Molecular Characterization of Stage 1-3 Melanoma: Are we close to accurate prognostication and prediction? I have no relevant conflicts of interest to disclose. John T. Seykora MD PhD Departments of Dermatology & Pathology and Laboratory Medicine
COI Disclosure: I have received support from the following companies: Merck Samumed Novartis Rohto Pharmaceuticals Galderma Piqur Co-owner of patent with UPenn to use SMKIs topically to treat skin cancers Myriad Genetics, speaker
Objectives: Briefly discuss some recent changes to AJCC staging system for early stage melanoma Discuss genetic and molecular tests for stage 1-3 melanomas
Changes for 2018 Mitoses are no longer used to stratify tumor staging Instead, for lesions <1mm, thickness of 0.8mm is used for stratification Stage IA: no ulceration, <0.8mm Stage IB: ulceration OR 0.8 1mm <0.8mm, -ulceration = IA <0.8mm, +ulceration = IB 0.8-1mm (regardless of ulceration) = IB
Mitoses remain significant Among patients with clinically node-negative (cn0) primary melanoma in the 8th edition database, increasing mitotic rate was significantly associated with decreasing MSS in univariate analysis Gershenwald et al, Ca Cancer J Clin 2017
Early prognostic models: Model predicting survival in stage I melanoma based on tumor progression. Clark WH Jr 1, Elder DE, Guerry D 4th, Braitman LE, Trock BJ, Schultz D, Synnestvedt M, Halpern AC. J Nat Cancer Inst. 1989. 264 patients with VGP stage 1 melanomas with over 100 month follow up Found that 6 variables: mitotic rate/mm 2, thickness, TILs, anatomic site, patient sex and regression predicted 8-year survival with 89% accuracy.
Molecular analysis of melanoma for diagnosis and prognosis: 1) B-Raf mutational analysis 2) Comparative genomic hybridization (CGH) 3) Fluorescence in situ hybridization (FISH) 4) Gene expression profiling to determine if melanocytic lesion is melanoma 5) Gene expression profiling to determine metastatic risk
Why is BRAF testing performed? 50-60% of melanomas harbor mutations at codon 600 Valine (V) to glutamic acid (E) substitution most common mutation at position 600 = V600E (90%) 2 nd most common mutation is V600K (valine lysine) Mutation results in constitutive activation of the MAP kinase signaling pathway = dysregulated tumor growth V600E/K mutations confer increased sensitivity to BRAF inhibitors (vemurafenib, dabrafenib)
How is BRAF testing performed? PCR-based BRAF V600 mutation test Used on formalin-fixed, paraffin-embedded tissue, so biopsy specimens sent for routine histopathology are used Sensitive detection of the BRAF V600E mutation May also detect other mutations such as V600D, V600K, V600R
Additional methods for BRAF mutations testing Immunohistochemistry using anti-braf V600E antibody FIGURE 1. IHC staining with the VE1 antibody visualized using the chromogen diaminobenzene (brown staining) in metastatic melanoma. A, BRAF wild-type (wt) melanoma, which is negative for VE1. B, BRAF V600K-mutated melanoma, which is negative for VE1 (brown melanin pigment is present in a few melanoma cells). C, BRAF V600E-mutated melanoma strongly positive for VE1. D, Discordant case that was strongly VE1 positive and BRAF wt on original mutation testing. Upon retesting, a BRAF V600E mutation was detected. E, Lymph node containing scattered single and small clusters of strongly VE1-positive melanoma cells in a background of numerous lymphocytes. This case was BRAF wt on original and repeat mutation testing. F, Discordant fine-needle biopsy case showing negative VE1 staining in the cell block preparation. Mutation testing detected a BRAF V600E mutation. The cell block was prepared and fixed using techniques that differed from those used for all other cases (which utilized FFPE tissues). Long et al, Am J Surgical Pathology 2013
Additional formats for BRAF testing Targeted next generation sequencing panels Ability to assay for mutations in multiple oncogenes Used on formalin-fixed, paraffin-embedded tissue http://www.pennmedicine.org/personalized-diagnostics/services.html
Comparative Genomic Hybridization (CGH) CGH developed in the early 1990 s to expedite the identification and mapping of genome-wide DNA copy number alterations Deviations from normal copy numbers are now applied to cancer research by enabling the identification of genetic aberrations that occur in cancers the loss of tumor suppressor genes or amplification of oncogenes can be identified by comparing DNA from normal tissue to that from tumors March et al., JAAD 2015
Traditional CGH Arraybased CGH March et al., JAAD 2015
CGH in melanoma Determine if genetic criteria can distinguish melanoma from nevi. Examined 132 melanomas and 54 nevi 127 (96.2%) melanomas has genomic alterations 7 (13%) of nevi had gene rearrangements
CGH in melanoma The 7 nevi were Spitz nevi with gains on 11p Acral melanomas with alterations in 5p, 11q, 12q and 15 LMM loses at 17p and 13q
Busam, Seminars in Diagnostic Pathology 2013
CGH in Melanoma Multiple gains and losses (losses of 6q, 8p, 9p, and 10q, gains of 1q, 6p, 7, 8q, 17q, and 20q) In particular: Losses on 9p and 10, gains in 7 This is in contrast to nevi most lack genomic aberrations or have isolated changes Spitz nevi exhibit gains in chromosome 11p which is not seen in melanoma
Pitfalls Some melanomas are CGH negative Test sensitivity enough DNA for analysis, or admixture of melanoma cells with background nevus, stroma, etc True lack of copy number changes Benign lesions with copy number changes Differentiating homozygous from heterozygous deletions (better detected on FISH) Busam, Seminars in Diagnostic Pathology 2013
Fluorescence In Situ Hybridization (FISH) Method for determining the copy number of specific regions or sequences of DNA Short strands of fluorescently labeled DNA (probes) are used to label complementary target sequences in a given tissue sample Unlike CGH, FISH can detect balanced chromosomal translocations and single-point mutations BUT - the probes limit detection to prespecified DNA sequences (usually 4 probes) each probe must be tagged with a distinct fluorochrome that emits wavelengths of light that do not overlap with those emitted from the other fluorochromes CyTOF mass spectrometry of heavy metal tagged probes March et al., JAAD 2015
March et al., JAAD 2015
FISH for (unambiguous) melanocytic tumors Gerami et al., Am J Surg Path 2009 The algorithm correctly classified 72/83 melanomas 82/86 nevi Sensitivity 86.7% Specificity 95.4% 4/86 nevi were FISH positive (FP) 11/83 melanomas were FISH negative (FN) Probe set 5 yielded best results
What about for ambiguous melanocytic lesions? Histopath alone vs outcome: sensitivity 95%, specificity 52% FISH alone vs outcome: sensitivity 43%, specificity 80% Histopath AND FISH vs outcome: sensitivity 90% vs. specificity 76% Vergier et al., Modern Pathology 2011
Traditional probe set 6p25, 6q23, CEP6, 11q13 Sensitivity 75%, specificity 94% Enhanced probe set 6p25 (RREB1), 11q13 (CCND1), 9p21 (p16 and p15), 8q24 (Myc) Sensitivity 96%, specificity 98%
Advantages of FISH Permits detection of abnormal subpopulations within a heterogeneous tissue mixture Identification of much smaller populations of emerging clones of chromosomally aberrant cells is possible compared to CGH Visual correlation between a cell nucleus with an abnormal number of chromosomal loci and cytologic features is possible, which permits the verification of the identity of an affected cell Only small amounts of tissue are necessary for FISH
Limitations of FISH It only tests for aberrations in the targeted areas, which is usually limited to four chromosomal loci it doesn t analyze the entire set of chromosomes the way CGH does Focal view of the tumor False positives and false negatives occur, the latter varies based on the subtype of melanoma Tetraploidy may confuse the issue
Evaluating benign nevi and melanomas using a gene expression signature Candidate biomarker genes identified, based on differential expression in benign vs primary malignant melanocytic lesions reported in the literature or observed in practice Using a training set of 464 melanocytic lesions, a 23 gene signature yields an area under the curve of (AUC) 96% RT-PCR of RNA from FFPE tissue Clarke et al, Journal of Cutaneous Pathology 2015
Evaluating benign nevi and melanomas using a gene expression signature Clarke et al, Journal of Cutaneous Pathology 2015
Evaluating benign nevi and melanomas using a gene expression signature Clarke et al, Journal of Cutaneous Pathology 2015
Evaluating benign nevi and melanomas using a gene expression signature Histopathologically ambiguous lesions All 9 cases deemed to be malignant upon review by expert dermatopathologists were classified as malignant by the gene signature 4/8 cases deemed to be benign were classified as benign by gene signature Role for an indeterminate score? Metastatic lesions excluded from this study Not included in original cohorts. Clarke et al, Journal of Cutaneous Pathology 2015
Follow up study 1400 cases, divided into benign or malignant by 3 dermatopathologists. 91.5% sensitivity 92.5 specificity
Non-invasive test for melanoma
Assessing disease progression in stage 1 and 2 melanoma: Gene expression profile should predict biological behavior of melanoma Stage I and II melanomas represent a heterogeneous group with disease progression and death ranging from 3-55% within 5 years of diagnosis.
Assessing disease progression in stage 1 and 2 melanoma: Assessed published genomic analyses to develop prognostic genetic signature: Genes were selected based on differential expression Identified 28 prognostic genes and 3 control genes Assessed 260 primary melanomas used RT-PCR for these genes Analyzed levels of mrna using algorithm
Prognostic gene expression profiling (GEP) test 27 gene signature identified from microarray analysis: primary cutaneous melanoma vs. metastasis RT-PCR of RNA extracted from melanoma FFPE tissue Gerami et al, Clin Cancer Research 2015
Cellular functions represented in GEP signature Migration/chemotaxis/ metastasis CXCL14 SPP1 CLCA2 S100A9 S100A8 Differentiation/ proliferation CRABP2 SPRRIB BTG1 Chemokine/secreted molecules CCL14 MGP SPP1 Cell surface receptors TACST D2 CLCA2 ROBO1 Gap junction/cellular adhesion GJA1 DSC1 PPL Structural proteins MGP SPP1 CST6 Lymphocytic invasion LTA4H Angiogenesis regulator CXCL14 Transcription factor TRIM29 Extracellular functions KRT6B KRT14 Gerami, Clin Cancer Res 2013
Prognostic gene expression profiling (GEP) test Gene expression algorithm classifies tumors into: Class I: low metastatic risk Class 2: high metastatic risk Development set 5 year DFS Class 1-100%, validation-97% Class 2-38%, validation-31%
Prognostic gene expression profiling (GEP) test Median follow up time without evidence of LN involvement or distant metastasis = 7.6 years Gerami et al, Clin Cancer Research 2015
Acknowledgements: Emily Chu MD PhD Michael Ming MD PhD john.seykora@uphs.upenn.edu