Amelanotic Melanoma and Basal Cell Carcinoma
Dr Victoria Mar MBBS FACD PhD Overview
• Amelanotic presentations in different subtypes • Amelanosis and diagnostic accuracy • Insights from genetic sequencing • Take home messages Amelanosis / Hypomelanosis
• Up to 20% of all melanomas are only partially pigmented (hypomelanotic)
• NM, DM, ALM >40% hypomelanotic • SSM, LMM 10-25%
• Hypomelanotic melanomas may mimic basal cell carcinoma
Dermoscopic clues
Amelanotic MM Basal cell carcinoma • Vascular pattern: – ‘Atypical’ vessels • Vascular pattern: • Dotted • Linear irregular – Arborising vessels • Hair pin • Large diameter vessels
• Pigment – Periph black dots/globules • Pigment – Irreg dots/globules – Irreg blotches (black/brown/grey) – Maple leaf structures – Blue-grey ovoid nests • scar-like depigmentation • inverse network – Multiple bue-grey globules • irregular blue grey dots • blue-white veil • milky pink areas
Menzies, JAAD 2013 Menzies, Arch Derm, 2008 Superficial Spreading Melanoma
Hypomelanotic SSM
Subtle pigment
0.5mm SSM
Image courtesy of Victorian Melanoma Service
1.0mm SSM
1.8mm SSM
0.4mm SSM 1.1mm amelanotic SSM R forearm
Images courtesy of Victorian Melanoma Service Lentigo Maligna Melanoma
Rapid development of a thick amelanotic nodule (LMM) within a long standing ‘freckle’ (LM)
Images courtesy of Victorian Melanoma Service Image courtesy of Victorian Melanoma Service Nodular Melanoma- hypomelanotic
Pigment evident on dermoscopy
ulceration Acral Lentiginous Melanoma
• ALM may present as a wart-like lesion
• If ALM are mistakenly pared down = friable and lack typical pin-point vessels seen in a wart
Images courtesy of Victorian Melanoma Service Pigment may be subtle!!
Images courtesy of Victorian Melanoma Service Images courtesy of Victorian Melanoma Service 3.1mm thick, SLNB+ Desmoplastic Melanoma
<1% melanomas Elderly patients Head and neck / CSD
Often amelanotic Dermoscopic signs may be subtle Poorly demarcated Firm / scar-like
Image courtesy of Victorian Melanoma Service
Subtle pigment 11mm DM Longstanding lesion on leg = dermatofibroma Images courtesy of Victorian Melanoma Service Scalp Melanoma
More common in older men More likely to be nodular subtype More commonly amelanotic
More rapid growth Thicker at diagnosis (2.8 vs 1.2mm)
Less likely to be first noticed by patient
C.Xie et al., AJD 2015 30
Overview
• Amelanotic presentations in different subtypes • Amelanosis and diagnostic accuracy • Insights from genetic sequencing • Take home messages Challenges for early diagnosis
• Atypical clinical presentation – Doctor delay – Patient delay
• Rapid growth of primary tumor
Median growth rate of melanomas by subtype
Nodular melanomas grow faster than other types: Median melanoma growth in mm per month 0.63
0.50
0.49 0.38
mm/month 0.25
0.13 0.12 0.13 0.00 SSM LMM NM
Liu et al, Arch Dermatol. 2006; 142: 1551-1558 JAAD, 2014
Faster ROG significantly associated with: amelanosis symptoms (eg. itch) tumor thickness mitotic rate tumor subtype The impact of Nodular Melanoma
S Smithson, MJA 2015 37 What is the impact of NM in Victoria?
Subtype % Cases % Deaths
SSM 56 30
NM 14 43
ALM 1 3
Desmo 1 3
– Risk of mortality from NM compared to SSM • Crude HR = 7.6 (95%CI 6.3, 9.2 P<0.001)
– When thickness, age and sex taken into account • Adjusted HR=1.5 (95% CI 1.2-1.8, p=0.002)
Diagnostic Accuracy
• SSM 76.6% (95% CI 68.7-84.5%) • NM 41.3% (95% CI 32.5-50.1%) • DM 20.9% (95% CI 8.7-33.1%)
• Amelanosis (lack of pigment) was associated with lower diagnostic accuracy for all subtypes
• NM and DM most commonly misdiagnosed as BCC and SCC. • Other Dx: angioma, dermatofibroma, pyogenic granuloma, cyst, scar
Dr Matthew Lin, AJD 2013
39 Opportunities missed
Biopsy at visit SSM NM Percentages Percentage p-value number (n=60) (n=60) DETECTED MISSED (cumulative) (subtractive)
SSM vs NM SSM vs NM 1 42 26 70% vs 44% 30% vs 56% 0.01 2 14 14 93% vs 67% 7% vs 33% 3 4 7 100% vs 78% 0% vs 22% 4 0 5 -- vs 86% -- vs 14% 5 0 5 -- vs 95% -- vs 5% 6 0 3 -- vs 100% -- vs 0%
Dr Mark Cicchiello, AJD 2015
40 Doctor behaviour
SSM NM p-value (n=60) (n=60)
Reassurance that it was most likely benign 20% (16) 50% (30) 0.01 (n=46)
Immediate biopsy (n=53) 56% (34) 31% (19) 0.01
Dr Mark Cicchiello, AJD 2015
41 How can we improve diagnostic accuracy? • History –Listen to the patient!! –History of change over time
• Dermoscopy – sensitivity ~90% for pigmented lesions – much lower for hypomelanotic lesions
• Wood’s lamp Overview
• Amelanotic presentations in different subtypes • Amelanosis and diagnostic accuracy • Insights from genetic sequencing • Take home messages What causes amelanosis??
Images courtesy of Victorian Melanoma Service Oncogene 2015
Proliferative Invasive
MITF MITF Brn2 Brn2 TGFβ pigment MYC
Notch EZH2
pigment
Pinner Cancer Res 2009 Pouryazdanparast Mod Path 2012 Amelanosis and mutation status
• Cohort of 330 primary melanomas 20% amelanotic Of the 330 tumors, 188 tested for BRAF/NRAS mutations:
WT BRAF+ NRAS+ p Pigmented 36% 47% 17% 0.005 Amelanotic 63% 22% 15%
Clin Can Res, 2013
Pigmentation and mutation status 31% 41% 19% 9%
amelanotic pigmented
BRAF/NRAS WT 80% 50% p=0.09
Mutation rate/Mb 16 12 p=0.9
Low High mutation mutation load load
BRAF mutant NRAS mutant BRAF/NRAS WT Low UV damage UV damage ++
[amelanotic?]
Targeted therapy Immunotherapy Key points
• Not all melanomas look like the classic ‘ABCD’ melanomas • Not all melanomas are ‘ugly ducklings’
• Rapid growth and atypical clinical appearance are major challenges for early diagnosis
• Molecular sequencing has enabled us to understand more about how melanomas spread and which melanomas are likely to behave more aggressively
• Significant reductions in melanoma mortality can be achieved by improving early detection, particularly of ‘atypical’ lesions Acknowledgements • Melbourne Melanoma Project • VMS/Alfred – Ms Sonia Mailer – Prof John Kelly – Ms Anne Fennessy – Prof Catriona McLean – Dr Charles Xie • MIA – Dr Matthew Lin – Prof Richard Scolyer – Dr Mark Cicchello – Hojabr Kakavand – Dr Wenyuan Liu • ONJCI – Ms Karen Scott, Data Manager – Prof Jonathan Cebon – Ms Caroline Hedt, Clinical Photographer – Andreas Behren
• PMC • Bioinformatics – Prof Grant McArthur – Jason Li – Stephen Wong – Tony Papenfuss – Alex Dobrovic – Aleks Logan – Sue Sturrock