Discrepancies in cancer genomic sequencing highlight opportunities for driver mutation discovery

Coexistence of Multiple Gene Variants in Some Patients with Erythrocytoses

Background

Erythrocytosis is a relatively common condition; however, a large proportion of these patients (70%) remain without a clear etiologic explanation.

Methods

We set up a targeted NGS panel for patients with erythrocytosis, and 118 sporadic patients with idiopathic erythrocytosis were studied.

Results

In 40 (34%) patients, no variant was found, while in 78 (66%), we identified at least one germinal variant; 55 patients (70.5%) had 1 altered gene, 18 (23%) had 2 alterations, and 5 (6.4%) had 3. An altered HFE gene was observed in 51 cases (57.1%), EGLN1 in 18 (22.6%) and EPAS1, EPOR, JAK2, and TFR2 variants in 7.7%, 10.3%, 11.5%, and 14.1% patients, respectively. In 23 patients (19.45%), more than 1 putative variant was found in multiple genes.

Conclusions

Genetic variants in patients with erythrocytosis were detected in about 2/3 of our cohort. An NGS panel including more candidate genes should reduce the number of cases diagnosed as "idiopathic" erythrocytosis in which a cause cannot yet be identified. It is known that HFE variants are common in idiopathic erythrocytosis. TFR2 alterations support the existence of a relationship between genes involved in iron metabolism and impaired erythropoiesis. Some novel multiple variants were identified. Erythrocytosis appears to be often multigenic.

RNA Sequencing of Hepatobiliary Cancer Cell Lines: Data and Applications to Mutational and Transcriptomic Profiling

Cancer cell lines allow the identification of clinically relevant alterations and the prediction of drug response. However, sequencing data for hepatobiliary cancer cell lines in general, and particularly gallbladder cancer (GBC), are sparse. Here, we apply RNA sequencing to characterize 10 GBC, eight hepatocellular carcinoma, and five cholangiocarcinoma (CCA) cell lines. RNA extraction, quality control, library preparation, sequencing, and pre-processing of sequencing data were implemented using state-of-the-art techniques. Public data from the MSK-IMPACT database and a large cohort of Japanese biliary tract cancer patients were used to illustrate the usage of the released data. The total number of exonic mutations varied from 7207 for the cell line NOZ to 9760 for HuCCT1. Researchers planning experiments that require TP53 mutations could use the cell lines NOZ, OCUG-1, SNU308, or YoMi. Mz-Cha-1 showed mutations in ATM, SNU308 presented SMAD4 mutations, and the only investigated cell line that showed ARID1A mutations was GB-d1. SNU478 was the cell line with the global gene expression pattern most similar to GBC, intrahepatic CCA, and extrahepatic CCA. EGFR, KMT2D, and KMT2C generally presented a higher expression in the investigated cell lines than in Japanese primary GBC tumors. We provide the scientific community with detailed mutation and gene expression data, together with three showcase applications, with the aim of facilitating the design of future in vitro cell culture assays for research on hepatobiliary cancer.

Evaluating the consistency of large-scale pharmacogenomic studies

Recent years have seen an increase in the availability of pharmacogenomic databases such as Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) that provide genomic and functional characterization information for multiple cell lines. Studies have alluded to the fact that specific characterizations may be inconsistent between different databases. Analysis of the potential discrepancies in the different databases is highly significant, as these sources are frequently used to analyze and validate methodologies for personalized cancer therapies. In this article, we review the recent developments in investigating the correspondence between different pharmacogenomics databases and discuss the potential factors that require attention when incorporating these sources in any modeling analysis. Furthermore, we explored the consistency among these databases using copulas that can capture nonlinear dependencies between two sets of data.

CCLA: an accurate method and web server for cancer cell line authentication using gene expression profiles

Cancer cell lines (CCLs) as important model systems play critical roles in cancer research. The misidentification and contamination of CCLs are serious problems, leading to unreliable results and waste of resources. Current methods for CCL authentication are mainly based on the CCL-specific genetic polymorphism, whereas no method is available for CCL authentication using gene expression profiles. Here, we developed a novel method and homonymic web server (CCLA, Cancer Cell Line Authentication, http://bioinfo.life.hust.edu.cn/web/CCLA/) to authenticate 1291 human CCLs of 28 tissues using gene expression profiles. CCLA showed an excellent speed advantage and high accuracy for CCL authentication, a top 1 accuracy of 96.58 or 92.15% (top 3 accuracy of 100 or 95.11%) for microarray or RNA-Seq validation data (719 samples, 461 CCLs), respectively. To the best of our knowledge, CCLA is the first approach to authenticate CCLs using gene expression data. Users can freely and conveniently authenticate CCLs using gene expression profiles or NCBI GEO accession on CCLA website.

False-negative errors in next-generation sequencing contribute substantially to inconsistency of mutation databases

Background

More than 11,000 laboratories and companies developed their own next-generation sequencing (NGS) for screening and diagnosis of various diseases including cancer. Although inconsistencies of mutation calls as high as 43% in databases such as GDSC (Genomics of Drug Sensitivity in Cancer) and CCLE (Cancer Cell Line Encyclopedia) have been reported, not many studies on the reasons for the inconsistencies have been published. Methods: Targeted-NGS analysis of 151 genes in 35 cell lines common to GDSC and CCLE was performed, and the results were compared with those from GDSC and CCLE wherein whole-exome- or highly-multiplex NGS were employed.

Results

In the comparison, GDSC and CCLE had a high rate (40–45%) of false-negative (FN) errors which would lead to high rate of inconsistent mutation calls, suggesting that highly-multiplex NGS may have high rate of FN errors. We also posited the possibility that targeted NGS, especially for the detection of low-level cancer cells in cancer tissues might suffer significant FN errors.

Conclusion

FN errors may be the most important errors in NGS testing for cancer; their evaluation in laboratory-developed NGS tests is needed.

Monitoring circulating tumor DNA by analyzing personalized cancer-specific rearrangements to detect recurrence in gastric cancer

Circulating tumor DNA (ctDNA) has emerged as a candidate biomarker for cancer screening. However, studies on the usefulness of ctDNA for postoperative recurrence monitoring are limited. The present study monitored ctDNA in postoperative blood by employing cancer-specific rearrangements. Personalized cancer-specific rearrangements in 25 gastric cancers were analyzed by whole-genome sequencing (WGS) and were employed for ctDNA monitoring with blood up to 12 months after surgery. Personalized cancer-specific rearrangements were identified in 19 samples. The median lead time, which is the median duration between a positive ctDNA detection and recurrence, was 4.05 months. The presence of postoperative ctDNA prior to clinical recurrence was significantly correlated with cancer recurrence within 12 months of surgery (P = 0.029); in contrast, no correlation was found between cancer recurrence and the presence of preoperative ctDNA, suggesting the clinical usefulness of postoperative ctDNA monitoring for cancer recurrence in gastric cancer patients. However, the clinical application of ctDNA can be limited by the presence of ctDNA non-shedders (42.1%, 8/19) and by inconsistent postoperative ctDNA positivity.

Fragments of tumor DNA, or circulating tumor DNA (ctDNA), in blood can help predict stomach cancer recurrence within 12 months of surgery. Kyeong-Man Hong at the National Cancer Center, in Goyang-si, South Korea, and colleagues, carried out whole genome sequencing of stomach tumor samples from 25 patients to identify personalized cancer-specific rearranged DNA sequences. When they used this information to monitor ctDNA in blood samples obtained after surgical removal of the tumor, they found a significant correlation between the presence of ctDNA and cancer recurrence. In most cases, ctDNA was detected around four months prior to clinical recurrence, highlighting the potential usefulness of ctDNA monitoring. The lack of correlation between ctDNA levels and tumor size suggests that further research into the factors determining ctDNA levels is needed.

Mutation pattern analysis reveals polygenic mini-drivers associated with relapse after surgery in lung adenocarcinoma

The genomic lesions found in malignant tumours exhibit a striking degree of heterogeneity. Many tumours lack a known driver mutation, and their genetic basis is unclear. By mapping the somatic mutations identified in primary lung adenocarcinomas onto an independent coexpression network derived from normal tissue, we identify a critical gene network enriched for metastasis-associated genes. While individual genes within this module were rarely mutated, a significant accumulation of mutations within this geneset was predictive of relapse in lung cancer patients that have undergone surgery. Since it is the density of mutations within this module that is informative, rather than the status of any individual gene, these data are in keeping with a 'mini-driver' model of tumorigenesis in which multiple mutations, each with a weak effect, combine to form a polygenic driver with sufficient power to significantly alter cell behaviour and ultimately patient outcome. These polygenic mini-drivers therefore provide a means by which heterogeneous mutation patterns can generate the consistent hallmark changes in phenotype observed across tumours.

Inherent and toxicant-provoked reduction in DNA repair capacity: A key mechanism for personalized risk assessment, cancer prevention and intervention, and response to therapy

Genomic investigations reveal novel evidence which indicates that genetic predisposition and inherent drug response are key factors for development of cancer and for poor response to therapy. However, mechanisms for these outcomes and interactions with environmental factors have not been well-characterized. Therefore, cancer risk, prevention, intervention and prognosis determinations have still mainly been based on population, rather than on individualized, evaluations. The objective of this review was to demonstrate that a key mechanism which contributes to the determination is inherent and/or toxicant-provoked reduction in DNA repair capacity. In addition, functional and quantitative determination of DNA repair capacity on an individual basis would dramatically change the evaluation and management of health problems from a population to a personalized basis. In this review, justifications for the scenario were delineated. Topics to be presented include assays for detection of functional DNA repair deficiency, mechanisms for DNA repair defects, toxicant-perturbed DNA repair capacity, epigenetic mechanisms (methylation and miRNA expression) for alteration of DNA repair function, and bioinformatics approach to analyze large amount of genomic data. Information from these topics has recently been and will be used for better understanding of cancer causation and of response to therapeutic interventions. Consequently, innovative genomic- and mechanism-based evidence can be increasingly used to develop more precise cancer risk assessment, and target-specific and personalized medicine.

HuH-7 reference genome profile: complex karyotype composed of massive loss of heterozygosity

Human cell lines represent a valuable resource as in vitro experimental models. A hepatoma cell line, HuH-7 (JCRB0403), has been used extensively in various research fields and a number of studies using this line have been published continuously since it was established in 1982. However, an accurate genome profile, which can be served as a reliable reference, has not been available. In this study, we performed M-FISH, SNP microarray and amplicon sequencing to characterize the cell line. Single cell analysis of metaphases revealed a high level of heterogeneity with a mode of 60 chromosomes. Cytogenetic results demonstrated chromosome abnormalities involving every chromosome in addition to a massive loss of heterozygosity, which accounts for 55.3% of the genome, consistent with the homozygous variants seen in the sequence analysis. We provide empirical data that the HuH-7 cell line is composed of highly heterogeneous cell populations, suggesting that besides cell line authentication, the quality of cell lines needs to be taken into consideration in the future use of tumor cell lines.

Robust in-silico identification of cancer cell lines based on next generation sequencing

Cancer cell lines (CCL) are important tools for cancer researchers world-wide. However, handling of cancer cell lines is error-prone, and critical errors such as misidentification and cross-contamination occur more often than acceptable. Based on the fact that CCL today very often are sequenced (partly or entirely) anyway as part of the studies performed, we developed Uniquorn, a computational method that reliably identifies CCL samples based on variant profiles derived from whole exome or whole genome sequencing. Notably, Uniquorn does neither require a particular sequencing technology nor downstream analysis pipeline but works robustly across different NGS platforms and analysis steps. We evaluated Uniquorn by comparing more than 1900 CCL profiles from three large CCL libraries, embracing 1585 duplicates, against each other. In this setting, our method achieves a sensitivity of 97% and specificity of 99%. Errors are strongly associated to low quality mutation profiles. The R-package Uniquorn is freely available as Bioconductor-package.

Extracting Fitness Relationships and Oncogenic Patterns among Driver Genes in Cancer

Driver mutation provides fitness advantage to cancer cells, the accumulation of which increases the fitness of cancer cells and accelerates cancer progression. This work seeks to extract patterns accumulated by driver genes (“fitness relationships”) in tumorigenesis. We introduce a network-based method for extracting the fitness relationships of driver genes by modeling the network properties of the “fitness” of cancer cells. Colon adenocarcinoma (COAD) and skin cutaneous malignant melanoma (SKCM) are employed as case studies. Consistent results derived from different background networks suggest the reliability of the identified fitness relationships. Additionally co-occurrence analysis and pathway analysis reveal the functional significance of the fitness relationships with signaling transduction. In addition, a subset of driver genes called the “fitness core” is recognized for each case. Further analyses indicate the functional importance of the fitness core in carcinogenesis, and provide potential therapeutic opportunities in medicinal intervention. Fitness relationships characterize the functional continuity among driver genes in carcinogenesis, and suggest new insights in understanding the oncogenic mechanisms of cancers, as well as providing guiding information for medicinal intervention.

Comparing the genomes of cutaneous melanoma tumors to commercially available cell lines

Insulated culture environment and prolonged propagation contribute to known limitations of cell lines, and selection is often limited to availability or favorable growth characteristics. To better characterize and improve selection of cell lines, we compared 60 melanoma cell lines profiled by the Cancer Cell Line Encyclopedia and 472 cutaneous melanoma tumors profiled by The Cancer Genome Atlas by DNA sequence and copy number alterations. All samples were scored for stromal and immune cell composition by the ESTIMATE algorithm, and 412 tumors with ≥ 60% tumor cell fraction were compared to cell lines. Uncharacterized early passage cell lines that lacked BRAF, NRAS, or NF1 mutations had near zero mean Pearson correlation of copy number alterations per gene to tumors and also tended to have higher stromal scores. The Comet Exact Test was applied to tumors and cell lines identifying three pairs of genes mutated in a mutually exclusive pattern in tumors but not cell lines: BRAF and NRAS, BRAF and NF1, as well as NRAS and PTEN. Additionally, 31 genes were more frequently mutated in cell lines than tumors. Avoiding cell lines with co-occurring mutually exclusive mutations and the fewest differentially mutated genes within a known distribution of genetic similarity to tumors by copy number alterations may optimize selection.

Identifying CpG sites with different differential methylation frequencies in colorectal cancer tissues based on individualized differential methylation analysis

A big challenge to clinical diagnosis and therapy of colorectal cancer (CRC) is its extreme heterogeneity, and thus it would be of special importance if we could find common biomarkers besides subtype-specific biomarkers for CRC. Here, with DNA methylation data produced by different laboratories, we firstly revealed that the relative methylation-level orderings (RMOs) of CpG sites within colorectal normal tissues are highly stable but widely disrupted in the CRC tissues. This finding provides the basis for using the RankComp algorithm to identify differentially methylated (DM) CpG sites in every individual CRC sample through comparing the RMOs within the individual sample with the stable RMOs predetermined in normal tissues. For 75 CRC samples, RankComp detected averagely 4,062 DM CpG sites per sample and reached an average precision of 91.34% in terms that the hypermethylation or hypomethylation states of the DM CpG sites detected for each cancer sample were consistent with the observed differences between this cancer sample and its paired adjacent normal sample. Finally, we applied RankComp to identify DM CpG sites for each of the 268 CRC samples from The Cancer Genome Atlas and found 26 and 143 genes whose promoter regions included CpG sites that were hypermethylated and hypomethylated, respectively, in more than 95% of the 268 CRC samples. Individualized pathway analysis identified six pathways that were significantly enriched with DM genes in more than 90% of the CRC tissues. These universal DNA methylation biomarkers could be important diagnostic makers and therapy targets for CRC.

Analysis of renal cancer cell lines from two major resources enables genomics-guided cell line selection

The utility of cancer cell lines is affected by the similarity to endogenous tumour cells. Here we compare genomic data from 65 kidney-derived cell lines from the Cancer Cell Line Encyclopedia and the COSMIC Cell Lines Project to three renal cancer subtypes from The Cancer Genome Atlas: clear cell renal cell carcinoma (ccRCC, also known as kidney renal clear cell carcinoma), papillary (pRCC, also known as kidney papillary) and chromophobe (chRCC, also known as kidney chromophobe) renal cell carcinoma. Clustering copy number alterations shows that most cell lines resemble ccRCC, a few (including some often used as models of ccRCC) resemble pRCC, and none resemble chRCC. Human ccRCC tumours clustering with cell lines display clinical and genomic features of more aggressive disease, suggesting that cell lines best represent aggressive tumours. We stratify mutations and copy number alterations for important kidney cancer genes by the consistency between databases, and classify cell lines into established gene expression-based indolent and aggressive subtypes. Our results could aid investigators in analysing appropriate renal cancer cell lines.

Synonymous Somatic Variants in Human Cancer Are Not Infamous: A Plea for Full Disclosure in Databases and Publications

Single-nucleotide variants (SNVs) are the most frequent genetic changes found in human cancer. Most driver alterations are missense and nonsense variants localized in the coding region of cancer genes. Unbiased cancer genome sequencing shows that synonymous SNVs (sSNVs) can be found clustered in the coding regions of several cancer oncogenes or tumor suppressor genes suggesting purifying selection. sSNVs are currently underestimated, as they are usually discarded during analysis. Furthermore, several public databases do not display sSNVs, which can lead to analytical bias and the false assumption that this mutational event is uncommon. Recent progress in our understanding of the deleterious consequences of these sSNVs for RNA stability and protein translation shows that they can act as strong drivers of cancer, as demonstrated for several cancer genes such as TP53 or BCL2L12. It is therefore essential that sSNVs be properly reported and analyzed in order to provide an accurate picture of the genetic landscape of the cancer genome.

Revisiting inconsistency in large pharmacogenomic studies

In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. We present a new analysis using these expanded data, where we address the most significant suggestions for improvements on our published analysis - that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should be compared across cell lines, and that the software analysis tools provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. Using new statistics to assess data consistency allowed identification of two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

Revisiting inconsistency in large pharmacogenomic studies

In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. We present a new analysis using these expanded data, where we address the most significant suggestions for improvements on our published analysis - that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should be compared across cell lines, and that the software analysis tools provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. Using new statistics to assess data consistency allowed identification of two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

Revisiting inconsistency in large pharmacogenomic studies

In 2013, we published a comparative analysis mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. Here we present a new analysis using these expanded data in which we address the most significant suggestions for improvements on our published analysis - that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should both be compared across cell lines, and that the software analysis tools we provided should have been easier to run, particularly as the GDSC and CCLE released additional data.             Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. The use of new statistics to assess data consistency allowed us to identify two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs.             Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

Inactivation of TGFβ receptors in stem cells drives cutaneous squamous cell carcinoma

Melanoma patients treated with oncogenic BRAF inhibitors can develop cutaneous squamous cell carcinoma (cSCC) within weeks of treatment, driven by paradoxical RAS/RAF/MAPK pathway activation. Here we identify frequent TGFBR1 and TGFBR2 mutations in human vemurafenib-induced skin lesions and in sporadic cSCC. Functional analysis reveals these mutations ablate canonical TGFβ Smad signalling, which is localized to bulge stem cells in both normal human and murine skin. MAPK pathway hyperactivation (through Braf(V600E) or Kras(G12D) knockin) and TGFβ signalling ablation (through Tgfbr1 deletion) in LGR5(+ve) stem cells enables rapid cSCC development in the mouse. Mutation of Tp53 (which is commonly mutated in sporadic cSCC) coupled with Tgfbr1 deletion in LGR5(+ve) cells also results in cSCC development. These findings indicate that LGR5(+ve) stem cells may act as cells of origin for cSCC, and that RAS/RAF/MAPK pathway hyperactivation or Tp53 mutation, coupled with loss of TGFβ signalling, are driving events of skin tumorigenesis.

Assessment of pharmacogenomic agreement

In 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE), were inconsistent. The GDSC and CCLE investigators recently reported that their respective studies exhibit reasonable agreement and yield similar molecular predictors of drug response, seemingly contradicting our previous findings. Reanalyzing the authors' published methods and results, we found that their analysis failed to account for variability in the genomic data and more importantly compared different drug sensitivity measures from each study, which substantially deviate from our more stringent consistency assessment. Our comparison of the most updated genomic and pharmacological data from the GDSC and CCLE confirms our published findings that the measures of drug response reported by these two groups are not consistent. We believe that a principled approach to assess the reproducibility of drug sensitivity predictors is necessary before envisioning their translation into clinical settings.

Rare ATAD5 missense variants in breast and ovarian cancer patients

ATAD5/ELG1 is a protein crucially involved in replication and maintenance of genome stability. ATAD5 has recently been identified as a genomic risk locus for both breast and ovarian cancer through genome-wide association studies. We aimed to investigate the spectrum of coding ATAD5 germ-line mutations in hospital-based series of patients with triple-negative breast cancer or serous ovarian cancer compared with healthy controls. The ATAD5 coding and adjacent splice site regions were analyzed by targeted next-generation sequencing of DNA samples from 273 cancer patients, including 114 patients with triple-negative breast cancer and 159 patients with serous epithelial ovarian cancer, and from 276 healthy females. Among 42 different variants identified, twenty-two were rare missense substitutions, of which 14 were classified as pathogenic by at least one in silico prediction tool. Three of four novel missense substitutions (p.S354I, p.H974R and p.K1466N) were predicted to be pathogenic and were all identified in ovarian cancer patients. Overall, rare missense variants with predicted pathogenicity tended to be enriched in ovarian cancer patients (14/159) versus controls (11/276) (p = 0.05, 2df). While truncating germ-line variants in ATAD5 were not detected, it remains possible that several rare missense variants contribute to genetic susceptibility toward epithelial ovarian carcinomas.

ApoCanD: Database of human apoptotic proteins in the context of cancer

In the past decade, apoptosis pathway has gained a serious consideration being a critical cellular process in determining the cancer progression. Inverse relationship between cancer progression and apoptosis rate has been well established in the literature. It causes apoptosis proteins under the investigative scanner for developing anticancer therapies, which certainly got a success in the case of few apoptosis proteins as drug targets. In the present study, we have developed a dedicated database of 82 apoptosis proteins called ApoCanD. This database comprises of crucial information of apoptosis proteins in the context of cancer. Genomic status of proteins in the form of mutation, copy number variation and expression in thousands of tumour samples and cancer cell lines are the major bricks of this database. In analysis, we have found that TP53 and MYD88 are the two most frequently mutated proteins in cancer. Availability of other information e.g. gene essentiality data, tertiary structure, sequence alignments, sequences profiles, post-translational modifications makes it even more useful for the researchers. A user-friendly web interface is provided to ameliorate the use of ApoCanD. We anticipate that, this database will facilitate the research community working in the field of apoptosis and cancer. The database can be accessed at: http://crdd.osdd.net/raghava/apocand.

Identification of genomic mutations associated with clinical outcomes of induction chemotherapy in patients with head and neck squamous cell carcinoma

PURPOSE

We performed deep sequencing of target genes in head and neck squamous cell carcinoma (HNSCC) tumors to identify somatic mutations that are associated with induction chemotherapy (IC) response.

METHODS

Patients who were diagnosed with HNSCC were retrospectively identified. Patients who were treated with IC were divided into two groups: good responders and poor responders by tumor response and progression-free survival. Targeted gene sequencing for 2404 somatic mutations of 44 genes was performed on HNSCC tissues. Mutations with total coverage of <500 were excluded, and the cutoff for altered allele frequency was >10 %.

RESULTS

Of the 71 patients, 45 were treated upfront with IC. Mean total coverage was 1941 per locus, and 42.2 % of tumors had TP53 mutations. Thirty-three mutations in TP53, NOTCH3, FGFR2, FGFR3, ATM, EGFR, MET, PTEN, FBXW7, SYNE1, and SUFU were frequently altered in poor responders. Among the patients who were treated with IC, those with unfavorable genomic profiles had significantly poorer overall survival than those without unfavorable genomic profiles (hazard ratio 6.45, 95 % confidence interval 2.07-20.10, P < 0.001).

CONCLUSIONS

Comprehensive analysis of mutation frequencies identified unfavorable genomic profiles, and the patients without unfavorable genomic profiles can obtain clinical benefits from IC in patients with HNSCC.

Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset

Identifying genetic alterations that prime a cancer cell to respond to a particular therapeutic agent can facilitate the development of precision cancer medicines. Cancer cell-line (CCL) profiling of small-molecule sensitivity has emerged as an unbiased method to assess the relationships between genetic or cellular features of CCLs and small-molecule response. Here, we developed annotated cluster multidimensional enrichment analysis to explore the associations between groups of small molecules and groups of CCLs in a new, quantitative sensitivity dataset. This analysis reveals insights into small-molecule mechanisms of action, and genomic features that associate with CCL response to small-molecule treatment. We are able to recapitulate known relationships between FDA-approved therapies and cancer dependencies and to uncover new relationships, including for KRAS-mutant cancers and neuroblastoma. To enable the cancer community to explore these data, and to generate novel hypotheses, we created an updated version of the Cancer Therapeutic Response Portal (CTRP v2).

SIGNIFICANCE

We present the largest CCL sensitivity dataset yet available, and an analysis method integrating information from multiple CCLs and multiple small molecules to identify CCL response predictors robustly. We updated the CTRP to enable the cancer research community to leverage these data and analyses.

Using large-scale genomics data to identify driver mutations in lung cancer: methods and challenges

Lung cancer is the commonest cause of cancer death in the world and carries a poor prognosis for most patients. While precision targeting of mutated proteins has given some successes for never- and light-smoking patients, there are no proven targeted therapies for the majority of smokers with the disease. Despite sequencing hundreds of lung cancers, known driver mutations are lacking for a majority of tumors. Distinguishing driver mutations from inconsequential passenger mutations in a given lung tumor is extremely challenging due to the high mutational burden of smoking-related cancers. Here we discuss the methods employed to identify driver mutations from these large datasets. We examine different approaches based on bioinformatics, in silico structural modeling and biological dependency screens and discuss the limitations of these approaches.

Personalized targeted therapy for esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma continues to heavily burden clinicians worldwide. Researchers have discovered the genomic landscape of esophageal squamous cell carcinoma, which holds promise for an era of personalized oncology care. One of the most pressing problems facing this issue is to improve the understanding of the newly available genomic data, and identify the driver-gene mutations, pathways, and networks. The emergence of a legion of novel targeted agents has generated much hope and hype regarding more potent treatment regimens, but the accuracy of drug selection is still arguable. Other problems, such as cancer heterogeneity, drug resistance, exceptional responders, and side effects, have to be surmounted. Evolving topics in personalized oncology, such as interpretation of genomics data, issues in targeted therapy, research approaches for targeted therapy, and future perspectives, will be discussed in this editorial.

GOLPH3 links the Golgi, DNA damage, and cancer

GOLPH3 is the first example of an oncogene that functions in secretory trafficking at the Golgi. The discovery of GOLPH3's roles in both cancer and Golgi trafficking raises questions about how GOLPH3 and the Golgi contribute to cancer. Our recent investigation of the regulation of GOLPH3 revealed a surprising response by the Golgi upon DNA damage that is mediated by DNA-PK and GOLPH3. These results provide new insight into the DNA damage response with important implications for understanding the cellular response to standard cancer therapeutic agents.

Cancer-associated protein kinase C mutations reveal kinase's role as tumor suppressor

Protein kinase C (PKC) isozymes have remained elusive cancer targets despite the unambiguous tumor promoting function of their potent ligands, phorbol esters, and the prevalence of their mutations. We analyzed 8% of PKC mutations identified in human cancers and found that, surprisingly, most were loss of function and none were activating. Loss-of-function mutations occurred in all PKC subgroups and impeded second-messenger binding, phosphorylation, or catalysis. Correction of a loss-of-function PKCβ mutation by CRISPR-mediated genome editing in a patient-derived colon cancer cell line suppressed anchorage-independent growth and reduced tumor growth in a xenograft model. Hemizygous deletion promoted anchorage-independent growth, revealing that PKCβ is haploinsufficient for tumor suppression. Several mutations were dominant negative, suppressing global PKC signaling output, and bioinformatic analysis suggested that PKC mutations cooperate with co-occurring mutations in cancer drivers. These data establish that PKC isozymes generally function as tumor suppressors, indicating that therapies should focus on restoring, not inhibiting, PKC activity.

COSMIC: exploring the world's knowledge of somatic mutations in human cancer

COSMIC, the Catalogue Of Somatic Mutations In Cancer (http://cancer.sanger.ac.uk) is the world's largest and most comprehensive resource for exploring the impact of somatic mutations in human cancer. Our latest release (v70; Aug 2014) describes 2 002 811 coding point mutations in over one million tumor samples and across most human genes. To emphasize depth of knowledge on known cancer genes, mutation information is curated manually from the scientific literature, allowing very precise definitions of disease types and patient details. Combination of almost 20 000 published studies gives substantial resolution of how mutations and phenotypes relate in human cancer, providing insights into the stratification of mutations and biomarkers across cancer patient populations. Conversely, our curation of cancer genomes (over 12 000) emphasizes knowledge breadth, driving discovery of unrecognized cancer-driving hotspots and molecular targets. Our high-resolution curation approach is globally unique, giving substantial insight into molecular biomarkers in human oncology. In addition, COSMIC also details more than six million noncoding mutations, 10 534 gene fusions, 61 299 genome rearrangements, 695 504 abnormal copy number segments and 60 119 787 abnormal expression variants. All these types of somatic mutation are annotated to both the human genome and each affected coding gene, then correlated across disease and mutation types.