Open access peer-reviewed chapter
The protocol for routine clinical examination of melanoma.
Abstract
In recent years, the direction of personalized medicine, which is based on a disease-specific targeting therapy, as well as the early diagnosis of tumors and the identification of high-risk individuals, is rapidly developing in the world. Invasive melanoma is a tumor with high impact for its rapidly growing incidence, high mortality, increased complexity, and high care costs in advanced stages. Recent studies demonstrated the significant value of both conventional histopathological characteristics and genetic alterations in melanoma. This review focuses on the value of conventional histopathological characteristics including histological tumor subtype, Clark level, Breslow thickness, solar elastosis, ulceration, regression, lymphovascular invasion, mitotic counts, peritumoral lymphocyte infiltration, clinical characteristics such as age, gender, length of follow-up after surgery, recurrence, or metastasis, and progression-free survival, and tumor BRAF and NRAS mutations.
Keywords
- melanoma
- histopathology
- tumor infiltration lymphocytes
- BRAF
- NRAS
1. Introduction
More than 97% of all melanomas are diagnosed with a known primary site, most often on the skin [1, 2, 3]. Melanoma can also present within the eye or in the mucosae of internal organs [3]. In the rare cases in which it is diagnosed without an obvious primary site, it is referred to as melanoma of unknown primary (MUP) [3]. The predominant hypothesis to explain MUP involves the spontaneous regression of melanoma from a known primary site [3]. Metastatic melanoma could develop synchronously with a subclinical or otherwise unrecognized cutaneous, ocular, or mucosal melanoma.
Ultraviolet (UV) radiation is the most significant risk factor in the pathogenesis of melanoma, directly damaging DNA [1, 2, 3]. Multiple somatic and epigenetic alterations have also been implicated in the pathogenic process, along with the immune response and disturbances of immune tolerance [3].
There is a little evidence for early detection and risk stratification in malignant melanoma [4, 5]. The gold standard for melanoma diagnosis is still histopathological examination of tissues. Histopathological diagnosis involving the qualitative and quantitative assessment of biomarkers is susceptible to substantial interobserver variability, limiting its usefulness for individual patients. Specialized dermatopathologists are likely to be more consistent; however, their expertise is not widely available. Therefore, the standardization of the assessment is important [3].
Deep learning, an automated approach using labeled images to train a network with no other assumptions, has proven useful in many similar areas of digital pathology. In recent years, significant progress has been made in proteomics, metabolomics, and genomics. However, histopathological examination remains the gold standard for the diagnosis and prognosis of melanoma [3, 5, 6, 7].
The current World Health Organization (WHO) classification of skin tumors subdivides melanoma on the basis of solar elastosis assessed by dermal elastic fibers to measure cumulative sun damage (CSD) [3]. According to this WHO classification, there are currently three classes of melanomas: those associated with high CSD, those associated with low CSD, and those associated with nodular melanomas [3]. Solar elastosis is usually apparent in superficially spreading melanoma and lentigo maligna melanoma, the so-called high CSD melanoma. Desmoplastic melanoma is associated with increased solar elastosis. The most common subtype of high CSD melanoma is superficially spreading melanoma, which usually begins with early radial growth, followed by vertical growth and invasion of the dermis.
Acral, mucosal, uveal, and spitzoid melanomas are not associated with CSD or are characterized by low CSD. Nodular melanoma usually characterized as a low CSD type with early progression to vertical growth [3].
The development of melanoma is closely related to somatic and epigenetic changes. Different mutations have been implicated in its pathogenesis and evolution. Recent genomic classification subdivides melanoma into four subtypes based on the pattern of the most prevalent significantly mutated genes: BRAF, RAS and NF1 mutants, and triple-WT (wild type) [3, 5].
BRAF, CDKN2, and NRAS mutations are the most important and clinically relevant. The advent of novel personalized treatment for melanoma based on BRAF inhibitors and immunotherapy has reduced the mortality rate over the last decade, but advanced and metastatic melanomas remain difficult to treat [8, 9, 10]. Immune tolerance mechanisms are also important in the progression of melanoma.
Germline mutations in the cyclin-dependent kinase inhibitor 2A gene (CDKN2A) are frequently identified in familial melanoma; in 20–50% of such cases, three or more family members are diagnosed with melanoma [11]. Germline mutations in CDKN2A have also been associated with familial atypical multiple mole melanoma (FAMMM) syndrome, an autosomally dominant condition exemplified by a family history of melanoma and large numbers of atypical nevi [3, 11],
Immune responses are important in the pathogenesis of melanoma. Programmed cell death protein 1 ligand 1 (PDL1) and PDL2 are usually expressed by melanoma cells, T cells, B cells, and natural killer cells. This observation led to the development of specific antibodies against programmed cell death protein 1 (PD1) for the personalized treatment of melanoma (for example, nivolumab and pembrolizumab). Combinations of different targeting treatments that influence immune response mechanisms had beneficial effects on melanoma treatment, including PDL1 and CTLA4 targeting and immunotherapy with oncolytic viruses [8, 9, 10, 11, 12].
Clinicopathological characteristics, such as tumor size, tumor type, tumor invasiveness (Breslow thickness, Clark level, lymphovascular invasion, and neurotropisms), ulceration, and tumor mitotic activity, are significant prognostic factors for the development and progression of melanoma [3, 11]. In addition, it has been demonstrated that tumor-infiltrating lymphocytes can stratify melanoma into low- and high-risk progression types [13, 14, 15].
Diagnostic and therapeutic molecular markers have been increasingly used to assist in the histopathological assessment of melanoma [16]. These markers are helpful not only for diagnosing the condition, but also for distinguishing certain subtypes that could otherwise be difficult to identify [17, 18, 19, 20, 21, 22, 23, 24]. BRAF-mutated melanoma is mostly associated with superficial spreading melanoma, younger patients, and non-CSD skin, whereas NRAS mutational melanoma is a nodular subtype associated with CSD skin [20, 25].
Generally, NRAS mutations are independent of BRAF mutations, but dual expression has been reported [25]. The association of NRAS mutations with the degree of solar elastosis suggests that NRAS is closely related to the mutations induced by UV irradiation. Previous studies showed that NRAS mutation is also associated with decreased immune responses in peritumoral melanoma tissue and a more advanced tumor stage [26]. However, the prognostic value of NRAS mutation is still controversial, especially in early-stage melanoma.
1.1 Histopathological assessment of melanoma
At present, the histopathological examination of melanoma is based on the current WHO classification and the College of American Pathologists (CAP) guidelines [3]. Such criteria as tumor type, ulceration, peritumoral lymphocytes, Clark invasion level, Breslow invasion level, lymphovascular invasion, neurotropism, regression, and mitotic activity are routinely assessed. In addition, the excision lines and distance from the tumor are recorded. The pathological tumor node metastasis (pTNM) staging is determined on the basis of histopathological assessment. Table 1 summarizes the histopathological characteristics for assessing invasive melanoma.
| Characteristics | |
| Tumor site | Head and neck, arms, back, trunk, limb |
| Tumor size | |
| Histological type, Invasive melanoma | Invasive melanoma Superficial spreading melanoma (low-cumulative sun damage (CSD) melanoma) Lentigo maligna melanoma Desmoplastic melanoma Pure desmoplastic melanoma Mixed desmoplastic melanoma Acral melanoma Melanoma arising in a blue nevus (blue nevus-like melanoma) Melanoma arising in a giant congenital nevus Spitz melanoma (malignant Spitz tumor) |
| Ulceration | Present/Absent |
| Tumor Regression | Not identified Present, involving less than 75% of lesion Present, involving 75% or more of lesion |
| Maximum Tumor (Breslow) Thickness | mm |
| Anatomic (Clark) Level | Clark I-V level |
| Mitotical activity | Mitoses/mm2 |
| Solar elastosis | 0–3 |
| Microsatellite(s) | Present/Absent |
| Lymphovascular Invasion | Present/Absent |
| Neurotropism | Present/Absent |
| Tumor-Infiltrating Lymphocytes | The lymphocyte distribution score 0 = absence of lymphocytes within the tissue, 1 = presence of lymphocytes occupying <25% of the tissue, 2 = presence of lymphocytes occupying 25 to 50% of the tissue, and 3 = presence of lymphocytes occupying >50% of tissue |
| Margins | Distance from Invasive Melanoma to Peripheral Margin, mm Distance from Invasive Melanoma to Deep Margin, mmm |
| Regional lymph nodes status | Total Number of Lymph Nodes Size of Largest Nodal Metastatic Deposit, mm Extranodal involvement Total Number of Lymph Nodes with Tumor Sentinel Lymph Nodes with Tumor |
| Distant metastasis | Not identified Site |
| pTNM |
Table 1.
Since Breslow thickness is of particular importance for TNM staging, digital slide analysis could provide better evidence for the measurement of invasions, especially in borderline cases. During recent years, digital pathology has been extensively used not only in research but also in clinical practice. Slide digitalization, scanning, and analysis by artificial intelligence have been suggested as a comprehensive tool to help pathologists construct a final report [27].
Figure 1 shows superficial spreading melanoma. The slide was stained with hematoxylin and eosin, magnification ×100. The tumor cells are located in the epidermis and papillary dermis, with moderate cellular pleomorphism, epidermotropism, and asymmetry. There is prominent peritumoral lymphocyte infiltration.
Figure 1.
Representative photomicrograph demonstrated superficial spreading melanoma. Hematoxylin-eosin staining method, magnification: ×100, and scale bar: 20 μm.
Melanomas with an amelanotic appearance are more difficult to diagnose. Immunohistochemical staining positive for S100, SOX-10, HMB-45, Melan-A, Mart-1, and tyrosinase supports a diagnosis of melanoma [3].
Some melanomas, especially if regressed and metastatic, can cease to express HMB-45, Melan-A, and tyrosinase. In such cases, the immunohistochemical assessment of melanoma is straightforward; usually, only S-100 and vimentin expression is characteristic.
Figure 2A demonstrates S-100 expression in melanoma immunohistochemically. The arrow indicates positively stained cells. Note cytoplasmic biomarker expression. Figure 2B demonstrates SOX-10 expression in melanoma tissue immunohistochemically. The arrow indicates positively stained cells. Note the positive nuclear staining of melanoma cells.
Figure 2.
Representative photomicrograph of biomarker expression melanoma. A. S-100, B. SOX-10. Immunohistochemical staining method, magnification: ×200, and scale bar: 50 μm.
Recently, it has been shown that p16 expression in melanoma is significantly lower than nevus [28]. PRAME has also been demonstrated as an immunohistochemical marker to aid the diagnosis of malignant melanoma [29].
1.2 Artificial intelligence in the histopathological assessment of melanoma
Artificial intelligence (AI) and its subdisciplines of machine learning (ML) and deep learning (DL) are emerging as key technologies in healthcare with the potential to change lives and improve patient outcomes in many areas of medicine. While there is considerable promise for AI technologies in health, there are challenges ahead. These include recognition that it will be extremely difficult for AI to achieve full automation in the diagnostic/clinical pathway. Most efforts to date have focused on the development of neural network architectures to enhance the performance of different computational pathology tasks. U-Net has been used in several applications.
Recently, a deep learning network called MVPNet—multiviewing path deep learning neural networks for magnification invariant diagnosis in breast cancer—has been proposed for the digital analysis of breast cancer. MVPNet has significantly fewer parameters than standard deep learning models and combines local and global features.
During the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable patients to be selected and stratified for treatment.
In the global market, there is a high demand for digital pathology and artificial intelligence software for consultations and automated data analysis. Recently, the Food and Drug Administration (FDA) approved the first digital pathology software for automated prostate cancer assessment.
The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence and machine learning tools in digital pathology, which enable subvisual morphometric phenotypes to be mined and could ultimately improve patient management [30].
1.3 Tumor-infiltrating lymphocytes for stratifying the risk of melanoma progression
Tumor-infiltrating lymphocytes (TILs) are considered a manifestation of the host immune response to the tumor [13, 14, 15].
Cell membrane-bound antigens different from those of normal cells are characteristic of tumor cells. These antigens are recognized as nonself by antigen-presenting cells, with subsequent activation of cellular and humoral immune responses. The key cells for cytotoxic immune responses are CD4, CD8, and NK cells; for humoral responses, they are B lymphocytes and plasma cells. However, a tumor can escape immune surveillance by unmasking its antigens and inducing apoptosis in the immune cells. The key characteristic of tumor immunity is the presence of peritumoral and intratumoral inflammatory cells. Tumor-infiltrating lymphocytes (TILs) arise from different inflammatory cells, mainly CD4 and CD8 T cells, plus CD20 B lymphocytes and NK cells. These cells have been extensively described in antitumor immunity. T-regulatory lymphocytes, which form the key cell population of peritumoral and intratumoral lymphocytes, have immunoregulatory features. They suppress the immune response and commonly express FOXP3, CD4, and CD25 [13, 14, 15].
It has been shown that peritumoral lymphocyte infiltration (TIL) is valuable for melanoma prognosis. It is also closely associated with tumor metastasis to lymph nodes. Patients with increased TIL infiltrate have a better prognosis [13]. Furthermore, increased TIL infiltration is a sign of longer progression-free survival and overall survival, and a lower mortality rate [31].
However, American Joint Committee for Cancer (AJCC) manuals have not included the assessment of TIL for tumor staging and prognosis, and some pathology guidelines do not require peritumoral lymphocyte infiltration to be assessed [3]. The College of American Pathologists (CAP) and the Royal College of Pathologists of Australasia (RCPA) protocols suggest that peritumoral lymphocyte infiltration be assessed as brisk and nonbrisk infiltration. The association of TIL with an improved prognosis for melanoma remains controversial [32, 33, 34]. Previous studies have shown that an increased TIL infiltrate is associated with more favorable survival outcomes [13, 30, 31].
A recent study showed that melanoma patients with high TIL grade had significantly better progression-free survival than patients with low TIL grade [15]. The authors recommend incorporating the assessment of TIL into a scoring system, for example from 0 to 3, by estimating the percentage cellular infiltration of the tissue. The scoring system was defined as follows: 0 = absence of lymphocytes from the tissue, 1 = lymphocytes occupying <25% of the tissue, 2 = lymphocytes occupying 25–50% of the tissue, and 3 = lymphocytes occupying >50% of tissue. Low TIL infiltration was defined as scores of 0 and 1. High TIL infiltration was defined as scores of 2 and 3 [15]. This scoring system correlated significantly with progression-free survival and showed perfect concordance among pathologists; therefore, it could be recommended for routine clinical practice.
1.4 Assessment of BRAF gene mutation for stratifying the risk of melanoma progression
The BRAF gene is located on the seventh chromosome and encodes BRAF protein, one of the signaling kinases in the MAPK pathway. BRAF mutations are the most common genetic alterations in cutaneous melanoma. The prevalence of BRAF mutations among the different melanoma subtypes and populations ranges from 40% to 60% of cases [16, 17, 18, 19, 25]. BRAF mutations lead to the constitutive activation of the MAPK pathway. The most common BRAF mutation (80% of all alterations in the gene) is V600E [20]. V600K and V600R mutations are other examples [21].
Previous studies have shown that the BRAF V600E mutation is associated with the superficial spreading melanoma subtype, solar elastosis, younger patients, and melanoma localization on the extremities and back. In contrast, BRAF V600K mutations are correlated with skin sites with high CSD, such as the head and neck, and with older patients [14, 15, 16, 17, 18, 19, 35, 36, 37, 38, 39, 40].
Recently, whole-genome sequencing of benign melanocytic nevi revealed BRAF mutations in addition to NRAS mutations, the mutational load being positively correlated with UV exposure. The mutational loads in congenital nevi were lower [23].
A recent study revealed associations between BRAF V600 mutational status and younger patient age, Clark invasion level, Breslow thickness, lymphovascular invasion, female gender, and TIL [15].
1.5 Assessment of NRAS gene mutation for stratifying the risk of melanoma progression
The importance of NRAS mutations for the progression of melanoma is controversial. Some studies have shown associations between NRAS mutation and melanoma prognosis, while others found that NRAS mutations have no value for assessing the prognosis [3, 11, 41, 42].
The RAS gene family includes genes that encode the G proteins responsible for cell growth and cell cycle regulation. Three major members of the RAS gene family are NRAS, KRAS, and HRAS. NRAS-mutant melanomas often have dysregulated cell cycles, characterized by the upregulation of cyclin D1 and loss of the tumor suppressor p16INK4A [43].
The NRAS gene is most frequently mutated at hotspots in exon 2 (codons 12 and 13) and exon 3 (codon 61) [42, 44, 45, 46, 47]. Mutations of NRAS have previously been associated with the nodular subtype of the primary tumor and localization in sun-damaged skin [45].
Some studies have shown that NRAS mutation is associated with a favorable prognosis [46]. In contrast, others have demonstrated that this mutation is associated with a worse prognosis [48, 49], and some found no significant association at all between NRAS mutation and a prognosis of melanoma [45, 50, 51].
Recent evidence showed that in up to 20–30% of cases, NRAS mutations coexisted with BRAF mutations. Patients with both BRAF and NRAS mutations had worse prognoses than those with BRAF mutant melanoma alone [25, 26]. Since the prognosis for co-mutations is worse, routine NRAS assessment of all the primary melanoma cases would seem to be beneficial.
The assessment of NRAS mutation in melanoma, especially in BRAF-wild-type melanoma, is beneficial since targeted treatment is considered for NRAS mutant melanoma [52]. Immune checkpoint inhibitors (anti-CTLA4 and/or anti-PD1) are the standard treatment in these cases. However, a recent clinical trial also showed promising results from targeted treatments of PI3K-AKT-mTOR, MEK, and CDK4/6.
Advertisement
2. Conclusion
In recent years, the direction of personalized medicine, which is based on disease-specific targeting therapy, along with the early diagnosis of tumors and identification of high-risk individuals, has developed rapidly around the world.
The gold standard for melanoma diagnosis is histopathological investigation and routine evaluation of, e.g., tumor type and tumor invasiveness. Histopathological slide digitalization seems to be beneficial for standardizing the assessment of histopathological characteristics. In addition, the assessment of peritumoral lymphocyte infiltration and BRAF and NRAS mutation status in early-stage melanoma has proved to be of significant value for the risk stratification of disease progression and for personalized treatment.
The assessment of BRAF and NRAS mutations in melanomas is important not only for personalized targeting treatment, but also for prognosis and surveillance strategy. BRAF and NRAS mutations correlate with primary tumor type and disease stage. NRAS mutant melanoma has a significantly worse prognosis than BRAF mutant melanoma, and an active surveillance strategy should be applied to patients with this condition.
Advertisement
Acknowledgments
The study was supported by project “Strengthening of the capacity of doctoral studies at the University of Latvia within the framework of the new doctoral model” identification No. 8.2.2.0/20/I/006. The assistance of BioMedES UK (www.biomedes.biz) in the final drafting of this chapter is acknowledged.
References
- 1.
Yang K, ASW O, Slominski RM, Brożyna AA, Slominski AT. Current molecular markers of melanoma and treatment target. International Journal of Molecular Sciences. 2020; 16 :3535 - 2.
Forsea AM. Melanoma epidemiology and early detection in Europe: Diversity and disparities. Dermatology Practical & Conceptual. 2020; 10 (3):e2020033 - 3.
Scolyer R, Massi D, Willemze R, Elder DE. WHO Classification of Skin Tumours. WHO, International Agency for Research on Cancer (IARC); 2018 - 4.
Shellenberger R, Nabhan M. Kakaraparthi S: Melanoma screening: A plan for improving early detection. Annals of Medicine. 2016; 48 :142-148 - 5.
Mandalà M, Massi D. Tissue prognostic biomarkers in primary cutaneous melanoma. Virchows Archiv. 2014; 464 (3):265-281 - 6.
Elder DE, Bastian BC, Cree IA, Massi D, Scolyer RA. The 2018 World Health Organization classification of cutaneous, mucosal, and uveal melanoma: Detailed analysis of 9 distinct subtypes defined by their evolutionary pathway. Archives of Pathology & Laboratory Medicine. 2020; 144 :500-522 - 7.
Fong L. Small EJ anti-cytotoxic T-lymphocyte Antigen-4 antibody: The first in an emerging class of immunomodulatory antibodies for cancer treatment. Journal of Clinical Oncology. 2008; 26 :5275-5283 - 8.
Hodi FS, O’Day SJ, McDermott DF, Weber RW, Sosman JA, Haanen JB, et al. Improved survival with Ipilimumab in patients with metastatic melanoma. The New England Journal of Medicine. 2010; 363 :711-723 - 9.
Topalian SL, Sznol M, McDermott DF, Kluger HM, Carvajal RD, Sharfman WH, et al. Survival, durable tumor remission, and Long-term safety in patients with advanced melanoma receiving Nivolumab. Journal of Clinical Oncology. 2014; 32 :1020-1030 - 10.
Dummer R, Ascierto PA, Gogas HJ, Arance A, Mandala M, Liszkay G, et al. Encorafenib plus Binimetinib versus Vemurafenib or Encorafenib in patients with Braf-mutant melanoma (Columbus): A multicentre, open-label, randomised phase 3 trial. The Lancet Oncology. 2018; 19 :603-615 - 11.
Bastian BC. The molecular pathology of melanoma: An integrated taxonomy of melanocytic neoplasia. Annual Review of Pathology. 2014; 9 :239-271 - 12.
Sorokins V, Tilgase A, Olmane E, Isajevs S, Zablocka T, Rasa A, et al. A progressive stage IIIB melanoma treated with oncolytic ECHO-7 virus: A case report. SAGE Open Medical Case Reports. 2020; 8 [eCollection] - 13.
Park CK, Kim SK. Clinicopathological significance of intratumoral and peritumoral lymphocytes and lymphocyte score based on the histologic subtypes of cutaneous melanoma. Oncotarget. 2017; 8 (9):14759-14769 - 14.
Maibach F, Sadozai H, Morteza S, Jafari S, Hunger RE, Schenk M. Tumor-infiltrating lymphocytes and their prognostic value in cutaneous melanoma. Frontiers in Immunology. 2020; 11 :2105 - 15.
Zablocka T, Nikolajeva A, Kreismane M, Pjanova D, Isajevs S. Addressing the importance of melanoma tumor-infiltrating lymphocytes in disease progression and clinicopathological characteristics. Molecular and Clinical Oncology. 2021; 15 (6):255 - 16.
Network CGA. Genomic classification of cutaneous melanoma. Cell. 2015; 161 :1681-1696 - 17.
Melis C, Rogiers A, Bechter O, van den Oord JJ. Molecular genetic and immunotherapeutic targets in metastatic melanoma. Virchows Archiv. 2017; 471 :281-293 - 18.
Pracht M, Mogha A, Lespagnol A, Fautrel A, Mouchet N, Le Gall F, et al. Prognostic and predictive values of oncogenic Braf, Nras, C-kit and Mitf in cutaneous and mucous melanoma. Journal of the European Academy of Dermatology and Venereology. 2015; 29 :1530-1538 - 19.
Ny L, Hernberg M, Nyakas M, Koivunen J, Oddershede L, Yoon M, et al. BRAF mutational status as a prognostic marker for survival in malignant melanoma: A systematic review and meta-analysis. Acta Oncologica. 2020; 59 (7):833-844 - 20.
Eigentler T, Assi Z, Hassel JC, Heinzerling L, Starz H, Berneburg M, et al. Which melanoma patient carries a BRAF-mutation? A comparison of predictive models. Oncotarget. 2016; 7 (24):36130-36137 - 21.
Davies H, Bignell GR, Cox C, Stephens P, Edkins S, Clegg S, et al. Mutations of the Braf gene in human cancer. Nature. 2002; 417 :949-954 - 22.
Long GV, Menzies AM, Nagrial AM, Haydu LE, Hamilton AL, Mann GJ, et al. Prognostic and Clinicopathologic associations of oncogenic Braf in metastatic melanoma. Journal of Clinical Oncology. 2011; 29 :1239-1246 - 23.
Menzies AM, Haydu LE, Visintin L, Carlino MS, Howle JR, Thompson JF, et al. Distinguishing Clinicopathologic features of patients with V600e and V600k Braf-mutant metastatic melanoma. Clinical Cancer Research. 2012; 18 :3242-3249 - 24.
Colebatch AJ, Ferguson P, Newell F, Kazakoff SH, Witkowski T, Dobrovic A, et al. Molecular genomic profiling of melanocytic nevi. Journal of Investigative Dermatology. 2019; 139 :1762-1768 - 25.
Rose EE, Egyhazi S, Omholt K, Mansson-Brahme E, Platz A, Hansson J, et al. NRAS and BRAF mutations in melanoma tumours in relation to clinical characteristics: A study based on mutation screening by pyrosequencing. Melanoma Research. 2006; 6 (6):471-478 - 26.
Chiappetta C, Proietti I, Soccodato V, Puggioni C, Zaralli R, Pacini L, et al. Braf and Nras mutations are heterogeneous and not mutually exclusive in nodular melanoma. Applied Immunohistochemistry & Molecular Morphology. 2015; 23 :172-177 - 27.
De Logu F, Ugolini F, Maio V, Simi S, Cossu A, Massi D, et al. Recognition of cutaneous melanoma on digitized histopathological slides via artificial intelligence algorithm. Frontiers in Oncology. 2020; 10 :1559 - 28.
Milman T, Zhang Q , Ang S, Elder D, Ida CM, Salomao DR, et al. Conjunctival nevi and melanoma: Multiparametric immunohistochemical analysis, including p16, SOX10, HMB45, and Ki-67. Human Pathology. 2020; 103 :107-119 - 29.
Lezcano C, Jungbluth AA, Busam KJ. PRAME immunohistochemistry as an ancillary test for the assessment of melanocytic lesions. Surgery Pathology Clinical. 2021; 14 (2):165-175 - 30.
Hekler A, Utikal JS, Enk AH, Berking C, Klode J, Schadendorf D, et al. Pathologist-level classification of histopathological melanoma images with deep neural networks. European Journal of Cancer. 2019; 115 :79-83 - 31.
Azimi F, Scolyer RA, Rumcheva P, Moncrieff M, Murali R, McCarthy SW, et al. Tumor-infiltrating lymphocyte grade is an independent predictor of sentinel lymph node status and survival in patients with cutaneous melanoma. Journal of Clinical Oncology. 2012; 30 (21):2678-2683 - 32.
Taylor RC, Patel A, Panageas KS, Busam KJ, Brady MS. Tumor-infiltrating lymphocytes predict sentinel lymph node positivity in patients with cutaneous melanoma. Journal of Clinical Oncology. 2007; 25 (7):869-875 - 33.
Burton AL, Roach BA, Mays MP, Chen AF, Ginter BA, Vierling AM, et al. Prognostic significance of tumor infiltrating lymphocytes in melanoma. The American Surgeon. 2011; 77 (2):188-192 - 34.
Mandala M, Imberti GL, Piazzalunga D, Belfiglio M, Labianca R, Barberis M, et al. Clinical and histopathological risk factors to predict sentinel lymph node positivity, disease-free and overall survival in clinical stages I-II AJCC skin melanoma: Outcome analysis from a single-institution prospectively collected database. European Journal of Cancer. 2009; 45 (14):2537-2545 - 35.
Tas F, Erturk K. Clinical and prognostic significance of BRAF V600E mutation in non-metastatic cutaneous melanoma patients. Neoplasma. 2019; 66 (4):631-636 - 36.
Bezić J, Kuret S, Vrbičić B, Smolić J, Borić I, Škifić I, et al. Clinicopathological characteristics of BRAF V600E mutated melanomas in the Dalmatian region of Croatia. Acta Dermatovenerologica Croatica. 2019; 27 (4):225-230 - 37.
Spathis A, Katoulis AC, Damaskou V, Liakou AI, Kottaridi C, Leventakou D, et al. BRAF mutation status in primary, recurrent, and metastatic malignant melanoma and its relation to histopathological parameters. Dermatology Practical & Conceptual. 2019; 9 (1):54-62 - 38.
Kim SY, Kim SN, Hahn HJ, Lee YW, Choe YB, Ahn KJ. Meta analysis of BRAF mutations and clinicopathologic characteristics in primary melanoma. Journal of the American Academy of Dermatology. 2015; 72 (6):1036-1046.e2 - 39.
Estrozi B, Machado J, Rodriguez R, Bacchi CE. Clinicopathologic findings and BRAF mutation in cutaneous melanoma in young adults. Applied Immunohistochemistry & Molecular Morphology. 2014; 22 (1):57-64 - 40.
Aksenenko MB, Kirichenko AK, Ruksha TG. Russian study of morphological prognostic factors characterization in BRAF-mutant cutaneous melanoma. Pathology, Research and Practice. 2015; 211 (7):521-527 - 41.
Thomas NE, Edmiston SN, Alexander A, Groben PA, Parrish E, Kricker A, et al. Association between Nras and Braf mutational status and melanoma-specific survival among patients with higher-risk primary melanoma. JAMA Oncology. 2015; 1 :359-368 - 42.
Lee JH, Choi JW, Kim YS. Frequencies of Braf and Nras mutations are different in histological types and sites of origin of cutaneous melanoma: A meta-analysis. The British Journal of Dermatology. 2011; 164 :776-784 - 43.
Randic T, Kozar I, Margue C, Utikal J, Kreis S. NRAS mutant melanoma: Towards better therapies. Cancer Treatment Reviews. 2021; 99 :102238 - 44.
Platz A, Egyhazi S, Ringborg U, Hansson J. Human cutaneous melanoma; a review of NRAS and BRAF mutation frequencies in relation to histogenetic subclass and body site. Molecular Oncology. 2008; 1 :395-405 - 45.
Jakob JA, Bassett RL Jr, Ng CS, Curry JL, Joseph RW, Alvarado GC, et al. NRAS mutation status is an independent prognostic factor in metastatic melanoma. Cancer. 2012; 118 (16):4014-4023 - 46.
Devitt B, Liu W, Salemi R, Wolfe R, Kelly J, Tzen CY, et al. Clinical outcome and pathological features associated with NRAS mutation in cutaneous melanoma. Pigment Cell & Melanoma Research. 2011; 24 (4):666-672 - 47.
Ugurel S, Thirumaran RK, Bloethner S, Gast A, Sucker A, Mueller-Berghaus J, et al. B-RAF and N-RAS mutations are preserved during short time in vitro propagation and differentially impact prognosis. PLoS One. 2007; 2 (2):e236 - 48.
Heppt MV, Siepmann T, Engel J, Schubert-Fritschle G, Eckel R, Mirlach L, et al. Prognostic significance of BRAF and NRAS mutations in melanoma: A German study from routine care. BMC Cancer. 2017; 17 (1):536 - 49.
Ellerhorst JA, Greene VR, Ekmekcioglu S, Warneke CL, Johnson MM, Cooke CP, et al. Clinical correlates of NRAS and BRAF mutations in primary human melanoma. Clinical Cancer Research. 2011; 17 (2):229-235 - 50.
Schlaak M, Bajah A, Podewski T, Kreuzberg N, von Bartenwerffer W, Wardelmann E, et al. Assessment of clinical parameters associated with mutational status in metastatic malignant melanoma: A single-Centre investigation of 141 patients. The British Journal of Dermatology. 2013; 168 (4):708-716 - 51.
Bucheit AD, Syklawer E, Jakob JA, Bassett RL Jr, Curry JL, Gershenwald JE, et al. Clinical characteristics and outcomes with specific BRAF and NRAS mutations in patients with metastatic melanoma. Cancer. 2013; 119 (21):3821-3829 - 52.
Grimaldi AM, Simeone E, Festino L, Vanella V, Strudel M, Ascierto PA. MEK inhibitors in the treatment of metastatic melanoma and solid tumors. American Journal of Clinical Dermatology. 2017; 18 (6):745-754