Project description:Purpose: To determine the 8-week disease control rate (DCR) of sorafenib monotherapy in patients with advanced non-small-cell lung cancer (NSCLC) in the BATTLE trial. Methods: Patients with pre-treated NSCLC consented to baseline biopsies for pre-specified biomarkers assessment and biomarker discovery. Sorafenib was given at 400 mg orally twice daily until tumor progression or unacceptable toxicity. Outcomes by pre-specified biomarkers were analyzed and a Sorafenib sensitivity signature developed using high-throughput gene expression profiles of NSCLC cell lines and baseline biopsies. Results: 105 patients were eligible and 98 patients were evaluable. Median age was 62 (range 34-81) years, 51% of patients were male, 75% were former/current smokers, and 89% had an ECOG performance status of 0-1. Median prior chemotherapies for stage IV NSCLC were two. Median follow-up was 9.4 (range: 1.3-32.2) months. Overall, 8-week DCR was 58.2%. Patients with EGFR mutations had significantly lower 8-week DCR compared to patients with wild-type tumors (23.1% vs. 64.2%, P=0.0119), and patients with K-RAS mutations had the highest 8-week DCR (67%). Most commonly reported treatment-related adverse events include hand-foot syndrome (59.6%), fatigue (42.3%), rash (40.4%), diarrhea (38.5%), and weight loss (38.5%). Sorafenib sensitivity signature developed in cell lines was associated with an improved outcome. Conclusion: Patients with wild-type EGFR, including those with K-RAS mutation, may benefit from sorafenib as opposed to patients with EGFR mutation. We identify a gene expression signature associated with an improved outcome in patients with wild-type EGFR treated with sorafenib. BATTLE-2 trial is ongoing to validate those results. ClinicalTrials.gov number, NCT00411671. Gene expression profiles were measured in 37 core biopsies from patients with refractory non-small cell lung cancer treated with sorafenib in the Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial. We report a Sorafenib sensitivity gene expression signature trained in vitro (separate GEO submission), and tested in baseline biopsies collected in the BATTLE trial.

Project description:BackgroundClear cell RCC (ccRCC) accounts for approximately 75% of the renal cancer cases. Surgery treatment seems to be the best efficacious approach for the majority of patients. However, a consistent fraction (30%) of cases progress after surgery with curative intent. It is currently largely debated the use of adjuvant therapy for high-risk patients and the clinical and molecular parameters for stratifying beneficiary categories. In addition, the treatment of advanced forms lacks reliable driver biomarkers for the appropriated therapeutic choice. Thus, renal cancer patient management urges predictive molecular indicators and models for therapy-decision making.MethodsHere, we developed and optimized new models and tools for ameliorating renal cancer patient management. We isolated from fresh tumor specimens heterogeneous multi-clonal populations showing epithelial and mesenchymal characteristics coupled to stem cell phenotype. These cells retained long lasting-tumor-propagating capacity provided a therapy monitoring approach in vitro and in vivo while being able to form parental tumors when orthotopically injected and serially transplanted in immunocompromised murine hosts.ResultsIn line with recent evidence of multiclonal cancer composition, we optimized in vitro cultures enriched of multiple tumor-propagating populations. Orthotopic xenograft masses recapitulated morphology, grading and malignancy of parental cancers. High-grade but not the low-grade neoplasias, resulted in efficient serial transplantation in mice. Engraftment capacity paralleled grading and recurrence frequency advocating for a prognostic value of our developed model system. Therefore, in search of novel molecular indicators for therapy decision-making, we used Reverse-Phase Protein Arrays (RPPA) to analyze a panel of total and phosphorylated proteins in the isolated populations. Tumor-propagating cells showed several deregulated kinase cascades associated with grading, including angiogenesis and m-TOR pathways.ConclusionsIn the era of personalized therapy, the analysis of tumor propagating cells may help improve prediction of disease progression and therapy assignment. The possibility to test pharmacological response of ccRCC stem-like cells in vitro and in orthotopic models may help define a pharmacological profiling for future development of more effective therapies. Likewise, RPPA screening on patient-derived populations offers innovative approach for possible prediction of therapy response.

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The BATTLE trial: personalizing therapy for lung cancer.

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The BATTLE trial: personalizing therapy for lung cancer.

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