bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 5310: Chromatin accessibility landscape and transcriptome of castration resistant prostate cancers reveals novel subtypes and diverse master regulators
Authors: Tang Fanying , Wong Chen Khuan , Cohen Sandra , Lee Cindy , Liu Minwei , Bareja Rohan , Eng Kenneth , Beg Shaham , Puca Loredana , Sternberg Cora , Mosquera Juan Miguel , Beltran Himisha , Sboner Andrea , Chen Yu , Khurana Ekta 
Published: September 2020

Abstract
Castration-resistant prostate cancer (CRPC) is a heterogeneous disease with diverse drivers and mechanisms of resistance to androgen receptor (AR) therapy.
We generated ATAC-seq and RNA-seq data for twenty-four metastatic human prostate cancer organoids and cell lines. Integration of chromatin accessibility profiles and transcriptomes revealed four subtypes: androgen-receptor(AR)-dependent, neuroendocrine, Wnt-dependent and epithelial mesenchymal transition (EMT). The transcriptomic signatures obtained from these four subtypes enable the classification of 100 metastatic prostate cancer patient samples from Institute Precision Medicine (IPM) and 270 published samples from SU2C study, revealing potential therapeutic vulnerabilities. Furthermore, using novel computational algorithms we constructed regulatory networks and identified the master regulators of each subtype. Currently we're carrying out western blot and quantitative PCR to confirm the subtypes of all prostate cancer models we use, and using drug sensitivity test, CRISPR knockout and cell competition assay to validate the functions of candidates in each subtype.
Our study has characterized global chromatin accessibility landscape and transcriptome in the largest number of metastatic prostate cancer models, which revealed novel subtypes and corresponding tumor drivers. Collectively, these organoids, cell lines and matching sequence data provide a resource to the community to study various CRPC models. The molecular classification and corresponding master regulators reveal new drug targets and could potentially guide future therapeutic studies.
Citation Format: Fanying Tang, Chen Khuan Wong, Sandra Cohen, Cindy Lee, Minwei Liu, Rohan Bareja, Kenneth Eng, Shaham Beg, Loredana Puca, Cora Sternberg, Juan Miguel Mosquera, Himisha Beltran, Andrea Sboner, Yu Chen, Ekta Khurana. Chromatin accessibility landscape and transcriptome of castration resistant prostate cancers reveals novel subtypes and diverse master regulators [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5310.
Abstract 5323: Chemical inhibitors of quiescin sulfhydryl oxidase (QSOX1) suppress tumor invasion and metastasis
Authors: Fifield Amber , Ho Thai H. , Faigel Douglas O. , Svarovsky Sergei , Sergienko Eduard , Chung Thomas C. , Lake Douglas F. 
Published: September 2020

Abstract
Purpose: Quiescin Sulfhydryl Oxidase 1 (QSOX1) is an enzyme over-expressed by tumor cells. Our group and others have shown that QSOX1 is involved in invasion and metastasis. We previously reported that a small molecule, SBI-183, inhibited the enzymatic activity of QSOX1 and suppressed tumor cell invasion in vitro and metastasis of MDA-MB-231 breast tumor cells in vivo. In our current work we tested SPX-009, an analog of SBI-183 with increased potency, to suppress invasion and metastasis in vitro and in vivo.
Methods: Commercially available analogs for SBI-183 were screened in a cell-free fluorescent screening assay for the ability to inhibit QSOX1. Analog compounds that inhibited QSOX1 in a concentration-dependent manner were then tested for the ability to inhibit growth and invasion of triple negative breast cancer (TNBC) cells (MDA-MB-231), sarcomatoid kidney cancer cells (RCJ-41T2) and pancreatic adenocarcinoma cells (MIAPaCa2). MTT assays were used to measure the effect of the compounds on tumor growth. 2D invasion assays and 3D tumor spheroid assays were employed to measure the effect of the analogs on invasion. One potent analog (SPX-009) was evaluated for the ability to suppress tumor growth and metastasis in mice bearing MDA-MB-231-luc breast cancer orthotopic xenografts.
Results: Three of 53 analogs inhibited the enzymatic activity of QSOX1 at levels similar to the parent compound. However, one of those compounds, SPX-009, suppressed invasion of breast, kidney and pancreas tumor cells in 2D and 3D invasion assays at &gt;10-fold lower concentrations than the parent compound (SBI-183). In a non-metastatic xenograft of kidney cancer SBI-183 suppressed primary kidney tumor growth by 51%, but in a metastatic xenograft breast cancer model (MDA-MB-231-luc TNBC), SBI-183 reduced lung metastasis by 76% compared to control. Human-mouse xenograft studies are ongoing with the more potent analog (SPX-009) and will also be presented.
Conclusions: Chemical inhibitors of QSOX1 have a minor effect on tumor cell viability, but a major effect on tumor cell invasion in vitro and metastasis in vivo. This finding suggests that combining a QSOX1 inhibitor with a cytotoxic agent might provide a means to inhibit metastasis of cells that are resistant to cytotoxic agents.
Citation Format: Amber Fifield, Thai H. Ho, Douglas O. Faigel, Sergei Svarovsky, Eduard Sergienko, Thomas C. Chung, Douglas F. Lake. Chemical inhibitors of quiescin sulfhydryl oxidase (QSOX1) suppress tumor invasion and metastasis [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5323.
Abstract 5337: Analysis of drug combinations with the PKC inhibitor IDE196 support dual MEK and PKC inhibition as a rational combination in metastatic uveal melanoma
Authors: Frey Christian R. , Wagle Marie-Claire , Vaidya Kedar , Hambleton Julie , Lackner Mark , Mounir Zineb 
Published: September 2020

Abstract
Uveal melanoma (UM) is the most common primary intraocular tumor. The majority (&gt;90%) of uveal melanoma harbor activating mutations in the GNAQ and GNA11 Ga subunits of G protein coupled receptors. These genetic driver mutations, which are also detected in other solid tumors, lead to continuous activation of protein kinase C (PKC) and downstream signaling pathways such as the mitogen-activated protein kinase (MAPK) signaling pathway. Inhibition of PKC with small molecule inhibitors is an attractive therapeutic approach in UM. IDE196 is a selective and potent protein kinase C inhibitor targeting the novel (δ, ϵ, η, θ) and classical (α, β) PKC isoforms and has shown anti-tumor activity as monotherapy in patients with metastatic uveal melanoma (MUM). Inhibiting downstream signaling pathways in addition to PKC inhibition with IDE196 may increase the anti-tumor activity in MUM patients. To address this question, we assessed pathway-focused drug combination partners for their potential to exert synergy with IDE196. In this study, we evaluated baseline-expression of different PKC isoforms in uveal melanoma or control cell lines and the response to PKC inhibition with IDE196 by western blotting (WB). We measured the sensitivity to several drug combinations utilizing a concentration matrix in a 3-day cell titer glow assay to measure cell viability and determined synergy of drug combinations. Modulation of PKC, MAPK pathway activity and other downstream markers were assessed by WB. The evaluated uveal melanoma cell lines expressed different phospho-PKC isoforms and were sensitive to increasing concentrations of IDE196 as demonstrated by decreased levels of PKC substrate p-MARCKS and reduced cell viability. Furthermore, we did not detect synergy of IDE196 with cyclin-dependent kinase (CDK) 4/6 inhibitors Palbociclib or Ribociclib in viability assay. In contrast, combination of IDE196 with MEK inhibitors Binimetinib or Trametinib showed synergy at clinically relevant doses for each compound. The observed synergy of IDE196 with MEK inhibitors is further substantiated by modulation of MARCKS and MAPK pathway activity as measured by WB. Additional work is underway in in vitro as well as in vivo models to clarify the potential benefit of combining IDE196 with MEK inhibitors in the clinic.
Citation Format: Christian R. Frey, Marie-Claire Wagle, Kedar Vaidya, Julie Hambleton, Mark Lackner, Zineb Mounir. Analysis of drug combinations with the PKC inhibitor IDE196 support dual MEK and PKC inhibition as a rational combination in metastatic uveal melanoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5337.
Abstract 5373: Circulating tumor cell as a predictive marker for immunotherapy in advnaced non-small cell lung cancer
Authors: Park Cheol-Kyu , Yoon Joon-Young , Kim Min-Suk , Koh Bo-Gun , Cho Hyun-Ju , Oh In-Jae , Kim Young-Chul 
Published: September 2020

Abstract
Background: Circulating tumor cells (CTCs) have potential to provide minimally invasive way for the response monitoring of various cancer. This study aimed to investigate the feasibility of CTCs as a predictive marker for anti-PD-L1 immunotherapy in patients with advanced non-small cell lung cancer (NSCLC).
Methods: This study included 37 patients with advanced NSCLC receiving atezolizumab monotherapy as second-line or more from July to November 2019. Blood was collected in a K2-EDTA tube and CTCs were isolated and enriched by using CD-PRIMETM system which is antibody-independent size-based isolation method. By Bioview CCBS system, total CTCs (tCTCs) were identified by a sum of single EpCAM/CK-positive (sCTCs) and double EpCAM/CK-CD45-positive cells (dCTCs).
Results: Among 26 response-evaluable patients, objective response rate (ORR) and disease control rate (DCR) were 11.5% (3/26) and 57.7% (15/26), respectively. At cycle 1 (C1), patients with progression (PD) showed higher sCTC count (median, 8.6 vs. 4.3, p=0.065) and sCTC-to-tCTC ratio (median %, 35.9 vs. 9.8, p=0.024) than those without PD. During treatment, tCTC and sCTC count showed significant increase from C1 to C2 (p=0.019 and p=0.022). The subgroup with high sCTC increase from C1 to C2 (more than 2-folds) showed higher DCR than subgroup with low sCTC increase (less than 2-folds) (88.9% vs. 44.4%, p=0.066). Patients with high sCTC-to-tCTC ratio at C1 showed worse progression-free survival (median, 2.1 months) than those with low sCTC-to-tCTC ratio (not reached, hazard ratio 2.87, 95% confidence interval: 1.06-7.75, p=0.082).
Conclusion: The baseline sCTC count and sCTC increase (more than 2-folds) at the first 3-week after atezolizumab could be potential biomarkers to predict the disease progression and poor survival in advanced NSCLC.
Citation Format: Cheol-Kyu Park, Joon-Young Yoon, Min-Suk Kim, Bo-Gun Koh, Hyun-Ju Cho, In-Jae Oh, Young-Chul Kim. Circulating tumor cell as a predictive marker for immunotherapy in advnaced non-small cell lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5373.
Abstract 5420: Multiomics approach identifies the SP8-FGF8 axis as an important promoter of metastatic features in hepatoblastoma
Authors: Wagner Alexandra , Häberle Beate , Schmid Irene , Vokuhl Christian , von Schweinitz Dietrich , Kappler Roland 
Published: September 2020

Abstract
Introduction
Hepatoblastoma (HB) is the most common pediatric liver tumor with a general good prognosis, but if associated with multifocal growth, vessel invasion or metastasis, outcome is still poor. The molecular mechanisms causing this high-risk profile are still unknown. The aim of our study was to gain insight into the neoplastic continuation towards more aggressive tumor phenotypes and to aid identification of biomarkers for high-risk patients.
Experimental procedures
Tumor specimens of seven non-metastasized and four metastasized HB patients were analyzed by RNA-sequencing and the Human Methylation 450 BeadChip microarray to assess global expression and methylation levels. Integration of both datasets identified new candidate genes, which were further verified in a cohort of 34 HB patients using real-time PCR. Gain and loss of function experiments of SP8 were conducted in five HB cell lines to evaluate its effects on proliferation, mobility, self renewal, migration and invasion. Chromatin-immunoprecipitation was used to assess binding of SP8 to the FGF8 promoter. Kaplan-Meier estimates of specific survival time in various groups were compared using the log-rank Mantel-Cox test.
Results
Global integration of expression and methylation levels identified the transcription factor SP8 and the fibroblast growth factor FGF8 as significantly upregulated genes accompanied with strong methylation changes in metastatic HB. In an independent cohort of 34 HB patients expression of both candidates was highly correlated (R² = 0.74, P &lt; 0.0001) and could be positively associated with metastasis, the aggressive C2 subtype of the 16-gene signature and poor overall survival. Chromatin-immunoprecipitation identified binding of SP8 to the FGF8 promoter. Following SP8 overexpression in liver tumor cells, we found a significant increase in cell motility, self renewal and invasive growth together with the acquisition of a mesenchymal phenotype. In turn, CRISPRi-mediated knockdown of FGF8 rescued the phenotype.
Conclusions
We showed that elevated levels of SP8 induced cell motility, self renewal and mesenchymal characteristics by targeting FGF8, thereby implicating an important functional role of the SP8-FGF8 axis for the progression and metastasis of malignant childhood liver tumors. Moreover, SP8 might serve as a new biomarker in metastatic HB with poor prognosis.
Citation Format: Alexandra Wagner, Beate Häberle, Irene Schmid, Christian Vokuhl, Dietrich von Schweinitz, Roland Kappler. Multiomics approach identifies the SP8-FGF8 axis as an important promoter of metastatic features in hepatoblastoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5420.
Abstract 5483: An image-based mechanistic computational model for early prediction of organ-confined untreated prostate cancer growth
Authors: Lorenzo Guillermo , Hughes Thomas J. , Reali Alessandro , Gomez Hector , Yankeelov Thomas E. 
Published: September 2020

Abstract
According to the American Cancer Society, prostate cancer (PCa) is the most common newly-diagnosed cancer and the second leading cancer-related cause of death among men in the US in 2019. The current clinical protocols of PCa are based on two key strategies: regular screening of men over fifty and patient triaging in risk groups. Prostatic tumors are usually detected at an early organ-confined stage, which poses a low to intermediate risk to the patient and may not produce any symptoms or require treatment for long time. However, most patients are prescribed a radical treatment immediately after diagnosis (e.g., surgery or radiation therapy), which may adversely impact their lives without necessarily prolonging their longevity or quality of life. Moreover, some patients who initially delay their treatment ultimately succumb to PCa due to an inaccurate initial diagnosis. Thus, while regular screening enables the detection of the majority of tumors at an early and mild stage, the limited individualization of patient monitoring and treatment has led to significant rates of both overtreatment and undertreatment.
To overcome these fundamental limitations in the clinical management of PCa, we propose the use of a mechanistic mathematical model for which we can perform computer simulations to forecast patient-specific tumor growth. This computational model is based on a set of partial differential equations that describe the main mechanisms involved in organ-confined PCa growth. The model is parameterized using longitudinal multiparametric magnetic resonance (MR) images and the available clinical data for each patient. Tumor growth is simulated over the patient's prostate extracted from T2-weighted MR images. We use isogeometric analysis to accurately and efficiently address the computational challenges arising in this application.
Our preliminary simulation results show that our computational technology can predict tumor growth and associated serum Prostate Specific Antigen (PSA, a key biomarker in clinical management of PCa) with reasonable accuracy. We also explore the potential of model parameters and variables to characterize tumor aggressivity. Thus, we believe that our imaging-based modeling approach could be a promising tool capable of being implemented in current PCa protocols to assist physicians in the clinical management of PCa.
Citation Format: Guillermo Lorenzo, Thomas J. Hughes, Alessandro Reali, Hector Gomez, Thomas E. Yankeelov. An image-based mechanistic computational model for early prediction of organ-confined untreated prostate cancer growth [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5483.
Abstract 5488: Prediction of target receptor occupancy for ALX148, a CD47 blocker, using mechanistic PK/RO modeling
Authors: Vasileva Elena , Demin Oleg 
Published: September 2020

Abstract
Background: ALX148 is one of the anti-CD47 antibodies that are currently in clinical development. Understanding the target receptor occupancy (RO) in tumor can help with dose selection for ALX148 and other anti-CD47 antibodies. However, RO can be challenging to determine through direct experimental measurements. A mechanistic PK/RO model was developed to predict CD47 occupancy in tumor tissues and periphery for ALX148 based on its properties (PK, structure, and affinity).
Methods: Developed mechanistic PK/RO model describes PK of ALX148 in blood plasma, distribution to tumor tissues of Non-Hodgkin lymphoma (lymph nodes, spleen), non-linear clearance of ALX148 due to the binding with CD47 and further complex internalization. RO was described on red blood cells and tumor cells. The model takes into account CD47 expression (molecules per cell) and a number of cells expressing CD47. Parameters were fitted against clinical PK data. Clinical data on RO in the periphery was used for model validation.
Results: The model successfully described ALX148 clinical data on PK and RO in blood plasma. Concentrations in lymph nodes and spleen were predicted to be similar and in approximately 3 times lower than in blood plasma. Such high concentration in tumor tissues is caused by small size of ALX148 (molecular weight is 78 kDa). Dose and regimen chosen for combination treatment (10 mg/kg QW) result in 90-95% trough steady-state RO in tumor tissues. The model predicted that trough steady-state RO in lymph nodes and spleen was more than 95% after administration of maximal dose 30 mg/kg Q2W.
Conclusions: A mechanistic PK/RO model was built to capture ALX148 clinical PK data and then predicted more than 90% RO at the dose used in combination treatment cohort. This approach has the potential to be used to inform dose selection for other anti-CD47 antibodies taking into account their specific features as binding properties, size, etc.
Citation Format: Elena Vasileva, Oleg Demin Jr. Prediction of target receptor occupancy for ALX148, a CD47 blocker, using mechanistic PK/RO modeling [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5488.
Abstract 5490: Scales and dynamics of intratumor heterogeneity
Authors: Ferrall-Fairbanks Meghan C. , Kimmel Gregory J. , Altrock Philipp M. 
Published: September 2020

Abstract
Current dogma stipulates that more heterogenous tumor cell populations results from compounding genetic and epigenetic changes and instability, ultimately driving unfavorable outcomes for these patients. Nonetheless, some cancers, including many pediatric cancers and some leukemias, have limited genomic diversity. As a result, we have a limited number of biomarkers available to stratify patients into high versus low-risk groups by their mutational cellular diversity or heterogeneity. For these cases, we have developed a method to quantify intratumor heterogeneity, for example at the transcriptomic level, using a generalized diversity index (GDI) from ecology. This index allows control of a scaling parameter called the order of diversity, q, to tailor the scale or resolution of the cellular diversity to a given context. However, the question remains, what role does the order of diversity play in the context of relative tumor age or disease stage? We show that the order of diversity parameter allows to either emphasize clonal richness (at low values of q), while high values shift the analysis toward the abundance of potential drivers of the tumor evolution. To gain deeper quantitative insight, we use evolutionary dynamics approaches that model the dynamics of heterogeneous cell populations over time, to understand how a patient's tumor diversity could emerge, persist, and change over time. Applying the replicator-mutator dynamics framework, we explore the effects and magnitude of frequency-dependent (positive or negative) selection on GDI. These analyses suggest that GDI often peaks at intermediate time points and, when run over a sufficiently long timescale, they reduce as the tumor population presumable becomes clonally dominant or otherwise stable before a major transition (e.g. metastasis). We explore GDI changes over time further in vitro in sequential single-cell RNA sequencing samples of fused breast cancer cells, where at earlier passages after fusion, the GDI peaks, and at later passages after fusion the GDI returns to levels similar to that of the parental breast cancer cell lines. Overall, GDI, as a means to quantify intratumor heterogeneity at select time points, can be a powerful tool to stratify high-risk patients. Our analyses show how computational analyses and mathematical modeling tools can be coupled with clinical and experimental data to understand eco-evolutionary dynamics of a patient's tumor.
Citation Format: Meghan C. Ferrall-Fairbanks, Gregory J. Kimmel, Philipp M. Altrock. Scales and dynamics of intratumor heterogeneity [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5490.