Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis

Tumour content plays a pivotal role in directing the bioinformatic analysis of molecular profiles such as copy number variation (CNV). In clinical application, tumour purity estimation (TPE) is achieved either through visual pathological review [conventional pathology (CP)] or the deconvolution of molecular data. While CP provides a direct measurement, it demonstrates modest reproducibility and lacks standardisation. Conversely, deconvolution methods offer an indirect assessment with uncertain accuracy, underscoring the necessity for innovative approaches. SoftCTM is an open-source, multiorgan deep-learning (DL) model for the detection of tumour and non-tumour cells in H&E-stained slides, developed within the Overlapped Cell on Tissue Dataset for Histopathology (OCELOT) Challenge 2023. Here, using three large multicentre colorectal cancer (CRC) cohorts (N = 1,097 patients) with digital pathology and multi-omic data, we compare the utility and accuracy of TPE with SoftCTM versus CP and bioinformatic deconvolution methods (RNA expression, DNA methylation) for downstream molecular analysis, including CNV profiling. SoftCTM showed technical repeatability when applied twice on the same slide (r = 1.0) and excellent correlations in paired H&E slides (r > 0.9). TPEs profiled by SoftCTM correlated highly with RNA expression (r = 0.59) and DNA methylation (r = 0.40), while TPEs by CP showed a lower correlation with RNA expression (r = 0.41) and DNA methylation (r = 0.29). We show that CP and deconvolution methods respectively underestimate and overestimate tumour content compared to SoftCTM, resulting in 6-13% differing CNV calls. In summary, TPE with SoftCTM enables reproducibility, automation, and standardisation at single-cell resolution. SoftCTM estimates (M = 58.9%, SD ±16.3%) reconcile the overestimation by molecular data extrapolation (RNA expression: M = 79.2%, SD ±10.5, DNA methylation: M = 62.7%, SD ±11.8%) and underestimation by CP (M = 35.9%, SD ±13.1%), providing a more reliable middle ground. A fully integrated computational pathology solution could therefore be used to improve downstream molecular analyses for research and clinics. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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Publication Details

Journal
The Journal of Pathology
Published
2024-12-22
DOI
https://doi.org/10.1002/path.6376
Citations
7
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
2.00

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article

Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis

Viktor Hendrik Koelzer, Claire Hardy, Celina Whalley, Susan D. Richman et al.
7 citations
The Journal of Pathology
Cancer Genomics and Diagnostics
2.00
article

Tumour purity assessment with deep learning in colorectal cancer and impact on molecular analysis

Viktor Hendrik Koelzer, Claire Hardy, Celina Whalley, Susan D. Richman, Jens Rittscher, Lydia A. Schoenpflug, Leslie Samuel, Korsuk Sirinukunwattana, Ian Tomlinson, Andrew D. Beggs, Kirsten D. Mertz, Philip Quirke, Michel Seymour, Aikaterini Chatzipli, Philip D. Dunne, Graeme I. Murray, Ultan McDermott, Simon J. Leedham, Enric Domingo, Tim Maughan, Clare Verrill, James Robineau, Steven Walker, Andrew Blake, Keara L. Redmond
article en
7 citations

Abstract

Tumour content plays a pivotal role in directing the bioinformatic analysis of molecular profiles such as copy number variation (CNV). In clinical application, tumour purity estimation (TPE) is achieved either through visual pathological review [conventional pathology (CP)] or the deconvolution of molecular data. While CP provides a direct measurement, it demonstrates modest reproducibility and lacks standardisation. Conversely, deconvolution methods offer an indirect assessment with uncertain accuracy, underscoring the necessity for innovative approaches. SoftCTM is an open-source, multiorgan deep-learning (DL) model for the detection of tumour and non-tumour cells in H&E-stained slides, developed within the Overlapped Cell on Tissue Dataset for Histopathology (OCELOT) Challenge 2023. Here, using three large multicentre colorectal cancer (CRC) cohorts (N = 1,097 patients) with digital pathology and multi-omic data, we compare the utility and accuracy of TPE with SoftCTM versus CP and bioinformatic deconvolution methods (RNA expression, DNA methylation) for downstream molecular analysis, including CNV profiling. SoftCTM showed technical repeatability when applied twice on the same slide (r = 1.0) and excellent correlations in paired H&E slides (r > 0.9). TPEs profiled by SoftCTM correlated highly with RNA expression (r = 0.59) and DNA methylation (r = 0.40), while TPEs by CP showed a lower correlation with RNA expression (r = 0.41) and DNA methylation (r = 0.29). We show that CP and deconvolution methods respectively underestimate and overestimate tumour content compared to SoftCTM, resulting in 6-13% differing CNV calls. In summary, TPE with SoftCTM enables reproducibility, automation, and standardisation at single-cell resolution. SoftCTM estimates (M = 58.9%, SD ±16.3%) reconcile the overestimation by molecular data extrapolation (RNA expression: M = 79.2%, SD ±10.5, DNA methylation: M = 62.7%, SD ±11.8%) and underestimation by CP (M = 35.9%, SD ±13.1%), providing a more reliable middle ground. A fully integrated computational pathology solution could therefore be used to improve downstream molecular analyses for research and clinics. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The Journal of PathologyVol. 265(2)
Queen's University Belfast (GB), University of Leeds (GB), Centre for Human Genetics (GB), University of Liverpool (GB), University of Aberdeen (GB), University of Zurich (CH), Transport Research Laboratory (United Kingdom) (GB), Aberdeen Royal Infirmary (GB), Wellcome Sanger Institute (GB), University Hospital of Basel (CH), John Radcliffe Hospital (GB), University of Oxford (GB), Kantonsspital Baselland (CH), Queens University (BD), University Hospital of Zurich (CH), Psychiatry Baselland (CH), Ludwig Cancer Research (GB), Theological Seminary Bienenberg (CH), Cancer Research UK Clinical Trials Unit (GB), Almac (United Kingdom) (GB), Health Data Research UK (GB), Oxford BioMedica (United Kingdom) (GB), Science Oxford (GB), Oxford University Hospitals NHS Trust (GB), University of Birmingham (GB), University of Edinburgh (GB)
National Science Foundation, Cancer Research UK, National Institute for Health and Care Research, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Medical Research Council, Engineering and Physical Sciences Research Council, Biomedical Research Council, NIHR Oxford Biomedical Research Centre, National Cancer Institute
Openalex Percentile: Top 12%
Cancer Genomics and Diagnostics
2.00
DOIOpen Access PDFOpenAlex
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