Purpose: Up to one third of ovarian cancer patients are intrinsically resistant to platinum-based treatment. However, predictive and therapeutic strategies are lacking due to a poor understanding of the underlying molecular mechanisms. This study aimed to identify key molecular characteristics that are associated with primary chemoresistance in epithelial ovarian cancers.Experimental Design: Gene expression profiling was performed on a discovery set of 85 ovarian tumors with clinically well-defined response to chemotherapies as well as on an independent validation dataset containing 138 ovarian patients from the chemotreatment arm of the ICON7 trial.Results: We identified a distinct "reactive stroma" gene signature that is specifically associated with primary chemoresistant tumors and was further upregulated in posttreatment recurrent tumors. Immunohistochemistry (IHC) and RNA in situ hybridization (RNA ISH) analyses on three of the highest-ranked signature genes (POSTN, LOX, and FAP) confirmed that modulation of the reactive stroma signature genes within the peritumoral stromal compartments was specifically associated with the clinical chemoresistance. Consistent with these findings, chemosensitive ovarian cells grown in the presence of recombinant POSTN promoted resistance to carboplatin and paclitaxel treatment in vitro. Finally, we validated the reactive stroma signature in an independent dataset and demonstrated that a high POSTN expression level predicts shorter progression-free survival following first-line chemotherapy.Conclusions: Our findings highlight the important interplay between cancer and the tumor microenvironment in ovarian cancer biology and treatment. The identified reactive stromal components in this study provide a molecular basis to the further development of novel diagnostic and therapeutic strategies for overcoming chemoresistance in ovarian cancer.
Purpose: Non-small cell lung cancers (NSCLC) comprise multiple distinct biologic groups with different prognoses. For example, patients with epithelial-like tumors have a better prognosis and exhibit greater sensitivity to inhibitors of the epidermal growth factor receptor (EGFR) pathway than patients with mesenchymal-like tumors. Here, we test the hypothesis that epithelial-like NSCLCs can be distinguished from mesenchymal-like NSCLCs on the basis of global DNA methylation patterns.Experimental Design: To determine whether phenotypic subsets of NSCLCs can be defined on the basis of their DNA methylation patterns, we combined microfluidics-based gene expression analysis and genomewide methylation profiling. We derived robust classifiers for both gene expression and methylation in cell lines and tested these classifiers in surgically resected NSCLC tumors. We validate our approach using quantitative reverse transcriptase PCR and methylation-specific PCR in formalin-fixed biopsies from patients with NSCLC who went on to fail front-line chemotherapy.Results: We show that patterns of methylation divide NSCLCs into epithelial-like and mesenchymal-like subsets as defined by gene expression and that these signatures are similarly correlated in NSCLC cell lines and tumors. We identify multiple differentially methylated regions, including one in ERBB2 and one in ZEB2, whose methylation status is strongly associated with an epithelial phenotype in NSCLC cell lines, surgically resected tumors, and formalin-fixed biopsies from patients with NSCLC who went on to fail frontline chemotherapy.Conclusions: Our data show that patterns of DNA methylation can divide NSCLCs into two phenotypically distinct subtypes of tumors and provide proof of principle that differences in DNA methylation can be used as a platform for predictive biomarker discovery and development.
Purpose: We developed a method to monitor copy number variations (CNV) in plasma cell-free DNA (cfDNA) from patients with metastatic squamous non-small cell lung cancer (NSCLC). We aimed to explore the association between tumor-derived cfDNA and clinical outcomes, and sought CNVs that may suggest potential resistance mechanisms. Experimental Design: Sensitivity and specificity of low-pass whole-genome sequencing (LP-WGS) were first determined using cell line DNA and cfDNA. LP-WGS was performed on baseline and longitudinal cfDNA of 152 patients with squamous NSCLC treated with chemotherapy, or in combination with pictilisib, a pan-PI3K inhibitor. cfDNA tumor fraction and detected CNVs were analyzed in association with clinical outcomes. Results: LP-WGS successfully detected CNVs in cfDNA with tumor fraction !10%, which represented approximately 30% of the first-line NSCLC patients in this study. The most frequent CNVs were gains in chromosome 3q, which harbors the PIK3CA and SOX2 oncogenes. The CNV landscape in cfDNA with a high tumor fraction generally matched that of corresponding tumor tissue. Tumor fraction in cfDNA was dynamic during treatment, and increases in tumor fraction and corresponding CNVs could be detected before radiographic progression in 7 of 12 patients. Recurrent CNVs, such as MYC amplification, were enriched in cfDNA from posttreatment samples compared with the baseline, suggesting a potential resistance mechanism to pictilisib. Conclusions: LP-WGS offers an unbiased and highthroughput way to investigate CNVs and tumor fraction in cfDNA of patients with cancer. It may also be valuable for monitoring treatment response, detecting disease progression early, and identifying emergent clones associated with therapeutic resistance.
The insulin-like growth factor-I receptor (IGF-IR) pathway is required for the maintenance of the transformed phenotype in neoplastic cells and hence has been the subject of intensive drug discovery efforts. A key aspect of successful clinical development of targeted therapies directed against IGF-IR will be identification of responsive patient populations. Toward that end, we have endeavored to identify predictive biomarkers of response to an anti-IGF-IR-targeting monoclonal antibody in preclinical models of breast and colorectal cancer. We find that levels of the IGF-IR itself may have predictive value in these tumor types and identify other gene expression predictors of in vitro response. Studies in breast cancer models suggest that IGF-IR expression is both correlated and functionally linked with estrogen receptor signaling and provide a basis for both patient stratification and rational combination therapy with antiestrogen-targeting agents. In addition, we find that levels of other components of the signaling pathway such as the adaptor proteins IRS1 and IRS2, as well as the ligand IGF-II, have predictive value and report on the development of a pathway-focused panel of diagnostic biomarkers that could be used to test these hypotheses during clinical development of IGF-IR-targeting therapies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.