Clinical tool predicts adoptive therapy response in eye cancer patients

Researchers from the University of Pittsburgh Medical Center (UPMC) have developed a predictive model to forecast metastatic uveal melanoma patients’ response to adoptive therapy, according to a study published today in Nature Communications.

The research team indicated that uveal melanoma is resistant to conventional immunotherapies, but that adoptive therapy – a type of immunotherapy in which a patient’s T-cells are extracted, multiplied in a laboratory and reinfused – provides a promising approach for treating the disease.

“The dogma was that uveal melanoma is a ‘cold’ cancer, meaning that T cells can’t get into these tumors,” explained senior author Udai Kammula, MD, associate professor of surgery at Pitt and director of the Solid Tumor Cell Therapy Program at UPMC Hillman Cancer Center, in a press release shared with HealthITAnalytics. “We show that T cells are in fact infiltrating metastases and they’re getting activated, but they’re just sitting there in a dormant state because something in the tumor is suppressing them. Adoptive therapy allows us to rescue these cells from the suppressive tumor microenvironment and successfully treat some patients.”

Uveal melanoma originates in the eye’s uveal tract, but the cancer often spreads aggressively throughout the body, particularly to the liver. Upon metastasis, the cancer is extremely difficult to treat, leading to poor prognoses for many patients.

Researchers have been working to uncover why uveal melanoma is resistant to immunotherapy, unlike other types of melanoma.

“Cutaneous melanoma, which affects the skin, is the poster child of immunotherapy. It responds incredibly well to immune checkpoint inhibitor drugs,” said Kammula. “None of these conventional immunotherapies work for uveal melanoma, but we hadn’t known why — until now.”

In previous work, the research team successfully utilized adoptive therapy to surgically remove tumor tissues from 19 uveal melanoma patients, which were used to grow T cells in the lab. Upon reinfusion, roughly 35 percent of patients experienced either partial or complete regression of their cancer, contradicting the prevailing theory that cancer-fighting cells – known as tumor-infiltrating lymphocytes (TILs) – are not present in uveal melanoma.

Despite these promising findings, researchers still did not know why immune checkpoint inhibitors remain ineffective in treating the disease.

The team investigated this phenomenon using data from a large repository of uveal melanoma samples, corresponding tissues and clinical information.

After analyzing 100 metastases from 84 patients, the researchers determined that over half of these tumors had a significant number of T cells. From there, they conducted single-cell ribonucleic acid (RNA) sequencing to assess gene expression in nearly 100,000 cells across six metastases.

The analysis revealed that the TILs in some tumors were activated and capable of attacking tumor cells in a lab setting, but that something was preventing them from proliferating within the tumor itself.

“We found that TILs from metastatic uveal melanoma have the potential to attack the tumor, but something in the tumor microenvironment is shutting them down, so they’re in a dormant, or quiescent, state,” Kammula stated. “By liberating these cells from the suppressive environment and growing them in the lab, we can rescue their tumor-fighting capacity when infused back into the patient.” 

However, since this type of therapy was demonstrated to only be effective for some patients, the research team sought to develop an approach to help predict which patients were likely to respond well to adoptive therapy.

The tool, known as the Uveal Melanoma Immunogenic Score (UMIS), works by measuring the activity of over 2,000 genes expressed by tumor, immune and other types of cells in the tumor microenvironment. The higher the UMIS value, the more potent the tumor’s TILs.

Across the 100 metastases analyzed in the study, UMIS scores ranged from 0.114 to 0.347. When looking at participants from the earlier study who had undergone adoptive therapy, the researchers found that those with higher UMIS scores had improved tumor regression.

Further, patients whose metastases scored above 0.246 had significantly improved progression-free survival and overall survival compared to others with lower scores, highlighting the potential predictive value of UMIS scores.

“If a patient’s UMIS level is below this threshold, we think that adoptive therapy is not appropriate. Using a biopsy to calculate a patient’s UMIS could help avoid futile therapies and unnecessarily subjecting patients to invasive operations,” said Kammula. “But the immune system is not static. UMIS offers a window into the tumor that could also help us find the optimal time to treat a patient with adoptive therapy, like picking a fruit when it’s at its ripest.”

The researchers are now working to evaluate UMIS in an ongoing TIL therapy clinical trial for patients with metastatic uveal melanoma, and they are building a pan-cancer version of UMIS to help forecast how well patients with any type of cancer may respond to adoptive therapy.

Predictive analytics approaches, like the one detailed in this research, continue to show promise in improving cancer care.

Last week, researchers revealed that machine learning models can use patient-generated health data from wearable devices to accurately forecast an unplanned hospitalization event during concurrent chemoradiotherapy (CRT).

The toxic effects of CRT can lead to significant treatment interruptions and hospitalizations, resulting in increased costs and reduced treatment efficacy.

The research team indicated that physical activity monitoring data could help flag CRT patients at higher risk for hospitalization and sought to test the hypothesis via machine learning analysis.

The models incorporated daily step counts from the wearable devices of cancer patients undergoing CRT. Using a combination of patient-generated activity data and clinical information, the tools successfully predicted risk of short-term hospitalization.