PhD Scientific Days

Poster Presentation: Oncology

P41: Predicting lymph node status in breast cancer based on gene expression signature of the primary tumor

Előadó neve

Dr. Sztupinszki, Zsófia, PhD

Előadó munkahelye

2nd Dept. of Pediatrics, Semmelweis University, Budapest, Hungary

Előadó telefonszáma

+36203561862

Előadó e-mail címe

sztup@hotmail.com

Az előadás címe

Predicting lymph node status in breast cancer based on gene expression signature of the primary tumor

Szerző(k) neve és munkahelye

Zsófia Sztupinszki1, Balázs Győrffy1,2

1. 2nd Dept. of Pediatrics, Semmelweis University, Budapest, Hungary
2. MTA TTK Lendület Cancer Biomarker Research Group, Budapest, Hungary

Szekció

Poster Presentation: Oncology

Data of the presenter

Doctoral School: Doctoral School of Pathological Sciences
Program: Oncology
Supervisor: Dr. Balázs Győrffy
E-mail address: sztup@hotmail.com

Text of the abstract

Introduction: Generally, 40% - 60% of patients have no disease in axillary lymph nodes (ALN) other than the sentinel node itself, thus these patients are undergoing unnecessary ALN dissection with no additional therapeutic benefit or further staging information provided. Our aim was to determine the presence of lymph node metastasis based on the primary tumor’s gene-expression signature
Materials and methods: Using publicly available datasets from the GEO repository we established a database containing clinical and microarray data for 2341 breast cancer patients. The patients were classified into three subgroups: ER-negative, ER-positive / MKI67 positive and ER-positive / MKI67-negative based on gene expression. To identify differently expressed genes between LN negative and positive cases, we used RankProduct algorithm per cohorts, then used these gene sets to build a boosted decision tree predictive model. To validate our model, we collected 100 independent validation samples of breast cancer patients who underwent surgical resection between 2004 and 2010.
Results: We aimed to optimize our classification based on negative predictive value, thus for patients with negative LN prediction the axillary dissection and the adjuvant chemotherapy may be omitted. In our internal validation set of ER-negative patients we achieved negative predictive value (NPV) of 0.85 and accuracy (ACC) of 0.88 and in the ER-positive and MKI67-positive group a NPV=0.77 and ACC=0.90. In case of our independent, external validation set of 100 patients our prediction model performed well. We predicted involvement in the ER-negative cohort with a NPV=0.92, ACC=0.73, and in the ER-positive and MKI67-positive group with a NPV=1.0, ACC=0.86.
Conclusion: In summary, here we present a gene-expression based prediction for lymph node status for ER-negative and ER-positive and MKI67-positive patients.
Fundings: supported by the ÚNKP-16-3-III New National Excellence Program of The Ministry of Human Capacities and the MTA Lendület programme.

Azonosító

P41

Kind

Szabad

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

1233

Authors (legacy)