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Kalla, J.

Publications and source records attributed to Kalla, J..

3 recordsLinked to original sources

Biobank of genetically defined murine prostate cancer tumoroids uncovers oncogenic pathways and drug vulnerabilities driven by PTEN-loss

Prostate cancer (PCa) is the second most common cancer in men and shows high inter- and intra-patient heterogeneity. Consequently, treatment options are limited and there is a lack of representative preclinical models. Here we establish a comprehensive biobank of murine organoids and tumoroids that reflect common patient mutations. We demonstrate that the deletion of Pten alone, or in combination with Stat3, or Tp53, drives the activation of cancer-related pathways in both prostate organoids and tumour-derived tumoroids. A medium-throughput drug screen identified two potent compounds, the PDPK1/AKT/FLT dual pathway inhibitor and the sirtuin inhibitor tenovin-6, which effectively suppressed tumoroid proliferation. Notably, these compounds also inhibited the growth of several human PCa cell lines and displayed synergistic effects when combined with the standard-of-care antiandrogen enzalutamide. Together, our findings provide evidence that murine tumoroids are versatile preclinical models for studying PCa tumorigenesis and drug sensitivities to develop novel therapeutic options for PCa patients.

cancer biology↗

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↗

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↗