Abstract P5-11-04: Therapy-induced priming of natural killer cells predicts patient-specific tumor rejection in multiple breast cancer indications

Abstract

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.

Evolution of Organoids in Oncology

“…The major biological complexities make the treatment methods difficult which in turn varies with the metastatic sites among individual patients [7]. The microenvironment of tumor cells includes normal stroma, malignant cells, and immune response based on every individual [8]. The durability and patient-to-patient response extremely varies based on each patient and hence very difficult to predict the consequences [9].…”