Literature DB >> 32739409

An enhanced prognostic score for overall survival of patients with cancer derived from a large real-world cohort.

T Becker1, J Weberpals1, A M Jegg2, W V So3, A Fischer1, M Weisser2, F Schmich1, D Rüttinger2, A Bauer-Mehren4.   

Abstract

BACKGROUND: By understanding prognostic biomarkers, we gain insights into disease biology and may improve design, conduct, and data analysis of clinical trials and real-world data. In this context, we used the Flatiron Health Electronic Health Record-derived deidentified database that provides treatment outcome and biomarker data from >280 oncology centers in the USA, organized into 17 cohorts defined by cancer type. PATIENTS AND METHODS: In 122 694 patients, we analyzed demographic, clinical, routine hematology, and blood chemistry parameters within a Cox proportional hazard framework to derive a multivariable prognostic risk model for overall survival (OS), the 'Real wOrld PROgnostic score (ROPRO)'. We validated ROPRO in two independent phase I and III clinical studies.
RESULTS: A total of 27 variables contributed independently and homogeneously across cancer indications to OS. In the largest cohort (advanced non-small-cell lung cancer), for example, patients with elevated ROPRO scores (upper 10%) had a 7.91-fold (95% confidence interval 7.45-8.39) increased death hazard compared with patients with low scores (lower 10%). Median survival was 23.9 months (23.3-24.5) in the lowest ROPRO quartile Q1, 14.8 months (14.4-15.2) in Q2, 9.4 months (9.1-9.7) in Q3, and 4.7 months (4.6-4.8) in Q4. The ROPRO model performance indicators [C-index = 0.747 (standard error 0.001), 3-month area under the curve (AUC) = 0.822 (0.819-0.825)] strongly outperformed those of the Royal Marsden Hospital Score [C-index = 0.54 (standard error 0.0005), 3-month AUC = 0.579 (0.577-0.581)]. We confirmed the high prognostic relevance of ROPRO in clinical Phase 1 and III trials.
CONCLUSIONS: The ROPRO provides improved prognostic power for OS. In oncology clinical development, it has great potential for applications in patient stratification, patient enrichment strategies, data interpretation, and early decision-making in clinical studies.
Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Cox regression; Flatiron Health; overall survival; prognostic score (ROPRO); real world data (RWD)

Year:  2020        PMID: 32739409     DOI: 10.1016/j.annonc.2020.07.013

Source DB:  PubMed          Journal:  Ann Oncol        ISSN: 0923-7534            Impact factor:   32.976


  4 in total

1.  Artificial Intelligence for Prognostic Scores in Oncology: a Benchmarking Study.

Authors:  Hugo Loureiro; Tim Becker; Anna Bauer-Mehren; Narges Ahmidi; Janick Weberpals
Journal:  Front Artif Intell       Date:  2021-04-16

2.  Real-world data prognostic model of overall survival in patients with advanced NSCLC receiving anti-PD-1/PD-L1 immune checkpoint inhibitors as second-line monotherapy.

Authors:  Cristina Julian; Robson J M Machado; Sandhya Girish; Pascal Chanu; Dominik Heinzmann; Chris Harbron; Anda Gershon; Shannon M Pfeiffer; Wei Zou; Valerie Quarmby; Qing Zhang; Yachi Chen
Journal:  Cancer Rep (Hoboken)       Date:  2022-01-24

3.  Discovery and Evaluation of Protein Biomarkers as a Signature of Wellness in Late-Stage Cancer Patients in Early Phase Clinical Trials.

Authors:  Bethany Geary; Erin Peat; Sarah Dransfield; Natalie Cook; Fiona Thistlethwaite; Donna Graham; Louise Carter; Andrew Hughes; Matthew G Krebs; Anthony D Whetton
Journal:  Cancers (Basel)       Date:  2021-05-18       Impact factor: 6.639

4.  Machine Learning for Prediction of Immunotherapy Efficacy in Non-Small Cell Lung Cancer from Simple Clinical and Biological Data.

Authors:  Sébastien Benzekry; Mathieu Grangeon; Mélanie Karlsen; Maria Alexa; Isabella Bicalho-Frazeto; Solène Chaleat; Pascale Tomasini; Dominique Barbolosi; Fabrice Barlesi; Laurent Greillier
Journal:  Cancers (Basel)       Date:  2021-12-09       Impact factor: 6.639

  4 in total

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