# Multimodal machine learning models identify chemotherapy drugs with prospective clinical efficacy in dogs with relapsed B-cell lymphoma

## TL;DR

A machine learning model helps choose effective chemotherapy drugs for dogs with relapsed B-cell lymphoma, leading to better treatment outcomes.

## Contribution

Multimodal machine learning models predict effective chemotherapy drugs for relapsed B-cell lymphoma in dogs using tumor profiling data.

## Key findings

- Dogs receiving model-predicted effective drugs had higher overall and complete response rates.
- Duration of complete response and survival times were longer for dogs treated with predicted effective drugs.
- Outcomes were better than historical controls and other dogs in the study.

## Abstract

Dogs with B-cell lymphoma typically respond well to first-line CHOP-based chemotherapy, but there is no standard of care for relapsed patients. To help veterinary oncologists select effective drugs for dogs with lymphoid malignancies such as B-cell lymphoma, we have developed multimodal machine learning models that integrate data from multiple tumor profiling modalities and predict the likelihood of a positive clinical response for 10 commonly used chemotherapy drugs. Here we report on clinical outcomes that occurred after oncologists received a prediction report generated by our models. Remarkably, we found that dogs that received drugs predicted to be effective by the models experienced better clinical outcomes by every metric we analyzed (overall response rate, complete response rate, duration of complete response, patient survival times) relative to other dogs in the study and relative to historical controls.