Tailoring neoadjuvant treatment of HR-positive/HER2-negative breast cancers: Which role for gene expression assays?

Garufi, Giovanna; Carbognin, Luisa; Arcanà, Concetta; Parola, Sara; Ventriglia, Anna; Doronzo, Antonio; Garutti, Mattia; Orlandi, Armando; Palazzo, Antonella; Fabi, Alessandra; Bria, Emilio; Arpino, Grazia; Giuliano, Mario; Mastro, Lucia Del; Laurentiis, Michelino De; Puglisi, Fabio

doi:10.1016/j.ctrv.2022.102454

Cited by 17 publications

(10 citation statements)

References 87 publications

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“…Despite numerous advancements in comprehensive treatment strategies for LBC, there remains a deficiency in effective prognostic markers and targeted molecular therapies. 37 , 38 The transformation of normal cells into malignant cells is accompanied by various biological alterations, with metabolic reprogramming being the most evident, including alterations in glycolysis, glutamate-driven anabolism, and irregular lipid synthesis. 39 Evidence has revealed that dysregulated metabolism in cancer cells and TME is crucial in cancer progression, treatment, recurrence, and metastasis.…”

Section: Discussionmentioning

confidence: 99%

Development of an obesity-related multi-gene prognostic model incorporating clinical characteristics in luminal breast cancer

Zhang,

Ma,

Wang

et al. 2024

iScience

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Section: Discussionmentioning

confidence: 99%

Development of an obesity-related multi-gene prognostic model incorporating clinical characteristics in luminal breast cancer

Zhang,

Ma,

Wang

et al. 2024

iScience

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“…Beyond these variables, the inclusion of other features of interest could increase the discriminatory power of the model. A high recurrence risk score of gene expression panels such as the 21-gene assay on diagnostic biopsy has been shown to predict pCR 47,48 , as well as the assessment of circulating tumor DNA and particularly its early dynamic changes during over the course of NCT 49 . Furthermore, in the BC NCT setting, ML and deep learning models have been explored to predict the pathological response based on pre-NCT histopathological and radiological images [50][51][52] .…”

Section: Discussionmentioning

confidence: 99%

Accessible Machine Learning and Deep Learning Models Predict Response and Survival in Early Stage Hormone Receptor-Positive/HER2-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy

Garufi,

Mastrantoni,

Giordano

et al. 2024

Preprint

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Hormone receptor-positive/HER2 negative breast cancer (BC) is the most common subtype of BC and typically occurs as an early, operable disease. In patients receiving neoadjuvant chemotherapy (NACT), pathological complete response (pCR) is rare and multiple efforts have been made to predict disease recurrence and survival. We developed a framework to predict pCR, disease-free survival (DFS) and overall survival (OS) using clinicopathological characteristics widely available at diagnosis and after surgery. The machine learning (ML) model trained to predict pCR (n = 463) was evaluated in an internal validation cohort (n = 109) and validated in an external validation cohort (n = 171), achieving an area under the curve (AUC) of respectively 0.86 and 0.81. The models trained to predict DFS and OS were evaluated in the internal validation cohort, achieving a concordance index of 0.70 and 0.69. Our results emphasize the value of including accessible ML algorithms in clinical practice and provide a framework for the development of risk-adapted clinical trials based on ML models.

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“…Indeed, gene expression panels evaluated on core needle biopsy have demonstrated a predictive role of pathologic response to systemic therapy for LBCs, with a higher likelihood of pCR following NACT for high genomic risk tumors and a higher likelihood of clinicopathological response to neoadjuvant ET for low genomic risk tumors. 53 International guidelines suggest the use of gene expression signatures to consider preoperative ET for LBC patients selected for comorbidities and clinical and genomic features of low risk of recurrence. 48,49 We have to acknowledge that the retrospective and non-randomized nature of this analysis represents a limit.…”

Section: Therapeutic Advances Inmentioning

confidence: 99%

Development of a nomogram for predicting pathological complete response in luminal breast cancer patients following neoadjuvant chemotherapy

et al. 2023

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Background: Given the low chance of response to neoadjuvant chemotherapy (NACT) in luminal breast cancer (LBC), the identification of predictive factors of pathological complete response (pCR) represents a challenge. A multicenter retrospective analysis was performed to develop and validate a predictive nomogram for pCR, based on pre-treatment clinicopathological features. Methods: Clinicopathological data from stage I–III LBC patients undergone NACT and surgery were retrospectively collected. Descriptive statistics was adopted. A multivariate model was used to identify independent predictors of pCR. The obtained log-odds ratios (ORs) were adopted to derive weighting factors for the predictive nomogram. The receiver operating characteristic analysis was applied to determine the nomogram accuracy. The model was internally and externally validated. Results: In the training set, data from 539 patients were gathered: pCR rate was 11.3% [95% confidence interval (CI): 8.6–13.9] (luminal A-like: 5.3%, 95% CI: 1.5–9.1, and luminal B-like: 13.1%, 95% CI: 9.8–13.4). The optimal Ki67 cutoff to predict pCR was 44% (area under the curve (AUC): 0.69; p < 0.001). Clinical stage I–II (OR: 3.67, 95% CI: 1.75–7.71, p = 0.001), Ki67 ⩾44% (OR: 3.00, 95% CI: 1.59–5.65, p = 0.001), and progesterone receptor (PR) <1% (OR: 2.49, 95% CI: 1.15–5.38, p = 0.019) were independent predictors of pCR, with high replication rates at internal validation (100%, 98%, and 87%, respectively). According to the nomogram, the probability of pCR ranged from 3.4% for clinical stage III, PR > 1%, and Ki67 <44% to 53.3% for clinical stage I–II, PR < 1%, and Ki67 ⩾44% (accuracy: AUC, 0.73; p < 0.0001). In the validation set (248 patients), the predictive performance of the model was confirmed (AUC: 0.7; p < 0.0001). Conclusion: The combination of commonly available clinicopathological pre-NACT factors allows to develop a nomogram which appears to reliably predict pCR in LBC.

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Tailoring neoadjuvant treatment of HR-positive/HER2-negative breast cancers: Which role for gene expression assays?

Cited by 17 publications

References 87 publications

Development of an obesity-related multi-gene prognostic model incorporating clinical characteristics in luminal breast cancer

Development of an obesity-related multi-gene prognostic model incorporating clinical characteristics in luminal breast cancer

Accessible Machine Learning and Deep Learning Models Predict Response and Survival in Early Stage Hormone Receptor-Positive/HER2-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy

Development of a nomogram for predicting pathological complete response in luminal breast cancer patients following neoadjuvant chemotherapy

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