Biology subjects

Pimentel, H.

Publications and source records attributed to Pimentel, H..

3 recordsLinked to original sources

bioRxiv2018

Intron retention as a novel source of cancer neoantigens

Personalized cancer vaccine strategies directed at tumor neoantigens derived from somatic mutations in the DNA are currently under prospective evaluation1, 2. Alterations in tumor RNA, rather than DNA, may also represent a previously-unexplored source of neoantigens. Here, we show that intron retention, a widespread feature of cancer transcriptomes3, 4, represents a novel source of tumor neoantigens. We developed an in silico approach to identify retained intron neoantigens from RNA sequencing data and applied this methodology to tumor samples from patients with melanoma treated with immune checkpoint blockade5, 6, discovering that the retained intron neoantigen burden in these samples augments the DNA-derived, somatic neoantigen burden. We validated the existence of retained intron derived neoantigens by implementing this technique on cancer cell lines with mass spectrometry-derived immunopeptidome data7, 8, revealing that retained intron neoantigens were complexed with MHC I experimentally. Unexpectedly, we observed a trend toward lack of clinical benefit from immune checkpoint blockade in high retained intron load-tumors, which harbored transcriptional signatures consistent with cell cycle dysregulation and DNA damage repair. Our results demonstrate the contribution of transcriptional dysregulation to the overall burden of tumor neoantigens, provide a foundation for augmenting personalized cancer vaccine development with a new class of tumor neoantigens, and demonstrate how global transcriptional dysregulation may impact selective response to immune checkpoint blockade.\n\nStatement of significanceWe developed and experimentally validated a computational pipeline to identify a novel class of tumor neoantigens derived from RNA-based intron retention, which is prevalent throughout cancer transcriptomes. The discovery of transcriptionally-derived tumor neoantigens expands the tumor immunopeptidome and contributes potential substrates for personalized cancer vaccine development.

cancer biology↗

bioRxiv2017

Gene-level differential analysis at transcript-level resolution

Gene-level differential expression analysis based on RNA-Seq is more robust, powerful and biologically actionable than transcript-level differential analysis. However aggregation of transcript counts prior to analysis results can mask transcript-level dynamics. We demonstrate that aggregating the results of transcript-level analysis allow for gene-level analysis with transcript-level resolution. We also show that p-value aggregation methods, typically used for meta-analyses, greatly increase the sensitivity of gene-level differential analyses. Furthermore, such aggregation can be applied directly to transcript compatibility counts obtained during pseudoalignment, thereby allowing for rapid and accurate model-free differential testing. The methods are general, allowing for testing not only of genes but also of any groups of transcripts, and we showcase an example where we apply them to perturbation analysis of gene ontologies.

bioinformatics↗

bioRxiv2017

Fusion detection and quantification by pseudoalignment

RNA sequencing in cancer cells is a powerful technique to detect chromosomal rearrangements, allowing for de novo discovery of actively expressed fusion genes. Here we focus on the problem of detecting gene fusions from raw sequencing data, assembling the reads to define fusion transcripts and their associated breakpoints, and quantifying their abundances. Building on the pseudoalignment idea that simplifies and accelerates transcript quantification, we introduce a novel approach to fusion detection based on inspecting paired reads that cannot be pseudoaligned due to conflicting matches. The method and software, called pizzly, filters false positives, assembles new transcripts from the fusion reads, and reports candidate fusions. With pizzly, fusion detection from raw RNA-Seq reads can be performed in a matter of minutes, making the program suitable for the analysis of large cancer gene expression databases and for clinical use. pizzly is available at https://github.com/pmelsted/pizzly

bioinformatics↗