Osimertinib + Savolitinib to Overcome Acquired MET-Mediated Resistance in Epidermal Growth Factor Receptor-Mutated, MET-Amplified Non-Small Cell Lung Cancer: TATTON.

MET-inhibitor and EGFR tyrosine kinase inhibitor (EGFR-TKI) combination therapy could overcome acquired MET-mediated osimertinib resistance. We present the final phase Ib TATTON (NCT02143466) analysis (Part B, n = 138/Part D, n = 42) assessing oral savolitinib 600 mg/300 mg once daily (q.d.) + osimertinib 80 mg q.d. in patients with MET-amplified, EGFR-mutated (EGFRm) advanced non-small cell lung cancer (NSCLC) and progression on prior EGFR-TKI. An acceptable safety profile was observed. In Parts B and D, respectively, objective response rates were 33% to 67% and 62%, and median progression-free survival (PFS) was 5.5 to 11.1 months and 9.0 months. Increased antitumor activity may occur with MET copy number ≥10. EGFRm circulating tumor DNA clearance on treatment predicted longer PFS in patients with detectable baseline ctDNA, while acquired resistance mechanisms to osimertinib + savolitinib were mediated by MET, EGFR, or KRAS alterations. SIGNIFICANCE: The savolitinib + osimertinib combination represents a promising therapy in patients with MET-amplified/overexpressed, EGFRm advanced NSCLC with disease progression on a prior EGFR-TKI. Acquired resistance mechanisms to this combination include those via MET, EGFR, and KRAS. On-treatment ctDNA dynamics can predict clinical outcomes and may provide an opportunity to inform earlier decision-making. This article is highlighted in the In This Issue feature, p. 1.

Analysis of cancer metabolism with high-throughput technologies.

BACKGROUND: Recent advances in genomics and proteomics have allowed us to study the nuances of the Warburg effect--a long-standing puzzle in cancer energy metabolism--at an unprecedented level of detail. While modern next-generation sequencing technologies are extremely powerful, the lack of appropriate data analysis tools makes this study difficult. To meet this challenge, we developed a novel application for comparative analysis of gene expression and visualization of RNA-Seq data. RESULTS: We analyzed two biological samples (normal human brain tissue and human cancer cell lines) with high-energy, metabolic requirements. We calculated digital topology and the copy number of every expressed transcript. We observed subtle but remarkable qualitative and quantitative differences between the citric acid (TCA) cycle and glycolysis pathways. We found that in the first three steps of the TCA cycle, digital expression of aconitase 2 (ACO2) in the brain exceeded both citrate synthase (CS) and isocitrate dehydrogenase 2 (IDH2), while in cancer cells this trend was quite the opposite. In the glycolysis pathway, all genes showed higher expression levels in cancer cell lines; and most notably, digital gene expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and enolase (ENO) were considerably increased when compared to the brain sample. CONCLUSIONS: The variations we observed should affect the rates and quantities of ATP production. We expect that the developed tool will provide insights into the subtleties related to the causality between the Warburg effect and neoplastic transformation. Even though we focused on well-known and extensively studied metabolic pathways, the data analysis and visualization pipeline that we developed is particularly valuable as it is global and pathway-independent.

Promoter prediction in E. coli based on SIDD profiles and Artificial Neural Networks.

BACKGROUND: One of the major challenges in biology is the correct identification of promoter regions. Computational methods based on motif searching have been the traditional approach taken. Recent studies have shown that DNA structural properties, such as curvature, stacking energy, and stress-induced duplex destabilization (SIDD) are useful in promoter prediction, as well. In this paper, the currently used SIDD energy threshold method is compared to the proposed artificial neural network (ANN) approach for finding promoters based on SIDD profile data. RESULTS: When compared to the SIDD threshold prediction method, artificial neural networks showed noticeable improvements for precision, recall, and F-score over a range of values. The maximal F-score for the ANN classifier was 62.3 and 56.8 for the threshold-based classifier. CONCLUSIONS: Artificial neural networks were used to predict promoters based on SIDD profile data. Results using this technique were an improvement over the previous SIDD threshold approach. Over a wide range of precision-recall values, artificial neural networks were more capable of identifying distinctive characteristics of promoter regions than threshold based methods.

Acquired Resistance to the Mutant-Selective EGFR Inhibitor AZD9291 Is Associated with Increased Dependence on RAS Signaling in Preclinical Models.

Resistance to targeted EGFR inhibitors is likely to develop in EGFR-mutant lung cancers. Early identification of innate or acquired resistance mechanisms to these agents is essential to direct development of future therapies. We describe the detection of heterogeneous mechanisms of resistance within populations of EGFR-mutant cells (PC9 and/or NCI-H1975) with acquired resistance to current and newly developed EGFR tyrosine kinase inhibitors, including AZD9291. We report the detection of NRAS mutations, including a novel E63K mutation, and a gain of copy number of WT NRAS or WT KRAS in cell populations resistant to gefitinib, afatinib, WZ4002, or AZD9291. Compared with parental cells, a number of resistant cell populations were more sensitive to inhibition by the MEK inhibitor selumetinib (AZD6244; ARRY-142886) when treated in combination with the originating EGFR inhibitor. In vitro, a combination of AZD9291 with selumetinib prevented emergence of resistance in PC9 cells and delayed resistance in NCI-H1975 cells. In vivo, concomitant dosing of AZD9291 with selumetinib caused regression of AZD9291-resistant tumors in an EGFRm/T790M transgenic model. Our data support the use of a combination of AZD9291 with a MEK inhibitor to delay or prevent resistance to AZD9291 in EGFRm and/or EGFRm/T790M tumors. Furthermore, these findings suggest that NRAS modifications in tumor samples from patients who have progressed on current or EGFR inhibitors in development may support subsequent treatment with a combination of EGFR and MEK inhibition.