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. 2022 Nov;196(2):299-310.
doi: 10.1007/s10549-022-06729-7. Epub 2022 Sep 10.

A comparative analysis of recurrence risk predictions in ER+/HER2- early breast cancer using NHS Nottingham Prognostic Index, PREDICT, and CanAssist Breast

Aparna Gunda  1 , Mallikarjuna S Eshwaraiah  1 , Kiran Gangappa  1 , Taranjot Kaur  1 , Manjiri M Bakre  2
Affiliations

A comparative analysis of recurrence risk predictions in ER+/HER2- early breast cancer using NHS Nottingham Prognostic Index, PREDICT, and CanAssist Breast

Aparna Gunda et al. Breast Cancer Res Treat. 2022 Nov.

Abstract

Aims: Clinicians use multi-gene/biomarker prognostic tests and free online tools to optimize treatment in early ER+/HER2- breast cancer. Here we report the comparison of recurrence risk predictions by CanAssist Breast (CAB), Nottingham Prognostic Index (NPI), and PREDICT along with the differences in the performance of these tests across Indian and European cohorts.

Methods: Current study used a retrospective cohort of 1474 patients from Europe, India, and USA. NPI risk groups were categorized into three prognostic groups, good (GPG-NPI index ≤ 3.4) moderate (MPG 3.41-5.4), and poor (PPG > 5.4). Patients with chemotherapy benefit of < 2% were low-risk and ≥ 2% high-risk by PREDICT. We assessed the agreement between the CAB and NPI/PREDICT risk groups by kappa coefficient.

Results: Risk proportions generated by all tools were: CAB low:high 74:26; NPI good:moderate:poor prognostic group- 38:55:7; PREDICT low:high 63:37. Overall, there was a fair agreement between CAB and NPI[κ = 0.31(0.278-0.346)]/PREDICT [κ = 0.398 (0.35-0.446)], with a concordance of 97%/88% between CAB and NPI/PREDICT low-risk categories. 65% of NPI-MPG patients were called low-risk by CAB. From PREDICT high-risk patients CAB segregated 51% as low-risk, thus preventing over-treatment in these patients. In cohorts (European) with a higher number of T1N0 patients, NPI/PREDICT segregated more as LR compared to CAB, suggesting that T1N0 patients with aggressive biology are missed out by online tools but not by the CAB.

Conclusion: Data shows the use of CAB in early breast cancer overall and specifically in NPI-MPG and PREDICT high-risk patients for making accurate decisions on chemotherapy use. CAB provided unbiased risk stratification across cohorts of various geographies with minimal impact by clinical parameters.

Keywords: CanAssist Breast; Early breast cancer; Nottingham Prognostic Index; PREDICT; Prognostication.

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Conflict of interest statement

There is no conflict of interest. All authors are employees of OncoStem Diagnostics Pvt Ltd.

References

    1. Early Breast Cancer Trialists’ Collaborative Group (EBCTCG) Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomized trials. Lancet. 2005;365:1687–1717. - PubMed
    1. Chen LL, Nolan ME, Silverstein MJ, Mihm MC, Jr, Sober AJ, Tanabe KK, et al. The impact of primary tumor size, lymph node status, and other prognostic factors on the risk of cancer death. Cancer. 2009;115(21):5071–5083. - PubMed
    1. Wazir U, Mokbel K, Carmichael A, Mokbel K. Are online prediction tools a valid alternative to genomic profiling in the context of systemic treatment of ER-positive breast cancer? Cell Mol Biol Lett. 2017;22:20. - PMC - PubMed
    1. Mokbel K, Wazir U, El Hage CH, Manson A, Choy C, Moye V, et al. A comparison of the performance of EndoPredict clinical and NHS PREDICT in 120 patients treated for ER-positive Breast Cancer. Anticancer Res. 2017;37(12):6863–6869. - PubMed
    1. van Maaren MC, van Steenbeek CD, Pharoah PDP, Witteveen A, Sonke GS, Strobbe LJA, et al. Validation of the online prediction tool PREDICT v. 2.0 in the Dutch breast cancer population. Eur J Cancer. 2017;86:364–372. - PubMed