Multimodal integration of blood RNA and ctDNA reflects response to immunotherapy in metastatic urothelial cancer
Sandra van Wilpe, … , Pedro Romero, Niven Mehra
Sandra van Wilpe, … , Pedro Romero, Niven Mehra
Published January 30, 2025
Citation Information: JCI Insight. 2025;10(5):e186062. https://doi.org/10.1172/jci.insight.186062.
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Clinical Research and Public Health Immunology Oncology

Multimodal integration of blood RNA and ctDNA reflects response to immunotherapy in metastatic urothelial cancer

Abstract

BACKGROUND. Previously, we demonstrated that changes in circulating tumor DNA (ctDNA) are promising biomarkers for early response prediction (ERP) to immune checkpoint inhibitors (ICIs) in metastatic urothelial cancer (mUC). In this study, we investigated the value of whole-blood immunotranscriptomics for ERP-ICI and integrated both biomarkers into a multimodal model to boost accuracy. METHODS. Blood samples of 93 patients were collected at baseline and after 2–6 weeks of ICI for ctDNA (n = 88) and immunotranscriptome (n = 79) analyses. ctDNA changes were dichotomized into increase or no increase, the latter including patients with undetectable ctDNA. For RNA model development, the cohort was split into discovery (n = 29), test (n = 29), and validation sets (n = 21). Finally, RNA- and ctDNA-based predictions were integrated in a multimodal model. Clinical benefit (CB) was defined as progression-free survival beyond 6 months. RESULTS. Sensitivity (SN) and specificity (SP) of ctDNA increase for predicting non-CB (N-CB) was 59% and 92%, respectively. Immunotranscriptome analysis revealed upregulation of T cell activation, proliferation, and interferon signaling during treatment in the CB group, in contrast with N-CB patients. Based on these differences, a 10-gene RNA model was generated, reaching an SN and SP of 73% and 79%, respectively, in the test and 67% and 67% in the validation set for predicting N-CB. Multimodal model integration led to superior performance, with an SN and SP of 79% and 100%, respectively, in the validation cohort. CONCLUSION. The combination of whole-blood immunotranscriptome and ctDNA in a multimodal model showed promise for ERP-ICI in mUC and accurately identified patients with N-CB. FUNDING. Eurostars grant E! 114908 - PRECISE, Paul Speth Foundation (Bullseye project).

Authors

Sandra van Wilpe, Davide Croci, Sara S. Fonseca Costa, Iris B.A.W. te Paske, Sofie H. Tolmeijer, Jolique van Ipenburg, Leonie I. Kroeze, Simona Pavan, Sylvain Monnier-Benoit, Guido Coccia, Noushin Hadadi, Irma M. Oving, Tineke J. Smilde, Theo van Voorthuizen, Marieke Berends, Mira D. Franken, Marjolijn J.L. Ligtenberg, Sahar Hosseinian Ehrensberger, Laura Ciarloni, Pedro Romero, Niven Mehra

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Figure 3

Blood-based immunotranscriptome predictive model forecasts CB in an independent cohort.

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Blood-based immunotranscriptome predictive model forecasts CB in an inde...
(A) Modeling approach schematic: Biomarker discovery was performed in the discovery cohort (patients with paired BL and OT RNA-seq data, n = 29) by DEA. Model training was performed in the same cohort by multiple iterations of random features reduction of the biomarker/gene list, followed by model testing in the independent test cohort (patients with paired BL and OT RNA-seq data, n = 29). The best CB predictive model was selected by area under the curve (AUC) ranking of each model receiver operating characteristics (ROC) curve and by ranking the difference in median PFS between the predicted CB and N-CB groups in the test cohort (n = 29). Last, the best performing model was validated in the validation cohort (patients with paired BL and OT RNA-seq data, n = 21). (B) ROC curve showing model performance of the best performing model in the independent test cohort (n = 29). Specificity is calculated with respect to CB patients (true negative cases), while sensitivity to N-CB (true positive cases). (C) Kaplan-Meier (KM) curve comparing the PFS of model-based predicted CB population (red) and predicted N-CB population (blue) in the independent test cohort (n = 29). (D) Attention map contextualizing the biology of the 10 genes used to craft the model shown in B and C showing in which DEA the genes were identified. The genes have also been mapped to a selection of significantly enriched pathways of different ontologies in the longitudinal CB DEA (enrichment-adjusted P ≤ 0.05) and to the STRING network clusters shown in Figure 2B. Genes included in the DEGs of the longitudinal CB DEA or the OT DEA are highlighted in orange (upregulated, based on edgeR FC ≥ 0) or in blue (downregulated, based on edgeR FC < 0). Genes associated with enriched pathways or STRING clusters are highlighted in yellow. (E) ROC curve showing model performance assessment in the independent blinded validation cohort (n = 21). Specificity is calculated with respect to CB patients (true negative cases) and sensitivity to N-CB (true positive cases). (F) KM curve comparing the PFS of RNA model–based predicted CB population (red) and N-CB population (blue) in the independent blinded validation cohort (n = 21). P values were determined by a Mantel-Haenszel test. HR, hazard ratio (predicted CB population as reference); CI, confidence interval.