High-throughput data production technologies, particularly 'next-generation' DNA sequencing, have ushered in widespread and disruptive changes to biomedical research. Making sense of the large datasets produced by these technologies requires sophisticated statistical and computational methods , as well as substantial computational power. This has led to an acute crisis in life sciences, as researchers without informatics training attempt to perform computation-dependent analyses. Since 2005, the Galaxy project has worked to address this problem by providing a framework that makes advanced computational tools usable by non experts. Galaxy seeks to make data-intensive research more accessible , transparent and reproducible by providing a Web-based environment in which users can perform computational analyses and have all of the details automatically tracked for later inspection, publication , or reuse. In this report we highlight recently added features enabling biomedical analyses on a large scale.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update

Combination anti–cytotoxic T lymphocyte antigen 4 (CTLA-4) and anti–programmed cell death protein 1 (PD-1) therapy promotes antitumor immunity and provides superior benefit to patients with advanced-stage melanoma compared with either therapy alone. T cell immunity requires recognition of antigens in the context of major histocompatibility complex (MHC) class I and class II proteins by CD8+ and CD4+ T cells, respectively. We examined MHC class I and class II protein expression on tumor cells from previously untreated melanoma patients and correlated the results with transcriptional and genomic analyses and with clinical response to anti–CTLA-4, anti–PD-1, or combination therapy. Most (>50% of cells) or complete loss of melanoma MHC class I membrane expression was observed in 78 of 181 cases (43%), was associated with transcriptional repression of HLA-A, HLA-B, HLA-C, and B2M, and predicted primary resistance to anti–CTLA-4, but not anti–PD-1, therapy. Melanoma MHC class II membrane expression on >1% cells was observed in 55 of 181 cases (30%), was associated with interferon-γ (IFN-γ) and IFN-γ–mediated gene signatures, and predicted response to anti–PD-1, but not anti–CTLA-4, therapy. We conclude that primary response to anti–CTLA-4 requires robust melanoma MHC class I expression. In contrast, primary response to anti–PD-1 is associated with preexisting IFN-γ–mediated immune activation that includes tumor-specific MHC class II expression and components of innate immunity when MHC class I is compromised. The benefits of combined checkpoint blockade may be attributable, in part, to distinct requirements for melanoma-specific antigen presentation to initiate antitumor immunity.

MHC proteins confer differential sensitivity to CTLA-4 and PD-1 blockade in untreated metastatic melanoma

Cancer immunotherapy has gained significant momentum from recent clinical successes of checkpoint blockade inhibition. Massively parallel sequence analysis suggests a connection between mutational load and response to this class of therapy. Methods to identify which tumor-specific mutant peptides (neoantigens) can elicit anti-tumor T cell immunity are needed to improve predictions of checkpoint therapy response and to identify targets for vaccines and adoptive T cell therapies. Here, we present a flexible, streamlined computational workflow for identification of personalized Variant Antigens by Cancer Sequencing (pVAC-Seq) that integrates tumor mutation and expression data (DNA- and RNA-Seq). pVAC-Seq is available at https://github.com/griffithlab/pVAC-Seq
 .

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pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens.

Purpose: Cyclin-dependent kinase (CDK) 4/6 drives cell proliferation in estrogen receptor-positive (ER+) breast cancer. This single-arm phase II neoadjuvant trial (NeoPalAna) assessed the antiproliferative activity of the CDK4/6 inhibitor palbociclib in primary breast cancer as a prelude to adjuvant studies.Experimental Design: Eligible patients with clinical stage II/III ER+/HER2- breast cancer received anastrozole 1 mg daily for 4 weeks (cycle 0; with goserelin if premenopausal), followed by adding palbociclib (125 mg daily on days 1-21) on cycle 1 day 1 (C1D1) for four 28-day cycles unless C1D15 Ki67 > 10%, in which case patients went off study due to inadequate response. Anastrozole was continued until surgery, which occurred 3 to 5 weeks after palbociclib exposure. Later patients received additional 10 to 12 days of palbociclib (Cycle 5) immediately before surgery. Serial biopsies at baseline, C1D1, C1D15, and surgery were analyzed for Ki67, gene expression, and mutation profiles. The primary endpoint was complete cell cycle arrest (CCCA: central Ki67 ≤ 2.7%).Results: Fifty patients enrolled. The CCCA rate was significantly higher after adding palbociclib to anastrozole (C1D15 87% vs. C1D1 26%, P < 0.001). Palbociclib enhanced cell-cycle control over anastrozole monotherapy regardless of luminal subtype (A vs. B) and PIK3CA status with activity observed across a broad range of clinicopathologic and mutation profiles. Ki67 recovery at surgery following palbociclib washout was suppressed by cycle 5 palbociclib. Resistance was associated with nonluminal subtypes and persistent E2F-target gene expression.Conclusions: Palbociclib is an active antiproliferative agent for early-stage breast cancer resistant to anastrozole; however, prolonged administration may be necessary to maintain its effect. Clin Cancer Res; 23(15); 4055-65. ©2017 AACR.

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NeoPalAna: Neoadjuvant Palbociclib, a Cyclin-Dependent Kinase 4/6 Inhibitor, and Anastrozole for Clinical Stage 2 or 3 Estrogen Receptor-Positive Breast Cancer.

Summary: Visualizing and summarizing data from genomic studies continues to be a challenge. Here, we introduce the GenVisR package to addresses this challenge by providing highly customizable, publication-quality graphics focused on cohort level genome analyses. GenVisR provides a rapid and easy-to-use suite of genomic visualization tools, while maintaining a high degree of flexibility by leveraging the abilities of ggplot2 and Bioconductor.

Availability and Implementation: GenVisR is an R package available via Bioconductor (https://bioconductor.org/packages/GenVisR) under GPLv3. Support is available via GitHub (https://github.com/griffithlab/GenVisR/issues) and the Bioconductor support website.

Contacts: ude.ltsuw@htiffirgibo or ude.ltsuw@tiffirgm

Supplementary information: Supplementary data are available at Bioinformatics online.

/pdf/genvisr-genomic-visualizations-in-r-3gojpjosd7.pdf

GenVisR: Genomic Visualizations in R

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.

/pdf/genome-modeling-system-a-knowledge-management-platform-for-4xe1qm1x8w.pdf

Thomas P. Mooney

Papers

Genome Modeling System: A Knowledge Management Platform for Genomics.