Cancer Immunotherapy and Personalized Medicine: Emerging Technologies and Biomarker-Based Approaches

Purpose of review The vision and strategy for the 21st century treatment of cancer calls for a personalized approach in which therapy selection is designed for each individual patient. While genomics has led the field of personalized cancer medicine over the past several decades by connecting patient-specific DNA mutations with kinase-targeted drugs, the recent discovery that tumors evade immune surveillance has created unique challenges to personalize cancer immunotherapy. In this mini-review we will discuss how personalized medicine has evolved recently to accommodate the emerging era of cancer immunotherapy. Moreover, we will discuss novel platform technologies that have been engineered to address some of the persisting limitations. Recent finding Beginning with early evidence in personalized medicine, we discuss how biomarker-driven approaches to predict clinical success have evolved to account for the heterogeneous tumor ecosystem. In the emerging field of cancer immunotherapy, this challenge requires the use of a novel set of tools, distinct from the classic approach of next-generation genomic sequencing-based strategies. We will introduce new techniques that seek to tailor immunotherapy by re-programming patient-autologous T-cells, and new technologies that are emerging to predict clinical efficacy by mapping infiltration of lymphocytes, and harnessing fully humanized platforms that reconstruct and interrogate immune checkpoint blockade, ex-vivo. Summary While cancer immunotherapy is now leading to durable outcomes in difficult-to-treat cancers, success is highly variable. Developing novel approaches to study cancer immunotherapy, personalize treatment to each patient, and achieve greater outcomes is penultimate to developing sustainable cures in the future. Numerous techniques are now emerging to help guide treatment decisions, which go beyond simple biomarker-driven strategies, and are now we are seeking to interrogate the entirety of the dynamic tumor ecosystem

Abstract LB-346: Case study: Non-uniform response to therapy in multiple metastatic is predicted using CANscriptTM, a live tissue, ex - vivo , platform

Abstract Background: It is now clear that the tumor microenvironment drives response or resistance to therapy. More specifically, the stroma, vasculature and immune compartment shape tumor response to therapy. In addition, heterogeneity within a tumor and between metastatic sites will invariably affect the outcome of treatment. Due in large part to these biological complexities, predicting how treatment response varies among multiple metastatic sites may impact overall outcome remains poorly established. Methods: Here, we interrogated the genomic and transcriptomic profile of three metastatic lesions from a patient diagnosed with relapsed head and neck squamous cell carcinoma (HNSCC), refractory to second-line Pembrolizumab (PD-1 checkpoint blockade). In addition, we employed CANscript™, a patient-derived ex-vivo model, which uses live tissue to recapitulate the native 3D tumor microenvironment coupled with an algorithm-driven strategy to predict clinical response in the form of an S-Score (Majumder et al., Nat. Comm., 2015). Using this platform, we tested two combination therapies; carboplatin with gemcitabine, and adriamycin with cyclophosphamide. Moreover, we characterized the tumor microenvironment, following combination treatment, using a multiplexed immunohistochemistry (IHC) panel (Ki67, PanCK, CD3, CD4, CD8, DAPI). Results: We determined that the three metastatic sites displayed distinct transcription and whole exome signatures, prior to CANscriptTM. Based on CANscriptTM predicted responses (S-Score) for the two combinations tested, all three sites responded in a non-uniform manner. Interestingly, each site also displayed distinct patterns of proliferative immune subsets (Ki67 staining) and CD4:CD8 ratios, following treatment with each combination therapy. Conclusion: Together, these findings demonstrate that, due to the underlying genetic and tumor microenvironment heterogeneity, metastatic sites might each confer distinct clinical responses to the same drug regimen, even in immunotherapy-resistant disease. Moreover, we highlight the utility of ex-vivo profiling as a tool to predict therapeutic response - not only at the individual patient level, but also at the level of multiple metastatic sites from a single patient. These findings underscore the importance of characterizing the entire tumor-immune contexture under pressure anticancer drugs. Such information can revise our understanding of personalized cancer care, and may impact rational treatment options. Citation Format: Chukwuemeka Ikpeazu, Munisha Smalley, Baraneedharan Ulaganathan, Allen Thayakumar, Laura Majeiko, Jyothsana Ganesh, Basavaraja Shanthappa, Hans Gertje, Mark Lawson, Sara Lapomarda, Aaron Goldman. Case study: Non-uniform response to therapy in multiple metastatic is predicted using CANscriptTM, a live tissue, ex-vivo, platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-346

Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy.

Oncolytic viruses (OVs) are emerging cancer immunotherapy. Despite notable successes in the treatment of some tumors, OV therapy for central nervous system cancers has failed to show efficacy. We used an ex vivo tumor model developed from human glioblastoma tissue to evaluate the infiltration of herpes simplex OV rQNestin (oHSV-1) into glioblastoma tumors. We next leveraged our data to develop a computational, model of glioblastoma dynamics that accounts for cellular interactions within the tumor. Using our computational model, we found that low stromal density was highly predictive of oHSV-1 therapeutic success, suggesting that the efficacy of oHSV-1 in glioblastoma may be determined by stromal-to-tumor cell regional density. We validated these findings in heterogenous patient samples from brain metastatic adenocarcinoma. Our integrated modeling strategy can be applied to suggest mechanisms of therapeutic responses for central nervous system cancers and to facilitate the successful translation of OVs into the clini