A data processing pipeline for the AACR project GENIE biopharma collaborative data with the {genieBPC} R package.

MOTIVATION: Data from the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE BPC) represent comprehensive clinical data linked to high-throughput sequencing data, providing a multi-institution, pan-cancer, publicly available data repository. GENIE BPC data provide detailed demographic, clinical, treatment, genomic and outcome data for patients with cancer. These data result in a unique observational database of molecularly characterized tumors with comprehensive clinical annotation that can be used for health outcomes and precision medicine research in oncology. Due to the inherently complex structure of the multiple phenomic and genomic datasets, the use of these data requires a robust process for data integration and preparation in order to build analytic models. RESULTS: We present the {genieBPC} package, a user-friendly data processing pipeline to facilitate the creation of analytic cohorts from the GENIE BPC data that are ready for clinico-genomic modeling and analyses. AVAILABILITY AND IMPLEMENTATION: {genieBPC} is available on CRAN and GitHub.

Bioinformatically Expanded Next-Generation Sequencing Analysis Optimizes Identification of Therapeutically Relevant MET Copy Number Alterations in >50,000 Tumors.

PURPOSE: Clinical relevance thresholds and laboratory methods are poorly defined for MET amplification, a targetable biomarker across malignancies. EXPERIMENTAL DESIGN: The utility of next-generation sequencing (NGS) in assessing MET copy number alterations was determined in >50,000 solid tumors. Using fluorescence in situ hybridization as reference, we validated and optimized NGS analysis. RESULTS: Incorporating read-depth and focality analyses achieved 91% concordance, 97% sensitivity, and 89% specificity. Tumor heterogeneity, neoplastic cell proportions, and genomic focality affected MET amplification assessment. NGS methodology showed superiority in capturing overall amplification status in heterogeneous tumors and defining amplification focality among other genomic alterations. MET copy gains and amplifications were found in 408 samples across 23 malignancies. Total MET copy number inversely correlated with amplified segment size. High-level/focal amplification was enriched in certain genomic subgroups and associated with targeted therapy response. CONCLUSIONS: Leveraging our integrated bioinformatic approach, targeted therapy benefit was observed across diverse MET amplification contexts.

Genomic Determinants of Early Recurrences in Low-Stage, Low-Grade Endometrioid Endometrial Carcinoma.

Low-stage, low-grade endometrioid endometrial carcinoma (EEC), the most common histologic type of endometrial cancer, typically has a favorable prognosis. A subset of these cancers, however, displays an aggressive clinical course with early recurrences, including distant relapses. All statistical tests were 2-sided. Using a combination of whole-exome and targeted capture sequencing of 65 FIGO stage IA and IB grade 1 EECs treated with surgery alone, we demonstrate that chromosome 1q gain (odds ratio [OR] = 8.09, 95% confidence interval [CI] = 1.59 to 54.6; P = .02), PIK3CA mutation (OR = 9.16, 95% CI = 1.95 to 61.8; P = .01), and DNA mismatch repair-deficient molecular subtype (OR = 7.92, 95% CI = 1.44 to 87.6; P = .02) are independent predictors of early recurrences within 3 years in this patient population. Chromosome 1q gain was validated in an independent dataset of stage I grade 1 EECs subjected to whole-exome sequencing. Our findings expand on the repertoire of genomic parameters that should be considered in the evaluation of patients with low-stage, low-grade EEC.

Clinical reliability of genomic data obtained from spinal metastatic tumor samples.

BACKGROUND: The role of tumor genomic profiling is rapidly growing as it results in targeted, personalized, cancer therapy. Though routinely used in clinical practice, there are no data exploring the reliability of genomic data obtained from spine metastases samples often leading to multiple biopsies in clinical practice. This study compares the genomic tumor landscape between spinal metastases and the corresponding primary tumors as well as between spinal metastases and visceral metastases. METHODS: Spine tumor samples, obtained for routine clinical care from 2013 to 2019, were analyzed using MSK-IMPACT, a next-generation sequencing assay. These samples were matched to primary or metastatic tumors from the corresponding patients. A concordance rate for genomic alterations was calculated for matching sample pairs within patients for the primary and spinal metastatic tumor samples as well as for the matching sample pairs within patients for the spinal and visceral metastases. For a more robust and clinically relevant estimate of concordance, subgroup analyses of previously established driver mutations specific to the main primary tumor histologies were performed. RESULTS: Eighty-four patients contributed next-generation sequencing data from a spinal metastasis and at least one other site of disease: 54 from the primary tumor, 39 had genomic tumor data from another, nonspinal metastasis, 12 patients participated in both subsets. For the cohort of matched primary tumors and spinal metastases (n = 54) comprised of mixed histologies, we found an average concordance rate of 96.97% for all genetic events, 97.17% for mutations, 100% for fusions, 89.81% for deletions, and 97.01% for amplifications across all matched samples. Notably, >25% of patients harbored at least one genetic variant between samples tested, though not specifically for known driver mutations. The average concordance rate of driver mutations was 96.99% for prostate cancer, 95.69% (P = .0004513) for lung cancer, and 96.43% for breast cancer. An average concordance of 99.02% was calculated for all genetic events between spine metastases and non-spinal metastases (n = 41) and, more specifically, a concordance rate of 98.91% was calculated between spine metastases and liver metastases (n = 12) which was the largest represented group of nonspine metastases. CONCLUSION: Sequencing data performed on spine tumor samples demonstrate a high concordance rate for genetic alterations between the primary tumor and spinal metastasis as well as between spinal metastases and other, visceral metastases, particularly for driver mutations. Spine tumor samples may be reliably used for genomic-based decision making in cancer care, particularly for prostate, NSCLC, and breast cancer.

Implications of Selection Bias Due to Delayed Study Entry in Clinical Genomic Studies.

IMPORTANCE: Real-world data sets that combine clinical and genomic data may be subject to left truncation (when potential study participants are not included because they have already passed the milestone of interest at the time of study recruitment). The lapse between diagnosis and molecular testing can present analytic challenges and threaten the validity and interpretation of survival analyses. OBSERVATIONS: Effects of ignoring left truncation when estimating overall survival are illustrated using data from the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE BPC), and a straightforward risk-set adjustment approach is described. Ignoring left truncation results in overestimation of overall survival: unadjusted median survival estimates from diagnosis among patients with stage IV non-small cell lung cancer or stage IV colorectal cancer were overestimated by more than 1 year. CONCLUSIONS AND RELEVANCE: Clinicogenomic data are a valuable resource for evaluation of real-world cancer outcomes and should be analyzed using appropriate methods to maximize their potential. Analysts must become adept at application of appropriate statistical methods to ensure valid, meaningful, and generalizable research findings.

Frequency of synchronous malignant liver lesions initially detected by ultrasound in patients with newly diagnosed underlying non-hematologic malignant disease: a retrospective study in 434 patients.

INTRODUCTION: This study described the frequency of malignant synchronous focal liver lesions (FLLs) in patients with newly diagnosed non-hematologic malignant disease. METHODS: From June 2013 to January 2017, 434 patients with synchronous FLLs and newly diagnosed underlying malignant disease were included in the study. The diagnosis of the FLLs was made by histology and/or contrast-enhanced imaging. The final medical report was used for analysis in the study. RESULTS: Of the 434 liver lesions, 258 (59.4%) were malignant and 176 (40.6%) benign. All malignant lesions were metastases. The benign liver lesions were comprised of 93 cysts (21.4%), 52 hemangiomas (12.0%), 20 focal fatty sparing (4.6%), 4 focal nodular hyperplasia (0.9%), 3 unspecified benign lesions (0.7%), 2 regenerative nodules (0.5%), 1 calcification (0.2%), and 1 adenoma (0.2%). Diseases with the highest percentage frequency of synchronous malignant FLLs were cholangiocarcinoma with 86.7%, neuroendocrine tumor with 72.7%, and pancreatic carcinoma with 72.0%. Gastric carcinoma (33.3%), breast carcinoma (44.4%), and urothelial cell carcinoma (45.5%) were the diseases with the lowest percentage frequency of synchronous malignant FLLs. CONCLUSION: In total, the frequency of malignant synchronous FLLs in newly diagnosed non-hematologic malignant disease was 59.4%. In particular, cholangiocarcinoma, neuroendocrine tumor, and pancreatic carcinoma were the diseases with the highest rate of synchronous malignant FLLs.

Identifying prognostic pairwise relationships among bacterial species in microbiome studies.

In recent literature, the human microbiome has been shown to have a major influence on human health. To investigate this impact, scientists study the composition and abundance of bacterial species, commonly using 16S rRNA gene sequencing, among patients with and without a disease or condition. Methods for such investigations to date have focused on the association between individual bacterium and an outcome, and higher-order pairwise relationships or interactions among bacteria are often avoided due to the substantial increase in dimension and the potential for spurious correlations. However, overlooking such relationships ignores the environment of the microbiome, where there is dynamic cooperation and competition among bacteria. We present a method for identifying and ranking pairs of bacteria that have a differential dichotomized relationship across outcomes. Our approach, implemented in an R package PairSeek, uses the stability selection framework with data-driven dichotomized forms of the pairwise relationships. We illustrate the properties of the proposed method using a published oral cancer data set and a simulation study.

The use of a next-generation sequencing-derived machine-learning risk-prediction model (OncoCast-MPM) for malignant pleural mesothelioma: a retrospective study.

BACKGROUND: Current risk stratification for patients with malignant pleural mesothelioma based on disease stage and histology is inadequate. For some individuals with early-stage epithelioid tumours, a good prognosis by current guidelines can progress rapidly; for others with advanced sarcomatoid cancers, a poor prognosis can progress slowly. Therefore, we aimed to develop and validate a machine-learning tool-known as OncoCast-MPM-that could create a model for patient prognosis. METHODS: We did a retrospective study looking at malignant pleural mesothelioma tumours using next-generation sequencing from the Memorial Sloan Kettering Cancer Center-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT). We collected clinical, pathological, and routine next-generation sequencing data from consecutive patients with malignant pleural mesothelioma treated at the Memorial Sloan Kettering Cancer Center (New York, NY, USA), as well as the MSK-IMPACT data. Together, these data comprised the MSK-IMPACT cohort. Using OncoCast-MPM, an open-source, web-accessible, machine-learning risk-prediction model, we integrated available data to create risk scores that stratified patients into low-risk and high-risk groups. Risk stratification of the MSK-IMPACT cohort was then validated using publicly available malignant pleural mesothelioma data from The Cancer Genome Atlas (ie, the TCGA cohort). FINDINGS: Between Feb 15, 2014, and Jan 28, 2019, we collected MSK-IMPACT data from the tumour tissue of 194 patients in the MSK-IMPACT cohort. The median overall survival was higher in the low-risk group than in the high-risk group as determined by OncoCast-MPM (30·8 months [95% CI 22·7-36·2] vs 13·9 months [10·7-18·0]; hazard ratio [HR] 3·0 [95% CI 2·0-4·5]; p<0·0001). No single factor or gene alteration drove risk differentiation. OncoCast-MPM was validated against the TCGA cohort, which consisted of 74 patients. The median overall survival was higher in the low-risk group than in the high-risk group (23·6 months [95% CI 15·1-28·4] vs 13·6 months [9·8-17·9]; HR 2·3 [95% CI 1·3-3·8]; p=0·0019). Although stage-based risk stratification was unable to differentiate survival among risk groups at 3 years in the MSK-IMPACT cohort (31% for early-stage disease vs 30% for advanced-stage disease; p=0·90), the OncoCast-MPM-derived 3-year survival was significantly higher in the low-risk group than in the high-risk group (40% vs 7%; p=0·0052). INTERPRETATION: OncoCast-MPM generated accurate, individual patient-level risk assessment scores. After prospective validation with the TCGA cohort, OncoCast-MPM might offer new opportunities for enhanced risk stratification of patients with malignant pleural mesothelioma in clinical trials and drug development. FUNDING: US National Institutes of Health/National Cancer Institute.

Mining mutation contexts across the cancer genome to map tumor site of origin.

The vast preponderance of somatic mutations in a typical cancer are either extremely rare or have never been previously recorded in available databases that track somatic mutations. These constitute a hidden genome that contrasts the relatively small number of mutations that occur frequently, the properties of which have been studied in depth. Here we demonstrate that this hidden genome contains much more accurate information than common mutations for the purpose of identifying the site of origin of primary cancers in settings where this is unknown. We accomplish this using a projection-based statistical method that achieves a highly effective signal condensation, by leveraging DNA sequence and epigenetic contexts using a set of meta-features that embody the mutation contexts of rare variants throughout the genome.

A Genomic-Pathologic Annotated Risk Model to Predict Recurrence in Early-Stage Lung Adenocarcinoma.

Importance: Recommendations for adjuvant therapy after surgical resection of lung adenocarcinoma (LUAD) are based solely on TNM classification but are agnostic to genomic and high-risk clinicopathologic factors. Creation of a prediction model that integrates tumor genomic and clinicopathologic factors may better identify patients at risk for recurrence. Objective: To identify tumor genomic factors independently associated with recurrence, even in the presence of aggressive, high-risk clinicopathologic variables, in patients with completely resected stages I to III LUAD, and to develop a computational machine-learning prediction model (PRecur) to determine whether the integration of genomic and clinicopathologic features could better predict risk of recurrence, compared with the TNM system. Design, Setting, and Participants: This prospective cohort study included 426 patients treated from January 1, 2008, to December 31, 2017, at a single large cancer center and selected in consecutive samples. Eligibility criteria included complete surgical resection of stages I to III LUAD, broad-panel next-generation sequencing data with matched clinicopathologic data, and no neoadjuvant therapy. External validation of the PRecur prediction model was performed using The Cancer Genome Atlas (TCGA). Data were analyzed from 2014 to 2018. Main Outcomes and Measures: The study end point consisted of relapse-free survival (RFS), estimated using the Kaplan-Meier approach. Associations among clinicopathologic factors, genomic alterations, and RFS were established using Cox proportional hazards regression. The PRecur prediction model integrated genomic and clinicopathologic factors using gradient-boosting survival regression for risk group generation and prediction of RFS. A concordance probability estimate (CPE) was used to assess the predictive ability of the PRecur model. Results: Of the 426 patients included in the analysis (286 women [67%]; median age at surgery, 69 [interquartile range, 62-75] years), 318 (75%) had stage I cancer. Association analysis showed that alterations in SMARCA4 (clinicopathologic-adjusted hazard ratio [HR], 2.44; 95% CI, 1.03-5.77; P = .042) and TP53 (clinicopathologic-adjusted HR, 1.73; 95% CI, 1.09-2.73; P = .02) and the fraction of genome altered (clinicopathologic-adjusted HR, 1.03; 95% CI, 1.10-1.04; P = .005) were independently associated with RFS. The PRecur prediction model outperformed the TNM-based model (CPE, 0.73 vs 0.61; difference, 0.12 [95% CI, 0.05-0.19]; P < .001) for prediction of RFS. To validate the prediction model, PRecur was applied to the TCGA LUAD data set (n = 360), and a clear separation of risk groups was noted (log-rank statistic, 7.5; P = .02), confirming external validation. Conclusions and Relevance: The findings suggest that integration of tumor genomics and clinicopathologic features improves risk stratification and prediction of recurrence after surgical resection of early-stage LUAD. Improved identification of patients at risk for recurrence could enrich and enhance accrual to adjuvant therapy clinical trials.

Concurrent Mutations in STK11 and KEAP1 Promote Ferroptosis Protection and SCD1 Dependence in Lung Cancer.

Concurrent loss-of-function mutations in STK11 and KEAP1 in lung adenocarcinoma (LUAD) are associated with aggressive tumor growth, resistance to available therapies, and early death. We investigated the effects of coordinate STK11 and KEAP1 loss by comparing co-mutant with single mutant and wild-type isogenic counterparts in multiple LUAD models. STK11/KEAP1 co-mutation results in significantly elevated expression of ferroptosis-protective genes, including SCD and AKR1C1/2/3, and resistance to pharmacologically induced ferroptosis. CRISPR screening further nominates SCD (SCD1) as selectively essential in STK11/KEAP1 co-mutant LUAD. Genetic and pharmacological inhibition of SCD1 confirms the essentiality of this gene and augments the effects of ferroptosis induction by erastin and RSL3. Together these data identify SCD1 as a selective vulnerability and a promising candidate for targeted drug development in STK11/KEAP1 co-mutant LUAD.

Rb and p53-Deficient Myxofibrosarcoma and Undifferentiated Pleomorphic Sarcoma Require Skp2 for Survival.

Myxofibrosarcoma (MFS) and undifferentiated pleomorphic sarcoma (UPS) are highly genetically complex soft tissue sarcomas. Up to 50% of patients develop distant metastases, but current systemic therapies have limited efficacy. MFS and UPS have recently been shown to commonly harbor copy number alterations or mutations in the tumor suppressor genes RB1 and TP53. As these alterations have been shown to engender dependence on the oncogenic protein Skp2 for survival of transformed cells in mouse models, we sought to examine its function and potential as a therapeutic target in MFS/UPS. Comparative genomic hybridization and next-generation sequencing confirmed that a significant fraction of MFS and UPS patient samples (n = 94) harbor chromosomal deletions and/or loss-of-function mutations in RB1 and TP53 (88% carry alterations in at least one gene; 60% carry alterations in both). Tissue microarray analysis identified a correlation between absent Rb and p53 expression and positive expression of Skp2. Downregulation of Skp2 or treatment with the Skp2-specific inhibitor C1 revealed that Skp2 drives proliferation of patient-derived MFS/UPS cell lines deficient in both Rb and p53 by degrading p21 and p27. Inhibition of Skp2 using the neddylation-activating enzyme inhibitor pevonedistat decreased growth of Rb/p53-negative patient-derived cell lines and mouse xenografts. These results demonstrate that loss of both Rb and p53 renders MFS and UPS dependent on Skp2, which can be therapeutically exploited and could provide the basis for promising novel systemic therapies for MFS and UPS. SIGNIFICANCE: Loss of both Rb and p53 renders myxofibrosarcoma and undifferentiated pleomorphic sarcoma dependent on Skp2, which could provide the basis for promising novel systemic therapies.See related commentary by Lambert and Jones, p. 2437.

Genetic variants and cognitive functions in patients with brain tumors.

BACKGROUND: Patients with brain tumors treated with radiotherapy (RT) and chemotherapy (CT) often experience cognitive dysfunction. We reported that single nucleotide polymorphisms (SNPs) in the APOE, COMT, and BDNF genes may influence cognition in brain tumor patients. In this study, we assessed whether genes associated with late-onset Alzheimer's disease (LOAD), inflammation, cholesterol transport, dopamine and myelin regulation, and DNA repair may influence cognitive outcome in this population. METHODS: One hundred and fifty brain tumor patients treated with RT ± CT or CT alone completed a neurocognitive assessment and provided a blood sample for genotyping. We genotyped genes/SNPs in these pathways: (i) LOAD risk/inflammation/cholesterol transport, (ii) dopamine regulation, (iii) myelin regulation, (iv) DNA repair, (v) blood-brain barrier disruption, (vi) cell cycle regulation, and (vii) response to oxidative stress. White matter (WM) abnormalities were rated on brain MRIs. RESULTS: Multivariable linear regression analysis with Bayesian shrinkage estimation of SNP effects, adjusting for relevant demographic, disease, and treatment variables, indicated strong associations (posterior association summary [PAS] ≥ 0.95) among tests of attention, executive functions, and memory and 33 SNPs in genes involved in: LOAD/inflammation/cholesterol transport (eg, PDE7A, IL-6), dopamine regulation (eg, DRD1, COMT), myelin repair (eg, TCF4), DNA repair (eg, RAD51), cell cycle regulation (eg, SESN1), and response to oxidative stress (eg, GSTP1). The SNPs were not significantly associated with WM abnormalities. CONCLUSION: This novel study suggests that polymorphisms in genes involved in aging and inflammation, dopamine, myelin and cell cycle regulation, and DNA repair and response to oxidative stress may be associated with cognitive outcome in patients with brain tumors.

Harnessing Clinical Sequencing Data for Survival Stratification of Patients with Metastatic Lung Adenocarcinomas.

PURPOSE: Broad panel sequencing of tumors facilitates routine care of people with cancer as well as clinical trial matching for novel genome-directed therapies. We sought to extend the use of broad panel sequencing results to survival stratification and clinical outcome prediction. PATIENTS AND METHODS: Using sequencing results from a cohort of 1,054 patients with advanced lung adenocarcinomas, we developed OncoCast, a machine learning tool for survival risk stratification and biomarker identification. RESULTS: With OncoCast, we stratified this patient cohort into four risk groups based on tumor genomic profile. Patients whose tumors harbored a high-risk profile had a median survival of 7.3 months (95% CI 5.5-10.9), compared to a low risk group with a median survival of 32.8 months (95% CI 26.3-38.5), with a hazard ratio of 4.6 (P<2e-16), far superior to any individual gene predictor or standard clinical characteristics. We found that co-mutations of both STK11 and KEAP1 are a strong determinant of unfavorable prognosis with currently available therapies. In patients with targetable oncogenes including EGFR/ALK/ROS1 and received targeted therapies, the tumor genetic background further differentiated survival with mutations in TP53 and ARID1A contributing to a higher risk score for shorter survival. CONCLUSION: Mutational profile derived from broad-panel sequencing presents an effective genomic stratification for patient survival in advanced lung adenocarcinoma. OncoCast is available as a public resource that facilitates the incorporation of mutational data to predict individual patient prognosis and compare risk characteristics of patient populations.

Positron emission tomography-computed tomography in liver imaging.

The relatively new technique of positron emission tomography (PET) that incorporates both anatomy and function is increasingly being utilized in oncological imaging. This review assesses the use of PET of the liver for optimal and appropriate patient clinical management. The role of PET in both liver-lesion detection and characterization is discussed (including the context of primary liver malignancy, hepatocellular carcinoma, cholangiocarcinoma, metastatic disease from colorectal cancer and other tumor types). The use of functional PET imaging for prognostication, efficacy pre-liver transplantation, response assessment including post-radio frequency ablation and Yttrium-90 radioembolization, as well as the emerging and future roles of novel PET biomarkers, is also highlighted.

Imaging in oncology--over a century of advances.

Over the past 120 years, the discipline of oncology has evolved so that a multitude of anatomical and increasingly complex functional imaging techniques are now applicable in both clinical and research platforms. This Timeline article revisits the achievements of the pioneer techniques in cancer imaging, discusses how these techniques have changed over time, provides some examples of clinical importance, and ventures to explain how imaging will remodel the future of modern oncology.