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Residual risk models may improve patient selection for breast cancer clinical trials
Measuring patient eligibility for breast cancer clinical trials based on residual risk for recurrence rather than tumor size and nodal status alone may improve reliable power estimates in future adjuvant trials, according to published findings.
“This approach guarantees that the statistical power of the study is adequate to demonstrate if the new treatment is really effective or not,” Lajos Pusztai, MD, DPhil, professor of medicine at Yale University and chief of the breast medical oncology section at Yale Cancer Center, said in a press release. “We can also expose fewer patients to the side effects of the new treatment, [which is] good for the patients.”
Historically, patients with breast cancer are selected to participate in clinical trials based on tumor size and lymph node status. However, patients in control groups commonly experience fewer events, including recurrence and death, limiting the ability to conduct statistically conclusive comparisons with patients who receive investigational therapy.
“If there are not enough events, one can miss a truly effective drug that could help patients who remain at risk for recurrence despite current best therapies,” Pusztai said.
Pusztai and colleagues sought to determine whether the use of a single risk score to select patients with predefined minimum risk for recurrence — calculating by combining tumor size, lymph node status and benefit from standard-of-care therapy — would increase the reliability of clinical trial power calculations and, therefore, be superior to criteria based on tumor size and nodal status alone.
Researchers reviewed medical records of 500 consecutive patients with stage I to stage III breast cancer treated at the Breast Center at Smilow Cancer Hospital and included 443 patients (mean age, 56.1 years; range, 23-89) in the study.
Researchers simulated two-arm, 1:1 randomized clinical trials with a target HR for RFS of 0.7. For the simulations, researchers used eligibility criteria based on tumor size and nodal status from a currently recruiting trial for patients with triple-negative breast cancer. The tumor size and nodal status categories were divided into smaller prognostic groups to form uniform risk cohorts for four accrual scenarios.
The accrual scenarios included:
- the proportion of patients in each tumor and nodal status cohort and their corresponding 5-year survival rates from The University of Texas MD Anderson Cancer Center Department of Breast Medical Oncology database (scenario 1);
- increasing the number of low-risk patients (from 40% to 50% and 70%) and correspondingly lowering the number of high-risk patients (scenarios 2 and 3); and
- increasing the number of low-risk patients and their 5-year survival (scenario 4).
The clinical trial power of each scenario was based on 5,000 simulated trials.
The researchers aimed to compare current patient eligibility methods — tumor size and lymph node status — with patient selection based on minimal risk for recurrence created from Adjuvant! Online, a risk prediction model.
Residual risk was defined as risk for recurrence that remained regardless of standard-of-care treatment.
At baseline, 115 patients had intermediate risk for recurrence (10%-20%) and 328 patients had high risk for recurrence (> 20%). After adjustment for treatment effect, 124 had low risk (< 10%), 199 had intermediate risk and 120 patients had high risk.
Researchers evaluated residual risk distribution in three patient cohorts that corresponded to patients eligible for three ongoing adjuvant randomized clinical trials: NRG BR003 (n = 12), NSABP-B55/BIG 6-13 (n = 96) and NSABP-B47 (n = 93).
The median residual risk was 28% (interquartile range [IQR], 25-31) in the NRG BR003 trial and 22% (IQR, 15-28) for both the NSABP-B55/BIG 6-13 and NSABP-B47 trials.
“We showed that the range of event rates varies very broadly if you use the traditional method of selecting patients, and a particular trial may end up with substantially lower events than intended by the statistical design,” Pusztai said.
As the number of low-risk patients increased and the proportion of higher-risk cohorts decreased in scenarios 2 and 3, the power of the trial decreased. Power further reduced if the 5-year survival of the low-risk group increases, as in scenario 4. With a sample size of 800, the power of scenario 1 was 0.87, scenario 2 was 0.84, scenario 3 was 0.8 and scenario 4 was 0.76.
“We emphasize that these changes in power occur despite all patients meeting eligibility criteria based on tumor size and nodal status, the researchers wrote.
Under the four scenarios, an eligibility of a minimum 10-year risk for recurrence of less than 40% had little impact on power because the mean 5-year risk for recurrence in the lowest-risk group was 36% under scenario 1.
However, the power of a trial increased if eligibility was defined as greater than 40% residual risk. Using that threshold for eligibility would yield 82% power for an 800-patient randomized clinical trial, even when 70% of patients had low-risk disease and 5-year survival for these patients exceeds observed survival in the MD Anderson Cancer Center cohort, as in scenario 4.
The residual disease for eligibility approach may benefit not only patients and clinical trials, but also pharmaceutical companies, Pusztai said.
“Our residual risk-based selection method also renders trials more efficient and cost-effective for drug companies, because it allows smaller studies with more guaranteed statistically power to succeed,” he said in the release. – by Melinda Stevens
Disclosures: The authors report no relevant financial disclosures.