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Liu, Y. L.

Publications and source records attributed to Liu, Y. L..

2 recordsLinked to original sources
bioRxiv2021

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↗
bioRxiv2021

Geometric Network Analysis Provides Prognostic Information in Patients with High Grade Serous Carcinoma of the Ovary Treated with Immune Checkpoint Inhibitors

PurposeNetwork analysis methods can potentially quantify cancer disturbances in gene networks without introducing fitted parameters or variable selection. A new network curvature-based method is introduced to provide an integrated measure of variability within cancer gene networks. The method is applied to high grade serous ovarian cancers (HGSOCs) to predict response to immune checkpoint inhibitors (ICIs), and to rank key genes associated with prognosis. MethodsCopy number alterations (CNAs) from targeted and whole exome sequencing data were extracted for HGSOC patients (n = 45) treated with ICIs. CNAs at a gene level were represented on a protein-protein interaction network to define patient-specific networks with a fixed topology. A version of Ollivier-Ricci curvature was used to identify genes that play a potentially key role in response to immunotherapy and further to stratify patients at high risk of mortality. Overall survival (OS) was defined as the time from the start of ICI treatment to either death or last follow-up. Kaplan-Meier analysis with log-rank test was performed to assess OS between the high and low curvature classified groups. ResultsThe network curvature analysis stratified patients at high risk of mortality with p=0.00047 in Kaplan-Meier analysis. Genes with high curvature were in accordance with CNAs relevant to ovarian cancer. ConclusionNetwork curvature using CNAs has the potential to be a novel predictor for OS in HGSOC patients treated with immunotherapy.

cancer biology↗