Power estimation in biomarker studies where events are already observed

Development and validation of a novel combinational index of liquid biopsy biomarker for longitudinal lung cancer patient management

Objectives

Many cancer biomarkers such as the circulating tumor cells/microemboli (CTCs/CTM) have been reported significant associations with clinical outcomes. However, different biomarkers have different sensitivities and specificities for cancer types and cohort patients, and synergistic effects between certain biomarkers have also been observed, leading to the inaccurate, fluctuating, and even controversial results when multiple biomarkers are analyzed together. In this paper, a novel combinational index, P-score, was developed for monitoring and predicting the disease condition of lung cancer patients during follow-up visits.

Materials and methods

There were totally 13 return patients with 54 blood samples involved in this study to examine the number of CTC and CTM. Information from one group of 7 patients including 27 blood samples with published clinical data was employed to develop while those from another group of 4 patients containing 14 blood samples with unpublished clinical data were used to validate the P score in prediction. Enumerations were based on immunofluorescent staining images. Distributions of CTC/CTM and their frequencies in stratified patients were carefully examined and analyzed the ROC curve and AUC value to develop the P score and P score-based prediction model.

Results and conclusion

We found that the predictive power of P-score was not only comparable to the traditional cancer marker, in comparison with individual CTC/CTM, more false positives could be corrected by using P-score, thereby to improve the accuracy of analysis. From our preliminary validation tests, the prognosis and disease progression monitored longitudinally by P-score were further confirmed by clinical outcome data from physicians and its sensitivity was even better than those from individual biomarkers. We believe that this novel combinational indicator could be a promising tool to interpret clinical outcomes more accurately from multiple factors, particularly useful for the early prognosis and longitudinal monitoring in cancer patient management.

Current drug development and trial designs in neuro-oncology: report from the first American Society of Clinical Oncology and Society for Neuro-Oncology Clinical Trials Conference

Successful drug development for people with cancers of the CNS has been challenging. There are multiple barriers to successful drug development including biological factors, rarity of the disease, and ineffective use of clinical trials. Based upon a series of presentations at the First Central Nervous System Clinical Trials Conference hosted by the American Society of Clinical Oncology and the Society for Neuro-Oncology, we provide an overview on drug development and novel trial designs in neuro-oncology. This Review discusses the challenges of therapeutic development in neuro-oncology and proposes strategies to improve the drug discovery process by enriching the pipeline of promising therapies, optimising trial design, incorporating biomarkers, using external data, and maximising efficacy and reproducibility of clinical trials.

Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.