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Article

Higher Mutation Burden in High Proliferation Compartments of Heterogeneous Melanoma Tumors

by
Tomasz M. Grzywa
1,2,3,†,
Agnieszka A. Koppolu
4,5,†,
Wiktor Paskal
1,†,
Klaudia Klicka
1,2,
Małgorzata Rydzanicz
4,
Jarosław Wejman
6,
Rafał Płoski
4 and
Paweł K. Włodarski
1,*
1
Center for Preclinical Research, The Department of Methodology, Medical University of Warsaw, 1B Banacha Str., 02-097 Warsaw, Poland
2
Doctoral School, Medical University of Warsaw, 61 Zwirki and Wigury Str., 02-091 Warsaw, Poland
3
Department of Immunology, Medical University of Warsaw, 5 Nielubowicza Str., 02-097 Warsaw, Poland
4
Department of Medical Genetics, Medical University of Warsaw, 3C Pawinskiego Str., 02-106 Warsaw, Poland
5
Postgraduate School of Molecular Medicine, Medical University of Warsaw, 02-091 Warsaw, Poland
6
Department of Pathology, Medical Center of Postgraduate Education, 00-416 Warsaw, Poland
*
Author to whom correspondence should be addressed.
These authors contributed equally.
Int. J. Mol. Sci. 2021, 22(8), 3886; https://doi.org/10.3390/ijms22083886
Submission received: 28 February 2021 / Revised: 2 April 2021 / Accepted: 7 April 2021 / Published: 9 April 2021
(This article belongs to the Special Issue Precision Oncology in Melanoma Progression)

Abstract

:
Melanoma tumors are the most heterogeneous of all tumor types. Tumor heterogeneity results in difficulties in diagnosis and is a frequent cause of failure in treatment. Novel techniques enable accurate examination of the tumor cells, considering their heterogeneity. The study aimed to determine the somatic variations among high and low proliferating compartments of melanoma tumors. In this study, 12 archival formalin-fixed paraffin-embedded samples of previously untreated primary cutaneous melanoma were stained with Ki-67 antibody. High and low proliferating compartments from four melanoma tumors were dissected using laser-capture microdissection. DNA was isolated and analyzed quantitatively and qualitatively. Libraries for amplicon-based next-generation sequencing (NGS) were prepared using NEBNext Direct Cancer HotSpot Panel. NGS detected 206 variants in 42 genes in melanoma samples. Most of them were located within exons (135, 66%) and were predominantly non-synonymous single nucleotide variants (99, 73.3%). The analysis showed significant differences in mutational profiles between high and low proliferation compartments of melanoma tumors. Moreover, a significantly higher percentage of variants were detected only in high proliferation compartments (39%) compared to low proliferation regions (16%, p < 0.05). Our results suggest a significant functional role of genetic heterogeneity in melanoma.

1. Introduction

Melanoma is a neoplasm arising from melanocytes, neural crest-derived pigment cells [1]. It is the deadliest type of skin tumor with highly metastatic capabilities and aggressive behavior. Incidence rates continue to increase. Melanoma is the fifth most common skin neoplasm type, with 95,710 new cases in the United States in 2020 [2]. While early-stage melanomas can be cured by surgical excision, advanced metastatic melanoma is associated with short overall survival. Because of the significant development of new therapies, such as checkpoint inhibitors, immunotherapies, and targeted therapies, the mortality rates decrease [2]. Nonetheless, the number of total deaths is estimated for nearly 10,000 cases each year [3].
Melanoma is characterized by high intratumor heterogeneity, i.e., the existence of multiple populations of neoplastic cells with distinct features within one tumor. Intratumor heterogeneity occurs on different levels such as the genome, transcriptome, proteome, and epigenome [4]. The main cause of tumor genetic heterogeneity is the accumulation of mutations by neoplastic cells caused by genomic instability induced by environmental factors, including ultraviolet (UV) radiation or insufficient DNA damage response [4,5]. That genetic and phenotypic variation between cells leads to the selection of new subclones that are resistant to applied therapy [6].
Tumor heterogeneity is one of the causes of therapy failure in patients [4]. However, currently, most of the standard diagnostic procedures underestimate actual clonal tumor composition. Recently, next-generation sequencing (NGS) emerged as a promising tool to determine the profile of mutations and quantify mutational burden [7,8]. Thus, NGS is useful in selecting the appropriate personalized, targeted therapy for patients with melanoma [7,8]. It was demonstrated that the quantity and quality of DNA from archival formalin-fixed and paraffin-embedded (FFPE) tissue is suitable for NGS analysis [9,10,11].
In this study, we used the NGS method to detect genetic variants in a panel of cancer-related genes in compartments of high and low proliferation within primary cutaneous melanoma tumors.

2. Results

Samples of 12 previously untreated primary cutaneous melanoma were included in the study (Figure 1). Resected tumors were formalin-fixed and paraffin-embedded according to the standard protocol. The samples were cut on a microtome and stained with hematoxylin and eosin for the pathologist examination according to the seventh edition of AJCC Melanoma Staging and Classification. Subsequent slices were stained with anti-Ki67 antibodies to determine proliferation patterns within a tumor. Melanoma tumors obtained from four patients exhibited significant heterogeneity of Ki-67 staining with both high and low proliferation compartments of tumor cells and were subjected to further studies (Table 1). Tumors resected from eight patients exhibited homogeneously high intensity of Ki-67 staining and were excluded from the study (Supplementary Table S1).
Subsequent sections of melanoma tumors were stained with hematoxylin and subjected to laser-capture microdissection (LCM)-aided dissection of two regions of each tumor tissue—exhibiting high proliferation (HP) and low proliferation (LP). The pattern of proliferation was determined based on the density of Ki-67-positive cells revealed by immunohistochemical staining with anti-Ki-67 antibodies (Figure 2). HP and LP compartments were defined as areas of tumor cells with higher (HP) or lower (LP) density of Ki-67-positive cells compared to mean density for the whole tumor slice. Regions of more than 10% Ki-67-positive tumor cells were considered HP (preferably and mainly areas of >50% positive staining were dissected), and compartments of less than 10% Ki-67-positive cells were considered LP.
Next-generation sequencing (NGS) of a commercial panel of cancer-related genes (NEBNext Direct® Cancer HotSpot Panel Table 2) was used to detect mutations (single nucleotide variants (SNVs) and small insertion/deletions). This panel includes oncogenes and tumor suppressor genes with a well-established role in melanoma that regulate hallmarks of cancer, including sustaining proliferative potential and evading growth suppression [12,13,14].
We detected 206 variants in a total number of 42 genes. Most of them (135, 66%) were located within exons. Most variants within exons were non-synonymous single nucleotide variants (SNVs) (99, 73.3%). Synonymous SNVs (25, 18.5%), stopgain SNVs (4, 3.0%), frameshift deletions (3, 2.2%), non-synonymous multi-nucleotide variants (MNVs) (2, 1.4%), frameshift insertions (1, 0.7%), and non-synonymous deletions (1, 0.7%) within the exon were detected in a smaller number. Intronic variants (71, 34%) were predominantly SNVs (55, 77.5%), followed by deletions (11, 15.5%) and insertions (2, 2.8%) as well as untranslated region (UTR) variants (3, 4.2%). A total of 35 (17%) of all variants were classified as UV-signature mutations (C>T, CC>TT [15]).
We found 16 variants in 15 genes in the tumor tissue of Patient 1 (Table 3). Six of them were detected only in HP compartments, two only in LP compartments, and eight variants were detected in both HP and LP regions (shared variants).
We detected 84 variants within 32 genes in the tumor tissue of Patient 2 (Table 4). A total of 39 variants were found only in the HP compartment, 29 were detected only in the LP compartment, and 16 variants were detected in both compartments.
We detected 54 variants within 25 genes in the tumor tissue of Patient 3 (Table 5). A total of 25 variants were found only in the HP compartment, 5 were detected only in the LP compartment, and 24 variants were detected in both compartments.
We detected 52 variants within 25 genes in the tumor tissue of Patient 5 (Table 6). A total of 14 variants were found only in the HP compartment, 3 were detected only in the LP compartment, and 35 variants were detected in both compartments.
Analysis of variants detected in selected compartments revealed higher numbers of variants in HP compared to LP compartments (Figure 3a). Importantly, the percentage of variants detected only in HP compartments was significantly higher than of those detected only in LP (Figure 3b). Numbers and percentages of variants detected only in HP compartments were similar to those detected in both tumor sections (shared variants). Detailed analysis of variant allele frequency (VAF) of each patient revealed significantly higher VAFs in HP compartments than LP (Figure 3c).
Most of the mutations were detected only in HP compartments (Figure 4). Nonetheless, shared variants detected in both HP and LP also constituted a substantial percentage of variants. In contrast, variants detected only in LP compartments were rare.
Variants detected in HP compartments (Figure 5a) were commonly absent in LP (Figure 5b). Nonetheless, shared variants that were detected in both HP and LP constitute a substantial percentage of mutations (Figure 4). Shared variants exhibited either similar VAF (Figure 5c) or VAF was higher in HP compared to LP compartment (Figure 5d).
Our report demonstrates a higher mutational burden in high proliferating compartments of melanoma tumors compared to low proliferating ones.

3. Discussion

The development of targeted therapies for melanoma significantly prolonged the overall survival of patients with malignant melanoma [2]. However, a substantial group of patients either do not respond to the therapy or develop acquired resistance and eventually progress [16]. Genetic intratumor heterogeneity is one of the major obstacles to the successful clinical outcome of patients treated with targeted therapies [17].
Melanoma tumors exhibit one of the highest numbers of clones within tumors from all types of neoplasms [18]. Tumor heterogeneity has relevant clinical implications [19], is associated with worsened prognosis [19], and is an important cause of resistance to cancer therapies [20]. In melanoma, tumor heterogeneity is caused by many factors [4], including high mutational load caused by UV radiation [21,22,23]. We found that 17% of variants detected within a panel of cancer-related genes had UV-signature, which was substantially lower compared to over 82% of mutation with UV-signature detected by whole-exome sequencing of melanoma tumors [21]. Nonetheless, in our analysis non-synonymous SNVs constituted the majority of detected variants (73.3%), which is consistent with the result whole-exome sequencing of melanoma tumors [21].
In our study, we reported that high proliferation compartments of melanoma tumors have a higher mutation load in genes with a crucial role in oncogenesis compared to low proliferation regions. So far, it was reported for breast cancer that a higher mutation burden is associated with higher proliferation ability and aggressive clinical features [24]. Likewise, in uveal melanoma higher mutation burden was observed in small tumors that exhibited higher proliferation rates [25]. However, it remained unknown whether tumor cells population with a high proliferation rate have a higher mutational load. Here, we demonstrated that HP compartments of melanoma tumors have higher numbers of mutations as well as higher VAFs compared to LP compartments. For instance, we observed numerous mutations in the Rb gene in HP compartments in three patients (5, 7, and 6 variants in Patients 2–4, respectively). On the contrary, Rb variants in LP were less common (2, 1, and 1 in Patients 2–4, respectively). Similarly, mutations in the TP53 gene were detected mostly in HP compartments (1, 3, 1, and 4 variants in Patients 1–4, respectively). Rb and TP53 are tumor suppressor genes that are critical targets of mutagenesis in melanoma [26,27].
Mutations in platelet-derived growth factor receptor α (PDGFRA) were detected only in the HP compartment or were shared in both compartments. PDGFRA is a proto-oncogene, and mutations within its gene are detected in about 5% of melanoma tumors [28,29]. Moreover, variants in the ERBB4 gene, a commonly mutated proto-oncogene in melanoma [30], were detected in higher numbers in HP compartments (1, 5, 2, and 1 variant in Patients 2–4, respectively) than in LP compartments (0, 4, 3, and 0 variant in Patients 2–4, respectively).
Importantly, we observed that low proliferation compartments of melanoma tumors have a different mutational profile compared to high proliferation regions. Within melanoma tumors, the slow-cycling cells exhibit increased resistance to therapies and may trigger a relapse of the disease [31]. Because of the high resistance of melanoma slow-cycling subpopulation to conventional as well as targeted therapies and their ability to reconstitute tumor mass after treatment, there is a need for a better understanding of their mutation profile, which may result in the development of novel, more effective targeted therapies [4,32].
The main limitation of our study was the low number of analyzed patients and a low number of sequenced genes. Moreover, we did not investigate the biological effects of these mutations in melanoma cells. Further studies are required to determine the mutational landscape of distinct regions of melanoma tumors with different features on the whole genome level to provide insights regarding the functional role of genetic heterogeneity of melanoma tumors. Our study suggests that proto-oncogenes and tumor suppressor genes are more commonly mutated in compartments of high proliferating melanoma cells, which may contribute to the accelerated growth.

4. Materials and Methods

4.1. Patients Tissue

The study was performed on archival formalin-fixed, paraffin-embedded (FFPE) primary cutaneous melanoma tumors originating from 12 patients from the Department of Pathology, Medical Center of Postgraduate Education, Warsaw, Poland. The clinical and histopathological data of patients are presented in Table 1 and Supplementary Table S1. The study was conducted in accordance with the Declaration of Helsinki. The study was approved by the Bioethical Committee Medical University of Warsaw (AKBE/301/2019). The detailed protocol of the study is presented in Figure 1.

4.2. Hematoxylin and Eosin Staining

Resected skin tumors were formalin-fixed and paraffin-embedded according to the standard protocol in the tissue processor. The FFPE samples were cut on a microtome and stained with hematoxylin and eosin according to the standard diagnostic protocol. Subsequently, they were examined by a board-certified pathologist and reanalyzed by the second pathologist (J.W.) according to the seventh edition of AJCC Melanoma Staging and Classification.

4.3. Immunohistochemistry Staining

For immunohistochemical staining, the samples were cut on a microtome (Leica, RM2055 model, Buffalo Grove, IL, USA) on 3 µm slices. In the next step, samples were deparaffinized and rehydrated using xylene and ethanol. To determine the compartments of high and low proliferation patterns, tumors were stained with anti-Ki-67 antibody (NB110-90592, NovusBio, Centennial, CO, USA) at final dilution 1:3200. To confirm the melanocytic origin of the neoplastic cells, samples were stained with anti-MITF antibody (PA538294, Thermofisher Scientific, Waltham, MA, USA) at final dilution 1:300. Immunohistochemistry staining was performed using EnVision™ FLEX DAB+ Substrate Chromogen System (Dako, Agilent, Santa Clara, CA, USA) according to the manufacturer’s protocol.

4.4. Preparation for Laser-Capture Microdissection (LCM)

All samples for LCM were cut with a microtome to 10 µm slices (Leica, RM2055) and were mounted on glass slides (SuperFrost Ultra Plus, Menzel Gläser, Thermofisher Scientific [33]) with a drop of DNAse/RNAse-free water. Next, samples were incubated in a fume hood at 56 °C for 1 h to increase adherence to slides. Mounted slices were hematoxylin stained according to the standard protocol in a set of alcohol solutions, xylene, and stain.

4.5. Laser-Capture Microdissection

Stained and dehydrated sections of tissues were subjected to LCM-aided dissection, as described before [33,34]. Two regions of each melanoma tissue were selected depending on the intensity of proliferation based on Ki-67 staining (areas with low and high proliferation). Melanomas that exhibited a homogeneous density of Ki-67-positive cells within tumors were excluded from the study. Tumors that had areas with different densities of Ki-67-positive cells (heterogeneous Ki-67 staining) were included for further examination. At least two researchers chose by consensus compartments exhibiting higher density (high proliferation, >20 mitoses in HPF, >10% Ki-67-positive cells, usually >50%) and lower density (low proliferation, <10% Ki-67-positive cells) of Ki-67-positive tumor cells compared to mean density Ki-67-positive cells of whole tumor tissue. Representative scans of high and low proliferation compartments are presented in Figure 2b and Figure 5a. The neoplastic character of dissected tissues was assessed based on pathomorphological features by a board-certified pathologist and confirmed by MITF staining. Subsequently, 5 µm2 of each region were marked to dissect with the LCM system (PALM Robo, Zeiss, Oberkochen, Germany). The conditions of LCP (Laser Catapulting Pressure) were as follows: LCP energy—82–92, LCP spot distance—25 μm, magnification—5×, tissue collected in 20 μL of Digestion Buffer (Norgen Biotek FFPE RNA/DNA Purification Plus Kit, Thorold, ON, Canada) in 500 μL sterile PCR-tube cap. Each LCM was preceded by optimization of LCP energy and spot distance to provide an effective dissection of marked areas. Caps were sealed back with tubes, centrifuged briefly, and placed on ice until further steps.

4.6. DNA Isolation and Quality Verification

In the next step, samples were digested with proteinase K for 48 h at 37 °C followed by DNA isolation using Norgen Biotek FFPE RNA/DNA Purification Plus Kit according to the manufacturer’s protocol (Cat. 54300). DNA was eluted with 15 µL of ultrapure H2O preheated to 90 °C. Quantity of DNA was measured using Qubit Fluorometer and Qubit™ dsDNA HS Assay Kit (ThermoFisher Scientific, Waltham, MA USA). DNA quality was verified using Bioanalyzer 2100 according to the manufacturer protocol.

4.7. Library Preparation and Next-Generation Sequencing

A total of 10 ng of isolated DNA was fragmented using Covaris M220 Focused ultrasonicator to obtain 200 bp fragment size. Libraries were prepared according to the protocol of NEBNext Direct® Cancer HotSpot Panel provided by the manufacturer. Accordingly, steps were as follows: denaturation and probe hybridization, 3′ blunting of DNA, dA-tailing, ligation of 3′ adaptor, 5′ blunting of DNA, ligation of 5′UMI adaptor, adaptor cleaving, and PCR amplification. The next-generation sequencing (NGS) was performed using Illumina HiSeq 1500cancer. All steps were conducted according to the manufacturer’s protocol. Reads within 50 cancer-related genes (Table 2) were aligned to the hg38 reference genome sequence. Integrative Genomics Viewer v.2.8 was used to visualize NGS results (IGV, http://software.broadinstitute.org/software/igv/, accessed on 15 January 2021). Pathogenicity of variants was determined with DANN [35].

4.8. Statistical Analysis and Data Presentation

Statistical analyses were conducted with GraphPad Prism 8.4.3 (GraphPad Software Inc., San Diego, CA, USA) using the repeated-measures ANOVA with Tukey’s post-hoc test and paired t-test. All values are represented as mean ± SD. A p-value of <0.05 was considered statistically significant. Figure 1 and Figure 4 were created with Biorender.com.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/ijms22083886/s1, Table S1: Main clinical and histopathological data of the eight patients excluded from the study.

Author Contributions

Conceptualization, T.M.G., W.P., and P.K.W.; methodology, T.M.G., A.A.K., W.P., K.K., M.R., R.P., and P.K.W.; software, T.M.G., A.A.K., W.P., M.R., and R.P.; validation, T.M.G., A.A.K., W.P., K.K., M.R., R.P., and P.K.W.; formal analysis, T.M.G. and K.K.; investigation, T.M.G., A.A.K., W.P., K.K., and M.R.; resources, T.M.G., A.A.K., W.P., M.R., J.W., R.P., and P.K.W.; data curation, T.M.G. and W.P.; writing—original draft preparation, T.M.G. and K.K.; writing—review and editing, T.M.G., A.A.K., W.P., K.K., M.R., J.W., R.P., and P.K.W.; visualization, T.M.G.; supervision, R.P. and P.K.W.; project administration, T.M.G. and W.P.; funding acquisition, W.P. All authors have read and agreed to the published version of the manuscript.

Funding

Research was supported by the grant from the Medical University of Warsaw: 1M15/NM3/16 (W.P.). The APC was funded by the Medical University of Warsaw.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Ethics Committee of the Medical University of Warsaw (AKBE/301/2019, 07.10.2019).

Informed Consent Statement

Patient consent was waived due to the retrospective character of the study and the anonymization of patients.

Data Availability Statement

Data is contained within the article or Supplementary Material.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. General overview of sample processing. (1) Preparation of archival formalin-fixed paraffin-embedded (FFPE) originated from primary cutaneous melanoma tumors. (2) Staining with hematoxylin and eosin (HE) for the pathologist examination. (3) Immunohistochemistry staining with anti-Ki67 antibodies to determine the compartments of high and low proliferation. (4) Hematoxylin staining and laser-capture microdissection. (5) The laser-capture microdissection of compartments of high and low proliferation based on Ki-67 staining. (6) Digestion with proteinase K and DNA isolation followed by DNA quantity and quality assessment. (7) Preparation of libraries for amplicon-based next-generation sequencing of a panel of cancer-related genes. (8) Targeted next-generation sequencing.
Figure 1. General overview of sample processing. (1) Preparation of archival formalin-fixed paraffin-embedded (FFPE) originated from primary cutaneous melanoma tumors. (2) Staining with hematoxylin and eosin (HE) for the pathologist examination. (3) Immunohistochemistry staining with anti-Ki67 antibodies to determine the compartments of high and low proliferation. (4) Hematoxylin staining and laser-capture microdissection. (5) The laser-capture microdissection of compartments of high and low proliferation based on Ki-67 staining. (6) Digestion with proteinase K and DNA isolation followed by DNA quantity and quality assessment. (7) Preparation of libraries for amplicon-based next-generation sequencing of a panel of cancer-related genes. (8) Targeted next-generation sequencing.
Ijms 22 03886 g001
Figure 2. Laser-capture microdissection of chosen compartments of tumor tissue. (a). Representative Ki-67 staining of melanoma tissue. Magnification 5×. (b). Representative Ki-67 staining of high proliferation (HP) and low proliferation (LP) compartments. HP and LP fragments were defined as a compartment of tumor tissue with a higher or lower density of Ki-67-positive cells compared to the mean density of whole tumor slices. Magnification 20×. (c) Scans of the samples before and after laser-capture microdissection (LCM). Magnification 5×.
Figure 2. Laser-capture microdissection of chosen compartments of tumor tissue. (a). Representative Ki-67 staining of melanoma tissue. Magnification 5×. (b). Representative Ki-67 staining of high proliferation (HP) and low proliferation (LP) compartments. HP and LP fragments were defined as a compartment of tumor tissue with a higher or lower density of Ki-67-positive cells compared to the mean density of whole tumor slices. Magnification 20×. (c) Scans of the samples before and after laser-capture microdissection (LCM). Magnification 5×.
Ijms 22 03886 g002
Figure 3. Higher mutation burden in high proliferation compartments. a,b. Number (a) and percentage (b) of variants detected only in high proliferation (HP) compartment, low proliferation (LP) compartment, and variants detected in both compartments (shared). p-value calculated using repeated-measures ANOVA with Tukey’s post-hoc test. (c) Variants’ allele frequency detected in high proliferation (HP) compartments compared to low proliferation (LP) compartments in each patient. p-value was calculated using paired t-test. * p < 0.05, **** p < 0.0001
Figure 3. Higher mutation burden in high proliferation compartments. a,b. Number (a) and percentage (b) of variants detected only in high proliferation (HP) compartment, low proliferation (LP) compartment, and variants detected in both compartments (shared). p-value calculated using repeated-measures ANOVA with Tukey’s post-hoc test. (c) Variants’ allele frequency detected in high proliferation (HP) compartments compared to low proliferation (LP) compartments in each patient. p-value was calculated using paired t-test. * p < 0.05, **** p < 0.0001
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Figure 4. Variants detected in compartments with higher proliferation constitute the majority of variants detected in melanoma tumors. Numbers and percentages of variants detected in high proliferation (HP), low proliferation (LP), and in both compartments (shared).
Figure 4. Variants detected in compartments with higher proliferation constitute the majority of variants detected in melanoma tumors. Numbers and percentages of variants detected in high proliferation (HP), low proliferation (LP), and in both compartments (shared).
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Figure 5. Representative molecular characteristics of patient 1. (a) Representative microphotographs of Ki-67 staining of chosen compartments of high proliferation (HP) and low proliferation (LP) of melanoma tissue. Magnification 20×. (b) p.Arg718Gln mutation in PDGRFA gene as a representative variant detected only in HP compartment. (c) p.Cys482Arg mutation in KDR gene as a representative variant detected in similar variant allele frequency (VAF) in both compartments. (d) p.Ser607Phe variant in BRAF gene as a representative variant detected in both compartments but with substantially higher VAF in HP (31%) than LP (4%) compartment.
Figure 5. Representative molecular characteristics of patient 1. (a) Representative microphotographs of Ki-67 staining of chosen compartments of high proliferation (HP) and low proliferation (LP) of melanoma tissue. Magnification 20×. (b) p.Arg718Gln mutation in PDGRFA gene as a representative variant detected only in HP compartment. (c) p.Cys482Arg mutation in KDR gene as a representative variant detected in similar variant allele frequency (VAF) in both compartments. (d) p.Ser607Phe variant in BRAF gene as a representative variant detected in both compartments but with substantially higher VAF in HP (31%) than LP (4%) compartment.
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Table 1. Main clinical and histopathological data of the four patients included in the study.
Table 1. Main clinical and histopathological data of the four patients included in the study.
PatientPatient 1
Mel001
Patient 2
Mel002
Patient 3
Mel010
Patient 4
Mel011
SexFMFF
Age78818276
Anatomical locationLeft cheekRight cheekLeft eyebrowLeft crus
Histological subtypeFusocellular NMFusocellular NMSSMLMM
TNMpT4bpT4bpT1bpT1a
ClarkVVIIII
Breslow8 mm7 mm0.9 mm0.29 mm
UlcerationYesYesYesNo
Mitotic index3–6/mm27/mm22/mm21/mm2
Lymphoid infiltrationYesYesBriskBrisk
Satellite tumorsIn subcutaneous fat tissueNoNoNo
Lymph nodesn.d.Clearn.d.n.d.
Ki-67HeterogeneousHeterogeneousHeterogeneousHeterogeneous
HPF—high power field, LMM—lentigo maligna melanoma, MF—mitotic figures, n.d.—no data, NM—nodular melanoma, SSM—superficial spreading melanoma.
Table 2. List of cancer-related genes covered by next-generation sequencing panel.
Table 2. List of cancer-related genes covered by next-generation sequencing panel.
NEBNext Direct® Cancer HotSpot Panel
ABL1, AKT1, ALK, APC, ATM, BRAF, CDH1, CDKN2A, CSF1R, CTNNB1, EGFR, ERBB2, ERBB4, EZH2, FBXW7, FGFR1, FGFR2, FGFR3, FLT3, GNA11, GNAQ, GNAS, HNF1A, HRAS, IDH1, IDH2, JAK2, JAK3, KDR, KIT, KRAS, MET, ML1, MPL, NOTCH1, NPM1, NRAS, PDGFRA, KIP3CA, PTEN, PTPN11, RB1, RET, SMAD4, SMARCB1, SMO, SRC, STK11, TP53, VHL
Table 3. Molecular characteristics of variants detected in tumor tissue of Patient 1.
Table 3. Molecular characteristics of variants detected in tumor tissue of Patient 1.
GeneType of AlterationVariantAmino Acid ChangePathogenicityVAF HPVAF LP
Shared variants
KDRIntronic SNVc.*27T>C--100%100%
KDRNon-synonymous MNVc.3433GG>AAp.Gly1145Lys0.988923%23%
KDRNon-synonymous SNVc.2699A>Gp.Asn900Ser0.999863%20%
KDRNon-synonymous SNVc.1444T>Cp.Cys482Arg0.999843%53%
KDRNon-synonymous SNVc.1416A>Tp.Gln472His0.079745%50%
NPM1Intronic deletionc.*165delT--100%100%
FLT3Intronic SNVc.1310T>C-0.0232100%100%
TP53Non-synonymous SNVc.215C>Gp.Pro72Arg0.363696%31%
High proliferation only
BRAFNon-synonymous MNVc.1820CC>TTp.Ser607Phe0.989831%4%
CDKN2ANon-synonymous SNVc.341C>Tp.Pro114Leu0.999959%n.d.
ERBB4Non-synonymous SNVc.518C>Tp.Ser173Phe0.989928%8%
KDRNon-synonymous SNVc.2836C>Tp.Arg946Cys0.977925%n.d.
PDGFRANon-synonymous SNVc.2153G>Ap.Arg718Gln0.999944%n.d.
SMAD4Intronic SNVc.1882+4811C>A--22%n.d.
Low proliferation only
BRAFNon-synonymous SNVc.1406G>Ap.Gly469Glu0.9999n.d.45%
STK11Non-synonymous SNVc.968C>Ap.Pro323Gln0.9989n.d.21%
HP—high proliferation, LP—low proliferation, n.d.—not detected, MNV—multi-nucleotide variant, SNV—single nucleotide variant, VAF—variant allele frequency.
Table 4. Molecular characteristics of variants detected in tumor tissue of Patient 2.
Table 4. Molecular characteristics of variants detected in tumor tissue of Patient 2.
GeneType of AlterationVariantAmino Acid ChangePathogenicityVAF HPVAF LP
Shared variants
EGFRNon-synonymous SNVc.298C>Tp.Pro100Ser0.999812%15%
EZH2Non-synonymous SNVc.1922A>Tp.Tyr641Phe0.987415%17%
PTENNon-synonymous SNVc.804C>Ap.Asp268Glu0.586918%32%
PTENNon-synonymous SNVc.810G>Tp.Met270Ile0.992121%26%
HRASNon-synonymous SNVc.145G>Ap.Glu49Lys0.998926%19%
ATMNon-synonymous SNVc.8094A>Tp.Leu2698Phe0.99855%7%
HNF1AFrame shifting insertionc.864_865insCp.Gly288_Pro289 4%4%
FLT3Intronic SNVc.1310-3T>C-0.5447100%100%
RB1Frame shifting deletionc.2107delAp.Ile703 6%5%
TP53Non-synonymous SNVc.215C>Gp.Pro72Arg0.5704100%100%
ERBB4Intronic insertionc.884_885insT- 20%24%
ERBB4Intronic deletionc.884delT- 19%19%
ERBB4Non-synonymous SNVc.490C>Ap.Gln164Lys0.90557%6%
FAIMIntronic SNVc.-60T>C-0.983929%20%
PIK3CANon-synonymous SNVc.881A>Tp.Tyr294Phe0.989756%13%
APCNon-synonymous SNVc.3355C>Tp.His1119Tyr0.99912%15%
High proliferation only
APCNon-synonymous SNVc.2876C>Tp.Ser959Phe0.985722%n.d.
APCNon-synonymous SNVc.3479C>Ap.Thr1160Lys0.97026%n.d.
APCNon-synonymous SNVc.3485A>Tp.Tyr1162Phe0.983912%n.d.
APCNon-synonymous SNVc.4749G>Cp.Met1583Ile0.996310%n.d.
BRAFNon-synonymous SNVc.1768G>Tp.Val590Leu0.99828%n.d.
CSF1RNon-synonymous SNVc.985C>Ap.Pro329Thr0.99719%n.d.
ERBB4Stopgain SNVc.2798T>Ap.Leu933*0.994412%n.d.
ERBB4Non-synonymous SNVc.1829C>Ap.Pro610Gln0.74987%n.d.
FBXW7Non-synonymous SNVc.1186G>Tp.Val396Phe0.99567%n.d.
FGFR3Non-synonymous SNVc.1918C>Tp.Arg640Trp0.99696%n.d.
GNA11Non-synonymous SNVc.629G>Ap.Arg210Gln0.998410%n.d.
HNF1ANon-synonymous SNVc.955G>Ap.Gly319Ser0.99665%n.d.
HRASNon-synonymous SNVc.121C>Tp.Arg41Trp0.99893%n.d.
KDRStopgain SNVc.2959G>Tp.Glu987*0.99717%n.d.
KITNon-synonymous SNVc.1463C>Tp.Thr488Met0.999213%n.d.
KITNon-synonymous SNVc.2056C>Tp.Arg686Cys0.99918%n.d.
METNon-synonymous SNVc.439C>Tp.Pro147Ser0.923732%n.d.
METNon-synonymous SNVc.681G>Tp.Met227Ile0.96395%n.d.
METIntronic SNVc.1201T>C-0.927912%n.d.
METNon-synonymous SNVc.3650C>Gp.Thr1217Arg0.99494%n.d.
NOTCH1Non-synonymous SNVc.7436C>Ap.Ala2479Asp0.926918%n.d.
NOTCH1Non-synonymous SNVc.6972C>Ap.Asn2324Lys0.975319%n.d.
NOTCH1Non-synonymous SNVc.6229G>Ap.Ala2077Thr0.99913%n.d.
NOTCH1Non-synonymous SNVc.5069C>Tp.Ser1690Leu0.99688%n.d.
NOTCH1Non-synonymous SNVc.7602G>Tp.Glu2534Asp0.99166%n.d.
PDGFRANon-synonymous SNVc.2470G>Ap.Val824Ile0.99915%n.d.
PIK3CANon-synonymous SNVc.2152A>Tp.Ile718Phe0.72115%n.d.
PIK3CANon-synonymous SNVc.995G>Tp.Ser332Ile0.97366%n.d.
RB1Intronic SNVc.1696G>T-0.99565%n.d.
RB1Non-synonymous SNVc.2002C>Tp.Arg668Cys0.99514%n.d.
RB1Non-synonymous SNVc.2032C>Ap.His678Asn0.99114%n.d.
RB1Non-synonymous SNVc.2242G>Ap.Glu748Lys0.995611%n.d.
SMAD4Non-synonymous SNVc.1486C>Tp.Arg496Cys0.999315%n.d.
SMONon-synonymous SNVc.1198C>Tp.Arg400Cys0.99944%n.d.
SMONon-synonymous SNVc.595C>Tp.Arg199Trp0.99924%n.d.
STK11Non-synonymous SNVc.589G>Tp.Val197Leu0.99759%n.d.
STK11Intronic SNVc.598C>A-0.91485%n.d.
TP53Non-synonymous SNVc.839G>Ap.Arg280Lys0.997713%n.d.
TP53Intronic SNVc.673G>T-0.99445%n.d.
Low proliferation only
ERBB4Non-synonymous SNVc.1003G>Tp.Asp335Tyr0.9837n.d.4%
VHLNon-synonymous SNVc.4C>Ap.Pro2Thr0.9225n.d.9%
KITNon-synonymous SNVc.311G>Tp.Ser104Ile0.9838n.d.4%
KDRNon-synonymous SNVc.1473C>Ap.Phe491Leu0.5998n.d.6%
APCNon-synonymous SNVc.3192G>Tp.Glu1064Asp0.9648n.d.9%
APCNon-synonymous SNVc.4749G>Tp.Met1583Ile0.9961n.d.9%
EGFRNon-synonymous SNVc.1804G>Ap.Glu602Lys0.9989n.d.4%
EGFRNon-synonymous SNVc.2492G>Ap.Arg831His0.9965n.d.4%
EGFRNon-synonymous SNVc.2495G>Ap.Arg832His0.9972n.d.5%
METStopgain SNVc.760G>Tp.Glu254*0.9963n.d.13%
METNon-synonymous SNVc.1147G>Tp.Val383Leu0.9872n.d.8%
SMONon-synonymous SNVc.1246G>Tp.Gly416Cys0.9962n.d.4%
FGFR1Non-synonymous SNVc.936G>Tp.Lys312Asn0.9988n.d.7%
NOTCH1Non-synonymous SNVc.6733G>Ap.Gly2245Arg0.9296n.d.3%
NOTCH1Non-synonymous SNVc.4987C>Tp.Arg1663Trp0.9989n.d.6%
NOTCH1Non-synonymous SNVc.4793G>Tp.Arg1598Leu0.9909n.d.4%
PTENNon-synonymous SNVc.25G>Tp.Val9Phe0.9949n.d.17%
FGFR2Non-synonymous SNVc.1273C>Tp.Arg425Trp0.9992n.d.4%
ATMNon-synonymous SNVc.3853G>Tp.Asp1285Tyr0.99n.d.4%
PTPN11Non-synonymous SNVc.1462A>Tp.Ile488Phe0.9901n.d.13%
HNF1ANon-synonymous SNVc.528G>Tp.Gln176His0.9952n.d.7%
RB1Stopgain SNVc.585G>Ap.Trp195*0.9946n.d.9%
AKT1Non-synonymous SNVc.73C>Tp.Arg25Cys0.9992n.d.4%
TP53Non-synonymous SNVc.845G>Ap.Arg282Gln0.9994n.d.6%
STK11Non-synonymous SNVc.196G>Tp.Val66Leu0.9916n.d.5%
STK11Non-synonymous SNVc.758A>Gp.Tyr253Cys0.9984n.d.3%
GNASNon-synonymous SNVc.654C>Ap.Asn218Lys0.9924n.d.4%
GNASNon-synonymous SNVc.674G>Tp.Gly225Val0.9977n.d.5%
GNASNon-synonymous SNVc.718G>Ap.Asp240Asn0.9966n.d.3%
HP—high proliferation, LP—low proliferation, n.d.—not detected, SNV—single nucleotide variant, VAF—variant allele frequency.
Table 5. Molecular characteristics of variants detected in tumor tissue of Patient 3.
Table 5. Molecular characteristics of variants detected in tumor tissue of Patient 3.
GeneType of AlterationVariantAmino Acid ChangePathogenicityVAF HPVAF LP
Shared mutations
NRASNon-synonymous SNVc.38G>Tp.Gly13Val0.997335%29%
HRASIntronic SNVc.111+15G>A-0.819247%50%
KRASNon-synonymous SNVc.283C>Ap.His95Asn0.903522%23%
FLT3Intronic SNVc.1310-3T>C-0.544745%76%
RB1Intronic SNVc.137+86T>C-0.5128100%100%
VHLSynonymous SNVc.216C>Ap.Ser72=0.959533%30%
MLH1Intronic SNVc.1039-8T>A-0.777431%46%
TP53Non-synonymous SNVc.215G>Cp.Arg72Pro0.5704100%100%
PIK3CAIntronic SNVg.2756T>G-0.5439100%100%
PDGFRASynonymous SNVc.1701A>Gp.Pro592=0.3955100%100%
ERBB4Intronic SNVc.742-37T>A-0.660723%21%
PIK3CANon-synonymous SNVc.1173A>Gp.Ile391Met0.929641%62%
PIK3CAIntronic SNVc.2016-27A>T-0.162860%54%
FGFR3Synonymous SNVc.1956G>Ap.Thr652=0.7994100%100%
FGFR3Intronic SNVc.1959+22G>A-0.911568%59%
KITNon-synonymous SNVc.1621A>Gp.Met541Val0.490860%52%
KDR3′ UTR Variantc.*27=-0.5891100%100%
APCSynonymous SNVc.4425G>Ap.Thr1475=0.771542%47%
METNon-synonymous SNVc.3029C>Tp.Thr1010Ile0.999290%43%
SMOIntronic SNVc.538-26C>A-0.6804100%100%
SMOIntronic SNVc.747+24G>C-0.5792100%100%
SMOSynonymous SNVc.1164G>Cp.Gly258=0.7710100%100%
EZH2Intronic SNVc.1852-21A>G-0.5855100%100%
NOTCH1Non-synonymous deletionc.5015delGpArg1431Pro0.997329%14%
High proliferation only
ATMFrame shifting deletionc.911delApGlu304Gly0.239633%n.d.
FLT3Intronic SNVc.1253-6G>A-0.470335%n.d.
RB1Synonymous SNVc.1071A>TpPro357= 19%n.d.
RB1Intronic deletionc.1389+8delA-0.669129%n.d.
RB1Intronic SNVc.1389+16T>A- 40%n.d.
RB1Intronic deletionc.2106+54_2106+56delTTC-0.8097100%n.d.
RB1Intronic SNVc.2211+32T>A-0.642218%n.d.
RB1Intronic SNVc.2325+18T>C-0.668325%n.d.
SMAD4Intronic SNVc.956-18C>T-0.296929%n.d.
SMAD4Intronic SNVc.1309-35A>T-0.679318%n.d.
GNA11Synonymous SNVc.771C>Tp.Thr257=0.9983100%n.d.
ERBB4Non-synonymous SNVc.242G>Ap.Arg81Gln0.749325%n.d.
SRCSynonymous SNVc.1508C>Tp.Arg503=0.958423%n.d.
MLH1Intronic SNVc.1409+2T>A- 16%n.d.
PIK3CAFrame shifting deletionc.57delAp.Arg190.952815%n.d.
PIK3CANon-synonymous SNVc.990T>Ap.Ile330Lys0.445821%n.d.
PIK3CAIntronic SNVc.1404+19T>A- 70%n.d.
FGFR3Intronic deletionc.1076-44delG-0.562535%n.d.
KITIntronic SNVc.2484+78T>C-0.890575%n.d.
APCNon-synonymous SNVc.3112A>Tp.Ile1038Leu0.905533%n.d.
APCNon-synonymous SNVc.7820 G>Tp.Ser2607Ile0.8807100%n.d.
FGFR1Synonymous SNVc.2130C>Tp.Phe710=0.801967%n.d.
CDKN2ANon-synonymous SNVc.371G>Tp.Arg73Leu0.587718%n.d.
NOTCH1Intronic SNVc.5018+79 G>T-0.5021100%n.d.
NOTCH1Intronic SNVc.5018+55C>T-0.239622%n.d.
Low proliferation only
ATMSynonymous SNVc.8015_c.8018delACCp.Asp2672= n.d.13%
ATMNon-synonymous SNVc.8021insTp.Gly2675Trp n.d.13%
ERBB4Intronic SNVc.1717-10G>A-0.7227n.d.13%
ERBB4Intronic SNVc.1717-16G>A-0.3892n.d.15%
FGFR4Intronic SNVc.728-12C>T-0.7287n.d.27%
HP—high proliferation, LP—low proliferation, n.d.—not detected, SNV—single nucleotide variant, VAF—variant allele frequency.
Table 6. Molecular characteristics of variants detected in tumor tissue of Patient 4.
Table 6. Molecular characteristics of variants detected in tumor tissue of Patient 4.
GeneType of AlterationVariantAmino Acid ChangePathogenicityVAF HPVAF LP
Shared mutations
PDGFRASynonymous SNVc.1701A>Gp.Pro567=0.3955100%100%
TP53Non-synonymous SNVc.98C>Gp.Pro72Arg0.5704100%100%
KDR3′ UTR Variantc.*27=-0.5891100%100%
SMOIntronic SNVc.747+24G>C-0.5792100%100%
NOTCH1Synonymous SNVc.6555C>Tp.Asp1944=0.837266%50%
FGFR3Synonymous SNVc.1956G>Ap.Thr651=0.7994100%100%
NOTCH1Intronic SNVc.5018+55C>T-0.502158%60%
FLT3Intronic SNVc.1310-3T>C-0.5447100%100%
SMOIntronic SNVc.538-26C>T-0.6804100%100%
SMOSynonymous SNVc.1164G>Cp.Gly258=0.7710100%100%
RB1Intronic SNVc.137+86T>C 0.5128100%100%
RETNon-synonymous SNVc.2071G>Ap.Gly691Ser0.859539%14%
PDGFRASynonymous SNVc.2472C>Tp.Val824=0.758139%39%
PI3KCAIntronic SNVg.2756T>G-0.543950%46%
RETSynonymous SNVc.2712C>Gp.Ser650=0.741425%5%
HRASSynonymous SNVc.81T>Cp.His27=0.816550%31%
APCSynonymous SNVc.4425G>Ap.Thr1475=0.771539%58%
PI3KCAIntronic SNVg.2645G>A-0.845635%35%
EZH2Intronic SNVc.1852-21T>C-0.585536%50%
RETSynonymous SNVc.2307G>Tp.Leu769=0.730830%5%
HRASIntronic deletionc.-53-35_ -53-40delCCCAGC- 67%67%
NOTCH1Synonymous SNVc.5094C>Tp.Asp1457=0.922247%48%
KDRNon-synonymous SNVc.1416A>Tp.Gln472His0.733833%31%
EGFRSynonymous SNVc.2361G>Ap.Gln787=0.943950%35%
FGFR4Intronic SNVc.728-35G>A-0.6556100%100%
HNF1ASynonymous SNVc.864G>Cp.Gly288=0.895136%64%
HNF1AIntronic SNVc.955+94T>G-0.371350%50%
PTENIntronic SNVc.1026+32T>G-0.3730100%100%
CLEC2D3′ UTR Variantc.*1413=-0.881929%45%
PDGFRAIntronic SNVc.2440-50T>TA--80%57%
TP53Intronic SNVc.672+62A>G-0.6480100%100%
KDRIntronic SNVc.798+54C>T-0.6404100%100%
ATMNon-synonymous SNVc.7391T>Ap.Leu2463Phe0.988721%47%
HNF1AIntronic SNVc.527-23C>T-0.604625%67%
NOTCH3Synonymous SNVc.4563A>Tp.Pro1469=0.6682100%100%
Only high proliferation
ALKSynonymous SNVc.27C>Gp.Leu9=0.5627100%n.d.
ERBB4Intronic deletionc.884-7_884-8delAA--16%n.d.
KDRIntronic SNVc.3405-92A>C-0.546767%n.d.
KDRIntronic insertionc.2615-37insC--100%n.d.
KITIntronic SNVc.2484+78T>C-0.5625100%n.d.
NPM1Intronic deletionc.847-17delT--32%n.d.
PIK3CANon-synonymous SNVc.989T>Ap.Ile330Lys0.952822%n.d.
PTENIntronic deletionc.802-17delT--24%n.d.
PTENNon-synonymous SNVc.810G>Tp.Gln97His0.997329%n.d.
RB1Intronic SNVc.2211+32T>A-0.809715%n.d.
RB1Intronic SNVc.2212-15A>C-0.271021%n.d.
RETSynonymous SNVc.2307G>Tp.Leu769=0.730830%n.d.
TP53Intronic SNVc.-28-13A>G-0.788667%n.d.
TP53Intronic deletionc.96+48_97-58del CCCCAGCCCTCCAGGT--100%n.d.
Low proliferation
PTENNon-synonymous SNVc.983C>Ap.Ala327Glu n.d.57%
PIK3CAIntronic deletionc.2667-13_2667-14delTA--n.d.13%
CDKN2AIntronic SNVc.457+18C>T--n.d.33%
HP—high proliferation, LP—low proliferation, n.d.—not detected, SNV—single nucleotide variant, VAF—variant allele frequency.
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Grzywa, T.M.; Koppolu, A.A.; Paskal, W.; Klicka, K.; Rydzanicz, M.; Wejman, J.; Płoski, R.; Włodarski, P.K. Higher Mutation Burden in High Proliferation Compartments of Heterogeneous Melanoma Tumors. Int. J. Mol. Sci. 2021, 22, 3886. https://doi.org/10.3390/ijms22083886

AMA Style

Grzywa TM, Koppolu AA, Paskal W, Klicka K, Rydzanicz M, Wejman J, Płoski R, Włodarski PK. Higher Mutation Burden in High Proliferation Compartments of Heterogeneous Melanoma Tumors. International Journal of Molecular Sciences. 2021; 22(8):3886. https://doi.org/10.3390/ijms22083886

Chicago/Turabian Style

Grzywa, Tomasz M., Agnieszka A. Koppolu, Wiktor Paskal, Klaudia Klicka, Małgorzata Rydzanicz, Jarosław Wejman, Rafał Płoski, and Paweł K. Włodarski. 2021. "Higher Mutation Burden in High Proliferation Compartments of Heterogeneous Melanoma Tumors" International Journal of Molecular Sciences 22, no. 8: 3886. https://doi.org/10.3390/ijms22083886

APA Style

Grzywa, T. M., Koppolu, A. A., Paskal, W., Klicka, K., Rydzanicz, M., Wejman, J., Płoski, R., & Włodarski, P. K. (2021). Higher Mutation Burden in High Proliferation Compartments of Heterogeneous Melanoma Tumors. International Journal of Molecular Sciences, 22(8), 3886. https://doi.org/10.3390/ijms22083886

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