Download Prediction of Sensitivity of Rectal Cancer Cells in Response to

Prediction of Sensitivity of Rectal Cancer Cells
in Response to Preoperative Radiotherapy by
DNA Microarray Analysis of
Gene Expression Profiles
Toshiaki Watanabe, Yasuhiro Komuro, Tomomichi Kiyomatsu, Takamitsu
Kanazawa,Yoshihiro Kazama, Junichiro Tanaka, Toshiaki Tanaka, Yoko
Yamamoto,Masatoshi Shirane, Tetsuichiro Muto, and Hirokazu Nagawa
Department of Surgical Oncology, University of Tokyo Hospital, Tokyo
Cancer Res 2006; 66
Background
• Rectal cancer
– Preoperative radiotherapy : major treatment modality
– Response to radiotherapy : differs among individual tumors
Radiotherapy
Prediction
Responders
Nonresponders
Chemoradiotherapy
• Advances in expression genomics by DNA microarray
 Genes expression profiles of cancer cells
 Distinguishing responders & nonresponders
• Purpose
– Responders and nonresponders
to preoperative radiotherapy in rectal cancer
 Identify a set of discriminating genes
Materials and Methods
• Patient Samples
– Fifty-two rectal cancer patients
– Preoperative radiotherapy
– Prospectively collecting biopsy specimens during colonoscopic
examination before starting preoperative radiotherapy
– Histologic examination & RNA extraction
– RTx.: total dose of 50.4 Gy of radiation
– Standardized curative resection, following an interval of 4 weeks
after radiotherapy
Figure 1. Endoscopic view of rectal cancers before and after
preoperative radiotherapy
• Response to Radiotherapy
– Histopathologic examination of surgically
resected specimens
– Semiquantitative classification system
– Responder : regression grade 2 or
– Nonresponder : regression grade 0 or 1
• RNA Isolation and Microarray Procedures
TISSUE
lysis
Total RNA
Sepasol-RNA I
Reverse
Transcription
T7-(dT)24
primer
cDNA
MEGAscript
Transcription
Staining
&
Scanning
Hybridization
to human
U95Av2
GebeChip
(In Vitro
Transcript Kit)
Biotin-labeled cRNA :
50 to 100 nucleotides
• Statistical Analysis
– Class prediction
• Training set (35 samples)
– To build a predictive model
– Distinguish a gene set & prognostic signature
• Testing set (17 samples)
– Independent validation
– Gene functional category analysis
Results
• Gene expression profiling: class comparison
between responders and nonresponders
– 35 training samples
• 7 : responders, 28 : nonresponders
– 17 testing samples
• 6 : responders, 11 : nonresponders
– Responders and nonresponders
• Clinicopathologic factors
: gender, age, histologic classification, preoperative tumor stage
 No significant difference
Related apoptosis
Inhibition of
apoptosis
13
:responder
20
:responder
; Signal transduction
Inducing of
apoptosis
; Cell proliferation
; Cell adhesion
Figure 2.
Supervised clustering
of rectal cancer and 77 genes
Red: overexpression
Green : underexpression
Yellow : nonresponders
Red : responders
Figure 3.
Discriminating genes
were used to generate
a three-dimensional
(from 33-dimensional)
plot of the data
• Gene functional category analysis
– To investigate the biological functions
– Gene Ontology category analysis
• cell growth, signal transduction,
cell differentiation, receptor activity
– Selected, discriminating genes
• cell adhesion molecule activity
: significantly higher proportion
• Gene expression profiling: class prediction of
responders and nonresponders
– k-nearest-neighbor method
Training sample
Test sample
Accuracy of class
prediction
88.6 %
82.4 %
Sensitivity
71.4 %
50 %
Specificity
92.9 %
100 %
Positive predictive
value
71.4 %
100 %
Negative predictive
value
92.9 %
78.6 %
Discussion
• In rectal cancer
– Gene expression profiling
: Predicting response to preoperative chemoradiotherapy
- Ghadimi BM et al.J Clin Oncol 2005;23
• Chemoradiotherapy
– Postoperative morbidity rate : high
 Radiotherapy : more feasible modality in conducting
neoadjuvant therapy for rectal cancer
• Response to radiotherapy  Radiotherapy
Nonresponse to radiotherapy  Chemoradiotherapy
• Expression differed significantly between responders
and nonresponders : 33 novel genes
– 20 genes : higher & 13 genes : lower in responders
• Gene Ontology category analysis
– Transcription, Cell growth, Signal transduction, Apoptosis
– Induction of apoptosis
: Important factor in determining the response to radiotherapy
• Five apoptosis-related genes
– Lumican
• Member of the small leucine-rich proteoglycan family
• Increasing Bax expression & suppresses cell proliferation
– Thrombospondin
• Potent endogenous inhibitor of tumor growth & angiogenesis
• Inhibits cell proliferation and induces apoptosis
– Galectin-1
• Initiate cell apoptosis
Induce apoptosis
Significantly higher expression in responders
– Cyclophilin 40 & Glutathione peroxidase
• Inhibitory effects on apoptosis
significantly lower expression in responders
Conclusion
• Gene expression profiling
 Useful in predicting response to radiotherapy
to establish an individualized tailored therapy
for rectal cancer.