Ensemble-based classification using microRNA expression identifies a breast cancer patient subgroup with an ultralow...

Area of Science:

• Oncology
• Genomics
• Biomarker Discovery

Background:

• Current clinical markers overestimate recurrence risk in lymph node negative (LNN) breast cancer (BC) patients.
• This leads to overtreatment in a majority of low-risk BC patients.
• Differential microRNA expression may offer a more accurate risk classification.

Purpose of the Study:

• To identify indolent LNN BC patients using differential microRNA expression.
• To develop a classification tool for reducing overtreatment in LNN BC.
• To improve risk stratification for breast cancer patients.

Main Methods:

• Collected primary tumors from 160 LNN BC patients (80 recurrent, 80 recurrence-free).
• Used pairwise matched samples from systemically untreated patients.
• Developed a microRNA expression-based classifier using seven methods and validated in two independent cohorts.

Main Results:

• The classifier identified 37 of 80 indolent patients as ultralow-risk with 100% sensitivity for recurrence.
• MicroRNA expression results were independent of current clinical markers.
• Validated cohorts showed successful classification of ultralow-risk BC patients with prolonged recurrence-free survival.

Conclusions:

• Differential microRNA expression profiles can identify LNN BC patients who may not need systemic treatment.
• This approach offers a potential alternative to current classifications for reducing overtreatment.
• Further validation studies are necessary for clinical implementation.