Looking at ‘RCHOP+X’ trials, we used clinical data on each ‘drug X’ to predict the clinical trial results.
Only Pola-R-CHP, and Tucidinostat plus R-CHOP, were expected to succeed, and indeed they did
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10/n

We calibrated the model to reproduce Progression-Free Survival for the CHOP and RCHOP regimens for Diffuse Large B-Cell Lymphoma.

Simulated tumor population shrinkage agreed well with observed changes in circulating tumor DNA after the first cycle of RCHOP:

9/n

From this ‘bottom-up’ model of tumor heterogeneity, simulating treatment responses in a cohort of patients produces a Kaplan-Meier curve of Progression-Free Survival:

8/n

This extends to combination therapy by using a different dimension of heterogeneity for each drug

This way, patients and cells vary in their sensitivity to different drugs; for example, some patients can be more sensitive to one drug than another, or sensitive to both, etc.

7/n

Extending to patient variability, a group of patients - say in a clinical trial - also have a distribution of drug response phenotypes, with each patient’s cancer containing a range of cellular heterogeneity around the average drug sensitivity of that individual.

6/n

In this model of heterogeneity as a distribution of states, each cycle of chemotherapy progressively shifts the distribution to increasingly drug-resistant states

5/n

Many insightful models of tumor heterogeneity described drug-sensitive and drug-resistant subpopulations.

Based on clone-tracing data, we modelled cellular heterogeneity as a distribution of sensitivity phenotypes, reflecting many complex influences on drug response

4/n