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Clinical Models to Define Response and Survival With Anti-PD-1 Antibodies Alone or Combined With Ipilimumab in Metastatic Melanoma

  • Ines Pires Da Silva
  • , Tasnia Ahmed
  • , Jennifer L. McQuade
  • , Caroline A. Nebhan
  • , John J. Park
  • , Judith M. Versluis
  • , Patricio Serra-Bellver
  • , Yasir Khan
  • , Tim Slattery
  • , Honey K. Oberoi
  • , Selma Ugurel
  • , Lauren E. Haydu
  • , Rudolf Herbst
  • , Jochen Utikal
  • , Claudia Pfohler
  • , Patrick Terheyden
  • , Michael Weichenthal
  • , Ralf Gutzmer
  • , Peter Mohr
  • , Rajat Rai
  • Jessica L. Smith, Richard A. Scolyer, Ana M. Arance, Lisa Pickering, James Larkin, Paul Lorigan, Christian U. Blank, Dirk Schadendorf, Michael A. Davies, Matteo S. Carlino, Douglas B. Johnson, Georgina V. Long, Serigne N. Lo, Alexander M. Menzies
  • The University of Sydney
  • Blacktown Hospital
  • German Cancer Research Center
  • University of Texas MD Anderson Cancer Center
  • Vanderbilt University
  • Macquarie University
  • Netherlands Cancer Institute
  • The Christie NHS Foundation Trust
  • Royal Marsden NHS Foundation Trust
  • Hospital Clínic de Barcelona
  • University of Duisburg-Essen
  • Fresenius AG
  • Heidelberg University 
  • Saarland University
  • University of Lübeck
  • University Hospital Schleswig-Holstein
  • Ruhr University Bochum
  • Elbe-Klinikum Buxtehude
  • Royal Prince Alfred Hospital
  • University of Manchester
  • Royal North Shore and Mater Hospitals

Research output: Contribution to journal › Article › peer-review

73 Citations (Scopus)

Abstract

PURPOSE Currently, there are no robust biomarkers that predict immunotherapy outcomes in metastatic melanoma. We sought to build multivariable predictive models for response and survival to anti-programmed cell death protein 1 (anti-PD-1) monotherapy or in combination with anticytotoxic T-cell lymphocyte-4 (ipilimumab [IPI]; anti-PD-1 6 IPI) by including routine clinical data available at the point of treatment initiation. METHODS One thousand six hundred forty-four patients withmetastaticmelanoma treated with anti-PD-16 IPI at 16 centers from Australia, the United States, and Europe were included. Demographics, disease characteristics, and baseline blood parameters were analyzed. The end points of this study were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). The final predictive models for ORR, PFS, and OS were determined through penalized regression methodology (least absolute shrinkage and selection operator method) to select the most significant predictors for all three outcomes (discovery cohort, N =633). Each model was validated internally and externally in two independent cohorts (validation-1 [N5 419] and validation-2 [N5 592]) and nomograms were created. RESULTS The final model for predicting ORR (area under the curve [AUC] = 0.71) in immunotherapy-treated patients included the following clinical parameters: Eastern Cooperative Oncology Group Performance Status, presence/absence of liver and lung metastases, serum lactate dehydrogenase, blood neutrophil-lymphocyte ratio, therapy (monotherapy/ combination), and line of treatment. The final predictive models for PFS (AUC5 0.68) and OS (AUC5 0.77) included the same variables as those in the ORR model (except for presence/absence of lung metastases), and included presence/absence of brain metastases and blood hemoglobin. Nomogram calculators were developed from the clinical models to predict outcomes for patients with metastatic melanoma treated with anti-PD-1 ± IPI. CONCLUSION Newly developed combinations of routinely collected baseline clinical factors predict the response and survival outcomes of patients with metastatic melanoma treated with immunotherapy and may serve as valuable tools for clinical decision making.

Original languageEnglish
Pages (from-to)1068-1080
Number of pages13
JournalJournal of Clinical Oncology
Volume40
Issue number10
DOIs
Publication statusPublished - 1 Apr 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 American Society of Clinical Oncology. All rights reserved.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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