In a study, researchers developed a gene expression predictor that can indicate whether melanoma in a specific patient is likely to respond to treatment with immune checkpoint inhibitors, a novel type of immunotherapy. The predictor was developed by Dr Noam Auslander, with other researchers in the Centre for Cancer Research (CCR) at the National Cancer Institute (NCI), part of the National Institutes of Health, and colleagues at Harvard University, the University of Pennsylvania and the University of Maryland.
"There is a critical need to be able to predict how cancer patients will respond to this type of immunotherapy," said Dr Eytan Ruppin, of NCI's newly established Cancer Data Science Laboratory, who led the study. "Being able to predict who is highly likely to respond and who isn't will enable us to more accurately and precisely guide patients' treatment."
Treatment with checkpoint inhibitors is effective for some patients with late-stage melanoma and certain other types of cancer. However, not all patients with melanoma respond to this treatment, and it can have considerable side effects. Being able to predict which patients are likely to respond and which are not would be a major clinical advance. But developing a predictor of response has been challenging, partly because of the limited number of patients who have received this relatively new form of treatment.
In this study, the investigators developed a predictor by first looking for clues in cases where the immune system appears to mount an unprompted, successful immune response to cancer, causing spontaneous tumour regression. They analysed neuroblastoma, a type of cancer that frequently undergoes spontaneous regression in young children, and were able to define gene expression features that separated patients with non-regressing disease from those with regressing disease.
These features enabled the researchers to compute what they called an IMmuno-PREdictive Score (IMPRES) for each patient sample. The higher the IMPRES score for a sample, the more likely it was to undergo spontaneous regression. To see if IMPRES could be used to predict melanoma patients' responses to checkpoint inhibitors, the authors analysed 297 samples from several studies. They found that the predictor could identify nearly all patients who responded to the inhibitors and more than half of those who did not, making it significantly superior to all other existing published predictors. Importantly, unlike other existing predictors, IMPRES was accurate across many different melanoma patient data sets.
"We now know that immunotherapy works, but we do not understand well why a particular therapy will work for some patients but not others," said Tom Dr Misteli, director of CCR at NCI. "This study is a step forward in developing tools to address this challenge, which is of practical importance to patients."
Ruppin said that while the results obtained are encouraging, they will need to be carefully evaluated in additional patient datasets. The authors also wrote that further study of this kind of predictor is now warranted in other cancer types for which checkpoint inhibitors have been approved.
Abstract
Immune checkpoint blockade (ICB) therapy provides remarkable clinical gains and has been very successful in treatment of melanoma. However, only a subset of patients with advanced tumors currently benefit from ICB therapies, which at times incur considerable side effects and costs. Constructing predictors of patient response has remained a serious challenge because of the complexity of the immune response and the shortage of large cohorts of ICB-treated patients that include both ‘omics’ and response data. Here we build immuno-predictive score (IMPRES), a predictor of ICB response in melanoma which encompasses 15 pairwise transcriptomics relations between immune checkpoint genes. It is based on two key conjectures: (i) immune mechanisms underlying spontaneous regression in neuroblastoma can predict melanoma response to ICB, and (ii) key immune interactions can be captured via specific pairwise relations of the expression of immune checkpoint genes. IMPRES is validated on nine published datasets1,2,3,4,5,6 and on a newly generated dataset with 31 patients treated with anti-PD-1 and 10 with anti-CTLA-4, spanning 297 samples in total. It achieves an overall accuracy of AUC = 0.83, outperforming existing predictors and capturing almost all true responders while misclassifying less than half of the nonresponders. Future studies are warranted to determine the value of the approach presented here in other cancer types.
Authors
Noam Auslander, Gao Zhang, Joo Sang Lee, Dennie T Frederick, Benchun Miao, Tabea Moll, Tian Tian, Zhi Wei, Sanna Madan, Ryan J Sullivan, Genevieve Boland, Keith Flaherty, Meenhard Herlyn, Eytan Ruppin
[link url="https://www.cancer.gov/news-events/press-releases/2018/melanoma-immunotherapy-predictor"]NIH/National Cancer Institute material[/link]
[link url="https://www.nature.com/articles/s41591-018-0157-9"]Nature Medicine abstract[/link]