SpliceMutr Enables Pan-Cancer Analysis of Splicing-Derived Neoantigen Burden in Tumors.

SpliceMutr shows that splicing antigenicity changes in response to ICI therapies and that native modulation of the splicing machinery through mutations increases the contribution of splicing to the neoantigen load of some The Cancer Genome Atlas cancer subtypes. Future studies of the relationship between splicing antigenicity and immune checkpoint inhibitor response pan-cancer are essential to establish the interplay between antigen heterogeneity and immunotherapy regimen on patient response.

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces.

Recent advances in spatial transcriptomics (STs) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor as well as the tumor microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as it occurs in tumor-immune interactions.

Deconvolution of B cell receptor repertoire in multiple sclerosis patients revealed a delay in tBreg maturation.

Background: B lymphocytes play a pivotal regulatory role in the development of the immune response. It was previously shown that deficiency in B regulatory cells (Bregs) or a decrease in their anti-inflammatory activity can lead to immunological dysfunctions. However, the exact mechanisms of Bregs development and functioning are only partially resolved.

Spatial correlation statistics enable transcriptome-wide characterization of RNA structure binding.

Molecular interactions at identical transcriptomic locations or at proximal but non-overlapping sites can mediate RNA modification and regulation, necessitating tools to uncover these spatial relationships. We present nearBynding, a flexible algorithm and software pipeline that models spatial correlation between transcriptome-wide tracks from diverse data types. nearBynding can process and correlate interval as well as continuous data and incorporate experimentally derived or predicted transcriptomic tracks.

Landscape of allele-specific transcription factor binding in the human genome.

Sequence variants in gene regulatory regions alter gene expression and contribute to phenotypes of individual cells and the whole organism, including disease susceptibility and progression. Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Differential transcription factor binding in heterozygous genomic loci provides a natural source of information on such regulatory variants.

Newly Identified Members of FGFR1 Splice Variants Engage in Cross-talk with AXL/AKT Axis in Salivary Adenoid Cystic Carcinoma.

Adenoid cystic carcinoma (ACC) is the second most common malignancy of the salivary gland. Although characterized as an indolent tumor, ACC often leads to incurable metastatic disease. Patients with ACC respond poorly to currently available therapeutic drugs and factors contributing to the limited response remain unknown.

Extracellular Vesicles Released by Tumor Endothelial Cells Spread Immunosuppressive and Transforming Signals Through Various Recipient Cells.

Head and neck squamous cell carcinoma (HNSCC) has a high recurrence and metastatic rate with an unknown mechanism of cancer spread. Tumor inflammation is the most critical processes of cancer onset, growth, and metastasis. We hypothesize that the release of extracellular vesicles (EVs) by tumor endothelial cells (TECs) induce reprogramming of immune cells as well as stromal cells to create an immunosuppressive microenvironment that favor tumor spread.

HPV E2, E4, E5 drive alternative carcinogenic pathways in HPV positive cancers.

The dominant paradigm for HPV carcinogenesis includes integration into the host genome followed by expression of E6 and E7 (E6/E7). We explored an alternative carcinogenic pathway characterized by episomal E2, E4, and E5 (E2/E4/E5) expression. Half of HPV positive cervical and pharyngeal cancers comprised a subtype with increase in expression of E2/E4/E5, as well as association with lack of integration into the host genome.

Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets.

While the methods available for single-cell ATAC-seq analysis are well optimized for clustering cell types, the question of how to integrate multiple scATAC-seq data sets and/or sequencing modalities is still open. We present an analysis framework that enables such integration across scATAC-seq data sets by applying the CoGAPS Matrix Factorization algorithm and the projectR transfer learning program to identify common regulatory patterns across scATAC-seq data sets. We additionally integrate our analysis with scRNA-seq data to identify orthogonal evidence for transcriptional regulators predicted by scATAC-seq analysis.

Rational genomic optimization of DNA detection for human papillomavirus type 16 in head and neck squamous cell carcinoma.

Background: We aimed to use genomic data for optimizing polymerase chain reaction (PCR) primer/probe sets for detection of human papillomavirus (HPV)-16 in body fluids of patients with HPV-related head and neck squamous cell carcinoma (HPV-HNSCC).

Methods: We used genomic HPV-HNSCC sequencing data from a single institutional and a TCGA cohort. Optimized primer/probe sets were designed and tested for analytical performance in CaSki HPV-16 genome and confirmed in salivary rinse samples from patients with HPV-HNSCC.

What Do Neighbors Tell About You: The Local Context of Cis-Regulatory Modules Complicates Prediction of Regulatory Variants.

Many problems of modern genetics and functional genomics require the assessment of functional effects of sequence variants, including gene expression changes. Machine learning is considered to be a promising approach for solving this task, but its practical applications remain a challenge due to the insufficient volume and diversity of training data. A promising source of valuable data is a saturation mutagenesis massively parallel reporter assay, which quantitatively measures changes in transcription activity caused by sequence variants.

Allele-specific nonstationarity in evolution of influenza A virus surface proteins.

Influenza A virus (IAV) is a major public health problem and a pandemic threat. Its evolution is largely driven by diversifying positive selection so that relative fitness of different amino acid variants changes with time due to changes in herd immunity or genomic context, and novel amino acid variants attain fitness advantage. Here, we hypothesize that diversifying selection also has another manifestation: the fitness associated with a particular amino acid variant should decline with time since its origin, as the herd immunity adapts to it.

Differential Variation Analysis Enables Detection of Tumor Heterogeneity Using Single-Cell RNA-Sequencing Data.

Tumor heterogeneity provides a complex challenge to cancer treatment and is a critical component of therapeutic response, disease recurrence, and patient survival. Single-cell RNA-sequencing (scRNA-seq) technologies have revealed the prevalence of intratumor and intertumor heterogeneity. Computational techniques are essential to quantify the differences in variation of these profiles between distinct cell types, tumor subtypes, and patients to fully characterize intratumor and intertumor molecular heterogeneity.

Publisher Correction: Chromatin dysregulation and DNA methylation at transcription start sites associated with transcriptional repression in cancers.

The original version of this Article contained an error in the author affiliations. Trey Ideker was incorrectly associated with 'Department of Medicine (Oncology), Stanford University School of Medicine, 875 Blake Wilbur Dr, Palo Alto, CA 94304, USA.' This has now been corrected in both the PDF and HTML versions of the Article.

Chromatin dysregulation and DNA methylation at transcription start sites associated with transcriptional repression in cancers.

Although promoter-associated CpG islands have been established as targets of DNA methylation changes in cancer, previous studies suggest that epigenetic dysregulation outside the promoter region may be more closely associated with transcriptional changes. Here we examine DNA methylation, chromatin marks, and transcriptional alterations to define the relationship between transcriptional modulation and spatial changes in chromatin structure. Using human papillomavirus-related oropharyngeal carcinoma as a model, we show aberrant enrichment of repressive H3K9me3 at the transcriptional start site (TSS) with methylation-associated, tumor-specific gene silencing.

Enter the Matrix: Factorization Uncovers Knowledge from Omics.

Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis.

Functional characterization of alternatively spliced GSN in head and neck squamous cell carcinoma.

We have recently performed the characterization of alternative splicing events (ASEs) in head and neck squamous cell carcinoma, which allows dysregulation of protein expression common for cancer cells. Such analysis demonstrated a high ASE prevalence among tumor samples, including tumor-specific alternative splicing in the GSN gene.In vitro studies confirmed that overall expression of either ASE-GSN or wild-type GSN (WT-GSN) isoform inversely correlated with cell proliferation, whereas the high ratio of ASE-GSN to WT-GSN correlated with increased cellular invasion.

Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance.

Background: Targeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients' treatment options.

Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer.

Motivation: Current bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants.

DNA methylation regulates TMEM16A/ANO1 expression through multiple CpG islands in head and neck squamous cell carcinoma.

ANO1 is a calcium-activated chloride channel that is frequently overexpressed in head and neck squamous cell carcinoma (HNSCC) and other cancers. While ANO1 expression negatively correlates with survival in several cancers, its epigenetic regulation is poorly understood. We analyzed HNSCC samples from TCGA and a separate dataset of HPV+ oropharyngeal squamous cell carcinoma (OPSCC) samples to identify differentially methylated regions.

StereoGene: rapid estimation of genome-wide correlation of continuous or interval feature data.

Motivation: Genomics features with similar genome-wide distributions are generally hypothesized to be functionally related, for example, colocalization of histones and transcription start sites indicate chromatin regulation of transcription factor activity. Therefore, statistical algorithms to perform spatial, genome-wide correlation among genomic features are required.

Results: Here, we propose a method, StereoGene, that rapidly estimates genome-wide correlation among pairs of genomic features.

Epigenetic regulation of gene expression in cancer: techniques, resources and analysis.

Cancer is a complex disease, driven by aberrant activity in numerous signaling pathways in even individual malignant cells. Epigenetic changes are critical mediators of these functional changes that drive and maintain the malignant phenotype. Changes in DNA methylation, histone acetylation and methylation, noncoding RNAs, posttranslational modifications are all epigenetic drivers in cancer, independent of changes in the DNA sequence.

Integrated Analysis of Whole-Genome ChIP-Seq and RNA-Seq Data of Primary Head and Neck Tumor Samples Associates HPV Integration Sites with Open Chromatin Marks.

Chromatin alterations mediate mutations and gene expression changes in cancer. Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) has been utilized to study genome-wide chromatin structure in human cancer cell lines, yet numerous technical challenges limit comparable analyses in primary tumors. Here we have developed a new whole-genome analytic pipeline to optimize ChIP-Seq protocols on patient-derived xenografts from human papillomavirus-related (HPV) head and neck squamous cell carcinoma (HNSCC) samples.