A Prediction Model for Preoperative Risk Assessment in Endometrial Cancer Utilizing Clinical and Molecular Variables
Abstract
:1. Introduction
2. Results
2.1. Survival Analysis
2.2. Variable Selection for Prediction Modeling
2.3. Prediction Models
2.3.1. UI Prediction Models
2.3.2. TCGA/GEO Replication
2.3.3. TCGA Validation
3. Discussion
4. Materials and Methods
4.1. Classification of EEC Risk
4.2. Patients and Clinical Data Collection
4.2.1. University of Iowa (UI)
4.2.2. The Cancer Genome Atlas (TCGA)
4.2.3. Gene Expression Omnibus (GEO)
4.3. Biological Data
University of Iowa (UI)
4.4. Statistical Analysis
4.4.1. Survival Analysis
4.4.2. Variable Selection for Prediction Modeling
4.4.3. Prediction Model Construction
4.4.4. The Cancer Genome Atlas Replication and Validation
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
EEC | Endometrioid endometrial cancer |
AUC | Area under the (receiver operating characteristic) curve |
SLN | Sentinel lymph nodes |
LN | Lymph nodes |
TCGA | The Cancer Genome Atlas |
GEO | Gene Expression Omnibus |
UI | University of Iowa |
RNA-seq | RNA sequencing |
CNV | Gene copy number variation |
miRNA | Micro RNA |
NPV | Negative predictive value |
PCR | Polymerase chain reaction |
GOG | Gynecologic Oncology Group |
Appendix A
Weights for Each miRNA Included in the Model | ||
---|---|---|
miRNA | M2-B | M3-D |
MIR125B1 | 3.01 | 1.07 |
MIR181A1 | 1.67 | 1.27 |
MIR181A2HG | 1.53 | 1.07 |
MIR188 | 1.86 | 1.31 |
MIR301B | 1.98 | - |
MIR30B | 0.35 | 0.61 |
MIR3142 | 12.08 | - |
MIR345 | 0.38 | - |
MIR3690 | 1.36 | 1.15 |
MIR4269 | 2.09 | 1.58 |
MIR4307 | 14.25 | 1.19 |
MIR4463 | 8.68 | - |
MIR492 | 0.95 | - |
MIR5692A1 | 0.41 | - |
MIR578 | 2.01 | - |
MIR601 | 2.59 | - |
MIR633 | 14.71 | 1.20 |
MIR6503 | 0.21 | - |
MIR6769A | 1.51 | - |
MIR6820 | 0.29 | - |
MIR876 | 0.22 | 0.97 |
Weights for Each Somatic Mutation Included in the Model | |||
---|---|---|---|
M2-C | M3-C | M3-D | |
AARS2 | 1.55 | 1.1 | |
ABCD1 | 83.96 | 35.65 | |
ADAMTS13 | 3.9 | 4.57 | 3.2 |
ATL1 | 2.58 | 2.87 | |
C14orf37 | 13.02 | 1.05 | 10.85 |
CEP350 | 0.98 | 0.88 | |
CGNL1 | 0.94 | 0.93 | |
COL9A3 | 4.23 | 6.64 | |
CR2 | 3.22 | ||
CTAGE8 | 0.71 | 0.61 | 0.74 |
DAGLA | 12.51 | 4.28 | |
ENTPD1 | 1.01 | ||
FAM111A | 1.42 | 1.34 | |
HIP1R | 4.12 | 4.29 | 2.81 |
HSD17B8 | 1.56 | ||
KIF20B | 1.13 | ||
KIZ | 1.34 | 1.04 | |
LCORL | 3.63 | 4.41 | |
MAP3K12 | 1.33 | 2.06 | |
MAPKBP1 | 0.93 | 0.94 | |
MPHOSPH8 | 0.9 | 0.86 | |
NOTCH4 | 2.38 | 1.95 | |
NR2C2 | 17.57 | 5.83 | 12.5 |
PANK2 | 0.86 | ||
PCSK5 | 4.34 | 2.33 | |
PIGN | 1.81 | 1.45 | 1.62 |
PVR | 2.07 | 3.15 | |
RPAP1 | 1.17 | ||
RSF1 | 2.67 | 2.55 | 2.41 |
SHROOM2 | 5.45 | 4.91 | 6.33 |
TMEM41B | 1.37 | ||
VDR | 1.41 | 1.32 | 1.52 |
ZDHHC24 | 1.74 | ||
ZNF780B | 3.65 | 3.47 | 2.78 |
Weights for Each Somatic Mutation Included in the Model | |
---|---|
M3-C | |
AQP2 | 1.04 |
C1QL4 | 1.19 |
C5orf17 | 1.18 |
CDH19 | 1.33 |
COLCA2 | 0.98 |
FAIM2 | 1.12 |
FGF18 | 1.83 |
HAS3 | 1.07 |
IGFL2 | 1.27 |
IGFL4 | 1.05 |
IL23R | 0.82 |
LINC01128 | 1.19 |
LOC101927701 | 1.28 |
LOC101929529 | 1.71 |
LONP2 | 0.55 |
MAN2A2 | 2.01 |
MRPS28 | 0.58 |
P4HA2 | 0.57 |
SCARNA4 | 1.33 |
SLC25A21 | 0.65 |
SPATA4 | 0.95 |
TAC1 | 1.21 |
TBATA | 1.45 |
TFAP2A.AS1 | 1.16 |
TGFA-IT1 | 1.41 |
TUBAL3 | 1.48 |
VAX2 | 1.18 |
ZNF398 | 0.66 |
Appendix B
Appendix B.1. Clinical Data Available in Databases Used to Validate the UI Prediction Models
Appendix B.1.1. University of Iowa (UI)
Appendix B.1.2. The Cancer Genome Atlas (TCGA)
Low Risk (N = 206) | High Risk (N = 194) | p-Value | ||
---|---|---|---|---|
Preoperative characteristics | Age (mean) | 60 | 62 | <0.001 |
BMI (mean) | 36.1 | 33.6 | 0.064 | |
Grade | <0.001 | |||
1 | 80 | 17 | ||
2 | 57 | 59 | ||
3 | 69 | 118 | ||
Postoperative characteristics | Myometrial invasion | 0.984 | ||
<50% | 204 | 54 | ||
>50% | 0 | 17 | ||
2009 FIGO Stage | 0.984 | |||
I | 204 | 71 | ||
II | 0 | 33 | ||
III | 0 | 70 | ||
IV | 0 | 13 | ||
Lymph nodes (positive) | 0 (0%) | 40 (27%) | <0.001 | |
Peritoneal Cytology (positive) | 3 (2%) | 24 (16%) | <0.001 |
Appendix B.1.3. Gene Expression Omnibus (GEO)
Low Risk (N = 49) | High Risk (N = 22) | p-Value | ||
---|---|---|---|---|
Preoperative characteristics | Age (mean) | 58 | 67 | 0.002 |
Grade | <0.001 | |||
1 | 26 | 0 | ||
2 | 17 | 13 | ||
3 | 6 | 9 |
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Clinical/Pathological Variables | Low Risk (N = 70) | High Risk (N = 56) | p-Value | |
---|---|---|---|---|
Preoperative characteristics | Age (mean) | 58.7 | 64.8 | 0.003 * |
BMI (mean) | 38.5 | 32.6 | <0.001 * | |
Charlson Morbidity Index (mean) | 4.7 | 5 | 0.012 * | |
Grade | <0.001 * | |||
1 | 38 | 7 | ||
2 | 21 | 27 | ||
3 | 8 | 22 | ||
Postoperative characteristics | Invasion (mean) | 19 | 62 | <0.001 * |
2009 FIGO Stage | 0.991 | |||
I | 70 | 23 | ||
II | - | 7 | ||
III | - | 20 | ||
IV | - | 6 | ||
Lymph nodes (% positive) | 0 (0%) | 13 (27%) | 0.987 | |
Peritoneal Cytology (% positive) | 2 (3%) | 31 (56%) | 0.011 * | |
Lymphovascular involvement (% positive) | 2 (3%) | 10 (19%) | <0.001 * | |
ER (% positive) | 38 (93%) | 31 (78%) | 0.066 | |
PR (% positive) | 38 (93%) | 30 (75%) | 0.040 * | |
Postoperative complications (% positive) | 12 (17%) | 17 (32%) | 0.056 | |
LOS (mean days) | 3.3 | 6.1 | 0.002 * | |
Adjuvant Treatment (yes) (% positive) | 8 (11%) | 39 (74%) | <0.001 * | |
Outcomes | 5-year Survival (%) | 98% | 75% | <0.001 * |
Recurrence (% positive) | 2 (3%) | 19 (37%) | <0.001 * | |
Death due to disease (% positive) | 1 (1%) | 15 (30%) | 0.001 * |
Model Number | Data Class | # Input Variables | # Resulting Variables | AUC | 95% CI |
M1-A | Clinical | 17 | 7 | 0.88 | 0.84, 0.92 |
M1-B | mRNAs | 255 | 38 | 0.79 | 0.73, 0.85 |
M1-C | miRNAs | 55 | 28 | 0.84 | 0.76, 0.93 |
M1-D | Mutations | 398 | 35 | 0.68 | 0.63, 0.73 |
M1-E | CNVs | 846 | 65 | 0.67 | 0.56, 0.77 |
Prediction Models Using Two Data Classes | |||||
Model Number | Data Classes Included Clinical + | # Input Variables | # Resulting Variables | AUC | 95% CI |
M2-A | mRNAs | 7 + 38 | 37 | 0.93 | 0.90, 0.96 |
* M2-B | miRNAs | 7 + 28 | 24 | 0.97 | 0.96, 0.99 |
* M2-C | Mutations | 7 + 35 | 35 | 1 | 1, 1 |
M2-D | CNVs | 7 + 65 | 61 | 0.92 | 0.89, 0.94 |
Prediction Models Using Three Data Classes | |||||
Model Number | Data Classes Included Clinical + | # Input Variables | # Resulting Variables | AUC | 95% CI |
M3-A | mRNAs + miRNAs | 7 + 38 + 28 | 37 | 0.83 | 0.74, 0.91 |
M3-B | Mutations + CNVs | 7 + 35 + 65 | 48 | 0.94 | 0.91, 0.97 |
M3-C | mRNAs + Mutations | 7 + 38 + 35 | 41 | 0.95 | 0.92, 0.98 |
M3-D | miRNAs + Mutations | 7 + 28 + 35 | 36 | 0.94 | 0.91, 0.97 |
M3-E | miRNAs + CNVs | 7 + 28 + 65 | 46 | 0.86 | 0.81, 0.91 |
M3-F | mRNAs + CNVs | 7 + 38 + 65 | 44 | 0.93 | 0.91, 0.95 |
Prediction Models Using Four Data Classes | |||||
Model Number | Data classes Included Clinical + | # Input Variables | # Resulting Variables | AUC | 95% CI |
M4-A | mRNAs + miRNAs + Mutations | 7 + 38 + 28 + 35 | 42 | 0.94 | 0.91, 0.96 |
M4-B | mRNAs + miRNAs + CNVs | 7 + 38 + 28 + 65 | 40 | 0.91 | 0.88, 0.93 |
M4-C | mRNAs + Mutations + CNVs | 7 + 38 + 35 + 65 | 42 | 0.91 | 0.88, 0.95 |
M4-D | miRNAs + Mutations + CNVs | 7 + 28 + 35 + 65 | 53 | 0.88 | 0.84, 0.92 |
Prediction Models Using Five Data Classes | |||||
Model Number | Data Classes Included Clinical + | # Input Variables | # Resulting Variables | AUC | 95% CI |
M5-A | mRNAs + miRNAs + Mutations + CNVs | 7 + 38 + 28 + 35 + 65 | 47 | 0.89 | 0.86, 0.92 |
Replication of Prediction Models Using One Data Class | |||||
Model Number | Data Class | # Input Variables | # Resulting Variables | AUC | 95% CI |
UI model M1-A | Clinical | 17 | 7 | 0.88 | 0.84, 0.92 |
TCGA model M1-A | Clinical | 2 * | 2 | 0.75 | 0.73, 0.78 |
GEO model M1-A | Clinical | 2 * | 2 | 0.84 | 0.79, 0.89 |
UI model M1-B | mRNAs | 255 | 38 | 0.79 | 0.73, 0.85 |
TCGA model M1-B | mRNAs | 36 * | 23 | 0.60 | 0.57, 0.63 |
GEO model M1-B | mRNAs | 14 * | 5 | 0.60 | 0.53, 0.68 |
UI model M1-C | miRNAs | 55 | 28 | 0.84 | 0.76, 0.93 |
TCGA model M1-C | miRNAs | 28 | 4 | 0.57 | 0.54, 0.60 |
UI model M1-D | Mutations | 398 | 35 | 0.68 | 0.63, 0.73 |
TCGA model M1-C | Mutations | 34 * | 18 | 0.59 | 0.57, 0.62 |
UI model M1-C | CNVs | 846 | 65 | 0.67 | 0.56, 0.77 |
TCGA model M1-E | CNVs | 65 | 2 | 0.63 | 0.59, 0.67 |
Replication of Prediction Models Using Two Data Classes | |||||
Model Number | Data Classes Included Clinical + | # Input Variables | # Resulting Variables | AUC | 95% CI |
UI model M2-A | mRNAs | 7 + 38 | 37 | 0.93 | 0.90, 0.96 |
TCGA model M2-A | mRNAs | 2 + 36 * | 15 | 0.75 | 0.72, 0.78 |
GEO model M2-A | mRNAs | 2 + 14 * | 2 | 0.92 | 0.90, 0.95 |
UI model M2-B | miRNAs | 7 + 28 | 24 | 0.97 | 0.96, 0.99 |
TCGA model M2-B | miRNAs | 2 + 28 * | 3 | 0.75 | 0.72, 0.77 |
UI model M2-C | Mutations | 7 + 35 | 35 | 1 | 1, 1 |
TCGA model M2-C | Mutations | 2 + 34 * | 30 | 0.75 | 0.73, 0.77 |
UI model M2-D | CNVs | 7 + 65 | 61 | 0.92 | 0.89, 0.94 |
TCGA model M2-D | CNVs | 2 + 65 * | 3 | 0.75 | 0.71, 0.79 |
Replication of Prediction Models Using Three Data Classes | |||||
Model Number | Data Classes Included Clinical + | # Input Variables | # Resulting Variables | AUC | 95% CI |
UI model M3-A | mRNAs + miRNAs | 7 + 38 + 28 | 37 | 0.83 | 0.74, 0.91 |
TCGA model M3-A | mRNAs + miRNAs | 2 + 36 + 28 * | 4 | 0.75 | 0.72, 0.78 |
UI model M3-B | Mutations + CNVs | 7 + 35 + 65 | 48 | 0.94 | 0.91, 0.97 |
TCGA model M3-B | Mutations + CNVs | 2 + 34 + 65 * | 24 | 0.78 | 0.75, 0.80 |
UI model M3-C | mRNAs + Mutations | 7 + 38 + 35 | 41 | 0.95 | 0.92, 0.98 |
TCGA model M3-C | mRNAs + Mutations | 2 + 36 + 34 * | 2 | 0.74 | 0.71, 0.77 |
UI model M3-D | miRNAs + Mutations | 7 + 28 + 35 | 36 | 0.94 | 0.91, 0.97 |
TCGA model M3-D | miRNAs + Mutations | 2 + 28 + 34 * | 2 | 0.74 | 0.72, 0.75 |
UI model M3-E | miRNAs + CNVs | 7 + 28 + 65 | 46 | 0.86 | 0.81, 0.91 |
TCGA model M3-E | miRNAs + CNVs | 2 + 28 + 65 * | 5 | 0.76 | 0.73, 0.79 |
UI model M3-F | mRNAs + CNVs | 7 + 38 + 65 | 44 | 0.93 | 0.91, 0.95 |
TCGA model M3-F | mRNAs + CNVs | 2 + 36 + 65 * | 2 | 0.75 | 0.72, 0.78 |
Replication of Prediction Models Using Four Data Classes | |||||
Model Number | Data Classes Included Clinical + | # Input Variables | # Resulting Variables | AUC | 95% CI |
UI model M4-A | mRNAs + miRNAs + Mutations | 7 + 38 + 28 + 35 | 42 | 0.94 | 0.91, 0.96 |
TCGA model M4-A | mRNAs + miRNAs + Mutations | 2 + 36 + 28 + 34 * | 2 | 0.74 | 0.71, 0.77 |
UI model M4-B | mRNAs + miRNAs + CNVs | 7 + 38 + 28 + 65 | 40 | 0.91 | 0.88, 0.93 |
TCGA model M4-B | mRNAs + miRNAs + CNVs | 2 + 36 + 28 + 65 * | 2 | 0.76 | 0.73, 0.79 |
UI model M4-C | mRNAs + Mutations + CNVs | 7 + 38 + 35 + 65 | 42 | 0.91 | 0.88, 0.95 |
TCGA model M4-C | mRNAs + Mutations + CNVs | 2 + 36 + 34 + 65 * | 10 | 0.75 | 0.73, 0.78 |
UI model M4-D | miRNAs + Mutations + CNVs | 7 + 28 + 35 + 65 | 53 | 0.88 | 0.84, 0.92 |
TCGA model M4-D | miRNAs + Mutations + CNVs | 2 + 28 + 34 + 65 * | 9 | 0.77 | 0.74, 0.80 |
Replication of Prediction Models using Five Data Classes | |||||
Model Number | Data classes included Clinical + | # Input variables | # Resulting variables | AUC | 95% CI |
UI model M5-A | mRNAs + miRNAs + Mutations + CNVs | 7 + 38 + 28 + 35 + 65 | 47 | 0.8 | 0.85, 0.91 |
TCGA model M5-A | mRNAs + miRNAs + Mutations + CNVs | 2 + 36 + 28 + 34 + 65 * | 8 | 0.76 | 0.73, 0.78 |
Model M2-B Clinical + miRNAs | Model M2-C Clinical + Mutations | Model M3-C Clinical + mRNAs + Mutations | Model M3-D Clinical + miRNAs + Mutations | |||||
---|---|---|---|---|---|---|---|---|
Recurrence probability scale * | Cut-off = 0.5004 | Cut-off = 0.4984 | Cut-off = 0.7309 | Cut-off = 0.5151 | ||||
Value | 95% CI | Value | 95% CI | Value | 95% CI | Value | 95% CI | |
Sensitivity | 90% | 85%, 94% | 90% | 86%, 94% | 90% | 82%, 98% | 90% | 86%, 94% |
Specificity | 38% | 31%, 44% | 16% | 8%, 26% | 10% | 1%, 23% | 30% | 23%, 37% |
Positive Predictive Value (PPV) | 56% | 51%, 61% | 49% | 47%, 52% | 13% | 12%, 15% | 53% | 50%, 57% |
Negative Predictive Value (NPV) | 79% | 70%, 84% | 64% | 47%, 74% | 87% | 45%, 94% | 76% | 66%, 81% |
Accuracy | 62% | 54%, 68% | 51% | 47%, 56% | 20% | 13%, 32% | 58% | 52%, 63% |
Prediction Model | M2-B | M2-C | M3-C | M3-D |
---|---|---|---|---|
Clinical variables | Weight of clinical variables * | |||
Age | 1.03 | - | - | - |
History of other cancers | 0.93 | - | - | - |
Grade | 12.99 | 1.27 | 1.01 | 1.48 |
BMI | - | 0.99 | - | - |
Molecular variables | Log2 transformed and normalized miRNA expression **: | |||
miRNAs | MIR125B1, MIR181A1, MIR181A2HG, MIR188, MIR301B, MIR30B, MIR3142, MIR345, MIR3690, MIR4269, MIR4307, MIR4463, MIR492, MIR5692A1, MIR578, MIR601, MIR633, MIR6503, MIR6769A, MIR6820 | MIR125B1, MIR181A1, MIR181A2HG, MIR188, MIR30B, MIR3690, MIR4269, MIR4307, MIR633, MIR876 | ||
Somatic mutations | Number of mutations per gene and person #: | |||
AARS2, ABCD1, ADAMTS13, ATL1, C14orf37, CEP350, CGNL1, COL9A3, CR2, CTAGE8, DAGLA, ENTPD1, FAM111A, HIP1R, HSD17B8, KIF20B, KIZ, LCORL, MAP3K12, MAPKBP1, MPHOSPH8, NOTCH4, NR2C2, PANK2, PCSK5, PIGN, PVR, RPAP1, RSF1, SHROOM2, VDR, ZDHHC24, ZNF780B | ADAMTS13, C14orf37, CEP350, CTAGE8, HIP1R, MAPKBP1, NR2C2, PIGN, RSF1, SHROOM2, VDR, ZNF780B | AARS2, ABCD1, ADAMTS13, ATL1, C14orf37, CGNL1, COL9A3, CTAGE8, DAGLA, FAM111A, HIP1R, KIZ, LCORL, MAP3K12, MPHOSPH8, NOTCH4, NR2C2, PCSK5, PIGN, PVR, RSF1, SHROOM2, TMEM41B, VDR, ZNF780B | ||
Gene expression | Log2 transformed and normalized gene expression ##: | |||
AQP2, C1QL4, C5orf17, CDH19, COLCA2, FAIM2, FGF18, HAS3, IGFL2, IGFL4, IL23R, LINC01128, LOC101927701, LOC101929529, LONP2, MAN2A2, MRPS28, P4HA2, SCARNA4, SLC25A21, SPATA4, TAC1, TBATA, TFAP2A-AS1, TGFA.IT1, TUBAL3, VAX2, ZNF398 |
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Salinas, E.A.; Miller, M.D.; Newtson, A.M.; Sharma, D.; McDonald, M.E.; Keeney, M.E.; Smith, B.J.; Bender, D.P.; Goodheart, M.J.; Thiel, K.W.; et al. A Prediction Model for Preoperative Risk Assessment in Endometrial Cancer Utilizing Clinical and Molecular Variables. Int. J. Mol. Sci. 2019, 20, 1205. https://doi.org/10.3390/ijms20051205
Salinas EA, Miller MD, Newtson AM, Sharma D, McDonald ME, Keeney ME, Smith BJ, Bender DP, Goodheart MJ, Thiel KW, et al. A Prediction Model for Preoperative Risk Assessment in Endometrial Cancer Utilizing Clinical and Molecular Variables. International Journal of Molecular Sciences. 2019; 20(5):1205. https://doi.org/10.3390/ijms20051205
Chicago/Turabian StyleSalinas, Erin A., Marina D. Miller, Andreea M. Newtson, Deepti Sharma, Megan E. McDonald, Matthew E. Keeney, Brian J. Smith, David P. Bender, Michael J. Goodheart, Kristina W. Thiel, and et al. 2019. "A Prediction Model for Preoperative Risk Assessment in Endometrial Cancer Utilizing Clinical and Molecular Variables" International Journal of Molecular Sciences 20, no. 5: 1205. https://doi.org/10.3390/ijms20051205