LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features Journal Article

   

Authors:	Chang, T. G.; Cao, Y.; Sfreddo, H. J.; Dhruba, S. R.; Lee, S. H.; Valero, C.; Yoo, S. K.; Chowell, D.; Morris, L. G. T.; Ruppin, E.
Article Title:	LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features
Abstract:	Despite the revolutionary impact of immune checkpoint blockade (ICB) in cancer treatment, accurately predicting patient responses remains challenging. Here, we analyzed a large dataset of 2,881 ICB-treated and 841 non-ICB-treated patients across 18 solid tumor types, encompassing a wide range of clinical, pathologic and genomic features. We developed a clinical score called LORIS (logistic regression-based immunotherapy-response score) using a six-feature logistic regression model. LORIS outperforms previous signatures in predicting ICB response and identifying responsive patients even with low tumor mutational burden or programmed cell death 1 ligand 1 expression. LORIS consistently predicts patient objective response and short-term and long-term survival across most cancer types. Moreover, LORIS showcases a near-monotonic relationship with ICB response probability and patient survival, enabling precise patient stratification. As an accurate, interpretable method using a few readily measurable features, LORIS may help improve clinical decision-making in precision medicine to maximize patient benefit. LORIS is available as an online tool at https://loris.ccr.cancer.gov/. Chang et al. performed a pan-cancer multimodal data integration analysis and devised a model, LORIS, that can predict objective responses to immunotherapy and patient survival across many cancer types and allow for patient stratification.
Keywords:	neutrophil; immunotherapy; solid tumors; criteria; response evaluation; cancer; prognosis; tumor mutational burden
Journal Title:	Nature Cancer
Volume:	5
Issue:	7
ISSN:	2662-1347
Publisher:	Nature Research
Date Published:	2024-08-01
Start Page:	1158
End Page:	1175
Language:	English
ACCESSION:	WOS:001238211600002
DOI:	10.1038/s43018-024-00772-7
PROVIDER:	wos
PUBMED:	38831056
PMCID:	PMC11962634
Notes:	Article -- MSK corresponding author is Luc Morris -- Source: Wos

https://synapse.mskcc.org/synapse/works/has_related_data_chk/218494