The concordance of treatment decision guided by OncotypeDX and the PREDICT tool in real-world early-stage breast cancer.


Journal

Cancer medicine
ISSN: 2045-7634
Titre abrégé: Cancer Med
Pays: United States
ID NLM: 101595310

Informations de publication

Date de publication:
07 2020
Historique:
received: 19 09 2019
revised: 10 04 2020
accepted: 11 04 2020
pubmed: 7 5 2020
medline: 4 5 2021
entrez: 7 5 2020
Statut: ppublish

Résumé

Decision-making regarding adjuvant chemotherapy for early-stage breast cancer can be guided by genomic assays such as OncotypeDX. The concordance of expected clinical decisions guided by OncotypeDX and prognostication online tools such as PREDICT is unknown. We performed a retrospective single-center cohort study comprising all women with estrogen receptor (ER) positive, human epidermal growth factor receptor 2 (HER2) negative, node negative disease, whose tumors were sent for OncotypeDX analysis. Expected decision on adjuvant chemotherapy was evaluated using OncotypeDX and using PREDICT. The concordance between these two tools was calculated. The impact on concordance of prespecified features was assessed, including age, tumor size, intensity of ER and progesterone receptor (PR), grade, Ki67 and perineural and lymphovascular invasion. A total of 445 women were included. Overall concordance was 75% (K = 0.284). The concordance was significantly higher for grade 1 disease compared to grade 2-3 (93% vs 72%, P < .001), tumor ≤ 1 cm compared to >1 cm (85% vs 72%, P = .009), PR positive compared to PR negative (78% vs 58%, P < .001) and ki67 < 10% compared to ≥10% (92% vs 63%, P < .001). The intensity of ER and the presence of perineural or lymphovascular invasion had no significant impact on concordance. Compared to PREDICT, using OncotypeDx in node negative, ER positive disease is expected to change the clinical decision in a quarter of patients. The concordance between OncotypeDx and PREDICT is influenced by pathological features. In patients with very low risk, treatment decisions may be made based solely on clinical risk assessment.

Sections du résumé

BACKGROUND
Decision-making regarding adjuvant chemotherapy for early-stage breast cancer can be guided by genomic assays such as OncotypeDX. The concordance of expected clinical decisions guided by OncotypeDX and prognostication online tools such as PREDICT is unknown.
METHODS
We performed a retrospective single-center cohort study comprising all women with estrogen receptor (ER) positive, human epidermal growth factor receptor 2 (HER2) negative, node negative disease, whose tumors were sent for OncotypeDX analysis. Expected decision on adjuvant chemotherapy was evaluated using OncotypeDX and using PREDICT. The concordance between these two tools was calculated. The impact on concordance of prespecified features was assessed, including age, tumor size, intensity of ER and progesterone receptor (PR), grade, Ki67 and perineural and lymphovascular invasion.
RESULTS
A total of 445 women were included. Overall concordance was 75% (K = 0.284). The concordance was significantly higher for grade 1 disease compared to grade 2-3 (93% vs 72%, P < .001), tumor ≤ 1 cm compared to >1 cm (85% vs 72%, P = .009), PR positive compared to PR negative (78% vs 58%, P < .001) and ki67 < 10% compared to ≥10% (92% vs 63%, P < .001). The intensity of ER and the presence of perineural or lymphovascular invasion had no significant impact on concordance.
CONCLUSIONS
Compared to PREDICT, using OncotypeDx in node negative, ER positive disease is expected to change the clinical decision in a quarter of patients. The concordance between OncotypeDx and PREDICT is influenced by pathological features. In patients with very low risk, treatment decisions may be made based solely on clinical risk assessment.

Identifiants

pubmed: 32372569
doi: 10.1002/cam4.3088
pmc: PMC7333833
doi:

Substances chimiques

Receptors, Estrogen 0
Receptors, Progesterone 0
Receptor, ErbB-2 EC 2.7.10.1

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

4603-4612

Informations de copyright

© 2020 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

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Auteurs

Daniel A Goldstein (DA)

Davidoff Cancer Center, Beilinson Hospital, Rabin Medical Center, Petach-Tikva, Israel.
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Chen Mayer (C)

Department of Pathology, Sheba Medical Center, Ramat Gan, Israel.

Tzippy Shochat (T)

Statistical Consulting Unit, Beilinson Hospital, Rabin Medical Center, Petach-Tikva, Israel.

Daniel Reinhorn (D)

Davidoff Cancer Center, Beilinson Hospital, Rabin Medical Center, Petach-Tikva, Israel.
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

Assaf Moore (A)

Davidoff Cancer Center, Beilinson Hospital, Rabin Medical Center, Petach-Tikva, Israel.
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

Michal Sarfaty (M)

Davidoff Cancer Center, Beilinson Hospital, Rabin Medical Center, Petach-Tikva, Israel.
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

Rinat Yerushalmi (R)

Davidoff Cancer Center, Beilinson Hospital, Rabin Medical Center, Petach-Tikva, Israel.
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

Hadar Goldvaser (H)

Davidoff Cancer Center, Beilinson Hospital, Rabin Medical Center, Petach-Tikva, Israel.
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

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Classifications MeSH