@article {335, title = {Clinical Significance and Molecular Annotation of Cellular Morphometric Subtypes in Lower Grade Gliomas discovered by Machine Learning.}, journal = {Neuro Oncol}, year = {2022}, month = {2022 Jun 18}, abstract = {

BACKGROUND: Lower grade gliomas (LGG) are heterogenous diseases by clinical, histological, and molecular criteria. We aimed to personalize the diagnosis and therapy of LGG patients by developing and validating robust cellular morphometric subtypes (CMS) and to uncover the molecular signatures underlying these subtypes.

METHODS: Cellular morphometric biomarkers (CMBs) were identified with artificial intelligence technique from TCGA-LGG cohort. Consensus clustering was used to define CMS. Survival analysis was performed to assess the clinical impact of CMBs and CMS. A nomogram was constructed to predict 3- and 5- year overall survival (OS) of LGG patients. Tumor mutational burden (TMB), and immune cell infiltration between subtypes were analyzed using the Mann-Whitney test. The double-blinded validation for important immunotherapy-related biomarkers were executed using immunohistochemistry (IHC).

RESULTS: We developed a machine learning pipeline to extract CMBs from whole slide images of tissue histology; identifying and externally validating robust CMS of LGGs in multi-center cohorts. The subtypes had independent predicted OS across all three independent cohorts. In the TCGA-LGG cohort, patients within the poor-prognosis subtype responded poorly to primary and follow-up therapies. LGGs within the poor-prognosis subtype were characterized by high mutational burden, high frequencies of copy number alterations, and high levels of tumor-infiltrating lymphocytes and immune checkpoint genes. Higher levels of PD-1/PD-L1/CTLA-4 were confirmed by immunohistochemical staining. In addition, the subtypes learned from LGG demonstrates translational impact on glioblastoma (GBM).

CONCLUSIONS: We developed and validated a framework (CMS-ML) for CMS discovery in LGG associated with specific molecular alterations, immune micro-environment, prognosis, and treatment response.

}, issn = {1523-5866}, doi = {10.1093/neuonc/noac154}, author = {Liu, Xiao-Ping and Jin, Xiaoqing and Ahmadian, Saman Seyed and Yang, Xu and Tian, Su-Fang and Cai, Yu-Xiang and Chawla, Kuldeep and Snijders, Antoine M and Xia, Yankai and van Diest, Paul J and Weiss, William A and Mao, Jian-Hua and Li, Zhi-Qiang and Vogel, Hannes and Chang, Hang} } @article {300, title = {High-Dimensional Phenotyping Identifies Age-Emergent Cells in Human Mammary Epithelia.}, journal = {Cell Rep}, volume = {23}, year = {2018}, month = {2018 Apr 24}, pages = {1205-1219}, abstract = {

Aging is associated with tissue-level changes in\ cellular composition that are correlated with increased susceptibility to disease. Aging human mammary tissue shows skewed progenitor cell potency, resulting in diminished tumor-suppressive cell types and the accumulation of defective epithelial progenitors. Quantitative characterization of these age-emergent human cell subpopulations is lacking, impeding our understanding of the relationship between age and cancer susceptibility. We conducted single-cell resolution proteomic phenotyping of healthy breast epithelia from 57 women, aged 16-91 years, using mass cytometry. Remarkable heterogeneity was quantified within the two mammary epithelial lineages. Population partitioning identified a subset of aberrant basal-like luminal cells that accumulate with age and originate from age-altered progenitors. Quantification of age-emergent phenotypes\ enabled robust classification of breast tissues by age in healthy women. This high-resolution mapping highlighted specific epithelial subpopulations that\ change with age in a manner consistent with increased susceptibility to breast cancer.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2018.03.114}, author = {Pelissier Vatter, Fanny A and Schapiro, Denis and Hang Chang and Borowsky, Alexander D and Lee, Jonathan K and Parvin, Bahram and Stampfer, Martha R and LaBarge, Mark A and Bodenmiller, Bernd and Lorens, James B} }