ABSTRACT
Whereas CD4+ T cells conventionally mediate antitumor immunity by providing help to CD8+ T cells, recent clinical studies have implied an important role for cytotoxic CD4+ T cells in cancer immunity. Using an orthotopic melanoma model, we provide a detailed account of antitumoral CD4+ T cell responses and their regulation by major histocompatibility complex class II (MHC II) in the skin. Intravital imaging revealed prominent interactions of CD4+ T cells with tumor debris-laden MHC II+ host antigen-presenting cells that accumulated around tumor cell nests, although direct recognition of MHC II+ melanoma cells alone could also promote CD4+ T cell control. CD4+ T cells stably suppressed or eradicated tumors even in the absence of other lymphocytes by using tumor necrosis factor-α and Fas ligand (FasL) but not perforin-mediated cytotoxicity. Interferon-γ was critical for protection, acting both directly on melanoma cells and via induction of nitric oxide synthase in myeloid cells. Our results illustrate multifaceted and context-specific aspects of MHC II-dependent CD4+ T cell immunity against cutaneous melanoma, emphasizing modulation of this axis as a potential avenue for immunotherapies.
Subject(s)
Melanoma , Skin Neoplasms , Humans , CD8-Positive T-Lymphocytes , CD4-Positive T-Lymphocytes , Histocompatibility Antigens Class II , HLA AntigensABSTRACT
SUMMARY: StructuralVariantAnnotation is an R/Bioconductor package that provides a framework for decoupling downstream analysis of structural variant breakpoints from upstream variant calling methods. It standardizes the representational format from BEDPE, or any of the three different notations supported by VCF into a breakpoint GRanges data structure suitable for use by the wider Bioconductor ecosystem. It handles both transitive breakpoints and duplication/insertion notational differences of identical variants-both common scenarios when comparing short/long read-based call sets that confound downstream analysis. StructuralVariantAnnotation provides the caller-agnostic foundation needed for a R/Bioconductor ecosystem of structural variant annotation, classification and interpretation tools able to handle both simple and complex genomic rearrangements. AVAILABILITY AND IMPLEMENTATION: StructuralVariantAnnotation is implemented in R and available for download as the Bioconductor StructuralVariantAnnotation package. Details can be found at https://www.bioconductor.org/packages/release/bioc/html/StructuralVariantAnnotation.html. It has been released under a GPL license.
Subject(s)
Ecosystem , Software , Genomics/methods , GenomeABSTRACT
Nuclear integration of mitochondrial genomes and retrocopied transcript insertion are biologically important but often-overlooked aspects of structural variant (SV) annotation. While tools for their detection exist, these typically rely on reanalysis of primary data using specialised detectors rather than leveraging calls from general purpose structural variant callers. Such reanalysis potentially leads to additional computational expense and does not take advantage of advances in general purpose structural variant calling. Here, we present svaRetro and svaNUMT; R packages that provide functions for annotating novel genomic events, such as nonreference retrocopied transcripts and nuclear integration of mitochondrial DNA. The packages were developed to work within the Bioconductor framework. We evaluate the performance of these packages to detect events using simulations and public benchmarking datasets, and annotate processed transcripts in a public structural variant database. svaRetro and svaNUMT provide modular, SV-caller agnostic tools for downstream annotation of structural variant calls.
ABSTRACT
Recently, efforts have been made toward the harmonization of transcriptomic data structures and workflows using the concept of data tidiness, to facilitate modularisation. We present tidybulk, a modular framework for bulk transcriptional analyses that introduces a tidy transcriptomic data structure paradigm and analysis grammar. Tidybulk covers a wide variety of analysis procedures and integrates a large ecosystem of publicly available analysis algorithms under a common framework. Tidybulk decreases coding burden, facilitates reproducibility, increases efficiency for expert users, lowers the learning curve for inexperienced users, and bridges transcriptional data analysis with the tidyverse. Tidybulk is available at R/Bioconductor bioconductor.org/packages/tidybulk .
