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Biology subjects

Long Roche, K.

Publications and source records attributed to Long Roche, K..

2 recordsLinked to original sources

Immune and malignant cell phenotypes of ovarian cancer are determined by distinct mutational processes

High-grade serous ovarian cancer (HGSOC) is an archetypal cancer of genomic instability patterned by distinct mutational processes, intratumoral heterogeneity and intraperitoneal spread. We investigated determinants of immune recognition and evasion in HGSOC to elucidate co- evolutionary processes underlying malignant progression and tumor immunity. Mutational processes and anatomic sites of tumor foci were key determinants of tumor microenvironment cellular phenotypes, inferred from whole genome sequencing, single-cell RNA sequencing, digital histopathology and multiplexed immunofluorescence of 160 tumor sites from 42 treatment-naive HGSOC patients. Homologous recombination-deficient (HRD)-Dup (BRCA1 mutant-like) and HRD- Del (BRCA2 mutant-like) tumors harbored increased neoantigen burden, inflammatory signaling and ongoing immunoediting, reflected in loss of HLA diversity and tumor infiltration with highly- differentiated dysfunctional CD8+ T cells. Foldback inversion (FBI, non-HRD) tumors exhibited elevated TGF{beta} signaling and immune exclusion, with predominantly naive/stem-like and memory T cells. Our findings implicate distinct immune resistance mechanisms across HGSOC subtypes which can inform future immunotherapeutic strategies. HIGHLIGHTSO_LIMulti-region, multi-modal profiling of malignant and immune cell phenotypes in ovarian cancer C_LIO_LIAnatomic site specificity is a determinant of cancer cell and intratumoral immune phenotypes C_LIO_LITumor mutational processes impact mechanisms of immune control and immune evasion C_LIO_LISpatial topology of HR-deficient tumors is defined by immune interactions absent from immune inert HR-proficient subtypes C_LI

cancer biology

Machine-Perception Nanosensor Platform to Detect Cancer Biomarkers

Conventional molecular recognition elements, such as antibodies, present issues for the development of biomolecular assays for use in point-of-care devices, implantable/wearables, and under-resourced settings. Additionally, antibody development and use, especially for highly multiplexed applications, can be slow and costly. We developed a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids. We demonstrated this platform in gynecologic cancers, which are often diagnosed at advanced stages, leading to low survival rates. We investigated the platform for detection in uterine lavage samples, which are enriched with cancer biomarkers compared to blood. We found that the method enables the simultaneous detection of multiple biomarkers in patient samples, with F1-scores of ~0.95 in uterine lavage samples from cancer patients. This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements.

bioengineering