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 4

Integration of ctDNA- and RNA-based biomarkers boosts the performance of a multimodal model in an independent blinded validation cohort.

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Integration of ctDNA- and RNA-based biomarkers boosts the performance of...
(A) Prediction comparison: Patients of the independent test cohort (n = 27, where both RNA-seq and ctDNA data were available) were categorized based on the RNA and ctDNA model predictions, highlighting convergent or divergent readouts by the 2 approaches. Column color coding reflects the actual CB group defined by clinical assessment (red = CB, blue = N-CB). (B) Model performance comparison of the different model approaches (ctDNA model in orange, RNA model in green, multimodal model in violet) in the independent test cohort (circles, n = 27, where both RNA-seq and ctDNA data were available) and blinded validation cohort (triangles, n = 19, where both RNA-seq and ctDNA data were available). (C) Hazard ratio (HR) for PFS of the 3 modeling approaches used for patient stratification (ctDNA model in orange, RNA model in green, multimodal model in violet) in the independent test (circles, n = 27, where both RNA-seq and ctDNA data were available) and blinded validation cohorts (triangles, n = 19, where both RNA-seq and ctDNA data were available). The bars represent the confidence of interval for each HR. The dashed line represents HR = 1. (D and E) Kaplan-Meier curves comparing the PFS of the multimodal model–based predicted CB population (red) and N-CB population (blue) in (D) the independent test cohort (n = 27, where both RNA-seq and ctDNA data were available) and (E) in an additional blinded and independent validation cohort (n = 19, where both RNA-seq and ctDNA data were available). P values were determined by a Mantel-Haenszel test. HR, hazard ratio (predicted CB population as reference); CI, confidence interval.