Predicting Triple-Negative Breast Cancer Subtype Using Multiple Single Nucleotide Polymorphisms for Breast Cancer Risk and Several Variable Selection Methods

Abstract

Introduction Studies of triple-negative breast cancer have recently been extending the inclusion criteria and incorporating additional molecular markers into the selection criteria, opening up scope for targeted therapies. The screening phases required for studies of this type are often prolonged, since the process of determining the molecular subtype and carrying out additional biomarker assessment is time-consuming. Parameters such as germline genotypes capable of predicting the molecular subtype before it becomes available from pathology might be helpful for treatment planning and optimizing the timing and cost of screening phases. This appears to be feasible, as rapid and low-cost genotyping methods are becoming increasingly available. The aim of this study was to identify single nucleotide polymorphisms (SNPs) for breast cancer risk capable of predicting triple negativity, in addition to clinical predictors, in breast cancer patients.

Methods This cross-sectional observational study included 1271 women with invasive breast cancer who were treated at a university hospital. A total of 76 validated breast cancer risk SNPs were successfully genotyped. Univariate associations between each SNP and triple negativity were explored using logistic regression analyses. Several variable selection and regression techniques were applied to identify a set of SNPs that together improve the prediction of triple negativity in addition to the clinical predictors of age at diagnosis and body mass index (BMI). The most accurate prediction method was determined by cross-validation.

Results The SNP rs10069690 (TERT, CLPTM1L) was the only significant SNP (corrected p = 0.02) after correction of p values for multiple testing in the univariate analyses. This SNP and three additional SNPs from the genes RAD51B, CCND1, and FGFR2 were selected for prediction of triple negativity. The addition of these SNPs to clinical predictors increased the cross-validated area under the curve (AUC) from 0.618 to 0.625. Age at diagnosis was the strongest predictor, stronger than any genetic characteristics.

Conclusion Prediction of triple-negative breast cancer can be improved if SNPs associated with breast cancer risk are added to a prediction rule based on age at diagnosis and BMI. This finding could be used for prescreening purposes in complex molecular therapy studies for triple-negative breast cancer.

Zusammenfassung

Einleitung Studien bei triple-negativem Brustkrebs haben die Einschlusskriterien durch die Aufnahme zusätzlicher molekularer Marker erweitert. Im Rahmen des Screenings für diese Therapiestudien wird sowohl für die Bestimmung des molekularen Subtyps als als auch für zusätzliche Biomarker-Untersuchungen ein längerer Zeitraum beansprucht, was die Behandlung verzögert. Keimbahn-Genotypen könnten bei der Vorhersage des molekularen Subtyps helfen, zumal schnelle und günstige Genotypisierungsmethoden zunehmend zur Verfügung stehen. Ziel dieser Studie war es deswegen, zu prüfen, ob Einzelnukleotid-Polymorphismen (SNPs) der Keimbahn dabei helfen können, Brustkrebspatientinnen mit triple-negativem Mammakarzinom zu identifizieren.

Methoden In dieser Querschnittsstudie wurden 1271 Patientinnen mit invasivem Mammakarzinom eingeschlossen. Insgesamt wurden 76 validierte Brustkrebsrisiko-SNPs erfolgreich genotypisiert. Univariate Assoziationen zwischen jedem SNP und Triple-Negativität wurden mittels logistischer Regression geprüft. Verschiedene Variablenselektions- und Regressionsmethoden wurden angewandt, um eine Gruppe von SNPs zu identifizieren, die zusammen mit den klinischen Prädiktoren Alter bei Diagnose und BMI die Prädiktion der Triple-Negativität verbessern. Mittels Kreuzvalidierung wurde die Methode mit der höchsten Genauigkeit bestimmt.

Ergebnisse Der SNP rs10069690 (TERT, CLPTM1L) war der einzige einzelne SNP, der nach p-Wert-Korrektur für multiples Testen signifikant mit Triple-Negativität assoziiert war (p = 0,02). Dieser SNP und 3 weitere in den Genen RAD51B, CCND1 und FGFR2 wurden ausgewählt, um gemeinsam in einem Prädiktionsmodell Triple-Negativität vorherzusagen. Die Hinzunahme dieser 4 SNPs erhöhte die kreuzvalidierte AUC von 0,618 auf 0,625. Alter bei Diagnose war bei Weitem der stärkste Prädiktor.

Schlussfolgerung Die Vorhersage von triple-negativem Mammakarzinom kann verbessert werden, wenn sie nicht nur auf den klinischen Prädiktoren Alter bei Diagnose und BMI basiert, sondern auch auf Brustkrebsrisiko-SNPs. Das Prädiktionsmodell könnte bei der Rekrutierung von Patientinnen für aufwendige molekulare Therapiestudien eingesetzt werden.

Key words

breast cancer - SNPs - triple-negative - subtype prediction - prediction model - variable selection

Schlüsselwörter

Brustkrebs - SNPs - triple-negativ - Subtypvorhersage - Prädiktionsmodell - Variablenselektion

^* Shared last authorship.

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