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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.

Widespread, comprehensive sequencing of patient tumors has facilitated the usage of precision medicine (PM) drugs to target specific genomic alterations. Therapeutic clinical trials are necessary to test new PM drugs to advance precision medicine, however, the abundance of patient sequencing data coupled with complex clinical trial eligibility has made it challenging to match patients to PM trials. To facilitate enrollment onto PM trials, we developed MatchMiner, an open-source platform to computationally match genomically profiled cancer patients to PM trials. Here, we describe MatchMiner’s capabilities, outline its deployment at Dana-Farber Cancer Institute (DFCI), and characterize its impact on PM trial enrollment. MatchMiner’s primary goals are to facilitate PM trial options for all patients and accelerate trial enrollment onto PM trials. MatchMiner can help clinicians find trial options for an individual patient or provide trial teams with candidate patients matching their trial’s eligibility criteria. From March 2016 through March 2021, we curated 354 PM trials containing a broad range of genomic and clinical eligibility criteria and MatchMiner facilitated 166 trial consents (MatchMiner consents, MMC) for 159 patients. To quantify MatchMiner’s impact on trial consent, we measured time from genomic sequencing report date to trial consent date for the 166 MMC compared to trial consents not facilitated by MatchMiner (non-MMC). We found MMC consented to trials 55 days (22%) earlier than non-MMC. MatchMiner has enabled our clinicians to match patients to PM trials and accelerated the trial enrollment process.

BACKGROUND: Pathogenic POLE exonuclease domain mutations (pPOLE) undermine mismatch error correction during DNA replication, causing somatic ultramutation and response to immunotherapy. We examined the pan-cancer landscape of POLE mutations and applied a POLE-specific variant classification model. METHODS: Comprehensive genomic profiling was performed during clinical care. Mutational signature calling was performed via decomposition using the 96-feature single-base substitution COSMIC reference signatures. A POLE-specific classification model encompassing mutation position in the exonuclease domain, TMB, presence of POLE signature, absence of other signatures, germline frequency, and other features was applied to identify pPOLE mutations causative of ultramutation. RESULTS: POLE mutation status was evaluated in 458,437 samples (425,520 tissue biopsies (TB) and 32,917 liquid biopsies (LB)). One or more POLE alterations, including pathogenic alterations and variants of unknown significance (VUS), were detected in 3.8% of samples. 19,470 total alterations were identified, 84.8% of which were missense substitutions. Application of the POLE-specific classification model identified 35 unique pathogenic variants, many of which were VUS prior to this study. 749 samples harbored a pPOLE, more than half (56.6%) of which were either p.P286R (n=245) or p.V411L (n=179). pPOLE were found in 1.4% (199/13,688) of endometrial cancers (EC) and 0.5% (270/55,981) of colorectal cancers (CRC) and were rarer in a long tail of other malignancies. The overall pPOLE rate was significantly lower in LB than TB (0.02% vs 0.17%, P CONCLUSIONS: pPOLE were seen in both TB and LB across cancer types. The high rate of passenger mutations underscores the utility of this POLE-specific variant classification model. Because TMB can be underestimated when tumor purity is near the limit of detection for the assay, accurate detection and classification of pPOLE is critical for identifying patients who may benefit from immunotherapy. Citation Format: Rachel B. Keller, James Haberberger, Tyler Janovitz, Alexa B. Schrock, Hanna Tukachinsky, Lei Zhong, Douglas A. Mata, Lyle V. Lopez, Zoe Fleischmann, Radwa Sharaf, Ethan S. Sokol, Garrett M. Frampton, Nimesh R. Patel, Douglas I. Lin, Geoff R. Oxnard, Erik A. Williams, Julia A. Elvin, Brennan Decker. POLE-specific variant classification strategy is critical for identifying patients who may benefit from immunotherapy [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 305.

We conducted next-generation DNA sequencing on 335 biliary tract cancers and characterized the genomic landscape by anatomic site within the biliary tree. In addition to frequent FGFR2 fusions among patients with intrahepatic cholangiocarcinoma (IHCC), we identified FGFR2 extracellular domain in-frame deletions (EID) in 5 of 178 (2.8%) patients with IHCC, including two patients with FGFR2 p.H167_N173del. Expression of this FGFR2 EID in NIH3T3 cells resulted in constitutive FGFR2 activation, oncogenic transformation, and sensitivity to FGFR inhibitors. Three patients with FGFR2 EIDs were treated with Debio 1347, an oral FGFR1/2/3 inhibitor, and all showed partial responses. One patient developed an acquired L618F FGFR2 kinase domain mutation at disease progression and experienced a further partial response for 17 months to an irreversible FGFR2 inhibitor, futibatinib. Together, these findings reveal FGFR2 EIDs as an alternative mechanism of FGFR2 activation in IHCC that predicts sensitivity to FGFR inhibitors in the clinic.Significance:FGFR2 EIDs are transforming genomic alterations that occur predominantly in patients with IHCC. These FGFR2 EIDs are sensitive to FGFR inhibition in vitro, and patients with these alterations benefited from treatment with FGFR inhibitors in the clinic.This article is highlighted in the In This Issue feature, p. 2355

Approximately 8-10% of pancreatic cancers do not harbor mutations in KRAS. Understanding the unique molecular and clinical features of this subset of pancreatic cancer (PC) is important to guide patient stratification for clinical trials of molecularly targeted agents. To this end we investigated a cohort of 795 PC patients from Dana-Farber Cancer Institute who had undergone somatic genomic characterization with OncoPanel, a targeted next-generation sequencing panel with coverage of more than 400 cancer-associated genes. A total of 9.2% (73/795) of cases in our cohort were KRAS WT. The KRAS WT cohort was statistically enriched for MSI-H PC and acinar cell carcinomas (p = 0.0035, p < 0.0001 respectively). Actionable alterations in alternative MAPK drivers were identified in 44% (32/73) of KRAS WT cases. BRAF alterations accounted for 56% (18/32) of detected alternative MAPK drivers, the majority of which (72%) were Class II which exhibit dimer-dependent constitutive activity. Receptor Tyrosine Kinase (RTK) fusion events in BRAF, NTRK1, NRG1, NTRK3, ROS1, and FGFR2 accounted for 25% (8/32) of detected alternative MAPK drivers in KRAS WT tumors. BRAF in-frame deletions showed increased sensitivity to dual pan-RAF and MEK inhibition in organoid models and one patient with a ROS1 fusion received prolonged clinical benefit from targeted therapy. In addition to alternative MAPK drivers, mutations in GNAS (p = 0.0014) and ARID2 (p = 0.045) were significantly enriched in KRAS WT PC, whereas TP53 mutations were significantly less frequent in KRAS WT cases. Interestingly, although not statistically significant, rates of mutation in the other canonical tumor suppressor genes (CDKN2A, SMAD4) were also lower in KRAS WT PC. Clinically, KRAS Mutant (MUT) PC were associated with a decreased overall survival (OS) compared to the KRAS WT cohort [median OS 17.5 vs 24.0 months, HR 1.38, p = 0.036], however this relationship was no longer significant after accounting for other clinical factors. For patients with KRAS WT PC, those with SMAD4 alterations had a significantly decreased OS (HR 6.24, p < 0.001), whereas presence of TP53 or CDKN2A mutations had no significant impact. Lastly, we found that KRAS WT PC was associated with a younger age of onset. Interestingly, we noted that KRAS WT PC patients with a younger age of onset had tumors with few oncogenic alterations whereas no such association was seen in KRAS MUT patients. Validation of this finding in a separate dataset is required and is currently ongoing. In summary, our clinical and genomic characterization of KRAS WT PC identifies a high prevalence of alternative MAPK drivers that are amenable to targeted therapies. Our cohort also recapitulates the previously reported clinical characteristics of KRAS WT PC and identifies the presence of SMAD4 alterations as significantly associated with decreased overall survival in KRAS WT PC. Additional analysis from multiple sources will be critical to risk stratify these patients further and to validate age-related findings. Citation Format: Harshabad Singh, Rachel B. Keller, Kevin S. Kapner, Julien Dilly, Srivatsan Raghavan, Chen Yuan, Eizabeth Cohen, Michael Tolstorukov, Emma Hill, Elizabeth Andrews, Lauren K. Brais, Annacarolina Da Silva, Kimberly Perez, Douglas A. Rubinson, Benjamin L. Schlechter, Michael H. Rosenthal, Jason L. Hornick, Valentina Nardi, Yvonne Li, Hersh Gupta, Andrew Cherniack, Mathew L. Meyerson, James M. Cleary, Jonathan A. Nowak, Brian M. Wolpin, Andrew A. Aguirre. Clinical-genomic analysis of KRAS wild-type pancreatic cancer confirms alternative targetable drivers and provides insight for age and risk related clinical stratification [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A001.

Purpose: Receptor tyrosine kinase fusions in colorectal cancers are rare, but potentially therapeutically relevant. We describe clinical, molecular, and pathologic attributes of RTK fusion–associated colorectal cancer. Experimental Design: We identified all cases with RTK fusions in patients with colorectal cancer seen at Dana-Farber Cancer Institute (Boston, MA) who underwent OncoPanel testing between 2013 and 2018. Clinical, histologic, and molecular features were extracted from the patient charts and molecular testing results. Results: We identified 12 driver oncogenic fusions in various RTKs. These fusions occurred exclusively in BRAF and RAS wild-type tumors and were enriched in right-sided and mismatch repair–deficient (MMR-D) colorectal cancers. All of the MMR-D colorectal cancers with RTK fusions were found in tumors with acquired MMR-D due to MLH1 promoter hypermethylation and one was associated with a sessile serrated polyp. Molecular profiles of MMR-D colorectal cancer with RTK fusions largely resembled BRAF V600E–mutated MMR-D colorectal cancer, rather than those secondary to Lynch syndrome. We describe two patients with fusion-associated microsatellite stable (MSS) colorectal cancer who derived clinical benefit from therapeutic targeting of their translocation. The first harbored an ALK-CAD fusion and received sequential crizotinib and alectinib therapy for a total of 7.5 months until developing an ALK L1196Q gatekeeper mutation. The second patient, whose tumor contained an ROS1-GOPC fusion, continues to benefit from entrectinib after 9 months of therapy. Conclusions: RTK fusions in colorectal cancer are a rare, but important disease subgroup that occurs in RAS and BRAF wild-type tumors. Despite enrichment in acquired MMR-D tumors, RTK fusions also occur in MSS colorectal cancer and provide an important therapeutic target.

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.

Precision oncology is fast becoming the new paradigm for cancer treatment. This approach involves molecular profiling of patient tumors and tailoring of a treatment regimen based on the unique combination of targetable alterations identified therein. At Dana-Farber Cancer Institute (DFCI), over 24,000 patient tumors have been sequenced using the NGS platform OncoPanel since 2013. OncoPanel is a comprehensive genomic characterization of a tumor and it can be time-consuming to discern those alterations that are interesting from an academic or research perspective from those that can be leveraged to direct patient care. To address this issue, the GI TARGET (Treatment Assistance Regarding Genomic Evaluation of Tumors) project was initiated to provide decision support for oncologists within the DFCI Gastrointestinal Cancer Center (GCC). GI TARGET is a collaboration between the DFCI GCC, the Brigham & Women’s Hospital (BWH) Center for Advanced Molecular Diagnostics (CAMD), and the DFCI Knowledge Systems Group, developers of the precision medicine clinical trial matching platform MatchMiner. Report generation involves review of OncoPanel data in the context of patient clinical history via one of two workflows; complex genomic cases are reviewed in a weekly Molecular Tumor Board comprised of oncologists, molecular pathologists, a PhD-level Clinical Genomics Scientist, and representatives from the MatchMiner team, while ‘simple’ genomic cases are reviewed by both an oncologist and a PhD-level Clinical Genomics Scientist. Since the launch of the project in January 2018, we have signed out over 1000 GI TARGET reports. We are assessing the utility of these reports using a number of metrics including clinical trial enrollment tracking via MatchMiner, follow-up on recommendations via chart abstraction, and oncologist feedback. The aims of GI TARGET are 1) to reduce the burden of genomic analysis on the treating oncologist by providing expert-led deep review of OncoPanel and exploration of associated therapy/additional patient care considerations, 2) to support clinical trial enrollment by matching the results of somatic testing with ongoing clinical studies, and 3) to provide an example framework for how precision oncology can be incorporated into standard clinical cancer care, which may be helpful to guide efforts in other care settings, particularly in community hospitals where the vast majority (an estimated 80%-85%) of cancer patients in the United States receive their care. Citation Format: Rachel B Keller, Tali Mazor, Marios Giannakis, Jonathan Nowak, Lynette Sholl, Andrew Aguirre, Adam Bass, Nilay Sethi, Ankur Nagaraja, Lauren Brais, Emma Reilly, Ethan Cerami, Brian Wolpin. Precision oncology decision support within the Gastrointestinal Cancer Center at Dana-Farber Cancer Institute (GI TARGET) [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 C130. doi:10.1158/1535-7163.TARG-19-C130

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 C19MC gene on chromosome band 19q13.4 encodes a cluster of 46 microRNAs; those microRNAs are normally only expressed from the paternal allele and in the placenta. Placental expression correlates with selective demethylation of the paternal C19MC promoter, in contrast to methylation of both maternal and paternal alleles in nonplacental tissues. Prior investigations demonstrated “ectopic” activation of this gene in most hepatic mesenchymal hamartomas, including sporadic tumors and others with androgenetic-biparental mosaicism (subset of cells are diploid, but contain only paternally derived chromosomes). In the present investigation of C19MC promoter methylation status in a series of 14 mesenchymal hamartomas, a demethylated allele was identified in 6 tumors, including all 4 with androgenetic-biparental mosaicism. Conversely, only methylated alleles were cloned from sporadic hamartomas, including 3 tumors with chromosomal rearrangements thought likely to activate C19MC expression independent of the native promoter. In conjunction with published data, the findings suggest multiple molecular mechanisms for C19MC activation in hepatic mesenchymal hamartoma, including the existence of a normal placental imprinting pattern in mesenchymal cells in a subset of cases. Some or all of the latter hamartomas may result from placental “grafting,” a hypothesis supported by endothelial expression of the placental vascular marker, glucose transporter-1, in 1 of the 6 cases with a demethylated allele.