AI predicts kidney cancer therapy response

An artificial intelligence (AI)-based model developed by UT Southwestern Medical Centre researchers can accurately predict which kidney cancer patients will benefit from anti-angiogenic therapy, a class of treatments that’s only effective in some cases. Their findings, published in Nature Communications, could lead to viable ways to use AI to guide treatment decisions for this and other types of cancer.

“There’s a real unmet need in the clinic to predict who will respond to certain therapies. Our work demonstrates that histopathological slides, a readily available resource, can be mined to produce state-of-the-art biomarkers that provide insight on which treatments might benefit which patients,” said Satwik Rajaram, Ph.D., Assistant Professor in the Lyda Hill Department of Bioinformatics and member of the Harold C. Simmons Comprehensive Cancer Center at UT Southwestern.

Dr. Rajaram co-led the study with Payal Kapur, M.D., Professor of Pathology and Urology and a co-leader of the Kidney Cancer Program (KCP) at the Simmons Cancer Centre.

Every year, nearly 435,000 people are diagnosed with clear cell renal cell carcinoma (ccRCC), making it the most common subtype of kidney cancer. When the disease metastasises, anti-angiogenic therapies are often used for treatment. These drugs inhibit new blood vessels from forming in tumours, limiting access to molecules that fuel tumour growth. Although anti-angiogenic drugs are widely prescribed, fewer than 50% of patients benefit from them, Dr. Kapur explained, exposing many to unnecessary toxicity and financial burden.

“No biomarkers are clinically available to accurately assess which patients are most likely to respond to anti-angiogenic drugs”, she added, “although a clinical trial conducted by Genentech suggested that the Angioscore (a test that assesses the expression of six blood vessel-associated genes) may have promise. However, this genetic test is expensive, is hard to standardise among clinics, and introduces delays in treatment. It also tests a limited part of the tumour, and ccRCC is quite heterogeneous, with variable gene expression in different regions of the cancer.”

To overcome these challenges, Drs. Kapur, Rajaram and their colleagues at the KCP developed a predictive method using AI to assess histopathological slides—thinly cut tumour tissue sections stained to highlight cellular features. These slides are nearly always part of a patient’s standard workup at diagnosis, and their images are increasingly available in electronic health records, said Dr. Rajaram, also Assistant Professor in the Centre for Alzheimer’s and Neurodegenerative Diseases and the Department of Pathology.

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