Article Text

Download PDFPDF

761 Potential predictive biomarkers of rapid progression and response to anti-PD1 treatment by gene profiling analysis in metastatic melanoma patients
  1. Maria Grazia Vitale1,
  2. Domenico Mallardo1,
  3. Antonio Grimaldi1,
  4. Ncholas Bayless2,
  5. Mariaelena Capone1,
  6. Gabriele Madonna1,
  7. Vito Vanella1,
  8. Lucia Festino1,
  9. Claudia Trojaniello1,
  10. Marcello Curvietto1,
  11. Luigi Scarpato1,
  12. Sarah Warren3,
  13. SuFey Ong3,
  14. Ernesta Cavalcanti1,
  15. Corrado Caracò1,
  16. Alessandra Cesaro3,
  17. Ester Simeone1 and
  18. Paolo Ascierto1
  1. 1Instituto Nazionale Tumori I.R.C.C.S. Pascale, Napoli, Italy
  2. 2Parker Institute for Cancer Immunotherapy, San Francisco, USA
  3. 3Nanostring Technologies, Seattle, WA, USA


Background Immunotherapy dramatically changed the landscape of melanoma treatment. Even if nearly 40% of patients has a long-term benefit from anti-PD-1 agents, nearly 30% relapse in the first year of treatment, showing in some cases very rapid disease progression. Actually, there are no effective biomarkers that could predict patient‘s clinical benefit. Aim of this study is to identify gene profiling biomarkers that could help to select melanoma patients who most likely respond to anti-PD-1 therapy.

Methods We defined as fast responder (FR) or fast progressor (FP) patients who got clinical response or clinical progression within eight weeks from first cycle of therapy. We retrospectively collected data from 51 metastatic melanoma patients (25 FR and 26 FP) treated from October 2016 to June 2020 in first-line with anti-PD1 monotherapy (nivolumab or pembrolizumab) at National Cancer Institute of Naples, Italy. Gene expression profiling analysis was performed using NanoString® IO 360 panels on PBMCs collected at baseline from 18 patients (10 FR and 8 FP). Patients with ECOG≥2 were excluded. They were all IV stage (5 M1a, 1 M1b, 12 M1c) of which 15 were B-RAF wild-type (83%) and 3 were B-RAF mutated (17%). Statistical associations between treatment response and gene score variables were estimated through Bonferroni correction for multiple comparisons and Benjamini-Hochberg.

Results Patterns of gene expression were assessed for correlation to response. We compared PBMCs Nanostring analysis between FR and FP patients. We found a higher expression of KRas, CD39, IFI16, IL18, FCGR2A, IL1RN, MAP3K8, TLR5, TLR8, MyD88 and NF-kB in FP patients (all with p-value ≤0.005), most of them related to cell proliferation and immunosuppressive mechanism. Instead we found a higher expression of PRF1, PIK3R1, HLA-DPA1, HLA-DRB1, HLA-DOA, CD45RA, LDHB, KIR3DL2, CD2, CD28, CD7, CD27 in FR patients (all with p-value ≤0.01), most of them related to priming and cytolysis.

Conclusions Our study suggests that a specific gene signature may discriminate FR or FP patients. These preliminary data provide a rationale for further investigating gene profiling signature as a potential biomarker of response to immunotherapy.

Acknowledgements The study was supported by the Institutional Project ‘Ricerca Corrente’ of Istituto Nazionale Tumori IRCCS Fondazione ‘G. Pascale’ of Napoli, Italy.

Ethics Approval The study was approved by the internal ethics board of the Istituto Nazionale Tumori IRCCS Fondazione ‘G. Pascale’ of Napoli Italy, approval number of registry 17/17 OSS.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:

Statistics from

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.