Shreyansh Priyadarshi

Cancer progression is driven by a set of well-defined biological principles—collectively termed the “hallmarks of cancer”—yet current diagnostic approaches seldom incorporate these distinct molecular features into clinical practice. Despite substantial progress in molecular oncology, traditional methods like histopathological grading and immunohistochemical assays often fail to capture the complex interplay between cancer cells and the tumor microenvironment, emphasizing the need for robust computational frameworks capable of systematically quantifying hallmark-specific activity. Here, we address this gap by developing OncoMark, a high-throughput neural multi-task learning (N-MTL) framework designed to simultaneously quantify hallmark activities in tumor biopsies using transcriptomics data. We show that OncoMark achieves near-perfect accuracy, precision, recall, and F1 scores (>99%) in cross-validation, with external validation consistently exceeding 96.6% on five independent datasets. Further evaluation on eight additional datasets—including large-scale cancer cohorts (TCGA, MET500, CCLE, TARGET, PCAWG, POG570) and normal tissue datasets (GTEx, ANTE)—demonstrated high specificity for normal samples and robust sensitivity for hallmark prediction in cancer. By delivering a comprehensive and cost-effective molecular portrait of tumor biology and providing a user-friendly web platform accessible at [https://oncomark-ai.hf.space/](https://oncomark-ai.hf.space/), OncoMark has the potential to guide tailored treatment strategies and advance precision oncology. More broadly, this framework signifies a transformative step toward routine hallmark-based diagnostics, promising to improve patient outcomes by facilitating timely and precise tumor profiling.

Cancer stem cells (CSCs), a distinct subpopulation within tumors, are pivotal in driving treatment resistance and tumor recurrence, posing substantial challenges to conventional therapeutic strategies. Precise quantification and profiling of these cells are essential for improving cancer treatment outcomes. We present ACSCeND, an advanced deep neural network model accompanied by a robust workflow, specifically developed to quantify cellular compositions from bulk RNA-seq data, enabling accurate CSC profiling. By integrating bulk RNA-seq data with insights derived from single-cell RNA-seq datasets, ACSCeND effectively captures the diversity and hierarchical organization of tumor-resident cell states, alongside cell-specific gene expression profiles (GEPs). Compared to current tissue deconvolution models, ACSCeND exhibits superior performance, achieving significantly higher Concordance Correlation Coefficient (CCC) values and lower Root Mean Square Error (RMSE) across various pseudobulk and real-world bulk tissue samples. Application of ACSCeND to TCGA and PRECOG datasets reveals a strong association between CSC abundance and poorer disease-free survival outcomes, underscoring the clinical relevance of CSCs in cancer progression. Furthermore, cell-specific GEPs for distinct CSC states unveil novel molecular signatures and illuminate the origins of CSC-driven tumor heterogeneity. In summary, ACSCeND provides a powerful, scalable platform for high-throughput quantification of cellular compositions and distinct potency states within normal tissues as well as highly heterogeneous tissues, such as tumors.

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.