Cisplatin-resistant triple-negative breast cancer subtypes: multiple mechanisms of resistance.

BACKGROUND: Understanding mechanisms underlying specific chemotherapeutic responses in subtypes of cancer may improve identification of treatment strategies most likely to benefit particular patients. For example, triple-negative breast cancer (TNBC) patients have variable response to the chemotherapeutic agent cisplatin. Understanding the basis of treatment response in cancer subtypes will lead to more informed decisions about selection of treatment strategies. METHODS: In this study we used an integrative functional genomics approach to investigate the molecular mechanisms underlying known cisplatin-response differences among subtypes of TNBC. To identify changes in gene expression that could explain mechanisms of resistance, we examined 102 evolutionarily conserved cisplatin-associated genes, evaluating their differential expression in the cisplatin-sensitive, basal-like 1 (BL1) and basal-like 2 (BL2) subtypes, and the two cisplatin-resistant, luminal androgen receptor (LAR) and mesenchymal (M) subtypes of TNBC. RESULTS: We found 20 genes that were differentially expressed in at least one subtype. Fifteen of the 20 genes are associated with cell death and are distributed among all TNBC subtypes. The less cisplatin-responsive LAR and M TNBC subtypes show different regulation of 13 genes compared to the more sensitive BL1 and BL2 subtypes. These 13 genes identify a variety of cisplatin-resistance mechanisms including increased transport and detoxification of cisplatin, and mis-regulation of the epithelial to mesenchymal transition. CONCLUSIONS: We identified gene signatures in resistant TNBC subtypes indicative of mechanisms of cisplatin. Our results indicate that response to cisplatin in TNBC has a complex foundation based on impact of treatment on distinct cellular pathways. We find that examination of expression data in the context of heterogeneous data such as drug-gene interactions leads to a better understanding of mechanisms at work in cancer therapy response.

Factors that influence response classifications in chemotherapy treated patient-derived xenografts (PDX).

In this study, we investigated the impact of initial tumor volume, rate of tumor growth, cohort size, study duration, and data analysis method on chemotherapy treatment response classifications in patient-derived xenografts (PDXs). The analyses were conducted on cisplatin treatment response data for 70 PDX models representing ten cancer types with up to 28-day study duration and cohort sizes of 3-10 tumor-bearing mice. The results demonstrated that a 21-day dosing study using a cohort size of eight was necessary to reliably detect responsive models (i.e., tumor volume ratio of treated animals to control between 0.1 and 0.42)-independent of analysis method. A cohort of three tumor-bearing animals led to a reliable classification of models that were both highly responsive and highly nonresponsive to cisplatin (i.e., tumor volume ratio of treated animals to control animals less than 0.10). In our set of PDXs, we found that tumor growth rate in the control group impacted treatment response classification more than initial tumor volume. We repeated the study design factors using docetaxel treated PDXs with consistent results. Our results highlight the importance of defining endpoints for PDX dosing studies when deciding the size of cohorts to use in dosing studies and illustrate that response classifications for a study do not differ significantly across the commonly used analysis methods that are based on tumor volume changes in treatment versus control groups.

Mutations in TP53, PIK3CA, PTEN and other genes in EGFR mutated lung cancers: Correlation with clinical outcomes.

INTRODUCTION: The degree and duration of response to epidermal growth factor receptor (EGFR) inhibitors in EGFR mutated lung cancer are heterogeneous. We hypothesized that the concurrent genomic landscape of these tumors, which is currently unknown in view of the prevailing single gene assay diagnostic paradigm in clinical practice, could play a role in clinical outcomes and/or mechanisms of resistance. METHODS: We retrospectively probed our institutional lung cancer database for tumors with EGFR kinase domain mutations that were also evaluated by more comprehensive molecular profiling, and evaluated tumor response to EGFR tyrosine kinase inhibitors (TKIs). RESULTS: Out of 171 EGFR mutated tumor-patient cases, 20 were sequenced using at least a limited comprehensive genomic profiling platform. 50% harbored concurrent TP53 mutation, 10% PIK3CA mutation, 5% PTEN mutation, among others. The response rate to EGFR TKIs, the median progression-free survival (PFS) to TKIs, the percentage of EGFR-T790M TKI resistance and survival had higher trends in EGFR mutant/TP53 wild-type cases when compared to EGFR mutant/TP53 mutant tumors (all p >0.05 without statistical significance); with a significantly longer median PFS in EGFR-exon 19 deletion mutant/TP53 wild-type cancers treated with 1st generation EGFR TKIs (p=0.035). CONCLUSIONS: Concurrent mutations, specifically TP53, are common in EGFR mutated lung cancer and may alter clinical outcomes. Additional cohorts will be needed to determine if comprehensive molecular profiling adds clinically relevant information to single gene assay identification in oncogene-driven lung cancers.

Development and validation of the JAX Cancer Treatment Profile™ for detection of clinically actionable mutations in solid tumors.

BACKGROUND: The continued development of targeted therapeutics for cancer treatment has required the concomitant development of more expansive methods for the molecular profiling of the patient's tumor. We describe the validation of the JAX Cancer Treatment Profile™ (JAX-CTP™), a next generation sequencing (NGS)-based molecular diagnostic assay that detects actionable mutations in solid tumors to inform the selection of targeted therapeutics for cancer treatment. METHODS: NGS libraries are generated from DNA extracted from formalin fixed paraffin embedded tumors. Using hybrid capture, the genes of interest are enriched and sequenced on the Illumina HiSeq 2500 or MiSeq sequencers followed by variant detection and functional and clinical annotation for the generation of a clinical report. RESULTS: The JAX-CTP™ detects actionable variants, in the form of single nucleotide variations and small insertions and deletions (≤50 bp) in 190 genes in specimens with a neoplastic cell content of ≥10%. The JAX-CTP™ is also validated for the detection of clinically actionable gene amplifications. CONCLUSIONS: There is a lack of consensus in the molecular diagnostics field on the best method for the validation of NGS-based assays in oncology, thus the importance of communicating methods, as contained in this report. The growing number of targeted therapeutics and the complexity of the tumor genome necessitate continued development and refinement of advanced assays for tumor profiling to enable precision cancer treatment.