Extended Abstracts for the 49th International Symposium of
the Princess Takamatsu Cancer Research Fund, 2018

DECIPHERING, SIMULATING AND
EDITING OF THE CANCER GENOME
November 6-8, 2018
Tokyo, Japan

Edited by :Tatsuhiro Shibata, Matthew Meyerson, Peter Campbell,and Hiroyuki Aburatani

Princess Takamatsu Cancer Research Fund

CONTENTS
CURRENT STATUS OF CANCER GENOMICS INCLUDINGDATA ANALYSISInterrogating the Architecture of Cancer GenomesPeter Campbell.................................................................................................................................1

Molecular Genomic Landscape of Liver Cancer
Tatsuhiro Shibata ............................................................................................................................3
Germline Determinants of the Somatic Mutation Landscape in 2,642
Cancer Genomes
Jan O. Korbel .....................................................................................................................................9
Molecular Basis of Pediatric T-cell Acute Lymphoblastic Leukemia
Junko Takita .....................................................................................................................................11

Decoding Challenging Cancers
Sean Grimmond ............................................................................................................................15
Comprehensive Genomic Analysis of Rare Gastroenterological Cancers
Shinichi Yachida ............................................................................................................................22
Genomic and Epigenomic Profiles of Asian Endemic Cancers
Patrick Tan.........................................................................................................................................27
Coding and Non-coding Drivers in >2,500 Whole Cancer Genomes
Gad Getz .............................................................................................................................................31iSystematic Identification of Splicing Associated Variants toward
Precision Medicine
Yuichi Shiraishi ..............................................................................................................................34
Coding and Non-coding Cancer Mutations
Núria López-Bigas .......................................................................................................................37
Mutations and Their Interactions in Cancer and Beyond
Ben Lehner ........................................................................................................................................43
Long Noncoding RNA and Wnt Signaling in Cancer
Hiroyuki Aburatani ....................................................................................................................47

TUMOR HETELOGENEITY AND IMMUNOGENOMICS

Analysis of Non-coding Genome Alterations in Human Cancer
Matthew Meyerson .....................................................................................................................49
A Unique Mechanism of Cancer Immune Evasion via the Disruption of
PD-L1 3’-UTR
Seishi Ogawa ...................................................................................................................................56
Genomic Pathology of Diffuse-type Gastric Carcinoma
Shumpei Ishikawa .......................................................................................................................60
Cancer Evolution and Immune Escape: TRACERxCharles Swanton ...........................................................................................................................63
Quantifying and Forecasting Tumor Progression
Christina Curtis .............................................................................................................................72ii

Novel Evolutionary Principle Shaping Intratumor Heterogeneity in
Colorectal Cancer
Koshi Mimori ..................................................................................................................................77
Whole Genome Sequencing and Immuno-genomic Analysis for Liver
Cancer
Hidewaki Nakagawa .................................................................................................................80
Genetic Alterations in T-cell Lymphomagenesis
Keisuke Kataoka ...........................................................................................................................84
A Single Cell Lens into Tumor Heterogeneity, Metastasis and Tumor
Ecosystems
Dana Pe’er .........................................................................................................................................87

Whole-organism Clone-tracing Using Single-cell Sequencing
Alexander van Oudenaarden ..............................................................................................89
In Vivo Mechanisms of Resistance to Genotoxic Chemotherapy
Michael T. Hemann .....................................................................................................................91

NAKAHARA MEMORIAL LECTURE

Signatures of Mutational Processes in Cancer
Michael Stratton ............................................................................................................................98iii

CLINICAL GENOMICS AND CANCER GENOME EDITINGIn Vivo and Ex Vivo Engineering of Cancer-associated Chromosomal via
Somatic Genome Editing
Andrea Ventura ...........................................................................................................................104
Systematic Mapping of Genetic Vulnerabilities in Cancer
Prashant Mali ................................................................................................................................113
Matched Tumor-normal Mutation Profiling to Identify Therapeutic
Biomarkers and Guide Clinical Care
Michael F. Berger ........................................................................................................................119
A Systems Level View of Breast Cancer
Carlos Caldas ................................................................................................................................123
The International Cancer Genome Consortium – Argo Initiative:
Bringing a Million Patient Years of Precision Oncology Knowledge to theWorld
Andrew V. Biankin ....................................................................................................................129
High-throughput Annotation of Variants of Unknown SignificanceHiroyuki Mano ............................................................................................................................133

CONCLUDING REMARKS

Tatsuhiro Shibata ........................................................................................................................137

List of Participants ................................................................................................................................140
List of Previous International Symposia Series .............................................................160

iv

1

INTERROGATING THE ARCHITECURE OF CANCERGENOMESPeter Campbell

Cancer Genome Project
Wellcome Sanger InstituteWellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK(pc8@sanger.ac.uk)

Cancer is driven by mutation. Using massively parallel sequencing technology, we can

now sequence the entire genome of cancer samples, allowing the generation ofcomprehensive catalogues of somatic mutations of all classes. Bespoke algorithms havebeen developed to identify somatically acquired point mutations, copy number changes
and genomic rearrangements, which require extensive validation by confirmatory testing.

The findings from our first handful of genomes illustrate the potential for next-generation

sequencing to provide unprecedented insights into mutational processes, cellular repair
pathways and gene networks associated with cancer development. I will also review

possible applications of these technologies in a diagnostic and clinical setting, and thepotential routes for translation.

2

P. CampbellPeter Campbell, PhD

2003
2007
2010-present

PhD, University of Cambridge
Post-doctoral Fellowship, Sanger InstituteGroup Leader, Wellcome Sanger Institute3

MOLECULAR GENOMIC LANDSCAPE OF LIVER CANCER

Tatsuhiro Shibata

Laboratory of Molecular Medicine
The Institute of Medical Science, The University of Tokyo (IMSUT)
4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan(tshibata@ims.u-tokyo.ac.jp)
Division of Cancer Genomics
National Cancer Center Research Institute5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan(tashibat@ncc.go.jp)

1. Whole genome landscape of human cancer towards deconvolution of the cancer
Recent advances in sequencing technologies can afford whole genome sequencing
genomeapproach to cancer at quite reasonable cost. This includes whole genome sequencing(WGS), whole transcriptome sequencing, whole methylation sequencing or other epigenetic

approaches such as Chromatin-immunoprecipitation sequencing etc. These analyses

identify somatic genetic (nucleotide substitution, insertion/deletion, copy number change,

structural rearrangement, mobilization of transposable elements, etc.) and epigenetic(aberrant methylation of CpG, change of active and inactive histone modifications)alterations in cancer cells. Recent studies have further identified the interactions between

genetic and epigenetic alterations that include promoter/enhancer hijacking, driver genes

regulating epigenetic landscape (IDH1/2, TET2 etc.) and oncogenic non-coding RNA.

These whole-genome scale characterizations of the cancer genome can explore thefollowing two questions; trace of positive and negative selections such as identification ofcancer driver and anti-driver genes, predisposing germline variations, and trace of

carcinogenesis processes in human cancer, including mutational and othergenetic/epigenetic signatures, precancerous clonal events, cancer evolution andheterogeneity (Figure 1).

4

T. Shibata

Mutagenic factors or processes leave unique substitutions with characteristic sequence

2. Landscape of mutational signatures in hepatocellular carcinoma (HCC)

contexts on DNA, that are called mutational signatures. We attempted to examine therelationship between mutational signature with epigenetic contexts. Eight mutationalsignatures (W1-8) were extracted from WGS data from 266 HCC samples (Figure 2a), andthe contribution of each signature was compared between the active and inactive
chromatin status (Figure 2b, c). A significant enrichment of W2 (corresponding to COSMIC

signature 16) in active chromatin areas (odds ratio: 6.32) was observed irrespective ofhepatitis virus status. Signatures W6 (odds ratio: 3.09) and W5 (which corresponds to

COSMIC signature 19) (odds ratio: 1.24) were enriched in the active histone-marked
genome, whereas signatures W7 (odds ratio: 2.30), W4, W1 (which corresponds to COSMIC

signature 1), W3 (which corresponds to COSMIC signature 12) and W8 occurred more

frequently in inactive chromatin segments.

The HBV genome integration process is associated with host DNA nicking and3. HBV integration and epigenetic state

recombination. However, the DNA methylation of integrated HBV sequences and the HCC

genome around HBV integration sites remain to be thoroughly explored. Analyzing a totalof 108 non-cancerous liver tissues and 102 HCC samples from the same patients, 1,010 non-clonal integration events in non-cancerous tissues and 476 clonal integration events in
HCCs were identified. Non-clonal HBV integrations in non-cancerous liver tissues weresignificantly (P < 2.2e-16) enriched in the transcriptionally active chromatin regions,whereas the clonal integration sites in tumors were located significantly more frequently in

the inactive chromatin regions (P = 0.0085). This suggests that although substantial HBV

integration occurs in open and transcribing epigenetic regions, these integration events arenegatively selected for in cancerous tissues, probably because of their deleterious effects on

hepatocyte growth.

We explored whether structural alterations of HBV genomes in the tumor genome may

4. Structural rearrangements of integrated HBV genomes

play a role in the host genome structural rearrangements. Paired reads that both mapped to

the HBV genome were used to detect HBV genome rearrangements in five HCC genomes,

and the frequency of these rearrangements (per Mb) was extremely higher than that of thetumor genome (42,815-fold increase on average, P < 2.2e-16) (Figure 3a). To verify therearrangements in the HBV genomes, we obtained long-read (the average length of asequenced read was 1,783 kb) WGS data for one pair of HCC and non-cancerous liver

genomes (HX25). Based on the 16x coverage of PacBio long-read WGS, we de novo

Current Status of Cancer Genomics Including Data Analysis

5

Deconvolution of Cancer genome
Germline & Somatic Genetic

Epigenetic

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Trace of carcinogenesis processes:! Mutation and other genetic signatures! Background clonal events (clonal
hematopoiesis…)! Cancer evolution and heterogeneity

Trace of positive (and negative) selections:! Identification of driver genes (and antidriver (ghost) genes)! Germline contribution

Figure 1

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Distribution of mutational signatures across different chromatina. Eight mutational signatures identified from active or inactive chromatin states of 266
HCCs. b. Contribution of the eight mutational signatures to each tumor in active and inactive
chromatin states. HBV: hepatitis B virus-related HCCs, HCV: hepatitis C virus-related
HCCs, NBNC: HCCs without HBV or HCV infection. c. Odds ratio of the contribution of thesignature in active and inactive chromatin.

6

T. Shibata

constructed six independent long consensus reads (5,425-10,251 kb) of the integrated HBVgenome. These reads perfectly confirmed the presence of the rearrangements (deletions,inversions and duplications) detected by the Illumina short reads (Figure 3b).
a. Detection by Illumina short reads
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To further confirm this finding, we performed HBV capture sequencing of a larger HCC

cohort and identified more than five rearrangements in 74% (45/61) of cases.

Characteristically, tumors with frequent HBV genome rearrangements harboredsignificantly fewer total somatic mutations (P = 0.04), suggesting that HBV genome

instability-associated genetic alterations may complement part of the driver events in HCC.

This integrative analysis identified interdependency between genetic, viral and5. Conclusion

epigenetic alterations in liver cancer. Understanding the underlying molecular mechanisms

would facilitate the identification of epigenetic driver events as well as carcinogenic
processes and ultimately contribute to genome-based treatment and prevention.

FCurrent Status of Cancer Genomics Including Data Analysis

7

References
1. Hama N, Totoki Y, Miura F, Tatsuno K, Saito-Adachi M, Nakamura H, Arai Y, Hosoda F,
Urushidate T, Ohashi S, Mukai W, Hiraoka N, Aburatani H, Ito T, Shibata T. Epigenetic
landscape influences the liver cancer genome architecture. Nat Commun. 2018 Apr
24;9(1):1643. doi: 10.1038/s41467-018-03999-y.
2. Jusakul A, Cutcutache I, Yong CH, Lim JQ, Huang MN, Padmanabhan N, Nellore V,
Kongpetch S, Ng AWT, Ng LM, Choo SP, Myint SS, Thanan R, Nagarajan S, Lim WK, Ng
CCY, Boot A, Liu M, Ong CK, Rajasegaran V, Lie S, Lim AST, Lim TH, Tan J, Loh JL,
McPherson JR, Khuntikeo N, Bhudhisawasdi V, Yongvanit P, Wongkham S, Totoki Y,
Nakamura H, Arai Y, Yamasaki S, Chow PK, Chung AYF, Ooi LLPJ, Lim KH, Dima S,
Duda DG, Popescu I, Broet P, Hsieh SY, Yu MC, Scarpa A, Lai J, Luo DX, Carvalho AL,
Vettore AL, Rhee H, Park YN, Alexandrov LB, Gordân R, Rozen SG, Shibata T,
Pairojkul C, Teh BT, Tan P. Whole-Genome and Epigenomic Landscapes of
Etiologically Distinct Subtypes of Cholangiocarcinoma. Cancer Discov. 2017 Oct;7(10):1116-1135.
3. Cancer Genome Atlas Research Network. Comprehensive and Integrative Genomic
Characterization of Hepatocellular Carcinoma. Cell. 2017 Jun 15;169(7):1327-1341.
4. Fujimoto A, Furuta M, Totoki Y, Tsunoda T, Kato M, Shiraishi Y, Tanaka H, Taniguchi H,
Kawakami Y, Ueno M, Gotoh K, Ariizumi SI, Wardell CP, Hayami S, Nakamura T,
Aikata H, Arihiro K, Boroevich KA, Abe T, Nakano K, Maejima K, Sasaki-Oku A,
Ohsawa A, Shibuya T, Nakamura H, Hama N, Hosoda F, Arai Y, Ohashi S, Urushidate T,
Nagae G, Yamamoto S, Ueda H, Tatsuno K, Ojima H, Hiraoka N, Okusaka T, Kubo M,
Marubashi S, Yamada T, Hirano S, Yamamoto M, Ohdan H, Shimada K, Ishikawa O,
Yamaue H, Chayama K, Miyano S, Aburatani H, Shibata T, Nakagawa H. Whole
genome mutational landscape and characterization of non-coding and structural mutations in liver cancer. Nat Genet, 2016, 48:500-9.
5. Nakamura H, Arai Y, Totoki Y, Shirota T, Elzawahry A, Kato M, Hama N, Hosoda F,
Urushidate T, Ohashi S, Hiraoka N, Ojima H, Shimada K, Okusaka T, Kosuge T,
Miyagawa S, Shibata T. Genomic spectra of biliary tract cancer. Nat Genet, 2015,
47:1003-10.

8

T. Shibata

6. Totoki Y, Tatsuno K, Covington KR, Ueda H, Creighton CJ, Kato M, Tsuji S,
Donehower LA, Slagle BL, Nakamura H, Yamamoto S, Shinbrot E, Hama N, Lehmkuhl
M, Hosoda F, Arai Y, Walker K, Dahdouli M, Gotoh K, Nagae G, Gingras MC, Muzny
DM, Ojima H, Shimada K, Midorikawa Y, Goss JA, Cotton R, Hayashi A, Shibahara J,
Ishikawa S, Guiteau J, Tanaka M, Urushidate T, Ohashi S, Okada N, Doddapaneni H,
Wang M, Zhu Y, Dinh H, Okusaka T, Kokudo N, Kosuge T, Takayama T, Fukayama M,
Gibbs RA, Wheeler DA, Aburatani H, Shibata T. Trans-ancestry mutational landscape of
hepatocellular carcinoma genomes. Nat Genet, 2014, 46:1267-73.

Tatsuhiro Shibata, MD, PhD

1990-1994

1995-1997

2010-present

2014-present

Graduated from Graduate School of Medicine andFaculty of Medicine, The University of Tokyo(Pathology)
Postdoctoral Researcher, Developmental Biology
Center, University of California, Irvine
Chief, Division of Cancer Genomics, National Cancer
Center Research Institute
Professor, Laboratory of Molecular Medicine, The
Institute of Medical Science, The University of Tokyo

9

GERMLINE DETERMINANTS OF THE SOMATIC MUTATIONLANDSCAPE IN 2,642 CANCER GENOMESJan O. Korbel

Genome Biology
European Molecular Biology Laboratory (EMBL)
Meyerhofstraße 169117 Heidelberg, Germany(korbel@embl.de)
My presentation will cover research from our group on polymorphic genome structuralvariation, and on genomic somatic DNA rearrangements in cancer. I will provide an
update on our efforts to reconstruct patterns of polymorphic genome structural variation

through analysis of DNA sequencing data within the Human Genome Structural Variation

Consortium, which uses a range of different sequencing platforms including long read andstrand-specific sequencing to assess the full spectrum of genetic variation in humangenomes. Furthermore, we recently developed computational including cloud-based

approaches [1] for performing an analysis of >2,800 deeply sequenced tumor/normal
paired genomes in the context of the Pan Cancer Analysis of Whole Genomes (PCAWG)

project, to search for commonalities and differences in molecular processes leading tocancer in different tumor entities. I will specifically highlight scientific results of the
PCAWG Germline Cancer Genome working group [2,3], which has reconstructed thegermline genomes of >2,800 cancer patients to examine how somatic mutation patterns

associate with germline genotypes. Based on pan-cancer analysis we recently inferred andsubsequently verified experimentally a new mechanism of cancer gene activation involving

somatic neo topological association domain (neo-TAD) formation mediated by recurrent
tandem duplications, which activate the IGF2 oncogene locus. [4]10

J. O. Korbel

References
1. Yakneen, S., Waszak, S. M., Gertz, M. & Korbel, J. O. Enabling rapid cloud-based analysisof thousands of human genomes via Butler. Nat Biotechnol in press (2018).
2. Campbell, P., Getz, G., Stuart, J. M., Korbel, J. O. & Stein, L. D. The PanCancer Analysis of
Whole Genomes. bioRxiv, doi:http://sci-hub.tw/10.1101/185736 (2017).
3. Waszak, S. M., ; et al. Germline determinants of the somatic mutation landscape in 2,642
cancer genomes. BioRxiv (2017).
4. Weischenfeldt, J. et al. Pan-cancer analysis of somatic copy-number alterations implicates IRS4 and IGF2 in enhancer hijacking. Nat Genet 49, 65-74, doi:10.1038/ng.3722(2017).

Jan O. Korbel, PhD

1994-2001

2001-2005

2005-2007

2008-20162016-present
2018

Dipl.-Ing. (German engineering degree) degree in
Biotechnology, Specialization Medical Biotechnology,
from Technical University (TU) Berlin
Doctoral Research Structural & Computational Biology
Unit, EMBL Heidelberg
Marie Curie and EMBO Postdoc at Yale University,
New Haven, CT
Group Leader at EMBL Heidelberg (since 2014:
Additionally Leader of Research Group in the Context
of the MMPU of EMBL & Heidelberg University)
Group Leader and Senior Scientist at EMBL Heidelberg(Secondary EMBL affiliation with the EMBL-European
Bioinformatics Institute, UK)
Winner of the 2018 Pezcoller Foundation - EACR
Cancer Researcher Award

11

MOLECULAR BASIS OF PEDIATRIC T-CELL ACUTE
LYMPHOBLASTIC LEUKEMIA

Junko Takita

Department of Pediatrics, Graduate School of Medicine
Kyoto University
54 Kawahara-cho, Shogoin Sakyo-ku, Kyoto 606-8507, Japan(jtakita@kuhp.kyoto-u.ac.jp)

T-cell acute lymphoblasic leukemia (T-ALL) is an aggressive malignancy which

accounts for 10-15% of pediatric leukemia. With appropriately intensive therapy, children

with T-ALL have an outcome approaching that of children with B-lineage ALL. However,

the outcome of T-ALL patients with primary resistant or relapsed leukemia remains

extremely poor [1]. Therefore, current research efforts are focused on the development oftherapeutic targets and relevant biomarkers for intractable T-ALL.

Constitutive activation of NOTCH1 signaling is the most prominent oncogenic pathway

in T cell transformation [2]. Furthermore, up to 40% of pediatric T-ALL cease carry TAL1gene aberrations mainly due to small deletions in chromosome 1p32 and translocations
involving TAL1 locus [3]. In addition, recently, a TAL1 super enhancer abnormality wasreported, which shows aberrant expression of TAL1 without gene fusion [4]. However,since the prognostic significance of these genetic alterations in T-ALL is not clear, genetic

basis which contributes aggressive phenotype or progression of pediatric T-ALL is still tobe elucidated.

Here, we report comprehensive profiling of 121 cases of pediatric T-ALL using RNA

sequencing and/or targeted capture sequencing through which we identified newrecurrent gene fusions involving SPI1 (STMN1-SPI1 and TCF7-SPI1) [5] (Fig. 1A). Cases

positive for fusions involving SPI1 (encoding PU.1), which accounted for 3.9% (7/181) of

the total examined pediatric T-ALL cases. The fusion-positive samples invariably showedmarkedly elevated PU.1 expression (Fig. 1B), most likely reflecting high-level fusiontranscripts from the rearranged allele under the control of a heterologous promoter from

TCF7 or STMN1, which was demonstrated to be highly expressed in the fusion-positive

12

J. Takita

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Figure 1

SPI1 fusions in pediatric T-ALL(A) Schematic representation of SPI1 fusions. (B) Comparison of expression levels of SPI1
between SPI1 fusion-positive cases (n=7) and negative cases (n=116). (C) Kaplan-Meiersurvival curves of overall survival for SPI1 fusion positive cases (n=7) and negative cases(n=113).

samples. To address the functional activities of SPI1 fusions, we performed luciferase

assays in HeLa cells carrying a reporter containing the SPI1-responsive promoter sequence.

In comparison to empty vector, transfection with the SPI1 fusion constructs resulted in amarked increase in luciferase activity comparable to that of wild type SPI1. This increase

was almost completely absent when the sequence encoding the DNA-binding ETS domain
was deleted from SPI1. This result suggested that SPI1 fusions retain the transcriptional

activity comparable to wild type SPI1. Then next, we evaluated the effect of SPI1 fusions on
T cell proliferation. DN T cells from wild-type mice thymus were transduced withconstructs encoding SPI1 fusions and seeded to in vitro cell culture to examine the effect of

SPI1 fusions on cell proliferation. SPI1 fusion transduced cells showed significantly higher

proliferation rates than cells transduced with empty vector. We next investigated the effects

of SPI1 fusion on T cell development. Mouse DN1 and DN2 thymocytes were transduced

to express wild-type SPI1 or each SPI1 fusion and cultured on TSt4/DLL1 mouse stromalcells. Cells expressing SPI1 fusions and those expressing wild-type SPI1 showed aCurrent Status of Cancer Genomics Including Data Analysis

13

differentiation block during DN T cell development. There were significantly reduced
numbers of DP, SP, and DN4 T cells in comparison to control cells, with an increase in thenumber of immature DN1 and DN2 T cells. We also confirmed similar results bytransplantation of SPI1 fusion-transduced primary bone marrow cells.

To characterize SPI1-fusion-positive T-ALL, we investigated gene expression profile of

our T-ALL cases. Using two-step unsupervised consensus clustering, we obtained 5 stableclusters. Among these clusters, four clusters, TAL1-RA, -RB, TLX, and ETP, largely

recapitulated distinct T-ALL subtypes characterized in previous expression array studies.

However, the remaining cluster was newly identified and consisted of only SPI1-fusion-

positive cases, suggesting that these cases represent a unique subtype of pediatric T-ALL

distinguishable from the known T-ALL subtypes. Although their small sample size

precluded accurate evaluation, SPI1 fusion-positive T-ALL shared several genetic

abnormalities with other T-ALL subtypes, such as frequent NOTCH1 mutations and
CDKN2A deletions, while other mutations commonly found in other T-ALL subtypes did

not seem to be frequent, except for altered RAS pathway genes, which were found in 57%

cases with SPI1 fusions.

Finally, we also evaluated the effects of SPI1 fusions on the clinical outcomes. When

compared to SPI1 fusion negative cases or cases in non SPI fusion clusters, SPI1 fusion
positive cases showed significantly shorter overall survival, with a median survival time of

2.2 years (Fig. 1B). Six out of the seven cases died within 3 years from diagnosis with early

relapse.

In conclusion, we have described novel recurrent fusions involving SPI1 associated withpediatric T-ALL. Exhibiting unique cytological and gene expression profiles, SPI1 fusion-

positive T-ALL had a uniformly dismal clinical outcome. To the best of our knowledge,
these are the first genetic lesions associated with a very poor prognosis in pediatric T-ALL,although their impacts on survival need to be confirmed in additional cases. Patients withSPI1 fusions seem to be incurable with current standard chemotherapy, which underscores

the importance of detecting this subset of patients for more intensive therapy, including
allogeneic stem-cell transplantation, to improve their clinical outcomes.
References
1. Herold, R., von Stackelberg, A., Hartmann, R., Eisenreich, B. & Henze, G. Acute lymphoblastic leukemia-relapse study of the Berlin-Frankfurt-Munster Group (ALL-REZ
BFM) experience: early treatment intensity makes the difference. J Clin Oncol 2004; 22,569-570.

14

J. Takita

2. Weng AP, Ferrando AA, Lee W, Morris JP 4th, Silverman LB, Sanchez-Irizarry C,
Blacklow SC, Look AT, Aster JC. Activating mutations of NOTCH1 in human T cell
acute lymphoblastic leukemia. Science 2004; 306:269-271.
3. Ferrando AA, Neuberg DS, Staunton J, Loh ML, Huard C, Raimondi SC, Behm FG, Pui
CH, Downing JR, Gilliland DG, Lander ES, Golub TR, Look AT. Gene expression signatures define novel oncogenic pathways in T cell acute lymphoblastic leukemia. Cancer
Cell 2002; 1, 75-87.
4. Mansour MR, Abraham BJ, Anders L, Berezovskaya A, Gutierrez A, Durbin AD, Etchin J,
Lawton L, Sallan SE, Silverman LB, Loh ML, Hunger SP, Sanda T, Young RA, Look AT.
Oncogene regulation. An oncogenic super-enhancer formed through somatic mutation ofa noncoding intergenic element.Science. 2014; 346:1373-1377.
5. Seki M, Kimura S, Isobe T, Yoshida K, Ueno H, Nakajima-Takagi Y, Changshan Wang,
Lin L, Kon A, Suzuki H, Shiozawa Y, Kataoka K, Fujii Y, Shiraishi Y, Chiba K, Tanaka H,
Shimamura T, Masuda K, Kawamoto H, Ohki K, Kato M, Arakawa Y, Koh K, Hanada R,
Moritake H, Akiyama M, Kobayashi R, Deguchi T, Hashii Y, Imamura T, Sato A,
Kiyokawa A, Oka A, Hayashi Y, Takagi M, Manabe A, Ohara A, Horibe K, Sanada M,
Iwama A, Mano H, Miyano S, Ogawa S, and Takita J. Recurrent PU.1 (SPI1) fusions inhigh-risk pediatric T cell acute lymphoblastic leukemia. Nature Genet. 2017; 49:1274-1281.

Junko Takita, MD, PhD

1991-1992
1992-1993
1993-1996
2003-2005
2005-20132013-20182018-present

Resident, Tokyo University Hospital
Resident, Yaizu City Hospital
Research Resident, National Cancer Research Institute
Assistant Professor, Tokyo University Hospital
Lecutuer, Tokyo University Hospital
Associate Professor, Tokyo University Hospital
Professor, Kyoto University

15

DECODING CHALLENGING CANCERS

Sean GrimmondCenter for Cancer Research
University of Melbourne
Victorian Comprehensive Cancer Centre
Level 10, 305 Grattan Street, Melbourne, VIC 3000, Australia(sean.grimmond@unimelb.edu.au)
Over the past 8 years, Prof Grimmond has led Australia’s International Cancer Genome

Consortium (ICGC) efforts, carrying out integrated whole-genome & transcriptome-based

surveys of large Pancreatic, Ovarian, Neuroendocrine, Esophageal cancer and Melanoma

cohorts and using them to determine the major root causes of somatic mutation, thecommon somatic mutations driving oncogenesis, mapping out clinically-relevant moleculartaxonomies and providing a global survey of readily actionable mutations in this disease[1-4].

The greatest area of focus of the Australian ICGC effort has been on Pancreatic Cancer.

Pancreatic Adenocarcinoma (PAAD) is currently the 4th leading cause of cancer death inWestern societies and is projected to become the 2nd leading cause within a decade [5].

Having a median survival measured in months, 5-year survival prospects of 7%, andtherapeutic advances over the last 30+ years on providing only incremental improvement,

there is an urgent need to better understand the molecular pathology and oncogenesis of
Pancreatic Cancer to improve patient selection for current treatments, and to develop novel
therapeutic strategies.

These findings have been the impetus behind molecularly-targeted PAAD clinical trials[6] and provided foundations for in-silico modelling of cancer evolution and tumour

cellular make up [1]. While the genome-exploratory efforts of the ICGC and The Cancer
Genome Atlas (TCGA) have revolutionized our understanding of many cancers, thesestudies are far from complete. The original experimental design adopted had significant

limitations: - (i) small cohort sizes and higher than expected somatic mutation rates leftmany studies underpowered, compromising the comprehensive detection of infrequent but

16

S. Grimmond

potent mutations in key genes, (ii) a reliance on exome sequencing left us blind to the roleof non-coding / regulatory driver mutations, (iii) genomic analyses on a single early -stage

tumour sample per patient hampered on our ability to study intramural heterogeneity andthe temporal order of somatic damage, (iv) a reliance on standard bulk tumour tissue
sequencing confounded our ability to accurately assign genomic events and transcriptomic

features to specific cellular compartments – tumour vs. stroma – within malignancies.

Given these challenges, our recent activities have focused on :- i)Dramatically

expanding our PAAD mutational atlas to include > 1000 whole genomes, exomes andtranscriptomes of pancreatic pre-malignant lesions, primary resected tumours andmetastases, ii) Studying uncommon molecular subtypes in pancreatic cancer (such as

KRAS wildtype and long term survivors) iii) Use single cell sequencing approaches toresolve intra-tumour genomic and transcriptomic heterogeneity to define the cellularcomposition and cell-specific activation states during PAAD progression and iv) coupling

genomic analysis of prospective pancreatic cancers drug sensitivity screening using patient

matched organoid models.

Early ICGC-based studies into Pancreatic Cancer revealed a complex mutationallandscape. While the classical instigators of premalignant progression (KRAS, CDKN2A,

TP53 and SMAD4 mutations) are common place, they frequently occur in combination withinactivating mutations in ARID1A, TGFBR2, RNF43, KDM6A (5-15% of patients).

Furthermore, studies of 450 tumours via exome and whole genome analysis discovered a“long tail” of 40 genes under selection, attracting highly-impactful mutations at low

prevalence (1-5% of patients). Focal copy number changes and chromosomal

rearrangements, often caused by genomic catastrophes, add additional key tumour
suppressor (TP53, CDKN2A) and oncogenes (e.g. GATA6, CCNE1, ERRB2, MET, MYC,

MIB1) [1] to this landscape. Intriguingly, the function of these recurrently mutated genes

fall into 12 core processes we now consider central to Pancreatic Cancer tumorigenesis.

In an effort to see whether we had saturated driver gene discovery based on position

selection / recurrent mutation, we expanded this study to include the CAN-ICGC &TCGA

alongside the expanded AUS-ICGC cohort identifies 60 significantly mutated genes. Whenthese genes are viewed in the context of “pathways”, we find the mutated genes underselection fall into MAP kinase signalling, TGF-beta signalling, WNT signalling, NOTCH

signalling, ROBO-SLIT signalling, Cell Cycle, DNA damage repair, the SWI-SNF complex,
Chromatin modification, and RNA processing.

With so much of early ICGC & the TCGA’s focus on exome rather than whole-genome

sequencing, the exploration of noncoding driver mutations has been limited. CIA-

Grimmond and colleagues recently carried out a genome-wide study of recurrent
mutations in known regulatory regions resulting in coordinate perturbation of proximal

Current Status of Cancer Genomics Including Data Analysis

17

gene expression in 305 PAAD whole cancer genomes from the Australian and Canadian

ICGC-PAAD efforts [7]. This study showed that mutations in regulatory elements is agenuine alternate mechanism for damaging core pancreatic cancer pathways like WNTsignalling and Axon guidance, as well as impacting on new processes like Cell adhesion,

Regulation of transcription and Homeobox regulation.

More recently, others have shown that structural rearrangements are capable ofactivating cancer-promoting genes through promoting ectopic enhancer activity [8,9].

Exploratory studies into these so called “enhancer hijacking” events within the AUS-PAAD

cohort have found recurrent duplication and amplification of the super enhancer locatedupstream of the MYC oncogene which have been shown to promote its overexpression inbreast cancer [9]. Taken together these exploratory findings warrant analysis of larger

cohorts to validate these early explorations and appropriately power deeper investigations.

Studying driver gene composition in clinically significant segments of the PAAD cohort:This cohort will be suitably powered to investigate the underlying genetics in two

uncommon but clinically significant segments: - (i) KRAS+/+ PAADs. Making up only 7 %of all PAAD, these tumours must rely on an alternate oncogenic mutation to drive them. In

the recent TCGA study [10], 3/11 KRAS +/+ PAADs possessed oncogenic BRAF fusions &mutations. This cohort contains WGS/WTS for >100 KRAS wildtype tumours allowing us

to robustly screen for oncogenic events (gain of function mutations, gene fusions and gene

amplifications) and contains a wealth of MAPK signalling gain-of-function mutations (egBRAF fusions & in frame indels, NRG fusions, RAF1 fusions, N and HRAS hotspot

mutations) opening up the opportunity to re-purpose BRAFi strategies or EGFRi targetingof MAPK independent tumours as testable therapeutic avenues KRAS+/+ tumours.

Over recent years, the TCGA and ICGC efforts have also demonstrated that global RNA

expression profiling is a powerful way to gain insight into the consequences of somatic

mutation, the transcriptional programs promoting malignancy and molecular taxonomies

present in otherwise histologically-homogenous cancers. We recently carried out a global
analysis of bulk tissue from >260s resectable ICGC PAADs via Whole Transcriptome

Sequencing (WTS) and/or microarray expression profiling and identified 4 robust PAAD
expression subtypes [5], now known as: Pancreatic Progenitor, ADEX (AbnormallyDifferentiated-Endocrine & eXocrine), Squamous, and Immunogenic PAADs. While

traditional whole-tissue RNAseq studies focus on deconvolving cell-specific gene

expression patterns from the admixtures of tumour, stromal, immune, endothelial cell etc.

expression in a tumour. Single-cell sequencing technology now makes this possible. We areusing single-nucleus RNASeq from cryopreserved tissues to study surplus material fromthe extensively characterised PAADs studied as part of the AUS_ICGC program.

18

S. Grimmond

Given that PAAD is such an aggressive malignancy (median survival is 7 months),

testing multiple therapeutic strategies in patients is rarely possible. As a consequence,systematic screening of drug sensitivity in patient-matched, ex-vivo tumour models has

been proposed as an alternative. Prof Grimmond and colleagues have previously generated

xenograft models for patients consented into the Australian ICGC program. Unfortunately,

the lag time and costs render them clinically impractical.

Organoid tumour models are an increasing popular alternative for solid tumours Overthe last 2 years, Prof Grimmond has led an Avner Foundation supported program withresearchers from University of Melbourne &WEHI to establish generation of pancreaticcancer organoid cultures, carry genomic analysis and plate-based drug sensitivity profilingto the standard of care agents within 6-8 weeks of biopsy (See Figure 1). Where extreme
drug sensitivities are observed, the study’s ethical framework permits return data topatients. Similar to published studies, the models maintain genomic fidelity to theirprimary lesion their drug sensitivity profile recapitulates patient therapeutic responses. The

next step for this program is to escalate the scale of drug screening performed on eachpatient-matched organoid generated, creating a much-needed knowledgebase of thespectrum of responses to dozens-100s of agents, which includes all standard of care drugs,

re-purposed cancer drugs for which actionable mutations have been seen in PAAD cancer

atlases novel agents targeting potential druggable leads that are identified in the previous
aims. This will determine how the allelic series of mutations seen in driver genes, and theircommon combinations influence drug sensitivity.

Figure 1

Current Status of Cancer Genomics Including Data Analysis

19

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Bruxner TJ, Quinn MC, Nourse C, Murtaugh L, Harliwong I, Idrisoglu S, Manning S,
Nourbakhsh E, Wani S, Fink L, Holmes O, Chin V, Anderson MJ, Kazakoff S, Leonard C,
Newell F, Waddell N, Wood S, Xu Q, Wilson PJ, Cloonan N, Kassahn KS, Taylor D,
Quek K, Robertson A, Pantano L, Mincarelli L, Sanchez LN, Evers L, Wu J, Pinese M,
Cowley MJ, Jones MD, Colvin EK, Nagrial AM, Humphrey ES, Chantrill LA, Mawson A,
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Samra JS, Kench JG, Lovell JA, Merrett ND, Toon CW, Epari K, Nguyen NQ, Barbour A,
Zeps N, Moran-Jones K, Jamieson NB, Graham JS, Duthie F, Oien K, Hair J, Grützmann R,
Maitra A, Iacobuzio-Donahue CA, Wolfgang CL, Morgan RA, Lawlor RT, Corbo V,
Bassi C, Rusev B, Capelli P, Salvia R, Tortora G, Mukhopadhyay D, Petersen GM;
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Eshleman JR, Hruban RH, Pilarsky C, Morton JP, Sansom OJ, Scarpa A, Musgrove EA,
Bailey UM, Hofmann O, Sutherland RL, Wheeler DA, Gill AJ, Gibbs RA, Pearson JV,
Waddell N, Biankin AV, Grimmond SM. Genomic analyses identify molecular subtypesof pancreatic cancer. Nature. 2016;531:47-52.
2. Patch AM, Christie EL, Etemadmoghadam D, Garsed DW, George J, Fereday S, Nones K,
Cowin P, Alsop K, Bailey PJ, Kassahn KS, Newell F, Quinn MC, Kazakoff S, Quek K,
Wilhelm-Benartzi C, Curry E, Leong HS; Australian Ovarian Cancer Study Group,
Hamilton A, Mileshkin L, Au-Yeung G, Kennedy C, Hung J, Chiew YE, Harnett P,
Friedlander M, Quinn M, Pyman J, Cordner S, O'Brien P, Leditschke J, Young G,
Strachan K, Waring P, Azar W, Mitchell C, Traficante N, Hendley J, Thorne H,
Shackleton M, Miller DK, Arnau GM, Tothill RW, Holloway TP, Semple T, Harliwong I,
Nourse C, Nourbakhsh E, Manning S, Idrisoglu S, Bruxner TJ, Christ AN, Poudel B,
Holmes O, Anderson M, Leonard C, Lonie A, Hall N, Wood S, Taylor DF, Xu Q, Fink JL,
Waddell N, Drapkin R, Stronach E, Gabra H, Brown R, Jewell A, Nagaraj SH, Markham E,
Wilson PJ, Ellul J, McNally O, Doyle MA, Vedururu R, Stewart C, Lengyel E, Pearson JV,
Waddell N, deFazio A, Grimmond SM, Bowtell DD. Whole Genome characterization ofchemoresistant ovarian cancer. Nature. 2015;521:489-494.
3. Scarpa A, Chang DK, Nones K, Corbo V, Patch AM, Bailey P, Lawlor RT, Johns AL,
Miller DK, Mafficini A, Rusev B, Scardoni M, Antonello D, Barbi S, Sikora KO,
Cingarlini S, Vicentini C, McKay S, Quinn MC, Bruxner TJ, Christ AN, Harliwong I,
Idrisoglu S, McLean S, Nourse C, Nourbakhsh E, Wilson PJ, Anderson MJ, Fink JL,
Newell F, Waddell N, Holmes O, Kazakoff SH, Leonard C, Wood S, Xu Q, Nagaraj SH,
Amato E, Dalai I, Bersani S, Cataldo I, Dei Tos AP, Capelli P, Davì MV, Landoni L,
Malpaga A, Miotto M, Whitehall VL, Leggett BA, Harris JL, Harris J, Jones MD,

20

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Humphris J, Chantrill LA, Chin V, Nagrial AM, Pajic M, Scarlett CJ, Pinho A, Rooman I,
Toon C, Wu J, Pinese M, Cowley M, Barbour A, Mawson A, Humphrey ES, Colvin EK,
Chou A, Lovell JA, Jamieson NB, Duthie F, Gingras MC, Fisher WE, Dagg RA, Lau LM,
Lee M, Pickett HA, Reddel RR, Samra JS, Kench JG, Merrett ND, Epari K, Nguyen NQ,
Zeps N, Falconi M, Simbolo M, Butturini G, Van Buren G, Partelli S, Fassan M;
Australian Pancreatic Cancer Genome Initiative, Khanna KK, Gill AJ, Wheeler DA,
Gibbs RA, Musgrove EA, Bassi C, Tortora G, Pederzoli P, Pearson JV, Waddell N,
Biankin AV, Grimmond SM. Whole-genome landscape of pancreatic neuroendocrine
tumours. Nature. 2017;543:65–71.
Hayward NK, Wilmott JS, Waddell N, Johansson PA, Field MA, Nones K, Patch AM,
Kakavand H, Alexandrov LB, Burke H, Jakrot V, Kazakoff S, Holmes O, Leonard C,
Sabarinathan R, Mularoni L, Wood S, Xu Q, Waddell N, Tembe V, Pupo GM, De PaoliIseppi R, Vilain RE, Shang P, Lau LMS, Dagg RA, Schramm SJ, Pritchard A, DuttonRegester K, Newell F, Fitzgerald A, Shang CA, Grimmond SM, Pickett HA, Yang JY,
Stretch JR, Behren A, Kefford RF, Hersey P, Long GV, Cebon J, Shackleton M, Spillane AJ,
Saw RPM, López-Bigas N, Pearson JV, Thompson JF, Scolyer RA, Mann GJ. Wholegenome landscapes of major melanoma subtypes. Nature. 2017; 545:175-180.
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Chantrill L, Nagrial A, Watson C, Johns A, Martyn-Smith M, Simpson S, Mead S, Jones M,
Samra JS, Gill A, Watson N, Chin V, Humphris J, Chou A, Brown B, Morey A, Pajic M,
Grimmond SM, Chang DK, Thomas D, Sebastian L, Sjoquist K, Yip S, Pavlakis N,
Asghari R, Harvey S, Grimison P, Simes J, Biankin AV. Precision Medicine forAdvanced Pancreas Cancer: The Individualized Molecular Pancreatic Cancer Therapy(IMPaCT) Trial. Clin Cancer Res. 2017;21:2029-2037.
Feigin ME, Garvin T, Bailey P, Waddell N, Chang DK, Kelley DR, Shuai S, Gallinger S,
McPherson JD, Grimmond SM, Khurana E, Stein LD, Biankin AV, Schatz MC, Tuveson
DA. Recurrent noncoding regulatory mutations in pancreatic ductal adenocarcinoma.
Nat Genet 2017;49:825-833.
Northcott PA, Lee C, Zichner T, Stütz AM, Erkek S, Kawauchi D, Shih DJ, Hovestadt V,
Zapatka M, Sturm D, Jones DT, Kool M, Remke M, Cavalli FM, Zuyderduyn S, Bader GD,
VandenBerg S, Esparza LA, Ryzhova M, Wang W, Wittmann A, Stark S, Sieber L,
Seker-Cin H, Linke L, Kratochwil F, Jäger N, Buchhalter I, Imbusch CD, Zipprich G,
Raeder B, Schmidt S, Diessl N, Wolf S, Wiemann S, Brors B, Lawerenz C, Eils J, Warnatz
HJ, Risch T, Yaspo ML, Weber UD, Bartholomae CC, von Kalle C, Turányi E, Hauser P,
Sanden E, Darabi A, Siesjö P, Sterba J, Zitterbart K, Sumerauer D, van Sluis P, Versteeg R,
Volckmann R, Koster J, Schuhmann MU, Ebinger M, Grimes HL, Robinson GW, Gajjar A,

Current Status of Cancer Genomics Including Data Analysis

21

Mynarek M, von Hoff K, Rutkowski S, Pietsch T, Scheurlen W, Felsberg J, Reifenberger G,
Kulozik AE, von Deimling A, Witt O, Eils R, Gilbertson RJ, Korshunov A, Taylor MD,
Lichter P, Korbel JO, Wechsler-Reya RJ, Pfister SM. Enhancer hijacking activates GFI1
family oncogenes in medulloblastoma. Nature. 2014;511:428-434.
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Lakhani SR, Thompson AM, Birney E, Stunnenberg HG, van de Vijver MJ, Martens JW,
Børresen-Dale AL, Richardson AL, Kong G, Viari A, Easton D, Evan G, Campbell PJ,
Stratton MR, Nik-Zainal S. A somatic-mutational process recurrently duplicates
germline susceptibility loci and tissue-specific super-enhancers in breast cancers. Nat
Genet. 2017;49:341-348.
10. Cancer Genome Atlas Consortium: Integrated Genomic Characterization of Pancreatic
Ductal Adenocarcinoma. Cancer Cell. 2017; 32:185-203.

Sean Grimmond, BSc, PhD, FFS, RCPA

2009-20142013-20152014-20152016-present

Director, Queensland Centre for Medical Genomics
Chair of Medical Genomics, University of Glasgow
Co-director of the Scottish Genome Partnership
Director & The Bertalli Chair in Cancer Medicine
University of Melbourne Centre for Cancer ResearchHead, Department of Clinical Pathology
University of Melbourne Centre for Cancer Research

22

COMPREHENSIVE GENOMIC ANALYSIS OF RAREGASTROENTEROLOGICAL CANCERS

Shinichi Yachida

Department of Cancer Genome Informatics
Graduate School of Medicine, Osaka University
2-2 Yamadaoka, Suita, Osaka 565-0871, Japan(syachida@cgi.med.osaka-u.ac.jp)

Comprehensive genetic and epigenetic analyses for major cancers have been performed

as TCGA (The Cancer Genome Atlas) and ICGC (International Cancer Genome
Consortium) projects. We have learned a lot of organ-specific and non-organ-specificalterations of cancer genome from these projects. However, genomic features of rare
cancers are still unknown.

Among rare cancers, we conducted an in-depth genomic analysis on ampullarycarcinomas from Japanese and American patient cohorts [1]. Carcinoma of the ampulla of

Vater is a highly malignant neoplasm. Three distinct epithelial linings (duodenal, biliary,and pancreatic) converge at the ampulla of Vater, with pancreatic and biliary epithelium

merging within the ampulla of Vater to form the epithelium of the ampulla. Ampullarycarcinomas can be classified into two histological phenotypes, intestinal-type andpancreatobiliary-type. Carcinomas of the pancreatobiliary subtype are found to be moreaggressive than those of the intestinal subtype in most studies [2]. These phenotypes havedifferent pathogenic and clinical characteristics.

To gain insight into the genetic basis of this tumor type, we performed the exome

sequencing in a discovery set of 60 ampullary carcinomas and ten duodenal carcinomas.

We next selected 92 genes which were recurrently altered in the discovery screen, or whichwere well-documented components of a pathway, or which were potentially targetable,

since alterations in these genes are most likely to be clinically relevant. In total, 172

ampullary carcinomas and 18 non-ampullary duodenal carcinomas were investigated.

Twenty-four genes were significantly mutated driver genes in 172 ampullarycarcinomas. We identified a characteristic significantly mutated driver gene (ELF3) as wellCurrent Status of Cancer Genomics Including Data Analysis

23

as previously known driver genes (TP53, KRAS, APC, and others) (Figure). We comparedsignificantly mutated genes between Japanese and American patients. We found that ELF3

mutations occur in ampullary carcinomas across racial lines. There were differences

between the genomic landscapes of the intestinal phenotype and those of thepancreatobiliary phenotype. Among the significantly mutated genes, high-ranking genes

based on the prevalence of mutations were similar between intestinal-type ampullarycarcinomas and colorectal carcinomas, i.e., APC, TP53, KRAS and between

pancreaticobiliary-type carcinomas and pancreatic carcinomas, i.e., KRAS, TP53, SMAD4.

Functional studies demonstrated that ELF3 silencing in normal human epithelial cells

enhances their motility and invasion. Since an immortalized normal epithelial cell line has

not been established from ampullary cells, we used an immortalized normal epithelial cellline of common bile duct origin, designated HBDEC2-3H10, and an immortalized normal
epithelial cell line of duodenal mucosa origin, designated HDuodEC3. These lines wereselected for functional analyses because ELF3 mutations have also been observed in 7/74

(9.5%) common bile duct carcinomas in our study [3] and 1/18 (5.6%) duodenal carcinomas

in the present study. To investigate the consequences of the loss-of-function mutation in

ELF3, three human ELF3-specific small interfering RNA (siRNA) oligonucleotides were

utilized to knock down ELF3 expression in the HBDEC2-3H10 cells. Invasion/migration

assay using Matrigel invasion chambers and control inserts demonstrated that invasive
activities and motilities in ELF3 knockdown cells were significantly increased comparedwith control cells. HDuodEC3 cells treated with ELF3 siRNAs showed similar phenotypicchanges in terms of cell invasion and motility. Consistent with the present data, aggressive

invasion phenotype (extended cell bodies into the Matrigel matrix) of ELF3 knockdown
cells was observed in time-lapse images of 3D cell invasion assay. Quantitative RT-PCR

analysis for the expression of matrix metalloproteinase-1 and -9 (MMP1 and MMP9) further

supported this observation, showing higher expression levels of MMP1 and MMP9 in ELF3

knockdown cells compared with control cells. Knockdown of ELF3 is associated withepithelial-to-mesenchymal transition (EMT): immunofluorescence and quantitative RT-PCR

analysis showed that the expression of vimentin, which is a mesenchymal marker of EMT

and a regulator of cell migration, was increased in ELF3 knockdown cells compared withthe control cells. By contrast, the expression of the epithelial marker, cytokeratin 19 (CK19),

was decreased in cells with ELF3 deficiency. In addition, key regulators of EMT, such as

ZEB1, ZEB2, and TWIST1, were upregulated in ELF3 knockdown cells.

Neuroendocrine neoplasms (NENs) are rare and heterogenous malignancies, whicharise across organs (e.g., esophagus, stomach, duodenum, pancreas, bile ducts andcolorectum), with two major subtypes: neuroendocrine tumor (NET) and neuroendocrine
carcinoma (NEC). Previous studies have shown that NETs and NECs have distinct

24

S. Yachidaprognoses and responses to treatment. The genomic features of pancreatic NET have been

identified, then DAXX/ATRX, MEN1, and mTOR pathway are frequently altered [4,5]. The
histology of pancreatic NECs is similar to that of small/large cell lung carcinomas. We

previously investigated alterations of KRAS, CDKN2A/p16, TP53, SMAD4/DPC4, DAXX,ATRX, PTEN, Bcl2, and RB1 by immunohistochemistry and/or targeted exomic sequencing

in 19 pancreatic NECs, indicating small cell NECs of pancreas are genetically similar tolarge cell NECs, and these genetic changes are distinct from those reported pancreatic

NETs [6].

Currently, we are focusing on gastroenterological NECs. While in this process, we have

performed an in-depth analysis of the genomic abnormalities of these carcinomas througha multi-center collaboration to establish a potential basis for treatments of this disease.
References

1. Yachida S, Wood LD, Suzuki M, Takai E, Totoki Y, Kato M, Luchini C, Arai Y,
Nakamura H, Hama N, Elzawahry A, Hosoda F, Shirota T, Morimoto N, Hori K,
Funazaki J, Tanaka H, Morizane C, Okusaka T, Nara S, Shimada K, Hiraoka N,

Taniguchi H, Higuchi R, Oshima M, Okano K, Hirono S, Mizuma M, Arihiro K,
Yamamoto M, Unno M, Yamaue H, Weiss MJ, Wolfgang CL, Furukawa T, Nakagama H,
Vogelstein B, Kiyono T, Hruban RH, Shibata T. Genomic sequencing identifies ELF3 as a

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driver of ampullary carcinoma. Cancer Cell. 2016;29: 229-240.
Okano K, Oshima M, Yachida S, Kushida Y, Kato K, Kamada H, Wato M, Nishihira T,
Fukuda Y, Maeba T, Inoue H, Masaki T, Suzuki Y. Factors predicting survival andpathological subtype in patients with ampullary adenocarcinoma. J Surg Oncol.
2014;110: 156-162.
Nakamura H, Arai Y, Totoki Y, Shirota T, Elzawahry A, Kato M, Hama N, Hosoda F,
Urushidate T, Ohashi S, Hiraoka N, Ojima H, Shimada K, Okusaka T, Kosuge T,
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Jiao Y, Shi C, Edil BH, de Wilde RF, Klimstra DS, Maitra A, Schulick RD, Tang LH,
Wolfgang CL, Choti MA, Velculescu VE, Diaz LA Jr, Vogelstein B, Kinzler KW, Hruban
RH, Papadopoulos N. DAXX/ATRX, MEN1, and mTOR pathway genes are frequently
altered in pancreatic neuroendocrine tumors. Science 2011; 331: 1199-1203.
Scarpa A, Chang DK, Nones K, Corbo V, Patch AM, Bailey P, Lawlor RT, Johns AL,
Miller DK, Mafficini A, Rusev B, Scardoni M, Antonello D, Barbi S, Sikora KO,
Cingarlini S, Vicentini C, McKay S, Quinn MC, Bruxner TJ, Christ AN, Harliwong I,
Idrisoglu S, McLean S, Nourse C, Nourbakhsh E, Wilson PJ, Anderson MJ, Fink JL,
Newell F, Waddell N, Holmes O, Kazakoff SH, Leonard C, Wood S, Xu Q, Nagaraj SH,
Amato E, Dalai I, Bersani S, Cataldo I, Dei Tos AP, Capelli P, Davì MV, Landoni L,
Malpaga A, Miotto M, Whitehall VL, Leggett BA, Harris JL, Harris J, Jones MD,
Humphris J, Chantrill LA, Chin V, Nagrial AM, Pajic M, Scarlett CJ, Pinho A, Rooman I,
Toon C, Wu J, Pinese M, Cowley M, Barbour A, Mawson A, Humphrey ES, Colvin EK,
Chou A, Lovell JA, Jamieson NB, Duthie F, Gingras MC, Fisher WE, Dagg RA, Lau LM,
Lee M, Pickett HA, Reddel RR, Samra JS, Kench JG, Merrett ND, Epari K, Nguyen NQ,
Zeps N, Falconi M, Simbolo M, Butturini G, Van Buren G, Partelli S, Fassan M;
Australian Pancreatic Cancer Genome Initiative, Khanna KK, Gill AJ, Wheeler DA,
Gibbs RA, Musgrove EA, Bassi C, Tortora G, Pederzoli P, Pearson JV, Waddell N,
Biankin AV, Grimmond SM. Whole-genome landscape of pancreatic neuroendocrine
tumours. Nature 2017;543: 65-71.
Yachida S, Vakiani E, White CM, Zhong Y, Saunders T, Morgan R, de Wilde RF, Maitra A,
Hicks J, Demarzo AM, Shi C, Sharma R, Laheru D, Edil BH, Wolfgang CL, Schulick RD,
Hruban RH, Tang LH, Klimstra DS, Iacobuzio-Donahue CA. Small cell and large cell neuroendocrine carcinomas of the pancreas are genetically similar and distinct from well-differentiated pancreatic neuroendocrine tumors. Am J Surg Pathol. 2012;36: 173-184.

26

S. Yachida

Shinichi Yachida, MD, PhD

1988-1994
1994-1998
1998-1999
1999-2002
2002-2007
2007-20102011-20122012-20172017-present

M.D., Tottori University
Ph.D. in Surgery, Kagawa University
Clinical Fellow, Kagawa University Hospital
Resident, National Cancer Center Hospital
Assistant Professor, Surgery, Kagawa University
Postdoctoral Fellow, Johns Hopkins Medical
Institutions
Assistant Professor, Surgery, Kagawa University
Laboratory Head, National Cancer Center ResearchInstitute
Professor, Graduate School of Medicine, Osaka
University

27

GENOMIC AND EPIGENOMIC PROFILES OF ASIAN
ENDEMIC CANCERS

Patrick Tan

Programme in Cancer & Stem Cell Biology
Duke-NUS Medical School
8 College Road, Singapore 169857(gmstanp@duke-nus.edu.sg)

In 2012, 14 million new cancer cases were diagnosed and there were 8.2 million cancer-

related deaths. Of these, almost half (49%) of newly diagnosed cancer cases were from Asia,

contributing to 55% of all global cancer mortality. This number is expected to dramaticallyincrease in coming years, and will be disproportionately felt in Asia. Unfortunately,

because different cancers can display tremendous geographic and regional variation, manyof the major cancers relevant to Asia are different from those in Western countries, and forthese Asian cancers comparatively little is known about their underlying molecular

genetics. For example, both liver and gastric cancer are endemic to Asia but have few

targeted treatment options. There are also certain types of cancer such as peripheral T-cell

lymphomas, where Asian-dominant subentities (Natural Killer T-cell Lymphoma (NKTCL)

are associated with dismal prognosis.

Notably, many malignancies with high prevalence in Asia are caused by exposures tocarcinogens, such as infectious agents and chemical toxins. Such cancers provide important“natural experiments” for understanding how environmental perturbations can disrupt

normal cellular processes to ultimately drive tumor development, at both the genetic andepigenetic level. Over the past decade, our lab has taken a “team science” approach

towards the molecular dissection of Asian cancers, working with Prof Bin Tean Teh(National Cancer Centre Singapore) and Prof Steven Rozen (Duke-NUS Medical School
Singapore). Our results have elucidated new cancer genes associated with biliary tract

cancer [1-3], breast fibroepithelial tumors [4,5] and NKTCL [6]. We have also gained

insights into how natural compounds such as aristolochic acid, which is found in certain
traditional Chinese medicines, can contribute to urinary tract and liver cancers [7].

28

P. TanIn this talk, I will focus on gastric cancer, which is highly endemic in Asia and a leading

cause of global cancer mortality. Earlier work from our team revealed that epigenomic

alterations are prevalent in gastric cancer, as revealed through driver mutations inchromatin modifier genes such as ARID1A (Figure 1, ref. 8). More recent work from ourgroup has revealed a surprising link between epigenomic alterations in gastric cancer andthe ability of tumors to evade host anti-tumor activity. Specifically, by mapping epigenomic

histone modifications associated with gene promoters, we found that gastric cancers
pervasively utilize alternative promoters, resulting in shortened proteins which exhibit

lower immunogenicity (Figure 2, ref 9). We have also focused on the precision preventionof gastric cancer, by asking if molecular profiles of gastric pre-malignant conditions(intestinal metaplasia) can predict which patients will progress to gastric cancer
development. This work was performed by leveraging on the Gastric Cancer Epidemiology

Figure 1

ARID1A mutations in gastric cancer. (a) Eight percent (9/110) of tumors harbor ARID1A
somatic mutations. Triangles indicate inactivating mutations. ARID, ATrich interactive
domain(green hexagon); LXXLL, C-terminal leucine-rich LXXLL motif (yellow ovals).
From Zang et al., (2012).

Figure 2

Epigenomic Alterations in Promoter Usage Facilitate Evasion of Host Immunity (fromQamra et al., 2017).

Current Status of Cancer Genomics Including Data Analysis

29

Program (GCEP), a 10-year prospective cohort study (Figure 3). By analyzing GCEP

samples, we were able to identify a subgroup of patients with high-risk of gastric cancer

progression, and patients with previously-undetected Helicobacter pylori infection, thecausative agent of gastric cancer [10].

Figure 3

Overview of Gastric Cancer Epidemiology Program

References
1. Ong CK, Subimerb C, Pairojkul C, Wongkham S, Cutcutache I, Yu W, et al. Exome
sequencing of liver fluke-associated cholangiocarcinoma. Nature Genetics. 2012
Jun;44(6):690-3
2. Chan-On W, Nairismagi ML, Ong CK, Lim WK, Dima S, Pairojkul C, et al. Exome
sequencing identifies distinct mutational patterns in liver fluke-related and non-infectionrelated bile duct cancers. Nature Genetics. 2013 Dec;45(12):1474-8
3. Jusakul A, Cutcutache I, Yong CH, Lim JQ, Huang MN, et al. Whole-Genome andEpigenomic Landscapes of Etiologically Distinct Subtypes of Cholangiocarcinoma.
Cancer Discovery 2017 Oct;7(10):1116-1135.
4. Lim WK, Ong CK, Tan, Thike AA, Ng CC, Rajasegaran V, et al. Exome sequencing
identifies highly recurrent MED12 somatic mutations in breast fibroadenoma. Nature
Genetics. 2014 Aug; 46(8):877-80.

30

P. Tan5. Tan J, Ong CK, Lim WK, Ng CC, Thike AA, et al. Genomic landscapes of breast fibroepithelial tumors. Nature Genetics. 2015 Nov;47(11):1341-5.
6. Koo GC, Tan SY, Tang T, Poon SL, Allen GE et al. Janus kinase 3-activating mutations
identified in natural killer/T-cell lymphoma. Cancer Discovery. 2012 Jul;2(7):591-7
7. Poon SL, Pang ST, McPherson JR, Yu W, Huang KK, Guan P, et al. Genome-wide mutational signatures of aristolochic acid and its application as a screening tool. Sciencetranslational medicine. 2013 Aug 7;5(197):197ra01.
8. Zang ZJ, Cutcutache I, Poon SL, Zhang SL, McPherson JR, Tao J, et al. Exome sequencing
of gastric adenocarcinoma identifies recurrent somatic mutations in cell adhesion andchromatin remodeling genes. Nature genetics. 2012 May;44(5):570-4.
9. Qamra A, Xing M, Padmanabhan N, Kwok JJT, Zhang S, et al. Epigenomic Promoter
Alterations Amplify Gene Isoform and Immunogenic Diversity in Gastric
Adenocarcinoma. Cancer Discovery 2017 Jun;7(6):630-651.
10. Huang KK, Ramnarayanan K, Zhu F, Srivastava S, Xu C, et al. Genomic andEpigenomic Profiling of High-Risk Intestinal Metaplasia Reveals Molecular
Determinants of Progression to Gastric Cancer. Cancer Cell. 2018 Jan 8;33(1):137-150.

Patrick Tan, MD, PhD

1988-1992
1992-2000

2006-present

2009-present
2012-present
2012-present
2015-present

2016-present

Harvard University, B.A. (Biochemistry)
Stanford University School of Medicine, M.D., Ph.D
(Developmental Biology)
Principal Investigator (Adjunct), National Cancer
Centre, Singapore
Director, Duke-NUS Genome Biology Facility
Senior Principal Investigator, Cancer Science Instituteof Singapore (NUS)
Professor (Tenured), Cancer and Stem Cell Biology,
Duke-NUS Medical School
Director, SingHealth Duke-NUS Institute of Precision
Medicine (PRISM)
Deputy Executive Director, Biomedical ResearchCouncil, A*STAR31

CODING AND NON-CODING DRIVERS IN >2,500 WHOLECANCER GENOMESGad Getz

Cancer Center and Dept. of Pathology, Massachusetts General Hospital
55 Fruit St., Boston, MA 02114, USA
Cancer Program, Broad Institute of Harvard and MIT
415 Main St., Cambridge, MA 02142, USA
Harvard Medical School
250 Longwood Ave, Boston, MA 02115, USA(gadgetz@broadinstitute.org)
Discovery of cancer genes has traditionally focused on the identification of protein-

coding drivers [1]. In the past few years, a few non-coding drivers were discovered andfunctionally validated, including mutations in the promoter of TERT across many cancers[2] and promoter of FOXA1 in breast cancer [3]. Here, I will present the work of the Drivers

and Functional Interpretation Group [4] within the ICGC/TCGA Pan-Cancer Analysis of

Whole Genomes (PCAWG) project [5], in which we performed a comprehensive analysis ofputative cancer driver mutations in both protein-coding and non-coding genomic regions

across >2,500 whole cancer genomes. We developed a statistically rigorous strategy for

combining significance levels from multiple driver discovery methods that can overcome

limitations of individual methods. Our analyses confirm previously reported elements,

raise doubts about others, and identify novel candidate elements across 27 cancer types and

15 pan-cancer sets. Novel recurrent events were found in the promoters or 5’UTRs of TP53,

RNF34, and MTG2; in the 3’UTRs of NFKBIZ and TOB1; and in the RNA gene RMRP. We

provide evidence that recurrent mutations in the RNA genes NEAT1 and MALAT1 aresubject to a localized mutational process and are not under positive selection.

Perhaps the most striking finding is the relative paucity of point mutations (single

nucleotide variations or short insertions and deletions) driving cancer in non-coding genes

and regulatory elements. While larger studies will provide greater statistical power andlikely identify novel non-coding driver events (as well as additional coding drivers),

aggregate analyses of the promoters of known cancer genes predict a modest yield, roughly
at a 1 to 10 ratio, of driver mutations in non-coding regulatory sequences compared to32

G. Getzcoding regions. This is in contrast to the many non-coding germline variants that affectcommon diseases and may be explained by smaller territory of strong functional effects in

non-coding elements compared coding ones.

References
1. Lawrence MS, Stojanov P, Mermel CH, Robinson JT, Garraway LA, Golub TR,
Meyerson M, Gabriel SB, Lander ES, Getz G. Nature. 2014 Jan 23;505(7484):495-501. doi:
10.1038/nature12912. Epub 2014 Jan 5.
2. Rheinbay E, Parasuraman P, Grimsby J, Tiao G, Engreitz JM, Kim J, Lawrence MS,
Taylor-Weiner A, Rodriguez-Cuevas S, Rosenberg M, Hess J, Stewart C, Maruvka YE,
Stojanov P, Cortes ML, Seepo S, Cibulskis C, Tracy A, Pugh TJ, Lee J, Zheng Z, Ellisen
LW, Iafrate AJ, Boehm JS, Gabriel SB, Meyerson M, Golub TR, Baselga J, HidalgoMiranda A, Shioda T, Bernards A, Lander ES, Getz G. Nature. 2017 Jul 6;547(7661):55-60.
doi: 10.1038/nature22992. Epub 2017 Jun 28.
3. Rheinbay E, Nielsen MM, Abascal F, Tiao G, Hornshøj H, Hess JM, Pedersen RI,
Feuerbach L, Sabarinathan R, Madsen HT, Kim J, Mularoni L, Shuai S, Camaioni AAL,
Herrmann C, Maruvka YE, Shen C, Amin SB, Bertl J, Dhingra P, Diamanti K, GonzalezPerez A, Guo Q, Haradhvala NJ, Isaev K, Juul M, Komorowski K, Sushant K, Lee D,
Lochovsky L, Liu EM, Pich O, Tamborero D, Umer HM, Uusküla-Reimand L, Wadelius C,
Wadi L, Zhang J, Boroevich KA, Carlevaro-Fita J, Chakravarty D, Yiu CW, Chan YW,
Fonseca NA, Hamilton MP, Hong C, Kahles A, Kim Y, Lehmann K-V, Andrew T,
Johnson A, Kahraman A, Park K, Saksena G, Sieverling L, Sinnott-Armstrong NA,
Campbell PJ, Hobolth A, Kellis M, Lawrence MS, Raphael B, Rubin MA, Sander C,
Stein LD, Stuart J, Tsunoda T, Wheeler DA, Johnson R, Reimand J, Gerstein MB,
Khurana E, Lopez-Bigas N, Martincorena I, Pedersen JS, Getz G, PCAWG Drivers andFunctional Interpretation Group, ICGC/TCGA Pan-Cancer Analysis of Whole
Genomes Network. bioRxiv 237313. doi: 10.1101/237313
4. Campbell PJ, Getz G, Stuart JM, Korbel JO, Stein LD, ICGC/TCGA Pan-Cancer
Analysis of Whole Genomes Network. bioRxiv 162784. doi: 10.1101/162784

Current Status of Cancer Genomics Including Data Analysis

Gad Getz, PhD

2003

2004-2007

2008-20102010-present

2013-present
2013-20182018-present

33

Ph.D. Physics, Weizmann Institute of Science, Rehovot,
IsraelPostdoctoral Associate, Cancer Program, Broad Instituteof Harvard and MIT
Head, Cancer Genome Computational Analysis
Director, Cancer Genome Computational Analysis,
Broad Institute
Director of Bioinformatics Program, Massachusetts
General Hospital Cancer Center & Department of
Pathology
Assoc. Professor of Pathology, Mass. General Hospitaland Harvard Medical School
Professor of Pathology, Mass. General Hospital andHarvard Medical School

34

SYSTEMATIC IDENTIFICATION OF SPLICING ASSOCIATEDVARIANTS TOWARD PRECISION MEDICINE

Yuichi Shiraishi

Center for Cancer Genomics and Advanced Therapeutics
National Cancer Center
5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan(yuishira@ncc.go.jp)

Splicing defects caused by somatic variants have long been implicated in cancer

development. The advances in high-throughput sequencing technology have provided theopportunity to investigate the relationship between somatic variants and splicing

alterations, especially beyond the variants affecting highly conserved canonical splice sites(GT-AG dinucleotides). However, due to the complexity of splicing regulation, systematiccharacterization of somatic variants inducing splicing alterations has been a challenging

issue.

We have developed a novel approach (SAVNet, https://github.com/friend1ws/SAVNet)

based on a rigorous Bayesian theory to identify somatic variants causing splicing

alterations by disrupting or creating splicing donor/acceptor motifs. Through thisapproach, we performed a comprehensive analysis of 8,976 primary cancer samples across

31 cancer types deposited in The Cancer Genome Atlas (TCGA), constructing a catalogue of

14,438 splicing-associated variants (SAVs) [1]. Such a large collection of SAVs enabledcharacterization of their positional distribution, genomic features, underlying mutational

processes and list of frequently affected genes. We have generated a high-resolution profile
of SAVs disrupting or creating splice sites. Notably, in addition to those disrupting GT-AG

canonical splice bases, a substantial number of somatic variants affect non-canonical basesof splice sites (including previously unidentified +3 and +5 bases of donor sites, Figure 1)
or newly create splice sites. Mutation signature analysis has revealed the relative

contribution of each signature to SAV generation: smoking signature is more frequently

associated with SAVs, whereas ultraviolet exposure and aberrant activity of the error-prone

polymerase POLE have less impact on SAV generation. We showed that as many as 14.7%

of samples harbored at least one SAVs in cancer-related genes (Figure 2), particularly inCurrent Status of Cancer Genomics Including Data Analysis

35

Figure 1

Base substitution patterns of SAVs at each exonic and intronic position of splice donor andacceptor sites. Extracted and modified from Shiraishi et al., Genome research, 2018.

Figure 2

Landscape of SAVs in frequently altered genes across cancer types. The point size indicatesthe number of affected samples. Genes are sorted by the total number of SAVs in all cancer
types and known cancer-related genes are shown in red. Extracted and modified from
Shiraishi et al., Genome research, 2018.

36

Y. Shiraishi

tumor suppressor genes (TSG). Importantly, among these, 6.7% of samples had SAVs

disrupting non-canonical bases or creating novel splice motifs, which would not be
identified by the conventional analysis focusing on those at canonical splice sites. In

addition, we comprehensively described the type and position of SAVs identified in well-known TSGs, such as TP53, PIK3R1, and CDKN2A, which will help understanding thebiological properties of these genes.

Additionally, SAVNet have been used in the PCAWG (Pan-Cancer Analysis of Whole

Genomes) project, especially focusing on somatic variants in deep intronic regions, andhave contributed to identification of several novel mechanisms of new exon generation by

deep intronic variants [2].

Collectively, these findings obtained by systematic analysis of SAVs will give highly

valuable insights into cancer genetic, biology, and medicine, especially into precision
medicine.

References
1. Shiraishi Y, Kataoka K, Chiba K, Okada A, Kogure Y, Tanaka H, Ogawa S, Miyano S. A
comprehensive characterization of cis-acting splicing-associated variants in human cancer.
Genome Research, 2018 Jul 16. doi: 10.1101/gr.231951.117
2. PCAWG Transcriptome Core Group. Genomic basis for RNA alterations revealed bywhole-genome analyses of 27 cancer types. BioRxiv, 2018.

Yuichi Shiraishi, PhD

2005-2008

2008-20102010-20182018-present

Graduate Student, The Graduate University for
Advanced Studies
Postdoctoral Researcher, RIKEN Research Center forAllergy and Immunology
Postdoctoral Researcher (2010-2012), Project AssistantProfessor (2012-2014), Assistant Professor (2014-2018),
Institute of Medical Science, The University of Tokyo
Unit Leader, National Cancer Center

37

CODING AND NON-CODING CANCER MUTATIONS

Núria López-Bigas

Barcelona Biomedical Genomics Lab
Institute for Research in Biomedicine (IRB Barcelona)
Baldiri Reixac, 10, Barcelona 08012, Spain(nuria.lopez@irbbarcelona.org)

The genome of cancer cells contains a large number of somatic mutations that are

distributed unevenly along its sequence. Several studies have shown that the mutation rate

(at the megabase scale) across the genome correlates with the level of chromatincompaction, DNA accessibility and replication timing. However, the genomic features that

can influence the mutation rate variation at nucleotide resolution are not yet studied indetail. By analysing the whole-genome somatic mutations of different cancer types, we

showed that the mutation rate vary locally around DNA protein-binding sites, such astranscription factor binding sites (TFBS) and nucleosome-covered DNA [1]. In particular,

melanomas and lung tumours contain larger number of mutations at TFBS than at theirflanks. We further showed that the abnormally high mutation rate at these sites is caused

by a decrease of the levels of nucleotide excision repair (NER) activity (Figure 1). We havealso discovered that in many tumors exons receive fewer mutations than expected. We

Figure 1

38

N. López-Bigasdemonstrated that this is not due to negative selection of exonic mutations and instead it iscaused by a differential mismatch repair in exons and introns [2]. This is revealed by thefact that tumors deficient of mismatch repair do not show reduced mutation burden inexons (Figure 2). We found evidences that support the notion that differences in chromatinfeatures between exons and introns, more specifically the content of H3K36me3, play a rolein this differential mismatch repair function.

We have also observed that somatic mutation rate in melanoma and other cancer types

follows a periodicity of around 190 bps, which coincides with the distance between
adjacent nucleosome dyads [1,3]. In particular, melanomas, esophageal and gastriccarcinomas and others, show a higher rate of somatic mutations at nucleosomes than at

linkers. In contrast, in lung adenocarcinomas and lung squamous cell carcinomas, theperiodicity is exactly the opposite, in other words, there is a higher rate of mutations at#
linkers than at nucleosomes (Figure 3).

Furthermore, we found that mutations accumulate with a 10bp periodicity within

nucleosome-covered DNA. The phase of the periodicity is different depending on the#mutational processes contributing the majority of the mutations in each cancer type (Figure#4). For instance, the somatic mutations in melanomas
follow a 10bp periodicity with higher#

mutation rate at places where the minor groove of the DNA faces away from histones,#
while somatic mutations in esophageal cancer also show a strong periodic pattern
but with##maxima at sites where the minor groove faces the histones. We explored whole-genome

maps # of different types of DNA damage and repair and discovered that the rate ofaccumulation of some DNA lesions (i.e. UV light damage) and of DNA repair (in particular

Nucleotide Excision Repair and Base Excision Repair) are periodic, providing a mechanistic

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Current Status of Cancer Genomics Including Data Analysis

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40

N. López-Bigas

The genomes of eukaryotes and certain archeabacteria exhibit a detectable 10bp

sequence periodicity consisting of AT, TT, TA, and AA di-nucleotides (WW periodicity).
This has long been associated to the presence of nucleosomes. This WW periodicity has

been speculated to have arisen throughconducive
to sequences*#* * # selection of mutations** * stabilizing the DNA bending around nucleosomes. Based on our results, we propose thatthe interplay between damage and repair would contribute to the generation of the WW*

periodic pattern in the genomes of eukaryotes (Figure 5) [3].

The findings presented here have strong implications for understanding mutational andrepair processes in human DNA, understanding the evolution of eukaryotic genomes, and*for the identification of coding and non-coding cancer driver mutations.

*

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Given the evolutionary principles of cancer, one effective way to identify genomic

elements involved in cancer is by tracing the signals left by the positive selection of driver**

mutations across tumors. We analyze thousands of tumor genomes to identify driver
mutations in coding and non-coding** * *regions of the genome [4,5]. More specifically, we* **have analyzed the contributionof genomic alterations in the ubiquitin-mediatedproteolysis system in tumorigenesis, including mutations in E3-ligases
target** and in**degrons (Martinez et al., unpublished).
The analysis of tumor cohorts provides valuable** * information to improve the interpretation of individual variants detected in newly*

sequenced tumors in clinical or research settings. We have also developed***

CancerGenomeInterpreter.org, a tool designed to identify driver mutations and biomarkers
of drug response in individual tumors [6].
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Current Status of Cancer Genomics Including Data Analysis

41

References
1. Sabarinathan R, Mularoni L, Deu-Pons J, Gonzalez-Perez A, Lopez-Bigas N. Nucleotide
excision repair is impaired by binding of transcription factors to DNA. Nature, 2016;
532 (7598): 264-7.
2. Frigola J, Sabarinathan R, Mularoni L, Muiños F, Gonzalez-Perez A, López-Bigas N.
Reduced mutation rate in exons due to differential mismatch repair. Nature Genetics, 201749(12), pp. 1684-1692, doi:10.1038/ng.3991
3. Pich O, Muiños F, Sabarinathan R, Reyes-Salazar I, Gonzalez-Perez A, Lopez-Bigas N,
Somatic and Germline Mutation Periodicity Follow the Orientation of the DNA Minor
Groove around Nucleosomes. Cell, Vol. 175, no4, 1, pp 1074-1087.e18
4. Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, Tamborero D, Schroeder MP, JeneSanz A, Santos A, Lopez-Bigas N. IntOGen-mutations identifies cancer drivers acrosstumor types. Nat Methods, 2013; 10 (11): 1081-1082
5. Sabarinathan R, Pich O, Martincorena I, Rubio-Perez C, Juul M, Wala J, Schumacher S,
Shapira O, Sidiropoulos N, Waszak S, Tamborero D, Mularoni L, Rheinbay E, Hornshoj
H, Deu-Pons J, Muinos F, Bertl J, Guo Q, Weischenfeldt J, Korbel JO, Getz G, Campbell PJ,
Pedersen JS, Beroukhim R, Perez-Gonzalez A, Lopez-Bigas N, PCAWG Drivers andFunctional Interpretation Group, ICGC/TCGA Pan-Cancer Analysis of Whole
Genomes Net. The whole-genome panorama of cancer drivers. 2017, Pre-print. doi:
10.1101/190330
6. Tamborero D, Rubio-Perez C, Deu-Pons J, Schroeder MP, Vivancos A, Rovira A,
Tusquets I, Albanell J, Rodon J, Tabernero J, de Torres C, Dienstmann R, GonzalezPerez A, Lopez-Bigas N. Cancer Genome Interpreter Annotates The Biological andClinical Relevance of Tumor Alterations. 2017, Genome Medicine. doi.org/ 10.1186/
s13073-018-0531-8

42

N. López-Bigas

Núria López-Bigas, PhD

1994-1998
1998-2002

2002-2005
2005-2006

2006-20112011-20162016-present

B.Sc. Biology, University of Barcelona
PhD. Biology Oncologic Research Institute, Universityof Barcelona
Postdoctoral Researcher (Human Frontiers Science
Program - HFSP) European Bioinformatics Institute(EBI)
Postdoctoral Researcher (Human Frontiers Science
Program - HFSP) Centre for Genomic Regulation(CRG)
Group Leader (Ramon y Cajal Researcher) Universitat
Pompeu Fabra
Group Leader (ICREA Research Professor), Universitat
Pompeu Fabra
Group Leader (ICREA Research Professor), Institute for
Research in Biomedicine (IRB Barcelona)

43

MUTATIONS AND THEIR INTERACTIONS IN CANCER AND
BEYOND

Ben Lehner

Systems Biology Program, Centre for Genomic Regulation (CRG)
The Barcelona Institute of Science and Technology
Institució Catalana de Recerca i Estudis Avançats (ICREA)
Dr. Aiguader 88, Barcelona 08003, Spain(lehner.ben@gmail.com)
Our goal is to understand and be able to predict how phenotypes vary amongst

individuals.

As part of our interest in stochastic processes, we have been using data from cancer

genome sequencing projects to better understand the distribution and spectra of somatic
mutations in human cells [1,2]. Many processes can cause the same nucleotide change in agenome, making the identification of the mechanisms causing mutations a difficult

challenge. We have used rare clustered mutations as more precise fingerprints ofmutagenic processes. We discovered a mutation signature matching the spectrum oftranslesion DNA polymerase eta (POLH) that is associated with UV-exposure and alcohol

consumption and targets the H3K36me3 chromatin of active genes in a mismatch repair(MMR)-dependent manner. These regions normally have low mutation rates because error-

free MMR also targets H3K36me3 chromatin. Carcinogens and error-prone repair therefore
redistribute mutations to the more important regions of the genome, contributing asubstantial mutation load in many tumors, including driver mutations [1].

We are also extremely interested in understanding interactions between mutations. The

genetic causes of cancer include both somatic mutations and inherited germline variants.

Large-scale tumour sequencing has revolutionized the identification of somatic driveralterations but has had limited impact on the identification of cancer predisposition genes(CPGs). We developed a statistical method, ALFRED, that tests Knudson's two-hit

hypothesis to systematically identify CPGs from cancer genome data [3]. Applied to~10,000 tumour exomes the approach identifies known and putative CPGs - including thechromatin modifier NSD1 - that contribute to cancer through a combination of rare

44

B. Lehner

germline variants and somatic loss-of-heterozygosity (LOH). We estimate that rare
germline variants in these genes contribute to a median of 2% of tumours across 17 cancer
types.

In parallel, we have been using deep mutagenesis to better understand how mutations

interact within [4] and between [5] genes to affect a range of molecular functions andphenotypes. These include the activity of individual proteins and RNAs, protein
interactions, splicing, and gene circuits. We find that both pairwise and higher-orderinteractions between mutations are common, with mutations quite often switching frompositive to negative effects, even in closely related genotypes, as illustrated for a yeasttRNA (Figure 1 and reference [4]). These genetic (epistatic) interactions are of two classes:

non-specific interactions resulting from non-linearities in genotype-phenotype maps andspecific interactions related to 3D structure [5].

Indeed, we have found that quantifying

how mutations interact within and between proteins can provide sufficient information todetermine their high-resolution structures (Figure 2 and reference [6]). Deep mutagenesis
combined with selection and sequencing can therefore be used to solve the structures ofmacromolecules [6].

Figure 1

Combinatorial mutagenesis of a tRNA(A) A library consisting of all combinations of 14 substitutions observed across yeast
species in 10 positions consists of >5,000 unique genotypes of a tRNA. (B) The fitness of thesegenotypes in a restrictive condition was measured using selection and deep sequencing.
(C) All 14 mutations have beneficial, detrimental and neutral effects in different genetic
backgrounds of the tRNA. The effect of each mutation was quantified in ~1,500 different
genotypes.

Current Status of Cancer Genomics Including Data Analysis

Figure 2

45

Solving protein structures using deep mutagenesis(A) Quantifying the genetic interactions between mutations in different positions in a protein
domain (protein G B1 domain) by combining two different metrics predicts structural contacts. (B) These predicted contacts are sufficient restraints to determine the backbone 3D
structure of the domain. The predicted structure is compared to a reference crystal structure.
RMSD – root-mean-square deviation. (C) Top predicted combined score contacts shown on
the structure.

Finally, we are using C. elegans as a model system to understand how epigenetic

inheritance can impact physiology and mutation outcomes in isogenic individuals [7-9]. We

discovered an example of transgenerational epigenetic memory of an environmental

perturbation and dissected the underlying molecular mechanism [8]. We have also foundthat impaired DNA replication during the fast cell divisions in an early embryo can drive

directional and long lasting changes in chromatin [9]. We speculate that similar effects may

occur during tumour development.

References
1. Supek F, Lehner B. Clustered mutation signatures reveal that error-prone DNA repair targets mutations to active genes. Cell 170(3):534-547. 2017.
2. Supek F, Lehner B. Differential DNA mismatch repair underlies mutation rate variation
across the human genome. Nature 521:81-4. 2015.
3. Park S, Supek F, Lehner B. Systematic discovery of germline cancer predisposition
genes through the identification of somatic second hits. Nature Communications
4;9(1):2601. 2018.

46

B. Lehner

4. Domingo J, Diss G, Lehner B. Pairwise and higher-order genetic interactions during
the evolution of a tRNA. Nature 558(7708):117-121. 2018.
5. Diss G, Lehner B. The genetic landscape of a physical interaction. Elife 11;7. pii: e32472.
2018.
6. Schmiedel J, Lehner B. Determining protein structures using genetics. bioRxiv.
https://www.biorxiv.org/content/early/2018/04/20/303875.
7. Perez MF, Francesconi M, Hidalgo-Carcedo C, Lehner B. Maternal age generates phenotypic variation in Caenorhabditis elegans. Nature 7;552(7683):106-109. 2017.
8. Klosin A, Casas E, Hidalgo-Carcedo C, Vavouri T, Lehner B. Transgenerational transmission of environmental information in C. elegans. Science 21;356(6335):320-323. 2017.
9. Klosin A, Reis K, Hidalgo-Carcedo C, Casas E, Vavouri T, Lehner B. Impaired DNA
replication derepresses chromatin and generates a transgenerationally inherited epigenetic
memory. Science Advances 16;3(8):e1701143. 2017.

Ben Lehner, PhD

1997-2000
2000-2004
2004-2006

2006-present
2017-present

BA, Natural Sciences, University of Cambridge, UK
PhD, University of Cambridge, UK
Postdoctoral Fellow, Wellcome Trust Sanger Institute,
UK
Group Leader, CRG, Barcelona
Coordinator of Systems Biology Program, CRG

47

LONG NONCODING RNA AND WNT SIGNALING IN
CANCER

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)
Long noncoding RNAs (lncRNAs) have been found to be involved in cell growth andapoptosis, in part through epigenetic regulation. Wnt signal, which is frequently activatedin various cancers, induces the expression of genes that regulate cell cycle and proliferation.

While many genes have been identified as the targets of b-catenin, it is not fully understood

how activated b-catenin regulates its downstream targets. We identified a b-catenin-target

lncRNA, lnc12R, by combination of RNA-seq and ChIP-seq for b-catenin and histone

modification. Lnc12R is overexpressed in colorectal tumors with APC mutation in In TCGA

dataset. Knockout of lnc12R using CRISPR/Cas9 inhibits tumorigenesis in vivo. Repressionof lnc12R resulted in H3K27 deacetylation and repressed b-catenin target genes, such as
LGR5, without affecting the binding of b-catenin on their promoter.

48

H. Aburatani

Hiroyuki Aburatani, MD, PhD

1980-1981

1983-1988

1988-1991

1988-1994

1995-1999

1999-2001

2001-present

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

49

ANALYSIS OF NON-CODING GENOME ALTERATIONS IN
HUMAN CANCER

Matthew Meyerson

Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
Broad Institute, Cambridge, MA, USA(Matthew_Meyerson@dfci.harvard.edu)

The advent of high-throughput genome sequencing has enabled systematic discovery ofsomatic genome alterations through genome, exome, and transcriptome sequencing of

paired human cancer and germline nucleic acids. These discoveries have led to theidentification of somatic genome alterations in new pathways including epigenetic

regulators, RNA splicing factors, and immune response regulators, in addition topreviously known signal transduction, cell cycle, and transcriptional pathways. Here, I
describe alterations in these pathways by copy number disruption, mutation, and re-arrangement, especially as seen through the lens of recent and ongoing large-scale studies

of lung adenocarcinoma and squamous cell lung carcinoma genomes. I connect thesegenomic analyses with functional studies, and discuss the implications for theunderstanding of cancer pathogenesis and for the development of targeted andimmunomodulatory therapies.

This report focuses particularly on the analysis of non-coding alterations in cancer

Introduction

genomes. After a brief introduction, I first discuss copy number alterations in humancancer, with a particular focus on the analysis of aneuploidy [1]. Second, I describe thephenomenon of super-enhancer duplications in the vicinity of oncogenes such as MYC [2],KLF5 [3], and the androgen receptor gene [4]. Third, I discuss the findings of gene bodyinsertions and deletions in lineage-expressed genes such as surfactant protein genes in lungcancer [5]. Then I consider the future of cancer genome sequencing with a concentration on
the analysis of linked reads and long reads for complete characterization of genome

50

M. Meyerson

structure. This summary does not include a comprehensive description of the scientificfield, but is rather limited to the work of my own laboratory group, presented at the 49th

International Symposium of the Princess Takamatsu Cancer Research Fund that took placein Tokyo, Japan in November, 2019.

Cancer is a disease of the genome. Cancer is caused by both germline, or inherited, andsomatic, or acquired, alterations in the genome. These alterations include single nucleotide

substitutions, small insertions, deletions or duplications, chromosomal rearrangements that

may occur either within or between chromosomes, and copy number alterations, including

focal gains or losses of DNA that may be restricted to particular regions of a chromosome,or losses or gains of entire chromosomes or chromosome arms. Cancer may also be causedby viral or bacterial infection, which could also considered to be the acquisition of novel

genomes.

During the time from 2000 to 2015, following the initial sequencing of the humangenome and during a time of rapid improvement in genome sequencing technology, mostpopulation-level cancer genome studies, such as The Cancer Genome Atlas project, TCGA,

in the United States of America, have focused on the sequence of the coding regions of the

genome. The sequence of all exons of the coding genome, or the exome, in over 10,000

pairs of human cancer-derived DNA samples, in comparison to the corresponding

sequences of their matched normal DNA samples, has permitted the delineation of themost common, recurrent coding mutations across human cancer.

However, the coding genome accounts for only 1% of all of the genome, and the vast

majority of somatic genome alterations, as well as germline genome alterations, occur in the

99% of the genome that does not code for proteins. In the last several years, the sequencingof whole genomes has begun to elucidate the alterations in non-coding regions of cancer

genomes, such as promoter mutations, and the enhancer duplications described below. In

addition, the continued improvement in genome sequencing technology, and thedevelopment of new technologies for long reads and linked reads, now enables a more

complete description of somatic alterations in the cancer genome, as described below.

Aneuploidy refers to the loss or gain of entire chromosomes or chromosome arms, andAneuploidy is a universal feature of cancer genomes

is one of the most common features of cancer genomes, being almost universal in

carcinomas, or cancers of the epithelium, such as lung, breast, colon, pancreatic, ovarian,and stomach cancers. The recognition of aneuploidy is not new—indeed, using earlychromosome staining techniques, Theodor Boveri described aneuploidy as a feature ofcancer more than 100 years ago, in 1914. Our recent studies have shown that wholechromosome or chromosome arms or losses are the most common of the currently

Tumor Hetelogeneity and Immunogenomics

51

detectable features of cancer genomes, more common than TP53 mutation or focal
alterations in copy number [1, 6, 7].

However, even after 100 years of study, we do not yet know the role of aneuploidy incancer pathogenesis. Does aneuploidy cause human cancer, or is it merely a bystander,
occurring because it is tolerated because of the other genome alterations in human cancers?

After completing a survey of the role of aneuploidy in human cancers through The

Cancer Genome Atlas, and confirming that chromosome level and chromosome arm level

genome alterations are almost universal in epithelial cancers and exhibit recurrent cancer-

specific patterns, Alison Taylor in my laboratory developed a method to generate

aneuploidy experimental in lung epithelial cells. She used CRISPR methods to cut DNA on

chromosome arm 3p near the centromere together with introduction of a plasmid that has

homology to the cut site and contains an artificial telomere (a gift from Professor Mitsuo

Oshimura at Tottori Universty), to mimic the 3p loss that is so common in squamous celllung cancers. In so doing, Dr. Taylor generated cells that had lost chromosome arm 3p.
Interestingly, these cells grew more slowly than parental cells, and were eventually over-grown by cells that gained an extra copy of chromosome arm 3p, becoming functionallycells with a gain of chromosome arm 3q [1]. This work provides an experimental methodby which to study aneuploidy but it still leaves unresolved the key question: does
aneuploidy promote cancer? Further studies will be required to answer this question.

Until recently, we believed that focal amplifications of oncogenes were always targeting

Duplication of super-enhancer elements near oncogenes

the coding gene body itself together with the promoter elements of the gene, causing gene

over-expression through increased number of coding gene copies. One of the biggest

surprises in whole genome sequencing of cancer is that this is not always the case.

Instead, the amplification of non-coding elements near oncogenes has now been foundto be a common type of activation of oncogene expression. Dr. Xiaoyang Zhang in mylaboratory first discovered such alterations near the MYC gene. Different enhancer regions,both showing lineage specific histone H3 lysine acetylation which is a marker of enhancers,

were found to be amplified in endometrial cancer or in lung adenocarcinoma. In bothcases, the amplifications were only in the enhancer regions, a few hundred kilobases from
the MYC gene body, and not in the gene itself. Working with Drs. Peter Choi and Joshua

Francis in my laboratory, Dr. Zhang went on to study the impact of enhancer deletion or

repression in a lung cancer cell line bearing MYC enhancer duplication. Using either

CRISPR-mediated deletion of an enhancer sub-region, or KRAB-mediated repression of thisregion, Dr. Zhang and colleagues showed that the enhancer activity promotes MYC

expression and cancer cell growth [2].

52

M. Meyerson

Analysis of enhancer duplication also helped enable us to find another oncogene, theKLF5 gene. We discovered KLF5 enhancer duplication in a variety of squamous cell cancer

types including lung, esophageal and cervical squamous carcinomas. KLF5 is also

activated in other specimens of these same cancer types, by point mutations either in a

DNA-binding domain or in a degradation domain. Here, Dr. Zhang again went on to show

an oncogenic phenotype for enhancer duplication that was reversed by CRISP-mediateddetion [3].

Finally, in our most recent discovery on this topic, Drs. Srinivas Viswanathan, Gavin Ha

and Andreas Hoff performed linked-read sequencing of metastatic castrate-resistant
prostate cancers and searched for novel regions of copy number alteration. Here, theyfound duplication of a region several hundred kilobases from the gene body of theandrogen receptor gene, AR. AR enhancer duplications occurred especially in the

population of prostate cancers resistant to nuclear hormone receptor inhibitors, and wereassociated with increased levels of AR expression [4].

One possible gene for discovering additional such super-enhancer duplications is the

SOX2 gene subjected to focal amplification in squamous carcinomas of the lung, esophagus

and cervix. As shown in Figure 1, copy number analysis of data from The Cancer Genome

Atlas reveals multiple regions of amplification near the SOX2 gene body, suggestive ofpotential enhancer duplication. The NKX2-1 gene that is amplified in lung adenocarcinoma

also shows evidence of non-coding duplications (data not shown).

Figure 1

Copy number analysis of the region near the SOX2 gene across human cancers shows multiple non-coding deletions in the region (red and pink bars)

Tumor Hetelogeneity and Immunogenomics

53

Together, these findings suggest that enhancer duplication is a common mechanism of

oncogene activation and could often be a mechanism by which alterations in the cancer

genome induce expression of these oncogenes. This implies that whole genome
sequencing, which can reveal such duplications, may be key for genome-based cancer

diagnosis.

Whole genome sequencing of cancer can reveal surprises. One such surprise was ourDeletions within or near genes expressed at high levels in a lineage-unique mannerrecent discovery of deletions within the 3’ untranslated regions, or sometimes the coding

gene bodies, of highly expressed genes within specific cancer lineages. The genes subjected

to lineage specific deletion mutations include the surfactant protein genes SFTPA, SFTPB,

and SFTPC in lung adenocarcinoma, the albumin gene ALB in hepatocellular carcinoma,

the lipase gene LIPF in gastric carcinoma, and the thyroglobulin gene TG in thyroid
carcinoma [5]. These deletions do not appear to affect gene expression. One possibility isthat these alterations are actually passenger mutations that are a consequence of theextremely high transcriptional level in these tissues, but this question remains to beresolved.

Towards the future—complete cancer genome sequencing with long reads or linked
As described above, we are beginning to learn that cancer genomes harbor criticalreads

alterations beyond the coding genome. Even though our current technology represents a

revolutionary leap from previous generations of genome sequencing technology, we do not

yet have the capability to find all cancer genome alterations at once. The advent of newtechnologies such as linked-read sequencing methods and long read sequencing

technologies such as nanopore sequencing, now gives us the opportunity to describe theabsolutely complete sequence and structure of cancer genomes. Once we make thiscomplete description, we will find far more causative alterations in cancer genomes, which
in turn will help us to develop new cancer therapies.
References
1. Taylor AM, Shih J, Ha G, Gao GF, Zhang X, Berger AC, Schumacher SE, Wang C, Hu H,
Liu J, Lazar AJ; Cancer Genome Atlas Research Network, Cherniack AD, Beroukhim R,
Meyerson M. Genomic and functional approaches to understanding cancer aneuploidy.
Cancer Cell. 2018 Apr 9;33(4):676-89.

54

M. Meyerson

2. Zhang X, Choi PS, Francis JM, Imielinski M, Watanabe H, Cherniack AD, Meyerson M.
Identification of focally amplified lineage-specific super-enhancers in human epithelial
cancers. Nat Genet. 2016 Feb;48(2):176-82.
3. Zhang X, Choi PS, Francis JM, Gao GF, Campbell JD, Ramachandran A, Mitsuishi Y, Ha
G, Shih J, Vazquez F, Tsherniak A, Taylor AM, Zhou J, Wu Z, Berger AC, Giannakis M,
Hahn WC, Cherniack AD, Meyerson M. Somatic superenhancer duplications andhotspot mutations lead to oncogenic activation of the KLF5 transcription factor. Cancer
Discov. 2018 Jan 8(1):108-25.
4. Viswanathan SR, Ha G, Hoff AM, Meyerson M. Structural alterations driving castration-resistant prostate cancer revealed by linked-read genome sequencing. Cell 2018 Jul
12;174(2):433-447.
5. Imielinski M, Guo G, Meyerson M. Insertions and deletions target lineage-defining
genes in human cancers. Cell. 2017 Jan 26;168(3):460-472.
6. 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 across human cancers. Nature. 2010 Feb 18;463(7283):899-905.
7. Zack TI, Schumacher SE, Carter SL, Cherniack AD, Saksena G, Tabak B, Lawrence MS,
Zhang CZ, Wala J, Mermel CH, Sougnez C, Gabriel SB, Hernandez B, Shen H, Laird
PW, Getz G, Meyerson M, Beroukhim R. Pan-cancer patterns of somatic copy number
alteration. Nat Genet. 2013 Sep 26;45(10):1134-40.

Tumor Hetelogeneity and Immunogenomics

Matthew Meyerson, MD, PhD

1993-1996
1995-1998
1998-2005
2005-20092009-present
2017-present

Resident, Massachusetts General Hospital
Research Fellow, Whitehead InstituteAsst. Professor, Harvard Medical School
Assoc. Professor, Harvard Medical School
Professor, Harvard Medical School
Director of Cancer Genomics, Dana-Farber Cancer
Institute and Broad Institute

55

56

A UNIQUE MECHANISM OF CANCER IMMUNE EVASION
VIA THE DISRUPTION OF PD-L1 3’-UTR

Seishi Ogawa

Department of Pathology and Tumor Biology, Graduate School of Medicine
Kyoto University, Japan
Department of Molecular Hematology, Karolinska Institute, Sweden
Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan(sogawa-tky@umin.ac.jp)
Successful treatment of many advanced cancer patients using antibodies against

programmed cell death 1 (PD-1) and its ligand (PD-L1) has highlighted the criticalimportance of PD-1/PD-L1-mediated immune escape in cancer development. However,

the genetic basis for the immune escape has not been fully elucidated, with the exception ofelevated PD-L1 expression by gene amplification and utilization of an ectopic promoter bytranslocation, as reported in Hodgkin and other B-cell lymphomas, as well as stomach

adenocarcinoma. Here we show a unique genetic mechanism of immune escape caused bystructural variations (SVs) commonly disrupting the 3’ region of the PD-L1 gene. Widely

affecting multiple common human cancer types, including adult T-cellleukemia/lymphoma (27%), diffuse large B-cell lymphoma (8%), and stomach
adenocarcinoma (2%), and other solid cancers (Figure 1), these SVs invariably lead to a

Figure 1

Aberrant expression of PD-L1 in ATL samples with SVs involving PD-L1 3’-UTR
a, Different types of SVs commonly affecting 3’ part of PD-L1 are shown by indicated colors.
b, PD-L1 exon 4 expression (reads per kb of exon per million mapped reads, RPKM) in 43
ATL samples, colored by PD-L1 SV status. Welch’s t-test.

57

marked elevation of aberrant PD-L1 transcripts that are stabilized by truncation of the 3’untranslated region (UTR) (Figure 2). We further interrogated PD-L1/PD-L2-involving

somatic aberrations in 387 samples from various lymphoma subtypes using high-throughput sequencing, particularly focusing on virus-associated lymphomas. A highfrequency of PD-L1-involving genetic aberrations was confirmed in this large set oflymphoma samples (Figure 3). In addition, we found recurrent SVs affecting 3’-UTR of PD-

L2. PD-L1/PD-L2-involving abnormalities, including SVs and focal amplification, wasespecially common in EBV-positive lymphomas [36 (24%) of 151 cases], including EBV-

positive diffuse large B-cell lymphoma (DLBCL, 19%) and peripheral T-cell lymphoma-not

otherwise specified (15%), and mature NK/T-cell neoplasms (17-57%) (Figure 4). In
particular, PD-L1-involving alterations represented the most prevalent somatic lesions in
extranodal NK/T-cell lymphoma. By contrast, the frequency was much lower in EBV-

negative lymphomas regardless of histology type [12 (5%) of 236 cases]. The majority ofthese abnormalities were explained by truncations of their 3’-untranslated regions, which

are thought to result in overexpression of the involved gene, contributing to tumor immune

evasion. Disruption of Pd-l1 3’-UTR in mice enables immune evasion of EG7-OVA tumor

cells with elevated Pd-l1 expression in vivo, which is effectively inhibited by Pd-1/Pd-l1

blockade, supporting the role of relevant SVs in clonal selection through immune evasion.

Our findings not only unmask a novel regulatory mechanism of expression of these PD-1

ligands, but also suggest that PD-L1/PD-L2 3’-UTR disruption could serve as a genetic

marker to identify cancers that actively evade anti-tumor immunity through PD-L1
overexpression.

Figure 2

PD-L1 SVs associated with overexpression of aberrant PD-L1 transcripts in multiple cancers
PD-L1 expression in each TCGA cancer type containing PD-L1 SV cases. Each bar represents
the 10th percentile. BLCA, bladder urothelial carcinoma; COAD, colon adenocarcinoma;
ESCA, esophageal squamous cell carcinoma; KIRC, kidney renal clear cell carcinoma;
LUAD, lung adenocarcinoma; READ, rectal adenocarcinoma; SKCM, skin cutaneous
melanoma; UCEC, uterine corpus endometrioid carcinoma.

58

S. Ogawa

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Frequency of genetic alterations involving PD-L1 and/or PD-L2 in each lymphoma subtype. Type of alterations is indicated by color. Cases harboring both SV and focal CNA> > corresponding> > SV group. Multiple repaffecting PD-L1 and/or PD-L2 are combined into the
resents cases harboring both PD-L1 and PD-L2 SVs. DLBCL, diffuse large B-cell lymphoma; FL, follicular lymphoma; MALT, mucosa-associated lymphoid tissue lymphoma;
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EBV high group corresponds to the EBV-positive group in the TCGA classification of stomach adenocarcinoma

Tumor Hetelogeneity and Immunogenomics

59

References
1. Kataoka K, Shiraishi Y, Takeda Y, Sakata S, Matsumoto M, Nagano S, Maeda T, Nagata Y,
Kitanaka A, Mizuno S, Tanaka H, Chiba K, Ito S, Watatani Y, Kakiuchi N, Suzuki H,
Yoshizato T, Yoshida K, Sanada M, Itonaga H, Imaizumi Y, Totoki Y, Munakata W,
Nakamura H, Hama N, Shide K, Kubuki Y, Hidaka T, Kameda T, Masuda K, Minato N,
Kashiwase K, Izutsu K, Takaori-Kondo A, Miyazaki Y, Takahashi S, Shibata T,
Kawamoto H, Akatsuka Y, Shimoda K, Takeuchi K, Seya T, Miyano S, Ogawa S.
Aberrant PD-L1 expression through 3'-UTR disruption in multiple cancers. Nature.
2016;534(7607):402-406.
2. Kataoka K, Miyoshi H, Sakata S, Dobashi A, Couronne L, Kogure Y, Sato Y, Nishida K,
Gion Y, Shiraishi Y, Tanaka H, Chiba K, Watatani Y, Kakiuchi N, Shiozawa Y,
Yoshizato T, Yoshida K, Makishima H, Sanada M, Onozawa M, Teshima T, Yoshiki Y,
Ishida T, Suzuki K, Shimada K, Tomita A, Kato M, Ota Y, Izutsu K, Demachi-Okamura A,
Akatsuka Y, Miyano S, Yoshino T, Gaulard P, Hermine O, Takeuchi K, Ohshima K,
Ogawa S. Frequent structural variations involving programmed death ligands in
Epstein-Barr virus-associated lymphomas. Leukemia. 2019.

Seishi Ogawa, MD, PhD

1989-1993
1983-1988

1988-1989
1994-1995
1995-1996
1996-2002

2002-20132013-present

2017-present

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

60

GENOMIC PATHOLOGY OF DIFFUSE-TYPE GASTRICCARCINOMA

Shumpei Ishikawa

Department of Molecular Preventive Medicine
Graduate School of Medicine, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan(isikawas-tky@umin.org)

Diffuse-type gastric carcinoma (DGC) is one of the major subtypes of stomach cancers

and has unique biological & clinical features such as single cell infiltration, strong fibrotic

stroma, and massive metastasis/dissemination potential. Except for CDH1 (E-cadherin),

DGC-specific drivers that explain these unique features and thus, could be moleculartargets, had not been discovered so far. Recent comprehensive cancer genomics hasrevealed its genomic stability, lack of popular cancer drivers, and unique RHOA gene

driver mutations [1,2,3]. DGC has low mutation burdens, and unlike other chromosomal

instable-type gastric cancers, harbors low frequencies of receptor tyrosine kinase

amplifications including HER2 gene aberrations. Therefore, DGC is not a good target ofcurrently available molecular drugs like tyrosine kinase inhibitors. The newly discovered

RHOA mutations did not show a random distribution but exhibited several hot spots,suggesting that it is a cancer driver. The driver nature of mutant RHOA was supported byseveral experiments, where the survivals of RHOA-mutant cell lines were strongly
dependent on their mutant RHOAs. Although the exact biochemistry of mutant RHOAs

would be complex, one of its mechanisms is suggested to be their dominant negative

nature of mutant proteins, which inactivate ROCK pathway to promote cell survival [4].

Histologically, DGCs with mutant RHOA are composed of the major component of poorly
cohesive carcinoma with limited tubular differentiation, and show penetrative growth

patterns at the edges of the mucosal area [5]. Likewise, mutant RHOA is macroscopicallylinked with Borrman type3 appearance and is not frequent among linitis-plastica-likecancers showing wide spread submucosal infiltration.

Tumor Hetelogeneity and Immunogenomics

61

While global genomic analysis so far has revealed heterogeneity and subgroups ofgastric cancer, tumor immune microenvironment is also very heterogeneous among gastriccancers. Applying recently developed deconvolution program for large scale transcriptome

data showed a characteristic immune microenvironment of DGCs, where T-cell frequently

tended to be inactive and B-cell infiltration was relatively increased. DGC was suggested tobe categorized into “immunologically cold tumors”, which is compatible with its low

tumor mutation burden and a recent report showing its low responsiveness to immune

checkpoint inhibitors in a clinical trial. Based on these observation, comprehensive

profiling of immune repertoires of T-cell and B-cell in clinical gastric cancer tissues was
performed [7]. It revealed different patterns of responses of T-cell and B-cell repertoires in

the cancer environment and clonal expansion of tumor-specific B-cells were frequently

observed. The CDR (Complementarity Determining Region) of immunoglobulins were
substantially variable and it was difficult to find common features of cancer-specificimmunoglobulin sequences. In order to investigate what kinds of antigens were recognizedby such tumor-specific dominant clones we discoverd, human IgG antibodies werereconstructed based on the sequences of tumor-specific immunoglobulin clones. While

many of the tested clones were shown to recognise autoantigens which were also reported

in autoimmune diseases, several clones commonly recognized a unique subgroup ofcarbohydrates, sulfated glycosaminoglycan (s-GAG). By reconstituting more numbers of
immunoglobulin clone, it was revealed that around 30 to 40% of the most dominantly

infiltrated B-cell clones in individual cases recognized s-GAG; therefore, s-GAG was

suggested to be the major humoral cancer antigens among DGC. The anti-s-GAG

antibodies synthesized based on the sequences of dominant clones exhibited tumor cellbinding properties, and moreover, some of which also showed growth suppressivefunctions. These clones were well incorporated into cancer cells and induced cell death byusing antibody-drug conjugate technologies.

These results have unraveled the unique genomic and immunogenomic features of

DGC, which have also provided us with new concepts for designing molecular targeteddrugs as well as the possibility of another-type of cancer immunotherapy by modulating
the humoral cancer immune system for this refractory cancer.
References
1. Kakiuchi M, Nishizawa T, Ueda H, Gotoh K, Tanaka A, Hayashi A, Yamamoto S,
Tatsuno K, Katoh H, Watanabe Y, Ichimura T, Ushiku T, Funahashi S, Tateishi K, Wada I,
Shimizu N, Nomura S, Koike K, Seto Y, Fukayama M, Aburatani H, Ishikawa S*:
Recurrent gain-of-function mutations of RHOA in diffuse-type gastric carcinoma. Nat.
Genet. 46(6):583-7,2014 (* Correspondence)

62

S. Ishikawa

2. Wang K, Yuen ST, Xu J, Lee SP, Yan HH, Shi ST, Siu HC, Deng S, Chu KM, Law S,
Chan KH, Chan AS, Tsui WY, Ho SL, Chan AK, Man JL, Foglizzo V, Ng MK, Chan AS,
Ching YP, Cheng GH, Xie T, Fernandez J, Li VS, Clevers H, Rejto PA, Mao M, Leung SY.
Whole-genome sequencing and comprehensive molecular profiling identify new driver
mutations in gastric cancer. Nat Genet. 46(6):573-82, 2014
3. The Cancer Genome Atlas Research Network. Comprehensive molecular characterizationof gastric adenocarcinoma. Nature. 513(7517):202-9, 2014
4. Nishizawa T, Nakano K, Harada A, Kakiuchi M, Funahashi SI, Suzuki M, Ishikawa S,
Aburatani H. DGC-specific RHOA mutations maintained cancer cell survival and promoted cell migration via ROCK inactivation. Oncotarget. 9(33):23198-23207,2018
5. Ushiku T, Ishikawa S*, Kakiuchi M, Tanaka A, Katoh H, Aburatani H, Lauwers GY,
Fukayama M: RHOA mutation in diffuse-type gastric cancer: A comparative clinicopathological analysis of 87 cases. Gastric Cancer 19(2):403-11,2016 (* Correspondence)
6. Katoh H, Komura D, Konishi H, Suzuki R, Yamamoto A, Kakiuchi M, Sato R, Ushiku T,
Yamamoto S, Tatsuno K, Oshima T, Nomura S, Seto Y, Fukayama M, Aburatani H,
Ishikawa S*: Immunogenetic Profiling for Gastric Cancers Identifies Sulfated
Glycosaminoglycans as Major and Functional B Cell Antigens in Human Malignancies.
Cell Rep. 1;20(5):1073-1087,2017 (* Correspondence)

Shumpei Ishikawa, MD, PhD

2000

2004

2004-2007
2007-20132013-20182018-present

Graduated from Faculty of Medicine, The University ofTokyo
Graduated from Graduate School of Medicine,
The University of Tokyo
Assistant Professor, Genomescience Division, ResearchCenter for Advanced Science and Technology,
The University of Tokyo
Assistant and Associate Professor, Department of
Pathology, Graduate School of Medicine,
The University of Tokyo
Professor, Department of Genomic Pathology, Medical
Research Institute, Tokyo Medical and Dental
University
Professor, Department of Molecular Preventive
Medicine, Graduate School of Medicine, The Universityof Tokyo

63

CANCER EVOLUTION AND IMMUNE ESCAPE: TRACERxCharles Swanton

Translational Cancer Therapeutics Lab
The Francis Crick Institute
1 Midland Road, London NW1 1AT, UK(charles.swanton@crick.ac.uk)

Over the last decade, there has been an increasing mismatch between cost and benefit ofIntroduction

cancer drugs. In an overview of 12 years of drug approvals by the FDA, of 71 anticancer

drugs approved, the median overall survival improvement was only 2.1 months [1]. The

cost of cancer drugs has doubled in the last 10 years, now estimated to be over $10,000 per
month. Taken together with the modest improvement in overall survival associated withthese therapies, it is now estimated that the cost per life year saved has reached $2.7million[2]. Such increments in cost that are not matched by survival improvements or cures in themetastatic setting are unsustainable within socialised health economies in Europe.

Cancer genome instability from the single nucleotide to the whole chromosomal level

results in cell-to-cell variation and provides a substrate for selection, ultimately

contributing to intratumour heterogeneity and cancer evolution. Intratumour heterogeneity

is now considered to be one of the major hurdles in cancer drug development and results inthe acquisition of cancer drug resistance, treatment failure and death, contributing to thefailure of new therapies to deliver meaningful survival benefits and the excessive healtheconomic cost of new therapies [3-5].

Through advances in tumour evolutionary genome analysis and longitudinal clinicalcancer evolutionary studies (TRACERx and PEACE autopsy studies) combined withdetailed analysis of the tumour microenvironment through mass cytometry, the laboratory
focussed on firstly, deciphering drivers of cancer genome instability and intercellular

heterogeneity that form the substrate upon which selection can act and secondly,

understanding how the propagation of unstable cancer genomes is initiated, tolerated and64

C. Swanton

sensed by the immune system. We are endeavouring to understand cancer evolution by

combining our genomic analyses with functional studies to understand the roles of driver

events in cancer evolution (both clonal and subclonal) utilising genome editing techniques,

live cell microscopy and the generation of animal models that more faithfully recapitulate

cancer evolutionary processes. By building on our tumour phylogenetics work throughbespoke circulating free DNA monitoring, we are attempting to identify and treat diseaseearlier when tumour heterogeneity is at a minimum and leverage the host immune
response, with a deeper understanding of immune escape pathways, in order to constrain

tumour adaptation and maximise tumour cell attrition by targeting multiple clonal neoantigens.

Our cancer evolutionary interests have contributed to defining the extent of branched

The Clinical Challenges of Cancer Cell Diversity and Selection

evolution and selection across solid tumours including renal [6-9], colorectal [10, 11], lung[12-15], glioblastoma [16] and oesophageal cancers [17]. Our work has contributed to theelucidation of branched tumour evolution across tumour types and the prevalence of the

APOBEC mutational process contributing to subclonal evolution [18]. We have developed

tools to understand mechanisms of immune evasion, that have demonstrated HLA Loss ofheterozygosity [15] in 40% of early non-small cell lung cancers. We have found evidence forthe spatial and temporal separation of driver and passenger genetic events and on-going

subclonal expansions that may confound the success of targeted therapies [8, 9, 15, 19]. We

have found extensive evidence for parallel evolution with the same gene, signaltransduction pathway or protein complex subject to distinct alterations in different

subclones of the same tumour [6, 7, 12, 13, 18]. These data provide evidence for constraints

to tumour evolution and suggest the presence of “evolutionary rule books” that might

govern the sequence of somatic events present in individual tumours that might be
exploitable therapeutically [20, 21].

We have begun to elucidate the impact of intratumour heterogeneity on drug resistancewith evidence of heterogeneous tumour responses driven by subclonal driver events,

spatially separated within distinct subclones of the same tumour [6]. From a clinicalperspective, tumours may harbour both good and poor prognostic signatures in the same

tumour, illustrating major challenges to biomarker discovery and qualification for clinicaluse [22]. In this regard, us and others have found that low frequency events in a tumourthat may not be detectable at diagnosis may influence clinical outcome later in the disease
course as subclonal events begin to dominate the disease at recurrence [7, 14, 16].

Tumor Hetelogeneity and Immunogenomics

65

In order to derive deeper insights into cancer evolutionary constraints, the mechanistic

Tracking Cancer Evolution: The TRACERx and PEACE studies

basis for cancer genomic instability and the impact of tumour heterogeneity on the hostimmune response, drug resistance and clinical outcome, we have initiated the TRACERx(TRAcking Cancer Evolution through Therapy/Rx) national lung and renal cancer
evolution studies. Lung TRACERx is a national study that has recruited approximately 700of the planned 842 patients, employing multi-region and longitudinal deep exome

sequencing approaches to decipher the timing of genomic events in relation to distinct

genomic instability processes and derive tumour phylogenies for each tumour [19]. The

study is collecting prospective clinical data over 10 years (2014-2024) that will be integrated

with tumour phylogenetic, immunological and microenvironment analysis to attempt toidentify therapeutic and environmental selection pressures that constrain cancer evolution,

the origins of the lethal metastatic subclones and mechanisms of immune escape andmetastasis. The primary endpoint of TRACERx is to establish whether there is arelationship between genetic intratumour heterogeneity and clinical outcome.

Analysis of the first 100 patients in TRACERx lung has revealed that chromosomal

instability (CIN) assessed by somatic copy number heterogeneity is a major determinant ofclinical outcome independent of tumour stage [13]. We have developed an approach toquantify chromosome instability from cancer exome TRACERx datasets through haplotype
and allelic imbalance analysis at subclonal resolution that is revealing the role of CIN laterin tumour evolution.

Using these tools, TRACERx has revealed four findings that may contribute to the poor

prognosis associated with CIN. Firstly, through the LOHHLA software tool developed in

our laboratory, we have found that CIN results in copy loss of class I HLA alleles andpermits branched evolution through the expansion of subclones harbouring neo-antigens

predicted to have bound to the lost HLA allele [15] and loss of clonal neoantigens(Rosenthal manuscript in revision). Secondly through mirrored subclonal allelic imbalance(MSAI), and parallel evolution of driver amplification events occurring recurrently in

distinct subclones of the same tumour[13]. Thirdly, renal TRACERx studies have revealed
that CIN results in DNA copy number loss of chromosomes 9p and 14q, high risk events

selected during metastatic dissemination [8, 9, 23]. Finally, we have found that genome

doubling, that occurs as an early event in >70% of patients with primary NSCLC [13],accelerates cancer evolution and the propagation of aneuploid cancer cell populations,accelerating cancer cell diversity [11].

66

C. Swanton

Prompted by our preliminary findings of contingencies and constraints to renal cancer

Developing Insights into the Primary-Metastatic Transition

evolution converging upon PI3K pathway activation [6, 20], we initiated the Renal
TRACERx study. In this study we have been investigating tumour evolution from primary

to metastatic sites through extensive multi-region sequencing analysis of 100 prospectively

recruited patients with clear cell carcinoma of the kidney and two validation cohorts ofprimary-metastases pairs from HUC (Hospital Unversitario Cruces) and MSKCC [8, 9, 23].

A principle of the TRACERx studies is that deep analysis of the primary tumour andmetastatic sites will enable the metastatic competent subclone to be distinguished frommetastatic incompetent subclones, that may shed light on the cell intrinsic processes
inherent to tumour metastasis and latency.

We found no evidence for the selection of single mutational driver events in themetastatic competent subclone. However, cancer cell ploidy, chromosomal complexity(wGII score: weighted genome instability index; the average proportion of the genome

with aberrant copy number weighted on each of the 22 autosomal chromosomes) andproliferation indices were enriched in metastatic competent compared to incompetentsubclones. Loss of chromosome 9p encoding CDKN2A/B was the most frequently selected

event at the metastatic transition. Comparing the clinical phenotype to genomic patterns ofevolution in the TRACERx cohort, we find that tumours dominated by high chromosomal

complexity selected early in tumour evolution in the most recent common ancestor and low

driver event heterogeneity, are associated with more rapid metastatic dissemination tomultiple organ sites. In contrast, tumours with late onset chromosomal complexity (i.e.

present subclonally) and with higher intratumour heterogeneity (assessed by the ratio of

subclonal drivers:clonal drivers) were associated with a more protracted clinical course anddissemination to single sites of disease over years [8, 9]. Therefore, both TRACERx studies

in renal and NSCLC have revealed the importance of chromosomal instability rather thanpoint mutational driver diversity as the major factor associated with poor metastasis freesurvival. Importantly, these data suggest that a detailed understanding of cancer evolution
may shed light on distinct patterns of future clinical behaviour and outcome.

An increased understanding of cancer evolution has led to an appreciation of the

Clonal Neoantigens: relevance to tumour control and checkpoint inhibitor response

importance of targeting clonal events that are present in every tumour cell [18, 24], and therequirements for new cancer biomarkers to account for tumour heterogeneity. Early resultsfrom the TRACERx program indicate that therapeutically relevant activating mutations in
EGFR and rearrangements in EML4-ALK are always clonal founder events, present inevery tumour cell [13]. Despite progress in targeted therapy development in non-small cellTumor Hetelogeneity and Immunogenomics

67

lung cancer, and the ability to target single driver founder events effectively, progression of

disease is inevitable in almost all patients due to selection of subclones harbouringresistance somatic events (eg T790M gatekeeper mutation following EGFR tyrosine kinase

inhibitor resistance [25]). These clinical data indicate that targeting a single genetic driver of

disease is unlikely to prove sufficient to maintain long-term disease control in a patientwith advanced lung cancer. An ideal therapeutic would target multiple clonal events in an

individual tumour, present and specific to every tumour cell, whilst minimising normal
tissue toxicity.

Our TRACERx work over the last 3 years indicates that the high mutational burdenpresent in a tobacco-associated non-small cell lung cancer (accounting for >40,000 deaths in

the UK per year) may represent a specific tumour vulnerability that may be taken

advantage of by augmenting the host immune response. We have found evidence that theburden of clonal mutations encoding tumour HLA Class I putative neo-antigens, isassociated with improved survival outcome in the absence of immune-therapy intervention

in both a TCGA discovery cohort [26] and the TRACERx validation cohort; patients withtumours harbouring a high clonal neo-antigen burden (upper quartile) rarely suffer

metastatic relapse following surgery [26] and (Rosenthal et al manuscript in revision).

We have found that insertion-deletion frameshift mutations (fs-indels) likely comprise apotent class of neo-antigen for therapeutic exploitation and the burden of such mutations

appears to correlate with checkpoint inhibitor response [27]. Intriguingly, renal cell

carcinoma has a particularly high burden of fs-indels that may explain the uniquesensitivity of this tumour type to immunotherapy, despite its overall low mutationalburden.

These data suggest that the clonal or subclonal status of a tumour neo-antigen isimportant, with clonal neo-antigens present in every tumour cell representing a major

cancer cell vulnerability subject to immune surveillance. Similarly, patients with relatively

homogeneous tumours with a large clonal burden appear to benefit from anti-PD1 or anti-

CTLA4 immune checkpoint inhibitor treatment [26], recently independently validated by
the Van Allen laboratory [28]. The importance of targeting clonal neo-antigens has also

been emphasised by animal studies from the Scheinberg laboratory demonstrating that theclonal fraction of cells expressing a neo-antigen is important for optimal immune-mediatedcontrol; immune rejection is impaired when the fraction of cells harbouring a neo-antigen islow [29]. These data suggest that optimal immune surveillance may not occur until a cancer
population size is reached, sufficient to present a “dose” of neo-antigen required for

immune activation. This argument, taken to its logical conclusion, implies that immune

surveillance early in tumour evolution may be sub-optimal due to low neo-antigen dosage
for early control of tumour initiation.

68

C. Swanton

In a collaboration with the Quezada and Hadrup laboratories, through multi-region

tumour sequencing and phylogenetic and MHC tetramer analysis, we have detected CD8+

T cells that recognise clonal neo-antigens present in every tumour cell [26, 30]. Analysis ofpatients from TRACERx, classified by smoking status revealed that tobacco associated

NSCLC harbour 5-10 fold more clonal non-synonymous mutations compared to non-Tobacco associated lung cancer suggesting this group of patients may be ideal candidates

for adoptive cell therapy or vaccine programs targeting clonal neo-antigens.

This work has resulted in the founding of a Francis Crick Institute biotechnology

company, Achilles Therapeutics, which through the Wellcome Trust and CRUK is

attempting to transform our laboratory findings into clinical practice and patient benefit. In

this approach, multiple, unique clonal events in lung cancers are targeted throughimmunotherapy (vaccine and T cell therapy) with the hope of limiting the evolution ofresistance and avoiding tumour growth. We intend to expand multiple distinct T cell clones

reactive against clonal neoantigens, in a patient-specific manner ex vivo, and reinfuse themwithin two first-in-human clinical trials in melanoma and NSCLC sponsored by Achilles
Therapeutics in 2019

Given emerging evidence of the importance of clonal neo-antigens in tumour evolution

Immune Editing of Neo-antigen presentation during Tumour Evolution

and immune surveillance, we have endeavoured to understand mechanisms of immune
escape during NSCLC and renal cancer evolution. Loss of human leukocyte antigens haslong been known to occur in cancer [31]. However, its prevalence, timing, and relationships

with clonal expansions in solid tumours was unclear. The polymorphic nature of the HLA

locus had prevented accurate HLA allele specific copy number assessment. With this inmind, Mcgranahan and Rosenthal in the laboratory developed a software tool- LOHHLA-

which uses the patient’s own germline reference reads on chromosome 6p to infer allele
specific copy number of class I HLA from tumour exome data. Through this approach we
found that HLA class I loss occurs in up to 40% of early stage, untreated NSCLC [15],approximately 10 times more frequently than B2M mutations. Intriguingly, HLA class I

LOH commonly occurs as a subclonal event, consistent with the hypothesis that an optimal

cancer cell population size driving increased antigen dosage is required before an effective
T cell response can be mounted to limit tumour growth [29]. We find that subclones

harbouring HLA LOH appear to have a higher non-synonymous mutational burden thanclosely related subclones in the tumour that have all 6 alleles intact, consistent with thepermissive role of HLA LOH in ongoing branched evolution. Our future TRACERx work
will explore how clonal neo-antigens are recognised, repressed or deleted during thedisease course, in order to inform clonal neo-antigen directed approaches to limit tumour
growth.

Tumor Hetelogeneity and Immunogenomics

69

References
1. Fojo, T., S. Mailankody, and A. Lo, Unintended consequences of expensive cancer therapeutics-the pursuit of marginal indications and a me-too mentality that stifles innovationand creativity: the John Conley Lecture. JAMA Otolaryngol Head Neck Surg, 2014.
140(12): p. 1225-36.
2. Kantarjian, H.M., et al., Cancer drugs in the United States: Justum Pretium--the justprice. J Clin Oncol, 2013. 31(28): p. 3600-4.
3. Yates, L.R. and P.J. Campbell, Evolution of the cancer genome. Nat Rev Genet, 2012.
13(11): p. 795-806.
4. Burrell, R.A., et al., The causes and consequences of genetic heterogeneity in cancer
evolution. Nature, 2013. 501(7467): p. 338-45.
5. Siravegna, G., et al., Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nat Med, 2015. 21(7): p. 827.
6. Gerlinger, M., et al., Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med, 2012. 366(10): p. 883-92.
7. Gerlinger, M., et al., Genomic architecture and evolution of clear cell renal cell carcinomas
defined by multiregion sequencing. Nat Genet, 2014. 46(3): p. 225-33.
8. Turajlic, S., et al., Tracking Cancer Evolution Reveals Constrained Routes to Metastases:
TRACERx Renal. Cell, 2018. 173(3): p. 581-594 e12.
9. Turajlic, S., et al., Deterministic Evolutionary Trajectories Influence Primary Tumor
Growth: TRACERx Renal. Cell, 2018. 173(3): p. 595-610 e11.
10. Burrell, R.A., et al., Replication stress links structural and numerical cancer chromosomal
instability. Nature, 2013. 494(7438): p. 492-6.
11. Dewhurst, S.M., et al., Tolerance of whole-genome doubling propagates chromosomal
instability and accelerates cancer genome evolution. Cancer Discov, 2014. 4(2): p. 175-85.
12. de Bruin, E.C., et al., Spatial and temporal diversity in genomic instability processes
defines lung cancer evolution. Science, 2014. 346(6206): p. 251-6.
13. Jamal-Hanjani, M., et al., Tracking the Evolution of Non-Small-Cell Lung Cancer. N
Engl J Med, 2017. 376(22): p. 2109-2121.
14. Abbosh, C., et al., Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution.
Nature, 2017. 545(7655): p. 446-451.
15. McGranahan, N., et al., Allele-Specific HLA Loss and Immune Escape in Lung Cancer
Evolution. Cell, 2017. 171(6): p. 1259-1271 e11.
16. Favero, F., et al., Glioblastoma adaptation traced through decline of an IDH1 clonal
driver and macroevolution of a double minute chromosome. Ann Oncol, 2015.
17. Murugaesu, N., et al., Tracking the genomic evolution of esophageal adenocarcinoma
through neoadjuvant chemotherapy. Cancer Discov, 2015.

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C. Swanton

18. McGranahan, N., et al., Clonal status of actionable driver events and the timing of
mutational processes in cancer evolution. Sci Transl Med, 2015. 7(283): p. 283ra54.
19. Jamal-Hanjani, M., et al., Tracking Genomic Cancer Evolution for Precision Medicine: The
Lung TRACERx Study. PLoS Biol, 2014. 12(7): p. e1001906.
20. Fisher, R., et al., Development of synchronous VHL syndrome tumors reveals contingencies and constraints to tumor evolution. Genome Biol, 2014. 15(8): p. 433.
21. McGranahan, N. and C. Swanton, Biological and Therapeutic Impact of Intratumor
Heterogeneity in Cancer Evolution. Cancer Cell, 2015. 27(1): p. 15-26.
22. Gulati, S., et al., Systematic Evaluation of the Prognostic Impact and Intratumour
Heterogeneity of Clear Cell Renal Cell Carcinoma Biomarkers. Eur Urol, 2014.
23. Mitchell, T.J., et al., Timing the Landmark Events in the Evolution of Clear Cell Renal Cell
Cancer: TRACERx Renal. Cell, 2018. 173(3): p. 611-623 e17.
24. Yap, T., et al., Intratumour Heterogeneity: Seeing the wood for the trees. Science
Translational Medicine, 2012. In press 2012.
25. Piotrowska, Z., et al., Heterogeneity Underlies the Emergence of EGFRT790 Wild-Type
Clones Following Treatment of T790M-Positive Cancers with a Third-Generation EGFR
Inhibitor. Cancer Discov, 2015. 5(7): p. 713-22.
26. McGranahan, N., et al., Clonal neoantigens elicit T cell immunoreactivity and sensitivity
to immune checkpoint blockade. Science, 2016. 351(6280): p. 1463-9.
27. Turajlic, S., et al., Insertion-and-deletion-derived tumour-specific neoantigens and theimmunogenic phenotype: a pan-cancer analysis. Lancet Oncol, 2017. 18(8): p. 1009-1021.
28. Miao, D., et al., Genomic correlates of response to immune checkpoint blockade in
microsatellite-stable solid tumors. Nat Genet, 2018. 50(9): p. 1271-1281.
29. Gejman, R.S., et al., Rejection of immunogenic tumor clones is limited by clonal fraction.
Elife, 2018. 7.
30. Bentzen, A.K., et al., Large-scale detection of antigen-specific T cells using peptideMHC-I multimers labeled with DNA barcodes. Nat Biotechnol, 2016. 34(10): p. 10371045.
31. Campoli, M. and S. Ferrone, HLA antigen changes in malignant cells: epigenetic mechanisms and biologic significance. Oncogene, 2008. 27(45): p. 5869-85.

Tumor Hetelogeneity and Immunogenomics

Charles Swanton, MD, PhD, FMedSci, FRS

1999

2008
2011

2015
2016

71

Completed MD, PhD Training, Imperial Cancer
Research Fund, UK (this became Cancer Research UK
in 2002)
Cancer Research UK Clinician Scientist/Medical
Oncology Training
Established Lab at the London Research Institute,
Cancer Research UK
Appointed Chair of Personalised Cancer Medicine at
UCL Hospitals
Group Leader, the Francis Crick Institute, London
Appointed Royal Society Napier Professor of Cancer
Medicine

72

QUANTIFYING AND FORECASTING TUMOR PROGRESSION

Christina Curtis

Departments of Medicine and Genetics, Stanford University School of Medicine
265 Campus Drive, Suite G2120C, Stanford, CA 94305, USA(cncurtis@stanford.edu)

Cancer results from the acquisition of somatic alterations in an evolutionary process thattypically occurs over many years, much of which is occult. Understanding theevolutionary dynamics that are operative at different stages of progression in individual
tumors might inform the earlier detection, diagnosis, and treatment of cancer. Although
direct observations of human tumor evolution are impractical, the spatiotemporal patternsof somatic alterations amongst cells within a tumor faithfully encode their evolutionary
histories.

Whereas it has traditionally been assumed that tumor progression results from ongoing

sequential selection for ‘driver’ mutations that confer a stringent fitness advantage,

unexpectedly, we recently found that after transformation, some tumors grow in theabsence of stringent selection, compatible with effectively neutral evolution. This led to ourdescription of a Big Bang model of colorectal tumor growth where the neoplasm grows as aterminal expansion propagated by numerous heterogeneous—and effectively equally fit

subclones [1] (Figure 1). This new model explains the early origins of intra-tumor

heterogeneity (Figure 2) and the dynamics of tumor growth with implications for earlier

detection, treatment resistance and metastasis. For example, we demonstrate that subclonalvariants are largely undetectable using current sequencing strategies, and yet may provide

a rich substrate for the emergence of resistance under treatment selective pressure.

Moreover, the data suggest that some tumors are born to be bad, wherein malignant

potential is specified early.

Big Bang dynamics have since been reported in other tumor types, implying that

effective neutrality is relatively common and that at least two dominant ‘modes’ of evolution

Tumor Hetelogeneity and Immunogenomics

73

are operative in established solid tumors. Despite the implications of tumor dynamics for

precision medicine, methods to infer the strength of selection from cancer genomic data arelacking. We therefore developed a population genetics framework to simulate spatial

tumor growth and infer the mode of evolution from patient genomic data based on

patterns of genetic divergence [2, 3] (Figure 3). By applying this approach to diverse solid

tumors, we demonstrate variability in the mode of evolution within and between tumor

types and at different stages of disease (Figure 4). Further, we illustrate the utility of thisclassification scheme for delineating the true ‘drivers’ of tumor progression.

Building on our established tumor evolutionary dynamic framework, we subsequently

developed a theoretical and computational approach to ‘time’ metastasis from primarytumors and paired metastases. This new method leverages a 3-dimensional computationalmodel to simulate the spatial growth of realistically sized tumors (composed of ~109 cells)
and a robust statistical inference framework to measure evolutionary parameters fromgenomic data in a patient-specific manner [4]. Application of this quantitative method topaired primary colorectal cancers and distant metastases enables the systematic analysis of

the rates and routes of metastasis and reveals fundamental insights into the drivers of thislethal process with implications for the approach to systemic therapy and earlier detection.

Figure 1

Schematic illustration of the sequential clonal evolution model versus Big Bang model of
effectively neutral tumor evolution
In both models driver mutations are strongly selected for during tumor initiation ultimately
leading to transformation of the founding malignant clone. In the sequential clonal evolution
model, ongoing subclonal selection and clonal expansion occurs after transformation as aresult of additional ‘driver’ alterations that confer a fitness advantage. In contrast, in the Big
Bang model, after transformation additional mutations accrue but do not result in
detectable subclonal expansions because the background population is already highly fit(adapted from Sottoriva et al, Nature Genetics, 2015).

74

C. Curtis

Figure 2

Multi-region profiling of colorectal cancers and statistical inference on the genomic data
verify the predictions of the Big Bang model
A. Multi-region sampling and integrated genomic profiling. B. Single gland copy number
profiles reveal extensive ITH and subclone mixing. Collectively, the genomic data indicatethat recent clonal expansions are rare and subclone mixing is common in CRC. C. Inference ofthe mutational timeline for classes of somatic alterations indicates that the majority ofdetectable subclonal alterations occur early during growth (data from Sottoriva et al. Nature
Genetics, 2015).

Figure 3

Schematic overview of spatial tumor growth model (from Sun et al. Nature Genetics, 2017)

Tumor Hetelogeneity and Immunogenomics

Figure 475

Classification of the ‘mode’ of tumor evolution for diverse solid tumors
Independent component analysis of virtual tumors (transparent circles) and patient tumors(small filled circles) based on multiple measures of ITH distinguishes tumors that are
evolving in an effectively neutral fashion, from those that are evolving under stringent
subclonal selection (from Sun et al. Nature Genetics, 2017).

References
1. Sottoriva A, Kang H, Ma Z, Graham TA, Salomon M, Zhao J, Marjoram P, Siegmund K,
Press MF, Shibata D and Curtis C. A Big Bang model of human colorectal tumor growth.
Nature Genetics, 2015, 47(3)209-216.
2. Sun R, Hu Z, Sottoriva A, Graham TA, Ma Z, Fischer JM, Shibata D and Curtis C.,
Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nature
Genetics, 2017, 49(7):1015-1024.
3. Hu, Z., R. Sun, and C. Curtis, A population genetics perspective on the determinants of intratumor heterogeneity. Biochim Biophys Acta – Reviews on Cancer, 2017, pii: S0304419X(17)30046-X.
4. Hu Z, Ding J, Ma Z, Sun R, Seoane JA, Suarez CJ, Berghoff AS, Cremolini C, Falcone A,
Loupakis, Birner P, Preusser M, Lenz HJ and Curtis C. Quantitative evidence for earlymetastatic seeding in colorectal cancer. In revision

76

C. Curtis

Christina Curtis, PhD, MSc

2003-2007
2007-20102010-20142014-present

2014-present

2018

Graduate Student, University of Southern California
Postdoctoral Fellow, University of Cambridge
Assistant Professor, University of Southern California,
Keck School of Medicine
Assistant Professor, Stanford University School of
Medicine
Co-director, Molecular Tumor Board, Stanford Cancer
Institute
NIH Director’s Pioneer Award Recipient

77

NOVEL EVOLUTIONARY PRINCIPLE SHAPINGINTRATUMOR HETEROGENEITY IN COLORECTAL CANCER

Koshi Mimori

Department of Surgery
Kyushu University Beppu Hospital
4546 Tsurumihara, Beppu 874-0838, Japan(kmimori@beppu.kyushu-u.ac.jp)

Cancer evolution and intratumor heterogeneity (ITH) have attracted increasing

attention in the cancer research field because ITH generated during cancer evolution
presumably contributes to the therapeutic and diagnostic difficulties of cancer. The recent

technological innovations enabled us to conduct the multiregion sequencing approach,which has been popularly used to understand ITH.

In the view point of the development of colorectal cancer (CRC), adenoma first forms apolyp and then partially progress to early carcinoma, which subsequently grows beyond

the muscularis mucosa to invade surrounding tissues. To examine ITH in advanced CRC(ACRC), we previously implemented multiregion sequencing of nine locally advanced ormetastatic tumors [1]. While most of the known driver events represented by APC and

KRAS mutations were observed as ubiquitous mutations, branched or parallel evolution

was rarely observed in evolutionary histories of ACRC. By additionally performing acomputational simulation of cancer evolution, we demonstrated possibility that ITH in
ACRC could be generated by neutral evolution [2].

Subsequent to our previous study, we perform multiregion whole-exome sequencing on

10 precancerous lesions of colorectal cancers (PCRCs), which contained adenoma andcarcinoma in situ [3]. By comparing with sequencing data from ACRC, we show that theearly tumors accumulate a higher proportion of subclonal driver mutations than theadvanced tumors, which is highlighted by subclonal mutations in KRAS and APC. We also

demonstrate that variant allele frequencies of subclonal mutations tend to be higher inearly tumors, suggesting that the subclonal mutations are subject to selective sweep in earlytumorigenesis while neutral evolution is dominant in advanced ones. This study

78

K. Mimoriestablishes that the evolutionary principle underlying intratumor heterogeneity shifts from

Darwinian to neutral evolution during colorectal tumor progression.

In addition to the SNV study, copy number aberrations were progressively increased

from adenoma through early carcinoma to ACRC. The increase in ubiquitous CNAs was

especially prominent in ACRC. Therefore, CNAs play more critical roles in the progression

from PCRC to ACRC.

Multi-regional sequencing analysis of solid clinical samples provides a major

breakthrough in disclosing ITH. The level of uniformity depends on the type of cancer, andthe causes of diversity vary among cancers. The actual application of the findings in ourstudies for clinical diagnosis and treatment might require furthermore time to save patientsof intractable cancers eventually.

In terms of “cancer evolution”, I have proposed an alternative evolution pathway which

was engendered by depressed type tumor other than adeno-carcinoma sequence. At last, I

have introduced “therapy driven pathway” which was observed in recurrence tumor after
the chemoradiotherapy in esophageal cancer cases. The shape of evolution looks like apunctuated manner.

References
1. Uchi R, Takahashi Y, Niida A, Shimamura T, Hirata H, Sugimachi K, Sawada G, Iwaya T,
Kurashige J, Shinden Y, Iguchi T, Eguchi H, Chiba K, Shiraishi Y, Nagae G, Yoshida K,
Nagata Y, Haeno H, Yamamoto H, Ishii H, Doki Y, Iinuma H, Sasaki S, Nagayama S,
Yamada K, Yachida S, Kato M, Shibata T, Oki E, Saeki H, Shirabe K, Oda Y, Maehara Y,
Komune S, Mori M, Suzuki Y, Yamamoto K, Aburatani H, Ogawa S, Miyano S, Mimori K.
Integrated Multiregional Analysis Proposing a New Model of Colorectal Cancer
Evolution. PLoS Genet. 2016 12(2):e1005778..
2. Niida A, Nagayama S, Miyano S, Mimori K. Understanding intratumor heterogeneity by
combining genome analysis and mathematical modeling. Cancer Sci. 2018 Apr;109(4):884-892.
3. Saito T, Niida A, Uchi R, Hirata H, Komatsu H, Sakimura S, Hayashi S, Nambara S,
Kuroda Y, Ito S, Eguchi H, Masuda T, Sugimachi K, Tobo T, Nishida H, Daa T, Chiba K,
Shiraishi Y, Yoshizato T, Kodama M, Okimoto T, Mizukami K, Ogawa R, Okamoto K,
Shuto M, Fukuda K, Matsui Y, Shimamura T, Hasegawa T, Doki Y, Nagayama S,
Yamada K, Kato M, Shibata T, Mori M, Aburatani H, Murakami K, Suzuki Y, Ogawa S,
Miyano S, Mimori K. A temporal shift of the evolutionary principle shaping intratumor
heterogeneity in colorectal cancer. Nat Commun. 2018 Jul 23;9(1):2884.

Tumor Hetelogeneity and Immunogenomics

Koshi Mimori, MD, PhD

1991
1994-1997

1997-2000

2000-2006

2010-20112012-present

79

Graduated from the Medical College of Oita
Resident, Department of Surgery, Medical Institute of
Bioregulation, Kyushu University
Postdoctoral Fellow, Kimmel Cancer Institute, Thomas
Jefferson University in Philadelphia
Research Associate, Department of Surgery, Medical
Institute of Bioregulation, Kyushu University
Associate Professor, Department of Surgery, Kyushu
University Beppu Hospital
Professor, Department of Surgery, Kyushu University
Beppu Hospital

80

WHOLE GENOME SEQUENCING AND IMMUNO-GENOMICANALYSIS FOR LIVER CANCER

Hidewaki Nakagawa

Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences
4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan(hidewaki@ims.u-tokyo.ac.jp)

Cancer is essentially a “disease of the genome” which develops and evolves with theaccumulation of a variety of mutations, based on the background of its genomic instability,

and some driver mutations were successfully targeted for treatment. Cancer also has been

proved to have a feature of “immune reaction” and have been affected by immune editing

in carcinogenic steps. Now immune therapies are a real in most types of cancer. To explore
whole genomic pictures and immuno-genomic features of cancer, we have been addressing

cancer whole genome sequencing (WGS) analysis for liver cancer and other types of cancer.

These approaches combined with mathematical analysis and other -omics analysis canclarify the underlying carcinogenesis and cancer immunology and achieve molecular sub-classification of cancer, which facilitates discovery of genomic biomarkers and personalized

cancer medicine. I here present discovery from cancer WGS and immuno-genomic analysis

of liver cancers, and discuss its utility and limitation of an analysis platform and mutationinterpretation for cancer genomics and cancer immunology.

As one of the Japanese ICGC projects, we sequenced whole genomes of 300 liver cancer,(A) Whole genome sequencing analysis of liver cancer

which were manly affected by virus infection [1]. The median number of somatic mutationof liver cancer was approximately 10,000. We identified several mutated driver genes andpathways in liver cancer, including TERT, TP53 pathway, Wnt/CTNNB1 pathway, and

ARID1A/ARID2, which is summarized in Figure 1. We found several non-coding
mutational clusters, such as TERT promoter, NEAT1/MALAT lincRNA, and WDR74

promoter, and genome-wide mutational clustering analysis also found several mutational

Tumor Hetelogeneity and Immunogenomics

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81

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Summary of liver cancer Driver genes
Fujimoto, et al. Nat Genet 48:500-509, 2016

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OLQF51$clusters in CTCF-binding sites. WGS and RNA-seq detected virus integrations of HBV andnew virus, AAV, in liver cancer genome. Additional deep sequencing analysis targeting

HBV also showed 1,684 HBV integration sites in cancer and liver tissues, which

preferentially occurred in the open chromatin regions and mitochondria genome in mouse

HBV infection model [2]. As one of the drivers, TERT promoter regions were frequently

affected by the hotspot mutations, SV, and virus integrations in liver cancer. From WGS
data, we extracted several mutational signature such as smoking signature (Sig4) andalcohol signature (Sig16), and estimated cell-of-origin through whole genome mutation

distribution, which indicated some of intrahepatic cholangiocarcinomas are likely to beoriginated from hepatocyte, as well as HCC [3].

The PCAWG (PanCancer Analysis of Whole Genomes) study is an international(B) Pan-cancer immuno-genomic analysis (PCAWG-15)

collaboration to identify common patterns of mutation in more than 2,800 cancer wholegenomes from the International Cancer Genome Consortium. As PCAWG-15 group, wecharacterized the immunological feature of theses PCAWG samples by analyzing HLA

genotype, immuno-signature, deconvolution of immune cells, neo-antigen prediction using
WGS and RNA-Seq data (Figure 2). We developed the algorism to precisely call HLA

82

H. Nakagawa

genotype from WGS and found somatic mutations and copy number change of HLA (chr6),

which frequently occurred in colorectal cancer, lymphoma, and pancreatic cancer. SV

affected 10 immune-related genes, including PD-L1 and PD-L2, and lead to theiroverexpression in some types of tumor. To estimate the immuno-editing history of eachcancer genome, we defined the immuno-editing index (IEI) by comparing the number of

neo-antigens in pseudogenes with that in exonic regions. IEI analysis indicated that

immuno-edited tumor (IEI-low) was enriched in MSI-positive CRC, and IEI was

statistically related to overall survival for lung cancer and pancreatic cancer [4].

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Figure 2

PCAWG-15 HLA/Immunogenomics Group

Liver cancer develops in chronic hepatitis where various types of immune cells are(C) Immuno-genomic analysis of liver cancer

activated and suppressed. Although the background liver is highly inflamed, liver cancer isgenerally considered as immune suppressive. We analyzed RNA and WGS of 234 livercancers and matched non-tumorous livers with chronic hepatitis, and characterized theirimmunological feature by comparing the immune profiles in liver cancers and hepatitis

livers. Anti-tumor immunity was associated with significantly better prognosis. Tumor hadTumor Hetelogeneity and Immunogenomics

83

lower expression levels of immune genes than adjacent hepatitis liver, indicating

predominant immune suppression in tumor. Gene signature for Treg were overexpressedin tumor. Three tumor subclasses of proliferation, CTNNB1, interferon were

immunologically characterized by high Treg signature, less immune infiltrates, anddeficient immune suppression, respectively.

References
1. Fujimoto A, Furuta M, Totoki Y, et al. Whole genome mutational landscape and characterization of non-coding and structural mutations in liver cancer. Nat Genet
2016;48:500-509.
2. Furuta M, et al. Characterization of HBV integration patterns and timing in liver cancerand HBV-infected liver. Oncotarget 2018;9:25075-25088.
3. Wardell CP, Fujita M, et al. Genomic characterization of biliary tract cancers identifies theirdriver genes and predisposing mutations. J Hepatol 2018;68:959-969,
4. Mizuno S, Yamaguchi R, Hasegawa T, Hayashi S, Fujita M, Zhang F, Koh Y, Lee SY,
Yoon SS, Shimizu E, Komura M, Fujimoto A, Nagai M, Kato M, Liang H, Miyano S,
Zhang Z, Nakagawa H, Imoto S, on behalf of the PCAWG Mitochondrial Genome and
Immunogenomics Working Group and The PCAWG Network. Immuno-genomic PanCancer landscape reveals diverse immune escape mechanisms and immuno-editing
histories. bioRxiv 285338 (2018). doi:10.1101/285338

Hidewaki Nakagawa, MD, PhD

1991
1991-1992
1992-1993
1993-1996

1996-1999
1999-2003

2003-2008

2008-20132013-present

MD, Osaka University, School of Medicine
Resident, Osaka University Hospital, General Surgery
Resident, Osaka University Hospital, ICU
Resident, National Osaka Hospital, GI and Breast
SurgeyPhD, Osaka University, Graduate School of Medicine
Postdoc Fellow, The Ohio State University, Human
Cancer Genetics Program
Assitant Professor, Institute of Medical Science,
The University of Tokyo
Team Leader, RIKEN Center for Genomic Medicine
Team Leader, RIKEN Center for Integrative Medical
Sciences

84

GENETIC ALTERATIONS IN T-CELL LYMPHOMAGENESIS

Keisuke Kataoka

Division of Molecular Oncology
National Cancer Center Research Institute5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan(kekataok@ncc.go.jp)

Peripheral T-cell lymphomas (PTCLs) represent a clinically, histologically, andmolecularly heterogeneous group of non-Hodgkin lymphomas derived from mature post-

thymic T cells. Among them, the most common entity in Japan is adult T-cellleukemia/lymphoma (ATL), which is an aggressive peripheral T-cell lymphoma associatedwith human T-cell leukemia virus type-1 (HTLV-1) infection. To delineate the genetic

landscape of ATL, we carried out an integrated molecular study, in which whole genome,

exome, transcriptome, and targeted resequencing, as well as array-based copy number andmethylation analyses were performed in more than 400 ATL cases [1]. We found recurrent

genetic alterations in T-cell receptor/NF-kB signaling, T-cell trafficking, and other T-cell-

related pathways as well as immunosurveillance. A conspicuous feature of ATL genome isthe predominance of gain-of-function alterations, including activating mutations (inPLCG1, PRKCB, CARD11, VAV1, IRF4, CCR4, and CCR7) and gene fusions (CTLA4-/ICOS-

CD28). We also discovered frequent intragenic deletions involving IKZF2, CARD11 andTP73 and mutations in GATA3, HNRNPA2B1, GPR183, CSNK2A1, CSNK2B and CSNK1A1.

Besides these alterations, we identified a unique genetic mechanism of immune evasion

caused by structural variations (SV) disrupting 3'-untranslated region (UTR) of the PD-L1

gene [2]. These SVs were also found in multiple common human cancer types, such as

diffuse large B-cell lymphoma and stomach adenocarcinoma, and caused a marked
elevation of aberrant PD-L1 transcripts, leading to immune evasion of tumor cells in vivo.

Aggressive (acute/lymphoma) subtypes were associated with an increased burden ofgenetic and epigenetic alterations, higher frequencies of TP53 and IRF4 mutations, andmany copy number alterations (CNAs), including PD-L1 amplifications and CDKN2A

Tumor Hetelogeneity and Immunogenomics

85

deletions, compared with indolent (chronic/smoldering) subtypes [3]. By contrast, STAT3

mutations were more characteristic of indolent ATL. Higher numbers of somatic mutations

and CNAs significantly correlated with worse survival. In addition, ATL subtypes arefurther classified into molecularly distinct subsets with different prognosis by genetic

profiling. Particularly, somatic alterations characterizing aggressive diseases predict worse

prognosis in indolent ATL, among which PD-L1 amplifications are a strong genetic

predictor in both aggressive and indolent ATL.

Among PTCLs, the most common in Western countries is PTCL, not otherwise specified(NOS), accounting for approximately 30% of all PTCLs. Combining whole-exome and deeptargeted-capture sequencing of 133 cases, we delineated the entire picture of genetic

alterations in PTCL, NOS. Of note is the identification of a previously undescribedmolecular subtype characterized by TP53 and/or CDKN2A mutations and deletions in

PTCL, NOS without showing a T follicular helper cell phenotype. This subtype exhibited

different prognosis and unique genetic features, including extensive chromosomal
instability, which preferentially affected molecules involved in immune escape andtranscriptional regulation.

Taken together, our findings provide novel insights into genetic and molecular

heterogeneity in PTCLs, which should help to devise a novel molecular classification and to

exploit a new therapeutic strategy for these malignancies.
References

1. Kataoka K, Nagata Y, Kitanaka A, Shiraishi Y, Shimamura T, Yasunaga J, et al.
Integrated molecular analysis of adult T-cell leukemia/lymphoma. Nat Genet. 2015;
47(11):1304-15.
2. Kataoka K, Shiraishi Y, Takeda Y, Sakata S, Matsumoto M, Nagano S, et al. Aberrant PDL1 expression through 3’ -UTR disruption in multiple cancers. Nature. 2016;
534(7607):402-6
3. Kataoka K, Iwanaga M, Yasunaga J, Nagata Y, Kitanaka A, Kameda T, et al. Prognostic
relevance of integrated genetic profiling in adult T-cell leukemia/lymphoma. Blood.
2018;131(2):215-225.

86

K.Kataoka

Keisuke Kataoka, MD, PhD

2005-2007
2007-2008
2008-20122009-20122012-20132013-20172017-present

Resident, Toranomon Hospital
Medical Staff, The University of Tokyo Hospital
Graduated from The University of Tokyo, Graduate
School of Medicine
Japan Society for the Promotion of Science Research
Fellow
Project Assistant Professor, The University of Tokyo
Project Assistant Professor, Kyoto University
Chief, National Cancer Center Research Institute87

A SINGLE CELL LENS INTO TUMOR HETEROGENEITY,METASTASIS AND TUMOR ECOSYSTEMS

Dana Pe’er

Program in Computational and Systems Biology
Sloan Kettering Institute
Memorial Sloan Kettering Cancer Center
417 E 68th Street, New York, NY 10027, USA(peerster@gmail.com)
Single-cell RNA-sequencing is fast becoming a major technology that is revolutionizing

the studies of tumor heterogeneity, tumor immune ecosystem and metastasis. Studies haveshown that cancer cell dysregulate regeneration pathways, creating new phenotypes andmalignant capabilities. We combine single cell RNA-seq with computational analysis tobuild a map tumor heterogeneity in AML and lung adenocarcinoma, connecting between

cancer and healthy phenotypes.

Acute myeloid leukemia (AML) evolution is a multistep process in which cells evolvefrom hematopoietic stem and progenitor cells (HSPCs) that acquire genetic anomalies
leading to to expansions of stem and progenitor cells with myeloid lineage bias. Many

recurring mutations in AML involve known and putative epigenetic and transcriptional

regulators, including TET2, IHD1/2 and NPM1. We profiled multiple AML samples withsingle-cell RNA sequencing creating an atlas that demonstrates demonstrating large scaleinter and intra patient heterogeneity. While the patients show a large degree of inter-patient heterogeneity, we find a cluster shared by 11/12 samples that contains cells with a

HSPC-like phenotype. We developed a manifold based algorithm to map trajectories ofdifferentiating cells, to map and compare altered differentiation trajectories, starting fromthe shared HSPC-like cluster, across the patients. We found NPM1 to be a late driver in thisdisease with sub-clonal appearance, hence we could find trajectories with and without

NPM1 within the same patient, to better understand its role. Interestingly, these mutations
were detected at various frequencies in erythroid cells, suggesting that NPM1 mutations

are acquired in cells with different lineage commitment in different patients. Most notably,

the HSPC-like also contained a subpopulation of cells that have acquired NPM1 mutations

88

D. Pe’er

and are transcriptionally different from wild-type cells.

The understanding of cancer as a chronic, non-healing, wound invokes a central role fordevelopmental pathways that regulate tissue regeneration and repair. How these pathways

relate to tumor progression and metastasis remains poorly understood. Here we employ

single-cell RNA sequencing to primary and metastatic patient lung adenocarcinomas. We

show that primary human tumors demonstrate a striking degree of developmental

plasticity, regenerating most normal lung epithelial lineages of the alveolar and bronchial

airway and expressing key embryonic lineage-determining transcription factors, SOX2 andSOX9. Conversely, metastases exhibit a significant reduction in lineage diversity and are
predominantly restricted to a Wnt-responsive, SOX9high epithelial progenitor. To dissect

potential mechanisms underlying this lineage-specific escape, cancer cells from a mousemodel of delayed lung cancer metastasis were transcriptionally profiled at different stagesof disease progression. Lineage constraint was alleviated upon natural killer (NK)-cell
depletion, demonstrating a dynamic interplay between developmental plasticity andimmune-mediated pruning during lung cancer progression and the evolution of metastasis.

Dana Pe’er, PhD

1999-2003

2003-2006
2006-20162016-present

2016-present

PhD, Computer Science, Hebrew University ofJerusalem
Postdoctoral Fellow, Genetics, Harvard Medical School
Professor, Biological Sciences, Columbia University
Chair, Computational and Systems Biology, Sloan
Kettering Institute
Director, Gerry Center for Metastasis and Tumor
Ecosystems

89

WHOLE-ORGANISM CLONE-TRACING USING SINGLE-CELL
SEQUENCING

Alexander van Oudenaarden

Hubrecht Institute
Uppsalalaan 8, 3584 CT Utrecht, The Netherlands(a.vanoudenaarden@hubrecht.eu)

Embryonic development is one of the most crucial periods in the life of a multicellularorganism. A limited set of embryonic progenitors gives rise to all cells in the adult body.

Determining which fate these progenitors acquire in adult tissue is a major challenge andrequires the simultaneous measurement of clonal history and cell-type at single-cellresolution. Clonal history has traditionally been quantified by microscopically tracking

cells during development, monitoring the heritable expression of genetically encoded

fluorescent proteins and, most recently, by utilizing next generation sequencing technology

exploiting somatic mutations, transposon tagging, viral barcoding, and CRISPR/Cas9

genome editing strategies. Single-cell transcriptomics on the other hand, provides a

powerful technology platform for cell-type classification in an unbiased manner. However,integrating both measurements for many single cells has been a major hurdle. Here, we
present ScarTrace, a single-cell sequencing strategy that allows us to simultaneously

quantify information on clonal history and cell type for thousands of single cells obtained

from different organs from adult zebrafish. Using this approach we show that all blood

cells in the kidney marrow arise from a small set of multipotent embryonic progenitors thatgive rise to all blood cell types. In contrast, we find that cells in the eyes, brain, and caudal
tail fin arise from many embryonic progenitors, which are more restricted and produce

specific cell types in the adult tissue. Next we use ScarTrace to explore when embryoniccells commit to forming either left or right organs using the eyes and brain as a model

system. Lastly we monitor regeneration of the caudal tail fin and identify a subpopulationof resident macrophages that have a clonal origin that is distinct from other blood cell

types. We envision that ScarTrace will have major applications in other experimental model

90

A. van Oudenaarden

systems to match embryonic clonal origin to adult cell-type to ultimately reconstruct how

the adult body was built from a single cell.

References
1. Alemany A, Florescu M, Baron CS, Peterson-Maduro J, van Oudenaarden A. Wholeorganism clone tracing using single-cell sequencing. Nature. Apr 5;556 (7699):108-112.
doi: 10.1038/nature25969. Epub 2018 Mar 28.

Alexander van Oudenaarden, PhD

1998
1998-1999
2000-2004
2004-2008
2008-20142013-present
2012-present

Delft University of Technology, PhD Physics
Postdoctoral Research, Stanford
Assistant Professor, MIT, Cambridge
Associate Professor, MIT, Cambridge
Professor, MIT, Cambridge
Professor, Utrecht University and UMC Utrecht
Director Hubrecht Institute (KNAW), Utrecht

91

IN VIVO MECHANISMS OF RESISTANCE TO GENOTOXICCHEMOTHERAPY

Michael T. Hemann

Koch Institute for Integrative Cancer Research at MIT
Cambridge, MA, USA(hemann@mit.edu)

Many human cancers fail to respond to DNA damaging chemotherapy, and cancers that

initially respond frequently acquire drug resistance and relapse. This process of tumor

relapse is particularly confounding, as patients can be in remission for years following

treatment prior to the reemergence of a cancer. Additionally, tumors that relapse havegenerally acquired resistance to the initial treatment. While conventional anti-cancer
therapies have been in clinical use for decades, little is known about the mechanisms bywhich a tumor cell can survive treatment and persist in a patient for extended period oftime. In fact, a major cause of cancer deaths is the inability to eradicate small sets ofsurviving tumor cells, termed “minimal residual disease” or “MRD”. Work from ourlaboratory has found that chemotherapy paradoxically elicits a pro-survival response incertain anatomical cites [1]. Specifically, normal cells proximal to tumor cells secrete factorsthat counter the effects of the chemotherapy. This survival response likely serves to protect

normal progenitor and stem cells to allow for tissue regeneration following damage, but it
is coopted by tumor cells that find themselves in these specialized sites.

There is now considerable data supporting the idea that the response of cancer cells to

Microenvironment mediated chemoprotection

chemotherapy fundamentally differs in cell culture versus the native tumor

microenvironment. Indeed, we can quite easily eradicate cells from culture plates at drug

doses that are unable to clear persistent disease in mice or humans. While the mechanisms

long thought to underlie this differential response relate to drug target interaction or drug

pharmacokinetics, there has been significant recent evidence that tumor

92

M. T. Hemannmicroenvironments can protect tumors from the consequences of drug action. These

correlative data have prompted us to investigate the basic mechanisms of paracrine
survival signaling that exist in specific microenvironments, during normal development

and physiological stress responses, and whether these strategies are coopted by tumor cells
to evade cancer therapy.

Our first work in the area focused on stress-induced signaling in the endothelial

compartment [2-4]. In response to genotoxic damage, specific sets of endothelial cells are

induced to engage secretory response that involves the release of pro-survival factors,

including IL-6 and Timp-1. These factors act through the Stat/Jak pathway tumor cells

lying immediately adjacent to the tumor vasculature, a process that leads to induction of

the anti-apoptotic Bcl2 family member Bclxl. Importantly, the protective microenvironmentthat contains endothelial cells does not function by blocking DNA damage induced bychemotherapy, rather it counteracts the pro-apoptotic signals activated by DNA damage.

This mechanism of chemoprotection has significant therapeutic implications, as blocking

IL-6 activity with neutralizing antibodies eliminates the protective effect of endothelial cells

and ablates the persistent MRD following chemotherapy.

We have gone on to further characterize the basic molecular mechanisms of this therapy

related secretory response. Specifically, doxorubicin induces endothelial IL-6 mRNAstabilization and release via reactive oxygen species (ROS)-induced p38 signaling. This

release is accompanied in endothelial cells by many of the hallmarks of cellular senescence,

an irreversible cell cycle arrest that is accompanied by a robust secretory response.

However, this therapy related secretory response is an atypical senescence-associatedsecretory response that is characterized by an acute, transient release of paracrine factorsthat does not produce a systemic inflammatory response. Thus, we identified a specialized

chemotherapy-induced secretory response that protects subsets of persisting tumor cells.

This data also show how protective secretory responses – the kinds of responses that

protect repopulating cells in regenerative organs and persistent disease in cancer – canoccur in the absence of systemic inflammatory responses (Figure 1). We believe that thisregulation represents a fundamental mechanism of “shielding” paracrine pro-survival
processes that underlie tissue regrowth in metazoans.

In addition to conventional chemotherapy, we have recently shown that selectmicroenvironments can underlie resistance to antibody-based therapy. Using a humanizedmodel of treatment-refractory B-cell leukemia, we have identified the bone marrow as a siteof persistent MRD [5]. Specifically, we find that infiltration of leukemia cells into the bonemarrow rewires the tumor microenvironment to inhibit engulfment of antibody-targeted

tumor cells. Resistance to macrophage-mediated killing can be overcome by combination
regimens involving therapeutic antibodies and genotoxic chemotherapy. These dataTumor Hetelogeneity and Immunogenomics

93

+ +
678+

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:62+ 4;+<#=>#$-(+!#..,+!#..,+

455+
012+?-,).(!#@+
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Figure 1!"#$%&"#'()*+)"*,-%.%/-!(.+,&'#,,+Development of protective cancer niches. Following the induction of exogenous stress,
proximal cells – including endothelial cells – release factors that recruit stem and progenitor
cells to sites of damage, promote the expansion of these cells and the subsequent regenerationof the hematopoietic system. Lymphoma and leukemia cells can displace normal stemfrom protective microenvironments, coopt pro-survival paracrine signaling and persist following chemotherapy. Abbreviations: EC are endothelial cells, HSC is hematopoietic stem
cells, MPP is multi-potent progenitor, B/T are B and T cells, GR is granulocyte, RBC is red
blood cell and MO is monocyte.

suggest that effective cancer therapy may not only involve targeting cancer cells but also

may require inhibiting survival signals emanating from surrounding cells. By generating

multiple models of MRD, we have not only identified mechanisms of tumor cell
persistence, but also strategies to sensitize chemorefractory tumors to both front-line andtargeted chemotherapy.

The data described above reveal that genotoxic chemotherapies that are presumed to

Mechanisms of action of genotoxic chemotherapy

exert their effect primarily through the induction of DNA breaks may show profoundmechanistic distinctions. In the case of doxorubicin, genotoxic damage promotes cytokine
mRNA stabilization in release. In the case of cyclophosphamide, treated cells undergosystemic changes that promote innate immune activation. To more closely examine theprecise mechanism of diverse genotoxic chemotherapies, we have developed an RNA

interference (RNAi)-based approach to characterize chemotherapeutic function in

mammalian cells [6-7]. Briefly, have used RNAi-induced phenotypic “signatures” tocharacterize drug action or the effects of distinct tumor microenvironments on drug action
with unprecedented resolution. These robust RNAi-based “signatures” can be used tocluster unknown drugs into functional categories and define mechanisms of action for94

M. T. Hemannuncharacterized cytotoxic agents. We have subsequently used this signature approach forapplications that were previously not feasible using other drug characterization metrics.

A major focus of these efforts has been the study of platinum compounds that arecommonly used as components of chemotherapeutic drug regimens [8]. Strikingly, we

found that oxaliplatin and cisplatin, two agents that are thought to exert their effects via thesame mechanism of action, are mechanistically quite distinct. While cisplatin promotes cell

death via inducing DNA crosslinks and a DNA damage response, oxaliplatin kills cells bypromoting inhibiting ribosome synthesis and translation. Thus, platinum drugs may not

function interchangeably with their derivatives in cancer regimens. This phenomenon may

explain a lack of efficacy for oxaliplatin in the treatment of malignancies conventionallytreated by cisplatin, and the initially unanticipated value of oxaliplatin as a treatment forcolorectal cancer. This work also highlights how changes in small molecule structure canalter the genetic determinants of chemotherapeutic response. For example, instead ofcisplatin, oxaliplatin has begun to be used as a front-line treatment for pancreas cancer, andrecent sequencing data show that greater than 20% of these tumors show defects inhomologous recombination. Our data predict that these mutant tumors may be better
treated by cisplatin than oxaliplatin. Thus, tumor mutations may represent importantbiomarkers underlying the choice between related platinum drugs (Figure 2).

Additionally, whereas conventional chemotherapeutics are commonly thought of as!"#$%&'()*+,(-$+,.#/0)*#&)1,)'2,3)%&)#)$#.4,$,3)(#&&,.

Figure 2)*1232*45'#(')*'+#(,$#'%-(*#..%/'0.

!C#+%#(*6#(/0+

>9=)

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89)

6%,&$#'%(!"#$%&$#'%(

Platinum-containing compounds can function as tumor vulnerability directed targeted
therapeutics. Cisplatin shows particular efficacy in tumors, like ovarian cancer, that show frequent defects in Homologous Repair (HR). Conversely, oxaliplatin promotes ribosomal
dysfunction and shows efficacy in cancers, like colon cancer, with high translation requirements. Tumors like breast or pancreas cancer that may have HR or translation vulnerabilities
should be treated in a targeted fashion with platinum compounds targeted towards specificpathway defects.

Tumor Hetelogeneity and Immunogenomics

95“generic” cytotoxic agents, our data highlight fundamental mechanistic distinctions thatexist between highly similar molecules and support a personalized and genetically directed

use of these compounds.

The challenges in identifying mechanisms of action and mechanisms of resistance for

The convergence of resistance and mechanism

genotoxic chemotherapy are intimately linked (Figure 3). Indeed, in the context of targeted

therapy, much of what we know about drug action is inferred from drug resistancemechanisms. For example, the identification of resistance mutations that alter putative sitesof drug target interaction provide strong evidence that the mutant protein is indeed therelevant drug target. Moreover, understanding a drug’s putative mechanism of action

allows one to perform a focused search for mutations or compensatory gene expression

changes in specific signaling pathways. These kinds of analyses have not been informativein the context of conventional genotoxic chemotherapy for two major reasons. First, as

described above, resistance to genotoxic chemotherapy is generally not conferred by cell

intrinsic, genetically encoded alterations. In fact, numerous large sequencing studies havefailed to identify clear resistance mutations in relapsed tumors following genotoxic
chemotherapy. Thus, resistance mutations cannot be used to define drug mechanism.

Second, the obligate use of combination drug regimens has impeded the analysis of single

component drugs within these regimens. These barriers have significantly blocked efforts

to identify biomarkers that can predict the efficacy of genotoxic chemotherapy or strategies

to rationally combine agents into multi-drug regimens.

,%-.(*(/0,1(-%2&

Figure 3

34+5(+*#4+%60
31(74*1(-%2&

2-"$*,.'
3*4*.3*.+,*#!""#$%$&

560"-'+*%%'/6%.*-(7,%,$,*#!"#$%&'())*+$
8-69:5(-9*$';.$*-(+$,".

'()#)*%+$(!"#$%&'())*+$!,+-"*./,-".0*.$1+*%%'#$($*The efficacy and resistance landscapes of targeted versus conventional (frequently termed
genotoxic) chemotherapy. Targeted therapies show efficacy in tumors expressing a specifictarget mutation, and resistance is generally driven by mutations that impact drug target
interaction or eliminate the target. Conversely sensitivity to conventional chemotherapy islikely driven by general tumor cell vulnerabilities, ie defects in DNA damage repair.
Resistance to these agents is conferred by protective tumor microenvironments or cell statechanges that globally alter tumor cell sensitivities.

96

M. T. Hemann

These challenges involved in understanding resistance mechanisms to conventional

chemotherapy also, paradoxically, highlight the unique utility and tremendous potential ofthese compounds. The lack of clear resistance-driving mutations in relapsed tumors

suggests that, unlike targeted therapy, individual mutations in tumor cells may beinsufficient to promote chemoresistance. Thus, tumor cells have difficulty evading theaction of genotoxic agents when present at sufficient doses. Additionally, the fact that asubstantial component of drug resistance is microenvironmental provides a strategy forenhancing the effective doses of these agents. Specifically, by introducing agents that blockparacrine pro-survival signaling in select microenvironments, one can effectively deliver

potent dose of chemotherapy selectively to tumors in a manner that does not promote
systemic toxicity.

References
1. Gilbert LA, Hemann MT. (2010) DNA damage-mediated induction of a chemoresistantniche. Cell, 143(3), 355-366.
2. Bent EH, Gilbert LA, Hemann MT. (2016). A senescence secretory switch mediated by
3.

4.

5.

6.
7.
8.

PI3K/AKT/mTOR activation controls chemoprotective endothelial secretory responses. Genes Dev. 30(16):1811–21
Gilbert LA, Hemann MT. (2011). Chemotherapeutic resistance: surviving stressful situations. Cancer Res. 71(15):5062–66
Corre I, Paris F, Huot J. (2017). The p38 pathway, a major pleiotropic cascade that transduces stress and metastatic signals in endothelial cells. Oncotarget 8(33):55684–714
Pallasch C, Leskov I, Braun C, Vorholt D, Drake A, Soto-Feliciano Y, Bent E, Schwamb J,
Iliopoulou B, Kutsch N, van Rooijen N, Frenzel L, Wendtner C, Heukamp L, Kreuzer K,
Hallek M, Chen J, Hemann M. (2014) Sensitizing Protective Tumor Microenvironments to
Antibody-Mediated Therapy. Cell. 156(3):590-602. PMCID: PMC3975171
Jiang H, Pritchard JR, Williams RT, Lauffenburger DA, Hemann MT. (2011) A mammalian functional-genetic approach to characterizing cancer therapeutics. Nat. Chem.
Biol., 7(2), 92-100. PMCID: PMC3070540
Pritchard JR, Bruno PM, Gilbert LA, Capron KL, Lauffenburger DA, Hemann MT.
(2013) Defining principles of combination drug mechanisms of action. Proc Natl Acad Sci.
110(2):E170-9.
Bruno PM, Liu Y, Park Y, Murai J, Koch CE, Eisen TJ, Pritchard JR, Pommier Y, Lippard SJ
and Michael T Hemann. (2017) A subset of platinum-containing chemotherapeutic
agents kills cells by inducing ribosome biogenesis stress. Nature Medicine. 23(4):461471

Tumor Hetelogeneity and Immunogenomics

Michael T. Hemann, PhD

1996-2001
2001-2006
2006-20102013-present

97

PhD from Johns Hopkins School of Medicine
Postdoctoral Fellow at Cold Spring Harbor Laboratory
Assistant Professor of Biology at MIT
Tenured Associate Professor of Biology at MIT

98

SIGNATURES OF MUTATIONAL PROCESSES IN CANCER

Michael Stratton

Wellcome Sanger InstituteWellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, UK(mrs@sanger.ac.uk)

Somatic mutations are believed to occur in all cells and all cancers are thought to arise

due to somatic driver mutations that occur in cancer genes. However, the repertoire ofmutational processes that contributes to the somatic mutation burden in normal and cancer
cells has been poorly understood. Furthermore, although it has been recognised for

decades that exposures such as tobacco smoking or ultraviolet light contribute to thedevelopment of lung and skin cancers respectively, probably through increasing thesomatic mutation burden in exposed cells, the causes of many other cancers, for example

breast, glioma or pancreatic, and their relationships to mutation loads have remained

obscure.

Early studies of mutations in the TP53 gene in the 1990s, following the discovery of

frequent driver mutations in this gene in many human cancers, showed that patterns ofsomatic base substitution mutation differ according to the environmental or lifestyle

exposures that contribute to a particular cancer type. Notably, the predominant pattern ofbase substitution mutations in lung cancers from smokers (C>A) is different from thepredominant pattern of substitutions in ultraviolet light induced skin cancers (C>T) andboth of these mutation types were consistent with the nature of the DNA damage known to

be induced by the exposures. These studies therefore indicated that characterisation ofpatterns of somatic mutation, now called “mutational signatures”, present in cancers and innormal cell genomes could in principle provide insight into the repertoire of mutational
processes that have been operative in them and potentially provide clues to endogenousand exogenous exposures contributing to cancer.

Nakahara Memorial Lecture99

Although the TP53 gene provided transformative insights of principle, the small

number of mutations from each cancer genome in TP53 (usually just a single mutation),provided limited ability to resolve different mutational signatures. The advent of next

generation sequencing, in particular the large and comprehensive catalogues of somatic

mutation from whole cancer genome sequences, changed the landscape in this regard withthousands to millions of mutations commonly detected in individual cancer genomes. In

order to optimally use this information, however, two further major analytic developments

required introduction. First, new subclassifications of base substitutions, doublet basesubstitutions, indels, rearrangements and copy number changes in order to provide

sufficient resolution between closely related mutational signatures. For example theprevious classification of base substitutions used for TP53 into six subtypes (C>A, C>G,

C>T, T>A, T>C, T>G) has commonly now been elaborated into a classification based on 96

subtypes in which the base immediately 5’ and 3’ to the mutated base is also taken into

account (generating 16 X 6 = 96 subtypes). Second, on the assumption that multiple

mutational processes (and therefore mutational signatures) have contributed to the somatic

mutations found in a single cell or cancer, methods were needed that could separate eachmutational signature from the others present. These methods work on the assumption thatthe contribution of each signature to each cell/cancer can differ. A commonly usedapproach employs non-negative matrix factorisation (NMF) [1], although other methods

have been developed. Further developments of these methods also allow estimation of themutation burden of each signature in each sample.

The first application of these approaches to large numbers of cancer genomes ofmultiple types revealed 21 base substitution mutational signatures [2] and, in a subsequent

update, 30 signatures [3]. Recently, as part of the Pan Cancer Analysis of Whole Cancer

Genomes (PCAWG), which includes ~4,000 whole cancer genome and ~15,000 exome

sequences, this list of signatures has been extended and now incorporates 49 basesubstitution signatures [4]. These include signatures of likely endogenous origin that are

ubiquitous in normal and cancer cells and which accumulate in a clocklike, linear mannerwith age of the individual (single base substitution signatures (SBS) SBS1 and SBS5);

signatures due to known exogenous exposures (including tobacco smoke (SBS4), ultraviolet

light (SBS7a, SBS7b, SBS7c and SBS7d); signatures due to previous chemotherapeutic

treatments (including temozolomide (SBS11), a cocktail of chemotherapeutics (SBS25),

cisplatin (SBS31 and SBS35), azothiaprine (SBS32)); signatures of defective DNA repair(defective DNA mismatch repair (SBS6, SBS14, SBS15, SBS20, SBS21, SBS26, SBS40),

defective base excision repair (SBS30, SBS36), defective homologous recombination based

repair (SBS3)); signatures of overactive DNA editing by enzymes of the APOBEC family(SBS2, SBS13 and SBS84); signatures of defective proof reading by replicative polymerases

100

M. Stratton(SBS10a, SBS10b and SBS28); and many signatures of unknown origins (for example, SBS17

which is found predominantly in oesophageal adenocarcinoma (although also in othercancers)) and many of speculated origin (eg SBS18 which may be due to reactive oxygen

species). In the course of this more highly powered analysis many signatures have been

better separated from each other and some have split into multiple constituents. For

example, SBS7, attributable to ultraviolet light exposure, has split into four components(SBS7a, SBS7b, SBS7c and SBS7d) suggesting that there are four distinct, but closely

correlated, mutational processes operating. Many signatures show evidence oftranscriptional strand bias indicating that some may have been initiated by bulky adducts

binding to DNA, with transcription coupled nucleotide excision repair being recruited.

Although single base substitutions account for most somatic mutations there are otherclasses of mutation which require incorporation into the framework in order to rendermutational signature analysis comprehensive. These include doublet base substitutions,indels, rearrangements and copy number changes. All these types of somatic mutation arepresent at much lower frequencies than single base substitutions and therefore offer lesspower for resolution of related signatures. In the recent PCAWG analyses doublet basesubstitutions and indels were analysed, either as separate classes or as part of composite

signatures including all the different types (and rearrangements/structural variation wereanalysed by another group). Seventeen indel and 11 doublet base substitution signatures

were found. The mutation loads attributable to some of these signatures closely correlatewith the mutation loads of certain base substitution signatures suggesting that they arecaused by the same or closely related mutational processes (eg DBS1, characterised
predominantly by CC>TT mutations, correlates with SBS7a due to ultraviolet light

exposure; and DBS2, characterised predominantly by CC>AA mutations, correlates with

SBS4 due to tobacco smoke exposure).

The sets of somatic mutation present in a normal cell or cancer represent the aggregate

accumulation of mutations over time since the fertilised egg of that individual wasconstituted. However, these mutations could, in principle, have been accumulated

continuously during the complete lifetime of the individual or, alternatively, all-at-one-go,with any intermediate formulation between these two extremes being a possibility. It is
often not possible to reconstruct well these temporal patterns of activity from mutation

catalogues in cancer or normal cell genomes. Therefore, to explore temporal patterns ofmutation accumulation of different signatures further, we have recently employedimmortal cancer cell lines which are derived from naturally occurring human cancers [5].

Using previously generated exome somatic mutational catalogues from ~1000 humancancer cell lines we first characterised the mutational signatures present in each stock cellline. Many of these signatures will have been generated during the part of the life history of

102

M. Stratton

Acknowledgements: many have contributed to this work over the years but I would

particularly like to thank colleagues on the PCAWG mutational signatures working group,

Ludmil Alexandrov, Jaegil Kim, Nick Haradvala, Gaddy Getz and Steve Rozen and Mia
Petljak for leading the APOBEC episodic mutagenesis study.
References

1. Alexandrov LB, Nik-Zainal S, Wedge DC, Campbell PJ, Stratton MR. Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 2013 Jan
31;3(1):246-59. doi: 10.1016/j.celrep.2012.12.008. Epub 2013 Jan 10. PubMed PMID:
23318258; PubMed Central PMCID: PMC3588146.
2. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, Bignell GR,
Bolli N, Borg A, Børresen-Dale AL, Boyault S, Burkhardt B, Butler AP, Caldas C, Davies
HR, Desmedt C, Eils R, Eyfjörd JE, Foekens JA, Greaves M, Hosoda F, Hutter B, Ilicic T,
Imbeaud S, Imielinski M, Jäger N, Jones DT, Jones D, Knappskog S, Kool M, Lakhani SR,
López-Otín C, Martin S, Munshi NC, Nakamura H, Northcott PA, Pajic M,
Papaemmanuil E, Paradiso A, Pearson JV, Puente XS, Raine K, Ramakrishna M,
Richardson AL, Richter J, Rosenstiel P, Schlesner M, Schumacher TN, Span PN, Teague
JW, Totoki Y, Tutt AN, Valdés-Mas R, van Buuren MM, van 't Veer L, Vincent-Salomon A,
Waddell N, Yates LR; Australian Pancreatic Cancer Genome Initiative; ICGC Breast
Cancer Consortium; ICGC MMML-Seq Consortium; ICGC PedBrain, Zucman-Rossi J,
Futreal PA, McDermott U, Lichter P, Meyerson M, Grimmond SM, Siebert R, Campo E,
Shibata T, Pfister SM, Campbell PJ, Stratton MR. Signatures of mutational processes inhuman cancer. Nature. 2013 Aug 22;500(7463):415-21. doi: 10.1038/nature12477. Epub
2013 Aug 14. Erratum in: Nature. 2013 Oct 10;502(7470):258. Imielinsk, Marcin [corrected
to Imielinski, Marcin]. PubMed PMID: 23945592; PubMed Central PMCID:
PMC3776390.
3. Alexandrov LB, Jones PH, Wedge DC, Sale JE, Campbell PJ, Nik-Zainal S, Stratton MR.
Clock-like mutational processes in human somatic cells. Nat Genet. 2015
Dec;47(12):1402-7. doi: 10.1038/ng.3441. Epub 2015 Nov 9. PubMed PMID: 26551669;
PubMed Central PMCID: PMC4783858.
4. Alexandrov LB, Kim J, Haradhvala NJ, Huang M, Ng AWT, Boot A, Covington KR,
Gordenin DA, Bergstrom E, Lopez-Bigas N, Klimczak LJ, McPherson JR, Morganella S,
Sabarinathan R, Wheeler DA, Mustonen V, Getz G, Rozen SG, Stratton MR, PCAWG
Mutational Signatures Working Group, ICGC/TCGA Pan-Cancer Analysis of Whole
Genomes Network. The Repertoire of Mutational Signatures in Human Cancer doi:
https://doi.org/10.1101/322859
5. Cell (in press 2019).

Nakahara Memorial Lecture

Michael Stratton, FMedSir FRS

1996

1997

1999
2000
2007
2008
2009

2010
2012

2013

103

Leader in the Molecular Genetics of Cancer, Institute ofCancer Research
Professor of Cancer Genetics and Chair of the newly
constitute section of Cancer Genetics at the Institute ofCancer Research
Fellow, Academy of Medical Sciences
Member, the Sanger Institute's Board of Management
Initiated the Cancer Genome Project and became theHead of this Programme
Deputy Director of the Sanger Institute
Fellow, the Royal Society
Member of the European Molecular Biology
Organization
Director of the Sanger InstituteChief Executive Officer of the Wellcome Genome
CampusKnighted by the Queen

104

IN VIVO AND EX VIVO ENGINEERING OF CANCERASSOCIATED CHROMOSOMAL VIA SOMATIC GENOMEEDITING

Andrea Ventura

Cancer Biology and Genetics Program
Memorial Sloan Kettering Cancer Center
1275 York Avenue, Box 524, New York, NY 10065, USA(venturaa@mskcc.org)

The advent of high throughput sequencing methods has profoundly changed ourIntroduction

understanding of cancer, revealing its genetic complexity, paving the way to novel

therapeutic strategies, and ushering the era of precision medicine. This technologicalrevolution, however, has also uncovered limitations in the experimental approaches
available to model human cancers and characterize gene function in vivo. Althoughconventional gene targeting approaches based on homologous recombination in murine
embryonic stem cells allow the generation of sophisticated genetically engineered mice,these technologies are not easily scalable, being costly and time-consuming. Furthermore,chromosomal rearrangements, a common class of cancer associated mutations that often
result in the formation of therapeutically actionable gene fusions, have proven challenging

to model in mice by homologous recombination.

To overcome this major limitation, we have developed a general CRISPR-Cas9-based

strategy that allows the rapid generation of complex chromosomal rearrangements(deletions, inversions, duplications, and translocations) directly in somatic cells of liveorganisms [1, 2]. The approach is based on the concomitant introduction of Cas9 and two

guide RNAs (gRNAs) designed to generate double stranded breaks (DSBs) at the desired
chromosomal breakpoints [3]. The idea is that in response to the two DSBs, the endogenous

DNA repair machinery will occasionally erroneously repair the two breaks generating thedesired chromosomal rearrangement s [4].

Clinical Genomics and Cancer Genome Editing105

As initial proof of concept, we used this approach to model a chromosomal inversion ofModeling EML4-ALK driven lung cancers

approximately 11 Mbp that results in the formation of a gene fusion between EML4 and the
ALK kinase (EML4-ALK) and is detected in 3-5% of non-small cell lung cancers patients(NSCLCs). The EML4-ALK fusion protein is a known driver of NSCLCs and confers
resistwith a recombinant adenovirus encoding Cas9 and two gRNAs designed to generate

the most common EML4-ALK variant. By 8 weeks post-infection, all injected micepresented multiple bilateral lung lesions that upon examinations were diagnosed as lungadenoma. These lesions progressed over the next 8-10 weeks to lung adenocarcinomas.

Importantly, each tumor examined displayed signs of constitutive activation of signaling
pathways downstream of ALK (MAPK, AKT and STAT3), expressed the Eml4-Alk cDNA

and was positive for the Eml4-Alk inversion (Figure 1).

To further confirm that the resulting tumors were in fact driven by the Eml4-Alk fusion

protein, a cohort of animals was infected with the recombinant virus and imaged by

microCT 12 weeks later to determine tumor load. Tumor bearing animals were then treateddaily with Crizotinib or vehicle and reimaged 2 weeks later. All crizotinib treated animal

displayed complete or near complete tumor regression, while all vehicle-treated mice
showed marked disease progression (Figure 2).

These results demonstrate that in vivo somatic CRISPR-Cas9-mediated chromosomal

engineering can be used to model lung adenocarcinomas driven by a recurrent

chromosomal inversion.

In principle CRISPR-based somatic chromosomal engineering could be used to rapidly

Modeling chromosomal rearrangements in human brain cancers

test the functional relevance of newly identified gene fusions and generate relevant

preclinical models to test new therapeutic approaches. For this new set of studies, we chose

to focus on human brain cancers because they represent a major clinical challenge withlimited therapeutic options and because a large number of rare gene fusions of unclear

functional significance have been reported in these tumors. Among the chromosomal

rearrangements sporadically observed in human gliomas we first attempted to model a~600 kb deletion resulting in the fusion between BCAN and NTRK1. This gene fusion has

been reported in only a few patients but its pathogenic role was unclear. To model thisrearrangement in mice, we chose two complementary approaches. First, we isolated adultneuronal stem cells (aNSCs) from the brain of p53-null mice (the p53 pathway is nearly

always inactivated in human glioblastomas) and generated the BCAN-NTRK1

rearrangement by transfecting them with plasmids encoding the two gRNAs and Cas9. We

then orthotopically injected the transfected cells with into the sub-ventricular zone (SVZ) of

106

A. Ventura

a
Ad5

U6

b Intra-tracheal
injection

Eml4
sgRNA
A

Alk
sgRNA
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1 week

1-12 weeks

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12 weeks

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FLAG Cas9

iii

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3
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300bp

Eml4
Eml4-Alk

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Ad-EA(tumors)
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Observed GGACCTAAAGTGTACCGTCG

dH2O

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dH20

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Eml4-Alk+

NIH3T3
Ad-EA(tumors)

WT

e

MWM

Control lungi’Eml4-Alk

300bp

Gapdh

(a) Schematic of the Adenoviral vector used to deliver Cas9 and sgRNAs targeting Eml4 andAlk to the lung of adult mice. (b) Schematic of the experiment. Mice are injected intratracheally with 1.5x10^8 PFU of recombinant adenoviruses and euthanized at increasing
intervals between 1 and 12 weeks post-infeciton. (c) Hematoxylin-eo-sin staining of lungs
from mice at the indicated times after intratracheal instillation of Ad-EA. (d) Break-apartinterphase FISH showing the presence of the Eml4-Alk inversion in a tumor from an Ad-EAinfected mouse (8 weeks post-infection). (i) Bright Field. (i', ii, iii increasing magnifications ofmerged fluorescent channels). (e) Detection of the wild type Eml4 locus and Eml4-Alk
inversion in micro-dissected tumors from Ad-EA-infected mice using a three-primer PCR
strategy. (f) RT-PCR detection (left) of the full-length Eml4-Alk cDNA (~3.2Kb) in thetumors shown in (e). The full-length PCR products were sequenced on both strands. A
chromatogram of the Eml4-Alk junction is shown (right).
cDNA

Clinical Genomics and Cancer Genome Editing

Ad-EA infectionµCT
imaging

9-13
weeks

c

CrizotiniborvehiclebµCT
imaging

crizotiniba

14 daysdcrizotinib

OP1300OP1290Figure 2

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vehiclevehiclecrizotinib

107

vehiclee

day 0

day 14200 µm

200µm

50µm

50µm

OP1259OP1280(a) Schematic of the experiment. (b) Representative mCT of the lungs of mice treated withcrizotinib or vehicle at day 0 and after 2 weeks of treatement. Lung tumors are indicated byarrows. Red asterisks mark the hearts. (c) Macroscopic appearance of the lungs after 2
weeks of treatment. (d) Low magnification of lung sections from two crizotinib- and 2
vehicle-treated mice (hematoxylin eosin). (e) Higher magnification of representative hematoxylin-eosin stained lung sections from crizotinib-treated mice showing residual atrophic
foci of tumor cells (left) or necrotic-inflammatory debris (right).

108

A. Ventura

adult immune-deficient mice to determine whether cells harboring the BCAN-NTRK1

rearrangement would be capable of forming brain tumors in vivo. 3-4 months later, themajority of animals injected with the transfected aNSCs, but not control animals injectedwith control p53-null aNSC, developed neurological signs consistent with the presence ofan intracranial mass. At necropsy, each mouse showed infiltrating masses that upon
histologically resulted to be high grade gliomas. Importantly, genomic analysis revealed
the presence, in each lesion examined, of the BCAN-NTRK1 rearrangements (Figure 3).

In parallel to the ex-vivo somatic chromosomal engineering approach just described, we

also generated autochthonous BCAN-NTRK1 driven gliomas by in vivo chromosome
engineering. To do so, we injected p53 f l / f l mice intracranially with recombinantadenoviruses expressing the Cre recombinase (AdCre, to inactivate p53) and withadenoviruses expressing Cas9 and the two gRNAs designed to engineer the BCAN-NTRK1

deletion. This approach further emphasizes the flexibility of CRISPR-based chromosomal
engineering, as it can be easily combined with the large number of already available

genetically engineered mouse models (GEMMs).

As expected, within 4 months, most of the injected animals had developed high grade

gliomas that were histologically indistinguishable from the ones generated using the exvivo approach, invariably harbored the BCAN-NTRK1 rearrangements, and showedrecombination of both floxed p53 alleles (Figure 4).

Finally, we tested whether BCAN-NTRK1 gliomas generated by chromosomal

engineering would be sensitive to a novel experimental TRKA inhibitor (Entrectinib,

Ignyta). Cell based experiments indicated that BCAN-NTRK1-positive murine gliomas aremarkedly sensitive to this drug in vitro at nanomolar concentrations. A cohort of miceharboring the BCAN-NTRK1 driven gliomas was therefore treated daily with Entrectinib orvehicle. During the treatment window, all vehicle control animals succumbed to thedisease, while all Entrectinib treated mice were still alive at the end of the treatment,

showing wither disease regression or stable disease (Figure 5).

In summary, we have developed a simple, flexible, and rapid strategy to generate awide range of chromosomal rearrangements in cells and in mice. We are currently

employing this strategy to model and investigate additional cancer types, including

pediatric brain tumors driven by BRAF fusions. We expect that this approach will

accelerate the development of better therapeutic strategies, will provide new insights into

the pathogenesis of a wide range of human cancers driven by chromosomal

rearrangements, and will facilitate the functional characterization of gene rearrangements
involving the non-coding fraction of the human genome.

Clinical Genomics and Cancer Genome Editing

109

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Induction of Bcan-Ntrk1 deletion in murine cells using CRISPR-Cas9
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chromosome
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 used to determine  of GFP-positive +/-SEM, (C) Top, schematic  targeted region  and Ntrk1.
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performed on genomic DNA extracted from aNSCs nucleofected with the indiPCRs
were
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two independent clones and a representative chromatogram are shown in the lower rightpanel. (D) RT-PCR (left panel) on total RNA extracted from aNSCs nucleofected with theindicated constructs using using primer designed to detected the wild type Bcan cDNAs or
the Bcan-Ntrk1 fusion transcript. The band corresponding to the fusion transcript was subcloned and sequenced (right panel).
  

110

A. VenturaagBcan_1

Ad-BNbgNtrk1_1

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FLAG

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high-grade
gliomasby in vivo somatic chromosomal

  
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 of the adenoviral   vector (Ad-BN) expressing the BN1 gRNA paira. Schematic
recombinant
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and FLAG-Cas9.
b.
Immunostaining
with antiFLAGandanti-Ki67 antibodiesofbrainsec 
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tions from
adult
wild
48 hours
stereotacticalintracranial  type mice
  after  injection with  Ad-BN(scale bar: 0.1mm).
c.  Survival

 


  
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
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 with
curvesof p53fl/fl
mice
injected
intracraniallya 1:1 mix

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of Ad-BN
Ad-CREoralone(n=4). d. H&E staining and  ture

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  (n=7)
 withAd-CRE
immunostaining with the indicated antibodies of a representative tumor observed in mice
infected with the Ad-BN virus (scale bar: 0.5mm). e. PCR analysis of genomic DNA purified
from three representative gliomas observed in the Ad-BN/Ad-Cre-infected mice (Tumor 1-3),
a Bcan-Ntrk1-positive glioma generated by orthotopic implantation of aNSC harboring the
Bcan-Ntrk1 rearrangement (BN Tumor) and a PDGF-beta-driven mouse glioma were
included as controls. Primers to detect the Bcan-Ntrk1 deletion or the wilde type Bcanallele were used. f. RT-PCR using primers designed to detect the Bcan-Ntrk1 fusion transcript
was performed on total RNAs extracted from the indicated tumors. g. Detection of therecombined p53 allele by genomic PCR on the indicated tumors and control tissues.

Clinical Genomics and Cancer Genome Editing

111

b

a
Impla nta
(500K cells)

Day: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

14-days EntrecVĞŚŝĐůĞŽntrol (n=9)

Vehicle ControlĞŶƚƌĞĐƟŶŝď ;ϱϬŵŐͬŬŐͿŽƌǀĞŚŝĐůĞ

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Day 13

Day 20Day 13Figure 5Ɖ<d^ϰϳϯŶƚƌĞĐƟŶŝďdƵŵŽƌϭsĞŚŝĐůĞŽŶƚƌŽůdƵŵŽƌϭƉ<d^ϰϳϯVĞŚŝĐůĞControl

Entrectinib inhibits Bcan-Ntrk1-positive gliomas growth in vivo

schematic of the experiment.
Mice were injected intracranially with cells  panel:   a. Upper  treatment  at dayrearrangement the  Bcan-Ntrk1  a glioma harboring derivedfrom
0. Daily
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   withentrectiniborvehiclewas
initiated
at
day
12
and
continued
for
14
days.
Lower
panel:

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 
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 

 

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
 
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Kaplan-Meierof mice(p-value=log-ranktest).
b.



 
 vehicle
 or
 entrectinibreceiving curve   cells (right and quantification  (left panel)

 for






  Immunofluorescencestaining
Ki67
of Ki67+
 


 
 



 
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 
   
panel) oftreatedand control
tumorsharvested
on day 26 post-implantation.
Scale
  


 



 



 


bar=0.1mm. p-value=two tailed t-test Error bars represent mean +/-S.E.M. c. representative
MRI analysis performed on entrectinib- or vehicle-treated mice on day 15 and 22 postimplantation. d. Histological analysis of animals receiving either entrectinib or vehicle for twodays (day 14 post tumor cell implantation). Immunostaining for pAkt1 (S473) of tumors
treated with entrectinib of vehicle for two days. Scale bar=0.1mm.

112

A. Ventura

References
1. Maddalo, D., E. Manchado, C.P. Concepcion, C. Bonetti, J.A. Vidigal, Y.C. Han, P.
Ogrodowski, A. Crippa, N. Rekhtman, E. de Stanchina, S.W. Lowe, and A. Ventura, In
vivo engineering of oncogenic chromosomal rearrangements with the CRISPR/Cas9 system.
Nature, 2014. 516(7531): p. 423-7.
2. Cook, P.J., R. Thomas, R. Kannan, E.S. de Leon, A. Drilon, M.K. Rosenblum, M. Scaltriti, R.
Benezra, and A. Ventura, Somatic chromosomal engineering identifies BCAN-NTRK1 as apotent glioma driver and therapeutic target. Nat Commun, 2017. 8: p. 15987.
3. Choi, P.S. and M. Meyerson, Targeted genomic rearrangements using CRISPR/Cas technology.
Nat Commun, 2014. 5: p. 3728.
4. Maddalo, D. and A. Ventura, Somatic Engineering of Oncogenic Chromosomal
Rearrangements: A Perspective. Cancer Res, 2016. 76(17): p. 4918-23.

Andrea Ventura, MD, PhD
1997
2003

2003-2008

2008-20172014-present

MD, Catholic University Medical School, RomePhD in Molecular and Cellular Biology, European
Institute of Oncology, Milan
Postdoctoral Fellow, MIT in the Laboratory of Dr.
Tyler Jacks
Assistant Member, Dept. of Cancer Biology andGenetics, Memorial Sloan Kettering Cancer Center(MSKCC)
Associate Member, Dept. of Cancer Biology & Genetics

113

SYSTEMATIC MAPPING OF GENETIC VULNERABILITIES IN
CANCER

Prashant Mali

Department of Bioengineering, University of California San Diego
9500 Gilman Drive 0412, La Jolla, CA 92093, USA(pmali@ucsd.edu)

A vast number of mutations contribute to cancer, but the observed non-random

combinations of these leading to transformation highlight the importance of hallmark

pathways and networks in cancer progression. While many pathways have been implicatedin cancer, tumor heterogeneity stemming from different mutagens, tissue of origin, degreeof progression, etc. leads to each case exhibiting a unique subset of altered pathways.

Taken together, this diversity among cancer types and their origins, compounded by thehuge genetic and epigenetic heterogeneity of tumors has complicated the development oftargeted cancer treatments. The central premise of our studies is that cancer mutations

converge into genetic interaction networks, and these networks bring together mutations ofall varieties, including genes with low frequency of oncogenic mutations, and tumor

suppressor profiles [1,2]. We thus hypothesize that by systematically mapping these
networks in both cell lines and in clinically relevant ex vivo and in vivo models, newmolecular targets for cancer therapy can be identified. Correspondingly, we develop anduse state-of-the-art high-throughput epistasis mapping via CRISPR-Cas [3,4] and coupled

single-cell analyses to enable systematic interrogation of the functions of individual genes

and combinations while also assaying the impact of tumor heterogeneity. Coupled with invitro and in vivo drug and targeted genetic validations in preclinical models, we anticipate

this integrated experimental and computational framework will result in new insights into
the underlying tumor biology, as well as unraveling of clinically actionable genetic

vulnerabilities to advance the practice of precision oncology.

To enable systematic mapping of genetic interaction networks, we have developed a

CRISPR-Cas9 based screening methodology for targeting single and pairs of genes in high-114

P. Malithroughput (Nature Methods, 2017; Molecular Cell 2018). Below I reproduce and summarize
some of the textual descriptions, experiments, and results from these studies.

In the CRISPR-Cas9 system, a guide-RNA (gRNA), in complex with the Cas9 protein,targets genomic sequences homologous to the gRNA. Targeting new genomic elements
entails modifying the gRNA sequence, thus enabling many targeted genome editing andregulation capabilities. Notably, Cas9 also enables facile multiplex targeting via delivery ofmultiple gRNAs per cell. Here, we combined multiplex targeting with array-basedoligonucleotide synthesis to create dual-gRNA libraries covering up to 105 defined genepairs (Figure 1a). In these libraries, each construct bears two gRNAs, with each gRNA
designed to target either a gene or a scrambled non-targeting sequence absent from thegenome. Thus, all combinations of gene-gene (double gene perturbation) and gene-scramble (single gene perturbation) are exhaustively assayed for effects on cell growth.
Notably, both spacers for a dual-gRNA construct are directly specified duringoligonucleotide synthesis thereby enabling the library constituents to be exactly defined tofacilitate custom gRNA-pairing. By enabling determination and comparison of single-geneand dual-gene perturbation effects in the same assay, this approach allows for thesystematic quantification of genetic interactions in human cells.

Figure 1(a) Combinatorial genetic screening experimental approach to de novo map functional
genetic interactions. (b) Overall analysis workflow: CRISPR screens are runs as two independent replicate experiments, with cells harvested at 4 time points and gRNA frequencies
determined by next-generation sequencing (NGS). All gRNAs below a threshold (red dash)
are excluded from further analysis. A fit of log relative abundance vs. time represents fitness,and probes are subsequently ranked by absolute fitness and weighted. Finally, a numerical
Bayesian method is used to test for presence of a genetic interaction. (Figure reproduced fromNature Methods 2017).

Clinical Genomics and Cancer Genome Editing

115

We conduct genetic interaction screens by transducing the dual-gRNA lentiviral library

into a population of cells stably expressing Cas9, maintaining these cells in exponential

growth over the course of four weeks, and then sampling the relative changes in gRNAs atdays 3, 14, 21 and 28 post-transduction. To robustly quantify gene fitness and genetic

interactions, we have developed a computational analysis framework that integrates all

samples across the multiple days of the experiment. This method: one, detects and removes

gRNA constructs with insufficient read coverage; two, fits growth curves to the measured

log2 abundances of each construct over time, the slopes of which reflect fitness; three,integrates data from the multiple gRNA constructs to derive a robust fitness value fordisruption of each gene, fg, and gene pair, fg,g’ ; and four, computes the genetic interaction

score pgg, as the difference of the observed from the expected fitness of the double geneknockout (Figure 1b). Significant departures from expected (p < 3s or p > 3s) are called as

negative or positive genetic interactions, respectively. A negative interaction indicatesslower-than-expected growth, suggesting synthetic sickness or lethality, while a positive

interaction indicates faster-than-expected growth, suggesting epistasis.

As an exemplar, using this method, we evaluated all pairwise gene knockout

combinations among a panel of 73 genes divided between tumor-suppressor genes (TSG)

and cancer-relevant drug targets (DT), a subset of which were also verified oncogenes(Figure 2). Experiments were performed in three somatic cell lines: HeLa, a cervical cancer
cell line driven by Human Papilloma Virus (HPV); and A549, a lung cancer cell line driven

by KRAS G12S mutation. With nine gRNA pairs per combination, the library comprised
23,652 double gene knockout constructs and 657 single gene constructs; testing two

replicates in each cell line yielded a total of 94,608 unique tests of interaction.

Measurements of gene fitness (fg) were well correlated between biological replicates in thesame cell line, as were the p scores for significant genetic interactions. Moreover, weobserved a significant correlation between the number of genetic interactions identified fora gene and its single gene fitness suggesting that network ‘hubs’ may have increased

functional importance to cells relative to genes with fewer interactions; such a relationshiphas been previously observed in model organisms but not before in humans. Comparing

the two cell lines, we found lower but significant correlation of the single gene fitness

scores between HeLa and A549. Differences in these fitness scores recapitulated known

biological differences between cell lines, including the large positive growth effect of TP53

knockout in A549 but not HeLa, in which TP53 is already inactivated by HPV. Knockout ofDTs was generally more deleterious to growth than knockout of TSGs, suggesting thatcancer cell lines are particularly dependent on the signaling pathways for which targeteddrugs have been successfully developed thus far. Furthermore, we found that the genetic

interactions identified from these data were remarkably different between cell lines. ~20%

116

P. Maliof the interactions we observed had been previously identified, including thetherapeutically relevant interactions EGFR-BRAF and PTEN-MTOR. Finally, the differencesin genetic interaction across cell lines seen by systematic CRISPR could be largely
reproduced as drug-drug interactions in small-scale assays.

Figure 2(a) A panel of 73 oncogenes, tumor suppressors, and cancer relevant drug targets was
screened. (b) Correlation of the single-gene fitness, and (c) pi-scores between HeLa andA549 is depicted. (d) Combined synthetic-lethal network for three cell lines. Circles indicate
TSG, squares DTs. Node colors indicate single gene knockout fitness effect, red: positive fitness effect, blue: negative fitness effect. Black edge around node indicates that the protein
product of the gene is the target of an FDA approved drug. Color of edge indicates the cellline in which the interaction was identified, blue: HeLa, red: A549, green: 293T. Blackedges were identified in multiple cell lines. (e) Example dose-response curve: adding the
ADA inhibitor Fludarabine shifts the dose-response curve of CHEK1 inhibitor MK-8776 to
the left. (f) Summary of the eight synthetic lethal interactions validated in this pilot study.
(Figure reproduced from Nature Methods 2017).

Clinical Genomics and Cancer Genome Editing117

As a second exemplar, we have also developed an integrated platform (Figure 3)

combining genetic, transcriptomic, and flux measurements, to improve elucidation of

metabolic network alterations, and guide precision targeting of metabolic vulnerabilities
based on tumor genetics. Specifically, we conducted a screen of isozymes involved incarbohydrate metabolism in different non-small cell lung cancer cells, we characterized

how loss or mutation of the KEAP1 tumor suppressor influences the susceptibility of cells

to knockout of oxidative pentose phosphate pathway enzymes.

In summary, we have successfully established a combinatorial CRISPR-Cas9 genetic

interaction mapping technology that successfully identifies genetic interactions in humancancer cells and shows the great importance of cellular context on the architecture of thegenetic interaction network.

Figure 3

Combinatorial CRISPR screens reveal metabolic network dependencies. (a) SKO fitness
scores for HeLa cells, plotted as fg (day-1), with a more negative score representing a loss in fitness with SKO. Plotted as mean ± SD. (b) Comparison of fitness scores versus gene expression across multi-isoform families in HeLa. (c) Genetic interaction map computed based on
the combinatorial knockouts. (Figure reproduced from Molecular Cell 2018).

118

P. Mali

References
1. Dongxin Zhao, Mehmet G. Badur, Jens Luebeck, Jose H. Magana, Amanda
Birmingham, Roman Sasik, Christopher S. Ahn, Trey Ideker, Christian Metallo,
Prashant Mali, "Combinatorial CRISPR-Cas9 metabolic screens reveal critical redox
control points dependent on the KEAP1-NRF2 regulatory axis," Molecular Cell 69, pp.
699-708 (2018).
2. John Paul Shen, Dongxin Zhao, Roman Sasik, Jens Luebeck, Amanda Birmingham, Ana
Bojorquez-Gomez, Katherine Licon, Kristin Klepper, Daniel Pekin, Alex Beckett, Kyle
Sanchez, Alex Thomas, Chih-Chung Kuo, Dan Du, Assen Roguev, Nathan E. Lewis,
Aaron N. Chang, Jason F. Kreisberg, Nevan Krogan, Lei Qi, Trey Ideker, Prashant Mali,"Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions,"
Nature Methods 14, pp. 573-576 (2017).
3. Prashant Mali, John Aach, P. Benjamin Stranges, Kevin M. Esvelt, Mark Moosburner,
Sriram Kosuri, Luhan Yang, George M. Church, "CAS9 transcriptional activators fortarget specificity screening and paired nickases for cooperative genome engineering,"
Nature Biotechnology 31, pp. 833-838 (2013).
4. Prashant Mali, Luhan Yang, Kevin M. Esvelt, John Aach, Marc Guell, James E. DiCarlo,
Julie E. Norville, George M. Church, “ RNA-guided human genome engineering via
Cas9,” Science 339, pp. 823-826 (2013).

Prashant Mali, PhD

2004-20112011-20142014-present

PhD, Biomedical Engineering, Johns Hopkins
University, School of Medicine
Postdoctoral Fellow Department of Genetics, Harvard
Medical School
Assistant Professor Bioengineering Department,
University of California, San Diego

119

MATCHED TUMOR-NORMAL MUTATION PROFILING TO
IDENTIFY THERAPEUTIC BIOMARKERS AND GUIDECLINICAL CARE

Michael F. Berger

Department of Pathology and Center for Molecular Oncology
Memorial Sloan Kettering Cancer Center
1275 York Avenue, New York, NY 10065, USA(bergerm1@mskcc.org)

Tumor molecular profiling is a fundamental component of precision oncology, enabling

the identification of oncogenomic mutations that can be targeted therapeutically. To

accelerate enrollment to clinical trials of molecularly targeted agents and guide treatment

selection, we have established an enterprise-wide, prospective clinical sequencing programat Memorial Sloan Kettering Cancer Center using a custom, paired tumor-normal

sequencing assay (MSK-IMPACT). MSK-IMPACT is a deep-coverage, targeted sequencing

panel designed to identify somatic mutations, copy number alterations, and recurrent

structural rearrangements in 468 cancer-associated genes with high sensitivity, and isauthorized for clinical use by the US Food and Drug Administration [1]. As of November

2018, we have prospectively profiled the tumors of more than 28,000 active patients at our

institution to inform their clinical care. More than 40% of patients harbor at least one
somatic alteration that can be targeted by an approved or investigational therapy.

Matched tumor-normal sequencing has allowed us to characterize not only individual

somatic alterations but also complex mutational signatures, germline cancer susceptibility

alleles, and clonal hematopoiesis, all of which may have important clinical implications(Figure 1). We have detected microsatellite instability, a signature that predicts response to
immune checkpoint inhibitors, in many unexpected cancer types, often associated with

underlying Lynch Syndrome-associated germline mutations [2]. Other pathogenic germline

mutations are also observed more frequently than previously thought, often undergoingtumor-specific zygosity alterations, and in genes and tumor types not encompassed bycurrent clinical guidelines [3]. Mutations associated with clonal hematopoiesis in patientswith solid tumors are associated with smoking history, prior therapy, shorter survival, and120

M. F. Berger

Figure 1

Different classes of alterations detected by matched tumor/normal MSK-IMPACT sequencing

the development of secondary hematological malignancies [4]. Moreover, our clinicalsequencing strategy has produced a comprehensive genomic and clinical dataset that canbe mined to discover molecular biomarkers that predict therapeutic response or outcome

within and across cancer types, such as whole genome doubling [5]. The high depth of

MSK-IMPACT enables precise estimates of mutant allele fraction and the inference ofzygosity, revealing oncogenic mutations that undergo allelic imbalance. Our analysis

showed that imbalance typically favors enrichment of the oncogenic allele and can serve asa predictive biomarker [6]. By providing broad access to genomic data and accompanying

clinical annotation to researchers through initiatives such as the American Association forCancer Research (AACR) Genomics Evidence Neoplasia Information Exchange (GENIE)

consortium [7], we hope to accelerate translational research programs throughout thescientific community.

Comprehensive molecular profiling across all cancer types has revealed clinicallyactionable alterations and facilitated enrollment on genomically matched clinical trials for

many, but certainly not all, individuals. Current efforts to characterize complex genomic

features, develop improved clinical decision support tools, and establish additional

molecular tests including liquid biopsies promise to further expand the benefits of genomedriven oncology to cancer patients.

References
1. Zehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR, Srinivasan P, Gao J,
Chakravarty D, Devlin SM, Hellmann MD, Barron DA, Schram AM, Hameed M, Dogan S,
Ross DS, Hechtman JF, DeLair DF, Yao J, Mandelker DL, Cheng DT, Chandramohan R,
Mohanty AS, Ptashkin RN, Jayakumaran G, Prasad M, Syed MH, Rema AB, Liu ZY,
Nafa K, Borsu L, Sadowska J, Casanova J, Bacares R, Kiecka IJ, Razumova A, Son JB,

Clinical Genomics and Cancer Genome Editing

2.

3.

4.

5.

6.

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Stewart L, Baldi T, Mullaney KA, Al-Ahmadie H, Vakiani E, Abeshouse AA, Penson
AV, Jonsson P, Camacho N, Chang MT, Won HH, Gross BE, Kundra R, Heins ZJ, Chen
HW, Phillips S, Zhang H, Wang J, Ochoa A, Wills J, Eubank M, Thomas SB, Gardos SM,
Reales DN, Galle J, Durany R, Cambria R, Abida W, Cercek A, Feldman DR, GounderMM, Hakimi AA, Harding JJ, Iyer G, Janjigian YY, Jordan EJ, Kelly CM, Lowery MA,
Morris LGT, Omuro AM, Raj N, Razavi P, Shoushtari AN, Shukla N, Soumerai TE,
Varghese AM, Yaeger R, Coleman J, Bochner B, Riely GJ, Saltz LB, Scher HI, Sabbatini PJ,
Robson ME, Klimstra DS, Taylor BS, Baselga J, Schultz N, Hyman DM, Arcila ME, Solit
DB, Ladanyi M, Berger MF. Mutational landscape of metastatic cancer revealed fromprospective clinical sequencing of 10,000 patients. Nature Medicine. 2017; 23(6):703-713.
Middha S, Zhang L, Nafa K, Jayakumaran G, Wong D, Kim HR, Sadowska J, Berger
MF, Delair DF, Shia J, Stadler Z, Klimstra DS, Ladanyi M, Zehir A, Hechtman, JF.
Reliable Pan-Cancer Microsatellite Instability Assessment by Using Targeted NextGeneration Sequencing Data. JCO Precision Oncology. 2017 Nov.
Mandelker D, Zhang L, Kemel Y, Stadler ZK, Joseph V, Zehir A, Pradhan N, Arnold A,
Walsh MF, Li Y, Balakrishnan AR, Syed A, Prasad M, Nafa K, Carlo MI, Cadoo KA,
Sheehan M, Fleischut MH, Salo-Mullen E, Trottier M, Lipkin SM, Lincoln A, Mukherjee S,
Ravichandran V, Cambria R, Galle J, Abida W, Arcila ME, Benayed R, Shah R, Yu K,
Bajorin DF, Coleman JA, Leach SD, Lowery MA, Garcia-Aguilar J, Kantoff PW, Sawyers
CL, Dickler MN, Saltz L, Motzer RJ, O'Reilly EM, Scher HI, Baselga J, Klimstra DS, Solit
DB, Hyman DM, Berger MF, Ladanyi M, Robson ME, Offit K. Mutation Detection in
Patients With Advanced Cancer by Universal Sequencing of Cancer-Related Genes in
Tumor and Normal DNA vs Guideline-Based Germline Testing. JAMA. 2017;
318(9):825-835.
Coombs CC, Zehir A, Devlin SM, Kishtagari A, Syed A, Jonsson P, Hyman DM, Solit DB,
Robson ME, Baselga J, Arcila ME, Ladanyi M, Tallman MS, Levine RL*, Berger MF*.
Therapy-Related Clonal Hematopoiesis in Patients with Non-hematologic Cancers Is
Common and Associated with Adverse Clinical Outcomes. Cell Stem Cell. 2017;21(3):374-382.e4.
Bielski CM, Zehir A, Penson AV, Donoghue MTA, Chatila W, Armenia J, Chang MT,
Schram AM, Jonsson P, Bandlamudi C, Razavi P, Iyer G, Robson ME, Stadler ZK,
Schultz N, Baselga J, Solit DB, Hyman DM, Berger MF, Taylor BS. Genome doubling
shapes the evolution and prognosis of advanced cancers. Nature Genetics. 2018;50(8):1189-1195.
Bielski CM, Donoghue MTA, Gadiya M, Hanrahan AJ, Won HH, Chang MT, Jonsson P,
Penson AV, Gorelick A, Harris C, Schram AM, Syed A, Zehir A, Chapman PB, Hyman
DM, Solit DB, Shannon K, Chandarlapaty S, Berger MF, Taylor BS. Widespread
Selection for Oncogenic Mutant Allele Imbalance in Cancer. Cancer Cell. 2018; 34(5):852862.e4.

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M. F. Berger

7. AACR Project GENIE Consortium. AACR Project GENIE: Powering precision medicine
through an international consortium. Cancer Discovery 7(8):818-831, 2017.

Michael F. Berger, PhD

1997-2001
2002-2008
2008-20102010-20172013-present

2017-present

BA in Physics, Princeton University
PhD in Biophysics, Harvard University
Research Scientist, The Broad Institute
Assistant Member, Memorial Sloan Kettering Cancer
Center
Associate Director, Marie Josée and Henry Kravis
Center for Molecular Oncology
Associate Member, Memorial Sloan Kettering Cancer
Center

123

A SYSTEMS LEVEL VIEW OF BREAST CANCER

Carlos Caldas

Cancer Research UK Cambridge Institute
University of Cambridge
Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK(Carlos.Caldas@cruk.cam.ac.uk)

We have redefined the molecular taxonomy of breast cancer showing it is a constellationof 11 distinct genomic subtypes (Integrative Clusters- IntClust). These IntClust havedistinct drivers, genomic architectures and TMEs. Explant models representing these

IntClust have been generated and have been used for pre-clinical drug testing and forstudying drug resistance. Using a combination of genomics, digital pathology, liquidbiopsies and explant models allows us to have an integrated view of breast cancers andconstitutes a platform for studying its biology and explore novel therapeutics.

My talk covered the following topics:

・ Driver-based classification of breast cancer

・ Breast cancers as communities of clones and communities of cells

・ TME and tumour neighbourhoods

・ PBCP: prospective WGS and RNAseq for characterization of 2,250 breast cancers in theclinic・ Cancer cell/TME/therapy ‘trialogue’・ Modelling breast cancer- PDTXs/PDTCs

・ Integrated breast cancer medicine- a systems level platform

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C. Caldas

Driver-based classification of breast cancer

Breast cancers as communities of clones and communities of cells

Clinical Genomics and Cancer Genome Editing

TME and tumor neighborhoods

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C. Caldas

PBCP: prospective WGS and RNAseq for characterization of 2,250 breast cancers in theclinicCancer cell/TME/therapy ‘trialogue’Clinical Genomics and Cancer Genome Editing

Modelling breast cancer- PDTXs/PDTCs

Integrated breast cancer medicine- a systems level platform

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C. Caldas

References
1. Curtis C, et al. The genomic and transcriptomic architecture of 2,000 breast tumours
reveals novel subgroups. Nature, 486: 346-352, 2012
2. Dawson S-J, et al. Analysis of circulating tumor DNA to monitor metastatic breast cancer.
The New England Journal of Medicine, 368: 1199-1209, 2013
3. Murtaza M, et al. Noninvasive analysis of acquired resistance to cancer therapy byexome sequencing of plasma DNA. Nature, 497: 108-112, 2013 [*co-corresponding/co-senior author]
4. Dvinge H, et al. The shaping and functional consequences of the miRNA landscape inbreast cancer. Nature, 497: 378-382, 2013
5. Murtaza M, et al. Multifocal clonal evolution characterized using circulating tumour
DNA in a case of metastatic breast cancer. Nature Communications, 6: 8760, 2015
6. Pereira B, et al. The somatic mutation profiles of 2433 primary breast cancers refine
their genomic and transcriptomic landscapes. Nature Communications, 7: 11479, 2016
7. Bruna A, et al. A biobank of breast cancer explants with preserved intra-tumour heterogeneity to screen anticancer compounds. Cell, 167: 260-274, 2016
8. Ali HR, et al. Patterns of immune infiltration in breast cancer and their clinical implications: a gene expression-based retrospective study. PLOS Medicine, 13: e1002194, 2016

Carlos Caldas, MD, FMedSci

2002-present
2006-present

2016-present

Professor of Cancer Medicine, University of Cambridge
Senior Group Leader, Cancer Research UK Cambridge
Institute
Director, Breast Cancer Programme, Cambridge Cancer
Centre, University of Cambridge

129

THE INTERNATIONAL CANCER GENOME CONSORTIUM– ARGO INITIATIVE: BRINGING A MILLION PATIENT YEARSOF PRECISION ONCOLOGY KNOWLEDGE TO THE WORLD

Andrew V. Biankin

Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences
University of Glasgow
Garscube Estate, Switchback Road, Bearsden, Glasgow Scotland G61 1BD, UK(Andrew.Biankin@glasgow.ac.uk)

The most valuable information for someone with the diagnosis of cancer is the ability to

predict which treatment will be most effective for them, the likely outcome, and theiroverall prognosis. Our ability to do this is currently limited to a few examples that as proof

of concept demonstrate improved outcomes and efficiency. The most effective way togenerate the knowledge required to do this for all people affected by cancer is to amass

large datasets of deep molecular and detailed phenotypic data through health systems as

part of routine care. However, the pathways and processes are not in place and requiredevelopment, with the paramount challenge of acquisition and application of rich, reliable

clinical data including treatment and response. ICGC-ARGO will build these datasets,initially from clinical trials and from well-annotated cohorts, and together with healthsystems develop the frameworks that integrate robust clinical data acquired throughroutine patient care with molecular data. Although multiple regional platforms have been

established to generate cancer genomics data, no platform exists that can capture,

aggregate, harmonize and appropriately share data globally to enable granular analysis ofpooled individual participant data.

ICGC-ARGO is an international network of cancer clinicians, researchers and clinical

trials groups that aims to provide a million patient-years of precision oncology

knowledge in a manner that allows for broad, but ethically responsible, data sharing andresearch. Clinical and genomic data generated by ICGC-ARGO members will be

exclusively available to its membership for a short period of time before being released tothe broader research community.

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A. V. Biankin

ICGC-ARGO will acquire detailed treatment and response information from patientsalong with genomic, transcriptomic and proteomic data from their tumours and healthy

tissues. This data will allow for precise correlation of molecular aberrations with clinicalfeatures, allowing for the discovery of molecular markers of response, resistance andtoxicity of treatment. This knowledge will translate into new approaches to improveoutcomes for people affected by cancer.

ICGC-ARGO is the second phase of the International Cancer Genome Consortium. The

The ARGO Project (Accelerating Research in Genomic Oncology)

first initiative comprehensively mapped the structural aberrations of cancer genomes andadvanced our understanding of the molecular basis of cancer. ICGC focused on primarycancers prior to therapy, and to date has collected and distributed knowledge on nearly

21,000 primary cancers that have been characterised at the genomic, transcriptomic andepigenomic levels. Data generated through the ICGC has transformed research strategies inacademia and industry alike, with hundreds of seminal works arising from ICGC data, andlandmark articles appearing in the world’s elite scientific journals. No therapeutic is

developed today without, in some way, applying the knowledge that ICGC has provided

the world. Although ICGC has achieved much, pivotal outstanding challenges remain to beaddressed; unanswered questions that are vital in our quest to defeat cancer.
1. How do we use current treatments better?

The key questions addressed by ARGO are:

2. How does a cancer change with time and treatment?

3. How do we practically implement these approaches in healthcare and therapeutic development?

4. How do we advance early detection and ultimately prevent cancer?

The ICGC and its membership have established global networks, genomic sequencing

capacity, and computational expertise for the collection, integration, analysis anddistribution of large cancer genome data sets. Tens of thousands of scientists, clinicians andtechnologists have built the methodologies and mechanisms to advance genomic health.

The ICGC forms a strong foundation on which to build and advance cancer researchthrough integrating the expertise and capacity of the ICGC with clinical research andcancer care.

Over the next ten years ICGC-ARGO will generate, acquire and integrate genomic andclinical data on more than 200,000 patients (1 million patient-years). Clinical data will

include information concerning lifestyle, co-morbidity, diagnostics, toxicity, response totherapy and survival. Using this large-scale integrated data, researchers, scientists,

Clinical Genomics and Cancer Genome Editing131

policymakers and clinicians will be able to work with patients, health care providers,industry, and others to advance therapeutic development with interventions based onmatching the patient’s disease molecular subtype with the most effective treatment;

develop preventative strategies; markers for early detection of disease; and more specificcriteria and methods for diagnosis and prognostication.

Any researcher or research group from academia or industry may become an ICGC-

Joining ICGC-ARGO

ARGO member. Members must commit to the acquisition of molecular and clinical datafrom a minimum number of cancer patient donors; there are several levels of membershipthat correspond to the number of donors committed. The sources of cohorts of patients thatwould constitute ICGC-ARGO projects may include:

・ Biospecimens from participants enrolled in active clinical trials;

・ Analyses of banked samples from past clinical trials;

・ Analyses of samples from clinically well-annotated cohorts that satisfy ICGC-ARGO
clinical data requirements;

・ Longitudinal cohort studies;・ Autopsy studies with detailed clinical data

・ Population-based studies with detailed clinical and lifestyle data

・ Real World Data acquired through health systems.

To apply, prospective members are asked to fill in an expression of interest that

describes their project and level of commitment.

ICGC-ARGO members will be able to submit patient genomic, transcriptomic andBenefits of Membershipclinical data to one of a series of ARGO regional data processing centres, where it will be

subjected to state-of-the-art QC, alignment, variant calling, annotation and clinicalharmonization. The harmonized data will be returned to members in a form that allows it

to be compared to data collected by all other ARGO participants, and a copy of the data

will be retained by the processing centre for merging into a central compute cloud-accessible database of all ARGO results. Members will have exclusive access to the data

they generate for a period of 12 months, after which it will become available to all membersof the consortium. After 24 months, the data will be available to external parties.

Industry partners are welcome to be part of ICGC-ARGO. If organisations wish to

Industry Partners

contribute data, then they may also become ICGC Members as detailed above. For

132

A. V. Biankinprospective partners that will not contribute data, in-kind contributions of either $5 million

or $10 million US gives them Gold or Platinum Industry partner status, which includes

membership of Working Groups and select committees.

Andrew V. Biankin, BMedSc, MBBS(HONS), FRACS,FFS(RCPA), FRCS(Glasg), FRCS(Edin), PhD, FRSE,
FMedSci
2011

2013
2013

2016

2017

Specialist Consultant in Precision Oncology andTherapeutic Development
Regius Chair of Surgery, University of Glasgow
Director, Wolfson Wohl Cancer Research Centre,
University of Glasgow
Chairman, Precision-Panc Therapeutic Development
Platform
Executive Director, International Cancer Genome
Consortium

133

HIGH-THROUGHPUT ANNOTATION OF VARIANTS OFUNKNOWN SIGNIFICANCE

Hiroyuki Mano

Division of Cellular Signaling, National Cancer Center Research Institute5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan(hmano@ncc.go.jp)

Numerous variants of unknown significance (VUS) have been identified through large-

scale cancer genome projects, although their functional relevance remains uninvestigated.

The L858R substitution or exon 19 deletions are, for instance, known to confer activation of

epidermal growth factor receptor (EGFR) kinase, but there are more than one thousand of

nonsynonymous mutations within EGFR reported in the COSMIC database. It is still
obscure whether such mutations have some relevance to EGFR kinase activity or are

merely passenger mutations (Figure 1).

To drastically increase the speed of functional annotation, here we have developed amixed-all-nominated-mutants-in-one (MANO) method to evaluate the transforming

potential and drug sensitivity of oncogene VUS in a high-throughput manner. Each mutant

cDNA is stably integrated into the genome of assay cells (such as mouse 3T3 fibroblasts andan IL-3–dependent, murine pro-B cell line, Ba/F3) along with 6 bp-barcode sequences

Figure 1

More than 1,000 nonsynonymous mutations are reported for EGFR

134

H. Mano(Figure 2). Separately transduced assay cells are then pooled together, and cultured in acompetitive manner to evaluate their transforming potential or drug sensitivity in vitro orin vivo. At the end of the expansion period, genomic DNA is extracted from the assay cells

to PCR-amplify the barcode sequences that are then subjected to deep sequencing with theIllumina MiSeq platform in order to quantitate the relative abundance.

To validate its usefulness, we treated with various tyrosine kinase inhibitors (TKIs) apool of 16 Ba/F3 cells expressing active EGFR mutants (n = 11) or other oncoproteins (n =5). Whereas treatment with a cytotoxic compound, puromycin, induced uniform cell deathacross the cell clones, that with EGFR TKIs (gefitinib, erlotinib, afatinib, osimertinib androciletinib) resulted in the dose-dependent death of cells for five TKIs-sensitive EGFR
mutant (L858R, E746_A750 del, G719S, E861Q, and S768I) in the pool (Figure 3). As

expected, Ba/F3 expressing EGFR(T790M) was resistant against the first- and secondgeneration EGFR TKIs (gefitinib, erlotinib and afatinib), but sensitive to the third-

generation EGFR TKIs (osimeritinib and rociletinib). On the contrary, cells expressing
EGFR(T790M/C797S) were shown resistant to such third-generation TKIs. Similarly,

crizotinib, a TKI for ALK and ROS1, suppressed the growth of the cells expressing EML4-

ALK or CD74-ROS1, and another inhibitor for ALK and RET, alectinib, inhibited growth ofthe cells expressing EML4-ALK or KIF5B-RET.

Figure 2

The MANO method

Clinical Genomics and Cancer Genome Editing135

We further applied this method to 101 nonsynonymous EGFR mutants, and discovered anumber of mutations conferring resistance to EGFR TKI, including gefitinib- and erlotinib-

insensitive missense mutations within exon 19 and other gefitinib-resistant mutations suchas L833V, A839T, V851I, A871T, and G873E. Importantly, 12.8% of EGFR(L858R)-positive

tumors harbored compound mutations in the cis allele, which decrease the gefitinib
sensitivity of these tumors. The MANO method further revealed that some EGFR mutants
that are highly resistant to all types of TKIs are sensitive to cetuximab [1].

In addition, the MANO method was used to assess comprehensively the transforming

activities as well as drug sensitivities of ERBB2 mutations. We evaluated 55 nonsynonymous ERBB2 mutations which are reported recurrently in COSMIC database, anddiscovered several novel activating mutations which probably drive tumorigenesis.

Furthermore, ERBB2 mutations showed varying drug sensitivities to ERBB2-targeted

inhibitors. Thus, the MANO method may be a novel approach for assessing VUS, and our

findings in this study will be beneficial to deliver personalized medicine to cancer patients

harboring ERBB2 mutations [2]. Thus, these data support the importance of examining theclinical relevance of uncommon mutations within oncogenes and of evaluating thefunctions of such mutations in combination.

Figure 3

Proof-of-concept of the MANO method

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H. ManoReferences
1. Kohsaka S, Nagano M, Ueno T, Suehara Y, Hayashi T, Shimada N, Takahashi K, Suzuki
K, Takamochi K, Takahashi F, Mano H. A method of high-throughput functional evaluation of EGFR gene variants of unknown significance in cancer. Sci Transl Med 9,
eaan6556, 2017.
2. Nagano M, Kohsaka S, Ueno T, Kojima S, Saka K, Iwase H, Kawazu M, Mano H. Highthroughput functional evaluation of variants of unknown significance in ERBB2. Clin
Cancer Res, doi: 10.1158/1078-0432.CCR-18-0991, 2018.

Hiroyuki Mano, MD, PhD

1984

1986-1989
1989-1991

1991-1993

1993-2001

2001-20132013-20182016-present

2018-present

Graduated 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
Chief, Division of Cellular Signaling, Director,
National Cancer Center Research Institute
Director, Center for Cancer Genomics and Advanced
Therapeutics, National Cancer Center

137

CONCLUDING REMARKS

Tatsuhiro Shibata

Laboratory of Molecular Medicine
The Institute of Medical Science, The University of Tokyo (IMSUT)
4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan(tshibata@ims.u-tokyo.ac.jp)
Division of Cancer Genomics
National Cancer Center Research Institute5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan(tashibat@ncc.go.jp)
The 49th International Symposium of the Princess Takamatsu Cancer Research Fund,

entitled “Deciphering, Simulating and Editing of the Cancer Genome”, focused on thecurrent status of cancer genomics, data informatics, and the potential application of gene

editing technology in the cancer research area. Since cancer is a disease of the genome,

exploration of somatic and germline genetic and epigenetic alterations in the cancer

genome is fundamental and essential for the diagnosis, treatment and prevention of cancer.

Here, 30 speakers, who are experts in cancer genomics, tumor heterogeneity, or gene

editing, and 196 participants have joined together and discussed the current status andfuture directions in this research area. In particular, this meeting aimed to accomplish thefollowing three purposes: Understanding and sharing information on the current cuttingedge developments in the field of cancer genomics, Promoting communication, interactions
and future collaborations among participants, especially young Japanese scientists andspeakers from abroad, and Discussion about the future of cancer genomics.

In this meeting, a wide range of topics related to cancer genome research have been

intensively discussed, including coding and non-coding cancer driver gene landscapes,

genetic changes in precancerous lesions, germline contribution, mutational signatures incarcinogenesis processes, international and trans-ethnic-scale cancer genome projects,

analytical informatics, immunogenomics, deciphering tumor genome heterogeneity at thesingle cell level, and application of gene editing for cancer research. Hereafter, several

topics are highlighted.

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T. Shibata

Large-scale collections of cancer genomes by the International Cancer Genome

Consortium (ICGC) and The Cancer Genome Atlas (TCGA) has now enabled inference ofthe saturating list of cancer driver genes, especially in the coding genome. Most cancer
types have long-tailed lists of driver genes with a minority of frequently altered ones andmany rare ones. Functional annotations of these cancer drivers enable identification of

essential and therapeutically targetable molecules, and these efforts have led to theapplication of cancer genome sequencing in the clinic. To further promote this, aninternational consortium, called ICGC-ARGO, is planning to collect and share more suchdetailed clinical information together with cancer genome information. On the other hand,driver events in the non-coding genome, such as promoter mutations and, more frequently,

structural alterations that affect regulatory regions, such as enhancers, remain to be well

explored, and hence, are likely to be some of the hottest fields in future research.

Recent multi-region sequencings of cancer tissues have uncovered clonal evolution

processes during carcinogenesis. In addition to the natural selection of the fittest clones,

neutral evolution or branched evolution in cancers have also been reported, which lead togenetic heterogeneity in the cancer cell population. Recent advances in single-cell genomics

have enabled exploration of the detailed processes and status of intra-tumor heterogeneity.

Accumulation of clonal and subclonal mutations during tumor evolution naturally

generates non-self-antigens, called neo-antigens, which are targets of host immune

surveillance. Consequently, cancer cells would evade immune cell attack through multiple

ways, including by activating immune checkpoint molecules, such as PD-L1, orinactivating the antigen presenting system. These are new types of cancer driver genes andhave been intensively discussed together with co-existing cancer-immune
microenvironments.

Cancer is also an accumulative process of genetic alterations. Large-scale cancer genome

data has elucidated that there exist characteristic patterns of these processes. In the case ofsomatic substitutions, more than 40 mutational patterns, called mutational signatures, havebeen currently identified in human cancer genomes, and, importantly, these signatures

mirror unique carcinogenesis processes, such as exposure to carcinogens (smoking,Aflatoxins, etc.) or DNA repair deficiencies. Understanding and elucidating mutational

processes would lead to efficient cancer prevention. Mutational processes also operate inprecancerous lesions, and deep sequencing of precancerous lesions in the liver haveuncovered unique mutational processes, patchy clonal structures, and non-random virus

genome integrations. In the near future, more detailed genomic views of precancerous

lesions will be uncovered, which will contribute to advancing our understanding of theprocesses involved in human carcinogenesis.

Since key scientists in the field of cancer genomics have participated in this meeting, weConcluding Remarks

139

circulated a questionnaire on the future directions of this research field, which was
generated following a discussion with Ms. Potenski, a senior editor of the Nature Genetics

journal. Many valuable suggestions and opinions were voiced. As an example, responses tothe question ‘What topics/challenges in the cancer genomics field will be important in thenext 2-5 years?’ included: deciphering the complete structure of cancer genomes and theimpact of non-coding elements (Aneuploidy, structural alterations, regulatory elements),

determining how cancer genomes are different from those of benign neoplasms, genetic

changes and clonal structure of precancerous tissue, understanding the degree of plasticity

of cancer genomes and their impact on cancer evolution, understanding the genomic

characteristics of the immune response to cancer, early detection and interception of cancer,

interplay between germline variants, somatic alterations and lineage, cancer epigenome,

and artificial intelligence for supporting clinical decision making, some of which have beenfurther discussed in this meeting.

At this meeting, we also introduced the system of asking participants to submit adocument pledging their non-disclosure of confidential data that was discussed during thismeeting, which would promote the presentation of unpublished data by speakers. This is

the first trial of this kind of a disclosure, but we believe that this should be continued insubsequent meetings.

Finally, I would like to express my sincere appreciation to the co-organizers of thismeeting, Drs. Matthew Meyerson, Peter Campbell and Hiroyuki Aburatani, for theirconception of the meeting sessions and selection of speakers, and all the speakers andparticipants for the active and fruitful discussions, all of which have greatly contributed tothe success of the meeting. I also thank the general secretary of the meeting, Dr. Shinichi

Yachida, staffs from the National Cancer Center, Osaka University, The University ofTokyo, and the secretarial office of the Princess Takamatsu Cancer Research Fund for theirefficient management. I hope that this meeting will promote future international

collaborations and help young cancer scientists to join this very exciting field.