Citations

Purpose Prior reports have indicated that patients with colon cancer who demonstrate high-level microsatellite instability (MSI-H) or defective DNA mismatch repair (dMMR) have improved survival and receive no benefit from fluorouracil (FU) -based adjuvant therapy compared with patients who have microsatellite-stable or proficient mismatch repair (pMMR) tumors. We examined MMR status as a predictor of adjuvant therapy benefit in patients with stages II and III colon cancer. Methods MSI assay or immunohistochemistry for MMR proteins were performed on 457 patients who were previously randomly assigned to FU-based therapy (either FU + levamisole or FU + leucovorin; n = 229) versus no postsurgical treatment (n = 228). Data were subsequently pooled with data from a previous analysis. The primary end point was disease-free survival (DFS). Results Overall, 70 (15%) of 457 patients exhibited dMMR. Adjuvant therapy significantly improved DFS (hazard ratio [HR], 0.67; 95% CI, 0.48 to 0.93; P = .02) in patients with pMMR tumors. Patients with dMMR tumors receiving FU had no improvement in DFS (HR, 1.10; 95% CI, 0.42 to 2.91; P = .85) compared with those randomly assigned to surgery alone. In the pooled data set of 1,027 patients (n = 165 with dMMR), these findings were maintained; in patients with stage II disease and with dMMR tumors, treatment was associated with reduced overall survival (HR, 2.95; 95% CI, 1.02 to 8.54; P = .04). Conclusion Patient stratification by MMR status may provide a more tailored approach to colon cancer adjuvant therapy. These data support MMR status assessment for patients being considered for FU therapy alone and consideration of MMR status in treatment decision making.

Presented are the efforts of the Pharmaceutical Research and Manufacturing Association PFS Working Group to address concerns related to blinded independent central review of locally evaluated progression times and the proposed audit methodology to minimize potential biases, along with the U.S. Food and Drug Administration's comments on this work.

Purpose In March 1998, Common Toxicity Criteria (CTC) version 2.0 introduced the collection of attribution of adverse events (AEs) to study drug. We investigate whether attribution adds value to the interpretation of AE data. Patients and Methods Patients in the placebo arm of two phase III trials—North Central Cancer Treatment Group Trial 97-24-51 (carboxyamino-triazole v placebo in advanced non–small-cell lung cancer) and American College of Surgeons Oncology Group Trial Z9001 (imatinib mesylate v placebo after resection of primary gastrointestinal stromal tumors)—were studied. Attribution was categorized as unrelated (not related or unlikely) and related (possible, probable, or definite). Results In total, 398 patients (84 from Trial 97-24-51 and 314 from Trial Z9001) and 7,736 AEs were included; 47% and 50% of the placebo-arm AEs, respectively, were reported as related. When the same AE was reported in the same patient on multiple visits, the attribution category changed at least once 36% and 31% of the time. AE type and sex (Trial Z9001) and AE type and performance status (Trial 97-24-51) were associated with a higher likelihood of AEs being deemed related. Conclusion Nearly 50% of AEs were reported as attributed to study drug on the placebo arm of two randomized clinical trials. These data provide strong evidence that AE attribution is difficult to determine, unreliable, and of questionable value in interpreting AE data in randomized clinical trials.

Background With the advent of targeted therapies, biomarkers provide a promising means of individualizing therapy through an integrated approach to prediction using the genetic makeup of the disease and the genotype of the patient. Biomarker validation has therefore become a central topic of discussion in the field of medicine, primarily due to the changing landscape of therapies for treatment of a disease and these therapies purported mechanism(s) of action. Purpose In this report, we discuss the merits and limitations of some of the clinical trial designs for predictive biomarker validation using examples from ongoing or completed clinical trials. Methods The designs are broadly classified as retrospective (i.e., using data from previously well-conducted randomized controlled trials (RCT)) versus prospective (enrichment or targeted, unselected or all-comers, hybrid, and adaptive analysis). We discuss some of these designs in the context of real trials. Results Well-designed retrospective analysis of prospective RCT can bring forward effective treatments to marker defined subgroup of patients in a timely manner. An example is the KRAS gene status in colorectal cancer — the benefit from cetuximab and panitumumab was demonstrated to be restricted to patients with wild type status based on prospectively specified analyses using data from previously conducted RCTs. Prospective enrichment designs are appropriate when compelling preliminary evidence suggests that not all patients will benefit from the study treatment under consideration; however, this may sometimes leave questions unanswered. An example is the established benefit of trastuzumab as adjuvant therapy for breast cancer; a clear definition of HER2-positivity and the assay reproducibility have, however, remained unanswered. An all-comers design is optimal where preliminary evidence regarding treatment benefit and assay reproducibility is uncertain (e.g., EGFR expression and tyrosine kinase inhibitors in lung cancer), or to identify the most effective therapy from a panel of regimens (e.g., chemotherapy options in breast cancer). Limitations The designs discussed here rest on the assumption that the technical feasibility, assay performance metrics, and the logistics of specimen collection are well established and that initial results demonstrate promise with regard to the predictive ability of the marker(s). Conclusions The choice of a clinical trial design is driven by a combination of scientific, clinical, statistical, and ethical considerations. There is no one size fits all solution to predictive biomarker validation. Clinical Trials 2010; 7: 567—573. http://ctj.sagepub.com

Abstract The need to practice evidence-based medicine is the current prevailing paradigm within the medical community. Evidence to guide practice can and should come from a variety of sources, including clinical trials, observational studies, and meta-analyses of both or either. This paper discusses the relative strengths and weaknesses of data that arise from these various sources. The different types of evidence required to demonstrate “efficacy” versus “effectiveness,” a critical and often overlooked distinction, are discussed. In the genomic age, in which targeted therapies with or without specific biomarkers are emerging in cancer care, new approaches are necessary to generate the evidence required for decision making.

Abstract The optimal design of phase II studies continues to be the subject of vigorous debate, especially studies of newer molecularly targeted agents. The observations that many new therapeutics “fail” in definitive phase III studies, coupled with the numbers of new agents to be tested as well as the increasing costs and complexity of clinical trials, further emphasize the critical importance of robust and efficient phase II design. The Clinical Trial Design Task Force (CTD-TF) of the National Cancer Institute (NCI) Investigational Drug Steering Committee (IDSC) has published a series of discussion papers on phase II trial design in Clinical Cancer Research. The IDSC has developed formal recommendations about aspects of phase II trial design that are the subject of frequent debate, such as endpoints (response versus progression-free survival), randomization (single-arm designs versus randomization), inclusion of biomarkers, biomarker-based patient enrichment strategies, and statistical design (e.g., two-stage designs versus multiple-group adaptive designs). Although these recommendations in general encourage the use of progression-free survival as the primary endpoint, randomization, inclusion of biomarkers, and incorporation of newer designs, we acknowledge that objective response as an endpoint and single-arm designs remain relevant in certain situations. The design of any clinical trial should always be carefully evaluated and justified based on characteristic specific to the situation. Clin Cancer Res; 16(6); 1764–9

AbstractPurpose: Obesity is associated with an increased risk of colon cancer. However, the influence of body mass index (BMI) on the prognosis of colon cancer survivors and its relationship to gender remains unknown.Experimental Design: BMI (kg/m2) was categorized in patients with tumor-node-metastasis stage II and III colon carcinomas (n = 4,381) enrolled in seven randomized trials of 5-fluorouracil–based adjuvant chemotherapy. Cox proportional hazards models were used to determine the association of BMI with disease-free survival (DFS) and overall survival (OS).Results: Among colon cancer patients, 868 (20%) were obese (BMI, ≥30 kg/m2), of which 606 were class 1 (BMI, 30-34 kg/m2) and 262 were class 2,3 (BMI, ≥35 kg/m2). Obese versus normal-weight patients were more likely to be younger, have distal tumors, show intact DNA mismatch repair, and have more lymph node metastases (P < 0.017). In a multivariate analysis, BMI was significantly associated with both DFS (P = 0.030) and OS (P = 0.0017). Men with class 2,3 obesity showed reduced OS compared with normal-weight men [hazard ratio, 1.35; 95% confidence interval, 1.02-1.79; P = 0.039]. Women with class I obesity had reduced OS [hazard ratio, 1.24; 95% confidence interval, 1.01-1.53; P = 0.045] compared with normal-weight women. Overweight status was associated with improved OS in men (P = 0.006), and underweight women had significantly worse OS (P = 0.019). BMI was not predictive of therapeutic benefit.Conclusions: Obesity is an independent prognostic variable in colon cancer survivors and shows gender-related differences. These data suggest that obesity-related biological factors can influence clinical outcome. Clin Cancer Res; 16(6); 1884–93

Purpose To improve the understanding of the appropriate design of phase II oncology clinical trials, we compared error rates in single-arm, historically controlled and randomized, concurrently controlled designs. Patients and Methods We simulated error rates of both designs separately from individual patient data from a large colorectal cancer phase III trials and statistical models, which take into account random and systematic variation in historical control data. Results In single-arm trials, false-positive error rates (type I error) were 2 to 4 times those projected when modest drift or patient selection effects (eg, 5% absolute shift in control response rate) were included in statistical models. The power of single-arm designs simulated using actual data was highly sensitive to the fraction of patients from treatment centers with high versus low patient volumes, the presence of patient selection effects or temporal drift in response rates, and random small-sample variation in historical controls. Increasing sample size did not correct the over optimism of single-arm studies. Randomized two-arm design conformed to planned error rates. Conclusion Variability in historical control success rates, outcome drifts in patient populations over time, and/or patient selection effects can result in inaccurate false-positive and false-negative error rates in single-arm designs, but leave performance of the randomized two-arm design largely unaffected at the cost of 2 to 4 times the sample size compared with single-arm designs. Given a large enough patient pool, the randomized phase II designs provide a more accurate decision for screening agents before phase III testing.