A Digital Environment for Multi-modal Relational Management, Representation, and Analysis of Cancer Clinical Studies
- 1The Institute of Cancer Research
Abstract
Biologically rich clinical trials generate heterogeneous datasets whose relationships and clinical context must be preserved. Here we present the POETIC-AI environment, a trial-centric digital environment for integrating, analysing, and exploring multimodal data from POETIC, a randomised phase III trial of peri-operative aromatase inhibition in hormone receptor-positive breast cancer (NCT02338310).
We implemented a graph-based ontology linking patients, treatments, sampling timepoints, specimens, assays, images, spatial regions, and derived measurements; and designed code-based and graphical analytical workflows. Using proliferation as a test case, we reproduced POETICs established reduction in Ki67% and examined variation underlying sample-level measurements. In response-enriched cohorts, mean Ki67% decreased while relative variation among pathologist-assessed fields increased (paired Wilcoxon, P < 0.0001). Pathologist-assessed Ki67% correlated strongly with an 18-gene transcriptomic proliferation signature (r = 0.834), and both decreased significantly during treatment (P < 0.0001). An annotation-free pixel-wise Ki67 H-score showed weaker correlations and a similar but non-significant reduction (r = 0.327-0.472; P = 0.15), highlighting differences between tumour-focused and whole-section image quantification. Digital pathology and spatial molecular measurements remained linked to their source images and annotated tissue regions, enabling exploration within their morphological and spatial context using bespoke interactive applications.
POETIC-AI demonstrates how the structure, provenance, and tissue context of evolving multimodal clinical-trial data can be preserved within a connected research environment, supporting reproducible analyses and the laying the groundwork for future development of biologically interpretable computational biomarkers.
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BibTeX
@misc{rodriguez2026digital,
title = {{A Digital Environment for Multi-modal Relational Management, Representation, and Analysis of Cancer Clinical Studies}},
author = {F. Cardoso Rodriguez and X. Zhu and E. Schuster and L. Kilburn and H. Tovey and A. Espinasse and R. Sardinha and T. Lund and A. Ring and M. Salto-Tellez and M. C. U. Cheang},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.01.26364400},
url = {https://www.medrxiv.org/content/10.64898/2026.10.01.26364400v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A Digital Environment for Multi-modal Relational Management, Representation, and Analysis of Cancer Clinical Studies
AU - F. Cardoso Rodriguez
AU - X. Zhu
AU - E. Schuster
AU - L. Kilburn
AU - H. Tovey
AU - A. Espinasse
AU - R. Sardinha
AU - T. Lund
AU - A. Ring
AU - M. Salto-Tellez
AU - M. C. U. Cheang
PY - 2026
DA - 2026/10/01
PB - medRxiv
DO - 10.64898/2026.10.01.26364400
UR - https://www.medrxiv.org/content/10.64898/2026.10.01.26364400v1
AB - Biologically rich clinical trials generate heterogeneous datasets whose relationships and clinical context must be preserved. Here we present the POETIC-AI environment, a trial-centric digital environment for integrating, analysing, and exploring multimodal data from POETIC, a randomised phase III trial of peri-operative aromatase inhibition in hormone receptor-positive breast cancer (NCT02338310). We implemented a graph-based ontology linking patients, treatments, sampling timepoints, specimens, assays, images, spatial regions, and derived measurements; and designed code-based and graphical analytical workflows. Using proliferation as a test case, we reproduced POETICs established reduction in Ki67% and examined variation underlying sample-level measurements. In response-enriched cohorts, mean Ki67% decreased while relative variation among pathologist-assessed fields increased (paired Wilcoxon, P < 0.0001). Pathologist-assessed Ki67% correlated strongly with an 18-gene transcriptomic proliferation signature (r = 0.834), and both decreased significantly during treatment (P < 0.0001). An annotation-free pixel-wise Ki67 H-score showed weaker correlations and a similar but non-significant reduction (r = 0.327-0.472; P = 0.15), highlighting differences between tumour-focused and whole-section image quantification. Digital pathology and spatial molecular measurements remained linked to their source images and annotated tissue regions, enabling exploration within their morphological and spatial context using bespoke interactive applications. POETIC-AI demonstrates how the structure, provenance, and tissue context of evolving multimodal clinical-trial data can be preserved within a connected research environment, supporting reproducible analyses and the laying the groundwork for future development of biologically interpretable computational biomarkers.
ER -