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The role for routine whole genome and transcriptome analysis (WGTA) for poor prognosis pediatric cancers remains undetermined. Here, we characterize somatic mutations, structural rearrangements, copy number variants, gene expression, immuno-profiles and germline cancer predisposition variants in children and adolescents with relapsed, refractory or poor prognosis malignancies who underwent somatic WGTA and matched germline sequencing. Seventy-nine participants with a median age at enrollment of 8.8 y (range 6 months to 21.2 y) are included. Germline pathogenic/likely pathogenic variants are identified in 12% of participants, of which 60% were not known prior. Therapeutically actionable variants are identified by targeted gene report and whole genome in 32% and 62% of participants, respectively, and increase to 96% after integrating transcriptome analyses. Thirty-two molecularly informed therapies are pursued in 28 participants with 54% achieving a clinical benefit rate; objective response or stable disease ≥6 months. Integrated WGTA identifies therapeutically actionable variants in almost all tumors and are directly translatable to clinical care of children with poor prognosis cancers., (© 2024. The Author(s).)

Background: Tumor mutation burden (TMB) is a key characteristic used in a tumor-type agnostic context to inform the use of immune checkpoint inhibitors (ICI). Accurate and consistent measurement of TMB is crucial as it can significantly impact patient selection for therapy and clinical trials, with a threshold of 10 mutations/Mb commonly used as an inclusion criterion. Studies have shown that the most significant contributor to variability in mutation counts in whole genome sequence (WGS) data is differences in analysis methods, even more than differences in extraction or library construction methods. Therefore, tools for improving consistency in whole genome TMB estimation are of clinical importance., Methods: We developed a distributable TMB analysis suite, TMBur, to address the need for genomic TMB estimate consistency in projects that span jurisdictions. TMBur is implemented in Nextflow and performs all analysis steps to generate TMB estimates directly from fastq files, incorporating somatic variant calling with Manta, Strelka2, and Mutect2, and microsatellite instability profiling with MSISensor. These tools are provided in a Singularity container downloaded by the workflow at runtime, allowing the entire workflow to be run identically on most computing platforms. To test the reproducibility of TMBur TMB estimates, we performed replicate runs on WGS data derived from the COLO829 and COLO829BL cell lines at multiple research centres. The clinical value of derived TMB estimates was then evaluated using a cohort of 90 patients with advanced, metastatic cancer that received ICIs following WGS analysis. Patients were split into groups based on a threshold of 10/Mb, and time to progression from initiation of ICIs was examined using Kaplan-Meier and cox-proportional hazards analyses., Results: TMBur produced identical TMB estimates across replicates and at multiple analysis centres. The clinical utility of TMBur-derived TMB estimates were validated, with a genomic TMB ≥ 10/Mb demonstrating improved time to progression, even after correcting for differences in tumor type (HR = 0.39, p = 0.012)., Conclusions: TMBur, a shareable workflow, generates consistent whole genome derived TMB estimates predictive of response to ICIs across multiple analysis centres. Reproducible TMB estimates from this approach can improve collaboration and ensure equitable treatment and clinical trial access spanning jurisdictions., (© 2022. The Author(s).)

Background: Recent advances are enabling delivery of precision genomic medicine to cancer clinics. While the majority of approaches profile panels of selected genes or hotspot regions, comprehensive data provided by whole-genome and transcriptome sequencing and analysis (WGTA) present an opportunity to align a much larger proportion of patients to therapies., Patients and Methods: Samples from 570 patients with advanced or metastatic cancer of diverse types enrolled in the Personalized OncoGenomics (POG) program underwent WGTA. DNA-based data, including mutations, copy number and mutation signatures, were combined with RNA-based data, including gene expression and fusions, to generate comprehensive WGTA profiles. A multidisciplinary molecular tumour board used WGTA profiles to identify and prioritize clinically actionable alterations and inform therapy. Patient responses to WGTA-informed therapies were collected., Results: Clinically actionable targets were identified for 83% of patients, of which 37% of patients received WGTA-informed treatments. RNA expression data were particularly informative, contributing to 67% of WGTA-informed treatments; 25% of treatments were informed by RNA expression alone. Of a total 248 WGTA-informed treatments, 46% resulted in clinical benefit. RNA expression data were comparable to DNA-based mutation and copy number data in aligning to clinically beneficial treatments. Genome signatures also guided therapeutics including platinum, poly-ADP ribose polymerase inhibitors and immunotherapies. Patients accessed WGTA-informed treatments through clinical trials (19%), off-label use (35%) and as standard therapies (46%) including those which would not otherwise have been the next choice of therapy, demonstrating the utility of genomic information to direct use of chemotherapies as well as targeted therapies., Conclusions: Integrating RNA expression and genome data illuminated treatment options that resulted in 46% of treated patients experiencing positive clinical benefit, supporting the use of comprehensive WGTA profiling in clinical cancer care., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In this study, we evaluate the impact of whole genome and transcriptome analysis (WGTA) on predictive molecular profiling and histologic diagnosis in a cohort of advanced malignancies. WGTA was used to generate reports including molecular alterations and site/tissue of origin prediction. Two reviewers analyzed genomic reports, clinical history, and tumor pathology. We used National Comprehensive Cancer Network (NCCN) consensus guidelines, Food and Drug Administration (FDA) approvals, and provincially reimbursed treatments to define genomic biomarkers associated with approved targeted therapeutic options (TTOs). Tumor tissue/site of origin was reassessed for most cases using genomic analysis, including a machine learning algorithm (Supervised Cancer Origin Prediction Using Expression [SCOPE]) trained on The Cancer Genome Atlas data. WGTA was performed on 652 cases, including a range of primary tumor types/tumor sites and 15 malignant tumors of uncertain histogenesis (MTUH). At the time WGTA was performed, alterations associated with an approved TTO were identified in 39 (6%) cases; 3 of these were not identified through routine pathology workup. In seven (1%) cases, the pathology workup either failed, was not performed, or gave a different result from the WGTA. Approved TTOs identified by WGTA increased to 103 (16%) when applying 2021 guidelines. The histopathologic diagnosis was reviewed in 389 cases and agreed with the diagnostic consensus after WGTA in 94% of non-MTUH cases (n = 374). The remainder included situations where the morphologic diagnosis was changed based on WGTA and clinical data (0.5%), or where the WGTA was non-contributory (5%). The 15 MTUH were all diagnosed as specific tumor types by WGTA. Tumor board reviews including WGTA agreed with almost all initial predictive molecular profile and histopathologic diagnoses. WGTA was a powerful tool to assign site/tissue of origin in MTUH. Current efforts focus on improving therapeutic predictive power and decreasing cost to enhance use of WGTA data as a routine clinical test., (© 2022 The Authors. The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland & John Wiley & Sons, Ltd.)

Tumor mutation burden (TMB) is a measure to predict patient responsiveness to immune checkpoint immunotherapy because with increased mutation frequency, the likelihood of a greater neoantigen burden is increased. Although neoantigen prediction tools exist, tumor neoantigen burden has not been adopted as a measure to predict immunotherapy response. With both measures, current guidelines are limited to the coding regions, but ectopic expression of sequences in the noncoding space may potentially be a source of neoantigens. A pan-cancer cohort of 574 advanced disease stage patients with whole genome and transcriptome sequencing was analyzed to report mutation burden and neoantigen counts within the coding and noncoding regions. The efficacy of tumor neoantigen burden, reported as tumor neoantigen count (TNC), including neoantigens derived from the expression of noncoding regions, compared with TMB as a predictor of response to immunotherapy for 80 patients who had received treatment, was evaluated. TMB was found to be the best predictor of response to immunotherapy, whereas expression-derived TNC from the noncoding regions did not improve prediction of response. Therefore, there is minimal benefit in extending the calculation of TNC to the noncoding space for the purposes of predicting response. However, it is likely that there is a wealth of neoantigens derived from the noncoding space that may impact patient outcomes and treatments., (Copyright © 2022 Association for Molecular Pathology and American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Manual interpretation of variants remains rate limiting in precision oncology. The increasing scale and complexity of molecular data generated from comprehensive sequencing of cancer samples requires advanced interpretative platforms as precision oncology expands beyond individual patients to entire populations. To address this unmet need, we introduce a Platform for Oncogenomic Reporting and Interpretation (PORI), comprising an analytic framework that facilitates the interpretation and reporting of somatic variants in cancer. PORI integrates reporting and graph knowledge base tools combined with support for manual curation at the reporting stage. PORI represents an open-source platform alternative to commercial reporting solutions suitable for comprehensive genomic data sets in precision oncology. We demonstrate the utility of PORI by matching 9,961 pan-cancer genome atlas tumours to the graph knowledge base, calculating therapeutically informative alterations, and making available reports describing select individual samples., (© 2022. The Author(s).)

Background: Single-arm trials are common in precision oncology. Owing to the lack of randomized counterfactual, resultant data are not amenable to comparative outcomes analyses. Difference-in-difference (DID) methods present an opportunity to generate causal estimates of time-varying treatment outcomes. Using DID, our study estimates within-cohort effects of genomics-informed treatment versus standard care on clinical and cost outcomes., Methods: We focus on adults with advanced cancers enrolled in the single-arm BC Cancer Personalized OncoGenomics program between 2012 and 2017. All individuals had a minimum of 1-year follow up. Logistic regression explored baseline differences across patients who received a genomics-informed treatment versus a standard care treatment after genomic sequencing. DID estimated the incremental effects of genomics-informed treatment on time to treatment discontinuation (TTD), time to next treatment (TTNT), and costs. TTD and TTNT correlate with improved response and survival., Results: Our study cohort included 346 patients, of whom 140 (40%) received genomics-informed treatment after sequencing and 206 (60%) received standard care treatment. No significant differences in baseline characteristics were detected across treatment groups. DID estimated that the incremental effect of genomics-informed versus standard care treatment was 102 days (95% CI: 35, 167) on TTD, 91 days (95% CI: -9, 175) on TTNT, and CAD$91,098 (95% CI: $46,848, $176,598) on costs. Effects were most pronounced in gastrointestinal cancer patients., Conclusions: Genomics-informed treatment had a statistically significant effect on TTD compared to standard care treatment, but at increased treatment costs. Within-cohort evidence generated through this single-arm study informs the early-stage comparative effectiveness of precision oncology., (© 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Purpose: Gene fusions are important oncogenic drivers and many are actionable. Whole-genome and transcriptome (WGS and RNA-seq, respectively) sequencing can discover novel clinically relevant fusions., Experimental Design: Using WGS and RNA-seq, we reviewed the prevalence of fusions in a cohort of 570 patients with cancer, and compared prevalence to that predicted with commercially available panels. Fusions were annotated using a consensus variant calling pipeline (MAVIS) and required that a contig of the breakpoint could be constructed and supported from ≥2 structural variant detection approaches., Results: In 570 patients with advanced cancer, MAVIS identified 81 recurrent fusions by WGS and 111 by RNA-seq, of which 18 fusions by WGS and 19 by RNA-seq were noted in at least 3 separate patients. The most common fusions were EML4-ALK in thoracic malignancies (9/69, 13%), and CMTM8-CMTM7 in colorectal cancer (4/73, 5.5%). Combined genomic and transcriptomic analysis identified novel fusion partners for clinically relevant genes, such as NTRK2 (novel partners: SHC3, DAPK1) , and NTRK3 (novel partners: POLG, PIBF1 )., Conclusions: Utilizing WGS/RNA-seq facilitates identification of novel fusions in clinically relevant genes, and detected a greater proportion than commercially available panels are expected to find. A significant benefit of WGS and RNA-seq is the innate ability to retrospectively identify variants that becomes clinically relevant over time, without the need for additional testing, which is not possible with panel-based approaches., (©2020 American Association for Cancer Research.)

Purpose: Immune checkpoint inhibitors (ICI) have revolutionized the treatment of solid tumors with dramatic and durable responses seen across multiple tumor types. However, identifying patients who will respond to these drugs remains challenging, particularly in the context of advanced and previously treated cancers., Experimental Design: We characterized fresh tumor biopsies from a heterogeneous pan-cancer cohort of 98 patients with metastatic predominantly pretreated disease through the Personalized OncoGenomics program at BC Cancer (Vancouver, Canada) using whole genome and transcriptome analysis (WGTA). Baseline characteristics and follow-up data were collected retrospectively., Results: We found that tumor mutation burden, independent of mismatch repair status, was the most predictive marker of time to progression ( P = 0.007), but immune-related CD8 + T-cell and M1-M2 macrophage ratio scores were more predictive for overall survival (OS; P = 0.0014 and 0.0012, respectively). While CD274 [programmed death-ligand 1 (PD-L1)] gene expression is comparable with protein levels detected by IHC, we did not observe a clinical benefit for patients with this marker. We demonstrate that a combination of markers based on WGTA provides the best stratification of patients ( P = 0.00071, OS), and also present a case study of possible acquired resistance to pembrolizumab in a patient with non-small cell lung cancer., Conclusions: Interpreting the tumor-immune interface to predict ICI efficacy remains challenging. WGTA allows for identification of multiple biomarkers simultaneously that in combination may help to identify responders, particularly in the context of a heterogeneous population of advanced and previously treated cancers, thus precluding tumor type-specific testing., (©2020 American Association for Cancer Research.)

Background: Randomized controlled trials (RCTs) are uncommon in precision oncology. We provide an introduction and illustrative example of matching methods for evaluating precision oncology in the absence of RCTs. We focus on British Columbia's Personalized OncoGenomics (POG) program, which applies whole-genome and transcriptome analysis (WGTA) to inform advanced cancer care., Methods: Our cohort comprises 230 POG patients enrolled between 2014 and 2015 and matched POG-naive controls. We generated our matched cohort using 1:1 propensity score matching (PSM) and genetic matching prior to exploring survival differences., Results: We find that genetic matching outperformed PSM when balancing covariates. In all cohorts, overall survival did not significantly differ across POG and POG-naive patients (p > 0.05). Stratification by WGTA-informed treatment indicated unmatched survival differences. Patients whose WGTA information led to treatment change were at a reduced hazard of death compared to POG-naive controls in all cohorts, with estimated hazard ratios ranging from 0.33 (95% CI: 0.13, 0.81) to 0.41 (95% CI: 0.17, 0.98)., Conclusion: These results signal that clinical effectiveness of precision oncology approaches will depend on rates of genomics-informed treatment change. Our study will guide future evaluations of precision oncology and support reliable effect estimation when RCT data are unavailable., (© 2020 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals LLC.)

Purpose: Structural variants (SVs) may be an underestimated cause of hereditary cancer syndromes given the current limitations of short-read next-generation sequencing. Here we investigated the utility of long-read sequencing in resolving germline SVs in cancer susceptibility genes detected through short-read genome sequencing., Methods: Known or suspected deleterious germline SVs were identified using Illumina genome sequencing across a cohort of 669 advanced cancer patients with paired tumor genome and transcriptome sequencing. Candidate SVs were subsequently assessed by Oxford Nanopore long-read sequencing., Results: Nanopore sequencing confirmed eight simple pathogenic or likely pathogenic SVs, resolving three additional variants whose impact could not be fully elucidated through short-read sequencing. A recurrent sequencing artifact on chromosome 16p13 and one complex rearrangement on chromosome 5q35 were subsequently classified as likely benign, obviating the need for further clinical assessment. Variant configuration was further resolved in one case with a complex pathogenic rearrangement affecting TSC2., Conclusion: Our findings demonstrate that long-read sequencing can improve the validation, resolution, and classification of germline SVs. This has important implications for return of results, cascade carrier testing, cancer screening, and prophylactic interventions.

Background The IND.226 study was a phase Ib study to determine the recommended phase II dose of durvalumab + tremelimumab in combination with standard platinum-doublet chemotherapy. Sequential administration of multiple agents increases total chair time adding costs overall and inconvenience for patients. This cohort of the IND.226 study evaluated the safety and tolerability of durvalumab + tremelimumab given either sequentially (SEQ) or concurrently (CON). Methods Patients with advanced solid tumours were enrolled and randomised to either SEQ tremelimumab 75 mg IV over 1 h followed by durvalumab 1500 mg IV over 1 h q4wks on the same day, or CON administration over 1 h. The serum pharmacokinetic profile of SEQ versus CON of durvalumab and tremelimumab administration was also evaluated. Results 14 patients either received SEQ (n = 7pts) or CON (n = 7 pts). There were no infusion related reactions. Drug related adverse events (AEs) were mainly low grade and manageable, and comparable in frequency between SEQ/CON- fatigue (43%/57%), rash (43%/43%), pruritus (43%/29%) and nausea (14%/29%). One patient in each cohort discontinued treatment due to toxicity. The PK profiles of durvalumab and tremelimumab were similar between CON and SEQ, and to historical reference data. Conclusions Concurrent administration of durvalumab and tremelimumab over 1 h is safe with a comparable PK profile to sequential administration.

Introduction Given the high level of uncertainty surrounding the outcomes of early phase clinical trials, whole genome and transcriptome analysis (WGTA) can be used to optimize patient selection and study assignment. In this retrospective analysis, we reviewed the impact of this approach on one such program. Methods Patients with advanced malignancies underwent fresh tumor biopsies as part of our personalized medicine program (NCT02155621). Tumour molecular data were reviewed for potentially clinically actionable findings and patients were referred to the developmental therapeutics program. Outcomes were reviewed in all patients, including those where trial selection was driven by molecular data (matched) and those where there was no clear molecular rationale (unmatched). Results From January 2014 to January 2018, 28 patients underwent WGTA and enrolled in clinical trials, including 2 patients enrolled in two trials. Fifteen patients were matched to a treatment based on a molecular target. Five patients were matched to a trial based upon single-gene DNA changes, all supported by RNA data. Ten cases were matched on the basis of genome-wide data (n = 4) or RNA gene expression only (n = 6). With a median follow-up of 6.7 months, the median time on treatment was 8.2 weeks. Discussion When compared to single-gene DNA-based data alone, WGTA led to a 3-fold increase in treatment matching. In a setting where there is a high level of uncertainty around both the investigational agents and the biomarkers, more data are needed to fully evaluate the impact of routine use of WGTA.

This multicenter phase Ib study aimed to establish a recommended phase II dose for durvalumab (Du) ± tremelimumab (Tr) in combination with standard platinum-doublet chemotherapy. Eligible patients were enrolled into one of six dose levels (DL) of Du ± Tr which included concomitant treatment with standard platinum-doublet regimens; (pemetrexed, gemcitabine, etoposide, (each with cisplatin or carboplatin) or nab-paclitaxel (with carboplatin)). Dose escalation was according to a Rolling Six type design. Both weight-based and fixed dosing of Du and Tr were explored. Du was continued until progression. Tr dosing was finite (up to 6 doses) with increasing dose and/or frequency by DL. 136 patients were enrolled. The majority of drug-related adverse events (AEs) were ≤ grade 2 and attributable to chemotherapy. AEs considered related to immunotherapy were mainly ≤ grade 2; the most frequent (occurring ≥10 %) were colitis/diarrhea, skin, and thyroid dysfunction. Seven patients had DLTs including pneumonitis, myocarditis, diarrhea, encephalitis, motor neuropathy, and enterocolitis. There were 2 treatment-related deaths. Tr and Du exposures did not appear affected by chemotherapy. Among the 73 non-small cell lung cancer (NSCLC) patients treated, the objective response rate was 51 % (95 %CI = 38.7-62.6 %) with a median progression-free and overall survival of 6.5 months (95 % CI = 5.5-9.4 months) and 19.8 months (95 % CI = 14.8 months - not yet reached) respectively. Anti-tumour activity was observed across PD-L1 subtypes. Du 1500 mg q3w and Tr 75 mg q3wx5 can be safely combined with platinum-doublet chemotherapy. Efficacy among NSCLC patients appears comparable to results from other immunotherapy and chemotherapy combination trials. NCT02537418., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Advanced and metastatic tumors with complex treatment histories drive cancer mortality. Here we describe the POG570 cohort, a comprehensive whole-genome, transcriptome and clinical dataset, amenable for exploration of the impacts of therapies on genomic landscapes. Previous exposure to DNA-damaging chemotherapies and mutations affecting DNA repair genes, including POLQ and genes encoding Polζ, were associated with genome-wide, therapy-induced mutagenesis. Exposure to platinum therapies coincided with signatures SBS31 and DSB5 and, when combined with DNA synthesis inhibitors, signature SBS17b. Alterations in ESR1, EGFR, CTNNB1, FGFR1, VEGFA and DPYD were consistent with drug resistance and sensitivity. Recurrent noncoding events were found in regulatory region hotspots of genes including TERT, PLEKHS1, AP2A1 and ADGRG6. Mutation burden and immune signatures corresponded with overall survival and response to immunotherapy. Our data offer a rich resource for investigation of advanced cancers and interpretation of whole-genome and transcriptome sequencing in the context of a cancer clinic., (© 2020. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Importance: Pediatric cancers are epigenetic diseases; therefore, considering tumor gene expression information is necessary for a complete understanding of the tumorigenic processes., Objective: To evaluate the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer., Design, Setting, and Participants: This cohort study was conducted as a consortium between the University of California, Santa Cruz (UCSC) Treehouse Childhood Cancer Initiative and clinical genomic trials. RNA sequencing (RNA-Seq) data were obtained from the following 4 clinical sites and analyzed at UCSC: British Columbia Children's Hospital (n = 31), Lucile Packard Children's Hospital at Stanford University (n = 80), CHOC Children's Hospital and Hyundai Cancer Institute (n = 46), and the Pacific Pediatric Neuro-Oncology Consortium (n = 24). The study dates were January 1, 2016, to March 22, 2017., Exposures: Participants underwent tumor RNA-Seq profiling as part of 4 separate clinical trials at partner hospitals. The UCSC either downloaded RNA-Seq data from a partner institution for analysis in the cloud or provided a Docker pipeline that performed the same analysis at a partner institution. The UCSC then compared each participant's tumor RNA-Seq profile with more than 11 000 uniformly analyzed tumor profiles from pediatric and young adult patients with cancer, downloaded from public data repositories. These comparisons were used to identify genes and pathways that are significantly overexpressed in each patient's tumor. Results of the UCSC analysis were presented to clinical partners., Main Outcomes and Measures: Feasibility of a third-party institution (UCSC Treehouse Childhood Cancer Initiative) to obtain tumor RNA-Seq data from patients, conduct comparative analysis, and present analysis results to clinicians; and proportion of patients for whom comparative tumor gene expression analysis provided useful clinical and biological information., Results: Among 144 samples from children and young adults (median age at diagnosis, 9 years; range, 0-26 years; 72 of 118 [61.0%] male [26 patients sex unknown]) with a relapsed, refractory, or rare cancer treated on precision medicine protocols, RNA-Seq-derived gene expression was potentially useful for 99 of 144 samples (68.8%) compared with DNA mutation information that was potentially useful for only 34 of 74 samples (45.9%)., Conclusions and Relevance: This study's findings suggest that tumor RNA-Seq comparisons may be feasible and highlight the potential clinical utility of incorporating such comparisons into the clinical genomic interpretation framework for difficult-to-treat pediatric and young adult patients with cancer. The study also highlights for the first time to date the potential clinical utility of harmonized publicly available genomic data sets.

Importance: A molecular diagnostic method that incorporates information about the transcriptional status of all genes across multiple tissue types can strengthen confidence in cancer diagnosis., Objective: To determine the practical use of a whole transcriptome-based pan-cancer method in diagnosing primary and metastatic cancers and resolving complex diagnoses., Design, Setting, and Participants: This cross-sectional diagnostic study assessed Supervised Cancer Origin Prediction Using Expression (SCOPE), a machine learning method using whole-transcriptome RNA sequencing data. Training was performed on publicly available primary cancer data sets, including The Cancer Genome Atlas. Testing was performed retrospectively on untreated primary cancers and treated metastases from volunteer adult patients at BC Cancer in Vancouver, British Columbia, from January 1, 2013, to March 31, 2016, and testing spanned 10 822 samples and 66 output classes representing untreated primary cancers (n = 40) and adjacent normal tissues (n = 26). SCOPE's performance was demonstrated on 211 untreated primary mesothelioma cancers and 201 treatment-resistant metastatic cancers. Finally, SCOPE was used to identify the putative site of origin in 15 cases with initial presentation as cancers with unknown primary of origin., Results: A total of 10 688 adult patient samples representing 40 untreated primary tumor types and 26 adjacent-normal tissues were used for training. Demographic data were not available for all data sets. Among the training data set, 5157 of 10 244 (50.3%) were male and the mean (SD) age was 58.9 (14.5) years. Testing was performed on 211 patients with untreated primary mesothelioma (173 [82.0%] male; mean [SD] age, 64.5 [11.3] years); 201 patients with treatment-resistant cancers (141 [70.1%] female; mean [SD] age, 55.6 [12.9] years); and 15 patients with cancers of unknown primary of origin; among the treatment-resistant cancers, 168 were metastatic, and 33 were the primary presentation. An accuracy rate of 99% was obtained for primary epithelioid mesotheliomas tested (125 of 126). The remaining 85 mesotheliomas had a mixed etiology (sarcomatoid mesotheliomas) and were correctly identified as a mixture of their primary components, with potential implications in resolving subtypes and incidences of mixed histology. SCOPE achieved an overall mean (SD) accuracy rate of 86% (11%) and F1 score of 0.79 (0.12) on the 201 treatment-resistant cancers and matched 12 of 15 of the putative diagnoses for cancers with indeterminate diagnosis from conventional pathology., Conclusions and Relevance: These results suggest that machine learning approaches incorporating multiple tumor profiles can more accurately identify the cancerous state and discriminate it from normal cells. SCOPE uses the whole transcriptomes from normal and tumor tissues, and results of this study suggest that it performs well for rare cancer types, primary cancers, treatment-resistant metastatic cancers, and cancers of unknown primary of origin. Genes most relevant in SCOPE's decision making were examined, and several are known biological markers of respective cancers. SCOPE may be applied as an orthogonal diagnostic method in cases where the site of origin of a cancer is unknown, or when standard pathology assessment is inconclusive.

Understanding the impact of treatment policies on patient outcomes is essential in improving all aspects of patient care. The BC Cancer Agency is a provincial program that provides cancer care on a population basis for 4.5 million residents. The Lung and Head & Neck Tumour Groups planned to create a generic yet comprehensive software infrastructure that could be used by all Tumour Groups: the Outcomes and Surveillance Integration System (OaSIS). The primary goal was the development of an integrated database that will amalgamate existing provincial data warehouses of varying datasets and provide the infrastructure to support additional routes of data entry, including clinicians from multiple-disciplines, quality of life and survivorship data from patients, and three dimensional dosimetric information archived from the radiotherapy planning and delivery systems. The primary goal is to be able to capture any data point related to patient characteristics, disease factors, treatment details and survivorship, from the point of diagnosis onwards. Through existing and novel data-mining techniques, OaSIS will support unique population based research activities by promoting collaborative interactions between the research centre, clinical activities at the cancer treatment centres and other institutions. This will also facilitate initiatives to improve patient outcomes, decision support in achieving operational efficiencies and an environment that supports knowledge generation.

Background Neuregulin-1 gene (NRG1) fusions function as oncogenic drivers across various solid tumors, most notably in invasive mucinous adenocarcinoma (IMA) of the lung, and represent a rational potential target for treatment. NRG1 is a growth factor, which binds to ErbB3 or ErbB4 inducing the formation of ErbB3 or 4-containing homo- or heterodimers and activating downstream ErbB-family signaling pathways. Therefore, the ErbB-family blocker afatinib may be a potential treatment option for patients with solid tumors harboring NRG1 fusions. Methods We report a case series of all known patients with NRG1 fusion-positive solid tumors who were treated with afatinib; afatinib therapy is ongoing for some patients. Results To date, 18 patients with NRG1 fusion-positive solid tumors have been treated with afatinib (Table). These include 12 cases of non-small cell lung cancer (NSCLC; 7 of which were reported as IMA), 5 cases of gastrointestinal (GI) cancer (primarily pancreatic ductal adenocarcinoma [PDAC]) and 1 case of ovarian cancer. Various NRG1 fusion partners were identified, most commonly CD74 in patients with NSCLC (n = 7; 58%) and ATP1B1 in patients with gastrointestinal cancer (n = 3; 60%). Best response with afatinib among patients with NSCLC was partial response (PR) lasting 24 months (10 months among those specifically with IMA of the lung). Patients with PDAC experienced PR of 3 and 5.5 months' duration, and one patient has an ongoing PR after 7 months. One patient with cholangiocarcinoma had a PR lasting 8 months; another patient with ovarian cancer had stable disease (SD) of unknown duration. Conclusions Afatinib is a potential treatment option for some patients with solid tumors harboring NRG1 fusions. The efficacy and safety of afatinib will be evaluated in ongoing/planned prospective non-randomized clinical trials of targeted drugs in patients with advanced cancer with potentially actionable genomic variants (NCT02925234 and NCT02693535). Table: 63O Tumor type    NRG1 fusion partner  Best response, physician assessed  Duration of response, mos  Refs  NSCLC  IMA  CD74  PR  10  1      CD74  PR  6.5  2      CD74  PD*    3      CD74  SD  3  3      SDC4  PD    3      CD74  PD    3      CD74  PR  -  4    ADC  SLC3A2  PR  12  1      SDC4  PR  12  5      -  PR  24  6      CD74  PR  14+  4, 6      SDC4  SD  4  6  GI  PDAC  ATP1B1  PR  3  7      ATP1B1  PR  5.5  8      APP  PR  7+  6, 8    Cholangiocarcinoma  ATP1B1  PR  8  5    Colorectal (KRASm +ve)  POMK  SD  4  9  Ovarian    CLU  SD  -  10  Tumor type    NRG1 fusion partner  Best response, physician assessed  Duration of response, mos  Refs  NSCLC  IMA  CD74  PR  10  1      CD74  PR  6.5  2      CD74  PD*    3      CD74  SD  3  3      SDC4  PD    3      CD74  PD    3      CD74  PR  -  4    ADC  SLC3A2  PR  12  1      SDC4  PR  12  5      -  PR  24  6      CD74  PR  14+  4, 6      SDC4  SD  4  6  GI  PDAC  ATP1B1  PR  3  7      ATP1B1  PR  5.5  8      APP  PR  7+  6, 8    Cholangiocarcinoma  ATP1B1  PR  8  5    Colorectal (KRASm +ve)  POMK  SD  4  9  Ovarian    CLU  SD  -  10  * PD on an anti-ErbB3 mAb prior to afatinib. -, not reported; ADC, adenocarcinoma; PD, progressive disease.1Gay. JTO 2017, 2Cheema. JTO 2017, 3Drilon. Cancer Discov 2018, 4Duruisseaux. WCLC 2019, 5Jones. Ann Oncol 2017, 6Laskin. JSMO 2019, 7Heining. Cancer Discov 2018, 8Jones. Clin Cancer Res 2019, 9Weinberg. ESMO GI 2019, 10Murumagi. AACR 2019. Table: 63O Tumor type    NRG1 fusion partner  Best response, physician assessed  Duration of response, mos  Refs  NSCLC  IMA  CD74  PR  10  1      CD74  PR  6.5  2      CD74  PD*    3      CD74  SD  3  3      SDC4  PD    3      CD74  PD    3      CD74  PR  -  4    ADC  SLC3A2  PR  12  1      SDC4  PR  12  5      -  PR  24  6      CD74  PR  14+  4, 6      SDC4  SD  4  6  GI  PDAC  ATP1B1  PR  3  7      ATP1B1  PR  5.5  8      APP  PR  7+  6, 8    Cholangiocarcinoma  ATP1B1  PR  8  5    Colorectal (KRASm +ve)  POMK  SD  4  9  Ovarian    CLU  SD  -  10  Tumor type    NRG1 fusion partner  Best response, physician assessed  Duration of response, mos  Refs  NSCLC  IMA  CD74  PR  10  1      CD74  PR  6.5  2      CD74  PD*    3      CD74  SD  3  3      SDC4  PD    3      CD74  PD    3      CD74  PR  -  4    ADC  SLC3A2  PR  12  1      SDC4  PR  12  5      -  PR  24  6      CD74  PR  14+  4, 6      SDC4  SD  4  6  GI  PDAC  ATP1B1  PR  3  7      ATP1B1  PR  5.5  8      APP  PR  7+  6, 8    Cholangiocarcinoma  ATP1B1  PR  8  5    Colorectal (KRASm +ve)  POMK  SD  4  9  Ovarian    CLU  SD  -  10  * PD on an anti-ErbB3 mAb prior to afatinib. -, not reported; ADC, adenocarcinoma; PD, progressive disease.1Gay. JTO 2017, 2Cheema. JTO 2017, 3Drilon. Cancer Discov 2018, 4Duruisseaux. WCLC 2019, 5Jones. Ann Oncol 2017, 6Laskin. JSMO 2019, 7Heining. Cancer Discov 2018, 8Jones. Clin Cancer Res 2019, 9Weinberg. ESMO GI 2019, 10Murumagi. AACR 2019. Editorial acknowledgement Greg Plosker of GeoMed, an Ashfield company, part of UDG Healthcare plc. Legal entity responsible for the study The authors. Funding Boehringer Ingelheim. Disclosure Y. Goto: Advisory / Consultancy, Speaker Bureau / Expert testimony: AstraZeneca; Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (self): Eli Lilly; Advisory / Consultancy, Speaker Bureau / Expert testimony: Chugai; Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (self): Taiho Pharmaceutical; Advisory / Consultancy, Speaker Bureau / Expert testimony: Boehringer Ingelheim; Speaker Bureau / Expert testimony, Research grant / Funding (self): Ono Pharmaceutical; Speaker Bureau / Expert testimony, Research grant / Funding (self): Bristol-Myers Squibb; Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (self): Pfizer; Speaker Bureau / Expert testimony: MSD; Speaker Bureau / Expert testimony: Shionogi Pharma; Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (self): Novartis; Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: Glaxo Smith Kline; Research grant / Funding (self): AbbVie; Research grant / Funding (self): Daiichi Sankyo; Research grant / Funding (self): Kyorin. J. Cadranel: Advisory / Consultancy, Research grant / Funding (institution), Non-remunerated activity/ies: AZ; Advisory / Consultancy, Non-remunerated activity/ies: BI; Advisory / Consultancy: MSD; Advisory / Consultancy: BMS; Advisory / Consultancy: Takeda; Advisory / Consultancy, Research grant / Funding (institution): Pfizer; Advisory / Consultancy: Lilly; Advisory / Consultancy, Non-remunerated activity/ies: Roche; Advisory / Consultancy, Research grant / Funding (institution): Novartis. B.A. Weinberg: Speaker Bureau / Expert testimony: Lilly; Speaker Bureau / Expert testimony: Bayer; Research grant / Funding (institution): Novartis; Research grant / Funding (institution): Ipsen; Travel / Accommodation / Expenses: Caris Life Sciences; Travel / Accommodation / Expenses: Boehringer Ingelheim. M. Duruisseaux: Honoraria (self): Roche; Honoraria (self): Takeda; Honoraria (self): Pfizer; Honoraria (self): Novartis; Honoraria (self): AstraZeneca; Honoraria (self): BMS; Honoraria (self): MSD; Honoraria (self): AbbVie; Honoraria (self): Boerhinger ingelheim. S.V. Liu: Advisory / Consultancy: Apollomics; Advisory / Consultancy, Research grant / Funding (institution): AstraZeneca; Advisory / Consultancy: Boehringer Ingelheim; Advisory / Consultancy, Research grant / Funding (institution): Bristol-Myers Squibb; Advisory / Consultancy: Celgene; Advisory / Consultancy: G1 Therapeutics; Advisory / Consultancy, Research grant / Funding (institution): Genentech/Roche; Advisory / Consultancy: Guardant Health; Advisory / Consultancy: Heron; Advisory / Consultancy, Research grant / Funding (institution): Ignyta; Advisory / Consultancy: Inivata; Advisory / Consultancy: Janssen; Advisory / Consultancy, Research grant / Funding (institution): Lilly; Advisory / Consultancy, Research grant / Funding (institution): Merck; Advisory / Consultancy, Research grant / Funding (institution): Pfizer; Advisory / Consultancy: Regeneron; Advisory / Consultancy: Taiho (DSMB); Advisory / Consultancy: Takeda/Ariad; Advisory / Consultancy: Tempus; Research grant / Funding (institution): Bayer; Research grant / Funding (institution): Blueprint; Research grant / Funding (institution): Clovis; Research grant / Funding (institution): Corvus; Research grant / Funding (institution): Esanex; Research grant / Funding (institution): Lycera; Research grant / Funding (institution): Molecular Partners; Research grant / Funding (institution): OncoMed; Research grant / Funding (institution): Rain Therapeutics; Research grant / Funding (institution): Threshold. K. Tolba: Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: Merck; Advisory / Consultancy: Boehringer Ingelheim; Honoraria (self): Foundation One. R.C. Doebele: Advisory / Consultancy, Shareholder / Stockholder / Stock options, Licensing / Royalties, Licenses are licensing fees from patents or biological materials : Rain Therapeutics; Honoraria (self): Guardant; Advisory / Consultancy: Pfizer; Advisory / Consultancy: Trovagene; Advisory / Consultancy, Licensing / Royalties, Licenses are licensing fees from patents or biological materials : Ariad; Advisory / Consultancy: Takeda; Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: Genentech/Roche; Advisory / Consultancy, Research grant / Funding (self), Licenses are licensing fees from patents or biological materials : Ignyta; Advisory / Consultancy, Research grant / Funding (self), Licenses are licensing fees from patents or biological materials: Loxo; Research grant / Funding (self): Mirati; Licensing / Royalties, Licensing fees from patents or biological materials : Abbott Molecular; Licensing / Royalties, Licensing fees from patents or biological materials : GVKbio; Licensing / Royalties, Licensing fees from patents or biological materials: Chugai; Licensing / Royalties, Licensing fees from patents or biological materials : Genentech; Licensing / Royalties, Licensing fees from patents or biological materials: Foundation Medicine; Licensing / Royalties, Licensing fees from patents or biological materials: Black Diamond. R.F. Schlenk: Research grant / Funding (self): Boehringer Ingelheim. J.J. Laskin: Honoraria (self), Research grant / Funding (self): Roche Canada; Honoraria (self): BI Canada; Honoraria (self): AstraZeneca Canada; Research grant / Funding (self): Pfizer Canada. P.K. Cheema: Honoraria (self), Advisory / Consultancy: Astrazeneca; Honoraria (self), Advisory / Consultancy: BI; Honoraria (self), Advisory / Consultancy: BMS; Honoraria (self), Advisory / Consultancy: Novartis; Honoraria (self), Advisory / Consultancy: Takeda; Honoraria (self), Advisory / Consultancy: genomic Health; Honoraria (self), Advisory / Consultancy: Pfizer; Honoraria (self), Advisory / Consultancy: Merck. M.R. Jones: Full / Part-time employment: Qiagen. L.A. Muscarella: Honoraria (self): AstraZeneca; Honoraria (self): Roche; Honoraria (self): Boehringer Ingelheim. A. Cseh: Full / Part-time employment: Boehringer Ingelheim. F. Solca: Full / Part-time employment: Boehringer Ingelheim. D.J. Renouf: Honoraria (self): Celgene; Honoraria (self): Tahio; Honoraria (self): Bayer; Honoraria (self): Ipsen; Honoraria (self): Servier. All other authors have declared no conflicts of interest. [ABSTRACT FROM AUTHOR]