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Biology subjects

Croucher, D. C.

Publications and source records attributed to Croucher, D. C..

2 recordsLinked to original sources

Single-cell transcriptional analysis of the immune tumour microenvironment during myeloma disease evolution

Multiple myeloma is universally preceded by a premalignant disease state. However, efforts to develop preventative therapeutic strategies are hindered by an incomplete understanding of the immune mechanisms associated with progression. Using single-cell RNA-sequencing, we profiled 104,880 cells derived from the bone marrow of V{kappa}*MYC mice across the myeloma progression spectrum, of which 97,720 were identified as non-malignant cells of the tumour microenvironment. Analysis of the non-malignant cells comprising the immune microenvironment identified mechanisms associated with disease progression in innate and adaptive immune cell populations. This included activation of IL-17 signaling in myeloid cells from precursor mice, accompanied by upregulation of Il6 gene expression in basophils. In the T/Natural killer cell compartment, we identified Tox-expressing CD8+ T cells enriched in the tumour microenvironment of mice with overt disease, with co-expression of LAG3 and PD-1, as well as elevated T cell exhaustion signatures in mice with early disease. We subsequently showed that early intervention with combinatorial blockade of LAG3 and PD-1 using neutralizing monoclonal antibodies delayed tumor progression and improved survival of V{kappa}*MYC mice. Together, this work provides insight into the biology of myeloma evolution and nominates a treatment strategy for early disease.

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↗