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1. Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation

Author

Wubing Zhang, Shourya S. Roy Burman, Jiaye Chen, Katherine A. Donovan, Yang Cao, Chelsea Shu, Boning Zhang, Zexian Zeng, Shengqing Gu, Yi Zhang, Dian Li, Eric S. Fischer, Collin Tokheim, and X. Shirley Liu

Subjects
Targeted protein degradation, Degradability, Protein-intrinsic feature, Ubiquitination, Machine learning, Biology (General), QH301-705.5, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell’s endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by TPD approaches, termed “degradability”, is largely unknown. Here, we developed a machine learning model, model-free analysis of protein degradability (MAPD), to predict degradability from features intrinsic to protein targets. MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds [with an area under the precision–recall curve (AUPRC) of 0.759 and an area under the receiver operating characteristic curve (AUROC) of 0.775] and is likely generalizable to independent non-kinase proteins. We found five features with statistical significance to achieve optimal prediction, with ubiquitination potential being the most predictive. By structural modeling, we found that E2-accessible ubiquitination sites, but not lysine residues in general, are particularly associated with kinase degradability. Finally, we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins (including proteins encoded by 278 cancer genes) that may be tractable to TPD drug development.

Published
2022
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2. Genetic fusions favor tumorigenesis through degron loss in oncogenes

Author

Jing Liu, Collin Tokheim, Jonathan D. Lee, Wenjian Gan, Brian J. North, X. Shirley Liu, Pier Paolo Pandolfi, and Wenyi Wei

Subjects
Science
Abstract

The impact of genetic fusions on degrons, which are motifs for ubiquitin-mediated protein degradation, has not been fully explored. Here, the authors analyse fusion genes affecting degrons in pan-cancer genomics data, validate their functional impact and find enrichment for both internal and C-terminal degron losses.

Published
2021
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3. Clonal tracing reveals diverse patterns of response to immune checkpoint blockade

Author

Shengqing Stan Gu, Xiaoqing Wang, Xihao Hu, Peng Jiang, Ziyi Li, Nicole Traugh, Xia Bu, Qin Tang, Chenfei Wang, Zexian Zeng, Jingxin Fu, Cliff Meyer, Yi Zhang, Paloma Cejas, Klothilda Lim, Jin Wang, Wubing Zhang, Collin Tokheim, Avinash Das Sahu, Xiaofang Xing, Benjamin Kroger, Zhangyi Ouyang, Henry Long, Gordon J. Freeman, Myles Brown, and X. Shirley Liu

Subjects
Clonal tracing, Immune checkpoint blockade, Heterogeneity, Tumor microenvironment, Mathematical modeling, Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

Abstract Background Immune checkpoint blockade (ICB) therapy has improved patient survival in a variety of cancers, but only a minority of cancer patients respond. Multiple studies have sought to identify general biomarkers of ICB response, but elucidating the molecular and cellular drivers of resistance for individual tumors remains challenging. We sought to determine whether a tumor with defined genetic background exhibits a stereotypic or heterogeneous response to ICB treatment. Results We establish a unique mouse system that utilizes clonal tracing and mathematical modeling to monitor the growth of each cancer clone, as well as the bulk tumor, in response to ICB. We find that tumors derived from the same clonal populations showed heterogeneous ICB response and diverse response patterns. Primary response is associated with higher immune infiltration and leads to enrichment of pre-existing ICB-resistant cancer clones. We further identify several cancer cell-intrinsic gene expression signatures associated with ICB resistance, including increased interferon response genes and glucocorticoid response genes. These findings are supported by clinical data from ICB treatment cohorts. Conclusions Our study demonstrates diverse response patterns from the same ancestor cancer cells in response to ICB. This suggests the value of monitoring clonal constitution and tumor microenvironment over time to optimize ICB response and to design new combination therapies. Furthermore, as ICB response may enrich for cancer cell-intrinsic resistance signatures, this can affect interpretations of tumor RNA-seq data for response-signature association studies.

Published
2020
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4. Determinants of transcription factor regulatory range

Author

Chen-Hao Chen, Rongbin Zheng, Collin Tokheim, Xin Dong, Jingyu Fan, Changxin Wan, Qin Tang, Myles Brown, Jun S. Liu, Clifford A. Meyer, and X. Shirley Liu

Subjects
Science
Abstract

Characterization of the distance over which TF binding influences gene expression is important for inferring target genes. Here the authors study this relationship using thousands of genomic data sets, finding two classes of TFs with distinct ranges of regulatory influence modulated by chromatin states of topologically associated domains.

Published
2020
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5. 700 Increasing MHC-I expression to potentiate immune checkpoint blockade therapy

Author

Peng Jiang, Xia Bu, Michael Manos, Ana Lako, Scott Rodig, Yingtian Xie, Shengqing Gu, Henry Long, Paloma Cejas, Gordon Freeman, Wubing Zhang, Xiaoqing Wang, Nicole Traugh, Ziyi Li, Clifford Meyer, Blair Stewig, Alba Font-Tello, Klothilda Lim, Jake Conway, Alok Tewari, Zexian Zeng, Avinash Das Sahu, Collin Tokheim, Jason Weirather, Jingxin Fu, Benjamin Kroger, Jin Hua Liang, Benjamin Gewurz, F. Stephen Hodi, Myles Brown, and X Shirley Liu

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Published
2021
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6. PrimerSeq: Design and Visualization of RT-PCR Primers for Alternative Splicing Using RNA-seq Data

Author

Collin Tokheim, Juw Won Park, and Yi Xing

Subjects
Alternative splicing, RNA-seq, Primer design, Transcriptome, Visualization, Biology (General), QH301-705.5, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

The vast majority of multi-exon genes in higher eukaryotes are alternatively spliced and changes in alternative splicing (AS) can impact gene function or cause disease. High-throughput RNA sequencing (RNA-seq) has become a powerful technology for transcriptome-wide analysis of AS, but RT-PCR still remains the gold-standard approach for quantifying and validating exon splicing levels. We have developed PrimerSeq, a user-friendly software for systematic design and visualization of RT-PCR primers using RNA-seq data. PrimerSeq incorporates user-provided transcriptome profiles (i.e., RNA-seq data) in the design process, and is particularly useful for large-scale quantitative analysis of AS events discovered from RNA-seq experiments. PrimerSeq features a graphical user interface (GUI) that displays the RNA-seq data juxtaposed with the expected RT-PCR results. To enable primer design and visualization on user-provided RNA-seq data and transcript annotations, we have developed PrimerSeq as a stand-alone software that runs on local computers. PrimerSeq is freely available for Windows and Mac OS X along with source code at http://primerseq.sourceforge.net/. With the growing popularity of RNA-seq for transcriptome studies, we expect PrimerSeq to help bridge the gap between high-throughput RNA-seq discovery of AS events and molecular analysis of candidate events by RT-PCR.

Published
2014
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7. Discovery of Targets for Immune–Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha

Author

Avinash Sahu, Xiaoman Wang, Phillip Munson, Jan P.G. Klomp, Xiaoqing Wang, Shengqing Stan Gu, Ya Han, Gege Qian, Phillip Nicol, Zexian Zeng, Chenfei Wang, Collin Tokheim, Wubing Zhang, Jingxin Fu, Jin Wang, Nishanth Ulhas Nair, Joost A.P. Rens, Meriem Bourajjaj, Bas Jansen, Inge Leenders, Jaap Lemmers, Mark Musters, Sanne van Zanten, Laura van Zelst, Jenny Worthington, Jun S. Liu, Dejan Juric, Clifford A. Meyer, Arthur Oubrie, X. Shirley Liu, David E. Fisher, and Keith T. Flaherty

Subjects
Oncology
Abstract

Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefits. Here, we first developed an in silico multiomics approach (BipotentR) to find cancer cell–specific regulators that simultaneously modulate tumor immunity and another oncogenic pathway and then used it to identify 38 candidate immune–metabolic regulators. We show the tumor activities of these regulators stratify patients with melanoma by their response to anti–PD-1 using machine learning and deep neural approaches, which improve the predictive power of current biomarkers. The topmost identified regulator, ESRRA, is activated in immunotherapy-resistant tumors. Its inhibition killed tumors by suppressing energy metabolism and activating two immune mechanisms: (i) cytokine induction, causing proinflammatory macrophage polarization, and (ii) antigen-presentation stimulation, recruiting CD8+ T cells into tumors. We also demonstrate a wide utility of BipotentR by applying it to angiogenesis and growth suppressor evasion pathways. BipotentR (http://bipotentr.dfci.harvard.edu/) provides a resource for evaluating patient response and discovering drug targets that act simultaneously through multiple mechanisms.Significance:BipotentR presents resources for evaluating patient response and identifying targets for drugs that can kill tumors through multiple mechanisms concurrently. Inhibition of the topmost candidate target killed tumors by suppressing energy metabolism and effects on two immune mechanisms.This article is highlighted in the In This Issue feature, p. 517

Published
2023

8. Figure S11 from Discovery of Targets for Immune–Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha

Author

Keith T. Flaherty, David E. Fisher, X. Shirley Liu, Arthur Oubrie, Clifford A. Meyer, Dejan Juric, Jun S. Liu, Jenny Worthington, Laura van Zelst, Sanne van Zanten, Mark Musters, Jaap Lemmers, Inge Leenders, Bas Jansen, Meriem Bourajjaj, Joost A.P. Rens, Nishanth Ulhas Nair, Jin Wang, Jingxin Fu, Wubing Zhang, Collin Tokheim, Chenfei Wang, Zexian Zeng, Phillip Nicol, Gege Qian, Ya Han, Shengqing Stan Gu, Xiaoqing Wang, Jan P.G. Klomp, Phillip Munson, Xiaoman Wang, and Avinash Sahu

Abstract

Machine learning evaluation of immune-metabolic targets in predicting patient response to anti-PD1 and anti-CTLA4 combination therapy.

Published
2023

9. Data from High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets

Author

Rachel Karchin, Kymberleigh A. Pagel, Valsamo Anagnostou, Victor E. Velculescu, Angelika B. Riemer, Maria Bonsack, Ashton Omdahl, Benjamin Kaminow, Dylan Hirsch, Lily Zheng, Collin Tokheim, I.K. Ashok Sivakumar, Justin Huang, Rohit Bhattacharya, and Xiaoshan M. Shao

Abstract

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low predictive value for actual peptide presentation, inadequate support for rare MHC alleles, and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method that predicts peptide–MHC binding. MHCnuggets can predict binding for common or rare alleles of MHC class I or II with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is faster than other methods. When compared with methods that integrate binding affinity and MHC-bound peptide (HLAp) data from mass spectrometry, MHCnuggets yields a 4-fold increase in positive predictive value on independent HLAp data. We applied MHCnuggets to 26 cancer types in The Cancer Genome Atlas, processing 26.3 million allele–peptide comparisons in under 2.3 hours, yielding 101,326 unique predicted immunogenic missense mutations (IMM). Predicted IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration (P < 2 × 10−16), including CD8+ T cells. Only 0.16% of predicted IMMs were observed in more than 2 patients, with 61.7% of these derived from driver mutations. Thus, we describe a method for neoantigen prediction and its performance characteristics and demonstrate its utility in data sets representing multiple human cancers.

Published
2023

12. Data from Discovery of Targets for Immune–Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha

Author

Keith T. Flaherty, David E. Fisher, X. Shirley Liu, Arthur Oubrie, Clifford A. Meyer, Dejan Juric, Jun S. Liu, Jenny Worthington, Laura van Zelst, Sanne van Zanten, Mark Musters, Jaap Lemmers, Inge Leenders, Bas Jansen, Meriem Bourajjaj, Joost A.P. Rens, Nishanth Ulhas Nair, Jin Wang, Jingxin Fu, Wubing Zhang, Collin Tokheim, Chenfei Wang, Zexian Zeng, Phillip Nicol, Gege Qian, Ya Han, Shengqing Stan Gu, Xiaoqing Wang, Jan P.G. Klomp, Phillip Munson, Xiaoman Wang, and Avinash Sahu

Abstract

Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefits. Here, we first developed an in silico multiomics approach (BipotentR) to find cancer cell–specific regulators that simultaneously modulate tumor immunity and another oncogenic pathway and then used it to identify 38 candidate immune–metabolic regulators. We show the tumor activities of these regulators stratify patients with melanoma by their response to anti–PD-1 using machine learning and deep neural approaches, which improve the predictive power of current biomarkers. The topmost identified regulator, ESRRA, is activated in immunotherapy-resistant tumors. Its inhibition killed tumors by suppressing energy metabolism and activating two immune mechanisms: (i) cytokine induction, causing proinflammatory macrophage polarization, and (ii) antigen-presentation stimulation, recruiting CD8+ T cells into tumors. We also demonstrate a wide utility of BipotentR by applying it to angiogenesis and growth suppressor evasion pathways. BipotentR (http://bipotentr.dfci.harvard.edu/) provides a resource for evaluating patient response and discovering drug targets that act simultaneously through multiple mechanisms.Significance:BipotentR presents resources for evaluating patient response and identifying targets for drugs that can kill tumors through multiple mechanisms concurrently. Inhibition of the topmost candidate target killed tumors by suppressing energy metabolism and effects on two immune mechanisms.This article is highlighted in the In This Issue feature, p. 517

Published
2023

14. Supplementary Notes from Discovery of Targets for Immune–Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha

Author

Keith T. Flaherty, David E. Fisher, X. Shirley Liu, Arthur Oubrie, Clifford A. Meyer, Dejan Juric, Jun S. Liu, Jenny Worthington, Laura van Zelst, Sanne van Zanten, Mark Musters, Jaap Lemmers, Inge Leenders, Bas Jansen, Meriem Bourajjaj, Joost A.P. Rens, Nishanth Ulhas Nair, Jin Wang, Jingxin Fu, Wubing Zhang, Collin Tokheim, Chenfei Wang, Zexian Zeng, Phillip Nicol, Gege Qian, Ya Han, Shengqing Stan Gu, Xiaoqing Wang, Jan P.G. Klomp, Phillip Munson, Xiaoman Wang, and Avinash Sahu

Abstract

We detailed a comparison of BipotentR with competing and alternative approaches in Supplementary notes 1, 2, 6, and 7. Supplementary Note 3-5 describes the in vitro findings of ESRRA, the potential clinical relevance of ESRRA across cancer-types, and the association of tumor activity of ESRRA with immune infiltration.

Published
2023

15. Table S2 from Therapeutically Increasing MHC-I Expression Potentiates Immune Checkpoint Blockade

Author

X. Shirley Liu, Myles Brown, F. Stephen Hodi, Benjamin E. Gewurz, Henry W. Long, Scott Rodig, Gordon J. Freeman, Paloma Cejas, Jin Hua Liang, Benjamin Kroger, Yi Zhang, Jingxin Fu, Jason L. Weirather, Collin Tokheim, Avinash Das Sahu, Zexian Zeng, Alok K. Tewari, Jake Conway, Klothilda Lim, Ana Lako, Evisa Gjini, Alba Font-Tello, Michael P. Manos, Xia Bu, Yingtian Xie, Blair Stewig, Clifford Meyer, Ziyi Li, Nicole Traugh, Peng Jiang, Xiaoqing Wang, Wubing Zhang, and Shengqing Stan Gu

Abstract

RNA-seq and ATAC-seq of TRAF3-normal and -deficient B16F10 cells treated with vehicle control or IFNγ

Published
2023

16. Table S1 from Whole-Genome Sequencing of Salivary Gland Adenoid Cystic Carcinoma

Author

Nishant Agrawal, Patrick K. Ha, Bert Vogelstein, Kenneth W. Kinzler, Nickolas Papadopoulos, Carole Fakhry, Shizhang Ling, Michael Considine, Luigi Marchionni, Sarah J. Wheelan, Elana J. Fertig, Rachel Karchin, Noushin Niknafs, Collin Tokheim, Laura D. Wood, Justin A. Bishop, Sian Jones, Mark Sausen, C. Conover Talbot, and Eleni M. Rettig

Abstract

Summary of sequence analysis of adenoid cystic carcinoma.

Published
2023

17. Figure S2 from Whole-Genome Sequencing of Salivary Gland Adenoid Cystic Carcinoma

Author

Nishant Agrawal, Patrick K. Ha, Bert Vogelstein, Kenneth W. Kinzler, Nickolas Papadopoulos, Carole Fakhry, Shizhang Ling, Michael Considine, Luigi Marchionni, Sarah J. Wheelan, Elana J. Fertig, Rachel Karchin, Noushin Niknafs, Collin Tokheim, Laura D. Wood, Justin A. Bishop, Sian Jones, Mark Sausen, C. Conover Talbot, and Eleni M. Rettig

Abstract

Axonal guidance signaling pathway alterations in adenoid cystic carcinoma.

Published
2023

18. Data from Therapeutically Increasing MHC-I Expression Potentiates Immune Checkpoint Blockade

Author

X. Shirley Liu, Myles Brown, F. Stephen Hodi, Benjamin E. Gewurz, Henry W. Long, Scott Rodig, Gordon J. Freeman, Paloma Cejas, Jin Hua Liang, Benjamin Kroger, Yi Zhang, Jingxin Fu, Jason L. Weirather, Collin Tokheim, Avinash Das Sahu, Zexian Zeng, Alok K. Tewari, Jake Conway, Klothilda Lim, Ana Lako, Evisa Gjini, Alba Font-Tello, Michael P. Manos, Xia Bu, Yingtian Xie, Blair Stewig, Clifford Meyer, Ziyi Li, Nicole Traugh, Peng Jiang, Xiaoqing Wang, Wubing Zhang, and Shengqing Stan Gu

Abstract

Immune checkpoint blockade (ICB) therapy revolutionized cancer treatment, but many patients with impaired MHC-I expression remain refractory. Here, we combined FACS-based genome-wide CRISPR screens with a data-mining approach to identify drugs that can upregulate MHC-I without inducing PD-L1. CRISPR screening identified TRAF3, a suppressor of the NFκB pathway, as a negative regulator of MHC-I but not PD-L1. The Traf3-knockout gene expression signature is associated with better survival in ICB-naïve patients with cancer and better ICB response. We then screened for drugs with similar transcriptional effects as this signature and identified Second Mitochondria-derived Activator of Caspase (SMAC) mimetics. We experimentally validated that the SMAC mimetic birinapant upregulates MHC-I, sensitizes cancer cells to T cell–dependent killing, and adds to ICB efficacy. Our findings provide preclinical rationale for treating tumors expressing low MHC-I expression with SMAC mimetics to enhance sensitivity to immunotherapy. The approach used in this study can be generalized to identify other drugs that enhance immunotherapy efficacy.Significance:MHC-I loss or downregulation in cancer cells is a major mechanism of resistance to T cell–based immunotherapies. Our study reveals that birinapant may be used for patients with low baseline MHC-I to enhance ICB response. This represents promising immunotherapy opportunities given the biosafety profile of birinapant from multiple clinical trials.This article is highlighted in the In This Issue feature, p. 1307

Published
2023

19. Supplementary Figures from Therapeutically Increasing MHC-I Expression Potentiates Immune Checkpoint Blockade

Author

X. Shirley Liu, Myles Brown, F. Stephen Hodi, Benjamin E. Gewurz, Henry W. Long, Scott Rodig, Gordon J. Freeman, Paloma Cejas, Jin Hua Liang, Benjamin Kroger, Yi Zhang, Jingxin Fu, Jason L. Weirather, Collin Tokheim, Avinash Das Sahu, Zexian Zeng, Alok K. Tewari, Jake Conway, Klothilda Lim, Ana Lako, Evisa Gjini, Alba Font-Tello, Michael P. Manos, Xia Bu, Yingtian Xie, Blair Stewig, Clifford Meyer, Ziyi Li, Nicole Traugh, Peng Jiang, Xiaoqing Wang, Wubing Zhang, and Shengqing Stan Gu

Abstract

Supplementary Figures S1-S8

Published
2023

20. Figure S6 from The Genetic Evolution of Treatment-Resistant Cutaneous, Acral, and Uveal Melanomas

Author

Christine A. Iacobuzio-Donahue, Suzanne L. Topalian, Barry S. Taylor, Timothy A. Chan, David B. Solit, Nicholas D. Socci, Rachel Karchin, Paul B. Chapman, Michael A. Postow, Nadeem Riaz, Vladimir Makarov, Zachary Kohutek, Rajya Kappagantula, Priscilla Baez, Collin Tokheim, Akimasa Hayashi, Craig M. Bielski, Jungeui Hong, Amanda Zucker, Jody E. Hooper, Evan J. Lipson, and Alvin P. Makohon-Moore

Abstract

Figure S6 shows phylogenies based on point mutations with UV signature mutations removed.

Published
2023

22. Supplementary Tables 1-9 from Exome-Scale Discovery of Hotspot Mutation Regions in Human Cancer Using 3D Protein Structure

Author

Rachel Karchin, David L. Masica, Michael Ryan, Rick Kim, Derek M. Gygax, Noushin Niknafs, Rohit Bhattacharya, and Collin Tokheim

Abstract

Table S1: Hotspot regions identified by 3D HotMAPS and overlap with 1D HotMAPS algorithm. Table S2: Tumor-type specific hotspot regions identified by 3D HotMAPS, overlap with 1D HotMAPS, and charactersitic features with naïve bayes score. Table S3: Sample and coverage information for missense mutations mapping onto protein structures or models. Table S4: Genes with HotMAPS regions identified in TCGA tumor types without previous literature support. Table S5: Hotspot regions identified by the 1D algorithm only utilizing primary sequence. Table S6: Tumor-type specifc hotspot regions identified by the 1D algorithm only utilizing primary sequence. Table S7: Residues in 3D HotMAPS regions that overlap with UniProtKB annotations. Table S8: Contigency table of hotspot residues at protein-protein interfaces versus other mutated residues. Table S9: Blacklisted residues that have ambiguous mappings caused by tBLASTn.

Published
2023

23. Tables S1-S8 from The Genetic Evolution of Treatment-Resistant Cutaneous, Acral, and Uveal Melanomas

Author

Christine A. Iacobuzio-Donahue, Suzanne L. Topalian, Barry S. Taylor, Timothy A. Chan, David B. Solit, Nicholas D. Socci, Rachel Karchin, Paul B. Chapman, Michael A. Postow, Nadeem Riaz, Vladimir Makarov, Zachary Kohutek, Rajya Kappagantula, Priscilla Baez, Collin Tokheim, Akimasa Hayashi, Craig M. Bielski, Jungeui Hong, Amanda Zucker, Jody E. Hooper, Evan J. Lipson, and Alvin P. Makohon-Moore

Abstract

Supplementary Tables S1-S8 show the samples used for study (Table S1), the somatic single nucleotide variants, small insertions and small deletions (Table S2), the somatic copy number alterations anallyzed (Table S3), the gene list used to analyze somatic mutations (Table S4), putative driver genes, immunotherapy related genes, and mutation-targeted therapy genes affected by mutations in each case (Table S5), mutation signatures detected with a cosine score {greater than or equal to}0.80 (Table S6), Jaccard similarity coefficients (Table S7), and putative neoantigens in each case (Table S8).

Published
2023

24. Data from Exome-Scale Discovery of Hotspot Mutation Regions in Human Cancer Using 3D Protein Structure

Author

Rachel Karchin, David L. Masica, Michael Ryan, Rick Kim, Derek M. Gygax, Noushin Niknafs, Rohit Bhattacharya, and Collin Tokheim

Abstract

The impact of somatic missense mutation on cancer etiology and progression is often difficult to interpret. One common approach for assessing the contribution of missense mutations in carcinogenesis is to identify genes mutated with statistically nonrandom frequencies. Even given the large number of sequenced cancer samples currently available, this approach remains underpowered to detect drivers, particularly in less studied cancer types. Alternative statistical and bioinformatic approaches are needed. One approach to increase power is to focus on localized regions of increased missense mutation density or hotspot regions, rather than a whole gene or protein domain. Detecting missense mutation hotspot regions in three-dimensional (3D) protein structure may also be beneficial because linear sequence alone does not fully describe the biologically relevant organization of codons. Here, we present a novel and statistically rigorous algorithm for detecting missense mutation hotspot regions in 3D protein structures. We analyzed approximately 3 × 105 mutations from The Cancer Genome Atlas (TCGA) and identified 216 tumor-type–specific hotspot regions. In addition to experimentally determined protein structures, we considered high-quality structural models, which increase genomic coverage from approximately 5,000 to more than 15,000 genes. We provide new evidence that 3D mutation analysis has unique advantages. It enables discovery of hotspot regions in many more genes than previously shown and increases sensitivity to hotspot regions in tumor suppressor genes (TSG). Although hotspot regions have long been known to exist in both TSGs and oncogenes, we provide the first report that they have different characteristic properties in the two types of driver genes. We show how cancer researchers can use our results to link 3D protein structure and the biologic functions of missense mutations in cancer, and to generate testable hypotheses about driver mechanisms. Our results are included in a new interactive website for visualizing protein structures with TCGA mutations and associated hotspot regions. Users can submit new sequence data, facilitating the visualization of mutations in a biologically relevant context. Cancer Res; 76(13); 3719–31. ©2016 AACR.

Published
2023

25. Data from The Genetic Evolution of Treatment-Resistant Cutaneous, Acral, and Uveal Melanomas

Author

Christine A. Iacobuzio-Donahue, Suzanne L. Topalian, Barry S. Taylor, Timothy A. Chan, David B. Solit, Nicholas D. Socci, Rachel Karchin, Paul B. Chapman, Michael A. Postow, Nadeem Riaz, Vladimir Makarov, Zachary Kohutek, Rajya Kappagantula, Priscilla Baez, Collin Tokheim, Akimasa Hayashi, Craig M. Bielski, Jungeui Hong, Amanda Zucker, Jody E. Hooper, Evan J. Lipson, and Alvin P. Makohon-Moore

Abstract

Purpose:Melanoma is a biologically heterogeneous disease composed of distinct clinicopathologic subtypes that frequently resist treatment. To explore the evolution of treatment resistance and metastasis, we used a combination of temporal and multilesional tumor sampling in conjunction with whole-exome sequencing of 110 tumors collected from 7 patients with cutaneous (n = 3), uveal (n = 2), and acral (n = 2) melanoma subtypes.Experimental Design:Primary tumors, metastases collected longitudinally, and autopsy tissues were interrogated. All but 1 patient died because of melanoma progression.Results:For each patient, we generated phylogenies and quantified the extent of genetic diversity among tumors, specifically among putative somatic alterations affecting therapeutic resistance.Conclusions:In 4 patients who received immunotherapy, we found 1–3 putative acquired and intrinsic resistance mechanisms coexisting in the same patient, including mechanisms that were shared by all tumors within each patient, suggesting that future therapies directed at overcoming intrinsic resistance mechanisms may be broadly effective.

Published
2023

26. Genetic fusions favor tumorigenesis through degron loss in oncogenes

Author

Collin Tokheim, Jonathan D. Lee, Wenyi Wei, Jing Liu, X. Shirley Liu, Wenjian Gan, Pier Paolo Pandolfi, and Brian J. North

Subjects
Male, Oncogene Proteins, Fusion, Ubiquitylation, Carcinogenesis, Science, Amino Acid Motifs, Transplantation, Heterologous, General Physics and Astronomy, Mice, Nude, Computational biology, Protein degradation, Biology, medicine.disease_cause, General Biochemistry, Genetics and Molecular Biology, Article, Transcription (biology), Cell Line, Tumor, Neoplasms, medicine, Cancer genomics, Animals, Humans, Gene, Cancer genetics, Cells, Cultured, Mice, Knockout, Multidisciplinary, Oncogene, Models, Genetic, Mechanism (biology), Computational Biology, General Chemistry, Oncogenes, HCT116 Cells, Fusion protein, Gene Expression Regulation, Neoplastic, HEK293 Cells, Ubiquitin ligases, Mutation, Proteolysis, Degron, HeLa Cells
Abstract

Chromosomal rearrangements can generate genetic fusions composed of two distinct gene sequences, many of which have been implicated in tumorigenesis and progression. Our study proposes a model whereby oncogenic gene fusions frequently alter the protein stability of the resulting fusion products, via exchanging protein degradation signal (degron) between gene sequences. Computational analyses of The Cancer Genome Atlas (TCGA) identify 2,406 cases of degron exchange events and reveal an enrichment of oncogene stabilization due to loss of degrons from fusion. Furthermore, we identify and experimentally validate that some recurrent fusions, such as BCR-ABL, CCDC6-RET and PML-RARA fusions, perturb protein stability by exchanging internal degrons. Likewise, we also validate that EGFR or RAF1 fusions can be stabilized by losing a computationally-predicted C-terminal degron. Thus, complementary to enhanced oncogene transcription via promoter swapping, our model of degron loss illustrates another general mechanism for recurrent fusion proteins in driving tumorigenesis., The impact of genetic fusions on degrons, which are motifs for ubiquitin-mediated protein degradation, has not been fully explored. Here, the authors analyse fusion genes affecting degrons in pan-cancer genomics data, validate their functional impact and find enrichment for both internal and C-terminal degron losses.

Published
2021

27. Charting the Spatial Landscape of Cancer Hallmarks

Author

Mustafa Sibai, Sergi Cervilla, Daniela Grases, Eva Musulen, Arola Fortian, Margarita Romeo, Adrià Bernat, Collin Tokheim, Manel Esteller, Jordi Barretina, Li Ding, Matthew Bailey, and Eduard Porta-Pardo

Abstract

Cancer Hallmarks have been indispensable to rationalize the complex phenotypic properties acquired by cancer cells and their surrounding tumor microenvironment (TME) as they progress from normalcy to malignancy. While great insights have been gained from their use to understand bulk tissue transcriptomics, our knowledge remained incomplete, as it was not possible to determine the origin and relative localization of the activity of each Cancer Hallmark within tumor tissues. Thanks to recent advances in spatial transcriptomics, we can now explore the spatial contribution of each compartment within a tumor to each Cancer Hallmark at near single-cell resolution.Here, we used spatial transcriptomics from fifteen different primary tumor samples from seven tissues to quantify the contribution of the main tumor compartments -cancer cells and the TME- to each of thirteen Cancer Hallmarks. Our results revealed a Pan-Cancer architecture: with the activity of some hallmarks consistently coming from the cancer compartment across all cancer types (Cancer-associated hallmarks) and the activity of others consistently coming from the TME (TME-associated hallmarks). Furthermore, we observed that even within each compartment (cancer or TME) there was a consistent internal architecture, as many of these hallmarks exhibited higher activity at the border between compartments. Our study provides the first spatial map of Cancer Hallmark activities in tumors at almost cellular resolution, paving the way for a better understanding of the underpinnings of hallmark acquisition by tumor cell types.

Published
2022

28. Therapeutically Increasing MHC-I Expression Potentiates Immune Checkpoint Blockade

Author

Henry W. Long, Blair Stewig, Alba Font-Tello, Xiaoqing Wang, Michael Manos, Ziyi Li, X. Shirley Liu, Jingxin Fu, Paloma Cejas, Gordon J. Freeman, Alok K. Tewari, Scott J. Rodig, Yi Zhang, Zexian Zeng, F. Stephen Hodi, Clifford A. Meyer, Yingtian Xie, Xia Bu, Wubing Zhang, Collin Tokheim, Avinash Das Sahu, Jason L. Weirather, Jake Conway, Klothilda Lim, Myles Brown, Jin Hua Liang, Evisa Gjini, Shengqing Stan Gu, Ana Lako, Peng Jiang, Benjamin Kroger, Nicole Traugh, and Benjamin E. Gewurz

Subjects
0301 basic medicine, T cell, medicine.medical_treatment, B7-H1 Antigen, Article, 03 medical and health sciences, 0302 clinical medicine, Downregulation and upregulation, Neoplasms, MHC class I, Tumor Microenvironment, Data Mining, Humans, Medicine, Immune Checkpoint Inhibitors, biology, business.industry, Gene Expression Profiling, Histocompatibility Antigens Class I, Cancer, Immunotherapy, medicine.disease, Immune checkpoint, Blockade, 030104 developmental biology, medicine.anatomical_structure, Oncology, 030220 oncology & carcinogenesis, Cancer cell, Cancer research, biology.protein, business
Abstract

Immune checkpoint blockade (ICB) therapy revolutionized cancer treatment, but many patients with impaired MHC-I expression remain refractory. Here, we combined FACS-based genome-wide CRISPR screens with a data-mining approach to identify drugs that can upregulate MHC-I without inducing PD-L1. CRISPR screening identified TRAF3, a suppressor of the NFκB pathway, as a negative regulator of MHC-I but not PD-L1. The Traf3-knockout gene expression signature is associated with better survival in ICB-naïve patients with cancer and better ICB response. We then screened for drugs with similar transcriptional effects as this signature and identified Second Mitochondria-derived Activator of Caspase (SMAC) mimetics. We experimentally validated that the SMAC mimetic birinapant upregulates MHC-I, sensitizes cancer cells to T cell–dependent killing, and adds to ICB efficacy. Our findings provide preclinical rationale for treating tumors expressing low MHC-I expression with SMAC mimetics to enhance sensitivity to immunotherapy. The approach used in this study can be generalized to identify other drugs that enhance immunotherapy efficacy. Significance: MHC-I loss or downregulation in cancer cells is a major mechanism of resistance to T cell–based immunotherapies. Our study reveals that birinapant may be used for patients with low baseline MHC-I to enhance ICB response. This represents promising immunotherapy opportunities given the biosafety profile of birinapant from multiple clinical trials. This article is highlighted in the In This Issue feature, p. 1307

Published
2021

29. Cross-Site Concordance Evaluation of Tumor DNA and RNA Sequencing Platforms for the CIMAC-CIDC Network

Author

Junko Tsuji, Jin Wang, David Cohen, Jianjun Zhang, Jijun Yu, Ignacio I. Wistuba, Tomas Vilimas, Len Taing, Li Chen, Chunhua Yan, Qing-Rong Chen, Magdalena Thurin, Rebecca A. Enos, Xiaoman Wang, Peng Jiang, Candace Patterson, Mohamed Uduman, Catherine J. Wu, Beatriz Sanchez-Espiridion, Aashna Jhaveri, Yang Liu, Andrew Futreal, Jingxin Fu, Jiexin Zhang, Jack Lee, Donna Neuberg, Curtis Gumbs, Xin Huang, Sylvie Janssens, Collin Tokheim, Zexian Zeng, Avinash Das Sahu, Ming Tang, Carrie Cibulskis, James Lindsay, Cu Nguyen, Jason L. Weirather, Joyce Yu, Stacey Gabriel, Ethan Cerami, Dzifa Y. Duose, Daoud Meerzaman, Chris Karlovich, Sharmistha Sarkar, X. Shirley Liu, Biswajit Das, Sachet A. Shukla, and Jianhua Zhang

Subjects
Nonsynonymous substitution, Cancer Research, Base Sequence, Concordance, RNA, DNA, Neoplasm, Computational biology, Human leukocyte antigen, Biology, Article, chemistry.chemical_compound, Oncology, chemistry, Monitoring, Immunologic, Immune infiltration, Neoplasms, Exome Sequencing, Fresh frozen, Humans, RNA, Neoplasm, International HapMap Project, DNA
Abstract

Purpose: Whole-exome (WES) and RNA sequencing (RNA-seq) are key components of cancer immunogenomic analyses. To evaluate the consistency of tumor WES and RNA-seq profiling platforms across different centers, the Cancer Immune Monitoring and Analysis Centers (CIMAC) and the Cancer Immunologic Data Commons (CIDC) conducted a systematic harmonization study. Experimental Design: DNA and RNA were centrally extracted from fresh frozen and formalin-fixed paraffin-embedded non–small cell lung carcinoma tumors and distributed to three centers for WES and RNA-seq profiling. In addition, two 10-plex HapMap cell line pools with known mutations were used to evaluate the accuracy of the WES platforms. Results: The WES platforms achieved high precision (> 0.98) and recall (> 0.87) on the HapMap pools when evaluated on loci using > 50× common coverage. Nonsynonymous mutations clustered by tumor sample, achieving an index of specific agreement above 0.67 among replicates, centers, and sample processing. A DV200 > 24% for RNA, as a putative presequencing RNA quality control (QC) metric, was found to be a reliable threshold for generating consistent expression readouts in RNA-seq and NanoString data. MedTIN > 30 was likewise assessed as a reliable RNA-seq QC metric, above which samples from the same tumor across replicates, centers, and sample processing runs could be robustly clustered and HLA typing, immune infiltration, and immune repertoire inference could be performed. Conclusions: The CIMAC collaborating laboratory platforms effectively generated consistent WES and RNA-seq data and enable robust cross-trial comparisons and meta-analyses of highly complex immuno-oncology biomarker data across the NCI CIMAC-CIDC Network.

Published
2020

30. Integrated Informatics Analysis of Cancer-Related Variants

Author

Ben Busby, Kyle Moad, Rachel Karchin, Rick Kim, Lily Zheng, Collin Tokheim, Kymberleigh A. Pagel, and Michael T. Ryan

Subjects
MEDLINE, Computational biology, Biology, Workflow, User-Computer Interface, 03 medical and health sciences, 0302 clinical medicine, Neoplasms, Databases, Genetic, medicine, Humans, 030304 developmental biology, 0303 health sciences, Extramural, Genetic variants, Computational Biology, Cancer, ORIGINAL REPORTS, General Medicine, medicine.disease, Neoplasm Proteins, Neoplasms diagnosis, Informatics, Mutation, Mutation (genetic algorithm), Special Series: Informatics Tools For Cancer Research and Care, Software, 030217 neurology & neurosurgery
Abstract

PURPOSE The modern researcher is confronted with hundreds of published methods to interpret genetic variants. There are databases of genes and variants, phenotype-genotype relationships, algorithms that score and rank genes, and in silico variant effect prediction tools. Because variant prioritization is a multifactorial problem, a welcome development in the field has been the emergence of decision support frameworks, which make it easier to integrate multiple resources in an interactive environment. Current decision support frameworks are typically limited by closed proprietary architectures, access to a restricted set of tools, lack of customizability, Web dependencies that expose protected data, or limited scalability. METHODS We present the Open Custom Ranked Analysis of Variants Toolkit 1 (OpenCRAVAT) a new open-source, scalable decision support system for variant and gene prioritization. We have designed the resource catalog to be open and modular to maximize community and developer involvement, and as a result, the catalog is being actively developed and growing every month. Resources made available via the store are well suited for analysis of cancer, as well as Mendelian and complex diseases. RESULTS OpenCRAVAT offers both command-line utility and dynamic graphical user interface, allowing users to install with a single command, easily download tools from an extensive resource catalog, create customized pipelines, and explore results in a richly detailed viewing environment. We present several case studies to illustrate the design of custom workflows to prioritize genes and variants. CONCLUSION OpenCRAVAT is distinguished from similar tools by its capabilities to access and integrate an unprecedented amount of diverse data resources and computational prediction methods, which span germline, somatic, common, rare, coding, and noncoding variants.

Published
2020

31. 700 Increasing MHC-I expression to potentiate immune checkpoint blockade therapy

Author

Zexian Zeng, Xia Bu, Paloma Cejas, Shengqing Gu, Alba Font-Tello, Ana Lako, Scott J. Rodig, X. Shirley Liu, Nicole Traugh, Yi Zhang, Ziyi Li, Clifford A. Meyer, Klothilda Lim, Jake Conway, Wubing Zhang, Xiaoqing Wang, Michael Manos, Benjamin E. Gewurz, Gordon J. Freeman, Yingtian Xie, Collin Tokheim, Jingxin Fu, Jason L. Weirather, Avinash Das Sahu, Evisa Gjini, Benjamin Kroger, Henry W. Long, Blair Stewig, Peng Jiang, F. Stephen Hodi, Jin Hua Liang, Alok K. Tewari, and Myles Brown

Subjects
Pharmacology, Cancer Research, biology, Immunology, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Immune checkpoint, Blockade, Oncology, MHC class I, Cancer research, biology.protein, Molecular Medicine, Immunology and Allergy, RC254-282
Abstract

BackgroundCancer immunotherapy, especially immune checkpoint blockade (ICB) therapy, is leading to a paradigm shift in cancer treatment, as a small percentage of cancer patients have obtained durable remission following ICB treatment. Successful ICB responses rely on cancer cells presenting antigens to the cell surface via the major histocompatibility complex (MHC), which activates antigen-specific T-lymphocytes to kill cancer cells. Type-I MHC (MHC-I) is wildly expressed in all cell types and mediates the interaction with cytotoxic CD8 T cells. However, over 65% of cancer patients are estimated to show defects in MHC-I-mediated antigen presentation, including downregulation of its expression that can lead to primary or acquired resistance to ICB therapy, and therapeutic strategies to effectively restore or boost MHC-I are limited.MethodsHere, we employed a CRISPR screening approach with dual-marker FACS sorting to identify factors that decouple the regulation of MHC-I and PD-L1. The experimentally validated target was used to generate a KO differential expression signature. Using this signature, we analyzed transcriptome data from drug perturbation studies to identify drugs that regulate MHC-I but not PD-L1. Finally, we validated the effect of the identified drug to enhance ICB response in a T-cell-dependent manner in vivo.ResultsCRISPR screens identified TRAF3, a suppressor of the NF-κB pathway, as a negative regulator of MHC-I but not PD-L1. The Traf3-knockout (Traf3-KO) gene expression signature is associated with better survival in ICB-naive cancer patients and better ICB response. We then screened for drugs with similar transcriptional effects as this signature and identified SMAC mimetics. We experimentally validated that the SMAC mimetic birinapant upregulates MHC-I, sensitizes cancer cells to T-cell-dependent killing, and adds to ICB efficacy. However, in cancer cells with high NF-κB activity, the effect of birinapant on MHC-I is weak, indicating context-dependent MHC-I regulation.ConclusionsIn summary, Traf3 deletion specifically upregulates MHC-I without inducing PD-L1 in response to various cytokines and sensitizes cancer cells to T-cell-driven cytotoxicity. The SMAC mimetic birinapant phenocopies Traf3-knockout and sensitizes MHC-I-low melanoma to ICB therapy. Further studies are needed to elucidate the context-dependencies of MHC-I regulation. Our findings provide preclinical rationale for treating some tumors expressing low MHC-I with SMAC mimetics to enhance sensitivity to immunotherapy. The approach used in this study can be generalized to identify other drugs that enhance immunotherapy efficacy.AcknowledgementsThis study was supported by grants from the NIH (R01CA234018 to XSL, R01AI137337 to BEG, P50CA101942-12 and P50CA206963 to GJF), Breast Cancer Research Foundation (BCRF-19-100 to XSL), Burroughs Wellcome Career Award in Medical Sciences (to BEG), and Sara Elizabeth O'Brien Trust Fellowship (to SG).We thank Drs. Kai Wucherpfennig and Deng Pan for their insightful suggestions on this study.Ethics ApprovalAll mice were housed in standard cage in Dana-Farber Cancer Institute Animal Resources Facility (ARF). All animal procedures were carried out under the ARF Institutional Animal Care and Use Committee (IACUC) protocol and were in accordance with the IACUC standards for the welfare of animals.

Published
2021

32. Data-driven discovery of targets for bipotent anticancer drugs identifies Estrogen Related Receptor Alpha

Author

Laura van Zelst, Sanne van Zanten, Shengqing Gu, Joost Rens, Phillip B. Nicol, Nishanth Ulhas Nair, Collin Tokheim, Chenfei Wang, Avinash Das Sahu, Xiaoqing Wang, Xiaoman Wang, Dejan Juric, Mark Musters, Wubing Zhang, Myles Brown, Keith T. Flaherty, Gege Qian, Cliff Meyer, David E. Fisher, Zexian Zeng, Jenny Worthington, Inge Leenders, Jan Klomp, Arthur Oubrie, Bas Jansen, Jin Wang, Phillip Munson, Jun Liu, Jaap Lemmers, Meriem Bourajjaj, Jingxin Fu, and X. Shirley Liu

Subjects
Drug, Angiogenesis, medicine.medical_treatment, media_common.quotation_subject, Macrophage polarization, Biology, Proinflammatory cytokine, Transcriptome, Estrogen-related receptor alpha, Cytokine, medicine, Cancer research, CD8, media_common
Abstract

Drugs that kill tumors through multiple mechanisms have potential for broad clinical benefits, with a reduced propensity to resistance. We developed BipotentR, a computational approach to find cancer-cell-specific regulators that simultaneously modulate tumor immunity and another oncogenic pathway. Using tumor metabolism as proof-of-principle, BipotentR identified 38 candidate immune-metabolic regulators by combining epigenomes with bulk and single-cell tumor transcriptomes from patients. Inhibition of top candidate ESRRA (Estrogen Related Receptor Alpha) killed tumors by direct effects on energy metabolism and two immune mechanisms: (i) cytokine induction, causing proinflammatory macrophage polarization (ii) antigen-presentation stimulation, recruiting CD8+T cells into tumors. ESRRA is activated in immune-suppressive and immunotherapy-resistant tumors of many types, suggesting broad clinical relevance. We also applied BipotentR to angiogenesis and growth-suppressor pathways, demonstrating a widely applicable approach to identify drug targets that act simultaneously through multiple mechanisms. BipotentR is publicly available at http://bipotentr.dfci.harvard.edu/.One-Sentence SummaryBipotentR identifies targets for bipotent anticancer drugs, as shown by the energy and immune effects of ESRRA inhibition.

Published
2021

33. Machine learning modeling of protein-intrinsic features predicts tractability of targeted protein degradation

Author

Collin Tokheim, Wubing Zhang, Yibin B. Zhang, Yang Cao, Zexian Zeng, Eric S. Fischer, Di Li, S. S. Roy Burman, Juxiang Chen, Xiaole Shirley Liu, Katherine A. Donovan, and Boning Zhang

Subjects
biology, Chemistry, Kinase, business.industry, Lysine, Protein degradation, Machine learning, computer.software_genre, Protein expression, Ubiquitin, Drug development, Proteome, biology.protein, Kinome, Artificial intelligence, business, computer
Abstract

Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell’s endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by TPD approaches, termed “degradability”, is largely unknown. Recent systematic studies to map the degradable kinome have shown differences in degradation between kinases with similar drug-target engagement, suggesting yet unknown factors influencing degradability. We therefore developed a machine learning model, MAPD (Model-based Analysis of Protein Degradability), to predict degradability from protein features that encompass post-translational modifications, protein stability, protein expression and protein-protein interactions. MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds (auPRC=0.759) and is likely generalizable to independent non-kinase proteins. We found five features with statistical significance to achieve optimal prediction, with ubiquitination potential being the most predictive. By structural modeling, we found that E2-accessible ubiquitination sites, but not lysine residues in general, are particularly associated with kinase degradability. Finally, we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins, including 278 cancer genes, that may be tractable to TPD drug development.

Published
2021

34. Systematic characterization of mutations altering protein degradation in human cancers

Author

Jun Ge, Stephen J. Elledge, Myles Brown, X. Shirley Liu, Cynthia Chen, Wubing Zhang, Binbin Wang, Boning Zhang, Xiaoqing Wang, Jun Chu, Elijah L. Mena, Collin Tokheim, Richard T. Timms, Timms, Richard [0000-0001-7275-597X], and Apollo - University of Cambridge Repository

Subjects
Protein degradation, Biology, medicine.disease_cause, Article, 03 medical and health sciences, 0302 clinical medicine, Deep Learning, Cell Line, Tumor, Neoplasms, medicine, Humans, Molecular Biology, Transcription factor, 030304 developmental biology, 0303 health sciences, Tumor microenvironment, Mutation, Models, Genetic, HEK 293 cells, GATA3, Cancer, Cell Biology, medicine.disease, Neoplasm Proteins, HEK293 Cells, Proteolysis, Cancer research, Degron, 030217 neurology & neurosurgery
Abstract

The Ubiquitin-Proteasome System (UPS) is the primary route for selective protein degradation in human cells. The UPS represents an attractive target for novel cancer therapeutics, but the precise UPS genes and substrates important for cancer growth are incompletely understood. Leveraging multi-omics data across more than 9,000 human tumors and 33 cancer types, we found that over 19% of all cancer driver genes impact UPS function. We implicate transcription factors as important substrates, and show that c-Myc stability is modulated by CUL3. Moreover, we developed a deep learning model (deepDegron) to identify mutations that result in degron loss, and experimentally validated predictions that gain-of-function truncating mutations in GATA3 and PPM1D result in increased protein stability. Lastly, we identified UPS driver genes associated with patient prognosis and the tumor microenvironment. This study demonstrates the important role of UPS dysregulation in human cancers and underscores the potential therapeutic utility of targeting the UPS.

Published
2021

35. In Vivo CRISPR Screens Identify E3 Ligase Cop1 as a Modulator of Macrophage Infiltration and Cancer Immunotherapy Target

Author

Peng Jiang, Shengqing Stan Gu, Qin Tang, Henry W. Long, Boning Zhang, Xiaole Shirley Liu, Klothilda Lim, Nicole Traugh, Tengfei Xiao, Wei Li, Yi Zhang, Xiaoqing Wang, Jun Chu, Paloma Cejas, Ziyi W Li, Binbin Wang, Jingxin Fu, Clifford A. Meyer, Yihao Li, Myles Brown, and Collin Tokheim

Subjects
Tumor microenvironment, Chemokine, biology, Chemistry, medicine.medical_treatment, Immune checkpoint, Ubiquitin ligase, Immune system, Cancer immunotherapy, Chemokine secretion, Cancer cell, biology.protein, Cancer research, medicine
Abstract

SUMMARYDespite remarkable clinical efficacies of immune checkpoint blockade (ICB) in cancer treatment, ICB benefits in triple-negative breast cancer (TNBC) remain limited. Through pooled in vivo CRISPR knockout (KO) screens in syngeneic TNBC mouse models, we found that inhibition of the E3 ubiquitin ligase Cop1 in cancer cells decreases the secretion of macrophage-associated chemokines, reduces tumor macrophage infiltration, and shows synergy in anti-tumor immunity with ICB. Transcriptomics, epigenomics, and proteomics analyses revealed Cop1 functions through proteasomal degradation of the C/ebpδ protein. Cop1 substrate Trib2 functions as a scaffold linking Cop1 and C/ebpδ, which leads to polyubiquitination of C/ebpδ. Cop1 inhibition stabilizes C/ebpδ to suppress the expression of macrophage chemoattractant genes. Our integrated approach implicates Cop1 as a target for improving cancer immunotherapy efficacy by regulating chemokine secretion and macrophage levels in the TNBC tumor microenvironment.HighlightsLarge-scale in vivo CRISPR screens identify new immune targets regulating the tumor microenvironmentCop1 knockout in cancer cells enhances anti-tumor immunityCop1 modulates chemokine secretion and macrophage infiltration into tumorsCop1 targets C/ebpδ degradation via Trib2 and influences ICB response

Published
2020

36. The Genetic Evolution of Treatment-Resistant Cutaneous, Acral, and Uveal Melanomas

Author

Amanda Zucker, Christine A. Iacobuzio-Donahue, Rachel Karchin, Jody E. Hooper, Vladimir Makarov, Nicholas D. Socci, Timothy A. Chan, Paul B. Chapman, Barry S. Taylor, Jungeui Hong, Suzanne L. Topalian, Michael A. Postow, Priscilla Baez, Rajya Kappagantula, David B. Solit, Akimasa Hayashi, Zachary Kohutek, Nadeem Riaz, Collin Tokheim, Alvin Makohon-Moore, Craig M. Bielski, and Evan J. Lipson

Subjects
0301 basic medicine, Oncology, Uveal Neoplasms, Cancer Research, medicine.medical_specialty, Skin Neoplasms, Somatic cell, medicine.medical_treatment, Autopsy, Disease, Article, Metastasis, Evolution, Molecular, 03 medical and health sciences, 0302 clinical medicine, Genetic Evolution, Internal medicine, Biomarkers, Tumor, Medicine, Humans, Treatment resistant, Melanoma, business.industry, Immunotherapy, medicine.disease, Prognosis, 030104 developmental biology, Drug Resistance, Neoplasm, 030220 oncology & carcinogenesis, Mutation, business
Abstract

Purpose: Melanoma is a biologically heterogeneous disease composed of distinct clinicopathologic subtypes that frequently resist treatment. To explore the evolution of treatment resistance and metastasis, we used a combination of temporal and multilesional tumor sampling in conjunction with whole-exome sequencing of 110 tumors collected from 7 patients with cutaneous (n = 3), uveal (n = 2), and acral (n = 2) melanoma subtypes. Experimental Design: Primary tumors, metastases collected longitudinally, and autopsy tissues were interrogated. All but 1 patient died because of melanoma progression. Results: For each patient, we generated phylogenies and quantified the extent of genetic diversity among tumors, specifically among putative somatic alterations affecting therapeutic resistance. Conclusions: In 4 patients who received immunotherapy, we found 1–3 putative acquired and intrinsic resistance mechanisms coexisting in the same patient, including mechanisms that were shared by all tumors within each patient, suggesting that future therapies directed at overcoming intrinsic resistance mechanisms may be broadly effective.

Published
2020

37. Determinants of transcription factor regulatory range

Author

Changxin Wan, Jun Liu, Myles Brown, Collin Tokheim, Clifford A. Meyer, Qin Tang, Rongbin Zheng, Jingyu Fan, Xin Dong, X. Shirley Liu, and Chen-Hao Chen

Subjects
Epigenomics, 0301 basic medicine, Science, Quantitative Trait Loci, General Physics and Astronomy, Context (language use), Computational biology, Biology, Polymorphism, Single Nucleotide, Chromatin structure, Article, General Biochemistry, Genetics and Molecular Biology, Cell Line, Histones, Mice, 03 medical and health sciences, 0302 clinical medicine, Gene density, Animals, lcsh:Science, Transcription factor, Gene, Regulation of gene expression, Multidisciplinary, Models, Genetic, Lysine, Acetylation, General Chemistry, Chromatin, Gene regulation, 030104 developmental biology, Histone, Gene Expression Regulation, 030220 oncology & carcinogenesis, biology.protein, lcsh:Q, Data integration, Gene expression, Transcription Initiation Site, Chromatin immunoprecipitation, Genome-Wide Association Study, Protein Binding, Transcription Factors
Abstract

Characterization of the genomic distances over which transcription factor (TF) binding influences gene expression is important for inferring target genes from TF chromatin immunoprecipitation followed by sequencing (ChIP-seq) data. Here we systematically examine the relationship between thousands of TF and histone modification ChIP-seq data sets with thousands of gene expression profiles. We develop a model for integrating these data, which reveals two classes of TFs with distinct ranges of regulatory influence, chromatin-binding preferences, and auto-regulatory properties. We find that the regulatory range of the same TF bound within different topologically associating domains (TADs) depend on intrinsic TAD properties such as local gene density and G/C content, but also on the TAD chromatin states. Our results suggest that considering TF type, binding distance to gene locus, as well as chromatin context is important in identifying implicated TFs from GWAS SNPs., Characterization of the distance over which TF binding influences gene expression is important for inferring target genes. Here the authors study this relationship using thousands of genomic data sets, finding two classes of TFs with distinct ranges of regulatory influence modulated by chromatin states of topologically associated domains.

Published
2020

38. Clonal tracing reveals diverse patterns of response to immune checkpoint blockade

Author

Paloma Cejas, Peng Jiang, Chenfei Wang, Xiaoqing Wang, Benjamin Kroger, Shengqing Stan Gu, Jingxin Fu, Wubing Zhang, Zhangyi Ouyang, Xihao Hu, Qin Tang, Gordon J. Freeman, Xiaofang Xing, Ziyi Li, Collin Tokheim, Yi Zhang, Nicole Traugh, Avinash Das Sahu, X. Shirley Liu, Klothilda Lim, Myles Brown, Cliff Meyer, Henry W. Long, Xia Bu, Jin Wang, and Zexian Zeng

Subjects
lcsh:QH426-470, Pharmacogenomic Variants, Clone (cell biology), Biology, Models, Biological, Interferon, Neoplasms, medicine, Biomarkers, Tumor, Clonal tracing, Animals, lcsh:QH301-705.5, Gene, Immune Checkpoint Inhibitors, Tumor microenvironment, Mice, Inbred BALB C, Research, Cancer, medicine.disease, Immune checkpoint, Human genetics, Clone Cells, Mice, Inbred C57BL, lcsh:Genetics, lcsh:Biology (General), Cancer cell, Cancer research, Mathematical modeling, Heterogeneity, Immune checkpoint blockade, medicine.drug
Abstract

BackgroundImmune checkpoint blockade (ICB) therapy has improved patient survival in a variety of cancers, but only a minority of cancer patients respond. Multiple studies have sought to identify general biomarkers of ICB response, but elucidating the molecular and cellular drivers of resistance for individual tumors remains challenging. We sought to determine whether a tumor with defined genetic background exhibits a stereotypic or heterogeneous response to ICB treatment.ResultsWe establish a unique mouse system that utilizes clonal tracing and mathematical modeling to monitor the growth of each cancer clone, as well as the bulk tumor, in response to ICB. We find that tumors derived from the same clonal populations showed heterogeneous ICB response and diverse response patterns. Primary response is associated with higher immune infiltration and leads to enrichment of pre-existing ICB-resistant cancer clones. We further identify several cancer cell-intrinsic gene expression signatures associated with ICB resistance, including increased interferon response genes and glucocorticoid response genes. These findings are supported by clinical data from ICB treatment cohorts.ConclusionsOur study demonstrates diverse response patterns from the same ancestor cancer cells in response to ICB. This suggests the value of monitoring clonal constitution and tumor microenvironment over time to optimize ICB response and to design new combination therapies. Furthermore, as ICB response may enrich for cancer cell-intrinsic resistance signatures, this can affect interpretations of tumor RNA-seq data for response-signature association studies.

Published
2020

39. In vivo CRISPR Screens Identify E3 Ligase Cop1 as a Modulator of Macrophage Infiltration and Cancer Immunotherapy Target

Author

Xiaole Shirley Liu, Myles Brown, Klothilda Lim, Henry W. Long, Qin Tang, Paloma Cejas, Tengfei Xiao, Shengqing Stan Gu, Nicole Traugh, Collin Tokheim, Binbin Wang, Yihao Li, Jingxin Fu, Clifford A. Meyer, Wei Li, Yi Zhang, Boning Zhang, Xiaoqing Wang, Jun Chu, Ziyi Li, and Peng Jiang

Subjects
Tumor microenvironment, Chemokine, biology, Chemistry, medicine.medical_treatment, fungi, Immunotherapy, Immune checkpoint, Ubiquitin ligase, Cancer immunotherapy, Chemokine secretion, Cancer cell, medicine, biology.protein, Cancer research
Abstract

Despite remarkable clinical efficacies of immune checkpoint blockade (ICB) in cancer treatment, ICB benefits in triple-negative breast cancer (TNBC) remain limited. Through pooled in vivo CRISPR knockout (KO) screens in syngeneic TNBC mouse models, we found that inhibition of the E3 ubiquitin ligase Cop1 in cancer cells decreases the secretion of macrophage-associated chemokines, reduces tumor macrophage infiltration, and shows synergy in anti-tumor immunity with ICB. Transcriptomics, epigenomics, and proteomics analyses revealed Cop1 functions through proteasomal degradation of the C/ebpδ protein. Cop1 substrate Trib2 functions as a scaffold linking Cop1 and C/ebpδ, which leads to polyubiquitination of C/ebpδ. Cop1 inhibition stabilizes C/ebpδ to suppress the expression of macrophage chemoattractant genes. Our integrated approach implicates Cop1 as a target for improving cancer immunotherapy efficacy by regulating chemokine secretion and macrophage levels in the TNBC tumor microenvironment.

Published
2020

40. Abstract P108: In vivo CRISPR screens identify E3 ligase COP1 as a modulator of macrophage infiltration and cancer immunotherapy target

Author

Xiaoqing Wang, Collin Tokheim, Shengqing S. Gu, Binbin Wang, Qin Tang, Yihao Li, Nicole Traugh, Zexian Zeng, Yi Zhang, Boning Zhang, Jingxin Fu, Tengfei Xiao, Wei Li, Clifford Meyer, Peng Jiang, Paloma Cejas, Klothilda Lim, Henry Long, Myles Brown, and X. Shirley Liu

Subjects
Cancer Research, Oncology
Abstract

Despite remarkable clinical efficacies of immune checkpoint blockade (ICB) in cancer treatment, ICB benefits in triple-negative breast cancer (TNBC) remain limited. Through pooled in vivo CRISPR knockout (KO) screens in syngeneic TNBC mouse models, we found that inhibition of the E3 ubiquitin ligase COP1 in cancer cells decreases the secretion of macrophage-associated chemokines, reduces tumor macrophage infiltration, enhances tumor immunity and ICB response. Transcriptomics, epigenomics, and proteomics analyses revealed COP1 functions through proteasomal degradation of the C/ebpδ protein. COP1 substrate TRIB2 functions as a scaffold linking COP1 and C/ebpδ, which leads to polyubiquitination of C/ebpδ. COP1 inhibition stabilizes C/ebpδ to suppress the expression of macrophage chemoattractant genes. Our integrated approach implicates COP1 as a target for improving cancer immunotherapy efficacy by regulating chemokine secretion and macrophage infiltration in the TNBC tumor microenvironment. Citation Format: Xiaoqing Wang, Collin Tokheim, Shengqing S. Gu, Binbin Wang, Qin Tang, Yihao Li, Nicole Traugh, Zexian Zeng, Yi Zhang, Boning Zhang, Jingxin Fu, Tengfei Xiao, Wei Li, Clifford Meyer, Peng Jiang, Paloma Cejas, Klothilda Lim, Henry Long, Myles Brown, X. Shirley Liu. In vivo CRISPR screens identify E3 ligase COP1 as a modulator of macrophage infiltration and cancer immunotherapy target [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P108.

Published
2021

41. In vivo CRISPR screens identify the E3 ligase Cop1 as a modulator of macrophage infiltration and cancer immunotherapy target

Author

Tengfei Xiao, Peng Jiang, Yi Zhang, Binbin Wang, Boning Zhang, Jingxin Fu, Wei Li, Zexian Zeng, Shengqing Stan Gu, Paloma Cejas, Ziyi Li, Xiaoqing Wang, Jun Chu, Henry W. Long, Myles Brown, Clifford A. Meyer, Collin Tokheim, Qin Tang, Nicole Traugh, Yihao Li, X. Shirley Liu, and Klothilda Lim

Subjects
CCAAT-Enhancer-Binding Protein-delta, Ubiquitin-Protein Ligases, medicine.medical_treatment, Triple Negative Breast Neoplasms, Article, General Biochemistry, Genetics and Molecular Biology, Substrate Specificity, Cancer immunotherapy, Ubiquitin, CRISPR-Associated Protein 9, Cell Line, Tumor, Neoplasms, medicine, Animals, Humans, Clustered Regularly Interspaced Short Palindromic Repeats, Gene Library, Immune Evasion, Mice, Inbred BALB C, Tumor microenvironment, biology, Chemotaxis, Macrophages, fungi, Nuclear Proteins, Immunotherapy, Immune checkpoint, Ubiquitin ligase, Mice, Inbred C57BL, Disease Models, Animal, Proteolysis, Chemokine secretion, Cancer cell, biology.protein, Cancer research, Chemokines
Abstract

Despite remarkable clinical efficacy of immune checkpoint blockade (ICB) in cancer treatment, ICB benefits in triple-negative breast cancer (TNBC) remain limited. Through pooled in vivo CRISPR knockout (KO) screens in syngeneic TNBC mouse models, we found that deletion of the E3 ubiquitin ligase Cop1 in cancer cells decreases the secretion of macrophage-associated chemokines, reduces tumor macrophage infiltration, enhances anti-tumor immunity, and strengthens ICB response. Transcriptomics, epigenomics, and proteomics analyses revealed that Cop1 functions through proteasomal degradation of the C/ebpδ protein. Cop1 substrate Trib2 functions as a scaffold linking Cop1 and C/ebpδ, which leads to polyubiquitination of C/ebpδ. In addition, deletion of the E3 ubiquitin ligase Cop1 in cancer cells stabilizes C/ebpδ to suppress the expression of macrophage chemoattractant genes. Our integrated approach implicates Cop1 as a target for improving cancer immunotherapy efficacy in TNBC by regulating chemokine secretion and macrophage infiltration in the tumor microenvironment.

Published
2021

42. Abstract 65: Therapeutically increasing MHC-I expression potentiates immune checkpoint blockade

Author

Shengqing Gu, Wubing Zhang, Xiaoqing Wang, Peng Jiang, Nicole Traugh, Ziyi Li, Clifford Meyer, Blair Stewig, Yingtian Xie, Xia Bu, Michael Manos, Alba Font-Tello, Evisa Gjini, Ana Lako, Klothilda Lim, Jake Conway, Alok Tewari, Zexian Zeng, Avinash Das Sahu, Collin Tokheim, Jason L. Weirather, Jingxin Fu, Yi Zhang, Benjamin Kroger, Jin Hua Liang, Paloma Cejas, Gordon J. Freeman, Scott J. Rodig, Henry Long, Benjamin E. Gewurz, F. Stephen Hodi, Myles Brown, and X. Shirley Liu

Subjects
Cancer Research, medicine.medical_specialty, Myeloid, Cell growth, medicine.medical_treatment, Cellular differentiation, Biology, Cytokine, Endocrinology, medicine.anatomical_structure, Oncology, Cell culture, Internal medicine, medicine, Cancer research, Tumor necrosis factor alpha, Clone (B-cell biology), Interleukin 4
Abstract

Immune checkpoint blockade (ICB) therapy revolutionized cancer treatment, but many patients with impaired MHC-I expression remain refractory. Here, we combined FACS-based genome-wide CRISPR screens with a data-mining approach to identify drugs that can upregulate MHC-I without inducing PD-L1. CRISPR screening identified TRAF3, a critical suppressor of the NF-κB pathway, as a negative regulator of MHC-I but not PD-L1. The Traf3-knockout (Traf3-KO) gene expression signature is associated with better survival in ICB-naive cancer patients and better ICB response. We then screened for drugs with similar transcriptional effects as this signature and identified SMAC mimetics. We experimentally validated that the SMAC mimetic birinapant upregulates MHC-I, sensitizes cancer cells to T-cell-dependent killing, and synergizes with ICB. Our findings provide preclinical rationale for treating tumors expressing low MHC-I expression with SMAC mimetics to enhance sensitivity to immunotherapy. The approach used in this study can be generalized to identify other drugs that enhance immunotherapy efficacy. Citation Format: Shengqing Gu, Wubing Zhang, Xiaoqing Wang, Peng Jiang, Nicole Traugh, Ziyi Li, Clifford Meyer, Blair Stewig, Yingtian Xie, Xia Bu, Michael Manos, Alba Font-Tello, Evisa Gjini, Ana Lako, Klothilda Lim, Jake Conway, Alok Tewari, Zexian Zeng, Avinash Das Sahu, Collin Tokheim, Jason L. Weirather, Jingxin Fu, Yi Zhang, Benjamin Kroger, Jin Hua Liang, Paloma Cejas, Gordon J. Freeman, Scott J. Rodig, Henry Long, Benjamin E. Gewurz, F. Stephen Hodi, Myles Brown, X. Shirley Liu. Therapeutically increasing MHC-I expression potentiates immune checkpoint blockade [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 65.

Published
2021

43. OpenCRAVAT, an open source collaborative platform for the annotation of human genetic variation

Author

Michael T. Ryan, Ben Busby, Rick Kim, Matthew Hynes-Grace, Lily Zheng, Rachel Karchin, Collin Tokheim, Kyle Moad, and Kymberleigh A. Pagel

Subjects
Annotation, Decision support system, Workflow, business.industry, Computer science, Scalability, Human genetic variation, Modular design, business, Gene, Data science
Abstract

PURPOSEThe modern researcher is confronted with hundreds of published methods to interpret genetic variants. There are databases of genes and variants, phenotype-genotype relationships, algorithms that score and rank genes, and in silico variant effect prediction tools. Because variant prioritization is a multi-factorial problem, a welcome development in the field has been the emergence of decision support frameworks, which make it easier to integrate multiple resources in an interactive environment. Current decision support frameworks are typically limited by closed proprietary architectures, access to a restricted set of tools, lack of customizability, web dependencies that expose protected data, or limited scalability.METHODSWe present OpenCRAVAT, a new open source, scalable decision support system for variant and gene prioritization. We have designed the resource catalog to be open and modular to maximize community and developer involvement, and as a result the catalog is being actively developed and growing every month. Resources made available via the store are well-suited for analysis of cancer, as well as Mendelian and complex diseases.RESULTSOpenCRAVAT offers both command line utility and dynamic GUI, allowing users to install with a single command, easily download tools from an extensive resource catalog, create customized pipelines, and explore results in a richly detailed viewing environment. We present several case studies to illustrate the design of custom workflows to prioritize genes and variants.CONCLUSIONOpenCRAVAT is distinguished from similar tools by its capabilities to access and integrate an unprecedented amount of diverse data resources and computational prediction methods, which span germline, somatic, common, rare, coding and non-coding variants. OpenCRAVAT is freely available at https://opencravat.org

Published
2019
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44. High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

Author

Lily Zheng, Benjamin Kaminow, Maria Bonsack, Dylan Hirsch, Xiaoshan M. Shao, I K Ashok Sivakumar, Angelika B. Riemer, Rohit Bhattacharya, Rachel Karchin, Victor E. Velculescu, Justin C. Huang, Valsamo Anagnostou, Kymberleigh A. Pagel, Collin Tokheim, and Ashton Omdahl

Subjects
Cancer Research, medicine.medical_treatment, In silico, Immunology, Mutation, Missense, Plasma protein binding, Computational biology, CD8-Positive T-Lymphocytes, Major histocompatibility complex, Cancer Vaccines, Article, 03 medical and health sciences, 0302 clinical medicine, Antigen, Cancer immunotherapy, Antigens, Neoplasm, Artificial Intelligence, Predictive Value of Tests, Neoplasms, MHC class I, medicine, Data Mining, Humans, Missense mutation, Allele, Gene, 030304 developmental biology, 0303 health sciences, biology, Histocompatibility Antigens Class I, Histocompatibility Antigens Class II, Computational Biology, 3. Good health, 030220 oncology & carcinogenesis, biology.protein, Biomarker (medicine), Neural Networks, Computer, Algorithms, Software, CD8, Protein Binding, 030215 immunology
Abstract

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins is an emerging biomarker for predicting patient response to cancer immunotherapy. Current neoantigen predictors focus onin silicoestimation of MHC binding affinity and are limited by low positive predictive value for actual peptide presentation, inadequate support for rare MHC alleles and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method to predict peptide-MHC binding. MHCnuggets is the only method to handle binding prediction for common or rare alleles of MHC Class I or II, with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is capable of faster performance than other methods. When compared to methods that integrate binding affinity and HLAp data from mass spectrometry, MHCnuggets yields a fourfold increase in positive predictive value on independent MHC-bound peptide (HLAp) data. We applied MHCnuggets to 26 cancer types in TCGA, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique candidate immunogenic missense mutations (IMMs). Predicted-IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted-IMM load was significantly associated with increased immune cell infiltration (p2 patients, with 61.7% of these derived from driver mutations. Our results provide a new method for neoantigen prediction with high performance characteristics and demonstrate its utility in large data sets across human cancers.SynopsisWe developed a newin silicopredictor of Major Histocompatibility Complex (MHC) ligand binding and demonstrated its utility to assess potential neoantigens and immunogenic missense mutations (IMMs) in 6613 TCGA patients.

Published
2019

45. Exome-Scale Discovery of Hotspot Mutation Regions in Human Cancer Using 3D Protein Structure

Author

Derek M. Gygax, David L. Masica, Rohit Bhattacharya, Michael C. Ryan, Collin Tokheim, Noushin Niknafs, Rick Kim, and Rachel Karchin

Subjects
0301 basic medicine, Cancer Research, Protein Conformation, Protein domain, Genomics, Biology, medicine.disease_cause, Article, 03 medical and health sciences, Protein structure, Neoplasms, Biomarkers, Tumor, medicine, Humans, Missense mutation, Exome, Gene, Genetics, Computational Biology, High-Throughput Nucleotide Sequencing, 030104 developmental biology, Oncology, Mutation, Mutation testing, Carcinogenesis
Abstract

The impact of somatic missense mutation on cancer etiology and progression is often difficult to interpret. One common approach for assessing the contribution of missense mutations in carcinogenesis is to identify genes mutated with statistically nonrandom frequencies. Even given the large number of sequenced cancer samples currently available, this approach remains underpowered to detect drivers, particularly in less studied cancer types. Alternative statistical and bioinformatic approaches are needed. One approach to increase power is to focus on localized regions of increased missense mutation density or hotspot regions, rather than a whole gene or protein domain. Detecting missense mutation hotspot regions in three-dimensional (3D) protein structure may also be beneficial because linear sequence alone does not fully describe the biologically relevant organization of codons. Here, we present a novel and statistically rigorous algorithm for detecting missense mutation hotspot regions in 3D protein structures. We analyzed approximately 3 × 105 mutations from The Cancer Genome Atlas (TCGA) and identified 216 tumor-type–specific hotspot regions. In addition to experimentally determined protein structures, we considered high-quality structural models, which increase genomic coverage from approximately 5,000 to more than 15,000 genes. We provide new evidence that 3D mutation analysis has unique advantages. It enables discovery of hotspot regions in many more genes than previously shown and increases sensitivity to hotspot regions in tumor suppressor genes (TSG). Although hotspot regions have long been known to exist in both TSGs and oncogenes, we provide the first report that they have different characteristic properties in the two types of driver genes. We show how cancer researchers can use our results to link 3D protein structure and the biologic functions of missense mutations in cancer, and to generate testable hypotheses about driver mechanisms. Our results are included in a new interactive website for visualizing protein structures with TCGA mutations and associated hotspot regions. Users can submit new sequence data, facilitating the visualization of mutations in a biologically relevant context. Cancer Res; 76(13); 3719–31. ©2016 AACR.

Published
2016

46. Abstract 4879: Identification of degron motifs and mutations by analyzing a deep neural network

Author

Collin Tokheim, Shirley Liu, and Cynthia Chen

Subjects
Cancer Research, Oncology, Artificial neural network, Computer science, Identification (biology), Computational biology, Degron
Abstract

Degrons, short amino acid sequences on protein substrates, play a critical role in the ubiquitin-proteasome system (UPS), which regulates over 80 percent of protein degradation in cells. When recognized by E3 ubiquitin ligases, degrons signal the destruction of its substrate protein, determining the regulatory specificity of the UPS. While a few degrons have been reported to be affected by mutations driving cancer growth, the effect of specific mutations on degrons remains largely unidentified. In this study, we analyze a deep neural network model that predicts the likelihood of an input protein sequence to contain a degron. To characterize the important C-terminal degron motifs learned by the model, we ran computational methods including sequence ranking and selection, enrichment analyses, and sequence logo visualization. This revealed the discovery of 101 significant degron motifs across lengths 2, 3, and 4. While previous studies suggested that di-amino acid motifs might be the primary source of c-end degron, the new motifs suggest that additional complexity might exist with a longer extended degron. Using these putative motifs as targets for mutations, we performed in silico mutagenesis to identify high-impact amino acid mutations that cause degron loss or gain. Our discovery of degron motif-mutation pairs could inform the development of targeted small-molecule drugs for cancer treatments and identification mutational biomarkers used for early detection of cancers. Citation Format: Cynthia Chen, Collin Tokheim, Shirley Liu. Identification of degron motifs and mutations by analyzing a deep neural network [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 4879.

Published
2020

47. Abstract A33: High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

Author

Angelika B. Riemer, Ashton Omdahl, Dylan Hirsch, Rachel Karchin, Xiaoshan M. Shao, Collin Tokheim, Victor E. Velculescu, Rohit Bhattacharya, Ben Kaminow, Kymberleigh A. Pagel, Lily Zheng, Justin C. Huang, Ashok Sivakumar, Maria Bonsack, and Valsamo Anagnostou

Subjects
Cancer Research, Class (computer programming), biology, Computer science, Immunology, MHC class I, biology.protein, Computational biology, Throughput (business)
Abstract

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins is an emerging biomarker for predicting patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low positive predictive value for actual peptide presentation, inadequate support for rare MHC alleles, and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method to predict peptide-MHC binding. MHCnuggets is the only method to handle binding prediction for common or rare alleles of MHC Class I or II, with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is capable of faster performance than other methods. When compared to methods that integrate binding affinity and HLAp data from mass spectrometry, MHCnuggets yields a fourfold increase in positive predictive value on independent MHC-bound peptide (HLAp) data. We applied MHCnuggets to 26 cancer types in TCGA, processing 52.6 million allele-peptide comparisons in under 2.3 hours, yielding 103,587 candidate immunogenic missense mutations (IMMs). IMM hotspots occurred in 36 genes, including 22 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration (p2 patients, with 65% of these derived from driver mutations. Our results provide a new method for neoantigen prediction with high performance characteristics and demonstrate its utility in large data sets across human cancers. Citation Format: Xiaoshan M. Shao, Rohit Bhattacharya, Justin Huang, Ashok Sivakumar, Collin Tokheim, Lily Zheng, Dylan Hirsch, Ben Kaminow, Ashton Omdahl, Maria Bonsack, Angelika B. Riemer, Victor E. Velculescu, Valsamo Anagnostou, Kymberleigh Pagel, Rachel Karchin. High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr A33.

Published
2020

48. CHASMplus reveals the scope of somatic missense mutations driving human cancers

Author

Collin Tokheim and Rachel Karchin

Subjects
Histology, Somatic cell, Carcinogenesis, Mutation, Missense, Datasets as Topic, Computational biology, Biology, Genome, Models, Biological, Article, Pathology and Forensic Medicine, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Cancer Medicine, Cancer genome, Neoplasms, medicine, Missense mutation, Humans, Gene, 030304 developmental biology, 0303 health sciences, Cancer type, Cancer, Computational Biology, Cell Biology, medicine.disease, Frequency spectrum, 3. Good health, Benchmarking, 030220 oncology & carcinogenesis, human activities, 030217 neurology & neurosurgery, Algorithms
Abstract

SummaryLarge-scale cancer sequencing studies of patient cohorts have statistically implicated many genes driving cancer growth and progression, and their identification has yielded substantial translational impact. However, a remaining challenge is to increase the resolution of driver prediction from the gene level to the mutation level, because mutation-level predictions are more closely aligned with the goal of precision cancer medicine. Here we present CHASMplus, a computational method, that is uniquely capable of identifying driver missense mutations, including those specific to a cancer type, as evidenced by significantly superior performance on diverse benchmarks. Applied to 8,657 tumor samples across 32 cancer types in The Cancer Genome Atlas, CHASMplus identifies over 4,000 unique driver missense mutations in 240 genes, supporting a prominent role for rare driver mutations. We show which TCGA cancer types are likely to yield discovery of new driver missense mutations by additional sequencing, which has important implications for public policy.SignificanceMissense mutations are the most frequent mutation type in cancers and the most difficult to interpret. While many computational methods have been developed to predict whether genes are cancer drivers or whether missense mutations are generally deleterious or pathogenic, there has not previously been a method to score the oncogenic impact of a missense mutation specifically by cancer type, limiting adoption of computational missense mutation predictors in the clinic. Cancer patients are routinely sequenced with targeted panels of cancer driver genes, but such genes contain a mixture of driver and passenger missense mutations which differ by cancer type. A patient’s therapeutic response to drugs and optimal assignment to a clinical trial depends on both the specific mutation in the gene of interest and cancer type. We present a new machine learning method honed for each TCGA cancer type, and a resource for fast lookup of the cancer-specific driver propensity of every possible missense mutation in the human exome.

Published
2018
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49. Comprehensive Characterization of Cancer Driver Genes and Mutations

Author

Matthew H. Bailey, Collin Tokheim, Eduard Porta-Pardo, Sohini Sengupta, Denis Bertrand, Amila Weerasinghe, Antonio Colaprico, Michael C. Wendl, Jaegil Kim, Brendan Reardon, Patrick Kwok-Shing Ng, Kang Jin Jeong, Song Cao, Zixing Wang, Jianjiong Gao, Qingsong Gao, Fang Wang, Eric Minwei Liu, Loris Mularoni, Carlota Rubio-Perez, Niranjan Nagarajan, Isidro Cortés-Ciriano, Daniel Cui Zhou, Wen-Wei Liang, Julian M. Hess, Venkata D. Yellapantula, David Tamborero, Abel Gonzalez-Perez, Chayaporn Suphavilai, Jia Yu Ko, Ekta Khurana, Peter J. Park, Eliezer M. Van Allen, Han Liang, Michael S. Lawrence, Adam Godzik, Nuria Lopez-Bigas, Josh Stuart, David Wheeler, Gad Getz, Ken Chen, Alexander J. Lazar, Gordon B. Mills, Rachel Karchin, Li Ding, Samantha J. Caesar-Johnson, John A. Demchok, Ina Felau, Melpomeni Kasapi, Martin L. Ferguson, Carolyn M. Hutter, Heidi J. Sofia, Roy Tarnuzzer, Zhining Wang, Liming Yang, Jean C. Zenklusen, Jiashan (Julia) Zhang, Sudha Chudamani, Jia Liu, Laxmi Lolla, Rashi Naresh, Todd Pihl, Qiang Sun, Yunhu Wan, Ye Wu, Juok Cho, Timothy DeFreitas, Scott Frazer, Nils Gehlenborg, David I. Heiman, Pei Lin, Sam Meier, Michael S. Noble, Gordon Saksena, Doug Voet, Hailei Zhang, Brady Bernard, Nyasha Chambwe, Varsha Dhankani, Theo Knijnenburg, Roger Kramer, Kalle Leinonen, Yuexin Liu, Michael Miller, Sheila Reynolds, Ilya Shmulevich, Vesteinn Thorsson, Wei Zhang, Rehan Akbani, Bradley M. Broom, Apurva M. Hegde, Zhenlin Ju, Rupa S. Kanchi, Anil Korkut, Jun Li, Shiyun Ling, Wenbin Liu, Yiling Lu, Kwok-Shing Ng, Arvind Rao, Michael Ryan, Jing Wang, John N. Weinstein, Jiexin Zhang, Adam Abeshouse, Joshua Armenia, Debyani Chakravarty, Walid K. Chatila, Ino de Bruijn, Benjamin E. Gross, Zachary J. Heins, Ritika Kundra, Konnor La, Marc Ladanyi, Augustin Luna, Moriah G. Nissan, Angelica Ochoa, Sarah M. Phillips, Ed Reznik, Francisco Sanchez-Vega, Chris Sander, Nikolaus Schultz, Robert Sheridan, S. Onur Sumer, Yichao Sun, Barry S. Taylor, Jioajiao Wang, Hongxin Zhang, Pavana Anur, Myron Peto, Paul Spellman, Christopher Benz, Joshua M. Stuart, Christopher K. Wong, Christina Yau, D. Neil Hayes, Joel S. Parker, Matthew D. Wilkerson, Adrian Ally, Miruna Balasundaram, Reanne Bowlby, Denise Brooks, Rebecca Carlsen, Eric Chuah, Noreen Dhalla, Robert Holt, Steven J.M. Jones, Katayoon Kasaian, Darlene Lee, Yussanne Ma, Marco A. Marra, Michael Mayo, Richard A. Moore, Andrew J. Mungall, Karen Mungall, A. Gordon Robertson, Sara Sadeghi, Jacqueline E. Schein, Payal Sipahimalani, Angela Tam, Nina Thiessen, Kane Tse, Tina Wong, Ashton C. Berger, Rameen Beroukhim, Andrew D. Cherniack, Carrie Cibulskis, Stacey B. Gabriel, Galen F. Gao, Gavin Ha, Matthew Meyerson, Steven E. Schumacher, Juliann Shih, Melanie H. Kucherlapati, Raju S. Kucherlapati, Stephen Baylin, Leslie Cope, Ludmila Danilova, Moiz S. Bootwalla, Phillip H. Lai, Dennis T. Maglinte, David J. Van Den Berg, Daniel J. Weisenberger, J. Todd Auman, Saianand Balu, Tom Bodenheimer, Cheng Fan, Katherine A. Hoadley, Alan P. Hoyle, Stuart R. Jefferys, Corbin D. Jones, Shaowu Meng, Piotr A. Mieczkowski, Lisle E. Mose, Amy H. Perou, Charles M. Perou, Jeffrey Roach, Yan Shi, Janae V. Simons, Tara Skelly, Matthew G. Soloway, Donghui Tan, Umadevi Veluvolu, Huihui Fan, Toshinori Hinoue, Peter W. Laird, Hui Shen, Wanding Zhou, Michelle Bellair, Kyle Chang, Kyle Covington, Chad J. Creighton, Huyen Dinh, HarshaVardhan Doddapaneni, Lawrence A. Donehower, Jennifer Drummond, Richard A. Gibbs, Robert Glenn, Walker Hale, Yi Han, Jianhong Hu, Viktoriya Korchina, Sandra Lee, Lora Lewis, Wei Li, Xiuping Liu, Margaret Morgan, Donna Morton, Donna Muzny, Jireh Santibanez, Margi Sheth, Eve Shinbrot, Linghua Wang, Min Wang, David A. Wheeler, Liu Xi, Fengmei Zhao, Julian Hess, Elizabeth L. Appelbaum, Matthew Bailey, Matthew G. Cordes, Catrina C. Fronick, Lucinda A. Fulton, Robert S. Fulton, Cyriac Kandoth, Elaine R. Mardis, Michael D. McLellan, Christopher A. Miller, Heather K. Schmidt, Richard K. Wilson, Daniel Crain, Erin Curley, Johanna Gardner, Kevin Lau, David Mallery, Scott Morris, Joseph Paulauskis, Robert Penny, Candace Shelton, Troy Shelton, Mark Sherman, Eric Thompson, Peggy Yena, Jay Bowen, Julie M. Gastier-Foster, Mark Gerken, Kristen M. Leraas, Tara M. Lichtenberg, Nilsa C. Ramirez, Lisa Wise, Erik Zmuda, Niall Corcoran, Tony Costello, Christopher Hovens, Andre L. Carvalho, Ana C. de Carvalho, José H. Fregnani, Adhemar Longatto-Filho, Rui M. Reis, Cristovam Scapulatempo-Neto, Henrique C.S. Silveira, Daniel O. Vidal, Andrew Burnette, Jennifer Eschbacher, Beth Hermes, Ardene Noss, Rosy Singh, Matthew L. Anderson, Patricia D. Castro, Michael Ittmann, David Huntsman, Bernard Kohl, Xuan Le, Richard Thorp, Chris Andry, Elizabeth R. Duffy, Vladimir Lyadov, Oxana Paklina, Galiya Setdikova, Alexey Shabunin, Mikhail Tavobilov, Christopher McPherson, Ronald Warnick, Ross Berkowitz, Daniel Cramer, Colleen Feltmate, Neil Horowitz, Adam Kibel, Michael Muto, Chandrajit P. Raut, Andrei Malykh, Jill S. Barnholtz-Sloan, Wendi Barrett, Karen Devine, Jordonna Fulop, Quinn T. Ostrom, Kristen Shimmel, Yingli Wolinsky, Andrew E. Sloan, Agostino De Rose, Felice Giuliante, Marc Goodman, Beth Y. Karlan, Curt H. Hagedorn, John Eckman, Jodi Harr, Jerome Myers, Kelinda Tucker, Leigh Anne Zach, Brenda Deyarmin, Hai Hu, Leonid Kvecher, Caroline Larson, Richard J. Mural, Stella Somiari, Ales Vicha, Tomas Zelinka, Joseph Bennett, Mary Iacocca, Brenda Rabeno, Patricia Swanson, Mathieu Latour, Louis Lacombe, Bernard Têtu, Alain Bergeron, Mary McGraw, Susan M. Staugaitis, John Chabot, Hanina Hibshoosh, Antonia Sepulveda, Tao Su, Timothy Wang, Olga Potapova, Olga Voronina, Laurence Desjardins, Odette Mariani, Sergio Roman-Roman, Xavier Sastre, Marc-Henri Stern, Feixiong Cheng, Sabina Signoretti, Andrew Berchuck, Darell Bigner, Eric Lipp, Jeffrey Marks, Shannon McCall, Roger McLendon, Angeles Secord, Alexis Sharp, Madhusmita Behera, Daniel J. Brat, Amy Chen, Keith Delman, Seth Force, Fadlo Khuri, Kelly Magliocca, Shishir Maithel, Jeffrey J. Olson, Taofeek Owonikoko, Alan Pickens, Suresh Ramalingam, Dong M. Shin, Gabriel Sica, Erwin G. Van Meir, Hongzheng Zhang, Wil Eijckenboom, Ad Gillis, Esther Korpershoek, Leendert Looijenga, Wolter Oosterhuis, Hans Stoop, Kim E. van Kessel, Ellen C. Zwarthoff, Chiara Calatozzolo, Lucia Cuppini, Stefania Cuzzubbo, Francesco DiMeco, Gaetano Finocchiaro, Luca Mattei, Alessandro Perin, Bianca Pollo, Chu Chen, John Houck, Pawadee Lohavanichbutr, Arndt Hartmann, Christine Stoehr, Robert Stoehr, Helge Taubert, Sven Wach, Bernd Wullich, Witold Kycler, Dawid Murawa, Maciej Wiznerowicz, Ki Chung, W. Jeffrey Edenfield, Julie Martin, Eric Baudin, Glenn Bubley, Raphael Bueno, Assunta De Rienzo, William G. Richards, Steven Kalkanis, Tom Mikkelsen, Houtan Noushmehr, Lisa Scarpace, Nicolas Girard, Marta Aymerich, Elias Campo, Eva Giné, Armando López Guillermo, Nguyen Van Bang, Phan Thi Hanh, Bui Duc Phu, Yufang Tang, Howard Colman, Kimberley Evason, Peter R. Dottino, John A. Martignetti, Hani Gabra, Hartmut Juhl, Teniola Akeredolu, Serghei Stepa, Dave Hoon, Keunsoo Ahn, Koo Jeong Kang, Felix Beuschlein, Anne Breggia, Michael Birrer, Debra Bell, Mitesh Borad, Alan H. Bryce, Erik Castle, Vishal Chandan, John Cheville, John A. Copland, Michael Farnell, Thomas Flotte, Nasra Giama, Thai Ho, Michael Kendrick, Jean-Pierre Kocher, Karla Kopp, Catherine Moser, David Nagorney, Daniel O’Brien, Brian Patrick O’Neill, Tushar Patel, Gloria Petersen, Florencia Que, Michael Rivera, Lewis Roberts, Robert Smallridge, Thomas Smyrk, Melissa Stanton, R. Houston Thompson, Michael Torbenson, Ju Dong Yang, Lizhi Zhang, Fadi Brimo, Jaffer A. Ajani, Ana Maria Angulo Gonzalez, Carmen Behrens, Jolanta Bondaruk, Russell Broaddus, Bogdan Czerniak, Bita Esmaeli, Junya Fujimoto, Jeffrey Gershenwald, Charles Guo, Christopher Logothetis, Funda Meric-Bernstam, Cesar Moran, Lois Ramondetta, David Rice, Anil Sood, Pheroze Tamboli, Timothy Thompson, Patricia Troncoso, Anne Tsao, Ignacio Wistuba, Candace Carter, Lauren Haydu, Peter Hersey, Valerie Jakrot, Hojabr Kakavand, Richard Kefford, Kenneth Lee, Georgina Long, Graham Mann, Michael Quinn, Robyn Saw, Richard Scolyer, Kerwin Shannon, Andrew Spillane, Jonathan Stretch, Maria Synott, John Thompson, James Wilmott, Hikmat Al-Ahmadie, Timothy A. Chan, Ronald Ghossein, Anuradha Gopalan, Douglas A. Levine, Victor Reuter, Samuel Singer, Bhuvanesh Singh, Nguyen Viet Tien, Thomas Broudy, Cyrus Mirsaidi, Praveen Nair, Paul Drwiega, Judy Miller, Jennifer Smith, Howard Zaren, Joong-Won Park, Nguyen Phi Hung, Electron Kebebew, W. Marston Linehan, Adam R. Metwalli, Karel Pacak, Peter A. Pinto, Mark Schiffman, Laura S. Schmidt, Cathy D. Vocke, Nicolas Wentzensen, Robert Worrell, Hannah Yang, Marc Moncrieff, Chandra Goparaju, Jonathan Melamed, Harvey Pass, Natalia Botnariuc, Irina Caraman, Mircea Cernat, Inga Chemencedji, Adrian Clipca, Serghei Doruc, Ghenadie Gorincioi, Sergiu Mura, Maria Pirtac, Irina Stancul, Diana Tcaciuc, Monique Albert, Iakovina Alexopoulou, Angel Arnaout, John Bartlett, Jay Engel, Sebastien Gilbert, Jeremy Parfitt, Harman Sekhon, George Thomas, Doris M. Rassl, Robert C. Rintoul, Carlo Bifulco, Raina Tamakawa, Walter Urba, Nicholas Hayward, Henri Timmers, Anna Antenucci, Francesco Facciolo, Gianluca Grazi, Mirella Marino, Roberta Merola, Ronald de Krijger, Anne-Paule Gimenez-Roqueplo, Alain Piché, Simone Chevalier, Ginette McKercher, Kivanc Birsoy, Gene Barnett, Cathy Brewer, Carol Farver, Theresa Naska, Nathan A. Pennell, Daniel Raymond, Cathy Schilero, Kathy Smolenski, Felicia Williams, Carl Morrison, Jeffrey A. Borgia, Michael J. Liptay, Mark Pool, Christopher W. Seder, Kerstin Junker, Larsson Omberg, Mikhail Dinkin, George Manikhas, Domenico Alvaro, Maria Consiglia Bragazzi, Vincenzo Cardinale, Guido Carpino, Eugenio Gaudio, David Chesla, Sandra Cottingham, Michael Dubina, Fedor Moiseenko, Renumathy Dhanasekaran, Karl-Friedrich Becker, Klaus-Peter Janssen, Julia Slotta-Huspenina, Mohamed H. Abdel-Rahman, Dina Aziz, Sue Bell, Colleen M. Cebulla, Amy Davis, Rebecca Duell, J. Bradley Elder, Joe Hilty, Bahavna Kumar, James Lang, Norman L. Lehman, Randy Mandt, Phuong Nguyen, Robert Pilarski, Karan Rai, Lynn Schoenfield, Kelly Senecal, Paul Wakely, Paul Hansen, Ronald Lechan, James Powers, Arthur Tischler, William E. Grizzle, Katherine C. Sexton, Alison Kastl, Joel Henderson, Sima Porten, Jens Waldmann, Martin Fassnacht, Sylvia L. Asa, Dirk Schadendorf, Marta Couce, Markus Graefen, Hartwig Huland, Guido Sauter, Thorsten Schlomm, Ronald Simon, Pierre Tennstedt, Oluwole Olabode, Mark Nelson, Oliver Bathe, Peter R. Carroll, June M. Chan, Philip Disaia, Pat Glenn, Robin K. Kelley, Charles N. Landen, Joanna Phillips, Michael Prados, Jeffry Simko, Karen Smith-McCune, Scott VandenBerg, Kevin Roggin, Ashley Fehrenbach, Ady Kendler, Suzanne Sifri, Ruth Steele, Antonio Jimeno, Francis Carey, Ian Forgie, Massimo Mannelli, Michael Carney, Brenda Hernandez, Benito Campos, Christel Herold-Mende, Christin Jungk, Andreas Unterberg, Andreas von Deimling, Aaron Bossler, Joseph Galbraith, Laura Jacobus, Michael Knudson, Tina Knutson, Deqin Ma, Mohammed Milhem, Rita Sigmund, Andrew K. Godwin, Rashna Madan, Howard G. Rosenthal, Clement Adebamowo, Sally N. Adebamowo, Alex Boussioutas, David Beer, Thomas Giordano, Anne-Marie Mes-Masson, Fred Saad, Therese Bocklage, Lisa Landrum, Robert Mannel, Kathleen Moore, Katherine Moxley, Russel Postier, Joan Walker, Rosemary Zuna, Michael Feldman, Federico Valdivieso, Rajiv Dhir, James Luketich, Edna M. 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B., Govindan R., Heath S., Ley T., Van Tine B., Westervelt P., Rubin M.A., Lee J.I., Aredes N.D., Mariamidze A., Godzik A., Lopez-Bigas N., Stuart J., Wheeler D., Chen K., and Karchin R.

Subjects
0301 basic medicine, Oncology, Entropy, Programmed Cell Death 1 Receptor, biochemistry, genetics, molecular Biology (all), Biochemistry, B7-H1 Antigen, structure analysi, Principal Component Analysi, Neoplasms, Databases, Genetic, LS2_1, LS4_6, Missense mutation, Statistical analysis, 610 Medicine & health, Exome sequencing, Confusion, Principal Component Analysis, Melanoma, Sciences bio-médicales et agricoles, structure analysis, Algorithm, oncology, Cancer gene, Adenocarcinoma, Microsatellite Instability, ALGORITMOS, medicine.symptom, Algorithms, Human, medicine.medical_specialty, driver gene discovery, Computational biology, Biology, Characterization (mathematics), General Biochemistry, Genetics and Molecular Biology, Article, NO, 03 medical and health sciences, Internal medicine, medicine, Humans, mutations of clinical relevance, Gene, Biochemistry, Genetics and Molecular Biology(all), Microsatellite instability, Computational Biology, Cancer, medicine.disease, Cancérologie, 030104 developmental biology, Precision oncology, Mutation, Neoplasm, Human genome, Colon adenocarcinoma, human activities, Genetics and Molecular Biology(all)
Abstract

Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%–85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors. A comprehensive analysis of oncogenic driver genes and mutations in >9,000 tumors across 33 cancer types highlights the prevalence of clinically actionable cancer driver events in TCGA tumor samples., 0, info:eu-repo/semantics/published

Published
2018

50. Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics

Author

Li Ding, Matthew H. Bailey, Eduard Porta-Pardo, Vesteinn Thorsson, Antonio Colaprico, Denis Bertrand, David L. Gibbs, Amila Weerasinghe, Kuan-lin Huang, Collin Tokheim, Isidro Cortés-Ciriano, Reyka Jayasinghe, Feng Chen, Lihua Yu, Sam Sun, Catharina Olsen, Jaegil Kim, Alison M. Taylor, Andrew D. Cherniack, Rehan Akbani, Chayaporn Suphavilai, Niranjan Nagarajan, Joshua M. Stuart, Gordon B. Mills, Matthew A. Wyczalkowski, Benjamin G. Vincent, Carolyn M. Hutter, Jean Claude Zenklusen, Katherine A. Hoadley, Michael C. Wendl, llya Shmulevich, Alexander J. Lazar, David A. Wheeler, Gad Getz, Samantha J. Caesar-Johnson, John A. Demchok, Ina Felau, Melpomeni Kasapi, Martin L. Ferguson, Heidi J. Sofia, Roy Tarnuzzer, Zhining Wang, Liming Yang, Jean C. Zenklusen, Jiashan (Julia) Zhang, Sudha Chudamani, Jia Liu, Laxmi Lolla, Rashi Naresh, Todd Pihl, Qiang Sun, Yunhu Wan, Ye Wu, Juok Cho, Timothy DeFreitas, Scott Frazer, Nils Gehlenborg, David I. Heiman, Michael S. Lawrence, Pei Lin, Sam Meier, Michael S. Noble, Gordon Saksena, Doug Voet, Hailei Zhang, Brady Bernard, Nyasha Chambwe, Varsha Dhankani, Theo Knijnenburg, Roger Kramer, Kalle Leinonen, Yuexin Liu, Michael Miller, Sheila Reynolds, Ilya Shmulevich, Wei Zhang, Bradley M. Broom, Apurva M. Hegde, Zhenlin Ju, Rupa S. Kanchi, Anil Korkut, Jun Li, Han Liang, Shiyun Ling, Wenbin Liu, Yiling Lu, Kwok-Shing Ng, Arvind Rao, Michael Ryan, Jing Wang, John N. Weinstein, Jiexin Zhang, Adam Abeshouse, Joshua Armenia, Debyani Chakravarty, Walid K. Chatila, Ino de Bruijn, Jianjiong Gao, Benjamin E. Gross, Zachary J. Heins, Ritika Kundra, Konnor La, Marc Ladanyi, Augustin Luna, Moriah G. Nissan, Angelica Ochoa, Sarah M. Phillips, Ed Reznik, Francisco Sanchez-Vega, Chris Sander, Nikolaus Schultz, Robert Sheridan, S. Onur Sumer, Yichao Sun, Barry S. Taylor, Jioajiao Wang, Hongxin Zhang, Pavana Anur, Myron Peto, Paul Spellman, Christopher Benz, Christopher K. Wong, Christina Yau, D. Neil Hayes, Joel S. Parker, Matthew D. Wilkerson, Adrian Ally, Miruna Balasundaram, Reanne Bowlby, Denise Brooks, Rebecca Carlsen, Eric Chuah, Noreen Dhalla, Robert Holt, Steven J.M. Jones, Katayoon Kasaian, Darlene Lee, Yussanne Ma, Marco A. Marra, Michael Mayo, Richard A. Moore, Andrew J. Mungall, Karen Mungall, A. Gordon Robertson, Sara Sadeghi, Jacqueline E. Schein, Payal Sipahimalani, Angela Tam, Nina Thiessen, Kane Tse, Tina Wong, Ashton C. Berger, Rameen Beroukhim, Carrie Cibulskis, Stacey B. Gabriel, Galen F. Gao, Gavin Ha, Matthew Meyerson, Steven E. Schumacher, Juliann Shih, Melanie H. Kucherlapati, Raju S. Kucherlapati, Stephen Baylin, Leslie Cope, Ludmila Danilova, Moiz S. Bootwalla, Phillip H. Lai, Dennis T. Maglinte, David J. Van Den Berg, Daniel J. Weisenberger, J. Todd Auman, Saianand Balu, Tom Bodenheimer, Cheng Fan, Alan P. Hoyle, Stuart R. Jefferys, Corbin D. Jones, Shaowu Meng, Piotr A. Mieczkowski, Lisle E. Mose, Amy H. Perou, Charles M. Perou, Jeffrey Roach, Yan Shi, Janae V. Simons, Tara Skelly, Matthew G. Soloway, Donghui Tan, Umadevi Veluvolu, Huihui Fan, Toshinori Hinoue, Peter W. Laird, Hui Shen, Wanding Zhou, Michelle Bellair, Kyle Chang, Kyle Covington, Chad J. Creighton, Huyen Dinh, HarshaVardhan Doddapaneni, Lawrence A. Donehower, Jennifer Drummond, Richard A. Gibbs, Robert Glenn, Walker Hale, Yi Han, Jianhong Hu, Viktoriya Korchina, Sandra Lee, Lora Lewis, Wei Li, Xiuping Liu, Margaret Morgan, Donna Morton, Donna Muzny, Jireh Santibanez, Margi Sheth, Eve Shinbrot, Linghua Wang, Min Wang, Liu Xi, Fengmei Zhao, Julian Hess, Elizabeth L. Appelbaum, Matthew Bailey, Matthew G. Cordes, Catrina C. Fronick, Lucinda A. Fulton, Robert S. Fulton, Cyriac Kandoth, Elaine R. Mardis, Michael D. McLellan, Christopher A. Miller, Heather K. Schmidt, Richard K. Wilson, Daniel Crain, Erin Curley, Johanna Gardner, Kevin Lau, David Mallery, Scott Morris, Joseph Paulauskis, Robert Penny, Candace Shelton, Troy Shelton, Mark Sherman, Eric Thompson, Peggy Yena, Jay Bowen, Julie M. Gastier-Foster, Mark Gerken, Kristen M. Leraas, Tara M. Lichtenberg, Nilsa C. Ramirez, Lisa Wise, Erik Zmuda, Niall Corcoran, Tony Costello, Christopher Hovens, Andre L. Carvalho, Ana C. de Carvalho, José H. Fregnani, Adhemar Longatto-Filho, Rui M. Reis, Cristovam Scapulatempo-Neto, Henrique C.S. Silveira, Daniel O. Vidal, Andrew Burnette, Jennifer Eschbacher, Beth Hermes, Ardene Noss, Rosy Singh, Matthew L. Anderson, Patricia D. Castro, Michael Ittmann, David Huntsman, Bernard Kohl, Xuan Le, Richard Thorp, Chris Andry, Elizabeth R. Duffy, Vladimir Lyadov, Oxana Paklina, Galiya Setdikova, Alexey Shabunin, Mikhail Tavobilov, Christopher McPherson, Ronald Warnick, Ross Berkowitz, Daniel Cramer, Colleen Feltmate, Neil Horowitz, Adam Kibel, Michael Muto, Chandrajit P. Raut, Andrei Malykh, Jill S. Barnholtz-Sloan, Wendi Barrett, Karen Devine, Jordonna Fulop, Quinn T. Ostrom, Kristen Shimmel, Yingli Wolinsky, Andrew E. Sloan, Agostino De Rose, Felice Giuliante, Marc Goodman, Beth Y. Karlan, Curt H. Hagedorn, John Eckman, Jodi Harr, Jerome Myers, Kelinda Tucker, Leigh Anne Zach, Brenda Deyarmin, Hai Hu, Leonid Kvecher, Caroline Larson, Richard J. Mural, Stella Somiari, Ales Vicha, Tomas Zelinka, Joseph Bennett, Mary Iacocca, Brenda Rabeno, Patricia Swanson, Mathieu Latour, Louis Lacombe, Bernard Têtu, Alain Bergeron, Mary McGraw, Susan M. Staugaitis, John Chabot, Hanina Hibshoosh, Antonia Sepulveda, Tao Su, Timothy Wang, Olga Potapova, Olga Voronina, Laurence Desjardins, Odette Mariani, Sergio Roman-Roman, Xavier Sastre, Marc-Henri Stern, Feixiong Cheng, Sabina Signoretti, Andrew Berchuck, Darell Bigner, Eric Lipp, Jeffrey Marks, Shannon McCall, Roger McLendon, Angeles Secord, Alexis Sharp, Madhusmita Behera, Daniel J. Brat, Amy Chen, Keith Delman, Seth Force, Fadlo Khuri, Kelly Magliocca, Shishir Maithel, Jeffrey J. Olson, Taofeek Owonikoko, Alan Pickens, Suresh Ramalingam, Dong M. Shin, Gabriel Sica, Erwin G. Van Meir, Hongzheng Zhang, Wil Eijckenboom, Ad Gillis, Esther Korpershoek, Leendert Looijenga, Wolter Oosterhuis, Hans Stoop, Kim E. van Kessel, Ellen C. Zwarthoff, Chiara Calatozzolo, Lucia Cuppini, Stefania Cuzzubbo, Francesco DiMeco, Gaetano Finocchiaro, Luca Mattei, Alessandro Perin, Bianca Pollo, Chu Chen, John Houck, Pawadee Lohavanichbutr, Arndt Hartmann, Christine Stoehr, Robert Stoehr, Helge Taubert, Sven Wach, Bernd Wullich, Witold Kycler, Dawid Murawa, Maciej Wiznerowicz, Ki Chung, W. Jeffrey Edenfield, Julie Martin, Eric Baudin, Glenn Bubley, Raphael Bueno, Assunta De Rienzo, William G. Richards, Steven Kalkanis, Tom Mikkelsen, Houtan Noushmehr, Lisa Scarpace, Nicolas Girard, Marta Aymerich, Elias Campo, Eva Giné, Armando López Guillermo, Nguyen Van Bang, Phan Thi Hanh, Bui Duc Phu, Yufang Tang, Howard Colman, Kimberley Evason, Peter R. Dottino, John A. Martignetti, Hani Gabra, Hartmut Juhl, Teniola Akeredolu, Serghei Stepa, Dave Hoon, Keunsoo Ahn, Koo Jeong Kang, Felix Beuschlein, Anne Breggia, Michael Birrer, Debra Bell, Mitesh Borad, Alan H. Bryce, Erik Castle, Vishal Chandan, John Cheville, John A. Copland, Michael Farnell, Thomas Flotte, Nasra Giama, Thai Ho, Michael Kendrick, Jean-Pierre Kocher, Karla Kopp, Catherine Moser, David Nagorney, Daniel O’Brien, Brian Patrick O’Neill, Tushar Patel, Gloria Petersen, Florencia Que, Michael Rivera, Lewis Roberts, Robert Smallridge, Thomas Smyrk, Melissa Stanton, R. Houston Thompson, Michael Torbenson, Ju Dong Yang, Lizhi Zhang, Fadi Brimo, Jaffer A. 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sciences, Medical Genetics, Medical Sociology, SAIC-F-Frederick, Inc, and Leidos Biomedical Research, Inc.

Subjects
0301 basic medicine, oncogenic process, INFORMATION, DNA Repair, Tumor Microenvironment/genetics, Carcinogenesis, Cancer Genome Atlas Research Network, medicine.disease_cause, Genome, Biochemistry, Germline, Transcriptome, oncogenic proce, Neoplasms, Databases, Genetic, LS2_1, Tumor Microenvironment, LS4_6, SOMATIC POINT MUTATIONS, 610 Medicine & health, 11 Medical and Health Sciences, Carcinogenesi, Metabolic Networks and Pathways/genetics, cancer genomic, CARCINOGÊNESE, Genomics, TUMORS, Neoplasms/genetics, omics, Proteome, Physique des particules élémentaires, Microsatellite Instability, Life Sciences & Biomedicine, Metabolic Networks and Pathways, Human, EXPRESSION, Biochemistry & Molecular Biology, cancer, cancer genomics, TCGA, omic, Computational biology, Biology, BREAST, CLASSIFICATION, General Biochemistry, Genetics and Molecular Biology, Article, NO, 03 medical and health sciences, DNA Repair/genetics, medicine, Humans, Tumor microenvironment, Science & Technology, LANDSCAPE, IDENTIFICATION, Biochemistry, Genetics and Molecular Biology(all), Metabolic Networks and Pathway, Cell Biology, Epigenome, 06 Biological Sciences, 030104 developmental biology, DISCOVERY, Mutation, DRIVER, Neoplasm, Carcinogenesis/genetics, Developmental Biology, Genetics and Molecular Biology(all), Genes, Neoplasm
Abstract

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing. A synthesized view on oncogenic processes based on PanCancer Atlas analyses highlights the complex impact of genome alterations on the signaling and multi-omic profiles of human cancers as well as their influence on tumor microenvironment., 0, info:eu-repo/semantics/published

Published
2018

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