For several years, the future of cancer care has revolved around “personalized medicine.” This process leverages all that can be learned about a patient’s tumor from genetic or protein characteristics, and treats the cancer with a therapy or cocktail that matches the tumor’s profile, based on clinical trials.

The development of highly sophisticated tests, which pair cancers with custom treatments, has become its own scientific frontier. Scientists and companies pushing these edges say when targeted therapy succeeds, it’s worth every dollar spent, not only in lives saved but in costs avoided—on expensive chemotherapies that would not have worked, or hospital costs from avoidable side effects, or both.

“Personalized medicine can change the trajectory costs in our healthcare system,” said Mark Capone, president of Myriad Genetic Laboratories. Based on his meetings with payers, he believes

they are seeing the value. But not everyone agrees. Many see the industry at a crossroads, with reimbursement issues at the center. The future of molecular diagnostics is both entwined in the broader discussion of paying America’s healthcare tab and its own separate beast, for reimbursement issues present a steep scientific and regulatory challenge. Almost everyone who spoke with 

predicted an industry shakeout would occur. Some promising small companies will join large ones, while others will disappear. (An example: Myriad Genetics’ acquisition of Crescendo Bioscience, and its rheumatoid arthritis test, for $270 million.

Right now, the divide looks like this: payers have seen a new cost category explode across their balance sheets; they are determined to understand what they are funding and whether tests are necessary. Molecular diagnostic companies, meanwhile, say they can’t understand what they call a penny-wise, pound-foolish approach. Why, they say, should insurers pay ever-rising sums for cancer drugs, with prices measured in tens and hundreds of thousands of dollars, but balk at a $3000 test

 that would tell doctors whether the drug will work?

“We overtreat people continuously in this country,” said Macey Johnson, vice president of managed care and reimbursement at bioTheranostics, based in San Diego, California. “It’s overkill to give people all these drugs, with oncology being the poster child.”

As both regulators and testing companies implement a new reimbursement law, many stakeholders see opportunities for change. “It brings the industry into the bright light,” said Mike Barlow,

vice president of operations at Palmetto GBA of South Carolina, the Medicare contractor that developed the MolDx program to create billing expertise around an emerging industry. “Too often, the lab industry has been operating as an afterthought.”

What’s harder to gauge is how much the recent reimbursement woes are driving the science—either by slowing new discoveries or directing research toward tests for which payment is perceived to be easier. Some say there’s no doubt that venture capitalists who make decisions on whether to invest in molecular diagnostic companies find the current landscape unsettling.

There’s a direct connection between science and the funding that pays for it, said Rina Wolf, vice president of commercialization strategies, consulting, and industry affairs at XIFIN, a California-based firm that provides research, technical, and health economics support for the molecular diagnostics industry. Today’s uncertainty can make venture capitalists nervous, Wolf said, especially as the bar for reimbursement gets higher. “You can have a company that has a tremendous protocol from a scientific standpoint, but if they can’t raise the money to validate

it, especially the clinical utility piece, it’s not going to go anywhere.”

Most of the focus on reimbursement for molecular diagnostic testing has been on CMS, for obvious reasons: first, most commercial payers use CMS payment schedules as a benchmark

second, many new, sophisticated tests involve cancer diagnoses, and cancer increasingly strikes Medicare eligible seniors.

The recent adoption of a federal law aims to put molecular diagnostics on a path to certainty.

 However, unhappiness over current CMS reimbursement policy prompted a lawsuit filed April 16, 2014.

 The California Clinical Laboratory Association, on behalf of some members and an unnamed Medicare beneficiary, sued HHS in federal court in the District of Columbia, asserting

that today’s interim billing practices place too much control in the hands of regional Medicare contractors. As a result, the suit alleges, contractors are using pricing policy gaps to make decisions about whether a test should be covered at all, which unfairly denies patients access.

As with any maturing industry, one challenge has been defining it: there are different types of tests with different levels of oversight. The FDA has jurisdiction over some tests but not others. FDA may give “clearance” to a companion diagnostic developed alongside a specific drug. Laboratory-developed tests, or LDTs, are not FDA-regulated but must be standards of the Clinical Laboratory Improvement Amendments (CLIA) before their results can be considered valid.

The pace of science, however, may mean that a companion diagnostic with FDA’s blessing

may be superseded by an LDT test if the approved therapy is found to have wider applicability. Wolf and others point to the example of a companion diagnostic for a BRAF mutation that Roche developed in tandem with vemurafenib for the treatment of late-stage skin cancer.

 “When Roche went through FDA, there was only 1 known mutation,” for which the drug was indicated, Wolf said. “Now there are 3.”

Then there is the emerging area of multi-analyte algorithmic tests, which determine risk factors based on algorithms that are often proprietary. Billing for these tests has been controversial—

some within CMS believe that Medicare should only cover elements that detect the presence of something in the body, not a calculation.

Molecular diagnostic testing presents 2 distinct regulatory challenges for CMS. First, it is never easy when scientific advances this important happen this quickly, so quickly that they threaten

to outpace the expertise within the agency that must fund them. Second, and perhaps more critically, long-term regulatory rhythms and lab fee schedules were never designed for such complicated tests, so CMS must now build a brand new framework for this industry, propelled by the provision tucked into the recent “patch” of the Sustainable Growth Rate (SGR).

 Even when change is good, any time a regulatory agency does something for the first time, there’s uncertainty during the transition from the old way of doing things to the new one. The fact that all this is occurring alongside the high-profile rollout of the Affordable Care Act (ACA) only complicates matters.

Molecular diagnostic testing is still a comparatively young discipline. The Association for Molecular Pathology (AMP) formed in 1995

; AMP cofounded the Journal of Molecular Diagnostics in 1999.

 Commercialization took hold over the next decade, with companies like Myriad Genetics and Genomic Health becoming well known, particularly in the field of breast cancer. By the time AMP secured its June 13, 2013, US Supreme Court victory over Myriad on the question of whether the company could patent a gene, Myriad’s footprint in BRCA1 and BRCA2 testing was firmly established.

Genomic Health, meanwhile, made successive splashes at the 2003 and 2004 June meetings of the American Society of Clinical Oncology (ASCO) with its Oncotype Dx test, which predicts both chemotherapy response and potential for recurrence in breast cancer. (Genomic Health’s 2004 results were featured at ASCO’s “Best of Oncology” session.)

For tests to gain widespread use, companies had to bill insurers and CMS. During the next decade, the industry grafted its fees around a billing system designed for less sophisticated clinical

laboratory tests. What emerged was a process called “stacking,” in which each step in the chain had its own code.

In a May 2013 presentation in New Orleans, Michael Longacre, reimbursement director for corporate/shared services, Becton Dickenson, offered an example in which a single test had 6 different codes.

What was worse, according to a UnitedHealthcare report, was that the same 5 or 6 steps could describe vastly different tests. A bill for a genetic test for Canavan disease might look just like a test for Tay-Sachs, leaving payers unable to tell what they were funding, much less whether it was medically necessary.

As molecular diagnostic testing became more common, payers winced at rising costs in a category that did not even exist a few years prior, coupled with their inability to fully track spending.

Many advocates for molecular diagnostics also concede that code stacking was not sustainable. Payers began to push back. As reported in 

front, around 2011 payers shifted from seeking proof of clinical validity to asking for evidence of clinical utility, or studies that show a given test directly guides or alters physician behavior. As

Beth Davis, senior director of health policy and reimbursement for MDx Health, told 

 at the time, “showing how doctors use a test in real-world settings can be difficult if payers will not cover it, because that has the practical effect of making it unavailable to most patients.”

By 2012, multiple efforts were afoot to address payment in molecular diagnostic testing. The rise of the clinical utility standard led the Baltimore-based Center for Medical Technology Policy (CMTP) to develop an Effectiveness Guidance Document, or EGD. This multi-stakeholder effort, supported by leaders in the pharmaceutical and diagnostic testing industries as well as by payers, represented an effort to develop criteria for evaluating tests in personalized medicine.

The bigger battle, however, involved getting rid of “stacking.” Commercial payers and CMS were both determined to get their arms around a market that CMS’ chief medical officer called a “tsunami,” which by 2010 had grown to $6.2 billion and was increasing by 15% to 20% a year.

Starting in 2009, the American Medical Association (AMA) Current Procedural Technology (CPT) editorial panel worked to collapse all the stacked codes into 127 new CPT codes. After a year of talks, CMS elected to adopt the new codes effective January 1, 2013.

 There was just one problem, however. The AMA cannot set prices; only CMS does that. And CMS punted.

There was hope that CMS would adopt the streamlined codes with existing prices, but that didn’t happen. Instead, in late 2012, CMS set off a year of uncertainty by letting its regional billing

contractors set their own prices, a process known as “gap-fill.”

 The expressed mission was to collect pricing data that would lead to new maximums, or National Limitation Amounts, by January 1, 2014.

 Trouble was, the old “stacking” system meant most contractors were in the dark about what they had been paying for a complete test, and chaos ensued.

Many molecular diagnostic companies spent the early months of 2013 not being paid at all. For some, payment did not start until after Forbes’ Scott Gottlieb, MD, wrote a March 27, 2013, column on the topic, stating, “This sort of bungling may be without precedent, even for the Medicare agency.”

Having developed MolDx, Palmetto GBA of South Carolina emerged as a potential solution for the nation. The MolDx program has unique identifiers to differentiate between various types of tests,

such as the difference between FDA-approved tests and LDTs. Some hoped that Palmetto GBA would fill the void for all, while others vehemently opposed this idea. As with all things in molecular diagnostics, opinions differ widely based on individual interests and experience, and that’s been one of the challenges in resolving the payment quagmire.

Of the vast clinical laboratory industry, sophisticated molecular diagnostic tests make up a small part, and they are a smaller part still of what gets paid by Medicare. Getting Congress’ involvement is difficult when the industry itself is split on what solutions should look like. Wolf said the message from Congress has been, “‘When you come to some consensus amongst yourselves, come talk to us.’ Different stakeholders have different agendas.”

Lobbying ensued throughout 2013 to address molecular diagnostic payment issues while CMS worked on the Medicare fee schedule, and, of course, the disastrous launch of the ACA website,

www.HealthCare.gov. In December 2013 came another surprise: plans to revamp the entire Clinical Laboratory Fee Schedule (CLFS), with an eye toward annual summer updates based on technological advances. The tumultuous year ended with as much uncertainty as it began.

What’s happening in molecular diagnostic reimbursement is happening alongside the broader movement in healthcare reform. The quest is on to get payers, and CMS in particular, away from

a fee-for-service model that rewards lots of procedures and instead pay for things that help patients. Molecular diagnostic testing companies insist they will have a good story to tell—they will prove their value in reducing waste and improving safety and cancer survival rates. Getting from here to there will be the hard part.

Of course, molecular diagnostic testing companies are just 1 part of cancer care and a sliver of the healthcare system. This spring, their cause was tucked into a louder drama over the effort to scrap the SGR in favor of value-based reimbursement. On April 1, 2014, Congress enacted a final fix, or “patch”, to forestall drastic cuts to Medicare payments, which would have covered shortfalls in forecasting.

The patch came with a provision, “Improving Medicare Policies for Clinical Diagnostic Laboratory Tests,” which stabilizes prices and gives everyone a timeout to develop a long-term payment

structure. The law calls for a transition to “market-based” pricing, which some have called “value-based.” Come January 1, 2015, the law will strip CMS of its authority to apply annual CLFS changes based on “technological changes,” as announced in December. But, the law comes with price tags, in the form of significant reporting requirements—and fines of up to $10,000 a day for failure to comply.

From afar, the law appears to provide more time, along with outside expertise and oversight, to the process CMS attempted in 2013. Key deadlines include:

• An expert advisory panel must be in place by July 1, 2015

• Labs must start reporting payer rates by January 1, 2016

• CMS must start filling in codes for certain existing tests now paid under miscellaneous codes by January 1, 2016

• A market-based system for advanced diagnostics will be effective January 1, 2017.

Overall, molecular diagnostic companies are cautious, but optimistic. A typical response came from Genomic Health: “We are encouraged to see value-based pricing included in the SGR Patch legislation that passed in March, with a new reimbursement methodology designed to align private managed care and public Medicare rates. We believe this will provide transparency and predictability to the reimbursement process under the Medicare program for diagnostic tests like ours,” said Emily Faucette, vice president of corporate communications and investor relations at Genomic Health.

Myriad’s Capone said attracting investment requires certainty in what he called “the three Rs” of the field—reimbursement, regulation, and “rights to intellectual property,” which covers whether a company’s discoveries can be patented and refers to interpretations from multiple Supreme Court rulings. While Capone believes these interpretations are a step in “the wrong direction,”

on the intellectual property front, he is more optimistic about progress on the first two Rs in light of the new law.

“As always the devil is in the details, but we’re very confident this legislation is good for the industry,” Capone said. A movement toward market-based pricing will take ambiguity out of  reimbursement. But that doesn’t mean the process will be easy. Some industry sources said they will be watching who makes up the advisory panel. Others said recent turnover at CMS, coupled with the agency’s duties to implement healthcare reform, may make it hard to retain focus. And that’s on top of a fundamental question: just what is “market-based” pricing?

Said Palmetto GBA’s Barlow, “This is not a service that is truly market driven. To say that you are developing market-based pricing is going to require significant effort to determine, ‘What is the market?’ ”

Apart from implementing payment, the process may yield discussion on what gets covered in the first place, and what levels of evidence should be required. This is where the industry may

see great divergence, with more established companies taking advantage of their ability to raise capital for studies. Barlow indicated that scientific bars will remain high, not go lower. “The days of ‘do more, get more’ have to come to an end,” Barlow said. “The utilization of services has to be based on need for the services. It has to be good for the patient.”

Articles

Evidence-Based Oncology

When Science Outpaces Payers: Reimbursement in Molecular Diagnostics

Everolimus in Elderly Hormone-Receptor—Positive Breast Cancer Patients

	I read with great interest the article entitled “Breast Cancer: Will Treatment Costs Outpace Effectiveness?” by MP Zimmerman et al, published in the December 2012 issue (Volume 18, Special Issue 5, SP200-SP202) of 

. In response to the authors’ discussion regarding the potential for treatment costs to outpace effectiveness in breast cancer, I would like to provide clarification, specifically with respect to the effectiveness of everolimus combined with exemestane to treat patients older than 65 years.

	The authors accurately reported the overall results from BOLERO-2, a phase III trial (N = 724) with everolimus, a mammalian target of rapamycin (mTOR) inhibitor, in postmenopausal women with hormone receptor—positive (HR+), human epidermal growth factor receptor 2–negative (HER2–) advanced breast cancer (ABC) that was refractory to nonsteroidal aromatase inhibitors (NSAIs). Results of this trial show that treatment with everolimus plus exemestane improved progression- free survival (PFS) by 4.6 months, from 3.2 months with exemestane alone to 7.8 months with the combination therapy (local assessment, hazard ratio: 0.45; 95% CI: 0.38-0.54; P <0.0001).

 Although overall survival at the time of interim analysis was not mature, results at the median 18-month follow- up assessment showed that fewer deaths occurred among patients treated with everolimus plus exemestane compared with those treated with exemestane alone (25.4% vs 32.2%).

	Exploratory subgroup analysis of the BOLERO-2 trial at a median 18-month follow-up visit showed that for patients younger than 65 years and patients 65 years and older, improvements in PFS, overall response rate (ORR), and clinical benefit rate (CBR) were observed with everolimus + exemestane versus placebo + exemestane (

 Additionally for patients younger than 70

	years and 70 years and older, improvements in PFS ORR and CBR were observed with everolimus + exemestane versus placebo + exemestane (

). The safety profile of EVE + EXE in elderly patients with advanced BC was consistent with the known overall profile of each agent.

	In conclusion, contrary to the claim put forth by Zimmerman et al, results from the BOLERO-2 trial show that patients younger than 65 years and those 65 years and older with HR+ HER2— BC

	have the potential to benefit from treatment with everolimus + exemestane. Currently, everolimus, in combination with exemestane, remains the only mTOR inhibitor approved by the FDA for managing patients with HR+ HER2—BC that is refractory to NSAI therapy.

		Thank you for your somewhat belated comments on our article, “Breast Cancer: Will Treatment Costs Outpace Effectiveness?”, published in the December 2012 issue (Volume 18, Special Issue 5, SP200-SP202) of 

. The information presented in the article from the BOLERO-2 study was acquired from information cited in the abstract by Martine J. Piccart-Gebhart, et al, presented at the June 2012 American Society of Clinical Oncology meeting and the press release from the FDA on July 23, 2012, announcing that it had approved everolimus.

 The information available at the time showed that everolimus had an overall positive effect when used in combination therapy for breast cancer. These 2 references did not contain any efficacy results stratified by age.

		Additional subgroup analysis is quite often performed after initial results of trials are presented. These subgroup analyses many times identify efficacy and safety information that is valuable to clinicians in treating and managing their patients. Thank you for providing further valuable information that will assist clinicians in determining optimal therapy for their patients.

Data discussed in this letter were presented at the 2012 American Society of Clinical Oncology Annual Meeting; June 1-5, 2012; Chicago, IL (Abstracts 551 and 559); and at the 2012 CTRC-AACR San Antonio Breast Cancer Symposium; December 4-8, 2012; San Antonio, TX (Poster P6-04-02). Everolimus in Elderly Hormone- Receptor—Positive Breast Cancer Patients.

 Dr Rogerio reports employment with Novartis Pharmaceuticals Corporation, which funded this study.

 Concept and design; acquisition of data; critical revision of the manuscript for important intellectual content.

 Jaqueline Rogerio, MD, Senior Medical Director, Novartis Pharmaceuticals Corporation, 1 Health Plaza, East Hanover, NJ 07936. E-mail: jaqueline .rogerio@novartis.com.

	1. Piccart M, Baselga J, Noguchi S et al. Final progression-free survival analysis of BOLERO-2: a phase III trial of everolimus for postmenopausal women with advanced breast cancer. Presented at the 2012 CTRC-AACR San Antonio Breast Cancer Symposium; December 4-8, 2012; San Antonio, TX.

	2. Pritchard KI, Burris HA, Rugo HS et al. Safety of everolimus for women over 65 years of age with advanced breast cancer (BC): 18-month follow-up of BOLERO-2 2012. Presented at the 2012 American Society of Clinical Oncology Annual Meeting; June 1-5, 2012; Chicago, IL.

		3. Piccart-Gebhart MJ, Noguchi S, Pritchard KI, et al. Everolimus for postmenopausal women with advanced breast cancer: updated results of the BOLERO-2 phase III trial. 

		4. Drugs: everolimus. FDA website. www.fda.gov/drugs/informationondrugs/approveddrugs/ucm313008.htm. Published July 23, 2012. Accessed September 12, 2012.

Everolimus in Elderly Hormone-Receptor-Positive Advanced Breast Cancer Patients

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.

 This, added to the failure rates of clinical trials (5 of the top 10 clinical trial failures in 2013 were of drugs for cancer indications

) 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.

 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).

 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).

 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.”

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).

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).

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.

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.

 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.

 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.

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.

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.

 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.

 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.

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&mdash;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.

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 years

), 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,

 and a recent Forbes analysis estimated the cost at $5 billion.

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.

 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).

 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.

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.

 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 treatment

—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).

, 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.

 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 bodies

) 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.

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 

creates a platform to bring all the data together, which can then be used to generate a treatment plan personalized to a patient (

). 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.”

 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.

 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).

 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.

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.

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.

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.

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.

 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.

 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.

 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 Manchester

) have now found their place in clinical trials as biomarkers (cancer,

) and are also being developed as “drugs” by companies like Mirna Therapeutics Inc.

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.”

 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.

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).

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.

 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.

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.

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.

The Role of Bioinformatics in Oncology Drug Development-and Precision Medicine

Genetic testing laboratories are using aggressive and manipulative tactics to capture market share in the BRCA testing market. Clinicians and payers are encouraged to utilize patient-centric criteria, including open access to data, to make decisions about genetic testing laboratories.The past 12 months in the world of cancer genetic counseling have been more notable, perhaps, than the past 12 years combined. In May 2013, Hollywood icon Angelina Jolie went public with her BRCA1+ status, thrusting the field of cancer genetic testing and counseling into the spotlight and increasing referral rates to clinics by as much as 40% (E. T. Matloff, MS, oral communication, July 2013).

 One month later, the US Supreme Court unanimously ruled against the validity of patents that lay claim to genomic DNA in Association for Molecular Pathology v Myriad Genetics, Inc.

Within hours, multiple laboratories began offering more comprehensive genetic testing for the hereditary breast and ovarian cancer genes BRCA1 and BRCA2, and at half the cost. The battle for the multi-million-dollar BRCA testing market had begun.

This battle has resulted in some genetic counseling centers reporting that referring clinicians have begun to request, or demand, that their patients’ BRCA testing be sent to a particular laboratory. In several instances it was discovered that these clinicians were either paid consultants for such laboratories, or received speaking fees or research funding from those entities. This is clearly a conflict of interest in violation of a physician’s ethical obligations “to regard responsibility to the patient as paramount” and to discharge their “responsibility to participate in activities contributing to the improvement of the community and the betterment of public health.”

 Concerns about such manipulative tactics led us to develop a position statement stipulating that decisions about which genetic testing laboratories to use should focus on test quality, turnaround time, cost, and whether or not the laboratory shares its data in public databases (

Without its patent-protected monopoly, Myriad now appears to be relying on trade secrets to maintain its share of the BRCA market.

 The “trade secrets” are actually a database of BRCA variants of uncertain significance (VUS) derived from the thousands of patient tests they have performed over the past decade.

 The company once contributed its data to a public database maintained by the National Institutes of Health, but ceased doing so in 2004.

 Several Myriad scientists and executives have stated that the public variant databases are not properly curated and that contributing to such resources would cause more harm than good; however, by at least 1 account, Myriad disclosed that it made its decision to stop sharing data for the purpose of retaining data as a trade secret.

In Myriad’s Q3 Earnings Call Transcript of May 6, 2014, the company publicly acknowledged that in the context of PARP inhibitors, “Our competitors’ reliance on public databases with high VUS and error rates will further restrict patient access to this life-saving medicine.”

The decision to hoard patient VUS data, even while recognizing that this decision will restrict patient access to lifesaving treatment, is incongruent with the American Medical Association’s (AMA’s) policy on Genome Analysis and Variant Identification (

) and is considered unethical behavior according to the AMA’s Resolution E-9.095 on “the use of patents, trade secrets…or other means to limit the availability of medical procedures.”

Although variant classification is important, few BRCA tests result in a variant of uncertain significance (ie, 4.4% at Ambry Genetics

). Myriad itself reports a 3% overall variant rate, slightly lower than other labs’ and presumably attributable to the superior testing experience enabled by its patents.

 Therefore, the vast majority of patients (~95%) are not impacted by a variant of uncertain significance, although the company describes its information as “vastly superior” and leverages variant classification ability as a major differentiator.

Other laboratories offering BRCA testing have teamed with clinicians, scientists, and patients to expand the pool of publicly available genetic information for the betterment of clinical care and research as part of the Free the Data movement (http://www.free-the-data.org). This movement recognizes that patients, clinicians, scientists, and insurers could all benefit from pooling such information. It has gained traction in patient communities, and some patients now request that their genetic testing be sent to laboratories that share data. Many of these competing laboratories are advertising their data-sharing policies as a way to gain market share. As this movement progresses, creating public genetic databases that feature proper curation of data, transparency on how variant classification decisions are made, and open forums for discussion will be critical.

With the growing number of laboratories offering testing, insurers are beginning to contract with particular laboratories for BRCA testing, designating certain laboratories as their in-network providers.

Before negotiating such partnerships, payers and regulators have the opportunity to choose to partner only with high-quality laboratories that pledge to share all past, present, and future data in public databases. As Cook-Deegan et al write, “National health systems and insurers, regulators, researchers, providers, and patients all have a strong interest in ensuring broad access to information about the clinical significance of variants discovered through genetic testing.”

 This is particularly relevant for publicly funded insurers that could create incentives or make data sharing a stipulation for coverage.

Our patients deserve for decisions regarding where their genetic testing is performed to be unbiased, free of conflict, and based upon considerations unrelated to the clinician’s self-interest. Moreover, sharing of genetic data will benefit patient care and clinical research, which may lead to lower healthcare costs for all moving forward. The choice to use only laboratories that are committed to quality, efficiency, and facilitating progress for all through sharing of data represents an important opportunity as our healthcare system evolves. 

Ellen T. Matloff and Rachel E. Barnett: Yale Cancer Genetic Counseling, Yale Cancer Center, Yale School of Medicine. Robert Nussbaum, MD: Division of Medical Genetics, University of California, San Francisco School of Medicine.

1. Jolie A. My medical choice. The New York Times. http://www.nytimes.com/2013/05/14/opinion/my-medical-choice.html/?_r=0. Published May 14, 2013.

2. Supreme Court of the United States. Association for Molecular Pathology v Myriad Genetics, 2013 WL 2631062 (June 13, 2013).

3. American Medical Association. Principles of medical ethics. http://www.ama-assn.org/ama/pub/physician-resources/medical-ethics/codemedical-ethics/principles-medical-ethics.page. Accessed May 7, 2014.

4. Cook-Deegan R, Conley JM, Evans JP, Vorhaus D. The next controversy in genetic testing:clinical data as trade secrets. Eur J Hum Genet. 2013;21:585-588.

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