AZD-8186

Colorectal cancer (CRC) is a highly prevalent cancer worldwide, but treatment outcomes can vary significantly among patients with similar clinical or historical stages. This study aimed to investigate the differences in immune cell abundance associated with malignant progression in CRC patients. We utilized data from patients with CRC obtained from The Cancer Genome Atlas as our training set. To assess immune cell infiltration levels, an immune cell risk score (ICRS) was calculated. Furthermore, we performed network analysis to identify effective T cell-related genes (ETRGs) and subsequently constructed an effective T cell prognostic index (ETPI). The performance of the ETPI was evaluated through external validation using four Gene Expression Omnibus datasets. Additionally, a nomogram analysis and drug sensitivity analysis were conducted to explore the clinical utility of the ETRGs. We also examined the expression of ETRGs in clinical samples. Based on the ICRS, we identified activated CD4+ and CD8+ T cells as protective factors in terms of prognosis. Six ETRGs were identified to develop the ETPI, which exhibited remarkable prognostic performance. In the external validation of immunotherapy, the low ETPI group demonstrated a significantly lower recurrence rate. To optimize therapeutic strategies, we developed a nomogram. Notably, patients with different ETPI values exhibited varying responses to tumor pathway inhibitors. Finally, we observed higher protein expression of certain ETRGs in normal tissues compared to tumors. Our findings suggest that the ETPI may contribute to the precise selection of patients based on tumor microenvironment and key genomic landscape interactions, thereby optimizing drug benefits and informing clinical strategies in future.

The gene-regulatory dynamics governing drug responses in cancer are yet to be fully understood. Here, we report a pipeline capable of producing high-throughput pharmacotranscriptomic profiling through live-cell barcoding using antibody–oligonucleotide conjugates. This pipeline combines drug screening with 96-plex single-cell RNA sequencing. We show the potential of this approach by exploring the heterogeneous transcriptional landscape of primary high-grade serous ovarian cancer (HGSOC) cells after treatment with 45 drugs, with 13 distinct classes of mechanisms of action. A subset of phosphatidylinositol 3-OH kinase (PI3K), protein kinase B (AKT) and mammalian target of rapamycin (mTOR) inhibitors induced the activation of receptor tyrosine kinases, such as the epithelial growth factor receptor (EGFR), and this was mediated by the upregulation of caveolin 1 (CAV1). This drug resistance feedback loop could be mitigated by the synergistic action of agents targeting PI3K–AKT–mTOR and EGFR for HGSOC with CAV1 and EGFR expression. Using this workflow could enable the personalized testing of patient-derived tumor samples at single-cell resolution.