Hae‐Ock Lee

Altered tumor signature and T‐cell profile after chemotherapy reveal new therapeutic opportunities in high‐grade serous ovarian carcinoma

AbstractChemotherapy combined with debulking surgery is the standard treatment protocol for high‐grade serous ovarian carcinoma (HGSOC). Nonetheless, a significant number of patients encounter relapse due to the development of chemotherapy resistance. To better understand and address this resistance, we conducted a comprehensive study investigating the transcriptional alterations at the single‐cell resolution in tissue samples from patients with HGSOC, using single‐cell RNA sequencing and T‐cell receptor sequencing techniques. Our analyses unveiled notable changes in the tumor signatures after chemotherapy, including those associated with epithelial–mesenchymal transition and cell cycle arrest. Within the immune compartment, we observed alterations in the T‐cell profiles, characterized by naïve or pre‐exhausted populations following chemotherapy. This phenotypic change was further supported by the examination of adjoining T‐cell receptor clonotypes in paired longitudinal samples. These findings underscore the profound impact of chemotherapy on reshaping the tumor landscape and the immune microenvironment. This knowledge may provide clues for the development of future therapeutic strategies to combat treatment resistance in HGSOC.

Single-cell mapping of combinatorial target antigens for CAR switches using logic gates

Identification of optimal target antigens that distinguish cancer cells from normal surrounding tissue cells remains a key challenge in chimeric antigen receptor (CAR) cell therapy for tumors with intratumoral heterogeneity. In this study, we dissected tissue complexity to the level of individual cells through the construction of a single-cell expression atlas that integrates ~1.4 million tumor, tumor-infiltrating normal and reference normal cells from 412 tumors and 12 normal organs. We used a two-step screening method using random forest and convolutional neural networks to select gene pairs that contribute most to discrimination between individual malignant and normal cells. Tumor coverage and specificity are evaluated for the AND, OR and NOT logic gates based on the combinatorial expression pattern of the pairing genes across individual single cells. Single-cell transcriptome-coupled epitope profiling validates the AND, OR and NOT switch targets identified in ovarian cancer and colorectal cancer.