Genomes Of Patients Research Articles

Updated Understanding of the Causes of Cancer, and a New Theoretical Perspective of Combinational Cancer Therapies, a Hypothesis.

We present an integrative understanding of cancer as a metabolic multifactorial, multistage disease. We focus on underlying genetics-environmental interactions, evidenced by telomere changes. A range of genetic and epigenetic factors, including physical agents and predisposing factors such as diet and lifestyle are included. We present a structured model of the causes of cancer, methods of investigations, approaches to cancer prevention, and polypharmaceutical multidisciplinary complex treatment within a framework of personalized medicine. We searched PubMed, National Cancer Institute online, and other databases for publications regarding causes of cancer, reports of novel mitochondrial reprogramming, epigenetic, and telomerase therapies and state-of-the-art investigations. We focused on multistep treatment protocols to enhance early detection of cancer, and elimination or neutralization of the causes and factors associated with cancer formation and progression. Our aim is to suggest a model therapeutic protocol that incorporates the patient's genome, metabolism, and immune system status; stage of tumor development; and comorbidity(ies), if any. Investigation and treatment of cancer is a challenge that requires further holistic studies that improve the quality of life and survival rates, but are most likely to aid prevention.

Genomic medicine in a community hospital setting

Clinical rapid genome sequencing (rGS), the sequencing and interpretation of variation in a patient's genome within ∼1 week, has helped to establish diagnoses and direct patient care in numerous studies, but has largely been restricted to an academic setting. In this volume of The Journal, Beuschel et al demonstrate that rGS is not only available in a community hospital but also directly affects patient care in this setting. In their retrospective analysis of 24 patients where a genetic diagnosis was suspected, or there was no clear explanatory cause of illness, medical management was altered in 67% of subjects as a result of rGS.

Therapy Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Is Predictive of Individual Acute Myeloid Leukemia (AML) Patient Probability of Clinical Response (CR) and Overall Survival (OS): Mycare-023

Therapy Biosimulation Using the Cellworks Computational Omics Biology Model (CBM) Is Predictive of Individual Acute Myeloid Leukemia (AML) Patient Probability of Clinical Response (CR) and Overall Survival (OS): Mycare-023

3Cnet: pathogenicity prediction of human variants using multitask learning with evolutionary constraints.

MotivationImprovements in next-generation sequencing have enabled genome-based diagnosis for patients with genetic diseases. However, accurate interpretation of human variants requires knowledge from a number of clinical cases. In addition, manual analysis of each variant detected in a patient's genome requires enormous time and effort. To reduce the cost of diagnosis, various computational tools have been developed to predict the pathogenicity of human variants, but the shortage and bias of available clinical data can lead to overfitting of algorithms.ResultsWe developed a pathogenicity predictor, 3Cnet, that uses recurrent neural networks to analyze the amino acid context of human variants. As 3Cnet is trained on simulated variants reflecting evolutionary conservation and clinical data, it can find disease-causing variants in patient genomes with 2.2 times greater sensitivity than currently available tools, more effectively discovering pathogenic variants and thereby improving diagnosis rates.Availability and implementationCodes (https://github.com/KyoungYeulLee/3Cnet/) and data (https://zenodo.org/record/4716879#.YIO-xqkzZH1) are freely available to non-commercial users.Supplementary information Supplementary data are available at Bioinformatics online.

Utility of Clinical-Grade Sequencing of Relapsed Multiple Myeloma Patients; Interim Analysis of the Multiple Myeloma Research Foundation (MMRF) Molecular Profiling Protocol

Utility of Clinical-Grade Sequencing of Relapsed Multiple Myeloma Patients; Interim Analysis of the Multiple Myeloma Research Foundation (MMRF) Molecular Profiling Protocol

Enabling Genomics Pipelines in Commodity Personal Computers With Flash Storage.

Analysis of a patient's genomics data is the first step toward precision medicine. Such analyses are performed on expensive enterprise-class server machines because input data sets are large, and the intermediate data structures are even larger (TB-size) and require random accesses. We present a general method to perform a specific genomics problem, mutation detection, on a cheap commodity personal computer (PC) with a small amount of DRAM. We construct and access large histograms of k-mers efficiently on external storage (SSDs) and apply our technique to a state-of-the-art reference-free genomics algorithm, SMUFIN, to create SMUFIN-F. We show that on two PCs, SMUFIN-F can achieve the same throughput at only one third (36%) the hardware cost and half (45%) the energy compared to SMUFIN on an enterprise-class server. To the best of our knowledge, SMUFIN-F is the first reference-free system that can detect somatic mutations on commodity PCs for whole human genomes. We believe our technique should apply to other k-mer or n-gram-based algorithms.

Nonsyndromic deafness due to compound heterozygous mutation of the CDH23 gene

Objective:To identify the pathogenic gene mutation of two patients with non-syndromic deafness（NSHL）. Methods:Two patient with NSHL and their parents were selected in the research object. Each participant provided 3-5 mL of peripheral venous blood, which was used to establish a DNA library. Next generation sequencing was used to detect the sequence of the patient's genome, and the sequencing results were compared with the human genome sequence （GRCh）37/hg19. Sanger sequencing was used to verify the parents' genome sequence. Finally the patient's pathogenic gene mutation was confirmed.Amino acid conservatism and single nucleotide polymorphisms of the mutant sites were analyzed using a variety of databases and software. Results:The mutation was located to CDH23 gene in the chromosomal location 10q21-q22. Complex heterozygous mutations consist of c. 1343T>C and c. 7991_7993delTCA. Parents are heterozygous carriers of a single mutation. Conclusion:The next generation sequencing technology were used to screen the pathogenic gene mutation of inherited deafness. Combined with the genetic sequencing results of parents, the specific pathogenic gene mutation of deafness patients can be identified. While the pathogenicity of complex heterozygous mutation were explained by various pathogenicity analysis methods.

Addendum: Array-based technology and recommendations for utilization in medical genetics practice for detection of chromosomal abnormalities

Laboratory evaluation of patients with developmental delay/intellectual disability, congenital anomalies, and dysmorphic features has changed significantly in the last several years with the introduction of microarray technologies. Using these techniques, a patient's genome can be examined for gains or losses of genetic material too small to be detected by standard G-banded chromosome studies. This increased resolution of microarray technology over conventional cytogenetic analysis allows for identification of chromosomal imbalances with greater precision, accuracy, and technical sensitivity. A variety of array-based platforms are now available for use in clinical practice, and utilization strategies are evolving. Thus, a review of the utility and limitations of these techniques and recommendations regarding present and future application in the clinical setting are presented in this study.

A Deep Learning Framework Identifies Pathogenic Noncoding Somatic Mutations from Personal Prostate Cancer Genomes.

Our understanding of noncoding mutations in cancer genomes has been derived primarily from mutational recurrence analysis by aggregating clinical samples on a large scale. These cohort-based approaches cannot directly identify individual pathogenic noncoding mutations from personal cancer genomes. Therefore, although most somatic mutations are localized in the noncoding cancer genome, their effects on driving tumorigenesis and progression have not been systematically explored and noncoding somatic alleles have not been leveraged in current clinical practice to guide personalized screening, diagnosis, and treatment. Here, we present a deep learning framework to capture pathogenic noncoding mutations in personal cancer genomes, which perturb gene regulation by altering chromatin architecture. We deployed the system specifically for localized prostate cancer by integrating large-scale prostate cancer genomes and the prostate-specific epigenome. We exhaustively evaluated somatic mutations in each patient's genome and agnostically identified thousands of somatic alleles altering the prostate epigenome. Functional genomic analyses subsequently demonstrated that affected genes displayed differential expression in prostate tumor samples, were vulnerable to expression alterations, and were convergent onto androgen receptor-mediated signaling pathways. Accumulation of pathogenic regulatory mutations in these affected genes was predictive of clinical observations, suggesting potential clinical utility of this approach. Overall, the deep learning framework has significantly expanded our view of somatic mutations in the vast noncoding genome, uncovered novel genes in localized prostate cancer, and will foster the development of personalized screening and therapeutic strategies for prostate cancer. SIGNIFICANCE: This study's characterization of the noncoding genome in prostate cancer reveals mutational signatures predictive of clinical observations, which may serve as a powerful prognostic tool in this disease.

The rare Alus element-mediated chimerism of multiple de novo complex rearrangement sequences in GAN result in giant axonal neuropathy

Giant axonal neuropathy (GAN) is a rare and grievous autosomal recessive neurodegenerative disease due to loss-of-function mutation in GAN. However, the chimerism of complex rearrangement sequences of GAN has not been reported so far, and the mechanism for its complex rearrangements remains to be determined. We identified a family with clinical symptoms of GAN and aimed to reveal a genetic cause underlying this disease. By whole-exome sequencing in the patient we identified a novel homozygous frameshift mutation with 1 bp deletion (c.27delC) in GAN. However, when analyzed the patient’s genomic DNA (gDNA) by quantitative real-time PCR and breakpoint DNA sequencing, we found the chimerism of multiple complex rearrangement sequences encompassing exon 1 of GAN in the patient's genome. The microhomology and localization of the breakpoint indicated that they may be caused by Alu repeat elements. We also found that the mRNA expression level of GAN in patient’s lymphocyte was decreased, confirming the pathogenicity of these mutations. Our study is the first reported on many complex rearrangement sequences mosaic in GAN mediated by Alu element. The patient here is not a simple homozygous frameshift mutation, but a compound heterozygous paternal c.27delC mutation and the chimerism of multiple de novo complex rearrangement sequences in GAN. Our results may also provide new insights into the formation and pathogenicity of complex rearrangement in GAN, and may be helpful to genetic counseling and genetic testing. It also enriches the Alu-mediated disease-associated database which are important for correct clinical interpretation.

A Prospective Analysis of the Genomic Landscape of Patients with Acute Myeloid Leukemia and Its Impact on Clinical Outcomes - Data from a Tertiary Care Center in India

BACKGROUND : Clinical Phenotype and outcomes of patients with Acute myeloid leukemia (AML) in the Indian subcontinent differs from published literature. A younger age at diagnosis and higher induction mortality complicate AML management in India(1). Metaphase Karyotyping represents the backbone of prognostication and risk stratification in AML. Optimal treatment strategies for the cohort of Cytogenetically normal AML are still under evaluation. Applications of Next generation Sequencing (NGS) techniques in AML have unravelled the genetic heterogeneity of this disease. Whole genome sequencing has identified many novel mutations leading to tremendous improvements in diagnosis and risk stratification. Development of therapies targeting these genetic alterations is enabling a gradual shift from non-specific approaches to personalised therapy tailored to an individual patient's genome. This will undoubtedly translate to better clinical outcomes for this disease, with otherwise poor prognosis. Whole genome sequencing is still in a nascent stage in Indian settings with no published literature on genomics in AML till date. We aimed to study the genomic landscape of AML in the Indian population and to co-relate this with clinical outcomes over the course of 1 year. METHODS: We recruited 34 newly diagnosed patients with AML who presented to our Centre (Mazumdar Shaw Medical Centre, Narayana Health City, Bangalore, India) between November 2017 and May 2018. Clinical and laboratory details of all patients were recorded. Bone marrow and paired peripheral blood samples were drawn before initiating therapy. Whole genome sequencing and Exome capture was done for each sample using Ilumina HiSeq platform. Patients were risk stratified as per ELN 2017 and treated as per NCCN guidelines. Patients were followed up prospectively for one year from initial diagnosis. Genetic results were stratified according to gene function and analysed with respect to predefined clinical outcomes (remission status post induction, relapse rates, progression free and overall survival). RESULTS: Amongst the 34 study participants, 5 patients failed QC during sequencing and were de-recruited. Hence 29 patients were available for final analysis. Median age of patients was 42 years with 13 patients (44.8%) less than 40 years of age.18 patients (60%) had normal cytogenetics at baseline.17 patients (58%) were classified as intermediate risk and 6 patients each as Standard and high risk, as per ELN 2017. 22 patients (79.3%) patients received standard Induction chemotherapy (3+7 regimen) while 6 patients received hypomethylating agents. Overall CR rate following induction at Day 28 was 50% and Induction mortality was 21.42%. 6 patients underwent an Allogenic Stem cell transplant. A total of 96 mutations (47 driver and 49 VUS mutations) in 123 genes were identified. The average number of Driver mutations was 1.48 per patient. IDH genes were the most frequently mutated Driver genes followed by FLT3 mutations. Frequency of NPM1 mutations was significantly low (17.25%). Highest frequency of VUS mutations was seen in the ETV6, ATM and CBLC genes. Highest frequency of somatic mutations were identified in the genes encoding for myeloid transcription factors and DNA methylation. Average driver mutations showed significant co-relation to Age (> 60 years) and high burden of Bone marrow blasts (>30%). An updated risk stratification incorporating mutation analysis findings resulted in re-stratification of 8 intermediate risk patients into high risk. 2 patients with detectable FLT3 ITD mutation by NGS were negative by PCR. Choice of consolidation therapy and Driver mutation status were found to show statistically significant association with both Event free survival and Overall survival at 1 year. Increased driver mutation burden was associated with increased refractoriness to chemotherapy and poor EFS and OS. Mutations in Tumour suppressor genes, were associated with suboptimal treatment outcomes and poor survival. CONCLUSIONS Genomic landscape of AML in Indian patients shows significant differences from published literature. This may hold clues to the differing biological characteristics of AML seen in this population. Genome based risk stratification and tailored therapy needs to be adapted into the management of AML. This data provides valuable insights into developing therapeutic strategies for Indian patients. Disclosures No relevant conflicts of interest to declare.

Clinical utility of next-generation sequencing in precision oncology.

The Precision Medicine Initiative (PMI) aims to change the way diseases are diagnosed and treated by taking into account a patient's genome, lifestyle, and environment. This type of research also uncovers potential biomarkers that can lead to the development of novel targeted therapies. Next-generation sequencing (NGS) is a new technology that facilitates collection of this genetic information by processing large amounts of DNA in an efficient and cost-effective way. NGS is particularly useful in oncology and has already begun to transform cancer management.

Male infertility in Sertoli cell-only syndrome: An investigation of autosomal gene defects.

To detect autosomal genetic defects and to determine candidate genes in Sertoli cell-only syndrome infertile men. Single-nucleotide polymorphism+comparative genomic hybridization microarray technology was carried out on 39 Sertoli cell-only syndrome infertile patients in the present study. Array comparative genomic hybridization compares the patient's genome against a reference genome, and identifies uncover deletions, amplifications and loss of heterozygosity. A link between defective spermatogenesis genes and infertility was examined, and amplifications and deletions in several genes were detected, including homeobox gene; synaptonemal complex element protein1; collagen, typeI, alpha1; imprinted maternally expressed transcript; and potassium voltage-gated channel subfamilyQ member1. The present data suggest that several genes can play an important role in spermatogenesis and progression of Sertoli cell-only syndrome.

Controversy and debate on clinical genomics sequencing—paper 1: genomics is not exceptional: rigorous evaluations are necessary for clinical applications of genomic sequencing

Next generation genomic sequencing (NGS) technologies—whole genome and whole exome sequencing—are now cheap enough to be within the grasp of many health care organizations. To many, NGS is symbolic of cutting edge health care, offering the promise of “precision” and “personalized” medicine. Historically, research and clinical application has been a two-way street in clinical genetics: research often driven directly by the desire to understand and try to solve immediate clinical problems affecting real, identifiable patients and families, accompanied by a low threshold of willingness to apply research-driven interventions without resort to formal empirical evaluations. However, NGS technologies are not simple substitutes for older technologies and need careful evaluation for use as screening, diagnostic, or prognostic tools. We have concerns across three areas. First, at the moment, analytic validity is unknown because technical platforms are not yet stable, laboratory quality assurance programs are in their infancy, and data interpretation capabilities are badly underdeveloped. Second, clinical validity of genomic findings for patient populations without pre-existing high genetic risk is doubtful, as most clinical experience with NGS technologies relates to patients with a high prior likelihood of a genetic etiology. Finally, we are concerned that proponents argue not only for clinically driven approaches to assessing a patient's genome, but also for seeking out variants associated with unrelated conditions or susceptibilities—so-called “secondary targets”—this is screening on a genomic scale. We argue that clinical uses of genomic sequencing should remain limited to specialist and research settings, that screening for secondary findings in clinical testing should be limited to the maximum extent possible, and that the benefits, harms, and economic implications of their routine use be systematically evaluated. All stakeholders have a responsibility to ensure that patients receive effective, safe health care, in an economically sustainable health care system. There should be no exception for genome-based interventions.

"Think metabolic" in adults with diagnostic challenges: Biotinidase deficiency as a paradigm disorder.

Neurologists should consider the possibility of an inherited metabolic disorder in adults with neurologic symptoms that may or may not mimic those seen in affected children, such as in the case of biotinidase deficiency. Because many of these disorders are treatable, they must be included in the differential diagnosis. Technologies, such as specific biochemical analysis and whole exomic sequencing, can assist the clinician by leading to the appropriate diagnosis and treatment. Whole exomic sequencing can identify known and putative mutations in a patient's genome. The neurologist must "think metabolic" in sorting out complex and difficult cases.

Harnessing Knowledge on Very Important Pharmacogenes CYP2C9 and CYP2C19 Variation for Precision Medicine in Resource-Limited Global Conflict Zones.

Pharmacogenomics harnesses the utility of a patient's genome (n = 1) in decisions on which therapeutic drugs and in what amounts should be administered. Often, patients with shared ancestry present with comparable genetic profiles that predict drug response. However, populations are not static, thus, often, population mobility through migration, especially enmasse as is seen for refugees, changes the pharmacogenetic profiles of resultant populations and therefore observed responses to commonly used therapeutic drugs. For example, in the aftermath of the Syrian civil war since 2011, millions have fled their homes to neighboring countries in the Middle East. The growing permanence of refugees and mass migrations is a call to shift our focus in the life sciences community from old models of pharmaceutical innovation. These seismic social changes demand faster decisions for "population-to-population bridging," whereby novel drugs developed in or for particular regions/countries can meet with rational regulatory decisions/approval in world regions impacted by migrant/refugee populations whose profiles are dynamic, such as in the Eastern Mediterranean region at present. Thus, it is important to characterize and report on the prevalence of pharmacogenes that affect commonly used medications and predict if population changes may call for attention to particular differences that may impact health of patients. Thus, we report here on four single-nucleotide polymorphism (SNP) variations in CYP2C9 and CYP2C19 genes among Mersin-Turkish healthy volunteers in the Mersin Province in the Eastern Mediterranean region that is currently hosting a vast number of migrant populations from Syria. Both CYP2C9 and CYP2C19 are very important pharmacogene molecular targets. We compare and report here on the observed SNP genetic variation in our sample with data on 12 world populations from dbSNP and discuss the feasibility of forecasting the pharmacokinetics of drugs utilized by migrant communities in Mersin and the Eastern Mediterranean region. This study can serve as a catalyst to invest in research in Syrian populations currently living in the Eastern Mediterranean. The findings have salience for rapid and rational regulatory decision-making for worldwide precision medicine and, specifically, "pharmacogenovigilance-guided bridging of pharmacokinetics" across world populations in the current era of planetary scale migration.

Abstract 681: Using a genome-editing approach for the stepwise establishment of zebrafish models of pediatric high-grade gliomas and MPNSTs

Abstract By high-throughput sequencing of cancer patient's genomes, driver mutations have recently been described in high-grade pediatric glioma, an aggressive type of brain tumor in children. The most prominent recurrent mutations were found in four genes, namely NF1, TP53, ATRX and H3F3A. NF1 and TP53 are tumor suppressor genes and their loss of function is characteristic for various human malignancies. However, the mechanisms through which precise missense mutations in H3F3A and loss-of-function of ATRX influence tumor biology remain largely unknown. In our project, we employ the genome-editing technology CRISPR-Cas9 to develop a zebrafish model of pediatric high-grade glioma, in order to examine the impact of these mutations on tumor onset and progression. We previously created a zebrafish line that is deficient for tp53 (tp53-/-) and has a loss-of-function mutation in 3 of 4 alleles of nf1 (nf1a+/-, nf1b-/-), which is duplicated in zebrafish. These fish develop high-grade gliomas with low penetrance and malignant peripheral nerve sheath tumors (MPNSTs) with high penetrance. By CRISPR-Cas9 we further incorporated early frameshift mutations of atrx in the background of tp53-/-, nf1a+/-, nf1b-/-. All analyzed fish injected with Cas9-mRNA and CRISPR guide-RNAs (gRNAs) targeting atrx coding sequences showed germline transmission of mutant alleles, of which one third were frameshift mutations. Primary CRISPR-Cas9 atrx-gRNA injected tp53-/-, nf1a+/-, nf1b-/- fertilized eggs developed as mosaics and exhibited increased tumor penetrance and faster onset of MPNSTs and these tumors harbored atrx mutations in 8/10 analyzed samples. This indicates that atrx loss-of-function mutations cooperate with loss of tp53 and nf1 tumor suppressors. Gliomas were not observed in mosaic atrx mutated fish, and these will be reanalyzed in stable atrx mutant lines. Next, we will add previously described missense mutations of h3f3a. To unravel the synergy between oncogenic mutations in atrx and h3f3a in tumor progression, mutant stable lines for both genes will be investigated independently and in a combined manner. The zebrafish model of pediatric high-grade gliomas and MPNSTs that we are developing will be useful to dissect the mechanisms underlying cooperation among driver mutations and for small molecule screens to identify specific inhibitors of cell growth and survival in these malignancies. Citation Format: Felix Oppel, Shuning He, Ting Tao, Mark W. Zimmerman, Adam D. Durbin, Nina Weichert, Dong H. Ki, A Thomas Look. Using a genome-editing approach for the stepwise establishment of zebrafish models of pediatric high-grade gliomas and MPNSTs. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 681.

A hot L1 retrotransposon evades somatic repression and initiates human colorectal cancer.

Although human LINE-1 (L1) elements are actively mobilized in many cancers, a role for somatic L1 retrotransposition in tumor initiation has not been conclusively demonstrated. Here, we identify a novel somatic L1 insertion in the APC tumor suppressor gene that provided us with a unique opportunity to determine whether such insertions can actually initiate colorectal cancer (CRC), and if so, how this might occur. Our data support a model whereby a hot L1 source element on Chromosome 17 of the patient's genome evaded somatic repression in normal colon tissues and thereby initiated CRC by mutating the APC gene. This insertion worked together with a point mutation in the second APC allele to initiate tumorigenesis through the classic two-hit CRC pathway. We also show that L1 source profiles vary considerably depending on the ancestry of an individual, and that population-specific hot L1 elements represent a novel form of cancer risk.

Information extraction from multi-institutional radiology reports.

The radiology report is the most important source of clinical imaging information. It documents critical information about the patient's health and the radiologist's interpretation of medical findings. It also communicates information to the referring physicians and records that information for future clinical and research use. Although efforts to structure some radiology report information through predefined templates are beginning to bear fruit, a large portion of radiology report information is entered in free text. The free text format is a major obstacle for rapid extraction and subsequent use of information by clinicians, researchers, and healthcare information systems. This difficulty is due to the ambiguity and subtlety of natural language, complexity of described images, and variations among different radiologists and healthcare organizations. As a result, radiology reports are used only once by the clinician who ordered the study and rarely are used again for research and data mining. In this work, machine learning techniques and a large multi-institutional radiology report repository are used to extract the semantics of the radiology report and overcome the barriers to the re-use of radiology report information in clinical research and other healthcare applications. We describe a machine learning system to annotate radiology reports and extract report contents according to an information model. This information model covers the majority of clinically significant contents in radiology reports and is applicable to a wide variety of radiology study types. Our automated approach uses discriminative sequence classifiers for named-entity recognition to extract and organize clinically significant terms and phrases consistent with the information model. We evaluated our information extraction system on 150 radiology reports from three major healthcare organizations and compared its results to a commonly used non-machine learning information extraction method. We also evaluated the generalizability of our approach across different organizations by training and testing our system on data from different organizations. Our results show the efficacy of our machine learning approach in extracting the information model's elements (10-fold cross-validation average performance: precision: 87%, recall: 84%, F1 score: 85%) and its superiority and generalizability compared to the common non-machine learning approach (p-value<0.05). Our machine learning information extraction approach provides an effective automatic method to annotate and extract clinically significant information from a large collection of free text radiology reports. This information extraction system can help clinicians better understand the radiology reports and prioritize their review process. In addition, the extracted information can be used by researchers to link radiology reports to information from other data sources such as electronic health records and the patient's genome. Extracted information also can facilitate disease surveillance, real-time clinical decision support for the radiologist, and content-based image retrieval.

Abstract CN01-01: Big challenges of big data: Biomedical science in the petabyte era

Abstract Next-generation sequencing technologies (NGS) have exploded in popularity in recent years thanks to their ability to produce an enormous volume of data quickly and at relatively low cost compared to traditional sequencing methods such as Sanger sequencing. The emergence of NGS has pushed forward the goal of personalized medicine, but looking at the entire scope of a study from experimental design to data analysis to the ultimate goal of dose selection based on a patient's genome reveals many more challenges. Sample size for RNA-seq studies has been a persistent problem that has only recently received attention. Sequencing data analysis packages are numerous and constantly in development, but without a clear gold standard, they can be more overwhelming than helpful. Finally, this genomic information must be seamlessly integrated with clinical information for the knowledge to be useable by practitioners before the goal of personalized medicine can become a reality. Citation Format: Yu Shyr. Big challenges of big data: Biomedical science in the petabyte era. [abstract]. In: Proceedings of the Thirteenth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2014 Sep 27-Oct 1; New Orleans, LA. Philadelphia (PA): AACR; Can Prev Res 2015;8(10 Suppl): Abstract nr CN01-01.