Faculty Opinions recommendation of Using DNA sequencing data to quantify T cell fraction and therapy response.

Abstract Background Cancer arises from an evolutionary process where somatic mutations give rise to clonal expansions. Reconstructing this evolutionary process is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. In particular, classifying a tumor’s evolutionary process as either linear or branched and understanding what cancer types and which patients have each of these trajectories could provide useful insights for both clinicians and researchers. While comprehensive cancer phylogeny inference from single-cell DNA sequencing data is challenging due to limitations with current sequencing technology and the complexity of the resulting problem, current data might provide sufficient signal to accurately classify a tumor’s evolutionary history as either linear or branched. Results We introduce the Linear Perfect Phylogeny Flipping (LPPF) problem as a means of testing two alternative hypotheses for the pattern of evolution, which we prove to be NP-hard. We develop Phyolin, which uses constraint programming to solve the LPPF problem. Through both in silico experiments and real data application, we demonstrate the performance of our method, outperforming a competing machine learning approach. Conclusion Phyolin is an accurate, easy to use and fast method for classifying an evolutionary trajectory as linear or branched given a tumor’s single-cell DNA sequencing data.

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Combined Histopathologic Risk Score using TP53 Protein Expression, CD8+ T‐cell Density, and Intratumoural Budding is an Independent Predictor of Neoadjuvant Therapy Response in Rectal Adenocarcinoma

Histopathology ◽

10.1111/his.14430 ◽

2021 ◽

Author(s):

Wei Chen ◽

Lama Farchoukh ◽

Lindsey Seigh ◽

Douglas J. Hartman ◽

Reetesh K. Pai

Keyword(s):

T Cell ◽

Protein Expression ◽

Neoadjuvant Therapy ◽

Cell Density ◽

Risk Score ◽

Independent Predictor ◽

Rectal Adenocarcinoma ◽

Therapy Response ◽

Cd8 T Cell ◽

Tp53 Protein

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Putatively adaptive genetic variation in the giant California sea cucumber ( Parastichopus californicus ) as revealed by environmental association analysis of restriction‐site associated DNA sequencing data

Molecular Ecology ◽

10.1111/mec.14942 ◽

2018 ◽

Vol 27 (24) ◽

pp. 5035-5048 ◽

Cited By ~ 13

Author(s):

Amanda Xuereb ◽

Christopher M. Kimber ◽

Janelle M. R. Curtis ◽

Louis Bernatchez ◽

Marie‐Josée Fortin

Keyword(s):

Genetic Variation ◽

Dna Sequencing ◽

Association Analysis ◽

Sea Cucumber ◽

Restriction Site ◽

Sequencing Data ◽

Adaptive Genetic Variation ◽

Environmental Association

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Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity

eLife ◽

10.7554/elife.38358 ◽

2018 ◽

Vol 7 ◽

Cited By ~ 42

Author(s):

William S DeWitt ◽

Anajane Smith ◽

Gary Schoch ◽

John A Hansen ◽

Frederick A Matsen ◽

...

Keyword(s):

T Cell ◽

T Cell Receptor ◽

Genetic Background ◽

Cell Receptor ◽

Sequencing Data ◽

Human T Cell ◽

Disease Associations ◽

Repertoire Sequencing ◽

Mhc Polymorphism ◽

Occurrence Patterns

The T cell receptor (TCR) repertoire encodes immune exposure history through the dynamic formation of immunological memory. Statistical analysis of repertoire sequencing data has the potential to decode disease associations from large cohorts with measured phenotypes. However, the repertoire perturbation induced by a given immunological challenge is conditioned on genetic background via major histocompatibility complex (MHC) polymorphism. We explore associations between MHC alleles, immune exposures, and shared TCRs in a large human cohort. Using a previously published repertoire sequencing dataset augmented with high-resolution MHC genotyping, our analysis reveals rich structure: striking imprints of common pathogens, clusters of co-occurring TCRs that may represent markers of shared immune exposures, and substantial variations in TCR-MHC association strength across MHC loci. Guided by atomic contacts in solved TCR:peptide-MHC structures, we identify sequence covariation between TCR and MHC. These insights and our analysis framework lay the groundwork for further explorations into TCR diversity.

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Accounting for B cell behaviour and sampling bias yields a superior predictor of anti-PD-L1 response in bladder cancer

10.1101/2021.03.04.433370 ◽

2021 ◽

Author(s):

Ilya A Dyugay ◽

Daniil K Lukyanov ◽

Maria A Turchaninova ◽

Andrew R Zaretsky ◽

Oybek A Khalmurzaev ◽

...

Keyword(s):

Bladder Cancer ◽

T Cell ◽

B Cells ◽

B Cell ◽

Molecular Subtype ◽

Specific Antigen ◽

Therapy Response ◽

Prognostic Indicator ◽

Molecular Signature ◽

Cell Functions

Tumor-infiltrating B cells and intratumorally-produced immunoglobulins (IG) play important roles in the tumor microenvironment and response to immunotherapy. IgG antibodies produced by intratumoral B cells may drive antibody-dependent cellular cytotoxicity (ADCC) and enhance antigen presentation by dendritic cells. Furthermore, B cells are efficient antigen-specific antigen presenters that can essentially modulate the behaviour of helper T cells. Here we investigated the role of intratumoral IG isotype and clonality in bladder cancer. Our results show that the IgG1/IgA ratio offers a strong and independent prognostic indicator for the Basal squamous molecular subtype and for the whole ImVigor210 cohort in anti-PD-L1 immunotherapy. Our findings also indicate that effector B cell functions, rather than clonally-produced antibodies, are involved in the antitumor response. High IgG1/IgA ratio was associated with relative abundance of cytotoxic genes and prominence of the IL-21/IL-21R axis suggesting importance of T cell/B cell interaction. We integrated the B, NK, and T cell components, employing immFocus-like normalization to account for the stochastic nature of tumor tissue sampling. Using a random forest model with nested cross-validation, we developed a tumor RNA-Seq-based predictor of anti-PD-L1 therapy response in muscle-invasive urothelial carcinoma. The resulting PRIMUS (PRedIctive MolecUlar Signature) predictor achieves superior sensitivity compared to PD-L1 expression scores or existing gene signatures, allowing for reliable identification of responders even within the desert patient subcohort analyzed as a hold out set.

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Splicing Express: a software suite for alternative splicing analysis using next-generation sequencing data

PeerJ ◽

10.7717/peerj.1419 ◽

2015 ◽

Vol 3 ◽

pp. e1419 ◽

Cited By ~ 6

Author(s):

Jose E. Kroll ◽

Jihoon Kim ◽

Lucila Ohno-Machado ◽

Sandro J. de Souza

Keyword(s):

Alternative Splicing ◽

Dna Sequencing ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Software Suite ◽

Next Generation Dna Sequencing ◽

Sequencing Technologies ◽

Body Map ◽

Sequencing Platforms

Motivation.Alternative splicing events (ASEs) are prevalent in the transcriptome of eukaryotic species and are known to influence many biological phenomena. The identification and quantification of these events are crucial for a better understanding of biological processes. Next-generation DNA sequencing technologies have allowed deep characterization of transcriptomes and made it possible to address these issues. ASEs analysis, however, represents a challenging task especially when many different samples need to be compared. Some popular tools for the analysis of ASEs are known to report thousands of events without annotations and/or graphical representations. A new tool for the identification and visualization of ASEs is here described, which can be used by biologists without a solid bioinformatics background.Results.A software suite namedSplicing Expresswas created to perform ASEs analysis from transcriptome sequencing data derived from next-generation DNA sequencing platforms. Its major goal is to serve the needs of biomedical researchers who do not have bioinformatics skills.Splicing Expressperforms automatic annotation of transcriptome data (GTF files) using gene coordinates available from the UCSC genome browser and allows the analysis of data from all available species. The identification of ASEs is done by a known algorithm previously implemented in another tool namedSplooce. As a final result,Splicing Expresscreates a set of HTML files composed of graphics and tables designed to describe the expression profile of ASEs among all analyzed samples. By using RNA-Seq data from the Illumina Human Body Map and the Rat Body Map, we show thatSplicing Expressis able to perform all tasks in a straightforward way, identifying well-known specific events.Availability and Implementation.Splicing Expressis written in Perl and is suitable to run only in UNIX-like systems. More details can be found at:http://www.bioinformatics-brazil.org/splicingexpress.

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