Tuomas K. Mirtti

The paper identifies site-dependent drug-biomarker associations in ccRCC using patient-derived models and multi-omic data, revealing discrepancies between genomic predictions and functional drug responses.

This study identifies shared drug vulnerabilities in adult Wilms tumor through multi-omics and functional assays, showing pathway-level dependencies predict drug responses better than single-gene biomarkers.

The paper develops a self-supervised deep learning framework to analyze multiplexed cancer tissue microscopy images, stratifying patients into prognostically distinct groups with different survival outcomes.

The paper evaluates immunomodulatory drug responses in patient-derived tumor explant models of breast and renal cancer, finding reproducible gene expression and cytokine changes across models.

The paper identifies club-like cells in the prostate tumor microenvironment that contribute to androgen deprivation therapy resistance through immune suppression.

The paper identifies FAP-expressing EMT-associated cancer cells in highly inflamed localized ccRCC as an aggressive, immunosuppressive subtype linked to poor prognosis and metastasis.

OSCAR uses an L0-pseudonorm approach to select sparse prognostic features in prostate cancer, balancing model accuracy and clinical implementation costs.

The paper uses spatial transcriptomics to analyze copy number variations in benign and malignant tissues, revealing clonal patterns and suggesting early cancer evolution mechanisms.