Using the pairwise relationships between 28 immune-related genes and clinical outcome data from neuroblastoma patients, Auslander et al. developed IMPRES, an immuno-predictive score for immune checkpoint blockade (ICB)-treated melanoma based on 15 predictive features. IMPRES was validated on nine published datasets of ICB-treated melanoma and its predictive ability was tested on a newly generated dataset. IMPRES captured almost all true responders and misclassified fewer than half of non-responders. High IMPRES scores correlated with improved PFS and OS, outperforming other transcriptome-based predictions.
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 datasets(1-6) 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.
Author Info: (1) Center for Bioinformatics and Computational Biology, Department of Computer Science, University of Maryland, College Park, MD, USA. Cancer Data Science Lab (CDSL), National Can
Author Info: (1) Center for Bioinformatics and Computational Biology, Department of Computer Science, University of Maryland, College Park, MD, USA. Cancer Data Science Lab (CDSL), National Cancer Institute, National Institutes of Health, Rockville, MD, USA. (2) Molecular and Cellular Oncogenesis Program and Melanoma Research Center, The Wistar Institute, Philadelphia, PA, USA. (3) Center for Bioinformatics and Computational Biology, Department of Computer Science, University of Maryland, College Park, MD, USA. Cancer Data Science Lab (CDSL), National Cancer Institute, National Institutes of Health, Rockville, MD, USA. (4) Massachusetts General Hospital Cancer Center, Boston, MA, USA. (5) Massachusetts General Hospital Cancer Center, Boston, MA, USA. (6) Department of Surgery, Massachusetts General Hospital, Boston, MA, USA. (7) Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA. (8) Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA. (9) Center for Bioinformatics and Computational Biology, Department of Computer Science, University of Maryland, College Park, MD, USA. Cancer Data Science Lab (CDSL), National Cancer Institute, National Institutes of Health, Rockville, MD, USA. (10) Massachusetts General Hospital Cancer Center, Boston, MA, USA. (11) Department of Surgery, Massachusetts General Hospital, Boston, MA, USA. (12) Massachusetts General Hospital Cancer Center, Boston, MA, USA. (13) Molecular and Cellular Oncogenesis Program and Melanoma Research Center, The Wistar Institute, Philadelphia, PA, USA. (14) Center for Bioinformatics and Computational Biology, Department of Computer Science, University of Maryland, College Park, MD, USA. eyruppin@gmail.com. Cancer Data Science Lab (CDSL), National Cancer Institute, National Institutes of Health, Rockville, MD, USA. eyruppin@gmail.com.
