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AACR Presentations Highlight BATTLE Trial as New Model for Biomarker-Based Drug Studies

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By Bernadette Toner

ORLANDO — Several presentations at this week's annual meeting of the American Association for Cancer Research cited the recently completed Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination, or BATTLE, trial as a promising example for evaluating personalized approaches to drug development.

The phase II trial, led by researchers at MD Anderson Cancer Center and published in the April issue of Cancer Discovery, was the first prospective, adaptively randomized study in pretreated non-small cell lung cancer patients that mandated tumor profiling with “real time” biopsies.

The 255 patients in the trial were adaptively randomized to four arms — erlotinib (Genentech/OSI's Tarceva); vandetanib (AstraZeneca's investigational Zactima); erlotinib plus bexarotene (Eisai's Targretin); or sorafenib (Bayer/Onyx's Nexavar) — based on their biomarker profiles, which included EGFR mutations and/or copy number, KRAS/BRAF mutations, VEGF/VEGFR-2 expression, and RXRs/Cyclin D1 expression and copy number. Primary end point was 8-week disease control, which served as a surrogate for outcome.

Erlotinib has been approved for use in non-small cell lung cancer, but bexarotene, sorafenib, vandetanib all failed in phase III trials that did not use biomarkers to select patients for treatment.

John Heymach, an oncologist at MD Anderson and a co-author of the study, explained during a presentation of the data at AACR that the “adaptive randomization” model meant that “outcomes were fed back into the model” as the trial progressed so that patients were treated with drugs that more likely to benefit them.

Jack Lee, a MD Anderson researcher who discussed the trial in a separate presentation, explained that the trial relied on a Bayesian adaptive design, which he summarized as “learn as you go.”

Specifically, the first 97 patients to enroll in the trial were randomized equally into the four trial arms. Once the results of these patients were assessed at the 8-week mark, the researchers used a Bayesian model to adjust future randomization so that patients were more likely to be assigned to a trial arm that would benefit them. This pattern continued during enrollment, so that as more patients with a particular signature did well on a given therapy, the likelihood would increase that subsequent patients with that signature would be placed on that trial arm.

The key finding from the study was that mutant-KRAS patients were more likely to benefit from sorafenib, with 79 percent of biomarker-positive patients exhibiting 8 week disease control rate, compared to the historical 8-week disease control rate of 30 percent.

In a presentation discussing the study following Heymach's talk, Thomas Lynch of the Yale Cancer Center called BATTLE “one of the most important studies to come out in non-small cell lung cancer.”

The MD Anderson team, he said, has “basically thrown down the gauntlet to everyone in lung cancer biology and lung cancer treatment that we need to begin to do this for all of our trials.”

Lynch did cite some shortcomings of the study, however. For one thing, he noted that it took approximately five years to accomplish. Not only is that timeframe “too long to be practical in the world of drug development,” but it was difficult for the researchers to keep pace with ongoing science during the course of the study. “Some of the markers that were hot and sexy in 2004 may be less so in 2011,” he said.

Another challenge was the fact that the researchers consolidated eleven different markers into only four “biomarker groupings” — for example, including EGFR expression with EGFR copy number, since at the time the study was designed, both markers were considered equally predictive for benefit from EGFR tyrosine kinase inhibitors, though subsequent research has shown that EGR mutations are more predictive. As the authors note in their Cancer Discovery paper, this strategy likely “diluted the impact of strong predictors in determining treatment probabilities.”

In a review of the study that was published alongside the paper in Cancer Discovery, Jeffrey Engelman of the Massachusetts General Hospital Cancer Center and co-authors cite this point as cause for “concern that a large and complex research structure such as BATTLE, with an umbrella framework and multiple nested treatment studies, does not have the ability to adapt quickly as knowledge outside the trial advances.”

As an example, Engelman and colleagues cite the “ALK translocation story” — the finding that a subset of lung cancer patients harbor an ELM4-ALK translocation and are therefore likely to respond to ALK inhibitors such as crizotinib — which “developed all the way from the bench to the bedside during the course of the BATTLE trial.”

Engelman and co-authors also questioned whether the BATTLE trial design would be preferable over more common designs, such as prospective biomarker-directed clinical trials, as in the case of crizotinib in ALK-translocated cancers, or retrospective studies of specific biomarkers in conventional targeted therapy trials. They posited that the BATTLE study design “may provide a more distinct advantage in the study of novel drugs without clearly understood mechanisms of action or in situations when the biologic characteristics of the target are uncertain,” while traditional designs would be more effective in cases where the drug target and its biological features are well understood.

One advantage of the BATTLE design over studies that examine biomarkers retrospectively, they noted, is that such studies typically look at tissue obtained at the time of diagnosis, which is not optimal for second-line studies since first-line treatment could alter biomarker status. The BATTLE trial, which performed biopsies at the time of treatment, “maximizes the chances of discovering the relationship between putative biomarkers and response to novel treatments,” Engelman and colleagues wrote.

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In another review of the study published in Cancer Discovery, Eric Rubin and colleagues from Merck Research Laboratories write that the BATTLE trial investigators "must be recognized and congratulated as bold innovators in their efforts to move beyond traditional clinical trial designs that are ineffective in simultaneously developing a new therapeutic and a matching diagnostic test."

They point out, however, that the investigators did not report the technological qualifications for the biomarker assays they used, "thus, choice of cutoffs for these assays (to determine whether a patient's tumor was "positive" or "negative" for a given biomarker group) could be questioned" — an issue that makes it "difficult to conclude that predictive biomarkers have been identified for the treatments."

Like Engelman and colleagues, the Merck reviewers also questioned BATTLE's adaptive randomization model, and suggested several alternative trial designs that they believed might have performed just as effectively, if not better.

BATTLE authors acknowledged the study's shortcomings. In his talk at AACR, MD Anderson's Lee noted that “one of the reasons that the trial was not more positive was because we bundled the markers into marker groups. We learned that rather than bundling them, we should treat markers separately.”

Furthermore, he noted that "preselecting markers is not a good idea, since we don't know what the best predictive marker is at the get-go."

Lee noted that the BATTLE team has already designed a follow-up validation study, called BATTLE-2, which has "a similar type of design" but is designed to overcome some of the limitations of BATTLE-1.

BATTLE-2 will also involve previously treated lung cancer patients and includes erlotinib and sorafenib treatment arms as well as a combination of the AKT inhibitor MK-2206 with the MEK inhibitor AZD6244, and a combination of MK-2206 with erlotinib.

BATTLE-2 will take a slightly different approach to biomarker selection and tumor classification. The researchers will first identify an "extremely limited set of markers" that they will use to conduct prospective testing on the first half of the study population, estimated to be around 200 patients. After completing analysis of the first phase of the study, the most predictive markers and signatures will be used to guide patient assignments to the most favorable matched treatments in the second half of the study, which will also be approximately 200 patients.

In addition to BATTLE-2, the MD Anderson investigators are also looking to extend the BATTLE findings in different directions. Heymach, for example, discussed an effort to use the data from the study to develop gene-expression signatures that would indicate which patients with wild-type EGFR might benefit from erlotinib.

Two signatures — one derived from genes associated with epithelial-to-mesenchymal transition and another derived using erlotinib-treated BATTLE patients with or without 8-week disease control — were found to be predictive of disease control in wild-type EGFR patients treated in BATTLE, and Heymach said these signatures will likely be explored in BATTLE-2.

MD Anderson's Waun Hong, the lead author on the BATTLE study, also discussed future directions for the approach during the AACR meeting. During Sunday's plenary session, he said that he and his colleagues are exploring the use of a "reverse-migration BATTLE strategy" that would identify targeted therapies that could be used in the adjuvant setting or even for prevention of lung cancer.

"Reverse migration from therapy to prevention is scientifically sound," he said. "The concept has been substantiated by use of tamoxifen in breast cancer."

In the case of a BATTLE adjuvant trial, he said, patients undergoing resection would be enrolled "and then we'd examine the molecular profile of the resected tumor as well as the adjacent field and randomize" based on the marker to a targeted therapy plus chemotherapy.

Designing a trial that would apply the reverse migration strategy in the prevention setting would be "more difficult," he acknowledged, largely because chemoprevention for lung cancer has so far been like "shooting in the dark." A successful chemoprevention agent "must be based on molecular targets," he said.

Nevertheless, Lee said, "we have to figure out how to integrate targeted therapy into each stage, so there's a BATTLE therapeutic approach, a BATTLE adjuvant approach, a BATTLE prevention approach, and they can be integrated and cross-fertilized to make a significant impact in lung cancer."


Have topics you'd like to see covered by Pharmacogenomics Reporter? Contact the editor at btoner [at] genomeweb [.] com.

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