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PURPOSEOur goal was to identify the opportunities and challenges in analyzing data from the American Association of Cancer Research Project Genomics Evidence Neoplasia Information Exchange (GENIE), a multi-institutional database derived from clinically driven genomic testing, at both the inter- and the intra-institutional level. Inter-institutionally, we identified genotypic differences between primary and metastatic tumors across the 3 most represented cancers in GENIE. Intra-institutionally, we analyzed the clinical characteristics of the Vanderbilt-Ingram Cancer Center (VICC) subset of GENIE to inform the interpretation of GENIE as a whole.METHODSWe performed overall cohort matching on the basis of age, ethnicity, and sex of 13,208 patients stratified by cancer type (breast, colon, or lung) and sample site (primary or metastatic). We then determined whether detected variants, at the gene level, were associated with primary or metastatic tumors. We extracted clinical data for the VICC subset from VICC’s clinical data warehouse. Treatment exposures were mapped to a 13-class schema derived from the HemOnc ontology.RESULTSAcross 756 genes, there were significant differences in all cancer types. In breast cancer, ESR1 variants were over-represented in metastatic samples (odds ratio, 5.91; q < 10−6). TP53 mutations were over-represented in metastatic samples across all cancers. VICC had a significantly different cancer type distribution than that of GENIE but patients were well matched with respect to age, sex, and sample type. Treatment data from VICC was used for a bipartite network analysis, demonstrating clusters with a mix of histologies and others being more histology specific.CONCLUSIONThis article demonstrates the feasibility of deriving meaningful insights from GENIE at the inter- and intra-institutional level and illuminates the opportunities and challenges of the data GENIE contains. The results should help guide future development of GENIE, with the goal of fully realizing its potential for accelerating precision medicine.

Background Approximately 10% of patients with SCLC develop a paraneoplastic syndrome (PNS). Neurologic PNS are thought to improve prognosis, which we hypothesized is related to increased tumor-infiltrating lymphocytes and immune recognition. Methods We queried 2,512,042 medical records from a single institution to identify patients who have SCLC with and without PNS and performed manual, retrospective chart review. We then performed multiplexed fluorescence immunohistochemistry and automated quantitative analysis (AQUA Technology) on tumors to assess CD3, CD4, and CD8 T cell infiltrates and programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) interactions. T cell infiltrates and PD-1/PD-L1 interaction scores were compared among patients with neurologic PNS, endocrinologic PNS, and a control group without PNS. Clinical outcomes were analyzed using the Kaplan-Meier method and Cox proportional hazards models. Results We evaluated 145 SCLC patients: 55 with PNS (25 neurologic and 30 endocrinologic) and 90 controls. Patients with neurologic PNS experienced improved overall survival compared to patients with endocrinologic PNS and controls (median overall survival of 24 months versus 12 months versus 13 months, respectively). Of the 145 patients, we identified tumor tissue from 34 patients that was adequate for AQUA analysis. Among 37 specimens from these 34 patients, patients with neurologic PNS had increased T cell infiltrates (p = 0.033) and PD-1/PD-L1 interaction (p = 0.014) compared to tumors from patients with endocrinologic PNS or controls. Conclusions Tumor tissue from patients with SCLC with neurologic PNS showed increased tumor-infiltrating lymphocytes and PD-1/PD-L1 interaction consistent with an inflamed tumor microenvironment.

When the COVID-19 pandemic began, formal frameworks to collect data about affected patients were lacking. The COVID-19 and Cancer Consortium (CCC19) was formed to collect granular data on patients with cancer and COVID-19 at scale and as rapidly as possible. CCC19 has grown from five initial institutions to 125 institutions with >400 collaborators. More than 5,000 cases with complete baseline data have been accrued. Future directions include increased electronic health record integration for direct data ingestion, expansion to additional domestic and international sites, more intentional patient involvement, and granular analyses of still-unanswered questions related to cancer subtypes and treatments.

Combination therapies have the potential to improve outcomes in melanoma patients but have not yet been clinically efficacious. Here, we used high-throughput flow cytometry-based screening to identify and characterize candidate therapies that might synergize with and augment T-cell immunotherapy efficacy. Two lead therapies, regorafenib (Reg) and NU7441, were selected based on their ability to alter a variety of immunomodulatory proteins, including CD55, CD73, CD155, programmed death-ligand 1 (PD-L1), nerve growth factor receptor (NGFR), and HLA class I in a heterogeneous panel of melanomas. The therapies also upregulated several melanoma antigens, inhibited proliferation, and perturbed activation of oncogenic signaling pathways in melanomas. T cells treated with the therapies proliferated normally and exhibited a favorably altered phenotype, including increased CD25, CD28, inducible T-cell costimulator (ICOS), and reduced expression of coinhibitory receptors. Cytokine production was also increased in treated T cells. When administered in mice, REg suppressed melanoma progression in a CD8+ T cell–dependent manner when used alone and with various immunotherapies. Additionally, Reg altered the number, phenotype, and function of various T-cell subsets in the tumor microenvironment. These studies reveal that Reg and NU7441 influence the immunobiology of both tumor cells and T cells and enhance the efficacy of various immunotherapies. Cancer Immunol Res; 5(9); 790–803. ©2017 AACR.

Melanoma represents the most serious type of skin cancer. Although recent years have seen advances using targeted and immunotherapies, most patients remain at high risk for tumor recurrence. Here we show that IRAK-M, a negative regulator of MyD88 signaling, is deficient or low in melanoma and expression levels correlate with patient survival. Inducing IRAK-M expression using genetic approaches or epigenetic modifiers initiates apoptosis by prompting its interaction with TRAF6 via IRAK-M’s C-terminal domain. This complex recruits and degrades calpastatin which stimulates calpain activity and triggers caspase-3-dependent but caspase-8,−9-independent apoptosis. Using a drug screen, we identified compounds that induced IRAK-M expression. Administration of IRAK-M-inducing drugs reduced tumor growth in mice but was ineffective against IRAK-M knock-down tumors. These results uncover a previously uncharacterized apoptosis pathway, emphasize IRAK-M as a potential therapeutic target and suggest that the anticancer activity of certain drugs could do so through their ability to induce IRAK-M expression., Geng et al find that low levels of the anti-inflammatory molecule IRAK-M in melanoma correlates with reduced patient survival and that induced expression of IRAK-M induces caspase-3 dependent apoptosis. The identification of cytotoxic compounds associated with IRAK-M induction suggests a route to melanoma drug development.

Background: As targeted cancer therapies and molecular profiling become widespread, the era of “precision oncology” is at hand. However, cancer genomes are complex, making mutation-specific outcomes difficult to track. We created a proof-of-principle, CUSTOM-SEQ: Continuously Updating System for Tracking Outcome by Mutation, to Support Evidence-based Querying, to automatically calculate and display mutation-specific survival statistics from electronic health record data.Methods: Patients with cancer genotyping were included, and clinical data was extracted through a variety of algorithms. Results were refreshed regularly and injected into a standard reporting platform. Significant results were highlighted for visual cueing. A subset was additionally stratified by stage, smoking status, and treatment exposure.Results: By August 2015, 4310 patients with a median follow-up of 17 months had sufficient data for survival calculation. As expected, epidermal growth factor receptor (EGFR) mutations in lung cancer were associated with superior overall survival, hazard ratio (HR) = 0.53 (P Interpretation: CUSTOM-SEQ represents a novel rapid learning system for a precision oncology environment. Retrospective studies are often limited by study of specific time periods and can lead to incomplete conclusions. Because data is continuously updated in CUSTOM-SEQ, the evidence base is constantly growing. Future work will allow users to interactively explore populations by demographics and treatment exposure, in order to further investigate significant mutation-specific signals.

e18572Background: Recurrent and metastatic cancers have been underrepresented in cancer genomics databases until the creation of AACR Project GENIE, a large multi-institutional database derived fro...

Background Precision cancer medicine (PCM) will require ready access to genomic data within the clinical workflow and tools to assist clinical interpretation and enable decisions. Since most electronic health record (EHR) systems do not yet provide such functionality, we developed an EHR-agnostic, clinico-genomic mobile app to demonstrate several features that will be needed for point-of-care conversations.Methods Our prototype, called Substitutable Medical Applications and Reusable Technology (SMART)® PCM, visualizes genomic information in real time, comparing a patient’s diagnosis-specific somatic gene mutations detected by PCR-based hotspot testing to a population-level set of comparable data. The initial prototype works for patient specimens with 0 or 1 detected mutation. Genomics extensions were created for the Health Level Seven® Fast Healthcare Interoperability Resources (FHIR)® standard; otherwise, the prototype is a normal SMART on FHIR app.Results The PCM prototype can rapidly present a visualization that compares a patient’s somatic genomic alterations against a distribution built from more than 3000 patients, along with context-specific links to external knowledge bases. Initial evaluation by oncologists provided important feedback about the prototype’s strengths and weaknesses. We added several requested enhancements and successfully demonstrated the app at the inaugural American Society of Clinical Oncology Interoperability Demonstration; we have also begun to expand visualization capabilities to include cancer specimens with multiple mutations.Discussion PCM is open-source software for clinicians to present the individual patient within the population-level spectrum of cancer somatic mutations. The app can be implemented on any SMART on FHIR-enabled EHRs, and future versions of PCM should be able to evolve in parallel with external knowledge bases.