Dataset Information

Multi-omic machine learning predictor of breast cancer therapy response.

ABSTRACT: Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment¹. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy². Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery³ were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.

SUBMITTER: Sammut SJ

PROVIDER: S-EPMC8791834 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Multi-omic machine learning predictor of breast cancer therapy response.

Sammut Stephen-John SJ Crispin-Ortuzar Mireia M Chin Suet-Feung SF Provenzano Elena E Bardwell Helen A HA Ma Wenxin W Cope Wei W Dariush Ali A Dawson Sarah-Jane SJ Abraham Jean E JE Dunn Janet J Hiller Louise L Thomas Jeremy J Cameron David A DA Bartlett John M S JMS Hayward Larry L Pharoah Paul D PD Markowetz Florian F Rueda Oscar M OM Earl Helena M HM Caldas Carlos C

Nature 20211207 7894

Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment<sup>1</sup>. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy<sup>2</sup>. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encod ...[more]

PMID: 34875674

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Project description:BackgroundBreast cancer remains a leading cause of mortality among women worldwide, necessitating innovative prognostic models to enhance treatment strategies.MethodsOur study retrospectively enrolled 9,439 breast cancer patients from 12 independent datasets and single-cell data from 12 patients (64,308 cells). Moverover, 30 in-house clinical cohort were collected for validation. We employed a comprehensive approach by combining ten distinct machine learning algorithms across 108 different combinations to scrutinize 88 pre-existing signatures of breast cancer. To affirm the efficacy of our developed model, immunohistochemistry assays were performed. Additionally, we investigated various potential immunotherapeutic and chemotherapeutic interventions.ResultsThis study introduces an Artificial Intelligence-aided Redox Signature (AIARS) as a novel prognostic tool, leveraging machine learning to identify critical redox-related gene signatures in breast cancer. Our results demonstrate that AIARS significantly outperforms existing prognostic models in predicting breast cancer outcomes, offering a robust tool for personalized treatment planning. Validation through immunohistochemistry assays on samples from 30 patients corroborated our results, underscoring the model's applicability on a wider scale. Furthermore, the analysis revealed that patients with low AIARS expression levels are more responsive to immunotherapy. Conversely, those exhibiting high AIARS were found to be more susceptible to certain chemotherapeutic agents, including vincristine.ConclusionsOur study underscores the importance of redox biology in breast cancer prognosis and introduces a powerful machine learning-based tool, the AIARS, for personalized treatment strategies. By providing a more nuanced understanding of the redox landscape in breast cancer, the AIARS paves the way for the development of redox-targeted therapies, promising to enhance patient outcomes significantly. Future work will focus on clinical validation and exploring the mechanistic roles of identified genes in cancer biology.

Dataset Information

Multi-omic machine learning predictor of breast cancer therapy response.

Publications

Multi-omic machine learning predictor of breast cancer therapy response.

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