The Untold Story of Qumran

Westwood214 p.: illus.; 28 c

Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution.

The success of targeted cancer therapy is limited by drug resistance that can result from tumor genetic heterogeneity. The current approach to address resistance typically involves initiating a new treatment after clinical/radiographic disease progression, ultimately resulting in futility in most patients. Towards a potential alternative solution, we developed a novel computational framework that uses human cancer profiling data to systematically identify dynamic, pre-emptive, and sometimes non-intuitive treatment strategies that can better control tumors in real-time. By studying lung adenocarcinoma clinical specimens and preclinical models, our computational analyses revealed that the best anti-cancer strategies addressed existing resistant subpopulations as they emerged dynamically during treatment. In some cases, the best computed treatment strategy used unconventional therapy switching while the bulk tumor was responding, a prediction we confirmed in vitro. The new framework presented here could guide the principled implementation of dynamic molecular monitoring and treatment strategies to improve cancer control

Molecular Landscape of BRAF-Mutant NSCLC Reveals an Association Between Clonality and Driver Mutations and Identifies Targetable Non-V600 Driver Mutations

IntroductionApproximately 4% of NSCLC harbor BRAF mutations, and approximately 50% of these are non-V600 mutations. Treatment of tumors harboring non-V600 mutations is challenging because of functional heterogeneity and lack of knowledge regarding their clinical significance and response to targeted agents.MethodsWe conducted an integrative analysis of BRAF non-V600 mutations using genomic profiles of BRAF-mutant NSCLC from the Guardant360 database. BRAF mutations were categorized by clonality and class (1 and 2: RAS-independent; 3: RAS-dependent). Cell viability assays were performed in Ba/F3 models. Drug screens were performed in NSCLC cell lines.ResultsA total of 305 unique BRAF mutations were identified. Missense mutations were most common (276, 90%), and 45% were variants of unknown significance. F468S and N581Y were identified as novel activating mutations. Class 1 to 3 mutations had higher clonality than mutations of unknown class (p < 0.01). Three patients were treated with MEK with or without BRAF inhibitors. Patients harboring G469V and D594G mutations did not respond, whereas a patient with the L597R mutation had a durable response. Trametinib with or without dabrafenib, LXH254, and lifirafenib had more potent inhibition of BRAF non-V600-mutant NSCLC cell lines than other MEK, BRAF, and ERK inhibitors, comparable with the inhibition of BRAF V600E cell line.ConclusionsIn BRAF-mutant NSCLC, clonality is higher in known functional mutations and may allow identification of variants of unknown significance that are more likely to be oncogenic drivers. Our data indicate that certain non-V600 mutations are responsive to MEK and BRAF inhibitors. This integration of genomic profiling and drug sensitivity may guide the treatment for BRAF-mutant NSCLC

Evolution and clinical impact of co-occurring genetic alterations in advanced-stage EGFR-mutant lung cancers

A widespread approach to modern cancer therapy is to identify a single oncogenic driver gene and target its mutant-protein product (for example, EGFR-inhibitor treatment in EGFR-mutant lung cancers). However, genetically driven resistance to targeted therapy limits patient survival. Through genomic analysis of 1,122 EGFR-mutant lung cancer cell-free DNA samples and whole-exome analysis of seven longitudinally collected tumor samples from a patient with EGFR-mutant lung cancer, we identified critical co-occurring oncogenic events present in most advanced-stage EGFR-mutant lung cancers. We defined new pathways limiting EGFR-inhibitor response, including WNT/β-catenin alterations and cell-cycle-gene (CDK4 and CDK6) mutations. Tumor genomic complexity increases with EGFR-inhibitor treatment, and co-occurring alterations in CTNNB1 and PIK3CA exhibit nonredundant functions that cooperatively promote tumor metastasis or limit EGFR-inhibitor response. This study calls for revisiting the prevailing single-gene driver-oncogene view and links clinical outcomes to co-occurring genetic alterations in patients with advanced-stage EGFR-mutant lung cancer