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p53 Immunohistochemical Patterns in HPV-Independent Squamous Cell Carcinomas of the Vulva and the Associated Skin Lesions: A Study of 779 Cases.

Natalia RakislovaLaia AlemanyOmar ClaveroAdela SacoAureli TornéMarta Del PinoMeritxell MunmanyMaria Teresa Rodrigo-CalvoJosé GuerreroLorena MarimonNaiara VegaBeatriz QuirósBelen LloverasInmaculada Ribera-CortadaMaria AlejoMichael PawlitaWim QuintSilvia de SanjoseJaume Ordinull Vvap Study Group
Published in: International journal of molecular sciences (2020)
Human papillomavirus (HPV)-independent vulvar squamous cell carcinomas (VSCC) and its precursors frequently harbour TP53 mutations. Recently, six p53 immunohistochemical (IHC) patterns have been defined, which have shown strong correlation with TP53 mutation status. However, few studies have applied this new six-pattern framework and none of them exhaustively compared p53 IHC positivity and patterns between invasive VSCC and adjacent skin lesion. We performed p53 IHC in a series of 779 HPV-independent VSCC with adjacent skin and evaluated the IHC slides following the newly described classification. Some 74.1% invasive VSCC showed abnormal p53 IHC staining. A skin lesion was identified in 450 cases (57.8%), including 254 intraepithelial precursors and 196 inflammatory/reactive lesions. Two hundred and ten of 450 (47%) VSCC with associated skin lesions showed an abnormal p53 IHC stain, with an identical staining pattern between the VSCC and the adjacent skin lesion in 80% of the cases. A total of 144/450 (32%) VSCC showed wild-type p53 IHC both in the invasive VSCC and adjacent skin lesion. Finally, 96/450 (21%) VSCC showed p53 IHC abnormal staining in the invasive VSCC but a wild-type p53 staining in the skin lesion. Most of the discordant cases (70/96; 73%) showed adjacent inflammatory lesions. In conclusion, the p53 IHC staining and pattern are usually identical in the VSCC and the intraepithelial precursor.
Keyphrases
  • high grade
  • soft tissue
  • wound healing
  • wild type
  • squamous cell
  • squamous cell carcinoma
  • flow cytometry
  • machine learning
  • early stage
  • deep learning
  • radiation therapy
  • lymph node
  • neoadjuvant chemotherapy
  • case control