaintaining tumorigenicity, as their removal strongly impairs tumor formation as we andothers have shown[31, 32]. Mirroring H3K36M mutagenesis, we show that loss of
H3K27me3 leads to redistribution of other epigenetic marks involved in ‘crosstalk’ with
H3K27 and H3K36 methylation, namely H3K36me2 and H3K27ac in H3K27M ([33]) andmanuscript submitted). Indeed, by isogenic modeling of this mutation in tumor-derived
contexts, we show that it leads to pervasive H3K27ac deposition across the genome and its
enrichment at repeat elements which normally partly rely on H3K27me3 deposition for
their silencing. Conversely, we show that active enhancers and promoters are not created denovo and instead reflect genes that define developmental states of origin between H3K27M
and H3K27WT HGGs[33] (Figure 2). Deposition of H3K27ac in the intergenome leads toelevated baseline expression of repeat elements in H3K27M cells and a level of viral
mimicry, which can be further amplified by DNA demethylation and histone deacetylase
inhibitors[9]. These agents may therefore modulate anti-tumor immune responses as anovel therapeutic modality for this untreatable disease. At least two clinical trials byinternational pediatric brain tumor consortia will open in the next year based on thesefindings.
The discovery of EZHIP indicates that H3 K-to-M mutants’ inhibition of
methyltransferases can occur in the absence of nucleosome incorporation. We hypothesizethat EZHIP mediates “PRC2 poising” during specific developmental windows and/or
progenitor states to prevent untimely repression of developmental transcriptional
programs and cell differentiation (Figure 2). This physiological stalling of PRC2 may be coopted by aberrant/persistent expression of EZHIP gene to promote oncogenesis. Whether
the extensive chromatin reprogramming induced by K-to-M mutations represents tumor
intrinsic and/or immune-related vulnerabilities remains underexplored. Based on our
preliminary findings that H3K27M and H3K36M mutations converge on aberrant

26

N. Jabado

activation of repeat elements and activation of innate immune response pathways, we
propose new mouse models that allow us to assess the immune infiltration landscape of Kto-M tumors and invent strategies to best therapeutically engage the immune system totreat these cancers.
Last, much less is known on H3.3G34 oncogenesis. We first identified somaticheterozygous mutations in H3F3A at H3.3G34 specifically in the parietal and frontoparietal cortex of 15-35y old patients [3] (Figure 1). 30% of cortical HGGs in these age groupharbor G34RH3.3 and less frequently G34VH3.3, and these mutations are always associatedwith loss-of-function genetic alterations in ATRX and TP53. Another aminoacid
substitution occurs in H3F3A/H3F3B changing glycine to tryptophan (G34W) and rarely toleucine (G34L). This was identified in >90% of GCTBs and is never found in brain tumors[9]. Glycine is the smallest aminoacid and its change to a bulky tryptophan, valine orarginine at position 34 at the base of H3 tail, just 2 residues away from K36, makes it likelythat this substitution affects the PTM or the recognition of a post-translationally modified
H3.3K36. The fact that this mutation occurs exclusively on H3.3 and accounts for 25% of thetotal H3.3 pool makes it likely that its effects will be in cis, on the mutated nucleosome
incorporated in chromatin, and not genome wide as shown for K-to-M oncohistones. One
in vitro study described that G34R and V mutants diminish H3K36me2/me3 in cis on thesame histone H3 tail but have no dominant effect on blocking K36 methylation on wild
type H3 tail [34]. Others found that binding of methyltransferases SETD2 and NSD as wellas the K36 demethylase (eraser) KDM4 critically rely on residue G34 for binding [35].
Additionally, a G34R mutant was modeled in fission yeast and found to downregulate both
H3K36me3 and H3K36ac levels, while H3K36me2 accumulated on the mutant tail [36].
However, despite these data ongoing efforts are needed to understand how local chromatinchanges due to G34 mutants act to promote tumorigenicity and why different mutants areencountered specific cancers (Figures 1 and 2).
In summary, oncohistones are a new model of oncogenesis that shows how the
deregulation of the epigenome during specific developmental windows can be co-opted fortumorigenesis. These mutations are unprecedented tools to explore the role of major
epigenetic marks during development. Indeed, stalled development is the main recurrent
theme that emerges from the studies we and other have performed on these mutations,hence the naming “The Peter Pan Syndrome” as these tumors are stuck in time unable toregress or further “age’ and differentiate. We propose that the genome wide rewiring ofmajor opposing marks is at the core of the oncogenic effect of these mutations and creates
exquisite vulnerabilities that could be taken advantage of. We suggest that the use of spefic
epigenetic drugs targeting these cancers may prove beneficial. Indeed, further inhibition of
PRC2 leading to further decrease in H3K27me2/me3 levels will prove mainly toxic forCancer Genomics and Epigenomics

27

K27M mutant cells, while the use of DNA demethylating agents can increase ERV
expression and further prime these tumors for immune targeting. Investigating the use of
combination therapies targeting the epigenome (EZH2 inhibitors, DNA demethylating
agents) in combination with agents affecting active pathways in these tumours (PDGFRA,
Ras and PI3K signaling, ACVR1 mutations) and/or agents aimed at modulating theimmune system (checkpoint inhibitors, vaccines, CART cells) will provide much needed
novel therapeutic avenues for patients with these deadly cancers.
References
1. Khuong-Quang, D.A., et al., K27M mutation in histone H3.3 defines clinically andbiologically distinct subgroups of pediatric diffuse intrinsic pontine gliomas. Acta
Neuropathol, 2012. 124(3): p. 439-47.
2. Schwartzentruber, J., et al., Driver mutations in histone H3.3 and chromatin remodellinggenes in paediatric glioblastoma. Nature, 2012. 482(7384): p. 226-31.
3. Wu, G., et al., Somatic histone H3 alterations in pediatric diffuse intrinsic pontine gliomas andnon-brainstem glioblastomas. Nat Genet, 2012. 44(3): p. 251-3.
4. Fontebasso, A.M., et al., Epigenetic dysregulation: a novel pathway of oncogenesis in pediatricbrain tumors. Acta Neuropathol, 2014. 128(5): p. 615-27.
5. Fontebasso, A.M., et al., Mutations in SETD2 and genes affecting histone H3K36 methylation
target hemispheric high-grade gliomas. Acta Neuropathol, 2013. 125(5): p. 659-69.
6. Liu, X.Y., et al., Frequent ATRX mutations and loss of expression in adult diffuse astrocytic
tumors carrying IDH1/IDH2 and TP53 mutations. Acta Neuropathol, 2012. 124(5): p. 61525.
7. Fontebasso, A.M., et al., Recurrent somatic mutations in ACVR1 in pediatric midline highgrade astrocytoma. Nat Genet, 2014. 46(5): p. 462-6.
8. Sturm, D., et al., Paediatric and adult glioblastoma: multiform (epi)genomic culprits emerge.
Nat Rev Cancer, 2014. 14(2): p. 92-107.
9. Behjati, S., et al., Distinct H3F3A and H3F3B driver mutations define chondroblastoma andgiant cell tumor of bone. Nat Genet, 2013. 45(12): p. 1479-82.
10. Gessi, M., et al., Evidence of H3 K27M mutations in posterior fossa ependymomas. Acta
Neuropathol, 2016. 132(4): p. 635-7.
11. Papillon-Cavanagh, S., et al., Impaired H3K36 methylation defines a subset of head and necksquamous cell carcinomas. Nat Genet, 2017. 49(2): p. 180-185.
12. Lu, C., et al., Histone H3K36 mutations promote sarcomagenesis through altered histone
methylation landscape. Science, 2016. 352(6287): p. 844-9.
13. Boileau, M., et al., Mutant H3 histones drive human pre-leukemic hematopoietic stem cell
expansion and promote leukemic aggressiveness. Nat Commun, 2019. 10(1): p. 2891.

28

N. Jabado

14. Lehnertz, B., et al., H3(K27M/I) mutations promote context-dependent transformation inacute myeloid leukemia with RUNX1 alterations. Blood, 2017. 130(20): p. 2204-2214.
15. Hubner, J.M., et al., EZHIP / CXorf67 mimics K27M mutated oncohistones and functions as
an intrinsic inhibitor of PRC2 function in aggressive posterior fossa ependymoma. Neuro
Oncol, 2019.
16. Jain, S.U., et al., PFA ependymoma-associated protein EZHIP inhibits PRC2 activity through a
H3 K27M-like mechanism. Nat Commun, 2019. 10(1): p. 2146.
17. Nacev, B.A., et al., The expanding landscape of 'oncohistone' mutations in human cancers.
Nature, 2019. 567(7749): p. 473-478.
18. Lewis, P.W., et al., Inhibition of PRC2 activity by a gain-of-function H3 mutation found inpediatric glioblastoma. Science, 2013. 340(6134): p. 857-61.
19. Bender, S., et al., Reduced H3K27me3 and DNA hypomethylation are major drivers of gene
expression in K27M mutant pediatric high-grade gliomas. Cancer Cell, 2013. 24(5): p. 660-72.
20. Bechet, D., et al., Specific detection of methionine 27 mutation in histone 3 variants (H3K27M)
in fixed tissue from high-grade astrocytomas. Acta Neuropathol, 2014. 128(5): p. 733-41.
21. Margueron, R. and D. Reinberg, The Polycomb complex PRC2 and its mark in life. Nature,
2011. 469(7330): p. 343-9.
22. Jiao, L. and X. Liu, Structural basis of histone H3K27 trimethylation by an active polycomb
repressive complex 2. Science, 2015. 350(6258): p. aac4383.
23. Justin, N., et al., Structural basis of oncogenic histone H3K27M inhibition of human polycomb
repressive complex 2. Nat Commun, 2016. 7: p. 11316.
24. Chan, K.M., et al., The histone H3.3K27M mutation in pediatric glioma reprograms H3K27
methylation and gene expression. Genes Dev, 2013. 27(9): p. 985-90.
25. Bernstein, B.E., et al., A bivalent chromatin structure marks key developmental genes inembryonic stem cells. Cell, 2006. 125(2): p. 315-26.
26. Mohammad, F., et al., EZH2 is a potential therapeutic target for H3K27M-mutant pediatricgliomas. Nat Med, 2017. 23(4): p. 483-492.
27. Piunti, A., et al., Therapeutic targeting of polycomb and BET bromodomain proteins in diffuse
intrinsic pontine gliomas. Nat Med, 2017. 23(4): p. 493-500.
28. Funato, K., et al., Use of human embryonic stem cells to model pediatric gliomas with
H3.3K27M histone mutation. Science, 2014. 346(6216): p. 1529-33.
29. Pathania, M., et al., H3.3(K27M) Cooperates with Trp53 Loss and PDGFRA Gain in Mouse
Embryonic Neural Progenitor Cells to Induce Invasive High-Grade Gliomas. Cancer Cell,2017. 32(5): p. 684-700 e9.
30. Mikkelsen, T.S., et al., Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature, 2007. 448(7153): p. 553-60.

Cancer Genomics and Epigenomics

29

31. Harutyunyan, A.S., et al., H3K27M induces defective chromatin spread of PRC2-mediatedrepressive H3K27me2/me3 and is essential for glioma tumorigenesis. Nat Commun, 2019.
10(1): p. 1262.
32. Silveira, A.B., et al., H3.3 K27M depletion increases differentiation and extends latency ofdiffuse intrinsic pontine glioma growth in vivo. Acta Neuropathol, 2019. 137(4): p. 637-655.
33. Krug, B., et al., Pervasive H3K27 Acetylation Leads to ERV Expression and a Therapeutic
Vulnerability in H3K27M Gliomas. Cancer Cell, 2019. 35(5): p. 782-797 e8.
34. Shi, L., et al., Histone H3.3 G34 Mutations Alter Histone H3K36 and H3K27 Methylation In
Cis. J Mol Biol, 2018. 430(11): p. 1562-1565.
35. Voon, H.P.J., et al., Inhibition of a K9/K36 demethylase by an H3.3 point mutation found in
paediatric glioblastoma. Nat Commun, 2018. 9(1): p. 3142.
36. Yadav, R.K., et al., Histone H3G34R mutation causes replication stress, homologous recombination defects and genomic instability in S. pombe. Elife, 2017. 6.

Nada Jabado, MD, PhD
1989-1999

1994-1998
1999-2003
2003-present

Medical Studies and Residency, University of Pierre etMarie Curie (Paris VI), France Graduate School of
Medicine University of Paris VI (Pediatrics,
Hematology)
Graduate Studies in Immunology, Pierre et Marie Curie(Paris VI), France
Postdoctoral Fellow, Department of Biochemistry,
McGill University
Professor, Departments of Pediatrics and Human
Genetics, McGill University
Senior Clinician Scientist, McGill University HealthCenter

30

MODELING TUMOR IMMUNITY FROM RNA-SEQ

X. Shirley Liu
Department of Data Sciences
Dana-Farber Cancer Institute, Harvard University
450 Brookline Ave, MC-CLS11007, Boston, MA 02215, USA(xsliu@ds.dfci.harvard.edu)

I will discuss two algorithms that our laboratory developed to extract useful cancer
immunology insights from treatment naïve RNA-seq samples in The Cancer Genome Atlas.
First, we developed a computational method TRUST that can assemble T cell receptor(TCR) and B cell receptor (BCR) complementarity-determining regions (CDR3s) from
unselected bulk tumor RNA-seq data (Li et al, Nat Genet 2016; Li et al, Nat Genet 2017).
The recent release of the TRUST4 algorithm has improved speed, memory efficiency,
sensitivity, and accuracy, as well as the added function to identify full length BCR
sequences (https://github.com/liulab-dfci/TRUST4). When applied to over 10K samples
in the TCGA, TRUST assembled ~3.3M TCR from TCGA tumor RNA-seq samples andrevealed association between tumor infiltrating TCR clonotype diversity and tumor
mutational load (Li et al, Nat Genet 2016). TRUST also assembled ~71M BCR CDR3
sequences from TCGA data, and revealed frequent BCR somatic hypermutations and Ig
class switch events (Hu et al, Nat Genet 2019). Although there have been inconsistentreports on tumor infiltrating B cells levels in different cancer types, TRUST analyses
suggest tumors in general have lower BCR diversity and higher IgG class switch thanadjacent normal. Specifically IgG1 and IgG3 B cells are associated with national killer cell
activity in the tumors, implicating their important roles in B-cell mediated tumor
immunity. Besides analysis of solid tumors, our analysis on acute myeloid leukemia (AML)
RNA-seq samples found significantly higher level of B cell activation and more secondary
Ig class switch events in adult AML than pediatric AML or non-tumor samples.
Furthermore, adult AML with highly expanded IgA2 B cells, which might represent an
immunosuppressive microenvironment, are associated with regulatory T cells and worse
overall survival.

Immunogenomics

31

Second, we derived Tumor Immune Dysfunction and tumor immune Exclusion gene
expression signatures (TIDE) from pretreatment tumors to predict patient response to antiPD1 and anti-CTLA4 treatment (Jiang et al, Nat Med 2018). Recent work has revealed two
broad categories of tumor immune evasion with distinct characteristics of the tumor
microenvironment (TME). The first subset shows a T cell inflamed phenotype consisting of
infiltrating T cells in dysfunctional state. The second subset lacks the T cell infiltration andresists immune attack through T cell exclusion. Based on these two mechanisms, wedeveloped a computational framework, Tumor Immune Dysfunction and Exclusion(TIDE), to identify factors underlying tumor immune escape. To model T-cell dysfunction,
we hypothesize that transcriptome profiles of treatment-naïve tumors with patient survival
outcome are informative. For example, in the TCGA melanoma tumors, a higher cytotoxic
CD8 T lymphocyte (CTL) level, indicates a better patient survival, but only when TGFB1
has a low expression level, as TGFB1 expression indicates T cell dysfunction and tumor
immune escape. To systematically evaluate genes with similar behavior as TGFB1, TIDE
uses a Cox-PH model to test how the interaction between the expression of a candidate
gene P and CTL affects death hazard (estimated from survival) in large-scale tumor
profiling cohorts. The TIDE output is a T-cell dysfunction score for every gene in the
genome, defined as the interaction coefficient “d” divided by its standard deviation. Ward
test in Cox-PH regression is used to identify dysfunction scores with statistically significant
p-values. Interestingly, TIDE dysfunctional scores derived from TCGA treatment naive
samples show good agreement with signature genes from eight published human or mousestudies on T cell dysfunction, especially fixed state of T cell dysfunction which is resistant
to reprogramming by anti-PD1 treatment. To model T-cell exclusion, TIDE considers
suppressive factors hypothesized to prevent T cells from infiltrating the tumor, namely
tumor-associated macrophage (TAM), myeloid derived suppressor cell (MDSC) and cancer
associated fibroblast (CAF). TIDE derives a T-cell exclusion signature by integrating
publicly available expression profiles of MDSC, TAM, and CAF, which indeed negatively
correlates with CTL in all the TCGA cancer cohorts. When predicting tumor response to
ICB from pre-treatment tumor expression profiles, TIDE evaluates whether T cell inflamed(high CTL) tumors have high level of dysfunctional T cells and whether non-inflamed (lowCTL) tumors have suppressive cells preventing T cell infiltration, as high correlations witheither T cell dysfunction or exclusion signatures predict poor responders. This TIDE
framework could predict the outcome of melanoma patients treated with anti-PD1 andanti-CTLA4 with a higher accuracy than other biomarkers such as PD-L1 level andmutation load, as well as recent computational predictions. We also present a data-driven
approach integrating large-scale omics data and biomarkers on published immune
checkpoint inhibitor trials, non-immunotherapy tumor profiles, and CRISPR screens on a

32

X. Shirley Liu

web platform (http://tide.dfci.harvard.edu). TIDE website has three functional modules tohelp hypothesis generation, biomarker optimization, and patient stratification respectively,
which demonstrates the utility of public data reuse in cancer research.
Together, our work demonstrates that tumor RNA-seq, even on treatment naïve tumors,
is cost effective to inform tumor microenvironment and immunity.
References
1. Hu X#, Zhang J#, Wang J, Fu J, Li T, Zheng X, Wang B, Gu S, Jiang P, Fan J, Ying X,
Zhang J, Carroll MC, Wucherpfennig KW, Hacohen N, Zhang F, Zhang P, Liu JS*, Li B*,
Liu XS*. Landscape of B cell immunity and related immune evasion in human cancers.
Nat Genet. 2019 Mar;51(3):560-567. 2019.
2. Wang B#, Wang M#, Zhang W#, Xiao T, Chen CH, Wu A, Wu F, Traugh N, Wang X, Li
Z, Mei S, Cui Y, Shi S, Lipp JJ, Hinterndorfer M, Zuber J, Brown M, Li W*, Liu XS*.
Integrative analysis of pooled CRISPR genetic screens using MAGeCKFlute. Nat
Protoc. 2019.
3. Jiang P#, Gu S#, Pan D#, Fu J, Sahu A, Hu X, Li Z, Traugh N, Bu X, Li B, Liu J, Freeman
GJ, Brown MA, Wucherpfennig KW*, Liu XS*. Signatures of T cell dysfunction andexclusion predict cancer immunotherapy response. Nat Med. 2018;
4. Hu X, Zhang J, Liu JS, Li B*, Liu XS*. Evaluation of immune repertoire inference
methods from RNA-seq data. Nat Biotechnol. 2018. 9;36(11):1034.
5. Pan D#, Kobayashi A#, Jiang P#, Ferrari de Andrade L, Tay RE, Luoma A, Tsoucas D,
Qiu X, Lim K, Rao P, Long HW, Yuan GC, Doench J, Brown M, Liu XS*, Wucherpfennig
KW*. A major chromatin regulator determines resistance of tumor cells to T cellmediated killing. Science 2018; 359(6377):770-5.
6. Liu XS*, Mardis ER*. Applications of immunogenomics to cancer. Cell 2017; 168(4): 600612
7. Li B*, Li T, Wang B, Dou R, Zhang J, Liu JS, Liu XS*. Ultrasensitive detection of TCR
hypervariable-region sequences in solid-tissue RNA-seq data. Nat Genet 2017; 49(4):4823.
8. Li B#, Li T#, Pignon JC, Wang B, Wang J, Shukla SA, Dou R, Chen Q, Hodi FS, Choueiri
TK, Wu C, Hacohen N, Signoretti S, Liu JS*, Liu XS*. Landscape of tumor-infiltrating T
cell repertoire of human cancers. Nat Genet 2016; 48(7):725-32.

Immunogenomics

33

X. Shirley Liu, PhD
1997-2002
2002-2003

2003-20122012-present

PhD in Biomedical Informatics, PhD Minor in
Computer Science, Stanford University
Independent Fellow, Department of Biostatistics,
Harvard School of Public Health, Dana-Farber Cancer
Institute
Assistant Professor and Associate Professor,
Department of Biostatistics and Computational Biology,
Dana-Farber Cancer Institute, Harvard T.H. Chan
School of Public HealthProfessor, Department of Data Sciences, Dana-Farber
Cancer Institute, Harvard University

34

IMMUNE SUPPRESSIVE MECHANISMS IN THE TUMORMICROENVIRONMENT

Hiroyoshi Nishikawa
Division of Cancer Immunology, Research Institute/ EPOC, National Cancer Center
5-1-1 Tsukiji, Chuo-Ku, Tokyo 104-0045, Japan
Department of Immunology, Nagoya University Graduate School of Medicine
65 Tsurumai, Showa-Ku, Nagoya 466-8550, Japan(hnishika@ncc.go.jp / nisihiro@med.nagoya-u.ac.jp)

One of the crucial processes involved in tumor development is a gain of immune escapemechanisms by cancers [1]. Therefore, the induction/recruitment of immunosuppressive
cells [e.g., regulatory T (Treg) cells, myeloid-derived suppressor cells and tumor-associatedmacrophages], and the increased expression of various immunosuppressive molecules[e.g., programmed cell death-1 (PD-1) and PD-ligand 1 (PD-L1)] are hallmarks of cancer
development and progression [1], indicating the importance of targeting these immune
suppressive machineries in the tumor microenvironment (TME) for successful cancer
therapy. The fields of immunology and oncology are now being linked, the clinicalapplication of cancer immunotherapy represented by immune checkpoint blockade (ICB)
therapies, such as anti-CTLA-4 monoclonal antibodies (mAbs) and anti-PD-1/PD-L1 mAbs,
revealed that the potential antitumor immune responses can be recovered by blocking
these immunosuppressive machineries, resulting in tumor regression and leading to aparadigm shift in cancer therapy across multiple cancer types [2]. Yet, more than half ofpatients treated with ICB therapy fail to respond, even in combination, uncovering alimited window of clinical responses. It is therefore necessary to develop more effective
cancer immunotherapeutic strategies and define biomarkers for stratifying responders andnon-responders via the detailed analysis of immune responses in patients [2].
There are two types of tumor antigens: tumor-specific antigens (TSAs), which are either
oncogenic viral proteins or abnormal proteins stemming from genetic alterations(neoantigens), and tumor-associated antigens (TAAs), which are highly or aberrantly
expressed normal proteins. It is not yet determined how the CD8+ T cells that are specificfor each type of antigen contribute to tumor regression, and whether activation of these selfImmunogenomics

35

vs non-self antigen-specific CD8 + T cells is controlled differently. Treg cells, animmunosuppressive subset of CD4+ T cells characterized by the expression of the mastertranscription factor forkhead box P3 (FoxP3), are a component of the immune system withessential roles in the maintenance of self-tolerance [3]. Treg cells protect hosts fromdeveloping autoimmune diseases and allergies, whereas they promote tumor development
and progression by suppressing antitumor immunity. A higher abundance of Treg cells
relative to effector T cells in the TME is associated with poor prognosis in patients withvarious types of cancer [3]. To address the differences in Treg-cell suppression modes,
Melan-A (a representative TAA)-, and virus (surrogate TSA)-specific CD8+ T cells were
stimulated in the presence of Treg cells. Treg cells rendered Melan-A-specific T cells
anergic. In contrast, virus-specific CD8+ T cells proliferated in the presence of Treg cells, yetthey highly expressed co-inhibitory molecules such as PD-1 [4] , suggesting the potential of
PD-blockade for selectively unleashing neoantigen-specific CD8+ T cells.
While PD-1 blockade is a cancer immunotherapy effective in various types of cancer, it
sometimes causes rapid cancer progression, called hyper-progressive disease (HPD), in a
fraction of treated patients. We explored how PD-1 blockade caused HPD in these patientsand how HPD could be treated and prevented. We show that tumor-infiltrating CD45RAFOXP3highCD4+ eTreg cells expressed PD-1 at equivalent levels as tumor-infiltrating effector
T cells and at much higher levels than eTreg cells in some GC patients. Functionally, PD-1+
eTreg cells in the TME were highly suppressive and proliferative than PD-1- eTreg cells.
Furthermore, PD-1 blockade significantly enhanced in vitro suppressive activity of eTreg
cells. When GC tissue samples were compared between before and after PD-1 blockade,tumor infiltrating Ki67+ eTreg cells were markedly increased by PD-1 blockade in HPD
patients, whereas they were reduced in non-HPD patients, indicating that PD-1 blockade
may facilitate the proliferation of highly suppressive PD-1+ eTreg cells in HPDs, resulting ininhibition of antitumor immunity [5] (Figure 1).
Clinical efficacy of ICB against cancers with oncogenic driver gene mutations, while
they frequently harbor small numbers of tumor mutation burden (TMB), is variable,suggesting different contributions to immune responses by each driver mutation. We
examined immunological phenotypes in the TME of EGFR-mutated lung adenocarcinomas(LUADs), to which ICB is largely ineffective [6]. While EGFR-mutated LUADs exhibited a
non-inflamed TME, CD4+ effector eTreg cells, that are generally detected with effector T
cells in the inflamed TME [7], highly infiltrated. EGFR signals activated cJun/JNK andreduced IRF; the former increased CCL22 recruiting eTreg cells and the latter decreased
CXCL10 and CCL5 inducing CD8+ T-cell infiltration. EGFR signal inhibitors decreased
eTreg cell infiltration in the TME, and combination with ICB provided better antitumor
effects compared with either of single treatment. This is a novel mechanism of Treg cell

36

H. Nishikawa

infiltration into the non-inflamed TME: signaling via driver mutations in EGFR promoted
Treg cell infiltration and inhibited effector T-cell infiltration into the TME by directly
targeting chemokine expression to establish the immune suppressive TME [8] (Figure 2).

Anti-PD-1 mAb
PD-L1/2

PD-L1/2
PD-1

APC

HLA TCR
CD80/CD86

PD-1

APC

Suppressive
activity

HLA TCR

CD80/CD86

CD28

Suppressive
activity

CD28

Treg cell

Treg cell

HLA

HLA
CD28

TCR

Anti-PD-1 mAb
Effector T cell

figure 1

TCR

CD28

Effector T cell

PD-1 expression inhibits TCR and CD28 signals in Treg cells and thereby attenuates Tregcell-mediated immune suppression (Left). PD-1 blockade by anti-PD-1 antibody increases
TCR and CD28 signal in Treg cells, and thereby enhances their proliferation andsuppressive activity. Strong immune suppression by such expanded and activated eTreg
cells hampers activation of effector T cells such as CD8+ T cells (Right).

EGFR mutated

EGFR mutated

EGFR signal

EGFR signal

AP‐1

CCL22

IRF1

CXCL10

AP‐1

CCL22

CCL5

eTreg infiltration

figure 2

CTL infiltration

EGFR tyrosine
kinase inhibitor

IRF1

CXCL10CCL5eTreg infiltration

CTL infiltration

EGFR-mutated lung adenocarcinomas cells themselves produce CCL22 to recruit eTreg
cells and inhibit to produce CXCL10 and CCL5 to avoid CD8+ T-cell infiltration as a mechanism of immune escape. EGFR-TKI changes the TME more favorable for cancer
immunotherapy: high CD8+ T cells and low eTreg cells.

Immunogenomics

37

Thus, integrated analyses of immunological and genomic assays are crucial forunderstanding comprehensive immune suppressive network in the TME. In addition,
developing novel strategies to control Treg cells could be important for effective cancer
immunotherapies.
References
1. Schreiber RD, Old LJ, Smyth MJ. Cancer immunoediting: integrating immunity's roles
in cancer suppression and promotion. Science 2011;331:1565-1570.
2. Zou W, Wolchok JD, Chen L. PD-L1 (B7-H1) and PD-1 pathway blockade for cancer
therapy: Mechanisms, response biomarkers, and combinations. Sci Transl Med
3.
4.

5.

6.

7.

8.

2016;8:328rv324.
Togashi Y, Shitara K, Nishikawa H. Regulatory T cells in cancer immunosuppression implications for anticancer therapy. Nat Rev Clin Oncol 2019;16:356-371.
Maeda Y, Nishikawa H, Sugiyama D, Ha D, Hamaguchi M, Saito T, Nishioka M, Wing
JB, Adeegbe D, Katayama I, Sakaguchi S. Detection of self-reactive CD8+ T cells with ananergic phenotype in healthy individuals. Science 2014;346:1536-1540.
Kamada T, Togashi Y, Tay C, Ha D, Sasaki A, Nakamura Y, Sato E, Fukuoka S, Tada Y,
Tanaka A, Morikawa H, Kawazoe A, Kinoshita T, Shitara K, Sakaguchi S, Nishikawa H.
PD-1+ regulatory T cells amplified by PD-1 blockade promote hyperprogression ofcancer. Proc Natl Acad Sci USA 2019;116:9999-10008.
Borghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, Chow LQ, Vokes EE,
Felip E, Holgado E, Barlesi F, Kohlhaufl M, Arrieta O, Burgio MA, Fayette J, Lena H,
Poddubskaya E, Gerber DE, Gettinger SN, Rudin CM, Rizvi N, Crino L, Blumenschein
GR, Jr., Antonia SJ, Dorange C, Harbison CT, Graf Finckenstein F, Brahmer JR.
Nivolumab versus Docetaxel in Advanced Nonsquamous Non-Small-Cell Lung
Cancer. N Engl J Med 2015;373:1627-1639.
Spranger S, Spaapen RM, Zha Y, Williams J, Meng Y, Ha TT, Gajewski TF. Upregulation of PD-L1, IDO, and T(regs) in the melanoma tumor microenvironment isdriven by CD8+ T cells. Sci Transl Med 2013;5:200ra116.
Sgiyama E, Togashi Y, Takeuchi Y, Shinya S, Tada Y, Kataoka K, Tane K, Sato E, Ishii G,
Goto K, Shintani Y, Okumura M, MTsuboi M, Nishikawa H. Blockade of EGFR
improves responsiveness to PD1 blockade in EGFR-mutated non-small cell lung cancer.
Sci Immunol In press.

38

H. Nishikawa

Hiroyoshi Nishikawa, MD, PhD
2003-2006
2006-20102010-2015
2015-present
2016-present

Research Fellow, Memorial Sloan Kettering Cancer
CenterAssistant Professor, Mie University Graduate School of
Medicine
Associate Professor, IFReC, Osaka University
Chief, Division of Cancer Immunology, National
Cancer Center
Professor, Nagoya University Graduate School of
Medicine

39

PROTEOGENOMICS AND IMMUNOPEPTIDOMICS FOR THE
DEVELOPMENT OF PERSONALIZED CANCERIMMUNOTHERAPY

Michal Bassani-Sternberg
Laboratory of Immunopeptidomics
Center of Experimental Therapeutics
Department of Oncology UNIL CHUV
Ludwig Institute for Cancer Research LausanneRue du Bugnon 25A, CH-1005 Lausanne, Switzerland(michal.bassani@chuv.ch)

The remarkable clinical efficacy of the immune checkpoint blockade therapies has
motivated researchers to discover immunogenic epitopes and exploit them for personalized
vaccines and T cell based therapies. Mutated human leukocyte antigen binding peptides(HLAp) are currently the leading targets. We and others have shown that the direct
identification of tissue-derived immunogenic neoantigens by mass spectrometry (MS) is
feasible [1]. However, most studies attempt to identify neoantigens based on HLA binding
prediction tools. We have compiled a large immunopeptidomics database across dozens of
HLA allotypes. By taking advantage of co-occurring HLA-I alleles, we rapidly andaccurately identified HLA-I binding motifs. We have shown that training HLA-I ligand
predictors on refined motifs significantly improves the identification of neoantigens [2].
Recently, we have acquired the largest reported HLA-II immunopeptidomics dataset. We
introduced novel algorithmic tools to analyze this data and developed for the first time
HLA-II epitope prediction tool trained on immunopeptidomics data that results in major
improvements in prediction accuracy [3].
In contrast to the private neoantigens, tumor-specific antigens that are shared acrosspatients may be more attractive for immunotherapy. Recent studies have focused on the
discovery of aberrantly-expressed non-canonical antigens, which expands the repertoire oftargetable epitopes through the translation and presentation of presumably non-coding
regions. Indeed, translation of 5′UTRs, exon–intron junctions, intronic regions, noncanonical reading frames or antisense transcripts can generate tumor antigens, to elicit
specific CD8 T cell responses and to promote tumor regression in humans. However, theiridentification requires highly sensitive and accurate MS-based proteogenomics approaches.

40

M. Bassani-Sternberg

figure 1

A schematic of the proteo-genomics workflow. Tumor tissue samples or tumor cell lines
were obtained and exome, RNA and Ribo-Seq are performed to provide a framework tointerrogate the non-canonical antigen repertoire. HLAp are immunoaffinity purified fromtumors and analyzed by MS. Immunopeptidomics spectra are searched against RNA andRibo-Seq based personalized protein sequence databases that contain non-canonicalsequences. Identified noncHLAIp are validated by targeted MS and tested for immunogenicity using autologous T cells or PBMCs. Different protein sequence databases
combining whole exome sequencing, and inferred from RNA-Seq and Ribo-Seq data areutilized. NewAnce is implemented by retaining the intersection of the two MS search tools
MaxQuant and Comet, and applying group-specific FDR calculations for protHLAp andnoncHLAp.

Immunogenomics

41

We have developed a novel analytical pipeline that can precisely characterize the noncanonical HLAp repertoire [4]. The workflow incorporates whole exome sequencing, bothbulk and single cell transcriptomics, ribosome profiling, and a combination of two MS/MS
search tools with group-specific false discovery rate calculations for accurate HLAp
identification. We identified more than 400 non-canonical HLAp derived from theexpressed lncRNAs, transposable elements and alternative open reading frames in ninemelanoma and lung cancer samples.
We employed several complementary methods to assess the accuracy of our approach.
First, we predicted the binding of peptides to their respective HLA allotypes. Across all 11
investigated samples, 90% of the noncHLAIp and 91% of the protHLAIp we identified,were predicted to bind the HLA allotypes. In addition, we correlated the observed mean
retention time (RT) of a given peptide against the predicted hydrophobicity index (HI) andshowed that the RT distribution of non-canonical peptides was on the diagonal line, andwas not significantly different from the distribution of proteome-derived peptides,
supporting their correct identification. We have validated experimentally their common
identification across multiple tumors through targeted MS of spiked-in synthetic
isotopically heavy labeled peptide counterparts. Furthermore, we validated theirtranslation from the correct open reading frame with Ribo-seq.
We retrieved their RNA expression levels in large databases of healthy tissues, such as
GTEx, and observed that about 20% of the non-canonical targets are not expressed in any of
the healthy tissues except testis (in GTEx), therefore are likely tumor specific. One noncanonical peptide KYKDRTNILF, derived from a novel downstream ORF (dORF) in thetranscript encoding the melanoma stem cell marker ATP-binding cassette sub-family B
member 5 (ABCB5) in melanoma primary cell line 0D5P, was found to be immunogenic inboth CD8+ TILs and CD8+ T cells from peripheral blood lymphocytes (PBLs). ABCB5
mediates chemotherapeutic drug resistance in stem-like tumor cell subpopulations inhuman malignant melanoma and is commonly over-expressed on circulating melanoma
tumor cells. T cells recognizing the noncHLAIp derived from the dORF of the ABCB5 gene,in both peripheral blood and TILs, suggests no central tolerance, and that this target could
allow immune-targeting of melanoma stem cell subpopulation to drastically affect tumor
growth. This analytical platform holds great promise for the discovery of novel cancer
antigens for cancer immunotherapy.

42

M. Bassani-Sternberg

figure 2

A. Through Ribo-seq analysis, we identified a novel downstream ORF (dORF) in the transcript encoding the melanoma stem cell marker ATP-binding cassette sub-family B
member 5 (ABCB5) gene in the melanoma 0D5P sample, which was the source of thepeptide KYKDRTNILF. B. ScRNA-Seq revealed non-coding transcriptional heterogeneity
in melanoma 0D5P. t-SNE plots of the 1,365 cells colored by the five identified clusters andof the ABCB5 gene expression that is enriched in cluster 0. The Heatmap shows the scaled
and centred expressions of marker genes in cluster 0. C. Immune reactivity was measured
in melanoma 0D5P by the IFNγ ELISpot assay using CD8+ T cells from PBLs that were rechallenged with autologous CD4+ blasts loaded with 1 μM peptide.

References
1. Bassani-Sternberg, M., Braunlein, E., Klar, R., Engleitner, T., Sinitcyn, P., Audehm, S.,
Straub, M., Weber, J., Slotta-Huspenina, J., Specht, K., Martignoni, M. E., Werner, A.,
Hein, R., D, H. Busch, Peschel, C., Rad, R., Cox, J., Mann, M., & Krackhardt, A. M.
(2016). Direct identification of clinically relevant neoepitopes presented on native
human melanoma tissue by mass spectrometry. Nat Commun, 7, 13404.
doi:10.1038/ncomms13404
2. Bassani-Sternberg, M., Chong, C., Guillaume, P., Solleder, M., Pak, H., Gannon, P. O.,
Kandalaft, L. E., Coukos, G., & Gfeller, D. (2017). Deciphering HLA-I motifs across
HLA peptidomes improves neo-antigen predictions and identifies allostery regulating
HLA specificity. PLoS Comput Biol, 13(8), e1005725. doi:10.1371/journal.pcbi.1005725

Immunogenomics

43

3. Julien Racle, Justine Michaux, Georg Alexander Rockinger, Marion Arnaud, Philippe
Guillaume, Sara Bobisse, George Coukos, Alexandre Harari, Camilla Jandus, Michal
Bassani-Sternberg* and David Gfeller* (2019). Deep motif deconvolution of HLA-II
peptidomes for robust epitope predictions. Nature Biotechnology. doi:10.1038/s41587019-0289-6
4. Chloe Chong, Markus Müller, HuiSong Pak, Dermot Harnett, Florian Huber, Delphine
Grun, Marion Leleu, Aymeric Auger, Brian J. Stevenson, Justine Michaux, Ilija Bilic,
Antje Hirsekorn, Lorenzo Calviello, Laia Simó-Riudalbas, Evarist Planet, Jan Lubiński,Marta Bryśkiewicz, Maciej Wiznerowicz, Ioannis Xenarios, Lin Zhang, Didier Trono,
Alexandre Harari, Uwe Ohler, George Coukos, Michal Bassani-Sternberg (2019).
Integrated Proteogenomic Deep Sequencing and Analytics Accurately Identify the
Tumor Non-Canonical Immunopeptidome. BioRxiv.

Michal Bassani-Sternberg, PhD
2004-20092010-20112012-2015

2015-present

PhD, Technion, Israel Institute of Technology (Biology)
Postdoctoral researcher, Technion, Israel Institute ofTechnology (Biology)
Postdoctoral Researcher, The Proteomics and Signal
Transduction Department, Max Planck Institute of
Biochemistry
Head of Immunopeptidomics Unit, the Center ofExperimental Therapeutics (CTE) at the University
Hospital of Lausanne (CHUV) and The Ludwig
Institute for Cancer Research, Lausanne44

INTERPRETING CANCER GENOMES THROUGH PHYSICALAND FUNCTIONAL MODELS OF CANCER CELLS

Trey Ideker
Department of Medicine
University of California at San Diego
9500 Gilman Drive, Mailcode 0688, La Jolla, California 92093, USA(tideker@ucsd.edu)

Recently we and other laboratories have launched the Cancer Cell Map Initiative [1](http://ccmi.org/) and have been building momentum. The goal of the CCMI is to
produce a complete map of the multi-protein molecular machines that are altered topromote transformation and growth of cancer cells. This map, currently missing, will forma critical component of the next generation of systems to decode a patient's cancer genome
in precision medicine applications. CCMI efforts are focused along several lines. The first isadministrative, related to coalition building. We have made notable progress in building a
dedicated consortium of CCMI investigators and institutions, within as well as external to
the University of California (UC) campuses, who provide expertise in data generation
and/or development of computational methodology required to build and use cancer cell
maps. Currently we have established a major collaborative axis between two UC campusesin particular, UC San Diego and UC San Francisco. These collaborations are also expanding
beyond cancer to cover cell mapping for psychiatric disease and other complex disorders[2]. Second, we are establishing platforms for systematic mapping of protein-protein
interactions in cancer, based on affinity purification mass spectrometry [3] (AP-MS). Our
recent efforts and progress along these lines were the main focus of my presentation,
described further below. Third, we are developing technology for mapping gene-gene
epistatic interactions rapidly using the CRISPR system. I did not cover this progress in mytalk on epistatic interaction mapping, beyond initial pilots which have been published [4, 5] .
A fourth focus area is on development of software and database technology to visualize
and store cancer cell maps [6, 7]. In terms of algorithm development, we are developing
machine learning systems for integrating protein and genetic interaction network data toCancer Evolution

45

create multi-scale models of cancer cells [8] . In a recent paper [9] , we have also shown how
a hierarchical map of cell structure can be embedded with a deep neural network, so thatthe model is able to accurately simulate the effect of mutations in genotype on the cellular
phenotype. Our machine learning efforts also include building causal network maps
connecting DNA mutations (somatic and germline, coding and noncoding) to the cancer
events they induce downstream [10] .
In my talk at the 50th International Symposium of the Princess Takamatsu Cancer
Research Fund, I reported recent progress of the CCMI related to the goal ofcomprehensive protein-protein interaction mapping. Thus far interactions have been
mapped for more than 50 cancer proteins, prioritizing those most frequently found to be
mutated in breast cancer and head and neck squamous carcinoma [11, 12] . This effort has
thus far yielded several thousand new protein interactions purified from a panel of breastand esophageal cell lines representing both tumor and normal cell models. We havecombined these data with previously measured protein interactions in the public domain
and analyzed this integrated dataset to reveal a hierarchy of protein complexes of different
sizes and stringencies of interaction. A significant number of these complexes aresomatically mutated in cancer, most in tumor-type specific patterns. The mutatedcomplexes, which number slightly over 300, include many expected based on previous
knowledge of cancer pathways and many that represent novel discoveries.
In the resulting discussion period, points were raised about the expression levels of theidentified protein complexes and, in particular, whether the complexes found to be
mutated in a population of tumors were also found to have high expression levels. In
general the answer is yes: the protein complexes mutated in a cancer subtype are also
typically expressed at appreciable levels in that subtype. Notable exceptions do emerge,
however. In at least one case, the discrepancy could be explained by the fact that asequenced tumor biopsy typically represents a mixture of cell types and/or tissues, suchthat the predominant cell type driving the bulk mRNA expression measurement is not thecell type in which the key mutations arise.
Another discussion point related to how we scored somatic mutations: were all single
nucleotide changes counted, and how would a more/less conservative policy affect ouranalysis? Here I replied that we had simply adopted the conventions used in analysis ofexomes in The Cancer Genome Atlas (TCGA) projects. These TCGA mutation analyses
generally focus on calling non-synonymous mutations in coding regions, which result in achange in the amino-acid sequence, correcting for external factors that have been shown to
influence local mutation rates such as gene length, expression level, replication timing, andtumor mutation burden. Thus, the intention of our CCMI analysis was not to recall
mutations or change the standard practice in that regard, but to extend the analysis from a46

T. Ideker

focus on genes and proteins to a focus on the multimeric protein complexes under positive
selection pressure for cancer mutations.
It is worth noting the significant synergy between my talk and several others presented
in the conference, including that of Dr. Garry Nolan, who spoke in the same session. Nolancovered a series of technological advances for mapping the structure of biological systems,
including mapping tissues at single-cell resolution and, remarkably, mapping cells at
single-atomic resolution. In both cases, he showed that the data reveal a hierarchy of
biological structures and functions and pointed out that this goal of hierarchical mapping
was very similar to the analysis of protein interaction networks described in my talk.
Particularly if Dr. Nolan is able to develop his new system for mapping the contents of cells
at atomic resolution, this technique could be very productively combined with our
informatics approaches to model the hierarchy of molecular machines under mutationalpressure in cancer, along with their corresponding cell types and locations.

figure 1

Pipeline for cancer cell network mapping and network interpretation of patient data

Databases of tumor genomes are mined to identify genes and cell types in whichalterations drive or predispose to development of cancer. Seeded by this information,systematic network mapping efforts enable assembly of the ‘‘Cancer Cell Map,’’ providing
a working scaffold of molecular interactions and the cell types and conditions under which
they are active. New patient data are assessed by query against this resource, whichtranslates alterations at the genetic and molecular level to reveal the impact thesealterations have on the hallmark networks of cancer. Key interactions and network
structures are explored for fine mapping of their basis in molecular structure or
implications to biomolecular mechanism.
References
1. Krogan, N. J., Lippman, S., Agard, D. A., Ashworth, A. & Ideker, T. The cancer cell
mapinitiative: defining the hallmark networks of cancer. Mol. Cell 58 , 690-698 (2015).

Cancer Evolution

47

2. Willsey, A. J. et al. The Psychiatric Cell Map Initiative: A Convergent Systems BiologicalApproach to Illuminating Key Molecular Pathways in Neuropsychiatric Disorders. Cell
174 , 505-520 (2018).
3. Eckhardt, M. et al. Multiple Routes to Oncogenesis Are Promoted by the Human
Papillomavirus?Host Protein Network. Cancer Discovery 8 , 1474-1489 (2018).
4. Du, D. et al. Genetic interaction mapping in mammalian cells using CRISPR
interference. Nat. Methods 14 , 577-580 (2017).
5. Shen, J. P. et al. Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic
interactions. Nature Methods 14 , 573-576 (2017).
6. Pratt, D. et al. NDEx, the Network Data Exchange. Cell Syst 1 , 302-305 (2015).
7. Shannon, P. et al. Cytoscape: a software environment for integrated models of
biomolecular interaction networks. Genome Res. 13 , 2498-2504 (2003).
8. Yu, M. K. et al. Visible Machine Learning for Biomedicine. Cell 173 , 1562-1565 (2018).
9. Ma, J. et al. Using deep learning to model the hierarchical structure and function of acell. Nat. Methods 15, 290-298 (2018).
10. Zhang, W. et al. A global transcriptional network connecting noncoding mutations tochanges in tumor gene expression. Nat. Genet. 50, 613-620 (2018).
11. Ciriello, G. et al. Comprehensive Molecular Portraits of Invasive Lobular Breast Cancer.
Cell 163 , 506-519 (2015).
12. Cancer Genome Atlas Network. Comprehensive genomic characterization of head andneck squamous cell carcinomas. Nature 517, 576-582 (2015).

Trey Ideker, PhD
1994
1995
2001
2003-present

BS (Electrical Engineering and Computer Science),
Massachusetts Institute of Technology
MS (Electrical Engineering and Computer Science),
Massachusetts Institute of Technology
PhD (Molecular Biotechnology), University ofWashington
Fellow, Whitehead Institute for Biomedical Research
Pres. Professor, Department of Medicine Adjunct
Professor, Departments of Bioengineering & Computer
Science, University of California San Diego

48

ORIGIN OF CANCER IN INFLAMMATORY TISSUES

Seishi Ogawa
Departments of Pathology and Tumor Biology
Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University
Department of Molecular Hematology, Karolinska InstituteYoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan(sogawa-tky@umin.ac.jp)

Chronic inflammation elicits a wide variety of responses in the involved tissues andmay cause severe organ damage. However, the tissue response to long-standing
inflammation is poorly understood in terms of positive selection in the target tissue. Here
we show that in patients with ulcerative colitis (UC), inflamed intestine undergoes
widespread remodelling by pervasive clones positively selected by acquiring mutations
that down-regulated IL-17 signalling, most frequently affecting NFKBIZ, ZC3H12A(Regnase-1), and PIGR. Substantially different mutation profiles in UC-epithelia andcancer indicate distinct mechanisms of positive selection between both. Particularly,
despite highly prevalent in UC, NFKBIZ and ZC3H12A mutations were rarely found incancer, suggesting a strong negative selection for these mutations during cancer evolution,
which was supported by significantly reduced colitis-associated tumour formation in
NFKBIZ-deficient mice. Our results provide insight into inflammation-associated tissue
remodelling and unmask unique cancer vulnerability, which might be utilized fortherapeutics of cancer.
Chronic inflammation is a major cause of morbidity and mortality in the humanpopulation and represents a major cancer risk therein. A common feature of chronic
inflammation is recurring tissue injury and repair fueled by long-lasting unregulated
immunity, terminating in tissue remodeling and debilitating organ dysfunction.
Meanwhile, it has recently been demonstrated that extensive tissue remodeling can takeplace even in physiologically normal tissues in an age-dependent manner, in which clones
carrying common cancer-related mutations replace existing normal tissues in the extreme
elderly. Substantially promoted by exposure to well-known cancer risks, clonal expansion

Cancer Evolution

49

of somatically mutated clones may also play a role in inflammation-associated tissue
remodeling. However, it is largely unknown whether this actually happens, howfrequently and extensively and with what mutations it ever occurs, and how it modifies the
inflammatory disease process and cancer development.
Ulcerative colitis (UC) is a common form of inflammatory bowel disease, which ischaracterized by persistent inflammation involving the large intestine, which causes a
severe destruction of intestinal mucosa and intractable ulcer formation. While
inflammation-induced mucosal damage is followed by the recruitment of repair
mechanisms, the repeated destruction and repair cycles for long years ultimately result in
the remodeling of the colorectal epithelium, which is associated with an increased risk
(approximately 15-20% depending on disease duration) of developing cancer (colitisassociated colorectal cancer; CAC). In this study, to understand how chronic inflammation
shapes tissue remodeling in terms of positive selection and how it correlates with cancer
development, we collected multiple specimens of colorectal epithelia from both UC andnon-UC individuals, which were analyzed for somatic mutations using unbiased
sequencing, followed by functional evaluations of clonal selection.
In the steady state after intestinal development, crypts in adults still divide to balancephysiological deficits of crypts; on the basis of sequencing of crypt clusters and assuming
an almost constant human colon length (and therefore crypt numbers in the colon), we
estimated that annually 100,000 crypts are estimated to be lost and replaced by new crypts
generated by fissions of adjacent crypts, which are equivalent to ~4% of the total intestinal
crypts per ten years or only ~0.0041 fissions/crypt/year. In UC patients, crypt fission isdramatically accelerated (~0.36 fissions/crypt/year) to amend the large deficits in the
injured epithelium, during which strong positive selection operates to allow for thedominance of clones carrying mutations in the IL-17 signalling pathway, although the exact
mechanism is still unclear. Importance of the IL-17 signalling in the pathogenesis of UC isalso supported by the previous GWAS studies that reported susceptibility associated with
the NFKBIZ and TRAF3IP2 loci. These two loci have also been associated withsusceptibility to psoriasis, a chronic inflammation in the epidermis, where aberrantly
activated IL-17 signalling is implicated on the basis of the critical role of NFKBIZ and theefficacy of anti-IL-17 antibody, suggesting a strong link between UC and psoriasis
pathophysiology.
As is the case with the remodelling in other tissues, like blood, skin, and oesophageal
epithelium, the positive selection also involves mutations commonly affected in CAC, but
overall frequencies of their mutations substantially differed between normal andinflammation-associated expansion, and respective cancers. Again, the critical difference inthe mechanism between positive selection in cancer and non-cancer tissues is highlighted.

50

S. Ogawa

A new finding from the study on UC epithelia would be that the mechanism that allows for
positive selection under chronic inflammation is not necessarily operate in neoplastic
growth; on the contrary, it imposes strong negative selection and thus, reveals new cancer
vulnerability, as evident from extremely rare mutations of NFKBIZ and ZC3H12A in CAC
and sporadic colorectal cancer, despite their pervasive nature in UC non-dysplasia.
Finally, one of the most important implications from our study would be a possibility
that chronic inflammation can evoke a tissue response by way of remodelling affected
tissues with positively selecting clones that acquired mutations, according to the Darwiniansurvival-of-the-fittest principle. Chronic inflammation is a highly prevalent disability
among the human population, potentially affecting almost every organs and tissues, wherea similar mechanism may operate and lead to tissue remodelling depending on the natureof inflammation, which may explain an important part of the disease picture.
Understanding of such responses is of potential importance for the better understanding of
the pathogenesis of inflammatory diseases and for the development of novel therapeutics.
References
1. Kakiuchi, N., K. Yoshida, M. Uchino, T. Kihara, K. Akaki, Y. Inoue, K. Kawada, S.
Nagayama, A. Yokoyama, S. Yamamoto, M. Matsuura, T. Horimatsu, T. Hirano, N.
Goto, Y. Takeuchi, Y. Ochi, Y. Shiozawa, Y. Kogure, Y. Watatani, Y. Fujii, S. K. Kim, A.
Kon, K. Kataoka, T. Yoshizato, M. M. Nakagawa, A. Yoda, Y. Nanya, H. Makishima, Y.
Shiraishi, K. Chiba, H. Tanaka, M. Sanada, E. Sugihara, T. A. Sato, T. Maruyama, H.
Miyoshi, M. M. Taketo, J. Oishi, R. Inagaki, Y. Ueda, S. Okamoto, H. Okajima, Y. Sakai,
T. Sakurai, H. Haga, S. Hirota, H. Ikeuchi, H. Nakase, H. Marusawa, T. Chiba, O.
Takeuchi, S. Miyano, H. Seno, and S. Ogawa. 2020. Frequent mutations that converge
on the NFKBIZ pathway in ulcerative colitis, Nature, 577: 260-65.
2. Yokoyama, A., N. Kakiuchi, T. Yoshizato, Y. Nannya, H. Suzuki, Y. Takeuchi, Y.
Shiozawa, Y. Sato, K. Aoki, S. K. Kim, Y. Fujii, K. Yoshida, K. Kataoka, M. M.
Nakagawa, Y. Inoue, T. Hirano, Y. Shiraishi, K. Chiba, H. Tanaka, M. Sanada, Y.
Nishikawa, Y. Amanuma, S. Ohashi, I. Aoyama, T. Horimatsu, S. Miyamoto, S.
Tsunoda, Y. Sakai, M. Narahara, J. B. Brown, Y. Sato, G. Sawada, K. Mimori, S.
Minamiguchi, H. Haga, H. Seno, S. Miyano, H. Makishima, M. Muto, and S. Ogawa.
2019. Age-related remodelling of oesophageal epithelia by mutated cancer drivers,
Nature, 565: 312-17.

Cancer Evolution

51

Seishi Ogawa, MD, PhD
1989-1993
1983-1988
1988-1989
1994-1995
1995-1996
1996-2002
2002-20132013-present
2017-present

MD, PhD Program, University of Tokyo
Undergraduate/MD Program, School of Medicine,
University of Tokyo
Postgraduate Clinical Training in Internal Medicine
Clinical Associate, University of Tokyo
Research Fellow
Assistant Professor of Medicine, University of Tokyo
Associate Professor, University of Tokyo
Professor, Department of Pathology and Tumor
Biology, Kyoto University
Guest Professor, Department of Molecular Hematology,
Karolinska Institute

52

PATHOLOGY FROM THE MOLECULAR SCALE ON UP

Garry P. Nolan
Rachford and Carlota A Harris Professor
Department of Microbiology & Immunology
Baxter Laboratory for Stem Cell Biology
Stanford University School of Medicine, Stanford, CA 94305, USA(gnolan@stanford.edu)

High parameter single cell analysis has driven deep understanding of immune
processes. Using a next-generation single-cell “mass cytometry” platform we quantify
surface and cytokine or drug responsive indices of kinase target with 45 or more parameter
analyses (e.g. 45 antibodies, viability, nucleic acid content, and relative cell size). Similarly,
we have developed two advanced technologies termed MIBI and CODEX that enable deep
phenotyping of solid tissue in both fresh frozen and FFPE formats (50–100 markers).
Collectively, the systems allows for subcellular analysis from the 70nm resolution scale towhole tissue in 3D.
I will present evidence of deep internal order in immune functionality demonstratingthat differentiation and immune activities have evolved with a definable “shape”. Further,
specific cellular neighborhoods of immune cells are now definable with unique abilities toaffect cellular phenotypes—and these neighborhoods alter in various cancer disease states.
In addition to cancer, these shapes and neighborhoods are altered during immune action
and “imprinted” during, and after, pathogen attack, traumatic injury, or auto-immune
disease. Hierarchies of functionally defined trans-cellular modules are observed that can beused for mechanistic and clinical insights in cancer and immune therapies.

Cancer Evolution

53

Garry P. Nolan, PhD
1983
1989
1990
1993
1993-1999
1995-1999

1999-20092009-present
2011-present

BS, Cornell University, Biology/Genetics
PhD, Stanford University, Genetics
Postdoctoral Fellowship, Stanford University, Genetics
Postdoctoral Fellowship, MIT, David Baltimore
Laboratory, Biochemistry
Assistant Professor, Department of Molecular
Pharmacology, Stanford University School of Medicine
Assistant Professor, Joint Appointment, Department ofMicrobiology and Immunology, Stanford University
School of Medicine
Associate Professor (Tenure), Department of Molecular
Pharmacology, Stanford University School of Medicine
Professor (Tenure), Department of Microbiology &Immunology, Stanford University School of Medicine
Rachford and Carlota A. Harris Professor, Departmentof Microbiology and Immunology, Stanford University
School of Medicine

54

LONG NON-CODING RNAS AND METASTATICPROGRESION OF COLORECTAL CANCER

Christopher A. Maher
Departments of Internal Medicine and Biomedical Engineering
McDonnell Genome InstituteWashington University School of Medicine
660 South Euclid Avenue, Campus Box 8069, St. Louis, MO 63110, USA(christophermaher@wustl.edu)

Colorectal cancer (CRC) is the most common gastrointestinal malignancy in the UnitedStates. At the time of initial diagnosis, 20% of patients present with metastasis, and of thosepatients with primary disease approximately 50% will eventually develop metastatic
disease. Although early stage colorectal cancer (CRC) is curable with surgery and adjuvant
therapy, metastatic CRC (mCRC) is usually lethal. Further, despite advances in our
understanding of primary CRC oncogenesis, the mechanisms of CRC metastasis andsubsequent patient death is poorly characterized. To date, CRC research has primarily
focused on the deregulation of protein-coding genes to identify oncogenes and tumor
suppressors as potential diagnostic and therapeutic targets. Therefore, the characterizationof long non-coding RNAs, elucidating their function, and assessing their clinicalapplicability could significantly impact mCRC diagnosis and treatment. Whiletranscriptome sequencing has provided an unbiased method for discovering lncRNAs,
existing large-scale sequencing projects such as The Cancer Genome Atlas Network(TCGA) are comprised of predominantly primary tumors lacking matched metastatic
samples. This represents a critical barrier to discovering novel lncRNAs throughout the
progression of primary to metastatic disease correlated to treatment response andresistance. To address this, we performed transcriptome sequencing and analysis ofnormal, primary, and distant metastatic CRC tissues and discovered 148 differentiallyexpressed RNAs Associated with Metastasis (RAMS). We prioritized a novel lncRNA,
RAMS11, due to its association with poor disease-free survival across independent patient
cohorts. To understand RAMS11 functional significance, we created a RAMS11 knockout
model by generating two CRISPR/Cas9 luciferase-tagged cell lines with a genomic deletionNoncoding RNA

55

of the last four exons of RAMS11 in the LoVo metastatic colon cancer cells. We confirmed
greater than a 99.9% reduction in our RAMS11 CRISPR knockout (KO) models relative towild type cells. We used these genetically engineered cell lines to confirm that the loss of
RAMS11 decreases cellular invasion/migration and anchorage independent growth.
Conversely, we overexpressed RAMS11 in HT29 cell (which lack endogenous RAMS11
expression) and observed an increase in cellular invasion/migration and anchorage
independent growth. Building upon our in vitro results, using our genetically engineered
cell lines we found that loss of RAMS11 reduces primary tumor growth and metastasis via
three mouse models: (1) a subcutaneous model to study tumor growth and metastases, (2) atail vein injection model to study the development of lung metastases, and (3) a hemisplenectomy model to study the development of liver metastases. To understand its role intreatment response, and implicate RAMS11 with specific signaling pathways, we
performed a high-throughput viability assay using FDA-approved drugs to reveal thatelevated RAMS11 expression increased resistance to topoisomerase inhibitors. We
subsequently confirmed that manipulating RAMS11 expression altered TOP2α mRNAlevels, protein levels, and downstream target gene expression. Due to the nuclear
localization of RAMS11, we hypothesized that it may interact with epigenetic regulators totranscriptionally activate TOP2 α expression. We found that RAMS11 interacts with
Chromobox protein 4 (CBX4), which possesses both activation and repressive activities, hasbeen found to interact with lncRNAs, and is known to regulate TOP2 α expression.
Subsequent experiments demonstrated RAMS11-dependent recruitment of Chromobox
protein 4 (CBX4) to transcriptionally activate Topoisomerase II alpha (TOP2α). A metaanalysis of ~7,000 patients across 22 cancer types revealed RAMS11 is overexpressed andpromotes oncogenic phenotypes in multiple cancer types, thereby broadening the impact ofour findings. Overall, our understanding of how lncRNAs promote metastasis in CRC
patients may have tremendous biological and clinical significance. To address this, ourstudy used patient samples to characterize the landscape of lncRNA expression throughout
the progression of primary to metastatic colorectal cancer. We also show that the novel
lncRNA RAMS11 directly affects mCRC biology, including promoting aggressive
phenotypes and correlating with treatment response and resistance.

56

C. A. Maher

Christopher A. Maher, PhD
1998-2002
2002-2006

2007-20092009-20112011-20182011-present
2018-present

Graduated with BS from Cornell University,
Biomedical Engineering
Graduated with PhD from shared program between
Stony Brook University and Cold Spring Harbor
Laboratory
Postdoctoral researcher, University of Michigan
Research Instructor, University of Michigan
Assistant Professor, Departments of Internal Medicine
and Biomedical Engineering, Washington University
School of Medicine
Assistant Director, McDonnell Genome Institute
Associate Professor, Departments of Internal Medicine
and Biomedical Engineering, Washington University
School of Medicine

57

ADVANCES IN DIAGNOSTIC AND THERAPEUTICAPPLICATION OF EXTRACELLULAR VESICLES IN CANCER

Takahiro Ochiya
Department of Molecular and Cellular Medicine
Tokyo Medical University
6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo 160-0023, Japan(tochiya@tokyo-med.ac.jp)

Growing evidence indicates that cancer cell-derived extracellular vesicles (EVs) serve as
regulatory agents in intercellular communications of the tumor microenvironment.
Whether and how this knowledge of EVs, known as small and large exosomes,
microvesicles, and oncosomes affect translational research and clinical practices havebecome pertinent questions.
EVs have been shown to be involved in the development of cancer metastasis [1] via
carrying pathogenic components such as proteins, messenger RNAs (mRNAs), microRNAs(miRNAs), DNAs, lipids, and transcriptional factors that can mediate paracrine signaling in
the tumor microenvironment [2, 3] (Figure 1). Moreover, these specific encapsulated
molecules in EVs derived from high-metastatic cancer cells enable the creation of theappropriate tumor microenvironment for cancer metastasis. Understanding the metastatic!"#$%&,%-2(21%',()%$*+$,1"&9%&(
!6>

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4"&35,6%7%$82#!"#$%&'()%$*+$%-6/0,:$;<,$7,6/0(=+,"*"$)'-+.*/!"#$%&'()%$*+$,%-./0,,

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1./0(,)*./0(,
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figure 1

Summary of Extracellular Vesicles for Biomarker Analysis Attached file (PDF)58

T. Ochiya

mechanisms through EV transfer may open up a new avenue for cancer diagnosis andtherapeutic strategies [4].
Our current understanding of cancer-EVs functions, cancer-EVs destroy our body’s
natural barriers and barrier structures. These examples are EVs-mediated brain metastasisof breast cancer cells [5], peritoneal dissemination of highly metastatic ovarian cancer cells[6] and late recurrence of breast cancer [7]. In case of a metastatic model of diffuse-type
gastric cancer (DGC), we investigated whether a difference in the fibroblast phenotypes
could be observed by comparing between high-metastatic and low-metastatic DGC cells.
Our study revealed that cancer cells with high metastatic capacity can generate at least two
distinct fibroblast subpopulations: α-SMA-expressing type and chemokine-expressing type[8]. EVs contribute to the formation of a specific subpopulation with chemokine expression
for contributing tumor metastasis.
Furthermore, we have demonstrated that RNA transcripts derived from an active
human L1 retrotransposon are packaged in EVs and can initiate retrotransposition inrecipient cells [9, 10]. Functional RNA intermediates are delivered to recipient cells, which
are translated into the encoded proteins, allowing the reverse transcription and genomic
integration of EGFP assisted by L1-encoded endonuclease and reverse transcriptase.
Characterization of recipient cells by PCR amplification and sequencing analysis confirmed
the precise splicing of the intron and the integration of the EGFP gene byretrotransposition. This study demonstrates that an active L1 retrotransposon can betransmitted to neighboring cells without direct cell-to-cell contact mediated by RNA
intermediates secreted by the cell. Additionally, we show that RNA transfer may influence
recipient transcriptional and post-transcriptional regulation. We demonstrate that L1derived RNA transcripts and translated proteins are targeted by intrinsic host factors suchas APOBEC3 family members, potentially restricting L1 activity to ensure genome stability
in the cell. Although further experiments are required to assess the biological consequencesof de novo L1 insertions in recipient cells, this study provides evidence for the horizontaltransmission of an active L1 retrotransposon mediated by EVs in cultured cells.
The tumor specific methylation of nucleic acids including tissue-specific or circulating
DNAs and microRNAs provide various messages on the physiological and pathologicalstatus of cancer patients [11]. The “Liquid Methylome” based on our ExoScreen [12] is amethod for detecting circulating EVs-based DNA methylation in the patients’ body fluids
and allows us for developing a novel liquid biopsy for early detection of cancer. We also
have several experiences on circulating microRNAs for early detection of cancer [13-16],thus EV is a promising agent for a liquid biopsy.
Currently, histopathological examination by specialists is the only method available forthe accurate diagnosis of early detection of tumors, and no biomarkers have been identified

Noncoding RNA

59

to date. To achieve early and accurate diagnosis of cancers, it will be necessary to develop
rapid, noninvasive, and straightforward diagnostic methods. The ideal biomarkers for thispurpose should have the capacity to detect cancers regardless of their histological subtype.
To this end, it is necessary to develop a biomarker identification strategy based on a novel
principle. Currently, circulating non-coding RNAs, including microRNAs and long noncoding RNAs, and the protein components of extracellular vesicles are promisingbiomarkers for the non-invasive detection of cancer at an early stage. The increasing
number of well-designed cancer biomarker-related studies that have been publishedworldwide. In many of these studies, high diagnostic accuracy, which is represented as thearea under the receiver operating characteristic curve being >0.8, could be achieved using
combinations of circulating microRNAs. In addition, similar diagnostic accuracies werereported using long non-coding RNAs or proteins present in extracellular vesicles,
although these evidences were based on a limited number of studies. In the present studyof our japan national project supported by AMED, we examined the expression profiles ofserum miRNA that were recently updated in miRBase (release 21) in several very large
cohorts that included more than 50,000 cancer patients and 20,000 controls to identify
circulating miRNA that can detect 13 species of early stage cancer. Our most current
updated result showed the circulating miRNA profiles can be indeed a powerful tool forblood based cancer diagnosis.
Finally, we currently developed a new retrograde virus-vector-based genetic approachto manipulate local autonomic nerves in a tumor-specific and sympathetic orparasympathetic fiber-type-specific manner [17]. A retrospective analysis of breast cancer
specimens revealed that increased sympathetic and decreased parasympathetic nerve
density in tumors were associated with poor clinical outcomes and correlated with higher
expression of immune checkpoint molecules. The EVs-mediated communication ofautonomic nervous system and cancer microenvironment is a novel target for controlling
cancer metastasis.
References
1. Kosaka, N., Iguchi, H., Yoshioka, Y., Takeshita, F., Matsuki, Y., *Ochiya, T.: Secretorymechanisms and intercellular transfer of microRNAs in living cells. J. Biol. Chem., 285:
17442-17452, 2010.
2. Kosaka N, Iguchi H, Hagiwara K, Yoshioka Y, Takeshita F, *Ochiya T. Neutral sphingomyelinase 2 (nSMase2)-dependent exosomal transfer of angiogenic microRNAs regulate cancer cell metastasis. J Biol Chem. 288(15):10849-10859, 2013.

60

T. Ochiya

3. Ageta H, Ageta-Ishihara N, Hitachi K, Karayel O, Onouchi T, Yamaguchi H, Kahyo T,
Hatanaka K, Ikegami K, Yoshioka Y, Nakamura K, Kosaka N, Nakatani M, Uezumi A,
Ide T, Tsutsumi Y, Sugimura H, Kinoshita M, *Ochiya T, Mann M, Setou M, Tsuchida
K. UBL3 modification influences protein sorting to small extracellular vesicles. Nat
Commun. 9(1):3936, 2018
4. Kosaka N, Fujita Y, Yoshioka Y, *Ochiya T. Versatile roles of extracellular vesicles incancer. J Clin Invest, 126(4):1163-1172, 2016
5. Tominaga N, Kosaka N, Ono M, Katsuda T, Yoshioka Y, Tamura K, Lötvall J,
Nakagama H, *Ochiya T. Brain metastatic cancer cells release microRNA-181ccontaining extracellular vesicles capable of destructing blood-brain barrier. Nat
Commun, 6:6716, 2015
6. Yokoi A, Yoshioka Y, Yamamoto Y, Ishikawa M, Ikeda SI, Kato T, Kiyono T, Takeshita
F, Kajiyama H, Kikkawa F, *Ochiya T. Malignant extracellular vesicles carrying MMP1mRNA facilitate peritoneal dissemination in ovarian cancer. Nature Commun, 8, 14470,
2017
7. Ono M, Kosaka N, Tominaga N, Yoshioka Y, Takeshita F, Takahashi RU, Yoshida M,
Tsuda H, Tamura K, *Ochiya T. Exosomes from bone marrow mesenchymal stem cells
contain a microRNA that promotes dormancy in metastatic breast cancer cells. Sci
Signal, 7:ra63, 2014 (2015 Signaling Breakthrough of the Year)
8. Naito Y, Yamamoto Y, Sakamoto N, Shimomura I, Kogure A, Kumazaki M, Yokoi A,
Yashiro M, Kiyono T, Yanagihara K, Takahashi RU, Hirakawa K, Yasui W, *Ochiya T.
Cancer extracellular vesicles contribute to stromal heterogeneity by inducing
chemokines in cancer-associated fibroblasts. Oncogene. 38(28): 5566-5579, 20199. Kawamura Y, Yamamoto Y, Sato TA, *Ochiya T. Extracellular vesicles as trans-genomic
agents: Emerging roles in disease and evolution. Cancer Sci. 108: 824-830, 2017
10. Kawamura Y, Sanchez Calle A, Yamamoto Y, Sato TA, *Ochiya T. Extracellular vesicles
mediate the horizontal transfer of an active LINE-1 retrotransposon. J Extracell Vesicles.
8(1):1643214, 2019
11. Konno M, Koseki J, Asai A, Yamagata A, Shimamura T, Motooka D, Okuzaki D,
Kawamoto K, Mizushima T, Eguchi H, Takiguchi S, Satoh T, Mimori K, *Ochiya T,
Doki Y, Ofusa K, Mori M, Ishii H. Distinct methylation levels of mature microRNAs in
gastrointestinal cancers. Nat Commun. 10(1):3888, 201912. Yoshioka Y, Kosaka N, Konishi Y, Ohta H, Okamoto H, Sonoda H, Nonaka R,
Yamamoto H, Ishii H, Mori M, Furuta K, Nakajima T, Hayashi H, Sugisaki H,
Higashimoto H, Kato T, Takeshita F, *Ochiya T. Ultra-sensitive liquid biopsy of circulating extracellular vesicles using ExoScreen. Nat Commun, 5:3591, 201413. Sudo K, Kato K, Matsuzaki J, Boku N, Abe S, Saito Y, Daiko H, Takizawa S, Aoki Y,
Sakamoto H, Niida S, Takeshita F, Fukuda T, *Ochiya T. Development and Validation

Noncoding RNA

61

of an Esophageal Squamous Cell Carcinoma Detection Model by Large-Scale
MicroRNA Profiling. JAMA Netw Open. 2(5):e194573, 201914. Urabe F, Matsuzaki J, Yamamoto Y, Kimura T, Hara T, Ichikawa M, Takizawa S, Aoki
Y, Niida S, Sakamoto H, Kato K, Egawa S, Fujimoto H, *Ochiya T. Large-scale
Circulating microRNA Profiling for the Liquid Biopsy of Prostate Cancer. Clin Cancer
Res. 2019 25(10):3016-3025, 2019
15. Asano N, Matsuzaki J, Ichikawa M, Kawauchi J, Takizawa S, Aoki Y, Sakamoto H,
Yoshida A, Kobayashi E, Tanzawa Y, Nakayama R, Morioka H, Matsumoto M,
Nakamura M, Kondo T, Kato K, Tsuchiya N, Kawai A, *Ochiya T. A serum microRNA
classifier for the diagnosis of sarcomas of various histological subtypes. Nat Commun.
10(1):1299, 201916. Yokoi A, Matsuzaki J, Yamamoto Y, Yoneoka Y, Takahashi K, Shimizu H, Uehara T,
Ishikawa M, Ikeda SI, Sonoda T, Kawauchi J, Takizawa S, Aoki Y, Niida S, Sakamoto H,
Kato K, Kato T, *Ochiya T. Integrated extracellular microRNA profiling for ovariancancer screening. Nat Commun. 29(1):4319, 2018
17. Kamiya A, Hayama Y, Kato S, Shimomura A, Shimomura T, Irie K, Kaneko R,
Yanagawa Y, Kobayashi K, *Ochiya T. Genetic manipulation of autonomic nerve fiber
innervation and activity and its effect on breast cancer progression. Nat Neurosci.
22(8):1289-1305, 2019

Takahiro Ochiya, PhD
1988
1988-1992
1991-1992
1992
1992-1998
1998-20102010-20182018-present

PhD, Osaka University
Research Assistant, Osaka University
Post-doctoral Fellow, La Jolla Cancer Research
Foundation (The SF Burnham Institute)
Chief Researcher, Genetics Div., National Cancer
Center Research Institute (NCCRI), Japan
Section Head, Genetics Div., NCCRI
Head, Section for Studies on Metastasis, NCCRI
Chief, Division of Molecular and Cellular Medicine,
NCCRI
Department of Molecular and Cellular Medicine, Tokyo
Medical University

62

LncRNAs: LOST IN TRANSLATION

Eleonora Leucci
Laboratory for RNA Cancer Biology
Department of Oncology, LKI
KU Leuven, Campus Gasthuisberg
Herestraat 49, O&N1 bus 818, 3000 Leuven, Belgium(eleonora.leucci@vib-kuleuven.be)

It is now widely accepted that although the eukaryotic genome is pervasivelytranscribed, less than 2% encodes for proteins [1]. The non-coding genome, hosts the vast
majority of recurrent somatic mutations [2], copy number alterations [3] and cancer-related
SNPs [4]. The so called “dark matter” of the genome gives rise to a broad spectrum oftranscripts including a large class of non-coding RNA longer than 200 nucleotides [5], thelncRNAs. Interestingly, while a minority are broadly expressed and evolutionary
conserved [6], the vast majority of them are primate-specific and represent cancer-specificvulnerabilities [5, 7]. The lack of conservation and consequently of appropriate animalmodels, has hampered the recognition of lncRNAs as key players in cancer. PatientDerived Xenografts or PDXs, where a human tumour is implanted in an
immunocompromised mouse, are increasingly employed as preclinical models [8], becauseof their ability to recapitulate the therapeutic responses and preserve the morphologicaland genetic heterogeneity of the original patients. They are therefore emerging aspreferential tools to explore the role of primate-specific lncRNAs during cancer progression
and therapy responses.
Although recent advances in targeted therapies and immune checkpoint blockade have
improve overall survival, cancer remains the second leading cause of death worldwide.
Therapy failure is mainly caused by intrinsic or acquired resistance to most anticancer
drugs. While most of the work in the field has focused on somatic mutations, emerging
evidence indicates that therapy exposure reprograms cancer cells into reversible drugtolerant states [9, 10]. In fact, as all the evolutionary processes, also cancer progression
strongly rely on plasticity to increase heterogeneity and face the environmental insults.
Occurring in a contest of global transcriptional repression, reversible reprogramming often

Noncoding RNA

63

involves the activation of adaptive responses through epigenetic and post-transcriptional
events. Although these responses evolved to restore homeostasis following environmental
stress, cancer cells hijack them to survive in unfavorable conditions [11]. LncRNA are
participating to the regulation of all the epigenetic and post-transcriptional events in thecell and therefore they are key components of the adaptive response pathways [11, 12], assuch they are emerging as modulators of therapy responses [13].
Being at the crossroad of several key metabolic processes, mitochondria are essential forthe integration of different adaptive responses. Accumulating evidence suggest that they
could play a major role in the development of resistance to targeted and immunotherapy[10, 13, 14]. Despite the fact that the metabolic profile of cancer cells varies across patients,
tumour types and cells within a tumour, most (if not all) of the cell states within a tumour
depend on mitochondria for their plasticity and survival [16]. This is not surprisingconsidering that they are not only the major source of ATP in the cell, but also essential for
membrane and nucleotide biosynthesis. Since the mitochondrial genome encodes only 13
subunits of the oxidative phosphorylation (OXPHOS) machinery, a fully functional
OXPHOS chain requires, proteins translated both by the mitochondrial and the cellularmachinery thus offering a unique opportunity to regulate cellular activities in response toenvironmental cues [12, 17]. The crosstalk between the cytosol and mitochondria duringribosome biogenesis and translational is essential for sensing and integrating different
stress responses [12, 17]. Emerging evidence indicates that several classes of ncRNAs including lncRNAs- impact indirectly and/or directly on mitochondrial biology [12,17]. For instance, we have recently demonstrated that the lncRNA SAMMSON is a key
guardian of proteostasis in melanoma cells, in which it rewires the compartmentalization of
specific RNA binding proteins to coordinately increase translation in the mitochondria andcytosol (Figure 1) [17]. This proof-of-concept study has paved the way to the identificationand characterization of other lncRNAs implicated in adaptive stress response andultimately may lead to the development of novel cancer-specific therapeutic tools that will
improve responses and overcome therapy resistance.

figure 1

SAMMSON rewire the RNA binding network to promote translation and melanoma
growth64

E. Leucci

References
1. The FANTOM Consortium. The Transcriptional Landscape of the Mammalian Genome.
Science 309, 1559–1563 (2005).
2. Melton, C. Reuter JA2, Spacek DV2, Snyder M2. Recurrent somatic mutations inregulatory regions of human cancer genomes. Nat Genet. 2015 Jul;47(7):710-6. doi:
10.1038/ng.3332. Epub 2015 Jun 8.
3. Beroukhim, R. Mermel CH, Porter D, Wei G, Raychaudhuri S, Donovan J, Barretina
J, Boehm JS, Dobson J, Urashima M, Mc Henry KT, Pinchback RM, Ligon AH, Cho
YJ, Haery L, Greulich H, Reich M, Winckler W, Lawrence MS, Weir BA, Tanaka
KE, Chiang DY, Bass AJ, Loo A, Hoffman C, Prensner J, Liefeld T, Gao Q, Yecies
D, Signoretti S, Maher E, Kaye FJ, Sasaki H, Tepper JE, Fletcher JA, Tabernero J, Baselga
J, Tsao MS, Demichelis F, Rubin MA, Janne PA, Daly MJ, Nucera C, Levine RL, Ebert
BL, Gabriel S, Rustgi AK, Antonescu CR, Ladanyi M, Letai A, Garraway LA, Loda
M, Beer DG, True LD, Okamoto A, Pomeroy SL, Singer S, Golub TR, Lander ES, Getz
G, Sellers WR, Meyerson M. The landscape of somatic copy-number alteration acrosshuman cancers. Nature. 2010 Feb 18;463(7283):899-905. doi: 10.1038/nature08822.
4. Cheetham, S.W. Gruhl F, Mattick JS, Dinger ME. Long noncoding RNAs and thegenetics of cancer. Br J Cancer. 2013 Jun 25;108(12):2419-25. doi: 10.1038/bjc.2013.233.
Epub 2013 May 9.
5. Leucci, E. Cancer development and therapy resistance: spotlights on the dark side of the
genome. Pharmacol. Ther. 189, 22–30 (2018).
6. Adriaens, C.*, Standaert, L.*, Barra J, Latil M, Verfaillie A, Kalev P, Boeckx B,
Wijnhoven PW, Radaelli E, Vermi W, Leucci E, Lapouge G, Beck B, van den Oord J,
Nakagawa S, Hirose T, Sablina AA, Lambrechts D, Aerts S, Blanpain C, Marine JC. p53
induces formation of NEAT1 lncRNA-containing paraspeckles that modulate
replication stress response and chemosensitivity. Nat Med. 2016 Aug;22(8):861-8. doi:
10.1038/nm.4135. Epub 2016 Jul 4.
7. Leucci, E. Vendramin R, Spinazzi M, Laurette P, Fiers M, Wouters J, Radaelli E,
Eyckerman S, Leonelli C, Vanderheyden K, Rogiers A, Hermans E, Baatsen P, Aerts S,
Amant F, Van Aelst S, van den Oord J, de Strooper B, Davidson I, Lafontaine DL,
Gevaert K, Vandesompele J, Mestdagh P, Marine JC. Melanoma addiction to the longnon-coding RNA SAMMSON. Nature 531, 518–522 (2016).
8. Byrne A Alférez DG, Amant F, Annibali D, Arribas J, Biankin AV, Bruna A, Budinská E,
Caldas C, Chang DK, Clarke RB, Clevers H, Coukos G, Dangles-Marie V, Eckhardt SG,
Gonzalez-Suarez E, Hermans E, Hidalgo M, Jarzabek MA, de Jong S, Jonkers J, Kemper
K, Lanfrancone L, Mælandsmo GM, Marangoni E, Marine JC, Medico E, Norum JH,
Palmer HG, Peeper DS, Pelicci PG, Piris-Gimenez A, Roman-Roman S, Rueda OM,

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65

Seoane J, Serra V, Soucek L, Vanhecke D, Villanueva A, Vinolo E, Bertotti A, Trusolino
L.Interrogating open issues in cancer precision medicine with patient-derived
xenografts. Nat Rev Cancer. 2017 Apr;17(4):254-268. doi: 10.1038/nrc.2016.140. Epub
2017 Jan 20. Review.
9. Rambow F*, Rogiers A*, Marin-Bejar O, Aibar S, Femel J, Dewaele M, Karras P, Brown
D, Chang YH, Debiec-Rychter M, Adriaens C, Radaelli E, Wolter P, Bechter O, Dummer
R, Levesque M, Piris A, Frederick DT, Boland G, Flaherty KT, van den Oord J, Voet T,
Aerts S, Lund AW, Marine JC. Toward Minimal Residual Disease-Directed Therapy in
Melanoma. Cell. 2018 Aug 9;174(4):843-855.e19. doi: 10.1016/j.cell.2018.06.025. Epub
2018 Jul 12.
10. Echeverria GV1, Ge Z1,2, Seth S3,4,5, Zhang X1, Jeter-Jones S1, Zhou X1, Cai S1, Tu Y1, McCoy
A1, Peoples M4,5, Sun Y4,5, Qiu H6, Chang Q4,5, Bristow C4,5, Carugo A4,5, Shao J1, Ma
X4,5, Harris A4,5, Mundi P7, Lau R8, Ramamoorthy V4,5, Wu Y8, Alvarez MJ7,9, Califano
A7, Moulder SL10, Symmans WF8, Marszalek JR4,5, Heffernan TP4,5, Chang JT2,6, PiwnicaWormsH11. Resistance to neoadjuvant chemotherapy in triple-negative breast cancer mediated by a reversible drug-tolerant state. Sci Transl Med. 2019 Apr 17;11(488). pii:
eaav0936. doi: 10.1126/scitranslmed.aav0936.
11. Verheyden Y, Goedert L, Leucci E. Control of nucleolar stress and translational reprogramming by lncRNAs. Cell stress. 2018 Dec 5;3(1):19-26.
12. Vendramin R, Marine JC, Leucci E. Non-coding RNAs: the dark side of nuclear-mitochondrial communication. EMBO J. 2017 May 2;36(9):1123-1133. doi:
10.15252/embj.201695546. Epub 2017 Mar 17. Review.
13. Nath A, Lau EYT, Lee AM, Geeleher P, Cho WCS, Huang RS. Discovering long noncoding RNA predictors of anticancer drug sensitivity beyond protein-coding genes.Proc
Natl Acad Sci U S A. 2019 Oct 29;116(44):22020-22029. doi: 10.1073/pnas.1909998116.
Epub 2019 Sep 23.
14. Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ, Melms JC, Leeson R, Kanodia A,
Mei S, Lin JR, Wang S, Rabasha B, Liu D, Zhang G, Margolais C, Ashenberg O, Ott PA,
Buchbinder EI, Haq R, Hodi FS, Boland GM, Sullivan RJ, Frederick DT, Miao B, Moll T,
Flaherty KT, Herlyn M, Jenkins RW, Thummalapalli R, Kowalczyk MS, Cañadas I,
Schilling B, Cartwright ANR, Luoma AM, Malu S, Hwu P, Bernatchez C, Forget MA,
Barbie DA, Shalek AK, Tirosh I, Sorger PK, Wucherpfennig K, Van Allen EM,
Schadendorf D, Johnson BE, Rotem A, Rozenblatt-Rosen O, Garraway LA, Yoon CH,
Izar B, Regev A. A Cancer Cell Program Promotes T Cell Exclusion and Resistance to
Checkpoint Blockade. Cell. 2018 Nov 1;175(4):984-997.e24. doi: 10.1016/j.cell.2018.
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15. Poźniak J, Nsengimana J, Laye JP, O'Shea SJ, Diaz JMS, Droop AP, Filia A, Harland M,
Davies JR, Mell T, Randerson-Moor JA, Muralidhar S, Hogan SA, Freiberger SN,
Levesque MP, Cook GP, Bishop DT, Newton-Bishop J. Genetic and Environmental
Determinants of Immune Response to Cutaneous Melanoma. Cancer Res. 2019 May
15;79(10):2684-2696. doi: 10.1158/0008-5472.CAN-18-2864. Epub 2019 Feb 17.
16. Chen H1, Chan DC2. Mitochondrial Dynamics in Regulating the Unique Phenotypes ofCancer and Stem Cells. Cell Metab. 2017 Jul 5;26(1):39-48. doi:10.1016/j.cmet.2017.05.
016. Epub 2017 Jun 22.
17. Vendramin R, Verheyden Y, Ishikawa H, Goedert L, Nicolas E, Saraf K, Armaos A,
Delli Ponti R, Izumikawa K, Mestdagh P, Lafontaine DLJ, Tartaglia GG, Takahashi N,
Marine JC, Leucci E. SAMMSON fosters cancer cell fitness by enhancing concertedly
mitochondrial and cytosolic translation. NSMB, 2018 Nov;25(11):1035-1046.

Eleonora Leucci, PhD
2003-2007
2008-20122012-20162016-20172017-present

PhD in Medical Biotechnology, Dept. of Pathology,
University of Siena
Postdoctoral Fellow, BRIC, University of Copenhagen
Postdoctoral Fellow, VIB-KU Leuven
Research Associate, ULB
Assistant Professor and Head of Trace, KU Leuven, LKI

67

PI 3-KINASE AND CANCER METABOLISM

Lewis C. Cantley
Meyer Cancer Center, Weill Cornell Medicine
New York Presbyterian Hospital
Belfer Research Building, 413 East 69th Street, New York, NY 10065, USA(lcantley@med.cornell.edu)

Phosphoinositide 3-Kinase isoform alpha (PI3Kα, encoded by the PIK3CA gene) is
activated by insulin and other growth factors to mediate cell growth during development
and regulate whole body glucose homeostasis both during development and in adults.
Although multiple PI3K genes exist in humans and other mammals and the enzymes
generated from these genes can respond to many types of growth factors and cytokines, theprimordial PI3Kα enzymes in lower organisms (flies and worms) are in a single defined
genetic pathway downstream of the insulin/IGF1 receptor. Consistent with PI3Kα
evolving to mediate insulin/IGF1 signaling, these two growth factors are the most effective
way to activate PI3Kα in most tissues and cancer cell lines that have been investigated.
The PIK3CA gene is one of the most frequently mutated oncogenes in human cancer,
especially in endometrial, breast, head and neck and bladder cancers. Several of the hot
spot mutations have been shown to enhance and/or prolong responses to insulin/IGF1 incell lines and in mouse tissues. More than 20 PI3K inhibitors have entered clinical trials fortreating various cancers and drugs targeting PI3Kd that drives growth of B cells (encodedby PIK3CD) have been approved for B cell lymphomas. However, with the exception of
alpelisib (Piqray), a PI3Kα inhibitor recently approved (in combination with the antiestrogen receptor drug Fulvestrant) for PIK3CA mutant ER positive, HER2 negative breast
cancer, most drugs targeting PI3Kα have failed in clinical trials due to toxicity or
insufficient efficacy. The major toxicity observed for drugs targeting PI3Kα is an acuteincrease in blood glucose levels immediately following drug dosing. This is an on-target
effect of turning off wild type PI3Kα in muscle and liver and a consequent release of
glucose from these tissues into the blood. This elevation in blood glucose causes a dramatic

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L. C. Cantley

elevation in blood insulin levels, that ultimately return blood glucose levels back to normal
over the subsequent 3 hours in most patience due to re-activating PI3Kα in liver andmuscle, despite the continued presence of the PI3Kα inhibitor.
Many patients with PIK3CA mutations had no benefit in the alpelisib approval trial,and some patients without mutations showed durable responses in phase 2 studies withthis drug. In addition, it has been difficult to identify mechanisms of resistance in thevarious clinical studies that have been published. In contrast to other targeted therapies
such as EGFR mutant tumors or BCR-ABL mutant tumors or BRAF mutant tumors, where
second mutations in the mutant allele provide resistance to the drugs, resistant mutations
in PIK3CA in tumors that acquire resistance to PI3K inhibitors have been difficult to find.
These results led us to speculate that variable responses to PI3Kα inhibitors amongst
patients with similar mutations and relatively short periods of response might be due tovariability in the patients’ diets, or due to variability in insulin sensitivity and or variability
in therapies used to manage hyperglycemia (several of which act by elevating serum
insulin.
Using mouse models of a variety of cancers, we explored the possibility that theelevation in blood insulin following PI3Kα inhibitor therapy might reactivate the mutant
PI3Kα in tumors and thereby compromise the effectiveness of these drugs. We found thatthe concentrations of insulin observed in human blood following PI3Kα inhibitor therapy
was high enough to restore PI3K signaling in ex vivo studies of human organoids of several
cancers, despite the presence of a therapeutic dose of various PI3Kα inhibitors. We
explored several approaches to lower blood insulin levels during PI3Kα inhibitor therapy
and found that the most effective approach was to place the mice on ketogenic diets (8%carbohydrate, 12% protein and 80% fat) that effectively blunted the elevation of glucose
following drug administration. This approach not only prevented reactivation of PI3Kα inthe tumors, but also caused dramatic tumor shrinkage in multiple mouse models of cancer,
including tumors that lacked PIK3CA mutations.
We also explored the ability of sodium-glucose co-transporter type 2 (SGLT2) inhibitors
to lower serum glucose and serum insulin during PI3Kalpha inhibitor therapy. These
drugs have been approved for treating insulin resistance and type 2 diabetes and do so by
blocking glucose reabsorption into the blood following ultrafiltration in the kidney. We
found that these inhibitors were more effective than the anti-diabetes drug metformin in
maintaining low glucose and insulin during PI3Kalpha inhibitory therapy and made thesedrugs more effective in shrinking tumors in mouse models, though they were not as
effective as the ketogenic diet.
On the basis of these pre-clinical studies, we are currently collaborating with multiple
pharmaceutical companies to execute clinical trials in which PI3Kα inhibitors are combined

Metabolic Targets in Cancer

69

with either a ketogenic diet or with an SGLT2 inhibitor to provide more effective anddurable responses in patients with PIK3CA mutations. As a minimum we expect that morepatients will be able to remain on therapy since hyperglycemia (an on-target toxicity) is the
major toxicity observed with PI3Kα inhibitors.
Over the past several decades it has become apparent that increased consumption ofsugars, especially in the form of sugary drinks, are driving increases in obesity andconsequent increases in insulin resistance and type 2 diabetes. Importantly this increase insugar consumption also correlates with increases in subsets of cancers, especiallyendometrial cancer, colorectal cancers and some types of breast cancers. A variety of
explanations for this correlation have been proposed, including increased inflammation in
the affected tissue, changes in the microbiome and elevations in insulin and IGF1. The
studies described above suggest that elevations in insulin and IGF1 in insulin-resistantpatients are likely to be causative of some of these cancers due to the ability of these growth
factors to stimulate PI3Kα.
We explored the possibility that consumption of sugary drinks could explain theincrease in colorectal cancers in young adults that have been observed in the last few
decades. We explored this correlation in mice genetically engineered to delete the APC
gene in distal small intestine or in the colon. In both models we found that a relatively
small bolus of high fructose corn syrup (60/40 fructose/glucose mixture) delivered orally
in water (proportionally comparable to a single 12 ounce sugary drink per day in humans)
was enough to double the size of polyps and dramatically increase the fraction of invasive
polyps in both of the genetic models. This quantity of sugary drink did not cause anincrease in weight or induce insulin resistance (though ad lib feeding on high fructose cornsyrup in water caused a major increase in obesity and insulin resistance and polyp growth).
This result showed that drinking moderate amounts of high fructose corn syrup drinks cancause increased polyp growth independent of obesity or increased serum insulin.
Interestingly, the polyps required both sugars simultaneously for accelerated growth.
Doubling the quantity of either sugar alone did not accelerate polyp growth compared to a
60/40 mixture of fructose + glucose or a 50/50 mixture of the two monosaccharides.
We utilized isotope tracing with 14C-labeled fructose, 13C-labeled fructose, 14C-labeled
glucose, or 13C-labeled glucose to determine how the polyps metabolized these two sugars.
Surprisingly, flux of glucose carbons into fatty acid production in the polyps was
dramatically accelerated when fructose was present. In contrast, very little fructose carbon
was incorporate into fatty acids in the polyps. The fructose was converted to fructose-1phosphate by ketohexokinase (fructokinase) and this species accumulated to very highlevels and correlated with a sharp drop in cellular ATP. The drop in ATP allowed PFK1
(known to be inhibited by cytosolic ATP) to become more active, explaining the70

L. C. Cantley

acceleration of glucose flux through glycolysis. Finally, we showed that deleting the
ketohexokinase gene in mice eliminated the ability of high fructose corn syrup to drive
polyp growth. This result shows that drinking even modest amounts of drinks containing
a combination of glucose and fructose (sodas or fruit juice) can drive polyp growth inpatients independent of obesity or insulin resistance.
In summary, our studies argue that limiting consumption of sugars is likely to bebeneficial in both the prevention and treatment of cancers.
References and Background Reading
1. Phase II trial of AKT inhibitor MK-2206 in patients with advanced breast cancer who
have tumors with PIK3CA or AKT mutations, and/or PTEN loss/PTEN mutation. Xing
Y, Lin NU, Maurer MA, Chen H, Mahvash A, Sahin A, Akcakanat A, Li Y, Abramson
V, Litton J, Chavez-MacGregor M, Valero V, Piha-Paul SA, Hong D, Do KA, Tarco E,
Riall D, Eterovic AK, Wulf GM, Cantley LC, Mills GB, Doyle LA, Winer E, Hortobagyi
GN, Gonzalez-Angulo AM, Meric-Bernstam F. Breast Cancer Res. 2019 Jul 5;21(1):78.
doi: 10.1186/s13058-019-1154-8. PMID: 31277699
2. High-fructose corn syrup enhances intestinal tumor growth in mice. Goncalves MD, Lu
C, Tutnauer J, Hartman TE, Hwang SK, Murphy CJ, Pauli C, Morris R, Taylor S, BoschK, Yang S, Wang Y, Van Riper J, Lekaye HC, Roper J, Kim Y, Chen Q, Gross SS, Rhee
KY, Cantley LC, Yun J. Science. 2019 Mar 22;363(6433):1345-1349. doi: 10.1126/
science.aat8515. PMID: 30898933
3. Treating cancer with phosphatidylinositol-3-kinase inhibitors: increasing efficacy andovercoming resistance. Paddock MN, Field SJ, Cantley LC. J Lipid Res. 2019
Apr;60(4):747-752. doi: 10.1194/jlr.S092130. Epub 2019 Feb 4. PMID: 30718284
4. Phosphatidylinositol 3-Kinase, Growth Disorders, and Cancer. Goncalves MD, Hopkins
BD, Cantley LC. N Engl J Med. 2018 Nov 22;379(21):2052-2062. doi: 10.1056/
NEJMra1704560. Review. No abstract available. PMID: 30462943
5. Suppression of insulin feedback enhances the efficacy of PI3K inhibitors. Hopkins BD,
Pauli C, Du X, Wang DG, Li X, Wu D, Amadiume SC, Goncalves MD, Hodakoski C,
Lundquist MR, Bareja R, Ma Y, Harris EM, Sboner A, Beltran H, Rubin MA, Mukherjee
S, Cantley LC. Nature. 2018 Aug;560(7719):499-503. doi: 10.1038/s41586-018-0343-4.
Epub 2018 Jul 4. Erratum in: Nature. 2018 Nov;563(7731):E24. doi: 10.1038/s41586-0180506-3. PMID: 30051890
6. A Phase Ib Study of Alpelisib (BYL719), a PI3Kα-Specific Inhibitor, with Letrozole in
ER+/HER2- Metastatic Breast Cancer. Mayer IA, Abramson VG, Formisano L, Balko
JM, Estrada MV, Sanders ME, Juric D, Solit D, Berger MF, Won HH, Li Y, Cantley LC,

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Winer E, Arteaga CL. Clin Cancer Res. 2017 Jan 1;23(1):26-34. doi: 10.1158/10780432.CCR-16-0134. Epub 2016 Apr 28. PMID: 27126994
7. The PI3K Pathway in Human Disease. Fruman DA, Chiu H, Hopkins BD, Bagrodia
S, Cantley LC, Abraham RT. Cell. 2017 Aug 10;170(4):605-635. doi: 10.1016/j.cell.2017.
07.029. Review. PMID: 28802037
8. Phosphorylation of TXNIP by AKT Mediates Acute Influx of Glucose in Response toInsulin. Waldhart AN, Dykstra H, Peck AS, Boguslawski EA, Madaj ZB, Wen J,
Veldkamp K, Hollowell M, Zheng B, Cantley LC, McGraw TE, Wu N. Cell Rep. 2017
Jun 6;19(10):2005-2013. doi: 10.1016/j.celrep.2017.05.041. PMID: 28591573

Lewis C. Cantley, PhD
1978-1981
1981-1985
1985-1992
1992-2003
1992-2012
2003-20122007-20122012-present
2012-present

Assistant Professor, Dept. of Biochemistry andMolecular Biology, Harvard Univ.
Associate Professor, Dept. of Biochemistry andMolecular Biology, Harvard Univ.
Professor of Physiology, Tufts University School of
Medicine
Professor of Cell Biology, Harvard Medical School
Chief, Division of Signal Transduction, Beth Israel
Deaconess Medical Center
Professor of Systems Biology, Harvard Medical School
Director, Beth Israel Deaconess Cancer Center
Professor of Medicine, Weill Cornell Medical College
Director, Sandra and Edward Meyer Cancer Center at
Weill Cornell Medical College/ Ronald P. Stanton
Clinical Cancer Program at NewYork-Presbyterian

72

THE MECHANISMS OF OBESITY-ASSOCIATED LIVERCANCER PROGRESSION

Naoko Ohtani
Department of Pathophysiology
Graduate School of Medicine, Osaka City University
1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan(ohtani.naoko@med.osaka-cu.ac.jp)

Recently, emerging evidences revealed that obesity is associated with the developmentof several types of cancer including hepatocellular carcinoma (HCC). However, the precise
mechanisms underlying obesity-associated cancer have remained unclear. We recently
reported that the dietary obesity accelerated the development of HCC in mice treated with
DMBA, a chemical carcinogen, at neonatal stage, whereas no tumors were developed inmice fed with normal diet. Interestingly, the hepatic stellate cells (HSCs) in tumor
microenvironment exhibited signs of cellular senescence and senescence-associatedsecretory phenotype (SASP), a phenotype that senescent cells secrete a series ofproinflammatory cytokines, chemokines or matrix-remodeling factors which could
contribute to chronic inflammation and tumorigenesis. We found that mice lacking IL-1b,
an upstream regulator of cytokine cascade, showed significantly reduced number of HCC
development and the suppression of SASP factors expression in senescent hepatic stellate
cells in tumor area, suggesting that IL-1b plays an important role in SASP-associatedinflammation and HCC development. Moreover, the enterohepatic circulation of obesityassociated gut microbial metabolite, deoxycholic acid (DCA), provokes DNA damage andcellular senescence and SASP in hepatic stellate cells, thereby creating HCC promoting
microenvironment. In this symposium, I have shown that the reciprocal activation of two
SASP factors, and a novel network between Treg cells and SASP factor producing senescenthepatic stellate cells in suppressing anti-tumor immunity in liver tumor microenvironment.

Metabolic Targets in Cancer

73

References
1. Takahashi A, Loo TM, Okada R, Kamachi F, Watanabe Y, Wakita M, Watanabe S,
Kawamoto S, Miyata K, Barber GN, Ohtani N, Hara E. Downregulation of cytoplasmic
DNases is implicated in cytoplasmic DNA accumulation and SASP in senescent cells.
Nat Commun. 2018; 9:1249. doi: 10.1038/s41467-018-03555-8
2. Loo TM, Kamachi F, Watanabe Y, Yoshimoto S, Kanda H, Arai Y, Nakajima-Takagi Y,
Iwama A, Koga T, Sugimoto Y, Ozawa T, Nakamura M, Kumagai M, Watashi K, Taketo
MM , Aoki T, Narumiya S, Oshima M, Arita M, Hara E and Ohtani N. Gut microbiota
promotes obesity-associated liver cancer through PGE2-mediated suppression of antitumor immunity. Cancer Discovery 7, 522-538, 2017 doi: 10.1158/2159-8290.CD-16-0932
3. Yoshimoto S, Loo TM, Atarashi K, Kanda H, Sato S, Oyadomari S, Iwakura Y, Oshima
K, Morita H, Hattori M, Honda K, Ishikawa Y, Hara E, Ohtani N. Obesity-induced gut
microbial metabolite promotes liver cancer through senescence secretome. Nature 499,
97-101 (2013)

Naoko Ohtani, MD, PhD
1995-1998
1998-2003
2003-2007
2008-20142014-20172017-present

Assistant Professor, Kyoto Prefectural University of
Medicine
Postdoctoral Fellow, University of Manchester,
Paterson Institute for Cancer Research
Lecturer and Associate Professor, Institute for Genome
Research, University of Tokushima
Senior Staff Scientist, Cancer Institute, JapaneseFoundation for Cancer Research
Professor, Dept. Applied Biological Science, Faculty of
Science and Technology, Tokyo University of Science
Professor, Department of Pathophysiology, Graduate
School of Medicine, Osaka City University

74

METABOLIC PHENOTYPES AND CANCER PROGRESSION

Ralph J. DeBerardinisChildren’s Medical Center Research Institute (CRI)
University of Texas Southwestern Medical Center
5323 Harry Hines Boulevard, Dallas, TX 75390-8502, USA(ralph.deberardinis@utsouthwestern.edu)

Metabolic reprogramming – the regulated alteration of metabolic activities in cancer
cells – is viewed as an essential component of malignancy and a potential source oftherapeutic targets. Classical cancer metabolism studies have focused for the most part oncell-autonomous aspects of reprogramming, often as a consequence of oncogenic
mutations, that confer enhanced cell survival and cell growth in the treatment-naïve state.
However, tumor metabolism responds to a combination of factors either intrinsic (e.g. theoncogenotype) or extrinsic (e.g. the tumor microenvironment) to malignant cells, and thesefactors culminate in marked metabolic heterogeneity of primary tumors within the tissue oforigin (1). Emerging evidence also suggests that metabolic preferences and liabilities evolve
throughout the course of cancer progression. A broad understanding of cancer metabolism
will therefore require approaches to understand how metabolic phenotypes are establishedin disease-relevant environments, and how these phenotypes relate to therapeutic
opportunities.
To develop a clinically-relevant understanding of tumor metabolism, we developed
methods to assess metabolic phenotypes in primary human cancers using intra-operative
infusions with stable isotope-labeled nutrients during surgical resection of solid tumors(Figure 1). During the infusion, the labeled nutrient is taken up by the tumor and adjacenttissue, and the isotope label is distributed to numerous intracellular metabolites as aconsequence of metabolic activity within the tissue. This allows pathway utilization to becompared between tumors and adjacent, nonmalignant tissue; between fragments acquired
from different tumors; and among regions with different biological properties within thesame tumor. The method is well-tolerated and sufficiently versatile to apply to manydifferent types of human cancer and several different metabolic tracers.

Metabolic Targets in Cancer

figure 1

75

Clinical cancer metabolism studies with intra-operative 13C infusions

Our work to this point demonstrates extensive metabolic heterogeneity among tumors
arising at different anatomic sites, and suggests that heterogeneity arises from a
combination of factors extrinsic and intrinsic to malignant cells. For example, tumors in thebrain and lung avidly oxidize fuels like glucose imported from the circulation (2-4). In
contrast, clear cell renal cell carcinomas (ccRCCs) exhibit glycolytic metabolism with
reduced glucose oxidation, similar to the classical description of aerobic glycolysis first
postulated by Warburg in the 1920s (Figure 2). Glycolytic metabolism in ccRCCs is likely
related to the loss of the Von Hippel Lindau (VHL) tumor suppressor in these tumors,
imposing a chronic pseudohypoxic state associated with elevated glycolysis andsuppressed oxidative metabolism in preclinical models (5). Thus, this isotope infusion
approach is sensitive enough to detect heterogeneous metabolic preferences among
different human tumors in vivo. Data from the cancers analyzed so far suggest that asystematic, in vivo assessment of metabolic phenotypes may help predict which tumors aremost likely to respond to therapies aimed against particular pathways.

figure 2

Metabolic heterogeneity of human tumors growing at different sites

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R. J. DeBerardinis

One surprising finding arising from analysis of human non-small cell lung cancer is thata subset of these tumors take up lactate from the circulation and use it as a carbon sourcefor oxidative metabolism (Figure 3) (Faubert et al., 2017). Patients whose tumors had
evidence of lactate import tended to undergo clinical progression (generally to metastatic
cancer) more rapidly than patients whose tumors lacked this metabolic property. This was
striking because the isotope infusions were performed months-to-years earlier than theclinical appearance of metastatic disease. I will discuss efforts to reverse translate thisfinding into mouse models to test whether the ability to import lactate supports metastatic
efficiency.

figure 3

Evidence for lactate oxidation by human non-small cell lung cancers

References
1. Kim, J., and DeBerardinis, R.J. Mechanisms and Implications of Metabolic
Heterogeneity in Cancer. Cell Metabolism 2019; 30:434-446.
2. Hensley, C.T., Faubert, B., Yuan, Q., Lev-Cohain, N., Jin, E., Kim, J., Jiang, L., Ko, B.,
Skelton, R., Loudat, L., Wodzak, M., Klimko, C., McMillan, E., Butt, Y., Ni, M., Oliver,D., Torrealba, J., Malloy, C.R., Kernstine, K., Lenkinski, R.E. and DeBerardinis, R.J.
Metabolic Heterogeneity in Human Lung Tumors. Cell 2016; 164:681-694.
3. Maher, E.A., Marin-Valencia, I., Bachoo, R.M., Mashimo, T., Raisanen, J., Hatanpaa,
K.J., Jindal, A., Jeffrey, F.M., Choi, C., Madden, C., Mathews, D., Pascual, J.M., Mickey,
B.E., Malloy, C.R. and DeBerardinis, R.J. Metabolism of [U-13 C]glucose in human brain
tumors in vivo. NMR Biomed 2012; 25:1234-1244.

Metabolic Targets in Cancer

77

4. Mashimo, T., Pichumani, K., Vemireddy, V., Hatanpaa, K.J., Singh, D.K.,
Sirasanagandla, S., Nannepaga, S., Piccirillo, S.G., Kovacs, Z., Foong, C., Huang, Z.,
Barnett, S., Mickey, B.E., DeBerardinis, R.J., Tu, B.P., Maher, E.A. and Bachoo, R.M..
Acetate is a bioenergetic substrate for human glioblastoma and brain metastases. Cell
2014:159;1603-1614.
5. Courtney, K.D., Bezwada, D., Mashimo, T., Pichumani, K., Vemireddy, V., Funk, A.M.,
Wimberly, J., McNeil, S.S., Kapur, P., Lotan, Y., Margulis, V., Caddedu, J.A., Pedrosa, I.,
DeBerardinis, R.J., Malloy, C.R., Bachoo, R.M. and Maher, E.A. Isotope Tracing of
Human Clear Cell Renal Cell Carcinomas Demonstrates Suppressed Glucose Oxidation
In Vivo. Cell Metabolism 2018; 28:793-800 e792.
6. Faubert, B., Li, K.Y., Cai, L., Hensley, C.T., Kim, J., Zacharias, L.G., Yang, C., Do, Q.N.,
Doucette, S., Burguete, D., Li, H., Huet, G., Yuan, Q., Wigal, T., Butt, Y., Ni, M.,
Torrealba, J., Oliver, D., Lenkinski, R.E., Malloy, C.R., Wachsmann, J.W., Young, J.D.,
Kernstine, K. and DeBerardinis, R.J. Lactate Metabolism in Human Lung Tumors. Cell
2017; 171:358-371 e359.

Ralph J. DeBerardinis, MD, PhD
2008-20132013-20172013-present

2017-present
2017-present
2018-present

Assistant Professor, Department of Pediatrics, UTSouthwestern
Associate Professor, CRI, UT Southwestern
Director, Genetic and Metabolic Disease Program andChief, Pediatric Genetics & Metabolism, UTSouthwestern
Robert L. Moody, Sr. Faculty Scholar
Professor, CRI, UT Southwestern
Investigator, Howard Hughes Medical Institute

78

MECHANISMS OF DRUG RESISTANCE IN LUNG CANCER

Katerina Politi
Departments of Pathology and Medicine (Section of Medical Oncology)
Yale Cancer Center
Yale University School of Medicine
333 Cedar Street, SHM-I 234D, New Haven, CT 06510, USA(katerina.politi@yale.edu)

Targeted therapies have transformed the landscape for the diagnosis and treatment ofmetastatic lung cancer. These tumors are now routinely tested for the presence ofmutations or rearrangements in specific oncogenic drivers that, if present, predict
sensitivity to targeted therapies directed to the genomic alterations present. Genotypedirected therapies have improved outcomes in specific subsets of patients with metastatic
lung cancer. Despite this success, targeted therapies are not curative and acquired
resistance is a major impediment to cures for patients treated with these therapies.
Moreover, there is heterogeneity in the durability and depth of responses between patientsand at different metastatic sites within individual patients.
A paradigm for the success of targeted therapies in lung cancer, comes from Epidermal
Growth Factor Receptor (EGFR) mutant lung cancer. Mutations in exons encoding thetyrosine kinase domain of EGFR confer sensitivity to tyrosine kinase inhibitors (TKIs) [1-3]and several TKIs (e.g. erlotinib, gefitinib, afatinib and, most recently, osimertinib) arecurrently approved for the first-line treatment of EGFR mutant lung cancer. Acquired drug
resistance, however, is a major challenge with all of these TKIs and especially forosimertinib we have very limited knowledge of the mechanisms of resistance given its
recent adoption in the clinic (Figure 1). Without knowledge about resistance mechanisms,
optimal post-osimertinib treatment strategies remain to be defined.
Several years ago, we developed genetically engineered mouse models of EGFR mutant
lung cancer (Figure 2) [4]. In these models, we showed that tumors are dependent on the
EGFR oncogene for their survival and that they regress upon TKI treatment. Moreover,long-term treatment of mice with EGFR-directed therapies led to the development of drugCancer Models (Organoids, PDX, GEM)

3rd Gen. mutant‐specific EGFR TKI:
Osimertinib

Acquired EGFR mutation
MET or HER2 amp
MAPK/PI3K alterations
SCLC transformation
EMT
Oncogenic Fusion
Unknown

PFS ~ 19 months
Response Rate ~ 80%

T790MTKI‐Naïve EGFR
mutant tumor

T790M

1st or 2nd Gen. EGFR TKI:
Erlotinib, gefitinib, afatinib

T790M+PFS ~ 10 ‐ 12 months
Response Rate ~ 70%

figure 179

No approved therapies

3rd Gen. EGFR TKI:
Osimertinib
PFS ~ 9 ‐ 13 months
Response Rate ~ 70%

Treatment Paradigm for EGFR Mutant Lung Cancer

Doxycycline withdrawal
CCSP-rtTA/TetOEGFRL858R
DOX

Mutant EGFR expressed in
Type II lung epithelial cells
when mice are given
doxycycline

H

H

T

43 days on dox

6 days off dox

 Immuno-competent mouse model Tetracycline inducible
 Reversible

figure 2

EGFR Mutant Tumors Depend on the Driver Oncogene

resistance mostly due to the emergence of the EGFRT790M mutation [5]. Since then we haveused these models to evaluate therapeutic strategies to overcome TKI resistance andidentify mechanisms of resistance to these new therapies [6-9]. Given that osimertinib is
now emerging as the preferred option for the first-line treatment of the disease, we used
these mice to model acquired resistance to first-line osimertinib treatment in transgenic
mouse models of EGFRL858R-induced lung adenocarcinoma. In these studies we found that itis mediated largely through secondary mutations in EGFR-either C797S or L718V/Q or
Kras mutations. Therapeutic testing in mice revealed that both erlotinib and afatinib caused
regression of osimertinib-resistant C797S-containing tumors, whereas only afatinib was
effective in L718Q mutant tumors. Further, combination first-line osimertinib plus erlotinib
treatment prevented the emergence of secondary mutations in EGFR.

80

K. Politi

These results highlight how these models can be valuable to identify mutational ontarget mechanisms of resistance to TKIs in vivo that recapitulate the human disease.
Moreover, they can be used to test therapeutic strategies to target these EGFR resistancemutations. However, in these models, the spectrum of resistance mechanisms observed, islimited whilst the spectrum of resistance mechanisms in patients is broader and themolecular properties of these resistance mechanisms are poorly understood. In patients, thedepth and duration of response is also more variable than in the transgenic mouse models.
It is increasingly becoming apparent that tumors without on-target EGFR resistancemutations have worst outcomes [10] therefore understanding the biology that underlies
this variability is critically important. We hypothesize that the presence of co-occurring
genetic alterations contributes to a more complex tumor phenotype and spectrum of
resistance mechanisms in vivo. To test this, we have developed new mouse models to
specifically evaluate the consequences of co-occurring tumor suppressor gene alterations
on the progression and TKI sensitivity of EGFR mutant tumors. In the new models, viraldelivery of cre recombinase leads to the expression of mutant EGFR in the lung epitheliumand concomitant deletion of the Trp53 tumor suppressor gene. TP53 is mutated or lost inover 50% of advanced EGFR mutant lung adenocarcinomas. As predicted, in the EGFR; p53
model, tumors develop that are more aggressive than in the p53 WT model. To further
include layer on additional alterations, we generated mice that carry a conditional Cas9
allele. This system allows us to deliver virus carrying both cre recombinase and an sgRNAtargeting a gene of interest. Using this new model, we tested a pool of lentiviruses targeting
10 commonly mutated tumor suppressor genes in lung adenocarcinoma. We found that theeffects of tumor suppressor gene inactivation fall into three different categories: 1)
promoting tumor growth, 2) no effect on tumor growth or 3) a detrimental effect on tumor
growth.
Collectively, our findings highlight how genetically engineered mouse models of lungcancer, including those with complex genotypes, can be leveraged to study tumor
progression and drug resistance in vivo.
References
1. Lynch, T.J., et al., Activating mutations in the epidermal growth factor receptor underlying
responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med, 2004. 350(21): p.
2129-39.
2. Paez, J.G., et al., EGFR mutations in lung cancer: correlation with clinical response to gefitinib
therapy. Science, 2004. 304(5676): p. 1497-500.

Cancer Models (Organoids, PDX, GEM)

81

3. Pao, W., et al., EGF receptor gene mutations are common in lung cancers from "neversmokers" and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl
Acad Sci U S A, 2004. 101(36): p. 13306-11.
4. Politi, K., et al., Lung adenocarcinomas induced in mice by mutant EGF receptors found in
human lung cancers respond to a tyrosine kinase inhibitor or to down-regulation of thereceptors. Genes Dev, 2006. 20(11): p. 1496-510.
5. Politi, K., et al., Erlotinib resistance in mouse models of epidermal growth factor receptorinduced lung adenocarcinoma. Dis Model Mech, 2010. 3(1-2): p. 111-9.
6. Pirazzoli, V., et al., Afatinib plus Cetuximab Delays Resistance Compared to Single-Agent
Erlotinib or Afatinib in Mouse Models of TKI-Naive EGFR L858R-Induced Lung
Adenocarcinoma. Clin Cancer Res, 2016. 22(2): p. 426-35.
7. Pirazzoli, V., et al., Acquired Resistance of EGFR-Mutant Lung Adenocarcinomas to Afatinib
plus Cetuximab Is Associated with Activation of mTORC1. Cell Rep, 2014. 7(4): p. 999-1008.
8. Pirazzoli, V. and K. Politi, Generation of drug-resistant tumors using intermittent dosing oftyrosine kinase inhibitors in mouse. Cold Spring Harb Protoc, 2014. 2014(2): p. 178-81.
9. Takezawa, K., et al., HER2 amplification: a potential mechanism of acquired resistance to
EGFR inhibition in EGFR-mutant lung cancers that lack the second-site EGFRT790M
mutation. Cancer Discov, 2012. 2(10): p. 922-33.
10. Oxnard, G.R., et al., Assessment of Resistance Mechanisms and Clinical Implications inPatients With EGFR T790M-Positive Lung Cancer and Acquired Resistance to Osimertinib.
JAMA Oncol, 2018. 4(11): p. 1527-1534.

Katerina Politi, PhD
2003
2003-20102010-present
2010-present

PhD in Genetics and Development, Columbia
University
Postdoctoral Researcher, Memorial Sloan Kettering
Cancer Center
Assistant /Associate (2016-) Professor, Department of
Pathology, Yale School of Medicine
Member, Research Program Leader (2019-), Yale
Cancer Center

82

BIOLOGICAL MECHANISM OF POLYCLONAL METASTASISOF COLORECTAL CANCER

Masanobu Oshima
Division of Genetics, Cancer Research InstituteKanazawa University
Kakuma-machi, Kanazawa, Ishikawa 920-1192, Japan(oshimam@staff.kanazawa-u.ac.jp)

Wnt signaling, TGF-b pathway, KRAS and p53 functions are frequently dysregulated inhuman colorectal cancer (CRCs) caused by the driver gene mutations, which induces
development and malignant progression of CRCs. However, mutation patterns vary inindividual patients, and the combinations of mutations that are responsible for thepromotion of each step of malignant progression, including invasion, intravasation andmetastasis, have not been fully understood. To understand biological mechanisms of CRC
development in vivo, we constructed mouse models that carried multiple driver mutations
in various combination by crossing GEM models of ApcD716 (A), KrasG12D (K), Tgfbr2-/- (T),Trp53R270H (P) or Fbxw7-/- (F) mutant mice. All mutant mice developed intestinal tumors
because of germline mutation of Apc gene (Figure 1A).
The initial histologically identified malignant phenotype is submucosal invasion. We
found that mice that carried AT or AP mutations in combination in their genotypes showed
submucosal invasion of intestinal tumors, indicating that either suppression of the TGF-b
pathway or expression of gain-of-function (GOF) mutant p53 plays a role in submucosal
invasion (Figure 1B) (1, 2). Moreover, additional KrasG12D activation mutation (i.e., AKT or
AKP combination) accelerates the EMT-like morphology and intravasation into
microcapillary vessels, although AK mutation only causes mucosal benign adenoma
development (Figure 1C) (3). These results indicate that Kras activation is responsible forthe important malignant phenotypes when combined with mutations in the TGF-b
pathway or p53.
We next established tumor-derived organoids from mice with all possible mutant
combinations and transplanted them to the mouse spleen to examine liver metastasis.

Cancer Models (Organoids, PDX, GEM)

A. tumor formation

B. submucosal invasion

AKTF
AKF
AF

AF

AKPF

AKPF
AKP

AKTPF

AK

A

AKTPF

AK

AKTP

AKTP

AKT

AKT

AP

AP
ATPF

ATPF

ATP

ATP

APC/CTNNB1(A)ATC. EMT‐like, intravasation

D. liver metastasis

AKTF

AKTF
AKF

AF

AF

AKPF

AKPF
AKP

AKP
AKTPF

AK

A

AKTPF

AK
AKTP

AKTP
AKT

AKT
AP

AP

ATPF

ATPF
ATP

ATP
AT

figure 1

TGF- suppression
TP53 (GOF/+)(AT/AP)

AT

AKF

A

AKTF

AKF

AKP
A

83

KRAS activation(AKT/AKP)AT

p53 (GOF/LOH)(AKTP/AKTF)

Genotype-phenotype relations of intestinal tumors developed in mouse models.
A, K, T, P, and F indicate genetic mutations for Apc, Kras, Tgfbr2, Trp53 and Fbxw7
in mouse models. Genotypes with gray color indicate mice that showedtumor/malignant phenotypes. A. All genotype mice carrying Apc mutation (A)developed intestinal tumors. B. TGF-b suppression or p53 GOF mutation (AT orAP) induced submucosal invasion. C. Additional Kras mutation (AKT or AKP)
caused EMT-like morphology and intravasation. D. Combination of AKTP withloss of wild-type p53 (LOH) or AKTF mutation induced liver metastasis withhigh multiplicity.

Similar to the primary tumors, tumor organoids with the combination of AKT or AKP in
their genotypes formed liver metastasis (3). Moreover, organoids with additional mutationsof AKTP, AKTF and AKTPF showed increased multiplicity of liver metastasis withadvanced malignant histology (Figure 1D). Using these organoid cells, we have recently
developed CRISPR/Cas9 screening system to identify novel driver genes (4). We further
identified that AKTP cells with loss of wild-type p53 gene by LOH were enriched in liver
metastatic lesions (Nakayama et al., in revision). These results indicate a genotypephenotype relationship in the primary intestinal tumors (Figure 1A-D).
We next examined metastasis mechanisms using established organoid systems.
Recently, it has been shown that tumor cell clusters detached from the primary sites canmetastasize to the distant organ more efficiently when compared with single cells. To

84

M. Oshima

examine a polyclonal metastasis mechanism in vivo, we differentially labeled AKTP
organoids and non-metastatic tumor-derived organoids with GFP and TdTomato,respectively. When adenoma-derived organoid cells were injected to the spleen together
with metastatic AKTP cells, only AKTP cells formed metastatic foci in the liver. However,to our surprise, adenocarcinoma-derived non-metastatic cells formed chimeric metastatic
foci when co-injected with AKTP cells (Figure 2). These results indicate that non-metastatic
cells can metastasize by polyclonal mechanism if they form polyclonal cell clusters withmetastatic cells by cooperative invasion.
We finally examined whether AKTP cells are continuously required for survival andproliferation of non-metastatic cells in the liver lesions by depletion of AKTP cells from themetastatic lesions by the DT-DTR system. Importantly, non-metastatic cells continued
proliferation and formed large tumors even after depletion of metastatic cells (Figure 2).
These results indicate that metastasis process cannot be explained not only by a concept ofgenetic alterations and clonal selections. Further study to understand a mechanistic insightof polyclonal metastasis is required for establishment of novel clinical strategy against
metastasis.
Intestine (primary site)

CTC

Liver (secondary site)
Colonization

metastatic:

Metastasis

non-metastatic:

polyclonal:

cooperative invasion

figure 2

polyclonal
CTC-cluster

depletion

Schematic drawing of metastasis of intestinal tumors from the primary site (left)
to the liver (right) via circulating tumor cells (CTC). When metastatic cells (gray,top) and non-metastatic cells (white, middle) are co-localized in the primarytumors (mixed, bottom), cell clusters containing both cell types can circulate as
CTC-cluster, disseminate to the liver and form polyclonal metastatic foci. Once
polyclonal metastasis lesions are developed, non-metastatic cells no longer
require support of metastatic cells.

Cancer Models (Organoids, PDX, GEM)

85

References
1. Oshima H, Nakayama M, Han TS, Naoi K, Ju X, Maeda Y, Robine S, Tsuchiya K, Sato T,
Taketo MM, Oshima M. Suppressing TGF-b signaling in regenerating epithelia in an
inflammatory microenvironment is sufficient to cause invasive intestinal cancer. Cancer
Res. 2015;75: 754-765.
2. Nakayama M, Sakai E, Echizen K, Yamada Y, Oshima H, Han TS, Ohki R, Fujii F,
Ochiai A, Robine S, Voon DC, Tanaka T, Taketo MM, Oshima M. Intestinal cancer progression by mutant p53 through the acquisition of invasiveness associated withcomplex glandular formation. Oncogene. 2017;36:5885-5896.
3. Sakai E, Nakayama M, Oshima H, Kouyama Y, Niida A, Fujii S, Ochiai A, Nakayama
KI, Mimori K, Suzuki Y, Hong CP, Ock CY, Kim SJ, Oshima M. Combined mutation ofApc, Kras and Tgfbr2 effectively drives metastasis of intestinal cancer. Cancer Res.
2018;78:1334-1346.
4. Takeda H, Kataoka S, Nakayama M, Ali MAE, Oshima H, Yamamoto D, Park JW,
Takegami Y, An T, Jenkins NA, Copeland NG, Oshima M. CRISPR-Cas9 mediated geneknockout in intestinal tumor organoids provides functional validation for colorectal
cancer driver genes. Proc Natl Acad Sci USA. 2019;116:15635-15644.

Masanobu Oshima, DVM, PhD
1986-1988
1988-1991
1992-1997
1997-1999
2000-2005
2005-present
2013-2017

Graduate School of Veterinary Medicine (MS),Hokkaido Univ.
Chugai Pharmaceutical Co., Ltd.
Banyu Pharmaceutical, Co., Ltd.
Research Associate, Merck Research Laboratories(Merck)Associate Professor, Kyoto University Graduate Schoolof Medicine
Professor, Cancer Research Institute, Kanazawa
University
Director, Cancer Research Institute, Kanazawa
University

86

SINGLE CELL ANALYSIS OF DIFFERENTIATION AND DRUGRESPONSE IN GLIOBLASTOMAPeter A. Sims
Department of Systems Biology
Columbia University Irving Medical Center
3960 Broadway, Lasker 203AC, New York, NY 10032, USA(pas2182@cumc.columbia.edu)

Glioblastoma (GBM) is a deadly form of malignant brain tumor against whichconventional targeted therapies and immunotherapies have had limited success. Bothintratumoral heterogeneity and the complexities of drug delivery in the brain are
formidable challenges in neuro-oncology. Furthermore, patient-derived models of GBM
often bear little resemblance to the corresponding primary tumor tissue and do notrecapitulate the microenvironment. Recent technological advances in microfluidic
implementations of single-cell RNA-seq (scRNA-seq) have enabled detailed analysis ofgene expression across thousands of individual cells. While scRNA-seq is revolutionizing
phenotypic characterization of complex tumors, it also opens new avenues for evaluating
patient-derived models. We recently reported the broad extent of neural lineage identityand differentiation associated with gliomas based on scRNA-seq [1, 2]. In a parallel effort,
we have analyzed matched, patient-derived glioma neurospheres from these tumors.
Glioma neurospheres represent the state-of-the-art patient-derived model of gliomas andthe basis of most drug screening and xenografts. While they are adept at preserving the
genotypic diversity of gliomas, we found that they fail to recapitulate the phenotypic
diversity of transformed cells in the tissue from which they originate.
To address these issues, we have developed a new approach to analyzing drug response
in primary GBM tissue with a readout that tracks the response of different cellularsubpopulations within the tumor. We are conducting small-scale drug screens in acute slice
cultures of GBM tissue and using scRNA-seq to deconvolve the cell type-specific responses
to drugs. We find that acute slice cultures recapitulate the phenotypic diversity of GBM
tissue, harbor an intact immune microenvironment, and allow us to inexpensively deliverCancer Models (Organoids, PDX, GEM)

87

single-cell resolved drug response information on up to 10 drugs in parallel within 72
hours.
Most recently, we have developed two experimental designs to facilitate analysis of cell
type-specific drug response in patient-derived acute slice cultures. To identify conserved
effects, we screen several drugs per resection along with controls where biologicalreplicates are distributed across patients. For analysis of cell type-specific responses withina patient, which could enable precision medicine, we screen a smaller number of drugs
with multiple biological replicates per patient resection.
Our most comprehensive analysis in acute slice culture to date has focused on theconserved effects of etoposide and panobinostat across four patients. Etoposide is achemotherapy and topoisomerase inhibitor that is thought to primarily target cycling cells.
Indeed, in acute slice cultures of both murine and human gliomas, we find robust loss of
proliferating tumor cells in slice cultures treated with etoposide and limited effects on nonneoplastic cells in the microenvironment relative to control slices. Panobinostat is a histone
deacetylase inhibitor, and in stark contrast to etoposide, has significant effects on gene
expression in all detectable cell types in acute slice cultures. Among the malignantly
transformed glioma cells, panobinostat appears to act as a differentiation therapy, and we
observe widespread induction of mature neuronal gene expression. More remarkably,panobinostat significantly alters the composition of the immune microenvironment oftreated slice cultures. The two major immune populations in GBM are microglia, which arecommonly found in a pro-inflammatory state, and macrophages, which are thought to be
immunosuppressive and associated with poor survival. While both of these myeloid
populations are detected in slice cultures from all four patients, macrophages are
selectively depleted from the panobinostat-treated slices, raising the possibility that a single
agent could drive differentiation of the transformed cells while ablating
immunosuppressive myeloid cells.
Overall, these initial studies show that scRNA-seq is a powerful tool not only forcharacterizing complex solid tumors, but also for assessing patient-derived models. We
find that acute slice cultures of human glioma surgical specimens effectively recapitulate
the cellular composition of the tumor tissue from which they are derived. In combination
with small-scale drug screening in acute slice cultures, scRNA-seq can be used to assess thecell type-specific response of tumor tissue to drugs, including both malignantly
transformed cells and non-neoplastic cells in the microenvironment. While promising,these studies also highlight a number of important analytical challenges with the acute slice
culture approach. For some drugs, rapid alteration of gene expression obscures the original
cellular identity of the tumor cells, complicating the determination of which cell types in
the untreated tumor tissue are perturbed by the drug. To address this issue, we are88

P. A. Sims

developing computational methods for projecting scRNA-seq profiles of perturbed cells
onto untreated control profiles. As we extend this platform to more patients and test its
predictive power, we envision potential utility for informing clinical trial enrollment andnovel drug combinations.
References
1

2

Yuan, J., Levitin, H. M., Frattini, V., Bush, E. C., Boyett, D. M., Samanamud, J.,
Ceccarelli, M., Dovas, A., Zanazzi, G., Canoll, P., Bruce, J. N., Lasorella, A., Iavarone, A.
& Sims, P. A. Single-cell transcriptome analysis of lineage diversity in high-grade
glioma. Genome Med 10, 57, (2018).
Levitin, H. M., Yuan, J., Cheng, Y. L., Ruiz, F. J. R., Bush, E. C., Bruce, J. N., Canoll, P.,
Iavarone, A., Lasorella, A., Blei, D. M. & Sims, P. A. De novo Gene Signature
Identification from Single-Cell RNA-seq with Hierarchical Poisson Factorization.
Molecular Systems Biology 15 (2019).

Peter A. Sims, PhD
2004-20092009-20112012-present

PhD, Chemistry, Harvard University
Postdoctoral Fellow, Dept. of Chemistry & Chemical
Biology, Harvard University
Assistant Professor, Department of Systems Biology,
Columbia University Irving Medical Center

89

NEUTRALIZING PROTUMOR INFLAMMATION: LESSONSLEARNED FORM PRECLINICAL MOUSE MODELS

Lisa M. Coussens
Department of Cell, Developmental & Cancer Biology
Basic Research, Knight Cancer InstituteOregon Health & Science University
Knight Cancer Research Building, Room 3030 | Mail Code KR-CDCB
2720 SW Moody Avenue, Portland, OR 97201, USA(coussenl@ohsu.edu)

The concept that leukocytes are critical components of solid tumors is now generally
accepted, however, their role(s) in regulating aspects of neoplastic progression, and/or
response to cytotoxic, targeted and/or immune therapy is only beginning to beunderstood. Utilizing de novo mouse models of organ-specific solid tumor development(mammary, cutaneous, and pancreas carcinomas and mesothelioma), we now appreciatethat adaptive leukocytes differentially regulate myeloid cell recruitment, activation andeffector function, and in turn, activated tumor-infiltrating myeloid cells engage tissuebased programs to foster malignancy, and repress anti-tumor immunity by a diversity ofmechanisms. Treatment of tumor-bearing mice with therapeutic agents that disrupt
lymphocyte-myeloid cell interaction, myeloid cell activation, or myeloid cell functionality
generally slow primary tumor growth kinetics when combined with cytotoxic therapy;
however, their impact on metastases is variable. Similar to organ-specific regulatory
programs co-opted to foster primary tumor growth, regulation of metastatic seeding andoutgrowth is also regulated by tissue- and organ-specific mechanisms. Based on this, it
stands to reason that therapeutic strategies may not be efficacious in both primary andmetastatic locations. To be presented will be our recent insights into organ and tissuespecific regulation of primary and metastatic cancer development by adaptive and innate
immune cells, how systemic regulation of humoral immunity and complement-mediatedpathways regulate pro- versus anti-tumor immune responses, and new studies evaluating
how attenuating protumor properties of select lymphoid and myeloid cells can be exploited
to enhance therapeutic responses to cytotoxic and immune-based therapy.

90

L. M. Coussens

LMC acknowledges generous support from the NIH / NCI, the Department of Defense
Breast Cancer Research Program, Susan G. Komen Foundation, the Breast Cancer Research
Foundation, the Brenden-Colson Center for Pancreatic Health.

Lisa M. Coussens, PhD
1980
1993
1997
2011-present

BA, San Francisco State University, San Francisco(Biology)
PhD, University of California, Los Angeles (Biological
Chemistry)
Postdoctoral Scholar, University of California, San
Francisco (Tumor Biology)
Professor and Chairwoman, Cell, Developmental andCancer Biology, Oregon Health and Science University(OHSU), Associate Director for Basic Research, Knight
Cancer Institute, OHSU

91

ROLE OF INTRAEPITHELIAL LYMPHOCYTES IN COLONCANCER

Bernardo Reis and Daniel Mucida
Laboratory of Mucosal Immunology
The Rockefeller University
1230 York Avenue, New York, NY 10065, USA(mucida@mail.rockefeller.edu)

Colorectal cancer (CRC) is the 4th most common cancer in the US. Although this cancer
arises at a key interface between the host and the environment, the role of the tumor
microenvironment and microbiome in CRC remains largely unexplored (Lawler et al.,2018). Up to 20% of IBD patients develop cancer within 30 years of disease onset and up to
50% of these patients die of CRC. Nevertheless, the majority of CRCs develop in patients
without underlying inflammation, primarily due to loss of tumor suppressor genes. In bothcases of CRC, tumor-elicited inflammation triggers epithelial cell damage resulting inadenoma invasion by microbes, in turn driving tumor growth by sustaining inflammation(Grivennikov et al., 2012). Therefore, immune surveillance of the epithelial barrier may
help prevent damaged epithelial cells from progressing into cancer.
Intestinal intraepithelial lymphocytes (IELs) comprise a large T cell population thatoccupies the intestinal epithelium. Dysregulation of IELs correlates with loss of mucosal
barrier and inflammatory bowel diseases (IBD)(Jabri and Sollid, 2009). IELs are aheterogeneous group of T lymphocytes characterized by high expression levels ofactivation markers, gut-homing molecules, NK-like receptors and cytotoxic T lymphocyte(CTL)-molecules such as granzyme B and perforin (Denning et al., 2007). Another common
characteristic of IELs is the surface expression of CD8αα homodimers, which can bind bothto classical MHC-I and to epithelial cell-associated non-classical MHC-I molecules,
presumably working as TCR “co-repressors” (Cheroutre and Lambolez, 2008; Cheroutre etal., 2011; Guy-Grand et al., 1991; Guy-Grand et al., 2013). Dissecting the role played bythese cells under homeostatic and pathophysiological conditions, including cancer, hasbeen hampered by a lack of genetic models for specific control of IEL development and92

D. Mucida et al.

function. Additionally, poor survival of IELs in culture and failure to simulate theirlocation within the epithelial compartment has further added to the difficulty in assessing
IEL function.
TCRgd+ cells form a major subpopulation of IELs, but their function is incompletely
understood [5, 8]. Nonetheless, studies using TCRgd -/- mice have reported increased
susceptibility to very distinct pathogens [5, 8], while studies investigating the use of TCRgd+
cells for cellular immunotherapy have confirmed their potent tumor reactivity in several
different malignancies, including CRC (Scheper et al., 2014). The use of multi-photon intravital microscopy (IVM) has provided insights into cell-cell dynamics and behavior at theintestinal epithelium that could not be achieved with previous imaging techniques.
Although the use of multi-photon microscopy in the gut is complicated by peristalsis anddifficult accessibility, we are able to track a large number of IELs in multiple villi
simultaneously. Using this method, we have observed that under homeostatic conditions
TCRgd+ IELs migrate rapidly within the intraepithelial (IE) compartment and survey theentire villous epithelium. This motility pattern is accompanied by continuous IEL
movement into the lateral intercellular spaces (we denominated this pattern “flossing”).
Additionally, we found that different enteric pathogens, including Salmonella andToxoplasma, trigger increased IEL flossing rates (Hoytema van Konijnenburg et al., 2017).
Importantly, a gd T cell gene signature was the most favorable prognostic factor among
tumor-infiltrating leukocytes across cancer types (Gentles et al., 2015). In addition, recent
studies in human CRC showed a favorable prognosis for tumors enriched in T cells
expressing cytotoxic genes normally associated with IEL function (Cheroutre et al., 2011;
Galon et al., 2006; Muller et al., 2000; Pages et al., 2010). We postulated that IELs play animportant role in surveillance of intestinal epithelium against initiation, or progression of
CRC. IELs provide a first line of immune defense against pathogens due to their location at
the critical interface between the intestinal lumen and the core of the body. Hence, IEL
immune surveillance of the epithelial layer may also help preventing damaged epithelial
cells (ECs) from progressing into cancer. We addressed whether a coordinated EC-IEL
response to environmental perturbation results in activation of IELs leading to removal(killing) of damaged or transformed epithelial cells.
To address EC-IEL dynamics in vivo during CRC, we used multi-photon intra-vital
microscopy as well as tissue clearing associated with light sheet microscopy. Using thesetechniques, we tracked IEL dynamics simultaneously in multiple villi during cancer or in
the steady state, and observed drastic changes in IEL behavior in tumor-bearing areas. We
observed significant changes in gd IEL movement patterns in tumor areas (disorganized,
heterogenous) when compared to healthy areas (highly organized, homogenous). Of note,
we also observed an accumulation of some gd IELs moving with very reduced speed withinTumor Microenvironment

93

tumor areas. Additionally, we observed a sharp increase in IEL inter-epithelial movements(flossing), especially around tumor areas. The observation of segregated behavior of gd IELs
in CRC indicated the existence of potentially responsive subsets. Flow cytometry analysis
indicated that, in naïve animals, there is a gradual increase in the frequency of CD8αα–TCRgd cells towards the distal small intestine and colon. While the small intestineepithelium is primarily occupied by Vg7+ IELs, we found that colon Vg usage is morediverse, including populations expressing Vg1, Vg4 and additional Vgs; CD8αα– gd IELs
rarely express the Vg7 chain. Additionally, by utilizing a cleared-tissue based imaging
analysis, we found that CD8αα+ gd IELs predominantly populate the tip of the villi, in close
proximity to the intestinal lumen, while CD8αα– gd IELs accumulate around the intestinal
crypts. Together, these data suggest regional specialization in IEL subset function(Di Marco
Barros et al., 2016), which could also be of relevance during cancer surveillance.
Further supporting this possibility, gd IELs infiltrating tumor regions significantly
expressed the immunoregulatory checkpoint molecule PD-1, while IELs in the neighboring
healthy tissue showed very low levels of PD-1 expression. Additionally, heightened PD-1
expression was mostly restricted to the CD8αα (and Vg7 – ) population, pointing to apossible distinct role for these cells in anti-tumor immunity, and also suggesting possible
new targets for immunotherapy intervention. These preliminary data also highlight themerit of multi-photon microscopy to gain insights into IEL dynamics and function in vivo
during epithelial transformation. Coupled with gene expression analysis, metabolic
profiling and novel genetic manipulation of IELs, these approaches will allow thecharacterization of novel mechanisms of epithelial surveillance. In preliminary
observations, we found that mice lacking TCRgd IELs display enhanced inflammation(reduced colon length, enhanced lipocalin-2) and develop tumors in a low-dose DSS(AOM) model, while wild-type mice do not (at low 1% DSS dose), suggesting a crucial rolefor TCRgd IELs in controlling tumor development and/or progression.
Overall, our data suggests a “division of labor” among IEL subsets in the regulation of
CRC development or progression; particular IEL subsets may be involved in the preventionof tumor initiation, while migrating IEL subsets that infiltrate the tumor may boos tumor
progression. Our data expands our understanding of adaptive immunity at the gut
epithelial barrier, contributing valuable information regarding the role of epithelial
immunity in cancer.
References
1. Cheroutre, H., and Lambolez, F. (2008). Doubting the TCR coreceptor function of
CD8alphaalpha. Immunity 28, 149-159.

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D. Mucida et al.

2. Cheroutre, H., Lambolez, F., and Mucida, D. (2011). The light and dark sides ofintestinal intraepithelial lymphocytes. Nat. Rev. Immunol. 11, 445-456.
3. Denning, T.L., Granger, S.W., Mucida, D., Graddy, R., Leclercq, G., Zhang, W., Honey,
K., Rasmussen, J.P., Cheroutre, H., Rudensky, A.Y., et al. (2007). Mouse
TCRalphabeta+CD8alphaalpha intraepithelial lymphocytes express genes that downregulate their antigen reactivity and suppress immune responses. J. Immunol. 178,
4230-4239.
4. Di Marco Barros, R., Roberts, N.A., Dart, R.J., Vantourout, P., Jandke, A., Nussbaumer,O., Deban, L., Cipolat, S., Hart, R., Iannitto, M.L., et al. (2016). Epithelia Use
Butyrophilin-like Molecules to Shape Organ-Specific gammadelta T Cell
Compartments. Cell 167, 203-218 e217.
5. Galon, J., Costes, A., Sanchez-Cabo, F., Kirilovsky, A., Mlecnik, B., Lagorce-Pages, C.,
Tosolini, M., Camus, M., Berger, A., Wind, P., et al. (2006). Type, density, and locationof immune cells within human colorectal tumors predict clinical outcome. Science 313,
1960-1964.
6. Gentles, A.J., Newman, A.M., Liu, C.L., Bratman, S.V., Feng, W., Kim, D., Nair, V.S., Xu,
Y., Khuong, A., Hoang, C.D., et al. (2015). The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat. Med. 21, 938-945.
7. Grivennikov, S.I., Wang, K., Mucida, D., Stewart, C.A., Schnabl, B., Jauch, D.,
Taniguchi, K., Yu, G.Y., Osterreicher, C.H., Hung, K.E., et al. (2012). Adenoma-linked
barrier defects and microbial products drive IL-23/IL-17-mediated tumour growth.
Nature 491, 254-258.
8. Guy-Grand, D., Malassis-Seris, M., Briottet, C., and Vassalli, P. (1991). Cytotoxic differentiation of mouse gut thymodependent and independent intraepithelial T
lymphocytes is induced locally. Correlation between functional assays, presence of perforin and granzyme transcripts, and cytoplasmic granules. J. Exp. Med. 173, 1549-1552.
9. Guy-Grand, D., Vassalli, P., Eberl, G., Pereira, P., Burlen-Defranoux, O., Lemaitre, F., Di
Santo, J.P., Freitas, A.A., Cumano, A., and Bandeira, A. (2013). Origin, trafficking, andintraepithelial fate of gut-tropic T cells. J. Exp. Med. 210, 1839-1854.
10. Hoytema van Konijnenburg, D.P., Reis, B.S., Pedicord, V.A., Farache, J., Victora, G.D.,
and Mucida, D. (2017). Intestinal Epithelial and Intraepithelial T Cell CrosstalkMediates a Dynamic Response to Infection. Cell.
11. Jabri, B., and Sollid, L.M. (2009). Tissue-mediated control of immunopathology incoeliac disease. Nat. Rev. Immunol. 9, 858-870.
12. Lawler, M., Alsina, D., Adams, R.A., Anderson, A.S., Brown, G., Fearnhead, N.S.,
Fenwick, S.W., Halloran, S.P., Hochhauser, D., Hull, M.A., et al. (2018). Critical researchgaps and recommendations to inform research prioritisation for more effective
prevention and improved outcomes in colorectal cancer. Gut 67, 179-193.

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13. Muller, S., Buhler-Jungo, M., and Mueller, C. (2000). Intestinal intraepithelial
lymphocytes exert potent protective cytotoxic activity during an acute virus infection. J.
Immunol. 164, 1986-1994.
14. Pages, F., Galon, J., Dieu-Nosjean, M.C., Tartour, E., Sautes-Fridman, C., and Fridman,
W.H. (2010). Immune infiltration in human tumors: a prognostic factor that should not
be ignored. Oncogene 29, 1093-1102.
15. Scheper, W., Sebestyen, Z., and Kuball, J. (2014). Cancer Immunotherapy Using
gammadeltaT Cells: Dealing with Diversity. Front. Immunol. 5, 601.

Daniel Mucida, PhD
2000
2005
2006-20102010-20162016-Present

BS in biochemistry and immunology, Federal
University of Minas Gerais
PhD, University of São Paulo and New York University
La Jolla Institute for Allergy and Immunology
Assistant Professor, The Rockefeller University
Associate Professor, The Rockefeller University

96

TGF-b FAMILY SIGNALING IN PROGRESSION OF CANCER

Kohei Miyazono
Department of Molecular Pathology, Graduate School of Medicine
The University of Tokyo
Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan(miyazono@m.u-tokyo.ac.jp)

Transforming growth factor-bs (TGF-bs) are multifunctional proteins that elicit bothanti-tumorigenic and pro-tumorigenic functions during progression of cancer. TGF-b
serves as an anti-oncogenic factor through growth inhibition and apoptosis induction ofvarious types of cells, including epithelial cells and endothelial cells. However, TGF-b
exhibits pro-tumorigenic functions by inducing epithelial-to-mesenchymal transition(EMT), as well as immune suppression and angiogenesis. Here, we present our findings on
TGF-b signaling in progression of cancer, focusing on induction of EMT by TGF-b. We also
show our findings on the role of bone morphogenetic proteins (BMPs) in the mostmalignant type of brain tumors, glioblastoma multiforme.
TGf-b-induced eMT in progression of cancer: TGF-b induces EMT through activationof Smad and non-Smad signaling pathways [1]. Through inhibition of TGF-b signaling
pathways by an inhibitory Smad, Smad7, cancer metastasis can be prevented in a mousemodel using mouse breast cancer JygMC(A) cells. TGF-b induces the expression of EMTtranscription factors (EMT-TFs), such as Snail, Slug, ZEB1, and ZEB2; these EMT-TFs play
crucial roles in induction of EMT. Importantly, Smad signaling cross-talks with K-Ras
signaling activated in some cancer cells, and induces certain crucial genes involved in EMT.
For example, in the presence of active K-Ras, TGF-b upregulates the expression of Snail,and thus induces an EMT phenotype in pancreatic cancer PANC1 cells. Althoughmutations of the SMAD4/DPC4 gene can be found in ~50% of advanced stages ofpancreatic cancer patients, mutations of KRAS occur in earlier phase of ~90% of pancreaticcancer patients; therefore, TGF-b may mainly play pro-tumorigenic functions during
progression of pancreatic cancer through cooperation with K-Ras signaling [1].

Nakahara Memorial Lecture97

We have developed a tissue-clearing technology, termed CUBIC-based cancer (CUBICCancer) analysis. CUBIC-Cancer analysis allows whole-body/organ imaging ofmacroscopic to microscopic analyses of cancer with single-cell resolution. By CUBICCancer analysis using human lung adenocarcinoma A549 cells, we revealed that EMT
induced by TGF-b accelerates not only the process of extravasation, but also cell survivaland extravasation of cancer cells at metastatic sites [2]. Moreover, TGF-b-treated A549 cellswith mesenchymal phenotype appeared to express E-cadherin at metastatic foci,suggesting that these cells had undergone mesenchymal-to-epithelial transition (MET)
when they formed metastatic foci [2].
EMT is known to be a reversible biological process. We found that chronic TGF-btreatment for more than 12 days induced stabilized EMT in mammary epithelial cells
transfected with H-Ras [3]. In the stabilized EMT, mammary carcinoma cells acquired stem
cell-like properties and chemoresistance. Chronic TGF-b treatment showed enhanced
mTOR signaling, and inhibition of mTOR signaling resulted in suppression of stem cell-like
properties and chemoresistance. Moreover, inhibition of mTOR signaling reduced
tumorigenicity of breast carcinoma cells in vivo, suggesting that mTOR signaling pathway
is a potentially interesting target for treatment of cancer metastasis [3].
roles of BMPs in glioblastoma: BMPs, members of the TGF-b family, are also
multifunctional proteins; in addition to induce bone and cartilage formation in vivo, BMPs
are involved in morphogenesis of various organs and tissues, including neuronal tissues,
blood and lymph vessels, and hair follicles. Recent findings revealed that BMPs play criticalroles in regulation of the progression of various cancers [4]. BMPs induce growth arrest,
apoptosis, and differentiation of glioma-initiating cells (GICs). Similar to TGF-bs, BMPs
transduce signals through type II and type I receptors and downstream Smad and nonSmad signaling pathways. Among four different type I receptors for BMPs, we found that
one of the BMP type I receptor known as ALK-2 (activin receptor-like kinase 2) plays acritical role in induction of apoptosis and differentiation of GICs [5].
Although BMPs exhibit anti-tumorigenic functions in GICs, BMPs do not always
prevent progression of glioblastoma in vivo, possibly because glioblastoma tissues arecomposed of heterogenous cell populations, and some glioblastoma cells do not respond to
BMPs. To further investigate the function of BMPs in GICs, we searched for genes andproteins regulated by BMPs using RNA-seq analysis and some other methods. We foundthat several nuclear proteins, such as Distal-less homeobox 2 (DLX2) and paired related
homeobox 1 (PRRX1), were induced by BMPs. DLX2 induced differentiation and apoptosis
of GICs [5]. Inoculation of GICs with ectopic expression of ALK-2 or DLX2 resulted in
prolonged survival of nude mice.
Using a phospho-receptor tyrosine kinase array, we also found that phosphorylation oferythropoietin-producing hepatocellular carcinoma receptor A6 (EphA6) is induced by

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K. Miyazono

BMP signaling [6]. Through a complex formation with ALK-2, EphA6 enhanced thesignaling activity of ALK-2. Although ALK-2 kinase activity was essential for induction of
GIC apoptosis, kinase activity of EphA6 was not required. Injection of GICs with ectopic
EphA6 resulted in prolonged survival of mice compared to that with control GICs,
suggesting that EphA6 is a putative tumor suppressor of glioblastoma. We have also
identified some genes regulated by BMP signaling in GICs. Further analyses may allow us
to identify potential target(s) for treatment of glioblastoma.
References
1. Miyazono K, Katsuno Y, Koinuma D, Ehata S, Morikawa M. Intracellular andextracellular TGF-b signaling in cancer: some recent topics. Front Med. 2018;12:387-411.
2. Kubota SI, Takahashi K, Nishida J, Morishita Y, Ehata S, Tainaka K, Miyazono K, Ueda
HR. Whole-body profiling of cancer metastasis with single-cell resolution. Cell Rep.
2017;20:236-250.
3. Katsuno Y, Meyer DS, Zhang Z, Shokat KM, Akhurst RJ, Miyazono K, Derynck R.
Chronic TGF-b exposure drives stabilized EMT, tumor stemness, and cancer drug
resistance with vulnerability to bitopic mTOR inhibition. Sci Signal. 2019;12:pii:
eaau8544.
4. Davis H, Raja E, Miyazono K, Tsubakihara Y, Moustakas A. Mechanisms of action ofbone morphogenetic proteins in cancer. Cytokine Growth Factor Rev. 2016;27:81-92.
5. Raja E, Komuro A, Tanabe R, Sakai S, Ino Y, Saito N, Todo T, Morikawa M, Aburatani
H, Koinuma D, Iwata C, Miyazono K. Bone morphogenetic protein signaling mediatedby ALK-2 and DLX2 regulates apoptosis in glioma-initiating cells. Oncogene,
2017;36:4963-4974.
6. Raja E, Morikawa M, Nishida J, Tanabe R, Takahashi K, Seeherman HJ, Saito N, Todo T,
Miyazono K. Tyrosine kinase Eph receptor A6 sensitizes glioma-initiating cells towards
bone morphogenetic protein-induced apoptosis. Cancer Sci. 2019;110:3486-3496.

Nakahara Memorial Lecture

Kohei Miyazono, MD, PhD
1988-1990
1990-1995
1995-2000
2000-present

Assistant of Professor, Third Dept. Internal Medicine,
The Univ. of Tokyo
Assistant Member, Ludwig Institute for Cancer
Research, Uppsala, Sweden
Chief, Department of Biochemistry, The Cancer
Institute, Japanese Foundation for Cancer Research
Professor, Department of Molecular Pathology,
Graduate School of Medicine, The Univ. of Tokyo

99

100

CAR T CELLS: THE EMERGENCE OF SYNTHETIC IMMUNITY

Michel Sadelain
Center for Cell Engineering
Memorial Sloan-Kettering Cancer Center
1275 York Avenue, New York, NY 10065, USA(m-sadelain@ski.mskcc.org)

Natural immune responses fall short of eradicating tumors in most cancer patients. T
cell engineering offers a means to repurpose immune cells to perform enhanced therapeuticfunctions. Chimeric antigen receptors (CARs) are synthetic receptors that redirect andreprogram T cells to mediate tumor rejection. CARs that target CD19 offer the prospect ofcomplete remissions in patients with chemorefractory, relapsed B cell malignancies,
especially acute lymphoblastic leukemia (ALL). In the first 53 adult patients with refractoryand relapsed ALL we treated with autologous peripheral blood T cells expressing the 1928z CAR, a complete remission (CR) was obtained in 83%. The US Food and Drug
Administration approved the first CD19 CAR therapies in 2017. Despite high CR rates, anumber of patients will eventually relapse, pointing to the need to further improve CAR
design and T cell engineering strategies to increase the functional persistence of CAR T
cells and reduce their toxicities. Using CRISPR/Cas9, we found that directing a CAR to the
T cell receptor alpha chain (TRAC) locus not only results in uniform CAR expression inhuman peripheral blood T cells, but enhances T cell potency by attenuating CAR tonic
signaling and T cell exhaustion, enabling TRAC-CAR T cells to outperform conventionallyengineered CAR T cells. Further analyses of the signaling properties of the 19-28z CAR
have identified signaling components that more effectively control T cell persistence andacquisition of effector functions, yielding new CAR designs that are better suited to balance
T cell memory and anti-tumor activity. Recent studies reveal that CAR T cells acquire the
CAR target and many other molecules from the tumor cell via trogocytosis, providing new
insights into the biology of CAR T cells and evolving strategies to further perfect the designof engineered T cell immunity to tackle solid tumors.

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101

Michel Sadelain, MD, PhD
1984
1989
1989
1994
2007-present

MD, University of Paris
PhD, University of Alberta
Postdoctoral Fellow, Whitehead Institute, MIT
Assistant Professor, Memorial Sloan Kettering Cancer
CenterDirector, Center for Cell Engineering; Stephen and
Barbara Friedman Chair, Memorial Sloan Kettering
Cancer Center

102

ADVANCES OF CAR-T CELL THERAPY FOR SOLID TUMORS

Koji Tamada
Department of Immunology
Yamaguchi University Graduate School of Medicine
1-1-1 Minami Kogushi, Ube, Yamaguchi 755-8505, Japan(ktamada@yamaguchi-u.ac.jp)

Gene-modified T cells utilizing tumor-specific T cell receptor (TCR) or chimeric antigen
receptor (CAR) have been rapidly developed and translated into clinic in recent years. In
particular, cancer immunotherapy using CAR-T cells has demonstrated the outstanding
clinical effects in B cell hematological malignancies, resulting in FDA and PMDA approvalof CD19 CAR-T cell therapy for children and young adult, refractory or relapsing B cell
acute lymphoblastic leukemia (ALL) and adult patients with relapsed or refractory B cell
lymphoma in 2017 and 2019, respectively. On the other hand, CAR-T cell therapy against
solid tumors has yet to be fully developed, as only a few exceptional cases have beenreported to achieve clinical efficacy. Potential hurdles for CAR-T cell therapy in solid
tumors include heterogeneity of tumor-associated targets, insufficient migration andinfiltration of CAR-T and endogenous immune cells into tumor tissues, andimmunosuppressive nature of tumor microenvironment. Various modifications andimprovement of CAR-T cells to overcome these hurdles have been actively investigated byworldwide researchers.
To this end, our group has developed novel CAR technology which enables CAR-T cells
to simultaneously produce interleukin-7 (IL-7) and CCL19, aiming at efficientaccumulation, expansion, and survival of immune cells inside solid tumor tissues (Figure1). Treatment of pre-established solid tumors with IL-7/CCL19-producing CAR-T cells
achieved complete regression followed by prolonged mouse survival and long-term
memory response specific to tumor (Figure 2). Tumor tissues from the mice treated with IL7/CCL19-producing CAR-T cells demonstrated massive accumulation of immune cells
including both the transferred CAR-T cells and endogenous T cells and DC (Figure 3). An

Novel Immunotherapeutics

103

figure 1

Generation of IL-7/CCL19-producing CAR-T cells.
(A) Schema of vector encoding IL-7/CCL19-producing CAR. (B) Production levels of IL-7
and CCL19 in the culture supernatants of IL-7/CCL19-producing CAR-T cells (hereafterreferred to as 7x19 CAR-T cells)

figure 2

Anti-tumor effects of IL-7/CCL19-producing CAR-T cells.
(A) Tumor growth in the mice treated with conventional CAR-T cells or IL-7/CCL19-producing CAR-T cells is shown. As control, tumor growth in the mice without treatment or
those treated with cyclophosphamide alone is shown. (B) Survival of the mice is shown.

104

K. Tamadafigure 3

Immune cell infiltration in tumor tissues of the mice treated with IL-7/CCL19-producing
CAR-T cells.
(A) Infiltration of CD4+ and CD8+ T cells (upper panels) and DEC205+ DC and CD3+ T
cells (lower panels) in the tumor tissues analyzed by IHC staining is shown. (B) Infiltrationof transferred CAR-T cells and host T cells in the tumor tissues analyzed by IHC staining isshown.

important role of endogenous T cells in the anti-tumor effects of IL-7/CCL19-producing
CAR-T cells was confirmed (Figure 4).
As another approach, we also developed CAR-T cells which produce anti-PD-1 scFv toattenuate PD-1 signal (Figure 5). Anti-PD-1 scFv-producing CAR-T cells demonstrated
improved therapeutic efficacy against solid tumors, along with enhanced responses ofendogenous tumor-specific T cells (Figure 6).

Novel Immunotherapeutics

105

figure 4

An important role of host T cells in the treatment efficacy of IL-7/CCL19-producing CART cells.
Tumor growth in the mice treated with 7x19 CAR-T cells with or without host T cell depletion is shown. As control, tumor growth in the mice without treatment or treated with conventional CAR-T cells is shown.

figure 5

Generation of anti-PD-1 scFv-producing CAR-T cells.
(A) Schema of vector encoding anti-PD-1 scFv-producing CAR (hereafter referred to as
scFv CAR). (B) Concentration of anti-PD-1 scFv in culture supernatant conventional or
scFv CAR-T cells is shown. ***; P<0.001. (C) Blockade of PD-L1/PD-1 interaction by anaddition of the culture supernatant from conventional or scFv CAR-T cells is shown.

106

K. Tamadafigure 6

Anti-tumor effect of anti-PD-1 scFv-producing CAR-T cells.
(A) Survival of mice treated with scFv CAR-T cells is shown. As control, survival of micetreated with activated T cells (non-CAR-T cells) or conventional CAR-T cells is shown. (B)
Number of CAR-T cells in the tumor tissues of the mice treated with conventional or scFv
CAR-T cells is shown. (C) P1A-specific TCR-transgenic T cells were injected into the miceand considered as host-derived tumor-specific T cells. After the treatment withconventional or scFv CAR-T cells, the number of P1A-specific TCR-transgenic T cells in thetumor tissues is shown. ***; P<0.001. **; P<0.005.

Thus, we developed next-generation CAR-T cell technologies effective for solid tumors.
Out studies suggest the importance of accumulation of anti-tumor immune cells in tumor
tissues, induction of endogenous polyclonal T cell responses against tumor likely due toepitope spreading, and attenuation of immune-inhibitory tumor microenvironment in thetreatment of solid tumors.

Koji Tamada, MD, PhD
1986-1992
1994-1998
1998-2005
2005-2008
2008-20112011-present

MD, Kyushu University
PhD, Kyushu University, Graduate School of Medicine
Postdoctoral researcher, Mayo Clinic
Assistant Professor, Johns Hopkins University
Associate Professor, University of Maryland
Professor, Department of Immunology, Yamaguchi
University Graduate School of Medicine

107

THE SYNTHETIC FUTURE OF CAR T CELL CANCERIMMUNOTHERAPY

Michael C. Jensen
University of Washington School of Medicine
Department of Pediatrics, Hematology-Oncology
Ben Towne Center for Childhood Cancer Research/Seattle Children's Research Institute1100 Olive Way, Suite 100, Seattle, WA 98101, USA(michael.jensen@seattlechildrens.org)

CD19-specific chimeric antigen receptor (CAR)-expressing autologous T cells
administered following lymphodepleting chemotherapy can induce clinical remissions in
B-lineage malignancies including refractory pediatric acute lymphoblastic leukemia (BALL), irrespective of disease burden and anatomic dissemination. Obstacles to broadclinical deployment of this treatment are (1) a high failure rate in CAR-T cellmanufacturing, (2) heterogeneity of anti-tumor responses, and (3) severe modalityassociated toxicities in some patients. While structure-function attributes of the various
CD19-specific CARs have been delineated in preclinical models, variations among thepolyclonal transduced CAR-T cell products tested in clinical trials have limited
opportunities for the systematic study of product attributes related to cellular composition,differentiation, and transgene expression that impact therapeutic potency and safety.
We engineered a CAR-T cell product that comprises a defined 1:1 ratio of CD4+/CD8+
CAR-T cells, selects for uniform high-level CAR expression, and limits activation-induced
differentiation of CD4 + /CD8 + T cells by using homeostatic cytokines for CAR-T cell
expansion. Herein we report a robust intent-to-treat (ITT) product manufacturing successrate in minimally selected heavily pre-treated patients, a 93% MRD-negative (MRD-neg)
complete remission (CR) rate resulting in 89% overall efficacy based on the ITT population,with a tolerable side effect profile. We report on duration of leukemic remissions, and theimpact of lymphodepleting regimen and CD19 antigen burden on sustained CAR
engraftment and durable remissions. These data establish the feasibility of this advanced
manufacturing platform and support further study of this highly defined CD19 CAR-T cell
product. This study also has revealed shortcomings of the technology in its current
iteration. Short engraftment as measured by duration of B cell aplasia less than 63 days is a

108

M. C. Jensen

risk factor for relapse (RR>33) and low CD19 antigen burden at the time of T cell transfer is
a risk for short BCA. Patients with longer engraftment are still at risk for CD19 antigen
escape relapse. I will describe new technologies such as T cell antigen presenting cells that
express CD19 as a booster product to reproliferate CD19CAR T cells in patients and ourstrategy to manufacture CD19xCD22 dual specific CAR T products that are anticipated toreduce antigen loss escape. These next generation trials have commenced at Seattle
Children’s and their current status will be described.
References
1. Finney, O.C. et al., 2019. CD19 CAR T cell product and disease attributes predictleukemia remission durability. The Journal of Clinical Investigation, 130.
2. Gardner, R. et al., 2016. Acquisition of a CD19-negative myeloid phenotype allows
immune escape of MLL-rearranged B-ALL from CD19 CAR-T-cell therapy. Blood,
127(20), pp.2406-2410.
3. Gardner, R.A. et al., 2017. Intent-to-treat leukemia remission by CD19 CAR T cells ofdefined formulation and dose in children and young adults. Blood, 129(25), pp.3322-3331.
4. Gust, J. et al., 2019. Glial injury in neurotoxicity after pediatric CD19-directed CAR-T
cell therapy. Annals of Neurology.
5. Lu, Y.J. et al., 2019. Preclinical Evaluation of Bispecific Adaptor Molecule Controlled
Folate Receptor CAR-T Cell Therapy With Special Focus on Pediatric Malignancies.
Frontiers in oncology, 9, p.151.
6. Taraseviciute, A. et al., 2018. Chimeric Antigen Receptor T Cell-Mediated Neurotoxicity
in Nonhuman Primates. Cancer discovery, 8(6), pp.750-763.
7. Turtle, C.J. et al., 2016. CD19 CAR-T cells of defined CD4+:CD8+ composition in adult
B cell ALL patients. The Journal of Clinical Investigation, 126(6), pp.2123-2138.

Michael C. Jensen, MD
1986-1990
1990-1993
1993-1997

2010-present
2011-present

MD; University of Pennsylvania School of Medicine
Intern and Resident in Pediatrics, Children’s Hospital ofNorthern California
Fellow in Pediatric Hematology-Oncology, Universityof Washington and the Fred Hutchinson Cancer
Research Center
Jim & Jan Sinegal Endowed Professor, Pediatrics,
University of Washington School of Medicine
Director, Ben Towne Center for Childhood Cancer
Research, Seattle Children’s Research Institute109

DECIPHERING AND TARGETING CLONAL EVOLUTION IN
MYELOID MALIGNANCIES

Linde A. Miles, Robert L. Bowman, Aaron Viny, and Ross L. Levine
Human Oncology & Pathogenesis Program
Department of Medicine
Center for Hematologic Malignancies
Memorial Sloan Kettering Cancer Center
1275 York Avenue, New York, NY 10065, USA(leviner@mskcc.org)

Genomic studies of myeloid malignancies (MM), including acute myeloid leukemia(AML), myeloproliferative neoplasms (MPN) and myelodysplasia (MDS), identified
mutations with different allele frequencies. Recent studies of clonal hematopoiesis (CH)discovered a subset of MM disease alleles, while other alleles are only observed in overt
MM. These observations suggest an important pathogenetic role for the chronology ofmutational acquisition. The most common CH mutations are in DNMT3A, TET2, and
ASXL1. Subjects with CH have reduced overall survival and carry an increased risk ofsubsequent hematologic malignancies and increased cardiovascular risk21,22. CHIP isobserved in 25% of advanced solid tumor patients and is associated with an increased riskof progression of their primary malignancy and with increased risk for subsequent myeloid
malignancies. These data resonate with in vivo studies showing that alterations in Tet2,
Dnmt3A, and Asxl1 in murine models lead to stem cell expansion.
Green and colleagues have used genomic studies of clonal colonies in MPN patientswith concurrent JAK2 and TET2 mutations to show that the order of mutational acquisition
can differ, such that either JAK2 or TET2 mutations could be the initiating or subsequent
event29. They also showed that the order of mutations (e.g. JAK2 first vs. TET2 first)
impacted clinical parameters, sensitivity to ruxolitinib in vitro, and transcriptional
programs. This seminal study demonstrates that the order of genetic events is a criticalfeature governing MPN pathogenesis and progression. Our studies aim to use similar
approaches to map evolution and assess the functional and therapeutic relevance ofevolution from CH to MPN and to AML. Although bulk sequencing informs
prognostication, it cannot distinguish which mutations occur in the same clone and cannot

110

R. L. Levine et al.

offer definitive evidence of mutational order. Delineation of clonal architecture at the single
cell level is key to understanding how the sequential/parallel acquisition of somatic
mutations contributes to myeloid transformation.
In order to elucidate the clonal structure of MM, we designed a custom single cell 109
amplicon panel of the most frequently mutated amplicons in 50 MM genes using theMission Bio Tapestri v2 platform. Viable cells were sorted from 90 samples from 78 patientswith CH, AML, and MPN/post-MPN AML followed by single cell amplification/
sequencing. Mutation calls were filtered based on read depth, quality, and alleles
genotyped per cell. We reconstructed a random distribution of clones by permuting
genotype calls across cells and generated empirical p values for each clone. To identify
dominant clones, we used a Poisson test to determine clones were significantly enrichedcompared to the mean clone size. Clones with significant p-values (p <0.05) were used togenerate plots of clonal architecture of each sample. Despite significant clonal complexity,
the majority of MM patients (80%;72/90) present with one (51/90; 56.7%) or two (21/90;
23.3%) dominant clones. These data show there are specific genotypic combinations whichlead to clonal dominance with increased fitness relative to other clones and/or suppressionof minor clones by dominant clone(s). We next investigated whether specific molecularly
defined AML subtypes had increased clonal complexity. FLT3-ITD mutant AML samples
had a significantly greater number of clones (p < 0.002) compared to AML samples withmultiple epigenetic modifier mutations. Similar findings were not observed whencomparing AML samples with epigenetic mutations to RAS pathway mutant samples.
We next investigated whether specific mutations were likely to co-occur/be mutually
exclusive at a single cell level. We observed evidence of oligoclonality in CH, including
parallel acquisition of DNMT3A mutations and clones with multiple mutations in theabsence of progression to MM. By contrast, in MM the dominant clone(s) almost always
harbored multiple epigenetic modifier mutations, suggesting cooperative epigenetic
remodeling in myeloid transformation. Mutations in signaling effectors (FLT3-ITD/TKD;
RAS/RAS) were mutually exclusive. We observed distinct FLT3-mutant clones in FLT3mutant AML patients and parallel acquisition of different RAS pathway mutations. We
used this data to develop clonal architecture trees in each patient, giving us a definitive
picture of mutational acquisition and transformation at a single cell level. We calculated a
Shannon diversity score and observed an increase in clonal complexity with disease
evolution; CH samples had the lowest clonal diversity and FLT3-ITD AML patients thehighest clonal diversity.We extended our findings by combining cell surface marker
assessment and single cell mutational analysis. Patient samples were stained with anantibody cocktail of 6 oligo-conjugated antibodies with barcode tags prior to single cell
sequencing, which allowed simultaneous acquisition of single cell immunophenotypic andBasic Mechanisms of Carcinogenesis

111

genotypic data. This allows us to identify distinct populations of stem/progenitor cellswith distinct clonal/mutational repertoires. This includes studies of CD34+ and CD34AML, which show striking differences in mutational representation in different
stem/progenitor compartments. In summary, these studies of clonal architecture at asingle cell level provide us novel insights into the pathogenesis of myeloid transformationand give us new insights into how clonal complexity contributes to disease progression.
We have also sought to identify novel targets in CH and in overt myeloid malignancies.
We have begun to investigate whether CHIP disease alleles render cells sensitive to specifictherapeutic targets. We recently described a conditional Jak1 allele and showed that Jak1 iscritical for stress hematopoiesis and for inflammatory signaling in hematopoietic
stem/progenitor cells34. We therefore investigated whether Jak1 was a liability in Jak2V617F
and Tet2 mutant stem cells by crossing Jak1 knockout mice to both disease alleles andassessing stem cell function in competitive transplant assays. Jak1 loss lead to a reversal of
the fitness advantage of Jak2V617F and Tet2 mutant stem cells, consistent with Jak1
dependency in both models of CHIP. We have now initiated preclinical therapeutic studies
in collaboration with Incyte (MTA) to test a clinical Jak1inhibitor (INCB39110) in order tobuild towards the first therapeutic trial in CHIP.
We have also adopted a stromal co-culture system developed by Shahih Rafii and Jason
Butler, which allows us to culture wild-type and CH/MPN/AML cells with defined
genotypes in culture for 2-3 weeks, with 10-100 fold expansion and retained ability forlong-term engraftment and disease propagation in vivo. This system can be used to test
candidate therapeutic agents and genetic targets (CRISPR screens) for their ability toabrogate the fitness advantage of CH/MPN/AML cells relative to wild-type cells in vitro
and in vivo. We can do this in CD45.2/1 co-culture systems where CH/MPN/AML arestudied in the same in vitro/in vivo context as wild-type cells, or in systems with >1
CH/MPN/AML clone in order to delineate therapeutic dependencies in a complex clonal
milieu.
In summary, our studies delineate the trajectories which drive hematopoietic
stem/progenitor cells towards CH and overt myeloid malignancies, and we are using thisinsight to identify novel therapeutic targets aimed to intercept clonal evolution fortherapeutic impact.

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R. L. Levine et al.

Disclosures

Genetics and Therapy of Clonal Hematopoiesis andMyeloid Malignancies• Supervisory Board/BoD (equity): Qiagen• Scientific Advisory Board (equity): Loxo (Lilly), C4 Therapeutics, Imago, Isoplexis, Auron• Consultant/Honoraria: Roche, Celgene, Novartis, Gilead, Incyte, Janssen, Morphosys, Astellas
Ross L. Levine M.D.
Molecular Cancer Medicine Service
Human Oncology and Pathogenesis Program
Leukemia Service, Department of Medicine
Center for Hematologic Malignancies
Center for Epigenetics Research
Memorial Sloan Kettering Cancer Center• Grant Support: Roche, Celgene, Prelude

AML Mutations and Their Effect on the Epigenetic state

Acute Myeloid Leukemia: Poor Overall Survival• Mutations can indirectly or directly alter theepigenetic state of cancer cells: leukemia and humanmalignancies in general• Many are associated with adverse outcome- need fornovel biologic and therapeutic insights• Mutations in epigenetic modifiers and gene
regulatory factors increase in incidence with age as
prognosis worsens• DNA Modifications: TET2, IDH1/2, DNMT3A• Chromatin Modulators: EZH2, MLL-PTD• DNA topology: cohesin mutations• RNA splicing: SRSF1, SF3B1…

• Even with intensive induction chemotherapy/transplantation most patients die of their disease newinsights are needed• Only 2 classes of agents approved for AML 1980-2017 (hypomethylating agents, gemtuzumab)• AML is a disease of the aging population: median age 68 years, worsening prognosis with increasingage
Issa, Kantarjian et al, Cancer 2008

Clonal Hematopoiesis

Mutations in Genes Which Regulate DNA Modifications in AML• Nonrandom X-inactivation rations in blood cells of female elderly subjects consistent withclonal outgrowth (Busque, Gilliland)• Mutations in TET2, DNMT3A
are common in AML (MDS,MPN…)• Age-dependent incidence(<5% pediatric AML, 40-50%
of AML in older adults)• How do these alleles
contribute to transformation?
• Are there therapeutic targets
specific to AML with
mutations in epigenetic
regulators?

• Increased myeloid bias, risk of myeloid leukemias with increasing age• Multiple hypotheses for mechanism:• Caused by mutations conferring selective growth advantage in stem cells.
• Stochastic clonal dominance secondary to stem cell depletion• Genetic trait•

Hypothesized clonal hematopoiesis due to somatic mutations-> exome sequencing of
granulocyte/normal DNA on elderly subjects with clonal hematopoiesis*Busque, Patel, et al. Nature Genetics 2012

Somatic TET2 Mutations in Clonal Hematopoiesis• TET2 is a commonly
mutated leukemia gene
(8-10% of AML) withage-dependent incidence• 3-5% of healthy
subjects>60 have TET2
mutations• Identical loss-of-function
mutations as those seen
in AML, MDS, MPN

Clonal Hematopoiesis and Somatic Mutations• Premalignant clonal state induced by somatic mutations in hematopoietic stem cells• Mutations in TET2 in CH (Busque et al. Nature Genetics 2012)• DNMT3A, TET2, IDH1/2 mutations in preleukemic stem cells (Jan et al. STM 2012, Shlush et al.
Nature 2014)• Somatic mutations in blood cells in population based cohorts (Jaiswal/Ebert et al., McCarroll et al.
NEJM)• Implications of this on genomic profiling in the clinical setting are expanding• Identification of somatic mutations in known leukemia disease alleles in patients with solid tumors• Following MRD: cannot follow a single mutation as residual CHmay not always drive relapse• Increased risk of cardiovascular disease (Ebert et al, Fuster/Walsh et al.)• May represent the first frontier for effective malignancy prevention studies• Identify and intervene on patients with somatic mutations to prevent hematopoietic malignancies
and other sequelae*Busque, Patel, et al. Nature Genetics 2012113

Basic Mechanisms of Carcinogenesis

Clonal Hematopoiesis and Evolution to Hematopoietic Malignancies
Clonal Hematopoiesis w/ Somatic
Mutations
DNMT3A, TET2, ASXL1, JAK2…

Delineating Clonal Heterogeneity

Myeloid
Malignancy
Number of patients

300

Acquisition of
Additional mutations

200

100

0
1

2

3

4

5

7

11

Number of Mutations per Patient•••

What are the events which drive CH •What is the implication of CH on
clinical outcome, including
cardiovascular disease and•epithelial tumors
How do we manage/follow patients
who are found with CH in theclinical context••
What drives the acquisition of
additional mutations (mutagenesis,•
selection)
Does the stem/progenitor
compartment which acquires
additional mutations dictate risk of •transformation
What microenvironmental factors
promote expansion of specificclones (Meisel et al. Nature 2018)• See CH/leukemia patients with 2, 3, 4+ mutations including co-occurring mutations in known,
validated leukemia alleles

Are CH mutations required formaintenance of the leukemic clone
How do subsequent mutations
cooperate with CH alleles to drive
disease phenotype
Are there therapeutic implications oftargeting different mutant alleles
including CH mutations• Can we delineate if in same/different clone->does this have biologic or clinical relevance• Can we use this to better model/understand pathogenesis of myeloid malignancies

Mission Bio Tapestri-Based DNA-sequencing To Delineate Mutations in Single
Cells

Single Cell Cell DNA Sequencing in Clonal Hematopoiesis• Barcode based sequencing of single
cells

HSS‐99HSS‐168

DNMT3A R736C
VAF 4%

DNMT3A Y528*
VAF 9%• Designed a custom panel of 100+
amplicons for top CH/myeloid genes
No co‐mutation

400

Cell Count

300

215
200

100

81
17

10

3

0

• Delineate clonal trajectories andarchitecture from HSC->CH>myeloid malignancies

2

DNMT3A:p.Y660C• Analysis of CH patients with multiple mutations to provide definitive evidence of clonalarchitecture and mutant order• mutational evolution in a single clone vs. expansion of >1 clone->incidence, clinicalrelevance

DNMT3A:p.Y481*

DNMT3A:p.S770L

DNMT3A:p.D579G

Genotype

WT

Heterozygous

Linde Miles

Homozygous

Bobby Bowman

2. Patients with ≥ 2 mutations in epigenetic modifiers – mutations are most commonly inthe same cell and same dominant clone->can track order of mutational acquisition

What We’ve Learned So Far With Single Cell DNA Sequencing
1. The majority of AML patients have 1-2 dominant clone(s)
800

800

620
600

639

Cell Count

Cell Count

600

400

400

200

200

10

0

3

2

2

1

1

1

1

1

52

44

31

30

30

21

17

0

8

6

WT

Heterozygous

6

4

3

3

3

2

2

1

1

KRAS:p.G60V

DNMT3A:p.W313*

KRAS:p.G12D

Mutation

Mutation

TET2:p.R1261H

RUNX1:p.R162G

NRAS:p.G13R

IDH2:p.R172K

DNMT3A:p.R899Afs*7

SETBP1:p.P1286R
PTPN11:p.G503A

IDH2:p.H175Mfs*74

Genotype

WT

Heterozygous

Homozygous

Genotype

Linde Miles; Bobby Bowman

3. Patterns assumed from biological studies hold true – FLT3-ITD, FLT3-TKD, RAS
pathway mutations mutually exclusive

1500

Homozygous

Linde Miles; Bobby Bowman

4. Clonal Diversity Increases with Disease Progression/Evolution

4

**

1394

***

1000

704

648

500

347
248

181

143

133

121

109

94

0
NPM1:p.W288Cfs*12
FLT3_INS_ 28608282_TTATCTGAGGA
WT1:p.P381Lfs*76
FLT3:p.D835Y

Shannon Diversity Score

Cell Count***

Mutation

M utation

DNMT3A R326C
VAF 1%• Can be used for informative samples
to provide single cell evidence of
clonal architecture and mutant order

368

***

3

***

2

1

NRAS:p.Q61R
FLT3:p.L601_K602insGGYDL

0

FLT3_INS_ 28608252_GAGATCATATCCCCC
PTPN11:p.E76Q

CH
Genotype

WT

Heterozygous

Post MPN

Epigenetic

RAS

FLT3−ITD

Patient Group

Homozygous

Linde Miles; Bobby Bowman

Linde Miles; Bobby Bowman

114

R. L. Levine et al.

Does Mutational Order Matter: Mutational Order Modulates Transformation by
Concurrent TET2/NPM1c Mutations

Where We’re Going->Single Cell Mutational/Cell Surface Marker Expression
Current Tapestri Workflow

Percent survival

100
75
50

TET2 first25

Npm1c first
0
0

10

20

30

Weeks Post Second Manipulation

K/uL

M/uL
5

0
t

• Perform library prep on both proteinderived library and DNA-derived library

Bead

Bead barcode

AbTag

CommonMission Bio

N

TE

T2

firs

fir
st

fir
st
TE
T2

firs
t

1c
N

pm

TE
T2

Common

200

0
fir
st

firs
t

firs
t
2

3a

1c

ry

st

ry
T2
TE

TE

T2

1c

2n

da

fir

st
fir
T2

0

t

20

N
pm
1c

0

Antibody withconjugated tag
oligo• Stain cells with oligo-conjugatedantibodies

400

firs

30

10

2

TE

y

3a
ry

st
1c

T2
TE

fir

TE
T2

2n
da
r

st
fir
T2
TE

pm
N

K/uL

K/uL
50

4

0

0

TE
T

10

pm

20

• Merge Ab-Seq technology with Tapestri
workflow

600

10

N

% of CD45.2 Lin- Cells

30

800

15

40

100

6

PLT

RBC

Monocyte #
50

12
10

1c

WBC

14

pm

WBM LSKs
16

Npm% of CD45.2 Lin- Cells

cKIT+ Cells in Blood
70
65
60
55
50

Linde Miles

Combining scDNA Sequencing with Single Cell Protein Profiling

Combining scDNA Sequencing with Single Cell Protein Profiling

Mutation% Mutated
Cells

scDNA VAF

Mutation% Mutated
Cells

scDNA VAFDNMT3A R635W

67%

37.2%

DNMT3A R635W

67%

37.2%

IDH2 R140Q

69%

38.3%

IDH2 R140Q

69%

38.3%

Linde Miles; Aik Ooi (MB); Pedro Mendez (MB)Integrated Analysis of Surface Marker/Mutational Data
Can identify trends in cell surface expressionthat correlate with mutational status:- CD34 & NPM1
- IDH2/DNMT3A co-mutation vs. CD34/CD38

Linde Miles; Aik Ooi (MB); Pedro Mendez (MB)Lessons from Functional Studies of CH Alleles to Date
• DNMT3A, TET2, ASXL1 loss leads to enhanced stem/progenitor numbers in vivo andto myeloid bias->critical features of CH• Variable phenotype in classical self-renewal assays• Not enough work done on specific mutations seen in CH/myeloid malignancies• Murine studies have been the backbone of mechanistic work to date->need similar
work in human systems (though murine phenotypes are consistent!)

0: homozygous ref (WT)
1 : heterozygous alt
2: homozygous alt
3: NOCALL (not enough information to make agenotype call)

Need for Better In Vitro Systems for CH/Leukemia Biology• Murine/Human hematopoietic cells historically do not culture well

• Little is known about what are the actual downstream targets which mediateenhanced fitness/self-renewal by these specific mutations->bulk RNA has not beenhighly instructive to date

Extended Culture over Bone Marrow Endothelial Cells Maintains Stem Cell Self-Renewal

Normal HSPC

MLL-AF9 Leukemia• Other than MLL-derived models, leukemia cells do not transplant backinto recipients after extended culture/transduction• Many biologic/therapeutic perturbations have longer-term readouts thancurrent culture systems except methocellulose which has limited
throughput/scale

Butler et al Rafii, Cell Stem Cell 2010

Poulos et al, Exp Hema 2014

Jason Butler, Shahin Rafii

Basic Mechanisms of Carcinogenesis

BMEC Cultures Preserve/Expand LT-HSCs With Preserved Self-Renewal

Assessing Relative Impact of CH Alleles on Fitness inCompetitive BMEC Assays• Can optimize
expansion/transplantability by alteringcytokines in culture40

20

1-4 weeks
Ex vivo

CD45.2 Mutant

Secondary Transplant, 4 weeks% Peripheral Chimerism

Transplant andevaluate chimerism

Tet2/Dnmt3a/Asxl1 KO

80

BMT

Figure 8: Schematic depicting co culture with bone marrow endothelial cells.
LT-HSC from mutant or wild type mice will be isolate and plated over BMEC andevaluated over the course of 1-4 weeks for their response to therapeutic perturbation.

Wildtype

100

Endothelial Cell

60
40
20

40

week 12 week 16

39week 836

week 4

38

0

0

37

% Chimerism, periphery

60

Expand, perturb andevaluate competition

Plate LT-HSC
CD45.1 Wildtype• Robust engraftment insecondary/tertiary transplants
Long term engraftment

115

Mouse Number

Bobby Bowman

ASXL Loss Promotes Expansion of LT-HSC>Progenitors

scRNA-seq of ASXL1 KO/WT BMEC Cultures• Can read out relative
fitness of mutant/WT
cells in vitro->in vivo

• Can see differences in
specific cell populationsin BMEC cultures• Can be done incompetitive setting withfull assessment ofhematopoietic ontogeny• Higher Ki67, lower MPO
in ASXL1 KO myeloid
progenitors

TET2 KO Cells Have Distinct Phenotype In BMEC-> In Vivo

Therapeutic Studies in BMEC Co-Cultures
Cytarabine
Hematopoietic Fraction

Decitabine
Hematopoietic Fraction

50

0

8

40

WT
TET2

20

% LSK

% LSK

TET2

4

Control 100nM 250nM 500nM 1000nM

N
e
L- g
K
+S
M
EP
G
M
LS
P
K
M
as
LS Cd
t
K 15
C 0+ LS
LS d1
C K
K 50+ d48C
LS d1 Cd K 50 48
C -C +
d1
50 d48
-C +
d4
8-

Lin

Venetoclax
LSK Fraction• Read out mutant vs.
wild-type impact on
viability, differentiation
including in defined
stem/progenitor states

4
2

0

0

0

Lessons from BMEC Studies to Date

Control 100nM 250nM 500nM 1000nM

8
6

20
10

2

• Assess efficacy of apanel of anti-leukemic
therapies in defined
CH/AML genotypes

500

Decitabine
LSK Fraction

30

6

WT

40

50

Control 100nM 250nM 500nM 1000nM

Cytarabine
LSK Fraction

80
60

100

0

Control 100nM 250nM 500nM 1000nM

Myeloid‐biased output in vivo

BMEC A Tet2

Venetoclax
Hematopoietic Fraction

100% Hematopoietic cells

TET2% Hematopoietic cells

100

WT

% Hematopoietic cells

Increased engraftment in vivo

TET2‐KO

% LSK

WT

0
Control 100nM 250nM 500nM 1000nM

Control

5nM

50nM 250nM 500nM• All experiments canproceed from ex vivo>in vivo readouts given
transplantability is
maintained

How Can We Therapeutically Target Clonal Hematopoiesis and Prevent Leukemia?
 Therapies which restore wild-type CH TSG function (vitamin C in Tet2-mutant patients) or target mutant

 Can investigate the biological function of specific CH/myeloid malignancy alleles in vitro on an

specific dependencies

extended time-frame, allowing studies of differentiation, self-renewal
 Therapies which target mutant oncogenes which drive CH (JAK2V617F, IDHm)

 Can maintain in vivo propagation after extended culture->allows for in vitro perturbations
followed by longer term in vivo readouts Reduce use of chemoradiotherapy in solid tumor patients at highest risk of leukemia->need prospective
studies Can co-culture mutant/wild-type cells and look at relative effect of specific perturbations(therapeutics, CRISPR) on specific genotypes in vitro->in vivo

 Can use this as a system to efficiently delete/activate knock-in/knock-out alleles

 Alter the fitness landscape and reduce the clonal advantage of mutant CH clones
 Microbial induced inflammation increases fitness advantage of TET2-mutant CH (Jabri) Can we specifically target inflammation and abrogate the fitness advantage of CH clones?
 Small molecules which attenuate inflammation
 Life-style/exercise interventions (Lee Jones)

116

R. L. Levine et al.

Studies of JAK1 loss in the Hematopoietic Compartment->Potential
New Therapeutic Opportunity in CH?

JAK1 Loss Results in Reduced Competitive Transplantation Capacity• Germline Jak1 knock-out mice die shortly after death due todevelopmental/neurologic alterations->developed a
conditional KO allele (Schreiber lab 1996)- Similar results with RosaCreRT – not a pIpC effect- Complete absence of JAK1-deficient stem cells in secondary/serial transplant assays and marked reduction incolony output in vitro- Reduced survival/protection in setting of 5FU-induced myelosuppression
Kleppe et al. Cell Stem Cell 2017

Constitutive Jak2 activation Cannot Fully Rescue Jak1-mediated Stem Cell DefectHow do we Target TET2 Mutant Pre-Leukemic/Leukemic cells: JAK1/cytokine Signaling

CD45.2 [%]

80

Ctrl
J1
J1/T2
T2

60
40
20

16

8

12

4

st
-p
Po

Pre

-p

Ip
C

Ip
C

0

- Polycythemic disease in primary mice is minimally affected by JAK1 loss

• JAK1 loss abrogates competitive advantage of TET2-mutant stem cells- Transplantablity of disease is markedly attenuated• Clinical JAK1 (JAK2 sparing) inhibitors are in clinical development->can see if genetic requirementtranslates to pharmacologic inhibitors- Complete loss of MPN cells in secondary->tertiary recipients
Conclusion: JAK1 is required for survival of JAK2-mutant MPN stem cells

JAK1 Inhibition Reduces Viability of TET2 and TET2/FLT3 Mutant Cells in Vitro

TET2 cells (9th plating)

TET2/FLT3 cells (9th plating)

500

60

e

ug
dr
N
O

M

0

cl

g
dr
u
O

Ve
hi
cl
e

• The mechanisms by which CH mutations promote clonal expansion and leukemictransformation in an aging hematopoietic environment remain to be delineated

20

N

5u
M

1u
M

0

1u

100

40

Ve
hi

Colony #

200

M

300

5u

Colony #400• In vivo studies currently ongoing in TET2-mutant stem cells and TET2-mutant leuekemia cells with aim
to move to clinical trials in CH setting• Testing in myeloid malignancies->including in combination with chemotherapy to see if cytokine/JAK1
signaling is used to allow AML cells to survive in setting of therapeutic stressors

Acknowledgements
Levine Lab• Bobby Bowman• Linde Miles• Michael Waarts• Wenbin Xiao
• Pablo Sanchez Vella• Aaron Viny
• Alan Shih• Andrew Dunbar• Fredericke Pastore• Sheng Cai• Tiffany Merlinsky• Nicole Delgaudio• Shira Redlich• Isabelle Csete• Jason Butler (Hackensack)• Sara Meyer (Jefferson)• Martin Carroll (Penn)• Iannis Aifantis (NYU)

MSKCC• Mike Berger• Ahmet Zemir• Kelly Bolton• Larry Norton• Elizabeth Comen• Elli Papaemanuil• Luis Diaz

Conclusions• Clonal hematopoiesis is common in aging healthy subjects and in patients withconcurrent solid tumors• Likely contribute to pathogenesis of many aging associated diseases• Common pre-leukemic state

Mission Bio• Robert Durruthy-Durruthy• Aik Ooi
• Pedro Mendez

NCI (R35, RO1), NIH (Mouse Models U54)MSK Precision Interception Program
Waxman Foundation• There may be specific therapeutic dependences in CH which can be used to abrogate
this interaction and to reduce the risk of secondary sequelae including secondary
malignancies, cardiovascular events, and other aging-related diseases

Basic Mechanisms of Carcinogenesis

117

References
1. Busque L, Patel JP, Figueroa ME, et al. Recurrent somatic TET2 mutations in normal
elderly individuals with clonal hematopoiesis. Nat Genet. 2012;44(11):1179-1181.
2. Xie M, Lu C, Wang J, et al. Age-related mutations associated with clonal hematopoietic
expansion and malignancies. Nat Med. 2014;20(12):1472-1478.
3. Jaiswal S, Fontanillas P, Flannick J, et al. Age-related clonal hematopoiesis associatedwith adverse outcomes. N Engl J Med. 2014;371(26):2488-2498.
4. Genovese G, Kahler AK, Handsaker RE, et al. Clonal hematopoiesis and blood-cancer
risk inferred from blood DNA sequence. N Engl J Med. 2014;371(26):2477-2487.
5. McKerrell T, Park N, Moreno T, et al. Leukemia-associated somatic mutations drive distinct patterns of age-related clonal hemopoiesis. Cell Rep. 2015;10(8):1239-1245.
6. Jaiswal S, Natarajan P, Silver AJ, et al. Clonal Hematopoiesis and Risk ofAtherosclerotic Cardiovascular Disease. N Engl J Med. 2017.
7. Coombs CC, Zehir A, Devlin SM, et al. Therapy-Related Clonal Hematopoiesis inPatients with Non-hematologic Cancers Is Common and Associated with Adverse
Clinical Outcomes. Cell Stem Cell. 2017;21(3):374-382 e374.
8. Abdel-Wahab O, Gao J, Adli M, et al. Deletion of Asxl1 results in myelodysplasia andsevere developmental defects in vivo. J Exp Med. 2013;210(12):2641-2659.
9. Ortmann CA, Kent DG, Nangalia J, et al. Effect of mutation order on myeloproliferative
neoplasms. N Engl J Med. 2015;372(7):601-612.
10. Kleppe M, Spitzer MH, Li S, et al. Jak1 Integrates Cytokine Sensing to Regulate
Hematopoietic Stem Cell Function and Stress Hematopoiesis. Cell Stem Cell.
2017;21(4):489-501 e487.
11. Butler JM, Rafii S. Generation of a vascular niche for studying stem cell homeostasis.
Methods Mol Biol. 2012;904:221-233.

Ross L. Levine, MD
2007-20112011-20142011-20152014-present
2015-present
2015-present
2016-present

Assistant Member, Memorial Sloan Kettering Cancer
Center, Assistant Professor of Medicine, Weill Cornell
Medical College
Associate Member, Memorial Sloan Kettering Cancer
Center
Associate Professor of Medicine, Weill Cornell Medical
College
Member, Memorial Sloan Kettering Cancer Center,
Tenure, Full Time
Professor of Medicine, Weill Cornell Medical College
Center for Epigenetics Research, MSKCC
Director, MSK Center for Hematologic Malignancies

118

CELL COMPETITION DURING CANCER DEVELOPMENT

Yasuyuki Fujita
Division of Molecular Oncology
Institute for Genetic Medicine, Hokkaido University
Kita-15, Nishi-8, Kita-ku, Sapporo 060-0815, Japan(yasu@igm.hokudai.ac.jp)

At the initial step of carcinogenesis, transformation occurs in a single cell within anepithelial sheet, and the emerging transformed cells grow while being surrounded bynormal epithelial cells. However, it was not clear what happens at the boundary between
normal and transformed cells. Using newly established cell culture and mouse model
systems, we have shown that various phenomena can occur at the interface between
normal and transformed epithelial cells [1-3]. For example, when Ras- or Src-transformed
cells are surrounded by normal epithelial cells, various signaling pathways are activated in
the transformed cells and they are often eliminated from the apical surface of the epithelialmonolayer (Figure 1) [4,5]. These phenomena are not observed when transformed cells
alone are present, suggesting that the presence of surrounding normal cells affects thesignaling pathways and fate of transformed cells. Furthermore, we have demonstrated that
normal epithelial cells can recognize and actively eliminate neighboring transformed cells
and named this process EDAC (Epithelial Defense Against Cancer) (Figure 2) [6].
For cancer progression, an additional oncogenic mutation should occur withintransformed clones, acquiring more malignant phenotypes. During cancer development,
double-transformed cells would expand their territory while being surrounded by singletransformed cells; however, it remains elusive what occurs at the boundary between them.
Recently, we have found that when RasV12 expression is induced within a monolayer of
Scribble-knockdown epithelial cells, the substantial number of Scribble-knockdown cells
surrounding the RasV12/Scribble-knockdown cells undergo apoptosis with fragmented
and/or condensed nuclei. Moreover, the dead Scribble-knockdown cells are often engulfed
by the neighboring RasV12/Scribble-knockdown. These results suggest that the sequential
accumulation of oncogenic mutations can affect the consequence of cell competition.

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figure 1

Apical extrusion of RasV12-transformed cells from a monolayer of normal epithelial cells.
Arrows indicate RasV12-transformed cells that are apically extruded.

figure 2

Schematics for EDAC ( Epithelial Defense Against Cancer)

A series of recent studies using next-generation sequencing have revealed that invarious epithelial tissues of the adult human there are a number of precancerous lesions
with a single or double oncogenic mutation(s) that apparently look normal. Indeed, thisissue was intensively discussed between the speakers and audience during the symposium.
The notion that epithelia possess the anti-tumor activity EDAC implies that cellcompetition study can be potentially applied to cancer prevention: early diagnosis andprophylactic eradication of these precancerous lesions. Several cytosolic proteins such asfilamin and vimentin accumulate at the interface between normal and transformed cells [6],suggesting the presence of plasma membrane proteins that recruit them to the intercellularadhesion sites. If such membrane proteins are identified, they can be used as a boundary
marker to detect the outer margin of pre-malignant cell groups. The establishment of120

Y. Fujita

diagnostic scheme for precancerous lesions would profoundly influence pathological andclinical science. It will enable us not only to detect precancerous lesions in our bodies, butalso to monitor their fate upon prophylactic treatment. That would boost the search forchemical drugs against the early stage of tumors that has just recently begun. For this end,
EDAC can be a potent candidate for drug target. Indeed, a cell competition-based highthroughput screening platform has been established for chemical compounds that enhanceelimination of transformed cells from the monolayer of normal epithelial cells [7]. Further
advance of cell competition studies would create a brand-new dimension in cancer biology
and clinical science.
References
1. Maruyama, T. and Fujita, Y. Cell competition in mammals – novel homeostatic
machinery for embryonic development and cancer prevention. Current Opinion in Cell
Biology 2017, 15;48:106-112.
2. Sasaki, A., Nagatake,T., Egami, R., Gu, G., Takigawa, I., Ikeda, W., Nakatani, T.,
Kunisawa, J. and Fujita, Y. Obesity suppresses cell competition-mediated apical elimination of RasV12-transformed cells from epithelial tissues. Cell Reports 2018, 23(4):974982.
3. Kon, S., Ishibashi, K., Katoh, H., Kitamoto, S., Shirai, T., Tanaka, S., Kajita, M., Ishikawa,S., Yamauchi, H., Yako, Y., Kamasaki, T., Matsumoto, T., Watanabe, H., Egami, R.,
Sasaki, A., Nishikawa, A., Kameda, I., Maruyama, T., Narumi, R., Morita, T., Sasaki, Y.,
Enoki, R., Honma, S., Imamura, H., Oshima, M., Soga, T., Miyazaki, J., Duchen, M. R.,
Nam, J.-M., Onodera, Y., Yoshioka, S., Kikuta, J., Ishii, M., Imajo, M., Nishida, E.,
Fujioka, Y., Ohba, Y., Sato, T., and Fujita, Y. Cell competition with normal epithelial
cells promotes apical extrusion of transformed cells through metabolic changes. Nature
Cell Biology 2017, 19(5):530-541.
4. Hogan, C., Dupré-Crochet, S., Norman, M., Kajita, M., Zimmermann, C., Pelling, A.E.,
Piddini, E., Baena-López, L.A., Vincent, J. P., Hosoya, H., Itoh, Y., Pichaud, F. andFujita, Y. Characterization of the interface between normal and transformed epithelial
cells. Nature Cell Biology 2009; 11 (4), 460-467.
5. Kajita, M., Hogan, C., Harris, A. R., Dupre-Crochet, S., Itasaki, N., Kawakami, K.,
Charras, G., Tada, M., and Fujita, Y. Interaction with surrounding normal cells
influences signaling pathways and behavior of Src-transformed cells. Journal of Cell
Science 2010; 123 (Pt2): 171-80.

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6. Kajita M., Sugimura, K., Ohoka, A., Burden, J., Suganuma, H., Ikegawa, M., Shimada, T.,
Kitamura, T., Shindoh, M., Ishikawa, S., Yamamoto, S., Saitoh, S., Yako, Y., Takahashi,R., Okajima, T., Kikuta, J., Maijima, Y., Ishii, M., Tada, M., and Fujita, Y. Filamin acts asa key regulator in epithelial defence against transformed cells. Nature Communications
2015; 5:4428
7. Yamauchi, H., Matsumaru, T., Morita, T., Ishikawa, S., Maenaka, K., Takigawa, I.,
Semba, K., Kon, S. and Fujita, Y. (2015) The cell competition-based high-throughput
screening identifies small compounds that promote the elimination of RasV12-transformed cells from epithelia. Scientific Reports, 5:15336. doi: 10.1038/srep15336.

Yasuyuki Fujita, MD, PhD
2002-20112010-presentGroup Leader, MRC, LMCB, University College
LondonProfessor, Hokkaido University, Institute for Genetic
Medicine, Division of Molecular Oncology

122

NEW CANCER VULNERABILITIES AND THERAPEUTICS IN
THE RNA WORLD

Trey Westbrook
Therapeutic Innovation Center
Department of Molecular & Human Genetics
Baylor College of Medicine
Houston, TX 77030, USA(thomasw@bcm.edu)

The Westbrook team leverages functional genomic and chemical biology approaches todiscover the gene networks driving breast cancer pathogenesis as well as tumor-selective
vulnerabilities. During tumorigenesis, breast cancers acquire genetic alterations that confer
widespread changes in transcriptional, metabolic, and biosynthetic processes. Suchprocesses fuel uncontrolled proliferation and other hallmarks of cancer. However, theselarge-scale changes in the synthesis of macromolecules like RNA and protein also create
new dependencies in breast cancer and other malignancies. Recently, the Westbrook lab
and others discovered a new cellular stress in cancer that we term RNA splicing stress, or
RSS (see Kessler et al, Science; Hsu et al, Nature). Many oncogenes and tumor suppressors
drive collateral RSS, and genetic or pharmacologic inhibition of RNA processing enzymes
can enhance this stress, thus tipping the balance toward selective cancer cell death andinhibiting progression of primary and metastatic breast cancer. Importantly, newtherapeutics that prime RSS have been developed and are advancing in the clinic. In thecurrent study, we have identified a surprising mechanism by which spliceosome-targeted
therapies engage the immune system and selectively kill breast cancers (Figure 1).
Specifically, we have discovered that spliceosome-targeted therapies induce a tumorselective activation of antiviral signaling and downstream recruitment of adaptive immune
compartments including CD8+ CTLs. This presentation will elaborate on the mechanisms
of RSS, the cancer genomic indications that drive RSS, and how this tumor-intrinsic stresscan be leveraged therapeutically for breast cancer patients.

Basic Mechanisms of Carcinogenesis

figure 1

123

Spliceosome-targeted therapies (STTs) kill triple-negative breast cancer (TNBC) throughantiviral (tumor-cell autonomous) signaling and downstream recruitment of adaptive antitumor immune cells.

Trey Westbrook, PhD
1997-2002
2003-2007
2008-present
2017-present

University of Rochester, School of Medicine, Program:
Molecular Virology & Immunology
Postdoctoral Fellow (Mentor: S. Elledge) Dept. of
Genetics, Harvard Medical School
Professor, Dept. of Molecular & Human Genetics,
Baylor College of Medicine
Executive Director, Therapeutic Innovation Center(THINC), Baylor College of Medicine

124

CONCLUDING REMARKS

Hiroyuki Mano
Center for Cancer Genomics and Advanced Therapeutics/Research InstituteNational Cancer Center
5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan(hmano@ncc.go.jp)

On 12 th –14 th November 2019, we had a pleasure to hold the 50 th Commemorative
International Symposium of The Princess Takamatsu Cancer Research Fund with 30
distinguished speakers and about 200 of discussants. We cancer researchers deeply
appreciate the long-standing support from The Princess Takamatsu Cancer Research Fundin many activities. To celebrate this 50th Symposium, the organizers decided to make thisevent special, not dealing with a specific topic, but including a wide range of areas fromground-breaking basic research to cutting-edge precision medicine techniques. The main
theme of the 50th Symposium was thus determined as “New Horizons for Cancer Research andPrecision Medicine”. It was also our privilege to welcome Dr. Margaret Foti, the CEO of
American Association for Cancer Research (AACR). It was also our joy to award The
Nakahara Memorial Lecture Prize to Dr. Kohei Miyazono for his unique and world-leading
research of the TGF-b world.
The distance from basic research to patients has never been closer than today. The way
to diagnose and treat cancer patients is incessantly revolutionized, and suchaccomplishments are swiftly translated to the clinics. In our Symposium, a novel andcomprehensive approach of precision medicine for pediatric cancer in USA wasdemonstrated, and a nation-wide precision medicine initiative under the Japanese healthinsurance system was discussed. Also, the results of large-scale clinical studies to stratify
breast cancer based on gene expression profiles were demonstrated, proving the utility ofthe outcome prediction with expression patterns.
Many facets of cancer genome/epigenome were discussed. The mismatch repair system
was shown to play a critical role in producing neoantigens and to be potentially regulated

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by therapeutic compounds. Chronic inflammation was proved to be a driver in producing
somatic mutations in colorectal carcinoma. Further, genomic/epigenomic status enabled
the classification of the tumors of hepatic lineage. Variants of histone H3 possess recurrent
somatic mutations in some brain tumors, and the role of such genetic anomalies was
hypothesized to shift global gene expression profiles. Resistance to targeted drugs forcancer is almost inevitable. We discussed how secondary mutations responsible for drug
resistance can be monitored and, potentially, be overcome.
Although cancer immunology is probably the most transformed field in the last decadeboth in research and clinics, lack of reliable biomarkers and presence of untargetable
tumors is still the major hurdle in this field. So, many innovative research activities were
presented in our Symposium, from biomarker analyses in tumor microenvironment tonovel CAR-T therapies. Further novel approaches for single cell bioimaging were
proposed.
Noncoding RNAs have been shown to play a plethora of function in onset and progressionof cancer. MicroRNAs can, for instance, affect microenvironment through extracellular
vesicles, and some long noncoding RNAs may regulate cancer metabolism and metastases.
PIK3CA is one of the most frequently mutated genes in cancer, and many inhibitors havebeen tried to suppress the growth of tumors with activating mutations within PIK3CA.
Recent studies indicate that treatments with such inhibitors result in an increase in blood
glucose levels, thereby stimulating insulin receptors and inducing resistance to theinhibitors. Other metabolites such as deoxycholic acid were shown to confer
carcinogenesis. Establishing cancer model systems is critical to simulate multistep
carcinogenesis and to screen effective drugs. In addition to cell lines, many other systems
have been developed to recapitulate cancer environment in non-human conditions. In our
Symposium, insights obtained from such model systems were presented.
Lastly, but never the least, as the chairman of the organizing committee, I deeply thankour co-organizer, Dr. Elaine R. Mardis, for her dedication to make this Symposium so
special. I also thank the other members of the committee to support this Symposium; Drs.
Kohzo Imai, Masanobu Oshima and Ryuzo Ueda. We owe entirely our Secretary General,
Dr. Toshikazu Ushijima, for running the Symposium flawlessly. We are deeply thankful tothe office of The Princess Takamatsu Cancer Research Fund and its chairman Dr. Takao
Sekiya to support this wonderful Symposium. We also appreciate young researchers in
National Cancer Center and Kanazawa University for helping the logistics of thisSymposium. We believe this meeting was priceless for such young scientists as well.
Finally, we deeply appreciate all speakers and discussants who made our Symposium
so memorable. We hope this Symposium has evoked further discussion and collaboration.