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Biology subjects

Chan, C. L.

Publications and source records attributed to Chan, C. L..

2 recordsLinked to original sources

Lipidic and senescent macrophages predict progression and response to combinatorial immunotherapy in triple-negative breast cancer

Immune cell subsets in the tumor predict prognosis. Identifying reliable subsets consistently in multiple patients is clinically important. Whats more advantageous to the field is if such subsets can be a target of immunotherapy. Here we analyzed single-cell RNA-sequencing datasets of patients with triple-negative breast cancer and identified APOE or FABP5-expressing macrophages correlated with poor prognosis. Further validation with TCGA-BRCA cohort detailed molecular signatures of these macrophages as lipidic and senescent. Our receptor-ligand mapping identified lipidic and senescent macrophages both suppress T-lymphoid and myeloid immunogenicity. Finally, we discovered that immune checkpoint therapy combined with chemotherapy reprogrammed anti-tumor microenvironment enriched with FOLR2+ macrophages facilitated T-cell activations. This suggests that lipidic and senescent macrophages could be a therapeutic target of immune checkpoint therapy.

cancer biology↗

R Functions for Analysis of Continuous Glucose Monitor Data

Continuous glucose monitoring (CGM) is an essential part of diabetes care. Real-time CGM data are beneficial to patients for daily glucose management, and aggregate summary statistics of CGM measures are valuable to direct insulin dosing and as a tool for researchers in clinical trials. Yet, the various commercial systems still report CGM data in disparate, non-standard ways. Accordingly, there is a need for a standardized, free, open-source approach to CGM data management and analysis. Functions were developed in the free programming language R to provide a rapid, easy, and consistent methodology for CGM data management and analysis. Summary variables calculated by our package compare well to those generated by various CGM software, and our functions provide a more comprehensive list of summary measures available to clinicians and researchers. Consistent handling of CGM data using our R package may facilitate collaboration between research groups and contribute to a better understanding of free-living glucose patterns.

bioinformatics↗