Addressing the Clinical Feasibility of Adopting Circulating miRNA for Breast Cancer Detection, Monitoring and Management with Artificial Intelligence and Machine Learning Platforms
Abstract
:1. Introduction
2. miRNA as Liquid Biopsy in Personalized Breast Cancer Management and Targeted Therapy
3. Current Trends and Research Outcomes of Circulating miRNA as Liquid Biopsy
3.1. Diagnostic Significance of Circulating miRNAs in Human Breast Cancer
3.2. Prognostic Significance of Circulating miRNAs in Human Breast Cancer
miRNAs | miRNAs Source | Diagnostic Significance | Significance in Grading/Classification | Prognostic Significance | Ref. | ||
---|---|---|---|---|---|---|---|
Response to Treatment | Overall Survival | Relapse/Recurrence | |||||
miR-21 | Serum | ↑ miR-21 in BC | ↑ miR-21 in advanced BC | ↑ miR-21 linked to ↑ radioresistance | ↑ miR-21 in BC ↓ survival | NIA | [36] |
miR-125b | Serum | NIA | ↑ miR-125b linked to ↑ disease staging | ↑ miR-125b linked to ↑ chemoresistance | NIA | NIA | [61] |
miR-140-5p | Plasma | ↓ miR-140-5p in BC as compared to CT | ↓ miR-140-5p linked to worst disease prognosis | ↓ miR-140-5p linked to ↑ chemoresistance | ↓ miR-140-5p linked to ↓ EFS | ↓ miR-140-5p linked to ↑ relapse/recurrence | [39] |
miR-335 | Serum | ↓ miR-335 in BC as compared to CT | ↓ miR-335 in TNBC | NIA | ↓ miR-335 linked to ↓ OS | ↓ miR-335 linked to ↑ relapse/recurrence | [54] |
miR-34a/b/c | Plasma | ↓ In the 3 miRNAs levels in BC as compared to CT | ↓ miR-34a levels linked to advanced clinical staging & histopathological grading | NIA | ↓ miR-34a levels linked to ↓ survival | NIA | [37] |
miR-21, miR-23b, miR-200c, miR-190 | Plasma | ↑ miR-21, miR-23b & miR-200c levels & ↓ miR-190 in BC | The 4 miRNAs distinguished relapsed & non-relapsed BC cases | NIA | ↑ miR-21 & miR-200c linked to short DFS | ↑ miR-21, miR-23b & miR-200c & ↓ miR-190 in relapsed as compared to non-relapsed case | [42] |
miR-16-5p, miR-17-3p, miR-451a, miR-940 | Serum | No significant difference in the 4 miRNAs levels between BC & CT cases | The 4 miRNAs distinguished metastatic & non-metastatic BC cases | ↓ In the 4 miRNAs in trastuzumab-resistant BC | ↑ In the 4 miRNAs in improved BC survival | ↑ In the 4 miRNAs in reduced incidence of relapse/recurrence | [50] |
miR-18b, miR-103, miR-107, miR-652 | Serum | ↑ In the 4 miRNAs levels in TNBC | ↑ In the 4 miRNAs levels linked to advanced clinical staging & histopathological grading | NIA | ↑ In the 4 miRNAs ↓ RFS & OS | ↑ In the 4 miRNAs in relapse group | [66] |
Let-7a, miR-10b, miR-21, miR-145, miR-181a | Plasma | ↑ miR-10b, miR-21 & miR-181a & ↓ let-7a & miR-145 in BC | ↑ miR-10b, miR-21 & miR-181a & ↓ let-7a & miR-145 in locally advanced BC | NIA | ↑ miR-10b & ↓ miR-21 linked to survival | ↑ miR-10b & miR-21 linked to ↑ relapse | [57] |
miR-26b-5p, miR-106b-5p, miR-142-3p, miR-142-5p, miR-185-5p, miR-362-5p | Whole blood | ↑ In the 6 miRNAs levels in BC | ↑ In the 6 miRNAs levels in early BC | NIA | ↑ In the 6 miRNAs levels linked to ↓ OS/DFS | NIA | [45] |
miR-19a, miR-19b-3p, miR-22-3p, miR-25-3p, miR-93-5p, miR-199a-3p, miR-210-3p | Plasma | ↑ In the 7 miRNAs levels in BC | These miRNAs predicted BC patient survival & relapse | The 7 miRNAs regulate chemotherapy & targeted therapy resistance | ↑ miR-19a, miR-19b, miR-93 & miR-201 linked to poor OS in TNBC patients | NIA | [30] |
miR-296-3p, miR-575, miR-3610-5p, miR-4483, miR-4710, miR-4755-3p, miR-5698, miR-8089 | Serum | ↑ In the 8 miRNAs levels in BC | The 8 miRNAs distinguished metastatic & non-metastatic BC cases | NIA | ↓ miR-5698 & miR-8089 linked to ↑ improved survival | The 8 miRNAs predicted distant metastases | [31] |
miRNAs | miRNAs Source | Diagnostic Significance | Significance in Grading/Classification | Prognostic Significance | Ref. | ||
---|---|---|---|---|---|---|---|
Response to Treatment | Overall Survival | Relapse/Recurrence | |||||
miR-24-3p | Plasma | ↑ miR-24-3p in BC | ↑ miR-24-3p linked to advanced clinical & histopathological grading | NIA | ↓ miR-24-3p linked to improved survival | NIA | [47] |
miR-363-5p | Plasma | ↓ miR-363-5p in BC | ↓ miR-363-5p in LN+ve BC cases as compared to LN –ve BC cases | NIA | ↑ miR-363-5p linked to ↑ survival | NIA | [25] |
miR-141, miR-200c | Plasma | ↑ miR-141 & miR-200c in BC | ↑ miR-141 in invasive BC; ↑ miR-141 & miR-200c in metastatic BC | NIA | ↑ miR-200c linked to short OS | NIA | [64] |
miR-155, miR-1246 | Plasma | ↑ Both miRNAs in trastuzumab-resistant BC | ↑ Both miRNAs advanced BC as compared to non-advanced BC | ↑ Both miRNAs in trastuzumab-resistant BC | ↑ Both miRNAs linked to poor survival | ↑ Both miRNAs linked to relapse & poor EFS | [38] |
miR-21, miR-105, miR-222 | Serum | ↑ In the 3 miRNAs levels linked to presence of circulating BC cells | ↑ miR-222 linked to advanced clinical staging & histopathological grading; ↑ miR-21 & miR-105 in metastatic than non-metastatic BC | ↑ miR-21 reduced NACT response | NIA | NIA | [58] |
miR-150-5p, miR-576-3p, miR-4665-5p | Plasma | ↑ In the 3 miRNAs levels in BC | The 3 miRNAs distinguished recurrence & non-recurrence in BC cases | NIA | NIA | ↑ In the 3 miRNAs levels in recurrent BC as compared to non-recurrent BC | [34] |
miR-16, miR-30b, miR-93 | Plasma | ↑ miR-16 in BC & ↑ miR-93 in DCIS | ↑ miR-93 in ER & PR +ve BC | NIA | NIA | ↓ miR-30b linked to recurrence | [63] |
miR-195-5p, miR-548ab, miR-2392, miR-2467-3p, miR-4448, miR-4800-3p | Serum | ↑ miR-2392, miR-2467-3p, miR-4448 & miR-4800-3p levels in BC | The 6 miRNAs distinguished recurrence & non-recurrence in BC cases | ↑ miR-2392, miR-2467-3p, miR-4448 & miR-4800-3p levels in BC with complete NACT response | ↑ miR-2392, miR-2467-3p, miR-4448 & miR-4800-3p levels linked to ↑ OS in BC | ↑ In miR-195-5p & ↓ miR-548ab in recurrent BC cases | [60] |
miR-30b, miR-34a, miR-127, miR-141, miR-182, miR-183, miR-328, miR-423 | Plasma | Dysregulation in the 8 miRNA levels in BC | ↓ miR-30b, miR-127 & miR-328 in invasive BC | ↑ miR-127 & miR-141 linked to complete NACT response; ↑ miR-34a, miR-182 & miR-183 linked to poor NACT response | ↓ miR-141, miR-34a, miR-423, miR-182 & miR-183 linked to ↑ OS | NIA | [59] |
3.3. Multifunctional Roles of Circulating miRNAs as Potential Biomarker for Human Breast Cancer
3.4. Sensitivity and Specificity Levels of miRNA Detection in BC Patients
4. Current Challenges and Issues in Circulating miRNAs as a Common Candidate for Liquid Biopsy in BC Management
4.1. Biological Parameters
4.2. Statistical Models
5. Machine Learning and Deep Learning Approaches in BC Research
5.1. Machine Learning and Deep Learning for Detection and Diagnosis
5.2. Studies with miRNAs as Breast Cancer Biomarkers with ML/DL Approaches
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Cancer Type | Benign | Pre-Malignant/ In-Situ (20–25%) [13] | Malignant/Invasive [IDC (80%), ILC (20%)] [13] | |||||
---|---|---|---|---|---|---|---|---|
Categories | Fibroadenoma Intraductal papilloma Lipoma | Early Breast Cancer Detection | Molecular Subtypes (St Gallen) | Recurrence/ Metastatic | ||||
Lubular Carcinoma In-situ (LCIS) | Ductal Carcinoma In-situ (DCIS) | Luminal A | Luminal B (HER2-) | Luminal B (HER2+) | HER2+ Enriched | TNBC | ||
Cancer/Bio markers [11,12] | ER, PR, HER2 & Ki67 (low < 10%); Germline test BRCA1 & 2 (High risk group) | ER+; PR+; HER2−; Ki67 low (<10–14%); Germline test BRCA1 & 2 (High Risk Group) | ER+; PR−; HER2−; Ki67 high (>14–30%); Germline test BRCA1 & 2 (High Risk Group) | ER+; PR+/−; HER2+; Ki67 high/low; Germline test BRCA1 & 2 (High Risk Group) | ER−; PR−; HER2+; Ki67 high; Germline test BRCA1 & 2 (High Risk Group) | ER−; PR−; HER2−; Ki67 high; (CK 5/6+; EGFR+); Germline test BRCA1 & BRCA2 (High Risk Group) | Metastatic Site: Bone, liver, lungs, brain ESCAT score: I = Good prognosis II = Poor Prognosis | |
Frequency of cases [14] | 20–25% | 40–50% | 20–30% | 20–30% | 15–20% | 10–20% | 4% | |
Histological grade (Majority) | Well differentiated (G1) | Moderately differentiated (G2) | Moderately differentiated (G2) | Poorly differentiated (G3) | Poorly differentiated (G3) | Poorly differentiated (G4) | ||
TNM Stage | NR | I-III | I-III | I-III | I-III | I-III | IV | |
Prognosis | NR | Good | Intermediate | Intermediate | Poor | Poor | Poor | |
Response to therapies [11,12,14] | Surgery Breast-conserving surgery (BCS) Radiotherapy Lumpectomy Mastectomy | Endocrine | Endocrine Chemotherapy | Endocrine Chemotherapy Targeted Therapy | Chemotherapy Targeted Therapy | Chemotherapy PARP inhibitors | Chemotherapy CKD4/6 Inhibitor Fulvestrant |
Study Name | Year Launched | Study ID | Location | Status |
---|---|---|---|---|
Onco-liq: Kit for Breast Cancer Diagnosis. | 2021 | NCT04906330 | Argentina | On-going |
Prospective Breast Cancer Biobanking (PBCB) | 2020 | NCT04488614 | Norway | On-going |
Reference | Function/Purpose | Methods | Accuracy of Model |
---|---|---|---|
[97] | Cancer Classification | Gradient Boosting | Accuracy 93.59% |
RF | Accuracy 93.24% | ||
LR | Accuracy 92.37% | ||
Passive Aggressive | Accuracy 88.31% | ||
SGD | Accuracy 90.35% | ||
SVM | Accuracy 91.54% | ||
Ridge | Accuracy 83.05% | ||
Bagging | Accuracy 91.1% | ||
[98] | Cancer Classification | NB | Accuracy 94.9% |
[99] | Cancer detection | RF | AUC 99.5–99.9% |
SVM | AUC 93.8–99.6% | ||
ANN | Accuracy 97.3% | ||
KNN | Accuracy 99.2% | ||
SVM | Accuracy 96.3% | ||
LR | Accuracy 95.8% | ||
[100] | Cancer Classification | Tree-based model | NIA |
[101] | Cancer Classification | DT | Accuracy 99.12% |
NB | Accuracy 93.86% | ||
ANN | Accuracy 100% | ||
DL | Accuracy 100% |
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Ling, L.; Aldoghachi, A.F.; Chong, Z.X.; Ho, W.Y.; Yeap, S.K.; Chin, R.J.; Soo, E.Z.X.; Khor, J.F.; Yong, Y.L.; Ling, J.L.; et al. Addressing the Clinical Feasibility of Adopting Circulating miRNA for Breast Cancer Detection, Monitoring and Management with Artificial Intelligence and Machine Learning Platforms. Int. J. Mol. Sci. 2022, 23, 15382. https://doi.org/10.3390/ijms232315382
Ling L, Aldoghachi AF, Chong ZX, Ho WY, Yeap SK, Chin RJ, Soo EZX, Khor JF, Yong YL, Ling JL, et al. Addressing the Clinical Feasibility of Adopting Circulating miRNA for Breast Cancer Detection, Monitoring and Management with Artificial Intelligence and Machine Learning Platforms. International Journal of Molecular Sciences. 2022; 23(23):15382. https://doi.org/10.3390/ijms232315382
Chicago/Turabian StyleLing, Lloyd, Ahmed Faris Aldoghachi, Zhi Xiong Chong, Wan Yong Ho, Swee Keong Yeap, Ren Jie Chin, Eugene Zhen Xiang Soo, Jen Feng Khor, Yoke Leng Yong, Joan Lucille Ling, and et al. 2022. "Addressing the Clinical Feasibility of Adopting Circulating miRNA for Breast Cancer Detection, Monitoring and Management with Artificial Intelligence and Machine Learning Platforms" International Journal of Molecular Sciences 23, no. 23: 15382. https://doi.org/10.3390/ijms232315382