Next-generation sequencing of paired tyrosine kinase inhibitor-sensitive and -resistant EGFR mutant lung cancer cell lines identifies spectrum of DNA changes associated with drug resistance

The role of APOBEC3B in lung tumor evolution and targeted cancer therapy resistance

In this study, the impact of the apolipoprotein B mRNA-editing catalytic subunit-like (APOBEC) enzyme APOBEC3B (A3B) on epidermal growth factor receptor (EGFR)-driven lung cancer was assessed. A3B expression in EGFR mutant (EGFRmut) non-small-cell lung cancer (NSCLC) mouse models constrained tumorigenesis, while A3B expression in tumors treated with EGFR-targeted cancer therapy was associated with treatment resistance. Analyses of human NSCLC models treated with EGFR-targeted therapy showed upregulation of A3B and revealed therapy-induced activation of nuclear factor kappa B (NF-κB) as an inducer of A3B expression. Significantly reduced viability was observed with A3B deficiency, and A3B was required for the enrichment of APOBEC mutation signatures, in targeted therapy-treated human NSCLC preclinical models. Upregulation of A3B was confirmed in patients with NSCLC treated with EGFR-targeted therapy. This study uncovers the multifaceted roles of A3B in NSCLC and identifies A3B as a potential target for more durable responses to targeted cancer therapy.

Overcoming Cancer Multi-drug Resistance (MDR): Reasons, mechanisms, nanotherapeutic solutions, and challenges

Multi-drug resistance (MDR) in cancer cells, either intrinsic or acquired through various mechanisms, significantly hinders the therapeutic efficacy of drugs. Typically, the reduced therapeutic performance of various drugs is predominantly due to the inherent over expression of ATP-binding cassette (ABC) transporter proteins on the cell membrane, resulting in the deprived uptake of drugs, augmenting drug detoxification, and DNA repair. In addition to various physiological abnormalities and extensive blood flow, MDR cancer phenotypes exhibit improved apoptotic threshold and drug efflux efficiency. These severe consequences have substantially directed researchers in the fabrication of various advanced therapeutic strategies, such as co-delivery of drugs along with various generations of MDR inhibitors, augmented dosage regimens and frequency of administration, as well as combinatorial treatment options, among others. In this review, we emphasize different reasons and mechanisms responsible for MDR in cancer, including but not limited to the known drug efflux mechanisms mediated by permeability glycoprotein (P-gp) and other pumps, reduced drug uptake, altered DNA repair, and drug targets, among others. Further, an emphasis on specific cancers that share pathogenesis in executing MDR and effluxed drugs in common is provided. Then, the aspects related to various nanomaterials-based supramolecular programmable designs (organic- and inorganic-based materials), as well as physical approaches (light- and ultrasound-based therapies), are discussed, highlighting the unsolved issues and future advancements. Finally, we summarize the review with interesting perspectives and future trends, exploring further opportunities to overcome MDR.

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.

Rif1-Dependent Control of Replication Timing

Successful duplication of the genome requires the accurate replication of billions of base pairs of DNA within a relatively short time frame. Failure to accurately replicate the genome results in genomic instability and a host of diseases. To faithfully and rapidly replicate the genome, DNA replication must be tightly regulated and coordinated with many other nuclear processes. These regulations, however, must also be flexible as replication kinetics can change through development and differentiation. Exactly how DNA replication is regulated and how this regulation changes through development is an active field of research. One aspect of genome duplication where much remains to be discovered is replication timing (RT), which dictates when each segment of the genome is replicated during S phase. All organisms display some level of RT, yet the precise mechanisms that govern RT remain are not fully understood. The study of Rif1, a protein that actively regulates RT from yeast to humans, provides a key to unlock the underlying molecular mechanisms controlling RT. The paradigm for Rif1 function is to delay helicase activation within certain regions of the genome, causing these regions to replicate late in S phase. Many questions, however, remain about the intricacies of Rif1 function. Here, we review the current models for the activity of Rif1 with the goal of trying to understand how Rif1 functions to establish the RT program.

Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs

Drugs produce their therapeutic effects by modulating specific targets, and there are 89 innovative targets of first-in-class drugs approved in 2004–17, each with information about drug clinical trial dated back to 1984. Analysis of the clinical trial timelines of these targets may reveal the trial-speed differentiating features for facilitating target assessment. Here we present a comprehensive analysis of all these 89 targets, following the earlier studies for prospective prediction of clinical success of the targets of clinical trial drugs. Our analysis confirmed the literature-reported common druggability characteristics for clinical success of these innovative targets, exposed trial-speed differentiating features associated to the on-target and off-target collateral effects in humans and further revealed a simple rule for identifying the speedy human targets through clinical trials (from the earliest phase I to the 1st drug approval within 8 years). This simple rule correctly identified 75.0% of the 28 speedy human targets and only unexpectedly misclassified 13.2% of 53 non-speedy human targets. Certain extraordinary circumstances were also discovered to likely contribute to the misclassification of some human targets by this simple rule. Investigation and knowledge of trial-speed differentiating features enable prioritized drug discovery and development.

An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability

Tumor heterogeneity is a primary cause of treatment failure and acquired resistance in cancer patients. Even in cancers driven by a single mutated oncogene, variability in response to targeted therapies is well known. The existence of additional genomic alterations among tumor cells can only partially explain this variability. As such, nongenetic factors are increasingly seen as critical contributors to tumor relapse and acquired resistance in cancer. Here, we show that both genetic and nongenetic factors contribute to targeted drug response variability in an experimental model of tumor heterogeneity. We observe significant variability to epidermal growth factor receptor (EGFR) inhibition among and within multiple versions and clonal sublines of PC9, a commonly used EGFR mutant nonsmall cell lung cancer (NSCLC) cell line. We resolve genetic, epigenetic, and stochastic components of this variability using a theoretical framework in which distinct genetic states give rise to multiple epigenetic "basins of attraction," across which cells can transition driven by stochastic noise. Using mutational impact analysis, single-cell differential gene expression, and correlations among Gene Ontology (GO) terms to connect genomics to transcriptomics, we establish a baseline for genetic differences driving drug response variability among PC9 cell line versions. Applying the same approach to clonal sublines, we conclude that drug response variability in all but one of the sublines is due to epigenetic differences; in the other, it is due to genetic alterations. Finally, using a clonal drug response assay together with stochastic simulations, we attribute subclonal drug response variability within sublines to stochastic cell fate decisions and confirm that one subline likely contains genetic resistance mutations that emerged in the absence of drug treatment.

Akt kinase LANCL2 functions as a key driver in EGFR-mutant lung adenocarcinoma tumorigenesis

Epidermal growth factor receptor (EGFR) is a key oncogene in lung adenocarcinoma (LUAD). Resistance to EGFR tyrosine kinase inhibitors is a major obstacle for EGFR-mutant LUAD patients. Our gene chip array, quantitative polymerase chain reaction validation, and shRNA-based high-content screening identified the Akt kinase lanthionine synthetase C-like protein 2 (LANCL2) as a pro-proliferative gene in the EGFR-mutant LUAD cell line PC9. Therefore, we investigated whether LANCL2 plays a role in promoting cell proliferation and drug resistance in EGFR-mutant LUAD. In silico clinical correlation analysis using the Cancer Genome Atlas Lung Adenocarcinoma dataset revealed a positive correlation between LANCL2 and EGFR expression and an inverse relationship between LANCL2 gain-of-function and survival in LUAD patients. The EGFR-mutant LUAD cell lines PC9 and HCC827 displayed higher LANCL2 expression than the non-EGFR-mutant cell line A549. In addition, LANCL2 was downregulated following gefitinib+pemetrexed combination therapy in PC9 cells. LANCL2 knockdown reduced proliferation and enhanced apoptosis in PC9, HCC827, and A549 cells in vitro and suppressed murine PC9 xenograft tumor growth in vivo. Notably, LANCL2 overexpression rescued these effects and promoted gefitinib + pemetrexed resistance in PC9 and HCC827 cells. Pathway analysis and co-immunoprecipitation followed by mass spectrometry of differentially-expressed genes in LANCL2 knockdown cells revealed enrichment of several cancer signaling pathways. In addition, Filamin A and glutathione S-transferase Mu 3 were identified as two novel protein interactors of LANCL2. In conclusion, LANCL2 promotes tumorigenic proliferation, suppresses apoptosis, and promotes gefitinib+pemetrexed resistance in EGFR-mutant LUAD cells. Based on the positive association between LANCL2, EGFR, and downstream Akt signaling, LANCL2 may be a promising new therapeutic target for EGFR-mutant LUAD.

KinaseMD: kinase mutations and drug response database

Mutations in kinases are abundant and critical to study signaling pathways and regulatory roles in human disease, especially in cancer. Somatic mutations in kinase genes can affect drug treatment, both sensitivity and resistance, to clinically used kinase inhibitors. Here, we present a newly constructed database, KinaseMD (kinase mutations and drug response), to structurally and functionally annotate kinase mutations. KinaseMD integrates 679 374 somatic mutations, 251 522 network-rewiring events, and 390 460 drug response records curated from various sources for 547 kinases. We uniquely annotate the mutations and kinase inhibitor response in four types of protein substructures (gatekeeper, A-loop, G-loop and αC-helix) that are linked to kinase inhibitor resistance in literature. In addition, we annotate functional mutations that may rewire kinase regulatory network and report four phosphorylation signals (gain, loss, up-regulation and down-regulation). Overall, KinaseMD provides the most updated information on mutations, unique annotations of drug response especially drug resistance and functional sites of kinases. KinaseMD is accessible at https://bioinfo.uth.edu/kmd/, having functions for searching, browsing and downloading data. To our knowledge, there has been no systematic annotation of these structural mutations linking to kinase inhibitor response. In summary, KinaseMD is a centralized database for kinase mutations and drug response.

Landscape of drug-resistance mutations in kinase regulatory hotspots

More than 48 kinase inhibitors (KIs) have been approved by Food and Drug Administration. However, drug-resistance (DR) eventually occurs, and secondary mutations have been found in the previously targeted primary-mutated cancer cells. Cancer and drug research communities recognize the importance of the kinase domain (KD) mutations for kinasopathies. So far, a systematic investigation of kinase mutations on DR hotspots has not been done yet. In this study, we systematically investigated four types of representative mutation hotspots (gatekeeper, G-loop, αC-helix and A-loop) associated with DR in 538 human protein kinases using large-scale cancer data sets (TCGA, ICGC, COSMIC and GDSC). Our results revealed 358 kinases harboring 3318 mutations that covered 702 drug resistance hotspot residues. Among them, 197 kinases had multiple genetic variants on each residue. We further computationally assessed and validated the epidermal growth factor receptor mutations on protein structure and drug-binding efficacy. This is the first study to provide a landscape view of DR-associated mutation hotspots in kinase's secondary structures, and its knowledge will help the development of effective next-generation KIs for better precision medicine.

DNA methylation-driven EMT is a common mechanism of resistance to various therapeutic agents in cancer

Background

Overcoming therapeutic resistance is one of the major hurdles in cancer care. One mechanism contributing to therapeutic resistance is a process in which epithelial cells switch to a mesenchymal state (epithelial-to-mesenchymal transition or EMT). The precise mechanisms driving EMT-mediated therapeutic resistance have, however, not been elucidated.

Results

Here, we study ten cell line pairs, for which parental cell lines were made resistant to either a targeted or chemotherapy-based treatment. First, we show by miRNA-200 overexpression that treatment resistance is driven by EMT. Next, we demonstrate that DNA methylation changes occur within each cell line pair and show that exposure to 5-azacytidine or knock down of DNA methyltransferases (DNMTs), both of which globally demethylate cells, result in EMT reversal and increased therapeutic sensitivity. This suggests DNA methylation to causally underlie EMT and treatment resistance. We also observe significant overlap in methylation profiles between resistant lines, suggesting a common epigenetic mechanism to cause resistance to therapy. In line with this hypothesis, cross-resistance to other targeted and chemotherapies is observed, while importantly, this is lost upon demethylation of the cells. Finally, we clinically validate that DNA methylation changes drive EMT-mediated resistance to sorafenib in patients with advanced hepatocellular carcinoma (HCC). Specifically, we develop a capture-based protocol to interrogate DNA methylation in low amounts of circulating tumor DNA (ctDNA). By interrogating the methylation status in liquid biopsies, longitudinally collected during sorafenib treatment, we assess whether DNA methylation changes also drive EMT and therapy resistance in a clinical setting. Particularly, by monitoring methylation changes in EMT genes, we are able to predict tumor response and acquired resistance to sorafenib.

Conclusions

We propose methylation changes underlying EMT to constitute a common resistance mechanism to cancer therapies. This process can be reversed pharmacologically and monitored non-invasively in ctDNA to predict resistance to treatment.

Investigation of efficacy and acquired resistance for EGFR-TKI plus bevacizumab as first-line treatment in patients with EGFR sensitive mutant non-small cell lung cancer in a Real world population

OBJECTIVES

We aimed to investigate the clinical efficacy of EGFR tyrosine kinase inhibitor (TKI, T) plus bevacizumab (an antiangiogenic therapy, A) in a real-world population and to provide insights into their mechanism of resistance.

METHODS

This study included 256 NSCLC patients harboring EGFR sensitizing mutations (EGFR 19del and L858R) who underwent nextgeneration sequencing (NGS) with 168-gene panel prior to treatment between Jan 2015 to Aug 2018. Cohort A included 60 patients treated with A + T; while cohort B consisted of 120 patients treated with EGFR-TKI monotherapy with the patients identified using Propensity Score Matching (Ratio of 1:2). Clinical outcomes and potential resistance mechanism were evaluated.

RESULTS

Baseline clinical characteristics were not significantly different between Cohort A and B. Compared with cohort B, cohort A had significantly better overall response rate (95% vs 74.2%, p = 0.001) and longer median progression-free survival (PFS, 16.5m vs.12.0 m, HR = 0.7, p = 0.001). Until Jan 2019, 31 and 103 patients in cohort A and B, respectively, were evaluated with progressive disease and underwent tissue re-biopsy and NGS profiling with 168-gene panel. In cohort B, T790M was the predominant acquired resistance mechanism, detected in 51.5% (53/103) of progressive tumors, followed by amplifications in EGFR (15.5%, 16/103) and MET (6.8%, 7/103). Contrastingly, cohort A had a significantly lower rate of T790 M mutation (35.5%, 11/31, p = 0.0003), followed by mutations in TP53 (29.0%, 9/31), RB1 (9.7%, 3/31), SMAD4 (3.2%, 1/31) and EGFR V834 L (3.2%, 1/31) and amplifications in EGFR (9.7%, 3/31), and MET(6.5%, 2/31).

CONCLUSION

Treatment with first-line A + T significantly extends the time to progression and increases the response rate with acceptable safety profile. T790 M was the most common acquired resistance mechanism but it was less common in patients who received A + T.

Decoupling of Nrf2 Expression Promotes Mesenchymal State Maintenance in Non-Small Cell Lung Cancer

Epithelial mesenchymal transition is a common mechanism leading to metastatic dissemination and cancer progression. In an effort to better understand this process we found an intersection of Nrf2/NLE2F2 (Nrf2), epithelial mesenchymal transition (EMT), and metabolic alterations using multiple in vitro and in vivo approaches. Nrf2 is a key transcription factor controlling the expression of redox regulators to establish cellular redox homeostasis. Nrf2 has been shown to exert both cancer inhibitory and stimulatory activities. Using multiple isogenic non-small cell lung cancer (NSCLC) cell lines, we observed a reduction of Nrf2 protein and activity in a prometastatic mesenchymal cell state and increased reactive oxygen species. Knockdown of Nrf2 promoted a mesenchymal phenotype and reduced glycolytic, TCA cycle and lipogenic output from both glucose and glutamine in the isogenic cell models; while overexpression of Nrf2 promoted a more epithelial phenotype and metabolic reactivation. In both Nrf2 knockout mice and in NSCLC patient samples, Nrf2^low was co-correlated with markedly decreased expression of glycolytic, lipogenic, and mesenchymal RNAs. Conversely, Nrf2^high was associated with partial mesenchymal epithelial transition and increased expression of metabolic RNAs. The impact of Nrf2 on epithelial and mesenchymal cancer cell states and metabolic output provide an additional context to Nrf2 function in cancer initiation and progression, with implications for therapeutic inhibition of Nrf2 in cancer treatment.

Quantifying Drug Combination Synergy along Potency and Efficacy Axes

Two goals motivate treating diseases with drug combinations: reduce off-target toxicity by minimizing doses (synergistic potency) and improve outcomes by escalating effect (synergistic efficacy). Established drug synergy frameworks obscure such distinction, failing to harness the potential of modern chemical libraries. We therefore developed multi-dimensional synergy of combinations (MuSyC), a formalism based on a generalized, multi-dimensional Hill equation, which decouples synergistic potency and efficacy. In mutant-EGFR-driven lung cancer, MuSyC reveals that combining a mutant-EGFR inhibitor with inhibitors of other kinases may result only in synergistic potency, whereas synergistic efficacy can be achieved by co-targeting mutant-EGFR and epigenetic regulation or microtubule polymerization. In mutant-BRAF melanoma, MuSyC determines whether a molecular correlate of BRAFi insensitivity alters a BRAF inhibitor's potency, efficacy, or both. These findings showcase MuSyC's potential to transform the enterprise of drug-combination screens by precisely guiding translation of combinations toward dose reduction, improved efficacy, or both.

Acquired Resistance to Tyrosine Kinase Inhibitors in Non-Small Cell Lung Cancers: The Role of Next-Generation Sequencing on Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration Samples

CONTEXT

- Molecular testing is essential for the diagnostic workup of patients with advanced non-small cell lung cancers. Cytology specimens from minimally invasive procedures, such as endobronchial ultrasound-guided transbronchial needle aspiration, are often the only available samples for these patients. The implementation of molecular diagnostic testing, and in particular next-generation sequencing-based testing, on these cytologic specimens is currently an evolving field for lung cytopathology. The application of these molecular analyses on tyrosine kinase inhibitor-resistant non-small cell lung cancers raises unique technical, biologic, and clinical challenges.

OBJECTIVE

- To provide an overview of the implementation of next-generation sequencing analysis on endobronchial ultrasound-guided transbronchial needle aspiration samples to detect the molecular aberrations underneath the phenomenon of acquired resistance in patients with non-small cell lung cancers progressing while on the EGFR/ALK tyrosine kinase inhibitor treatment.

DATA SOURCES

- Peer-reviewed original articles, review articles, and published guidelines and expert opinion reports were reviewed, together with our single-center experience.

CONCLUSIONS

- Next-generation sequencing analyses and the endobronchial ultrasound-guided transbronchial needle aspiration procedure may represent a valuable strategy to address the unique requirements of molecular testing on tyrosine kinase inhibitor-resistant non-small cell lung cancers.

The EGFR T790M Mutation Is Acquired through AICDA-Mediated Deamination of 5-Methylcytosine following TKI Treatment in Lung Cancer

: Almost all patients with EGFR-driven lung cancer who are treated with EGFR tyrosine kinase inhibitors (TKI) develop resistance to treatment. A single base (c.2369C>T) transition mutation, EGFR T790M, is the most frequent resistance event after first-generation exposure to EGFR TKIs. Whether T790M mutation is acquired or is selected from a preexisting clone has been a matter of significant debate. In this study, we show that treatment with EGFR TKIs leads to activation of the NFκB pathway, which in turn induces expression of activation-induced cytidine deaminase (AICDA). In turn, AICDA causes deamination of 5-methylcytosine to thymine at position c.2369 to generate the T790M mutation. Pharmacologic inhibition of the NFκB pathway or knockout of AICDA decreased the frequency or prevented the development of T790M mutation, respectively. In addition, patients treated with first-line EGFR TKI displayed increased expression of AICDA and detection of the T790M mutation upon progression. These results identify the mechanism of T790M acquisition and present an opportunity to target the process to delay or prevent it. SIGNIFICANCE: These findings identify the mechanism behind acquisition of a common resistance mutation to TKI treatment in lung cancer.

RNA Sequencing of Carboplatin- and Paclitaxel-Resistant Endometrial Cancer Cells Reveals New Stratification Markers and Molecular Targets for Cancer Treatment

Despite advances in surgical technique and adjuvant treatment, endometrial cancer has recently seen an increase in incidence and mortality in the USA. The majority of endometrial cancers can be cured by surgery alone or in combination with adjuvant chemo- or radiotherapy; however, a subset of patients experience recurrence for reasons that remain unclear. Recurrence is associated with chemoresistance to carboplatin and paclitaxel and consequentially, high mortality. Understanding the pathways involved in endometrial cancer chemoresistance is paramount for the identification of biomarkers and novel molecular targets for this disease. Here, we generated the first matched pairs of carboplatin-sensitive/carboplatin-resistant and paclitaxel-sensitive/paclitaxel-resistant endometrial cancer cells and subjected them to bulk RNA sequencing analysis. We found that 45 genes are commonly upregulated in carboplatin- and paclitaxel-resistant cells as compared to controls. Of these, the leukemia inhibitory factor, (LIF), the protein tyrosine phosphatase type IVA, member 3 (PTP4A3), and the transforming growth factor beta 1 (TGFB1) showed a highly significant correlation between expression level and endometrial cancer overall survival (OS) and can stratify the 545 endometrial cancer patients in the TCGA cohort into a high-risk and low-risk-cohorts. Additionally, four genes within the 45 upregulated chemoresistance-associated genes are ADAMTS5, MICAL2, STAT5A, and PTP4A3 codes for proteins for which small-molecule inhibitors already exist. We identified these proteins as molecular targets for chemoresistant endometrial cancer and showed that treatment with their correspondent inhibitors effectively killed otherwise chemoresistant cells. Collectively, these findings underline the utility of matched pair of chemosensitive and chemoresistant cancer cells to identify markers for endometrial cancer risk stratification and to serve as a pharmacogenomics model for identification of alternative chemotherapy approaches for treatment of patients with recurrent disease.

Overall survival in EGFR mutated non-small-cell lung cancer patients treated with afatinib after EGFR TKI and resistant mechanisms upon disease progression

PURPOSE

To determine survival in afatinib-treated patients after treatment with first-generation EGFR tyrosine kinase inhibitors (TKIs) and to study resistance mechanisms in afatinib-resistant tumors.

METHODS

Characteristics and survival of patients treated with afatinib after resistance to erlotinib or gefitinib in two large Dutch centers were collected. Whole exome sequencing (WES) and pathway analysis was performed on available pre- and post-afatinib tumor biopsies and normal tissue.

RESULTS

A total of 38 patients were treated with afatinib. T790M mutations were identified in 22/29 (76%) pre-afatinib treatment tumor samples. No difference in median progression-free-survival (2.8 months (95% CI 2.3-3.3) and 2.7 months (95% CI 0.9-4.6), p = 0.55) and median overall-survival (8.8 months (95% CI 4.2-13.4) and 3.6 months (95% CI 2.3-5.0), p = 0.14) were observed in T790M+ patients compared to T790M- mutations. Somatic mutations in TP53, ADAMTS2, CNN2 and multiple genes in the Wnt and PI3K-AKT pathway were observed in post-afatinib tumors of six afatinib-responding and in one non-responding patient. No new EGFR mutations were found in the post-afatinib samples of the six responding patients. Further analyses of post-afatinib progressive tumors revealed 28 resistant specific mutations in six genes (HLA-DRB1, AQP7, FAM198A, SEC31A, CNTLN, and ESX1) in three afatinib responding patients. No known EGFR-TKI resistant-associated copy number gains were acquired in the post-afatinib samples.

CONCLUSION

No differences in survival were observed in patients with EGFR-T790M treated with afatinib compared to those without T790M. Tumors from patients who had progressive disease during afatinib treatment were enriched for mutations in genes involved in Wnt and PI3K-AKT pathways.

Next generation sequencing: implications in personalized medicine and pharmacogenomics

A breakthrough in next generation sequencing (NGS) in the last decade provided an unprecedented opportunity to investigate genetic variations in humans and their roles in health and disease. NGS offers regional genomic sequencing such as whole exome sequencing of coding regions of all genes, as well as whole genome sequencing. RNA-seq offers sequencing of the entire transcriptome and ChIP-seq allows for sequencing the epigenetic architecture of the genome. Identifying genetic variations in individuals can be used to predict disease risk, with the potential to halt or retard disease progression. NGS can also be used to predict the response to or adverse effects of drugs or to calculate appropriate drug dosage. Such a personalized medicine also provides the possibility to treat diseases based on the genetic makeup of the patient. Here, we review the basics of NGS technologies and their application in human diseases to foster human healthcare and personalized medicine.

Breast Cancer Risk Associated with Genotype Polymorphisms of the Aurora Kinase a Gene (AURKA): a Case-Control Study in a High Altitude Ecuadorian Mestizo Population

Breast cancer (BC) is the leading cause of cancer related death among women in 2014. The AURKA gene that encodes the protein called Aurora kinase A plays an important role in the progression of the cell cycle, by controlling and promoting the entry into the phase of mitosis. The single nucleotide polymorphism AURKA T91A (rs2273535) (Phe21Ile) has been identified as functional alternator of this kinase, the Ile allele is associated with the occurrence of chromosome segregation errors and tumor progression. Therefore, it is essential to know how BC risk is associated with histopathological characteristics, immunohistochemical characteristics, and genotype polymorphism in a high altitude Ecuadorian mestizo population. In this retrospective case-control study 200 individuals were analyzed. DNA was extracted from 100 healthy and 100 affected women. Genotypes were determined by genomic sequencing. We found significant association between the AURKA T91A (rs2273535) (Phe21Ile) genotype and an increased risk of BC development: Phe/Ile (odds ratio [OR] = 2.6; 95% confidence interval [CI] = 1.4-4.9; P = 0.004), Ile/Ile (OR = 3.8; 95% CI = 1.6-9.0; P = 0.002), and Phe/Ile + Ile/Ile (OR = 2.9; 95% CI = 1.6-5.2; P = 0.001). Additionally, the rs2273535 variant was associated with the tumor grade SBR III (OR = 9.6; 95% CI = 1.0-91.9; P = 0.048) and the Ki-67 ≥ 20 (OR = 16.5; 95% CI = 2.7-101.3; P = 0.002). In brief, this study provides the first evidence where the Ile allele of the AURKA gene could act as potentially predictive biomarker of BC in the high altitude Ecuadorian mestizo population that lives at 2800 m above sea level (masl).

Perturbations in the Replication Program Contribute to Genomic Instability in Cancer

Cancer and genomic instability are highly impacted by the deoxyribonucleic acid (DNA) replication program. Inaccuracies in DNA replication lead to the increased acquisition of mutations and structural variations. These inaccuracies mainly stem from loss of DNA fidelity due to replication stress or due to aberrations in the temporal organization of the replication process. Here we review the mechanisms and impact of these major sources of error to the replication program.

Acriflavine Inhibits Acquired Drug Resistance by Blocking the Epithelial-to-Mesenchymal Transition and the Unfolded Protein Response

Epithelial-to-mesenchymal transition (EMT) is linked to tumor invasion, drug resistance and aggressive disease and this is largely dependent on the cell's microenvironment. Acriflavine (ACF) is an old antibacterial drug recently also suggested as anticancer agent and HIF inhibitor. We wanted to study the effect of acriflavine on EMT in different human cancer models. Pancreatic cancer cells (Panc-1) were exposed to TGF-β1 or cobalt chloride (to mimick severe hypoxia) to induce EMT. For our third model we exposed HepG2 liver cancer cells to sorafenib which resulted in development of acquired drug resistance with strong features of EMT and aggressive behavior. These models were morphologically and functionally (invasion assay) characterized. Markers of EMT were determined using qRT-PCR and Western blotting. Transcriptome analysis was performed following gene expression determination and combining the iRegulon tool and Gene Set Enrichment Analysis (GSEA). We made the following observations: (1) acriflavine inhibited EMT based on changes in cell morphology, invasive capacities and markers of EMT (at protein and gene expression level). (2) Transcriptome analysis revealed potent inhibition of ATF4 target genes and of the unfolded protein response. We showed that acriflavine blocked eIF2a phosphorylation and reduced ATF4 translation thereby inhibiting the PERK/eIF2a/ATF4 UPR pathway. (3) ACF restored drug sensitivity of cells that obtained acquired resistance.

CONCLUSIONS

We identified acriflavine as a potent inhibitor of EMT and the UPR, thereby re-sensitizing the cancer cells to antineoplastic drugs.

Genomic and transcriptomic analysis of imatinib resistance in gastrointestinal stromal tumors

Gastrointestinal stromal tumors represent the most common mesenchymal tumor of the digestive tract, driven by gain‐of‐function mutations in KIT. Despite its proven benefits, half of the patients treated with imatinib show disease progression within 2 years due to secondary resistance mutations in KIT. It remains unclear how the genomic and transcriptomic features change during the acquisition of imatinib resistance. Here, we performed exome sequencing and microarray transcription analysis for four imatinib‐resistant cell lines and one cell line briefly exposed to imatinib. We also performed exome sequencing of clinical tumor samples. The cell line briefly exposed to imatinib exhibited few single‐nucleotide variants and copy‐number alterations, but showed marked upregulation of genes related to detoxification and downregulation of genes involved in cell cycle progression. Meanwhile, resistant cell lines harbored numerous genomic changes: amplified genes related to detoxification and deleted genes with cyclin‐dependent kinase activity. Some variants in the resistant samples were traced back to the drug‐sensitive samples, indicating the presence of ancestral subpopulations. The subpopulations carried variants associated with cell death. Pre‐existing cancer cells with genetic alterations promoting apoptosis resistance may serve as a basis whereby cancer cells with critical mutations, such as secondary KIT mutations, can establish full imatinib resistance. © 2017 The Authors Genes, Chromosomes and Cancer Published by Wiley Periodicals, Inc.

Identifying anti-cancer drug response related genes using an integrative analysis of transcriptomic and genomic variations with cell line-based drug perturbations

Background

Clinical responses to anti-cancer therapies often only benefit a defined subset of patients. Predicting the best treatment strategy hinges on our ability to effectively translate genomic data into actionable information on drug responses.

Results

To achieve this goal, we compiled a comprehensive collection of baseline cancer genome data and drug response information derived from a large panel of cancer cell lines. This data set was applied to identify the signature genes relevant to drug sensitivity and their resistance by integrating CNVs and the gene expression of cell lines with in vitro drug responses. We presented an efficient in-silico pipeline for integrating heterogeneous cell line data sources with the simultaneous modeling of drug response values across all the drugs and cell lines. Potential signature genes correlated with drug response (sensitive or resistant) in different cancer types were identified. Using signature genes, our collaborative filtering-based drug response prediction model outperformed the 44 algorithms submitted to the DREAM competition on breast cancer cells. The functions of the identified drug response related signature genes were carefully analyzed at the pathway level and the synthetic lethality level. Furthermore, we validated these signature genes by applying them to the classification of the different subtypes of the TCGA tumor samples, and further uncovered their in vivo implications using clinical patient data.

Conclusions

Our work may have promise in translating genomic data into customized marker genes relevant to the response of specific drugs for a specific cancer type of individual patients.

Acriflavine Inhibits Acquired Drug Resistance by Blocking the Epithelial-to-Mesenchymal Transition and the Unfolded Protein Response

Epithelial-to-mesenchymal transition (EMT) is linked to tumor invasion, drug resistance and aggressive disease and this is largely dependent on the cell's microenvironment. Acriflavine (ACF) is an old antibacterial drug recently also suggested as anticancer agent and HIF inhibitor. We wanted to study the effect of acriflavine on EMT in different human cancer models. Pancreatic cancer cells (Panc-1) were exposed to TGF-β1 or cobalt chloride (to mimick severe hypoxia) to induce EMT. For our third model we exposed HepG2 liver cancer cells to sorafenib which resulted in development of acquired drug resistance with strong features of EMT and aggressive behavior. These models were morphologically and functionally (invasion assay) characterized. Markers of EMT were determined using qRT-PCR and Western blotting. Transcriptome analysis was performed following gene expression determination and combining the iRegulon tool and Gene Set Enrichment Analysis (GSEA). We made the following observations: (1) acriflavine inhibited EMT based on changes in cell morphology, invasive capacities and markers of EMT (at protein and gene expression level). (2) Transcriptome analysis revealed potent inhibition of ATF4 target genes and of the unfolded protein response. We showed that acriflavine blocked eIF2a phosphorylation and reduced ATF4 translation thereby inhibiting the PERK/eIF2a/ATF4 UPR pathway. (3) ACF restored drug sensitivity of cells that obtained acquired resistance. Conclusions: We identified acriflavine as a potent inhibitor of EMT and the UPR, thereby re-sensitizing the cancer cells to antineoplastic drugs.

Understanding Mechanisms of Resistance in the Epithelial Growth Factor Receptor in Non-Small Cell Lung Cancer and the Role of Biopsy at Progression

Molecular profiling and the discovery of drugs that target specific activating mutations have allowed the personalization of treatment for non-small cell lung cancer (NSCLC). The epithelial growth factor receptor (EGFR) is frequently over-expressed and/or aberrantly activated in different cancers, including NSCLC. The most common activating mutations of EGFR in NSCLC fall within the tyrosine kinase-binding domain. Three oral EGFR tyrosine kinase inhibitors (TKIs) have been approved by the U.S. Food and Drug Administration (FDA) for first-line use in patients with EGFR mutation-positive NSCLC (exon 19 deletions or exon 21 [L858R] substitution mutations), as detected by an FDA-approved test. However, disease progression is common and is often the result of secondary mutations, of which the EGFR T790M mutation is the most prevalent. Few options were available upon progression until the introduction of osimertinib, a kinase inhibitor that targets the T790M mutation, which was recently approved for use in patients with metastatic EGFR T790M mutation-positive NSCLC, as detected by an FDA-approved test, who progressed on or after EGFR TKI therapy. With the introduction of osimertinib, outcomes can now be improved in select patients. Therefore, performing a biopsy at progression to determine the underlying molecular cause of the acquired resistance is important for the enabling of individualized options that may provide the greatest opportunity for improved outcomes. This review discusses the latest updates in molecular testing at progression and outlines treatment options for this difficult-to-treat population.

THE ONCOLOGIST

2017;22:3-11 IMPLICATIONS FOR PRACTICE: Although the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs)-gefitinib, erlotinib, and afatinib-have changed the treatment paradigm for non-small cell lung cancer among those with EGFR mutation positive disease, most patients experience progression after approximately 12 months of treatment. Until recently, options were limited for patients who progressed, but improvements in molecular profiling and the approval of osimertinib, which targets the resistance mutation T790M, afford the opportunity for improved outcomes in many patients with this mutation. This article explains the options available after progression on initial EGFR TKI therapy and the importance of molecular testing at progression in making treatment decisions.

HNSCC cells resistant to EGFR pathway inhibitors are hypermutated and sensitive to DNA damaging substances

Despite remarkable successes with targeted therapies in the treatment of cancer, resistance can occur which limits the clinical outcome. In this study, we generated and characterized resistant cell clones derived from two different head and neck squamous cell carcinoma (HNSCC) cell lines (Cal27, UD-SCC-5) by long-term exposure to five targeted- and chemotherapeutics (afatinib, MK2206, BEZ235, olaparib and cisplatin). The resistant tumor cell clones showed an increased ERK1/2 expression and an altered expression of the stem-cell markers CD44, ALDH1, Oct4, Sox2, Nanog and Bmi1. None of the single markers alone was predictive for resistance to all five targeted- and chemotherapeutics. Furthermore, long-term exposure of tumor cells to these five drugs resulted in an eightfold increase in the mutational rate compared to untreated cells. Interestingly, targeted- and chemotherapy resistant cell clones remained sensitive to irradiation. Lastly, clones that were resistant to afatinib, MK2206 or BEZ235 showed cross-resistance to further treatment with therapeutics that affect the same signaling pathway, but remained sensitive to those affecting different pathways such as cisplatin and olaparib. In contrast, cell clones which were once resistant to cisplatin or olaparib were found to be multidrug-resistant. These data might indicate that patients with HNSCC benefit more by a first line targeted therapy followed by cisplatin as a second line therapy.

Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells

Cancer therapy has traditionally focused on eliminating fast-growing populations of cells. Yet, an increasing body of evidence suggests that small subpopulations of cancer cells can evade strong selective drug pressure by entering a 'persister' state of negligible growth. This drug-tolerant state has been hypothesized to be part of an initial strategy towards eventual acquisition of bona fide drug-resistance mechanisms. However, the diversity of drug-resistance mechanisms that can expand from a persister bottleneck is unknown. Here we compare persister-derived, erlotinib-resistant colonies that arose from a single, EGFR-addicted lung cancer cell. We find, using a combination of large-scale drug screening and whole-exome sequencing, that our erlotinib-resistant colonies acquired diverse resistance mechanisms, including the most commonly observed clinical resistance mechanisms. Thus, the drug-tolerant persister state does not limit--and may even provide a latent reservoir of cells for--the emergence of heterogeneous drug-resistance mechanisms.

Dose-Dependent Mutation Rates Determine Optimum Erlotinib Dosing Strategies for EGFR Mutant Non-Small Cell Lung Cancer Patients

BACKGROUND

The advent of targeted therapy for cancer treatment has brought about a paradigm shift in the clinical management of human malignancies. Agents such as erlotinib used for EGFR-mutant non-small cell lung cancer or imatinib for chronic myeloid leukemia, for instance, lead to rapid tumor responses. Unfortunately, however, resistance often emerges and renders these agents ineffective after a variable amount of time. The FDA-approved dosing schedules for these drugs were not designed to optimally prevent the emergence of resistance. To this end, we have previously utilized evolutionary mathematical modeling of treatment responses to elucidate the dosing schedules best able to prevent or delay the onset of resistance. Here we expand on our approaches by taking into account dose-dependent mutation rates at which resistant cells emerge. The relationship between the serum drug concentration and the rate at which resistance mutations arise can lead to non-intuitive results about the best dose administration strategies to prevent or delay the emergence of resistance.

METHODS

We used mathematical modeling, available clinical trial data, and different considerations of the relationship between mutation rate and drug concentration to predict the effectiveness of different dosing strategies.

RESULTS

We designed several distinct measures to interrogate the effects of different treatment dosing strategies and found that a low-dose continuous strategy coupled with high-dose pulses leads to the maximal delay until clinically observable resistance. Furthermore, the response to treatment is robust against different assumptions of the mutation rate as a function of drug concentration.

CONCLUSIONS

For new and existing targeted drugs, our methodology can be employed to compare the effectiveness of different dose administration schedules and investigate the influence of changing mutation rates on outcomes.

Cancer Cell Line Panels Empower Genomics-Based Discovery of Precision Cancer Medicine

Since the first human cancer cell line, HeLa, was established in the early 1950s, there has been a steady increase in the number and tumor type of available cancer cell line models. Cancer cell lines have made significant contributions to the development of various chemotherapeutic agents. Recent advances in multi-omics technologies have facilitated detailed characterizations of the genomic, transcriptomic, proteomic, and epigenomic profiles of these cancer cell lines. An increasing number of studies employ the power of a cancer cell line panel to provide predictive biomarkers for targeted and cytotoxic agents, including those that are already used in clinical practice. Different types of statistical and machine learning algorithms have been developed to analyze the large-scale data sets that have been produced. However, much work remains to address the discrepancies in drug assay results from different platforms and the frequent failures to translate discoveries from cell line models to the clinic. Nevertheless, continuous expansion of cancer cell line panels should provide unprecedented opportunities to identify new candidate targeted therapies, particularly for the so-called "dark matter" group of cancers, for which pharmacologically tractable driver mutations have not been identified.

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.

Inconsistency and features of single nucleotide variants detected in whole exome sequencing versus transcriptome sequencing: A case study in lung cancer

Whole exome sequencing (WES) and RNA sequencing (RNA-Seq) are two main platforms used for next-generation sequencing (NGS). While WES is primarily for DNA variant discovery and RNA-Seq is mainly for measurement of gene expression, both can be used for detection of genetic variants, especially single nucleotide variants (SNVs). How consistently variants can be detected from WES and RNA-Seq has not been systematically evaluated. In this study, we examined the technical and biological inconsistencies in SNV detection using WES and RNA-Seq data from 27 pairs of tumor and matched normal samples. We analyzed SNVs in three categories: WES unique - those only detected in WES, RNA-Seq unique - those only detected in RNA-Seq, and shared - those detected in both. We found a small overlap (average ∼14%) between the SNVs called in WES and RNA-Seq. The WES unique SNVs were mainly due to low coverage, low expression, or their location on the non-transcribed strand in RNA-Seq data, while the RNA-Seq unique SNVs were primarily due to their location out of the WES-capture boundary regions (accounting ∼71%), as well as low coverage of the regions, low coverage of the mutant alleles or RNA-editing. The shared SNVs had high locus-specific coverage in both WES and RNA-Seq and high gene expression levels. Additionally, WES unique and RNA-Seq unique SNVs showed different nucleotide substitution patterns, e.g., ∼55% of RNA-Seq unique variants were A:T→G:C, a hallmark of RNA editing. This study provides an important evaluation on the inconsistencies of somatic SNVs called in WES and RNA-Seq data.

Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy

The human kinome is gaining importance through its promising cancer therapeutic targets, yet no general model to address the kinase inhibitor resistance has emerged. Here, we constructed a systems biology-based framework to catalogue the human kinome, including 538 kinase genes, in the broader context of the human interactome. Specifically, we constructed three networks: a kinase-substrate interaction network containing 7,346 pairs connecting 379 kinases to 36,576 phosphorylation sites in 1,961 substrates, a protein-protein interaction network (PPIN) containing 92,699 pairs, and an atomic resolution PPIN containing 4,278 pairs. We identified the conserved regulatory phosphorylation motifs (e.g., Ser/Thr-Pro) using a sequence logo analysis. We found the typical anticancer target selection strategy that uses network hubs as drug targets, might lead to a high adverse drug reaction risk. Furthermore, we found the distinct network centrality of kinases creates a high anticancer drug resistance risk by feedback or crosstalk mechanisms within cellular networks. This notion is supported by the systematic network and pathway analyses that anticancer drug resistance genes are significantly enriched as hubs and heavily participate in multiple signaling pathways. Collectively, this comprehensive human kinome interactome map sheds light on anticancer drug resistance mechanisms and provides an innovative resource for rational kinase inhibitor design.

Unraveling tumor grading and genomic landscape in lung neuroendocrine tumors

Currently, grading in lung neuroendocrine tumors (NETs) is inherently defined by the histological classification based on cell features, mitosis count, and necrosis, for which typical carcinoids (TC) are low-grade malignant tumors with long life expectation, atypical carcinoids (AC) intermediate-grade malignant tumors with more aggressive clinical behavior, and large cell NE carcinomas (LCNEC) and small cell lung carcinomas (SCLC) high-grade malignant tumors with dismal prognosis. While Ki-67 antigen labeling index, highlighting the proportion of proliferating tumor cells, has largely been used in digestive NETs for assessing prognosis and assisting therapy decisions, the same marker does not play an established role in the diagnosis, grading, and prognosis of lung NETs. Next generation sequencing techniques (NGS), thanks to their astonishing ability to process in a shorter timeframe up to billions of DNA strands, are radically revolutionizing our approach to diagnosis and therapy of tumors, including lung cancer. When applied to single genes, panels of genes, exome, or the whole genome by using either frozen or paraffin tissues, NGS techniques increase our understanding of cancer, thus realizing the bases of precision medicine. Data are emerging that TC and AC are mainly altered in chromatin remodeling genes, whereas LCNEC and SCLC are also mutated in cell cycle checkpoint and cell differentiation regulators. A common denominator to all lung NETs is a deregulation of cell proliferation, which represents a biological rationale for morphologic (mitoses and necrosis) and molecular (Ki-67 antigen) parameters to successfully serve as predictors of tumor behavior (i.e., identification of pathological entities with clinical correlation). It is envisaged that a novel grading system in lung NETs based on the combined assessment of mitoses, necrosis, and Ki-67 LI may offer a better stratification of prognostic classes, realizing a bridge between molecular alterations, morphological features, and clinical behavior.

Stress-induced cellular adaptive strategies: ancient evolutionarily conserved programs as new anticancer therapeutic targets

Despite the remarkable achievements of novel targeted anti-cancer drugs, most therapies only produce remission for a limited time, resistance to treatment, and relapse, often being the ultimate outcome. Drug resistance is due to highly efficient adaptive strategies utilized by cancer cells. Exogenous and endogenous stress stimuli are known to induce first-line responses, capable of re-establishing cellular homeostasis and determining cell fate decisions. Cancer cells may also mount second-line adaptive strategies, such as the mutator response. Hypermutable subpopulations of cells may expand under severe selective stress, thereby accelerating the emergence of adapted clones. As with first-line protective responses, these strategies appear highly conserved, and are found in yeasts and bacteria. We hypothesize that evolutionarily conserved programs rheostatically regulate mutability in fluctuating environments, and contribute to drug resistance in cancer cells. Elucidating the conserved genetic and molecular mechanisms may present novel opportunities to increase the effectiveness of cancer therapies.

Complex correlations: replication timing and mutational landscapes during cancer and genome evolution

A recent flurry of reports correlates replication timing (RT) with mutation rates during both evolution and cancer. Specifically, point mutations and copy number losses correlate with late replication, while copy number gains and other rearrangements correlate with early replication. In some cases, plausible mechanisms have been proposed. Point mutation rates may reflect temporal variation in repair mechanisms. Transcription-induced double-strand breaks are expected to occur in transcriptionally active early replicating chromatin. Fusion partners are generally in close proximity, and chromatin in close proximity replicates at similar times. However, temporal enrichment of copy number gains and losses remains an enigma. Moreover, many conclusions are compromised by a lack of matched RT and sequence datasets, the filtering out of developmental variation in RT, and the use of somatic cell lines to make inferences about germline evolution.

CNVannotator: a comprehensive annotation server for copy number variation in the human genome

Copy number variation (CNV) is one of the most prevalent genetic variations in the genome, leading to an abnormal number of copies of moderate to large genomic regions. High-throughput technologies such as next-generation sequencing often identify thousands of CNVs involved in biological or pathological processes. Despite the growing demand to filter and classify CNVs by factors such as frequency in population, biological features, and function, surprisingly, no online web server for CNV annotations has been made available to the research community. Here, we present CNVannotator, a web server that accepts an input set of human genomic positions in a user-friendly tabular format. CNVannotator can perform genomic overlaps of the input coordinates using various functional features, including a list of the reported 356,817 common CNVs, 181,261 disease CNVs, as well as, 140,342 SNPs from genome-wide association studies. In addition, CNVannotator incorporates 2,211,468 genomic features, including ENCODE regulatory elements, cytoband, segmental duplication, genome fragile site, pseudogene, promoter, enhancer, CpG island, and methylation site. For cancer research community users, CNVannotator can apply various filters to retrieve a subgroup of CNVs pinpointed in hundreds of tumor suppressor genes and oncogenes. In total, 5,277,234 unique genomic coordinates with functional features are available to generate an output in a plain text format that is free to download. In summary, we provide a comprehensive web resource for human CNVs. The annotated results along with the server can be accessed at http://bioinfo.mc.vanderbilt.edu/CNVannotator/.

Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers

BACKGROUND

Driven by high throughput next generation sequencing technologies and the pressing need to decipher cancer genomes, computational approaches for detecting somatic single nucleotide variants (sSNVs) have undergone dramatic improvements during the past 2 years. The recently developed tools typically compare a tumor sample directly with a matched normal sample at each variant locus in order to increase the accuracy of sSNV calling. These programs also address the detection of sSNVs at low allele frequencies, allowing for the study of tumor heterogeneity, cancer subclones, and mutation evolution in cancer development.

METHODS

We used whole genome sequencing (Illumina Genome Analyzer IIx platform) of a melanoma sample and matched blood, whole exome sequencing (Illumina HiSeq 2000 platform) of 18 lung tumor-normal pairs and seven lung cancer cell lines to evaluate six tools for sSNV detection: EBCall, JointSNVMix, MuTect, SomaticSniper, Strelka, and VarScan 2, with a focus on MuTect and VarScan 2, two widely used publicly available software tools. Default/suggested parameters were used to run these tools. The missense sSNVs detected in these samples were validated through PCR and direct sequencing of genomic DNA from the samples. We also simulated 10 tumor-normal pairs to explore the ability of these programs to detect low allelic-frequency sSNVs.

RESULTS

Out of the 237 sSNVs successfully validated in our cancer samples, VarScan 2 and MuTect detected the most of any tools (that is, 204 and 192, respectively). MuTect identified 11 more low-coverage validated sSNVs than VarScan 2, but missed 11 more sSNVs with alternate alleles in normal samples than VarScan 2. When examining the false calls of each tool using 169 invalidated sSNVs, we observed >63% false calls detected in the lung cancer cell lines had alternate alleles in normal samples. Additionally, from our simulation data, VarScan 2 identified more sSNVs than other tools, while MuTect characterized most low allelic-fraction sSNVs.

CONCLUSIONS

Our study explored the typical false-positive and false-negative detections that arise from the use of sSNV-calling tools. Our results suggest that despite recent progress, these tools have significant room for improvement, especially in the discrimination of low coverage/allelic-frequency sSNVs and sSNVs with alternate alleles in normal samples.