Characterization of anticancer drug resistance by reverse-phase protein array: new targets and strategies

Transcriptomic, Proteomic, and Genomic Mutational Fraction Differences Based on HPV Status Observed in Patient-Derived Xenograft Models of Penile Squamous Cell Carcinoma

Metastatic penile squamous cell carcinoma (PSCC) has only a 50% response rate to first-line combination chemotherapies and there are currently no targeted-therapy approaches. Therefore, we have an urgent need in advanced-PSCC treatment to find novel therapies. Approximately half of all PSCC cases are positive for high-risk human papillomavirus (HR-HPV). Our objective was to generate HPV-positive (HPV+) and HPV-negative (HPV-) patient-derived xenograft (PDX) models and to determine the biological differences between HPV+ and HPV- disease. We generated four HPV+ and three HPV- PSCC PDX animal models by directly implanting resected patient tumor tissue into immunocompromised mice. PDX tumor tissue was found to be similar to patient tumor tissue (donor tissue) by histology and short tandem repeat fingerprinting. DNA mutations were mostly preserved in PDX tissues and similar APOBEC (apolipoprotein B mRNA editing catalytic polypeptide) mutational fractions in donor tissue and PDX tissues were noted. A higher APOBEC mutational fraction was found in HPV+ versus HPV- PDX tissues (p = 0.044), and significant transcriptomic and proteomic expression differences based on HPV status included p16 (CDKN2A), RRM2, and CDC25C. These models will allow for the direct testing of targeted therapies in PSCC and determine their response in correlation to HPV status.

Matrix Stiffness Triggers Lipid Metabolic Cross-talk between Tumor and Stromal Cells to Mediate Bevacizumab Resistance in Colorectal Cancer Liver Metastases

Bevacizumab is an anti-VEGF monoclonal antibody that plays an important role in the combination treatment of advanced colorectal cancer. However, resistance remains a major hurdle limiting bevacizumab efficacy, highlighting the importance of identifying a mechanism of antiangiogenic therapy resistance. Here, we investigated biophysical properties of the extracellular matrix (ECM) related to metabolic processes and acquired resistance to bevacizumab. Evaluation of paired pre- and posttreatment samples of liver metastases from 20 colorectal cancer patients treated with combination bevacizumab therapy, including 10 responders and 10 nonresponders, indicated that ECM deposition in liver metastases and a highly activated fatty acid oxidation (FAO) pathway were elevated in nonresponders after antiangiogenic therapy compared with responders. In mouse models of liver metastatic colorectal cancer (mCRC), anti-VEGF increased ECM deposition and FAO in colorectal cancer cells, and treatment with the FAO inhibitor etomoxir enhanced the efficacy of antiangiogenic therapy. Hepatic stellate cells (HSC) were essential for matrix stiffness-mediated FAO in colon cancer cells. Matrix stiffness activated lipolysis in HSCs via the focal adhesion kinase (FAK)/yes-associated protein (YAP) pathway, and free fatty acids secreted by HSCs were absorbed as metabolic substrates and activated FAO in colon cancer cells. Suppressing HSC lipolysis using FAK and YAP inhibition enhanced the efficacy of anti-VEGF therapy. Together, these results indicate that bevacizumab-induced ECM remodeling triggers lipid metabolic cross-talk between colon cancer cells and HSCs. This metabolic mechanism of bevacizumab resistance mediated by the physical tumor microenvironment represents a potential therapeutic target for reversing drug resistance.

SIGNIFICANCE

Extracellular matrix stiffening drives bevacizumab resistance by stimulating hepatic stellate cells to provide fuel for mCRC cells in the liver, indicating a potential metabolism-based therapeutic strategy for overcoming resistance.

Tumor-infiltrating immune cells based TMEscore and related gene signature is associated with the survival of CRC patients and response to fluoropyrimidine-based chemotherapy

Background

Tumor-infiltrating immune cells (TIICs) are associated with chemotherapy response. This study aimed to explore the prognostic value of a TIIC-related tumor microenvironment score (TMEscore) in patients with colorectal cancer (CRC) who underwent chemotherapy and construct a TMEscore-related gene signature to determine its predictive value.

Methods

Gene profiles of patients who underwent fluoropyrimidine-based chemotherapy were collected, and their TIIC fractions were calculated and clustered. Differentially expressed genes (DEGs) between clusters were used to calculate the TMEscore. The association between the TMEscore, chemotherapy response, and survival rate was analyzed. Machine learning methods were used to identify key TMEscore-related genes, and a gene signature was constructed to verify the predictive value.

Results

Two clusters based on the TIIC fraction were identified, and the TMEscore was calculated based on the DEGs of the two clusters. The TMEscore was higher in patients who responded to chemotherapy than in those who did not, and was associated with the survival rate of patients who underwent chemotherapy. Three machine learning methods, support vector machine (SVM), decision tree (DT), and Extreme Gradient Boosting (XGBoost), identified three TMEscore-related genes (ADH1C, SLC26A2, and NANS) associated with the response to chemotherapy. A TMEscore-related gene signature was constructed, and three external cohorts validated that the gene signature could predict the response to chemotherapy. Five datasets and clinical samples showed that the expression of the three TMEscore-related genes was increased in tumor tissues compared to those in control tissues.

Conclusions

The TIIC-based TMEscore was associated with the survival of CRC patients who underwent fluoropyrimidine-based chemotherapy, and predicted the response to chemotherapy. The TMEscore-related gene signature had a better predictive value for response to chemotherapy than for survival.