Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma
By Noam Auslander, Gao Zhang, J. S. Lee, Dennie T. Frederick, B. Miao, Tabea Moll, Tian Tian, Zhi Wei, Sanna Madan, R. Sullivan and 4 more. Nature Medicine, 2018
622 citations recorded by the source corpus.
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 datasets1–6 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. A gene signature identified in spontaneously regressing neuroblastoma identifies responders to immune checkpoint blockade among patients with melanoma with accuracy superior to previously reported biomarkers.
Subfields: Immunology, Neurology, Oncology
View Paper: https://www.semanticscholar.org/paper/416da46148b93b34708ba0bd38fe35abc3413bd3
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Citations in the corpus
- Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic (2018, background): “The T-cell-inflamed gene expression profile (GEP) [26], immune gene expression signatures [27, 28], as well as description of the microbiome [29–31] also represent emerging predictive biomarkers.”
- Network resilience (2020, background): “Plenty of efforts have been poured into identifying biomarkers for predicting the response to immune checkpoint blockade in cancer [476, 477, 478].”
- Integrative molecular and clinical modeling of clinical outcomes to PD-1 blockade in metastatic melanoma patients (2019, neutral): “We further evaluated various transcriptomic signatures 18,19,21,34-40 that have been proposed and demonstrated in various settings to be associated with response to immunotherapy (Methods), but found no statistically significant differences (p 0.05) overall in these signatures between responders vs…”