Scientific Session C: Solid Tumors Immunotherapeutics

Scientific Session C: Solid Tumors Immunotherapeutics

Talk 1: Epidemiology of Liver Cancer, a Population-based Case-control Study in Jiangsu Province, China

Zuo-Feng Zhang, Xing Liu, Ming Wu, Jian-Yu Rao, Jin-Yi Zhou, Jin-Kou Zhao

UCLA Department of Epidemiology, Fielding School of Public Health

Background: Although most of risk factors have been identified, the potential effect modification among these factors have not yet fully evaluated in Chinese population. We conducted a population-based case-control study of liver cancer with 2,011 cases and 7,933 controls in Jiangsu province, China from 2003 to 2010. Data on exposure to major risk or protective factors were collected by face-to-face interview. Unconditional logistic regression was used to examine the associations and effect modifications. We found that tobacco smoking, alcohol drinking, HBV infection, family history of cancer, as well as history of raw water drink were associated with liver cancer. We have identified effect modifications among these risk factors on the risk of liver cancer suggesting that more targeted intervention focused on sub-population for risk reduction and screening should be implemented.

Talk 2: An ex-vivo 3D platform to evaluate the efficacy of immunotherapy in melanoma patients

Dr Antonella Bresin

Istituto Dermopatico dell’Immacolata, Rome, Italy

INTRODUCTION: Despite significant improvements in advanced melanoma therapy, there is still a pressing need for innovative therapies. Here, we optimized a method for testing the drug sensitivity of patient-derived organotypic cultures (PDOCs) and evaluated whether this preclinical model could be a valid tool for rapidly determining the patient’s tumor response profile to approved and alternative therapies. METHODS PDOCs were generated from melanoma metastases after mechanical or enzymatic dissociation. Tissue fragments (<100µm) were harvested, embedded in 3D collagen beads (1.5 mg/mL type I collagen), and cultured in a 96-well plate for 6 days, maintaining tumor and stromal viability. More than 20 primary cell lines from the same samples were also stabilized. The melanoma cells or the PDOCs were left untreated or treated with the patient’s own therapy or with alternative drugs, and the efficacy of treatments was evaluated by MTT assay and flow cytometry. CD8+ T cell infiltration and cytokine secretion were also investigated. RESULTS The first experiments were set up to validate PDOCs as predictive preclinical models of patient response to therapy. We tested approved melanoma therapies (i.e., the combination of BRAF and MEK inhibitors, or anti-PD-1 antibodies) on PDOCs from 18 different patients, showing 83% match. Furthermore, drug response in PDOCs was comparable to the clinical response of matched patients undergoing the tested therapy, demonstrating PDOCs are reliable predictive tools. Then, we used primary cell lines to screen a pool of eigth different targeted agents selected through functional screening of a bioactive library of 512 compounds on patient-derived xenografts. The three best-performing compounds were subsequently studied in PDOCs as single agents or in combination with immunotherapy. We found the CDK inhibitor was the more effective compound as single agent in BRAFmut and NRASmut samples. Of note, in some cases, the combination with an anti-PD-1 inhibitor significantly improved the efficacy of one or more of the three drugs. CONCLUSIONS We optimized and validated PDOCs as personalized preclinical models suitable for assessingthe drug sensitivity/resistance profile of individual patient-derived melanomas. By retaining tumor stromal components and heterogeneity, PDOCs could be used to predict patients’ clinical response to currently approved agents, helping oncologists expedite decision-making when several treatment options are possible. Furthermore, the model could be used to evaluate the effectiveness of alternative treatments on tumors resistant to approved therapies, and to explore new drug combinations.