Literature DB >> 28661584

Clinical and palliative care outcomes for patients of poor performance status treated with antiprogrammed death-1 monoclonal antibodies for advanced melanoma.

Annie Wong1,2, Molly Williams3, Donna Milne4, Kortnye Morris1, Peter Lau1,2, Odette Spruyt2,3, Sonia Fullerton3, Grant McArthur1,2.   

Abstract

BACKGROUND: Antiprogrammed death-1 antibodies (anti-PD1) have response rates of 40% in metastatic melanoma. Patients with poor performance status (PS) were excluded from clinical trials, yet use of anti-PD1 is widespread in clinical practice. Literature regarding clinical and palliative care outcomes in patients with poor PS treated with anti-PD1 is lacking.
METHODS: Retrospective review of outcomes for all patients with advanced melanoma treated with anti-PD1 between 2012 and June 2015 at Peter MacCallum Cancer Centre, a tertiary specialist cancer center in Australia.
RESULTS: Between 2012 and 2015, 91 patients received anti-PD1: median age 63, 65% males, 77% elevated LDH>1xULN (37/48 patients). Fifty-eight patients had baseline ECOG PS of 0-1 (64%), 24 patients ECOG PS 2-3 (26%) and ECOG PS was not recorded in nine patients (10%). Median overall survival (OS) for the ECOG PS 0-1 group was 19.5 months and 1.8 months for ECOG PS 2-3 (HR 5.5; 95% CI, 9.1-50.3; P = 0.0001). Tumor response was 23/58 (39%) in ECOG PS 0-1, 2/16 (12%) in ECOG PS 2 and 0/8 in ECOG PS 3. Toxicity did not differ between different groups. ECOG PS 2-3 patients were more likely to be treated and hospitalized within the last month of life compared to ECOG PS 0-1 patients, RR 1.75 (95% CI, 1.04-2.56, P = 0.019) and RR 1.73 (95% CI, 1.10-2.16, P = 0.009), respectively. ECOG PS 2-3 patients were more likely to die in an acute hospital RR 2.68 (95% CI, 1.17-6.51, P = 0.016).
CONCLUSIONS: Patients with poor baseline PS have a significantly lower OS and reduced response to anti-PD1. Further quality of life and palliative care research is needed.
© 2017 John Wiley & Sons Australia, Ltd.

Entities:  

Keywords:  antiprogrammed death-1; immunotherapy; melanoma; palliative care

Mesh:

Substances:

Year:  2017        PMID: 28661584     DOI: 10.1111/ajco.12702

Source DB:  PubMed          Journal:  Asia Pac J Clin Oncol        ISSN: 1743-7555            Impact factor:   2.601


  11 in total

Review 1.  Immune checkpoint blockade in solid organ tumours: Choice, dose and predictors of response.

Authors:  Vishal Navani; Moira C Graves; Nikola A Bowden; Andre Van Der Westhuizen
Journal:  Br J Clin Pharmacol       Date:  2020-06-05       Impact factor: 4.335

Review 2.  An Approach to Drug-Induced Liver Injury from the Geriatric Perspective.

Authors:  Brian T Lee; Joseph A Odin; Priya Grewal
Journal:  Curr Gastroenterol Rep       Date:  2021-04-12

3.  Balancing the Hype with Reality: What Do Patients with Advanced Melanoma Consider When Making the Decision to Have Immunotherapy?

Authors:  Annie Wong; Alex Billett; Donna Milne
Journal:  Oncologist       Date:  2019-04-23

4.  Melanoma: An immunotherapy journey from bench to bedside.

Authors:  Vishal Navani; Moira C Graves; Hiren Mandaliya; Martin Hong; Andre van der Westhuizen; Jennifer Martin; Nikola A Bowden
Journal:  Cancer Treat Res       Date:  2022

Review 5.  Immunotherapy Versus Hospice: Treatment Decision-Making in the Modern Era of Novel Cancer Therapies.

Authors:  Amy An; David Hui
Journal:  Curr Oncol Rep       Date:  2022-02-03       Impact factor: 5.075

Review 6.  Immunotherapy use outside clinical trial populations: never say never?

Authors:  K Rzeniewicz; J Larkin; A M Menzies; S Turajlic
Journal:  Ann Oncol       Date:  2021-03-24       Impact factor: 51.769

7.  Quantified Kinematics to Evaluate Patient Chemotherapy Risks in Clinic.

Authors:  Zaki Hasnain; Tanachat Nilanon; Ming Li; Aaron Mejia; Anand Kolatkar; Luciano Nocera; Cyrus Shahabi; Frankie A Cozzens Philips; Jerry S H Lee; Sean E Hanlon; Poorva Vaidya; Naoto T Ueno; Sriram Yennu; Paul K Newton; Peter Kuhn; Jorge Nieva
Journal:  JCO Clin Cancer Inform       Date:  2020-06

8.  Immune checkpoint inhibitors in patients with solid tumors and poor performance status: A prospective data from the real-world settings.

Authors:  Akhil Kapoor; Vanita Noronha; Vijay M Patil; Nandini Menon; Amit Joshi; George Abraham; Kumar Prabhash
Journal:  Medicine (Baltimore)       Date:  2021-04-02       Impact factor: 1.817

9.  Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma.

Authors:  Paul Johannet; Nicolas Coudray; Douglas M Donnelly; George Jour; Irineu Illa-Bochaca; Yuhe Xia; Douglas B Johnson; Lee Wheless; James R Patrinely; Sofia Nomikou; David L Rimm; Anna C Pavlick; Jeffrey S Weber; Judy Zhong; Aristotelis Tsirigos; Iman Osman
Journal:  Clin Cancer Res       Date:  2020-11-18       Impact factor: 13.801

10.  Multiplex immunohistochemistry accurately defines the immune context of metastatic melanoma.

Authors:  H Halse; A J Colebatch; P Petrone; M A Henderson; J K Mills; H Snow; J A Westwood; S Sandhu; J M Raleigh; A Behren; J Cebon; P K Darcy; M H Kershaw; G A McArthur; D E Gyorki; P J Neeson
Journal:  Sci Rep       Date:  2018-07-24       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.