Chemistry-First Approach for Nomination of Personalized Treatment in Lung Cancer.

Diversity in the genetic lesions that cause cancer is extreme. In consequence, a pressing challenge is the development of drugs that target patient-specific disease mechanisms. To address this challenge, we employed a chemistry-first discovery paradigm for de novo identification of druggable targets linked to robust patient selection hypotheses. In particular, a 200,000 compound diversity-oriented chemical library was profiled across a heavily annotated test-bed of >100 cellular models representative of the diverse and characteristic somatic lesions for lung cancer. This approach led to the delineation of 171 chemical-genetic associations, shedding light on the targetability of mechanistic vulnerabilities corresponding to a range of oncogenotypes present in patient populations lacking effective therapy. Chemically addressable addictions to ciliogenesis in TTC21B mutants and GLUT8-dependent serine biosynthesis in KRAS/KEAP1 double mutants are prominent examples. These observations indicate a wealth of actionable opportunities within the complex molecular etiology of cancer.

Systematic identification of molecular subtype-selective vulnerabilities in non-small-cell lung cancer.

Context-specific molecular vulnerabilities that arise during tumor evolution represent an attractive intervention target class. However, the frequency and diversity of somatic lesions detected among lung tumors can confound efforts to identify these targets. To confront this challenge, we have applied parallel screening of chemical and genetic perturbations within a panel of molecularly annotated NSCLC lines to identify intervention opportunities tightly linked to molecular response indicators predictive of target sensitivity. Anchoring this analysis on a matched tumor/normal cell model from a lung adenocarcinoma patient identified three distinct target/response-indicator pairings that are represented with significant frequencies (6%-16%) in the patient population. These include NLRP3 mutation/inflammasome activation-dependent FLIP addiction, co-occurring KRAS and LKB1 mutation-driven COPI addiction, and selective sensitivity to a synthetic indolotriazine that is specified by a seven-gene expression signature. Target efficacies were validated in vivo, and mechanism-of-action studies informed generalizable principles underpinning cancer cell biology.

Genetic requirement for Mycl and efficacy of RNA Pol I inhibition in mouse models of small cell lung cancer.

Small cell lung cancer (SCLC) is a devastating neuroendocrine carcinoma. MYCL (L-Myc) is frequently amplified in human SCLC, but its roles in SCLC progression are poorly understood. We isolated preneoplastic neuroendocrine cells from a mouse model of SCLC and found that ectopic expression of L-Myc, c-Myc, or N-Myc conferred tumor-forming capacity. We focused on L-Myc, which promoted pre-rRNA synthesis and transcriptional programs associated with ribosomal biogenesis. Deletion of Mycl in two genetically engineered models of SCLC resulted in strong suppression of SCLC. The high degree of suppression suggested that L-Myc may constitute a therapeutic target for a broad subset of SCLC. We then used an RNA polymerase I inhibitor to target rRNA synthesis in an autochthonous Rb/p53-deleted mouse SCLC model and found significant tumor inhibition. These data reveal that activation of RNA polymerase I by L-Myc and other MYC family proteins provides an axis of vulnerability for this recalcitrant cancer.

Development of Human Adrenocortical Adenoma (HAA1) Cell Line from Zona Reticularis.

The human adrenal cortex is composed of distinct zones that are the main source of steroid hormone production. The mechanism of adrenocortical cell differentiation into several functionally organized populations with distinctive identities remains poorly understood. Human adrenal disease has been difficult to study, in part due to the absence of cultured cell lines that faithfully represent adrenal cell precursors in the early stages of transformation. Here, Human Adrenocortical Adenoma (HAA1) cell line derived from a patient's macronodular adrenocortical hyperplasia and was treated with histone deacetylase inhibitors (HDACis) and gene expression was examined. We describe a patient-derived HAA1 cell line derived from the zona reticularis, the innermost zone of the adrenal cortex. The HAA1 cell line is unique in its ability to exit a latent state and respond with steroidogenic gene expression upon treatment with histone deacetylase inhibitors. The gene expression pattern of differentiated HAA1 cells partially recreates the roster of genes in the adrenal layer that they have been derived from. Gene ontology analysis of whole genome RNA-seq corroborated increased expression of steroidogenic genes upon HDAC inhibition. Surprisingly, HDACi treatment induced broad activation of the Tumor Necrosis Factor (TNF) alpha pathway. This novel cell line we developed will hopefully be instrumental in understanding the molecular and biochemical mechanisms controlling adrenocortical differentiation and steroidogenesis.

A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images.

Digital pathology imaging of tumor tissues, which captures histological details in high resolution, is fast becoming a routine clinical procedure. Recent developments in deep-learning methods have enabled the identification, characterization, and classification of individual cells from pathology images analysis at a large scale. This creates new opportunities to study the spatial patterns of and interactions among different types of cells. Reliable statistical approaches to modeling such spatial patterns and interactions can provide insight into tumor progression and shed light on the biological mechanisms of cancer. In this article, we consider the problem of modeling a pathology image with irregular locations of three different types of cells: lymphocyte, stromal, and tumor cells. We propose a novel Bayesian hierarchical model, which incorporates a hidden Potts model to project the irregularly distributed cells to a square lattice and a Markov random field prior model to identify regions in a heterogeneous pathology image. The model allows us to quantify the interactions between different types of cells, some of which are clinically meaningful. We use Markov chain Monte Carlo sampling techniques, combined with a double Metropolis-Hastings algorithm, in order to simulate samples approximately from a distribution with an intractable normalizing constant. The proposed model was applied to the pathology images of $205$ lung cancer patients from the National Lung Screening trial, and the results show that the interaction strength between tumor and stromal cells predicts patient prognosis (P = $0.005$). This statistical methodology provides a new perspective for understanding the role of cell-cell interactions in cancer progression.

Integrative gene set enrichment analysis utilizing isoform-specific expression.

Gene set enrichment analysis (GSEA) aims at identifying essential pathways, or more generally, sets of biologically related genes that are involved in complex human diseases. In the past, many studies have shown that GSEA is a very useful bioinformatics tool that plays critical roles in the innovation of disease prevention and intervention strategies. Despite its tremendous success, it is striking that conclusions of GSEA drawn from isolated studies are often sparse, and different studies may lead to inconsistent and sometimes contradictory results. Further, in the wake of next generation sequencing technologies, it has been made possible to measure genome-wide isoform-specific expression levels, calling for innovations that can utilize the unprecedented resolution. Currently, enormous amounts of data have been created from various RNA-seq experiments. All these give rise to a pressing need for developing integrative methods that allow for explicit utilization of isoform-specific expression, to combine multiple enrichment studies, in order to enhance the power, reproducibility, and interpretability of the analysis. We develop and evaluate integrative GSEA methods, based on two-stage procedures, which, for the first time, allow statistically efficient use of isoform-specific expression from multiple RNA-seq experiments. Through simulation and real data analysis, we show that our methods can greatly improve the performance in identifying essential gene sets compared to existing methods that can only use gene-level expression.

Meta-analysis approaches to combine multiple gene set enrichment studies.

In the field of gene set enrichment analysis (GSEA), meta-analysis has been used to integrate information from multiple studies to present a reliable summarization of the expanding volume of individual biomedical research, as well as improve the power of detecting essential gene sets involved in complex human diseases. However, existing methods, Meta-Analysis for Pathway Enrichment (MAPE), may be subject to power loss because of (1) using gross summary statistics for combining end results from component studies and (2) using enrichment scores whose distributions depend on the set sizes. In this paper, we adapt meta-analysis approaches recently developed for genome-wide association studies, which are based on fixed effect and random effects (RE) models, to integrate multiple GSEA studies. We further develop a mixed strategy via adaptive testing for choosing RE versus FE models to achieve greater statistical efficiency as well as flexibility. In addition, a size-adjusted enrichment score based on a one-sided Kolmogorov-Smirnov statistic is proposed to formally account for varying set sizes when testing multiple gene sets. Our methods tend to have much better performance than the MAPE methods and can be applied to both discrete and continuous phenotypes. Specifically, the performance of the adaptive testing method seems to be the most stable in general situations.

Genome-wide copy number variation pattern analysis and a classification signature for non-small cell lung cancer.

The accurate classification of non-small cell lung carcinoma (NSCLC) into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is essential for both clinical practice and lung cancer research. Although the standard WHO diagnosis of NSCLC on biopsy material is rapid and economic, more than 13% of NSCLC tumors in the USA are not further classified. The purpose of this study was to analyze the genome-wide pattern differences in copy number variations (CNVs) and to develop a CNV signature as an adjunct test for the routine histopathologic classification of NSCLCs. We investigated the genome-wide CNV differences between these two tumor types using three independent patient datasets. Approximately half of the genes examined exhibited significant differences between LUAD and LUSC tumors and the corresponding non-malignant tissues. A new classifier was developed to identify signature genes out of 20 000 genes. Thirty-three genes were identified as a CNV signature of NSCLC. Using only their CNV values, the classification model separated the LUADs from the LUSCs with an accuracy of 0.88 and 0.84, respectively, in the training and validation datasets. The same signature also classified NSCLC tumors from their corresponding non-malignant samples with an accuracy of 0.96 and 0.98, respectively. We also compared the CNV patterns of NSCLC tumors with those of histologically similar tumors arising at other sites, such as the breast, head, and neck, and four additional tumors. Of greater importance, the significant differences between these tumors may offer the possibility of identifying the origin of tumors whose origin is unknown.

A statistical method for detecting differentially expressed SNVs based on next-generation RNA-seq data.

In this article, we propose a new statistical method-MutRSeq-for detecting differentially expressed single nucleotide variants (SNVs) based on RNA-seq data. Specifically, we focus on nonsynonymous mutations and employ a hierarchical likelihood approach to jointly model observed mutation events as well as read count measurements from RNA-seq experiments. We then introduce a likelihood ratio-based test statistic, which detects changes not only in overall expression levels, but also in allele-specific expression patterns. In addition, this method can jointly test multiple mutations in one gene/pathway. The simulation studies suggest that the proposed method achieves better power than a few competitors under a range of different settings. In the end, we apply this method to a breast cancer data set and identify genes with nonsynonymous mutations differentially expressed between the triple negative breast cancer tumors and other subtypes of breast cancer tumors.

ASCL1 is a lineage oncogene providing therapeutic targets for high-grade neuroendocrine lung cancers.

Aggressive neuroendocrine lung cancers, including small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), represent an understudied tumor subset that accounts for approximately 40,000 new lung cancer cases per year in the United States. No targeted therapy exists for these tumors. We determined that achaete-scute homolog 1 (ASCL1), a transcription factor required for proper development of pulmonary neuroendocrine cells, is essential for the survival of a majority of lung cancers (both SCLC and NSCLC) with neuroendocrine features. By combining whole-genome microarray expression analysis performed on lung cancer cell lines with ChIP-Seq data designed to identify conserved transcriptional targets of ASCL1, we discovered an ASCL1 target 72-gene expression signature that (i) identifies neuroendocrine differentiation in NSCLC cell lines, (ii) is predictive of poor prognosis in resected NSCLC specimens from three datasets, and (iii) represents novel "druggable" targets. Among these druggable targets is B-cell CLL/lymphoma 2, which when pharmacologically inhibited stops ASCL1-dependent tumor growth in vitro and in vivo and represents a proof-of-principle ASCL1 downstream target gene. Analysis of downstream targets of ASCL1 represents an important advance in the development of targeted therapy for the neuroendocrine class of lung cancers, providing a significant step forward in the understanding and therapeutic targeting of the molecular vulnerabilities of neuroendocrine lung cancer.

A genome-scale screen reveals context-dependent ovarian cancer sensitivity to miRNA overexpression.

Large-scale molecular annotation of epithelial ovarian cancer (EOC) indicates remarkable heterogeneity in the etiology of that disease. This diversity presents a significant obstacle against intervention target discovery. However, inactivation of miRNA biogenesis is commonly associated with advanced disease. Thus, restoration of miRNA activity may represent a common vulnerability among diverse EOC oncogenotypes. To test this, we employed genome-scale, gain-of-function, miRNA mimic toxicity screens in a large, diverse spectrum of EOC cell lines. We found that all cell lines responded to at least some miRNA mimics, but that the nature of the miRNA mimics provoking a response was highly selective within the panel. These selective toxicity profiles were leveraged to define modes of action and molecular response indicators for miRNA mimics with tumor-suppressive characteristics in vivo. A mechanistic principle emerging from this analysis was sensitivity of EOC to miRNA-mediated release of cell fate specification programs, loss of which may be a prerequisite for development of this disease.

A small-cell lung cancer genome with complex signatures of tobacco exposure.

Cancer is driven by mutation. Worldwide, tobacco smoking is the principal lifestyle exposure that causes cancer, exerting carcinogenicity through >60 chemicals that bind and mutate DNA. Using massively parallel sequencing technology, we sequenced a small-cell lung cancer cell line, NCI-H209, to explore the mutational burden associated with tobacco smoking. A total of 22,910 somatic substitutions were identified, including 134 in coding exons. Multiple mutation signatures testify to the cocktail of carcinogens in tobacco smoke and their proclivities for particular bases and surrounding sequence context. Effects of transcription-coupled repair and a second, more general, expression-linked repair pathway were evident. We identified a tandem duplication that duplicates exons 3-8 of CHD7 in frame, and another two lines carrying PVT1-CHD7 fusion genes, indicating that CHD7 may be recurrently rearranged in this disease. These findings illustrate the potential for next-generation sequencing to provide unprecedented insights into mutational processes, cellular repair pathways and gene networks associated with cancer.

High-throughput oncogene mutation profiling in human cancer.

Systematic efforts are underway to decipher the genetic changes associated with tumor initiation and progression. However, widespread clinical application of this information is hampered by an inability to identify critical genetic events across the spectrum of human tumors with adequate sensitivity and scalability. Here, we have adapted high-throughput genotyping to query 238 known oncogene mutations across 1,000 human tumor samples. This approach established robust mutation distributions spanning 17 cancer types. Of 17 oncogenes analyzed, we found 14 to be mutated at least once, and 298 (30%) samples carried at least one mutation. Moreover, we identified previously unrecognized oncogene mutations in several tumor types and observed an unexpectedly high number of co-occurring mutations. These results offer a new dimension in tumor genetics, where mutations involving multiple cancer genes may be interrogated simultaneously and in 'real time' to guide cancer classification and rational therapeutic intervention.

Microtubule affinity-regulating kinase 2 is associated with DNA damage response and cisplatin resistance in non-small cell lung cancer.

Microtubule affinity-regulating kinases (MARKs) are involved in several cellular functions but few studies have correlated MARK kinase expression with cancer, and none have explored their role in lung cancer. In this study, we identified MARK2 as frequently disrupted by DNA hypomethylation and copy gain, resulting in concordant overexpression in independent lung tumor cohorts and we demonstrate a role for MARK2 in lung tumor biology. Manipulation of MARK2 in lung cell lines revealed its involvement in cell viability and anchorage-independent growth. Analyses of both manipulated cell lines and clinical tumor specimens identified a potential role for MARK2 in cell cycle activation and DNA repair. Associations between MARK2 and the E2F, Myc/Max and NF-κB pathways were identified by luciferase assays and in-depth assessment of the NF-κB pathway suggests a negative association between MARK2 expression and NF-κB due to activation of non-canonical NF-κB signaling. Finally, we show that high MARK2 expression levels correlate with resistance to cisplatin, a standard first line chemotherapy for lung cancer. Collectively, our work supports a role for MARK2 in promoting malignant phenotypes of lung cancer and potentially modulating response to the DNA damaging chemotherapeutic, cisplatin.

Genome-scale analysis of DNA methylation in lung adenocarcinoma and integration with mRNA expression.

Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma is its most common histological subtype. Clinical and molecular evidence indicates that lung adenocarcinoma is a heterogeneous disease, which has important implications for treatment. Here we performed genome-scale DNA methylation profiling using the Illumina Infinium HumanMethylation27 platform on 59 matched lung adenocarcinoma/non-tumor lung pairs, with genome-scale verification on an independent set of tissues. We identified 766 genes showing altered DNA methylation between tumors and non-tumor lung. By integrating DNA methylation and mRNA expression data, we identified 164 hypermethylated genes showing concurrent down-regulation, and 57 hypomethylated genes showing increased expression. Integrated pathways analysis indicates that these genes are involved in cell differentiation, epithelial to mesenchymal transition, RAS and WNT signaling pathways, and cell cycle regulation, among others. Comparison of DNA methylation profiles between lung adenocarcinomas of current and never-smokers showed modest differences, identifying only LGALS4 as significantly hypermethylated and down-regulated in smokers. LGALS4, encoding a galactoside-binding protein involved in cell-cell and cell-matrix interactions, was recently shown to be a tumor suppressor in colorectal cancer. Unsupervised analysis of the DNA methylation data identified two tumor subgroups, one of which showed increased DNA methylation and was significantly associated with KRAS mutation and to a lesser extent, with smoking. Our analysis lays the groundwork for further molecular studies of lung adenocarcinoma by identifying novel epigenetically deregulated genes potentially involved in lung adenocarcinoma development/progression, and by describing an epigenetic subgroup of lung adenocarcinoma associated with characteristic molecular alterations.

Genome and transcriptome sequencing of lung cancers reveal diverse mutational and splicing events.

Lung cancer is a highly heterogeneous disease in terms of both underlying genetic lesions and response to therapeutic treatments. We performed deep whole-genome sequencing and transcriptome sequencing on 19 lung cancer cell lines and three lung tumor/normal pairs. Overall, our data show that cell line models exhibit similar mutation spectra to human tumor samples. Smoker and never-smoker cancer samples exhibit distinguishable patterns of mutations. A number of epigenetic regulators, including KDM6A, ASH1L, SMARCA4, and ATAD2, are frequently altered by mutations or copy number changes. A systematic survey of splice-site mutations identified 106 splice site mutations associated with cancer specific aberrant splicing, including mutations in several known cancer-related genes. RAC1b, an isoform of the RAC1 GTPase that includes one additional exon, was found to be preferentially up-regulated in lung cancer. We further show that its expression is significantly associated with sensitivity to a MAP2K (MEK) inhibitor PD-0325901. Taken together, these data present a comprehensive genomic landscape of a large number of lung cancer samples and further demonstrate that cancer-specific alternative splicing is a widespread phenomenon that has potential utility as therapeutic biomarkers. The detailed characterizations of the lung cancer cell lines also provide genomic context to the vast amount of experimental data gathered for these lines over the decades, and represent highly valuable resources for cancer biology.

Proteomic signatures associated with p53 mutational status in lung adenocarcinoma.

p53 is commonly mutated in lung adenocarcinoma. Mutant p53 loses wild-type function and some missense mutations further acquire oncogenic functions, while p53 wild-type may also induce pro-survival signaling. Therefore identification of signatures based on p53 mutational status has relevance to our understanding of p53 signaling pathways in cancer and identification of new therapeutic targets. To this end, we compared proteomic profiles of three cellular compartments (whole-cell extract, cell surface, and media) from 28 human lung adenocarcinoma cell lines that differ based on p53 mutational status. In total, 11,598, 11,569, and 9090 protein forms were identified in whole-cell extract, cell surface, and media, respectively. Bioinformatic analysis revealed that representative pathways associated with epithelial adhesion, immune and stromal cells, and mitochondrial function were highly significant in p53 missense mutations, p53 loss and wild-type p53 cell lines, respectively. Of note, mRNA levels of peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1-α), a transcription coactivator that promotes mitochondrial oxidative phosphorylation and mitochondrial biogenesis, was substantially higher in p53 wild-type cell lines compared to either cell lines with p53 loss or with missense mutation. Small interfering RNA targeting PGC1-α inhibited cell proliferation in p53 wild-type cell lines, indicative of PGC1-α and its downstream molecules as potential therapeutic targets in p53 wild-type lung adenocarcinoma.

Analysis of TP53 mutation status in human cancer cell lines: a reassessment.

Tumor-derived cell lines play an important role in the investigation of tumor biology and genetics. Across a wide array of studies, they have been tools of choice for the discovery of important genes involved in cancer and for the analysis of the cellular pathways that are impaired by diverse oncogenic events. They are also invaluable for screening novel anticancer drugs. The TP53 protein is a major component of multiple pathways that regulate cellular response to various types of stress. Therefore, TP53 status affects the phenotype of tumor cell lines profoundly and must be carefully ascertained for any experimental project. In the present review, we use the 2014 release of the UMD TP53 database to show that TP53 status is still controversial for numerous cell lines, including some widely used lines from the NCI-60 panel. Our analysis clearly confirms that, despite numerous warnings, the misidentification of cell lines is still present as a silent and neglected issue, and that extreme care must be taken when determining the status of p53, because errors may lead to disastrous experimental interpretations. A novel compendium gathering the TP53 status of 2,500 cell lines has been made available (http://p53.fr). A stand-alone application can be used to browse the database and extract pertinent information on cell lines and associated TP53 mutations. It will be updated regularly to minimize any scientific issues associated with the use of misidentified cell lines (http://p53.fr).

HER2 kinase domain mutation results in constitutive phosphorylation and activation of HER2 and EGFR and resistance to EGFR tyrosine kinase inhibitors.

HER2/Neu gene mutations have been identified in lung cancer. Expression of a HER2 mutant containing a G776(YVMA) insertion in exon 20 was more potent than wild-type HER2 in associating with and activating signal transducers, phosphorylating EGFR, and inducing survival, invasiveness, and tumorigenicity. HER2(YVMA) transphosphorylated kinase-dead EGFR(K721R) and EGFR(WT) in the presence of EGFR tyrosine kinase inhibitors (TKIs). Knockdown of mutant HER2 in H1781 lung cancer cells increased apoptosis and restored sensitivity to EGFR TKIs. The HER2 inhibitors lapatinib, trastuzumab, and CI-1033 inhibited growth of H1781 cells and cells expressing exogenous HER2(YVMA). These data suggest that (1) HER2(YVMA) activates cellular substrates more potently than HER2(WT); and (2) cancer cells expressing this mutation remain sensitive to HER2-targeted therapies but insensitive to EGFR TKIs.

Targeting ADAM-mediated ligand cleavage to inhibit HER3 and EGFR pathways in non-small cell lung cancer.

We describe here the existence of a heregulin-HER3 autocrine loop, and the contribution of heregulin-dependent, HER2-mediated HER3 activation to gefitinib insensitivity in non-small cell lung cancer (NSCLC). ADAM17 protein, a major ErbB ligand sheddase, is upregulated in NSCLC and is required not only for heregulin-dependent HER3 signaling, but also for EGFR ligand-dependent signaling in NSCLC cell lines. A selective ADAM inhibitor, INCB3619, prevents the processing and activation of multiple ErbB ligands, including heregulin. In addition, INCB3619 inhibits gefitinib-resistant HER3 signaling and enhances gefitinib inhibition of EGFR signaling in NSCLC. These results show that ADAM inhibition affects multiple ErbB pathways in NSCLC and thus offers an excellent opportunity for pharmacological intervention, either alone or in combination with other drugs.