沈洪兵 南京医科大学公共卫生学院 tel. (fax.): 86-25-86862756 email: hbshen@njmu
DESCRIPTION
血浆 miRNA 表达谱与肺癌预后关系研究. 沈洪兵 南京医科大学公共卫生学院 Tel. (Fax.): 86-25-86862756 Email: [email protected]. MicroRNAs and Cancer. (1) In the nucleus, pri-miRNAs processed by Drosha as pre-miRNAs; (2) pre-miRNAs exported by Exportin-5 into cytoplasm; (3) Processed by Dicer as mature miRNAs ; - PowerPoint PPT PresentationTRANSCRIPT
沈洪兵南京医科大学公共卫生学院Tel. (Fax.): 86-25-86862756Email: [email protected]
血浆 miRNA 表达谱与肺癌预后关系研究
(1) In the nucleus, pri-miRNAs processed by Drosha as pre-miRNAs;
(2) pre-miRNAs exported by Exportin-5 into cytoplasm;
(3) Processed by Dicer as mature miRNAs;
(4) Perfect complement with 3’UTR: mRNAs cleavage; in-perfect complement: translational repression.
Nat. Rev. Genet. 5(7):522-31,2004
MicroRNAs and Cancer
miRNA as Novel Important Tumor Suppresser Genes and Oncogenes
The discovery of microRNAs (miRNAs) has opened new avenues for cancer diagnosis and prediction of treatment response
Nature Reviews Cancer 6:259-269, 2006
Nature 435(7043):834-838,2005
miRNA expression profiles can classify human cancers
miRNA Profiling May Provide More Accurate Classification of Cancer
Subtypes than the Expression Profiles of Protein-coding Genes
In lung cancer, it has been shown that miRNA expression profiles and specific miRNAs in lung tissue are correlated with disease prognosis and clinical outcome
Cancer Cell. 2008;13:48-57. Cancer Cell. 2006;9:189-98.
Circulating microRNAs as stable blood-based markers for cancer detection
PNAS, 2008 Jul
miRNAs Exist in Serum?
Characterization of microRNAs in serum: a novel class of
biomarkers for diagnosis of cancer and other diseases
Cell Res. 2008 Oct
Why Serum Biomarkers are Important?
Easy to get (non-invasive)
Repeatable
In high risk population (cancer screening)
The discovery of stable serum miRNAs
Chen et al. 2007 Cell Res.
Solexa Whole Genome Sequencing
miRNAs (21-23nt) were most abundant sequences in serum RNAs
Serum miRNAs in Healthy People
Chen et al. 2007 Cell Res.
90 92
Exposure
Lung Cancer
Clinical Outcome
Serum miRNA expressions – prognosis?
Research Question:
Serum miRNA and Lung Cancer Survival
30 live cases34-68 months
30 dead cases2-22 months
120 patients 123 patients
Case-only study (303 cases)
DiscoverySolexa sequencing
Training setqRT-PCR
Validation setqRT-PCR
Randomly classified
Subjects and Assays
Study Population: Early stage (I-IIIa) adenocarcinoma and squamous cell lung cancer patients, treated with both operation and adjuvant chemotherapies
Discovery samples: “Long survival” patients: 30 live patients survival more than 30 months (34.6-61.8 months) (Stage I, 10; stage II, 10; stage III, 10 )“Short survival” group: 30 dead patients survival less than 25 months (2.0-22.5 months) (Stage I, 10; stage II, 10; stage III, 10 )
Training and testing sample sets:243 participants were classified as 120 for training and 123 for testing by computer-generated random numbers. qRT-PCR assay was conducted to quantify the serum levels of miRNAs.
Serum RNA Extraction Efficiency and the Reliability of qRT-PCR Assay
R2 = 0.9907
0
5
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25
30
-2 -1 0 1 2 3 4
log10 [the amount of ath-miR156a (unit: fmol)]
CT
valu
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0.1-1000 fmol synthesized ath-miR156a miRNA put into 250ul serum : R=0.99
R2 = 0.9798
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35
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0 5 10 15 20 25 30 35 40 45
Technical replicate 1
Tec
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32 miRNAs detected in 2 repeat samples : R=0.98
Copies of Solexa Sequencing and qRT-PCR Expression of the 11 miRNAs (30 LS vs 30 SS)
miRNA
Long survival group Short survival groupRatio P **
NCopies
*Mean±s.d. median N
Copies*
Mean±s.d. median
miR-486 30 163 0.658±0.361 0.669 30 3205 1.129±0.542 1.065 0.05 0.0002
miR-22 30 18 0.722±1.225 0.375 30 209 1.103±2.401 0.254 0.09 0.443
miR-30d 30 28 0.455±0.371 0.341 30 165 1.353±0.738 1.122 0.17 4.3×10-7
miR-21 30 230 1.490±0.544 1.480 30 44 1.239±0.501 1.239 5.23 0.069
miR-26b 30 56 1.423±1.337 0.943 30 10 1.240±0.973 0.793 5.60 0.545
let-7i 30 153 2.249±1.805 1.789 30 26 1.845±1.139 1.563 5.88 0.304
miR-378 30 212 2.946±5.963 0.740 30 34 4.407±11.335 1.042 6.24 0.535
miR-1 30 2918 2.918±3.818 1.182 30 407 0.408±0.608 0.180 7.17 0.001
miR-206 30 1979 1.471±0.898 1.450 30 196 1.500±1.023 1.37910.1
00.907
miR-146b 30 81 1.309±1.012 1.114 30 6 1.299±0.823 1.31513.5
00.966
miR-499 30 112 1.096±0.465 1.039 30 6 0.683±0.269 0.71218.6
70.0001* By Solexa sequencing on pooled samples
** Student t test between the two groups
The 4 miRNAs Expression and NSCLC Patients’ Survival of Discovery Data Set
miRNAs PatientsN=60
DeathsN=30
MST (Months)
Adjusted HR(95% CI)
TrendP
miR-486
Q1(<=0.470) 16 3 Not reached 1.00
Q2(0.470-0.830) 14 5 Not reached 3.34(0.75-14.94)
Q3(0.830-1.190) 15 10 9.97 7.46(1.95-28.58)
Q4(>1.190) 15 12 13.77 7.26(1.94-27.23) 0.0009miR-30d
Q1(<=0.325) 15 1 Not reached 1.00
Q2(0.325-0.760) 15 5 Not reached 6.51(0.75-56.83)
Q3(0.760-1.280) 15 10 13.77 22.70(2.69-191.85)
Q4(>1.280) 15 14 8.7 41.02(5.22-322.53) <0.0001miR-1
Q4(>1.465) 15 2 Not reached 1.00
Q3(0.525-1.465) 15 6 Not reached 6.03(0.70-52.29)
Q2(0.180-0.525) 15 11 12.27 12.70(1.54-104.98)
Q1(<=0.180) 15 11 9.8 45.17(5.73-355.82) <0.0001miR-499
Q4(>1.115) 15 1 Not reached 1.00
Q3(0.815-1.115) 15 5 Not reached 3.32(0.66-16.61)
Q2(0.605-0.815) 13 8 15.17 9.27(1.98-43.41)
Q1(<=0.605) 17 16 7.2 8.73(1.88-40.58) 0.0006
The 4 miRNAs Expression and NSCLC Patients’ Survival of Training/Testing Sets
miRNAsPatientsTraining /Testing
DeathsMST
(Months)
Adjusted HR1(95% CI) 1
Training
Adjusted HR2(95% CI) 1
Testing
miR-486
Low(<=0.795) 60/62 16/20 ----/39.93 1.00 1.00High (>0.795) 60/61 24/23 31.33/31.33 1.78(0.91-3.49) 1.70(0.91-3.18)
miR-30d
Low(<=0.680) 60/58 13/17 ----/42.6 1.00 1.00High(>0.680) 60/65 27/26 22.97/31.6 4.57(2.24-9.34) 3.27(1.63-6.55)
miR-1
High(>0.675) 60/62 11/16 ----/42.6 1.00 1.00Low(<=0.675) 60/61 29/27 22.97/31.8 3.04(1.49-6.22) 2.81(1.43-5.50)
miR-499
High(>0.750) 58/74 13/21 ----/43.17 1.00 1.00Low(<=0.750) 62/49 27/22 22.97/31.6 2.76(1.39-5.50) 2.14(1.10-4.14)
Number of high-risk miRNAs0-1 37/41 4/10 ----/43.17 1.00 1.002 43/48 8/12 41.67/36.77 2.62(0.76-9.08) 1.82(0.73-4.52)3 34/30 23/17 17.83/18.73 17.38(5.55-54.45) 11.59(4.40-30.53)4 6/4 5/4 17.03/17.08 24.39(5.96-99.75) 16.25(4.46-59.19)
0 10 20 30 40 50 60
Months
0.0
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1.0
Cu
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P < 0.0001
0 10 20 30 40 50 60 70
Months
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Cu
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Training Set (120 patients) Testing Set (123 patients)
Number of High-Risk miRNAs and NSCLC Patients’ Survival of Training/Testing Sets
P < 0.0001
MonthsMonths
Survival rate
43
2
1
4
3
2
1
The 4 miRNAs
Expression and
NSCLC Patients’
Survival of all Sample
Sets
Number of high-risk miRNAsPatientsN=303
DeathsN=113
MST (Months)Adjusted HR
(95% CI)
0-1 100 14 Not reached 1.002 107 28 Not reached 3.03(1.58-5.78)3 75 51 17.83 16.83(8.79-32.23)4 21 20 12.27 36.06(17.17-75.77)
0 10 20 30 40 50 60 70
Months
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Cu
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P < 0.0001 4
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Risk Score Analysis of 303 NSCLC Patients
Low risk miRNA signatures
miR-486miR-30dmiR-499 miR-1
High risk miRNA signatures
Risk Score=0.415
020
4060
Alive Death
Each patient was assigned a risk score according to a linear combination of the expression level of the miRNA, weighted by the regression coefficients from the training samples
Risk Score and NSCLC Survival
Risk score Patients Deaths MST (Months)Log-rank
PAdjusted HR
(95% CI)
Training set N=120 N=40
Low(<=0.415) 60 7 Not reached 1.00
High(>0.415) 60 33 20.3 <0.0001 10.74(4.28-26.96)
Testing set N=123 N=43
Low(<=0.415) 75 16 43.17 1.00
High(>0.415) 48 27 23.97 <0.0001 6.35(3.07-13.15)
All data sets N=303 N=113
Low(<=0.415) 167 26 Not reached 1.00
High(>0.415) 136 87 18.43 <0.0001 9.31(5.78-14.98)
Stepwise Cox Proportional Hazards Analyses on NSCLC Survival
Final variables β SE HR 95% CI P
Stage 0.51 0.12 1.67 1.32-2.12 <0.0001
miR-486 (high verse low) 0.90 0.22 2.47 1.61-3.79 <0.0001
miR-30d (high verse low) 1.41 0.23 4.10 2.62-6.42 <0.0001
miR-1 (low verse high) 1.17 0.23 3.22 2.04-5.08 <0.0001
miR-499 (low verse high) 1.02 0.22 2.78 1.81-4.27 <0.0001
Final variables β SE HR 95% CI P
Stage 0.48 0.12 1.62 1.29-2.03 <0.0001
The four-miRNA signature (>0.415 vs <0.415)
2.23 0.24 9.31 5.79-14.97 <0.0001
Conclusions
Serum miRNA signature can be also used to predict the subgroup of NSCLC patients with poor prognosis (short survival).
Journal of Clinical Oncology 2010 (IF=17.2)
Key Grant of National Natural Science Foundation of China (30730080);
National Outstanding Youth Science Foundation of China (30425001);
The China National Key Basic Research Program Grants (2002CB512902)
Dr. Dongxin Lin Cancer Institute/Hospital, Chinese Academy of Medical Sciences, Beijing, China
Dr. Chenyu Zhang Naning University, Nanjing, China
Dr. Lin Xu Jiangsu Cancer Hospital, Nanjing, China
Dr. Yijiang Chen First Affiliated Hospital of Nanjing Medical University, Nanjing, China
Acknowledgements
Thanks!