Predictive biomarkers in colorectal cancer: usage, validation, and design in clinical trials

A Signature Enrichment Design with Bayesian Adaptive Randomization

Clinical trials in the era of precision cancer medicine aim to identify and validate biomarker signatures which can guide the assignment of individually optimal treatments to patients. In this article, we propose a group sequential randomized phase II design, which updates the biomarker signature as the trial goes on, utilizes enrichment strategies for patient selection, and uses Bayesian response-adaptive randomization for treatment assignment. To evaluate the performance of the new design, in addition to the commonly considered criteria of type I error and power, we propose four new criteria measuring the benefits and losses for individuals both inside and outside of the clinical trial. Compared with designs with equal randomization, the proposed design gives trial participants a better chance to receive their personalized optimal treatments and thus results in a higher response rate on the trial. This design increases the chance to discover a successful new drug by an adaptive enrichment strategy, i.e., identification and selective enrollment of a subset of patients who are sensitive to the experimental therapies. Simulation studies demonstrate these advantages of the proposed design. It is illustrated by an example based on an actual clinical trial in non-small-cell lung cancer.

Strategies for power calculations in predictive biomarker studies in survival data

PURPOSE

Biomarkers and genomic signatures represent potentially predictive tools for precision medicine. Validation of predictive biomarkers in prospective or retrospective studies requires statistical justification of power and sample size. However, the design of these studies is complex and the statistical methods and associated software are limited, especially in survival data. Herein, we address common statistical design issues relevant to these two types of studies and provide guidance and a general template for analysis.

METHODS

A statistical interaction effect in the Cox proportional hazards model is used to describe predictive biomarkers. The analytic form by Peterson et al. and Lachin is utilized to calculate the statistical power for both prospective and retrospective studies.

RESULTS

We demonstrate that the common mistake of using only Hazard Ratio's Ratio (HRR) or two hazard ratios (HRs) can mislead power calculations. We establish that the appropriate parameter settings for prospective studies require median survival time (MST) in 4 subgroups (treatment and control in positive biomarker, treatment and control in negative biomarker). For the retrospective study which has fixed survival time and censored status, we develop a strategy to harmonize the hypothesized parameters and the study cohort. Moreover, we provide an easily-adapted R software application to generate a template of statistical plan for predictive biomarker validation so investigators can easily incorporate into their study proposals.

CONCLUSION

Our study provides guidance and software to help biostatisticians and clinicians design sound clinical studies for testing predictive biomarkers.

Biomarker-Guided Non-Adaptive Trial Designs in Phase II and Phase III: A Methodological Review

Biomarker-guided treatment is a rapidly developing area of medicine, where treatment choice is personalised according to one or more of an individual’s biomarker measurements. A number of biomarker-guided trial designs have been proposed in the past decade, including both adaptive and non-adaptive trial designs which test the effectiveness of a biomarker-guided approach to treatment with the aim of improving patient health. A better understanding of them is needed as challenges occur both in terms of trial design and analysis. We have undertaken a comprehensive literature review based on an in-depth search strategy with a view to providing the research community with clarity in definition, methodology and terminology of the various biomarker-guided trial designs (both adaptive and non-adaptive designs) from a total of 211 included papers. In the present paper, we focus on non-adaptive biomarker-guided trial designs for which we have identified five distinct main types mentioned in 100 papers. We have graphically displayed each non-adaptive trial design and provided an in-depth overview of their key characteristics. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. Our comprehensive review provides guidance for those designing biomarker-guided trials.

RNAi-mediated downregulation of DNA binding protein A inhibits tumorigenesis in colorectal cancer

DNA binding protein A (dbpA) belongs to the Y-box binding protein family and has been reported to play an important role in carcinogenesis. Our previous study demonstrated that the knockdown of dbpA in gastric cancer cells inhibited cell proliferation by modulating the cell cycle. However, the role of dbpA in human colorectal cancer (CRC) remains unclear. In this study, immunohistochemical (IHC) staining and clinicopathological parameter analysis were employed to detect dbpA expression in 44 paired CRC samples and 7 CRC cell lines. Lentivirus-mediated short hairpin RNA (shRNA) was used to silence dbpA, and the effects of dbpA knockdown on cell proliferation were determined by MTT assay, colony formation assay and flow cytometry. Furthermore, a xenograft model was established to observe tumor growth in vivo. Functional analysis indicated that dbpA was overexpressed in the CRC tissues and cell lines, and a high dbpA expression was associated with the depth of invasion (p<0.001), the degree of differentiation (p<0.001), lymphatic metastasis (p<0.001) and vessel invasion (p<0.001). The suppression of dbpA expression resulted in decreased cell proliferation in vitro and tumor growth in vivo, and it induced cell cycle arrest and promoted the apoptosis of the CRC cells. As a whole, our findings illustrate the crucial role of dbpA in colorectal tumorigenesis. Thus, dbpA may be used as a novel and potent therapeutic target in CRC.

Biomarker-Guided Adaptive Trial Designs in Phase II and Phase III: A Methodological Review

BACKGROUND

Personalized medicine is a growing area of research which aims to tailor the treatment given to a patient according to one or more personal characteristics. These characteristics can be demographic such as age or gender, or biological such as a genetic or other biomarker. Prior to utilizing a patient's biomarker information in clinical practice, robust testing in terms of analytical validity, clinical validity and clinical utility is necessary. A number of clinical trial designs have been proposed for testing a biomarker's clinical utility, including Phase II and Phase III clinical trials which aim to test the effectiveness of a biomarker-guided approach to treatment; these designs can be broadly classified into adaptive and non-adaptive. While adaptive designs allow planned modifications based on accumulating information during a trial, non-adaptive designs are typically simpler but less flexible.

METHODS AND FINDINGS

We have undertaken a comprehensive review of biomarker-guided adaptive trial designs proposed in the past decade. We have identified eight distinct biomarker-guided adaptive designs and nine variations from 107 studies. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. We have graphically displayed the current biomarker-guided adaptive trial designs and summarised the characteristics of each design.

CONCLUSIONS

Our in-depth overview provides future researchers with clarity in definition, methodology and terminology for biomarker-guided adaptive trial designs.

The potential of tumor-derived exosomes for noninvasive cancer monitoring

Tumor-derived exosomes (TEX) are emerging as a new type of cancer biomarker. TEX are membrane-bound, virus-size vesicles of endocytic origin present in all body fluids of cancer patients. Based on the expanding albeit incomplete knowledge of their biogenesis, secretion by tumor cells and cancer cell-specific molecular and genetic contents, TEX are viewed as promising, clinically-relevant surrogates of cancer progression and response to therapy. Preliminary proteomic, genetic and functional profiling of tumor cell-derived or cancer plasma-derived exosomes confirms their unique characteristics. Alterations in protein or nucleic acid profiles of exosomes in plasma of cancer patients responding to therapies appear to correlate with clinical endpoints. However, methods for TEX isolation and separation from the bulk of human plasma-derived exosomes are not yet established and their role as biomarkers remains to be confirmed. Further development and validation of TEX as noninvasive, liquid equivalents of tumor biopsies are necessary to move this effort forward.

Stage-specific frequency and prognostic significance of aneuploidy in patients with sporadic colorectal cancer--a meta-analysis and current overview

PURPOSE

Aneuploidy has long been suggested as an independent prognostic marker for colorectal cancer (CRC) patients and could thus aid for individualized medicine. However, due to a large spectrum of deviating studies, expert panels do not recommend ploidy assessment. In order to clarify a potential bias of stage-specific frequency of aneuploidy, we now conducted a meta-analysis combined with a systematic review regarding aneuploidy and prognosis.

METHODS

A systematic, web-based search process retrieved 1935 studies published in English between 1990 and 2011. The defined endpoint for the meta-analysis was an increase in aneuploidy frequency between early- (Dukes A, B and UICC I, II; n = 3632 samples) and late-stage (Dukes C, D and UICC III, IV; n = 3440 samples) colorectal carcinomas.

RESULTS

Of 1935 studies initially identified, 17 image (2130 patients) and 20 (7023 patients) flow cytometric studies were analyzed in detail. The meta-analysis (7072 patients) revealed late-stage CRC to be more frequently aneuploid than early-stage CRC (odds ratio 1.51, 95 % CI 1.37-1.67; p = 0.0007). Independent of tumor stage, the overall range of aneuploidy was 39 to 81 % (median 58 %), and altogether, 21 (54.1 %) studies described a significant prognostic impact of aneuploidy for overall, disease-specific, and recurrence-free survival, respectively.

CONCLUSIONS

A substantial number of studies showed a prognostic importance of aneuploidy in CRC. Furthermore, the higher frequency of aneuploidy in late-stage CRC implies an increase in genomic instability with CRC progression, indicating aneuploidy to be also a stage-specific prognostic marker.

Overexpressed RACK1 is positively correlated with malignant degree of human colorectal carcinoma

RACK1 is a crucial scaffold and anchoring protein, which plays a vital role in multiple signaling pathways of tumorigenesis. The aim of the present study was to identify the correlation between expressions of RACK1 and malignant degrees in colorectal carcinoma (CRC) patients. All together 157 CRC patients were enrolled, and their clinical data were analyzed. Expressions of RACK1 in CRC and pericarcinous tissues in these patients were determined by RT-PCR, Western-blot, and immunohistochemistry, respectively. The correlation between RACK1 expressions and histological grades, as well as lymph node metastasis was evaluated. Results showed that the expressions of RACK1 were positively correlated with differentiation level and lymph node metastasis in CRC patients.

A review of phase II trial designs for initial marker validation

Phase II clinical trials aim to identify promising experimental regimens for further testing in phase III trials. In this review article, we focus on phase II designs for initial predictive biomarker validation to determine if a drug should be developed for an unselected patient population or for a biomarker-defined patient subset only. Several prospective designs for biomarker-directed therapy have been proposed, differing primarily in the study population, or randomization scheme, or both. The design choice is driven by scientific rationale, marker prevalence, strength of preliminary evidence, assay performance, and turn-around times for marker assessment. The enrichment design is most appropriate when compelling preliminary evidence suggests treatment benefit in only certain marker-defined subgroups, the all-comers design is useful when preliminary evidence regarding treatment effects in marker subgroups is unclear, and adaptive designs have the most potential in the setting of multiple treatment options and multiple marker-defined subgroups. We recently proposed a 2-stage phase II design that has the option for direct assignment (i.e., stop randomization and assign all patients to the experimental arm in stage 2) based on interim analysis (IA) results. This design not only recognizes the need for randomization but also acknowledges the possibility of promising but inconclusive results after pre-planned IA. Simulation studies demonstrated that the direct assignment-option design has minimal power loss, marginal increase in type I error rates, and reasonable robustness to population shift effects. Systematic evaluation and implementation of these design strategies in the phase II setting are essential for accelerating the clinical validation of biomarker guided-therapy.

Activated leukocyte cell adhesion molecule (CD166)--its prognostic power for colorectal cancer patients

BACKGROUND

The activated leukocyte cell adhesion molecule (ALCAM, CD166) has been reported to be involved in tumorigenesis of colorectal cancer (CRC) and to function as a cancer stem cell marker. Controversial data exist regarding the prognostic power of ALCAM expression in CRC. Here, we evaluate the expression of ALCAM in a cohort of CRC patients and its usage as a prognostic marker for survival.

MATERIALS AND METHODS

Tissue specimens from 299 patients with CRC treated between 1993 and 2006 were analyzed via ALCAM immunohistochemistry (clone MOG/07) using a tissue microarray. Results were correlated with clinical, histopathological, and patient survival data (Chi-square test, Kaplan-Meier analysis, and log-rank test, respectively). Multivariate analysis also was performed (Cox regression).

RESULTS

ALCAM is expressed in most primary (76%) and secondary (62%) CRC lesions (P = 0.014). Immunohistochemistry revealed an inverse association with tumor grading (P = 0.002) but not with any other clinical or histopathological data. Kaplan-Meier survival analysis revealed a significant overall survival benefit in the group of ALCAM-positive patients (P = 0.019). Multivariate analysis showed that ALCAM is an independent positive prognostic marker for overall survival (P = 0.023).

CONCLUSIONS

ALCAM expression is a positive prognostic marker for overall survival of CRC patients, and its detection might help to optimize the existing prognostic staging system. Elevated expression in higher differentiated tumors might indicate a potential role in the early steps of tumorigenesis, and its loss might be associated with reduced cellular adhesion, resulting in a higher metastatic potential of the tumor. Further studies must be conducted investigating these hypotheses.