With much gratitude for discussions from Chris Chidley, Ash Alizadeh, Palmer lab members and others, and support from NCI, NIGMS, @unclineberger.bsky.social,
@unc-phco.bsky.social and UNC Computational Medicine Program

13/13

This model provides quantitative insight into how combination therapy overcomes heterogeneity within and between tumors to cure many patients with Large B-Cell Lymphoma.

We hope it is a useful tool to design new curative-intent combinations using clinical data on new drugs.

12/n

Importantly, first-author Amy Pomeroy had predicted the success of Pola-R-CHP *before* the trial read out, as she reported from the model prototype back in 2021:
www.amypomeroy.com/post/predicting-the-results-of-the-polarix-trial

11/n

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
asco.org/abstracts-pr...
nejm.org/doi/full/10....

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

We built a model that unifies intra-tumor and inter-patient heterogeneity in drug sensitivity to understand the clinical efficacy of curative-intent combination therapy for Large B-Cell Lymphoma.

3/n

Cell-to-cell and patient-to-patient heterogeneity both have a role in the success of drug combinations.

While inter-patient variation can explain better response rates of combos for incurable cancers, CURES need a regimen to also overcome cellular heterogeneity and evolution

2/n