Multi-Gene Prognostic Signatures and Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in ER-Positive, HER2-Negative Breast Cancer Patients
Abstract
:1. Introduction
2. Method
2.1. Systematic Search
2.2. Dataset
2.3. Predicting Probability of pCR Using Logistic Regression Analysis
3. Results
3.1. Correlation Analysis
3.2. Predicted Probability of pCR
4. Discussion
5. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
Abbreviations
BCI | Breast Cancer Index |
CI | Confidence Interval |
EP | EndoPredict |
GEO | Gene Expression Omnibus |
MTRs | Master Transcriptional Regulators |
OM | OncoMasTR |
pCR | Pathological Complete Response |
RS | Oncotype DX |
TILs | Tumor Infiltrating Lymphocytes |
EI | Enterprise Ireland |
IRC | Irish Research Council |
SFI | Science Foundation Ireland |
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GEO Dataset | Platform | Patients (N) | pCR (N) | Missing Genes | |||
---|---|---|---|---|---|---|---|
EP | RS | TILs | OM | ||||
GSE16716 | Affymetrix Human Genome U133A Array | 140 | 7 | PTRPC, KLRK1, EOMES, KIR3DL2, XCL2, CD8B | ZNF367 | ||
GSE20271 | Affymetrix Human Genome U133A Array | 89 | 6 | PTRPC, KLRK1, EOMES, KIR3DL2, XCL2, CD8B | ZNF367 | ||
GSE25066 | Affymetrix Human Genome U133A Array | 278 | 30 | PTRPC, KLRK1, EOMES, KIR3DL2, XCL2, CD8B | ZNF367 | ||
GSE32646 | Affymetrix Human Genome U133A Plus 2.0 Array | 55 | 5 | PTRPC, KLRK1, KIR3DL2, XCL2 | |||
GSE34138 | Illumina Human WG 6 v3.0 expression bead chip | 119 | 4 | MYBL2 | PTRPC, KLRK1, TPSB2, XCL2, NCR1, FOXP3 | ZNF367 | |
GSE41998 | Affymetrix Human U133A 2.0 Array | 93 | 10 | PTRPC, KLRK1, EOMES, KIR3DL2, XCL2, CD8B | ZNF367 | ||
GSE22226 GPL1708 | Agilent 012391 Whole Human Genome Oligo Microarray G4112A (Feature Number version) | 39 | 4 | CCNB1 | MYBL2 | PTRPC, EOMES, TPSB2, TPSB1, MS4A2, KIR3DL2, CD3E | |
813 | 66 |
Signatures | Overall r | Lowest r (Dataset) | Highest r (Dataset) |
---|---|---|---|
OM vs. RS | (GSE20271) | (GSE41998) | |
OM vs. EP | (GSE20271) | (GSE41998) | |
RS vs. EP | (GSE20271) | (GSE32646) | |
OM vs. TILs | (GSE20271) | (GSE32646) | |
EP vs. TILs | (GSE41998) | (GSE20271) | |
RS vs. TILs | (GSE41998) | (GSE20271) |
Signature | Odds Ratio ( CI) | p-Value | Model |
---|---|---|---|
Univariable Analysis | |||
OM | (1.29–2.16) | OM | |
RS | (1.44–2.35) | < | RS |
EP | (1.37–2.27) | < | EP |
TILs | (1.07–1.72) | TILs | |
Bivariable Analysis (adjusted for dataset or TILs) | |||
OM | (1.30–2.18) | < | OM + Dataset |
RS | (1.45–2.37) | < | RS + Dataset |
EP | (1.37–2.30) | < | EP + Dataset |
TILs | (1.08–1.73) | TILs + Dataset | |
OM | (1.26–2.12) | OM + TILs | |
TILs | (1.04–1.66) | ||
RS | (1.40–2.31) | < | RS + TILs |
TILs | (0.98–1.60) | ||
EP | (1.31–2.20) | < | EP + TILs |
TILs | (0.96–1.56) | ||
Trivariable Analysis (adjusted for dataset and TILs) | |||
OM | (1.28–2.14) | OM + TILs + Dataset | |
TILs | (1.04–1.69) | ||
RS | (1.41–2.33) | < | RS + TILs + Dataset |
TILs | () | ||
EP | (1.32–2.23) | < | EP + TILs + Dataset |
TILs | (0.97–1.59) |
Model | Null Deviance | df Null | LogLik | AIC | BIC | Deviance | df Residual |
---|---|---|---|---|---|---|---|
Univariable Analysis | |||||||
OM | 457.95 | 812 | −221.16 | 446.33 | 455.73 | 442.33 | 811 |
RS | 457.95 | 812 | −216.89 | 437.79 | 447.19 | 433.79 | 811 |
EP | 457.95 | 812 | −219.27 | 442.53 | 451.93 | 438.53 | 811 |
TILs | 457.95 | 812 | −225.80 | 455.59 | 464.99 | 451.59 | 811 |
Bivariable Analysis (adjusted for dataset or TILs) | |||||||
OM + Dataset | 457.95 | 812 | −215.84 | 447.68 | 485.29 | 431.68 | 805 |
RS + Dataset | 457.95 | 812 | −211.59 | 439.18 | 476.78 | 423.18 | 805 |
EP + Dataset | 457.95 | 812 | −213.94 | 443.89 | 481.49 | 427.89 | 805 |
TILs + Dataset | 457.95 | 812 | −220.55 | 457.09 | 494.70 | 441.09 | 805 |
OM + TILs | 457.95 | 812 | −218.65 | 443.30 | 457.41 | 437.30 | 810 |
RS + TILs | 457.95 | 812 | −215.22 | 436.44 | 450.55 | 430.44 | 810 |
EP + TILs | 457.95 | 812 | −217.86 | 441.71 | 455.82 | 435.71 | 810 |
Trivariable Analysis (adjusted for dataset and TILs) | |||||||
OM + TILs + Dataset | 457.95 | 812 | −213.21 | 444.43 | 486.73 | 426.43 | 804 |
RS + TILs + Dataset | 457.95 | 812 | −209.80 | 437.60 | 479.90 | 419.60 | 804 |
EP + TILs + Dataset | 457.95 | 812 | −212.40 | 442.80 | 485.11 | 424.80 | 804 |
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Mazo, C.; Barron, S.; Mooney, C.; Gallagher, W.M. Multi-Gene Prognostic Signatures and Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in ER-Positive, HER2-Negative Breast Cancer Patients. Cancers 2020, 12, 1133. https://doi.org/10.3390/cancers12051133
Mazo C, Barron S, Mooney C, Gallagher WM. Multi-Gene Prognostic Signatures and Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in ER-Positive, HER2-Negative Breast Cancer Patients. Cancers. 2020; 12(5):1133. https://doi.org/10.3390/cancers12051133
Chicago/Turabian StyleMazo, Claudia, Stephen Barron, Catherine Mooney, and William M. Gallagher. 2020. "Multi-Gene Prognostic Signatures and Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in ER-Positive, HER2-Negative Breast Cancer Patients" Cancers 12, no. 5: 1133. https://doi.org/10.3390/cancers12051133
APA StyleMazo, C., Barron, S., Mooney, C., & Gallagher, W. M. (2020). Multi-Gene Prognostic Signatures and Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in ER-Positive, HER2-Negative Breast Cancer Patients. Cancers, 12(5), 1133. https://doi.org/10.3390/cancers12051133