Blood-Based mRNA Tests as Emerging Diagnostic Tools for Personalised Medicine in Breast Cancer
Abstract
:Simple Summary
Abstract
1. Introduction
2. Molecular Diagnostic Tests and Companion Diagnostic Devices for Breast Cancer Approved by the US Food and Drug Administration (FDA)
3. Cancer Diagnostics Based on mRNA Can Offer Prognostically Useful Information beyond DNA Variation
4. Tissue-Based mRNA Expression Assays for Breast Cancer
4.1. Prosigna Breast Cancer Prognostic Gene Signature Assay (Formerly Called the PAM50 Test)
4.2. MammaPrint Test (also Called the 70-Gene Signature)
4.3. Oncotype DX Breast Recurrence Score Test
4.4. Breast Cancer Index, BCI
4.5. EndoPredict Breast Cancer Prognostic Test
4.6. GeneSearch Breast Lymph Node (BLN) Test Kit
Assay Trade Name (Manufacturer) | Number of Genes; Sample Type | Assay Indicated For | Description | Methodology/ Platform | FDA Numbers; References |
---|---|---|---|---|---|
Prosigna Breast Cancer Prognostic Gene Signature Assay (Veracyte, Inc.) | 50 genes; BC tissue (formalin-fixed paraffin embedded—FFPE) | HR+, LN-negative or 1–3 positive nodes, stage I or II cancers | Classification of intrinsic BC subtypes; prognostic; recurrence risk assessment; guides adjuvant endocrine and chemotherapy in postmenopausal patients | nCounter Dx Analysis System (mRNA hybridization to DNA probes) | FDA (K130010); [26,31] |
MammaPrint (Agendia, Inc.) | 70 genes; BC tissue (FFPE or fresh) | HR+ or HR−, LN-negative or 1–3 positive nodes, stage I or II cancers, ≤5.0 cm | Prognostic; recurrence risk assessment; guides adjuvant endocrine and chemotherapy in patients >50 or postmenopausal patients | Microarray-based assay; also available as a targeted RNA next-generation sequencing assay | FDA (K101454, K081092, K080252, K070675); [31,57,58] |
Oncotype DX Breast Recurrence Score Test (Genomic Health) | 21 genes (16 cancer-related and 5 reference genes); BC tissue (FFPE) | HR+/HER2−, LN-negative or 1–3 positive nodes, stage I, II or IIIa cancers, ≤5.0 cm | Prognosticates distant recurrence; predicts chemotherapy benefit/guides adjuvant endocrine and chemotherapy in postmenopausal or premenopausal patients | qRT-PCR-based assay | [31,34,35,36,59] |
Breast Cancer Index—BCI (Biotheranostic, Inc.) | 7 genes; BC tissue (FFPE) | HR+, LN-negative or 1–3 positive nodes, stage I–III cancers, invasive BC cases without evidence of recurrence | Prognosticates risk of distant recurrence; predicts likelihood of benefit from extended (>5 years) endocrine therapy; guides adjuvant endocrine and chemotherapy in patients >50 or postmenopausal patients | qRT-PCR-based assay | [31,60] |
EndoPredict (Myriad Genetics) | 12 genes (8 BC-related and 4 reference genes); BC tissue (FFPE) | HR+/HER2−, LN-negative or 1–3 positive nodes, tumour size T1–T3, grade 1–3 | Predicts distant recurrence at 10 years (and up to 15 years); guides adjuvant endocrine and chemotherapy in patients >50 or postmenopausal patients; identifies premenopausal patients who do not need chemotherapy | qRT-PCR-based assay | [31,46,61] |
GeneSearch Breast Lymph Node (BLN) Test Kit (Veridex, LLC.) | 3 genes (2 metastasis-related and 1 reference); lymph node(s) removed during surgery | Patients with invasive BC, scheduled for sentinel lymph node dissection | BC metastasis detection | qRT-PCR-based assay | FDA: P060017 S001–S004, [52] |
5. The Advantages of Using Peripheral Blood (i.e., a Blood-Based Liquid Biopsy) for Cancer Diagnostics
6. Research Focusing on BC-Specific Transcriptional Profiles in Peripheral Blood Has Established Their Diagnostic and Prognostic Value
6.1. Identification of Blood Predictor Genes That Can Distinguish BC Patients from Other Cancer Patients and Healthy Subjects
6.2. Identification of Expression Signatures That Can Distinguish between Breast Cancer and Benign Breast Disease
6.3. Identification of Blood Expression Signatures That Can Distinguish between Lymph-Node Positive and Negative BC
6.4. Blood Gene Expression Alterations Years before BC Diagnosis
6.5. Peripheral Blood Transcriptomics May Open the Way to Novel Immune BC Subtyping
7. Commercially Available Blood-Based mRNA Tests for Breast Cancer
7.1. Hereditary Breast Cancer Predisposition
7.2. Breast Cancer Screening
8. Current Status and Future Perspectives
8.1. The RNA Methods Used for Breast Cancer and Their Expanding Diagnostic Potential
8.2. The Capability of Supplemental Blood mRNA Analyses to Improve the Diagnostic Outcomes of Cancer DNA Testing
8.3. The Potential of Blood RNA Analyses to Enable Early Cancer Detection
8.4. The mRNA-Based Applications in Research and under Development for Personalised BC Management
8.5. The Issue of Centralised Testing and Restricted Availability of mRNA Diagnostics for BC
8.6. The Centralisation of Testing and the Associated Individualised Standardisation and Validation of RNA-Based Diagnostic Assays
8.7. The Technical Challenges Associated with mRNA Diagnostic Testing
9. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
- Dalmartello, M.; La Vecchia, C.; Bertuccio, P.; Boffetta, P.; Levi, F.; Negri, E.; Malvezzi, M. European cancer mortality predictions for the year 2022 with focus on ovarian cancer. Ann Oncol. 2022, 33, 330–339. [Google Scholar] [CrossRef] [PubMed]
- International Agency for Research on Cancer, WHO. Cancer Today. 2022. Available online: https://gco.iarc.fr/today/home. (accessed on 19 October 2022).
- Szymiczek, A.; Lone, A.; Akbari, M.R. Molecular intrinsic versus clinical subtyping in breast cancer: A comprehensive review. Clin. Genet. 2021, 99, 613–637. [Google Scholar] [PubMed]
- Čelešnik, H.; Potočnik, U. Peripheral Blood Transcriptome in Breast Cancer Patients as a Source of Less Invasive Immune Biomarkers for Personalized Medicine, and Implications for Triple Negative Breast Cancer. Cancers 2022, 14, 591. [Google Scholar]
- McAndrew, N.P.; Finn, R.S. Clinical Review on the Management of Hormone Receptor-Positive Metastatic Breast Cancer. JCO Oncol. Pract. 2022, 18, 319–327. [Google Scholar] [CrossRef] [PubMed]
- Ferrario, C.; Christofides, A.; Joy, A.A.; Laing, K.; Gelmon, K.; Brezden-Masley, C. Novel Therapies for the Treatment of HER2-Positive Advanced Breast Cancer: A Canadian Perspective. Curr. Oncol. 2022, 29, 2720–2734. [Google Scholar] [CrossRef]
- Bianchini, G.; De Angelis, C.; Licata, L.; Gianni, L. Treatment landscape of triple-negative breast cancer—expanded options, evolving needs. Nat. Rev. Clin. Oncol. 2022, 19, 91–113. [Google Scholar] [CrossRef]
- Čelešnik, H.S.; Potočnik, U. Immunotherapy in Breast Cancer. In: Encyclopedia. Available online: https://encyclopedia.pub/entry/21561 (accessed on 27 November 2022).
- Skok, K.; Gradišnik, L.; Čelešnik, H.; Milojević, M.; Potočnik, U.; Jezernik, G.; Gorenjak, M.; Sobočan, M.; Takač, I.; Kavalar, R.; et al. MFUM-BrTNBC-1, a Newly Established Patient-Derived Triple-Negative Breast Cancer Cell Line: Molecular Characterisation, Genetic Stability, and Comprehensive Comparison with Commercial Breast Cancer Cell Lines. Cells 2021, 11, 117. [Google Scholar] [CrossRef]
- Skok, K.; Gradišnik, L.; Čelešnik, H.; Potočnik, U.; Kavalar, R.; Takač, I.; Maver, U. Isolation and characterization of the first Slovenian human triple-negative breast cancer cell line. Breast J. 2020, 26, 328–330. [Google Scholar]
- O’Meara, T.A.; Tolaney, S.M. Tumor mutational burden as a predictor of immunotherapy response in breast cancer. Oncotarget. 2021, 12, 394–400. [Google Scholar]
- Chen, X.; Li, J.; Gray, W.H.; Lehmann, B.D.; Bauer, J.A.; Shyr, Y.; Pietenpol, J.A. TNBCtype: A Subtyping Tool for Triple-Negative Breast Cancer. Cancer Inform. 2012, 11, 147–156. [Google Scholar]
- Hartung, C.; Porsch, M.; Stückrath, K.; Kaufhold, S.; Staege, M.S.; Hanf, V.; Lantzsch, T.; Uleer, C.; Peschel, S.; John, J.; et al. Identifying High-Risk. Triple-Negative Breast Cancer Patients by Molecular Subtyping. Breast Care 2021, 16, 637–647. [Google Scholar] [PubMed]
- Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012, 490, 61–70. [Google Scholar] [CrossRef] [PubMed]
- Prat, A.; Adamo, B.; Cheang, M.C.; Anders, C.K.; Carey, L.A.; Perou, C.M. Molecular characterization of basal-like and non-basal-like triple-negative breast cancer. Oncologist 2013, 18, 123–133. [Google Scholar] [PubMed]
- U.S. Food & Drug Administration, FDA. 2022. Available online: www.fda.gov (accessed on 20 October 2022).
- Ravkin, H.D.; Givton, O.; Geffen, D.B.; Rubin, E. Direct comparison shows that mRNA-based diagnostics incorporate information which cannot be learned directly from genomic mutations. BMC Bioinform. 2020, 21, 196. [Google Scholar] [CrossRef]
- Karam, R.; Conner, B.; LaDuca, H.; McGoldrick, K.; Krempely, K.; Richardson, M.E.; Zimmermann, H.; Gutierrez, S.; Reineke, P.; Hoang, L.; et al. Assessment of Diagnostic Outcomes of RNA Genetic Testing for Hereditary Cancer. JAMA Netw. Open 2019, 2, e1913900. [Google Scholar]
- Chen, S.; Parmigiani, G. Meta-analysis of BRCA1 and BRCA2 penetrance. J. Clin. Oncol. 2007, 25, 1329–1333. [Google Scholar]
- Vietri, M.T.; Caliendo, G.; Casamassimi, A.; Cioffi, M.; De Paola, M.L.; Napoli, C.; Molinari, A.M. A novel PALB2 truncating mutation in an Italian family with male breast cancer. Oncol. Rep. 2015, 33, 1243–1247. [Google Scholar] [CrossRef]
- Piccolo, S.R.; Andrulis, I.L.; Cohen, A.L.; Conner, T.; Moos, P.J.; Spira, A.E.; Buys, S.S.; Johnson, W.E.; Bild, A.H. Gene-expression patterns in peripheral blood classify familial breast cancer susceptibility. BMC Med. Genomics. 2015, 8, 72. [Google Scholar] [CrossRef]
- Schaafsma, E.; Zhang, B.; Schaafsma, M.; Tong, C.Y.; Zhang, L.; Cheng, C. Impact of Oncotype DX testing on ER+ breast cancer treatment and survival in the first decade of use. Breast Cancer Res. 2021, 23, 74. [Google Scholar]
- Martín, M.; González-Rivera, M.; Morales, S.; de la Haba-Rodriguez, J.; González-Cortijo, L.; Manso, L.; Albanell, J.; González-Martín, A.; González, S.; Arcusa, A.; et al. Prospective study of the impact of the Prosigna assay on adjuvant clinical decision-making in unselected patients with estrogen receptor positive, human epidermal growth factor receptor negative, node negative early-stage breast cancer. Curr. Med. Res. Opin. 2015, 31, 1129–1137. [Google Scholar]
- Dowsett, M.; Sestak, I.; Lopez-Knowles, E.; Sidhu, K.; Dunbier, A.K.; Cowens, J.W.; Ferree, S.; Storhoff, J.; Schaper, C.; Cuzick, J. Comparison of PAM50 risk of recurrence score with oncotype DX and IHC4 for predicting risk of distant recurrence after endocrine therapy. J. Clin. Oncol. 2013, 31, 2783–2790. [Google Scholar] [PubMed]
- Gnant, M.; Filipits, M.; Greil, R.; Stoeger, H.; Rudas, M.; Bago-Horvath, Z.; Mlineritsch, B.; Kwasny, W.; Knauer, M.; Singer, C.; et al. Predicting distant recurrence in receptor-positive breast cancer patients with limited clinicopathological risk: Using the PAM50 Risk of Recurrence score in 1478 postmenopausal patients of the ABCSG-8 trial treated with adjuvant endocrine therapy alone. Ann. Oncol. 2014, 25, 339–345. [Google Scholar] [CrossRef] [PubMed]
- Veracyte. Prosigna Breast Cancer Assay. 2022. Available online: https://www.prosigna.com/ (accessed on 26 October 2022).
- Lænkholm, A.V.; Jensen, M.B.; Eriksen, J.O.; Rasmussen, B.B.; Knoop, A.S.; Buckingham, W.; Ferree, S.; Schaper, C.; Nielsen, T.O.; Haffner, T.; et al. PAM50 Risk of Recurrence Score Predicts 10-Year Distant Recurrence in a Comprehensive Danish Cohort of Postmenopausal Women Allocated to 5 Years of Endocrine Therapy for Hormone Receptor-Positive Early Breast Cancer. J. Clin. Oncol. 2018, 36, 735–740. [Google Scholar] [CrossRef]
- Soliman, H.; Shah, V.; Srkalovic, G.; Mahtani, R.; Levine, E.; Mavromatis, B.; Srinivasiah, J.; Kassar, M.; Gabordi, R.; Qamar, R.; et al. MammaPrint guides treatment decisions in breast Cancer: Results of the IMPACt trial. BMC Cancer 2020, 20, 81. [Google Scholar]
- van de Vijver, M.J.; He, Y.D.; van’t Veer, L.J.; Dai, H.; Hart, A.A.; Voskuil, D.W.; Schreiber, G.J.; Peterse, J.L.; Roberts, C.; Marton, M.J.; et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 2002, 347, 1999–2009. [Google Scholar] [CrossRef] [PubMed]
- Cardoso, F.; van’t Veer, L.J.; Bogaerts, J.; Slaets, L.; Viale, G.; Delaloge, S.; Pierga, J.Y.; Brain, E.; Causeret, S.; DeLorenzi, M.; et al. 70-Gene Signature as an Aid to Treatment Decisions in Early-Stage Breast Cancer. N. Engl. J. Med. 2016, 375, 717–729. [Google Scholar] [CrossRef]
- Andre, F.; Ismaila, N.; Allison, K.H.; Barlow, W.E.; Collyar, D.E.; Damodaran, S.; Henry, N.L.; Jhaveri, K.; Kalinsky, K.; Kuderer, N.M.; et al. Biomarkers for Adjuvant Endocrine and Chemotherapy in Early-Stage Breast Cancer: ASCO Guideline Update. J. Clin. Oncol. 2022, 40, 1816–1837. [Google Scholar] [CrossRef]
- Beumer, I.; Witteveen, A.; Delahaye, L.; Wehkamp, D.; Snel, M.; Dreezen, C.; Zheng, J.; Floore, A.; Brink, G.; Chan, B.; et al. Equivalence of MammaPrint array types in clinical trials and diagnostics. Breast Cancer Res. Treat. 2016, 156, 279–287. [Google Scholar]
- Krijgsman, O.; Roepman, P.; Zwart, W.; Carroll, J.S.; Tian, S.; de Snoo, F.A.; Bender, R.A.; Bernards, R.; Glas, A.M. A diagnostic gene profile for molecular subtyping of breast cancer associated with treatment response. Breast Cancer Res. Treat. 2012, 133, 37–47. [Google Scholar]
- Syed, Y.Y. Oncotype DX Breast Recurrence Score®: A Review of its Use in Early-Stage Breast Cancer. Mol. Diagn Ther. 2020, 24, 621–632. [Google Scholar]
- Kalinsky, K.; Barlow, W.E.; Gralow, J.R.; Meric-Bernstam, F.; Albain, K.S.; Hayes, D.F.; Lin, N.U.; Perez, E.A.; Goldstein, L.J.; Chia, S.K.L.; et al. 21-Gene Assay to Inform Chemotherapy Benefit in Node-Positive Breast Cancer. N. Engl. J. Med. 2021, 385, 2336–2347. [Google Scholar] [PubMed]
- Genomic Health. What is the Oncotype DX® Test, and What Makes it Unique? 2022. Available online: https://www.oncotypeiq.com/en-CA/breast-cancer/healthcare-professionals/oncotype-dx-breast-recurrence-score/about-the-test# (accessed on 7 November 2022).
- Trosman, J.R.; Van Bebber, S.L.; Phillips, K.A. Coverage policy development for personalized medicine: Private payer perspectives on developing policy for the 21-gene assay. J. Oncol. Pract. 2010, 6, 238–242. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sparano, J.A.; Gray, R.J.; Makower, D.F.; Pritchard, K.I.; Albain, K.S.; Hayes, D.F.; Geyer, C.E., Jr.; Dees, E.C.; Perez, E.A.; Olson, J.A., Jr.; et al. Prospective Validation of a 21-Gene Expression Assay in Breast Cancer. N. Engl. J. Med. 2015, 373, 2005–2014. [Google Scholar] [CrossRef]
- Sparano, J.A.; Gray, R.J.; Makower, D.F.; Pritchard, K.I.; Albain, K.S.; Hayes, D.F.; Geyer, C.E., Jr.; Dees, E.C.; Goetz, M.P.; Olson, J.A., Jr.; et al. Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Breast Cancer. N. Engl. J. Med. 2018, 379, 111–121. [Google Scholar]
- Sparano, J.A.; Gray, R.J.; Ravdin, P.M.; Makower, D.F.; Pritchard, K.I.; Albain, K.S.; Hayes, D.F.; Geyer, C.E., Jr.; Dees, E.C.; Goetz, M.P.; et al. Clinical and Genomic Risk to Guide the Use of Adjuvant Therapy for Breast Cancer. N. Engl. J. Med. 2019, 380, 2395–2405. [Google Scholar]
- Sparano, J.A.; Gray, R.J.; Makower, D.F.; Albain, K.S.; Saphner, T.J.; Badve, S.S.; Wagner, L.I.; Kaklamani, V.G.; Keane, M.M.; Gomez, H.L.; et al. Clinical Outcomes in Early Breast Cancer with a High 21-Gene Recurrence Score of 26 to 100 Assigned to Adjuvant Chemotherapy Plus Endocrine Therapy: A Secondary Analysis of the TAILORx Randomized Clinical Trial. JAMA Oncol. 2020, 6, 367–374. [Google Scholar] [PubMed]
- Sestak, I.; Zhang, Y.; Sgroi, D.; Schnabel, C.A.; Cuzick, J.M.; Dowsett, M. Residual risk assessment with the Breast Cancer Index (BCI) for prediction of late distant recurrence (DR) in patients from the TransATAC study. J. Clin. Oncol. 2018, 36 (Suppl. S15), 529. [Google Scholar]
- Habel, L.A.; Sakoda, L.C.; Achacoso, N.; Ma, X.J.; Erlander, M.G.; Sgroi, D.C.; Fehrenbacher, L.; Greenberg, D.; Quesenberry, C.P., Jr. HOXB13:IL17BR and molecular grade index and risk of breast cancer death among patients with lymph node-negative invasive disease. Breast Cancer Res. 2013, 15, R24. [Google Scholar]
- Sgroi, D.C.; Sestak, I.; Cuzick, J.; Zhang, Y.; Schnabel, C.A.; Schroeder, B.; Erlander, M.G.; Dunbier, A.; Sidhu, K.; Lopez-Knowles, E.; et al. Prediction of late distant recurrence in patients with oestrogen-receptor-positive breast cancer: A prospective comparison of the breast-cancer index (BCI) assay, 21-gene recurrence score, and IHC4 in the TransATAC study population. Lancet Oncol. 2013, 14, 1067–1076. [Google Scholar] [CrossRef]
- Müller, B.M.; Keil, E.; Lehmann, A.; Winzer, K.J.; Richter-Ehrenstein, C.; Prinzler, J.; Bangemann, N.; Reles, A.; Stadie, S.; Schoenegg, W.; et al. The EndoPredict Gene-Expression Assay in Clinical Practice—Performance and Impact on Clinical Decisions. PLoS ONE 2013, 8, e68252. [Google Scholar]
- Myriad Genetics. EndoPredict: One Test—Three Clinical Answers for Breast Cancer Patients. 2022. Available online: https://endopredict.eu/ (accessed on 27 November 2022).
- Filipits, M.; Rudas, M.; Jakesz, R.; Dubsky, P.; Fitzal, F.; Singer, C.F.; Dietze, O.; Greil, R.; Jelen, A.; Sevelda, P.; et al. A new molecular predictor of distant recurrence in ER-positive, HER2-negative breast cancer adds independent information to conventional clinical risk factors. Clin. Cancer Res. 2011, 17, 6012–6020. [Google Scholar] [CrossRef] [PubMed]
- Sestak, I.; Martín, M.; Dubsky, P.; Kronenwett, R.; Rojo, F.; Cuzick, J.; Filipits, M.; Ruiz, A.; Gradishar, W.; Soliman, H.; et al. Prediction of chemotherapy benefit by EndoPredict in patients with breast cancer who received adjuvant endocrine therapy plus chemotherapy or endocrine therapy alone. Breast Cancer Res. Treat. 2019, 176, 377–386. [Google Scholar] [PubMed]
- Filipits, M.; Dubsky, P.; Rudas, M.; Greil, R.; Balic, M.; Bago-Horvath, Z.; Singer, C.F.; Hlauschek, D.; Brown, K.; Bernhisel, R.; et al. Prediction of Distant Recurrence Using EndoPredict Among Women with ER+, HER2- Node-Positive and Node-Negative Breast Cancer Treated with Endocrine Therapy Only. Clin. Cancer Res. 2019, 25, 3865–3872. [Google Scholar] [PubMed]
- Buus, R.; Sestak, I.; Kronenwett, R.; Denkert, C.; Dubsky, P.; Krappmann, K.; Scheer, M.; Petry, C.; Cuzick, J.; Dowsett, M. Comparison of EndoPredict and EPclin With Oncotype DX Recurrence Score for Prediction of Risk of Distant Recurrence After Endocrine Therapy. J. Natl. Cancer Inst. 2016, 108, djw149. [Google Scholar] [PubMed]
- Constantinidou, A.; Marcou, Y.; Simmons, T.; Bernhisel, R.; Hughes, E.; Meek, S.; Kakouri, E.I.; Georgiou, G.; Zouvani, I.; Savvidou, G.; et al. Clinical validation of EndoPredict in premenopausal women with estrogen receptor-positive (ER+), human epidermal growth factor receptor 2-negative (HER2-) primary breast cancer. J. Clin. Oncol. 2021, 39 (Suppl. S15), 537. [Google Scholar] [CrossRef]
- Veridex. GeneSearch Breast Lymph Node (BLN) Test Kit, FDA Report/PMA P060017. 2022. Available online: https://fda.report/PMA/P060017/6/P060017B.pdf (accessed on 7 November 2022).
- Janssen Diagnostics. GeneSearch™ Breast Lymph Node (BLN) Assay Post Approval Study. ClinicalTrials.gov Identifier: NCT00595296; 2016. Available online: https://www.clinicaltrials.gov/ct2/show/record/NCT00595296?view=record (accessed on 2 November 2022).
- U.S. Food & Drug Administration. Premarket Approval, PMA Number P060017, Genesearch Breast Lymph Node (Bln) Assay. 2022. Available online: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMA/pma.cfm?start_search=1&PMANumber=P060017 (accessed on 2 November 2022).
- Cutress, R.; Agrawal, A.; Etherington, A.; Gabriel, F.G.; Jeffrey, M.; Lai, L.; Wise, M.; Cree, I.; Yiangou, C. Intra-operative assessment of axillary sentinel lymph nodes (SLN) using an RT-PCR based assay for Mammaglobin (MG) and Cytokeratin 19 (CK19). EJSO 2008, 34, 1159–1160. [Google Scholar]
- Mansel, R.E.; Goyal, A.; Douglas-Jones, A.; Woods, V.; Goyal, S.; Monypenny, I.; Sweetland, H.; Newcombe, R.G.; Jasani, B. Detection of breast cancer metastasis in sentinel lymph nodes using intra-operative real time GeneSearch BLN Assay in the operating room: Results of the Cardiff study. Breast Cancer Res. Treat. 2009, 115, 595–600. [Google Scholar] [CrossRef]
- Agendia. The Molecular Profile to Define and Defeat Her Unique Cancer. 2022. Available online: https://agendia.com/mammaprint/ (accessed on 27 November 2022).
- Mittempergher, L.; Delahaye, L.J.M.J.; Witteveen, A.T.; Spangler, J.B.; Hassenmahomed, F.; Mee, S.; Mahmoudi, S.; Chen, J.; Bao, S.; Snel, M.H.J.; et al. MammaPrint and BluePrint Molecular Diagnostics Using Targeted RNA Next-Generation Sequencing Technology. J. Mol. Diagn. 2019, 21, 808–823. [Google Scholar]
- Bou Zerdan, M.; Ibrahim, M.; Nakib, C.E.; Hajjar, R.; Assi, H.I. Genomic Assays in Node Positive Breast Cancer Patients: A Review. Front. Oncol. 2021, 10, 609100. [Google Scholar]
- Biotheranostics, I. Breast Cancer Index. 2022. Available online: www.breastcancerindex.com (accessed on 27 November 2022).
- Myriad Genetic Laboratories. Myriad EndoPredict Technical Specifications. 2021. Available online: https://myriad-library.s3.amazonaws.com/technical-specifications/EndoPredict-Technical-Specifications.pdf (accessed on 2 November 2022).
- Sun, L.; Legood, R.; Sadique, Z.; Dos-Santos-Silva, I.; Yang, L. Cost-effectiveness of risk-based breast cancer screening programme, China. Bull. World Health Organ. 2018, 96, 568–577. [Google Scholar] [CrossRef]
- Nøst, T.H.; Holden, M.; Dønnem, T.; Bøvelstad, H.; Rylander, C.; Lund, E.; Sandanger, T.M. Transcriptomic signals in blood prior to lung cancer focusing on time to diagnosis and metastasis. Sci. Rep. 2021, 11, 7406. [Google Scholar] [CrossRef] [PubMed]
- Holsbø, E.; Olsen, K.S. Metastatic Breast Cancer and Pre-Diagnostic Blood Gene Expression Profiles-The Norwegian Women and Cancer (NOWAC) Post-Genome Cohort. Front. Oncol. 2020, 10, 575461. [Google Scholar] [PubMed]
- Holden, M.; Holden, L.; Olsen, K.S.; Lund, E. Local in Time Statistics for detecting weak gene expression signals in blood—Illustrated for prediction of metastases in breast cancer in the NOWAC Post-genome Cohort. Adv. Genom. Genet. 2017, 7, 11–28. [Google Scholar]
- Chen, S.; Liu, M.; Liang, B.; Ge, S.; Peng, J.; Huang, H.; Xu, Y.; Tang, X.; Deng, L. Identification of human peripheral blood monocyte gene markers for early screening of solid tumors. PLoS ONE 2020, 15, e0230905. [Google Scholar]
- Han, M.; Liew, C.T.; Zhang, H.W.; Chao, S.; Zheng, R.; Yip, K.T.; Song, Z.Y.; Li, H.M.; Geng, X.P.; Zhu, L.X.; et al. Novel blood-based, five-gene biomarker set for the detection of colorectal cancer. Clin. Cancer Res. 2008, 14, 455–460. [Google Scholar] [CrossRef]
- Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer Statistics, 2021. CA Cancer J. Clin. 2021, 71, 7–33. [Google Scholar] [CrossRef]
- Thigpen, D.; Kappler, A.; Brem, R. The Role of Ultrasound in Screening Dense Breasts-A Review of the Literature and Practical Solutions for Implementation. Diagnostics 2018, 8, 20. [Google Scholar]
- Weedon-Fekjaer, H.; Lindqvist, B.H.; Vatten, L.J.; Aalen, O.O.; Tretli, S. Breast cancer tumor growth estimated through mammography screening data. Breast Cancer Res. 2008, 10, R41. [Google Scholar]
- DeSantis, C.E.; Ma, J.; Gaudet, M.M.; Newman, L.A.; Miller, K.D.; Goding Sauer, A.; Jemal, A.; Siegel, R.L. Breast cancer statistics, 2019. CA Cancer J. Clin. 2019, 69, 438–451. [Google Scholar]
- de Fraipont, F.; Gazzeri, S.; Cho, W.C.; Eymin, B. Circular RNAs and RNA Splice Variants as Biomarkers for Prognosis and Therapeutic Response in the Liquid Biopsies of Lung Cancer Patients. Front. Genet. 2019, 10, 390. [Google Scholar]
- Markou, A.; Tzanikou, E.; Lianidou, E. The potential of liquid biopsy in the management of cancer patients. Semin. Cancer Biol. 2022, 84, 69–79. [Google Scholar] [CrossRef] [PubMed]
- Duffy, M.J. Chapter 13—Circulating cancer biomarkers: Current status and future prospects. In Clinical Aspects and Laboratory Determination, Cancer Biomarkers; Ramanathan, L.V., Fleisher, M., Duffy, M.J., Eds.; Elsevier: Amsterdam, The Netherlands, 2022; pp. 409–443. [Google Scholar]
- Adashek, J.J.; Janku, F.; Kurzrock, R. Signed in Blood: Circulating Tumor DNA in Cancer Diagnosis, Treatment and Screening. Cancers 2021, 13, 3600. [Google Scholar] [PubMed]
- Li, J.; Guan, X.; Fan, Z.; Ching, L.M.; Li, Y.; Wang, X.; Cao, W.M.; Liu, D.X. Non-Invasive Biomarkers for Early Detection of Breast Cancer. Cancers 2020, 12, 2767. [Google Scholar] [PubMed]
- Jurj, A.; Zanoaga, O.; Braicu, C.; Lazar, V.; Tomuleasa, C.; Irimie, A.; Berindan-Neagoe, I. A Comprehensive Picture of Extracellular Vesicles and Their Contents. Molecular Transfer to Cancer Cells. Cancers 2020, 12, 298. [Google Scholar]
- Liyanage, U.K.; Moore, T.T.; Joo, H.G.; Tanaka, Y.; Herrmann, V.; Doherty, G.; Drebin, J.A.; Strasberg, S.M.; Eberlein, T.J.; Goedegebuure, P.S.; et al. Prevalence of regulatory T cells is increased in peripheral blood and tumor microenvironment of patients with pancreas or breast adenocarcinoma. J. Immunol. 2002, 169, 2756–2761. [Google Scholar]
- Liew, C.C.; Ma, J.; Tang, H.C.; Zheng, R.; Dempsey, A.A. The peripheral blood transcriptome dynamically reflects system wide biology: A potential diagnostic tool. J. Lab. Clin. Med. 2006, 147, 126–132. [Google Scholar]
- Twine, N.C.; Stover, J.A.; Marshall, B.; Dukart, G.; Hidalgo, M.; Stadler, W.; Logan, T.; Dutcher, J.; Hudes, G.; Dorner, A.J.; et al. Disease-associated expression profiles in peripheral blood mononuclear cells from patients with advanced renal cell carcinoma. Cancer Res. 2003, 63, 6069–6075. [Google Scholar]
- Stoiber, D.; Assinger, A. Platelet-Leukocyte Interplay in Cancer Development and Progression. Cells 2020, 9, 855. [Google Scholar] [CrossRef]
- Ward, M.P.; EKane, L.; ANorris, L.; Mohamed, B.M.; Kelly, T.; Bates, M.; Clarke, A.; Brady, N.; Martin, C.M.; Brooks, R.D.; et al. Platelets, immune cells and the coagulation cascade; friend or foe of the circulating tumour cell? Mol. Cancer 2021, 20, 59. [Google Scholar]
- Sharma, P.; Sahni, N.S.; Tibshirani, R.; Skaane, P.; Urdal, P.; Berghagen, H.; Jensen, M.; Kristiansen, L.; Moen, C.; Sharma, P.; et al. Early detection of breast cancer based on gene-expression patterns in peripheral blood cells. Breast Cancer Res. 2005, 7, R634–R644. [Google Scholar]
- Aarøe, J.; Lindahl, T.; Dumeaux, V.; Saebø, S.; Tobin, D.; Hagen, N.; Skaane, P.; Lönneborg, A.; Sharma, P.; Børresen-Dale, A.L. Gene expression profiling of peripheral blood cells for early detection of breast cancer. Breast Cancer Res. 2010, 12, R7. [Google Scholar]
- Čelešnik, H.S. Triple-Negative Breast Cancer and other Breast Cancer. In: Encyclopedia. Available online: https://encyclopedia.pub/entry/21678 (accessed on 27 November 2022).
- Dumeaux, V.; Ursini-Siegel, J.; Flatberg, A.; Fjosne, H.E.; Frantzen, J.O.; Holmen, M.M.; Rodegerdts, E.; Schlichting, E.; Lund, E. Peripheral blood cells inform on the presence of breast cancer: A population-based case-control study. Int. J. Cancer. 2015, 136, 656–667. [Google Scholar] [PubMed]
- Suzuki, E.; Sugimoto, M.; Kawaguchi, K.; Pu, F.; Uozumi, R.; Yamaguchi, A.; Nishie, M.; Tsuda, M.; Kotake, T.; Morita, S. Gene expression profile of peripheral blood mononuclear cells may contribute to the identification and immunological classification of breast cancer patients. Breast Cancer 2019, 26, 282–289. [Google Scholar] [PubMed]
- Zhang, F.; Deng, Y.; Drabier, R. Multiple biomarker panels for early detection of breast cancer in peripheral blood. Biomed. Res. Int. 2013, 2013, 781618. [Google Scholar] [PubMed]
- Hou, H.; Lyu, Y.; Jiang, J.; Wang, M.; Zhang, R.; Liew, C.C.; Wang, B.; Cheng, C. Peripheral blood transcriptome identifies high-risk benign and malignant breast lesions. PLoS ONE 2020, 15, e0233713. [Google Scholar]
- Srivastava, S.; Hanash, S. Pan-Cancer Early Detection: Hype or Hope? Cancer Cell 2020, 38, 23–24. [Google Scholar]
- Qi, F.; Gao, F.; Cai, Y.; Han, X.; Qi, Y.; Ni, J.; Sun, J.; Huang, S.; Chen, S.; Wu, C.; et al. Complex Age- and Cancer-Related Changes in Human Blood Transcriptome-Implications for Pan-Cancer Diagnostics. Front. Genet. 2021, 12, 746879. [Google Scholar]
- Varkey, J.; Nicolaides, T. Tumor-Educated Platelets: A Review of Current and Potential Applications in Solid Tumors. Cureus 2021, 13, e19189. [Google Scholar] [CrossRef]
- Best, M.G.; Sol, N.; Kooi, I.; Tannous, J.; Westerman, B.A.; Rustenburg, F.; Schellen, P.; Verschueren, H.; Post, E.; Koster, J.; et al. RNA-Seq of Tumor-Educated Platelets Enables Blood-Based Pan-Cancer, Multiclass, and Molecular Pathway Cancer Diagnostics. Cancer Cell 2015, 28, 666–676. [Google Scholar] [CrossRef]
- Yang, B.; Xu, Q.; Wu, F.; Liu, F.; Ye, X.; Liu, G.; Shao, Z.; Meng, X.; Mougin, B.; Wu, J. Using peripheral blood mRNA signature to distinguish between breast cancer and benign breast disease in non-conclusive mammography patients. Cancer Biol. Ther. 2010, 10, 1235–1239. [Google Scholar]
- Zuckerman, N.S.; Yu, H.; Simons, D.L.; Bhattacharya, N.; Carcamo-Cavazos, V.; Yan, N.; Dirbas, F.M.; Johnson, D.L.; Schwartz, E.J.; Lee, P.P. Altered local and systemic immune profiles underlie lymph node metastasis in breast cancer patients. Int. J. Cancer. 2013, 132, 2537–2547. [Google Scholar] [CrossRef] [PubMed]
- Lund, E.; Holden, L.; Bøvelstad, H.; Plancade, S.; Mode, N.; Günther, C.C.; Nuel, G.; Thalabard, J.C.; Holden, M. A new statistical method for curve group analysis of longitudinal gene expression data illustrated for breast cancer in the NOWAC postgenome cohort as a proof of principle. BMC Med. Res. Methodol. 2016, 16, 28. [Google Scholar] [CrossRef]
- Lund, E.; Dumeaux, V.; Braaten, T.; Hjartåker, A.; Engeset, D.; Skeie, G.; Kumle, M. Cohort profile: The Norwegian Women and Cancer Study--NOWAC--Kvinner og kreft. Int. J. Epidemiol. 2008, 37, 36–41. [Google Scholar] [PubMed]
- Čelešnik, H.S. Breast Cancer Biomarkers from Peripheral Blood Cells. In: Encyclopedia. 2022. Available online: https://encyclopedia.pub/entry/21624 (accessed on 8 November 2022).
- Balacescu, O.; Balacescu, L.; Virtic, O.; Visan, S.; Gherman, C.; Drigla, F.; Pop, L.; Bolba-Morar, G.; Lisencu, C.; Fetica, B.; et al. Blood Genome-Wide Transcriptional Profiles of HER2 Negative Breast Cancers Patients. Mediators Inflamm. 2016, 2016, 3239167. [Google Scholar] [PubMed]
- Foulds, G.A.; Vadakekolathu, J.; Abdel-Fatah, T.M.A.; Nagarajan, D.; Reeder, S.; Johnson, C.; Hood, S.; Moseley, P.M.; Chan, S.Y.T.; Pockley, A.; et al. Immune-Phenotyping and Transcriptomic Profiling of Peripheral Blood Mononuclear Cells from Patients with Breast Cancer: Identification of a 3 Gene Signature Which Predicts Relapse of Triple Negative Breast Cancer. Front. Immunol. 2018, 9, 2028. [Google Scholar] [PubMed]
- Ming, W.; Xie, H.; Hu, Z.; Chen, Y.; Zhu, Y.; Bai, Y.; Liu, H.; Sun, X.; Liu, Y.; Gu, W. Two Distinct Subtypes Revealed in Blood Transcriptome of Breast Cancer Patients with an Unsupervised Analysis. Front. Oncol. 2019, 9, 985. [Google Scholar] [CrossRef] [PubMed]
- Dumeaux, V.; Fjukstad, B.; Fjosne, H.E.; Frantzen, J.O.; Holmen, M.M.; Rodegerdts, E.; Schlichting, E.; Børresen-Dale, A.L.; Bongo, L.A.; Lund, E.; et al. Interactions between the tumor and the blood systemic response of breast cancer patients. PLoS Comput. Biol. 2017, 13, e1005680. [Google Scholar] [CrossRef] [Green Version]
- Thompson, D.; Easton, D. The genetic epidemiology of breast cancer genes. J. Mammary Gland. Biol. Neoplasia 2004, 9, 221–236. [Google Scholar] [CrossRef]
- Loke, S.Y.; Lee, A.S.G. The future of blood-based biomarkers for the early detection of breast cancer. Eur. J. Cancer 2018, 92, 54–68. [Google Scholar]
- Seale, K.N.; Tkaczuk, K.H.R. Circulating Biomarkers in Breast Cancer. Clin. Breast Cancer 2022, 22, e319–e331. [Google Scholar]
- Ambry Genetics. +RNAinsight, Expanded RNA Analysis for Better Variant Classification. 2021. Available online: https://www.ambrygen.com/file/material/view/1663/RNA_Flyer_FNL%20091521.pdf (accessed on 8 November 2022).
- Syantra. Syantra DX|Breast Cancer FAQs. 2022. Available online: https://ss-usa.s3.amazonaws.com/c/308494115/media/1619629a43fbddb7823212636145172/4115~collateral_FAQs_c.pdf (accessed on 8 November 2022).
- Syantra. 2022. Available online: https://www.syantra.com/ (accessed on 8 November 2022).
- StageZero. Aristotle®. 2022. Available online: https://www.stagezerolifesciences.com/aristotle-test.html (accessed on 8 November 2022).
- Tobin, D.; Karlsson, M.; Hagen, N.; Børresen-Dale, A.; Mydland, E.; Bårdsen, K.; Jensen, M. Use of the blood based, 96-assay set for breast cancer detection. Poster. EJC Suppl. 2010, 8, 164. [Google Scholar]
- Tobin, D.; Bårdsen, K.; Kauczynska, M.; Kumar, Y.; Shroff, C.; Punia, D.; Srinivasan, V.; Børresen Dale, A.; Sharma, P.; Hollingsworth, A. Performance of a blood-based gene-expression test, BCtect, for early breast cancer detection. J. Clin. Oncol. 2009, 27 (Suppl. S15), 11012. [Google Scholar]
- Mackay, J.; Szecsei, C.M. Genetic counselling for hereditary predisposition to ovarian and breast cancer. Ann. Oncol. 2010, 21 (Suppl. S7), vii334–vii338. [Google Scholar] [CrossRef] [PubMed]
- Karam, R.; Krempely, K.; Richardson, M.; McGoldrick, K.; Conner, B.R.; Landrith, T.; Allen, K.; Yussuf, A.; Rana, H.; Culver, S.; et al. RNA Genetic Testing in Hereditary Cancer Improves Variant Classification and Patient Management. In In Proceedings of the Annual Clinical Genetics Meeting (ACMG), Seattle, DC, USA, 2–6 April 2019; American College of Medical Genetics and Genomics: Bethesda, MD, USA, 2019. [Google Scholar]
- Ambry Genetics. First Prospective Study Shows +RNAinsight™ Identifies More Patients with Increased Risk for Hereditary Cancer Than DNA-Only Testing. 2020. Available online: https://www.ambrygen.com/company/press-release/128/first-prospective-study-shows-rnainsight-identifies-more-patients-with-increased-risk-for-hereditary-cancer-than-dna-only-testing (accessed on 24 January 2023).
- Bundred, N.; Fuh, K.; Asgarian, N.; Brown, S.; Simonot, D.; Wang, X.; Shepherd, R.; Quan, M.L.; Docktor, B.J.; Maxwell, A.; et al. Abstract P2-01-02: A whole blood assay to identify breast cancer: Interim analysis of the international identify breast cancer (IDBC) study evidence supporting the Syantra DX breast cancer test. Cancer Res. 2022, 82 (Suppl. S4), P2–01–02. [Google Scholar]
- Dempsey, A.A.; Tripp, J.H.; Chao, S.; Stamatiou, D.; Pilcz, T.; Ying, J.; Burakoff, R. Aristotle: A single blood test for pan-cancer screening. J. Clin. Oncol. 2020, 38 (Suppl. S15), e15037. [Google Scholar] [CrossRef]
- DiaGenic ASA and Applied Biosystems. Diagenic Announces Launch of First Breast Cancer Gene-Expression Blood Test. Medindia. 2008. Available online: https://www.medindia.net/health-press-release/Diagenic-Announces-Launch-of-First-Breast-Cancer-Gene-Expression-Blood-Test-37529-1.htm (accessed on 27 November 2022).
- Cision, C.E. Marking of the DiaGenic’s Blood Test for Early Breast Cancer Diagnosis. 2009. Available online: https://news.cision.com/nel-asa/r/ce-marking-of-the-diagenic-s-blood-test-for-early-breast-cancer-diagnosis,c434534 (accessed on 27 November 2022).
- Elvidge, S. Gene Expression Diagnostics: A New Approach. In: Life Science Leader. 2011. Available online: https://www.lifescienceleader.com/doc/gene-expression-diagnostics-a-new-approach-0001 (accessed on 27 November 2022).
- Park, A. Freenome, Siemens Join Forces to Develop Blood Test for Breast Cancer. 2021. Available online: https://www.fiercebiotech.com/medtech/freenome-siemens-join-forces-to-develop-blood-test-for-breast-cancer (accessed on 8 November 2022).
- Philippidis, A. Blood Stake: Roche Raises Freenome Investment to $360M. GEN Edge 2022, 4, 36–43. [Google Scholar] [CrossRef]
- Stephens, K. Freenome, Siemens Healthineers Collaborate for Breast Cancer Research. 2021. Available online: https://www.proquest.com/ (accessed on 27 November 2022).
- Byron, S.A.; Van Keuren-Jensen, K.R.; Engelthaler, D.M.; Carpten, J.D.; Craig, D.W. Translating RNA sequencing into clinical diagnostics: Opportunities and challenges. Nat. Rev. Genet. 2016, 17, 257–271. [Google Scholar]
- Bernard, P.S.; Wittwer, C.T. Real-time PCR technology for cancer diagnostics. Clin. Chem. 2002, 48, 1178–1185. [Google Scholar]
- Grätz, C.; Bui, M.L.U.; Thaqi, G.; Kirchner, B.; Loewe, R.P.; Pfaffl, M.W. Obtaining Reliable RT-qPCR Results in Molecular Diagnostics-MIQE Goals and Pitfalls for Transcriptional Biomarker Discovery. Life 2022, 12, 386. [Google Scholar] [CrossRef]
- Narrandes, S.; Xu, W. Gene Expression Detection Assay for Cancer Clinical Use. J. Cancer 2018, 9, 2249–2265. [Google Scholar] [CrossRef]
- Kamps, R.; Brandão, R.D.; Bosch, B.J.; Paulussen, A.D.; Xanthoulea, S.; Blok, M.J.; Romano, A. Next-Generation Sequencing in Oncology: Genetic Diagnosis, Risk Prediction and Cancer Classification. Int. J. Mol. Sci. 2017, 18, 308. [Google Scholar] [CrossRef] [PubMed]
- Liu, X.; Powell, C.A.; Wang, X. Forward single-cell sequencing into clinical application: Understanding of cancer microenvironment at single-cell solution. Clin. Transl. Med. 2022, 12, e782. [Google Scholar] [PubMed]
- Fang, H.; Zeng, Y.; Zhang, L.; Chen, C.; Powell, C.A.; Wang, X. Can single cell RNA sequencing reshape the clinical biochemistry of hematology: New clusters of circulating blood cells. Clin. Transl. Med. 2021, 11, e671. [Google Scholar] [CrossRef]
- Kamps-Hughes, N.; Carlton, V.E.H.; Fresard, L.; Osazuwa, S.; Starks, E.; Vincent, J.J.; Albritton, S.; Nussbaum, R.L.; Nykamp, K. A Systematic Method for Detecting Abnormal mRNA Splicing and Assessing Its Clinical Impact in Individuals Undergoing Genetic Testing for Hereditary Cancer Syndromes. J. Mol. Diagn. 2022. [Google Scholar] [CrossRef]
- Montalban, G.; Bonache, S.; Bach, V.; Gisbert-Beamud, A.; Tenés, A.; Moles-Fernández, A.; López-Fernández, A.; Carrasco, E.; Balmaña, J.; Diez, O.; et al. BRCA1 and BRCA2 whole cDNA analysis in unsolved hereditary breast/ovarian cancer patients. Cancer Genet. 2021, 258–259, 10–17. [Google Scholar]
- Murdock, D.R. Enhancing Diagnosis Through RNA Sequencing. Clin. Lab. Med. 2020, 40, 113–119. [Google Scholar] [CrossRef]
- Curry, P.D.K.; Broda, K.L.; Carroll, C.J. The Role of RNA-Sequencing as a New Genetic Diagnosis Tool. Curr. Genet. Med. Rep. 2021, 9, 13–21. [Google Scholar]
- Frésard, L.; Smail, C.; Ferraro, N.M.; Teran, N.A.; Li, X.; Smith, K.S.; Bonner, D.; Kernohan, K.D.; Marwaha, S.; Zappala, Z.; et al. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. Nat. Med. 2019, 25, 911–919. [Google Scholar]
- Cummings, B.B.; Marshall, J.L.; Tukiainen, T.; Lek, M.; Donkervoort, S.; Foley, A.R.; Bolduc, V.; Waddell, L.B.; Sandaradura, S.A.; O’Grady, L.G.; et al. Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Sci. Transl. Med. 2017, 9, eaal5209. [Google Scholar]
- Landrith, T.; Li, B.; Cass, A.A.; Conner, B.R.; LaDuca, H.; McKenna, D.B.; Maxwell, K.N.; Domchek, S.; Morman, N.A.; Heinlen, C.; et al. Splicing profile by capture RNA-seq identifies pathogenic germline variants in tumor suppressor genes. NPJ Precis Oncol. 2020, 4, 4. [Google Scholar]
- Tandy-Connor, S.; Guiltinan, J.; Krempely, K.; LaDuca, H.; Reineke, P.; Gutierrez, S.; Gray, P.; Tippin Davis, B. False-positive results released by direct-to-consumer genetic tests highlight the importance of clinical confirmation testing for appropriate patient care. Genet. Med. 2018, 20, 1515–1521. [Google Scholar] [CrossRef] [PubMed]
- Rotunno, M.; Hu, N.; Su, H.; Wang, C.; Bertazzi, P.A.; Caporaso, N.; Taylor, P.R.; Landi, M.T. A blood-based gene expression signature of early-stage non-small cell lung cancer. J. Clin. Oncol. 2012, 30 (Suppl. S30), 69. [Google Scholar]
- Wurdinger, T.; In ‘t Veld, S.G.J.G.; Best, M.G. Platelet RNA as Pan-Tumor Biomarker for Cancer Detection. Cancer Res. 2020, 80, 1371–1373. [Google Scholar] [PubMed]
- Malczewska, A.; Bodei, L.; Kidd, M.; Modlin, I.M. Blood mRNA Measurement (NETest) for Neuroendocrine Tumor Diagnosis of Image-Negative Liver Metastatic Disease. J. Clin. Endocrinol. Metab. 2019, 104, 867–872. [Google Scholar] [PubMed]
- Omar, H.; Lim, C.R.; Chao, S.; Lee, M.M.; Bong, C.W.; Ooi, E.J.; Yu, C.G.; Tan, S.S.; Abu Hassan, M.R.; Menon, J.; et al. Blood gene signature for early hepatocellular carcinoma detection in patients with chronic hepatitis B. J. Clin. Gastroenterol. 2015, 49, 150–157. [Google Scholar]
- Umu, S.U.; Langseth, H.; Zuber, V.; Helland, Å.; Lyle, R.; Rounge, T.B. Serum RNAs can predict lung cancer up to 10 years prior to diagnosis. Elife 2022, 11, e71035. [Google Scholar] [CrossRef]
- Umu, S.U.; Langseth, H.; Keller, A.; Meese, E.; Helland, Å.; Lyle, R.; Rounge, T.B. A 10-year prediagnostic follow-up study shows that serum RNA signals are highly dynamic in lung carcinogenesis. Mol. Oncol. 2020, 14, 235–247. [Google Scholar] [CrossRef]
- Burton, J.; Umu, S.U.; Langseth, H.; Grotmol, T.; Grimsrud, T.K.; Haugen, T.B.; Rounge, T.B. Serum RNA Profiling in the 10-Years Period Prior to Diagnosis of Testicular Germ Cell Tumor. Front Oncol. 2020, 10, 574977. [Google Scholar]
- Nielsen, T.; Wallden, B.; Schaper, C.; Ferree, S.; Liu, S.; Gao, D.; Barry, G.; Dowidar, N.; Maysuria, M.; Storhoff, J. Analytical validation of the PAM50-based Prosigna Breast Cancer Prognostic Gene Signature Assay and nCounter Analysis System using formalin-fixed paraffin-embedded breast tumor specimens. BMC Cancer 2014, 14, 177. [Google Scholar]
- Slembrouck, L.; Darrigues, L.; Laurent, C.; Mittempergher, L.; Delahaye, L.J.; Vanden Bempt, I.; Vander Borght, S.; Vliegen, L.; Sintubin, P.; Raynal, V.; et al. Decentralization of Next-Generation RNA Sequencing-Based MammaPrint® and BluePrint® Kit at University Hospitals Leuven and Curie Institute Paris. Transl. Oncol. 2019, 12, 1557–1565. [Google Scholar] [CrossRef]
- Kronenwett, R.; Bohmann, K.; Prinzler, J.; Sinn, B.V.; Haufe, F.; Roth, C.; Averdick, M.; Ropers, T.; Windbergs, C.; Brase, J.C.; et al. Decentral gene expression analysis: Analytical validation of the Endopredict genomic multianalyte breast cancer prognosis test. BMC Cancer 2012, 12, 456. [Google Scholar] [CrossRef] [PubMed]
- Wang, Y.S.; Liu, Y.H.; Tao, O.Y.; Yang, X.H.; Wu, J.; Su, F.X.; Sun, X.; Zhong, W.X.; Liao, N.; Yang, W.T. GeneSearch™ BLN Assay could replace frozen section and touch imprint cytology for intra-operative assessment of breast sentinel lymph nodes. Breast Cancer 2014, 21, 583–589. [Google Scholar] [PubMed]
- Matthijs, G.; Souche, E.; Alders, M.; Corveleyn, A.; Eck, S.; Feenstra, I.; Race, V.; Sistermans, E.; Sturm, M.; Weiss, M.; et al. Guidelines for diagnostic next-generation sequencing. Eur. J. Hum. Genet. 2016, 24, 1515. [Google Scholar] [PubMed]
- Naito, Y.; Aburatani, H.; Amano, T.; Baba, E.; Furukawa, T.; Hayashida, T.; Hiyama, E.; Ikeda, S.; Kanai, M.; Kato, M.; et al. Clinical practice guidance for next-generation sequencing in cancer diagnosis and treatment (edition 2.1). Int. J. Clin. Oncol. 2021, 26, 233–283. [Google Scholar] [CrossRef]
- Cardoso, F.; Piccart-Gebhart, M.; Van’t Veer, L.; Rutgers, E. TRANSBIG Consortium. The MINDACT trial: The first prospective clinical validation of a genomic tool. Mol. Oncol. 2007, 1, 246–251. [Google Scholar] [CrossRef]
- Viale, G.; de Snoo, F.A.; Slaets, L.; Bogaerts, J.; van’t Veer, L.; Rutgers, E.J.; Piccart-Gebhart, M.J.; Stork-Sloots, L.; Glas, A.; Russo, L.; et al. MINDACT investigators. Immunohistochemical versus molecular (BluePrint and MammaPrint) subtyping of breast carcinoma. Outcome results from the EORTC 10041/BIG 3-04 MINDACT trial. Breast Cancer Res. Treat. 2018, 167, 123–131. [Google Scholar]
- Albain, K.S.; Gray, R.J.; Makower, D.F.; Faghih, A.; Hayes, D.F.; Geyer, C.E.; Dees, E.C.; Goetz, M.P.; Olson, J.A.; Lively, T.; et al. Ethnicity, and Clinical Outcomes in Hormone Receptor-Positive, HER2-Negative, Node-Negative Breast Cancer in the Randomized TAILORx Trial. J. Natl. Cancer Inst. 2021, 113, 390–399. [Google Scholar] [CrossRef]
- Roy, S.; Coldren, C.; Karunamurthy, A.; Kip, N.S.; Klee, E.W.; Lincoln, S.E.; Leon, A.; Pullambhatla, M.; Temple-Smolkin, R.L.; Voelkerding, K.V.; et al. Standards and Guidelines for Validating Next-Generation Sequencing Bioinformatics Pipelines: A Joint Recommendation of the Association for Molecular Pathology and the College of American Pathologists. J. Mol. Diagn. 2018, 20, 4–27. [Google Scholar]
- Koh, E.J.; Yu, S.Y.; Kim, S.H.; Kim, S.J.; Lee, E.I.; Hwang, S.Y. Understanding Confounding Effects of Blood Handling Strategies on RNA Quality and Transcriptomic Alteration Using RNA Sequencing. BioChip J. 2021, 15, 187–194. [Google Scholar]
- Park, S.; Ahn, S.; Kim, J.Y.; Kim, J.; Han, H.J.; Hwang, D.; Park, J.; Park, H.S.; Park, S.; Kim, G.M.; et al. Blood Test for Breast Cancer Screening through the Detection of Tumor-Associated Circulating Transcripts. Int. J. Mol. Sci. 2022, 23, 9140. [Google Scholar] [CrossRef]
- Dubsky, P.; Van’t Veer, L.; Gnant, M.; Rudas, M.; Bago-Horvath, Z.; Greil, R.; Lujinovic, E.; Buresch, J.; Rinnerthaler, G.; Hulla, W.; et al. A clinical validation study of MammaPrint in hormone receptor-positive breast cancer from the Austrian Breast and Colorectal Cancer Study Group 8 (ABCSG-8) biomarker cohort. ESMO Open 2021, 6, 100006. [Google Scholar] [PubMed]
- Wylezinski, L.S.; Shaginurova, G.I.; Spurlock Iii, C.F. Longitudinal assessment and stability of long non-coding RNA gene expression profiles measured in human peripheral whole blood collected into PAXgene blood RNA tubes. BMC Res. Notes 2020, 13, 531. [Google Scholar] [CrossRef] [PubMed]
- Donohue, D.E.; Gautam, A.; Miller, S.A.; Srinivasan, S.; Abu-Amara, D.; Campbell, R.; Marmar, C.R.; Hammamieh, R.; Jett, M. Gene expression profiling of whole blood: A comparative assessment of RNA-stabilizing collection methods. PLoS ONE 2019, 14, e0223065. [Google Scholar]
- Asare, A.L.; Kolchinsky, S.A.; Gao, Z.; Wang, R.; Raddassi, K.; Bourcier, K.; Seyfert-Margolis, V. Differential gene expression profiles are dependent upon method of peripheral blood collection and RNA isolation. BMC Genom. 2008, 9, 474. [Google Scholar]
- Shen, Y.; Li, R.; Tian, F.; Chen, Z.; Lu, N.; Bai, Y.; Ge, Q.; Lu, Z. Impact of RNA integrity and blood sample storage conditions on the gene expression analysis. Onco. Targets Ther. 2018, 11, 3573–3581. [Google Scholar] [CrossRef] [PubMed]
- Ronaghi, M.; Karamohamed, S.; Pettersson, B.; Uhlén, M.; Nyrén, P. Real-time DNA sequencing using detection of pyrophosphate release. Anal. Biochem. 1996, 242, 84–89. [Google Scholar] [PubMed]
- Yang, Y.; Zhang, T.; Xiao, R.; Hao, X.; Zhang, H.; Qu, H.; Xie, B.; Wang, T.; Fang, X. Platform-independent approach for cancer detection from gene expression profiles of peripheral blood cells. Brief. Bioinform. 2020, 21, 1006–1015. [Google Scholar] [CrossRef]
Assay Trade Name (Manufacturer) | Number of Genes | Assay Indicated for | Description | Methodology/ Platform | References |
---|---|---|---|---|---|
+RNAinsight (Ambry Genetics) | Up to 91 genes (for maximum coverage) | Assessing hereditary cancer predisposition | +RNAinsight analyses functional RNA data to classify DNA variants and identify deep-intronic mutations; intended for paired RNA/DNA analyses, as a supplement to Ambry Genetics DNA-level hereditary cancer panels CancerNext, CancerNext-Expanded, CustomNext-Cancer. | RNA sequencing | [106] |
Syantra DX Breast Cancer (Syantra Inc.) | 12-gene multi-biomarker panel | Breast cancer screening for women aged 25–80 | Enables classification of a sample as positive or negative for BC signature; demonstrated utility for early cancer screening, for women with high breast density, and for women under 50. | qRT-PCR-based assay | [107,108] |
Multi-cancer blood test Aristotle (Stage Zero Life Sciences Ltd.) | Multi-biomarker panel | Pan-cancer screening (breast, bladder, colorectum, cervix, endometrium, liver, ovary, prostate, and stomach) | Enables detection of multiple cancer molecular signatures from a single blood sample (early cancer detection). | Microarray-based assay | [109] |
BCtect (DiaGenic ASA) | 96-assay signature | Breast cancer screening | Enables classification of a sample as positive or negative for BC signature; utility for early BC detection in both pre- and post-menopausal women, and across cancer stages and types. | qRT-PCR-based assay | [110,111,112] |
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Čelešnik, H.; Potočnik, U. Blood-Based mRNA Tests as Emerging Diagnostic Tools for Personalised Medicine in Breast Cancer. Cancers 2023, 15, 1087. https://doi.org/10.3390/cancers15041087
Čelešnik H, Potočnik U. Blood-Based mRNA Tests as Emerging Diagnostic Tools for Personalised Medicine in Breast Cancer. Cancers. 2023; 15(4):1087. https://doi.org/10.3390/cancers15041087
Chicago/Turabian StyleČelešnik, Helena, and Uroš Potočnik. 2023. "Blood-Based mRNA Tests as Emerging Diagnostic Tools for Personalised Medicine in Breast Cancer" Cancers 15, no. 4: 1087. https://doi.org/10.3390/cancers15041087
APA StyleČelešnik, H., & Potočnik, U. (2023). Blood-Based mRNA Tests as Emerging Diagnostic Tools for Personalised Medicine in Breast Cancer. Cancers, 15(4), 1087. https://doi.org/10.3390/cancers15041087