Impact of KRAS mutations and co-mutations on clinical outcomes in pancreatic ductal adenocarcinoma.

The relevance of KRAS mutation alleles to clinical outcome remains inconclusive in pancreatic adenocarcinoma (PDAC). We conducted a retrospective study of 803 patients with PDAC (42% with metastatic disease) at MD Anderson Cancer Center. Overall survival (OS) analysis demonstrated that KRAS mutation status and subtypes were prognostic (p < 0.001). Relative to patients with KRAS wildtype tumors (median OS 38 months), patients with KRASG12R had a similar OS (median 34 months), while patients with KRASQ61 and KRASG12D mutated tumors had shorter OS (median 20 months [HR: 1.9, 95% CI 1.2-3.0, p = 0.006] and 22 months [HR: 1.7, 95% CI 1.3-2.3, p < 0.001], respectively). There was enrichment of KRASG12D mutation in metastatic tumors (34% vs 24%, OR: 1.7, 95% CI 1.2-2.4, p = 0.001) and enrichment of KRASG12R in well and moderately differentiated tumors (14% vs 9%, OR: 1.7, 95% CI 1.05-2.99, p = 0.04). Similar findings were observed in the external validation cohort (PanCAN's Know Your Tumor® dataset, n = 408).

Pancreatic Cancer Action Network's SPARK: A Cloud-Based Patient Health Data and Analytics Platform for Pancreatic Cancer.

PURPOSE: Pancreatic cancer currently holds the position of third deadliest cancer in the United States and the 5-year survival rate is among the lowest for major cancers at just 12%. Thus, continued research efforts to better understand the clinical and molecular underpinnings of pancreatic cancer are critical to developing both early detection methodologies as well as improved therapeutic options. This study introduces Pancreatic Cancer Action Network's (PanCAN's) SPARK, a cloud-based data and analytics platform that integrates patient health data from the PanCAN's research initiatives and aims to accelerate pancreatic cancer research by making real-world patient health data and analysis tools easier to access and use. MATERIALS AND METHODS: The SPARK platform integrates clinical, molecular, multiomic, imaging, and patient-reported data generated from PanCAN's research initiatives. The platform is built on a cloud-based infrastructure powered by Velsera. Cohort exploration and browser capabilities are built using Velsera ARIA, a specialized product for leveraging clinicogenomic data to build cohorts, query variant information, and drive downstream association analyses. Data science and analytic capabilities are also built into the platform allowing researchers to perform simple to complex analysis. RESULTS: Version 1 of the SPARK platform was released to pilot users, who represented diverse end users, including molecular biologists, clinicians, and bioinformaticians. Included in the pilot release of SPARK are deidentified clinical (including treatment and outcomes data), molecular, multiomic, and whole-slide pathology images for over 600 patients enrolled in PanCAN's Know Your Tumor molecular profiling service. CONCLUSION: The pilot release of the SPARK platform introduces qualified researchers to PanCAN real-world patient health data and analytical resources in a centralized location.

Impact of KRAS Mutations and Co-mutations on Clinical Outcomes in Pancreatic Ductal Adenocarcinoma.

The relevance of KRAS mutation alleles to clinical outcome remains inconclusive in pancreatic adenocarcinoma (PDAC). We conducted a retrospective study of 803 PDAC patients (42% with metastatic disease) at MD Anderson Cancer Center. Overall survival (OS) analysis demonstrated that KRAS mutation status and subtypes were prognostic (p<0.001). Relative to patients with KRAS wildtype tumors (median OS 38 months), patients with KRASG12R had a similar OS (median 34 months), while patients with KRASQ61 and KRASG12D mutated tumors had shorter OS (median 20 months [HR: 1.9, 95% CI 1.2-3.0, p=0.006] and 22 months [HR: 1.7, 95% CI 1.3-2.3, p<0.001], respectively). There was enrichment of KRASG12D mutation in metastatic tumors (34% vs 24%, OR: 1.7, 95% CI 1.2-2.4, p=0.001) and enrichment of KRASG12R in well and moderately differentiated tumors (14% vs 9%, OR: 1.7, 95% CI 1.05-2.99, p=0.04). Similar findings were observed in the external validation cohort (PanCAN's Know Your Tumor® dataset, n=408).

A cloud-based resource for genome coordinate-based exploration and large-scale analysis of chromosome aberrations and gene fusions in cancer.

Cytogenetic analysis provides important information on the genetic mechanisms of cancer. The Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer (Mitelman DB) is the largest catalog of acquired chromosome aberrations, presently comprising >70 000 cases across multiple cancer types. Although this resource has enabled the identification of chromosome abnormalities leading to specific cancers and cancer mechanisms, a large-scale, systematic analysis of these aberrations and their downstream implications has been difficult due to the lack of a standard, automated mapping from aberrations to genomic coordinates. We previously introduced CytoConverter as a tool that automates such conversions. CytoConverter has now been updated with improved interpretation of karyotypes and has been integrated with the Mitelman DB, providing a comprehensive mapping of the 70 000+ cases to genomic coordinates, as well as visualization of the frequencies of chromosomal gains and losses. Importantly, all CytoConverter-generated genomic coordinates are publicly available in Google BigQuery, a cloud-based data warehouse, facilitating data exploration and integration with other datasets hosted by the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC) Resource. We demonstrate the use of BigQuery for integrative analysis of Mitelman DB with other cancer datasets, including a comparison of the frequency of imbalances identified in Mitelman DB cases with those found in The Cancer Genome Atlas (TCGA) copy number datasets. This solution provides opportunities to leverage the power of cloud computing for low-cost, scalable, and integrated analysis of chromosome aberrations and gene fusions in cancer.

Multi-omics inference of differential breast cancer-related transcriptional regulatory network gene hubs between young Black and White patients.

OBJECTIVE: Breast cancers (BrCA) are a leading cause of illness and mortality worldwide. Black women have a higher incidence rate relative to white women prior to age 40 years, and a lower incidence rate after 50 years. The objective of this study is to identify -omics differences between the two breast cancer cohorts to better understand the disparities observed in patient outcomes. MATERIALS AND METHODS: Using Standard SQL, we queried ISB-CGC hosted Google BigQuery tables storing TCGA BrCA gene expression, methylation, and somatic mutation data and analyzed the combined multi-omics results using a variety of methods. RESULTS: Among Stage II patients 50 years or younger, genes PIK3CA and CDH1 are more frequently mutated in White (W50) than in Black or African American patients (BAA50), while HUWE1, HYDIN, and FBXW7 mutations are more frequent in BAA50. Over-representation analysis (ORA) and Gene Set Enrichment Analysis (GSEA) results indicate that, among others, the Reactome Signaling by ROBO Receptors gene set is enriched in BAA50. Using the Virtual Inference of Protein-activity by Enriched Regulon analysis (VIPER) algorithm, putative top 20 master regulators identified include NUPR1, NFKBIL1, ZBTB17, TEAD1, EP300, TRAF6, CACTIN, and MID2. CACTIN and MID2 are of prognostic value. We identified driver genes, such as OTUB1, with suppressed expression whose DNA methylation status were inversely correlated with gene expression. Networks capturing microRNA and gene expression correlations identified notable microRNA hubs, such as miR-93 and miR-92a-2, expressed at higher levels in BAA50 than in W50. DISCUSSION/CONCLUSION: The results point to several driver genes as being involved in the observed differences between the cohorts. The findings here form the basis for further mechanistic exploration.

Whole Genome Variant Dataset for Enriching Studies across 18 Different Cancers.

Whole genome sequencing (WGS) has helped to revolutionize biology, but the computational challenge remains for extracting valuable inferences from this information. Here, we present the cancer-associated variants from the Cancer Genome Atlas (TCGA) WGS dataset. This set of data will allow cancer researchers to further expand their analysis beyond the exomic regions of the genome to the entire genome. A total of 1342 WGS alignments available from the consortium were processed with VarScan2 and deposited to the NCI Cancer Cloud. The sample set covers 18 different cancers and reveals 157,313,519 pooled (non-unique) cancer-associated single-nucleotide variations (SNVs) across all samples. There was an average of 117,223 SNVs per sample, with a range from 1111 to 775,470 and a standard deviation of 163,273. The dataset was incorporated into BigQuery, which allows for fast access and cross-mapping, which will allow researchers to enrich their current studies with a plethora of newly available genomic data.

PRoBE the cloud toolkit: finding the best biomarkers of drug response within a breast cancer clinical trial.

OBJECTIVES: In this paper, we discuss leveraging cloud-based platforms to collect, visualize, analyze, and share data in the context of a clinical trial. Our cloud-based infrastructure, Patient Repository of Biomolecular Entities (PRoBE), has given us the opportunity for uniform data structure, more efficient analysis of valuable data, and increased collaboration between researchers. MATERIALS AND METHODS: We utilize a multi-cloud platform to manage and analyze data generated from the clinical Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis 2 (I-SPY 2 TRIAL). A collaboration with the Institute for Systems Biology Cancer Gateway in the Cloud has additionally given us access to public genomic databases. Applications to I-SPY 2 data have been built using R Shiny, while leveraging Google's BigQuery tables and SQL commands for data mining. RESULTS: We highlight the implementation of PRoBE in several unique case studies including prediction of biomarkers associated with clinical response, access to the Pan-Cancer Atlas, and integrating pathology images within the cloud. Our data integration pipelines, documentation, and all codebase will be placed in a Github repository. DISCUSSION AND CONCLUSION: We are hoping to develop risk stratification diagnostics by integrating additional molecular, magnetic resonance imaging, and pathology markers into PRoBE to better predict drug response. A robust cloud infrastructure and tool set can help integrate these large datasets to make valuable predictions of response to multiple agents. For that reason, we are continuously improving PRoBE to advance the way data is stored, accessed, and analyzed in the I-SPY 2 clinical trial.