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Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.

ABSTRACT: Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (MET), are believed to play key roles in facilitating the metastatic cascade. Metastatic lesions often exhibit a similar epithelial-like state to that of the primary tumour, in particular, by forming carcinoma cell clusters via E-cadherin-mediated junctional complexes. However, the factors enabling mesenchymal-like micrometastatic cells to resume growth and reacquire an epithelial phenotype in the target organ microenvironment remain elusive. In this study, we developed a workflow using image-based cell profiling and machine learning to examine morphological, contextual and molecular states of individual breast carcinoma cells (MDA-MB-231). MDA-MB-231 heterogeneous response to the host organ microenvironment was modelled by substrates with controllable stiffness varying from 0.2kPa (soft tissues) to 64kPa (bone tissues). We identified 3 distinct morphological cell types (morphs) varying from compact round-shaped to flattened irregular-shaped cells with lamellipodia, predominantly populating 2-kPa and >16kPa substrates, respectively. These observations were accompanied by significant changes in E-cadherin and vimentin expression. Furthermore, we demonstrate that the bone-mimicking substrate (64kPa) induced multicellular cluster formation accompanied by E-cadherin cell surface localisation. MDA-MB-231 cells responded to different substrate stiffness by morphological adaptation, changes in proliferation rate and cytoskeleton markers, and cluster formation on bone-mimicking substrate. Our results suggest that the stiffest microenvironment can induce MET.

SUBMITTER: Rozova VS

PROVIDER: S-EPMC8336795 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:UnlabelledThere is increasing evidence that the physical environment is a critical mediator of tumor behavior. Hepatocellular carcinoma (HCC) develops within an altered biomechanical environment, and increasing matrix stiffness is a strong predictor of HCC development. The aim of this study was to establish whether changes in matrix stiffness, which are characteristic of inflammation and fibrosis, regulate HCC cell proliferation and chemotherapeutic response. Using an in vitro system of "mechanically tunable" matrix-coated polyacrylamide gels, matrix stiffness was modeled across a pathophysiologically relevant range, corresponding to values encountered in normal and fibrotic livers. Increasing matrix stiffness was found to promote HCC cell proliferation. The proliferative index (assessed by Ki67 staining) of Huh7 and HepG2 cells was 2.7-fold and 12.2-fold higher, respectively, when the cells were cultured on stiff (12 kPa) versus soft (1 kPa) supports. This was associated with stiffness-dependent regulation of basal and hepatocyte growth factor-stimulated mitogenic signaling through extracellular signal-regulated kinase, protein kinase B (PKB/Akt), and signal transducer and activator of transcription 3. β1-Integrin and focal adhesion kinase were found to modulate stiffness-dependent HCC cell proliferation. Following treatment with cisplatin, we observed reduced apoptosis in HCC cells cultured on stiff versus soft (physiological) supports. Interestingly, however, surviving cells from soft supports had significantly higher clonogenic capacity than surviving cells from a stiff microenvironment. This was associated with enhanced expression of cancer stem cell markers, including clusters of differentiation 44 (CD44), CD133, c-kit, cysteine-X-cysteine receptor 4, octamer-4 (CXCR4), and NANOG.ConclusionIncreasing matrix stiffness promotes proliferation and chemotherapeutic resistance, whereas a soft environment induces reversible cellular dormancy and stem cell characteristics in HCC. This has implications for both the treatment of primary HCC and the prevention of tumor outgrowth from disseminated tumor cells. (HEPATOLOGY 2011;).

Project description:BackgroundNeoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30-40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders.MethodsSerial sections from core needle biopsies (n = 76) were stained with H&E and immunohistochemically for the Ki67 and pH3 markers, followed by whole-slide image (WSI) generation. The serial section stains in H&E stain, Ki67 and pH3 markers formed WSI triplets for each patient. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67+, and pH3+ cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models and evaluating their performance by accuracy, area under curve, and confusion matrix analyses.ResultsHighest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67+, and pH3+ features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level.ConclusionsOverall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

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Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.

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