The BATTLE Trial: Personalizing Therapy for Lung Cancer
By Edward S Kim, R. Herbst, I. Wistuba, J. Lee, G. Blumenschein, A. Tsao, D. Stewart, M. Hicks, J. Erasmus, Sanjay Gupta and 14 more. Cancer Discovery, 2011
859 citations recorded by the source corpus.
Background Lung cancer is the leading cancer cause of mortality worldwide; large-scale trials have failed to improve clinical outcomes of patients with chemorefractory non-small-cell lung cancer (NSCLC). Methods Following an initial equal randomization period, BATTLE adaptively randomized patients with chemorefractory NSCLC to erlotinib, vandetanib, erlotinib plus bexarotene, or sorafenib based on molecular biomarkers of NSCLC pathogenesis in fresh core needle biopsy specimens. The primary end point was disease control rate (DCR) at 8 weeks. Results Of 255 patients randomly assigned to erlotinib (59 patients), vandetanib (54), erlotinib plus bexarotene (37), and sorafenib (105), 244 were eligible for the DCR analysis. Pneumothorax after lung biopsy occurred in 11.5% and treatment-related toxicities grade 3–4 in 6.5% of patients. Overall results were a 46% 8-week DCR, 1.9-month median progression-free survival, 9-month median overall survival, and 35% 1-year survival. Individual markers predicting a significantly superior DCR for a treatment included: epidermal growth factor receptor (EGFR) mutation (P=0.04) for erlotinib; cyclin D1 positivity (P=0.01) or EGFR amplification (P=0.006) for erlotinib plus bexarotene; vascular endothelial growth factor receptor 2 positivity (P=0.05) for vandetanib; and absence of EGFR mutation (P=0.01) or of EGFR high polysomy (P=0.05) for sorafenib. A better 8-week DCR occurred with sorafenib versus all other regimens (64% versus 33%; P<0.001) among EGFR wild-type patients and versus all other regimens (61% versus 32%; P=0.11) among mutant-KRAS patients. The prespecified biomarker groups were less predictive than the individual biomarkers analyzed in this study. Conclusions The first completed biopsy-mandated study in pretreated NSCLC, BATTLE confirmed our pre-specified hypotheses regarding biomarker and targeted treatment interactions, establishing a new paradigm for personalizing therapy for patients with NSCLC. (ClinicalTrials.gov numbers, NCT00409968, NCT00411671, NCT00411632, NCT00410059, NCT00410189.)
Subfields: Oncology, Pulmonary and Respiratory Medicine
View Paper: https://www.semanticscholar.org/paper/8c72b7fe8c26d8963f5b72f50a3f794d1de2e19f
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Citations in the corpus
- An epithelial-mesenchymal transition (EMT) gene signature predicts resistance to EGFR and PI3K inhibitors and identifies Axl as a therapeutic target for overcoming EGFR inhibitor resistance (2012, positive): “EMT signature in patients with relapsed or metastatic NSCLC Finally, we tested the EMT signature in previously treated NSCLC patients with advanced NSCLC enrolled in the BATTLE-1 trial (42).”
- Exploiting the Natural Exploration In Contextual Bandits (2017, background): “, a website may wish to tailor content based on an Internet user’s web history (Li et al. 2010), or a medical decision-maker may wish to choose treatments for patients based on their medical records (Kim et al. 2011). In these examples, the costs and benets of each decision depend on the individual ”
- Lessons from the cancer genome. (2013, neutral): “In these ‘‘basket trials,’’ patients are assigned to different therapeutic regimens based on the specific genetic profiles in their tumor (Kim et al., 2011).”
- Online Decision Making with High-Dimensional Covariates (2020, neutral): “For example, the BATTLE cancer trial found that only a few of many available patient biomarkers were predictive of the success of any given treatment (Kim et al. 2011).”
- Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines (2014, methodology): “Finally, we applied our approach to a dataset from the Biomarker-Integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) study (trial registration ID: NCT00409968) [17,34].”
- Offline Multi-Action Policy Learning: Generalization and Optimization (2018, background): “Introduction As a result of digitization of the economy, more and more decision-makers from a wide range of domains have gained the ability to target products, services, and information provision based on individual characteristics (Kim et al. (2011), Ozanne et al. (2014), Bertsimas and Kallus (2014”