Melanoma: Prognostic factors and factors predictive of response to therapy: Current Medicinal Chemistry

M. Strudel, L. Festino, V. Vanella, M. Berretta, F.M. Marincola, P.A. Ascierto

Research output: Contribution to journalArticlepeer-review


Background: A better understanding of prognostic factors and biomarkers that predict response to treatment is required in order to further improve survival rates in patients with melanoma. Prognostic Factors: The most important histopathological factors prognostic of worse outcomes in melanoma are sentinel lymph node involvement, increased tumor thickness, ulceration and higher mitotic rate. Poorer survival may also be related to several clinical factors, including male gender, older age, axial location of the melanoma, elevated serum levels of lactate dehydrogenase and S100B. Predictive Biomarkers: Several biomarkers have been investigated as being predictive of response to melanoma therapies. For anti-Programmed Death-1(PD-1)/Programmed Death-Ligand 1 (PD-L1) checkpoint inhibitors, PD-L1 tumor expression was initially proposed to have a predictive role in response to anti-PD-1/PD-L1 treatment. However, patients without PD-L1 expression also have a survival benefit with anti-PD-1/PD-L1 therapy, meaning it cannot be used alone to select patients for treatment, in order to affirm that it could be considered a correlative, but not a predictive marker. A range of other factors have shown an association with treatment outcomes and offer potential as predictive biomarkers for immunotherapy, including immune infiltration, chemokine signatures, and tumor mutational load. However, none of these have been clinically validated as a factor for patient selection. For combined targeted therapy (BRAF and MEK inhibition), lactate dehydrogenase level and tumor burden seem to have a role in patient outcomes. Conclusion: With increasing knowledge, the understanding of melanoma stage-specific prognostic features should further improve. Moreover, ongoing trials should provide increasing evidence on the best use of biomarkers to help select the most appropriate patients for tailored treatment with immuno-therapies and targeted therapies. © 2020 Bentham Science Publishers.
Original languageEnglish
Pages (from-to)2792-2813
Number of pages22
JournalCurr. Med. Chem.
Issue number17
Publication statusPublished - 2020


  • Biomarkers
  • BRAF inhibitors
  • Immunotherapy
  • MEK inhibitors
  • Melanoma
  • PD-1
  • PD-L1
  • Prognostic factors
  • B Raf kinase
  • B Raf kinase inhibitor
  • chemokine
  • cobimetinib
  • dabrafenib
  • dacarbazine
  • immune checkpoint inhibitor
  • immunological antineoplastic agent
  • ipilimumab
  • lactate dehydrogenase
  • mitogen activated protein kinase kinase inhibitor
  • nivolumab
  • pembrolizumab
  • programmed death 1 ligand 1
  • programmed death 1 receptor
  • trametinib
  • tumor marker
  • unclassified drug
  • vemurafenib
  • Breslow thickness
  • cancer immunotherapy
  • cancer patient
  • cancer prognosis
  • clinical feature
  • cutaneous melanoma
  • distant metastasis
  • drug efficacy
  • drug safety
  • gene expression
  • histopathology
  • human
  • immunohistochemistry
  • lymph node metastasis
  • lymph vessel metastasis
  • melanoma
  • melanoma cell
  • mitosis rate
  • molecularly targeted therapy
  • monotherapy
  • mutational load
  • neurotropism
  • overall survival
  • patient selection
  • phase 2 clinical trial (topic)
  • phase 3 clinical trial (topic)
  • predictive value
  • Review
  • risk factor
  • sentinel lymph node
  • survival rate
  • treatment response
  • tumor associated leukocyte
  • tumor immunity
  • tumor localization
  • tumor volume
  • ulcer
  • aged
  • immunotherapy
  • male
  • mutation
  • prognosis
  • treatment outcome
  • Aged
  • Biomarkers, Tumor
  • Humans
  • Male
  • Mutation
  • Prognosis
  • Treatment Outcome


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