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. 2019 May 27;57(6):901-910.
doi: 10.1515/cclm-2018-1065.

A novel machine learning-derived decision tree including uPA/PAI-1 for breast cancer care

Nathalie Reix  1   2 , Massimo Lodi  3   4 , Stéphane Jankowski  5 , Sébastien Molière  6 , Elisabeth Luporsi  6 , Suzanne Leblanc  7   8 , Louise Scheer  3 , Issam Ibnouhsein  5 , Julie-Charlotte Benabu  3 , Victor Gabriele  3 , Alberto Guggiola  5 , Jean-Marc Lessinger  7 , Marie-Pierre Chenard  4   8 , Fabien Alpy  4 , Jean-Pierre Bellocq  8 , Karl Neuberger  5 , Catherine Tomasetto  4 , Carole Mathelin  3   4   3
Affiliations

A novel machine learning-derived decision tree including uPA/PAI-1 for breast cancer care

Nathalie Reix et al. Clin Chem Lab Med. .

Abstract

Background uPA and PAI-1 are breast cancer biomarkers that evaluate the benefit of chemotherapy (CT) for HER2-negative, estrogen receptor-positive, low or intermediate grade patients. Our objectives were to observe clinical routine use of uPA/PAI-1 and to build a new therapeutic decision tree integrating uPA/PAI-1. Methods We observed the concordance between CT indications proposed by a canonical decision tree representative of French practices (not including uPA/PAI-1) and actual CT prescriptions decided by a medical board which included uPA/PAI-1. We used a method of machine learning for the analysis of concordant and non-concordant CT prescriptions to generate a novel scheme for CT indications. Results We observed a concordance rate of 71% between indications proposed by the canonical decision tree and actual prescriptions. Discrepancies were due to CT contraindications, high tumor grade and uPA/PAI-1 level. Altogether, uPA/PAI-1 were a decisive factor for the final decision in 17% of cases by avoiding CT prescription in two-thirds of cases and inducing CT in other cases. Remarkably, we noted that in routine practice, elevated uPA/PAI-1 levels seem not to be considered as a sufficient indication for CT for N≤3, Ki 67≤30% tumors, but are considered in association with at least one additional marker such as Ki 67>14%, vascular invasion and ER-H score <150. Conclusions This study highlights that in the routine clinical practice uPA/PAI-1 are never used as the sole indication for CT. Combined with other routinely used biomarkers, uPA/PAI-1 present an added value to orientate the therapeutic choice.

Keywords: breast cancer; chemotherapy; machine learning; over- and under-treatment; survival; uPA/PAI-1.

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