Desmoplastic Melanoma: A Clinicopathological Analysis of Three Cases in the Chinese Population

Local Recurrence Rates After Excision of Desmoplastic Melanoma: A Systematic Review and Meta-Analysis

BACKGROUND

Few prospective studies have evaluated local recurrence rates (LRR) after excision of desmoplastic melanoma (DM); however, several retrospective studies have reported high LRR.

OBJECTIVE

To determine LRR after excision of DM and evaluate factors affecting LRR.

MATERIALS AND METHODS

Systematic review of the PubMed, Embase, and Web of Science databases was performed to identify studies reporting local recurrence after excision of DM with conventional wide local excision (WLE), Mohs micrographic surgery (MMS), or staged excision (SE). Meta-analysis was performed to calculate summary LRR and pooled risk ratios (RR).

RESULTS

Literature search identified 4 studies evaluating MMS or SE (total n = 61 DM). 53 studies assessed WLE ( n = 3,080) and were analyzed quantitatively. The overall LRR after WLE of DM was 21% (95% CI, 0.16-0.28; n = 2,308). Local recurrence rate was higher with positive/unknown histologic excision margins (49%, 95% CI, 0.25-0.74; n = 91) versus negative histologic margins (11%, 95% CI, 0.07-0.17; n = 1,075; [ p < .01]). Neurotropism was also associated with increased LRR (RR, 1.79; 95% CI, 1.34-2.38, p < .01; n = 644).

CONCLUSION

DM has high LRR after WLE. Local recurrence risk was greatest with positive excision margins, indicating the importance of achieving negative microscopic margins. Greater study of MMS and SE for DM is required.

Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers

Structural variations (SVs) in cancer cells often impact large genomic regions with functional consequences. However, identification of SVs under positive selection is a challenging task because little is known about the genomic features related to the background breakpoint distribution in different cancers. We report a method that uses a generalized additive model to investigate the breakpoint proximity curves from 2,382 whole-genomes of 32 cancer types. We find that a multivariate model, which includes linear and nonlinear partial contributions of various tissue-specific features and their interaction terms, can explain up to 57% of the observed deviance of breakpoint proximity. In particular, three-dimensional genomic features such as topologically associating domains (TADs), TAD-boundaries and their interaction with other features show significant contributions. The model is validated by identification of known cancer genes and revealed putative drivers in cancers different than those with previous evidence of positive selection.