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Table S4

AACR GENIE Data Guide

Supplemental Methods. Supplemental Table 1: ââ,¬â€¹Genomic Data Characterization by Center. Supplemental Table 2: ââ,¬â€¹Gene Panels Submitted by Each Center. Figure S1: Number of putative germline SNPs per sample, before and after uniform germline filtering. Figure S2ââ,¬â€¹. Distribution of total somatic mutation burden per sample stratified by sequencing panel. Figure S3: ââ,¬â€¹Log-scale comparison of mutation frequencies at hotspot sites between GENIE (data aggregated from all sequencing panels) and cancerhotspots.org (CHS) using a binomial test. Figure S4:ââ,¬â€¹ Comparison of mutation frequencies at hotspot sites in each GENIE sequencing panel with cancerhotspots.org (CHS) using a binomial test.

Figures S1-S14

Supplementary Methods

Tables S1-S12

Purpose:In contrast to recurrence after initial diagnosis of stage I–III breast cancer [recurrent metastatic breast cancer (rMBC)], de novo metastatic breast cancer (dnMBC) represents a unique setting to elucidate metastatic drivers in the absence of treatment selection. We present the genomic landscape of dnMBC and association with overall survival (OS).Experimental Design:Targeted DNA sequencing (OncoPanel) was prospectively performed on either primary or metastatic tumors from 926 patients (212 dnMBC and 714 rMBC). Single-nucleotide variants, copy-number variations, and tumor mutational burden (TMB) in treatment-naïve dnMBC primary tumors were compared with primary tumors in patients who ultimately developed rMBC, and correlated with OS across all dnMBC.Results:When comparing primary tumors by subtype, MYB amplification was enriched in triple-negative dnMBC versus rMBC (21.1% vs. 0%, P = 0.0005, q = 0.111). Mutations in KMTD2, SETD2, and PIK3CA were more prevalent, and TP53 and BRCA1 less prevalent, in primary HR+/HER2− tumors of dnMBC versus rMBC, though not significant after multiple comparison adjustment. Alterations associated with shorter OS in dnMBC included TP53 (wild-type: 79.7 months; altered: 44.2 months; P = 0.008, q = 0.107), MYC (79.7 vs. 23.3 months; P = 0.0003, q = 0.011), and cell-cycle (122.7 vs. 54.9 months; P = 0.034, q = 0.245) pathway genes. High TMB correlated with better OS in triple-negative dnMBC (P = 0.041).Conclusions:Genomic differences between treatment-naïve dnMBC and primary tumors of patients who developed rMBC may provide insight into mechanisms underlying metastatic potential and differential therapeutic sensitivity in dnMBC. Alterations associated with poor OS in dnMBC highlight the need for novel approaches to overcome potential intrinsic resistance to current treatments.

PURPOSE With the growing number of available targeted therapeutics and molecular biomarkers, the optimal care of patients with cancer now depends on a comprehensive understanding of the rapidly evolving landscape of precision oncology, which can be challenging for oncologists to navigate alone. METHODS We developed and implemented a precision oncology decision support system, GI TARGET, (Gastrointestinal Treatment Assistance Regarding Genomic Evaluation of Tumors) within the Gastrointestinal Cancer Center at the Dana-Farber Cancer Institute. With a multidisciplinary team, we systematically reviewed tumor molecular profiling for GI tumors and provided molecularly informed clinical recommendations, which included identifying appropriate clinical trials aided by the computational matching platform MatchMiner, suggesting targeted therapy options on or off the US Food and Drug Administration–approved label, and consideration of additional or orthogonal molecular testing. RESULTS We reviewed genomic data and provided clinical recommendations for 506 patients with GI cancer who underwent tumor molecular profiling between January and June 2019 and determined follow-up using the electronic health record. Summary reports were provided to 19 medical oncologists for patients with colorectal (n = 198, 39%), pancreatic (n = 124, 24%), esophagogastric (n = 67, 13%), biliary (n = 40, 8%), and other GI cancers. We recommended ≥ 1 precision medicine clinical trial for 80% (406 of 506) of patients, leading to 24 enrollments. We recommended on-label and off-label targeted therapies for 6% (28 of 506) and 25% (125 of 506) of patients, respectively. Recommendations for additional or orthogonal testing were made for 42% (211 of 506) of patients. CONCLUSION The integration of precision medicine in routine cancer care through a dedicated multidisciplinary molecular tumor board is scalable and sustainable, and implementation of precision oncology recommendations has clinical utility for patients with cancer.

A growing number of longitudinal cohort studies are generating data with extensive patient observations across multiple timepoints. Such data offers promising opportunities to better understand the progression of diseases. However, these observations are usually treated as general events in existing visual analysis tools. As a result, their capabilities in modeling disease progression are not fully utilized. To fill this gap, we designed and implemented ThreadStates, an interactive visual analytics tool for the exploration of longitudinal patient cohort data. The focus of ThreadStates is to identify the states of disease progression by learning from observation data in a human-in-the-loop manner. We propose a novel Glyph Matrix design and combine it with a scatter plot to enable seamless identification, observation, and refinement of states. The disease progression patterns are then revealed in terms of state transitions using Sankey-based visualizations. We employ sequence clustering techniques to find patient groups with distinctive progression patterns, and to reveal the association between disease progression and patient-level features. The design and development were driven by a requirement analysis and iteratively refined based on feedback from domain experts over the course of a 10-month design study. Case studies and expert interviews demonstrate that ThreadStates can successively summarize disease states, reveal disease progression, and compare patient groups.

Tumor-infiltrating lymphocyte (TIL) density has been identified as a prognostic and predictive biomarker in select tumors treated with defined therapies. These observations suggest that TILs may be general markers of patient outcomes, but evidence in support of this hypothesis has been limited by small cohorts. We validated ImmunoPROFILE, a multiplexed immunofluorescence (MIF)-based assay coupled with machine-learning-based image analysis, to identify and quantify tumor cells (cytokeratin, PAX5, PAX8, SOX10), T cells (CD8), T-regulatory cells (FOXP3), exhausted cells (PD-1) and immunosuppressive tumor and immune cells (PD-L1). We applied the MIF panel to specimens from patients collected prospectively over three years and analyzed 2,023 cases across 27 tumor types. The association between biomarkers and overall survival (OS) was investigated using Cox models controlling for patient risk factors such as cancer type, metastatic vs. primary disease, age, and gender. Multivariable biomarker selection was based on likelihood ratios. The assay was highly robust (success rate 97%), reproducible (inter-scanning and intra-staining density controls within 1 SD, inter-staining PD-L1 scores ≤11% CV), and operator-independent (R2 >0.7 to >0.9 for each biomarker and 95% concordance in PD-L1 score-based interpretation between technicians). From whole slide images, a total of 11,932 individual regions of interest were analyzed across the cohort, resulting in >50 million spatially-resolved single cells which were summarized into cell population densities and PD-L1 scores. High densities of CD8+ (>64/mm2, p50/mm2, p30/mm2, p To our knowledge, this is the first enterprise-level immune biomarker assay using multiplexed staining, digital imaging, and machine learning to be applied in a prospective manner to clinical specimens at scale. We found that select immune cell densities are prognostic across cancer types and therapies and demonstrated that quantification of multiple cell populations yields better prognostic power than single marker analyses. Citation Format: James Lindsay, Bijaya Sharma, Kristen D. Felt, Anita Giobbie-Hurder, Ian Dryg, Jason L. Weirather, Jennifer Altreuter, Tali Mazor, Priti Kumari, Joao V. Alessi, Ajit J. Nirmal, Michael P. Manos, Ananth R. Kumar, William Lotter, Ethan Cerami, Burce E. Johnson, Neil I. Lindeman, Lynette M. Sholl, Jonathan A. Nowak, Scott J. Rodig. ImmunoPROFILE: A prospective implementation of clinically validated, quantitative immune cell profiling test identifies tumor-infiltrating CD8+ and PD-1+ cell densities as prognostic biomarkers across a 2,023 patient pan-cancer cohort treated with different therapies. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5706.

cBioPortal for Cancer Genomics is an open-source platform for interactive, exploratory analysis of large-scale clinico-genomic data sets. cBioPortal provides a suite of user-friendly visualizations and analyses, including OncoPrints, mutation “lollipop” plots, variant interpretation, group comparison, survival analysis, expression correlation analysis, alteration enrichment analysis, cohort and patient-level visualization. The public site (https://www.cbioportal.org) is accessed by >35,000 unique visitors each month and hosts data from >350 studies spanning individual labs and large consortia. In addition, at least 74 instances of cBioPortal are installed at academic institutions and companies worldwide. To better support all users, we unified our documentation (https://docs.cbioportal.org) and added a user guide and an ongoing series of ‘how-to’ videos to address common questions. In 2022 we added 32 studies (>38,000 samples) to the public site. In addition, we added a nonsynonymous tumor mutation burden (TMB) value for all samples and enhanced the TCGA PanCancer Atlas studies with DNA methylation and treatment data. All data is available in the cBioPortal Datahub: https://github.com/cBioPortal/datahub. We also host a dedicated instance for AACR Project GENIE, enabling access to the GENIE cohort of >165,000 clinically sequenced samples from 19 institutions (https://genie.cbioportal.org). The GENIE Biopharma Collaborative (BPC) enables the collection of comprehensive clinical annotations, including response, outcome, and treatment history. The first BPC cohorts are now available: ~2,000 non-small cell lung cancer samples and ~1,500 colorectal cancer samples. Support for multimodal data analysis has been a major focus, including several new integrations with external tools. Single cell data is now available in the CPTAC GBM study and can be visualized throughout cBioPortal, and via integration with cellxgene. On the patient page, H&E and mIF images can be visualized via integration with Minerva, and the genomic overview now integrates IGV. We continue to enhance existing features. In the study view, users can now add charts comparing categorical vs continuous data, and the plots tab includes a heatmap option. We replaced the existing fusion data type with a generalized structural variant data type that supports detailed information including breakpoints and orientation, to enable new visualizations and analyses. Pathway level analysis has been extended with a new integration with NDEx. cBioPortal is fully open source (https://github.com/cBioPortal/). Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children’s Hospital of Philadelphia, Princess Margaret Cancer Centre, Caris Life Sciences, Bilkent University and The Hyve. We welcome open source contributions from others in the cancer research community. Citation Format: Ino de Bruijn, Tali Mazor, Adam Abeshouse, Diana Baiceanu, Stephanie Carrero, Elena Garcia Lara, Benjamin Gross, David M. Higgins, Prasanna K. Jagannathan, Priti Kumari, Ritika Kundra, Bryan Lai, Xiang Li, James Lindsay, Aaron Lisman, Divya Madala, Ramyasree Madupuri, Angelica Ochoa, Yusuf Ziya Özgül, Oleguer Plantalech, Sander Rodenburg, Baby Anusha Satravada, Robert Sheridan, Lucas Sikina, Jessica Singh, S Onur Sumer, Yichao Sun, Pim van Nierop, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Sjoerd van Hagen, Ugur Dogrusoz, Allison Heath, Adam Resnick, Trevor J. Pugh, Chris Sander, Ethan Cerami, Jianjiong Gao, Nikolaus Schultz. cBioPortal for Cancer Genomics. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4256.

Purpose: In contrast to recurrence after initial diagnosis of stage I–III breast cancer [recurrent metastatic breast cancer (rMBC)], de novo metastatic breast cancer (dnMBC) represents a unique setting to elucidate metastatic drivers in the absence of treatment selection. We present the genomic landscape of dnMBC and association with overall survival (OS). Experimental Design: Targeted DNA sequencing (OncoPanel) was prospectively performed on either primary or metastatic tumors from 926 patients (212 dnMBC and 714 rMBC). Single-nucleotide variants, copy-number variations, and tumor mutational burden (TMB) in treatment-naïve dnMBC primary tumors were compared with primary tumors in patients who ultimately developed rMBC, and correlated with OS across all dnMBC. Results: When comparing primary tumors by subtype, MYB amplification was enriched in triple-negative dnMBC versus rMBC (21.1% vs. 0%, P = 0.0005, q = 0.111). Mutations in KMTD2, SETD2, and PIK3CA were more prevalent, and TP53 and BRCA1 less prevalent, in primary HR+/HER2− tumors of dnMBC versus rMBC, though not significant after multiple comparison adjustment. Alterations associated with shorter OS in dnMBC included TP53 (wild-type: 79.7 months; altered: 44.2 months; P = 0.008, q = 0.107), MYC (79.7 vs. 23.3 months; P = 0.0003, q = 0.011), and cell-cycle (122.7 vs. 54.9 months; P = 0.034, q = 0.245) pathway genes. High TMB correlated with better OS in triple-negative dnMBC (P = 0.041). Conclusions: Genomic differences between treatment-naïve dnMBC and primary tumors of patients who developed rMBC may provide insight into mechanisms underlying metastatic potential and differential therapeutic sensitivity in dnMBC. Alterations associated with poor OS in dnMBC highlight the need for novel approaches to overcome potential intrinsic resistance to current treatments.

794 Background: Tumor genomic testing (GT) has increased diagnostic accuracy and treatment options for patients (pts) with cancer. Dana-Farber Cancer Institute (DFCI) has made GT accessible as an institute-supported research effort for >10 yrs. We estimate 50% standard therapies and 15-35% clinical trials in Gastrointestinal Cancer Clinic (GCC) require GT to determine eligibility. Pts in GCC with certain cancers are eligible for GT as a clinical test – these include metastatic/locally advanced colorectal, gastric, pancreatic, or biliary cancers. Clinical testing requires CLIA lab certification and insurance reimbursement; research does not. Herein we ID gaps in our GT database. Methods: We reviewed data on GT uptake in GCC between 4/2015 - 6/2022. 20,096 pts were captured by the GT tracking system. Data included: testing ordered and completed (proportion, type, time to receiving tissue for testing [TR], time to testing completion [TC]). Demographic data is not captured in the tracking system; matching unique patient identifiers with electronic health record is pending. Results: Most pts received GT (57.6%); 12% were not eligible; 30.4% declined consent. Most testing was completed (67.6%), but 21.3% of tests failed (45.5% of these from insufficient tissue). Research testing (71%) comprised most tests, but clinical tests were completed faster (median 34 days research vs 20 days clinical). Ampullary (91%), anal (90%), colon (90%) had highest completion rates; pancreatic (59%), hepatocellular carcinoma (56%) had lowest (from insufficient viable tumor in submitted specimens). Conclusions: GCC has a robust recruitment program that has yielded high GT uptake. Given the frequency that GT is used for treatment and trials, building a demographically representative dataset is crucial, especially for pts with largest burden of morbidity and mortality from cancer. We ID'd data gaps in the GT tracking system, which lacks demographics and reason for not testing. Demographic data is available in the electronic health record but does not speak with the GT tracking system so this analysis is not routinely done. Ability to visualize this data is important to ensure equitable GT uptake. Future efforts will focus on improving rates of consent in genomics databases and cancer clinical trials. Genomic testing at Dana-Farber Cancer Institute Gastrointestinal Cancer Center, 4/2015 – 6/2022.[Table: see text]

Background: Lack of representation in genomic databases limits the generalizability of study findings, predictive models, and therapeutics. In cancer, the development of nearly 75% of cancer drugs have been informed by genomic data,1 yet there is persistent lack of diversity of genomic testing databases that inform clinical trials and lead to drug approval.2,3 The PROFILE project at Dana-Farber Cancer Institute (DFCI) is available to all adult cancer patients, offering free genomic testing for inclusion in their genomic database. Herein, we describe the enrollment ratios in PROFILE delineated by demographic group and cancer type. Methods: We conducted a cross-sectional analysis of the prevalence of PROFILE enrollment 7/8/2011 – 1/1/2022. Over this time period 177,788. Enrollment processes vary in each DFCI disease center, but all solid tumor patients are meant to be approached for enrollment around the time of their first visit. The electronic health record was queried to identify demographics, which are collected by registration staff at intake. Queried demographics included gender, age ( Citation Format: Nadine Jackson McCleary, Ellana K. Haakenstad, Bridget Neville, Arrien A. Bertram, Wendy Loeser, Joseph Grider, Andea Kruse, Alissa Gentile, Olga Kozyreva, Pedro Sanz-Altamira, Christopher Lathan, Michael J. Hassett, Ethan Cerami, Annette S. Kim, Marios Giannakis, R. Coleman Lindsley, Neal I. Lindeman, William C. Hahn, Janina Longtine, Barrett Rollins, Levi Garraway, Bruce E. Johnson. REAL variance in genomic testing by sex, age, race, ethnicity, and language: PROFILE database 7/2011 - 1/2022 [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C070.

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.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

PURPOSE The yield of comprehensive genomic profiling in recruiting patients to molecular-based trials designed for small subgroups has not been fully evaluated. We evaluated the likelihood of enrollment in a clinical trial that required the identification of a specific genomic change based on our institute-wide genomic tumor profiling. PATIENTS AND METHODS Using genomic profiling from archived tissue samples derived from patients with metastatic breast cancer treated between 2011 and 2017, we assessed the impact of systematic genomic characterization on enrollment in an ongoing phase II trial (ClinicalTrials.gov identifier: NCT01670877 ). Our primary aim was to describe the proportion of patients with a qualifying ERBB2 mutation identified by our institutional genomic panel (OncoMap or OncoPanel) who enrolled in the trial. Secondary objectives included median time from testing result to trial registration, description of the spectrum of ERBB2 mutations, and survival. Associations were calculated using Fisher’s exact test. RESULTS We identified a total of 1,045 patients with metastatic breast cancer without ERBB2 amplification who had available genomic testing results. Of these, 42 patients were found to have ERBB2 mutation and 19 patients (1.8%) were eligible for the trial on the basis of the presence of an activating mutation, 18 of which were identified by OncoPanel testing. Fifty-eight percent of potentially eligible patients were approached, and 33.3% of eligible patients enrolled in the trial guided exclusively by OncoPanel testing. CONCLUSION More than one half of eligible patients were approached for trial participation and, significantly, one third of those were enrolled in NCT01670877. Our data illustrate the ability to enroll patients in trials of rare subsets in routine clinical practice and highlight the need for these broadly based approaches to effectively support the success of these studies.

Squamous cell lung cancer (SqCLC) is a type of non-small cell lung cancer strongly associated with cigarette smoking and with known targetable mutations, however some patients do not have matching targeted drugs. The S1400I Phase III randomized LungMap sub-study of nivo+ipi versus nivo accrued 275 (252 eligible) previously-treated patients with stage IV SqCLC and absence of matched mutations (NCT02154490).Here, to investigate the longitudinal (baseline, C2 week 3, C4 week 7, C5 week 9) serum protein changes associated with treatment or response, we performed Olink proximity extension assay in 160 patients (561 samples) using an immuno-oncology panel of 92 analytes. We utilized mixed linear and joint models to identify differentially expressed proteins between treatment arms (nivo vs. nivo+ipi), response and quantify the effect of other potential prognostic factors. This approach quantifies the effect of covariates such as smoking status, demographics, prior radiation therapy, and metastases. We jointly modeled expression and survival to identify changes in proteins over time. The thresholds for significance in differentially expression tests were a log fold change of at least 0.5 and a false discovery rate of under 0.05 (FDR as a multiple testing adjustment method). The joint models used thresholds of log fold change of more than 1 and hazard ratio of more than 1. Results revealed increases in 40 serum proteins after treatment with either nivo alone or combined with ipi, including CXCL9, CXCL10, CXCL11, CXCL13, IL6, IL8, IL10, IFNg, and soluble PD-L1 and PDCD1. nivo+ipi treatment showed greater increases in IL8 and CXCL13 post-treatment compared to nivo alone. In addition, circulating IL6, CXCL13, and MIC-A/B were higher in patients with stable or progressive disease compared to those with objective response after treatment. The joint model revealed a worse hazard ratio with increases in 10 soluble proteins, including IL6, IL8, and CXCL13. Finally, for patients with similar values of IL8, those treated by combination had a better survival outcome than those treated by nivo alone. Using a data-modeling approach, we identified significant longitudinal changes in 40 serum proteins out of 92 tested in patients with SqCLC treated with nivo or nivo+ipi. Increases in serum inflammatory markers IL6 and CXCL13 were associated with worse disease. Although overall survival was not statistically different between arms, our modeling approaches suggested that IL-8 monitoring may help identify patients benefiting more from the combination vs. nivo alone. Citation Format: Edgar Gonzalez-Kozlova, Hsin-Hui Huang, Mary Redman, Roy Herbst, Scott Gettinger, Luda Bazhenova, Hui Xie, Manishkumar Patel, Kai Nie, Jocelyn Harris, Kimberly Argueta, Karen Kelly, Ethan Cerami, James Lindsay, Joyce Yu, Roshni Biswas, Stephen Van Nostrand, Radim Moravec, Diane Marie Del Valle, Seunghee Kim-schulze, Sacha Gnjatic. Dynamic changes in circulating protein levels reveal an association between ipilimumab and nivolumab combination treatment (SWOG Lung-MAP S1400I trial) with outcomes in squamous cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5026.

Background: In the Phase III randomized trial (SWOG Lung-MAP S1400I non match trial), adding ipilimumab (I) to nivolumab (N) did not improve survival of patients with advanced, pretreated, immune checkpoint inhibitor naive lung squamous cell carcinoma (SCC). The I+N combination demonstrated superiority in patients with a tumor mutation burden (TMB) ≥10 mt/MB and PD-L1 expression Methods: RNA extracted from baseline FFPE samples from 66/252 patients enrolled in the S1400I trial was received from the SWOG bank with 28 samples considered inadequate due to low input RNA. 38 samples were run on the ncounter platform using the PanCancer Immune Profiling panel using the manufacturer’s instructions with the addition of Human Reference RNA control. Data were processed and normalized using nSolver. All samples passed the post run QC with no batch effect. Using a log-rank test on dichotomized normalized data, we performed time to event analysis to identify genes that correlated with overall survival (OS) and PFS (Progression Free Survival) for all 38 samples and for each arm (23 N and 15 I+N). We ran a Cox model using significant genes identified independently and in association with TMB≥10 and PD-L1 ≥5 thresholds identified in the clinical studies. TIMER and nSolver advanced analysis were used to infer immune cells that correlated to clinical outcomes. Results: We observed that BLNK, CD163, FCGR2A associated with increased OS (p Conclusion: Despite a small dataset, this analysis shows a potential advantage in PFS and OS with increased presence of immune cells including exhausted CD8+ T cells and genes associated with myeloid cells. Validation of these findings is warranted and may refine patient populations that would benefit from this combination strategy. Acknowledgments: Scientific and financial support: U10CA180888, U10CA180819, U24CA224285, U24CA224316, PACT, PPP and FNIH Citation Format: Dzifa Y. Duose, Jiexin Zhang, Mary Redman, Baili Zhang, Ethan Cerami, James Lindsay, Joyce Yu, Roshni Biswas, Stephen Van Nostrand, Radim Moravec, Rajyalakshmi Luthra, Gheath Al-Atrash, Karen Kelly, Roy Herbst, Ignacio Wistuba, Scott Gettinger, Lyudmila Bazhenova, J. Jack Lee, Jianjun Zhang, Cara Haymaker. Immune gene expression signatures associated clinical benefit from nivolumab and ipilimumab for previously treated patients with stage IV squamous cell lung cancer: An immune biomarker analysis of phase III SWOG LungMAP S1400I trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1974.

Pediatric pan-cancer genome analyses do not capture the full range of diagnoses encountered in clinical practice. To inform basket trial design and real-world precision oncology practice, we classified diagnoses and assessed the landscape of mutations, including trial-matching, in an unselected cohort of pediatric solid tumors. Since 2013 all Dana-Farber/Boston Children’s patients have been offered participation in the Profile study. Participant tumor samples were sequenced with DFCI-OncoPanel, a targeted panel test sequencing exons of up to 447 cancer genes for single nucleotide variants, insertions and deletions and copy number alterations, and introns and exons of up to 60 genes for rearrangements. Patient diagnosis was classified according to ICD-O, version 3.2. Genomic alterations were analyzed for matching to the actionable mutation lists of precision oncology basket trials (NCI-COG Pediatric MATCH, NCI-MATCH, and the ASCO TAPUR Study v.3). Data will be shared with the Childhood Cancer Data Initiative. There were 888 pediatric patients with sequencing enrolled in Profile between January 2013 and March 2019; 512 (58%) with solid tumors and 376 (42%) with CNS tumors. Fifty-five percent (491/888) of patients had one of ten common pediatric cancer diagnoses: neuroblastoma (n=80), low-grade glioma (n=72), Wilms tumor (n=57), medulloblastoma (n=55), pilocytic astrocytoma (n=47), rhabdomyosarcoma (n=44), osteosarcoma (n=42), ependymoma (n=39), Ewing sarcoma (n=28) and glioblastoma (n=27). The remaining 45% (397/888) had one of 85 distinct rare malignancies with less than 25 cases per diagnosis. Most (80/85) of these rare diagnoses are not represented in prior pediatric pan-cancer sequencing studies. Recurrent (>5%) pathogenic alterations were, in common and rare diagnoses, TP53 mutations(m) and deletions(del) and BRAFm and rearrangements(r), in common diagnoses, MYC/MYCN amplification (amp) and EWSR1r and, in rare diagnoses, CTNNB1m, CDKN2A/Bdel and NF1m/del. We found that 31% (n=271/888) of patients had at least 1 variant matching a basket trial treatment arm. Genes with matching alterations include BRAF (10%), NF1 (4%), PI3KCA (3%), NRAS (2%), BRCA2 (2%), ALK (1%), and FGFR1 (1%). Sequencing of pediatric malignancies is increasing. This study highlights opportunities to use the resulting genomic data to inform genome-selected clinical trial design and uncover drivers in pediatric cancers. The proportion of cases in this cohort with genomic alterations meeting eligibility for basket trials is equivalent to that seen in the pediatric MATCH screening study. Due to the low prevalence of the diagnoses in the long tail of cancer types in this study, defining the genomic landscape of ultra-rare cancers will require data sharing. Classifying pediatric cancer diagnoses using the ICD-O standard ontology system is feasible and will facilitate data sharing. Citation Format: Suzanne J. Forrest, Hersh Gupta, Abigail Ward, Yvonne Li, Duong Doan, Alyaa Al-Ibraheemi, Sanda Alexandrescu, Pratiti Bandopadhayay, Suzanne Shusterman, Elizabeth A. Mullen, Natalie Collins, Susan N. Chi, Karen D. Wright, Priti Kumari, Tali Mazor, Keith L. Ligon, Priyanka Shivdasani, Phani Davineni, Monica Manam, Richard L. Schilsky, Suanna S. Bruinooge, Jaime M. Guidry Auvil, Ethan Cerami, Barrett J. Rollins, Matthew L. Meyerson, Neal I. Lindeman, Laura MacConaill, Bruce E. Johnson, Andrew D. Cherniack, Alanna J. Church, Katherine A. Janeway. Sequencing of 888 pediatric solid tumors informs precision oncology trial design and data sharing initiatives in pediatric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3890.

Background: Lung squamous cell carcinoma (SCC) is the second most common type of non-small cell lung cancer. Immunotherapy is a promising treatment for SCC. However, only a small proportion of unselected SCC patients are able to achieve durable clinical benefit. We leveraged the precious specimens from S1400I trial and sought to explore whether the levels and spatial distributions of tumor-associated immune cells (TAICs) in screening samples are associated with PFS, and OS. Methods: We utilized paraffin tumor tissue provided by the SWOG bank from screening on LungMAP. 82/252 eligible SCC sample from patients treated with nivolumab plus ipilimumab (nivo+ipi; n = 35) or with nivolumab alone (nivo; n = 47) were studied. Image analysis tissue immunoprofiling was conducted using 9 markers in 2 multiplex immunofluorescence panels against cytokeratin, CD3, CD8, granzyme B, CD45RO, and FOXP3, PD1, PD-L1, and CD68. Densities of cells phenotypes (cells/mm2) were assessed and dichotomized as being high (> median) or low (≤ median) in tumor, stroma, and total compartments. Distances from malignant cells (MCs) to cell populations were measured and dichotomized as close from MCs (≤ median) and faraway from MCs (> median) and associated with clinical variables. Clinicopathologic features and data on PFS, and OS were retrieved from the CIDC portal as provided by the LungMap team. Non-parametric tests where utilized to assessment associations of cell phenotypes versus continue or categorical variables and log-rank test for survival analysis. Results: Univariate analysis showed that higher densities of memory T cells in total compartment (HR:0.63, CI:0.40-1.00, P=0.041), antigen-experienced T cells in the stroma (HR:0.60, CI:0.37-0.96, P=0.024) and total compartment (HR:0.60, CI:0.38-0.95, P=0.023) and activated cytotoxic T cells in the tumor compartment (HR:0.55, CI:0.35-0.87, P=0.011) were associated with significative better PFS. In the nivo+ipi arm, higher ratios of cytotoxic T cells to regulatory T cells in the stroma were associated with better PFS. The spatial analysis of these TAICs showed that the presence of activated cytotoxic T cells close to the MCs (median, ≤19.27 µm) was associated with better PFS (P=0.037). Conclusions: Our findings suggest that patients who have a detectable immunosuppressive tumor axis (PD-L1/PD1) with adequate activated cytotoxic T cells close to the tumor cells are the ideal patients for immune therapy to be targetable with these types of treatments. Acknowledgments: Scientific and financial support for: U10CA180888, U10CA180819U24CA224285, U24CA224316, PACT, PPP and FNIH. Citation Format: Edwin Roger Parra, Jiexin Zhang, Mary Redman, Rossana Lazcano, Piubelli Mario, Caddie Laberiano Fernandez, Renganayaki K. Krishna, Shanyu Zhang, Hong Chen, Ganiraju Manyam, Radim Moravec, Ethan Cerami, James Lindsay, Joyce Yu, Roshni Biswas, Stephen Van Nostrand, Dzifa Y. Duose, Jianjun Zhang, Roy Herbst, Gheath Al-Atrash, Kasthuri Kannan, Ignacio I. Wistuba, Scott Gettinger, Lyudmila Bazhenova, Jack Lee, Jianhua Zhang, Cara Haymaker. Infiltration and spatial distribution of immune cells are associated clinical benefit from Nivolumab and Ipilimumab for previously treated patients with stage IV squamous cell lung cancer: an immune biomarker analysis of Phase III SWOG LungMAP S1400I trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2580.

Precision medicine (PM) drugs targeting alterations such as EGFR mutations and BCR-ABL fusions have provided great clinical benefit to patients. However, with an abundance of tumor sequencing data and trial eligibility criteria available, it can be challenging for clinicians to identify PM trial options for patients. To address this challenge at Dana-Farber Cancer Institute (DFCI), we developed MatchMiner, an open-source platform for computationally matching patients to PM trials. MatchMiner has three modes of use: (1) patient-centric, where clinicians can view all available trial matches for a patient, (2) trial-centric, where clinical trial teams identify patients for their trials based on genomic and clinical criteria, and (3) trial search, where clinicians search for available trials based on clinical and genomic eligibility. Trial matching is performed via the MatchEngine, which computes trial matches based on patient genomic and clinical data and PM trial eligibility criteria. To encode trial eligibility criteria, we developed a structured format called clinical trial markup language (CTML), which uses Boolean logic to encode inclusion and exclusion criteria. Here, we describe our implementation of MatchMiner at DFCI including strategies that were successful and MatchMiner’s impact on trial consent. Since MatchMiner first went live in March 2017, a number of strategies have helped facilitate utilization of MatchMiner. The biggest impact has come from targeted departmental collaborations (Gastrointestinal, Breast, and Center for Cancer Therapeutic Innovation or CCTI), where the MatchMiner team worked directly with key departmental stakeholders to develop customized workflows. To facilitate access to MatchMiner among individual clinicians, we integrated the patient-centric and trial search modes into the Epic electronic health record. Other implementation strategies were piloted, such as weekly emails to clinicians alerting them to potential trial matches, but were less impactful. Overall, departmental collaborations have resulted in several ongoing MatchMiner initiatives. Thus far at DFCI, we have curated 354 PM trials into MatchMiner and facilitated 220 patient consents. For PM trials, 222 genes, 7 mutational signatures and nearly all cancer types were represented, demonstrating that there is a wide range of PM trial options available to patients. We also examined the distribution of trial phases and disease centers running each trial. The majority were Phase I and Phase II trials run out of the CCTI, consistent with the frequency with which novel drugs do not progress to later phase trials. Lastly, we have identified 220 trial consents that benefitted from MatchMiner. Retrospective analysis of a subset of these trial consents (n=166) revealed a significant 22% decrease in time to consent relative to other consents to the same trials, demonstrating the clinical impact of MatchMiner. Citation Format: Harry Klein, Tali Mazor, Priti Kumari, Andrea Ovalle, Pavel Trukhanov, Jason Hansel, Joyce Yu, James Lindsay, Michael Hassett, Ethan Cerami. Design and adoption of MatchMiner at Dana-Farber Cancer Institute [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4091.

The Human Tumor Atlas Network (HTAN) is a National Cancer Institute (NCI) Cancer Moonshot Initiative to generate three-dimensional molecular and spatial atlases of diverse human tumors and characterize crucial transitions in cancer progression and treatment. A series of manuscripts describing an extensive array of genomics, transcriptomics, proteomics, and imaging datasets are emerging but careful curation is required to maximize the data’s utility. HTAN centers have generated data from over 30 assay types including more than a dozen imaging methods (e.g., fluorescence microscopy, metal-tagged imaging, digital pathology, spatial transcriptomics, and electron microscopy). The HTAN Data Coordinating Center (DCC) develops infrastructure and tools to ingest, curate, explore, and share these data in a findable, accessible, interoperable, and reusable (FAIR) manner. As of November 2021 the DCC had ingested over 150 TB of data, including nearly 150,000 imaging data files. HTAN metadata schemas were developed through a community-driven Request for Comments process, including schemas for raw, processed and QC-checked imaging data, segmentation masks and feature arrays. This work led to a set of Minimum Information for Highly Multiplexed Tissue Imaging (MITI) guidelines, which are complemented by detailed metadata on participants and biospecimens. The Schematic python package is used to provide a user-friendly interface for defining data-model schemas, generate (meta)data submission spreadsheets, and asset-store interfaces on various cloud platforms. Across assay types, the HTAN schema currently encompasses over 700 attributes from 35 components. The DCC manages the HTAN Data Portal, which provides community access to the atlases and enables filtering of released atlas data based on metadata fields, and pointers to data availability. To enable visualization and exploration of imaging data directly from the portal, centers can submit Minerva stories, which provide guided storytelling of multiplexed tissue images. We provide methods to automatically generate default stories with visually appropriate overlays of sequential four-channel groups. We developed Miniature to generate unsupervised and interpretable multiplexed image thumbnails to provide rapid contextual information when browsing image collections. Dimensionality reduction is used to reduce a multiplexed image pyramid to three dimensions. Pixels are recolored according to their coordinates in low-dimensional space, where Pixels with similar marker expression are assigned similar colours. Public data sharing requires careful consideration of data egress costs and de-identification of images and their metadata. The DCC is working closely with NCI Cancer Research Data Commons to ensure long term-legacy and reuse of HTAN data. Citation Format: Adam J. Taylor, Milen Nikolov, Ino de Brujin, Jeremy Muhlich, Mialy De Felice, Artem Sokolov, Denis Schapiro, Peter Sorger, Julie Bletz, Nikolaus Schultz, Vésteinn Thorsson, James Eddy, Ethan Cerami. Curating cartography: Enabling the harmonisation, visualisation, and reuse of diverse multiplexed imaging data through the Human Tumor Atlas Network Data Coordinating Center [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2131.

cBioPortal for Cancer Genomics is an open-source platform for interactive, exploratory analysis of large-scale cancer genomics data sets. cBioPortal provides a user-friendly interface that integrates genomic and clinical data, and provides a suite of visualizations and analyses, including OncoPrints, mutation “lollipop” plots, variant interpretation, group comparison, survival analysis, expression correlation analysis, alteration enrichment analysis, cohort and patient-level visualization. cBioPortal also integrates external tools including CIViC, Cancer Digital Slide Archive, Next-Generation Clustered Heat Map, IGV and Bioconductor to facilitate interpretation. The public site (https://www.cbioportal.org) is accessed by ~35,000 unique visitors each month and hosts data from >325 studies spanning individual labs and large consortia. In addition, >67 instances of cBioPortal are installed at academic institutions and pharmaceutical/biotechnology companies worldwide. In 2021 we added data from 32 studies, totaling >24,000 samples, to the public site. All data is also available in the cBioPortal Datahub: https://github.com/cBioPortal/datahub/. We also host a dedicated instance for AACR Project GENIE, enabling access to the GENIE cohort of >135,000 clinically sequenced samples from 19 institutions (https://genie.cbioportal.org). In addition, the GENIE Biopharma Collaborative (BPC) enables the collection of comprehensive clinical annotations, including response, outcome, and treatment histories. The first BPC release contains data from >1,800 non-small cell lung cancer samples and will be released in early 2022. The growing GENIE cohort and the BPC clinical data have driven a number of recent developments, including performance improvements (the load time for the GENIE cohort was reduced from minutes to seconds). To leverage the BPC clinical data, we enabled sample selection based on treatment status, extended support for outcome analysis, and enhanced the patient timeline representation to incorporate response data. Additional development work has focused on improvements to variant interpretation, enhancements to the Mutations tab, and support for novel molecular assays via the ‘generic assay’ data type. Documentation on these new features and many others is available at https://www.cbioportal.org/news. cBioPortal is fully open source (https://github.com/cBioPortal/) under a GNU Affero GPL license. Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children’s Hospital of Philadelphia, Princess Margaret Cancer Centre, Bilkent University and The Hyve. We welcome open source contributions from others in the cancer research community. Citation Format: Jianjiong Gao, Tali Mazor, Ino de Bruijn, Adam Abeshouse, Diana Baiceanu, Ziya Erkoc, Elena Garcia Lara, Benjamin Gross, David M. Higgins, Prasanna K. Jagannathan, Priti Kumari, Ritika Kundra, Xiang Li, James Lindsay, Aaron Lisman, Divya Madala, Ramyasree Madupuri, Angelica Ochoa, Oleguer Plantalech, Sander Rodenburg, Baby A. Satravada, Robert Sheridan, Lucas Sikina, Jessica Singh, S. Onur Sumer, Yichao Sun, Pim van Nierop, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Sjoerd van Hagen, Kees van Bochove, Ugur Dogrusoz, Allison Heath, Adam Resnick, Trevor J. Pugh, Chris Sander, Ethan Cerami, Nikolaus Schultz. cBioPortal for cancer genomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1155.

9044 Background: While Immune checkpoint blockade (ICB) is standard treatment for lung cancer there are limited biomarkers that predict benefit, pharmacokinetic on-treatment activity or explain progression. S1400I was a randomized Phase III trial of nivolumab(N)+ipilimumab(I) versus N (NCT02154490, PMID 34264316) for ICB naïve, previously treated stage IV or recurrent squamous cell lung cancer. We performed circulating serum protein analysis of serial blood specimens from patients enrolled in S1400I to evaluate if serum proteins levels changed over time, changes differed by treatment arm, and if they were associated with overall survival. Methods: 561 serial blood specimens (baseline, weeks (wk) 3, 7, 9, and progression [PD]) from 160 of 252 eligible patients enrolled to S1400I were analyzed for 92 immuno-oncology analytes with the Olink proximity extension assay. Protein levels were normalized with use of internal controls and quantified as log2 protein expression (denoted as NPX). Linear mixed models evaluated change in expression from baseline at each time point (wks 3,7,9 and PD), and NPX differences at baseline, wk3, and PD depending on best objective response. A Cox model was used to evaluate the association between baseline NPX and survival. Overall survival and longitudinal cytokine expression were jointly modeled using a linear mixed model to estimate dynamic biomarker changes in NPX and a Cox model for survival. The joint models for time-varying NPX values included a random intercept and modeled time using a natural spline with three knots. Significance was defined as P < 0.05. Results: Serum proteins PCDC1, CXCL9, and CXCL10 were increased from baseline at wks 3,7,9 and at PD. CCL19 was increased at wks 3 and 7 but not at wk 9 and PD. IL10 and IFNγ were increased at wk 3 but subsequently returned to baseline. Change in CXCL13 from baseline to PD was larger for N+I versus N. Baseline CCL23, CSF-1, IL6, and MUC-16 were associated with shorter survival (HR > 1). Joint modeling of survival with cytokines showed an increased risk of death (HR > 1) with higher longitudinal serum levels of CXCL13, MMP12, CSF-1, and IL8. Patients achieving objective response had higher IL4 and LAMP3 and lower IL6 and IL8 at baseline and wk 3 compared to non-responders. Conclusions: Measurements of blood circulating soluble proteins represent easily accessible biomarkers that may be useful as indicators of outcome, and that will need to be prospectively confirmed. Clinical trial information: NCT02154490.

Molecular profiling of patient tumors and liquid biopsies over time with next-generation sequencing technologies and new immuno-profile assays are becoming part of standard research and clinical practice. With the wealth of new longitudinal data, there is a critical need for visualizations for cancer researchers to explore and interpret temporal patterns not just in a single patient but across cohorts. To address this need we developed OncoThreads, a tool for the visualization of longitudinal clinical and cancer genomics and other molecular data in patient cohorts. The tool visualizes patient cohorts as temporal heatmaps and Sankey diagrams that support the interactive exploration and ranking of a wide range of clinical and molecular features. This allows analysts to discover temporal patterns in longitudinal data, such as the impact of mutations on response to a treatment, e.g. emergence of resistant clones. We demonstrate the functionality of OncoThreads using a cohort of 23 glioma patients sampled at 2-4 timepoints. OncoThreads is freely available at http://oncothreads.gehlenborglab.org and implemented in Javascript using the cBioPortal web API as a backend.

Purpose: Immunoprofiling to identify biomarkers and integration with clinical trial outcomes are critical to improving immunotherapy approaches for patients with cancer. However, the translational potential of individual studies is often limited by small sample size of trials and the complexity of immuno-oncology biomarkers. Variability in assay performance further limits comparison and interpretation of data across studies and laboratories. Experimental Design: To enable a systematic approach to biomarker identification and correlation with clinical outcome across trials, the Cancer Immune Monitoring and Analysis Centers and Cancer Immunologic Data Commons (CIMAC-CIDC) Network was established through support of the Cancer MoonshotSM Initiative of the National Cancer Institute (NCI) and the Partnership for Accelerating Cancer Therapies (PACT) with industry partners via the Foundation for the NIH. Results: The CIMAC-CIDC Network is composed of four academic centers with multidisciplinary expertise in cancer immunotherapy that perform validated and harmonized assays for immunoprofiling and conduct correlative analyses. A data coordinating center (CIDC) provides the computational expertise and informatics platforms for the storage, integration, and analysis of biomarker and clinical data. Conclusions: This overview highlights strategies for assay harmonization to enable cross-trial and cross-site data analysis and describes key elements for establishing a network to enhance immuno-oncology biomarker development. These include an operational infrastructure, validation and harmonization of core immunoprofiling assays, platforms for data ingestion and integration, and access to specimens from clinical trials. Published in the same volume are reports of harmonization for core analyses: whole-exome sequencing, RNA sequencing, cytometry by time of flight, and IHC/immunofluorescence.

The cBioPortal for Cancer Genomics is an open-source software platform that enables interactive, exploratory analysis of large-scale cancer genomics data sets with a user-friendly interface. It integrates genomic and clinical data, and provides a suite of visualization and analysis options, including OncoPrint, mutation diagram, variant interpretation, survival analysis, expression correlation analysis, alteration enrichment analysis, cohort and patient-level visualization, among others. The public site (https://www.cbioportal.org) hosts data from almost 300 studies spanning individual labs and large consortia. Data is also available in the cBioPortal Datahub (https://github.com/cBioPortal/datahub/). In 2020 we added data from 21 studies, totaling almost 30,000 samples. In addition, we added data to existing TCGA PanCancer Atlas studies, including MSI status, mRNA-seq z-scores relative to normal tissue, microbiome data, and RPPA-based protein expression. The cBioPortal also supports AACR Project GENIE with a dedicated instance hosting the GENIE cohort of 112,000 clinically sequenced samples from 19 institutions worldwide (https://genie.cbioportal.org). The site is accessed by over 30,000 unique visitors per month. To support these users, we hosted a five-part instructional webinar series. Recordings of these webinars are available on our website and have already been viewed thousands of times. In addition, more than 50 instances are installed at academic institutions and pharmaceutical/biotechnology companies. In support of these local instances, we continue to simplify the installation process: we now provide a docker compose solution which includes all microservices to run the web app as well as data validation, import and migration. We continue to enhance and expand the functionality of cBioPortal. This year we significantly enhanced the group comparison feature; it is now integrated into gene-specific queries and supports comparison of more data types including DNA methylation, microbiome, and any outcome measure. We also expanded support of longitudinal data: the existing patient timeline has been refactored and now supports a wider range of data and visualizations; a new “Genomic Evolution” tab highlights changes in mutation allele frequencies across multiple samples from a patient; and samples can now be selected based on pre- or post-treatment status. Other features released this year include: allowing users to add gene-level plots for continuous molecular profiles in study view, enabling users to select the desired transcript on the Mutations tab, and integration of PathwayMapper. The cBioPortal is fully open source (https://github.com/cBioPortal/) under a GNU Affero GPL license. Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children's Hospital of Philadelphia, Princess Margaret Cancer Centre, Bilkent University and The Hyve. Citation Format: Jianjiong Gao, Tali Mazor, Ino de Bruijn, Adam Abeshouse, Diana Baiceanu, Ziya Erkoc, Benjamin Gross, David Higgins, Prasanna K. Jagannathan, Karthik Kalletla, Priti Kumari, Ritika Kundra, Xiang Li, James Lindsay, Aaron Lisman, Pieter Lukasse, Divya Madala, Ramyasree Madupuri, Angelica Ochoa, Oleguer Plantalech, Joyce Quach, Sander Rodenburg, Anusha Satravada, Fedde Schaeffer, Robert Sheridan, Lucas Sikina, S. Onur Sumer, Yichao Sun, Paul van Dijk, Pim van Nierop, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Sjoerd van Hagen, Kees van Bochove, Ugur Dogrusoz, Allison Heath, Adam Resnick, Trevor J. Pugh, Chris Sander, Ethan Cerami, Nikolaus Schultz. The cBioPortal for Cancer Genomics [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 207.

Motivation Molecular profiling of patient tumors and liquid biopsies over time with next-generation sequencing technologies and new immuno-profile assays are becoming part of standard research and clinical practice. With the wealth of new longitudinal data, there is a critical need for visualizations for cancer researchers to explore and interpret temporal patterns not just in a single patient but across cohorts. Results To address this need we developed OncoThreads, a tool for the visualization of longitudinal clinical and cancer genomics and other molecular data in patient cohorts. The tool visualizes patient cohorts as temporal heatmaps and Sankey diagrams that support the interactive exploration and ranking of a wide range of clinical and molecular features. This allows analysts to discover temporal patterns in longitudinal data, such as the impact of mutations on response to a treatment, for example, emergence of resistant clones. We demonstrate the functionality of OncoThreads using a cohort of 23 glioma patients sampled at 2-4 timepoints. Availability and implementation Freely available at http://oncothreads.gehlenborglab.org. Implemented in Java Script using the cBioPortal web API as a backend. Supplementary information Supplementary data are available at Bioinformatics online.

Precision oncology relies on accurate discovery and interpretation of genomic variants, enabling individualized diagnosis, prognosis and therapy selection. We found that six prominent somatic cancer variant knowledgebases were highly disparate in content, structure and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. We developed a framework for harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations. We demonstrated large gains in overlap between resources across variants, diseases and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 57% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide a freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases., This analysis presents a harmonized meta-knowledgebase to facilitate clinical interpretation of somatic genomic variants in cancer. This community-based project highlights the need for cooperative efforts to curate clinical interpretations of somatic variants for robust practice of precision oncology.

PURPOSE: The yield of comprehensive genomic profiling in recruiting patients to molecular-based trials designed for small subgroups has not been fully evaluated. We evaluated the likelihood of enrollment in a clinical trial that required the identification of a specific genomic change based on our institute-wide genomic tumor profiling. PATIENTS AND METHODS: Using genomic profiling from archived tissue samples derived from patients with metastatic breast cancer treated between 2011 and 2017, we assessed the impact of systematic genomic characterization on enrollment in an ongoing phase II trial (ClinicalTrials.gov identifier: NCT01670877). Our primary aim was to describe the proportion of patients with a qualifying ERBB2 mutation identified by our institutional genomic panel (OncoMap or OncoPanel) who enrolled in the trial. Secondary objectives included median time from testing result to trial registration, description of the spectrum of ERBB2 mutations, and survival. Associations were calculated using Fisher’s exact test. RESULTS: We identified a total of 1,045 patients with metastatic breast cancer without ERBB2 amplification who had available genomic testing results. Of these, 42 patients were found to have ERBB2 mutation and 19 patients (1.8%) were eligible for the trial on the basis of the presence of an activating mutation, 18 of which were identified by OncoPanel testing. Fifty-eight percent of potentially eligible patients were approached, and 33.3% of eligible patients enrolled in the trial guided exclusively by OncoPanel testing. CONCLUSION: More than one half of eligible patients were approached for trial participation and, significantly, one third of those were enrolled in NCT01670877. Our data illustrate the ability to enroll patients in trials of rare subsets in routine clinical practice and highlight the need for these broadly based approaches to effectively support the success of these studies.

Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g. metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (i) tools to search this comprehensive resource, (ii) a download site offering integrated bulk sets of pathway data (e.g. tables of interactions and gene sets), (iii) reusable software libraries for working with pathway information in several programming languages (Java, R, Python and Javascript) and (iv) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4794 detailed human biochemical processes (i.e. pathways) and ∼2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.

Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g., metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (1) tools to search this comprehensive resource, (2) a download site offering integrated bulk sets of pathway data (e.g., tables of interactions and gene sets), (3) reusable software libraries for working with pathway information in several programming languages (Java, R, Python, and Javascript), and (4) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4,794 detailed human biochemical processes (i.e., pathways) and ∼2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.

Objective Misinterpretation of complex genomic data presents a major challenge in the implementation of precision oncology. We sought to determine whether interactive genomic reports with embedded clinician education and optimized data visualization improved genomic data interpretation. Materials and Methods We conducted a randomized, vignette-based survey study to determine whether exposure to interactive reports for a somatic gene panel, as compared to static reports, improves physicians’ genomic comprehension and report-related satisfaction (overall scores calculated across 3 vignettes, range 0–18 and 1–4, respectively, higher score corresponding with improved endpoints). Results One hundred and five physicians at a tertiary cancer center participated (29% participation rate): 67% medical, 20% pediatric, 7% radiation, and 7% surgical oncology; 37% female. Prior to viewing the case-based vignettes, 34% of the physicians reported difficulty making treatment recommendations based on the standard static report. After vignette/report exposure, physicians’ overall comprehension scores did not differ by report type (mean score: interactive 11.6 vs static 10.5, difference = 1.1, 95% CI, −0.3, 2.5, P = .13). However, physicians exposed to the interactive report were more likely to correctly assess sequencing quality (P Discussion and Conclusion Interactive genomic reports may improve physicians’ ability to accurately assess genomic data and increase report-related satisfaction. Additional research in users’ genomic needs and efforts to integrate interactive reports into electronic health records may facilitate the implementation of precision oncology.

With the advent of next generation sequencing in cancer care, patients’ tumors can be genomically profiled and specific genetic alterations can be targeted with precision medicine drugs. However, the abundance of patient sequencing data coupled with complex clinical trial eligibility has made it challenging to match patients to precision medicine trials. To facilitate interpretation of complex tumor sequencing data and clinical trial genomic eligibility criteria, we developed MatchMiner, an open-source platform to computationally match cancer patients to precision medicine clinical trials. MatchMiner has several modes of clinical use: (1) patient-centric, where clinicians look up trial matches for their patient, (2) trial-centric, where clinical trial investigators identify patients for their clinical trials, and (3) trial search, where clinicians identify available trials based on any criteria, including external genomic reports. To support users in all three modes, MatchMiner also displays full genomic reports for patients and detailed trial information in user-friendly formats. MatchMiner trial matching is performed via the MatchEngine, an algorithm that computes matches based on patient genomic and clinical data and trial eligibility criteria. The MatchEngine accepts many different data inputs for patient-trial matching, and is easily customized to work with data available at any institution. At Dana-Farber Cancer Institute (DFCI), MatchMiner supports the following data: 1) patient-specific genomic sequencing data, including mutations, copy number alterations, structural variants, tumor mutational burden and mutational signatures including mismatch repair deficiency or microsatellite instability, 2) patient-specific clinical data, including primary cancer type, gender, age, and vital status, and 3) trial eligibility criteria including genomic targets, cancer type, and age. Unique to MatchMiner, each trial’s eligibility criteria is encoded in clinical trial markup language (CTML), a structured format that encodes detailed information about a trial and utilizes boolean logic to encode inclusion and exclusion criteria. Although MatchMiner has been operational at DFCI since early 2017, its impact on patient care has not yet been extensively studied. Thus far, MatchMiner has facilitated 181 precision medicine trial consents (MatchMiner consents, MMC) for 159 patients. To quantify MatchMiner’s impact on trial consent, we retrospectively measured time from genomic sequencing report date to trial consent date for a subset of the 181 MMC (166 MMC). We compared time to trial consent date for the 166 MMC to a group of 353 consents for the same trials not facilitated by MatchMiner (non-MatchMiner consents, non-MMC). MMC consented to trials 22% faster (P=0.004, median=195 days, IQR=85-34) than non-MMC (median=250 days; IQR=99-491). Thus, clinical use of MatchMiner decreased time to enroll in a precision medicine study, and suggests that use of precision medicine trial matching tools such as MatchMiner are important for the future of patient care. Citation Format: Harry Klein, Tali Mazor, Priti Kumari, James Lindsay, Andrea Ovalle, Pavel Trukhanov, Joyce Yu, Michael Hassett, Ethan Cerami. MatchMiner: An open-source platform for cancer precision medicine [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 P160.

With the advent of next generation sequencing in cancer care, patients' tumors can be genomically profiled and specific genetic alterations can be targeted with precision medicine drugs. However, the abundance of patient sequencing data coupled with complex clinical trial eligibility has made it challenging to match patients to precision medicine trials. To facilitate interpretation of complex tumor sequencing data and clinical trial genomic eligibility criteria, we developed MatchMiner, an open-source platform to computationally match cancer patients to precision medicine clinical trials. MatchMiner supports two distinct workflows: (1) patient-centric mode, in which an oncologist can find clinical trial matches for a specific patient, and (2) trial-centric mode, in which a clinical trial investigator can identify and recruit patients for a specific trial. In MatchMiner at DFCI, there are currently 330+ precision medicine trials and genomic and genomic and clinical data from 39,000+ patients. Although MatchMiner has been operational at Dana-Farber Cancer Institute since early 2017, its impact on patient care has not yet been extensively studied. In this study, we analyzed temporal trends of 170 MatchMiner-driven trial enrollments. We compared these 170 MatchMiner-driven trial enrollments to non-MatchMiner-driven trial enrollments to determine how MatchMiner has impacted patient enrollments. To compare MatchMiner-driven trial enrollments to non-MatchMiner-driven enrollments, we limited the non-MatchMiner group by choosing patients who enrolled on the same trials. We also ensured that all patients in both enrollment groups had a genomic report present in MatchMiner before their consent date. We then analyzed temporal trends between genomic report dates, patient consent and on-study dates, and patient views in MatchMiner. MatchMiner-driven enrollments had a significant decrease in time from genomic report date to consent date compared to non-MatchMiner-driven enrollments. Thus, clinical use of MatchMiner decreased time to enroll in a precision medicine study, and suggests that use of precision medicine trial matching tools such as MatchMiner are important for the future of patient care. The MatchMiner open-source software package is available through GitHub (https://github.com/dfci/matchminer). We are committed to supporting MatchMiner as an open-source software; to our knowledge, at least five cancer centers are implementing MatchMiner. Citation Format: Harry Klein, Tali Mazor, Priti Kumari, James Lindsay, Andrea Ovalle, Ethan Siegel, Pavel Trukhanov, Joyce Yu, Michael Hassett, Ethan Cerami. MatchMiner: An open-source computational platform that accelerates patient enrollment on to precision medicine trials [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 1198.

Introduction: The Lung Precancer Atlas (PCA) is developing bulk and single cell RNA-seq pipelines to process Lung PCA sequencing data collected across our multi-institutional consortium. The pipelines will be publicly available to run using the Terra Cloud platform so that they can be used by other members of the NCI Human Tumor Atlas Network (HTAN) and the broader research community. Method: We built our single cell and bulk RNA-seq pipelines for integration in the Terra cloud platform. Terra utilizes a Google Cloud/BigQuery backend to store data with built-in security features. The Terra platform provides a uniform resource for pipeline development and data processing for investigators located across multiple institutions. Result: The RNA-seq pipeline in Terra used by GTeX and HTOP utilizes STAR to align reads to a reference genome, RSEM and RNASeQC-2 to quantify expression and compute quality metrics. We have added additional quality metrics using the FASTQC and RSEQC tools as well as GATK germline variant calling. Somalier is used to perform fast fingerprinting for multiple samples derived from the same patient. The pipeline was also modified to estimate TCR/BCR repertoires using TRUST to facilitate downstream analyses of the immunological status of lung premalignant lesions. We have also enhanced Terra pipelines for droplet and plate-based single cell RNA sequencing data. The single cell RNA-seq preprocessing pipeline for 10X data included steps from CellRanger for demultiplexing, alignment to a reference genome, and count matrix generation. We have added the quality control pipeline from the singleCellTK package, which generates and aggregates quality control metrics from 8 different tools including those for doublet detection and ambient RNA quantification. For plate-based CEL-seq2 data, we have built a pipeline utilizing the SCRUFF package for alignment and singleCellTK for quality control. The count matrices and QC metrics are aggregated into SingleCellExperiment or SummarizedExperiment R objects for downstream analyses. Conclusion: Our completed Terra pipelines will allow researchers in the Lung PCA to process RNA sequencing data using a consistent set of tools and gene annotation. These pipelines, and the standardization of data processing and quality control that they provide, may be of use to other investigators in the Human Tumor Atlas Network as well as to broader scientific community. Citation Format: Chris Husted, François Aguet, Conor Shea, Adam Gower, William Mischler, Yusuke Koga, Rui Hong, Steven Dubinett, Avrum Spira, Sarah A. Mazzilli, Ethan Cerami, Ignaty Leshchiner, Marc E. Lenburg, Gad Getz, Jennifer E. Beane, Joshua D. Campbell. Cloud-based bulk and single-cell RNAseq pipelines in the Terra platform for the Lung PCA [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 171.

The AACR Project GENIE is an international data-sharing consortium focused on generating an evidence base for precision cancer medicine by integrating clinical-grade cancer genomic data with clinical outcome data for tens of thousands of cancer patients treated at multiple institutions worldwide. In conjunction with the first public data release from approximately 19,000 samples, we describe the goals, structure, and data standards of the consortium and report conclusions from high-level analysis of the initial phase of genomic data. We also provide examples of the clinical utility of GENIE data, such as an estimate of clinical actionability across multiple cancer types (>30%) and prediction of accrual rates to the NCI-MATCH trial that accurately reflect recently reported actual match rates. The GENIE database is expected to grow to >100,000 samples within 5 years and should serve as a powerful tool for precision cancer medicine. Significance: The AACR Project GENIE aims to catalyze sharing of integrated genomic and clinical datasets across multiple institutions worldwide, and thereby enable precision cancer medicine research, including the identification of novel therapeutic targets, design of biomarker-driven clinical trials, and identification of genomic determinants of response to therapy. Cancer Discov; 7(8); 818–31. ©2017 AACR. See related commentary by Litchfield et al., p. 796. This article is highlighted in the In This Issue feature, p. 783

To facilitate interpretation of complex tumor sequencing data and clinical trial genomic eligibility criteria, we developed MatchMiner, an open-source platform to computationally match cancer patients to precision medicine clinical trials based on clinical and genomic features. MatchMiner supports two distinct workflows: (1) patient-centric mode, in which an oncologist can find clinical trial matches for a specific patient, and (2) trial-centric mode, in which a clinical trial investigator can identify and recruit patients for a specific trial. MatchMiner has been operational at Dana-Farber Cancer Institute since early 2017. There are currently 275+ trials curated in the system and genomic data from 26,000+ patients. Over 80% of living patients match to at least one open clinical trial, with an average of 6 trial matches per patient. At least 98 patients have enrolled on a clinical trial as a result of MatchMiner. To enable computational matching, we developed clinical trial markup language (CTML), a structured format to encode detailed information about a trial. CTML utilizes boolean logic to define clinical (e.g. cancer type), demographic (e.g. age) and genomic (e.g. specific mutations, copy number alterations, structural variants or mutational signatures) eligibility, which can be applied to individual arms of a trial. MatchMiner is an open-source two-tier web application with a Python-based REST API server and an AngularJS front-end. MatchMiner utilizes Security Assertion Markup Language (SAML)-based authentication and is fully HIPAA-compliant when hosted behind a secure institutional firewall. MatchMiner ingests clinical and genomic data, and connects to existing clinical systems, including clinical trial management systems for real-time trial status. We recently refactored the core matching algorithm (the matchengine), which improves upon the original matchengine in several ways: (1) increased granularity in reporting the reason for a match; (2) can match all patients/trials or individual patients/trials; (3) easily extensible to match based on additional data types. The MatchMiner open-source software package is available through GitHub (https://github.com/dfci/matchminer). We are committed to supporting MatchMiner as an open-source software; to our knowledge, at least five cancer centers are implementing MatchMiner at their own institutions. In summary, we have defined a standard for encoding clinical trial information in a structured and computable form, and we have developed an open-source computational trial matching platform to support patient-specific trial identification as well as trial-specific patient recruitment. We are actively collaborating with clinical groups at Dana-Farber Cancer Institute and other institutions to understand the role of MatchMiner in their clinical workflows, and we are committed to continuing to evolve MatchMiner to meet clinical needs. Citation Format: Tali Mazor, Rachel B. Keller, Priti Kumai, James Lindsay, Eric Marriott, Andrea Ovalle, Ethan Siegel, Joyce Yu, Michael Hassett, Ethan Cerami. MatchMiner: An open-source computational platform for genomically-driven matching of cancer patients to precision medicine clinical trials [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 3382.

The cBioPortal for Cancer Genomics is an open-source software platform that enables interactive, exploratory analysis of large-scale cancer genomics data sets with a biologist-friendly interface. It integrates genomic and clinical data, and provides a suite of visualization and analysis options, including OncoPrint, mutation diagram, variant interpretation, survival analysis, expression correlation analysis, alteration enrichment analysis, cohort and patient-level visualization, among others. The public site (https://www.cbioportal.org) hosts data from more than 280 studies from diverse sources including individual labs and large consortia. All data is also available in the cBioPortal Datahub (https://github.com/cBioPortal/datahub/). Data from 40 studies, totaling more than 10,000 samples, was added in 2019, including the latest release from the Cancer Cell Line Encyclopedia and the Pediatric Preclinical Testing Consortium. The site is accessed by over 30,000 unique visitors per month. cBioPortal also supports AACR Project GENIE with a dedicated instance hosting the GENIE cohort of 80,000 clinically sequenced samples from 19 institutions worldwide (http://genie.cbioportal.org). In addition, more than 40 instances are installed locally at academic institutions and pharmaceutical/biotechnology companies. In support of these local installations, cBioPortal now has improved documentation and simplified installation via container technologies such as Docker and Kubernetes. Building on our successful refactoring of the code base, we have released a variety of new features and enhancements to cBioPortal over the past year. Most notably, we released a group comparison feature, enabling users to define groups of interest based on any clinical or genomic features. User-defined groups can be compared simultaneously across genomic and clinical data, including survival analysis and genomic alteration enrichment analysis. Additional new features include: integration of mutation annotations from dbSNP, ClinVar and gnomAD; support for waterfall plots to enable treatment response analysis; saving user preferences for chart layout on study view. The cBioPortal remains under active development. The portal is fully open source (https://github.com/cBioPortal/) under a GNU Affero GPL license. Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children's Hospital of Philadelphia, Princess Margaret Cancer Centre, and The Hyve. Ongoing and future development is focused on: (1) building the open source community; (2) continued performance improvements; (3) expanding user support, documentation and training resources; (4) supporting longitudinal data analysis and visualization; (5) developing novel features to support immunogenomics and immunotherapy; (6) enhancing individual variants and overall patient interpretation; (7) supporting single cell data visualizations and analysis. Citation Format: Jianjiong Gao, Tali Mazor, Adam Abeshouse, Ino de Bruijn, Benjamin Gross, Karthik Kalletla, Priti Kumari, Ritika Kundra, Xiang Li, James Lindsay, Aaron Lisman, Pieter Lukasse, Ramyasree Madupuri, Angelica Ochoa, Oleguer Plantalech, Sander Rodenburg, Fedde Schaeffer, Robert Sheridan, Lucas Sikina, Jing Su, S. Onur Sumer, Yichao Sun, Paul van Dijk, Sjoerd van Hagen, Pim van Nierop, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Kelsey Zhu, Kees van Bochove, Ugur Dogrusoz, Allison Heath, Adam Resnick, Trevor J. Pugh, Chris Sander, Ethan Cerami, Nikolaus Schultz. The cBioPortal for Cancer Genomics [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 3209.

567 Background: Fibroblast growth factor receptor (FGFR) pathway alterations have been identified in approximately 20% of patients (pts) with intrahepatic cholangiocarcinoma (IHCC), most commonly by FGFR2 fusions. Early phase clinical trials have demonstrated encouraging efficacy of FGFR inhibitors in pts with FGFR2-translocated cholangiocarcinoma, but efficacy in pts with other FGFR2 activating alterations is less clear. Methods: Pts with cholangiocarcinoma underwent CLIA-certified next generation DNA sequencing (NGS) to identify actionable alterations. FGFR2 fusions and other FGFR2 genomic events were assessed, with genomic characterization performed before and after treatment with FGFR inhibitors in appropriate pts. Novel extracellular domain in-frame deletions (INDELs) of FGFR2 and apparent resistance mutations were investigated for oncogenic activity and inhibitor resistance in vitro and in vivo. Results: Cholangiocarcinomas from 284 pts (136 male, 148 female; median age, 64 [20-89], including 139 IHCCs, were sequenced. Among the IHCCs, 16 (11.5%) had FGFR2 fusions, with 9 different gene partners. Surprisingly, 5 (3.6%) IHCCs harbored extracellular domain FGFR2 INDELs. Two of these IHCCs harbored an exon 5 deletion FGFR2 p.H167_N173del. Expression of FGFR2 p.H167_N173del in 3T3 cells resulted in oncogenic transformation. In the clinic, two pts with FGFR2 p.H167_N173del were treated with Debio1347, an oral FGFR-1/2/3 inhibitor. Both patients achieved a durable partial response (PR) of 11 months, with one of the pts still on active treatment with Debio-1347. The patient who developed acquired resistance underwent repeat biopsy, and NGS identified a secondary mutation ( FGFR2 p. L617F) in the kinase domain. In vitro studies demonstrated that this mutation confers resistance to Debio1347. This patient was subsequently treated with another FGFR inhibitor and again experienced a PR lasting 17 months. A third biopsy after disease progression demonstrated a previously undetected L597Q BRAF mutation. Conclusions: Extracellular domain FGFR2 in-frame deletions are a novel genomic alteration in IHCC that are transforming and predict clinical sensitivity to FGFR inhibitors.