Shreyansh Priyadarshi

Tumor-resident stem cells, also known as cancer stem cells (CSCs), constitute a subgroup within tumors, play a crucial role in fostering resistance to treatment and the recurrence of tumors, and pose significant challenges for conventional therapeutic methods. Existing approaches for identifying CSCs face notable hurdles related to scalability, reproducibility, and technical consistency across different cancer types due to the adaptable nature of CSCs. In this context, we introduce OSCORP, an innovative machine-learning-driven approach. This methodology quantifies and identifies CSCs, achieving almost 99% accuracy using biopsy bulk RNAseq data. OSCORP leverages genetic similarities between normal and cancer stem cells. By categorizing CSCs into four distinct yet dynamic potency states, this approach provides insights into the differentiation landscape of CSCs, unveiling previously undisclosed facets of tumor heterogeneity. In evaluations conducted on patient samples across 22 cancer types, OSCORP revealed clinical, transcriptomic, and immunological signatures associated with each CSC state. It has emerged as a comprehensive tool for understanding and addressing the complexities of cancer stem cells. Ultimately, OSCORP opens up new possibilities for more effective personalized cancer therapies and holds the potential to serve as a clinical tool for monitoring patient-specific CSC changes during treatment or follow-up care.

Kindlins serve as mechanosensitive adapters, transducing extracellular mechanical cues to intracellular biochemical signals and thus, their perturbations potentially lead to cancer progressions. Despite the kindlin involvement in tumor development, understanding their genetic and mechanochemical characteristics across different cancers remains elusive. Here, we thoroughly examined genetic alterations in kindlins across more than 10,000 patients with 33 cancer types. Our findings reveal cancer-specific alterations, particularly prevalent in advanced tumor stage and during metastatic onset. We observed a significant co-alteration between kindlins and mechanochemical proteome in various tumors through the activation of cancer-related pathways and adverse survival outcomes. Leveraging normal mode analysis, we predicted structural consequences of cancer-specific kindlin mutations, highlighting potential impacts on stability and downstream signaling pathways. Our study unraveled alterations in epithelial–mesenchymal transition markers associated with kindlin activity. This comprehensive analysis provides a resource for guiding future mechanistic investigations and therapeutic strategies targeting the roles of kindlins in cancer treatment.