New AI model improves prediction of cancer immunotherapy success

Cancer immunotherapy drugs known as immune checkpoint inhibitors (ICIs) can be miracle drugs for cancer patients, curing some and turning deadly disease into a manageable chronic condition in others. But these drugs work for only a subset of patients, with few indications why - a knowledge gap that has detrimental effects on patient prognosis, clinical trial recruitment, and research that could lead to new therapies.

A new artificial intelligence model called COMPASS, developed by Harvard Medical School researchers and their colleagues, improves prediction of which patients are most likely to respond to ICIs. Using data from patients treated in the past, the model outperformed the best existing approaches by 8.5 percent. It makes its predictions based on patients' tumor gene activity and provides a rationale for its output.

If these results are validated in a future clinical trial, COMPASS could lead to better personalized medicine for cancer patients, more efficient trial enrollment for new therapies, and new drug targets for researchers to explore.

Results are detailed July 3 in Nature Medicine.

Marinka Zitnik, study senior author, associate professor of biomedical informatics, Blavatnik Institute at HMSICIs are an exciting therapeutic modality that has transformed cancer treatment over the past decade by engaging the immune system to fight cancer cells and destroy them. By leveraging cutting-edge AI capabilities, we can identify who would be most likely to respond to a particular ICI before that patient receives the drug."

Potentially powerful cancer therapy

The first ICIs were approved by the U.S. Food and Drug Administration in 2011. These drugs - made possible in part by research from HMS scientists - target proteins on the surface of tumor cells or T cells, including PD-L1, PD-1, and CTLA-4. These proteins can act as an invisibility cloak, shielding cancer cells from immune attack. ICIs disrupt this interaction, reopening cancer cells to being recognized and destroyed by the immune system.

For some patients with specific cancer types, ICIs have been a literal lifeline, extending survival far beyond what was considered possible in the past. For example, former U.S. president Jimmy Carter survived nine years after a diagnosis of stage IV melanoma that had spread to his liver and brain, an outcome largely credited to taking a PD-1 blocker called pembrolizumab.

However, President Carter and others who respond to ICIs represent only a fraction of patients who receive these drugs - clinical trials have shown that only 10 percent to 40 percent of patients find success with ICIs, depending on their cancer type. Nonresponders not only risk sometimes serious side effects but also waste time receiving noneffective treatment while their cancers progress.

Some machine learning approaches and biomarkers have been used to help predict which patients are most likely to respond to ICIs. For example, response has been associated with an immune-inflamed tumor microenvironment - marked by tumor infiltration of immune cells - while nonresponders' tumors are often so-called immune deserts.

But a significant number of patients respond to these drugs in unexpected ways, negatively impacting the reliability of these predictions.

"Understanding who will respond to ICIs is not a minor knowledge gap," said Zitnik, who is also associate faculty at the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University. "It is one of the central unsolved problems in oncology."

A COMPASS to point the way to responders

Zitnik and her colleagues developed COMPASS to help solve this problem. The model makes ICI response predictions by analyzing the activity of nearly 16,000 genes with known roles in immune cell states, tumor-microenvironment interaction, and signaling pathways.

COMPASS was designed with what's known as concept bottleneck transformer architecture: Rather than spitting out black-box predictions with no explanation, it provides human-interpretable results, delivering rationale for its outputs.

The researchers trained COMPASS using data from 10,184 tumors across 33 cancer types derived from the Cancer Genome Atlas, a public database containing genetic sequence and molecular data from primary cancer and matched normal samples. With this data, the AI program "learned" what gene activity correlated with responders and nonresponders to different types of ICIs.

The team then fine-tuned this training using the results from 16 clinical trials that tested the effects of different ICI regimens on seven cancer types. To evaluate the model's success, they removed individual clinical trials from this fine-tuning one by one and asked COMPASS to predict ICI responders and nonresponders in the missing trial.

Their results showed that COMPASS outperformed the best existing approach for predicting ICI response by nearly 10 percent on average. This boost in accuracy held true under a variety of conditions, including for different cancer types, ICI drugs, gene transcript sequencing platforms, and biopsy sites.

Because the results were interpretable, the team could explain unexpected results among ICI response outliers. For example, the gene expression of some nonresponders with immune-inflamed tumors correlated with processes that impeded immune response. Conversely, the gene expression signatures of responders with immune-desert tumors often suggested biological processes that encouraged other types of immune activity.

Future directions

If these results hold true in prospective clinical trials, Zitnik explained, COMPASS could find use in cancer clinics as a decision aid to help doctors decide which individuals would benefit most from ICIs.

This tool could also be a boon for ICI clinical trials by helping trial runners enroll the best-matched participants and giving those participants the greatest chance of a meaningful response.

And because COMPASS' results are interpretable, Zitnik added, they could generate new hypotheses on how the immune system fights cancer, which could in turn lead to new drug targets.

She and her colleagues plan to test whether incorporating additional data into COMPASS could further improve its accuracy. This might include details from patients' electronic health records - such as their medical history, disease comorbidities, and previous response to other drugs and treatments - or data from single-cell sequencing that could shed light on the role of different cell populations in ICI response.

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Harvard Medical School

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