Biotech Strategy Blog

Commentary on Science, Innovation & New Products with a focus on Oncology, Hematology & Cancer Immunotherapy

Posts tagged ‘predictive biomarkers’

Paths to success in cancer research

It’s time to pull together some notes, ideas, and clinical data on various biomarkers based on data available from clinical studies in oncology R&D and see how much progress we are making.

Are biomarkers a good path to success in cancer research or are they a gloomy red herring to the road less travelled?

Both answers can be equally true, but how do we tell the difference?  Are there any clues that we can use ahead of time to avoid later disappointment?

There have been several early studies that we’ve been following lately with readouts available from numerous cancer conferences, both positive and negative.

Can we learn from the failures and successes of the past to better interpret outcomes from future trials?

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It’s time for the August mailbag where we answer questions about cancer research and R&D from subscribers.

After the recent queries about immuno-oncology, it’s time to focus a little on targeted therapies again. Neither chemotherapies nor targeted therapies are going to go away – they are still the bedrock of many treatment approaches in the clinic today. Sadly though, much of the new data for the latter trials were easily swamped by the sheer tsunami of immunotherapy data in Philadelphia (AACR) and Chicago (ASCO).

One important area that we have been discussing on both blogs for some time is the value of well designed basket trials.  It’s time to revisit this concept in the light of new data relating to the BRAF V600 mutation outside of metastatic melanoma.

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What is a Biomarker?

According to the Biomarkers Definitions Working Group, a biomarker is:

“a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.”

An example of a common biomarker is blood pressure. High blood pressure is a surrogate for cardiovascular disease and risk of stroke.

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Why are Biomarkers important?

Biomarkers can be used for diagnosis and for monitoring the safety and effectiveness of treatments. They are increasingly becoming important in the selection of patients for clinical trials, and as potential surrogates for clinical endpoints that may take a long time to occur e.g. measuring how long someone will live in a cancer trial (overall survival).

Examples of the use of biomarkers include:

  • Diagnosis: high blood pressure is used as a biomarker for cardiovascular disease and risk of stroke.
  • Treatment Selection: CSF biomarkers that correlate with neurodegenerative diseases may help select the most appropriate treatment
  • Drug Effectiveness: biomarkers can be used to monitor treatment or drug effectiveness e.g. use of cholesterol levels as a measure of cardiovascular disease
  • Surrogate Clinical Endpoint: a biomarker based on scientific evidence that predicts or correlates with clinical benefit could be used as a surrogate for a clinical endpoint that may take a while to detect e.g. how long a patient lives or survives, and in the process speed up drug development. Recent prostate cancer trials sought to show that circulating tumor cell (CTC) counts correlated with the survival benefits seen. However, validation of a biomarker needs to take place before regulatory agencies will accept it as a surrogate endpoint in clinical trials.

Biomarkers can be divided into those which are prognostic and those that are predictive.

Prognostic Biomarker: a marker that provides information on the likely course of a disease in an untreated individual.

Prognostic biomarkers are used to identify high-risk cancer patients who should, therefore, receive adjuvant therapy.

Predictive Biomarker: a marker that provides information on how likely you are to respond to a particular therapy.

Predictive biomarkers are used to guide treatment choices i.e. selecting the therapy with the highest likelihood of success.

In breast cancer, estrogen and progesterone receptors are biomarkers that predict sensitivity to endocrine therapy, while HER2 levels predict response to Herceptin treatment. In colorectal cancer (CRC) patients, KRAS mutations have been shown to be a biomarker of resistance to EGFR targeting drugs such as cetuximab and panitumumab.

Predictive biomarkers allow expensive new cancer treatments to be given only to those patients who are likely to respond. As we move forward into the era of personalized medicine the aim is to develop more highly predictive biomarkers that will allow better detection, diagnosis and treatment of disease.

In addition, there’s also a need to develop biomarkers that can distinguish between subgroups of patients to separate those who might benefit from a therapy and those who have developed resistance. Biomarkers for resistance to cancer therapy is an increasingly important area of research.

For those readers interested in cancer biomarkers, the joint ASCO-EORTC-NCI “Markers in Cancer” 2012 meeting in Hollywood, FL (near Fort Lauderdale) from October 11-13 has an agenda that holds promise.

Some of the presentations that caught my attention and ones I particularly look forward to watching remotely via the “Virtual Meeting” include:

  • Biomarkers of Resistance to EGFR-Targeted Therapies in Lung Cancer
    Enriqueta Felip, MD, PhD – Vall d’Hebron University Hospital
  • Resistance Mechanisms to BRAF Inhibition in Melanoma
    Jeffrey Sosman, MD – Vanderbilt-Ingram Cancer Center
  • Complexities of Identifying Non-Mutational Biomarkers of Resistance:
    The VEGF Pathway Example
    Michael B. Atkins, MD – Georgetown University
  • Development of Biomarkers for PI3K Pathway Targeting
    Sherene Loi, MD, PhD – Jules Bordet Institute, Brussels
  • Emerging Functional Imaging Biomarkers
    Annick D. Van Den Abbeele, MD – Dana-Farber Cancer Institute

The next post in this mini-series will discuss new research that shows how a panel of 5 CSF biomarkers can be used to differentiate between neurodegenerative diseases that might otherwise be misdiagnosed. This is particularly important for clinical trial recruitment where early symptomatic patients could potentially be recruited in error if given the wrong diagnosis, and placed in trials that they will not respond to.

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