Oncology drug development, a burgeoning therapeutic field for pharmaceutical companies, is also extremely time consuming and expensive. Navigating a single drug moiety through the tedious process of preclinical studies, clinical trials, and of course the FDA’s approval process is a net investment of 12 to 15 years and over a billion dollars.1 This, added to the failure rates of clinical trials (5 of the top 10 clinical trial failures in 2013 were of drugs for cancer indications2) makes it imperative that the discovery and development process be streamlined to be cost-effective and timely.
GenBank, an all-inclusive, open-source database initiated by the National Center for Biotechnology Information (NCBI), has a very important role to play in this process. GenBank includes nucleotide sequences for more than 280,000 species and the supporting bibliographies, with submissions from individual laboratories as well as large-scale sequencing projects. Addi-tionally, sequences from issued patents are submitted by the US Patent and Trademark Office.3 Despite the open access to this database, researchers all over the world have actively contributed to building up the resource, realizing the vast potential of this knowledge-sharing database. The information either goes to GenBank or is submitted through its European counterpart, the European Bioinformatics Institute (EBI), or its Japanese counterpart, the DNA Data Bank of Japan (DDJB).4 All the leading journals need researchers to submit their sequences to GenBank and cite the corresponding access number in the published article. The new sequences can be directly submitted to EBI, DDJB, or GenBank, and the 3 databases are synchronized daily for easy access to all the information on all 3 databases. The data are virtually in real time, with minimal delay in access to the latest data, free of cost.
Other commonly used nucleotide databases include the European Molecular Biology Laboratory (EMBL; EBI is run by EMBL), SwissProt, PROSITE, and Human Genome Database (GDB).5 Taken together, these databases are essentially a bioinformatics tool that helps integrate biological information with computational software. The information gained can be applied to understand disease etiology (in terms of mutations in genes and proteins) and individual variables, and ultimately aid drug development.
According to the National Institutes of Health Biomedical Information Science and Technology Initiative, bioinformatics is defined as “research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral, or health data, including those to acquire, store, organize, archive, analyze, or visualize such data.”6
Development of GenBank
Initially called the Los Alamos Sequence Database, this resource was conceptualized in 1979 by Walter Goad, a nuclear physicist and a pioneer in bioinformatics at Los Alamos National Laboratory (LANL).7 GenBank followed in 1982 with funding from the National Institutes of Health, the National Science Foundation, and the Departments of Energy and Defense. LANL collaborated with various bioinformatics and technology companies for sequence data management and to promote open access communications. By 1992, GenBank transitioned to being managed by the National Center for Biotechnology information (NCBI).8
Submissions to the database include original mRNA sequences, prokaryotic and eukaryotic genes, rRNA, viral sequences, transposons, microsatellite sequences, pseudogenes, cloning vectors, noncoding RNAs, and microbial genome sequences. Following a submission (using the Web-based BankIt or Sequin programs), the GenBank staff reviews the documents for originality and then assigns an accession number to the sequence, followed by quality assurance checks (vector contamination, adequate translation of coding regions, correct taxonomy, correct bibliographic citation) and release to the public database.3,8
How Are Researchers Utilizing This Database?
BLAST (Basic Local Alignment Search Tool) software, a product of GenBank, allows for querying sequence similarities by directly entering their sequence of interest, without the need for the gene name or its synonyms.4 An orphan (unknown) or de novo nucleotide sequence, which may have been cloned in a laboratory, can gain perspective following a BLAST search and a match with another, better-characterized sequence in the database. Further, by adding restrictions to the BLAST search, only specific regions of the genome (such as gene-coding regions) can be examined instead of the 3 billion bases.4 BLAST can also translate a DNA sequence to a protein, which can then be used to search a protein database.
BLAST, which was developed at NCBI, works only with big chunks of nucleotide sequences, and not with shorter reads, according to Santosh Mishra, PhD, director of bioinformatics and codirector of the Collaborative Genomics Center at the Vaccine and Gene Therapy Institute (VGTI) of Florida. Mishra, who worked as a postdoctoral research associate with Goad at LANL, was actively involved in developing GenBank. His work contributed to the generation of the “flat file” format, and he also worked on improving the query-response time of the search engine. Additionally, he initiated the “feature table” in GenBank—the documentation within that helps GenBank, EMBL, and DDJB exchange data on a daily basis.
According to Mishra, the STAR aligner, developed at Cold Spring Harbor, works better with reference sequences, while Trinity, developed at the Broad Institute in Cambridge, Massachusetts, is useful for de novo sequences. (The Broad Institute made news last month with its work on identifying gene mutations that prevent diabetes in adults who have known risk factors, such as obesity.)
Advantages and Disadvantages of the GenBank Platform
The biggest single advantage of GenBank is the open-access format, which allows for a centralized repository in a uniform format. The tremendous amount of data generated by laboratories (such as from microarrays and microRNA arrays) cannot be published in a research article. However, the data, tagged and uploaded on GenBank, can be linked to the journals’ websites and the links can be provided in the print versions of the articles as well.4
On the flip side, the biggest advantage of being an open-access platform is also the biggest disadvantage of the software. There’s always the probability of scientists registering faulty genetic sequences on the website, which will not be caught unless they are peer reviewed. Despite the incorporation of several quality control mechanisms into the system, reuse of the data by other scientists alone can help discover glitches in the existing data. Additionally, GenBank encourages its users to submit feedback and update records, which unfortunately is not a very proactive process.4
Bioinformatics and Pharmacogenomics in Drug Discovery/Development
Accelerating the drug development process saves costs for the pharmaceutical industry, especially with the way the industry functions today. The company that discovers or invents a new chemical entity, which could metamorphose into a new drug candidate, can squeeze the maximum profit out of the drug before the patent expires and competitors catch on. Essentially, companies jump at every opportunity to accelerate any aspect of the discovery/development process. Resources like the GenBank and EBI are data mines that can speed up the entire process in the following ways:
Drug candidates can be identified (following a high-throughput screen of chemical libraries) and developed only after a “druggable target” is discovered for a disease condition. Typically, about 1 in 1000 synthesized compounds will progress to the clinic, and only 1 in 10 drugs undergoing clinical trials reaches the market.9 Optimizing/validating a target is essential due to the prohibitively high cost of conducting trials, and the potential targets for drug discovery are increasing exponentially.10 By mining and storing information from huge data sets, like the human genome sequence, the nucleotide sequence of the target proteins has become readily available, as has the potential to identify new targets. This can exponentially increase the content of the drug pipelines of pharmaceutical companies.10
According to Arathi Krishnakumar, PhD, a protein biochemist and a senior research investigator with the department of Exploratory Biology and Genomics, Bristol-Myers Squibb (BMS), “For compounds that have no obvious targets from a typical phenotypic screening, proteomics offers tools for target identification or target deconvolution. Monitoring the global phosphorylation status of proteins that are downstream of tyrosine kinase inhibitors—also termed phosphoproteomics—is a very attractive tool that can also be used for target as well as biomarker identification. These events can be used as reporters (biomarkers) for specific upstream kinase(s).”
Establishing a robust association between a likely target and the disease, to confirm that target modulation translates into a beneficial therapeutic outcome, would not only validate the drug development process but also help absorb the risks associated with clinical trial failure of the molecule being developed.10
Says Krishnakumar, “Target validation is typically done with knock-out or knock-down of the proposed target using RNAi and then monitoring the disease phenotype in relevant cellular models. Proteomics tools are also highly valuable in monitoring specific events on proteins like post translational modifications, including phosphorylation, methylation, oxidation, etc, new product generation, degradation products, protein-protein interaction, etc, all of which could be direct or indirect consequences of target activation or engagement.”
The drug development process is not just lengthy (product development can take 10 to 15 years9), but is prohibitively expensive as well. Averaging $140 million in the 1970s, the cost of developing a drug was estimated at a whopping $1.2 billion in the early 2000s,11 and a recent Forbes analysis estimated the cost at $5 billion.12
Worth noting is that the final cost of any drug, which includes the total costs from discovery to approval, includes the cost of absorbing all the clinical trial failures.10 Clearly, bioinformatics tools improve the efficiency of target discovery and validation processes, reduce the time spent on the discovery phase, and make the entire process more costeffective.
Mishra believes GenBank is a good starting point in the drug discovery process. When a new sequence (of known or unknown function) is identified/ isolated in the laboratory, a GenBank search will help identify homologues (human or in other organisms) with a 70% to 80% match. Functional studies would then ensue, along with cell and tissue distribution studies.
With the value of personalized medication gaining acceptance, the study of pharmacogenomics (genetic variants that determine a person’s drug response; one size does not fit all) is extremely helpful to tailor the optimal drug, dose, and treatment options for a patient to improve efficacy as well as avoid adverse events (AEs).10 According to the Agency for Healthcare Research and Quality of the HHS, AEs annually result in more than 770,000 injuries and deaths and may cost up to $5.6 million per hospital.13
To this end, EMBL-EBI is actively involved in industry partnerships (the partnerships were initiated in 1996), which include Astellas, Merck Serono, AstraZeneca, Novartis, GlaxoSmithKline, BMS, and several others.14 With the high-throughput data that research and development (R&D) activities generate, open-source software and informatics developed by organizations like the GenBank and EBI could greatly improve efficiency and reduce the cost of drug discovery and development.
Translational Bioinformatics and Precision Medicine
Healthcare today is primarily symptom driven, and intervention usually occurs late in the pathological process, when the treatment may not be as effective. Identifying predisease states that could provide a window into the forthcoming risk of developing a disease, identifying reliable markers, and developing useful therapies would be the key to managing disease treatment15—not just to improve efficiency but also to reduce healthcare costs, which it is estimated will steadily increase and by 2022 account for 19.9% of the gross domestic product (GDP).16
With precision medicine or personalized medicine, molecular profiles generated from a patient’s genomic (coupled with other “-omics” such as epigenomics, proteomics, and metabolomics) information could help accurately drive the diagnostic, prognostic, and therapeutic plans, tailored to the patient’s physiological status. Predictive models can also be developed for different biological contexts, such as disease, populations, and tissues.15 However, the deluge of data generated by bioinformatics tools requires a framework to regulate, compile, and interpret the information. Most importantly, the key stakeholders (government, research industry, biological community, pharmaceutical industry, insurance companies, patient groups, and regulatory bodies17) that would drive the widespread acceptance and implementation of precision medicine need to be brought up to speed with the enormous progress made in the field and the promise it brings. There would also be a revolutionary change in the approach to conducting clinical trials—the phase 3 studies conducted in the target population could focus on a more select patient group, which could improve both clinical and economic efficacy.17
At BMS, Krishnakumar’s group actively provides support to clinical trials by developing assays for clinical samples. When it comes to administration of biologics such as antibodies, individual variations such as expression levels of various proteins and their affinity for an antibody essentiate dose-titration in order to personalize treatment to improve efficacy.
The developing field of translational bioinformatics creates a platform to bring all the data together, which can then be used to generate a treatment plan personalized to a patient (Figure). It has been defined as “the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data into proactive, predictive, preventative, and participatory health.”15 The primary goal of translational bioinformatics is to connect the dots and develop disease networks that can be used as predictive models. In other words, harmonization of the data from different sources (genome, proteome, transcriptome, metabolome, and patient’s pathological data) could help in making better-informed treatment decisions.
Within medical R&D, a commonly held belief is that cures for diseases could be found residing within existing data, if only the data could be made to give up their secrets.18 The current status of the scientific, medical, and healthcare fields is that experts in each field have set their minds on developing the best technologies; unfortunately, the technologies are compartmentalized and they work in parallel. The great need, which has been recognized and implemented in limited areas, is to create platforms where the data can be merged to produce meaningful outcomes.
Data Integration Platforms to Boost Evidence-Based Decisions
Implementing these huge changes would necessitate that physicians and providers be more adept at interpreting molecular data, which essentially requires improved education models that include relevant courses during graduate training. Also, development of software that can interpret the data would provide a tremendous advantage to researchers, clinicians, scientists, pathologists, and maybe patients as well.
To this end, companies such as N-of-One are developing analyzers coupled with software that can provide molecular interpretation of next-generation sequencing data. The company recently announced the launch of Variant Interpreter, a cloud-based application, on Illumina’s BaseSpace Apps (applications store for genomic analysis).19 The app allows oncologists, pathologists, and researchers to access relevant biological and clinical information related to the tumor profile generated following sequencing. Additionally, the user can request a molecular interpretation of a variant or multiple variants in a tumor and receive a customized interpretive road map linking the variant data to scientific knowledge on it. With a plan for future expansion, the software currently includes 30 cancer-associated genes.19
An application developed by Remedy Informatics, TIMe, boosts the process further. TIMe merges data, registries, applications, analyses, and any other relevant content. TIMe promises to enable faster, more informed decisions in clinical practice, research, and business operations. It also is expected to improve treatment effectiveness, quality of care, and patient outcomes.20
The MD Anderson cancer center is working in collaboration with IBM, using the IBM Watson computing system to enable clinicians to reveal relevant research and patient data from the cancer center’s rich databases. The Oncology Expert Advisor, a product of this collaboration, will integrate clinical and research data to help physicians develop, observe, and fine-tune treatment plans for the patients, and also help them anticipate adverse events that may occur through the treatment period.21
Applications of Translational Bioinformatics
Once the genomic and/or proteomic data have been generated, what next? How are providers employing these data to their advantage and to guide treatment? Several reports on clinical studies are being successfully conducted on the foundation of precision as well as evidence-based medicine.
A study published in the New England Journal of Medicine highlighted the importance of using panitumumab (Vectibix; Amgen Inc) in combination with traditional chemotherapy only in those patients with metastatic colorectal cancer (mCRC) who do not have RAS mutations. The study found that the subset of mCRC patients who expressed wild-type RAS demonstrated improvements in progression-free as well as overall survival upon the inclusion of panitumumab in their treatment regimen.22 The protein KRAS functions downstream of the epidermal growth factor receptor (EGFR). Mutations in the KRAS gene entail receptor-independent functioning of the protein, so using panitumumab, which is an EGFR antagonist, would be completely fruitless in this context. Thus, prior knowledge of the patient’s genomic status helped in selecting the right cohort for successfully using this drug.
Proteomics has led to the development of a molecular diagnostic tool to detect precancerous cysts in the pancreas.23 Although computed tomography (CT) and magnetic resonance imaging (MRI) can detect cysts, identifying the ones that can develop into cancer is difficult. An important issue faced with pancreatic cancer is that the patient is indication-free as the tumors initially develop, and disease symptoms appear only following tumor metastasis to distant organs, at which stage the disease is usually difficult to treat. This has resulted in a poor prognosis of pancreatic cancer. Scientists at the Sahlgrenska Academy in Sweden have developed a method to identify precancerous cysts by detecting mucins in the cystic fluid as biomarkers. Following evaluation, the diagnostic tool could accurately predict the nature of the cysts examined with 97% accuracy. Additionally, the researchers tested existing tumors and could determine which tumors have developed into cancers with 90% certainty.
Bioinformatics studies have also yielded microRNAs, which are small (~22 nucleotides), noncoding RNA molecules that can repress the transcription of messenger RNA (mRNA) or promote its degradation, thereby silencing gene expression.24 Initially thought of as “junk” sequences on the DNA since they are non-coding nucleotides, miRNAs (about 24,521 listed in miRBase, a database maintained by the University of Manchester25) have now found their place in clinical trials as biomarkers (cancer,26 multiple sclerosis,27 psoriasis28) and are also being developed as “drugs” by companies like Mirna Therapeutics Inc.29
The “Adaptive” Clinical Trial Design
The “omic” revolution has also had a tremendous impact on clinical trial design. The FDA definition of an adaptive clinical study is “a study that includes a prospectively planned opportunity for modification of one or more specified aspects of the study design and hypotheses based on analysis of data from subjects in the study.”30 The trial design includes interim analysis points that would allow researchers to alter the trial (treatment dose or schedule, randomization) based on results from earlier study participants. Two of the 20 ongoing adaptive trials recently published positive results.
The i-SPY 2 trial launched in 2010 in patients with newly diagnosed, locally advanced breast cancer was designed to screen 12 cancer drugs from multiple pharmaceutical companies, by adding each individual drug to a standard neoadjuvant chemotherapy. Adaptation was based on examining the transcriptional profile of the patient’s tumor right when enrolled in the trial, evaluating the responses following treatment, and comparing them against the responses to the same treatment of previous patients with a similar genetic tumor signature. Based on the results, the patients would be randomized to various trial arms. Two of the 12 drugs in the trial, veliparib (AbbVie) and neratinib (Puma Biotechnology), proved promising in 2 different breast cancer subtypes.1
The BATTLE trial, ongoing at the MD Anderson Cancer Center, is evaluating the effectiveness of multiple drugs on multiple mutations in a single cancer type: non-small cell lung cancer (NSCLC). Only 40% of the patients in the first phase of the study were evaluated for biomarkers and assigned to 4 treatment arms. In the second phase of the trial, the remaining 60% of patients were evaluated for their biomarker status and then assigned to treatments following an assessment of the responses from patients with a similar tumor profile in the first phase. The result: the trial reported encouraging clinical activity in the sorafenib-treated cohort harboring a wild-type epidermal growth factor receptor (EGFR).1
Genetic Testing to Determine Disease Susceptibility
Another aspect of bioinformatics is genetic testing, which along with risk assessment is rapidly being streamlined into mainstream oncology practices, especially with the recommendations provided by the USPSTF.31 Genetic counseling has become the standard of care for patients with a personal or family history of breast, ovarian, or colon cancer, while genetic testing is appropriate for some patients with pancreatic, renal, skin, or thyroid cancers as well as with some rare cancer syndromes.32
Then you have J. Craig Venter, PhD, a biologist and entrepreneur, who competed with the Human Genome Project to sequence the human genome and who recently announced the launch of a new company, Human Longevity. The company plans to sequence 40,000 human genomes per year to gain insightsinto the molecular causes of aging and age-associated diseases such as cancer and heart disease.33
The Healthcare Equation
Insurance companies are rapidly adapting to this changing scene of “big data” in their own right. Back in 2011, Aetna announced a partnership with the Center for Biomedical Informatics at Harvard Medical School with the aim of improving the quality and affordability of healthcare (healthcare informatics). The researchers at Harvard aimed to: • Evaluate the outcomes of various treatments for specific conditions based on quality and cost • Determine factors that predict adherence for chronic diseases • Study how claims data and clinical data, available through electronic health records, can best be used to predict outcomes • Improve the ability to predict adverse events through a proactive study of claims and clinical data.34
The possibilities are enormous, with application in all disease fields. Translational bioinformatics integrates the various data sources and paves a path for precision medicine that would be immensely valuable to all stakeholders (patients, pharmaceutical companies, scientists, and physicians) alike.
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19. N-of-One launches ‘Variant Interpreter’ app to provide easy access to molecular interpretation of next generation sequencing data for oncologists and researchers. N-of-One website. http://www.n-of-one.com/news/2014-0312-VariantInterpreter.php. Published March 12, 2014. Accessed March 12, 2014.
20. TIMe™ — the informatics marketplace. Remedy Informatics website. http://remedyinformatics.com/mosaic-platform/time-the-informaticsmarketplace/. Accessed March 14, 2014.
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26. The role of microRNA-29b in the oral squamous cell carcinoma. ClinicalTrials.gov. http://clinicaltrials.gov/ct2/show/NCT02009852?term=microRNA&rank=1. Accessed March 14. 2014.
27. A pilot study to assess microRNA biomarkers in early and later stage multiple sclerosis. ClinicalTrials.gov. http://clinicaltrials.gov/ct2/show/NCT01737372?term=miRNA+multiple+sclerosis&rank=1. Accessed March 14, 2014.
28. miRNAs and mRNAs in Psoriasis. ClinicalTrials.gov. http://clinicaltrials.gov/ct2/show/NCT01604902?term=miRNA+psoriasis&rank=1. Accessed March 14. 2014.
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32. Matloff ET. Integrating genetic counseling into an oncology practice can benefit high-risk families. OncLive. http://www.onclive.com/publications/oncology-business-news/2014/February2014/Integrating-Genetic-Counseling-Into-an-Oncology-Practice-Can-Benefit-High-Risk-Families. Published February 26, 2014. Accessed March 13, 2014.
33. Pollack A. A genetic entrepreneur sets his sights on aging and death. The New York Times. March 5, 2014:B1.
34. Aetna and the center for biomedical informatics at Harvard Medical School announce research collaboration [press release]. Hartford, CT: Aetna; November 17, 2011. http://newshub.aetna.
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