Abstract 5086: Transcriptomic analysis in a renal cancer PDX model enables the deconvolution of additive and synergistic effects of six different standard of care compounds with anti-PD-1 treatment (2023)

Abstract Identifying how to optimally combine immunotherapies with other available anti-cancer therapies is a major challenge in oncology. We have utilized an implantable microdevice performing cassette (IMD) microdosing that measures intratumor drug responses and anti-tumor immunity for six agents in parallel. This approach was combined with the systemic administration of anti-PD1 treatment to examine whether immunogenic cell death (ICD) induced by a given drug potentiates the immunotherapy’s anti-tumor effect. Local tumor response was measured by multiplex immunohistochemistry (IHC) and transcriptomic analyses. The study was performed in a humanized mouse model of a clear cell renal cancer, patient derived xenograft (PDX) RXF488. RXF488 was implanted subcutaneously in 30 NSG mice. Animals were stratified into 6 groups with n= 4-6. Humanization was performed by the intravenous injection of 5 × 10e6 human peripheral blood mononuclear cells prior to the first treatment. Systemic aPD1 treatment was applied in the presence and absence of the microdevice loaded with six different drugs. Control groups received the empty IMD in the presence or absence of PBMC. Several agents showed a significant increase in apoptosis induction when aPD1 was added: The largest increase was observed for the panRAF inhibitor LXH254, Sorafenib, Oxaliplatin and Doxorubicin. The increased efficacy from immunotherapy administration has a strong positive correlation with increased induction of ICD in the tumor microenvironment determined by CD11b, ICAM-1 and MHC-II expression: drugs that showed the highest increase in apoptosis when combined with aPD-1 showed an increased likelihood for markers associated with ICD, namely Oxaliplatin and LXH254. A transcriptomic analysis on the tumor tissue revealed nine different clusters. The implantation of the empty device modulated the expression data in a way that these samples clustered together and separate from the untreated tumor samples. Other clusters were defined by presence of absence of aPD1 and the different local treatments. A more focused analysis using a subset of 19 genes described to be predictive for ICD in solid cancer confirmed the activity of LXH254 but identified Sunitinib in combination with anti PD1 as another potential inducer of ICD. An induction of ICD by Oxaliplatin as indicated by the IHC results could not be confirmed by the expression data. Overall treatment arms the systemic treatment with anti-PD1 led to an increased expression of the above mentioned 19 genes. Our results demonstrate that local tumor response signatures of ICD can be used to systemically identify synergistic combinations of a range of drugs with immunotherapy on a tumor specific basis. A deeper dive into the transcriptomic data will help to identify other predictive biomarker for efficacy beyond ICD. Citation Format: Kanstantsin Lashuk, Eva Oswald, Oliver Jonas, Julia Schueler. Transcriptomic analysis in a renal cancer PDX model enables the deconvolution of additive and synergistic effects of six different standard of care compounds with anti-PD-1 treatment. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5086.