# Novel mixed cancer-cell models designed to capture inter-patient tumor heterogeneity for accurate evaluation of drug combinations

## TL;DR

Researchers created mixed cancer-cell models to better reflect patient tumor diversity, improving drug combination testing accuracy.

## Contribution

The novel mixed-cell models capture inter-patient tumor heterogeneity for more accurate preclinical drug combination evaluation.

## Key findings

- Mixed-cell models showed in-vitro drug responses matching known clinical efficacy of tested combinations.
- Computationally predicted drug combinations demonstrated preclinical efficacy in heterogeneous models.
- Traditional screening methods failed to detect drug efficacy in mixed-cell models.

## Abstract

Disease heterogeneity across a diverse patient cohort poses challenges to cancer drug development due to inter-patient variability in treatment responses. However, current preclinical models fail to depict inter-patient tumor heterogeneity, leading to a high failure rate when translating preclinical leads into clinical successes.

We integrated the expression profiles of prostate cancer (PC) lines and castration-resistant PC (CRPC) patient tumors to identify cell-lines that transcriptomically match distinct tumor subtypes in a clinical cohort. Representative cell-lines were co-cultured to create “mixed-cell” models depicting inter-patient heterogeneity in CRPC, which were employed to assess drug combinations.

When drug combinations previously tested in CRPC clinical cohorts, were assessed to establish proof-of-concept, in-vitro responses measured in our models concurred with their known clinical efficacy. Additionally, novel drug combinations computationally predicted to be efficacious in heterogeneous tumors, were evaluated. They demonstrated preclinical efficacy in the mixed-cell models, suggesting they will likely benefit heterogeneous patient cohorts. Furthermore, we showed that the current practice of screening cell-lines/xenografts separately and aggregating their responses, failed to detect their efficacy.

We believe that the application of our models will enhance the accuracy of preclinical drug assessment, thereby improving the success rate of subsequent clinical trials.