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iCAN,

Publications and source records attributed to iCAN,.

2 recordsLinked to original sources

Sustained epithelial interferon signaling modulates incomplete pathologic response in colorectal cancer

Background & AimsPatients with colorectal cancer have heterogeneous clinical responses to chemotherapy, although clinical guidelines advise little variability in treatment selection based on molecular tumor features. Precision oncology research typically utilizes patient-derived tumor organoids (PDTO) to predict clinical outcomes, but such efforts are often not directed towards identification of molecular factors underlying differential responses to therapy. MethodsBulk RNA-sequencing was performed on treatment-naive PDTOs, and gene expression data was combined to drug sensitivity data to identify transcriptomic features associated with low in vitro sensitivity to chemotherapy. Whole-exome sequencing was performed on primary tumors to infer the somatic mutations of PDTOs and used to identify somatic mutations associated with differential in vitro drug responses. Publicly available gene expression and drug sensitivity data sets were used to validate the results. RNA interference was used for functional validation. ResultsPDTOs with low chemosensitivity had high JAK-STAT pathway activity resulting from high expression of interferon-stimulated genes. Evidence from single-cell RNA-sequencing confirmed chemotherapy-induced expression of interferon-stimulated genes in epithelial cells of cancers with partial response. EPSTI1 knockdown decreased cancer cell viability and sensitized cells to chemotherapy. ConclusionsSustained interferon signaling in epithelial cancer cells contributes to incomplete pathologic response in colorectal cancer. The findings highlight the potential of JAK-STAT inhibition or TRAIL pathway activation to enhance chemotherapy efficacy. Future studies investigating pharmacologic modulation of these pathways in preclinical CRC models are needed to determine their viability as therapeutic targets.

cancer biology↗

Self-supervised learning enables unbiased patient characterization from multiplexed microscopy images

Multiplexed immunofluorescence microscopy offers detailed insights into the spatial architecture of cancer tissue. However, classical single-cell analysis approaches are limited by segmentation accuracy, reliance on predefined features, and the inability to capture spatial interrelationships among cells. We developed a hierarchical self-supervised deep learning framework that learns spatial protein marker patterns from multiplexed microscopy images by encoding the tissue at both local (cellular) and global (tissue architecture) levels. Applied to lung, prostate, and renal cancer tissue microarray cohorts, our method stratified patients into prognostically distinct groups with significantly different survival outcomes. These groupings were consistent with prior expert-driven single-cell analyses, demonstrating the validity of our approach. Furthermore, attention maps extracted from these models highlighted biologically relevant tissue regions associated with specific marker patterns. Overall, our framework effectively profiles complex multiplexed microscopy images and offers a scalable, interpretable tool for improved biomarker discovery, with potential to support more informed cancer treatment decisions.

bioinformatics↗