Abstract 1751: Systematic bias in genomic breast cancer classification due to selecting cases with high tumor percentage and good RNA quality
Authors: Esther H. Lips, Antien L. Mooyaart, Maartje van Seijen, Lennart Mulder, Marlous Hoogstraat, Petra M. Nederlof, Lodewyk F. Wessels, Sjoerd Rodenhuis, Gabe S. Sonke, Jelle Wesseling
Journal: Cancer Research (2017)

Abstract Background Cancer classification, prognostication, and prediction of treatment sensitivity increasingly rely on DNA and RNA-based tests. This approach requires sufficiently high tumor cell percentages to yield enough DNA or RNA for reliable test results. As a consequence, samples of insufficient quality may drop out, e.g. due to poor cellularity or high numbers of tumor infiltrating lymphocytes. We hypothesized that requiring a high tumor cell percentage and high quality RNA causes systemic bias when interpreting genomic test results in breast cancer, as specific breast cancer subgroups may be over- or underrepresented. Patients and methods For this analysis, we used pre-treatment frozen samples from patients included in neoadjuvant chemotherapy trials at the Netherlands Cancer Institute between 2004 and 2012. Histological features and tumor cell percentage were reviewed and assessed by a consultant breast pathologist. Gene expression profiling was done if the tumor cell percentage exceeded 50% and RNA quantity (&amp;gt;1 µg) and quality (RIN value≥ 6.5) were sufficient. We compared patient and tumor characteristics between patients in whom gene expression profiling could be performed and those in whom it could not be performed. In addition, we performed a systematic review on gene expression profiling for breast cancer, to assess the percentage of sample dropout in published studies. Results Frozen biopsies were available from 658 patients (79% of the total study population) and gene expression profiling could be performed in 60% of the cases, a percentage comparable to what is reported in the literature. Reasons for drop out were a low cellularity, poor quality and quantity of the RNA, or a too small biopsy for processing. These patients had more grade 3 tumors (43% versus 34%, p=0.04) and were more node positive (76% versus 69%, p=0.03). Analysis of neoadjuvant chemotherapy response and survival in these patients is ongoing and will be presented at the AACR Annual Meeting 2017. Conclusion Breast cancers for which gene expression data were successfully obtained were associated with a higher grade and with lymph node metastasis. Such tumors represent a more aggressive phenotype and have a relatively poor prognosis, compared to the patients for whom gene expression data could not be measured. As gene expression arrays are now broadly used in a clinical context, it is important to acknowledge this systematic bias, and to be cautious in applying gene expression based tests on different patient populations than a test was developed on. Citation Format: Esther H. Lips, Antien L. Mooyaart, Maartje van Seijen, Lennart Mulder, Marlous Hoogstraat, Petra M. Nederlof, Lodewyk F. Wessels, Sjoerd Rodenhuis, Gabe S. Sonke, Jelle Wesseling. Systematic bias in genomic breast cancer classification due to selecting cases with high tumor percentage and good RNA quality [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1751. doi:10.1158/1538-7445.AM2017-1751