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Poster session 08

171P - A computational pathology collagen signature predictive of tamoxifen benefit in ductal carcinoma in situ: Results from a cohort within the UK/ANZ DCIS randomized trial

Date

14 Sep 2024

Session

Poster session 08

Topics

Cancer Research

Tumour Site

Breast Cancer

Presenters

Arpit Aggarwal

Citation

Annals of Oncology (2024) 35 (suppl_2): S238-S308. 10.1016/annonc/annonc1576

Authors

A. Aggarwal1, S.S. Badve2, J. Cuzick3, A. Madabhushi1, M.A. Thorat4

Author affiliations

  • 1 Biomedical Engineering, Emory University, 30322 - Atlanta/US
  • 2 Pathology Dept., Winship Cancer Institute of Emory University, 30322 - Atlanta/US
  • 3 Wolfson Institute Of Population Health, Cancer Research UK Barts Centre - Barts and The London School of Medicine and Dentistry, EC1M 6BQ - London/GB
  • 4 Wolfson Institute Of Population Health, Cancer Prevention Unit , Centre for Prevetion Detection and Diagnosis ,Wolfson Institute of Population Health ,Queen Mary University of London ,United Kingdom, E1 4NS - London/GB

Resources

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Abstract 171P

Background

Disorder in collagen fiber architecture, characterized by alterations in collagen organization, plays a critical role in tumor progression. In this study, we developed a collagen-based computational pathology biomarker and evaluated its association with the benefits of adjuvant treatment in a clinical trial cohort.

Methods

The study utilized H&E-stained Whole Slide Images (WSIs) of DCIS (n = 755) from the UK/ANZ DCIS randomized controlled trial, which included four treatment allocations: no adjuvant treatment, radiotherapy, tamoxifen, radiotherapy+tamoxifen. Two-thirds of ipsilateral breast events (IBE) from each treatment group were used for training, with each case (patient with IBE) matched to two controls by age (+/-7 years) and treatment. The collagen score was evaluable in 713 of 755 samples, including 270 of 288 in training set. For the development of the collagen-tamoxifen score (CTS), 102 samples (IBE=35) out of 270 formed the training set (TrS) and 140 samples (IBE=20) formed the validation set (VaS). Machine learning models were employed to extract a series of features relating to collagen arrangement from WSIs. A logistic regression model was trained using these features and generated a continuous score (CTS). A 66th percentile risk score threshold was applied on CTS in TrS to stratify patients into low or high-risk groups in TrS and VaS.

Results

In TrS+VaS, 194 of 713 (27%) patients were classified as CTS-high. Among 481 patients with ER (clonal) status data, a weak negative correlation (spearman rho -0.19, p<0.0001) was seen between ER (clonal) and CTS. Specifically, 61% (109/178) of ER-negative DCIS were CTS-low, while 21% (63/303) of ER-positive DCIS were CTS-high. In TrS and VaS, CTS-high DCIS had a greater than 3-fold risk of ipsilateral breast event (IBE) as compared with CTS-low DCIS [TrS: Hazard Ratio (HR) = 4.54; 95% Confidence Interval (95%CI), 2.27-9.06, p<0.0001; VaS: HR = 3.46; 95%CI, 1.41-8.48, p=0.0067).

Conclusions

Disorder in collagen architecture is associated with tamoxifen resistance in DCIS patients. Our computational pathology based collagen-tamoxifen score has a role, independent of ER-status, in predicting tamoxifen benefit in DCIS.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

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