Oncotype DX Predictive Nomogram for Recurrence Score Output: The Novel System ADAPTED01 Based on Quantitative...

Area of Science:

• Oncology
• Biostatistics
• Pathology

Background:

• Oncotype DX (ODX) is crucial for breast cancer recurrence risk assessment and guiding adjuvant treatment.
• Limited global access to ODX necessitates alternative methods for predicting recurrence risk.
• Quantitative immunohistochemistry (IHC) offers a potential surrogate for ODX recurrence score (RS) assessment.

Purpose of the Study:

• To develop a decision support system correlating phenotypical tumor characteristics, quantitative IHC, and ODX-assessed recurrence score (RS).
• To create a predictive model for identifying patients with specific ODX RS thresholds (≤25 or ≤20).
• To enhance clinical decision-making in breast cancer management where ODX testing is inaccessible.

Main Methods:

• Retrospective analysis of breast cancer patients who underwent ODX testing (2014-2018).
• Inclusion of age, menopausal status, and quantitative IHC features (estrogen receptor, progesterone receptor, Ki-67).
• Development and validation of logistic regression models using training (70%) and internal/external validation (30%) sets.

Main Results:

• The predictive model for RS ≤ 25 demonstrated high performance with an Area Under the Curve (AUC) of 92.2% (sensitivity 84.2%, specificity 80.1%) in the internal set.
• External validation confirmed model robustness with an AUC of 82.3% and a positive predictive value of 91%.
• Estrogen receptor, progesterone receptor, and Ki-67 levels were significantly associated with RS ≤ 25.

Conclusions:

• Quantitative IHC reliably correlates with ODX RS for patients with RS ≤ 25.
• A validated nomogram was developed to predict ODX RS, offering a cost-effective clinical decision support tool.
• Prospective studies are recommended to evaluate the nomogram's clinical utility in practice.