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1. Intracellular optical doppler phenotypes of chemosensitivity in human epithelial ovarian cancer

   https://doi.org/10.1038/s41598-020-74336-x  

   Li, Zhe; An, Ran; Swetzig, Wendy M.; Kanis, Margaux; Nwani, Nkechiyere; Turek, John; Matei, Daniela; Nolte, David (December 2020, Scientific Reports)

   null (Ed.)

   Abstract Development of an assay to predict response to chemotherapy has remained an elusive goal in cancer research. We report a phenotypic chemosensitivity assay for epithelial ovarian cancer based on Doppler spectroscopy of infrared light scattered from intracellular motions in living three-dimensional tumor biopsy tissue measured in vitro. The study analyzed biospecimens from 20 human patients with epithelial ovarian cancer. Matched primary and metastatic tumor tissues were collected for 3 patients, and an additional 3 patients provided only metastatic tissues. Doppler fluctuation spectra were obtained using full-field optical coherence tomography through off-axis digital holography. Frequencies in the range from 10 mHz to 10 Hz are sensitive to changes in intracellular dynamics caused by platinum-based chemotherapy. Metastatic tumor tissues were found to display a biodynamic phenotype that was similar to primary tissue from patients who had poor clinical outcomes. The biodynamic phenotypic profile correctly classified 90% [88–91% c.i.] of the patients when the metastatic samples were characterized as having a chemoresistant phenotype. This work suggests that Doppler profiling of tissue response to chemotherapy has the potential to predict patient clinical outcomes based on primary, but not metastatic, tumor tissue. 

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2. Explainable AI for Predicting Mortality Risk in Metastatic Cancer: Retrospective Cohort Study Using the Memorial Sloan Kettering-Metastatic Dataset

   https://doi.org/10.2196/74196  

   Nalela, Polycarp; Rao, Deepthi; Rao, Praveen (January 2026, JMIR Cancer)

   BackgroundMetastatic cancer remains one of the leading causes of cancer-related mortality worldwide. Yet, the prediction of survivability in this population remains limited by heterogeneous clinical presentations and high-dimensional molecular features. Advances in machine learning (ML) provide an opportunity to integrate diverse patient- and tumor-level factors into explainable predictive ML models. Leveraging large real-world datasets and modern ML techniques can enable improved risk stratification and precision oncology. ObjectiveThis study aimed to develop and interpret ML models for predicting overall survival in patients with metastatic cancer using the Memorial Sloan Kettering-Metastatic (MSK-MET) dataset and to identify key prognostic biomarkers through explainable artificial intelligence techniques. MethodsWe performed a retrospective analysis of the MSK-MET cohort, comprising 25,775 patients across 27 tumor types. After data cleaning and balancing, 20,338 patients were included. Overall survival was defined as deceased versus living at last follow-up. Five classifiers (extreme gradient boosting [XGBoost], logistic regression, random forest, decision tree, and naive Bayes) were trained using an 80/20 stratified split and optimized via grid search with 5-fold cross-validation. Model performance was assessed using accuracy, area under the curve (AUC), precision, recall, and F1-score. Model explainability was achieved using Shapley additive explanations (SHAP). Survival analyses included Kaplan-Meier estimates, Cox proportional hazards models, and an XGBoost-Cox model for time-to-event prediction. The positive predictive value and negative predictive value were calculated at the Youden index–optimal threshold. ResultsXGBoost achieved the highest performance (accuracy=0.74; AUC=0.82), outperforming other classifiers. In survival analyses, the XGBoost-Cox model with a concordance index (C-index) of 0.70 exceeded the traditional Cox model (C-index=0.66). SHAP analysis and Cox models consistently identified metastatic site count, tumor mutational burden, fraction of genome altered, and the presence of distant liver and bone metastases as among the strongest prognostic factors, a pattern that held at both the pan-cancer level and recurrently across cancer-specific models. At the cancer-specific level, performance varied; prostate cancer achieved the highest predictive accuracy (AUC=0.88), while pancreatic cancer was notably more challenging (AUC=0.68). Kaplan-Meier analyses demonstrated marked survival separation between patients with and without metastases (80-month survival: approximately 0.80 vs 0.30). At the Youden-optimal threshold, positive predictive value and negative predictive value were approximately 70% and 80%, respectively, supporting clinical use for risk stratification. ConclusionsExplainable ML models, particularly XGBoost combined with SHAP, can strongly predict survivability in metastatic cancers while highlighting clinically meaningful features. These findings support the use of ML-based tools for patient counseling, treatment planning, and integration into precision oncology workflows. Future work should include external validation on independent cohorts, integration with electronic health records via Fast Healthcare Interoperability Resources–based dashboards, and prospective clinician-in-the-loop evaluation to assess real-world use. 

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3. Matrix stiffness and cluster size collectively regulate dormancy versus proliferation in brain metastatic breast cancer cell clusters

   https://doi.org/10.1039/d0bm00969e  

   Kondapaneni, Raghu Vamsi; Rao, Shreyas S. (November 2020, Biomaterials Science)

   null (Ed.)

   Breast cancer cells can metastasize either as single cells or as clusters to distant organs from the primary tumor site. Cell clusters have been shown to possess higher metastatic potential compared to single cells. The organ microenvironment is critical in regulating the ultimate phenotype, specifically, the dormant versus proliferative phenotypes, of these clusters. In the context of breast cancer brain metastasis (BCBM), tumor cell cluster–organ microenvironment interactions are not well understood, in part, due to the lack of suitable biomimetic in vitro models. To address this need, herein, we report a biomaterial-based model, utilizing hyaluronic acid (HA) hydrogels with varying stiffnesses to mimic the brain microenvironment. Cell spheroids were used to mimic cell clusters. Using 100–10 000 MDA-MB-231Br BCBM cells, six different sizes of cell spheroids were prepared to study the impact of cluster size on dormancy. On soft HA hydrogels (∼0.4 kPa), irrespective of spheroid size, all cell spheroids attained a dormant phenotype, whereas on stiff HA hydrogels (∼4.5 kPa), size dependent switch between the dormant and proliferative phenotypes was noted ( i.e. , proliferative phenotype ≥5000 cell clusters < dormant phenotype), as tested via EdU and Ki67 staining. Furthermore, we demonstrated that the matrix stiffness driven dormancy was reversible. Such biomaterial systems provide useful tools to probe cell cluster–matrix interactions in BCBM. 

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4. Mechanisms of triple‐negative breast cancer extravasation: Impact of the physical environment and endothelial glycocalyx

   https://doi.org/10.1096/fj.202400380R  

   Okorafor, Chinedu_C; Shastri, Sanjana; Wen, Ke; Ebong, Eno_E (July 2024, The FASEB Journal)

   Abstract Cancer metastasis is the leading cause of death for those afflicted with cancer. In cancer metastasis, the cancer cells break off from the primary tumor, penetrate nearby blood vessels, and attach and extravasate out of the vessels to form secondary tumors at distant organs. This makes extravasation a critical step of the metastatic cascade. Herein, with a focus on triple‐negative breast cancer, the role that the prospective secondary tumor microenvironment's mechanical properties play in circulating tumor cells' extravasation is reviewed. Specifically, the effects of the physically regulated vascular endothelial glycocalyx barrier element, vascular flow factors, and subendothelial extracellular matrix mechanical properties on cancer cell extravasation are examined. The ultimate goal of this review is to clarify the physical mechanisms that drive triple‐negative breast cancer extravasation, as these mechanisms may be potential new targets for anti‐metastasis therapy. 

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5. Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach

   https://doi.org/10.1038/s41598-020-78780-7  

   Wang, Yafei; Brodin, Erik; Nishii, Kenichiro; Frieboes, Hermann B.; Mumenthaler, Shannon M.; Sparks, Jessica L.; Macklin, Paul (January 2021, Scientific Reports)

   Abstract Colorectal cancer and other cancers often metastasize to the liver in later stages of the disease, contributing significantly to patient death. While the biomechanical properties of the liver parenchyma (normal liver tissue) are known to affect tumor cell behavior in primary and metastatic tumors, the role of these properties in driving or inhibiting metastatic inception remains poorly understood, as are the longer-term multicellular dynamics. This study adopts a multi-model approach to study the dynamics of tumor-parenchyma biomechanical interactions during metastatic seeding and growth. We employ a detailed poroviscoelastic model of a liver lobule to study how micrometastases disrupt flow and pressure on short time scales. Results from short-time simulations in detailed single hepatic lobules motivate constitutive relations and biological hypotheses for a minimal agent-based model of metastatic growth in centimeter-scale tissue over months-long time scales. After a parameter space investigation, we find that the balance of basic tumor-parenchyma biomechanical interactions on shorter time scales (adhesion, repulsion, and elastic tissue deformation over minutes) and longer time scales (plastic tissue relaxation over hours) can explain a broad range of behaviors of micrometastases, without the need for complex molecular-scale signaling. These interactions may arrest the growth of micrometastases in a dormant state and prevent newly arriving cancer cells from establishing successful metastatic foci. Moreover, the simulations indicate ways in which dormant tumors could “reawaken” after changes in parenchymal tissue mechanical properties, as may arise during aging or following acute liver illness or injury. We conclude that the proposed modeling approach yields insight into the role of tumor-parenchyma biomechanics in promoting liver metastatic growth, and advances the longer term goal of identifying conditions to clinically arrest and reverse the course of late-stage cancer. 

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