Arash A. Alizadeh, Ph.D. - US grants

gene expression profiling; biomedical facility;

? DESCRIPTION (provided by applicant): Adaptive anti-tumor immune responses are important determinants of clinical outcomes in patients with diverse cancer types, and increasingly a target of emerging cancer therapies. A central unmet need is a better understanding of the targets of anti-tumor immune responses necessary for effective active immunotherapies and vaccination strategies. We are currently conducting a clinical trial of therapeutic vaccination for patients with Mantle Cell Lymphoma (NCT00490529), a heretofore incurable hematologic malignancy. In this application we will use a combination of novel technologies and bioinformatics platforms to discover the genetic and immunological determinants of the immune responses that we have induced. Our central hypothesis is that clinical and immunological responses in patients with this disease after their therapeutic vaccination are determined by the somatic mutations encoded in their tumor genomes. Alterations in the tumor proteome, such as novel (neo-) antigenic peptides generated in the process of somatic mutation, can serve as potent substrates for specific anti-tumor immune responses when appropriately presented in the context of major histocompatibility complex (MHC) to effector T-cells, and in turn recognized by their antigen receptors. We will test these hypotheses in close collaboration with ICBP@Stanford, first taking advantage of systematic methodologies for interrogation of the tumor coding genome, transcriptome, and MHC-peptidome to discover somatic mutations that are predicted (Aim 1) and/or observed (Aim 2) to bind cognate MHC. We will synthesize and assemble synthetic versions of these candidate peptide neoantigens with corresponding MHC molecules, and use them in large peptide-MHC `tetramer' panels to interrogate the T lymphocyte responses induced by tumor vaccination in the patients (Aim 3). In molecularly profiling, and functionally tracking these dynamic responses in serial blood specimens from patients before and after immunization, we aim to differentiate patients with or without clinical responses to this therapy, and to better predict their distinct outcomes. We anticipate that this integrated approach will reveal the interplay between nascent and induced immune responses and genetic factors in control of disease progression in MCL and inform new ways of battling this deadly disease. This approach should also have relevance to other cancers.

? DESCRIPTION (provided by applicant): Radiotherapy plays a critical role in the definitive management of early stage and locally advanced non- small cell lung cancer (NSCLC), the number one cause of cancer deaths in the United States. Two important challenges in the management of NSCLC patients treated with radiotherapy are the inability to detect microscopic residual disease and the difficulty of distinguishing between recurrent malignancy and post-radiation normal tissue changes on follow-up radiographic studies. These shortcomings prevent design and testing of individualized treatment strategies that could potentially increase cure rates and suggest that improved methods for following patients after treatment are needed. Our goal is to test a novel approach for detecting the presence of residual or recurrent disease in NSCLC patients treated with radiotherapy. Somatic alterations in the genomes of cancer cells represent ideal biomarkers, since they can define patient- and tumor-specific signatures that distinguish cancer cells from non-malignant counterparts. Cancers continually release genomic DNA into the circulation, potentially allowing noninvasive access to cancer genomes and quantitation of disease burden. We have designed and implemented a novel, high throughput sequencing-based technique for measurement of circulating tumor-derived DNA termed CAPP- Seq (CAncer Personalized Profiling in by deep Sequencing). We propose to evaluate the utility of ctDNA analysis via CAPP-Seq in NSCLC patients treated with radiotherapy and to explore mechanisms of ctDNA release and clearance in human patients. Taken together, these studies will lay the groundwork for prospective clinical trials that will test personalization of radiotherpy-based therapeutic strategies using ctDNA analysis.

? DESCRIPTION (provided by applicant): Our proposed research is aimed at developing catalytic molecules and methods that greatly improve the recovery of biomolecular information from formalin-fixed tissue specimens. Formalin (formaldehyde) treatment of tissue is universally used in preparation of hundreds of millions of biopsy and surgery specimens worldwide. Unfortunately, the formaldehyde forms many adducts and crosslinks with the biomolecules in the specimens, strongly inhibiting the ability to obtain sequences and quantification of RNAs and DNAs from such specimens, Standard heating-based methods of RNA/DNA extraction remove only a fraction of adducts, and they are harsh, degrading the nucleic acids in the process. In our preliminary work using model nucleotides in vitro, we have shown that bifunctional organic arylamine catalysts can be highly effective in removing hemiaminal formaldehyde adducts of RNA/DNA bases under mild conditions of pH and temperature. Recovery of undamaged nucleotides and RNAs is markedly enhanced as compared with standard methods, and the catalysts can be effective even at room temperature. This work takes an innovative, mechanistic chemistry-based approach to reversal of formaldehyde adducts. The goal of this 6-month collaborative research plan is to establish convincing proof-of-principle that our organocatalysts can be used to recover greater amounts of bimolecular signals from formalin-adducted RNA and DNA than literature-standard and commercial buffers and protocols do. The work will be carried out at cell Data Sciences Inc. and at Stanford School of Medicine. While our early work has been performed primarily with simple nucleotide models, here we plan to develop and optimize catalysts with full-length RNAs and DNAs, and secondly, to extend our protocols and catalysts to extracting, repairing and analyzing clinically relevant RNAs and DNAs in cell- and tissue-based FFPE specimens. We expect that this work will constitute a major breakthrough in retrieval of molecular data from formalin-fixed tissue, solving a widely recognized and decades-long problem. These catalysts will enable higher-yield retrieval of longer, less damaged RNAs and DNAs from tissue specimens, allowing access to sequence where it was not possible, and enhancing quantitative signals where they were previously weak or unquantifiable. Our approach will give stronger signals from PCR analysis and more accurate and complete sequencing data with fewer reads. It will also enable more robust signals from in situ hybridization. Finally, the catalysts may also remove crosslinks and adducts from proteins; this could yield stronger signals by immunohistochemistry and could enable more effective proteomics analyses from stored tissue.

? DESCRIPTION (provided by applicant): Adaptive anti-tumor immune responses are important determinants of clinical outcomes in patients with diverse cancer types, and increasingly a target of emerging cancer therapies. A central unmet need is a better understanding of the targets of anti-tumor immune responses necessary for effective active immunotherapies and vaccination strategies. We are currently conducting a clinical trial of therapeutic vaccination for patients with Mantle Cell Lymphoma (NCT00490529), a heretofore incurable hematologic malignancy. In this application we will use a combination of novel technologies and bioinformatics platforms to discover the genetic and immunological determinants of the immune responses that we have induced. Our central hypothesis is that clinical and immunological responses in patients with this disease after their therapeutic vaccination are determined by the somatic mutations encoded in their tumor genomes. Alterations in the tumor proteome, such as novel (neo-) antigenic peptides generated in the process of somatic mutation, can serve as potent substrates for specific anti-tumor immune responses when appropriately presented in the context of major histocompatibility complex (MHC) to effector T-cells, and in turn recognized by their antigen receptors. We will test these hypotheses in close collaboration with ICBP@Stanford, first taking advantage of systematic methodologies for interrogation of the tumor coding genome, transcriptome, and MHC-peptidome to discover somatic mutations that are predicted (Aim 1) and/or observed (Aim 2) to bind cognate MHC. We will synthesize and assemble synthetic versions of these candidate peptide neoantigens with corresponding MHC molecules, and use them in large peptide-MHC `tetramer' panels to interrogate the T lymphocyte responses induced by tumor vaccination in the patients (Aim 3). In molecularly profiling, and functionally tracking these dynamic responses in serial blood specimens from patients before and after immunization, we aim to differentiate patients with or without clinical responses to this therapy, and to better predict their distinct outcomes. We anticipate that this integrated approach will reveal the interplay between nascent and induced immune responses and genetic factors in control of disease progression in MCL and inform new ways of battling this deadly disease. This approach should also have relevance to other cancers.

PROJECT SUMMARY/ABSTRACT PIs: Ash Alizadeh, M.D./Ph.D. & Maximilian Diehn, M.D./Ph.D. For patients with Diffuse large B-cell lymphoma (DLBCL), the most common lymphoma subtype, curative outcomes are common. Unfortunately, despite many large clinical trials, survival has not significantly improved over the last 15 years and nearly a third of patients continue to succumb to this disease. For these patients, effective strategies to predict early treatment failures have been elusive. Our long-term goal is to study the ability of baseline and dynamic risk factors, including genetic mutations and circulating tumor DNA (ctDNA), to accurately predict treatment outcomes in DLBCL patients. Our central hypothesis is that novel biomarkers of cancer risk, such as detection of ctDNA and detailed genetic profiling, can be used for early detection of residual disease, to identify dynamic changes that anticipate treatment failure, and to provide early surrogate endpoints for future clinical trials. We will test our hypothesis via three specific aims: (1) To build an accurate and dynamic predictor of survival for patients newly diagnosed with DLBCL, (2) To test the validity and utility of this predictor in a large multi-institutional cohort of patients from around the globe, and (3) To assess the ability of this dynamic risk assessment tool to serve as an early surrogate endpoint in prospective clinical trials. We will apply our novel approach in both the frontline and relapse/refractory setting and to a variety of treatment types including immunochemotherapy, an antibody-drug conjugate and Chimeric Antigen Receptor (CAR) T cells. If successful, our project will lead to novel ways to select better therapies for patients at highest risk of failure. Our innovative approach, in which we will employ novel, blood-based methods for tumor genotyping and disease monitoring that were developed by our group, will lay the foundation for studies aimed at reducing risk of treatment failure in DLBCL patients. Demonstrating that this approach can serve as a robust, early surrogate endpoint for patients with aggressive lymphomas would be transformative for future trial design and for rapid evaluation of novel, personalized treatment approaches in patients at highest risk for recurrence. Our work will serve as proof-of-principle for an approach that could also be applied to other cancer types.

PROJECT SUMMARY/ABSTRACT Solid organ transplant recipients are an ideal population in which to study the link between oncogenic viral infections and cancer due to the deep immunosuppression required to prevent allograft rejection, which increases their risk of developing clinical complications such as infections and cancer. Our long-term goal is to study the relations among immunosuppression, infections, and cancer using transplantation as a model system. Our central hypothesis is that novel biomarkers of cancer risk such as detection of circulating tumor DNA, sequencing of circulating cell-free DNA, and detailed immune profiling can be used for early cancer detection, to identify changes in the virome that precede malignant transformation, and to quantify overall immunosuppression. We will test our hypothesis via three specific aims: (1) To evaluate circulating tumor DNA for early detection of post-transplant malignancies, focusing on post-transplant lymphoproliferative disorders (PTLDs). We will evaluate the performance of CAPP-Seq, an ultra-sensitive assay for early cancer detection, in existing cohorts of over 2000 heart and lung transplant recipients followed at Stanford University and 6 collaborating sites. We will study patients with PTLDs to (a) determine the kinetics of emerging somatic variants preceding tumor development, (b) define the window for accurate early prediction of cancer risk via circulating tumor DNA, and (c) relate these findings to oncotropic viral expansion and immune system suppression. Similar exploratory analyses will be performed in patients with post-transplant lung and colorectal cancers. (2) To profile oncoviruses in cell-free DNA and evaluate integration sites as cancer risk predictors. To distinguish features in the oncotropic virome preceding malignant transformation, we will enrich oncoviral cell-free DNA to enable identification of human:virus gene fusion by deep sequencing, and will determine whether read coverage is consistent with genome integration or with free DNA. We will then profile DNA from primary tumors and cell-free DNA, and will compare integration site coverage in tumor subtypes. (3) To quantify associations among immunosuppression, viral infection and cancer development. We will perform novel immune profiling assays at defined time points following transplantation and will correlate results with development of acute rejection, opportunistic infections, and cancer. Specifically, we will measure circulating Anellovirus load, will infer immune cell subsets from RNA-seq, and will sequence the B-cell antibody heavy chain. We will determine how these results relate to administered immunosuppression, and will build mathematical models to predict risk of clinical complications. This contribution is significant because knowledge of the molecular signatures associated with cancer risk and early detection may lead to novel ways to prevent, monitor, and treat malignant disease. Our innovative approach, in which we will employ novel methods developed by our group to study a very high-risk transplant patient cohort, will lay the foundation for studies aimed at prevention and early detection of cancer as a means of improving clinical outcomes.