Discovery of antibodies and cognate surface targets for ovarian cancer by surface profiling.

Although antibodies targeting specific tumor-expressed antigens are the standard of care for some cancers, the identification of cancer-specific targets amenable to antibody binding has remained a bottleneck in development of new therapeutics. To overcome this challenge, we developed a high-throughput platform that allows for the unbiased, simultaneous discovery of antibodies and targets based on phenotypic binding profiles. Applying this platform to ovarian cancer, we identified a wide diversity of cancer targets including receptor tyrosine kinases, adhesion and migration proteins, proteases and proteins regulating angiogenesis in a single round of screening using genomics, flow cytometry, and mass spectrometry. In particular, we identified BCAM as a promising candidate for targeted therapy in high-grade serous ovarian cancers. More generally, this approach provides a rapid and flexible framework to identify cancer targets and antibodies.

Reproducible and replicable comparisons using SummarizedBenchmark.

Summary: Benchmark studies are widely used to compare and evaluate tools developed for answering various biological questions. Despite the popularity of these comparisons, the implementation is often ad hoc, with little consistency across studies. To address this problem, we developed SummarizedBenchmark, an R package and framework for organizing and structuring benchmark comparisons. SummarizedBenchmark defines a general grammar for benchmarking and allows for easier setup and execution of benchmark comparisons, while improving the reproducibility and replicability of such comparisons. We demonstrate the wide applicability of our framework using four examples from different applications. Availability and implementation: SummarizedBenchmark is an R package available through Bioconductor (http://bioconductor.org/packages/SummarizedBenchmark). Supplementary information: Supplementary data are available at Bioinformatics online.

Abnormal Small Intestinal Epithelial Microvilli in Patients With Crohn's Disease.

BACKGROUND & AIMS: Crohn disease (CD) presents as chronic and often progressive intestinal inflammation, but the contributing pathogenic mechanisms are unclear. We aimed to identify alterations in intestinal cells that could contribute to the chronic and progressive course of CD. METHODS: We took an unbiased system-wide approach by performing sequence analysis of RNA extracted from formalin-fixed paraffin-embedded ileal tissue sections from patients with CD (n = 36) and without CD (controls; n = 32). We selected relatively uninflamed samples, based on histology, before gene expression profiling; validation studies were performed using adjacent serial tissue sections. A separate set of samples (3 control and 4 CD samples) was analyzed by transmission electron microscopy. We developed methods to visualize an overlapping modular network of genes dysregulated in the CD samples. We validated our findings using biopsy samples (110 CD samples for gene expression analysis and 54 for histologic analysis) from the UNITI-2 phase 3 trial of ustekinumab for patients with CD and healthy individuals (26 samples used in gene expression analysis). RESULTS: We identified gene clusters that were altered in nearly all CD samples. One cluster encoded genes associated with the enterocyte brush border, leading us to investigate microvilli. In ileal tissues from patients with CD, the microvilli were of decreased length and had ultrastructural defects compared with tissues from controls. Microvilli length correlated with expression of genes that regulate microvilli structure and function. Network analysis linked the microvilli cluster to several other down-regulated clusters associated with altered intracellular trafficking and cellular metabolism. Enrichment of a core microvilli gene set also was lower in the UNITI-2 trial CD samples compared with controls; expression of microvilli genes was correlated with microvilli length and endoscopy score and was associated with response to treatment. CONCLUSIONS: In a transcriptome analysis of formalin-fixed and paraffin-embedded ileal tissues from patients with CD and controls, we associated transcriptional alterations with histologic alterations, such as differences in microvilli length. Decreased microvilli length and decreased expression of the microvilli gene set might contribute to epithelial malfunction and the chronic and progressive disease course in patients with CD.

Statistical significance for hierarchical clustering.

Cluster analysis has proved to be an invaluable tool for the exploratory and unsupervised analysis of high-dimensional datasets. Among methods for clustering, hierarchical approaches have enjoyed substantial popularity in genomics and other fields for their ability to simultaneously uncover multiple layers of clustering structure. A critical and challenging question in cluster analysis is whether the identified clusters represent important underlying structure or are artifacts of natural sampling variation. Few approaches have been proposed for addressing this problem in the context of hierarchical clustering, for which the problem is further complicated by the natural tree structure of the partition, and the multiplicity of tests required to parse the layers of nested clusters. In this article, we propose a Monte Carlo based approach for testing statistical significance in hierarchical clustering which addresses these issues. The approach is implemented as a sequential testing procedure guaranteeing control of the family-wise error rate. Theoretical justification is provided for our approach, and its power to detect true clustering structure is illustrated through several simulation studies and applications to two cancer gene expression datasets.

SigFuge: single gene clustering of RNA-seq reveals differential isoform usage among cancer samples.

High-throughput sequencing technologies, including RNA-seq, have made it possible to move beyond gene expression analysis to study transcriptional events including alternative splicing and gene fusions. Furthermore, recent studies in cancer have suggested the importance of identifying transcriptionally altered loci as biomarkers for improved prognosis and therapy. While many statistical methods have been proposed for identifying novel transcriptional events with RNA-seq, nearly all rely on contrasting known classes of samples, such as tumor and normal. Few tools exist for the unsupervised discovery of such events without class labels. In this paper, we present SigFuge for identifying genomic loci exhibiting differential transcription patterns across many RNA-seq samples. SigFuge combines clustering with hypothesis testing to identify genes exhibiting alternative splicing, or differences in isoform expression. We apply SigFuge to RNA-seq cohorts of 177 lung and 279 head and neck squamous cell carcinoma samples from the Cancer Genome Atlas, and identify several cases of differential isoform usage including CDKN2A, a tumor suppressor gene known to be inactivated in a majority of lung squamous cell tumors. By not restricting attention to known sample stratifications, SigFuge offers a novel approach to unsupervised screening of genetic loci across RNA-seq cohorts. SigFuge is available as an R package through Bioconductor.

DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues.

Homeodomains (HDs) are the second largest class of DNA binding domains (DBDs) among eukaryotic sequence-specific transcription factors (TFs) and are the TF structural class with the largest number of disease-associated mutations in the Human Gene Mutation Database (HGMD). Despite numerous structural studies and large-scale analyses of HD DNA binding specificity, HD-DNA recognition is still not fully understood. Here, we analyze 92 human HD mutants, including disease-associated variants and variants of uncertain significance (VUS), for their effects on DNA binding activity. Many of the variants alter DNA binding affinity and/or specificity. Detailed biochemical analysis and structural modeling identifies 14 previously unknown specificity-determining positions, 5 of which do not contact DNA. The same missense substitution at analogous positions within different HDs often exhibits different effects on DNA binding activity. Variant effect prediction tools perform moderately well in distinguishing variants with altered DNA binding affinity, but poorly in identifying those with altered binding specificity. Our results highlight the need for biochemical assays of TF coding variants and prioritize dozens of variants for further investigations into their pathogenicity and the development of clinical diagnostics and precision therapies.

Whole-body CD8+ T cell visualization before and during cancer immunotherapy: a phase 1/2 trial.

Immune checkpoint inhibitors (ICIs), by reinvigorating CD8+ T cell mediated immunity, have revolutionized cancer therapy. Yet, the systemic CD8+ T cell distribution, a potential biomarker of ICI response, remains poorly characterized. We assessed safety, imaging dose and timing, pharmacokinetics and immunogenicity of zirconium-89-labeled, CD8-specific, one-armed antibody positron emission tomography tracer 89ZED88082A in patients with solid tumors before and ~30 days after starting ICI therapy (NCT04029181). No tracer-related side effects occurred. Positron emission tomography imaging with 10 mg antibody revealed 89ZED88082A uptake in normal lymphoid tissues, and tumor lesions across the body varying within and between patients two days after tracer injection (n = 38, median patient maximum standard uptake value (SUVmax) 5.2, IQI 4.0-7.4). Higher SUVmax was associated with mismatch repair deficiency and longer overall survival. Uptake was higher in lesions with stromal/inflamed than desert immunophenotype. Tissue radioactivity was localized to areas with immunohistochemically confirmed CD8 expression. Re-imaging patients on treatment showed no change in average (geometric mean) tumor tracer uptake compared to baseline, but individual lesions showed diverse changes independent of tumor response. The imaging data suggest enormous heterogeneity in CD8+ T cell distribution and pharmacodynamics within and between patients. In conclusion, 89ZED88082A can characterize the complex dynamics of CD8+ T cells in the context of ICIs, and may inform immunotherapeutic treatments.

A practical guide to methods controlling false discoveries in computational biology.

BACKGROUND: In high-throughput studies, hundreds to millions of hypotheses are typically tested. Statistical methods that control the false discovery rate (FDR) have emerged as popular and powerful tools for error rate control. While classic FDR methods use only p values as input, more modern FDR methods have been shown to increase power by incorporating complementary information as informative covariates to prioritize, weight, and group hypotheses. However, there is currently no consensus on how the modern methods compare to one another. We investigate the accuracy, applicability, and ease of use of two classic and six modern FDR-controlling methods by performing a systematic benchmark comparison using simulation studies as well as six case studies in computational biology. RESULTS: Methods that incorporate informative covariates are modestly more powerful than classic approaches, and do not underperform classic approaches, even when the covariate is completely uninformative. The majority of methods are successful at controlling the FDR, with the exception of two modern methods under certain settings. Furthermore, we find that the improvement of the modern FDR methods over the classic methods increases with the informativeness of the covariate, total number of hypothesis tests, and proportion of truly non-null hypotheses. CONCLUSIONS: Modern FDR methods that use an informative covariate provide advantages over classic FDR-controlling procedures, with the relative gain dependent on the application and informativeness of available covariates. We present our findings as a practical guide and provide recommendations to aid researchers in their choice of methods to correct for false discoveries.

Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma.

Comprehensive multiplatform analysis of 80 uveal melanomas (UM) identifies four molecularly distinct, clinically relevant subtypes: two associated with poor-prognosis monosomy 3 (M3) and two with better-prognosis disomy 3 (D3). We show that BAP1 loss follows M3 occurrence and correlates with a global DNA methylation state that is distinct from D3-UM. Poor-prognosis M3-UM divide into subsets with divergent genomic aberrations, transcriptional features, and clinical outcomes. We report change-of-function SRSF2 mutations. Within D3-UM, EIF1AX- and SRSF2/SF3B1-mutant tumors have distinct somatic copy number alterations and DNA methylation profiles, providing insight into the biology of these low- versus intermediate-risk clinical mutation subtypes.