Mark Jesus M Magbanua1, Laura H Hendrix2, Terry Hyslop2, William T Barry3,4, Eric P Winer5, Clifford Hudis6, Deborah Toppmeyer7, Lisa Anne Carey8, Ann H Partridge5, Jean-Yves Pierga9, Tanja Fehm10, José Vidal-Martínez11, Dimitrios Mavroudis12,13, Jose A Garcia-Saenz14, Justin Stebbing15, Paola Gazzaniga16, Luis Manso17, Rita Zamarchi18, María Luisa Antelo19, Leticia De Mattos-Arruda20, Daniele Generali21, Carlos Caldas22, Elisabetta Munzone23, Luc Dirix24,25, Amy L Delson26, Harold J Burstein5, Misbah Qadir8, Cynthia Ma27, Janet H Scott28, François-Clément Bidard9, John W Park28, Hope S Rugo28. 1. Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA. 2. Duke Cancer Institute, Duke University, Durham, NC, USA. 3. Alliance Statistics and Data Center, Dana-Farber/Partners CancerCare, Boston, MA, USA. 4. Rho Inc., Raleigh, NC, USA. 5. Dana-Farber/Partners CancerCare, Boston, MA, USA. 6. Memorial Sloan Kettering Cancer Center, New York, NY, USA. 7. Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA. 8. UNC Lineberger Comprehensive Cancer Center, Chapel Hill, NC, USA. 9. Department of Medical Oncology, Institut Curie, PSL Research University, Paris, France. 10. Department of Gynecology and Obstetrics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. 11. Clinical Laboratory, Hospital Arnau de Vilanova, Valencia, Spain. 12. Laboratory of Translational Oncology, School of Medicine, University of Crete, Heraklion, Greece. 13. Department of Medical Oncology, University Hospital of Heraklion, Greece. 14. DCIBERONC, IdISCC Madrid, Spain. 15. Division of Cancer, Department of Surgery and Cancer, Imperial College London, London, UK. 16. Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy. 17. Hospital 12 de Octubre, Madrid, Spain. 18. Veneto Institute of Oncology IOV-IRCCS, Padua, Italy. 19. Department of Hematology, Complejo Hospitalario de Navarra, Pamplona, Spain. 20. Val d'Hebron Institute of Oncology, Val d'Hebron University Hospital, and Universitat Autònoma de Barcelona, Barcelona, Spain. 21. Women Cancer Center, University of Trieste, Trieste, Italy. 22. Cancer Research UK Cambridge Institute and Department of Oncology Li Ka Shing Centre, University of Cambridge, Cambridge, UK. 23. Division of Medical Senology, European Institute of Oncology, IRCCS, Milano, Italy. 24. Translational Cancer Research Unit, GZA Hospitals Sint-Augustinus, Antwerp, Belgium. 25. University of Antwerp, Antwerp, Belgium. 26. Breast Science Advocacy Group, University of California San Francisco, San Francisco, CA, USA. 27. Washington University School of Medicine, St. Louis, MO, USA. 28. Division of Hematology Oncology, University of California San Francisco, San Francisco, CA, USA.
Abstract
BACKGROUND: We examined the prognostic significance of circulating tumor cell (CTC) dynamics during treatment in metastatic breast cancer (MBC) patients receiving first-line chemotherapy. METHODS: Serial CTC data from 469 patients (2202 samples) were used to build a novel latent mixture model to identify groups with similar CTC trajectory (tCTC) patterns during the course of treatment. Cox regression was used to estimate hazard ratios for progression-free survival (PFS) and overall survival (OS) in groups based on baseline CTCs, combined CTC status at baseline to the end of cycle 1, and tCTC. Akaike information criterion was used to select the model that best predicted PFS and OS. RESULTS: Latent mixture modeling revealed 4 distinct tCTC patterns: undetectable CTCs (56.9% ), low (23.7%), intermediate (14.5%), or high (4.9%). Patients with low, intermediate, and high tCTC patterns had statistically significant inferior PFS and OS compared with those with undetectable CTCs (P < .001). Akaike Information Criterion indicated that the tCTC model best predicted PFS and OS compared with baseline CTCs and combined CTC status at baseline to the end of cycle 1 models. Validation studies in an independent cohort of 1856 MBC patients confirmed these findings. Further validation using only a single pretreatment CTC measurement confirmed prognostic performance of the tCTC model. CONCLUSIONS: We identified 4 novel prognostic groups in MBC based on similarities in tCTC patterns during chemotherapy. Prognostic groups included patients with very poor outcome (intermediate + high CTCs, 19.4%) who could benefit from more effective treatment. Our novel prognostic classification approach may be used for fine-tuning of CTC-based risk stratification strategies to guide future prospective clinical trials in MBC.
BACKGROUND: We examined the prognostic significance of circulating tumor cell (CTC) dynamics during treatment in metastatic breast cancer (MBC) patients receiving first-line chemotherapy. METHODS: Serial CTC data from 469 patients (2202 samples) were used to build a novel latent mixture model to identify groups with similar CTC trajectory (tCTC) patterns during the course of treatment. Cox regression was used to estimate hazard ratios for progression-free survival (PFS) and overall survival (OS) in groups based on baseline CTCs, combined CTC status at baseline to the end of cycle 1, and tCTC. Akaike information criterion was used to select the model that best predicted PFS and OS. RESULTS: Latent mixture modeling revealed 4 distinct tCTC patterns: undetectable CTCs (56.9% ), low (23.7%), intermediate (14.5%), or high (4.9%). Patients with low, intermediate, and high tCTC patterns had statistically significant inferior PFS and OS compared with those with undetectable CTCs (P < .001). Akaike Information Criterion indicated that the tCTC model best predicted PFS and OS compared with baseline CTCs and combined CTC status at baseline to the end of cycle 1 models. Validation studies in an independent cohort of 1856 MBCpatients confirmed these findings. Further validation using only a single pretreatment CTC measurement confirmed prognostic performance of the tCTC model. CONCLUSIONS: We identified 4 novel prognostic groups in MBC based on similarities in tCTC patterns during chemotherapy. Prognostic groups included patients with very poor outcome (intermediate + high CTCs, 19.4%) who could benefit from more effective treatment. Our novel prognostic classification approach may be used for fine-tuning of CTC-based risk stratification strategies to guide future prospective clinical trials in MBC.
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