Download Time-Dependent ROC

Prognostic microRNA Signature of TripleNegative Breast Cancer Identified by CrossValidated Cox Model Development
Jianying Zhang, PhD
Charles Shapiro, MD
Center for Biostatistics
Department of Biomedical Informatics
Ohio State University, Columbus, OH
[email protected]
Background - TNBC
• ER (estrogen), PR (progesterone), HER2/neu
(epidermal growth factor receptor) all negative
• Annually 170K new TNBC diagnoses per year
(worldwide), accounting for 12-24% of all breast
cancers
• Poor prognosis: chemo-resistance and early
recurrence
• Limited treatment options
Background - microRNA
• Small (19-25 nucleotide), non-coding RNAs
• Regulatory role: reduce the abundance and
translational efficiency of mRNAs
• microRNAs play key roles in cancer progression and
drug resistance
• oncomiRs regulate tumor suppressor genes
• microRNA signature is necessary and predictive for
prognosis of TNBC
Research Goal
• Identify a miR signature for good prognosis of TNBC
over time, especially within 2-5 years
• Dichotomization of patients into high vs low risk
groups
• Primary endpoint:
time-to-progression (recurrence/death)
TNBC Cohort
• 159 TNBCs from the Wexner Medical Center of the
Ohio State University with first diagnosis from 19852005 (4 before 1995)
• Both survival/recurrence data and valid miRNA
profiling were available
• Demographic/pathologic/clinic outcome data were
collected from Information Warehouse
Clinical Data Summary
Progressi Recurren Progressi P value (cox
on-free ce/death on rate model)
Chemo
multi/single- agent
66
37
36%
no chemo
11
6
35%
White
69
39
36%
Other
7
4
36%
50-
35
23
40%
50+
42
20
32%
Negative
50
19
28%
Positive
26
20
43%
0
32
100%
no recurrence
77
11
13%
Death
Alive
Dead
77
0
2
41
3%
100%
Grade
II
11
2
15%
III
64
38
37%
IV
1
0
0%
Unknown
0
3
100%
Yes
27
17
39%
No
49
22
31%
1
4
80%
Race
Age (mean: 51)
Lymph nodes
Recurrence
Menopause status
recurrence
Unknown
0.985
0
0.554
0.048
0.702
0.11
miRNA Profiling
• Patient primary tumor tissues at diagnosis/surgery
• Nano String nCounter platform for miR profiling
• 6 positive controls, 8 negative controls, 5
housekeeping controls, and 734 miRs
• Technical Normalization by normalization factor (NF)
calculated from positive controls
• Samples with outbound NF were excluded
• Filtering low-expressed miRs by negative control
threshold (398 miRs remained).
• Global normalization – Quantile Normalization
Statistical Challenges/Pitfalls
Model over-fitting: a model fitting the training data
too well while fitting the test data poorly.
Reasons:
• Too many more-than-necessary predictors were
selected
• High-dimensional data: sample size n >> p
(#predictors)
• Same data used to develop the model and assess
the performance
• Training set/data is too small or too noisy
(A) Re-substitution estimates and (B) cross-validated estimates.
Simon et al 2011
Solution: Cross-Validation (CV)
• K-fold CV
- Training data is split into K partitions equally at random
- Each partition (1 to K) is left as the test set role in turns
- Remaining K-1 partitions plays the train set role: build the
model
- Model prediction (risk score for Cox) is evaluated on test set
- After all K folds run, overall model performance is evaluated:
cross-validated AUC, ROC, survival curves (KM), etc.
• Repeat above K-fold CV many times (e.g. 100 times)
Simon et al 2011
Model Building – Cox Regression
• Feature Screening/Selection (FS)
-
Univariate cox regression
Univariate two-group comparison
Clustering
Principle component analysis (PCA)
Linear discriminant analysis (LDA)
• Model Selection (MS)
- Traditional stepwise selection
- Penalized likelihood or shrinkage methods (Lasso/Ridge,
etc.)
- Machine learning
Cox Proportional Hazard Model
• Semi-parametric:
For observation i at time t
hi(t) = h0(t) exp(β1Xi1 + β2Xi2 + … + βpXip) p: # of
predictors
• Linear model:
f(xi) = log(hi(t)/h0(t)) = β1Xi1 + β2Xi2 + … + βpXip
• hi(t)/h0(t): proportional hazard
• f(x) = B’X is called linear risk score function
where B = (β1, β2, …,βp) for p predictors
Parameter Estimate
• OLS (Ordinary Least Square)
To minimize: 𝑛𝑖=1 𝑓 𝑥𝑖 − 𝑥𝑖 ′ β
2
• LASSO (Least absolute shrinkage and selection
operator)
To maximize the Cox’s partial likelihood:
𝐿(β) =
exp(β′ 𝑥𝑡 )
𝑟∈𝐷
′
𝑗∈𝑅𝑡 exp(β 𝑥𝑗 )
where D is set of event indices;
Rt is the set of indices at risk at time t
Subject to:
𝑝
𝑗=1 |β𝑗| ≤ s
Model/Variable Selection
•
•
•
•
•
Forward (AIC)
Backward (AIC)
Stepwise (AIC)
Penalized or shrinkage method (Lasso)
Cross-Validation is used to find the optimal tuning
parameter (maximum likelihood)
Model Assessment (MA)
• Continuous response:
Mean-square error: bias2 + variance
• Binary/categorical response:
AUC-area under the curve (ROC)
Sensitivity/specificity
• Time-to-event response:
Time-dependent ROC/AUC (Heagetty et al, 2000)
Time-Dependent ROC
• ROC and AUC are estimated in terms of landmark
time t
• Sensitivity: Pr[M ≥ c | T ≤ t]
• Specificity: Pr[M < c | T > t]
T: survival time;
M: test value (risk score)
C: threshold of positivity
• Two methods:
KM – Kaplan Meier
NNE – Nearest Neighbor Estimate
Heagerty et al 2000
CV Modeling on TNBC Data
N=159
5 exclusions: older
than 80 or no PF
N=154
Validation Set (N=34)
Training Set (N=120)
5-fold CV
Final model
100 iterations
FS
FS0: No FS
FS1: Univariate Cox
FS2: progression vs
progression-free
MS
MS1: CV stepwise
MS2: CV Lasso
Other covariates
(NODES Positive)
Add
Skip
MA
Time-dependent
ROC/AUC
Risk classification
Risk score prediction
Cross-validated ROC/AUC/risk
classification
FS0
FS1
so-b
.1
FS2
o.13
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5
o.1.
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uns
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t.l as
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-Ste
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se
Co x
-Las
so-b
.13
Co x
-Las
so-b
.1
Co x
-Las
so-b
.0
Uni
Uni
Uni
Uni
Non
-Las
onl y
so-b
.0
DE S
Non
-Las
NO
0.3
0.4
0.5
0.6
CV AUC (KM)
0.7
0.8
Cross-validated AUC by KM (100 runs)
FS2 + NODES
FS0
FS1
so-b
.1
FS2
o.13
.f1.1
5
o.1.
f1.1
5
o.0.
f1.1
5
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f1.1
5
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ta.s
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uns
c al e
d
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ta.l a
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pcv
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5
t.l as
s
t.l as
s
t.l as
s
Co x
-Ste
pwi
se
Co x
-Las
so-b
.13
Co x
-Las
so-b
.1
Co x
-Las
so-b
.0
Uni
Uni
Uni
Uni
Non
-Las
onl y
so-b
.0
DE S
Non
-Las
NO
0.4
0.5
0.6
CV AUC (NNE)
0.7
0.8
Cross-validated AUC by NNE (100 runs)
FS2 + NODES
High-Frequency miRs
T-FS w/ FC 1.15, Stepwise CV, plus NODES (scaled) T-FS w/ FC 1.15, Stepwise CV, plus NODES (unscaled)
hsa.miR.1913
hsa.miR.361.5p
hsa.miR.1305
hsa.miR.566
hsa.miR.623
hsa.miR.1280
hsa.miR.516a.3p
hsa.miR.146b.5p
hsa.miR.548a.3p
hsa.miR.423.5p
hsa.miR.205
hsa.miR.2115
hsa.let.7c
hsa.miR.199a.5p
hsa.miR.122
hsa.miR.1252
hsa.miR.874
hsa.miR.1253
hsa.miR.365
hsa.miR.525.5p
hsa.miR.578
hsa.miR.1286
hsa.miR.16
hsa.miR.1285
hsa.miR.142.3p
hsa.miR.1307
hsa.miR.142.5p
kshv.miR.K12.11
hsa.miR.363
hsa.miR.155
hsa.miR.1201
hsa.miR.31
hsa.miR.361.5p
hsa.miR.197
hsa.miR.1979
hsa.miR.613
hsa.miR.362.5p
hsa.miR.1280
hsa.miR.146b.5p
hsa.miR.1305
hsa.miR.423.5p
hsa.miR.2115
hsa.miR.516a.3p
hsa.miR.205
hsa.miR.199a.5p
hsa.let.7c
hsa.miR.1286
hsa.miR.874
hsa.miR.365
hsa.miR.578
hsa.miR.1253
hsa.miR.525.5p
hsa.miR.1285
hsa.miR.16
hsa.miR.142.3p
hsa.miR.1307
hsa.miR.142.5p
kshv.miR.K12.11
hsa.miR.363
hsa.miR.155
0
100
200
300
Freq (/500) each miR
400
0
100
200
300
Freq (/500) each miR
400
Significance Test of the AUC
• Hypothesis: AUC = 0.5 vs AUC > 0.5
• 500 random permutations of the time-to-event and
event status
• Five-fold cross-validated AUC was calculated within
each permutation based on the candidate model
development procedures
• For example, for the procedure with the highest mean
AUC:
P(AUC>= 0.61) = 0.05
P(AUC >= 0.63) = 0.04
P(AUC>= 0.68) = 0.032
Optimal Model
• Full training set (n=120) was used for modeling
• Feature selection/screening by comparing progression
vs progression-free with FC>1.15 and P<0.06
• 5-fold cross-validated stepwise cox regression was used
for model selection.
• Above 5-fold CV stepwise was repeated 100 times to
pick the most frequent model
• The miRs in the most frequent model plus NODES to fit
the final cox model
• Evaluate the final cox model on validation data set
Final model – Full train set
• Five miRs were selected
miR-363
miR-155
miR-142.5p
kshv.miR.K12.11
miR-1307
# miRs
5
4
2
miRs
freq
miR-363 + miR-155 + miR-142.5p + kshv.miR.K12.11 + miR-1307
27
miR-363 + miR-155 + miR-142.5p + kshv.miR.K12.11
24
miR-363 + miR-155
13
IPA Enrichment Pathway Analysis
IPA – Diseases or Functions
Categories
Diseases or Functions Annotation # Molecules
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
acute lymphocytic leukemia
advanced colorectal adenoma
advanced Dukes' stage colorectal cancer
ALK fusion negative anaplastic large cell lymphoma
B-cell lymphoma
Hematological Disease, Immunological Disease, Organismal Injury and Abnormalities
Gastrointestinal Disease, Organismal Injury and Abnormalities
Gastrointestinal Disease, Organismal Injury and Abnormalities
Hematological Disease, Immunological Disease, Organismal Injury and Abnormalities
Hematological Disease, Immunological Disease, Organismal Injury and Abnormalities
Cancer, Organismal Injury and Abnormalities, Reproductive System Disease
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
Cancer,
breast cancer
Hematological Disease, Immunological Disease, Organismal Injury and Abnormalities
Burkitt's lymphoma
Organismal Injury and Abnormalities, Reproductive System Disease
early stage invasive cervical squamous cell carcinoma
Organismal Injury and Abnormalities
EPCAM positive tumor
Organismal Injury and Abnormalities
growth of tumor
Gastrointestinal Disease, Hepatic System Disease, Organismal Injury and Abnormalities
hepatocellular carcinoma
Hematological Disease, Immunological Disease, Organismal Injury and Abnormalities
Hodgkin's disease
Endocrine System Disorders, Organismal Injury and Abnormalities, Respiratory Disease
large cell lung cancer
Gastrointestinal Disease, Hepatic System Disease, Organismal Injury and Abnormalities
liver metastasis
Gastrointestinal Disease, Organismal Injury and Abnormalities
metastatic colorectal cancer
Organismal Injury and Abnormalities
metastatic melanoma cancer
Hematological Disease, Immunological Disease, Organismal Injury and Abnormalities
multiple myeloma
Neurological Disease, Organismal Injury and Abnormalities
neuroblastoma
Endocrine System Disorders, Organismal Injury and Abnormalities, Respiratory Disease
neuroendocrine lung cancer
Organismal Injury and Abnormalities, Respiratory Disease
non-small cell lung cancer
Endocrine System Disorders, Gastrointestinal Disease, Organismal Injury and Abnormalities
pancreatic ductal adenocarcinoma
Hematological Disease, Immunological Disease, Organismal Injury and Abnormalities
precursor T-cell lymphoblastic leukemia-lymphoma
Hematological Disease, Immunological Disease, Neurological Disease, Organismal Injury and Abnormalities
primary central nervous system lymphoma
Organismal Injury and Abnormalities
primary melanoma
Organismal Injury and Abnormalities, Reproductive System Disease
progesterone receptor positive breast carcinoma
Cellular Development, Cellular Growth and Proliferation, Organismal Injury and Abnormalities, Respiratory Disease, Tumor
proliferation
Morphology
of lung adenocarcinoma cells
Hematological Disease, Immunological Disease, Organismal Injury and Abnormalities, Tissue Morphology
quantity of lymphoma
Hematological Disease, Immunological Disease, Organismal Injury and Abnormalities
sinonasal natural killer/T-cell lymphoma
Endocrine System Disorders, Organismal Injury and Abnormalities, Respiratory Disease
small cell lung cancer
Organismal Injury and Abnormalities, Respiratory Disease
squamous cell lung cancer
Gastrointestinal Disease, Organismal Injury and Abnormalities
stage 2 colorectal cancer
Organismal Injury and Abnormalities, Reproductive System Disease, Skeletal and Muscular Disorders
uterine leiomyoma
2
1
1
1
2
3
1
3
1
2
2
1
1
1
1
3
1
1
2
3
1
1
1
3
1
1
1
3
1
2
1
1
Recent Published Evidence
miR
hsa.miR.155
has-miR-142-5p
has-miR-363
Author/year
miR-155 supresses ErbB2-induced malignant
He et al, 2016
transforemation of breast epithelial cells
miR-155 represses matrix Gla protein to promote oncogenic
Tiago et al, 2016
signals in MCF-7 cells
Hemmatzadeh et al 2016 miR-155 is one oncomirs in treatment of breast cancer
Loss of miR-155 enhances C/EBP-β-mediated MDSC
Kim et al 2016
inflitration and tumor growth
miR-155 expression change is associated with breast cancer
lymph-node metastasis
Petrovic et al 2016
Autoregulatory response to oncogenic drivers for good
Tembe et al 2014
prognosis for melanoma
Jayawardana et al 2016 One of the prognostic biomarkers in metastatic melanoma
MiR-363-3p inhibits the epithelial-to-mesenchymal transition
Hu et al, 2016
and suppresses metastasis by targeting Sox4
Loss of tumor suppressive miR-363-3p overexpressed
Li et al, 2016
oncogenic cAMP responsive binding protein 1
Tumor suppressor role of miR-363-3p: inhibit cell
Song et al, 2015
growth/migration by targeting NOTCH1
Khuu et al, 2016
Anti-proliferative properties
Beltran et al , 2011
Suppresses breast tumor growth and metastasis
Zhang et al, 2014
Sensitizes cisplatin-induced apoptosis targeting in Mcl-1
kshv-miR-K12-11 Rainy et al, 2016
Dahlke et al, 2012
Skalsky et al, 2007
miR-1307
Findings
Zhou et al, 2015
Induce non-cell-autonomous target gene regulation by viral
oncomiR spreading between B and T cells
promotes B-cell expansion in vivo
Coded by a herpesvirus as an ortholog of miR-155 in B cell
lymphoma developmenbt
Up-regulated in chemoresistant epithelial ovarian cancer
tissues, but not associated with lymph node metastasis
Cancer
breast cancer
breast cancer cell line
breast cancer
breast cancer
melanoma
melanoma
colerecctal cancer
renal cancer
gastric cancer
carcinoma
breast cancer
breast cancer
Kaposi sarcoma
Kaposi sarcoma
Kspodi'd sarcoma
Ovarian cancer
Final Model Validation (n=34)
Event: Recurrence/death
Final model
P value (log-rank test) P value (log-rank test)
AUC (2-year) AUC (3-year) AUC (5-year) by validation median by train median
Scaled data
0.84
0.72
0.77
0.065
0.0008
Unscaled data
0.87
0.79
0.83
0.065
0.023
Event: Recurrence
Final model
P value (log-rank test)
AUC (2-year) AUC (3-year) AUC (5-year) by validation median
P value (log-rank test)
by train median
Scaled data
0.79
0.67
0.67
0.028
0.337
Unscaled data
0.79
0.68
0.68
0.028
0.154
0.6
0.4
0.2
sensitivity
0.8
1.0
Validation: ROC of 5-year on the 5-miR+NODES model
0.0
AUC=0.79
0.0
0.2
0.4
0.6
0.8
1.0
1-specificity
Sensitivity: P(high risk | event at time t);
Specificity = P(low risk | event-free at time t);
By cutoff of risk score = -0.04, sensitivity=80% and specificity = 64%.
Validation Set
Cutoff: median of train risk score
0
50
100
Time to event (months)
0.8
0.6
0.4
High risk
Low risk
P = 0.023
0.0
P = 0.065
0.2
Event-Free Probability
0.8
0.6
0.4
0.2
High risk
Low risk
0.0
Event-Free Probability
1.0
1.0
Cutoff: median of validation risk score
150
0
50
100
Time to event (months)
150
Ongoing …
•
•
•
•
•
•
•
•
Any other predictive covariates missing?
Non-molecule predictors parallel to miRNA predictors?
Any covariates competing with miRNA biomarkers?
Covariates forced into the model, or involving in
variable/selection?
Does the “final” model still need further variable
selection in terms of their parameter estimate and P
values?
More model selection methods?
Will the recommended model comparison procedure
work for other public/published data?
Can the proposed miRNA signature be validated in other
independent studies?
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Thanks to:
Dr. Charles L. Shapiro
Medicine, Hematology and Medical Oncology
Mount Sinai Health System
New York, NY
James Cancer Hospital and Solove Resarch Institute
The Ohio State University
Columbus, OH