Accelerating Therapeutic Development through Innovative Trial Design in Colorectal Cancer

Defining early recurrence in patients with resected primary colorectal carcinoma and its respective risk factors

PURPOSE

There is no evidence-based definition of early recurrence following resection of colorectal cancer. The purpose of this study is to define a point that discriminates between early and late recurrence in patients who have undergone colorectal cancer resection with curative intent and to analyze associated risk factors.

METHODS

A retrospective single-center cohort study was performed at a university hospital recognized as a comprehensive cancer center, specializing in colorectal cancer surgery. Patient data were retrieved from a prospectively maintained institutional database. Included patients underwent resection for primary, non-metastatic colorectal carcinomas with curative intent between 1995 and 2010. Aims of the study were (1) to define the optimal cut-off point of recurrence-free survival based on overall survival using a minimum p value approach and (2) to identify patterns of initial recurrence and putative risk factors for early recurrence using regression models.

RESULTS

Recurrence was diagnosed in 412 of 1893 patients. Statistical analysis suggested that a recurrence-free survival of 16 months could be used to distinguish between early and late recurrence based on overall survival (p < 0.001). Independent risk factors for early recurrence included advanced pT categories (pT3,4/ypT3,4) and positive lymph node status (pN+/ypN+). Early recurrence was independent of site of recurrence and was associated with worse prognosis.

CONCLUSIONS

Recurrence of colorectal carcinoma within 16 months after primary treatment should be labeled as "early." Tumor categories pT3,4/ypT3,4 and positive lymph node status pN+/ypN+ are predictive of early recurrence.

Circulating tumor DNA guided adjuvant chemotherapy in stage II colon cancer (MEDOCC-CrEATE): study protocol for a trial within a cohort study

BACKGROUND

Accurate detection of patients with minimal residual disease (MRD) after surgery for stage II colon cancer (CC) remains an urgent unmet clinical need to improve selection of patients who might benefit form adjuvant chemotherapy (ACT). Presence of circulating tumor DNA (ctDNA) is indicative for MRD and has high predictive value for recurrent disease. The MEDOCC-CrEATE trial investigates how many stage II CC patients with detectable ctDNA after surgery will accept ACT and whether ACT reduces the risk of recurrence in these patients.

METHODS/DESIGN

MEDOCC-CrEATE follows the 'trial within cohorts' (TwiCs) design. Patients with colorectal cancer (CRC) are included in the Prospective Dutch ColoRectal Cancer cohort (PLCRC) and give informed consent for collection of clinical data, tissue and blood samples, and consent for future randomization. MEDOCC-CrEATE is a subcohort within PLCRC consisting of 1320 stage II CC patients without indication for ACT according to current guidelines, who are randomized 1:1 into an experimental and a control arm. In the experimental arm, post-surgery blood samples and tissue are analyzed for tissue-informed detection of plasma ctDNA, using the PGDx elio™ platform. Patients with detectable ctDNA will be offered ACT consisting of 8 cycles of capecitabine plus oxaliplatin while patients without detectable ctDNA and patients in the control group will standard follow-up according to guideline. The primary endpoint is the proportion of patients receiving ACT when ctDNA is detectable after resection. The main secondary outcome is 2-year recurrence rate (RR), but also includes 5-year RR, disease free survival, overall survival, time to recurrence, quality of life and cost-effectiveness. Data will be analyzed by intention to treat.

DISCUSSION

The MEDOCC-CrEATE trial will provide insight into the willingness of stage II CC patients to be treated with ACT guided by ctDNA biomarker testing and whether ACT will prevent recurrences in a high-risk population. Use of the TwiCs design provides the opportunity to randomize patients before ctDNA measurement, avoiding ethical dilemmas of ctDNA status disclosure in the control group.

TRIAL REGISTRATION

Netherlands Trial Register: NL6281/NTR6455 . Registered 18 May 2017, https://www.trialregister.nl/trial/6281.

Effect size estimates from umbrella designs: Handling patients with a positive test result for multiple biomarkers using random or pragmatic subtrial allocation

Umbrella trials have been suggested to increase trial conduct efficiency when investigating different biomarker-driven experimental therapies. An overarching platform is used for patient screening and subsequent subtrial allocation according to patients’ biomarker status. Two subtrial allocation schemes for patients with a positive test result for multiple biomarkers are (i) the pragmatic allocation to the eligible subtrial with the currently fewest included patients and (ii) the random allocation to one of the eligible subtrials. Obviously, the subtrials compete for such patients which are consequently underrepresented in the subtrials. To address questions of the impact of an umbrella design in general as well as with respect to subtrial allocation and analysis method, we investigate an umbrella trial with two parallel group subtrials and discuss generalisations. First, we analytically quantify the impact of the umbrella design with random allocation on the number of patients needed to be screened, the biomarker status distribution and treatment effect estimates compared to the corresponding gold standard of an independent parallel group design. Using simulations and real data, we subsequently compare both allocation schemes and investigate weighted linear regression modelling as possible analysis method for the umbrella design. Our results show that umbrella designs are more efficient than the gold standard. However, depending on the biomarker status distribution in the disease population, an umbrella design can introduce differences in estimated treatment effects in the presence of an interaction between treatment and biomarker status. In principle, weighted linear regression together with the random allocation scheme can address this difference though it is difficult to assess if such an approach is applicable in practice. In any case, caution is required when using treatment effect estimates derived from umbrella designs for e.g. future trial planning or meta-analyses.