ColoGuide_Stage_III-Msig signature reporting file
Generated by ReProMSig version 1.0
Date created: 2022-03-23
Last update: 2023-07-18
Lead contact: Jianmin Wu
Email address: wujm@bjmu.edu.cn
Organization: Peking University Cancer Hospital & Institute
Reporting file URL: https://omics.bjcancer.org/prognosis/open/ColoGuide_Stage_III-Msig_4e0d91d3_report.html
I. Signature summary
Signature name: ColoGuide_Stage_III-Msig
Signature type: Prognostic molecular signature
Signature generation:
- 275 candidate genes (ColoGuide_Stage_III-Msig candidate genes) were used in analysis
- Selection of robust candidates by a bootstrapping approach (200 iterations, occurrence > 50% frequency) using supervised principal components approach (SPCA)
- The final prediction model built by multivariate Cox regression
Patient risk stratification: 2 Groups
Signature description: A 8-gene expression signature for stage III colorectal cancer prognosis, developed and evaluated by ReProMSig, using published gene expression datasets and clinicopathological information.
Training & Validation datasets
| Class | Dataset name | Primary site | Disease type | Molecular dataset | Endpoint | Pubmed | Clinical trial Registry |
|---|---|---|---|---|---|---|---|
| Training dataset | ProLT_stageIII | Colorectal | Adenomas and Adenocarcinomas (76) | GSE30378 , GSE24550 (76 in total) | DFS (37 events, 76 patients) | 22991413 | |
| Validation dataset | ProLT_stageII | Colorectal | Adenomas and Adenocarcinomas (96) | GSE30378 , GSE24550 (96 in total) | DFS (30 events, 96 patients) | 22991413 | |
| Validation dataset | ProV_stageII | Colorectal | Adenomas and Adenocarcinomas (108) | GSE14333 , GSE17538 (108 in total) | DFS (14 events, 108 patients) | 19996206 , 19914252 | |
| Validation dataset | ProV_stageIII | Colorectal | Adenomas and Adenocarcinomas (110) | GSE14333 , GSE17538 (110 in total) | DFS (37 events, 110 patients) | 19996206 , 19914252 |
II. Items of methods
Item 4. Source of data
Item 4a. Study design
Training dataset
- Study design or source of data
Sourced from 2 independent series of altogether 76 patients with stage III CRC.
- 43 samples from GSE30378 were taken from patients treated surgically at different hospitals in the Oslo region before adjuvant chemotherapy becoming standard treatment for stage III patients.
- 33 samples from GSE24550 was consecutively collected from patients treated by curative resection at one Norwegian hospital, Aker University Hospital, Oslo, Norway.
Validation dataset(s)
- Study design or source of data
ProLT_stageII: sourced from 2 published independent series of altogether 96 patients with stage II CRC.
- 52 samples from GSE30378 were taken from patients treated surgically at different hospitals in the Oslo region before adjuvant chemotherapy becoming standard treatment for stage III patients.
- 44 samples from GSE24550 were consecutively collected from patients treated by curative resection at a Norwegian hospital (Aker University Hospital, Oslo, Norway).
ProV_stageII: sourced from 2 independent series of altogether 108 patients with stage II CRC.
- 94 patients from GSE14333 were retrieved from the tissue banks of the Royal Melbourne Hospital and the H. Lee Moffitt Cancer Center in the United States.
- 14 patients from GSE17538 were treated at the Vanderbilt Medical Centre (Nashville, TN, USA).
ProV_stageIII: sourced from 2 independent series of altogether 110 patients with stage II CRC.
- 91 patients from GSE14333 were retrieved from the tissue banks of the Royal Melbourne Hospital and the H. Lee Moffitt Cancer Center in the United States.
- 19 patients from GSE17538 were treated at the Vanderbilt Medical Centre (Nashville, TN, USA).
Item 4b. Key study dates
Training dataset
43 patients (GSE30378) were recruited from 1987 to 1989, and 33 patients (GSE24550) were recruited from 2005 to 2008.
Validation dataset(s)
ProLT_stageII: 52 patients (GSE30378) were recruited from 1987 to 1989, and 44 patients (GSE24550) were recruited from 2005 to 2008.
ProV_stageII: Not mentioned in this dataset.
ProV_stageIII: Not mentioned in this dataset.
Item 5. Participants
Item 5a. Key study setting
Training dataset
- Study setting, number and location of centers
- 43 patients from GSE30378 were collected from hospitals in the Oslo region.
- 33 patients from GSE24550 were consecutively collected at Oslo University Hospital, Aker, Norway.
Validation dataset(s)
- Study setting, number and location of centers
ProLT_stageII:
- 52 patients from GSE30378 were collected from hospitals in the Oslo region.
- 44 patients from GSE24550 were consecutively collected at Oslo University Hospital, Aker, Norway.
ProV_stageII: Patients were collected at H Lee Moffitt Cancer Center, Royal Melbourne Hospital, and Vanderbilt University Medical Center.
ProV_stageIII: Same as the ProV_stageII dataset.
Item 5b. Eligibility criteria for participants
Training dataset
- Exclusion criteria
Clinical anlaysis
Patients with stage II tumors were excluded.
Statistical anlaysis
No patients were excluded by ReProMSig.
- Inclusion criteria
Clinical anlaysis
Patients with stage III tumors were selected.
Statistical anlaysis
All patients were included in ReProMSig.
Validation dataset(s)
- Exclusion criteria
Clinical anlaysis
ProLT_stageII: Patients with stage III tumors were excluded.
ProV_stageII:
- Patients with stage III tumors were excluded.
- Patients from GSE14333 who had received preoperative chemotherapy and/or radiotherapy or for whom tumor-derived total RNA was inadequate for microarray analysis (RNA integrity number [RIN] < 6) were excluded.
ProV_stageIII:
- Patients with stage II tumors were excluded.
- Patients from GSE14333 who had received preoperative chemotherapy and/or radiotherapy or for whom tumor-derived total RNA was inadequate for microarray analysis (RNA integrity number [RIN] < 6) were excluded.
Statistical anlaysis
No patients were excluded by ReProMSig.
- Inclusion criteria
Clinical anlaysis
ProLT_stageII: Stage II patients were selected.
ProV_stageII: Nonoverlapping stage II patients from GSE14333 and GSE17538 series were included.
ProV_stageIII: Nonoverlapping stage III patients from GSE14333 and GSE17538 series were included.
Statistical anlaysis
All patients were included in ReProMSig.
Similar as aforementioned.
Item 5c. Received treatments
Training dataset
- Treatment details
- Patients from GSE30378 had not received adjuvant chemotherapy, which was introduced as standard treatment for patients with stage III CRC aged <75 years in Norway in 1997.
- Patients from GSE24550 had not received preoperative radiotherapy and adjuvant chemotherapy was given in accordance with Norwegian guidelines to patients with stage III colon cancer aged <75 years, or to patients with stage II disease in whom <8 lymph nodes were examined, or to patients with preoperative or intraoperative tumour perforation. Furthermore, adjuvant chemotherapy is generally only given to physically fit patients, but not to patients with rectal cancer. All underwent curative resection and no bowel perforation was reported.Validation dataset(s)
- Treatment details
ProLT_stageII: Same as the training dataset.
ProV_stageII:
- Patients from GSE14333 series had not received preoperative chemotherapy and/or radiotherapy. Nine patients had received postoperative concurrent chemoradiotherapy (50.4 Gy in 28 fractions with concurrent 5-fluorouracil), 13 patients had only received adjuvant chemotherapy.
-14 patients from GSE17538 had no available information about received treatments.
ProV_stageIII:
- Patients from GSE14333 had not received preoperative chemotherapy and/or radiotherapy. 12 patients had received postoperative concurrent chemoradiotherapy (50.4 Gy in 28 fractions with concurrent 5-fluorouracil), one patient had only received adjuvant radiotherapy, 51 patients had only received adjuvant chemotherapy (either single agent 5-fluouracil/capecitabine or 5-fluouracil and oxaliplatin).
- 19 patients from GSE17538 had no available information about received treatments.
Item 6. Outcome
Item 6a. Clinical endpoints being analyzed
Outcome
Relapse or death from colorectal cancer was regarded as events, and patients with no events were censored. Specially, 10 years follow-up was done for the GSE30378 dataset, and 5 years follow-up was done for the GSE24550 dataset.
- How and when assessed
Not mentioned in this study.
Item 6b. Actions for blind assessment of the outcome
Not mentioned in this study.
Item 7. Predictors
Item 7a. Predictors used in developing the multivariable prediction model
Genome-wide expression at the exon-level for colorectal cancer tissue biopsies was analyzed using the Affymetrix Human Exon 1.0 ST platform.
Item 7b. Actions for blind assessment of predictors
Not mentioned in this study.
Item 8. Sample Size
How the study size arrived at
Not mentioned in this study.
Item 9. Missing Data
Training dataset
- Missing value handling
Assuming occurrence of missing values in gene expression profiles were random events, KNN method (impute.knn function in R package impute version 1.60.0) was applied to impute missing values by ReProMSig.
Validation dataset(s)
- Missing value handling
Assuming occurrence of missing values in gene expression profiles were random events, KNN method (impute.knn function in R package impute version 1.60.0) was applied to impute missing values by ReProMSig.
Item 10. Statistical Analysis Methods
Item 10a. Predictors handling method
Molecular profiles
Expression values were converted to log2 transformed. Using training dataset gene expression values as the reference, ComBat function in R package "sva" (version 3.34.0) was applied to reduce the likelihood of batch effects from nonbiological technical biases for each validation dataset profiles.
- Implausible observations
Extreme values (e.g., outliers) in expression profiles were regarded as NA, and imputed by KNN method, using impute.knn function in R package "impute " (version 1.60.0).
Item 10b. Model building procedures
Predictor selection before modeling
The following genes (n = 275) with expression variances higher than 0.2 and P-values from univariate Cox proportional hazards analyses below 0.1 were used for predictor selection and modelling. P-values (Wald test of predictive potential) were calculated in R 2.11.1 using the Bioconductor package Weighted Gene P-values (Wald test of predictive potential) were calculated in R 2.11.1 using the Bioconductor package Weighted Gene Co-expression Network Analysis (WGCNA).
GZMB NDRG1 SHISA6 FANK1 SYT17 ABHD12B LGR6 CXCL2 ABLIM1 PLAG1 NACA2 GYG2 LDHB CST6 TAS2R9 SLC15A2 TACSTD2 LIF IDO1 HIST2H2BE RAB3B ZMAT3 MAEL FRAS1 ASCL3 PRSS1 ICAM1 TPD52L1 FBP2 AOAH LAMP3 FSTL4 SLC1A4 SCML1 CXCL9 BMP2 NDP LRRN1 ABCA12 EYA2 TM4SF4 VSIG2 SELE TNIP3 UPK3A CTLA4 DND1 CYP24A1 CHST4 LONRF3 CD69 TNFRSF1B RPL23AP32 NPDC1 CXCL11 CYP2A7 VTCN1 DDT IDH2 C10orf107 OR2S2 ANAPC11 RBM43 ZNHIT3 UGT8 CLIC3 MDM2 RASSF9 TNFAIP3 SLC4A8 TSPAN15 LINGO1 ELAVL2 SACS GLDN ADAMTS9 ANO1 PSTPIP2 B4GALNT2 EIF4A3 APOL3 ABCC3 TPH1 SLC43A2 SUGT1P1 TNFRSF11B BCAS1 TPSG1 RGS17 TOR1B GDF15 TFAP2C CXCL10 ADRA2A IFNA7 ODC1 SOX6 INE1 ABCC2 MAK16 KCNQ1DN VGLL1 PADI2 FGGY GJB4 CYB5A RNASEH1 B3GALT5 MRPL19 CHDH ADPRH KIAA0319 FHDC1 GADD45B NTSR1 C10orf11 CENPA FOXQ1 MYO19 RSPH10B CD177 RBP4 EMILIN2 COX18 FAM167A IL17RB SLC5A6 VN1R2 PELI2 C5 CCL2 SNCAIP EMP2 IL6 NPBWR2 WDR66 GRAP SOD2 MTMR11 SFTA2 DKFZP434K028 GPRC5D PFN2 TIPIN PYY ATAD3C ALAD RPS19 GBP5 POLR2H TRIM2 ING4 EGLN3 SATB1 SPINT3 RANBP17 DEFB132 RHOH GEMIN6 TNFRSF11A SCNN1A SCNN1B IRAK2 XPNPEP3 REG3G C4orf27 TTF2 PNPT1 POLG2 BLMH TEAD2 SERF1A ZAK TCF12 DBH RPS4Y1 RASL11B ZFHX3 HSPA4L SYT1 OR51B6 USP46 APLP1 CXCL3 ALG6 FAM131B LRRC6 GCNT1 FZD3 GDPD2 PCBD1 PDIA4 AATF SERPINB7 ZNF215 SMCP VHL STYK1 DCTD TRPM3 RASD2 STARD5 TSEN2 CLMN GRB10 SH3GL2 LDLRAD3 UGT2B4 LRRC8A CHP2 MT1H PACRG FOXD1 ACTL7A DNER MUC1 C12orf5 ABO GJD2 MOCOS GPR18 GLYR1 XYLT1 ZNF165 MPP1 EXPH5 SRPX2 RABGGTB TGIF2 KLHL36 CHI3L1 WARS ROBO1 C1QBP SNRPA1 RCN1 SLC35E3 RNF122 GPR31 KLHL31 KLF4 SH3BGRL2 TAF1A GRAMD1B PPAP2B NELL2 PCDH1 BCL2A1 AKR1E2 GAN TMC7 RIMKLB AGBL3 PCDHB16 HLA-B FRG1 TTLL6 GRAPL KRT20 STT3B BTG2 HFE PTTG1IP RFX6 PLEK SNX25 MCFD2 NEDD8 ASB4 C3orf14 MPHOSPH10 MBOAT1 MYC PHC1 MRPL12
A bootstrapping procedure was applied to select molecular predictors associated with outcome from the list of candidate predictors shown above. For each bootstrap resample (samples were drawn with replacement keeping the same size as the original training dataset), supervised principal components approach (SPCA, R package "SPCA") was applied. The entire process was repeated 200 times, and predictors with frequency of more than 50% occurrence were taken as potential signature predictors for prognosis.
- Multivariable prediction model building
Univariate Cox regression model (R package "survival") was fitted to the expression data (training dataset) of the selected molecular predictors, to identify robust molecular signatures (using 0.05 as the significance level) associated with DFS, which were used to generate the multivariable prediction signature (R package "survival").
- Collinearity assessment
The correlations between each pair of selected molecular predictors were estimated using Pearson correlation analysis, to evaluate the presence of collinearity. However, we did not exclude any predictors even if potential collinearity found.
Item 10c. Prediction method for the validation dataset(s)
Signature model
Individual signature score was calculated by a weighted sum of the predictors in the generated multivariate Cox regression model, in which weights are the corresponding regression coefficients (i.e., log hazard ratios). The signature score for a patient is the log relative hazard compared with a hypothetical patient whose signature score is zero.
- Nomogram
Nomogram was not constructed as no clinicopathological variable was selected in the development form.
- Online research tool
An online research tool for single patient prediction of risk score (i.e., the exponential of signature score) and DFS probabilities at 12, 36, 60-months using the molecular signature is available from https://omics.bjcancer.org/prognosis/.
Item 10d. Model performance assessment
Independence test
Univariate and multivariate Cox regression analyses were performed to test whether individual prognosis factor is an independent factor in predicting patients DFS. Both the molecular signature prediction (risk groups) and clinicopathological variable (MSI_status) were included in the independence test.
- Interaction test
We did not examine interaction terms but relied on the main effects of the selected.
- Kaplan-Meier analysis
Discrimination is visually inspected from the spread of Kaplan-Meier curves for each predicted risk group (high-risk, and low-risk), in the training and validation datasets respectively. Differences in the probability of DFS between risk groups (high risk vs low risk) were tested by the two-sided log-rank test. In addition, Kaplan-Meier plots also presented the total number of patients, the number of events (outcome) for each risk group.
- Time dependent receiver operating characteristic (ROC) analysis
12, 36, 60-months time-dependent ROC analysis was performed to examine the prognostic accuracy of the developed model in the training dataset. ROC curves for molecular signature were plotted. An area under the ROC curve (AUC) of 0.5 indicates no discrimination, whereas an AUC of 1.0 indicates perfect discrimination.
- Prediction error (PE) curve analysis
PE curve analysis was applied in the training dataset to examine the prognosis prediction error rate, and ten-fold cross-validation cumulative prediction error was computed using Kaplan-Meier estimation as reference. Models with smaller area under the curve indicates a relatively lower error rate. PE curve for molecular signature was plotted.
- Calibration plots
The calibration plots at 12, 36, 60-months were used to assess the consistency between the actual and predicted DFS probabilities from the molecular signature in training dataset.
- DFS
DFS with 95% confidence intervals at 12, 36, 60-months in the training and validation datasets respectively, were calculated by Kaplan-Meier method for patients in each risk group.
Association analysis
To assess whether the developed prediction model is correlated with clinicopathological variables, two-sided Fisher's exact test (for categorical variables) and two-sided t test (for continuous variables) were applied to measure association or difference between predicted risk groups.
Item 10e. Model updating arising from the validation
Item 11. Risk Groups
Patients were stratified into two risk groups on the basis of signature score distribution in each dataset: low-risk (signature score < 0.368) and high-risk (signature score >= 0.368).
Item 12. Development vs. Validation
Differences in development and validation
The details are described at Item 5a.
The details are described at Item 5b.
The details are described at Item 6.
The mRNA expression profiles of predictors in training dataset and ProLT_stageII dataset were generated using Affymetrix Human Exon 1.0 ST platform, while those in ProV_stageII and ProV_stageIII dataset were determined using Affymetrix Human Genome U133Plus 2.0 arrays.
III. Items of results
Item 13. Participants
Item 13a. Study participants
Training dataset
In the training dataset, 76 out of 76 (100%) had follow-up of DFS with a median follow-up (by reverse Kaplan-Meier method) of 56.04 months (range: 2.04-120).
Validation dataset(s)
ProLT_stageII: 96 out of 96 (100%) had follow-up of DFS with a median follow-up (by reverse Kaplan-Meier method) of 59.64 months (range: 5.04-120).
ProV_stageII: 108 out of 108 (100%) had follow-up of DFS with a median follow-up (by reverse Kaplan-Meier method) of 44.8 months (range: 0.43-118.58).
ProV_stageIII: 110 out of 110 (100%) had follow-up of DFS with a median follow-up (by reverse Kaplan-Meier method) of 52.86 months (range: 0.49-112.33).
Item 13b/c. Comparison of participant characteristics between the training and validation datasets
Clinical and pathological characteristics
Key clinical and pathological characteristics of participants were included. P denotes the P value of the characteristics distribution comparison between the training dataset and each validation dataset. Categorical variables were compared using fisher'exact test, and continuous variables using t-test.
Expression profile distribution
The density plots present the distributions of the log2-transformed expression level for the training dataset ProLT_stageIII, and the validation datasets ProLT_stageII, ProV_stageII, ProV_stageIII.
Item 14. Model Development
Unadjusted association between each candidate predictor and outcome in the training dataset
Unadjusted association between top 100 candidate predictors and DFS.
TRIPOD relevance: This table reports the information needed suggested by Item 14a "Number of participants and outcome events involved in model development" and Item 14b "Unadjusted association between each candidate predictor and outcome".
Item 15. Model Specification
Item 15a. Details of the prediction model
Signature genes and the corresponding coefficients
8 variables comprising the developed signature and their corresponding coefficients were calculated by the Cox regression model.
The signature score of a patient can be calculated using the following formula:
Signature score = 0.388*ABHD12B - 0.511*CXCL2 - 0.181*GZMB - 0.096*IDO1 - 0.08*LDHB - 0.047*LGR6 + 0.696*NDRG1 + 0.288*TM4SF4.
A higher signature score indicates a relative poorer prognosis.
Pearson correlation plot for pairwise expression comparison among signature genes
The correlation plot shows the Pearson correlation coefficients of expression profiles between each pair of signature predictors in the training dataset. From the plot, the correlation pattern of signature predictors can be visually checked if their expression levels are independent. The correlation coefficient ranges from -1 to 1. A smaller absolute value implies that a lower linear dependency, indicating this pair of signature predictors maybe is independent.
Item 15b. Application of the prediction model
Nomogram was not constructed as no clinicopathological variable was selected in the development form.
Item 16. Model Performance
Cox regression analysis of signature and clinicopathological variables (training dataset)
Univariate hazard ratio and multivariate hazard ratio of each variable with a 95% confidence interval provided in parentheses, indicate multiplicative effects on the hazard. P denotes the statistical significances in the hazard ratio between a test group relative to a reference group, which were tested by the wald test. For multivariate Cox analysis, the relationship between the variable of interest and the outcome was evaluated after adjusting for potential confounding variables that may be related to the outcome.
Receiver operating characteristic (ROC) analysis (training dataset)
Time-dependent receiver operating characteristic (ROC) curves show the sensitivity and specificity of different variables in prognosis prediction. It could help to quantify and compare the discrimination ability of the signature and clinicopathological prognosis factors using the 12, 36, 60-months time-dependent ROC analysis in the training dataset. The area under the curve (AUC) ranges from 0.5 (no discrimination) to a theoretical maximum of 1, which were texted in the legend for each prognosis model. A prognosis model with larger AUC indicates a better performance.
Calibration of the molecular signature (training dataset)
Calibration plots at 12, 36, 60-months were generated to explore the performance of the signature. Signature-predicted probabilities and observed outcome were plotted on the X-axis and Y-axis, respectively. A calibration plot along the 45-degree line indicates perfect consistency between the actual and signature-predicted prognosis. The vertical bars represent 95% CIs.
Prediction error (PE) curve analysis (training dataset)
Prediction error (PE) curves were used to evaluate the performance of prediction models in the training dataset, and ten‐fold cross‐validation cumulative prediction error were computed using Kaplan-Meier estimation as reference. Integrated Brier score (IBScore) was defined as the area under a prediction error curve, which were texted in the legend for each prognosis model. A prognosis model with smaller IBScore indicates a better performance.
Association analysis of signature-predicted risk groups with clinicopathological variables
Association analysis was employed to investigate whether the signature is correlated with clinicopathological variables and other molecular features in both training and validation dataset. Samples with valid values (such as non-missing, non-unknown) were compared in the analysis. P denotes the P values comparing the associations between the clinicopathological/molecular variables and the risk groups in each dataset. Fisher'exact test was used for association analysis.
ProV_stageII, ProV_stageIII datasets were not shown due to lack of MSI_status variable.
Kaplan-Meier analysis of signature-predicted risk groups
Training dataset
Validation dataset 1
Validation dataset 2
Validation dataset 3
Kaplan-Meier survival curves for DFS in training and validation datasets, stratified by the prediction model (high-risk and low risk). The performance of the prognostic signature was evaluated by the two‐sided log‐rank test in both training dataset and validation datasets. P < 0.05 was considered as statistically significant, and 95% confidence intervals are presented in brackets. HR, hazard ratio.
12/36/60 months DFS for different signature-predicted risk groups
12/36/60 months DFS of patients in different risk groups for each dataset were predicted by the prediction model.