Transcriptomic-metabolomic reprogramming in EGFR-mutant NSCLC early adaptive drug escape linking TGFß2-bioenergetics-mitochondrial priming.

The impact of EGFR-mutant NSCLC precision therapy is limited by acquired resistance despite initial excellent response. Classic studies of EGFR-mutant clinical resistance to precision therapy were based on tumor rebiopsies late during clinical tumor progression on therapy. Here, we characterized a novel non-mutational early adaptive drug-escape in EGFR-mutant lung tumor cells only days after therapy initiation, that is MET-independent. The drug-escape cell states were analyzed by integrated transcriptomic and metabolomics profiling uncovering a central role for autocrine TGFß2 in mediating cellular plasticity through profound cellular adaptive Omics reprogramming, with common mechanistic link to prosurvival mitochondrial priming. Cells undergoing early adaptive drug escape are in proliferative-metabolic quiescent, with enhanced EMT-ness and stem cell signaling, exhibiting global bioenergetics suppression including reverse Warburg, and are susceptible to glutamine deprivation and TGFß2 inhibition. Our study further supports a preemptive therapeutic targeting of bioenergetics and mitochondrial priming to impact early drug-escape emergence using EGFR precision inhibitor combined with broad BH3-mimetic to interrupt BCL-2/BCL-xL together, but not BCL-2 alone.

Learning mixed graphical models with separate sparsity parameters and stability-based model selection.

BACKGROUND: Mixed graphical models (MGMs) are graphical models learned over a combination of continuous and discrete variables. Mixed variable types are common in biomedical datasets. MGMs consist of a parameterized joint probability density, which implies a network structure over these heterogeneous variables. The network structure reveals direct associations between the variables and the joint probability density allows one to ask arbitrary probabilistic questions on the data. This information can be used for feature selection, classification and other important tasks. RESULTS: We studied the properties of MGM learning and applications of MGMs to high-dimensional data (biological and simulated). Our results show that MGMs reliably uncover the underlying graph structure, and when used for classification, their performance is comparable to popular discriminative methods (lasso regression and support vector machines). We also show that imposing separate sparsity penalties for edges connecting different types of variables significantly improves edge recovery performance. To choose these sparsity parameters, we propose a new efficient model selection method, named Stable Edge-specific Penalty Selection (StEPS). StEPS is an expansion of an earlier method, StARS, to mixed variable types. In terms of edge recovery, StEPS selected MGMs outperform those models selected using standard techniques, including AIC, BIC and cross-validation. In addition, we use a heuristic search that is linear in size of the sparsity value search space as opposed to the cubic grid search required by other model selection methods. We applied our method to clinical and mRNA expression data from the Lung Genomics Research Consortium (LGRC) and the learned MGM correctly recovered connections between the diagnosis of obstructive or interstitial lung disease, two diagnostic breathing tests, and cigarette smoking history. Our model also suggested biologically relevant mRNA markers that are linked to these three clinical variables. CONCLUSIONS: MGMs are able to accurately recover dependencies between sets of continuous and discrete variables in both simulated and biomedical datasets. Separation of sparsity penalties by edge type is essential for accurate network edge recovery. Furthermore, our stability based method for model selection determines sparsity parameters faster and more accurately (in terms of edge recovery) than other model selection methods. With the ongoing availability of comprehensive clinical and biomedical datasets, MGMs are expected to become a valuable tool for investigating disease mechanisms and answering an array of critical healthcare questions.

Aberrant signaling pathways in squamous cell lung carcinoma.

Lung cancer is the second most commonly occurring non-cutaneous cancer in the United States with the highest mortality rate among both men and women. In this study, we utilized three lung cancer microarray datasets generated by previous researchers to identify differentially expressed genes, altered signaling pathways, and assess the involvement of Hedgehog (Hh) pathway. The three datasets contain the expression levels of tens of thousands genes in normal lung tissues and squamous cell lung carcinoma. The datasets were combined and analyzed. The dysregulated genes and altered signaling pathways were identified using statistical methods. We then performed Fisher's exact test on the significance of the association of Hh pathway downstream genes and squamous cell lung carcinoma.395 genes were found commonly differentially expressed in squamous cell lung carcinoma. The genes encoding fibrous structural protein keratins and cell cycle dependent genes encoding cyclin-dependent kinases were significantly up-regulated while the ones encoding LIM domains were down. Over 100 signaling pathways were implicated in squamous cell lung carcinoma, including cell cycle regulation pathway, p53 tumor-suppressor pathway, IL-8 signaling, Wnt-ß-catenin pathway, mTOR signaling and EGF signaling. In addition, 37 out of 223 downstream molecules of Hh pathway were altered. The P-value from the Fisher's exact test indicates that Hh signaling is implicated in squamous cell lung carcinoma.Numerous genes were altered and multiple pathways were dysfunctional in squamous cell lung carcinoma. Many of the altered genes have been implicated in different types of carcinoma while some are organ-specific. Hh signaling is implicated in squamous cell lung cancer, opening the door for exploring new cancer therapeutic treatment using GLI antagonist GANT 61.