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    Topic review

    Melanoma Biomarkers

    Subjects: Dermatology
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    Melanoma is the deadliest form of skin cancer and remains a diagnostic challenge in the dermatology clinic. Here, we categorize and review known melanoma diagnostic biomarkers into five categories including visual, histopathological, morphological, immunohistochemical, and serological/molecular biomarkers.

    1. Introduction

    The consequences of missing a malignant melanoma are grave. As such, many biopsies are needlessly performed on clinically suspicious but still benign lesions to rule out melanoma. In fact, it has been shown that for every positive case of melanoma, there are 15 to 30 biopsies of lesions later proven to be benign[1]. Thus, the current method for melanoma detection has placed a significant economic burden on the healthcare system. At present, melanoma diagnosis is based on clinical examination and the ABCDE evaluation by specialists, followed by the selection of lesions that look different than the majority of existing moles on the body, dermoscopy and total body photography, excisional biopsy, and histopathologic examination by an expert dermatopathologist, and less often molecular analysis, genetic analysis, testing when indicated, and a multidisciplinary approach when indicated.

    It is estimated that $32,594 dollars are spent for each melanoma detected​[2]. Due to the increasing incidence of melanoma and the high cost of melanoma detection, there is a public health need for skin cancer screening with precise, cost-efficient methods. Particularly useful would be a non-invasive imaging technique to aid in melanoma diagnosis and the decision to biopsy.

    Melanoma diagnostic biomarkers can be categorized into five categories including visual, histopathological, morphological, immunohistochemical, and serological/molecular biomarkers. Visual biomarkers are the specific features of melanoma that dermatologists recognize on the patient with a naked eye or with the use of a dermatoscope. Histopathology of melanoma refers to the features that pathologists and dermatopathologists look for under the microscope after a biopsy of a suspicious lesion has been performed. The morphologic features of melanoma refer to the overall layer architecture and cellular structure of the lesions. Immunohistochemistry refers to a method of staining lesions for specific key markers, which aid in differentiating benign from malignant lesions. Lastly, serological/molecular markers refer to markers that can be detected in the peripheral blood or serum as indicators for melanoma.

    2. Melanoma Progression

    The transition from normal healthy skin to melanoma is a topic that has been studied and debated for years. Cutaneous melanoma originates from melanocytes located in the basal layer of the epidermis. Melanocytes comprise only 1% to 2% of epidermal cells but produce all of the melanin in the skin. Melanin production is stimulated by melanocyte stimulating hormone (MSH) released from keratinocytes via a p53-mediated mechanism in response to ultraviolet (UV) light[3][4].

    There are two common types of melanin found in humans: (1) eumelanin—a brown-black pigmented melanin found in darker-skinned people, and (2) pheomelanin—a yellow-red pigmented melanin responsible for red hair and freckles[3]. Eumelanin has the ability to protect DNA more effectively than pheomelanin, absorbing more efficiently the harmful UV radiation and converting it to heat through a chemical process known as internal conversion (a process lacking in pheomelanin)[3]. This mechanism likely contributes to the higher incidence of skin cancer and melanoma observed in lighter-skinned individuals than in darker-skinned individuals.

    Cells typically respond to UV radiation-induced DNA damage in one of two ways: the cell either repairs the DNA or initiates apoptosis (rarely they undergo necrosis or mitotic catastrophe)[5]. DNA is repaired by a number of cellular mechanisms including direct repair, nucleotide and base excision repair, and recombinational and cross-linked repair[6]. However, these mechanisms are error-prone processes that can potentially lead to the formation of mutations resulting in melanoma formation[3]. UVB radiation damages pyrimidines, leading to the formation of cyclobutene pyrimidine dimers and (6-4) photoproducts[7]. Repeated carcinogenic exposure from UV light results in an accumulation of mutations within the skin. Invasive melanoma contains a larger number of UV-related mutations compared to those found in benign nevi[8]. In addition, inherited conditions such as xeroderma pigmentosum (XP), congenital melanocytic nevi, familial atypical multiple moles and melanoma (FAMMM) syndrome, and BRCA2 mutation all provide evidence for a genetic predisposition to the development of melanoma[9][10].

    Unlike non-melanoma skin cancers (NMSC), melanoma can develop in areas that rarely receive sun exposure, such as the palmar surfaces of the hands and feet, and mucosal surfaces[11]. These melanomas are understood to have distinct oncogenic mutations uncommon in melanomas in areas of chronic ultraviolet (UV) exposure. One study found that melanomas located in areas of minimal sun exposure commonly displayed mutations in BRAF or NRAS, while melanomas in chronically sun exposed areas are most commonly associated with mutations in TP53, evidencing that melanoma is a heterogeneous disease stemming from genetic risk factors and accumulated environmental exposures[11][12].

    Recently, the thought of a simple linear progression from nevus to melanoma in situ does not appear to occur[8]; rather, it is the result of an accumulation of multiple different mutations[13]. It has been found that melanoma associated mutations can be either somatic or due to environmental factors that are acquired over time[14].  In order to transform from benign nevus to melanoma, multiple mutations or “hits” must occur. Tsao et al. found that there is a 0.03% (men) and 0.009% (women) lifetime risk of a mole that is present by age 20 to later transform into cutaneous melanoma by age 80[15]. In the work by Bastian, he suggests that there is an inciting oncogenic event that is often a gain of function mutation involving one of the following: NRAS, HRAS, BRAF, KIT, GNAQ, GNA11, ALK, ROS1, RET, and NTRK1[13]. Given that 30% of cutaneous melanoma arise near a nevus, often with the BRAFV600E mutation[16], the initial oncogenic mutation is helpful in separating different lesions such as congenital nevi, pigmented lesions on chronic sun damaged (CSD) skin, non-CSD skin pigmented lesions, spitz tumors, and blue nevi[13]. Secondary and tertiary oncogenic events usually involve a loss of tumor suppressor genes such as CDKN2A, TP53, PTEN, or BAP1, and these can be used for determining disease progression within classes[13].

    Melanomas can be sorted into two categories based on the skin on which they arise: CSD and non-CSD. CSD melanomas develop on skin showing solar elastosis, deterioration of the dermal elastic fibers, and they are often found in individuals >55 years old after years of UV radiation often on the head and neck, while the non-CSD melanomas usually affect individuals <55 years old in areas with intermittent sun exposure such as the trunk[17]. Non-CSD melanomas are often superficial spreading melanomas that can develop within a previous nevi in younger patients[18]. Non-CSD melanomas are often associated with BRAFV600E mutations that are found in common nevi as well, while CSD melanomas are often seen to have NF1, NRAS, or BRAFnonV600E mutations[17][19].

    Within a nevus, limited proliferation occurs due to the initiating mutation. If additional mutations are acquired such as TERT promoter mutations on both non-CSD and CSD skin, this results in further proliferation toward melanoma[19]. The characteristic histologic pagetoid growth pattern is associated with non-CSD melanoma with BRAFV600E mutations[17]. In contrast, melanocytes with high cumulative sun exposure can result in the formation of lentigo maligna with its characteristic lentiginous growth pattern that can cover several centimeters of skin for years before generating a nodule and becoming invasive, making it more common in older individuals with years of sun damage[17]. Ultimately, loss of function in CDKN2A or SWI/SNF primes lesions to become invasive, with mutations in PTEN and TP53 promoting complete invasion[19]. As found in the study by Colebatch et al., a simple linear progression from nevus to invasive melanoma does not appear to occur, but instead, different branches of mutations occur later in the progression of melanoma with a resultant heterogeneity of neoplasms[8].

    3. Melanoma Biomarkers

    3.1 Visual 

    ​​​​​Differentiating a benign nevus from cutaneous melanoma is first done through visual inspection. Visual criteria for melanoma detection include the ABCDE criteria of asymmetry, border irregularity, color variation, diameter (>6 mm), and evolution, with “E” being officially added in 2004[20]. Thomas et al. found that using two criteria in combination leads to sensitivity of 89.3% and specificity of 65.3%, while utilizing three criteria brings sensitivity to 65.55% and specificity to 80%[21][22]. Identifying visual features can be difficult with lesions that are not pigmented such as nodular amelanotic melanoma[22]. Dermoscopy or dermatoscopy is a method of examining the skin using skin surface microscopy. Russo et al. presented a seven-point checklist of melanoma used in dermoscopy including (I) atypical network (indicating two types of pigment networks), (II) blue whitish veil (irregular area with blue pigmentation), (III) atypical vascular pattern (dotted and hairpin vessels indicating neoangiogenesis), (IV) atypical dots/globules (indicating clumps of melanocytes), (V) irregular streaks (indicating melanocytic nests in rete ridges), (VI) irregular blotches (pigmented keratinocytes or pagetoid melanocytosis), and (VII) regression structures (corresponding to thin epidermis and few melanophages)[23]. In dermoscopy of acral lesions, benign lesions often show a parallel furrow pattern (linear pigmentation in furrows of the sole) in comparison to malignant lesions with parallel ridge pattern (parallel band-like pigmentation in ridges of the sole (gold standard for diagnosing volar melanocytic nevus and malignant melanoma)[24]

    3.2. Histopathology

    While visual examination is limited to the horizontal plane of view (surface of the lesion), the next logical step is to examine the lesion in the vertical plane[25]. This is done by either a pathologist or dermatopathologist who analyzes the biopsied specimen stained with hematoxylin–eosin (H&E) staining to allow for the visualization of structures from the epidermis through the reticular dermis and subcutaneous tissues[26]. Criteria for the diagnosis of melanoma includes overall asymmetry and poor circumscription, poor or variably sized nests, single cells predominating over nests, upward scatter of melanocytes and nuclear pleomorphism, and morphologic changes of the nucleus and cytoplasm. Pathologists do have a set of mandatory histopathological qualities of melanoma that must be included in the pathology report of a melanoma including ulceration, mitotic rate, regression, lymphovascular invasion, perineural invasion, Breslow thickness, satellitosis, and status of surgical margins[27]

    3.3. Morphology

    Morphologic features can be examined through different non-invasive imaging modalities including Optical Coherence Tomography (OCT), Reflectance Confocal Microscopy (RCT), and Ultrasonography (see the complete list of these imaging modalities in [26][28][29][30][31], including quantitative dynamic infrared imaging, hyperspectral imaging, multispectral imaging, electrical impedance spectroscopy, and photoacoustic imaging (both microscopy and tomography)[32][33][34][35][36][37]. Raman spectrometry, real-time elastography, terahertz pulse imaging, multiphoton imaging, magnetic resonance imaging, positron emission tomography, fiber diffraction, Fourier transform infrared spectroscopy, and reflex transmission imaging. It should be noted that many of these imaging modalities are in the investigational phase (see [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62]); i.e., they are not regularly used in clinical practice as of yet, but they could provide promising options in the future when they are better understood and accessible.

    Rajabi-Estarabadi et al. reviewed the literature on the use of OCT to detect morphologic features of melanoma. These features included architectural disarray, stromal reaction, atypical melanocytes, vertical location of atypical melanocytes, pagetoid spread, junctional nests, and dermal nests[62]. Vessel morphology can also be examined through the use of speckle variance optical coherence tomography (SV-OCT) to detect the irregular organization of vessels found in melanoma[62].

    Reflectance Confocal Microscopy (RCT) is another non-invasive method to study skin cancer in vivo. RCT also allows for visualization of the tissue microstructure in tumorous lesions. Morphologic biomarkers such as pagetoid melanocytes can be detected by RCT[63]. Other morphologic features detected by RCT are broken down by skin layers by Waddell et al.[64]. In the superficial epidermis, atypical honeycomb pattern, atypical cobblestone pattern, and pagetoid cells are often seen in melanomas. In the basal cell layer and the dermo-epidermal layer (DEJ), cellular atypia, nonedged dermal papillae, and a disarranged DEJ can be appreciated. Lastly, the upper dermis can have cells distributed in sheet-like structures and sparse nests composed of round or pleomorphic cells[64].

    High-frequency ultrasound (HFUS) has also been utilized in the diagnosis of melanoma[65]. Dinnes et al. found in their analysis that melanotic lesions appear hypoechoic, homogenous, and well defined on ultrasound[65]. Doppler ultrasound can be utilized to assess tumor vascularity by characterizing vascularization and the number of vascular pedicles present[66]. Giovagnorio et al. found that hypervascularity had a sensitivity of 90% and specificity of 100% in contrast to the benign lesions that showed hypovascularity with a sensitivity of 100% and specificity of 90% [89]. Strain elastography can be utilized to assess tumor stiffness, which is likely due to increased cellularity and tumor infiltration, as noted by Botar et al.[66].

    One of the parameters that can be well studied using the above-mentioned modalities is the depth of the tumor in skin. The depth of tumor invasion correlates with the thickness of the tumor, which is strongly related to prognosis. Thickness of the tumor assists in the staging of the melanoma, which is based off of Breslow’s Depth, which was updated in 2017 in the 8th Edition of the AJCC Cancer Staging Manual. The depth is measured from the epidermal granular level to the deepest level of invasion[67]. The stages are as follows T1: ≤1.0 mm, T1a: <0.8 mm with no ulceration, T1b: 0.8–1.0 mm with or without ulceration or <0.8 mm with ulceration, T2: 1.01–2.0 mm, T3: 2.01–4.0 mm, and T4: >4.0 mm. Additionally, mitotic rate is no longer in the T category[67].

    3.4. Immunohistochemical Stains

    When the limitations of histologic examination are reached, special stains and immunohistochemical analysis provides tools to differentiate malignant lesions from benign nevi. Multiple targets have been noted in several reviews including but not limited to S100, Gp100, Anti-MART-2, Anti Melan-A, CSPG4 (Chondroitin Sulfate Proteoglycan 4), pHH3, and p16[68][69][70]. A comprehensive list of immunohistochemical biomarkers is given in the review by Abbas et al.[70]. As noted by Eisenstein et al., these biomarkers indicate the existence of melanoma, as opposed to separating it from other cancer types[69]. While there are many immunohistochemical markers that are currently known, we have focused on several of the most clinically utilized markers, as well as several markers that are utilized for the discernment of ambiguous lesions. These biomarkers include S100, HMB 45, Ki-67, Melan A (MART1-Melanoma antigen recognized by T cells 1), Chondroitin Sulfate Proteoglycan 4 (CSPG4), Tyrosinase, PNL2, MITF (Microphthalmia transcription factor), SOX10, MC1R (Melanocortin 1 Receptor), PRAME (preferential expressed antigen in melanoma), pHH3, and p16.

    S100 is a protein family with at least 25 identified members encoded by many genes, but most are located on chromosome 1q21 in a region called the epidermal differentiation cluster. These proteins have a known expression in melanoma[71]. S100 is involved in multiple cellular processes including cellular growth, cell cycle progression, cellular motility, calcium homeostasis, transcription, and protein phosphorylation[72][73]. Eisenstein et al. reported 90% sensitivity in the immunohistochemistry (IHC) stain of S100 in primary and metastatic lesions of melanoma[69]. This is in agreement with the work done by Nonaka et al., finding that S100 is the most sensitive marker for melanoma, particularly with the subtypes S100A1, S100A6, and S100B[74]. In their study[74], more than 90% of the malignant melanomas were found to express these proteins; S100A1 specifically was present in all types of melanomas but was not present in neurofibromas, schwannomas, or malignant peripheral nerve sheath tumors [72][74]. In contrast, S100A6 was strongly and diffusely positive in the junctional and dermal components of 100% (42/42) studied spitz nevi, positive in 56% melanocytic nevi (41/73), but only positive in 33% (35/105) of the dermal components of melanomas in the study done by Ribé and McNutt[75]; therefore, they proposed the idea of utilizing S100A6 for the differentiation of Spitz nevus from melanoma[75].

    HMB 45 is a monoclonal antibody against PMEL17, which is also called gp100 and plays a role in the organizational structure of melanoma[72][76]. While it can stain positively in nevi, the stain is usually limited to the epidermal and papillary dermal melanocytes in benign nevi[77], while in primary melanoma, the staining pattern is in both the superficial and the deep melanocytes of the lesion[72]. HMB 45 could be particularly useful in combination with Ki-67[72][77][78], which is discussed as an additional marker in this manuscript. In the past, there was discussion in regard to false positive results in other forms of cancer, but currently, Ordóñez states that other tumors such as epithelial, lymphoid, glial, and mesenchymal origin tumors are negative[72]. However, HMB 45 can be seen in other tumors such as angiomyolipoma, lymphangiomyomatosis, and the clear cell “sugar” tumor, and it has also been seen to be positive in post inflammatory hyperpigmentation, making it less reliable as a melanoma marker according to the review by Eisenstein et al.[68][69].

    Ki-67 is a non-histone nuclear protein and is useful as a marker of proliferation. Due to the detection of Ki-67 in all cell cycle phases except in the resting phase G0, Ki-67 is thought to be a more useful marker of proliferation than mitotic rate[70]. Ki-67 is found to be positive in <5% of common nevi, while being positive in 13%–30% of melanoma tumor cells, with cases showing 100% nuclear positivity[70][79][80].

    Melan A, also known as MART 1 (Melanoma antigen recognized by T cells-Cloned gene)[81] is found in both melanosomes and the endoplasmic reticulum, which aids in the processing and transportation of PMEL (premelanosome protein). PMEL is a key factor in the creation of melanosomes[82]. Rochaix et al. stated that “Immunohistochemical studies have shown Melan A expression in all (100%) dysplastic, junctional, intradermal, compound, Spitz, and congenital nevi, as well as in lymph node capsular nevi”[83]. Melan A is a highly sensitive marker that is not expressed in the dendritic cells of lymph nodes like S100 is, which makes Melan A an appropriate candidate for melanoma detection in lymph nodes. Melan A is also not expressed in histiocytes and is reported to be more sensitive than HMB 45[84].

    Chondroitin Sulfate Proteoglycan 4 (CSPG4) is involved in tissue development and can be a transmembrane receptor allowing for melanoma motility. Campoli et al. showed that the expression of CSPG4 is seen in 70% of superficial spreading and nodular human melanomas at multiple stages of melanoma progression[69][85].

    Tyrosinase is involved in melanin synthesis and is expressed in epidermal melanocytes as well as pigmented portions of the eye including the retina, iris, and ciliary body[72]. Tyrosinase is also expressed in junctional nevi as well as in the junctional zone of compound nevi, with decreasing expression in the deeper areas[86]. In the review done by Ordóñez, he noted that tyrosinase has been seen to also be positive in clear cell sarcomas, pigmented neurofibromas, and a low percentage of angiomyolipomas[72].

    PNL2 is a monoclonal antibody that does not have a target antigen known but reacts with normal melanocytes and neutrophils[83]. After Ordóñez performed multiple studies, he concluded that PNL2 is a highly sensitive and specific melanoma marker that is often positive in primary epithelioid melanomas and metastatic melanomas[72][83]. PNL2 has also been reported positive in clear cell sarcomas, renal angiomyolipomas, lymphangioleiomyomatosis, and melanocytic schwanommas[83][87].

    MITF, the microphthalmia transcription factor protein, plays a role in the differentiation of neural crest-derived melanocytes, mast cells, osteoclasts, and optic cup-derived retinal pigment epithelium[88]. MITF-M is the melanocyte specific isoform that does the transcription regulation of genes and controls melanogenesis, cell survival, and differentiation[87]. Ordóñez found that the sensitivity and specificity of MITF is lower than other melanoma biomarkers. MITF is similarly expressed in Schwann cells, stromal fibroblasts, dermal scars, and some mesenchymal and neural spindle cell neoplasms, which can easily be mixed up with desmoplastic melanoma[87][89]. MITF lacks specificity, so it is not beneficial for use in differentiating epithelioid melanomas from carcinomas but does have the advantage of being expressed in the nucleus, making the interpretation of IHC easier to read[72].

    SOX10 is involved in the embryonic determination of cell fate and is critical in the development and formation of melanocytes[90][91]. SOX10 is a sensitive biomarker for melanocytic tumors that can be expressed in both primary and metastatic melanomas[72][74]. SOX10 stains in a nuclear pattern and is not expressed in dendritic cells, making it more beneficial for lymph node staining [92][93]. SOX10 is not restricted to solely the melanocyte, and it is found in hepatocytes, renal tubular cells, adrenal medullary cells, and the myocardium[94].

    Melanocortin 1 Receptor (MC1R) is a melanocyte-stimulating hormone receptor in the GPCR (G protein-coupled receptor) family that controls pigment and plays a large role in the skin phenotype and sensitivity[95]. In two studies reviewed by Ordóñez[94][96], MC1R was present in 100% of the 44 melanomas.

    PRAME (preferential expressed antigen in melanoma) is a member of the cancer testis antigen family that has normal expression in the testis, ovaries, adrenals, endometrium, and placenta[97][98]. These proteins encode antigens that are subsequently recognized by T lymphocytes[97]. In a study done by Lezcano et al., they tested 110 melanocytic tumors with ambiguous features by PRAME immunohistochemistry (IHC) and cross-referenced them with fluorescent in situ hybridization (FISH) and single nucleotide polymorphism (SNP) array. They found agreement in PRAME IHC and final diagnostic interpretation in 102/110 samples (92.7%) [99]. In their previous study from 2018, Lezcano found that 88%–94% of non-spindle cell cutaneous melanomas showed nuclear immunoreactivity for PRAME in >75% of sampled cells. In comparison, benign nevi showed PRAME expression in 13.1% of the samples and was present in less than 50% of specimen cells[98]. These findings suggest the use of PRAME in the workup of ambiguous melanocytic lesions[99].

    Other studied immunomarkers include pHH3 and p16. Tissue growth is identified when stained for pHH3 and correlates with mitosis specifically by looking at the phosphorylation of histone H3 [70][100]. There is some concern that pHH3 may overestimate mitoses in both melanocytes and nonmelanocytic mitoses in the tissue[70]. The product of the cyclin-dependent kinase inhibitor 2A (CDKN2A) gene is p16 protein. In the review by Abbas et al., they found several studies that have shown a decrease in nuclear staining with p16 in melanomas (50%–98% show loss) and that p16 could be used for differentiating melanoma from spitz nevi[70]

    3.5. Serologic/Molecular Diagnosis

    In addition to studying markers within the tissue themselves, current research has shifted toward seeking out melanoma biomarkers within the serum. While LDH (lactate dehydrogenase) is the most widely known serum biomarker in melanoma as a strong prognostic factor[101], its use in diagnosis is limited. Deichmann et al. demonstrated that LDH is the most specific serum biomarker in melanoma with a 92% specificity and 79% sensitivity[102]. LDH is currently the only serum biomarker accepted by the American Joint Committee on Cancer staging system as having a prognostic value for melanoma[73]. Neagu et al. discussed research on microRNA. They showed that miRNA-200c, miRNA-205, and miRNA-23b were downregulated in melanoma, while miR-146a and miR-155 were upregulated[103][104]. Armand-Labit et al. did a study with miR-1246 and miR-185, finding them to be associated with metastatic melanoma. These microRNA biomarkers in the plasma have the potential to serve in early detection of melanoma[103][105].

    S100B in the serum has also been seen to correlate with the clinical stage of melanoma according to Fagnart et al.[69][106]. S100B has a direct action on TP53, a known tumor suppressor, and the effect of S100B allows for increased tumor growth in melanoma[8][16]. In a study done by Guo et al., serum S100B was normal in healthy people, and it increased in those with melanoma. In stages I/II, 1.3% of people were found to have elevated levels. In stage III, 8.7% had elevated levels, and in stage IV, 73.9% of patients had elevated levels of serum S100B[107]. Weinstein et al. suggests that S100 is not beneficial in early melanoma detection, but it is better suited for evaluation in patients with advanced disease[73].

    Another serologic test possibility on the horizon is the use of genetic screening for the identification and risk stratification of patients based on their likelihood of developing melanoma. This is especially pertinent in patients with conditions that predispose to the development of melanoma such as mutations in PTEN (Cowden syndrome), TP53 (Li Fraumeni syndrome), and multiple XP genes (xeroderma pigmentosum)[108]. Other genes including CDKN2A, CDK4, BAP1, POT1, ACD, TERF2IP, and TERT are known for their high penetrance as predisposing mutations for melanoma[109]. While CDKN2A, BRCA1 protein, and CDK4 genes are known susceptibility genes that are considered to be high risk for melanoma, a well-established clinical utility for testing these gene must first be established[110]. The genetic biomarkers are a promising niche that we continue to better understand each year.

    This entry is adapted from 10.3390/ijms21249583


    1. Rebekah L. Wilson; Brad A. Yentzer; Scott P. Isom; Steven Feldman; Alan B. Fleischer Jr; How good are US dermatologists at discriminating skin cancers? A number-needed-to-treat analysis. Journal of Dermatological Treatment 2011, 23, 65-69, 10.3109/09546634.2010.512951.
    2. Martha Matsumoto; Aaron Secrest; Alyce Anderson; Melissa I. Saul; Jonhan Ho; John M. Kirkwood; Laura K. Ferris; Estimating the cost of skin cancer detection by dermatology providers in a large health care system. Journal of the American Academy of Dermatology 2018, 78, 701-709.e1, 10.1016/j.jaad.2017.11.033.
    3. Lilit Garibyan; David E. Fisher; How Sunlight Causes Melanoma. Current Oncology Reports 2010, 12, 319-326, 10.1007/s11912-010-0119-y.
    4. Rutao Cui; Hans R. Widlund; Erez Feige; Jennifer Y. Lin; Dara L. Wilensky; Viven E. Igras; John D'orazio; Claire Y. Fung; Carl F. Schanbacher; Scott R. Granter; et al. Central Role of p53 in the Suntan Response and Pathologic Hyperpigmentation. Cell 2007, 128, 853-864, 10.1016/j.cell.2006.12.045.
    5. Wynand P. Roos; Bernd Kaina; DNA damage-induced cell death by apoptosis. Trends in Molecular Medicine 2006, 12, 440-450, 10.1016/j.molmed.2006.07.007.
    6. Aziz Sancar; Laura A. Lindsey-Boltz; Keziban Ünsal-Kaçmaz; Stuart Linn; Molecular Mechanisms of Mammalian DNA Repair and the DNA Damage Checkpoints. Annual Review of Biochemistry 2004, 73, 39-85, 10.1146/annurev.biochem.73.011303.073723.
    7. Giuseppina Giglia-Mari; Alain Sarasin; TP53 mutations in human skin cancers. Human Mutation 2003, 21, 217-228, 10.1002/humu.10179.
    8. Andrew J. Colebatch; Richard A. Scolyer; Trajectories of premalignancy during the journey from melanocyte to melanoma. Pathology 2017, 50, 16-23, 10.1016/j.pathol.2017.09.002.
    9. P.V. Gumaste; L.A. Penn; R.M. Cymerman; Tomas Kirchhoff; D. Polsky; B. McLellan; Skin cancer risk in BRCA1/2 mutation carriers.. British Journal of Dermatology 2015, 172, 1498-1506, 10.1111/bjd.13626.
    10. Estee L. Psaty; Alon Scope; Allan C. Halpern; Ashfaq A. Marghoob; Defining the patient at high risk for melanoma. International Journal of Dermatology 2010, 49, 362-376, 10.1111/j.1365-4632.2010.04381.x.
    11. John A. Curtin; Jane Fridlyand; Toshiro Kageshita; Hetal N. Patel; Klaus J. Busam; Heinz Kutzner; Kwang-Hyun Cho; Setsuya Aiba; Eva-Bettina Bröcker; Philip E. LeBoit; et al. Distinct Sets of Genetic Alterations in Melanoma. New England Journal of Medicine 2005, 353, 2135-2147, 10.1056/nejmoa050092.
    12. Elisa A Rozeman; Tim J. A. Dekker; John B A G Haanen; Christian U Blank; Advanced Melanoma: Current Treatment Options, Biomarkers, and Future Perspectives. American Journal of Clinical Dermatology 2017, 19, 303-317, 10.1007/s40257-017-0325-6.
    13. Boris C. Bastian; The Molecular Pathology of Melanoma: An Integrated Taxonomy of Melanocytic Neoplasia. Annual Review of Pathology: Mechanisms of Disease 2014, 9, 239-271, 10.1146/annurev-pathol-012513-104658.
    14. Genetic testing in melanoma: An interview with Dr. Diane McDowell, US Medical Affairs Lead, Oncology GSK . Melanoma Research Victoria . Retrieved 2021-1-6
    15. Hensin Tsao; Caroline Bevona; William Goggins; Timothy Quinn; The Transformation Rate of Moles (Melanocytic Nevi) Into Cutaneous Melanoma. Archives of Dermatology 2003, 139, 282-288, 10.1001/archderm.139.3.282.
    16. Jing Lin; Qingyuan Yang; Paul T. Wilder; France Carrier; David J. Weber; The Calcium-binding Protein S100B Down-regulates p53 and Apoptosis in Malignant Melanoma. Journal of Biological Chemistry 2010, 285, 27487-27498, 10.1074/jbc.m110.155382.
    17. A. Hunter Shain; Boris C. Bastian; From melanocytes to melanomas. Nature Cancer 2016, 16, 345-358, 10.1038/nrc.2016.37.
    18. Marco Rastrelli; Saveria Tropea; Carlo Riccardo Rossi; Mauro Alaibac; Melanoma: epidemiology, risk factors, pathogenesis, diagnosis and classification.. In Vivo 2014, 28, 1005-1011, .
    19. Adam N Guterres; Meenhard Herlyn; Jessie Villanueva; Melanoma. Encyclopedia of Life Sciences 2018, 1, 1-10, 10.1002/9780470015902.a0001894.pub3.
    20. Naheed R. Abbasi; Helen M. Shaw; Darrell S. Rigel; Robert J. Friedman; William H. McCarthy; Iman Osman; Alfred W. Kopf; David Polsky; Early Diagnosis of Cutaneous Melanoma. JAMA 2004, 292, 2771-2776, 10.1001/jama.292.22.2771.
    21. L. Thomas; P. Tranchand; Frédéric Bérard; T. Secchi; C. Colin; G. Moulin; Semiological value of ABCDE criteria in the diagnosis of cutaneous pigmented tumors.. Dermatology 1997, 197, 11-17, 10.1159/000017969.
    22. Hensin Tsao; Jeannette M. Olazagasti; Kelly M. Cordoro; Jerry D. Brewer; Susan C. Taylor; Jeremy S. Bordeaux; Mary-Margaret Chren; Arthur J. Sober; Connie Tegeler; Reva Bhushan; et al. Early detection of melanoma: Reviewing the ABCDEs. Journal of the American Academy of Dermatology 2015, 72, 717-723, 10.1016/j.jaad.2015.01.025.
    23. Teresa Russo; Vincenzo Piccolo; Gerardo Ferrara; Marina Agozzino; Roberto Alfano; Caterina Longo; Giuseppe Argenziano; Dermoscopy pathology correlation in melanoma. The Journal of Dermatology 2017, 44, 507-514, 10.1111/1346-8138.13629.
    24. Ling Jin; Eiichi Arai; Shinichi Anzai; Tetsunori Kimura; Tetsuya Tsuchida; Koji Nagata; Michio Shimizu; Reassessment of histopathology and dermoscopy findings in 145 Japanese cases of melanocytic nevus of the sole: Toward a pathological diagnosis of early-stage malignant melanomain situ. Pathology International 2010, 60, 65-70, 10.1111/j.1440-1827.2009.02483.x.
    25. Histopathological Correlation . Dermoscopedia . Retrieved 2021-1-19
    26. Gale Smith; Sheila MacNeil; State of the art in non-invasive imaging of cutaneous melanoma. Skin Research and Technology 2011, 17, 257-269, 10.1111/j.1600-0846.2011.00503.x.
    27. Alessandra Filosa; Giorgio Filosa; Melanoma Diagnosis: The Importance of Histopathological Report. Dermatopathology 2018, 5, 41-43, 10.1159/000486670.
    28. Yan Xing; Yulia Bronstein; Merrick I. Ross; Robert L. Askew; Jeffrey E. Lee; Jeffrey E. Gershenwald; Richard Royal; J. N. Cormier; Contemporary Diagnostic Imaging Modalities for the Staging and Surveillance of Melanoma Patients: a Meta-analysis. JNCI Journal of the National Cancer Institute 2010, 103, 129-142, 10.1093/jnci/djq455.
    29. I. Ak; M.P.M. Stokkel; W. Bergman; E.K.J. Pauwels; Cutaneous malignant melanoma: clinical aspects, imaging modalities and treatment.. European Journal of Nuclear Medicine and Molecular Imaging 2000, 27, 447-458, 10.1007/s002590050529.
    30. Wassef, C.; Rao, B.K.; Uses of non‐invasive imaging in the diagnosis of skin cancer: An overview of the currently available modalities. Int. J. Dermatol 2013, 52, 1481, .
    31. Alexandra Burke-Smith; Jonathan Collier; Isabel Jones; A comparison of non-invasive imaging modalities: Infrared thermography, spectrophotometric intracutaneous analysis and laser Doppler imaging for the assessment of adult burns. Burns 2015, 41, 1695-1707, 10.1016/j.burns.2015.06.023.
    32. Mohammadreza Nasiriavanaki; Jun Xia; Hanlin Wan; Adam Quentin Bauer; Joseph P. Culver; Lihong V. Wang; High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain. Proceedings of the National Academy of Sciences 2013, 111, 21-26, 10.1073/pnas.1311868111.
    33. Junjie Yao; Jun Xia; Konstantin I. Maslov; Mohammadreza Nasiriavanaki; Vassiliy Tsytsarev; Alexei V. Demchenko; Lihong V. Wang; Noninvasive photoacoustic computed tomography of mouse brain metabolism in vivo. NeuroImage 2012, 64, 257-266, 10.1016/j.neuroimage.2012.08.054.
    34. Afreen Fatima; Karl Kratkiewicz; Rayyan Manwar; Mohsin Zafar; Ruiying Zhang; Bin Huang; Neda Dadashzadeh; Jun Xia; Kamran Avanaki; Review of cost reduction methods in photoacoustic computed tomography. Photoacoustics 2019, 15, 100137, 10.1016/j.pacs.2019.100137.
    35. Karl Kratkiewicz; Rayyan Manwar; Yang Zhou; Moein Mozaffarzadeh; Kamran Avanaki; Technical Considerations when using Verasonics Research Ultrasound Platform for Developing a Translational Photoacoustic Imaging System. Biomedical Optics Express 2020, ., ., 10.1364/boe.415481.
    36. Rayyan Manwar; Matin Hosseinzadeh; Ali Hariri; Karl Kratkiewicz; Shahryar Noei; Kamran Avanaki; Photoacoustic Signal Enhancement: Towards Utilization of Low Energy Laser Diodes in Real-Time Photoacoustic Imaging. Sensors 2018, 18, 3498, 10.3390/s18103498.
    37. Rayyan Manwar; Xin Li; Sadreddin Mahmoodkalayeh; Eishi Asano; Dongxiao Zhu; Kamran Avanaki; Deep learning protocol for improved photoacoustic brain imaging. Journal of Biophotonics 2020, 13, e202000212, 10.1002/jbio.202000212.
    38. Karl Kratkiewicz; Rayyan Manwar; Ali Rajabi‐Estarabadi; Joseph Fakhoury; Jurgita Meiliute; Steven Daveluy; Darius Mehregan; Kamran Avanaki; Photoacoustic/Ultrasound/Optical Coherence Tomography Evaluation of Melanoma Lesion and Healthy Skin in a Swine Model.. Sensors 2019, 19, 2815, 10.3390/s19122815.
    39. Mohammad R.N. Avanaki; Ali Hojjat; Adrian Podoleanu; Investigation of computer-based skin cancer detection using optical coherence tomography. Journal of Modern Optics 2009, 56, 1536-1544, 10.1080/09500340902990007.
    40. Nasiri-Avanaki, M.R.; Sira, M.; Aber, A; Improved imaging of basal cell carcinoma using dynamic focus optical coherence tomography.. Journal of Investigative Dermatology 2011, 131, S38 , .
    41. Nasiri-Avanaki, M.; Aber, A.; Hojjatoleslami, S.; Sira, M.; Schofield, J.B.; Jones, C.; Podoleanu, A.G. Dynamic focus optical coherence tomography: Feasibility for improved basal cell carcinoma investigation. In Proceedings of the Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues X, San Francisco, CA, USA, 9 February 2012; p. 82252J.
    42. Ali Hojjatoleslami; Mohammad R. N. Avanaki; OCT skin image enhancement through attenuation compensation.. Applied Optics 2012, 51, 4927-4935, 10.1364/ao.51.004927.
    43. Mohammad R.N. Avanaki; Ali Hojjatoleslami; Skin layer detection of optical coherence tomography images. Optik 2013, 124, 5665-5668, 10.1016/j.ijleo.2013.04.033.
    44. Abad, A.T.K.; Adabi, S.; Soltanizadeh, H.; Daveluy, S.; Clayton, A.; Avanaki, M.R. A novel dermo-epidermal localization algorithm for swept source OCT images of human skin. In Proceedings of the Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXI, San Francisco, CA, USA, 17 February 2017; p. 100533C.
    45. Adabi, S.; Conforto, S.; Hosseinzadeh, M.; Noe, S.; Daveluy, S.; Mehregan, D.; Nasiriavanaki, M. Textural analysis of optical coherence tomography skin images: Quantitative differentiation between healthy and cancerous tissues. In Proceedings of the Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXI, San Francisco, CA, USA, 17 February 2017; p. 100533F.
    46. Adeleh Taghavikhalilbad; Saba Adabi; Anne Clayton; Hadi Soltanizadeh; Darius Mehregan; Kamran Avanaki; Semi-automated localization of dermal epidermal junction in optical coherence tomography images of skin. Applied Optics 2017, 56, 3116, 10.1364/ao.56.003116.
    47. Avanaki, M.R.; Podoleanu; A. En-face time-domain optical coherence tomography with dynamic focus for high-resolution imaging. . J. Biomed. Opt 2017, 22, 056009, .
    48. Saba Adabi; Zahra Turani; Emad Fatemizadeh; Anne Clayton; Mohammadreza Nasiriavanaki; Optical Coherence Tomography Technology and Quality Improvement Methods for Optical Coherence Tomography Images of Skin: A Short Review. Biomedical Engineering and Computational Biology 2016, 8, 1179597217713475, 10.1177/1179597217713475.
    49. Saba Adabi; Matin Hosseinzadeh; Shahryar Noie; Steven Daveluy; Anne Clayton; Darius Mehregan; Silvia Conforto; Mohammadreza Nasiriavanaki; Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms. Sci. Rep. 2017, 7, 1-11, .
    50. Tes, D.; Aber, A.; Zafar, M.; Horton, L.; Fotouhi, A.; Xu, Q.; Moiin, A.; Thompson, A.D.; Moraes Pinto Blumetti, T.C.; Daveluy, S; et al. S. Granular cell tumor imaging using optical coherence tomography. . Biomedical Engineering and Computational Biology 2018, 9, 1179597218790250, .
    51. Saba Adabi; Audrey Fotouhi; Qiuyun Xu; Steve Daveluy; Darius Mehregan; Adrian Podoleanu; Kamran Avanaki; An overview of methods to mitigate artifacts in optical coherence tomography imaging of the skin. Skin Research and Technology 2017, 24, 265-273, 10.1111/srt.12423.
    52. Qiuyun Xu; Saba Adabi; Anne Clayton; Steven Daveluy; Darius Mehregan; Mohammadreza Nasiriavanaki; Swept-Source Optical Coherence Tomography–Supervised Biopsy. Dermatologic Surgery 2018, 44, 768-775, 10.1097/dss.0000000000001475.
    53. S. O'leary; A. Fotouhi; D. Türk; P. Sriranga; A. Rajabi-Estarabadi; K. Nouri; S. Daveluy; D. Mehregan; M. Nasiriavanaki; OCT image atlas of healthy skin on sun-exposed areas. Skin Research and Technology 2018, 24, 570-586, 10.1111/srt.12468.
    54. Rakshita Panchal; Luke Horton; Peyman Poozesh; Javad Baqersad; Kamran Avanaki; Vibration analysis of healthy skin: toward a noninvasive skin diagnosis methodology. Journal of Biomedical Optics 2019, 24, 015001-11, 10.1117/1.jbo.24.1.015001.
    55. Zahra Turani; Emad Fatemizadeh; Tatiana Blumetti; Steven Daveluy; A. Moraes; Wei Chen; Darius Mehregan; Peter A. Andersen; Kamran Avanaki; Optical Radiomic Signatures Derived from Optical Coherence Tomography Images Improve Identification of Melanoma. Cancer Research 2019, 79, 2021-2030, 10.1158/0008-5472.can-18-2791.
    56. 78. Turani, Z.; Fatemizadeh, E.; Blumetti, T.; Daveluy, S.; Moraes, A.F.; Chen, W.; Mehregan, D.; Andersen, P.E.; Nasiriavanaki, M. Optical radiomic signatures derived from OCT images to improve identification of melanoma. In Proceedings of the European Conference on Biomedical Optics, Munich Germany, 23–25 June 2019; p. 11078_11023.
    57. Elmira Jalilian; Qiuyun Xu; Luke Horton; Audrey Fotouhi; Shriya Reddy; Rayyan Manwar; Steven Daveluy; Darius Mehregan; Juri Gelovani; Kamran Avanaki; et al. Contrast‐enhanced optical coherence tomography for melanoma detection: An in vitro study. Journal of Biophotonics 2020, 13, e201960097, 10.1002/jbio.201960097.
    58. M. Hossein Eybposh; Zahra Turani; Darius Mehregan; Mohammadreza Nasiriavanaki; Cluster-based filtering framework for speckle reduction in OCT images. Biomedical Optics Express 2018, 9, 6359-6373, 10.1364/boe.9.006359.
    59. Mohammad R. N. Avanaki; Adrian Podoleanu; John B. Schofield; Carole Jones; Manu Sira; Yan Liu; Ali Hojjat; Quantitative evaluation of scattering in optical coherence tomography skin images using the extended Huygens–Fresnel theorem. Applied Optics 2013, 52, 1574-1580, 10.1364/ao.52.001574.
    60. Mohammad R. N. Avanaki; P. Philippe Laissue; Ali Hojjatoleslami; De-Noising Speckled Optical Coherence Tomography Images Using an Algorithm Based on Artificial Neural Network. Journal of Neuroscience and Neuroengineering 2013, 2, 347-352, 10.1166/jnsne.2013.1066.
    61. Mohammad Almasganj; Saba Adabi; Emad Fatemizadeh; Qiuyun Xu; Hamid Sadeghi; Steven Daveluy; Mohammadreza Nasiriavanaki; A spatially-variant deconvolution method based on total variation for optical coherence tomography images. Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging 2017, 10137, 1013725, 10.1117/12.2255557.
    62. Ali Rajabi-Estarabadi; Julie M. Bittar; Caiwei Zheng; Vanessa Nascimento; Isabella Camacho; Lynn G. Feun; Mohammadreza Nasiriavanaki; Michael Kunz; Keyvan Nouri; Optical coherence tomography imaging of melanoma skin cancer. Lasers in Medical Science 2018, 34, 411-420, 10.1007/s10103-018-2696-1.
    63. Stefania Borsari; Riccardo Pampena; Aimilios Lallas; A Kyrgidis; Elvira Moscarella; Elisa Benati; Margherita Raucci; Giovanni Pellacani; Iris Zalaudek; Giuseppe Argenziano; et al. Clinical Indications for Use of Reflectance Confocal Microscopy for Skin Cancer Diagnosis. JAMA Dermatology 2016, 152, 1093-1098, 10.1001/jamadermatol.2016.1188.
    64. Andréanne Waddell; Phoebe Star; Pascale Guitera; Advances in the use of reflectance confocal microscopy in melanoma. Melanoma Management 2018, 5, MMT04, 10.2217/mmt-2018-0001.
    65. J. Dinnes; Jeffrey Bamber; Naomi Chuchu; Susan E Bayliss; Yemisi Takwoingi; Clare Davenport; Kathie Godfrey; Colette O'sullivan; R.N. Matin; Jonathan Deeks; et al. High-frequency ultrasound for diagnosing skin cancer in adults. Cochrane Database of Systematic Reviews 2018, 12, CD013188, 10.1002/14651858.cd013188.
    66. Carolina Botar Jid; Sorana Bolboaca; Rodica Cosgarea; Simona Şenilă; Liliana Rogojan; Manuela Lenghel; Dan Vasilescu; Sorin M Dudea; Doppler ultrasound and strain elastography in the assessment of cutaneous melanoma: preliminary results. Medical Ultrasonography 2015, 17, 509-14, 10.11152/mu.2013.2066.174.dus.
    67. Mahul B. Amin; Frederick L. Greene; Stephen B. Edge; Carolyn C. Compton; Jeffrey E. Gershenwald; Robert K. Brookland; Laura Meyer; Donna M. Gress; David R. Byrd; David P. Winchester; et al. The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging. CA: A Cancer Journal for Clinicians 2017, 67, 93-99, 10.3322/caac.21388.
    68. Adriano Piris; Martin C. Mihm; Progress in Melanoma Histopathology and Diagnosis. Hematology/Oncology Clinics of North America 2009, 23, 467-480, 10.1016/j.hoc.2009.03.012.
    69. Anna Eisenstein; Estela Chen Gonzalez; Rekha Raghunathan; Xixi Xu; Muzhou Wu; Emily O. McLean; Jean McGee; Byungwoo Ryu; Rhoda M. Alani; Emerging Biomarkers in Cutaneous Melanoma. Molecular Diagnosis & Therapy 2018, 22, 203-218, 10.1007/s40291-018-0318-z.
    70. Ossama Abbas; Daniel D. Miller; Jag Bhawan; Cutaneous Malignant Melanoma. The American Journal of Dermatopathology 2014, 36, 363-379, 10.1097/dad.0b013e31828a2ec5.
    71. Claus W Heizmann; S100 proteins structure functions and pathology. Frontiers in Bioscience 2001, 7, d1356-1368, 10.2741/a846.
    72. Nelson G. Ordóñez; Value of melanocytic-associated immunohistochemical markers in the diagnosis of malignant melanoma: a review and update. Human Pathology 2014, 45, 191-205, 10.1016/j.humpath.2013.02.007.
    73. David Weinstein; Jennifer Leininger; Carl Hamby; Bijan Safai; Diagnostic and Prognostic Biomarkers in Melanoma. The Journal of clinical and aesthetic dermatology 2014, 7, 13-24, .
    74. Daisuke Nonaka; Luis Chiriboga; Brian P. Rubin; Differential expression of S100 protein subtypes in malignant melanoma, and benign and malignant peripheral nerve sheath tumors. Journal of Cutaneous Pathology 2008, 35, 1014-1019, 10.1111/j.1600-0560.2007.00953.x.
    75. Adriana Ribé; N. Scott McNutt; Adriana Rib; S100A6 Protein Expression is Different in Spitz Nevi and Melanomas. Modern Pathology 2003, 16, 505-511, 10.1097/01.mp.0000071128.67149.fd.
    76. Alexander C. Theos; Steven T. Truschel; Graca Raposo; Michael S. Marks; The Silver locus product Pmel17/gp100/Silv/ME20: controversial in name and in function. Pigment Cell Research 2005, 18, 322-336, 10.1111/j.1600-0749.2005.00269.x.
    77. Victor G. Prieto; Christopher R. Shea; Use of immunohistochemistry in melanocytic lesions. Journal of Cutaneous Pathology 2008, 35, 1-10, 10.1111/j.1600-0560.2008.01130.x.
    78. Victor G. Prieto; Christopher R. Shea; Immunohistochemistry of melanocytic proliferations.. Archives of Pathology & Laboratory Medicine 2011, 135, 853-859, 10.1043/2009-0717-RAR.1.
    79. Joe A. Chorny; Ronald J. Barr; Ainura Kyshtoobayeva; James Jakowatz; Richard J. Reed; Ki-67 and p53 Expression in Minimal Deviation Melanomas as Compared with Other Nevomelanocytic Lesions. Modern Pathology 2003, 16, 525-529, 10.1097/01.mp.0000072747.08404.38.
    80. Ling-Xi L. Li; Kerry A. Crotty; Stanley W. McCarthy; Allan A. Palmer; Jillian J. Kril; A Zonal Comparison of MIB1-Ki67 Immunoreactivity in Benign and Malignant Melanocytic Lesions. The American Journal of Dermatopathology 2000, 22, 489-495, 10.1097/00000372-200012000-00002.
    81. Sonal Muzumdar; Melissa Argraves; Árni Kristjánsson; Katalin Ferenczi; Soheil S. Dadras; A quantitative comparison between SOX10 and MART-1 immunostaining to detect melanocytic hyperplasia in chronically sun-damaged skin. Journal of Cutaneous Pathology 2018, 45, 263-268, 10.1111/cup.13115.
    82. Margot Gaspard; L Lamant; Emilie Tournier; T. Valentin; Philippe Rochaix; Philippe Terrier; Dominique Ranchère-Vince; Jean-Michel Coindre; Thomas Filleron; Sophie Le Guellec; et al. Evaluation of eight melanocytic and neural crest-associated markers in a well-characterised series of 124 malignant peripheral nerve sheath tumours (MPNST): useful to distinguish MPNST from melanoma?. Histopathology 2018, 73, 969-982, 10.1111/his.13740.
    83. Philippe Rochaix; Magali Lacroix-Triki; L Lamant; Carole Pichereaux; Severine Valmary; Elena Puente; Talal Al Saati; Bernard Monsarrat; Christiane Susini; Louis Buscail; et al. PNL2, a New Monoclonal Antibody Directed against a Fixative-Resistant Melanocyte Antigen. Modern Pathology 2003, 16, 481-490, 10.1097/01.mp.0000067686.34489.50.
    84. Judit Zubovits; Elizabeth Buzney; Lawrence Yu; Lyn M Duncan; HMB-45, S-100, NK1/C3, and MART-1 in metastatic melanoma. Human Pathology 2004, 35, 217-223, 10.1016/j.humpath.2003.09.019.
    85. Michael Campoli; Soldano Ferrone; Xinhui Wang; Functional and Clinical Relevance of Chondroitin Sulfate Proteoglycan 4. Advances in Cancer Research 2009, 109, 73-121, 10.1016/b978-0-12-380890-5.00003-x.
    86. Achim A. Jungbluth; Kristin Iversen; Keren Coplan; Denise Kolb; Elisabeth Stockert; Yao-Tseng Chen; Lloyd J. Old; Klaus Busam; T311—An Anti-Tyrosinase Monoclonal Antibody for the Detection of Melanocytic Lesions in Paraffin Embedded Tissues. Pathology - Research and Practice 1999, 196, 235-242, 10.1016/s0344-0338(00)80072-2.
    87. Klaus J. Busam; Derya Kucukgöl; Eiichi Sato; Denise Frosina; Julie Teruya-Feldstein; Achim A Jungbluth; Immunohistochemical Analysis of Novel Monoclonal Antibody PNL2 and Comparison With Other Melanocyte Differentiation Markers. American Journal of Surgical Pathology 2005, 29, 400-406, 10.1097/01.pas.0000152137.81771.5b.
    88. Eirikur Steingrimsson; Neal G. Copeland; Zhubo Wei Neal G Copeland Nancy A Jenkins; Melanocytes and theMicrophthalmiaTranscription Factor Network. Annual Review of Genetics 2004, 38, 365-411, 10.1146/annurev.genet.38.072902.092717.
    89. Scott R. Granter; Katherine N. Weilbaecher; Catherine Quigley; Christopher D.M. Fletcher; David E. Fisher; Microphthalmia Transcription Factor. The American Journal of Dermatopathology 2001, 23, 185-189, 10.1097/00000372-200106000-00004.
    90. S.Brian Potterf; Ramin Mollaaghababa; Ling Hou; E.Michelle Southard-Smith; Thomas J. Hornyak; Heinz Arnheiter; William J. Pavan; Analysis of SOX10 Function in Neural Crest-Derived Melanocyte Development: SOX10-Dependent Transcriptional Control of Dopachrome Tautomerase. Developmental Biology 2001, 237, 245-257, 10.1006/dbio.2001.0372.
    91. Robert N. Kelsh; Sorting outSox10 functions in neural crest development. BioEssays 2006, 28, 788-798, 10.1002/bies.20445.
    92. Elen Blochin; Daisuke Nonaka; Diagnostic value of Sox10 immunohistochemical staining for the detection of metastatic melanoma in sentinel lymph nodes. Histopathology 2009, 55, 626-628, 10.1111/j.1365-2559.2009.03415.x.
    93. Charay Jennings; Jinah Kim; Identification of Nodal Metastases in Melanoma Using Sox-10. The American Journal of Dermatopathology 2011, 33, 474-482, 10.1097/dad.0b013e3182042893.
    94. F Salazar-Onfray; M López; A Lundqvist; A Aguirre; A Escobar; A Serrano; C Korenblit; M Petersson; V Chhajlani; O Larsson; et al. Tissue distribution and differential expression of melanocortin 1 receptor, a malignant melanoma marker. British Journal of Cancer 2002, 87, 414-422, 10.1038/sj.bjc.6600441.
    95. José C. García-Borrón; Berta Sanchez-Laorden; Celia Jiménez-Cervantes; Melanocortin-1 receptor structure and functional regulation. Pigment Cell Research 2005, 18, 393-410, 10.1111/j.1600-0749.2005.00278.x.
    96. M. N. López; C. Pereda; M. Ramirez; Ariadna Mendoza‐Naranjo; Antonio E. Serrano; A. Ferreira; R. Poblete; A. M. Kalergis; R. Kiessling; F. Salazar-Onfray; et al. Melanocortin 1 Receptor Is Expressed by Uveal Malignant Melanoma and Can Be Considered a New Target for Diagnosis and Immunotherapy. Investigative Opthalmology & Visual Science 2007, 48, 1219-27, 10.1167/iovs.06-0090.
    97. Steve Goodison; Virginia Urquidi; The cancer testis antigen PRAME as a biomarker for solid tumor cancer management. Biomarkers in Medicine 2012, 6, 629-632, 10.2217/bmm.12.65.
    98. Cecilia Lezcano; Achim A. Jungbluth; Kishwer S. Nehal; Travis J. Hollmann; Klaus J. Busam; PRAME Expression in Melanocytic Tumors. American Journal of Surgical Pathology 2018, 42, 1456-1465, 10.1097/pas.0000000000001134.
    99. Cecilia Lezcano; Achim A. Jungbluth; Klaus J. Busam; Comparison of Immunohistochemistry for PRAME With Cytogenetic Test Results in the Evaluation of Challenging Melanocytic Tumors. American Journal of Surgical Pathology 2020, 44(7), 893-900, 10.1097/pas.0000000000001492.
    100. David J Casper; Kate I Ross; Jane L Messina; Vernon K Sondak; Cheryl N Bodden; Tim W McCardle; L Frank Glass; Use of Anti-phosphohistone H3 Immunohistochemistry to Determine Mitotic Rate in Thin Melanoma. The American Journal of Dermatopathology 2010, 32, 650-654, 10.1097/dad.0b013e3181cf7cc1.
    101. Charles M. Balch; Jeffrey E. Gershenwald; Seng-Jaw Soong; John F. Thompson; Michael B. Atkins; David R. Byrd; Antonio C. Buzaid; Alistair J. Cochran; Daniel G. Coit; Shouluan Ding; et al. Final Version of 2009 AJCC Melanoma Staging and Classification. Journal of Clinical Oncology 2009, 27, 6199-6206, 10.1200/jco.2009.23.4799.
    102. Martin Deichmann; Axel Benner; Michael Bock; Andreas Jäckel; Karen Uhl; Volker Waldmann; Helmut Näher; S100-Beta, Melanoma-Inhibiting Activity, and Lactate Dehydrogenase Discriminate Progressive From Nonprogressive American Joint Committee on Cancer Stage IV Melanoma. Journal of Clinical Oncology 1999, 17, 1891-1891, 10.1200/jco.1999.17.6.1891.
    103. Monica Neagu; Carolina Constantin; Cristiana Tanase; Immune-related biomarkers for diagnosis/prognosis and therapy monitoring of cutaneous melanoma. Expert Review of Molecular Diagnostics 2010, 10, 897-919, 10.1586/erm.10.81.
    104. Demetra Philippidou; Martina J Schmitt; Dirk Moser; Christiane Margue; Petr V. Nazarov; Arnaud Muller; Laurent Vallar; Dorothee Nashan; Iris Behrmann; Stephanie Kreis; et al. Signatures of MicroRNAs and Selected MicroRNA Target Genes in Human Melanoma. Cancer Research 2010, 70, 4163-4173, 10.1158/0008-5472.can-09-4512.
    105. V Armand-Labit; Nicolas Meyer; A Casanova; H Bonnabau; V Platzer; E Tournier; B Sansas; S Verdun; B Thouvenot; B Hilselberger; et al. Identification of a Circulating MicroRNA Profile as a Biomarker of Metastatic Cutaneous Melanoma. Acta Dermato Venereologica 2015, 96, 29-34, 10.2340/00015555-2156.
    106. O C Fagnart; C J Sindic; C Laterre; Particle counting immunoassay of S100 protein in serum. Possible relevance in tumors and ischemic disorders of the central nervous system.. Clinical Chemistry 1988, 34, 1387-91, .
    107. Hua Bei Guo; B. Stoffel-Wagner; T. Bierwirth; J. Mezger; D. Klingmüller; Clinical significance of serum S100 in metastatic malignant melanoma. European Journal of Cancer 1995, 31, 1898-1902, 10.1016/0959-8049(95)00087-y.
    108. Sancy A. Leachman; Olivia M. Lucero; Jone E. Sampson; Pamela Cassidy; William Bruno; Paola Queirolo; Paola Ghiorzo; Identification, genetic testing, and management of hereditary melanoma. Cancer and Metastasis Reviews 2017, 36, 77-90, 10.1007/s10555-017-9661-5.
    109. Jazlyn Read; Karin Wadt; Nicholas K. Hayward; Melanoma genetics. Journal of Medical Genetics 2015, 53, 1-14, 10.1136/jmedgenet-2015-103150.
    110. 12. Cashin-Garbutt, A. Genetic testing in melanoma: An interview with Dr. Diane McDowell, US Medical Affairs Lead, Oncology GSK; McDowell, D.D., Ed.; Melanoma Research Victoria, Victoria Australia 2014.