Methods for Using Biomarker Information in Randomized Clinical Trials

Advances in high-throughput biological technologies have led to large numbers of potentially predictive biomarkers becoming routinely measured in modern clinical trials. Biomarkers which influence treatment efficacy may be used to find subgroups of patients who are most likely to benefit from a new treatment. Consequently, there is a growing interest in better approaches to identify biomarker signatures and utilize the biomarker information in clinical trials. The first focus of this thesis is on developing methods for detecting biomarker-treatment interactions in large-scale trials. Traditional interaction analysis, using regression models to test biomarker-treatment interactions one biomarker at a time, may suffer from poor power when there is a large multiple testing burden. I adapt recently proposed two-stage interaction detecting procedures for application in randomized clinical trials. I propose two new stage 1 multivariate screening strategies using lasso and ridge regressions to account for correlations among biomarkers. For these new multivariate screening strategies, I prove the asymptotic between-stage independence, required for family-wise error rate control. Simulation and real data application results are presented which demonstrate greater power of the new strategies compared with previously existing approaches. The second focus of this thesis is on developing methods for utilizing biomarker information during the course of a randomized clinical trial to improve the informativeness of results. Under the adaptive signature design (ASD) framework, I propose two new classifiers that more efficiently leverage biomarker signatures to select a subgroup of patients who are most likely to benefit from the new treatment. I provide analytical arguments and demonstrate through simulations that these two proposed classification criteria can provide at least as good, and sometimes significantly greater power than the originally proposed ASD classifier. Third, I focus on an important issue in the statistical analysis of interactions for binary outcomes, which is pertinent to both topics above. Testing for biomarker-treatment interactions with logistic regression can suffer from an elevated number of type I errors due to the asymptotic bias of the interaction regression coefficient under model misspecification. I analyze this problem in the randomized clinical trial setting and propose two new de-biasing procedures, which can offer improved family-wise error rate control in various simulated scenarios. Finally, I summarize the main contributions from the work above, discuss some practical limitations as well as their real world value, and prioritize future directions of research building upon the work in this thesis.Medical Research Council, grant ID: MR/R502303/

Nonnegative principal component analysis for mass spectral serum profiles and biomarker discovery

<p>Abstract</p> <p>Background</p> <p>As a novel cancer diagnostic paradigm, mass spectroscopic serum proteomic pattern diagnostics was reported superior to the conventional serologic cancer biomarkers. However, its clinical use is not fully validated yet. An important factor to prevent this young technology to become a mainstream cancer diagnostic paradigm is that robustly identifying cancer molecular patterns from high-dimensional protein expression data is still a challenge in machine learning and oncology research. As a well-established dimension reduction technique, PCA is widely integrated in pattern recognition analysis to discover cancer molecular patterns. However, its global feature selection mechanism prevents it from capturing local features. This may lead to difficulty in achieving high-performance proteomic pattern discovery, because only features interpreting global data behavior are used to train a learning machine.</p> <p>Methods</p> <p>In this study, we develop a nonnegative principal component analysis algorithm and present a nonnegative principal component analysis based support vector machine algorithm with sparse coding to conduct a high-performance proteomic pattern classification. Moreover, we also propose a nonnegative principal component analysis based filter-wrapper biomarker capturing algorithm for mass spectral serum profiles.</p> <p>Results</p> <p>We demonstrate the superiority of the proposed algorithm by comparison with six peer algorithms on four benchmark datasets. Moreover, we illustrate that nonnegative principal component analysis can be effectively used to capture meaningful biomarkers.</p> <p>Conclusion</p> <p>Our analysis suggests that nonnegative principal component analysis effectively conduct local feature selection for mass spectral profiles and contribute to improving sensitivities and specificities in the following classification, and meaningful biomarker discovery.</p

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images