Genome sequencing and cancer

Impact of genomic and epigenomic alterations of multigene on a multicancer pedigree

BACKGROUND

Germline mutations have been identified in a small number of hereditary cancers, but the genetic predisposition for many familial cancers remains to be elucidated.

METHODS

This study identified a Chinese pedigree that presented different cancers (breast cancer, BRCA; adenocarcinoma of the esophagogastric junction, AEG; and B-cell acute lymphoblastic leukemia, B-ALL) in each of the three generations. Whole-genome sequencing and whole-exome sequencing were performed on peripheral blood or bone marrow and cancer biopsy samples. Whole-genome bisulfite sequencing was conducted on the monozygotic twin brothers, one of whom developed B-ALL.

RESULTS

According to the ACMG guidelines, bioinformatic analysis of the genome sequencing revealed 20 germline mutations, particularly mutations in the DNAH11 (c.9463G > A) and CFH (c.2314G > A) genes that were documented in the COSMIC database and validated by Sanger sequencing. Forty-one common somatic mutated genes were identified in the cancer samples, displaying the same type of single nucleotide substitution Signature 5. Meanwhile, hypomethylation of PLEK2, MRAS, and RXRA as well as hypermethylation of CpG island associated with WT1 was shown in the twin with B-ALL.

CONCLUSIONS

These findings reveal genomic alterations in a pedigree with multiple cancers. Mutations found in the DNAH11, CFH genes, and other genes predispose to malignancies in this family. Dysregulated methylation of WT1, PLEK2, MRAS, and RXRA in the twin with B-ALL increases cancer susceptibility. The similarity of the somatic genetic changes among the three cancers indicates a hereditary impact on the pedigree. These familial cancers with germline and somatic mutations, as well as epigenomic alterations, represent a common molecular basis for many multiple cancer pedigrees.

Application of informatics in cancer research and clinical practice: Opportunities and challenges

Cancer informatics has significantly progressed in the big data era. We summarize the application of informatics approaches to the cancer domain from both the informatics perspective (e.g., data management and data science) and the clinical perspective (e.g., cancer screening, risk assessment, diagnosis, treatment, and prognosis). We discuss various informatics methods and tools that are widely applied in cancer research and practices, such as cancer databases, data standards, terminologies, high-throughput omics data mining, machine-learning algorithms, artificial intelligence imaging, and intelligent radiation. We also address the informatics challenges within the cancer field that pursue better treatment decisions and patient outcomes, and focus on how informatics can provide opportunities for cancer research and practices. Finally, we conclude that the interdisciplinary nature of cancer informatics and collaborations are major drivers for future research and applications in clinical practices. It is hoped that this review is instrumental for cancer researchers and clinicians with its informatics-specific insights.

Next-Generation Sequencing Concordance Analysis of Comprehensive Solid Tumor Profiling between a Centralized Specialty Laboratory and the Decentralized Personal Genome Diagnostics, Inc., Elio Tissue Complete Kitted Solution

Genomic tumor profiling by next-generation sequencing (NGS) allows for large-scale tumor testing to inform targeted cancer therapies and immunotherapies, and to identify patients for clinical trials. These tests are often underutilized in patients with late-stage solid tumors and are typically performed in centralized specialty laboratories, thereby limiting access to these complex tests. Personal Genome Diagnostics Inc., elio tissue complete NGS solution is a comprehensive DNA-to-report kitted assay and bioinformatics solution. Comparison of 147 unique specimens from >20 tumor types was performed using the elio tissue complete solution and Foundation Medicine's FoundationOne test, which is of similar size and gene content. The analytical performance of all genomic variant types was evaluated. In general, the overall mutational profile is highly concordant between the two assays, with agreement in sequence variants reported between panels demonstrating >95% positive percentage agreement for single-nucleotide variants and insertions/deletions in clinically actionable genes. Both copy number alterations and gene translocations showed 80% to 83% positive percentage agreement, whereas tumor mutation burden and microsatellite status showed a high level of concordance across a range of mutation loads and tumor types. The Personal Genome Diagnostics Inc., elio tissue complete assay is comparable to the FoundationOne test and will allow more laboratories to offer a diagnostic NGS assay in house, which will ultimately reduce time to result and increase the number of patients receiving molecular genomic profiling and personalized treatment.

PRDX2 removal inhibits the cell cycle and autophagy in colorectal cancer cells

Colorectal cancer (CRC) is a prevalent worldwide disease in which the antioxidant enzyme peroxiredoxin 2 (PRDX2) plays an important role. To investigate the molecular mechanism of PRDX2 in CRC, we performed bioinformatics analysis of The Cancer Genome Atlas (TCGA) datasets and Gene Expression Omnibus (GEO) DataSets (accession no. GSE81429). Our results suggest that PRDX2 is associated with cell-cycle progression and autophagy activated by the P38 MAPK/FOXO signaling pathway. Using a short-hairpin RNA vector, we verified that PRDX2 is essential for CRC cell proliferation and S-phase progression. Immunostaining, electron microscopy and western blotting assays verified the effect of PRDX2 knockdown on autophagy flux and p38 activation. The P38 activator dehydrocorydaline chloride partially rescued the effects of sh-PRDX2 on the expression of proteins related to cell-cycle progression and autophagy. We verified the correlation between PRDX2 expression and the expression of an array of cell-cycle and autophagy-related genes using data from an independent set of 222 CRC patient samples. A mouse xenoplast model was consistent with in vitro results. Our results suggest that PRDX2 promotes CRC cell-cycle progression via activation of the p38 MAPK pathway.

Whole exome sequencing study of a Chinese concurrent cancer family

Cancer is one of the leading causes of mortality in China, and poses a threat to public health due to its increasing incidence and mortality rates. Concurrent cancer is defined as one or more organs in the same individual having ≥2 primary malignancies occurring simultaneously or successively; however, concurrent cases are rare and poorly studied. The present study recruited a Chinese family presenting multiple cases of concurrent cancer and performed whole exome sequencing in one unaffected and two affected individuals to identify the causative mutations. DNA was extracted from peripheral blood and tumor tissue samples. Following an exome capture and quality test, the qualified library was sequenced as 100 bp paired-end reads on an Ion Torrent platform. Clean data were obtained by filtering out the low-quality reads. Subsequently, bioinformatics analyses were performed using the clean data. After mapping and annotating in 1000 Genomes Project database, the existing SNP database and the Cancer Gene Census (CGC) database, it was revealed that the NADH:ubiquinone oxidoreductase core subunit S7 gene was a candidate gene with somatic mutations, and a subset of 16 genes were candidate genes with germline mutations. The findings of the present study may improve the understanding of the molecular pathogenesis of concurrent cancer.

Preclinical models for precision oncology

Precision medicine approaches have revolutionized oncology. Personalized treatments require not only identification of the driving molecular alterations, but also development of targeted therapies and diagnostic tests to identify the appropriate patient populations for clinical trials and subsequent therapeutic implementation. Preclinical in vitro and in vivo models are widely used to predict efficacy of newly developed treatments. Here we discuss whether, and to what extent, preclinical models including cell lines, organoids and tumorgrafts recapitulate key features of human tumors. The potential of preclinical models to anticipate treatment efficacy and clinical benefit is also presented, using examples in different tumor types.

Sanger sequencing of MMR genes in a one-plate system

Both incidence and mortality of colorectal cancer (CRC) in Romania have shown a continuous increase during the last decades. Hereditary Non-Polyposic Colorectal Cancer (HNPCC), also known as Lynch syndrome, is mainly attributable to mismatch repair (MMR) genes MSH2, MSH6, and MLH1. Individuals carrying germ-line mutations of these genes present high lifetime risk of colorectal and other cancers, compared to non-carriers. Oncogenetics is developed worldwide nowadays, for identifying hereditary predisposition to cancer and offering appropriate clinical follow-up to patients and mutation carriers in Lynch families. Molecular oncogenetic diagnosis in Lynch syndrome is based on complete Sanger sequencing of entire MMR genes, which is time and resources consuming, therefore needing an appropriate and adapted optimization. Conventional sequencing requires a sufficient number of available samples to be processed simultaneously, which increases the waiting time for diagnostic results. Complete analysis for only one patient meets difficult technical problems due to the complex co-amplification of all gene regions of interest within the same conditions, therefore increasing the costs and reducing the cost-effectiveness of the test. Here we present an original and robust technical protocol for sequencing the entire MSH2, MSH6, and MLH1 coding sequence for one patient in a single PCR plate. Our optimized and verified system overcomes all technical problems and offers a quick, robust, and cost-effective possibility to personalize molecular oncogenetic diagnosis in Lynch syndrome.

Informatics for cancer immunotherapy

The rapid development of immunomodulatory cancer therapies has led to a concurrent increase in the application of informatics techniques to the analysis of tumors, the tumor microenvironment, and measures of systemic immunity. In this review, the use of tumors to gather genetic and expression data will first be explored. Next, techniques to assess tumor immunity are reviewed, including HLA status, predicted neoantigens, immune microenvironment deconvolution, and T-cell receptor sequencing. Attempts to integrate these data are in early stages of development and are discussed in this review. Finally, we review the application of these informatics strategies to therapy development, with a focus on vaccines, adoptive cell transfer, and checkpoint blockade therapies.

Distribution of circulating tumor DNA in lung cancer: analysis of the primary lung and bone marrow along with the pulmonary venous and peripheral blood

Circulating tumor DNA (ctDNA), extracted from plasma, is a non-invasive surrogate biomarker. However, the distribution of ctDNA in the body still remains to be elucidated. In this study, resected lung tumors, with simultaneous blood and bone marrow samples, were analyzed to elucidate the distribution of ctDNA. Rib bone marrow, pulmonary venous blood (Pul.V) and peripheral blood (Peri.B) were obtained from 30 patients. The liquid samples were divided into cell pellets and supernatant by centrifugation; a total of 212 DNA samples were subjected to massively parallel sequencing. ctDNA was detected in 5 patients. Given that the frequency of mutations in the primary tumor was considered to be 100%, those in the other specimens were as follows; Pul.V plasma 20%, Peri.B plasma 11%, and the other samples 0%. Furthermore, ctDNA reflected the predominant mutations in the primary lesion. Clinically, the presence of ctDNA was associated with significantly poorer survival. These results suggest ctDNA “spill over” into an immediate outflow tract (Pul.V), and from there is disseminated to the entire body. Thus, it can be inferred that ctDNA reflects the cancer progression and could function as a prognostic marker.

A novel network regularized matrix decomposition method to detect mutated cancer genes in tumour samples with inter-patient heterogeneity

Inter-patient heterogeneity is a major challenge for mutated cancer genes detection which is crucial to advance cancer diagnostics and therapeutics. To detect mutated cancer genes in heterogeneous tumour samples, a prominent strategy is to determine whether the genes are recurrently mutated in their interaction network context. However, recent studies show that some cancer genes in different perturbed pathways are mutated in different subsets of samples. Subsequently, these genes may not display significant mutational recurrence and thus remain undiscovered even in consideration of network information. We develop a novel method called mCGfinder to efficiently detect mutated cancer genes in tumour samples with inter-patient heterogeneity. Based on matrix decomposition framework incorporated with gene interaction network information, mCGfinder can successfully measure the significance of mutational recurrence of genes in a subset of samples. When applying mCGfinder on TCGA somatic mutation datasets of five types of cancers, we find that the genes detected by mCGfinder are significantly enriched for known cancer genes, and yield substantially smaller p-values than other existing methods. All the results demonstrate that mCGfinder is an efficient method in detecting mutated cancer genes.

NetNorM: Capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis

Genome-wide somatic mutation profiles of tumours can now be assessed efficiently and promise to move precision medicine forward. Statistical analysis of mutation profiles is however challenging due to the low frequency of most mutations, the varying mutation rates across tumours, and the presence of a majority of passenger events that hide the contribution of driver events. Here we propose a method, NetNorM, to represent whole-exome somatic mutation data in a form that enhances cancer-relevant information using a gene network as background knowledge. We evaluate its relevance for two tasks: survival prediction and unsupervised patient stratification. Using data from 8 cancer types from The Cancer Genome Atlas (TCGA), we show that it improves over the raw binary mutation data and network diffusion for these two tasks. In doing so, we also provide a thorough assessment of somatic mutations prognostic power which has been overlooked by previous studies because of the sparse and binary nature of mutations.

3D Culture as a Clinically Relevant Model for Personalized Medicine

Advances in understanding many of the fundamental mechanisms of cancer progression have led to the development of molecular targeted therapies. While molecular targeted therapeutics continue to improve the outcome for cancer patients, tumor heterogeneity among patients, as well as intratumoral heterogeneity, limits the efficacy of these drugs to specific patient subtypes, as well as contributes to relapse. Thus, there is a need for a more personalized approach toward drug development and diagnosis that takes into account the diversity of cancer patients, as well as the complex milieu of tumor cells within a single patient. Three-dimensional (3D) culture systems paired with patient-derived xenografts or patient-derived organoids may provide a more clinically relevant system to address issues presented by personalized or precision medical approaches. In this review, we cover the current methods available for applying 3D culture systems toward personalized cancer research and drug development, as well as key challenges that must be addressed in order to fully realize the potential of 3D patient-derived culture systems for cancer drug development. Greater implementation of 3D patient-derived culture systems in the cancer research field should accelerate the development of truly personalized medical therapies for cancer patients.

Precision oncology for acute myeloid leukemia using a knowledge bank approach

Underpinning the vision of precision medicine is the concept that causative mutations in a patient's cancer drive its biology and, by extension, its clinical features and treatment response. However, considerable between-patient heterogeneity in driver mutations complicates evidence-based personalization of cancer care. Here, by reanalyzing data from 1,540 patients with acute myeloid leukemia (AML), we explore how large knowledge banks of matched genomic-clinical data can support clinical decision-making. Inclusive, multistage statistical models accurately predicted likelihoods of remission, relapse and mortality, which were validated using data from independent patients in The Cancer Genome Atlas. Comparison of long-term survival probabilities under different treatments enables therapeutic decision support, which is available in exploratory form online. Personally tailored management decisions could reduce the number of hematopoietic cell transplants in patients with AML by 20-25% while maintaining overall survival rates. Power calculations show that databases require information from thousands of patients for accurate decision support. Knowledge banks facilitate personally tailored therapeutic decisions but require sustainable updating, inclusive cohorts and large sample sizes.

Translating leukemia stem cells into the clinical setting: Harmonizing the heterogeneity

Considerable evidence suggests that rare leukemia cells with stem cell features, including self-renewal capacity and drug resistance, are primarily responsible for both disease maintenance and relapses. Traditionally, these so-called leukemia stem cells (LSCs) have been identified in the laboratory by their ability to engraft acute myeloid leukemia (AML) into immunocompromised mice. For many years, only those rare AML cells characterized by a hematopoietic stem cell (HSC) CD34+CD38- phenotype were believed capable of generating leukemia in immunocompromised mice. However, more recently, significant heterogeneity in the phenotypes of those AML cells that can engraft immunocompromised mice has been demonstrated. AML cells that engraft immunocompromised mice have also been shown to not necessarily represent either the founder clone or those cells responsible for relapse. A recent study found that the most immature phenotype present in an AML correlated with genetically defined risk groups and outcomes, but was heterogeneous. Patients with AML cells expressing a primitive HSC phenotype (CD34+CD38- with high aldehyde dehydrogenase activity) manifested significantly lower complete remission rates, as well as poorer event-free and overall survivals. Leukemias in which the most primitive cells displayed more mature phenotypes were associated with better outcomes. The strong clinical correlations suggest that the most immature phenotype detectable within a patient's AML might serve as a biomarker for "clinically relevant" LSCs.

Capture-based next-generation sequencing reveals multiple actionable mutations in cancer patients failed in traditional testing

BACKGROUND

Targeted therapies including monoclonal antibodies and small molecule inhibitors have dramatically changed the treatment of cancer over past 10 years. Their therapeutic advantages are more tumor specific and with less side effects. For precisely tailoring available targeted therapies to each individual or a subset of cancer patients, next-generation sequencing (NGS) has been utilized as a promising diagnosis tool with its advantages of accuracy, sensitivity, and high throughput.

METHODS

We developed and validated a NGS-based cancer genomic diagnosis targeting 115 prognosis and therapeutics relevant genes on multiple specimen including blood, tumor tissue, and body fluid from 10 patients with different cancer types. The sequencing data was then analyzed by the clinical-applicable analytical pipelines developed in house.

RESULTS

We have assessed analytical sensitivity, specificity, and accuracy of the NGS-based molecular diagnosis. Also, our developed analytical pipelines were capable of detecting base substitutions, indels, and gene copy number variations (CNVs). For instance, several actionable mutations of EGFR,PIK3CA,TP53, and KRAS have been detected for indicating drug susceptibility and resistance in the cases of lung cancer.

CONCLUSION

Our study has shown that NGS-based molecular diagnosis is more sensitive and comprehensive to detect genomic alterations in cancer, and supports a direct clinical use for guiding targeted therapy.

Sustained proliferation in cancer: Mechanisms and novel therapeutic targets

Proliferation is an important part of cancer development and progression. This is manifest by altered expression and/or activity of cell cycle related proteins. Constitutive activation of many signal transduction pathways also stimulates cell growth. Early steps in tumor development are associated with a fibrogenic response and the development of a hypoxic environment which favors the survival and proliferation of cancer stem cells. Part of the survival strategy of cancer stem cells may manifested by alterations in cell metabolism. Once tumors appear, growth and metastasis may be supported by overproduction of appropriate hormones (in hormonally dependent cancers), by promoting angiogenesis, by undergoing epithelial to mesenchymal transition, by triggering autophagy, and by taking cues from surrounding stromal cells. A number of natural compounds (e.g., curcumin, resveratrol, indole-3-carbinol, brassinin, sulforaphane, epigallocatechin-3-gallate, genistein, ellagitannins, lycopene and quercetin) have been found to inhibit one or more pathways that contribute to proliferation (e.g., hypoxia inducible factor 1, nuclear factor kappa B, phosphoinositide 3 kinase/Akt, insulin-like growth factor receptor 1, Wnt, cell cycle associated proteins, as well as androgen and estrogen receptor signaling). These data, in combination with bioinformatics analyses, will be very important for identifying signaling pathways and molecular targets that may provide early diagnostic markers and/or critical targets for the development of new drugs or drug combinations that block tumor formation and progression.

Establishment of a novel human medulloblastoma cell line characterized by highly aggressive stem-like cells

Medulloblastoma is a highly aggressive brain tumor and one of the leading causes of morbidity and mortality related to childhood cancer. These tumors display differential ability to metastasize and respond to treatment, which reflects their high degree of heterogeneity at the genetic and molecular levels. Such heterogeneity of medulloblastoma brings an additional challenge to the understanding of its physiopathology and impacts the development of new therapeutic strategies. This translational effort has been the focus of most pre-clinical studies which invariably employ experimental models using human tumor cell lines. Nonetheless, compared to other cancers, relatively few cell lines of human medulloblastoma are available in central repositories, partly due to the rarity of these tumors and to the intrinsic difficulties in establishing continuous cell lines from pediatric brain tumors. Here, we report the establishment of a new human medulloblastoma cell line which, in comparison with the commonly used and well-established cell line Daoy, is characterized by enhanced proliferation and invasion capabilities, stem cell properties, increased chemoresistance, tumorigenicity in an orthotopic metastatic model, replication of original medulloblastoma behavior in vivo, strong chromosome structural instability and deregulation of genes involved in neural development. These features are advantageous for designing biologically relevant experimental models in clinically oriented studies, making this novel cell line, named USP-13-Med, instrumental for the study of medulloblastoma biology and treatment.

New concepts in breast cancer genomics and genetics

Massively parallel DNA and RNA sequencing approaches have generated data on thousands of breast cancer genomes. In this review, we consider progress largely from the perspective of new concepts and hypotheses raised so far. These include challenges to the multistep model of breast carcinogenesis and the discovery of new defects in DNA repair through sequence analysis. Issues for functional genomics include the development of strategies to differentiate between mutations that are likely to drive carcinogenesis and bystander background mutations, as well as the importance of mechanistic studies that examine the role of mutations in genes with roles in splicing, histone methylation, and long non-coding RNA function. The application of genome-annotated patient-derived breast cancer xenografts as a potentially more reliable preclinical model is also discussed. Finally, we address the challenge of extracting medical value from genomic data. A weakness of many datasets is inadequate clinical annotation, which hampers the establishment of links between the mutation spectra and the efficacy of drugs or disease phenotypes. Tools such as dGene and the DGIdb are being developed to identify possible druggable mutations, but these programs are a work in progress since extensive molecular pharmacology is required to develop successful ‘genome-forward’ clinical trials. Examples are emerging, however, including targeting HER2 in HER2 mutant breast cancer and mutant ESR1 in ESR1 endocrine refractory luminal-type breast cancer. Finally, the integration of DNA- and RNA-based sequencing studies with mass spectrometry-based peptide sequencing and an unbiased determination of post-translational modifications promises a more complete view of the biochemistry of breast cancer cells and points toward a new discovery horizon in our understanding of the pathophysiology of this complex disease.

A mutation profile for top-k patient search exploiting Gene-Ontology and orthogonal non-negative matrix factorization

MOTIVATION

As the quantity of genomic mutation data increases, the likelihood of finding patients with similar genomic profiles, for various disease inferences, increases. However, so does the difficulty in identifying them. Similarity search based on patient mutation profiles can solve various translational bioinformatics tasks, including prognostics and treatment efficacy predictions for better clinical decision making through large volume of data. However, this is a challenging problem due to heterogeneous and sparse characteristics of the mutation data as well as their high dimensionality.

RESULTS

To solve this problem we introduce a compact representation and search strategy based on Gene-Ontology and orthogonal non-negative matrix factorization. Statistical significance between the identified cancer subtypes and their clinical features are computed for validation; results show that our method can identify and characterize clinically meaningful tumor subtypes comparable or better in most datasets than the recently introduced Network-Based Stratification method while enabling real-time search. To the best of our knowledge, this is the first attempt to simultaneously characterize and represent somatic mutational data for efficient search purposes.

AVAILABILITY

The implementations are available at: https://sites.google.com/site/postechdm/research/implementation/orgos.

CONTACT

sael@cs.stonybrook.edu or hwanjoyu@postech.ac.kr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Achieving high-sensitivity for clinical applications using augmented exome sequencing

Background

Whole exome sequencing is increasingly used for the clinical evaluation of genetic disease, yet the variation of coverage and sensitivity over medically relevant parts of the genome remains poorly understood. Several sequencing-based assays continue to provide coverage that is inadequate for clinical assessment.

Methods

Using sequence data obtained from the NA12878 reference sample and pre-defined lists of medically-relevant protein-coding and noncoding sequences, we compared the breadth and depth of coverage obtained among four commercial exome capture platforms and whole genome sequencing. In addition, we evaluated the performance of an augmented exome strategy, ACE, that extends coverage in medically relevant regions and enhances coverage in areas that are challenging to sequence. Leveraging reference call-sets, we also examined the effects of improved coverage on variant detection sensitivity.

Results

We observed coverage shortfalls with each of the conventional exome-capture and whole-genome platforms across several medically interpretable genes. These gaps included areas of the genome required for reporting recently established secondary findings (ACMG) and known disease-associated loci. The augmented exome strategy recovered many of these gaps, resulting in improved coverage in these areas. At clinically-relevant coverage levels (100 % bases covered at ≥20×), ACE improved coverage among genes in the medically interpretable genome (>90 % covered relative to 10-78 % with other platforms), the set of ACMG secondary finding genes (91 % covered relative to 4-75 % with other platforms) and a subset of variants known to be associated with human disease (99 % covered relative to 52-95 % with other platforms). Improved coverage translated into improvements in sensitivity, with ACE variant detection sensitivities (>97.5 % SNVs, >92.5 % InDels) exceeding that observed with conventional whole-exome and whole-genome platforms.

Conclusions

Clinicians should consider analytical performance when making clinical assessments, given that even a few missed variants can lead to reporting false negative results. An augmented exome strategy provides a level of coverage not achievable with other platforms, thus addressing concerns regarding the lack of sensitivity in clinically important regions. In clinical applications where comprehensive coverage of medically interpretable areas of the genome requires higher localized sequencing depth, an augmented exome approach offers both cost and performance advantages over other sequencing-based tests.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-015-0197-4) contains supplementary material, which is available to authorized users.

Genome Modeling System: A Knowledge Management Platform for Genomics

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.

Somatic mutations, viral integration and epigenetic modification in the evolution of hepatitis B virus-induced hepatocellular carcinoma

Liver cancer in men is the second leading cause of cancer death and hepatocellular carcinoma (HCC) accounts for 70%-85% of the total liver cancer worldwide. Chronic infection with hepatitis B virus (HBV) is the major cause of HCC. Chronic, intermittently active inflammation provides “fertile field” for “mutation, selection, and adaptation” of HBV and the infected hepatocytes, a long-term evolutionary process during HBV-induced carcinogenesis. HBV mutations, which are positively selected by insufficient immunity, can promote and predict the occurrence of HCC. Recently, advanced sequencing technologies including whole genome sequencing, exome sequencing, and RNA sequencing provide opportunities to better under-stand the insight of how somatic mutations, structure variations, HBV integrations, and epigenetic modifications contribute to HCC development. Genomic variations of HCC caused by various etiological factors may be different, but the common driver mutations are important to elucidate the HCC evolutionary process. Genome-wide analyses of HBV integrations are helpful in clarifying the targeted genes of HBV in carcinogenesis and disease progression. RNA sequencing can identify key molecules whose expressions are epigenetically modified during HCC evolution. In this review, we summarized the current findings of next generation sequencings for HBV-HCC and proposed a theory framework of Cancer Evolution and Development based on the current knowledge of HBV-induced HCC to characterize and interpret evolutionary mechanisms of HCC and possible other cancers. Understanding the key viral and genomic variations involved in HCC evolution is essential for generating effective diagnostic, prognostic, and predictive biomarkers as well as therapeutic targets for the interventions of HBV-HCC.

Onco-STS: a web-based laboratory information management system for sample and analysis tracking in oncogenomic experiments

BACKGROUND

Whole genomes, whole exomes and transcriptomes of tumour samples are sequenced routinely to identify the drivers of cancer. The systematic sequencing and analysis of tumour samples, as well other oncogenomic experiments, necessitates the tracking of relevant sample information throughout the investigative process. These meta-data of the sequencing and analysis procedures include information about the samples and projects as well as the sequencing centres, platforms, data locations, results locations, alignments, analysis specifications and further information relevant to the experiments.

RESULTS

The current work presents a sample tracking system for oncogenomic studies (Onco-STS) to store these data and make them easily accessible to the researchers who work with the samples. The system is a web application, which includes a database and a front-end web page that allows the remote access, submission and updating of the sample data in the database. The web application development programming framework Grails was used for the development and implementation of the system.

CONCLUSIONS

The resulting Onco-STS solution is efficient, secure and easy to use and is intended to replace the manual data handling of text records. Onco-STS allows simultaneous remote access to the system making collaboration among researchers more effective. The system stores both information on the samples in oncogenomic studies and details of the analyses conducted on the resulting data. Onco-STS is based on open-source software, is easy to develop and can be modified according to a research group's needs. Hence it is suitable for laboratories that do not require a commercial system.

Characterization of structural variants with single molecule and hybrid sequencing approaches

MOTIVATION

Structural variation is common in human and cancer genomes. High-throughput DNA sequencing has enabled genome-scale surveys of structural variation. However, the short reads produced by these technologies limit the study of complex variants, particularly those involving repetitive regions. Recent 'third-generation' sequencing technologies provide single-molecule templates and longer sequencing reads, but at the cost of higher per-nucleotide error rates.

RESULTS

We present MultiBreak-SV, an algorithm to detect structural variants (SVs) from single molecule sequencing data, paired read sequencing data, or a combination of sequencing data from different platforms. We demonstrate that combining low-coverage third-generation data from Pacific Biosciences (PacBio) with high-coverage paired read data is advantageous on simulated chromosomes. We apply MultiBreak-SV to PacBio data from four human fosmids and show that it detects known SVs with high sensitivity and specificity. Finally, we perform a whole-genome analysis on PacBio data from a complete hydatidiform mole cell line and predict 1002 high-probability SVs, over half of which are confirmed by an Illumina-based assembly.

Detection of circulating tumor DNA in early- and late-stage human malignancies

The development of noninvasive methods to detect and monitor tumors continues to be a major challenge in oncology. We used digital polymerase chain reaction-based technologies to evaluate the ability of circulating tumor DNA (ctDNA) to detect tumors in 640 patients with various cancer types. We found that ctDNA was detectable in >75% of patients with advanced pancreatic, ovarian, colorectal, bladder, gastroesophageal, breast, melanoma, hepatocellular, and head and neck cancers, but in less than 50% of primary brain, renal, prostate, or thyroid cancers. In patients with localized tumors, ctDNA was detected in 73, 57, 48, and 50% of patients with colorectal cancer, gastroesophageal cancer, pancreatic cancer, and breast adenocarcinoma, respectively. ctDNA was often present in patients without detectable circulating tumor cells, suggesting that these two biomarkers are distinct entities. In a separate panel of 206 patients with metastatic colorectal cancers, we showed that the sensitivity of ctDNA for detection of clinically relevant KRAS gene mutations was 87.2% and its specificity was 99.2%. Finally, we assessed whether ctDNA could provide clues into the mechanisms underlying resistance to epidermal growth factor receptor blockade in 24 patients who objectively responded to therapy but subsequently relapsed. Twenty-three (96%) of these patients developed one or more mutations in genes involved in the mitogen-activated protein kinase pathway. Together, these data suggest that ctDNA is a broadly applicable, sensitive, and specific biomarker that can be used for a variety of clinical and research purposes in patients with multiple different types of cancer.

A Genomic Analysis Pipeline and Its Application to Pediatric Cancers

We present a cancer genomic analysis pipeline which takes as input sequencing reads for both germline and tumor genomes and outputs filtered lists of all genetic mutations in the form of short ranked list of the most affected genes in the tumor, using either the Complete Genomics or Illumina platforms. A novel reporting and ranking system has been developed that makes use of publicly available datasets and literature specific to each patient, including new methods for using publicly available expression data in the absence of proper control data. Previously implicated small and large variations (including gene fusions) are reported in addition to probable driver mutations. Relationships between cancer and the sequenced tumor genome are highlighted using a network-based approach that integrates known and predicted protein-protein, protein-TF, and protein-drug interaction data. By using an integrative approach, effects of genetic variations on gene expression are used to provide further evidence of driver mutations. This pipeline has been developed with the aim to be used in assisting in the analysis of pediatric tumors, as an unbiased and automated method for interpreting sequencing results along with identifying potentially therapeutic drugs and their targets. We present results that agree with previous literature and highlight specific findings in a few patients.

Next-generation sequencing: application in liver cancer-past, present and future?

Hepatocellular Carcinoma (HCC) is the third most deadly malignancy worldwide characterized by phenotypic and molecular heterogeneity. In the past two decades, advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. More recently, improvements of sophisticated next-generation sequencing (NGS) technologies have enabled complete and cost-efficient analyses of cancer genomes at a single nucleotide resolution and advanced into valuable tools in translational medicine. Although the use of NGS in human liver cancer is still in its infancy, great promise rests in the systematic integration of different molecular analyses obtained by these methodologies, i.e., genomics, transcriptomics and epigenomics. This strategy is likely to be helpful in identifying relevant and recurrent pathophysiological hallmarks thereby elucidating our limited understanding of liver cancer. Beside tumor heterogeneity, progress in translational oncology is challenged by the amount of biological information and considerable “noise” in the data obtained from different NGS platforms. Nevertheless, the following review aims to provide an overview of the current status of next-generation approaches in liver cancer, and outline the prospects of these technologies in diagnosis, patient classification, and prediction of outcome. Further, the potential of NGS to identify novel applications for concept clinical trials and to accelerate the development of new cancer therapies will be summarized.

Tumor haplotype assembly algorithms for cancer genomics

The growing availability of inexpensive high-throughput sequence data is enabling researchers to sequence tumor populations within a single individual at high coverage. But, cancer genome sequence evolution and mutational phenomena like driver mutations and gene fusions are difficult to investigate without first reconstructing tumor haplotype sequences. Haplotype assembly of single individual tumor populations is an exceedingly difficult task complicated by tumor haplotype heterogeneity, tumor or normal cell sequence contamination, polyploidy, and complex patterns of variation. While computational and experimental haplotype phasing of diploid genomes has seen much progress in recent years, haplotype assembly in cancer genomes remains uncharted territory. In this work, we describe HapCompass-Tumor a computational modeling and algorithmic framework for haplotype assembly of copy number variable cancer genomes containing haplotypes at different frequencies and complex variation. We extend our polyploid haplotype assembly model and present novel algorithms for (1) complex variations, including copy number changes, as varying numbers of disjoint paths in an associated graph, (2) variable haplotype frequencies and contamination, and (3) computation of tumor haplotypes using simple cycles of the compass graph which constrain the space of haplotype assembly solutions. The model and algorithm are implemented in the software package HapCompass-Tumor which is available for download from http://www.brown.edu/Research/Istrail_Lab/.

Studying cancer genomics through next-generation DNA sequencing and bioinformatics

Cancer is a complex disease driven by multiple mutations acquired over the lifetime of the cancer cells. These alterations, termed somatic mutations to distinguish them from inherited germline mutations, can include single-nucleotide substitutions, insertions, deletions, copy number alterations, and structural rearrangements. A patient's cancer can contain a combination of these aberrations, and the ability to generate a comprehensive genetic profile should greatly improve patient diagnosis and treatment. Next-generation sequencing has become the tool of choice to uncover multiple cancer mutations from a single tumor source, and the falling costs of this rapid high-throughput technology are encouraging its transition from basic research into a clinical setting. However, the detection of mutations in sequencing data is still an evolving area and cancer genomic data requires some special considerations. This chapter discusses these aspects and gives an overview of current bioinformatics methods for the detection of somatic mutations in cancer sequencing data.

Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing

As more clinically relevant cancer genes are identified, comprehensive diagnostic approaches are needed to match patients to therapies, raising the challenge of optimization and analytical validation of assays that interrogate millions of bases of cancer genomes altered by multiple mechanisms. Here we describe a test based on massively parallel DNA sequencing to characterize base substitutions, short insertions and deletions (indels), copy number alterations and selected fusions across 287 cancer-related genes from routine formalin-fixed and paraffin-embedded (FFPE) clinical specimens. We implemented a practical validation strategy with reference samples of pooled cell lines that model key determinants of accuracy, including mutant allele frequency, indel length and amplitude of copy change. Test sensitivity achieved was 95-99% across alteration types, with high specificity (positive predictive value >99%). We confirmed accuracy using 249 FFPE cancer specimens characterized by established assays. Application of the test to 2,221 clinical cases revealed clinically actionable alterations in 76% of tumors, three times the number of actionable alterations detected by current diagnostic tests.

Network-based stratification of tumor mutations

Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence.

Clinical applications of next-generation sequencing in colorectal cancers

Like other solid tumors, colorectal cancer (CRC) is a genomic disorder in which various types of genomic alterations, such as point mutations, genomic rearrangements, gene fusions, or chromosomal copy number alterations, can contribute to the initiation and progression of the disease. The advent of a new DNA sequencing technology known as next-generation sequencing (NGS) has revolutionized the speed and throughput of cataloguing such cancer-related genomic alterations. Now the challenge is how to exploit this advanced technology to better understand the underlying molecular mechanism of colorectal carcinogenesis and to identify clinically relevant genetic biomarkers for diagnosis and personalized therapeutics. In this review, we will introduce NGS-based cancer genomics studies focusing on those of CRC, including a recent large-scale report from the Cancer Genome Atlas. We will mainly discuss how NGS-based exome-, whole genome- and methylome-sequencing have extended our understanding of colorectal carcinogenesis. We will also introduce the unique genomic features of CRC discovered by NGS technologies, such as the relationship with bacterial pathogens and the massive genomic rearrangements of chromothripsis. Finally, we will discuss the necessary steps prior to development of a clinical application of NGS-related findings for the advanced management of patients with CRC.

Tracking the clonal origin of lethal prostate cancer

Recent controversies surrounding prostate cancer overtreatment emphasize the critical need to delineate the molecular features associated with progression to lethal metastatic disease. Here, we have used whole-genome sequencing and molecular pathological analyses to characterize the lethal cell clone in a patient who died of prostate cancer. We tracked the evolution of the lethal cell clone from the primary cancer to metastases through samples collected during disease progression and at the time of death. Surprisingly, these analyses revealed that the lethal clone arose from a small, relatively low-grade cancer focus in the primary tumor, and not from the bulk, higher-grade primary cancer or from a lymph node metastasis resected at prostatectomy. Despite being limited to one case, these findings highlight the potential importance of developing and implementing molecular prognostic and predictive markers, such as alterations of tumor suppressor proteins PTEN or p53, to augment current pathological evaluation and delineate clonal heterogeneity. Furthermore, this case illustrates the potential need in precision medicine to longitudinally sample metastatic lesions to capture the evolving constellation of alterations during progression. Similar comprehensive studies of additional prostate cancer cases are warranted to understand the extent to which these issues may challenge prostate cancer clinical management.

Whole-exome sequencing supports genetic heterogeneity in childhood apraxia of speech

Background

Childhood apraxia of speech (CAS) is a rare, severe, persistent pediatric motor speech disorder with associated deficits in sensorimotor, cognitive, language, learning and affective processes. Among other neurogenetic origins, CAS is the disorder segregating with a mutation in FOXP2 in a widely studied, multigenerational London family. We report the first whole-exome sequencing (WES) findings from a cohort of 10 unrelated participants, ages 3 to 19 years, with well-characterized CAS.

Methods

As part of a larger study of children and youth with motor speech sound disorders, 32 participants were classified as positive for CAS on the basis of a behavioral classification marker using auditory-perceptual and acoustic methods that quantify the competence, precision and stability of a speaker’s speech, prosody and voice. WES of 10 randomly selected participants was completed using the Illumina Genome Analyzer IIx Sequencing System. Image analysis, base calling, demultiplexing, read mapping, and variant calling were performed using Illumina software. Software developed in-house was used for variant annotation, prioritization and interpretation to identify those variants likely to be deleterious to neurodevelopmental substrates of speech-language development.

Results

Among potentially deleterious variants, clinically reportable findings of interest occurred on a total of five chromosomes (Chr3, Chr6, Chr7, Chr9 and Chr17), which included six genes either strongly associated with CAS (FOXP1 and CNTNAP2) or associated with disorders with phenotypes overlapping CAS (ATP13A4, CNTNAP1, KIAA0319 and SETX). A total of 8 (80%) of the 10 participants had clinically reportable variants in one or two of the six genes, with variants in ATP13A4, KIAA0319 and CNTNAP2 being the most prevalent.

Conclusions

Similar to the results reported in emerging WES studies of other complex neurodevelopmental disorders, our findings from this first WES study of CAS are interpreted as support for heterogeneous genetic origins of this pediatric motor speech disorder with multiple genes, pathways and complex interactions. We also submit that our findings illustrate the potential use of WES for both gene identification and case-by-case clinical diagnostics in pediatric motor speech disorders.

DNA sequencing methods in human genetics and disease research

DNA sequencing has revolutionized biological and medical research, and is poised to have a similar impact in medicine. This tool is just one of a number of developments in our capability to identify, quantitate and functionally characterize the components of the biological networks keeping us healthy or making us sick, but in many respects it has played the leading role in this process. The new technologies do, however, also provide a bridge between genotype and phenotype, both in man and model (as well as all other) organisms, revolutionize the identification of elements involved in a multitude of human diseases or other phenotypes, and generate a wealth of medically relevant information on every single person, as the basis of a truly personalized medicine of the future.

Patient-derived xenografts, the cancer stem cell paradigm, and cancer pathobiology in the 21st century

Cancer is a heterogeneous disease manifest in many forms. Tumor histopathology can differ significantly among patients and cellular heterogeneity within tumors is common. A primary goal of cancer biologists is to better understand tumorigenesis and cancer progression; however, the complex nature of tumors has posed a substantial challenge to unlocking cancer's secrets. The cancer stem cell (CSC) paradigm for the pathobiology of solid tumors appropriately acknowledges phenotypic and functional tumor cell heterogeneity observed in solid tumors and accounts for the disconnect between drug approval based on response and the general inability of approved therapies to meaningfully impact survival due to their failure to eradicate these most important of cellular targets. First proposed to exist decades ago, CSC have only recently begun to be precisely identified due to technical advancements that facilitate identification, isolation, and interrogation of distinct tumor cell subpopulations with differing ability to form and perpetuate tumors. Precise identification of CSC populations and the complete hierarchy of cells within solid tumors will facilitate more accurate characterization of patient subtypes and ultimately contribute to more personalized and effective therapies. Rapid advancement in the understanding of tumor biology as it exists in patients requires cooperation among institutions, surgeons, pathologists, cancer biologists and patients alike, primarily because this translational research is best done with patient-derived tissue grown in the xenograft setting as patient-derived xenografts. This review calls for a broader change in the approaches taken to study cancer pathobiology, highlights what implications the CSC paradigm has for pathologists and cancer biologists alike, and calls for greater collaboration between institutions, physicians and scientists in order to more rapidly advance our collective understanding of cancer.

Integrated platform for genome-wide screening and construction of high-density genetic interaction maps in mammalian cells

A major challenge of the postgenomic era is to understand how human genes function together in normal and disease states. In microorganisms, high-density genetic interaction (GI) maps are a powerful tool to elucidate gene functions and pathways. We have developed an integrated methodology based on pooled shRNA screening in mammalian cells for genome-wide identification of genes with relevant phenotypes and systematic mapping of all GIs among them. We recently demonstrated the potential of this approach in an application to pathways controlling the susceptibility of human cells to the toxin ricin. Here we present the complete quantitative framework underlying our strategy, including experimental design, derivation of quantitative phenotypes from pooled screens, robust identification of hit genes using ultra-complex shRNA libraries, parallel measurement of tens of thousands of GIs from a single double-shRNA experiment, and construction of GI maps. We describe the general applicability of our strategy. Our pooled approach enables rapid screening of the same shRNA library in different cell lines and under different conditions to determine a range of different phenotypes. We illustrate this strategy here for single- and double-shRNA libraries. We compare the roles of genes for susceptibility to ricin and Shiga toxin in different human cell lines and reveal both toxin-specific and cell line-specific pathways. We also present GI maps based on growth and ricin-resistance phenotypes, and we demonstrate how such a comparative GI mapping strategy enables functional dissection of physical complexes and context-dependent pathways.

Genome evolution during progression to breast cancer

Cancer evolution involves cycles of genomic damage, epigenetic deregulation, and increased cellular proliferation that eventually culminate in the carcinoma phenotype. Early neoplasias, which are often found concurrently with carcinomas and are histologically distinguishable from normal breast tissue, are less advanced in phenotype than carcinomas and are thought to represent precursor stages. To elucidate their role in cancer evolution we performed comparative whole-genome sequencing of early neoplasias, matched normal tissue, and carcinomas from six patients, for a total of 31 samples. By using somatic mutations as lineage markers we built trees that relate the tissue samples within each patient. On the basis of these lineage trees we inferred the order, timing, and rates of genomic events. In four out of six cases, an early neoplasia and the carcinoma share a mutated common ancestor with recurring aneuploidies, and in all six cases evolution accelerated in the carcinoma lineage. Transition spectra of somatic mutations are stable and consistent across cases, suggesting that accumulation of somatic mutations is a result of increased ancestral cell division rather than specific mutational mechanisms. In contrast to highly advanced tumors that are the focus of much of the current cancer genome sequencing, neither the early neoplasia genomes nor the carcinomas are enriched with potentially functional somatic point mutations. Aneuploidies that occur in common ancestors of neoplastic and tumor cells are the earliest events that affect a large number of genes and may predispose breast tissue to eventual development of invasive carcinoma.

Diagnostic applications of high-throughput DNA sequencing

Advances in DNA sequencing technology have allowed comprehensive investigation of the genetics of human beings and human diseases. Insights from sequencing the genomes, exomes, or transcriptomes of healthy and diseased cells in patients are already enabling improved diagnostic classification, prognostication, and therapy selection for many diseases. Understanding the data obtained using new high-throughput DNA sequencing methods, choices made in sequencing strategies, and common challenges in data analysis and genotype-phenotype correlation is essential if pathologists, geneticists, and clinicians are to interpret the growing scientific literature in this area. This review highlights some of the major results and discoveries stemming from high-throughput DNA sequencing research in our understanding of Mendelian genetic disorders, hematologic cancer biology, infectious diseases, the immune system, transplant biology, and prenatal diagnostics. Transition of new DNA sequencing methodologies to the clinical laboratory is under way and is likely to have a major impact on all areas of medicine.

Personalized immune-interception of cancer and the battle of two adaptive systems--when is the time right?

A growing body of evidence points to a coevolutionary model of cancer, wherein the cross-talk between tumor cells (or their subclones) and the host determine the malignant potential of individual tumors. Most of this natural history is clinically invisible and includes preneoplastic states. The capacity of the immune system to recognize these incipient lesions provides the basis for targeting them immunologically to arrest the development of preneoplasia toward clinical cancer. Kimura and colleagues provide evidence of immunogenicity of a potential cancer vaccine in patients with a history of advanced colon adenomas. These studies provide proof-of-principle or feasibility of such an approach in the clinic. Here, we discuss emerging opportunities and challenges in harnessing the immune system to "intercept" the precursor or preneoplastic lesions. Both cancer cells as well as the immune system represent independent and complex systems with plasticity and adaptive potential. It is therefore likely that specific aspects of the cross-talk between tumor cells and host may differ between individual tumors and determine the evolution of both tumors and the host response. We try to make the case to consider individualized approaches based on the genetic make-up of tumor cells and properties of the host response. Such strategies may be needed to optimally position the immune system to prevent cancers.

A survey of tools for variant analysis of next-generation genome sequencing data

Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers.

Chapter 6: Structural variation and medical genomics

Differences between individual human genomes, or between human and cancer genomes, range in scale from single nucleotide variants (SNVs) through intermediate and large-scale duplications, deletions, and rearrangements of genomic segments. The latter class, called structural variants (SVs), have received considerable attention in the past several years as they are a previously under appreciated source of variation in human genomes. Much of this recent attention is the result of the availability of higher-resolution technologies for measuring these variants, including both microarray-based techniques, and more recently, high-throughput DNA sequencing. We describe the genomic technologies and computational techniques currently used to measure SVs, focusing on applications in human and cancer genomics.

Bioinformatics for cancer immunology and immunotherapy

Recent mechanistic insights obtained from preclinical studies and the approval of the first immunotherapies has motivated increasing number of academic investigators and pharmaceutical/biotech companies to further elucidate the role of immunity in tumor pathogenesis and to reconsider the role of immunotherapy. Additionally, technological advances (e.g., next-generation sequencing) are providing unprecedented opportunities to draw a comprehensive picture of the tumor genomics landscape and ultimately enable individualized treatment. However, the increasing complexity of the generated data and the plethora of bioinformatics methods and tools pose considerable challenges to both tumor immunologists and clinical oncologists. In this review, we describe current concepts and future challenges for the management and analysis of data for cancer immunology and immunotherapy. We first highlight publicly available databases with specific focus on cancer immunology including databases for somatic mutations and epitope databases. We then give an overview of the bioinformatics methods for the analysis of next-generation sequencing data (whole-genome and exome sequencing), epitope prediction tools as well as methods for integrative data analysis and network modeling. Mathematical models are powerful tools that can predict and explain important patterns in the genetic and clinical progression of cancer. Therefore, a survey of mathematical models for tumor evolution and tumor-immune cell interaction is included. Finally, we discuss future challenges for individualized immunotherapy and suggest how a combined computational/experimental approaches can lead to new insights into the molecular mechanisms of cancer, improved diagnosis, and prognosis of the disease and pinpoint novel therapeutic targets.