On the Use of Marker Strategy Design to Detect Predictive Marker Effect in Cancer Immunotherapy

Indiana University-Purdue University Indianapolis (IUPUI)The marker strategy design (MSGD) has been proposed to assess and validate predictive markers for targeted therapies and immunotherapies. Under this design, patients are randomized into two strategies: the marker-based strategy, which treats patients based on their marker status, and the non-marker-based strategy, which randomizes patients into treatments independent of their marker status in the same way as in a standard randomized clinical trial. The strategy effect is then tested by comparing the response rate between the two strategies and this strategy effect is commonly used to evaluate the predictive capability of the markers. We show that this commonly used between-strategy test is flawed, which may cause investigators to miss the opportunity to discover important predictive markers or falsely claim an irrelevant marker as predictive. Then we propose new procedures to improve the power of the MSGD to detect the predictive marker effect. One is based on a binary response endpoint; the second is based on survival endpoints. We conduct simulation studies to compare the performance of the MSGD with the widely used marker stratified design (MSFD). Numerical studies show that the MSGD and MSFD has comparable performance. Hence, contrary to popular belief that the MSGD is an inferior design compared with the MSFD, we conclude that using the MSGD with the proposed tests is an efficient and ethical way to find predictive markers for targeted therapies

INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

Classification of clinical outcomes using high-throughput and clinical informatics.

It is widely recognized that many cancer therapies are effective only for a subset of patients. However clinical studies are most often powered to detect an overall treatment effect. To address this issue, classification methods are increasingly being used to predict a subset of patients which respond differently to treatment. This study begins with a brief history of classification methods with an emphasis on applications involving melanoma. Nonparametric methods suitable for predicting subsets of patients responding differently to treatment are then reviewed. Each method has different ways of incorporating continuous, categorical, clinical and high-throughput covariates. For nonparametric and parametric methods, distance measures specific to the method are used to make classification decisions. Approaches are outlined which employ these distances to measure treatment interactions and predict patients more sensitive to treatment. Simulations are also carried out to examine empirical power of some of these classification methods in an adaptive signature design. Results were compared with logistic regression models. It was found that parametric and nonparametric methods performed reasonably well. Relative performance of the methods depends on the simulation scenario. Finally a method was developed to evaluate power and sample size needed for an adaptive signature design in order to predict the subset of patients sensitive to treatment. It is hoped that this study will stimulate more development of nonparametric and parametric methods to predict subsets of patients responding differently to treatment

Syngeneic animal models of tobacco-associated oral cancer reveal the activity of in situ anti-CTLA-4.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. Tobacco use is the main risk factor for HNSCC, and tobacco-associated HNSCCs have poor prognosis and response to available treatments. Recently approved anti-PD-1 immune checkpoint inhibitors showed limited activity (≤20%) in HNSCC, highlighting the need to identify new therapeutic options. For this, mouse models that accurately mimic the complexity of the HNSCC mutational landscape and tumor immune environment are urgently needed. Here, we report a mouse HNSCC model system that recapitulates the human tobacco-related HNSCC mutanome, in which tumors grow when implanted in the tongue of immunocompetent mice. These HNSCC lesions have similar immune infiltration and response rates to anti-PD-1 (≤20%) immunotherapy as human HNSCCs. Remarkably, we find that >70% of HNSCC lesions respond to intratumoral anti-CTLA-4. This syngeneic HNSCC mouse model provides a platform to accelerate the development of immunotherapeutic options for HNSCC

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/

Role of noninvasive molecular imaging in determining response

The intersection of immunotherapy and radiation oncology is a rapidly evolving area of preclinical and clinical investigation. The strategy of combining radiation and immunotherapy to enhance local and systemic antitumor immune responses is intriguing yet largely unproven in the clinical setting because the mechanisms of synergy and the determinants of therapeutic response remain undefined. In recent years, several noninvasive molecular imaging approaches have emerged as a platform to interrogate the tumor immune microenvironment. These tools have the potential to serve as robust biomarkers for cancer immunotherapy and may hold several advantages over conventional anatomic imaging modalities and contemporary invasive tissue acquisition techniques. Given the key and expanding role of precision imaging in radiation oncology for patient selection, target delineation, image guided treatment delivery, and response assessment, noninvasive molecular-specific imaging may be uniquely suited to evaluate radiation/immunotherapy combinations. Herein, we describe several experimental imaging-based strategies that are currently being explored to characterize in vivo immune responses, and we review a growing body of preclinical data and nascent clinical experience with immuno-positron emission tomography molecular imaging as a putative biomarker for cancer immunotherapy. Finally, we discuss practical considerations for clinical translation to implement noninvasive molecular imaging of immune checkpoint molecules, immune cells, or associated elements of the antitumor immune response with a specific emphasis on its potential application at the interface of radiation oncology and immuno-oncology

Oncogene addiction as a foundational rationale for targeted anti-cancer therapy: promises and perils

A decade has elapsed since the concept of oncogene addiction was first proposed. It postulates that – despite the diverse array of genetic lesions typical of cancer – some tumours rely on one single dominant oncogene for growth and survival, so that inhibition of this specific oncogene is sufficient to halt the neoplastic phenotype. A large amount of evidence has proven the pervasive power of this notion, both in basic research and in therapeutic applications. However, in the face of such a considerable body of knowledge, the intimate molecular mechanisms mediating this phenomenon remain elusive. At the clinical level, successful translation of the oncogene addiction model into the rational and effective design of targeted therapeutics against individual oncoproteins still faces major obstacles, mainly due to the emergence of escape mechanisms and drug resistance. Here, we offer an overview of the relevant literature, encompassing both biological aspects and recent clinical insights. We discuss the key advantages and pitfalls of this concept and reconsider it as an illustrative principle to guide post-genomic cancer research and drug development

Determinants Of Adaptive Immunity In Cancer

Immune checkpoint blockade results in T cell antitumor responses but most patients fail to respond. This raises fundamental questions about mechanisms of tumor immune recognition and resistance. Here, I first report tumor regressions in a subset of patients with metastatic melanoma treated with anti-CTLA4 antibody and radiation (RT) and reproduced this effect in mouse models. Although combined treatment improved responses in irradiated and unirradiated tumors, resistance was common due to upregulation of PD-L1 on tumor cells and corresponding T cell exhaustion. Accordingly, optimal response in melanoma and other cancer types required RT, anti-CTLA4, and anti-PD-L1/PD1. When I investigated determinants of improved responses to combination therapy, I found that RT enhanced the antigenic diversity of intratumoral T cells, anti-CTLA4 predominantly inhibited regulatory T cells, and anti-PD-L1 reversed T cell exhaustion. I next extended my investigation of this combination therapy to pancreatic ductal adenocarcinoma (PDA), finding that optimal responses required addition of agonist CD40 monoclonal antibody