Download ORS 2017 Annual Meeting Poster No.1292

Signature genetic mutations related to refractory bone metastasis in non-small cell lung cancers.
Sungwook Seo1, MD, Yoonna Choi, MD1, Sehun Kim, MD2, Hyeon Lee, MD2
1
Professor, Department of Orthopaedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
2
Orthopedist, Department of Orthopaedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
Abstract
Introduction
Intractable bone metastasis refers to pathologic fractures in which all the treatment such as chemo and radiation therapy has failed. This fracture
and following pain increases morbidity and functional impairment, ultimately deteriorating quality of patient’s life. It has been known that there are
heterogeneous cancer cells which carry different mutant genes within a solitary occurrence and even between metastatic and the primary one. These
specific mutations of metastatic cancer are now emerging as explanation why these tumors migrate and resistant to chemotherapy. Therefore,
unearthing those specific genes that is responsible for metastasis is most pivotal to many studies. Employing next generation sequencing technique, this
study is to identify gene mutations of primary lung cancer and metastasized bones that discriminate them from normal cells and further analyze this
data to describe a least number of genes and algorithm to classify them systematically.
Methods
We collected a total number of 77 tissues from patients with non-small cell lung cancer (NSCLC) who were treated during January 2012 to
December, 2014. Samples were submitted and classified according to the site; 53 from primary lung cancers (non-bone), 24 were metastasized bones.
Using whole exome sequencing, we analyzed frozen tissue samples and blood for 81 genes that are commonly expressed in cancers. Lasso regression
method, one of the penalized regression techniques enables us to find minimal number of genes that are related to bone metastasis. We aimed to find
the best prediction model that classifies metastatic bone cancer with a minimal number of genes. There is one Classifier model that includes whole
genome, another one with genes extracted by LASSO method, and the last one with even lower number of genes resulted from cross validation of
LASSO method.
Results
LASSO regression model selected 32 genetic mutations for classifying metastatic lung cancer. To identify a least number of genes and to
minimize the effect on our classification validity, bootstrap samples were applied as train data into 6 different classifiers and compared accuracy with
test data #1. Based on the above results, we divided classifiers into three group; 80, 32, 4 genes and compared their performance with third-party test
data #2. This comparison study revealed a 4 gene signature that predicts bone tumor occurrence, and they were following: GNAQ, ARID1A, MET,
PTCH1. Using this prediction model, we could classify 80% of all bone cancers with a great accuracy.
Discussion
The four genes we have identified in our study are already known for their role on tumorigenesis and aggressiveness of cancer, however, not for
the metastasis to bone. Further studies will be needed to substantiate their role of biological pathways to bone metastasis
Significance
We tried to identify gene mutations of primary lung cancer and metastasized bones that discriminate them from normal cells and further analyze
this data to describe a least number of genes and algorithm to classify them using machine learning algorithm.
ORS 2017 Annual Meeting Poster No.1292