Combined tumor and immune signals from genomes or transcriptomes predict outcomes of checkpoint inhibition in melanoma

Freeman, Samuel S.; Sade-Feldman, Moshe; Kim, Jaegil; Stewart, Chip; Gonye, Anna L.K.; Ravi, Arvind; Arniella, Monica; Gushterova, Irena; LaSalle, Thomas J.; Blaum, Emily M.; Yizhak, Keren; Frederick, Dennie T.; Sharova, Tatyana; Leshchiner, Ignaty; Elagina, Liudmila; Spiro, Oliver; Livitz, Dimitri; Rosebrock, Daniel; Aguet, François; Carrot-Zhang, Jian; Ha, Gavin; Lin, Ziao; Chen, Jonathan; Barzily-Rokni, Michal; Hammond, Marc R.; Eckstaedt, Hans C Vitzthum von; Blackmon, Shauna; Jiao, Yunxin; Gabriel, Stacey; Lawrence, Donald P.; Duncan, Lyn M.; Stemmer‐Rachamimov, Anat; Wargo, Jennifer A.; Flaherty, Keith T.; Sullivan, Ryan J.; Boland, Genevieve M.; Meyerson, Matthew; Getz, Gad; Hacohen, Nir

doi:10.1016/j.xcrm.2021.100500

Cited by 15 publications

(25 citation statements)

References 107 publications

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“…9 ). Interestingly, we find only moderate correlation between the leukocyte-proliferation regulation classifier scores with B- and T-cell burden scores (BCB and TCB, respectively) that have been published recently 57 , supporting an independent prognostic value (Supplementary Fig. 11 ).…”

Section: Discussionsupporting

confidence: 75%

Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma

Patterson

Auslander

2022

Nat Commun

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Immune Checkpoint Inhibitor (ICI) therapy has revolutionized treatment for advanced melanoma; however, only a subset of patients benefit from this treatment. Despite considerable efforts, the Tumor Mutation Burden (TMB) is the only FDA-approved biomarker in melanoma. However, the mechanisms underlying TMB association with prolonged ICI survival are not entirely understood and may depend on numerous confounding factors. To identify more interpretable ICI response biomarkers based on tumor mutations, we train classifiers using mutations within distinct biological processes. We evaluate a variety of feature selection and classification methods and identify key mutated biological processes that provide improved predictive capability compared to the TMB. The top mutated processes we identify are leukocyte and T-cell proliferation regulation, which demonstrate stable predictive performance across different data cohorts of melanoma patients treated with ICI. This study provides biologically interpretable genomic predictors of ICI response with substantially improved predictive performance over the TMB.

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Section: Discussionsupporting

confidence: 75%

Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma

Patterson

Auslander

2022

Nat Commun

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“…In agreement with these findings, ten selected IMMUNETS genes that are upregulated in both MEL_PROG and VALID_PROG therapy response groups also portend a good prognosis in our analysis of MEL_TCGA ( Table 3 , Supplementary Figure S5 ). BIO_13 was further validated in a metastatic melanoma cohort ( n = 174, MIXED_ICI) who received either single or combined immune checkpoint inhibitors and was drawn from four separate studies [ 90 , 91 , 92 , 93 ]. MIXED_ICI includes diverse primary tumour sites, for instance, the uveal tract and skin are primary tumour sites in the Samuel et al study [ 90 ].…”

Section: Resultsmentioning

confidence: 99%

“…BIO_13 was further validated in a metastatic melanoma cohort ( n = 174, MIXED_ICI) who received either single or combined immune checkpoint inhibitors and was drawn from four separate studies [ 90 , 91 , 92 , 93 ]. MIXED_ICI includes diverse primary tumour sites, for instance, the uveal tract and skin are primary tumour sites in the Samuel et al study [ 90 ]. Patients in MIXED_ICI also received several different immune checkpoint inhibitors; for example, in the Gide et al study, 63 patients received anti-PD1 immunotherapy (Nivolumab/Pembrolizumab) and 57 patients were treated with combined anti-PD1 and anti-CTLA-4 (Ipilimumab with Nivolumab/Pembrolizumab) [ 93 ].…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, in a mixed immunotherapy cohort (MIXED_ICI), six of the BIO_13 stratified patients with diverse treatments (ADAM28, CD247, IKZF3, CIITA, CD79A, IL2RB). Only 5% of TCGA melanoma patients were identified as stage IV (metastatic), while MIXED_ICI represents metastatic melanoma patients [ 23 , 90 , 91 , 92 , 93 ]. Multivariate regularised Cox regression with stepwise feature selection produced a model containing the variables age, tumour stage, CIITA, and IKZF3 with significant p-values individually ( Supplementary Table S4 ), demonstrating added value for these genes over the clinical variables examined, and its significance was validated in a testing set of MEL_TCGA.…”

Section: Discussionmentioning

confidence: 99%

“…For univariate analysis, risk groups were defined by Gaussian mixture modelling (GMM) with unsupervised selection of cardinality [ 118 ] using the BIO_13 gene expression values in MEL_TCGA and MIXED_ICI ( n = 174) [ 90 , 91 , 92 , 93 ]. Log-rank test p-values with false discovery rate correction [ 89 ] identified significant genes ( q < 0.05).…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Immune Cell Networks Uncover Candidate Biomarkers of Melanoma Immunotherapy Response

McGleave

Overton

2022

JPM

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The therapeutic activation of antitumour immunity by immune checkpoint inhibitors (ICIs) is a significant advance in cancer medicine, not least due to the prospect of long-term remission. However, many patients are unresponsive to ICI therapy and may experience serious side effects; companion biomarkers are urgently needed to help inform ICI prescribing decisions. We present the IMMUNETS networks of gene coregulation in five key immune cell types and their application to interrogate control of nivolumab response in advanced melanoma cohorts. The results evidence a role for each of the IMMUNETS cell types in ICI response and in driving tumour clearance with independent cohorts from TCGA. As expected, ‘immune hot’ status, including T cell proliferation, correlates with response to first-line ICI therapy. Genes regulated in NK, dendritic, and B cells are the most prominent discriminators of nivolumab response in patients that had previously progressed on another ICI. Multivariate analysis controlling for tumour stage and age highlights CIITA and IKZF3 as candidate prognostic biomarkers. IMMUNETS provide a resource for network biology, enabling context-specific analysis of immune components in orthogonal datasets. Overall, our results illuminate the relationship between the tumour microenvironment and clinical trajectories, with potential implications for precision medicine.

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A Predictive Network‐Based Immune Checkpoint Blockade Immunotherapeutic Signature Optimizing Patient Selection and Treatment Strategies

Zhang,

Yang,

Yang

et al. 2024

Small Methods

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Immune checkpoint blockade (ICB) therapy has brought significant advancements to the field of oncology. However, the diverse responses among patients highlight the need for more accurate predictive tools. In this study, insights are drawn from tumor‐immunology pathways, and a novel network‐based ICB immunotherapeutic signature, termed ICBnetIS, is constructed. The signature is derived from advanced biological network‐based computational strategies involving co‐expression networks and molecular interactions networks. The efficacy of ICBnetIS is established through its association with enhanced patient survival and a robust immune response characterized by diverse immune cell infiltration and active anti‐tumor immune pathways. The validation process positions ICBnetIS as an effective tool in predicting responses to ICB therapy, analyzing ICB data from a broad collection of over 700 samples from multiple cancer types of more than 15 datasets. It achieves an aggregated prediction AUC of 0.784, which outperforms the other nine renowned immunotherapeutic signatures, indicating the superior predictive capability of ICBnetIS. To sum up, the findings suggest ICBnetIS as a potent tool in predicting ICB therapy responses, offering significant implications for patient selection and treatment optimization in oncology. The study highlights the role of ICBnetIS in advancing personalized treatment strategies, potentially transforming the clinical landscape of ICB therapy.

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Combined tumor and immune signals from genomes or transcriptomes predict outcomes of checkpoint inhibition in melanoma

Cited by 15 publications

References 107 publications

Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma

Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma

Immune Cell Networks Uncover Candidate Biomarkers of Melanoma Immunotherapy Response

A Predictive Network‐Based Immune Checkpoint Blockade Immunotherapeutic Signature Optimizing Patient Selection and Treatment Strategies

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