Genomic classification of the RAS network identifies a personalized treatment strategy for lung cancer

Transcriptome profiling reveals Silibinin dose-dependent response network in non-small lung cancer cells

Silibinin (SIL), a natural flavonolignan from the milk thistle (Silybum marianum), is known to exhibit remarkable hepatoprotective, antineoplastic and EMT inhibiting effects in different cancer cells by targeting multiple molecular targets and pathways. However, the predominant majority of previous studies investigated effects of this phytocompound in a one particular cell line. Here, we carry out a systematic analysis of dose-dependent viability response to SIL in five non-small cell lung cancer (NSCLC) lines that gradually differ with respect to their intrinsic EMT stage. By correlating gene expression profiles of NSCLC cell lines with the pattern of their SIL IC50 response, a group of cell cycle, survival and stress responsive genes, including some prominent targets of STAT3 (BIRC5, FOXM1, BRCA1), was identified. The relevancy of these computationally selected genes to SIL viability response of NSCLC cells was confirmed by the transient knockdown test. In contrast to other EMT-inhibiting compounds, no correlation between the SIL IC50 and the intrinsic EMT stage of NSCLC cells was observed. Our experimental results show that SIL viability response of differently constituted NSCLC cells is linked to a subnetwork of tightly interconnected genes whose transcriptomic pattern can be used as a benchmark for assessment of individual SIL sensitivity instead of the conventional EMT signature. Insights gained in this study pave the way for optimization of customized adjuvant therapy of malignancies using Silibinin.

Using Regularization to Infer Cell Line Specificity in Logical Network Models of Signaling Pathways

Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down the development of effective cures. The molecular differences between cell types or between healthy and diseased cellular states are usually determined by the wiring of regulatory networks. Understanding these molecular and cellular differences at the systems level would improve patient stratification and facilitate the design of rational intervention strategies. Models of cellular regulatory networks frequently make weak assumptions about the distribution of model parameters across cell types or patients. These assumptions are usually expressed in the form of regularization of the objective function of the optimization problem. We propose a new method of regularization for network models of signaling pathways based on the local density of the inferred parameter values within the parameter space. Our method reduces the complexity of models by creating groups of cell line-specific parameters which can then be optimized together. We demonstrate the use of our method by recovering the correct topology and inferring accurate values of the parameters of a small synthetic model. To show the value of our method in a realistic setting, we re-analyze a recently published phosphoproteomic dataset from a panel of 14 colon cancer cell lines. We conclude that our method efficiently reduces model complexity and helps recovering context-specific regulatory information.

In pursuit of synergy: An investigation of the PI3K/mTOR/MEK co-targeted inhibition strategy in NSCLC

Clinical PI3K inhibition has been somewhat disappointing, due to both inadequate patient stratification and compensatory cell signalling through bypass mechanisms. As such, investigation of PI3K-MEK co-targeted inhibition has been recommended. With high mortality rates and a clear need for new therapeutic intervention strategies, non-small cell lung cancer (NSCLC) is an important setting to investigate the effectiveness of this approach.

Here, 174 NSCLC tumours were screened for 150 mutations by Fluidigm technology, with 15 patients being profiled for phosphoprotein expression. The effects of GDC-0941 (a pan PI3K inhibitor), GDC-0980 (a dual PI3K/mTOR inhibitor) and GDC-0973 (a MEK inhibitor) alone and in combination were assessed in 3 NSCLC cell lines.

PIK3CA was mutated in 5.17% of NSCLC patients. GDC-0941 and GDC-0980 treatment induced anti-proliferative and pro-apoptotic responses across all NSCLC cell lines, while GDC-0973 treatment induced only anti-proliferative responses. GDC-0980 and GDC-0973 combined treatment led to significant increases in apoptosis and synergistic reductions in proliferation across the panel of cell lines.

This study found that the PI3K/MEK co-targeted inhibition strategy is synergistic in all 3 molecular subtypes of NSCLC investigated. Consequently, we would advocate clinical trials for NSCLC patients combining GDC-0980 and GDC-0973, each of which are separately under clinical investigation currently.

Receptor tyrosine kinases and downstream pathways as druggable targets for cancer treatment: the current arsenal of inhibitors

Searching for targets that allow pharmacological inhibition of cell proliferation in over-proliferative states, such as cancer, leads us to finely understand the complex mechanisms orchestrating the perfect control of mitosis number, frequency and pace as well as the molecular arrangements that induce cells to enter functional quiescence and brings them back to cycling in specific conditions. Although the mechanisms regulating cell proliferation have been described several years ago, never before has so much light been shed over this machinery as during the last decade when therapy targets have been explored and molecules, either synthetic or in the form of antibodies with the potential of becoming cancer drugs were produced and adjusted for specific binding and function. Proteins containing tyrosine kinase domains, either membrane receptors or cytoplasmic molecules, plus the ones activated by those in downstream pathways, having tyrosine kinase domains or not, such as RAS which is a GTPase and serine/threonine kinases such as RAF, play crucial role in conducting proliferation information from cell surroundings to the nucleus where gene expression takes place. Tyrosine kinases phosphorylate tyrosine residues in an activating mode and are found in important growth factor receptors, such as for ligands from families collectively known as VEGF, PDGF and EGF, to name a few and in intracellular downstream molecules. They all play important roles in normal physiology and are commonly found mutated or overexpressed in neoplastic states. Our objective here is to present such kinases as druggable targets for cancer therapy, highlighting the ones for which the pharmacological arsenal is available, discussing specificity, resistance mechanisms and treatment alternatives in cases of resistance, plus listing potential targets that have not been successfully worked yet.

Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes

BACKGROUND

The growth factor receptor network (GFRN) plays a significant role in driving key oncogenic processes. However, assessment of global GFRN activity is challenging due to complex crosstalk among GFRN components, or pathways, and the inability to study complex signaling networks in patient tumors. Here, pathway-specific genomic signatures were used to interrogate GFRN activity in breast tumors and the consequent phenotypic impact of GRFN activity patterns.

METHODS

Novel pathway signatures were generated in human primary mammary epithelial cells by overexpressing key genes from GFRN pathways (HER2, IGF1R, AKT1, EGFR, KRAS (G12V), RAF1, BAD). The pathway analysis toolkit Adaptive Signature Selection and InteGratioN (ASSIGN) was used to estimate pathway activity for GFRN components in 1119 breast tumors from The Cancer Genome Atlas (TCGA) and across 55 breast cancer cell lines from the Integrative Cancer Biology Program (ICBP43). These signatures were investigated for their relationship to pro- and anti-apoptotic protein expression and drug response in breast cancer cell lines.

RESULTS

Application of these signatures to breast tumor gene expression data identified two novel discrete phenotypes characterized by concordant, aberrant activation of either the HER2, IGF1R, and AKT pathways ("the survival phenotype") or the EGFR, KRAS (G12V), RAF1, and BAD pathways ("the growth phenotype"). These phenotypes described a significant amount of the variability in the total expression data across breast cancer tumors and characterized distinctive patterns in apoptosis evasion and drug response. The growth phenotype expressed lower levels of BIM and higher levels of MCL-1 proteins. Further, the growth phenotype was more sensitive to common chemotherapies and targeted therapies directed at EGFR and MEK. Alternatively, the survival phenotype was more sensitive to drugs inhibiting HER2, PI3K, AKT, and mTOR, but more resistant to chemotherapies.

CONCLUSIONS

Gene expression profiling revealed a bifurcation pattern in GFRN activity represented by two discrete phenotypes. These phenotypes correlate to unique mechanisms of apoptosis and drug response and have the potential of pinpointing targetable aberration(s) for more effective breast cancer treatments.

KRAS oncogene in lung cancer: focus on molecularly driven clinical trials

KRAS mutations are the most frequent molecular abnormalities found in one out of four nonsmall cell lung cancers (NSCLC). Their incidence increases in cases of adenocarcinoma, smokers and Caucasian patients. Their negative value in terms of prognosis and responsiveness to both standard chemotherapy and targeted therapies remains under debate. Many drugs have been developed specifically for KRAS-mutated NSCLC patients. Direct inhibition of RAS activation failed to show any clinical efficacy. Inhibition of downstream targets of the mitogen-activated protein kinase (MEK) pathway is a promising strategy: phase II combinations of MEK 1/2 kinase inhibitors with chemotherapy doubled patients' clinical outcomes. One phase III trial in such a setting is ongoing. Double inhibition of MEK and epidermal growth factor receptor proteins is currently being assessed in early-phase trials. The association with mammalian target of rapamycin pathway inhibition leads to non-manageable toxicity. Other strategies, such as inhibition of molecular heat-shock proteins 90 or focal adhesion kinase are currently assessed. Abemaciclib, a cyclin-dependent kinase 4/6 inhibitor, showed promising results in a phase I trial, with a 54% disease control rate. Results of an ongoing phase III trial are warranted. Immunotherapy might be the next relevant step in KRAS-mutated NSCLC management due to the high burden of associated mutations and neo-antigens.

The value of genomics in dissecting the RAS-network and in guiding therapeutics for RAS-driven cancers

The rise in genomic knowledge over the past decade has revealed the molecular etiology of many diseases, and has identified intricate signaling network activity in human cancers. Genomics provides the opportunity to determine genome structure and capture the activity of thousands of molecular events concurrently, which is important for deciphering highly complex genetic diseases such as cancer. In this review, we focus on genomic efforts directed towards one of cancer's most frequently mutated networks, the RAS pathway. Genomic tools such as gene expression signatures and assessment of mutations across the RAS network enable the capture of RAS signaling complexity. Due to this high level of interaction and cross-talk within the network, efforts to target RAS signaling in the clinic have generally failed, and we currently lack the ability to directly inhibit the RAS protein with high efficacy. We propose that the use of gene expression data can identify effective treatments that broadly inhibit the RAS network as this approach measures pathway activity independent of mutation status or any single mechanism of activation. Here, we review the genomic studies that map the complexity of the RAS network in cancer, and that show how genomic measurements of RAS pathway activation can identify effective RAS inhibition strategies. We also address the challenges and future directions for treating RAS-driven tumors. In summary, genomic assessment of RAS signaling provides a level of complexity necessary to accurately map the network that matches the intricacy of RAS pathway interactions in cancer.

Emerging role of long noncoding RNAs in lung cancer: Current status and future prospects

Lung cancer is the leading cause of cancer-related death worldwide with a 5-year survival rate of less than 15%, despite significant advances in both diagnostic and therapeutic approaches. Combined genomic and transcriptomic sequencing studies have identified numerous genetic driver mutations that are responsible for the development of lung cancer. Importantly, these approaches have also uncovered the widespread expression of "noncoding RNAs" including long noncoding RNAs (LncRNAs), which impact biologic responses through the regulation of mRNA transcription or translation. To date, most studies of the role of noncoding RNAs have focused on LncRNAs, which regulate mRNA translation via the RNA interference pathway. Although many of their attributes, such as patterns of expression, remain largely unknown, LncRNAs have key functions in transcriptional, post-transcriptional, and epigenetic gene regulation. Recent research showed that LncRNAs regulate flowering time in the lung cancer. In this review, we discuss these investigations into long noncoding RNAs were performed almost exclusively in lung cancer. Future work will need to extend these into lung cancer and to analyze how LncRNAs interact to regulate mRNA expression. From a clinical perspective, the targeting of LncRNAs as a novel therapeutic approach will require a deeper understanding of their function and mechanism of action.

Inferring pathway dysregulation in cancers from multiple types of omic data

Although in some cases individual genomic aberrations may drive disease development in isolation, a complex interplay among multiple aberrations is common. Accordingly, we developed Gene Set Omic Analysis (GSOA), a bioinformatics tool that can evaluate multiple types and combinations of omic data at the pathway level. GSOA uses machine learning to identify dysregulated pathways and improves upon other methods because of its ability to decipher complex, multigene patterns. We compare GSOA to alternative methods and demonstrate its ability to identify pathways known to play a role in various cancer phenotypes. Software implementing the GSOA method is freely available from https://bitbucket.org/srp33/gsoa.

Genomic classification of the RAS network identifies a personalized treatment strategy for lung cancer

Better approaches are needed to evaluate a single patient's drug response at the genomic level. Targeted therapy for signaling pathways in cancer has met limited success in part due to the exceedingly interwoven nature of the pathways. In particular, the highly complex RAS network has been challenging to target. Effectively targeting the pathway requires development of techniques that measure global network activity to account for pathway complexity. For this purpose, we used a gene-expression-based biomarker for RAS network activity in non-small cell lung cancer (NSCLC) cells, and screened for drugs whose efficacy was significantly highly correlated to RAS network activity. Results identified EGFR and MEK co-inhibition as the most effective treatment for RAS-active NSCLC amongst a panel of over 360 compounds and fractions. RAS activity was identified in both RAS-mutant and wild-type lines, indicating broad characterization of RAS signaling inclusive of multiple mechanisms of RAS activity, and not solely based on mutation status. Mechanistic studies demonstrated that co-inhibition of EGFR and MEK induced apoptosis and blocked both EGFR-RAS-RAF-MEK-ERK and EGFR-PI3K-AKT-RPS6 nodes simultaneously in RAS-active, but not RAS-inactive NSCLC. These results provide a comprehensive strategy to personalize treatment of NSCLC based on RAS network dysregulation and provide proof-of-concept of a genomic approach to classify and target complex signaling networks.