Shedding (Dim) Light on Clinical Benefit in Biomarker-Based Drug Development

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Despite the appeal of personalized medicine (that is treatment selection based on the presence of a particular marker), uncertainty remains regarding the broad utility of this selection strategy in oncology. A recent meta-analysis by Jardim et al. in the Journal of the National Cancer Institute attempted to provide some clarity by comparing efficacy outcomes between personalized and non-personalized clinical trial designs leading to the new FDA approval of drugs between 1998-2013. The publication concluded that using a biomarker-based selection strategy led to improved response rate, progression free survival and overall survival across a range of cancer subtypes and selection biomarkers.

The study should be applauded for its unique approach in trying to determine the benefit of personalized drug development, the paper’s conclusions are qualified by five issues.

No information about drugs that do not receive license 

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Personalized Drug Development

The study only evaluated efficacy outcomes for trials directly leading to the FDA approval of the drug (the authors acknowledge this). This may prevent generalizability of conclusions, as it does not capture drugs that failed during testing. However this search strategy also excluded studies earlier in the development of approved drugs, where they were explored unsuccessfully for various indications or biomarker subgroups. In contrast to FDA approval for non-personalized drugs, which just requires identifying the proper indication, personalized strategies in addition require finding optimal test conditions for biomarkers used in patient selection. It is therefore conceivable that greater failed exploration goes into the development of a personalized strategy and therefore that an overall comparison of efficacy outcomes between personalized and non-personalized designs may not reach the same conclusions as a comparison of the FDA approval trials.

Doesn’t address dangers of premature biomarker enrichment

While there may indeed be a benefit to using biomarker-based trial designs, the study does not encompass potential harm that can arise when trials prematurely enrich for a particular biomarker population. Early enrichment precludes evaluating the drug in biomarker ‘negative’ patients and can prolong uncertainty regarding a drug’s utility in biomarker negative groups. The approval of Trastuzumab for HER2+ breast cancer provides an example of this. The two clinical trials leading to the FDA approval of the drug were based on a personalized strategy, but now nearly 20 years later the biomarker originally used for patient selection is being reevaluated in a large-scale phase 3 study.

May not properly classify “personalized therapy”

A third issue concerns the authors’ classification of “personalized therapy”. The paper’s definition includes both trials selecting patients who express rare biomarkers along with studies in which at least 50% of the patient population is known to harbor the mutation (in the study just over half of the personalized trials fell into the latter category, with a number of those including markers present in nearly 100% of the patients). While a biomarker is implicated in the response to therapy in both situations, comparing these two groups may not be appropriate. As there was no selection process needed to identify patients from the overall population to include in 50% criteria trials, they more appropriately reflects a “population based-” rather than a “personalized-” medicine. One of the most pressing issues in developing personalized treatments is grappling with properly selecting the patients who have increased chance of benefit. It is conceivable that the risk/benefit of personalized trials using low frequency mutations (which requires applying often complex selection criteria to identify the proper population) may not be comparable to the “population” marker trials.

Doesn’t quantify clinical benefit post-approval

Another issue not addressed in their conclusion is the actual clinical impact of biomarker-based treatment selection once a treatment has been approved. There is general concern over the current unbalanced cost/benefit of drug development and as many biomarkers exist in low frequencies in the population it is conceivable that the net benefit of drugs approved based on personalized strategies is lower than that of non-personalized strategies – or that the impact of drugs approved based on the 50% criteria is greater than that of other biomarker-based drugs. It is therefore unclear whether a biomarker-based study design is just better for getting drugs approved, or better for getting better drugs approved.

May not predict the future of personalized medicine

Finally, several commentators have noted that large scale trials (such as those evaluated in this study) may not be sustainable for the future of personalized medicine drug development. There is a growing trend in the use of next generation clinical trials, which include N of 1 trials, basket designs and adaptive treatment allocation. Each of these enroll small populations because the frequency of patients expressing the biomarkers of interest is generally very low, and therefore one should be cautious in extrapolating the methods and conclusions of the publication (especially due to the inclusion of “population” markers) to future evaluations of the efficacy of personalized medicine.

While not complete, this publication is the first step in a much-needed rigorous evaluation of utility of biomarker-based strategies in cancer treatment and drug development.

BibTeX

@Manual{stream2016-986,
    title = {Shedding (Dim) Light on Clinical Benefit in Biomarker-Based Drug Development},
    journal = {STREAM research},
    author = {Brianna Barsanti-Innes},
    address = {Montreal, Canada},
    date = 2016,
    month = may,
    day = 9,
    url = {http://www.translationalethics.com/2016/05/09/shedding-dim-light-on-clinical-benefit-in-biomarker-based-drug-development/}
}

MLA

Brianna Barsanti-Innes. "Shedding (Dim) Light on Clinical Benefit in Biomarker-Based Drug Development" Web blog post. STREAM research. 09 May 2016. Web. 17 Oct 2019. <http://www.translationalethics.com/2016/05/09/shedding-dim-light-on-clinical-benefit-in-biomarker-based-drug-development/>

APA

Brianna Barsanti-Innes. (2016, May 09). Shedding (Dim) Light on Clinical Benefit in Biomarker-Based Drug Development [Web log post]. Retrieved from http://www.translationalethics.com/2016/05/09/shedding-dim-light-on-clinical-benefit-in-biomarker-based-drug-development/


Acting on “Actionable Mutations”

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The new buzzword in personalized cancer medicine is “actionable mutation”. This label is given to the genetic aberrations that are present in some patients’ tumors, and that are intended targets of new drugs. Increasingly, treatment decisions in routine clinical care, and enrollment in trials are being guided by the concept of “actionable mutations”.

However, determining “actionablility” is an ongoing challenge. The function of a particular mutation, to what degree it is responsible for driving a given malignancy, and how easily it can be targeted by a specific therapy, all affect how “actionable” it is. Further, a tumor’s genomic profiling can vary depending on which tissues are biopsied (ie primary and metastatic tumor sites), or when they are biopsied (before and after particular treatment regiments). “Actionability” of mutations may not be a stable variable that is easily transferred from one clinical setting to another. Instead, the concept of actionability invites further clarification on where a when and these genes should be screened.

Unfortunately, lack of clarity on the definition of “actionable mutations” has not prevented its uptake in either the commercial or scientific medical communities. For example, upon physician request, diagnostics company Foundation One will sequence over 300 genes that are known or likely targets of a specific therapy and provide clinicians with a report listing all of their patient’s actionable mutations. These are mutations they define as all those that can be targeted by both therapies currently approved for their indication as well as those approved for other malignancies (off-label). Further, some mutations are designated “Equivocal” signifying that there isDSC03420-B3 some, but not confirmed evidence, supporting an aberration in a patient’s sample, or “Subclonal” where an abnormality only exists in less than 10% of a patient’s tumor. However, absent clear guidelines or standards surrounding actionable mutations it can be extremely difficult for oncologists to interpret these often ambiguous results.

A number of next generation clinical trials currently underway are allocating patients to treatment arms using similar targeted strategies. Basket trials, like the NCI MATCH study, are assigning mixed-malignancy, advance cancer patients, to off-label targeted therapies based on the presence of “actionable mutations”. However, here too some concerns have been raised. There is little consensus on how to prioritize certain mutations over others – both in terms of their functional importance and rarity – and has raised issues in dealing with patient’s harboring co-mutations and optimizing allotment to ensure sufficient patient accrual to different arms.

These concerns along with the results of a recently published basket trial in the New England Journal of Medicine should lead researchers and physicians to be cautious in blindly treating “actionable mutations”. The phase 2 study looking at Vemurafenib in BRAF V600 mutation positive nonmelanoma patients found variable response among different malignancies – indicating that genomic signatures should not be the only factor playing into treatment selection.

In efforts to give some clarity to the current situation a couple of collaborations have been recently undertaken. Founded in late 2014, The Actionable Genome Consortium, is a collaboration of biotech company Illumina and four leading cancer centers (Dana-Farber Cancer Institute, Fred Hutchinson Cancer Research Center, MD Anderson Cancer Center and Memorial Sloan Kettering Cancer Center) with the goal to define the “actionable cancer genome” and create robust standards for next-generation sequencing and treatment decision making.

Another initiative, TAPUR (Targeted Agent and Profiling Utilization Registry), led by ASCO, is a prospective, observational, non-randomized clinical study that will track the off-label performance of commercially available targeted drugs in advanced cancer patients. These therapies are commonly prescribed off-label. However, this will be the first attempt to aggregate data to determine the usefulness of this strategy in regards to targeting actionable mutations.

In time these initiatives should go a long way in drawing the boundaries around “actionable mutations.” In
the interim, however, practicing oncologists and researchers alike are left wondering how, exactly, to interpret the “act” in “actionable.”

BibTeX

@Manual{stream2015-842,
    title = {Acting on “Actionable Mutations”},
    journal = {STREAM research},
    author = {Brianna Barsanti-Innes},
    address = {Montreal, Canada},
    date = 2015,
    month = sep,
    day = 30,
    url = {http://www.translationalethics.com/2015/09/30/acting-on-actionable-mutations/}
}

MLA

Brianna Barsanti-Innes. "Acting on “Actionable Mutations”" Web blog post. STREAM research. 30 Sep 2015. Web. 17 Oct 2019. <http://www.translationalethics.com/2015/09/30/acting-on-actionable-mutations/>

APA

Brianna Barsanti-Innes. (2015, Sep 30). Acting on “Actionable Mutations” [Web log post]. Retrieved from http://www.translationalethics.com/2015/09/30/acting-on-actionable-mutations/


Charting the Unpredictable: Using fMRI patterns to determine outcome in acutely comatose patients

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DMN Image

Every year in Canada around 50,000 people suffer brain injuries, with those experiencing severe traumas often becoming comatose for days or weeks post-incident. While there exists a battery of physiological prognostic indicators, such as pupillary light reflex (or lack thereof), and patterns of EEG activity, there remains a significant subset of patients who retain an indeterminate prognosis even after their completion. The use of sophisticated imaging techniques like fMRI has provided a modern way of mapping residual cognitive function in newly comatose patients. Currently, three fMRI studies have looked at the preservation of neural connectivity of two brain networks as potential markers of outcome. While all these studies found a (modest) positive correlation between the BOLD signal strength of the intact network and better patient outcome, significant further work is required before the technique could become clinically useful.

Dr. Charles Weijer of Western University, stresses, however, that this imminent research raises several ethical concerns: patients do not have decisional capacity, time restraints may not permit the proper procurement of surrogate informed consent, critically ill patients are clearly a vulnerable population, and it is not clear how the fMRI study results would impact patient prognosis and treatment decisions. As well, there exist practical concerns including the intra-hospital transport of patients to the fMRI machine, and the time needed outside of the ICU to perform the scans.

As a recent graduate in neuroscience another particular concern struck me – why had the researchers of the previous fMRI studies only considered two networks? The first mapped the preservation of activity in S1 after a stimulus to the hand, while the following two studies assessed the resting state connectivity of the default mode network. These are just two of several networks that have been mapped and are reliably found in healthy patients. I would be curious to see if there is prognostic contribution by analyzing other connectivity, like the auditory or executive resting state networks. Exploring the integrity of several neural networks as potential prognostic indices may allow future research to hone in on a target rather than just testing on a ‘one by one’ basis.

An analogous issue has emerged at STREAM regarding the trajectory of research in the field of cancer biomarkers and the proper method of exploring a new study space. Similar to the intended use of fMRI in previous situation, the biomarkers are being evaluated as predictive markers of outcome to specific cancer therapies. We have noticed that early studies in this field apply a very narrow set of research techniques to try and validate a biomarker. These methods are often suboptimal and it is only much later down the road that researchers branch out into other more successful methods. A notable example of this is can be seen in our evaluation of the research trajectory of one potential biomarker in lung cancer – ERCC1. A non-specific antibody had been routinely used to detect the presence of the marker, and it wasn’t until years later that basic research into a more appropriate antibody was initiated. This is likely part of the reason for the notably sluggish progress in the field. We propose that ideally, novel research programs would start with studies looking at a broad set of potential targets and then taper these down over time, as the accumulating evidence would warrant. Acutely comatose patients are a new and important population for fMRI studies, and to me it seems like this research program might benefit by encouraging future studies to evaluate and compare the predictive use of multiple networks so that they most rigorously map the study space.

Context: On January 12th, Charles Weijer, visiting from the Rotman Institute of Philosophy at Western University, gave the first talk in the new STREAM speaker series. He spoke on the ethical considerations involved in performing fMRI studies on acutely comatose patients in the ICU.

BibTeX

@Manual{stream2015-683,
    title = {Charting the Unpredictable: Using fMRI patterns to determine outcome in acutely comatose patients},
    journal = {STREAM research},
    author = {Brianna Barsanti-Innes},
    address = {Montreal, Canada},
    date = 2015,
    month = feb,
    day = 6,
    url = {http://www.translationalethics.com/2015/02/06/charting-the-unpredictable-using-fmri-patterns-to-determine-outcome-in-acutely-comatose-patients/}
}

MLA

Brianna Barsanti-Innes. "Charting the Unpredictable: Using fMRI patterns to determine outcome in acutely comatose patients" Web blog post. STREAM research. 06 Feb 2015. Web. 17 Oct 2019. <http://www.translationalethics.com/2015/02/06/charting-the-unpredictable-using-fmri-patterns-to-determine-outcome-in-acutely-comatose-patients/>

APA

Brianna Barsanti-Innes. (2015, Feb 06). Charting the Unpredictable: Using fMRI patterns to determine outcome in acutely comatose patients [Web log post]. Retrieved from http://www.translationalethics.com/2015/02/06/charting-the-unpredictable-using-fmri-patterns-to-determine-outcome-in-acutely-comatose-patients/


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