The tumor stroma, or the microenvironment surrounding solid tumors, can significantly impact the effectiveness of cancer therapies. The tumor microenvironment is characterized by high interstitial pressure, a consequence of leaky vasculature, and dense stroma created by excessive deposition of various macromolecules such as collagen, fibronectin, and hyaluronic acid (HA). In addition, non-cancerous cells such as cancer-associated fibroblasts (CAFs) and the extracellular matrix (ECM) itself can promote tumor growth. In recent years, there has been increased interest in combining standard cancer treatments with stromal-targeting strategies or stromal modulators to improve therapeutic outcomes. Furthermore, the use of nanomedicine, which can improve the delivery and retention of drugs in the tumor, has been proposed to target the stroma. This review focuses on how different stromal components contribute to tumor progression and impede chemotherapeutic delivery. Additionally, this review highlights recent advancements in nanomedicine-based stromal modulation and discusses potential future directions for developing more effective stroma-targeted cancer therapies.

Prostate cancer (PC) is the most frequently diagnosed malignancy and a leading cause of cancer deaths in US men. Many PC cases metastasize and develop resistance to systemic hormonal therapy, a stage known as castration-resistant prostate cancer (CRPC). Therefore, there is an urgent need to develop effective therapeutic strategies for CRPC. Traditional drug discovery pipelines require significant time and capital input, which highlights a need for novel methods to evaluate the repositioning potential of existing drugs. Here, we present a computational framework to predict drug sensitivities of clinical CRPC tumors to various existing compounds and identify treatment options with high potential for clinical impact. We applied this method to a CRPC patient cohort and nominated drugs to combat resistance to hormonal therapies including abiraterone and enzalutamide. The utility of this method was demonstrated by nomination of multiple drugs that are currently undergoing clinical trials for CRPC. Additionally, this method identified the tetracycline derivative COL-3, for which we validated higher efficacy in an isogenic cell line model of enzalutamide-resistant vs. enzalutamide-sensitive CRPC. In enzalutamide-resistant CRPC cells, COL-3 displayed higher activity for inhibiting cell growth and migration, and for inducing G1-phase cell cycle arrest and apoptosis. Collectively, these findings demonstrate the utility of a computational framework for independent validation of drugs being tested in CRPC clinical trials, and for nominating drugs with enhanced biological activity in models of enzalutamide-resistant CRPC. The efficiency of this method relative to traditional drug development approaches indicates a high potential for accelerating drug development for CRPC.

Metastatic castration-resistant prostate cancer (mCRPC) remains a deadly disease due to a lack of efficacious treatments. The reprogramming of cancer metabolism toward elevated glycolysis is a hallmark of mCRPC. Our goal is to identify therapeutics specifically associated with high glycolysis. Here, we established a computational framework to identify new pharmacological agents for mCRPC with heightened glycolysis activity under a tumor microenvironment, followed by in vitro validation. First, using our established computational tool, OncoPredict, we imputed the likelihood of drug responses to approximately 1900 agents in each mCRPC tumor from two large clinical patient cohorts. We selected drugs with predicted sensitivity highly correlated with glycolysis scores. In total, 77 drugs predicted to be more sensitive in high glycolysis mCRPC tumors were identified. These drugs represent diverse mechanisms of action. Three of the candidates, ivermectin, CNF2024, and P276-00, were selected for subsequent vitro validation based on the highest measured drug responses associated with glycolysis/OXPHOS in pan-cancer cell lines. By decreasing the input glucose level in culture media to mimic the mCRPC tumor microenvironments, we induced a high-glycolysis condition in PC3 cells and validated the projected higher sensitivity of all three drugs under this condition (p < 0.0001 for all drugs). For biomarker discovery, ivermectin and P276-00 were predicted to be more sensitive to mCRPC tumors with low androgen receptor activities and high glycolysis activities (AR(low)Gly(high)). In addition, we integrated a protein–protein interaction network and topological methods to identify biomarkers for these drug candidates. EEF1B2 and CCNA2 were identified as key biomarkers for ivermectin and CNF2024, respectively, through multiple independent biomarker nomination pipelines. In conclusion, this study offers new efficacious therapeutics beyond traditional androgen-deprivation therapies by precisely targeting mCRPC with high glycolysis.

Despite the initial success of single‐targeted chimeric‐antigen receptor (CAR) T‐cell therapy in hematological malignancies, its long‐term effectiveness is often hindered by antigen heterogeneity and escape. As a result, there is a growing interest in cell therapies targeting multiple antigens (≥2). However, the dose–exposure–response relationship and specific factors influencing the pharmacology of dual‐targeted CAR‐T‐cell therapy remain unclear. In this study, we have developed a multiscale cellular kinetic–pharmacodynamic (CK–PD) model using case studies from CD19/CD22 and GPRC5D/BCMA autologous CAR‐Ts. Initially, an in vitro tumor‐killing model characterized the impact of individual binder affinities and their contribution to overall potency across varying (1) effector: target (ET) ratios and (2) tumor‐associated antigen (TAA) expressing cell lines. Subsequently, an integrated CK–PD model was developed in pediatric acute lymphoblastic leukemia (ALL) patients, which accounted for CAR‐T‐cell product composition and relative antigen abundance in patients' tumor burden to characterize patient‐level multiphasic cellular kinetics using multiple bioanalytical assays (e.g., flow and qPCR‐based readouts). Global sensitivity analysis highlighted relative antigen expression, maximum killing rate constant, and CAR‐T expansion rate constant as major determinants for observed exposure of dual‐targeted CAR‐T‐cell therapy. This modeling framework could facilitate dose‐optimization and construct refinement for dual‐targeted bicistronic CAR‐T‐cell therapies, serving as a valuable tool for both forward and reverse translation in drug development.