Improvement in pancreatic cancer treatment represents an urgent medical goal that has been hampered by the lack of predictive biomarkers. Circulating Tumor Cells (CTCs) may be able to overcome this issue by allowing the monitoring of therapeutic response and tumor aggressiveness through ex vivo expansion. The successful expansion of CTCs is challenging, due to their low numbers in blood and the high abundance of blood cells. Here, we explored the utility of pancreatic CTC cultures as a preclinical model for treatment response. CTCs were isolated from ten patients with locally advanced pancreatic cancer using the Labyrinth, a biomarker independent, size based, inertial microfluidic separation device. Three patient-derived CTC samples were successfully expanded in adherent and spheroid cultures. Molecular and functional characterization was performed on the expanded CTC lines. CTC lines exhibited KRAS mutations, consistent with pancreatic cancers. Additionally, we evaluated take rate and metastatic potential in vivo and examined the utility of CTC lines for cytotoxicity assays. Patient derived expanded CTCs successfully generated patient derived xenograft (PDX) models with a 100% take rate. Our results demonstrate that CTC cultures are possible and provide a valuable resource for translational pancreatic cancer research, while also providing meaningful insight into the development of distant metastasis, as well as treatment resistance.

Epigenetic compounds have become attractive small molecules for targeting the multifaceted aspects of Alzheimer’s disease (AD). Although AD disproportionately affects women, most of the current literature investigating epigenetic compounds for the treatment of AD do not report sex-specific results. This is remarkable because there is rising evidence that epigenetic compounds intrinsically affect males and females differently. This manuscript explores the sexual dimorphism observed after chronic, low-dose administration of a clinically relevant histone deacetylase inhibitor, chidamide (Tucidinostat), in the 3xTg-AD mouse model. We found that chidamide treatment significantly improves glucose tolerance and increases expression of glucose transporters in the brain of males. We also report a decrease in total tau in chidamide-treated mice. Differentially expressed genes in chidamide-treated mice were much greater in males than females. Genes involved in the neuroinflammatory pathway and amyloid processing pathway were mostly upregulated in chidamide-treated males while downregulated in chidamide-treated females. This work highlights the need for drug discovery projects to consider sex as a biological variable to facilitate translation.

The term chondrosarcoma refers to a rare and heterogeneous group of malignant cartilaginous tumors that are typically resistant to chemotherapy and radiotherapy. Metastatic chondrosarcoma has a poor prognosis, and effective systemic therapies are lacking. Isocitrate dehydrogenase (IDH) mutations represent a potential therapeutic target, but IDH inhibitors alone have shown limited clinical efficacy to date. Although the role of conventional chemotherapy is still subject to debate, some evidence suggests it may provide therapeutic benefits in advanced cases. In this study, we aimed to identify effective compounds for combination therapy in chondrosarcoma. Using high-throughput screening, we evaluated a panel of anticancer agents in IDH1-mutant chondrosarcoma cell lines and their mutant IDH1 knockout derivatives. The top 20 most potent compounds were identified across all cell lines, irrespective of IDH mutation status. Representative drugs selected for further investigation included docetaxel, methotrexate, panobinostat, idarubicin, camptothecin, and pevonedistat. These drugs inhibited colony formation, induced apoptosis and cell cycle arrest, and exhibited synergistic antitumor activity in two-drug combinations. In conclusion, we identified several highly effective agents with potent anti-tumor activity in chondrosarcoma cells, independent of IDH mutation status. These agents represent promising candidates for chondrosarcoma therapy and warrant further preclinical investigation and potential inclusion in clinical trials.

Chondrosarcoma (CS) is a rare cancer comprising a heterogeneous group of malignant cartilage-forming tumors. CS is considered a “surgical disease” characterized by high levels of resistance to chemotherapy, radiation therapy, and limited response to immune checkpoint inhibitors. Due to the rarity of the disease and lack of research models, the impact of the CS microenvironment on tumor development and progression remains elusive. Nevertheless, a few recent studies have begun to interpret the composition and function of the CS tumor immune microenvironment (TME), elucidating its contribution to immune evasion and tumor progression. This review summarizes current findings on the immune cell landscape of CS, highlighting key immune cells, their characteristics, and their potential as therapeutic targets.

Precision medicine approaches using ex-vivo drug sensitivity testing (DST) have received attention in the cancer research community as a means to improve treatment stratification in populations where multiple treatment attempts are not feasible, or no standard-of-care treatment exists, such as ultra-rare cancers with a significant clinical need for effective treatment options, like osteosarcoma. DST has the potential to supplement existing patient stratification approaches by providing tumor-specific response data to aid in treatment selection at the time of treatment decision. We present the case of a pediatric osteosarcoma patient who was evaluated using DST at the time of standard-of-care treatment to evaluate treatment sensitivity. The DST screen indicated significant treatment sensitivity to anthracyclines and methotrexate, consistent with the first-line standard-of-care therapy (MAP). Clinical follow-up showed treatment sensitivity to standard-of-care MAP treatment and pathology results of 90% necrosis. The present case shows that DST screening is feasible from a technical standpoint, can be performed in a clinically relevant time frame that does not delay treatment start, and provides personalized drug sensitivity information on clinically available agents, and the DST results align with the clinical treatment response.

This study aims to develop and evaluate a trustworthy and ethical-by-design machine learning (ML) framework for predicting 5-year cancer survival across 10 Surveillance, Epidemiology, and End Results (SEER) cancer types. We assessed ML model(s) performance across localized, regional, and distant stages while examining ML fairness, ML explainability, and the added value of social determinants of health (SDOH) as features. The goal is to advance clinically interpretable and equity-centered survival prediction models suitable for translational use in health systems. Utilizing SEER Research Plus data, we built stage-stratified cohorts and defined a binary 5-year survival outcome. Four ML models were trained as follows: (1) base clinical models and (2) fairness-enhanced models incorporating SDOH features. Performance was evaluated using AUC and accuracy. Machine learning model(s) trustworthiness was assessed through SHapley Additive exPlanations and Local Interpretable Model-Agnostic Explanations and fairness auditing via Equalized Odds Difference across different subgroups. XGBoost showed the strongest discrimination across cancer types, and adding SDOH features generally maintained or modestly improved performance. Fairness evaluations revealed minimal to moderate bias across most SDOH strata, with larger disparities in specific cancer-stage cases. Machine learning explainability analysis demonstrated that clinical variables remained the primary predictors, while SDOH features added interpretable contextual risk without displacing core prognostic factors. This study demonstrates that trustworthy and ethical-by-design artificial intelligence for cancer survival prediction is achievable through a unified, explainable, and fairness-aware ML framework. Incorporating SDOH features enhances equity insights without diminishing predictive performance. The proposed framework offers a clinically meaningful and ethically aligned approach for precision medicine and population health applications.