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.

e20035 Background: Immunotherapy has emerged as a powerful treatment paradigm wherein therapies primarily target immune components. For example, blockade of PD-1 and PD-L1 offers effective treatment options for patients with aggressive tumors such as head and neck squamous cell carcinoma (HNSCC) and in non-small cell lung carcinoma (NSCLC). However, clinical responses to immotherapy vary widely among patients. There is an unmet need to understand these disparities at the individual patient level. Rationally combining checkpoint inhibitors may address many of these underlying challenges. Methods: Here, we describe a patient-derived ex-vivo platform technology CANscript™, which captures the 3D profiles of native tumor microenvironment by incorporating tumor tissue, autologous immune cells, and immune-targeted agents. Utilizing late stage HNSCC and NSCLC patient tumors we interrogated the phenotypic changes in the tumor-immune contexture in response to standard-of-care agents, PD-1 and PD-L1 inhibitors. Flow cytometry and immunohistochemistry profiling of CD8, CD45, FOXP3, CXCR4, CD68, PDL1, PD1), cytokine profile (IL6, IL8, IFN-g, IL12 and others), and tumor proliferation/apoptosis were measured. Results: The data suggest that PD-1 and PD-L1 blockade induced patient-specific response, which was characterized by differential distribution and infiltration of CD8+ and CD4+ lymphocytes, distinct patterning of cytokines linked to functional dysregulation, and changes in tumor proliferation and apoptosis. Interestingly, the data demonstrated unique immune signatures associated with single agent vs. combination therapy that imply functionally distinct mechanisms of orchestration of response. Conclusions: Our data highlights the translational underpinnings of of CANScript™ as an ex vivo platform for predicting patient driven therapeutic response of immune checkpoint inhibitors where distinct tumor-immune networks influence clinical response to therapy. Information obtained from this study can re-shape our understanding of patient selection and rational combinations for novel immune checkpoint inhibitors.

Background: Outgrowth of new blood vessels (neovascularization) allows tumors to supply themselves with oxygen and nutrients, and to rapidly metastasize throughout the body. Triple negative breast cancer (TNBC) is particularly susceptible to neovascularization. However, success with anti-angiogenics is highly variable and often patient-specific. This is particularly true as anti-angiogenics are being combined with immunotherapies. Thus, there is a huge unmet need for clinicians to test and predict clinical efficacy of anti-angiogenics at the individual patient level, prior to treatment. Methods: Here, we characterize a patient-autologous, ex-vivo tumor model, termed CANscript, as a platform to study the intratumor microvascular density (iMVD) of breast cancer samples (N=15). To profile iMVD we used immunohistochemical (IHC) analysis of CD34, an early biomarker of neovascularization. We then introduced anticancer and anti-angiogenic agents (e.g. Avastin) for 72 hours, and subsequently quantified phenotypic response to drugs by testing viability, cell death, proliferation and morphology. These quantitative data were then fed into a machine learning algorithm that provides a clinical response prediction (M-Score). Results: We determined that ex-vivo culture reliably retains baseline heterogeneity of iMVD based on expression of CD34+ nodes per visual field by IHC. Furthermore, we show that anticancer and anti-angiogenic agents will dynamically alter iMVD, ex-vivo, in a patient-specific manner. Finally, we show that prediction of clinical response using the 'M-Score' algorithm associates with diminished expression of CD34 per visual field of IHC after drug pressure. Summary: Neovascularization and iMVD are features of aggressive cancers, such as TNBC. CANscript provides a rapid assessment of clinical response to anticancer drugs, many of which induce their antitumor effect by targeting the tumor vasculature. We show that pharmacodynamics of antiangiogenics can be captured during acute ex-vivo culture under drug pressure, which associate to clinical response prediction. Therefore, we highlight the ability of CANscript as a platform to predict clinical response to anti-angiogenic drugs, and may therefore be a logical 'testing ground' to predict clinical efficacy of antiangiogenic drugs combined with immunotherapies. Citation Format: Smalley M, Alam N, Murmu N, Somashekhar S, Ulaganathan B, Thayakumar A, Maciejko L, Ganesh J, Lawson M, Gertje H, Shanthappa BU, Goldman A. A live tissue platform allows dynamic measurement of neovascularization and prediction of clinical response in human breast cancer samples, ex vivo [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P6-07-03.

Background: Predicting patient-specific clinical response to anticancer therapy is the holy grail of treatment-selection. It is now clear that response or resistance to therapy depends on the heterogeneous tumor microenvironment, which is comprised of malignant cells, normal stroma, soluble ligands, and tumor-immune contexture; attributes that are unique to each individual patient. This is particularly true for emerging anticancer drugs, such as immune checkpoint inhibitors, which recalibrate the body's own immune defense largely by modulating exhaustion of cytotoxic lymphocytes including T cells and natural killer (NK) cells. However, clinical response to therapy varies enormously. There is a critical gap in our understanding for the mechanisms that drive response or resistance to conventional drugs and immunotherapies at the individual patient level. Methods: Here, we used a fully patient-autologous, clinically-validated ex-vivo tumor model that recreates and preserves the native, patient tumor microenvironment (CANscriptTM), which incorporates an algorithm-driven method to predict clinical response to therapy (M-Score). Utilizing tissue from patients diagnosed with luminal, HER2 positive, and triple-negative (ER- PR- HER2-) breast cancers (N=10), we studied phenotypic alterations to the tumor-immune contexture under pressure of conventional standard-of-care regimens and immunotherapies including immune-checkpoint inhibitors, ex-vivo. To do this, we used a comprehensive panel of immunological assays to evaluate changes in cytotoxic lymphocytes by flow cytometry and multiplex immunohistochemistry (i.e. CD56, MHC class 1A/B, NKG2D/C, CD8, CD3, PD-1, CTLA-4, TIM-3, LAG-3, 4-1BB, granzyme A/B). In addition, we used multiplex cytokine analysis to study the soluble components of the tumor microenvironment. Results: We identified that tumor response, predicted by M-Score, correlates to increased infiltration of NK cells, which associated a pro-inflammatory cytokine signature from the tumor microenvironment. Interestingly, these evidences were concordant with induction of the tumor-expressing biomarker MICA/B, which is known to attract and recruit active NK cells. Furthermore, we determined that therapy-induced expression of protein biomarkers associated with NK cell exhaustion inversely correlated to the expression of cytotoxic granzyme B in the tumor microenvironment. Conclusions: Taken together, these data demonstrate an integral role that NK cells contribute to the antitumor effect of therapy including conventional and immuno-modulatory drugs. It further demonstrates how a novel ex-vivo platform can be harnessed to study the mechanisms of response and resistance, which couldn't otherwise be known in a drug naïve state. Such an advance in our preclinical methods to study anticancer drugs at the individual patient level can help guide treatment decisions for clinicians while simultaneously functioning as a platform to study clinical efficacy of novel and emerging agents. Citation Format: Smalley M, Shanthappa BU, Gertje H, Lawson M, Ulaganathan B, Thayakumar A, Maciejko L, Radhakrishnan P, Biswas M, Thiyagarajan S, Majumder B, Gopinath KS, K GB, Goldman A. Therapy-induced priming of natural killer cells predicts patient-specific tumor rejection in multiple breast cancer indications [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-11-04.

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