Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine
Abstract
:Simple Summary
Abstract
1. Introduction
2. Breast Cancer Subtypes and Intratumoral Heterogeneity
2.1. Molecular Classification and Intrinsic Subtypes
2.2. Intratumoral Heterogeneity in Breast Cancers
3. Resistance to Neoadjuvant Chemotherapy
3.1. Drug-Associated Resistance
3.2. Cancer Cell-Associated Resistance
4. Current Biomarkers Used for the Clinical Decision Making of Breast Cancer Patients
4.1. Ki-67 before NAC
4.2. Tumor Size
4.3. Surrogate Molecular Subtypes as Determined by Immunohistochemistry
4.4. Tumor-Infiltrating Lymphocytes (TILs)
4.5. PD-L1 Expression
Trials | Year of TILs Subanalysis | Number of Patients | Number of Patients for TILs Subanalysis | Subtypes (n) | NAC Regimens | pCR Rates |
---|---|---|---|---|---|---|
GeparDuo [66,86] | 2010 | 913 | 218 | All | 4× doxorubicin + docetaxel q2w (ADoc) vs. 4× doxorubicin + cyclophosphamide and 4× docetaxel q3w (ACDoc) | 7% (ADoc) vs. 14% (ACDoc) |
GeparTrio [66,87] | 2010 | 2090 | 840 | All | docetaxel + doxorubicin + cyclophosphamide (TAC) vs. vinorelbine + capecitabine (NX) | 5.3% (TAC) vs. 6% (NX) |
GeparQuattro [67,88] | 2016 | 1509 | 178 | HER2-negative (n = 1058) HER2-positive (n = 451) | 4× epirubicin + cyclophosphamide + 4× docetaxel + trastuzumab +/− capecitabine in HER2 positive 4× epirubicin + cyclophosphamide + 4× docetaxel +/− capecitabine in HER2 negative | 31.7% (HER2-positive) vs. 15.7% (HER2-negative) |
GeparQuinto [67,89] | 2016 | 615 | 320 | HER2-positive | 4× epirubicin + cyclophosphamide + 4× docetaxel + trastuzumab (T) vs. lapatinib (L) | 30.3% (T) vs. 22.7% (L) |
GeparSixto [73,90] | 2015 | 588 | 580 | HER2-positive (n = 273) TNBC (n = 315) | In HER2-positive: paclitaxel + doxorubicin + trastuzumab + lapatinib +/− carboplatin In TNBC: paclitaxel + doxorubicin +/− carboplatin +/− bevacizumab | |
NeoALTTO [91] | 2015 | 455 | 387 | HER2-positive | Lapatinib (L) vs. trastuzumab (T) vs. lapatinib + trastuzumab (LT) | 20% (L) vs. 27% (T) vs. 44% (LT) |
CherLOB [92] | 2016 | 121 | 121 | HER2-positive | Paclitaxel + FEC + trastuzumab (T) vs. lapatinib (L) vs. lapatinib + trastuzumab (LT) | 25% (T) vs. 26.3% (L) vs. 46.7% (LT) |
GeparSepto [71,93] | 2017 | 1206 | 1206 | HER2-negative (n = 810) HER2-positive (n = 396) | Nab-paclitaxel (nP) or paclitaxel (P) + EC +/− trastuzumab and pertuzumab | 38% (nP) vs. 29% (P) |
TRYPHAENA [70] | 2016 | 225 | 213 | HER2-positive | Arm A: FEC + trastuzumab + pertuzumab followed by docetaxel + trastuzumab + pertuzumab Arm B: FEC followed by docetaxel + trastuzumab + pertuzumab Arm C: docetaxel + carboplatin + trastuzumab + pertuzumab | 61.6% (arm A) vs. 57.3% (arm B) vs. 66.2% (arm C) |
NeoSphere [69] | 2015 | 417 | 350 | HER2-positive | Group A: trastuzumab + docetaxel Group B: trastuzumab + pertuzumab + docetaxel Group C: pertuzumab + trastuzumab Group D: pertuzumab + docetaxel | 29% (group A) vs. 45.8% (group B) vs.16.8% (group C) vs. 24% (group D) |
GeparNuevo [74] | 2019 | 174 | 171 | TNBC | Nab-paclitaxel +/− durvalumab followed by EC | 53.4% (durvalumab) vs. 44.2% (placebo) |
5. Predictive Biomarkers under Investigation
5.1. Imaging and Radiomics Biomarkers
5.1.1. MRI
5.1.2. Quantitative Ultrasound
5.1.3. 18F-FDG PET/CT
5.2. Plasmatic Biomarkers
5.2.1. Peripheral Blood Cells and Ratios
5.2.2. Liquid Biopsies
- ctDNA
- CTCs
5.3. Gene Signatures
5.3.1. EndoPredict—Molecular Score (MS)
5.3.2. Oncotype DX—Recurrence Score (RS)
5.3.3. Mammaprint
5.3.4. PAM50—Prosigna Assay
6. Future: Patients-Derived Tumor Organoids (PDTO)
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Breast Cancer Subtype | NAC Backbone | Drug Added to NAC Backbone | Indications | Side Effects |
---|---|---|---|---|
Luminal B | ||||
Sequential AC or EC—taxanes | Hormone-receptor-positive cancers larger than 2 cm and/or with axillary lymph node involvement | Cardiotoxicity, hair loss, peripheral neuropathy, febrile neutropenia, fatigue, nausea, diarrhea | ||
CMF | In elderly patients | Hair loss, peripheral neuropathy, febrile neutropenia, fatigue, nausea, diarrhea, hand-foot syndrome | ||
TC | If at risk of cardiac complications | Hair loss, peripheral neuropathy, febrile neutropenia, fatigue, nausea, diarrhea | ||
HER2-positive | ||||
Sequential AC or EC—taxanes | Trastuzumab | Node-negative | Chemotherapy side effects: Cardiotoxicity, hair loss, peripheral neuropathy, febrile neutropenia, fatigue, nausea, diarrhea Trastuzumab side effects: Transient cardiotoxicity, diarrhea | |
Sequential AC or EC—taxanes | Trastuzumab and pertuzumab | Node-positive | Chemotherapy side effects: Cardiotoxicity, hair loss, peripheral neuropathy, febrile neutropenia, fatigue, nausea, diarrhea Trastuzumab and pertuzumab side effects: Transient cardiotoxicity, diarrhea, peripheral neuropathy | |
TNBC | ||||
Sequential AC or EC—Carboplatin and taxanes | Pembrolizumab | Chemotherapy side effects: Cardiotoxicity, hair loss, peripheral neuropathy, febrile neutropenia, fatigue, nausea, diarrhea Pembrolizumab side effects: Cutaneous, endocrinopathy, cardiotoxicity, diarrhea, inflammatory pneumopathy, arthritis, hepatitis, nephritis |
Authors | Year | N | Subtypes | Timepoint | |||
---|---|---|---|---|---|---|---|
Before NAC | During NAC | Before Surgery | After Surgery | ||||
Garcia-Murillas et al. [142] | 2015 | 55 | All subtypes | Yes | Yes | ||
Riva et al. [143] | 2017 | 46 | TNBC | Yes | Yes | Yes | Yes |
Rothé et al. [144] | 2019 | 69 | HER2-positive | Yes | Yes | Yes | |
Butler et al. [145] | 2019 | 10 | All subtypes | Yes | Yes | Yes | Yes |
McDonald et al. [146] | 2019 | 22 | All subtypes | Yes | Yes | Yes | |
Magbanua et al. [136] | 2021 | 84 | All subtypes | Yes | Yes | Yes | |
Zhou et al. [147] | 2021 | 145 | HR+ and TNBC | Yes | Yes | Yes |
Gene Signature | Number of Genes | Genes | Validated Indications | Utilization |
---|---|---|---|---|
EndoPredict (MS) | 12 | BIRC5, UBE2C, DHCR7, RBBP8, IL6ST, AZGP1, MGP, STC2, CALM2, OAZ1, RPL37A | Evaluation of recurrence at 5–10 years | Score range from 0 to 15 <5: low risk ≥5: high risk |
Oncotype DX (RS) | 21 | CCNB1, MYBL2, MMP11, CTSL2, GRB2, ESR1, PGR, BCL2, BAG1, Ki-67, ACTB, GAPDH, RPLPO, GUS, TRFC, STK15, BIRC5, HER2, SCUBE2, GSTM1, CD68 | Evaluation of 10-year recurrence in patients | Score range from 0 to 100 (TAILORx) <11: low risk 11–25: intermediate risk >25: high risk |
Mammaprint | 70 | BBC3, EGLN1, TGFB3, ESM1, IGFBP5, FGF18, SCUBE2, TGFB3, WISP1, FLT1, HRASLS, STK32B, RASSF7, DCK, MELK, EXT1, GNAZ, EBF4, MTDH, PITRM1, QSCN6L1, CCNE2, ECT2, CENPA, LIN9, KNTC2, MCM6, NUSAP1, ORC6L, TSPYL5, RUNDC1, PRC1, RFC4, RECQL5, CDCA7, DTL, COL4A2, GPR180, MMP9, GPR126, RTN4RL1, DIAPH3, CDC42BPA, PALM2, ALDH4A1, AYTL2, OXCT1, PECI, GMPS, GSTM3, SLC2A3, FLT1, FGF18, COL4A2, GPR180, EGLN1, MMP9, LOC100288906, C9orf30, ZNF533, C16orf61, SERF1A, C20orf46, LOC730018, LOC100131053, AA555029_RC, LGP2, NMU, UCHL5, JHDM1D, AP2B1, MS4 A7, RAB6B | Early and distant relapse | Low risk High risk |
PAM50—Prosigna | 50 | UBE2C, PTTG1, MYBL2, BIRC5, CCNB1, TYMS, MELK, CEP55, KNTC2, UBE2T, RRM2, CDC6, ANLN, ORC6L, KIF2C, EXO1, CDCA1, CENPF, CCNE1, MKI-67, CDC20, MMP11, GRB7, ERBB2, TMEM45B, BAG1, PGR, MAPT, NAT1, GPR160, FOXA1, BLVRA, CXXC5, ESR1, SLC39A6, KRT17, KRT5, SFRP1, BCL2, KRT14, MLPH, MDM2, FGFR4, MYC, MIA, FOXC1, ACTR3B, PHGCH, CDH3, EGFR | -Risk of Recurrence Score (ROR) -Relapse at 10 years | -Risk of recurrence: low, intermediate, high -Relapse at 10 years in % |
Features | Cell Lines | PDTO | PDX |
---|---|---|---|
Establishment | + | ++ | ++ |
Maintenance | +++ | + | − |
Heterogeneity | − | + | ++ |
Patient-specific | − | +++ | +++ |
Environment interactions | − | − | +++ |
Preservation of tissue feature | − | ++ | +++ |
Co-culture | + | + | ++ |
Genetic manipulation | +++ | ++ | − |
High-throughput screening | +++ | +++ | − |
Cost | + | ++ | +++ |
Time-consuming | + | ++ | +++ |
Expertise | + | +++ | +++ |
Studies | Status | Type of Study | Aim |
---|---|---|---|
NCT04450706 | Recruiting | Interventional | Treatment decision based on genome sequencing (blood) and drug screening on organoids |
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NCT04531696 | Recruiting | Interventional | Post-mortem tissue donation program with multi-level and multi-region sample analysis to unravel metastatic breast cancer evolution, biology, heterogeneity and treatment resistance |
NCT04281641 | Recruiting | Interventional | Evaluation of the correlation between early changes in multiple markers and pathological complete response in patients with HER2-positive breast cancer receiving carboplatin, docetaxel and trastuzumab plus pertuzumab (TCHP) pre-operatively. Markers are examined by gene expression assays, 18F-FDG-PET, 68 Ga-Affibody HER-2 Imaging PET and organoid drug sensitivity |
NCT02732860 | Recruiting | Observational | Personalized patient-derived xenografts (pPDX) and organoids for drug screening |
NCT04703244 | Recruiting | Observational | Generate PDX and PDTO models from residual tumors after NAC for drug testing and the study of mechanisms of resistance |
NCT03896958 | Recruiting | Observational | Establish a data and tissue biobank |
NCT05134779 | Recruiting | Observational | Live biobank study with samples collected at inflection points in the course of the disease (at the time of initial diagnosis, at the time of surgery and during recurrence or metastasis) |
NCT04723316 | Recruiting | Observational | Create a national framework with molecular profiling of circulating tumor DNA and/or tumor tissue (optional) |
NCT04526587 | Recruiting | Observational | Investigate the clinical course of CDK4/6 inhibitor-treated patients in the real-world setting (cfDNA, organoids, PDX models) |
NCT05007379 | Not yet recruiting | Observational | Test the new CAR-macrophages drug on PDTO |
NCT04504747 | Not yet recruiting | Observational | Establishment of PDTO models from tumors exposed to NAC in parallel with the study of CTCs, along with tumors before and after NAC, to better identify mechanisms of resistance |
NCT05317221 | Not yet recruiting | Observational | Study of the heterogeneity and mechanisms of resistance |
NCT05381038 | Not yet recruiting | Interventional | QPOP drug selection followed by CURATE.AI-guided dose optimization for azacitidine combination therapy (docetaxel or paclitaxel or irinotecan) |
NCT04655573 | Not yet recruiting | Observational | Assess the feasibility of generating patient-derived micro-organospheres (PDMO) and drug screening |
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Share and Cite
Derouane, F.; van Marcke, C.; Berlière, M.; Gerday, A.; Fellah, L.; Leconte, I.; Van Bockstal, M.R.; Galant, C.; Corbet, C.; Duhoux, F.P. Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine. Cancers 2022, 14, 3876. https://doi.org/10.3390/cancers14163876
Derouane F, van Marcke C, Berlière M, Gerday A, Fellah L, Leconte I, Van Bockstal MR, Galant C, Corbet C, Duhoux FP. Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine. Cancers. 2022; 14(16):3876. https://doi.org/10.3390/cancers14163876
Chicago/Turabian StyleDerouane, Françoise, Cédric van Marcke, Martine Berlière, Amandine Gerday, Latifa Fellah, Isabelle Leconte, Mieke R. Van Bockstal, Christine Galant, Cyril Corbet, and Francois P. Duhoux. 2022. "Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine" Cancers 14, no. 16: 3876. https://doi.org/10.3390/cancers14163876