Background Despite accounting for the majority of available treatment options, there are currently no diagnostic tests capable of predicting combination immunotherapy response in kidney cancer. We have developed a fully human 3D immune-oncology (IO) model for clinical drug efficacy prediction through multivariate analysis of the tumor-immune microenvironment (TiME) combining functional assays, artificial intelligence and multi-omics. These immune-microtumors recapitulate a patient’s unique pathophysiology and allow 3D spatio-temporal analyses of functional metrics at both bulk and single-cell resolutions.

Methods Renal cell carcinoma (RCC) resections and matched blood were collected and processed for single cell isolation of tumor and PBMCs (N=20, PEAR-TREE ISRCTN10001405). Flow cytometry (FC) and immunofluorescence (IF) were used to characterize TiME cell subpopulations (cancer, immune, stromal, etc.) in pellets and 3D cultures. Target (tumor) and effector cells (PBMCs and CD8+) were stained with fluorescent probes, activated using ɑCD3 and encapsulated in TiME-mimicking 3D matrices. 3D immune-microtumors were treated with FDA-approved regimens including immunotherapies (ipilimumab, nivolumab, pembrolizumab) and receptor-tyrosine kinase inhibitors (TKIs) (cabozantinib, lenvatinib, axitinib) as monotherapies or combination therapies. Differential drug efficacies were functionally quantified over 4 days using 3D confocal microscopy and computer vision pipelines to extract cell behavior metrics such as immune cell infiltration, immune/tumor cell migration speed, and tumor killing/viability.

Results We devised a specialized RCC hydrogel formulation with superior viability compared to Matrigel^TM alone (N=7). FC/IF/RNAseq were used to quantify relative cell subpopulations after isolation and show protein/transcriptional signature retention after 3D culture (N=10). The platform was conducive to the testing of various drugs with varying MoAs including immunotherapies, TKIs, targeted therapies and cell therapies. Treatment with pembrolizumab led to 26% higher PBMC infiltration, 14% higher CD8+ infiltration and 15% increased tumor cell death compared to control. When in combination, pembrolizumab reduced cell viability by 10% and 19% with lenvatinib and axitinib respectively (N=1). Population analyses revealed intra-patient response heterogeneity (N=7). Furthermore, supporting Luminex and characterisation studies allowed for the demonstration and validation of the MoA and ex vivo dose determination of specific treatment classes, as well as optimisation and fine-tuning of the platform for future application.

Conclusions In conclusion, we showed MoA-agnostic, patient-dependent responses to FDA-approved clinical treatment regimens via molecular and functional spatio-temporal analyses. Further work is currently underway validating specific MoAs in our model to enable our advanced RCC study PEAR-TREE2 (NCT06264479) as well as our studies in breast NCT05435352, NCT06182306 and brain NCT06038760 malignancies.

Ethics Approval The study was approved by the Health Research Authority, approval number 22/YH/0068. All participants gave informed consent before being enrolled in the trial.