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From transcriptomics to tumor understanding: Charting malignant cells and cancer hallmarks from bulk and single-cell gene expression data.

From transcriptomics to tumor understanding: Charting malignant cells and cancer hallmarks from bulk and single-cell gene expression data.

Natalia Alonso

Centro de Investigación del Cáncer (CSIC, USAL, FICUS)

Date: 24/09/2026
Time: 12:30
CIC Lecture Hall
Host: Javier De las Rivas

Cancer is a highly heterogeneous disease, characterized by a complex tumor microenvironment (TME) formed through dynamic interactions between malignant and non-malignant cells. Despite substantial inter-patient heterogeneity, cancer development is driven by common key biological functions and processes called cancer hallmarks. Therefore, the identification of tumor and non-tumor cell composition together with the activity of the different cancer hallmarks may help predict tumor progression and disease development.

Single-cell RNA sequencing (scRNA-seq) enables the characterization of cellular heterogeneity within the TME at single-cell resolution. However, distinguishing tumor from non-tumor cells remains a major challenge, particularly when malignant and non-malignant cells share the same lineage. In addition, scRNA-seq is still limited by technical biases and does not capture all cells, genes and isoforms. For this reason, bulk or full-transcriptomic data remain essential for a more comprehensive characterization of tumor biology.

In our work, we developed a bioinformatic pipeline to identify malignant cells and cancer-associated programs from scRNA-seq data, and we applied it to single-cell atlases from breast cancer and head and neck cancer. Furthermore, we also worked on the development of a computational method to calculate cancer hallmarks scores in order to quantify hallmark activation from full-transcriptomics signals in cancer patients.