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Biology subjects

Mohanraj, S.

Publications and source records attributed to Mohanraj, S..

3 recordsLinked to original sources

Immunogenomic profiling of circulating T cells in pediatric cancer patients during standard-of-care

While pediatric cancer patients receive intensive chemotherapy, its impact on peripheral T cells and subsequently to disease outcomes are not fully characterized. Here, we assessed T-cell dynamics during treatment, identifying associations with outcomes through immune phenotyping and T-cell Receptor (TCR) sequencing in pediatric solid and hematologic malignancies. We show that while levels of immune checkpoint proteins (PD-1, LAG3, and TIM3) at baseline were highest in lymphomas compared to other cancer groups, they increased significantly in response to therapy in all cancers. Levels of Central Memory (CM) T cells increased in leukemias and solid tumors, while naive T cells and cell-free TCR diversity decreased in lymphomas. By combining immune cell and TCR repertoire features across all timepoints, we proposed the Dynamic Immunogenomic Score (DIS) to measure patient-specific effects of therapy on the peripheral T-cell population. Higher DIS was associated with high-risk cancer types and logistic regression analysis revealed it may predict incidence of relapse in leukemia patients. TCR specificity analysis revealed patient-specific clonal dynamics and differential detection of virally-associated TCRs in cancer patients compared to healthy individuals. Our results highlight the potential of early upfront immunogenomic profiling in identifying high-risk patients that may be predictive in light of emerging cellular immunotherapies.

cancer biology↗

A comparison of data integration methods for single-cell RNA sequencing of cancer samples

Tumours are routinely profiled with single-cell RNA sequencing (scRNA-seq) to characterize their diverse cellular ecosystems of malignant, immune, and stromal cell types. When combining data from multiple samples or studies, batch-specific technical variation can confound biological signals. However, scRNA-seq batch integration methods are often not designed for, or benchmarked, on datasets containing cancer cells. Here, we compare 5 data integration tools applied to 171,206 cells from 5 tumour scRNA-seq datasets. Based on our results, STACAS and fastMNN are the most suitable methods for integrating tumour datasets, demonstrating robust batch effect correction while preserving relevant biological variability in the malignant compartment. This comparison provides a framework for evaluating how well single-cell integration methods correct for technical variability while preserving biological heterogeneity of malignant and non-malignant cell populations.

bioinformatics↗

CReSCENT: CanceR Single Cell ExpressioN Toolkit

CReSCENTCanceR Single Cell ExpressioN Toolkit (https://crescent.cloud), is an intuitive and scalable web portal incorporating a containerized pipeline execution engine for standardized analysis of single-cell RNA sequencing (scRNA-seq) data. While scRNA-seq data for tumour specimens are readily generated, subsequent analysis requires high-performance computing infrastructure and user expertise to build analysis pipelines and tailor interpretation for cancer biology. CReSCENT uses public data sets and preconfigured pipelines that are accessible to computational biology non-experts and are user-editable to allow optimization, comparison, and reanalysis for specific experiments. Users can also upload their own scRNA-seq data for analysis and results can be kept private or shared with other users.

bioinformatics↗