Evaluation of somatic copy number variation detection by NGS technologies and bioinformatics tools on a hyper-diploid cancer genome.

Background: Copy number variation (CNV) is a key genetic characteristic for cancer diagnostics and can be used as a biomarker for the selection of therapeutic treatments. Using data sets established in our previous study, we benchmark the performance of cancer CNV calling by six most recent and commonly used software tools on their detection accuracy, sensitivity, and reproducibility. In comparison to other orthogonal methods, such as microarray and Bionano, we also explore the consistency of CNV calling across different technologies on a challenging genome.

Serial changes in liquid biopsy-derived variant allele frequency predict immune checkpoint inhibitor responsiveness in the pan-cancer setting.

Major immunotherapy challenges include a limited number of predictive biomarkers and the unusual imaging features post-therapy, such as pseudo-progression, which denote immune infiltrate-mediated tumor enlargement. Such phenomena confound clinical decision-making, since the cancer may eventually regress, and the patient should stay on treatment. We prospectively evaluated serial, blood-derived cell-free DNA (cfDNA) (baseline and 2-3 weeks post-immune checkpoint inhibitors [ICIs]) for variant allele frequency (VAF) and blood tumor mutation burden (bTMB) changes (next-generation sequencing) (N = 84 evaluable patients, diverse cancers).

Proteogenomic Annotation of Chinese Hamsters Reveals Extensive Novel Translation Events and Endogenous Retroviral Elements.

A high-quality genome annotation greatly facilitates successful cell line engineering. Standard draft genome annotation pipelines are based largely on de novo gene prediction, homology, and RNA-Seq data. However, draft annotations can suffer from incorrect predictions of translated sequence, inaccurate splice isoforms, and missing genes.

Assembling the Community-Scale Discoverable Human Proteome.

The increasing throughput and sharing of proteomics mass spectrometry data have now yielded over one-third of a million public mass spectrometry runs. However, these discoveries are not continuously aggregated in an open and error-controlled manner, which limits their utility. To facilitate the reusability of these data, we built the MassIVE Knowledge Base (MassIVE-KB), a community-wide, continuously updating knowledge base that aggregates proteomics mass spectrometry discoveries into an open reusable format with full provenance information for community scrutiny.

The Antibody Repertoire of Colorectal Cancer.

Immunotherapy is becoming increasingly important in the fight against cancers, using and manipulating the body's immune response to treat tumors. Understanding the immune repertoire-the collection of immunological proteins-of treated and untreated cells is possible at the genomic, but technically difficult at the protein level. Standard protein databases do not include the highly divergent sequences of somatic rearranged immunoglobulin genes, and may lead to miss identifications in a mass spectrometry search.

Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer.

To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy.

Advanced Proteogenomic Analysis Reveals Multiple Peptide Mutations and Complex Immunoglobulin Peptides in Colon Cancer.

Aiming toward an improved understanding of the regulation of proteins in cancer, recent studies from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) have focused on analyzing cancer tissue using proteomic technologies and workflows. Although many proteogenomics approaches for the study of cancer samples have been proposed, serious methodological challenges remain, especially in the identification of multiple mutational variants or structural variations such as fusion gene events. In addition, although immune system genes play an important role in cancer, identification of IgG peptides remains challenging in proteomic data sets.

Proteogenomic strategies for identification of aberrant cancer peptides using large-scale next-generation sequencing data.

Cancer is driven by the acquisition of somatic DNA lesions. Distinguishing the early driver mutations from subsequent passenger mutations is key to molecular subtyping of cancers, understanding cancer progression, and the discovery of novel biomarkers. The advances of genomics technologies (whole-genome exome, and transcript sequencing, collectively referred to as NGS (next-generation sequencing)) have fueled recent studies on somatic mutation discovery.

Proteogenomic database construction driven from large scale RNA-seq data.

The advent of inexpensive RNA-seq technologies and other deep sequencing technologies for RNA has the promise to radically improve genomic annotation, providing information on transcribed regions and splicing events in a variety of cellular conditions. Using MS-based proteogenomics, many of these events can be confirmed directly at the protein level. However, the integration of large amounts of redundant RNA-seq data and mass spectrometry data poses a challenging problem.