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Lunch and Poster Display session

242P - An artificial intelligence (AI)-based model to predict conversion from HER2-0 primary breast cancer (BC) to HER2-low phenotype at relapse

Date

16 May 2024

Session

Lunch and Poster Display session

Presenters

Federica Miglietta

Citation

Annals of Oncology (2024) 9 (suppl_4): 1-47. 10.1016/esmoop/esmoop103200

Authors

F. Miglietta1, A. Collesei1, T. Giarratano1, C.A. Giorgi2, F. Girardi1, M. Cacciatore3, D. Massa4, F. Zanghi4, M. Marino4, M. Fassan5, M.V. Dieci6, V. Guarneri7

Author affiliations

  • 1 IOV - Istituto Oncologico Veneto IRCCS, Padova/IT
  • 2 IOV - Istituto Oncologico Veneto IRCCS, 35128 - Padova/IT
  • 3 ULSS 9 - Treviso-Azienda ULSS 2 Marca Trevigiana, 31100 - Treviso/IT
  • 4 University of Padova, Padova/IT
  • 5 Azienda Universitaria Ospedaliera di Padova, Padova/IT
  • 6 University of Padua, Padova/IT
  • 7 University of Padua, 35128 - Padova/IT

Resources

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Abstract 242P

Background

We and others reported that HER2-low expression is unstable during disease evolution. The availability of anti-HER2 antibody-drug conjugates (ADCs) for HER2-low metastatic BC (MBC) patients (pts) commands attention to the identification of those with HER2-0 primary BC who may acquire HER2-low phenotype at relapse. Driven by the urgency of maximizing treatment access, we developed an AI-based model to predict this phenomenon.

Methods

We included a large multicentric retrospective cohort of pts with matched HER2 status on primary and MBC samples. All the variables in the dataset were used to build the model, including, among others: primary and MBC phenotype, timing and site of relapse and of relapse biopsy, treatments for primary BC. The dataset underwent preprocessing to address missingness: we applied Multiple Imputation by Chained Equations algorithm to observations without missing values in key relapse-related features. Features were tested for collinearity through the calculation of the Variance Inflation Factor, and one-hot-encoded due to the high representation of categorical variables. In the final dataset, pts were randomly assigned to training and test set. The training set (n=561) was subjected to random under-sampling to mitigate the target class imbalance. To prevent lack of explainability, a Generalized Linear Model was then fitted (10-fold repeated cross-validation to prevent overfitting).

Results

Among 749 pts (final dataset), 296 had HER2-0 primary BC, of which 109 (37%) gained HER2-low expression at relapse. The model was able to predict this switch with a 74% accuracy. Sensitivity and specificity were 74%. Hormone receptor expression and timing of relapse biopsy (≤ vs >2 years from relapse) were the variables associated with the highest importance for the AI model (p<0.05).

Conclusions

Our AI model, based on clinicopathological features, showed promising accuracy in predicting the conversion from HER2-0 primary BC to HER2-low phenotype at relapse. This model may help identifying pts with HER2-0 primary BC for whom a relapse biopsy should be prioritized to maximize treatment access to anti-HER2 ADCs for HER2-low BC.

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

F. Miglietta: Financial Interests, Personal, Invited Speaker: Roche, Novartis, Seagen, Pfizer, Lilly, Menarini; Financial Interests, Personal, Advisory Board: AstraZeneca, MSD. T. Giarratano: Financial Interests, Personal, Invited Speaker, outside the submitted work: Roche, Novartis, Seagen, Eli Lilly, Gliead, Daiichi Sankyo, Istituto Gentili. C.A. Giorgi: Financial Interests, Personal, Invited Speaker, outside the submitted work: Eli Lilly, Novartis, Seagen, AstraZeneca. F. Girardi: Financial Interests, Personal, Other, travel support; outside the submitted work: Eli Lilly, Gilead; Financial Interests, Personal, Invited Speaker, outside the submitted work: AstraZeneca, Eli Lilly, Gilead. D. Massa: Financial Interests, Personal, Other, travel support; outside the submitted work: Eli Lilly. M. Fassan: Financial Interests, Institutional, Research Grant, outside the submitted work: Astellas Pharma, QED therapeutics, Macrophage pharma, Diaceutics; Financial Interests, Personal, Invited Speaker, outside the submitted work: Astellas Pharma, AstraZeneca, Pierre Fabre, GSK, Roche, Merck Sharp & Dohme; Financial Interests, Personal, Invited Speaker, outside the: Johnson & Johnson, Bristol Myers Squibb, Amgen, Eli Lilly, Novartis, Incyte. M.V. Dieci: Financial Interests, Personal, Invited Speaker: Eli Lilly, Exact sciences, Gilead, Seagen, Daiichi Sankyo, Novartis; Financial Interests, Personal, Advisory Board: Novartis, Eli Lilly, Seagen, Exact Science, Daiichi Sankyo, Gilead; Financial Interests, Personal, Other, Consultancy: Pfizer; Financial Interests, Personal, Other, Consultancy on educational project: Roche. V. Guarneri: Financial Interests, Personal, Invited Speaker: Eli Lilly, Novartis, GSK, AstraZeneca, Gilead, Exact Sciences; Financial Interests, Personal, Advisory Board: Eli Lilly, Novartis, MSD, Gilead, Merck, Exact Sciences, Eisai, Olema Oncology, AstraZeneca, Daiichi Sankyo, Pfizer; Financial Interests, Personal, Expert Testimony: Eli Lilly; Financial Interests, Institutional, Invited Speaker: Eli Lilly, Roche, BMS, Novartis, AstraZeneca, MSD, Synton Biopharmaceuticals, Merck, GSK, Daiichi Sankyo, Nerviano, Pfizer; Non-Financial Interests, Member: ASCO. All other authors have declared no conflicts of interest.

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