Biomarker dynamics affecting neoadjuvant therapy response and outcome of HER2-positive breast cancer subtype
Articolo
Data di Pubblicazione:
2023
Citazione:
Biomarker dynamics affecting neoadjuvant therapy response and outcome of HER2-positive breast cancer subtype / OrrĂ¹, S; Pascariello, E; Pes, B; Rallo, V; Barbara, R; Muntoni, M; Notari, F; Fancello, G; Mocci, C; Muroni, Mr; Cossu Rocca, P; Angius, A; DE MIGLIO, Maria Rosaria. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - 13(1):12869:(2023). [10.1038/s41598-023-40071-2]
Abstract:
HER2+ breast cancer (BC) is an aggressive subtype genetically and biologically heterogeneous. We
evaluate the predictive and prognostic role of HER2 protein/gene expression levels combined with
clinico-pathologic features in 154 HER2+ BCs patients who received trastuzumab-based neoadjuvant
chemotherapy (NACT). The tumoral pathological complete response (pCR) rate was 40.9%. High
tumoral pCR show a scarce mortality rate vs subjects with a lower response. 93.7% of ypT0 were
HER2 IHC3+ BC, 6.3% were HER2 IHC 2+/SISH+ and 86.7% of ypN0 were HER2 IHC3+, the remaining
were HER2 IHC2+/SISH+. Better pCR rate correlate with a high percentage of infltrating immune
cells and right-sided tumors, that reduce distant metastasis and improve survival, but no incidence
diference. HER2 IHC score and laterality emerge as strong predictors of tumoral pCR after NACT from
machine learning analysis. HER2 IHC3+ and G3 are poor prognostic factors for HER2+ BC patients,
and could be considered in the application of neoadjuvant therapy. Increasing TILs concentrations,
lower lymph node ratio and lower residual tumor cellularity are associated with a better outcome. The
immune microenvironment and scarce lymph node involvement have crucial role in clinical outcomes.
The combination of all predictors might ofer new options for NACT efectiveness prediction and
stratifcation of HER2+ BC during clinical decision-making.
Tipologia CRIS:
1.1 Articolo in rivista
Keywords:
HER2-positive breast cancer; neoadjuvant therapy, immunohistochemistry, laterality, tumor infiltrating lymphocyte, machine learning
Elenco autori:
OrrĂ¹, S; Pascariello, E; Pes, B; Rallo, V; Barbara, R; Muntoni, M; Notari, F; Fancello, G; Mocci, C; Muroni, Mr; Cossu Rocca, P; Angius, A; DE MIGLIO, Maria Rosaria
Link alla scheda completa:
Pubblicato in: