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The American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) is an international pan-cancer registry with the goal to inform cancer research and clinical care worldwide. Founded in late 2015, the milestone GENIE 9.1-public release contains data from >110,000 tumors from >100,000 people treated at 19 cancer centers from the United States, Canada, the United Kingdom, France, the Netherlands, and Spain. Here, we demonstrate the use of these real-world data, harmonized through a centralized data resource, to accurately predict enrollment on genome-guided trials, discover driver alterations in rare tumors, and identify cancer types without actionable mutations that could benefit from comprehensive genomic analysis. The extensible data infrastructure and governance framework support additional deep patient phenotyping through biopharmaceutical collaborations and expansion to include new data types such as cell-free DNA sequencing. AACR Project GENIE continues to serve a global precision medicine knowledge base of increasing impact to inform clinical decision-making and bring together cancer researchers internationally.Significance:AACR Project GENIE has now accrued data from >110,000 tumors, placing it among the largest repository of publicly available, clinically annotated genomic data in the world. GENIE has emerged as a powerful resource to evaluate genome-guided clinical trial design, uncover drivers of cancer subtypes, and inform real-world use of genomic data.This article is highlighted in the In This Issue feature, p. 2007

Supplementary Table from AACR Project GENIE: 100,000 Cases and Beyond

Supplementary Figure from AACR Project GENIE: 100,000 Cases and Beyond

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AACR GENIE Data Guide

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Supplementary Methods

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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.

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.

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.

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.

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.

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.

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

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.

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) a patient-centric mode, in which an oncologist can find clinical trial matches for a specific patient, and (2) a 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 250+ trials curated in the system and genomic data from 24,000+ patients. Over 82% of living patients match to at least one open clinical trial, with an average of 6 trial matches per patient. At least 85 patients have enrolled on a clinical trial as a result of MatchMiner. The MatchMiner open-source software package is available through GitHub (https://github.com/dfci/matchminer). MatchMiner is a two-tier web application with a Python-based REST application programming interface (API) server and an AngularJS front-end. MatchMiner utilizes Security Assertion Markup Language (SAML)-based authentication and, when hosted behind a secure institutional firewall, is fully HIPAA-compliant. MatchMiner can connect to existing clinical systems, including clinical trial management systems for real-time trial status. To enable computational matching to clinical trials, 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. We recently refactored the core matching algorithm (the matchengine), which improves upon the original matchengine in several ways. While the original matchengine reported the reason for a patient-trial match, the refactored matchengine provides additional, more granular details. In addition, the original matchengine ran at the cohort level, matching all patients to all trials, whereas the refactored matchengine can also run against individual patients or trials, speeding up the matching process. The refactored matchengine is also easily extensible to match based on additional data types. 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 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 Kumari, James Lindsay, Eric Marriott, Andrea Ovalle, Ethan Siegel, Elizabeth H Williams, 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 AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr A024. doi:10.1158/1535-7163.TARG-19-A024

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 (http://www.cbioportal.org) hosts data from more than 200 studies from individual labs and large consortia, including the newly added TCGA Pan-Cancer Atlas data and the Count Me In project. These studies can be explored and queried individually or combined together into “virtual studies”. Users are now allowed to login and save virtual studies for query and analysis. The site is currently accessed by approximately 30,000 unique visitors per month. The software is also installed locally at dozens of academic institutions and pharmaceutical/biotechnology companies. A notable instance is the cBioPortal for AACR GENIE (http://www.cbioportal.org/genie/) hosting 60,000 clinically sequenced samples from multiple institutions. Over the past year, the code base has been fully refactored, resulting in a more responsive and interactive website. A new web API is in beta facilitating easier programmatic access to data. In addition, all public studies are available for download from the new datahub (https://github.com/cBioPortal/datahub/). 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) developing novel features to support immunogenomics and immunotherapy; (5) enhancing individual variants and overall patient interpretation; (6) creating a simplified query interface; and (7) enabling comparative analysis of user-defined patient cohorts. Citation Format: Jianjiong Gao, Tali Mazor, Adam Abeshouse, Ersin Ciftci, Ino de Bruijn, Benjamin Gross, Karthik Kalletla, Priti Kumari, Ritika Kundra, James Lindsay, Aaron Lisman, Pieter Lukasse, Ramyasree Madupuri, Angelica Ochoa, Oleguer Plantalech, Pichai Raman, Fedde Schaeffer, Robert Sheridan, Jing Su, S. Onur Sumer, Yichao Sun, Sander Tan, Sjoerd van Hagen, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Kelsey Zhu, Kees van Bochove, Ugur Dogrusoz, Trevor J. Pugh, Adam Resnick, Chris Sander, Ethan Cerami, Nikolaus Schultz. The cBioPortal for cancer genomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 910.

Background: Discordance in hormone receptor (HR) status between primary (p) tumors and metastatic (m) recurrences has been widely described. Loss of estrogen and progesterone receptor expression occurs in ˜12% of asynchronous recurrences, leading to triple-negative (TN) status in the metastasis. Genomic mechanisms driving HR loss and its prognostic and therapeutic implications have not been fully elucidated. Methods: Targeted NGS (Oncopanel, OP) at Dana-Farber Cancer Institute using multiplexed copy number variation and mutation (mut) detection across the full coding regions of 300 genes and selected intronic regions of 35 genes was prospectively performed on either archival primary or metastatic samples collected in patients (pts) with metastatic breast cancer (MBC). Receptor status at initial diagnosis and recurrence were reviewed using a 1% cutoff to define HR-positivity and excluding HER2+ cases. Fisher´s exact test was used to compare frequency of alterations. Tumor mut burden (TMB) was computed normalizing the sum of reported exon mut in each pt by the exonic-bait-set size of the panel. Results: Between 8/2013-9/2016, 929 pts with MBC underwent OP testing. Of 517 pts diagnosed with primary HR+/HER2- breast cancer, at time of recurrence 388 remained HR+/HER2- (pHR+/mHR+), 39 switched to HR-/HER2- (pHR+/mTN, of which 23 (59%) had initial HR expression >10%), 10 switched to HER2+ and 80 had unknown metastatic receptor status. Comparison between primary samples in pHR+/mHR+ (n=245) and pHR+/mTN (n=24) showed that pHR+/mTN was significantly more likely to harbor mut in TP53, STK11 and MSH6, amplifications (amp) in CCNE1 and FGFR2, and less likely to have PIK3CA mut or CCND1 amp. Median TMB in primary pHR+/mHR+ was 6.05 mut/Mb (0-37.5) and 5.68 mut/Mb (1.2-10.9) in pHR+/mTN (p=0.45). Metastatic samples in pHR+/mTN (n=15) were enriched in ARID1A, CRTC2 and CDH1 mut compared to metastases (n=40) in pts who remained TN (pTN/mTN). Deletions in CDKN2A/2B and RB1, and mut in TP53, NOTCH2 and ERCC2 were more prevalent in recurrent tumors of pHR+/mTN than pHR+/mHR+. In metastases, TMB was higher in pHR+/mTN than pTN/mTN or pHR+/mHR+ (10.9 vs. 7.0 vs. 7.3 mut/Mb, respectively; p=0.002). Median OS from initial diagnosis was 9.4 yrs in pHR+/mTN, less than pHR+/mHR+ (15.9 yrs; p=0.009) and greater than pTN/mTN (4.3 yrs; p=0.008). Median OS from MBC diagnosis was 1.8 yrs in pHR+/mTN, less than pHR+/mHR+ (6.4 yrs; p=0.001) but not significantly different than pTN/mTN (1.5 yrs, p=0.3). pHR+/mHR+ (n=245)pHR+/mTN (n=24)p value NFreq (%)NFreq (%) MutTP536325.72083.3 Conclusion: Targeted NGS shows that alterations in DNA damage and cell-cycle regulation pathways in primary HR+ tumors are associated with HR loss in the metastatic setting. Primary tumors that lose HR appear more similar to basal-like than luminal tumors, despite >10% baseline HR expression in most pts, and once metastatic, survival is comparable to pTN/mTN. Metastases with HR loss have higher TMB than those that remain HR+ or TN throughout the course of the disease. These findings, if confirmed, may influence treatment and pt selection for clinical trials. Citation Format: Garrido-Castro AC, Hughes ME, Cherniack A, Barroso-Sousa R, Bychkovsky BL, Di Lascio S, Berger A, Mittendorf EA, Files JL, Guo H, Kumari P, Cerami E, Krop IE, Wagle N, Lindeman NI, MacConaill LE, Dillon DA, Winer EP, Lin NU. Genomic alterations associated with loss of HR expression in metastatic breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr PD9-01.

The Metastatic Breast Cancer Project (MBCproject) is a research study that directly engages patients (pts) through social media and advocacy groups, and empowers them to share samples, clinical data, and experiences. The goal is to create a publicly available database of genomic, molecular, clinical, and patient-reported data to enable research. Working with pts and advocates, a website (MBCproject.org) was developed that allows pts with metastatic breast cancer (MBC) to register. Registered pts are sent an online consent form that asks for permission to obtain and analyze their medical records and samples. Once enrolled, pts are sent a saliva kit and asked to mail back a saliva sample, which is used to extract germline DNA. We contact participants' medical providers and obtain medical records and a portion of their stored tumor biopsies. Pts may be asked to mail in a blood sample, which is used to extract cell free DNA (cfDNA). Whole-exome sequencing (WES) is performed on tumor DNA, germline DNA, and cfNDA; transcriptome sequencing is performed on tumor RNA. Clinically annotated genomic data are used to study specific pt cohorts (including outliers) and to identify mechanisms of response and resistance to therapies. All de-identified data are shared via public databases. Study updates are shared with participants regularly. From 10/2015-11/2017, 4237 MBC pts registered, representing over 1,000 institutions. 95% answered the 16-question survey about their cancer, treatments, and demographic information. 2471 (58%) completed the consent form. 2,136 saliva kits were mailed to pts and 1,523 saliva samples were sent in (71%). 408 blood kits were mailed to pts and 175 blood samples have been received for cfDNA analysis. To date, we have obtained medical records from 311 pts and 190 tumors from 127 pts. In 10/2017, all data generated so far were publicly released on cbioportal.org, including WES for 103 tumors from 78 pts linked to clinical data including pathology (22 elements), medical record abstraction including all treatments and timelines/durations (67 elements), and patient-reported data (11 elements). 81% of biopsies included in this release were from the breast and 19% from metastatic sites. 75% were obtained prior to any therapy, 24% following therapy. New data will be released 4/2018 and every six months thereafter, as they are generated. Additional patient-reported data, including treatments, side effects, quality of life, family history, pregnancies, and sites of metastasis, will also be collected and shared. In summary, a patient-driven approach enabled rapid identification of thousands of MBC pts willing to share samples and clinical data. Remote acquisition of medical records, saliva, blood, and tumor tissue for pts across the U.S. is feasible. This shared clinico-genomic database should enable research in MBC and may serve as a model for patient-driven research in other cancers. Citation Format: Nikhil Wagle, Corrie Painter, Elana Anastasio, Michael Dunphy, Mary McGillicuddy, Rachel Stoddard, Esha Jain, Dewey Kim, Simona Di Lascio, Brett N. Tompson, Sara Balch, Beena Thomas, Priti Kumari, Shawn Johnson, Jamie Holloway, Ofir Cohen, Erik H. Knelson, Katie Larkin, Sam Pollock, Alicia Wong, Samira Bahl, Simone Maiwald, Andrew Zimmer, Esme O. Baker, Jen Hendry Lapan, Scott Sutherland, Scott Sassone, Viktor Adalsteinsson, Eric S. Lander, Todd R. Golub. The Metastatic Breast Cancer Project: Partnering with patients to accelerate progress in cancer research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5371.

The cBioPortal for Cancer Genomics provides intuitive visualization and analysis of complex cancer genomics data. The public site (http://cbioportal.org/) is accessed by more than 1,500 researchers per day, and there are now dozens of local instances of the software that host private data sets at cancer centers around the globe. We have recently released the software under an open source license, making it free to use and modify by anybody. The software and detailed documentation are available at https://github.com/cBioPortal/cbioportal. We are now establishing a multi-institutional software development network, which will coordinate and drive the future development of the software and associated data pipelines. This group will focus on four main areas: 1. New analysis and visualization features, including: a. Improved support for cross-cancer queries and cohort comparisons. b. Enhanced clinical decision support for precision oncology, including an improved patient view with knowledge base integration, patient timelines and improved tools for visualizing tumor evolution. 2. New data pipelines, including support for new genomic data types and streamlined pipelines for TCGA and the International Cancer Genome Consortium (ICGC). 3. Software architecture and performance improvements. 4. Community engagement: Documentation, user support, and training. This coordinated effort will help to further establish the cBioPortal as the software of choice in cancer genomics research, both in academia and the pharmaceutical industry. Furthermore, as the sequencing of tumor samples has entered clinical practice, we are expanding the features of the software so that it can be used for precision medicine at cancer centers. In particular, clean, web-accessible, interactive clinical reports integrating multiple sources of genome variation and clinical annotation over time has potential to improve clinical action beyond current text-based molecular reports. By making complex genomic data easily interpretable and linking it to information about drugs and clinical trials, the cBioPortal software has the potential to facilitate the use of genomic data in clinical decision making. Citation Format: Jianjiong Gao, James Lindsay, Stuart Watt, Istemi Bahceci, Pieter Lukasse, Adam Abeshouse, Hsiao-Wei Chen, Ino de Bruijn, Benjamin Gross, Dong Li, Ritika Kundra, Zachary Heins, Jorge Reis-Filho, Onur Sumer, Yichao Sun, Jiaojiao Wang, Qingguo Wang, Hongxin Zhang, Priti Kumari, M. Furkan Sahin, Sander de Ridder, Fedde Schaeffer, Kees van Bochove, Ugur Dogrusoz, Trevor Pugh, Chris Sander, Ethan Cerami, Nikolaus Schultz. The cBioPortal for cancer genomics and its application in precision oncology. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5277.