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Feb 1, 2013 (Vol. 33, No. 3)

Unlocking Biomarkers’ Full Potential

  • “Biomarker research is focused on on- target effects,” says Nick Dracopoli, Ph.D., vp, head of oncology biomarkers at Janssen Research and Development, a J&J company.

    “We look at indications and at patients with those indications that are most likely to respond to the drug candidates we’re developing. For oncology biomarkers, germ-line effects are weaker indicators than somatic changes in the tumor. As a consequence, SNP-based, genome-wide association studies are not very useful. It is better to focus on molecular changes within the tumor and define gene expression profiles and epigenetic modifications that correlate with the tumor phenotype. We are increasingly tracking patient immune response, particularly as more immuno-oncology products are moving into the drug development pipeline.”

    The number of biomarkers being developed varies from project to project. But it is very clear that to be successful in the clinic, the biomarkers and the assays need to be of low complexity. Of the 10 to 12 companion diagnostics that have been approved by the FDA to date, all measure the status of the drug target (on-target markers). For example, EGFR measures the level of receptor expression; Braf and Kras markers measure the presence of the mutation and translocation in the ALK gene measures gene knockout.

    It is important to realize that molecular profiles for first-in-class drugs are not optimal because they are based on only a few patients. Consequently they have weak predictive value overall.

    “Aside from that rule of thumb, if you have a greater than 50% response rate for your drug, it is unlikely that you need a biomarker to predict response. Biomarker utility is best for drugs that would have a difficult road to approval, where it is critical to enrich for the subpopulation of responders. For example, Pfizer’s crizontinib was approved for non-small-cell lung patients but is only effective for 5% of all patients. If Pfizer was unable to demonstrate the relationship between activation of the ALK gene and disease, this inhibitor would not have been approved,” says Dr. Dracopoli.

    “Drugs that are more broadly active can come to market without a companion diagnostic test. There is always a balance between the predictive values of the biomarker test and the response rates to treatment. That is, we should not treat if the chance of response is only 3–5%, rather than if it were 50% where the patient would want to take the chance if the drug were safe.”

    An important take-home message is that mutations are not unique to an indication. So if you find a driver mutation in indications for which the drug has not been approved, you could discover new indications for the drug.

    “At the end of the day, this is what cancer is—heterogeneous,” says Dr. Dracopoli. “We’d all love to treat one cancer with one drug and at one dose, but the story is more complex. The future of oncology is around understanding the molecular heterogeneity or underlying molecular pathology of the disease and the diversity of it, and then treat each patient accordingly.”

  • Clinical Considerations

    “Given the complexity of biology,” says Achim Plum, Ph.D., principal consultant, Siemens, “whether is it cancer, metabolic disease, or any other disease state, we have been forced to move away from the idea that a single biomarker can capture the entire ‘story’ or mechanistic view of any disease. Hence newly developed biomarkers will be made up of a panel of markers that serve as a profile. In addition, with the sheer volume of DNA and protein analytics data, the clinic will need to employ software tools and algorithms to help the decision making.”

    The task of getting broad profiling technologies that are analytical into a clinical setting and making them routine is difficult but not insurmontable. This will take a collaborative effort, something that Siemens among others are looking to develop. The key is to avoid technology hype and to establish good reliable software to process the data for decision making. “Data is not knowledge, and knowledge is not automatically decision making.”

    As an academic, Daniel Chan, Ph.D., has a view of the whole value chain for biomarkers from discovery to development to use in the clinic. Dr. Chan holds the titles of professor in pathology, oncology, radiology, and urology, and is the director of the clinical chemistry division lab at Johns Hopkins Hospital.

    Given his perspective from discovery to clinical use, Dr. Chan indicated that from the clinical point of view, “we need more markers.” He oversees the discovery of new biomarkers in his research lab, their validation in his translational research lab, and finally their utility in practice in his clinical chemistry lab. He is a strong advocate for collaboration of biomarker development from discovery to verification and validation to incorporation within the clinical practice.

    Beyond the use of biomarkers for patient stratification and correlation between marker and therapeutic choice, as is the focus of the biopharma industry, for the clinic the use of biomarkers is for prevention and early detection. The earlier the detection, the better the outcome. That is, provide the “cure” before you need to initiate treatment.

    To be successful in the future of biomarkers, we need to look beyond the biopharma focus and expand the horizon for early detection and monitor therapy later, says Dr. Chan. He describes a roadmap of developing bridges (to bridge the knowledge gaps), gates (decision gates for go/no go decisions as to whether a development path is viable), and partnerships (to collaborate with different points of view) for efficient new biomarker development.

    According to Dr. Chan, we must define the intended use of the biomarker, which identifies the specific application and sets up the clinical study and study population to meet the clinical needs. We need to define specific assays to monitor biomarkers that will work within a clinical setting, not a research lab setting that uses disease models (tissue culture cells or small animals) and not real patient samples.

    “The days when single markers are sufficient (PSA for prostate cancer or troponin for cardiovascular disease) are behind us. We need to develop a panel of markers or a profile pattern to address patient population heterogeneity and disease complexity that will guide our decision-making process,” remarks Dr. Chan. “Molecular biomarkers are giving way to protein biomarkers,” he adds.

    Prevention and early detection will require the use of whole-body scans, so the sampling technology and analytical tools to be developed are critical to realize this goal. Assay ease of use, automation, and analytical performance that is suitable for the clinical lab are fundamental.

    “An important future goal for biomarkers,” says Dr. Billings, “is to sample circulating tumor cells or circulating DNA in blood or plasma samples as a noninvasive measure of patient status. A decline in tumor biomarkers during chemotherapy, for example, could reflect the efficacy of the therapy. In contrast, an increase in tumor biomarkers, in a patient who had previously undergone surgery and therapy, might indicate disease recurrence, and is likely to do so before a tumor mass is detectable by imaging methods.”


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