Kalla, J. | Search bioRxiv

bioRxiv2024

Aggressive KRAS mutations direct TGF-β response towards partial EMT in patient-derived colorectal cancer tumoroids

Transforming growth factor beta (TGF-{beta}) exhibits complex and context-dependent cellular responses. While it mostly induces tumor-suppressive effects in early stages of tumorigenesis, its tumor promoting properties are evident in advanced disease. This TGF-{beta} duality is still not fully understood, and whether TGF-{beta} supports invasion and metastasis by influencing cancer cells directly, or rather through the stromal tumor compartment remains a matter of debate. Here, we utilized a library of colorectal cancer (CRC) patient-derived tumoroids (PDTs), representing a spectrum of tumor stages, to study cancer cell-specific responses to TGF-{beta}. Using medium conditions allowing for the differentiation of PDTs, we observed TGF-{beta} induced tumor-suppressive effects in early-stage tumoroids. PDTs with TGF-{beta} pathway mutations or PDTs derived from metastatic tumors were insensitive to the treatment. Notably, one tumoroid line harboring an atypical KRASQ22K mutation underwent partial epithelial-to-mesenchymal transition (EMT), associated with morphological changes and increased invasiveness. On a molecular level, this was accompanied by elevated expression of mesenchymal genes, as well as deregulation of pathways associated with matrix remodeling and cell adhesion. Our results suggest that tumor cell intrinsic responses to TGF-{beta} are critical in determining its tumor-suppressive or -promoting effects.

cancer biology↗

bioRxiv2023

Integrating weighted correlation network analysis and machine learning identifies common trajectories of prostate cancer

BackgroundProstate cancer diagnosis and prognosis is currently limited by the availability of sensitive and specific biomarkers. There is an urgent need to develop molecular biomarkers that allow for the distinction of indolent from aggressive disease, the sensitive detection of heterogeneous tumors, or the evaluation of micro-metastases. The availability of multi-omics datasets in publicly accessible databases provides a valuable foundation to develop computational workflows for the identification of suitable biomarkers for clinical management of cancer patients. ResultsWe combined transcriptomic data of primary localized and advanced prostate cancer from two cancer databases. Transcriptomic analysis of metastatic tumors unveiled a distinct overexpression pattern of genes encoding cell surface proteins intricately associated with cell-matrix components and chemokine signaling pathways. Utilizing an integrated approach combining machine learning and weighted gene correlation network modules, we identified the EZH2-TROAP axis as the main trajectory from initial tumor development to lethal metastatic disease. In addition, we identified and independently validated 58 promising biomarkers that were specifically upregulated in primary localized or metastatic disease. Among those biomarkers, 22 were highly significant for predicting biochemical recurrence. Notably, we confirmed TPX2 upregulation at the protein level in an independent cohort of primary prostate cancer and matched lymph node metastases. ConclusionsThis study demonstrates the effectiveness of using advanced bioinformatics approaches to identify the biological factors that drive prostate cancer progression. Furthermore, the targets identified show promise as prognostic biomarkers in clinical settings. Thus, integrative bioinformatics methods provide both deeper understanding of disease dynamics and open the doors for future personalized interventions.

bioinformatics↗