Intrinsic Resistance to MEK Inhibition in KRAS Mutant Lung and Colon Cancer through Transcriptional Induction of ERBB3

Summary There are no effective therapies for the ∼30% of human malignancies with mutant RAS oncogenes. Using a kinome-centered synthetic lethality screen, we find that suppression of the ERBB3 receptor tyrosine kinase sensitizes KRAS mutant lung and colon cancer cells to MEK inhibitors. We show that MEK inhibition results in MYC-dependent transcriptional upregulation of ERBB3, which is responsible for intrinsic drug resistance. Drugs targeting both EGFR and ERBB2, each capable of forming heterodimers with ERBB3, can reverse unresponsiveness to MEK inhibition by decreasing inhibitory phosphorylation of the proapoptotic proteins BAD and BIM. Moreover, ERBB3 protein level is a biomarker of response to combinatorial treatment. These data suggest a combination strategy for treating KRAS mutant colon and lung cancers and a way to identify the tumors that are most likely to benefit from such combinatorial treatment

Deciphering mechanisms of response and resistance in large-scale mouse cancer screens

Acquired resistance is a major limitation for the successful treatment of cancer patients. Although numerous efficacious cancer therapeutics have been developed in the past decades, resistance arises due to a variety of reasons including tumoral genetic alterations, or modulation of factors in the tumor environment. Understanding the mechanistic reasons for tumor relapse supports the identification of novel combination therapies that could lead to more durable responses. Here, we will review large-scale in vivo screens in pre-clinical cancer models that employed genetic and pharmacological agents toward elucidating acquired drug resistance and informing on beneficial combinations to be tested in clinical trials

Modelling signalling networks from perturbation data

Motivation: Intracellular signalling is realized by complex signalling networks, which are almost impossible to understand without network models, especially if feedbacks are involved. Modular Response Analysis (MRA) is a convenient modelling method to study signalling networks in various contexts. Results: We developed the software package STASNet (STeady-STate Analysis of Signalling Networks) that provides an augmented and extended version of MRA suited to model signalling networks from incomplete perturbation schemes and multi-perturbation data. Using data from the Dialogue on Reverse Engineering Assessment and Methods challenge, we show that predictions from STASNet models are among the top-performing methods. We applied the method to study the effect of SHP2, a protein that has been implicated in resistance to targeted therapy in colon cancer, using a novel dataset from the colon cancer cell line Widr and a SHP2-depleted derivative. We find that SHP2 is required for mitogen-activated protein kinase signalling, whereas AKT signalling only partially depends on SHP2. Availability and implementation: An R-package is available at https://github.com/molsysbio/STASNet. Supplementary information: Supplementary data are available at Bioinformatics online

Comparative Network Reconstruction using mixed integer programming

Motivation Signal-transduction networks are often aberrated in cancer cells, and new anti-cancer drugs that specifically target oncogenes involved in signaling show great clinical promise. However, the effectiveness of such targeted treatments is often hampered by innate or acquired resistance due to feedbacks, crosstalks or network adaptations in response to drug treatment. A quantitative understanding of these signaling networks and how they differ between cells with different oncogenic mutations or between sensitive and resistant cells can help in addressing this problem. Results Here, we present Comparative Network Reconstruction (CNR), a computational method to reconstruct signaling networks based on possibly incomplete perturbation data, and to identify which edges differ quantitatively between two or more signaling networks. Prior knowledge about network topology is not required but can straightforwardly be incorporated. We extensively tested our approach using simulated data and applied it to perturbation data from a BRAF mutant, PTPN11 KO cell line that developed resistance to BRAF inhibition. Comparing the reconstructed networks of sensitive and resistant cells suggests that the resistance mechanism involves re-establishing wild-type MAPK signaling, possibly through an alternative RAF-isoform. Availability and implementation CNR is available as a python module at https://github.com/NKI-CCB/cnr. Additionally, code to reproduce all figures is available at https://github.com/NKI-CCB/CNR-analyses. Supplementary information Supplementary data are available at Bioinformatics online.Pattern Recognition and Bioinformatic

PTPN11 Is a Central Node in Intrinsic and Acquired Resistance to Targeted Cancer Drugs

Most BRAF (V600E) mutant melanomas are sensitive to selective BRAF inhibitors, but BRAF mutant colon cancers are intrinsically resistant to these drugs because of feedback activation of EGFR. We performed an RNA-interference-based genetic screen in BRAF mutant colon cancer cells to search for phosphatases whose knockdown induces sensitivity to BRAF inhibition. We found that suppression of protein tyrosine phosphatase non-receptor type 11 (PTPN11) confers sensitivity to BRAF inhibitors in colon cancer. Mechanistically, we found that inhibition of PTPN11 blocks signaling from receptor tyrosine kinases (RTKs) to the RAS-MEK-ERK pathway. PTPN11 suppression is lethal to cells that are driven by activated RTKs and prevents acquired resistance to targeted cancer drugs that results from RTK activation. Our findings identify PTPN11 as a drug target to combat both intrinsic and acquired resistance to several targeted cancer drugs. Moreover, activated PTPN11 can serve as a biomarker of drug resistance resulting from RTK activation

A System-wide Approach to Monitor Responses to Synergistic BRAF and EGFR Inhibition in Colorectal Cancer Cells

Intrinsic and/or acquired resistance represents one of the great challenges in targeted cancer therapy. A deeper understanding of the molecular biology of cancer has resulted in more efficient strategies, where one or multiple drugs are adopted in novel therapies to tackle resistance. This beneficial effect of using combination treatments has also been observed in colorectal cancer patients harboring the BRAF(V600E) mutation, whereby dual inhibition of BRAF(V600E) and EGFR increases antitumor activity. Notwithstanding this success, it is not clear whether this combination treatment is the only or most effective treatment to block intrinsic resistance to BRAF inhibitors. Here, we investigate molecular responses upon single and multi-target treatments, over time, using BRAF(V600E) mutant colorectal cancer cells as a model system. Through integration of transcriptomic, proteomic and phosphoproteomics data we obtain a comprehensive overview, revealing both known and novel responses. We primarily observe widespread up-regulation of receptor tyrosine kinases and metabolic pathways upon BRAF inhibition. These findings point to mechanisms by which the drug-treated cells switch energy sources and enter a quiescent-like state as a defensive response, while additionally compensating for the MAPK pathway inhibition

Mapping phospho-catalytic dependencies of therapy-resistant tumours reveals actionable vulnerabilities

Phosphorylation networks intimately regulate mechanisms of response to therapies. Mapping the phospho-catalytic profile of kinases in cells or tissues remains a challenge. Here, we introduce a practical high-throughput system to measure the enzymatic activity of kinases using biological peptide targets as phospho-sensors to reveal kinase dependencies in tumour biopsies and cell lines. A 228-peptide screen was developed to detect the activity of >60 kinases, including ABLs, AKTs, CDKs and MAPKs. Focusing on BRAFV600E tumours, we found mechanisms of intrinsic resistance to BRAFV600E-targeted therapy in colorectal cancer, including targetable parallel activation of PDPK1 and PRKCA. Furthermore, mapping the phospho-catalytic signatures of melanoma specimens identifies RPS6KB1 and PIM1 as emerging druggable vulnerabilities predictive of poor outcome in BRAFV600E patients. The results show that therapeutic resistance can be caused by the concerted upregulation of interdependent pathways. Our kinase activity-mapping system is a versatile strategy that innovates the exploration of actionable kinases for precision medicine