Introduction: HER2 is expressed in a minority of patients with metastatic colorectal cancer (mCRC), yet it has proven to be a valuable therapeutic target for novel agents such as trastuzumab deruxtecan and tucatinib. Currently, testing is not mandatory in mCRC at baseline and a simple clinical tool to identify patients with a higher likelihood of being HER2 positive would be extremely helpful in guiding test requests and personalized medicine. Methods: Two machine learning (ML) algorithms were applied to analyze 30 variables available in routine clinical practice (clinicomics) for the prediction of both overall HER2 expression (immunohistochemistry [IHC] score 1–3) and HER2 positivity (IHC score 3 or score 2 with ERBB2 gene amplification). Variables identified as relevant in a training cohort were selected to build an easy-to-use predictive model, whose utility was validated in a separate validation cohort. Results: ML algorithms consistently showed that hemoglobin (Hb) <12 g/dL, carcinoembryonic antigen (CEA) >100 ng/mL, height >160 cm, and the presence of lymph node metastases were significantly associated with HER2 expression in the training cohort (n = 293). A model using these four variables had an area under the curve (AUC) of 67% (p = 0.0002). Patients with the presence of all predictive factors had a HER2 expression prevalence of 55%, compared to 15% in patients with none of the predictive factors (p < 0.0001), while HER2 positivity prevalence was 36% vs. 0%, respectively (p < 0.0001). The results were confirmed in the validation cohort (n = 96): AUC 68% (p = 0.004); difference in HER2 expression and positivity 58% vs. 12% (p = 0.005) and 47% vs. 0% (p = 0.0002), respectively. Conclusion: Hb <12 g/dL, CEA >100 ng/mL, height >160 cm, and the presence of lymph node metastases were associated with HER2 expression and positivity. HER2 testing should be considered mandatory when all these factors are present. The mechanisms linking these four factors to HER2 expression require further investigation.

Formica, V., Morelli, C., Rofei, M., Sansone, M., Vitale, J., Zurlo, I.v., et al. (2025). Clinicomics for Predicting HER2 Expression in Metastatic Colorectal Cancer: A Multicenter Machine Learning Analysis on Real-World Data. ONCOLOGY, 1-12 [10.1159/000549661].

Clinicomics for Predicting HER2 Expression in Metastatic Colorectal Cancer: A Multicenter Machine Learning Analysis on Real-World Data

Formica V.;Morelli C.;Rofei M.;Lucchetti J.;Cereda V.;
2025-01-01

Abstract

Introduction: HER2 is expressed in a minority of patients with metastatic colorectal cancer (mCRC), yet it has proven to be a valuable therapeutic target for novel agents such as trastuzumab deruxtecan and tucatinib. Currently, testing is not mandatory in mCRC at baseline and a simple clinical tool to identify patients with a higher likelihood of being HER2 positive would be extremely helpful in guiding test requests and personalized medicine. Methods: Two machine learning (ML) algorithms were applied to analyze 30 variables available in routine clinical practice (clinicomics) for the prediction of both overall HER2 expression (immunohistochemistry [IHC] score 1–3) and HER2 positivity (IHC score 3 or score 2 with ERBB2 gene amplification). Variables identified as relevant in a training cohort were selected to build an easy-to-use predictive model, whose utility was validated in a separate validation cohort. Results: ML algorithms consistently showed that hemoglobin (Hb) <12 g/dL, carcinoembryonic antigen (CEA) >100 ng/mL, height >160 cm, and the presence of lymph node metastases were significantly associated with HER2 expression in the training cohort (n = 293). A model using these four variables had an area under the curve (AUC) of 67% (p = 0.0002). Patients with the presence of all predictive factors had a HER2 expression prevalence of 55%, compared to 15% in patients with none of the predictive factors (p < 0.0001), while HER2 positivity prevalence was 36% vs. 0%, respectively (p < 0.0001). The results were confirmed in the validation cohort (n = 96): AUC 68% (p = 0.004); difference in HER2 expression and positivity 58% vs. 12% (p = 0.005) and 47% vs. 0% (p = 0.0002), respectively. Conclusion: Hb <12 g/dL, CEA >100 ng/mL, height >160 cm, and the presence of lymph node metastases were associated with HER2 expression and positivity. HER2 testing should be considered mandatory when all these factors are present. The mechanisms linking these four factors to HER2 expression require further investigation.
2025
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore MEDS-09/A - Oncologia medica
English
Clinicomics; Explainable machine learning; HER2; Metastatic colorectal cancer
Formica, V., Morelli, C., Rofei, M., Sansone, M., Vitale, J., Zurlo, I.v., et al. (2025). Clinicomics for Predicting HER2 Expression in Metastatic Colorectal Cancer: A Multicenter Machine Learning Analysis on Real-World Data. ONCOLOGY, 1-12 [10.1159/000549661].
Formica, V; Morelli, C; Rofei, M; Sansone, M; Vitale, J; Zurlo, Iv; Zoratto, F; Dell'Aquila, E; Lucchetti, J; Arrivi, G; Torsello, A; Picone, V; Emili...espandi
Articolo su rivista
File in questo prodotto:
File Dimensione Formato  
000549661.pdf

solo utenti autorizzati

Tipologia: Versione Editoriale (PDF)
Licenza: Copyright dell'editore
Dimensione 2.13 MB
Formato Adobe PDF
2.13 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/450903
Citazioni
  • ???jsp.display-item.citation.pmc??? 1
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact