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Tumor Mutation Burden and Structural Chromosomal Aberrations Are Not Associated with T-cell Density or Patient Survival in Acral, Mucosal, and Cutaneous Melanomas.

Jarem EdwardsPeter M FergusonSerigne N LôInês Pires da SilvaAndrew J ColebatchHansol LeeRobyn P M SawJohn F ThompsonAlexander M MenziesGeorgina V LongFelicity NewellJohn V PearsonNicola WaddellNicholas K HaywardPeter A JohanssonGraham J MannRichard A ScolyerUmaimainthan PalendiraJames S Wilmott
Published in: Cancer immunology research (2020)
Tumor mutation burden (TMB) has been proposed as a key determinant of immunogenicity in several cancers, including melanoma. The evidence presented thus far, however, is often contradictory and based mostly on RNA-sequencing data for the quantification of immune cell phenotypes. Few studies have investigated TMB across acral, mucosal, and cutaneous melanoma subtypes, which are known to have different TMB. It is also unknown whether chromosomal structural mutations [structural variant (SV) mutations] contribute to the immunogenicity in acral and mucosal melanomas where such aberrations are common. We stained 151 cutaneous and 35 acral and mucosal melanoma patient samples using quantitative IHC and correlated immune infiltrate phenotypes with TMB and other genomic profiles. TMB and SVs did not correlate with the densities of CD8+ lymphocytes, CD103+ tumor-resident T cells (Trm), CD45RO+ cells, and other innate and adaptive immune cell subsets in cutaneous and acral/mucosal melanoma tumors, respectively, including in analyses restricted to the site of disease and in a validation cohort. In 43 patients with stage III treatment-naïve cutaneous melanoma, we found that the density of immune cells, particularly Trm, was significantly associated with patient survival, but not with TMB. Overall, TMB and chromosomal structural aberrations are not associated with protective antitumor immunity in treatment-naïve melanoma.
Keyphrases
  • copy number
  • skin cancer
  • ulcerative colitis
  • case report
  • induced apoptosis
  • risk factors
  • cell proliferation
  • electronic health record
  • high resolution
  • machine learning
  • cell cycle arrest
  • case control