Abstract A051: Multi-omic explainable machine learning improves cancer treatment outcome prediction

Clinical Cancer Research, vol. 31, pp. A051

Abstract

Abstract Background: Advancements in multi-omics data integration and explainable Machine Learning (ML) have shown promise in precision oncology. Multi-omic data used to train ML models may include genomics, transcriptomics and histopathology to characterize cancer cells and the tumor microenvironment (TME). Explainability methods, such as SHAP, have enabled researchers and clinicians to unravel the decision-making rationale of ML models predicting cancer progression and treatment response. We developed an explainable ML framework that incorporates multi-omic features of cancer and the TME. This framework was applied to predict patient response to neoadjuvant chemotherapy (NAC) in breast cancer and immune checkpoint inhibitor (ICI) in melanoma. Methods: For breast cancer, we used the cohort from Sammut et al. [1] (n=157 training, n=75 test). For melanoma, we assembled a cohort comprising 229 patients (n=138 training, n=53 test cutaneous, n=38 test non-cutaneous) from five independent studies. We improved the performance of the ensemble ML models in Sammut et al. [1] by implementing a shared-learning architecture to enable component models to influence each other as training progresses. We applied this ensemble (Ens:LR+RF+SVM) to predict NAC response in breast cancer and ICI response in melanoma by integrating clinical, DNA sequencing, RNA sequencing and histopathology (only for breast cancer) data. For melanoma, we also trained three single ML models (LR, RF, and SVM) and another ensemble (Ens:LR+RF), and introduced a novel dual utility of SHAP for feature-selection during training and biomarker threshold identification during validation. Results: The ensemble model trained on the multi-omic breast cancer features achieved ROC-AUC of 0.88 and showed a potential 25% reduction in false positives (i.e., incorrect predictions of good response) compared to its predecessor from Sammut et al. [1]. In the melanoma cohort, the Ens:LR+RF model achieved ROC-AOC of 0.77 but was outperformed by the RF model, ROC-AUC 0.78. SHAP revealed unique interactions between each ML model and the feature space, resulting in distinct training feature sets per model. During validation, the intersection between feature values and SHAP scores revealed numerical thresholds underpinning good versus poor responses of clinically meaningful biomarkers such as neoantigen load (>2.25 good, <2.25 poor, values in log10 scale). Across these two studies, we developed and open-sourced a scalable and versatile ML workflow (xML-workFLow) for rapid experimentation in biomedical research. Conclusions: This work showcases the potential of multi-omics explainable ML in advancing precision oncology to improve treatment outcome prediction. With further experimental validation, the use of explainable ML to determine numerical thresholds could guide the development of companion diagnostics and inform combination therapeutic strategies. References: 1. Sammut, S.J., et al., Multi-omic machine learning predictor of breast cancer therapy response. Nature, 2022. 601(7894): p. 623-629. Citation Format: Khoa A. Tran, Venkateswar Addala, Lambros T. Koufariotis, Jia Zhang, Scott Wood, Conrad Leonard, Lotte L. Hoeijmakers, Christian U. Blank, Mireia Crispin-Ortuzar, Amy McCart. Reed, Po-ling Inglis, Sunil R. Lakhani, Elizabeth D. Williams, John V. Pearson, Olga Kondrashova, Nicola Waddell. Multi-omic explainable machine learning improves cancer treatment outcome prediction [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A051.

Authors 16

1. Khoa Anh Tran

   Queensland University of Technology · QIMR Berghofer Medical Research Institute

   Affiliation as printed

   1QIMR Berghofer Medical Research Institute; Queensland University of Technology (QUT), Brisbane, Australia,

2. Venkateswar Addala

   QIMR Berghofer Medical Research Institute

   Affiliation as printed

   2QIMR Berghofer Medical Research Institute, Brisbane, Australia,

3. Lambros T. Koufariotis

   QIMR Berghofer Medical Research Institute

   Affiliation as printed

   2QIMR Berghofer Medical Research Institute, Brisbane, Australia,

4. Jia Zhang

   QIMR Berghofer Medical Research Institute

   Affiliation as printed

   3Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Australia,

5. Scott Wood

   QIMR Berghofer Medical Research Institute

   Affiliation as printed

   2QIMR Berghofer Medical Research Institute, Brisbane, Australia,

6. Conrad Leonard

   QIMR Berghofer Medical Research Institute

   Affiliation as printed

   2QIMR Berghofer Medical Research Institute, Brisbane, Australia,

7. Lotte L. Hoeijmakers

   The Netherlands Cancer Institute

   Affiliation as printed

   4Netherlands Cancer Institute, Amsterdam, Netherlands,

8. Christian U. Blank Aachen

   Leiden University Medical Center · The Netherlands Cancer Institute

   Affiliation as printed

   5Netherlands Cancer Institute; Leiden University Medical Center, Amsterdam & Leiden, Netherlands,

9. Mireia Crispin‐Ortuzar

   University of Cambridge · Cancer Research UK · Cancer Research UK Cambridge Center

   Affiliation as printed

   6Cancer Research UK (CRUK) Cambridge Centre; University of Cambridge, Cambridge, United Kingdom,

10. Amy E. McCart Reed

    The University of Queensland

    Affiliation as printed

    7The University of Queensland Centre for Clinical Research (UQCCR), Brisbane, Australia,

11. Po-Ling Inglis

    Royal Brisbane and Women's Hospital

    Affiliation as printed

    8Royal Brisbane and Women's Hospital, Brisbane, Australia,

12. Sunil R. Lakhani

    The University of Queensland

    Affiliation as printed

    9The University of Queensland Centre for Clinical Research (UQCCR); Pathology Queensland, Brisbane, Australia,

13. Elizabeth D. Williams

    Queensland University of Technology

    Affiliation as printed

    10Queensland University of Technology (QUT); Australian Prostate Cancer Research Centre – Queensland (APCRC-Q) and Queensland Bladder Cancer Initiative (QBCI), Brisbane, Australia

14. John V. Pearson

    QIMR Berghofer Medical Research Institute

    Affiliation as printed

    2QIMR Berghofer Medical Research Institute, Brisbane, Australia,

15. Olga Kondrashova

    Queensland University of Technology · QIMR Berghofer Medical Research Institute

    Affiliation as printed

    1QIMR Berghofer Medical Research Institute; Queensland University of Technology (QUT), Brisbane, Australia,

16. Nicola Waddell

    QIMR Berghofer Medical Research Institute

    Affiliation as printed

    2QIMR Berghofer Medical Research Institute, Brisbane, Australia,