Pan-cancer pharmacogenetics : targeted sequencing panels or exome sequencing?

Aim:This study provides clinicians and researchers with an informed choice between current commercially available targeted sequencing panels and exome sequencing panels in the context of pan-cancer pharmacogenetics.Materials & methods:Nine contemporary commercially available targeted pan-cancer panels and the xGen Exome Research Panel v2 were investigated to determine to what extent they cover the pharmacogenetic variant-drug interactions in five available cancer knowledgebases, and the driver mutations and fusion genes in The Cancer Genome Atlas.Results:xGen Exome Research Panel v2 and TrueSight Oncology 500 target 71.0 and 68.9% of the pharmacogenetic interactions in the available knowledgebases, and 93.7 and 86.0% of the driver mutations in The Cancer Genome Atlas, respectively. All other studied panels target lower percentages.Conclusion:Exome sequencing outperforms pan-cancer targeted sequencing panels in terms of covered cancer pharmacogenetic variant-drug interactions and pharmacogenetic cancer variants

Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment.

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs\u27 recapitulation of human tumors

Multi-omics Integration for Gene Fusion Discovery and Somatic Mutation Haplotyping in Cancer

Cancer is a disease caused by changes to the genome and dysregulation of gene expression. Among many types of mutations, including point mutations, small insertions and deletions, large scale structural variants, and copy number changes, gene fusions are another category of genomic and transcriptomic alteration that can lead to cancer and which can serve as therapeutic targets. We studied gene fusion events using data from The Cancer Genome Atlas, including over 9,000 patients from 33 cancer types, finding patterns of gene fusion events and dysregulation of gene expression within and across cancer types. With data from the CoMMpass study (Multiple Myeloma Research Foundation), we generated the largest gene fusion study in multiple myeloma (742 patients), which is the second most common type of blood cancer, and which is driven by recurrent translocations. We then developed a novel tool for analyzing the haplotype context of somatic mutations. Linked-read whole genome sequencing enables haplotype resolution for analyzing somatic mutation patterns, which is lost during typical short-read sequencing and alignment. We analyzed a cohort of 14 multiple myeloma patients across disease stages, phasing three-quarters of high confidence somatic mutations and enabling us to interpret clonal evolution models at higher resolution. Finally, we also studied the co-evolution of the multiple myeloma tumor and microenvironment using single-cell RNA-sequencing, finding distinct patterns of tumor subclone evolution between disease stages in 14 patients. Our methods and results demonstrate the power of integrating data types to study complex and dynamic evolutionary pressures in cancer and point to future directions of research that aim to bridge gaps in research and clinical applications