Next Article in Journal
Development and Transferability of EST-SSR Markers for Pinus koraiensis from Cold-Stressed Transcriptome through Illumina Sequencing
Next Article in Special Issue
Distinct Tumor Microenvironments Are a Defining Feature of Strain-Specific CRISPR/Cas9-Induced MPNSTs
Previous Article in Journal
The Regulation of Homologous Recombination by Helicases
Previous Article in Special Issue
New Model Systems and the Development of Targeted Therapies for the Treatment of Neurofibromatosis Type 1-Associated Malignant Peripheral Nerve Sheath Tumors
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 11
Issue 5
10.3390/genes11050499
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Unmasking Intra-Tumoral Heterogeneity and Clonal Evolution in NF1-MPNST

by
Chang-In Moon
1,†,
William Tompkins
2,†,
Yuxi Wang
1,
Abigail Godec
3,
Xiaochun Zhang
1,
Patrik Pipkorn
4,5,
Christopher A. Miller
5,6,
Carina Dehner
7,
Sonika Dahiya
5,7 and
Angela C. Hirbe
1,5,*
1
Division of Medical Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
2
Washington University School of Medicine, St. Louis, MO 63110, USA
3
College of Human Medicine, Michigan State University, East Lansing, MI 48824, USA
4
Department of Otolaryngology, Division of Head and Neck Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA
5
Siteman Cancer Center, St. Louis, MO 63110, USA
6
McDonnell Genome Institute, Division of Oncology—Stem Cell Biology, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
7
Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally.
Genes 2020, 11(5), 499; https://doi.org/10.3390/genes11050499
Submission received: 6 March 2020 / Revised: 19 April 2020 / Accepted: 30 April 2020 / Published: 1 May 2020
(This article belongs to the Special Issue Genomics and Models of Nerve Sheath Tumors)
Browse Figures
Versions Notes

Abstract

:
Sarcomas are highly aggressive cancers that have a high propensity for metastasis, fail to respond to conventional therapies, and carry a poor 5-year survival rate. This is particularly true for patients with neurofibromatosis type 1 (NF1), in which 8%–13% of affected individuals will develop a malignant peripheral nerve sheath tumor (MPNST). Despite continued research, no effective therapies have emerged from recent clinical trials based on preclinical work. One explanation for these failures could be the lack of attention to intra-tumoral heterogeneity. Prior studies have relied on a single sample from these tumors, which may not be representative of all subclones present within the tumor. In the current study, samples were taken from three distinct areas within a single tumor from a patient with an NF1-MPNST. Whole exome sequencing, RNA sequencing, and copy number analysis were performed on each sample. A blood sample was obtained as a germline DNA control. Distinct mutational signatures were identified in different areas of the tumor as well as significant differences in gene expression among the spatially distinct areas, leading to an understanding of the clonal evolution within this patient. These data suggest that multi-regional sampling may be important for driver gene identification and biomarker development in the future.

1. Introduction

Malignant peripheral nerve sheath tumor (MPNSTs) is the sixth most common soft tissue sarcoma [1] and has an incidence rate of 0.1–0.2 per 100,000 persons per year [2]. MPNSTs are often associated with neurofibromatosis type 1 (NF1). The incidence rate of MPNSTs in patients with NF1 is much higher than that of the general population, estimated to be 1.6 per 1000 per year, or a lifetime risk of 8–13% [3]. Approximately 50% of MPNSTs occur in patients with neurofibromatosis [4,5,6,7], and the other 50% of MPNSTs occur sporadically or in the setting of previous radiation therapy [4,6]. In the setting of NF1, MPNSTs often arise within a pre-existing benign nerve sheath tumor (plexiform neurofibroma) [4,7].
Prognosis remains poor for patients with MPNST despite multi-modality therapy [2,5,6,7,8,9,10]. In the setting of metastatic disease, treatment is limited to cytotoxic chemotherapy, typically consisting of single agent doxorubicin or a combination of doxorubicin and ifosfamide [11,12,13].
A number of different genes have been implicated in the development of MPNSTs. One of the most commonly used models for preclinical testing was developed by Cichowski et al. and Vogel et al; they demonstrated that mice with germline variants in Nf1 and Tp53 develop MPNSTs, supporting a cooperative and causal role for these tumor suppressors in the context of MPNST formation [14,15]. Other groups have found a reduction in expression of PTEN, a tumor suppressor in the PI3K/AKT/mTOR pathway, in MPNSTs compared to benign nerve sheath tumors in a manner that is not regulated by NF1 [16]. Keng et al. went on to demonstrate the cooperative roles of Pten and Nf1 in the tumorigenesis of MPNSTs in vivo with transgenic mouse models [17]. Gregorian et al. further elucidated the cooperative relationship between k-ras activation and Pten deletion, showing that both variants in combination led to 100% penetrable development of MPNSTs [18]. Another gene implicated in MPNST pathogenesis is INK4A, a tumor suppressor encoding both p16 and p19. Deletions in this gene have been identified in MPNSTs but not in benign neurofibromas [19]. Lu et al. demonstrated a difference in aberrant expression of ATRX, a DNA helicase that plays a role in chromatin regulation and maintenance of telomeres, between MPNSTs and benign neurofibromas [20]. Additionally, variants in EED and SUZ12 have been observed in MPNST. These genes code for components of the PRC2 complex which is involved in transcriptional repression. Lee et al. showed loss-of-function somatic alterations of PRC2 components in 92% of sporadic, 70% of NF1-associated and 90% of radiotherapy-associated MPNSTs. Further, introduction of the lost PRC2 component in a PRC2-deficient MPNST cell line decreased cell growth [21]. Others have found alterations such as structural alterations of PDGFRA (platelet-derived growth factor-α) in 26% of MPNST samples [22]; increased expression of EGF-R (epidermal growth factor receptor) by immunohistochemistry in MPNSTs [23]; and IGFR1 gene amplification in 24% of MPNSTs [24].
Despite all of this research, no effective therapies have emerged from recent clinical studies based on this genomic data and subsequent preclinical studies. Intra-tumoral heterogeneity is a possible reason for these shortcomings. Prior studies have relied on a single sample from these tumors. All the subclones within a tumor may not be captured by this approach. Our aim in this study is to investigate intra-tumoral heterogeneity more thoroughly through analysis of samples taken from multiple sites of the same MPNST.

2. Materials and Methods

2.1. Study Approvals

Blood and tumor were obtained from an individual diagnosed with NF1 according to established criteria [25] and treated for a MPNST at Washington University/St. Louis Children’s Hospital NF Clinical Program (St. Louis, MO, USA). The human tumor samples were collected under an approved IRB protocol (#201203042) at Washington University, and the patient was appropriately consented.

2.2. Sample Collection

Samples were taken from three distinct areas within a single tumor from a patient with an NF1-MPNST immediately after surgical resection with guidance from a pathologist (SD). While area “1” represented solid, tan homogeneous tumor lacking hemorrhage and/or necrosis, areas “2” and “3” of the tumor grossly appeared necrotic and hemorrhagic respectively. 20 g of tissue was taken from each area. Each area was then divided to be used for RNA extraction, DNA extraction, and slide preparation to analyze the histology. A gross image of the tumor was taken at this time and is shown as Figure 1.

2.3. Histology

Images of the hematoxylin-eosin sections were taken (20X magnification) using an Olympus BX-51 microscope using an Olympus DP71 digital camera, and DP Controller software. Tumor purity was estimated based on morphologic review of the entire hematoxylin-eosin stained section estimating the number of tumor cells, stromal cells, lymphocytes, and extravasated red blood cells. Two pathologists reviewed these slides independently providing an estimated percentage of total tumors cells per slide.

2.4. Sequencing and Bioinformatics Analysis

Whole exome sequencing (WES), RNA sequencing (RNA-Seq), and copy number analysis (CNVkit) [26] were performed on each sample and compared to a blood sample as a germline DNA control. Both Illumina Whole Genome Sequencing (eWGS) of 3 tumor samples and 1 PBMC normal sample, and Illumina RNA Sequencing of the 3 tumor samples were generated from the sampled areas.

2.4.1. Library Construction and Sequencing

Each tumor had 2 enriched libraries constructed (n = 6), and the PBMCs had a single enriched library constructed (n = 1). Exome libraries were captured with an IDT exome reagent, then pooled with a WGS library for sequencing on an Illumina HiSeq4000 with at least 1000x coverage. RNA was prepared with a TrueSeq stranded total RNA library kit, then sequenced on an Illumina HISeq4000 with 72M reads per sample.

2.4.2. IDT Exome Sequencing Variant Detection

Genomic data were aligned against reference sequence hg38 via BWA-MEM [27] with Base Quality Score Recalibration (BQSR). Structural variants (SVs) and large indels were detected using manta [28]. SNVs and small indels were detected using VarScan2 [29], Strelka2 [30], MuTect2 [31], and Pindel [32] via the somatic pipelines available at https://github.com/genome/analysis-workflows, which includes best-practice variant filtering and annotation with VEP (Variant Effect Predictor, version 95) [33]. Manual review was used to remove additional sequencing artifacts. Germline variants and somatic variants reported on variant detecting pipeline were compared to see any intersection of variants. Any intersecting variants were removed from the somatic variant gene list, thus filtering out the germline variants. Common variants with 1000 genome MAF (minor allele frequency) > 0.05 were filtered out. Waterfall somatic variant plots were created with GenVisR [34] by including somatic variants that occurred in each area. Variants reported on the waterfall plot are most likely to be pathogenic, which is reported via VEP. These variants were not reported as a somatic variant in COSMIC (Catalogue Of Somatic Mutations In Cancer) [35] and ClinVar [36] archive, thus these variants are best classified as variants with unknown significance. In order to predict clinical significance and predictions of the functional effects of these variants, each variant was reviewed on SIFT [37] and Polyphen [38]. IMPACT rating was determined by VEP for each non-coding variant.

2.4.3. Copy Number Analysis

CNVkit was used to infer and visualize copy number from high-throughput DNA sequencing data. Coverage for each bait position in the exome reagent was calculated, then segments of constant copy number were identified using circular binary segmentation. Data were plotted to provide visualization of CNVs.

2.4.4. Inference of Clonal Phylogeny

SciClone [39] and ClonEvol [40] were utilized to attempt to perform a phylogeny inference. However, the analysis was complicated by the abundance of copy number-altered regions in these tumors, and these standard algorithms were unable to automatically perform that inference. Manual review of the shared and private single nucleotide variants and large copy number altered areas, though, revealed only one possible phylogeny for this tumor.

2.4.5. RNA Sequence Preprocessing

RNA-Sequence (RNA-seq) was trimmed from 3′-end with a minimum quality Phred score of 20 and aligned against hg38—Ensembl Transcripts release 99 via BWA-MEM. Pre/post quality control and full expectation-maximization (EM) quantification were run via Partek® Flow® [41]. Gene counts and transcript counts were normalized by CPM (counts per million) by using edgeR [42] package. Heatmap visualizations were created using gplots [43] R package (Warnes, G.R. Seattle, WA, USA).

2.4.6. Gene Differential Expression Analysis

The gene-specific analysis (GSA) method was used to test for differential expression of genes or transcript between sample regions in Partek® Flow® [44]. Differential expressed genes were defined as the following statistic parameters: p-value <= 0.05; FDR step up <= 0.05; Fold Change < −2 or >2. From differentially expressed genes, a GO enrichment test was used to functionally profile this set of genes, to determine which GO terms appear more frequently than would be expected by chance when examining the set of terms annotated to the input genes, each associated with a p-value.

2.4.7. Pathway Analysis

A list of genes in copy number aberrant (CNA) regions was extracted. CNA regions were defined as copy number regions greater than 3 or copy number regions less than 1. For each area, we intersected the list of genes that are located in the CNA regions with the differentially expressed gene list reported in the RNA differential expression analysis (p-value <= 0.05). PantherDB [45] was utilized to discover GO terms and pathways that may be affected by these genes.

3. Results

3.1. Patient Information

Patient characteristics can be seen in Table 1. The patient was a male with a history significant for a clinical diagnosis of neurofibromatosis type 1—patient had a plexiform neurofibroma, spinal neurofibromas, café au lait macules, and multiple first-degree relatives with neurofibromatosis type 1—and was 40 years old at the time of diagnosis of MPNST. He presented with a large tumor located in the left neck. Resection showed a high-grade malignant peripheral nerve sheath tumor, 10.2 cm in the largest dimension, with negative margins. The patient did not receive any adjuvant therapy for his MPNST following initial resection due to poor performance status. He recurred 21 months after the initial diagnosis and ultimately died secondary to complications from metastatic disease (33 months after initial diagnosis). Samples were taken in three different locations within the primary tumor immediately following the inititial resection for the purpose of this study.

3.2. Histology of Biopsy Sites

We first reviewed the H&E images of the tumor to correlate histology to the gross images of the tumor. H&E stained sections in Figure 2 show representative images of the three sampled areas. Area #1 demonstrates tissue of a spindle cell neoplasm of neural differentiation arranged in fascicles with elongated hyperchromatic nuclei and a mild to moderate amount of cytoplasm. The tumor purity of this sample was >95%. Area #2 shows spindled cells in a background of hemorrhage, a finding commonly seen in these high-grade tumors with a tumor purity of >95%. Area #3 represents an area of necrosis, another characteristic finding for MPNST. This sample showed >95% tumor purity.

3.3. Whole Exome Sequencing (WES), RNA Sequencing (RNA-Seq), and Copy Number Analysis

We first interrogated the sequencing data to identify the germline NF1 variant within this tumor. Figure 3 shows a lollipop plot identifying the patient’s likely NF1 germline variant based on exclusion of any variants with minor allele frequency >0.05 in the 1000 genomes database. Next, to investigate intra-tumoral heterogeneity within the sample, RNA sequencing of the three sample sites was performed and is shown in Figure 4.
Distinct gene expression profiles were observed in each of the areas sampled. The top 16 differentially expressed genes are listed in Table 2 and include a number of genes involved in transcription and translation. We next performed a copy number analysis of the three biopsy sites to determine whether or not different copy number alterations were observed in each area (Figure 5). Distinct copy number signatures can be appreciated in each of the three samples further illustrating intra-tumoral heterogeneity. Additionally, we evaluated the single nucleotide variants found in each of the samples. This broad overview of all somatic variants is depicted in the waterfall plot in Figure 6. Again, distinct somatic variants can be appreciated across different areas. We next explored the potential significance of these variants through further bioinformatics analysis. While the biological significance of each of these variants is uncertain, there is evidence that some of these variants may play a role in the pathogenesis. For each variant in a coding region, CBioPortal [47] was queried for each gene to determine if the somatic variant was in a functional domain. Additionally, the RNAseq data was queried to determine if the variant in a specific area of the tumor influenced the gene expression of that gene in a specific area. Finally, SIFT and Polyphen were used to predict pathogenicity. Table 3a,b list the somatic variants in the coding region that may play a role in the pathogenesis of this tumor based on the above criteria. For those mutaions in non-coding regions, the Ensembl Variant Effect Predictor [33] was used to determine whether or not the variant would be predicted to affect gene expression. All of the identified variants were classified as modifiers, indicating that pathogenicity prediction is difficult, thus the effects of these variants are unclear. (Table 3c). Further details of the somatic variants can be found in Supplemental Table S1. Next, a gene ontology analysis was performed. To do this, a list of genes in copy number aberrant (CNA) regions was extracted. For each area, the list of genes located in the CNA regions intersected with the differentially expressed gene list reported in the RNA differential expression analysis, and PantherDB [45] was utilized to identify pathways that may be affected by these genes. Table 4 displays the unique genes in each area with copy number aberrations and alterations in gene expression. Genes depicted in Area 1 have been reported in the literature to serve a myriad of functions in tumorigenesis, including base excision repair, nucleotide excision repair, and alternative splicing [48,49,50,51,52,53,54,55]. Those in Area 2 are involved in several different pathways, including transcriptional regulation in addition to ribosomal and proteasomal function [56,57,58,59,60]. Finally, the genes in Area 3 consist of several ribosomal subunits and small nucleolar RNAs, suggesting that both translation and transcription are uniquely affected compared to other areas [61,62,63]. This analysis suggests that there may be different functional programs at play across the three areas. Next, we manually reviewed the data to look for changes in other known drivers of MPNST including TP53, ATRX, EED, SUZ12, and CDKN2A. There were no copy number changes or somatic mutions in any of these genes. Finally, we performed a careful manual review of all of the shared and unique somatic variants and copy number alterations in each area in order to develop a predicted clonal evolution. Figure 7 depicts the predicted phylogenetic tree of the subclones from each area, representing the likely clonal evolution of the tumor.

4. Discussion

Despite advances in our understanding of the pathobiology of MPNST and the identification of seemingly promising therapeutic targets using a single model system in preclinical studies, no investigational agents have demonstrated efficacy following translation to human clinical trials. One element that has largely been ignored in the study of MPNST has been the possible existence of intra-tumoral heterogeneity. No single study in MPNST has focused on intra-tumoral heterogeneity. However, spatial intra-tumoral heterogeneity has become an area of interest in the study of other solid malignancies to begin to understand clonal evolution [91,92,93,94,95]. Within the NF1 field, researchers are beginning to appreciate the importance of understanding spatial and temporal heterogeneity. For example, Peacock et al. performed a genomic analysis of serial samples from one patient who developed an MPNST. Samples were taken at four timepoints (benign plexiform neurofibroma, MPNST pre-treatment, MPNST post-treatment, and MPNST at time of metastasis) [96]. They observed early hemizygous microdeletions in NF1 and TP53 with progressive amplifications of MET, HGF, and EGFR, highlighting the potential role of these pathways in progression. Additionally, Carriό et al. have started to examine intra-tumoral heterogeneity in PNF (plexiform neurofibromas), ANF (atypical neurofibroma) and ANNUBP (atypical neurofibromatous neoplasms with uncertain biological potential), the precursors to MPNST. They performed SNP-array analysis and exome sequencing on multiple biopsies of eight PNF, of which some had areas consistent with ANF or ANNUBP. Their data suggested that loss of a single copy of CDKN2A/B in NF1 null cells is sufficient to start ANF development and that total inactivation of both copies is necessary to form ANNUBP [97]. Our study represents the first look at spatial intra-tumoral heterogeneity within an MPNST. We have demonstrated differing mutational profiles, copy number alteration signatures, and gene expression profiles within the three areas sampled. The differing mutation profile includes a variety of single nucleotide variants, including missense, frameshift, and synonymous variants. The role of synonymous variants in the tumorigenesis of MPNST is uncertain. However, there is increasing evidence that synonymous variants can alter gene expression and protein function and thus cannot be simply disregarded [98,99,100,101]. Additionally, several of the genes in Table 3a,b have previously been implicated in cancer [102,103,104,105,106,107,108,109,110,111,112,113,114,115]. For example, in Area 2, CSK was found to have a frameshift variant in its functional domain. CSK encodes a C-terminal Src kinase that has previously been found to act as a tumor suppressor in both breast cancer and prostate cancer [112,113,114]. Interestingly, in the context of breast cancer, Smith et al. showed that C-terminal Src kinase loss facilitated tumorigenesis by altering expression of the PRC2 complex subunits, EZH2 and SUZ12 [113]. Based on these data, it is possible that alterations in CSK could be another way in which the PRC2 complex is affected in MPNST. Another gene, CCL16, is involved in chemotaxis of human monocytes and lymphocytes. This chemokine was shown to delay mammary tumor growth and reduce rates of metastasis in mouse models [115], raising the possibility of decreased immune surveillance of our patient’s MPNST secondary to a non-functional CCL16. In addition to the differences in single nucleotide variants, there were differences in copy number alterations across the three areas with Area 2 showing the most distinct signature in terms of copy number gains and losses. The degree to which each somatic variant, differentially expressed gene, and copy number aberration contributes to the biologic heterogeneity of the tumor remains uncertain. However, future work in our lab will be geared at elucidating this information. Finally, there was a distinct difference in gene expression among the three areas with gene ontology studies pointing toward differences in translation and protein targeting.
Taken together, these data point toward the existence of intra-tumoral heterogeneity and suggest that further investigation into this phenomenon is warranted. Additionally, these data suggest that there should be some caution taken in interpreting sequencing that comes from a single biopsy site. The advent of single cell sequencing has allowed for more rigorous evaluation of intra-tumoral heterogeneity in other cancers including acute leukemias [116,117], as well as in some solid malignancies [118,119]. Future work will be geared at using this data as the foundation to better understand clonal heterogeneity along with single cell sequencing to comprehensively evaluate intra-tumoral heterogeneity and clonal evolution of MPNST.

5. Conclusions

Significant intra-tumoral heterogeneity exists and may be a barrier to our ability to improve outcomes in patients with NF1-MPNST. These data suggest that multi-regional sampling may be necessary to understand clonal evolution, and for driver gene identification and biomarker development in the future.

Supplementary Materials

The following are available online at https://www.mdpi.com/2073-4425/11/5/499/s1, Supplemental Table S1: Comprehensive Genomic Information for Single Nucleotide Variants.

Author Contributions

Conceptualization, A.C.H.; Formal analysis, C.-I.M., Y.W., C.D., and A.G.; Funding acquisition, A.C.H.; Investigation, C.-I.M., W.T., C.D., Y.W. and X.Z.; Resources, A.G., X.Z., P.P. and S.D.; Software, C.-I.M.; C.A.M. Supervision, A.C.H.; Writing—original draft, C.-I.M. and W.T.; Writing—review & editing, Y.W., C.D., A.G., X.Z., P.P., S.D., C.A.M. and A.C.H. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the St. Louis Men’s Group Against Cancer. Hirbe is funded by a Francis Collins Scholar Award through NTAP.

Conflicts of Interest

The authors declare no potential conflicts of interest.

References

  1. Eilber, F.C.; Brennan, M.F.; Eilber, F.R.; Dry, S.M.; Singer, S.; Kattan, M.W. Validation of the Postoperative Nomogram for 12-Year Sarcoma-Specific Mortality. Cancer 2004, 101, 2270–2275. [Google Scholar] [CrossRef] [PubMed]
  2. Ng, V.Y.; Scharschmidt, T.J.; Mayerson, J.L.; Fisher, J.L. Incidence and Survival in Sarcoma in the United States: A Focus on Musculoskeletal Lesions. Anticancer Res. 2013, 33, 2597–2604. [Google Scholar] [PubMed]
  3. Evans, D.G.R.; Baser, M.E.; McGaughran, J.; Sharif, S.; Howard, E.; Moran, A. Malignant peripheral nerve sheath tumours in neurofibromatosis 1. J. Med. Genet. 2002, 39, 311–314. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Ducatman, B.S.; Scheithauer, B.W.; Piepgras, D.G.; Reiman, H.M.; Ilstrup, D.M. Malignant Peripheral Nerve Sheath Tumors. A Clinicopathologic Study of 120 Cases. Cancer 1986, 57, 2006–2021. [Google Scholar] [CrossRef]
  5. Porter, D.E.; Prasad, V.; Foster, L.; Dall, G.F.; Birch, R.; Grimer, R.J. Survival in malignant peripheral nerve sheath tumours: A comparison between sporadic and neurofibromatosis type 1-associated tumours. Sarcoma 2009, 2009, 1–5. [Google Scholar] [CrossRef]
  6. Zou, C.; Smith, K.D.; Liu, J.; Lahat, G.; Myers, S.; Wang, W.L.; Zhang, W.; McCutcheon, I.E.; Slopis, J.M.; Lazar, A.J.; et al. Clinical, pathological, and molecular variables predictive of malignant peripheral nerve sheath tumor outcome. Ann. Surg. 2009, 249, 1014–1022. [Google Scholar] [CrossRef]
  7. LaFemina, J.; Qin, L.X.; Moraco, N.H.; Antonescu, C.R.; Fields, R.C.; Crago, A.M.; Brennan, M.F.; Singer, S. Oncologic outcomes of sporadic, neurofibromatosis-associated, and radiation-induced malignant peripheral nerve sheath tumors. Ann. Surg. Oncol. 2013, 20, 66–72. [Google Scholar] [CrossRef]
  8. Farid, M.; Demicco, E.G.; Garcia, R.; Ahn, L.; Merola, P.R.; Cioffi, A.; Maki, R.G. Malignant Peripheral Nerve Sheath Tumors. Oncologist 2014, 19, 193–201. [Google Scholar] [CrossRef] [Green Version]
  9. Anghileri, M.; Miceli, R.; Fiore, M.; Mariani, L.; Ferrari, A.; Mussi, C.; Lozza, L.; Collini, P.; Olmi, P.; Casali, P.G.; et al. Malignant Peripheral Nerve Sheath Tumors: Prognostic Factors and Survival in a Series of Patients Treated at a Single Institution. Cancer 2006, 107, 1065–1074. [Google Scholar] [CrossRef]
  10. Stucky, C.C.; Johnson, K.N.; Gray, R.J.; Pockaj, B.A.; Ocal, I.T.; Rose, P.S.; Wasif, N. Malignant peripheral nerve sheath tumors (MPNST): The Mayo Clinic experience. Ann. Surg. Oncol. 2012, 19, 878–885. [Google Scholar] [CrossRef]
  11. Ferner, R.E.; Gutmann, D.H. International Consensus Statement on Malignant Peripheral Nerve Sheath Tumors in Neurofibromatosis. Cancer Res. 2002, 62, 1573–1577. [Google Scholar]
  12. Kroep, J.R.; Ouali, M.; Gelderblom, H.; Le Cesne, A.; Dekker, T.J.; Van Glabbeke, M.; Hogendoorn, P.C.; Hohenberger, P. First-Line Chemotherapy for Malignant Peripheral Nerve Sheath Tumor (MPNST) versus Other Histological Soft Tissue Sarcoma Subtypes and as a Prognostic Factor for MPNST: An EORTC Soft Tissue and Bone Sarcoma Group Study. Ann. Oncol. 2011, 22, 207–214. [Google Scholar] [CrossRef] [PubMed]
  13. James, A.W.; Shurell, E.; Singh, A.; Dry, S.M.; Eilber, F.C. Malignant Peripheral Nerve Sheath Tumor. Surg. Oncol. Clin. N. Am. 2016, 25, 789–802. [Google Scholar] [CrossRef] [PubMed]
  14. Cichowski, K.; Shih, T.S.; Schmitt, E.; Santiago, S.; Reilly, K.; McLaughlin, M.E.; Bronson, R.T.; Jacks, T. Mouse Models of Tumor Development in Neurofibromatosis Type 1. Science 1999, 286, 2172–2176. [Google Scholar] [CrossRef] [PubMed]
  15. Vogel, K.S.; Klesse, L.J.; Velasco-Miguel, S.; Meyers, K.; Rushing, E.J.; Parada, L.F. Mouse Tumor Model for Neurofibromatosis Type 1. Science 1999, 286, 2176–2179. [Google Scholar] [CrossRef]
  16. Bradtmoller, M.; Hartmann, C.; Zietsch, J.; Jäschke, S.; Mautner, V.F.; Kurtz, A.; Park, S.J.; Baier, M.; Harder, A.; Reuss, D.; et al. Impaired Pten Expression in Human Malignant Peripheral Nerve Sheath Tumours. PLoS ONE 2012. [Google Scholar] [CrossRef]
  17. Keng, V.W.; Rahrmann, E.P.; Watson, A.L.; Tschida, B.R.; Moertel, C.L.; Jessen, W.J.; Rizvi, T.A.; Collins, M.H.; Ratner, N.; Largaespada, D.A. PTEN and NF1 inactivation in Schwann cells produces a severe phenotype in the peripheral nervous system that promotes the development and malignant progression of peripheral nerve sheath tumors. Cancer Res. 2012, 72, 3405–3413. [Google Scholar] [CrossRef] [Green Version]
  18. Gregorian, C.; Nakashima, J.; Dry, S.M.; Nghiemphu, P.L.; Smith, K.B.; Ao, Y.; Dang, J.; Lawson, G.; Mellinghoff, I.K.; Mischel, P.S.; et al. PTEN dosage is essential for neurofibroma development and malignant transformation. Proc. Natl. Acad. Sci. USA 2009, 106, 19479–19484. [Google Scholar] [CrossRef] [Green Version]
  19. Kourea, H.P.; Orlow, I.; Scheithauer, B.W.; Cordon-Cardo, C.; Woodruff, J.M. Deletions of the INK4A gene occur in malignant peripheral nerve sheath tumors but not in neurofibromas. Am. J. Path. 1999, 155, 1855–1860. [Google Scholar] [CrossRef] [Green Version]
  20. Lu, H.C.; Eulo, V.; Apicelli, A.J.; Pekmezci, M.; Tao, Y.; Luo, J.; Hirbe, A.C.; Dahiya, S. Aberrant ATRX protein expression is associated with poor overall survival in NF1-MPNST. Oncotarget 2018, 9, 23018–23028. [Google Scholar] [CrossRef] [Green Version]
  21. Lee, W.; Teckie, S.; Wiesner, T.; Ran, L.; Prieto Granada, C.N.; Lin, M.; Zhu, S.; Cao, Z.; Liang, Y.; Sboner, A.; et al. PRC2 is recurrently inactivated through EED or SUZ12 loss in malignant peripheral nerve sheath tumors. Nat. Genet. 2014, 46, 1227–1232. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Holtkamp, N.; Okuducu, A.F.; Mucha, J.; Afanasieva, A.; Hartmann, C.; Atallah, I.; Estevez-Schwarz, L.; Mawrin, C.; Friedrich, R.E.; Mautner, V.F.; et al. Mutation and expression of PDGFRA and KIT in malignant peripheral nerve sheath tumors, and its implications for imatinib sensitivity. Carcinogenesis 2006, 27, 664–671. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. DeClue, J.E.; Heffelfinger, S.; Benvenuto, G.; Ling, B.; Li, S.; Rui, W.; Vass, W.C.; Viskochil, D.; Ratner, N. Epidermal growth factor receptor expression in neurofibromatosis type 1-related tumors and NF1 animal models. J. Clin. Investig. 2000, 105, 1233–1241. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Yang, J.; Ylipää, A.; Sun, Y.; Zheng, H.; Chen, K.; Nykter, M.; Trent, J.; Ratner, N.; Lev, D.C.; Zhang, W. Genomic and molecular characterization of malignant peripheral nerve sheath tumor identifies the IGF1R pathway as a primary target for treatment. Clin. Cancer Res. 2011, 17, 7563–7573. [Google Scholar] [CrossRef] [Green Version]
  25. Symposium on Linkage of von Recklinghausen Neurofibromatosis (NF1). Closing in on the gene for von Recklinghausen neurofibromatosis. Genomics 1987, 1, 335–383.
  26. Talevich, E.; Shain, A.H.; Botton, T.; Bastian, B.C. CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing. PLoS Comput. Biol. 2016, 12, e1004873. [Google Scholar] [CrossRef]
  27. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv Preprint 2013, arXiv:1303.3997. [Google Scholar]
  28. Chen, X.; Schulz-Trieglaff, O.; Shaw, R.; Barnes, B.; Schlesinger, F.; Källberg, M.; Cox, A.J.; Kruglyak, S.; Saunders, C.T. Manta: Rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 2015, 32, 1220–1222. [Google Scholar] [CrossRef]
  29. Koboldt, D.C.; Zhang, Q.; Larson, D.E.; Shen, D.; McLellan, M.D.; Lin, L.; Miller, C.A.; Mardis, E.R.; Ding, L.; Wilson, R.K. VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 2012, 22, 568–576. [Google Scholar] [CrossRef] [Green Version]
  30. Kim, S.; Scheffler, K.; Halpern, A.L.; Bekritsky, M.A.; Noh, E.; Källberg, M.; Chen, X.; Kim, Y.; Beyter, D.; Krusche, P.; et al. Strelka2: Fast and accurate calling of germline and somatic variants. Nat. Methods 2018, 15, 591–594. [Google Scholar] [CrossRef]
  31. Cibulskis, K.; Lawrence, M.S.; Carter, S.L.; Sivachenko, A.; Jaffe, D.; Sougnez, C.; Gabriel, S.; Meyerson, M.; Lander, E.S.; Getz, G. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 2013, 31, 213–219. [Google Scholar] [CrossRef] [PubMed]
  32. Ye, K.; Schulz, M.H.; Long, Q.; Apweiler, R.; Ning, Z. Pindel: A pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics (Oxford, England) 2009, 25, 2865–2871. [Google Scholar] [CrossRef] [PubMed]
  33. McLaren, W.; Gil, L.; Hunt, S.E.; Riat, H.S.; Ritchie, G.R.; Thormann, A.; Flicek, P.; Cunningham, F. The Ensembl Variant Effect Predictor. Genome Biol. 2016, 17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Bioconductor: Genomic Visualizations in R. Available online: https://bioconductor.org/packages/release/bioc/html/GenVisR.html (accessed on 24 February 2020).
  35. Bamford, S.; Dawson, E.; Forbes, S.; Clements, J.; Pettett, R.; Dogan, A.; Flanagan, A.; Teague, J.; Futreal, P.A.; Stratton, M.R.; et al. The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br. J. Cancer 2004, 91, 355–358. [Google Scholar] [CrossRef]
  36. Landrum, M.J.; Lee, J.M.; Benson, M.; Brown, G.; Chao, C.; Chitipiralla, S.; Gu, B.; Hart, J.; Hoffman, D.; Hoover, J.; et al. ClinVar: Public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 2016, 44, 862–868. [Google Scholar] [CrossRef] [Green Version]
  37. Pauline, C.; Ng, S.H. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003, 31, 3812–3814. [Google Scholar]
  38. Adzhubei, I.A.; Schmidt, S.; Peshkin, L.; Ramensky, V.E.; Gerasimova, A.; Bork, P.; Kondrashov, A.S.; Sunyaev, S.R. A method and server for predicting damaging missense mutations. Nat Methods 2010, 7, 248–249. [Google Scholar] [CrossRef] [Green Version]
  39. Miller, C.A.; White, B.S.; Dees, N.D.; Griffith, M.; Welch, J.S.; Griffith, O.L.; Vij, R.; Tomasson, M.H.; Graubert, T.A.; Walter, M.J.; et al. SciClone: Inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution. PLoS Comput. Biol. 2014. [Google Scholar] [CrossRef]
  40. Dang, H.X.; White, B.S.; Foltz, S.M.; Miller, C.A.; Luo, J.; Fields, R.C.; Maher, C.A. ClonEvol: Clonal ordering and visualization in cancer sequencing. Ann. Oncol. 2017, 28, 3076–3082. [Google Scholar] [CrossRef]
  41. Xing, Y.; Yu, T.; Wu, Y.N.; Roy, M.; Kim, J.; Lee, C. An expectation-maximization algorithm for probabilistic reconstructions of full-length isoforms from splice graphs. Nucleic Acids Res. 2006, 34, 3150–3160. [Google Scholar] [CrossRef] [Green Version]
  42. Robinson, M.D.; McCarthy, D.J.; Smyth, G.K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26, 139–140. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Warnes, G.R.; Bolker, B.; Bonebakker, L.; Gentleman, R.; Huber, W.; Liaw, A.; Lumley, T.; Maechler, M.; Magnusson, A.; Moeller, S.; et al. gplots: Various R Programming Tools for Plotting Data. Seattle, WA, USA, 2015. Available online: https://www.scienceopen.com/document?vid=0e5d8e31-1fe4-492f-a3d8-8cd71b2b8ad9 (accessed on 29 April 2020).
  44. Partek Flow Documentation: Gene-specific Analysis. Available online: https://documentation.partek.com/display/FLOWDOC/Gene-specific+Analysis (accessed on 24 February 2020).
  45. Thomas, P.D.; Campbell, M.J.; Kejariwal, A.; Mi, H.; Karlak, B.; Daverman, R.; Diemer, K.; Muruganujan, A.; Narechania, A. PANTHER: A library of protein families and subfamilies indexed by function. Genome Res. 2003, 13, 2129–2141. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Guillou, L.; Coindre, J.M.; Bonichon, F.; Nguyen, B.B.; Terrier, P.; Collin, F.; Vilain, M.O.; Mandard, A.M.; Le Doussal, V.; Leroux, A.; et al. Comparative study of the National Cancer Institute and French Federation of Cancer Centers Sarcoma Group grading systems in a population of 410 adult patients with soft tissue sarcoma. J. Clin. Oncol. 1997, 15, 350–362. [Google Scholar] [CrossRef] [PubMed]
  47. Gao, J.; Aksoy, B.A.; Dogrusoz, U.; Dresdner, G.; Gross, B.; Sumer, S.O.; Sun, Y.; Jacobsen, A.; Sinha, R.; Larsson, E.; et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal 2013. [Google Scholar] [CrossRef] [Green Version]
  48. Wang, T.; Wang, H.; Yang, S.; Guo, H.; Zhang, B.; Guo, H.; Wang, L.; Zhu, G.; Zhang, Y.; Zhou, H.; et al. Association of APEX1 and OGG1 gene polymorphisms with breast cancer risk among Han women in the Gansu Province of China. BMC Med. Genet. 2018, 19, 67. [Google Scholar] [CrossRef] [Green Version]
  49. Kim, H.B.; Lim, H.J.; Lee, H.J.; Park, J.H.; Park, S.G. Evaluation and Clinical Significance of Jagged-1-activated Notch Signaling by APEX1 in Colorectal Cancer. Anticancer Res. 2019, 39, 6097–6105. [Google Scholar] [CrossRef]
  50. Kim, H.B.; Cho, W.J.; Choi, N.G.; Kim, S.S.; Park, J.H.; Lee, H.J.; Park, S.G. Clinical implications of APEX1 and Jagged1 as chemoresistance factors in biliary tract cancer. Ann. Surg. Treat. Res. 2017, 92, 15–22. [Google Scholar] [CrossRef] [Green Version]
  51. Blazquez, L.; Emmett, W.; Faraway, R.; Pineda, J.M.B.; Bajew, S.; Gohr, A.; Haberman, N.; Sibley, C.R.; Bradley, R.K.; Irimia, M.; et al. Exon Junction Complex Shapes the Transcriptome by Repressing Recursive Splicing. Mol. Cell 2018, 72, 496–509. [Google Scholar] [CrossRef]
  52. Shen, Y.N.; Bae, I.S.; Park, G.H.; Choi, H.S.; Lee, K.H.; Kim, S.H. MicroRNA-196b enhances the radiosensitivity of SNU-638 gastric cancer cells by targeting RAD23B. Biomed. Pharmacother. 2018, 105, 362–369. [Google Scholar] [CrossRef]
  53. Linge, A.; Maurya, P.; Friedrich, K.; Baretton, G.B.; Kelly, S.; Henry, M.; Clynes, M.; Larkin, A.; Meleady, P. Identification and functional validation of RAD23B as a potential protein in human breast cancer progression. J. Proteome Res. 2014, 13, 3212–3222. [Google Scholar] [CrossRef]
  54. Luo, C.; Cheng, Y.; Liu, Y.; Chen, L.; Liu, L.; Wei, N.; Xie, Z.; Wu, W.; Feng, Y. SRSF2 Regulates Alternative Splicing to Drive Hepatocellular Carcinoma Development. Cancer Res. 2017, 77, 1168–1178. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Liang, Y.; Tebaldi, T.; Rejeski, K.; Joshi, P.; Stefani, G.; Taylor, A.; Song, Y.; Vasic, R.; Maziarz, J.; Balasubramanian, K. SRSF2 mutations drive oncogenesis by activating a global program of aberrant alternative splicing in hematopoietic cells. Leukemia 2018, 32, 2659–2671. [Google Scholar] [CrossRef] [PubMed]
  56. Gu, L.; Lu, L.; Zhou, D.; Liu, Z. Long Noncoding RNA BCYRN1 Promotes the Proliferation of Colorectal Cancer Cells via Up-Regulating NPR3 Expression. Cell Physiol. Biochem. 2018, 48, 2337–2349. [Google Scholar] [CrossRef]
  57. Li, X.; Li, J.; Li, F. P21 activated kinase 4 binds translation elongation factor eEF1A1 to promote gastric cancer cell migration and invasion. Oncol. Rep. 2017, 37, 2857–2864. [Google Scholar] [CrossRef]
  58. Shi, N.; Chen, X.; Liu, R.; Wang, D.; Su, M.; Wang, Q.; He, A.; Gu, H. Eukaryotic elongation factors 2 promotes tumor cell proliferation and correlates with poor prognosis in ovarian cancer. Tissue Cell 2018, 53, 53–60. [Google Scholar] [CrossRef] [PubMed]
  59. Wang, H.; He, Z.; Xia, L.; Zhang, W.; Xu, L.; Yue, X.; Ru, X.; Xu, Y. PSMB4 overexpression enhances the cell growth and viability of breast cancer cells leading to a poor prognosis. Oncol. Rep. 2018, 40, 2343–2352. [Google Scholar] [CrossRef] [Green Version]
  60. Liu, R.; Lu, S.; Deng, Y.; Yang, S.; He, S.; Cai, J.; Qiang, F.; Chen, C.; Zhang, W.; Zhao, S.; et al. PSMB4 expression associates with epithelial ovarian cancer growth and poor prognosis. Arch. Gynecol. Obstet. 2016, 293, 1297–1307. [Google Scholar] [CrossRef]
  61. Xu, X.; Xiong, X.; Sun, Y. The role of ribosomal proteins in the regulation of cell proliferation, tumorigenesis, and genomic integrity. Sci. China Life Sci. 2016, 59, 656–672. [Google Scholar] [CrossRef]
  62. Nallar, S.C.; Kalvakolanu, D.V. Regulation of snoRNAs in Cancer: Close Encounters with Interferon. J. Interferon Cytokine Res. 2013, 33, 189–198. [Google Scholar] [CrossRef] [Green Version]
  63. Falaleeva, M.; Welden, J.R.; Duncan, M.J.; Stamm, S. C/D-box snoRNAs form methylating and non-methylating ribonucleoprotein complexes: Old dogs show new tricks. Bioessays 2017, 39. [Google Scholar] [CrossRef]
  64. Dong, X.; Han, Y.; Sun, Z.; Xu, J. Actin γ 1, a new skin cancer pathogenic gene, identified by the biological feature-based classification. J. Cell Biochem. 2018, 119, 1406–1419. [Google Scholar] [CrossRef] [PubMed]
  65. Luo, Y.; Kong, F.; Wang, Z.; Chen, D.; Liu, Q.; Wang, T.; Xu, R.; Wang, X.; Yang, J.Y. Loss of ASAP3 destabilizes cytoskeletal protein ACTG1 to suppress cancer cell migration. Mol. Med. Rep. 2014, 9, 387–394. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Munschauer, M.; Nguyen, C.T.; Sirokman, K.; Hartigan, C.R.; Hogstrom, L.; Engreitz, J.M.; Ulirsch, J.C.; Fulco, C.P.; Subramanian, V.; Chen, J.; et al. The NORAD lncRNA assembles a topoisomerase complex critical for genome stability. Nature 2018, 561, 132–136. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Liang, L.; Li, Q.; Huang, L.Y.; Li, D.W.; Wang, Y.W.; Li, X.X.; Cai, S.J. Loss of ARHGDIA expression is associated with poor prognosis in HCC and promotes invasion and metastasis of HCC cells. Int. J. Oncol. 2014, 45, 659–666. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Riihilä, P.; Viiklepp, K.; Nissinen, L.; Farshchian, M.; Kallajoki, M.; Kivisaari, A.; Meri, S.; Peltonen, J.; Peltonen, S.; Kähäri, V.M. Tumour-cell-derived complement components C1r and C1s promote growth of cutaneous squamous cell carcinoma. Br. J. Dermatol. 2020, 182, 658–670. [Google Scholar] [CrossRef] [PubMed]
  69. Wheeler, L.J.; Watson, Z.L.; Qamar, L.; Yamamoto, T.M.; Post, M.D.; Berning, A.A.; Spillman, M.A.; Behbakht, K.; Bitler, B.G. CBX2 identified as driver of anoikis escape and dissemination in high grade serous ovarian cancer. Oncogenesis 2018, 7, 92. [Google Scholar] [CrossRef] [Green Version]
  70. Liu, J.; Shen, J.X.; Wu, H.T.; Li, X.L.; Wen, X.F.; Du, C.W.; Zhang, G.J. Collagen 1A1 (COL1A1) promotes metastasis of breast cancer and is a potential therapeutic target. Discov. Med. 2018, 25, 211–223. [Google Scholar]
  71. Menendez, J.A.; Lupu, R. Fatty acid synthase and the lipogenic phenotype in cancer pathogenesis. Nat. Rev. Cancer 2007, 7, 763–777. [Google Scholar] [CrossRef]
  72. Kamil, M.; Shinsato, Y.; Higa, N.; Hirano, T.; Idogawa, M.; Takajo, T.; Minami, K.; Shimokawa, M.; Yamamoto, M.; Kawahara, K.; et al. High filamin-C expression predicts enhanced invasiveness and poor outcome in glioblastoma multiforme. Br. J. Cancer 2019, 120, 819–826. [Google Scholar] [CrossRef] [Green Version]
  73. Zhu, Y.; Kakinuma, N.; Wang, Y.; Kiyama, R. Kank proteins: A new family of ankyrin-repeat domain-containing proteins. Biochim. Biophys. Act 2008, 1780, 128–133. [Google Scholar] [CrossRef]
  74. Trussart, C.; Pirlot, C.; Di Valentin, E.; Piette, J.; Habraken, Y. Melanoma antigen-D2 controls cell cycle progression and modulates the DNA damage response. Biochem. Pharmacol. 2018, 153, 217–229. [Google Scholar] [CrossRef] [PubMed]
  75. Wang, P.C.; Hu, Z.Q.; Zhou, S.L.; Zhan, H.; Zhou, Z.J.; Luo, C.B.; Huang, X.W. Downregulation of MAGE family member H1 enhances hepatocellular carcinoma progression and serves as a biomarker for patient prognosis. Future Oncol. 2018, 14, 1177–1186. [Google Scholar] [CrossRef] [PubMed]
  76. Qu, K.; Wang, Z.; Fan, H.; Li, J.; Liu, J.; Li, P.; Liang, Z.; An, H.; Jiang, Y.; Lin, Q.; et al. MCM7 promotes cancer progression through cyclin D1-dependent signaling and serves as a prognostic marker for patients with hepatocellular carcinoma. Cell Death Dis 2017, 8, e2603. [Google Scholar] [CrossRef] [PubMed]
  77. Nguyen, A.T.; Chia, J.; Ros, M.; Hui, K.M.; Saltel, F.; Bard, F. Organelle Specific O-Glycosylation Drives MMP14 Activation, Tumor Growth, and Metastasis. Cancer Cell 2017, 32, 639–653. [Google Scholar] [CrossRef] [Green Version]
  78. Hu, G.; Zhang, J.; Xu, F.; Deng, H.; Zhang, W.; Kang, S.; Liang, W. Stomatin-like protein 2 inhibits cisplatin-induced apoptosis through MEK/ERK signaling and the mitochondrial apoptosis pathway in cervical cancer cells. Cancer Sci. 2018, 109, 1357–1368. [Google Scholar] [CrossRef] [Green Version]
  79. Du, W.L.; Fang, Q.; Chen, Y.; Teng, J.W.; Xiao, Y.S.; Xie, P.; Jin, B.; Wang, J.Q. Effect of silencing the T-Box transcription factor TBX2 in prostate cancer PC3 and LNCaP cells. Mol. Med. Rep. 2017, 16, 6050–6058. [Google Scholar] [CrossRef] [Green Version]
  80. Czerwińska, P.; Mazurek, S.; Wiznerowicz, M. The complexity of TRIM28 contribution to cancer. J. Biomed. Sci. 2017, 29, 63. [Google Scholar] [CrossRef]
  81. Lan, B.; Chai, S.; Wang, P.; Wang, K. VCP/p97/Cdc48, A Linking of Protein Homeostasis and Cancer Therapy. Curr. Mol. Med. 2017, 17, 608–618. [Google Scholar] [CrossRef]
  82. Hwang, W.; Chiu, Y.F.; Kuo, M.H.; Lee, K.L.; Lee, A.C.; Yu, C.C.; Chang, J.L.; Huang, W.C.; Hsiao, S.H.; Lin, S.E.; et al. Expression of Neuroendocrine Factor VGF in Lung Cancer Cells Confers Resistance to EGFR Kinase Inhibitors and Triggers Epithelial-to-Mesenchymal Transition. Cancer Res. 2017, 77, 3013–3026. [Google Scholar] [CrossRef] [Green Version]
  83. De Blasio, A.; Vento, R.; Di Fiore, R. Mcl-1 targeting could be an intriguing perspective to cure cancer. J. Cell Physiol. 2018, 233, 8482–8498. [Google Scholar] [CrossRef]
  84. Louie, S.M.; Grossman, E.A.; Crawford, L.A.; Ding, L.; Camarda, R.; Huffman, T.R.; Miyamoto, D.K.; Goga, A.; Weerapana, E.; Nomura, D.K. GSTP1 Is a Driver of Triple-Negative Breast Cancer Cell Metabolism and Pathogenicity. Cell Chem. Biol. 2016, 23, 567–578. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Bianchi, M.; Crinelli, R.; Giacomini, E.; Carloni, E.; Radici, L.; Scarpa, E.S.; Tasini, F.; Magnani, M. A negative feedback mechanism links UBC gene expression to ubiquitin levels by affecting RNA splicing rather than transcription. Sci. Rep. 2019, 9, 18556. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. Lin, L.; Li, X.; Pan, C.; Lin, W.; Shao, R.; Liu, Y.; Zhang, J.; Luo, Y.; Qian, K.; Shi, M.; et al. ATXN2L upregulated by epidermal growth factor promotes gastric cancer cell invasiveness and oxaliplatin resistance. Cell Death Dis. 2019, 10, 173. [Google Scholar] [CrossRef] [PubMed]
  87. Livingstone, C. IGF2 and cancer. Endocr. Relat. Cancer 2013, 20, 321–339. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  88. Feng, W.; Wang, C.; Liang, C.; Yang, H.; Chen, D.; Yu, X.; Zhao, W.; Geng, D.; Li, S.; Chen, Z.; et al. The Dysregulated Expression of KCNQ1OT1 and Its Interaction with Downstream Factors miR-145/CCNE2 in Breast Cancer Cells. Cell Physiol. Biochem. 2018, 49, 432–446. [Google Scholar] [CrossRef]
  89. Zhang, Y.; Hu, J.F.; Wang, H.; Cui, J.; Gao, S.; Hoffman, A.R.; Li, W. CRISPR Cas9-guided chromatin immunoprecipitation identifies miR483 as an epigenetic modulator of IGF2 imprinting in tumors. Oncotarget 2017, 8, 34177–34190. [Google Scholar] [CrossRef] [Green Version]
  90. Gong, C.Y.; Tang, R.; Nan, W.; Zhou, K.S.; Zhang, H.H. Role of SNHG16 in human cancer. Clin. Chim. Acta 2020, 503, 175–180. [Google Scholar] [CrossRef]
  91. Gerlinger, M.; Horswell, S.; Larkin, J.; Rowan, A.J.; Salm, M.P.; Varela, I.; Fisher, R.; McGranahan, N.; Matthews, N.; Santos, C.R.; et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. 2014, 46, 225–233. [Google Scholar] [CrossRef]
  92. Yates, L.R.; Gerstung, M.; Knappskog, S.; Desmedt, C.; Gundem, G.; Van Loo, P.; Aas, T.; Alexandrov, L.B.; Larsimont, D.; Davies, H.; et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat. Med. 2015, 21, 751–759. [Google Scholar] [CrossRef]
  93. Hao, J.J.; Lin, D.C.; Dinh, H.Q.; Mayakonda, A.; Jiang, Y.Y.; Chang, C.; Jiang, Y.; Lu, C.C.; Shi, Z.Z.; Xu, X.; et al. Spatial intratumoral heterogeneity and temporal clonal evolution in esophageal squamous cell carcinoma. Nat. Genet. 2016, 48, 1500–1507. [Google Scholar] [CrossRef]
  94. Jamal-Hanjani, M.; Wilson, G.A.; McGranahan, N.; Birkbak, N.J.; Watkins, T.B.K.; Veeriah, S.; Shafi, S.; Johnson, D.H.; Mitter, R.; Rosenthal, R.; et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 2017, 376, 2109–2121. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Harbst, K.; Lauss, M.; Cirenajwis, H.; Isaksson, K.; Rosengren, F.; Törngren, T.; Kvist, A.; Johansson, M.C.; Vallon-Christersson, J.; Baldetorp, B.; et al. Multiregion whole-exome sequencing uncovers the genetic evolution and mutational heterogeneity of early-stage metastatic melanoma. Cancer Res. 2016, 76, 4765–4774. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  96. Peacock, J.D.; Pridgeon, M.G.; Tovar, E.A.; Essenburg, C.J.; Bowman, M.; Madaj, Z.; Koeman, J.; Boguslawski, E.A.; Grit, J.; Dodd, R.D.; et al. Genomic Status of MET Potentiates Sensitivity to MET and MEK Inhibition in NF1-Related Malignant Peripheral Nerve Sheath Tumors. Cancer Res. 2018, 78, 3672–3678. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Carriό, M.; Gel, B.; Terribas, E.; Zucchiatti, A.C.; Moliné, T.; Rosas, I.; Teulé, Á.; Ramón, Y.; Cajal, S.; López-Gutiérrez, J.C.; et al. Analysis of intratumor heterogeneity in Neurofibromatosis type 1 plexiform neurofibromas and neurofibromas with atypical features: Correlating histological and genomic findings. Hum. Mutat. 2018, 39, 1112–1125. [Google Scholar] [CrossRef] [PubMed]
  98. Nackley, A.G.; Shabalina, S.A.; Tchivileva, I.E.; Satterfield, K.; Korchynskyi, O.; Makarov, S.S.; Maixner, W.; Diatchenko, L. Human catechol-O-methyltransferase haplotypes modulate protein expression by altering mRNA secondary structure. Science 2006, 314, 1930–1933. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  99. Kimchi-Sarfaty, C.; Oh, J.M.; Kim, I.W.; Sauna, Z.E.; Calcagno, A.M.; Ambudkar, S.V.; Gottesman, M.M. A “silent” polymorphism in the MDR1 gene changes substrate specificity. Science 2007, 315, 525–528. [Google Scholar] [CrossRef] [Green Version]
  100. Kudla, G.; Murray, A.W.; Tollervey, D.; Plotkin, J.B. Coding-sequence determinants of gene expression in Escherichia coli. Science 2009, 324, 255–258. [Google Scholar] [CrossRef] [Green Version]
  101. Sauna, Z.E.; Kimchi-Sarfaty, C. Understanding the contribution of synonymous mutations to human disease. Nat. Rev. Genet. 2011, 12, 683–691. [Google Scholar] [CrossRef]
  102. Pey, J.; San José-Eneriz, E.; Ochoa, M.C.; Apaolaza, I.; de Atauri, P.; Rubio, A.; Cendoya, X.; Miranda, E.; Garate, L.; Cascante, M.; et al. In-silico gene essentiality analysis of polyamine biosynthesis reveals APRT as a potential target in cancer. Sci. Rep. 2017, 7, 14358. [Google Scholar] [CrossRef] [Green Version]
  103. Shen, L.; Ke, Q.; Chai, J.; Zhang, C.; Qiu, L.; Peng, F.; Deng, X.; Luo, Z. PAG1 promotes the inherent radioresistance of laryngeal cancer cells via activation of STAT3. Exp. Cell Res. 2018, 370, 127–136. [Google Scholar] [CrossRef]
  104. Agarwal, S.; Ghosh, R.; Chen, Z.; Lakoma, A.; Gunaratne, P.H.; Kim, E.S.; Shohet, J.M. Transmembrane adaptor protein PAG1 is a novel tumor suppressor in neuroblastoma. Oncotarget 2016, 7, 24018–24026. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  105. Jiang, D.; Hu, B.; Wei, L.; Xiong, Y.; Wang, G.; Ni, T.; Zong, C.; Ni, R.; Lu, C. High expression of vacuolar protein sorting 4B (VPS4B) is associated with accelerated cell proliferation and poor prognosis in human hepatocellular carcinoma. Pathol. Res. Pract. 2015, 211, 240–247. [Google Scholar] [CrossRef] [PubMed]
  106. Liu, Y.; Lv, L.; Xue, Q.; Wan, C.; Ni, T.; Chen, B.; Liu, Y.; Zhou, Y.; Ni, R.; Mao, G. Vacuolar protein sorting 4B, an ATPase protein positively regulates the progression of NSCLC via promoting cell division. Mol. Cell Biochem. 2013, 381, 163–171. [Google Scholar] [CrossRef] [PubMed]
  107. Lin, H.H.; Li, X.; Chen, J.L.; Sun, X.; Cooper, F.N.; Chen, Y.R.; Zhang, W.; Chung, Y.; Li, A.; Cheng, C.T.; et al. Identification of an AAA ATPase VPS4B-dependent pathway that modulates epidermal growth factor receptor abundance and signaling during hypoxia. Mol. Cell Biol. 2012, 32, 1124–1138. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  108. Hazawa, M.; Lin, D.C.; Handral, H.; Xu, L.; Chen, Y.; Jiang, Y.Y.; Mayakonda, A.; Ding, L.W.; Meng, X.; Sharma, A.; et al. ZNF750 is a lineage-specific tumour suppressor in squamous cell carcinoma. Oncogene 2017, 36, 2243–2254. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  109. Zhang, P.; He, Q.; Lei, Y.; Li, Y.; Wen, X.; Hong, M.; Zhang, J.; Ren, X.; Wang, Y.; Yang, X.; et al. m6A-mediated ZNF750 repression facilitates nasopharyngeal carcinoma progression. Cell Death Dis. 2018, 5, 1169. [Google Scholar] [CrossRef] [Green Version]
  110. Feng, T.; Sun, L.; Qi, W.; Pan, F.; Lv, J.; Guo, J.; Zhao, S.; Ding, A.; Qiu, W. Prognostic significance of Tspan9 in gastric cancer. Mol. Clin. Oncol. 2016, 5, 231–236. [Google Scholar] [CrossRef] [Green Version]
  111. Qi, Y.; Lv, J.; Liu, S.; Sun, L.; Wang, Y.; Li, H.; Qi, W.; Qiu, W. TSPAN9 and EMILIN1 synergistically inhibit the migration and invasion of gastric cancer cells by increasing TSPAN9 expression. BMC Cancer 2019, 19, 630. [Google Scholar] [CrossRef]
  112. Xiao, T.; Li, W.; Wang, X.; Xu, H.; Yang, J.; Wu, Q.; Huang, Y.; Geradts, J.; Jiang, P.; Fei, T.; et al. Estrogen-regulated feedback loop limits the efficacy of estrogen receptor–targeted breast cancer therapy. Proc. Natl. Acad. Sci. USA 2018, 115, 7869–7878. [Google Scholar] [CrossRef] [Green Version]
  113. Smith, H.W.; Hirukawa, A.; Sanguin-Gendreau, V.; Nandi, I.; Dufour, C.R.; Zuo, D.; Tandoc, K.; Leibovitch, M.; Singh, S.; Rennhack, J.P.; et al. An ErbB2/c-Src axis links bioenergetics with PRC2 translation to drive epigenetic reprogramming and mammary tumorigenesis. Nat. Commun. 2019, 10. [Google Scholar] [CrossRef]
  114. Yang, C.C.; Fazli, L.; Loguercio, S.; Zharkikh, I.; Aza-Blanc, P.; Gleave, M.E.; Wolf, D.A. Downregulation of c-SRC kinase CSK promotes castration resistant prostate cancer and pinpoints a novel disease subclass. Oncotarget 2015, 6, 22060–22071. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  115. Guiducci, C.; Di Carlo, E.; Parenza, M.; Hitt, M.; Giovarelli, M.; Musiani, P.; Colombo, M.P. Intralesional injection of adenovirus encoding CC chemokine ligand 16 inhibits mammary tumor growth and prevents metastatic-induced death after surgical removal of the treated primary tumor. J. Immunol. 2004, 172, 4026–4036. [Google Scholar] [CrossRef] [PubMed]
  116. Paulsson, K. Genomic heterogeneity in acute leukemia. Cytogenet. Genome Res. 2013, 139, 174–180. [Google Scholar] [CrossRef] [PubMed]
  117. Saadatpour, A.; Guo, G.; Orkin, S.H.; Yuan, G.C. Characterizing heterogeneity in leukemic cells using single-cell gene expression analysis. Genome Biol. 2014, 15, 525. [Google Scholar] [CrossRef] [Green Version]
  118. Navin, N.; Kendall, J.; Troge, J.; Andrews, P.; Rodgers, L.; McIndoo, J.; Cook, K.; Stepansky, A.; Levy, D.; Esposito, D.; et al. Tumor evolution inferred by single cell sequencing. Nature 2011, 472, 90–94. [Google Scholar] [CrossRef] [Green Version]
  119. Xu, X.; Hou, Y.; Yin, X.; Bao, L.; Tang, A.; Song, L.; Li, F.; Tsang, S.; Wu, K.; Wu, H.; et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 2012, 148, 886–895. [Google Scholar] [CrossRef] [Green Version]
Genes 11 00499 g001 550
Figure 1. Malignant peripheral nerve sheath tumor (MPNST) sampled areas. Area 1 shows an area centrally located in MPNST, Area 2 an area of hemorrhage, and Area 3 an area of necrosis.
Figure 1. Malignant peripheral nerve sheath tumor (MPNST) sampled areas. Area 1 shows an area centrally located in MPNST, Area 2 an area of hemorrhage, and Area 3 an area of necrosis.
Genes 11 00499 g001
Genes 11 00499 g002 550
Figure 2. H&E stained sections of the biopsy sites. H&E stained sections (20X) show areas (#1) of relatively uniform, spindled cells with fascicular growth pattern, characteristic for MPNST. Sampled area #2 shows evidence of hemorrhage within the tumor, a feature commonly seen in MPNST. Area #3 shows abundant tumor necrosis.
Figure 2. H&E stained sections of the biopsy sites. H&E stained sections (20X) show areas (#1) of relatively uniform, spindled cells with fascicular growth pattern, characteristic for MPNST. Sampled area #2 shows evidence of hemorrhage within the tumor, a feature commonly seen in MPNST. Area #3 shows abundant tumor necrosis.
Genes 11 00499 g002
Genes 11 00499 g003 550
Figure 3. Location of NF1 germline variant. One intronic germline variant, NC_000017.11:g.31296270C>T (rs11080149). was identified and is depicted in this figure.
Figure 3. Location of NF1 germline variant. One intronic germline variant, NC_000017.11:g.31296270C>T (rs11080149). was identified and is depicted in this figure.
Genes 11 00499 g003
Genes 11 00499 g004 550
Figure 4. RNA-Seq Heatmap. Normalized read counts by counts per million (CPM) in differentially expressed genes are depicted here. Distinct gene expression profiles can be appreciated in each biopsied area. Each column is depicted as list of genes.
Figure 4. RNA-Seq Heatmap. Normalized read counts by counts per million (CPM) in differentially expressed genes are depicted here. Distinct gene expression profiles can be appreciated in each biopsied area. Each column is depicted as list of genes.
Genes 11 00499 g004
Genes 11 00499 g005 550
Figure 5. Copy Number Variation Plot. Copy number variation plots for each biopsied site demonstrate distinct copy number signatures.
Figure 5. Copy Number Variation Plot. Copy number variation plots for each biopsied site demonstrate distinct copy number signatures.
Genes 11 00499 g005
Genes 11 00499 g006 550
Figure 6. Somatic Variant Waterfall Plot. All somatic variants displayed on a waterfall plot. Each row represents a gene. Distinct somatic variant signatures are appreciated.
Figure 6. Somatic Variant Waterfall Plot. All somatic variants displayed on a waterfall plot. Each row represents a gene. Distinct somatic variant signatures are appreciated.
Genes 11 00499 g006
Genes 11 00499 g007 550
Figure 7. Phylogenetic Tree. A predicted phylogenetic tree of the tumor subclones.
Figure 7. Phylogenetic Tree. A predicted phylogenetic tree of the tumor subclones.
Genes 11 00499 g007
Table
Table 1. Patient Characteristics.
Table 1. Patient Characteristics.
Age at Diagnosis, YearsSexTumor LocationTumor Size/GradeSurgical Margin StatusDisease StatusMetastasisAdjuvant TreatmentOS *, Months
40MaleLeft neck10.2 cm, Grade 3 1NegativeRecurredLungNone33
1 By French Federation of Cancer Centers Sarcoma Group Grading System (FNCLCC) [46]; * OS = Overall Survival-time from diagnosis of MPNST to death.
Table
Table 2. Top Differentially Expressed Genes. The gene-specific analysis was used to test for differential expression of genes or transcript between sample regions in Partek® Flow®. Statistical cutoff are made by these following parameters: p-value <= 0.05; FDR step up <= 0.05; Fold Change <−2 or >2.
Table 2. Top Differentially Expressed Genes. The gene-specific analysis was used to test for differential expression of genes or transcript between sample regions in Partek® Flow®. Statistical cutoff are made by these following parameters: p-value <= 0.05; FDR step up <= 0.05; Fold Change <−2 or >2.
Gene Symbolp-Value (1 vs. 2)Fold Change (1 vs. 2)p-Value (1 vs. 3)Fold Change (1 vs. 3)p-Value (2 vs. 3)Fold Change (2 vs. 3)
EEF1A12.04 × 10−84−3.323.33 × 10−162.201.35 × 10−1197.31
RPS274.32 × 10−24−2.517.64 × 10−133.014.27 × 10−467.55
RPS27A1.69 × 10−12−2.629.42 × 10−052.274.16 × 10−215.95
H3C37.46 × 10−12−4.515.05 × 10−0411.25.54 × 10−0950.6
RPLP12.36 × 10−10−2.577.25 × 10−042.132.43 × 10−175.48
SNORD133.24 × 10−103.008.25 × 10−62−4.913.52 × 10−66−14.8
RPLP01.05 × 10−09−2.261.60 × 10−042.091.73 × 10−184.72
TPI11.65 × 10−08−2.275.61 × 10−042.086.52 × 10−164.72
RPL23AP423.77 × 10−07−2.218.40 × 10−042.168.65 × 10−144.78
RPS235.34 × 10−06−2.461.17 × 10−032.929.16 × 10−117.19
MT-TI4.64 × 10−053.441.19 × 10−15−3.676.36 × 10−20−12.6
SNORA812.28 × 10−0433.34.00 × 10−11−3.395.12 × 10−07−11.3
RNY12.45 × 10−042.654.67 × 10−24−4.718.10 × 10−27−12.5
RNVU1-315.00 × 10−04−4.183.83 × 10−14−17.77.07 × 10−13−4.23
MT-TM6.37 × 10−043.702.29 × 10−07−2.892.69 × 10−11−10.7
TMSB4XP61.16 × 10−033.192.89 × 10−04−2.157.91 × 10−09−6.87
Table 3. (a) Details of the Tumor Related Somatic Variants in Coding Regions. Each gene with a somatic point variant is listed along with the area in which the variant occurred, the type of variant, the amino acid change, and whether or not the variant occurs in a putative functional domain. The final column lists whether or not the gene expression is altered in the area in which the variant occurred. The magnitude of gene expression is expressed as any of the following: NA indicates no change; “-” indicates 1-2 fold decrease in gene expression; “- -” indicates greater than 2 fold decrease in gene expression; “+” indicates 1-2 fold increase in gene expression; “+ +” indicates greater than 2 fold increase in gene expression compared to two other areas. Pathogenicity predictions are made based on SIFT and PolyPhen scores. (b) Details of the Tumor Related Somatic Frameshift Variants in Coding Regions. Each gene with a somatic point variant resulting in a frameshift is listed along with the area in which the frameshift variant occurred and whether or not the frameshift variant occurs in a putative functional domain. (c) Details of the Tumor Related Somatic Variants in Non-coding Transcript Exons, Untranslated Regions, Introns, and Upstream and Downstream Genes. Each gene with a somatic point variant is listed along with the area in which the variant occurred, the genomic location, the type of variant, and whether or not the gene expression is altered in the area in which the variant occurred. The magnitude of gene expression is expressed as: NA indicates no change; “-“ indicates 1-2 fold decrease in gene expression; “- -” indicates greater than 2 fold decrease in gene expression; “+” indicates 1-2 fold increase in gene expression; “+ +” indicates greater than 2 fold increase in gene expression compared to two other areas. The final column lists the potential impact rating as evaluated by VEP. All of these variants are listed as “modifier” indicating that predictions are difficult or there is no evidence of impact.
Table
(a)
GeneAreaGenomic LocationVariantAmino Acid ChangeFunctional Domain AffectedGene Expression AlteredPathogenicity Prediction
C2orf911Chr2:41953024missensep.(Arg91Ile)NNAPossibly damaging
CCL161Chr17:35978161missensep.(Cys60Ser)YNAProbably damaging
PAG11Chr8:80984896synonymousp.(Pro252=)YNAUnknown
VPS13D1Chr1:12283596missensep.(Phe1832Val)N-Probably damaging
VPS4B1Chr18:63400074missensep.(Lys255Thr)YNAProbably damaging
ZNF7501Chr17:82830337synonymousp.(Pro659=)NNAUnknown
RIMBP3C2Chr22:21546513missensep.(Arg1488Ser)NNAPossibly Damaging
SPATA31A52Chr9:60919364missensep.(Leu970Phe)N-Possibly Damaging
CCDC273Chr1:3752496synonymousp.(Ile5=)N+Unknown
LETM23Chr8:38400906synonymousp.(Leu279=)Y+Unknown
NTRK23Chr9:84670796missensep.(Trp16Cys)NNAPossibly Damaging
Table
(b)
GeneAreaGenomic LocationVariantAmino Acid ChangeFunctional Domain Affected
CSK2Chr15:74798671frameshiftp.(Glu25fs)Y
TSPAN92Chr12:3283047frameshiftp.(Leu218fs)Y
Table
(c)
GeneAreaGenomic LocationVariantGene Expression AlteredIMPACT
MAP3K21Chr2:127387525intron-Modifier
RIPK31Chr14:24332669 or Chr14:24332869downstream gene+Modifier
RNPS11Chr16:2266329intron-Modifier
SNX321Chr11:65832561upstream gene-Modifier
AC138649.12Chr15:22768761intronNAModifier
FAM157B2Chr9:138231054non-coding transcript exon+Modifier
FANCD2P22Chr3:11871392non-coding transcript exon+Modifier
LAIR12Chr19:54358582intronNAModifier
NFAM12Chr22:42432412upstream gene+Modifier
TET22Chr4:105241954intronNAModifier
TMEM1142Chr16:85697153 prime UTRNAModifier
MOCS23Chr5:531094555 prime UTR-Unknown
PSMB23Chr1:356415745 prime UTRNAModifier
RUFY13Chr5:179608552intron-Modifier
WDR63Chr3:49005134upstream geneNAModifier
Z82190.23Chr22:31821630intronNAModifier
Table
Table 4. Differentially Expressed Gene Pathway Analysis. These genes were located in copy number aberrant regions defined as copy number more than 3 or lower 1 and also demonstrated differential expression by RNA seq. Different pathways are implicated in the distinct sections.
Table 4. Differentially Expressed Gene Pathway Analysis. These genes were located in copy number aberrant regions defined as copy number more than 3 or lower 1 and also demonstrated differential expression by RNA seq. Different pathways are implicated in the distinct sections.
LocationChromosomeStart PositionEnd PositionRaw Copy NumberGenesRole in Tumorigenesis
Area1chr1781509970815238473.151914ACTG1Anti-apoptosis, motility [64,65]
Area1chr1781887843818915863.151914ALYREFGenomic stability [66]
Area1chr1420455190204577724.883921APEX1Base-excision repair [49]
Area1chr1781867720818714063.151914ARHGDIAInvasiveness, metastasis [67]
Area1chr12708020870926075.842557C1RInflammation [68]
Area1chr1779778131797879833.109085CBX2Transcription [69]
Area1chr1750183288502016323.060268COL1A1Metastasis [70]
Area1chr1782078332820983323.562293FASNMetabolism [71]
Area1chr71288303761288592743.66148FLNCInvasiveness [72]
Area1chr1782050690820574703.562293GPS1COP9 signalosome subunit/ubiquitin-proteasome pathway
Area1chr1911164266111977917.563794KANK2Cytoskeleton formation [73]
Area1chrX54807598548160123.320925MAGED2Cell-cycle regulator [74]
Area1chrX55452104554535663.320925MAGEH1Proliferation [75]
Area1chr71000927271001019404.16605MCM7Proliferation [76]
Area1chr1422836556228490274.136412MMP14Invasiveness, metastasis [77]
Area1chr1439175182391832183.038443PNNSplicing [51]
Area1chr910728313610733219414.61502RAD23BNucleotide-excision repair [53]
Area1chr1849488452494925233.095593RPL17Ribosome biogenesis, protein translation [61]
Area1chrX54814369548144973.320925SNORA11Maturation of ribosomal RNA [62]
Area1chr71021940751021941644.159154SNORA48Maturation of ribosomal RNA
Area1chr2569266657013853.929294SOX11Transcription
Area1chr1776734114767373743.109085SRSF2Splicing [54]
Area1chr935099775351031953.374564STOML2Anti-apoptosis [78]
Area1chr1761399895614094663.52571TBX2Transcription [79]
Area1chr1958544090585507223.012426TRIM28Proliferation [80]
Area1chr935056063350732493.374564VCPProtein degradation [81]
Area1chr71011625081011655934.159154VGFTranscription [82]
Area2chr247335314473355144.114423BCYRN1Transcription [56]
Area2chr673515749735237973.582945EEF1A1Translation [57]
Area2chr19397605539854693.359182EEF2Translation [58]
Area2chr11505745501505797384.140715MCL1Anti-apoptosis [83]
Area2chr11513995331514019444.140715PSMB4Proteasomal function [59]
Area2chr1167583594675866603.211531GSTP1Metabolism [84]
Area2chr1565296050652961663.976034RNU5A-1RNA processing
Area2chr1565304676653047923.976034RNU5B-1RNA processing
Area2chr71489837541489838563.383375RNY3RNA processing
Area2chr1327251308272566916.141368RPL21Ribosome biogenesis, protein translation
Area2chr919375714193802543.739665RPS6Ribosome biogenesis, protein translation
Area2chr224273613242737414.326829SCARNA21RNA processing
Area2chr1578091171780912973.898802SNORA63Maturation of ribosomal RNA
Area2chr112221147122212713.552826SNORA70Maturation of ribosomal RNA
Area2chr210446713104468494.496897SNORA80BMaturation of ribosomal RNA
Area2chr121249116031249173683.034233UBCUbiquitin homeostasis [85]
Area3chr1628823034288372375.159031ATXN2LStress granule regulator [86]
Area3chr91368621181368662863.830137EDF1Transcription
Area3chr11212911121412387.774932IGF2Proliferation [87]
Area3chr11260832726999947.774932KCNQ1OT1Transcription [88]
Area3chr11213413321342097.774932MIR483Transcription [89]
Area3chr91274476731274514053.212283RPL12Ribosome biogenesis, protein translation
Area3chr1949487553494923083.051258RPL13ARibosome biogenesis, protein translation
Area3chr1948615327486195363.174325RPL18Ribosome biogenesis, protein translation
Area3chr1618126862093893.792397RPL22Ribosome biogenesis, protein translation
Area3chr1774203581742106553.363835RPL38Ribosome biogenesis, protein translation
Area3chr118096468128803.117378RPLP2Ribosome biogenesis, protein translation
Area3chr1949496364494996893.051258RPS11Ribosome biogenesis, protein translation
Area3chr1939433206394359483.408557RPS16Ribosome biogenesis, protein translation
Area3chr16196205119648603.301972RPS2Ribosome biogenesis, protein translation
Area3chr19832115783233403.044231RPS28Ribosome biogenesis, protein translation
Area3chr1776557765765653483.374444SNHG16Transcription [90]
Area3chr16196233319624663.301972SNORA10Maturation of ribosomal RNA
Area3chr230187433301875663.83836SNORA10BMaturation of ribosomal RNA
Area3chr91367261041367262343.830137SNORA17BMaturation of ribosomal RNA
Area3chrY16138247161383793.968437SNORA20Maturation of ribosomal RNA
Area3chr16196518319653103.301972SNORA78Maturation of ribosomal RNA
Area3chr1910109756101098355.45924SNORD105BRibosomal RNA modification [63]
Area3chr1949490614494906993.051258SNORD33Ribosomal RNA modification
Area3chr1421397291213974013.835309SNORD8Ribosomal RNA modification
Area3chr1421392149213922533.835309SNORD9Ribosomal RNA modification

Share and Cite

MDPI and ACS Style

Moon, C.-I.; Tompkins, W.; Wang, Y.; Godec, A.; Zhang, X.; Pipkorn, P.; Miller, C.A.; Dehner, C.; Dahiya, S.; Hirbe, A.C. Unmasking Intra-Tumoral Heterogeneity and Clonal Evolution in NF1-MPNST. Genes 2020, 11, 499. https://doi.org/10.3390/genes11050499

AMA Style

Moon C-I, Tompkins W, Wang Y, Godec A, Zhang X, Pipkorn P, Miller CA, Dehner C, Dahiya S, Hirbe AC. Unmasking Intra-Tumoral Heterogeneity and Clonal Evolution in NF1-MPNST. Genes. 2020; 11(5):499. https://doi.org/10.3390/genes11050499

Chicago/Turabian Style

Moon, Chang-In, William Tompkins, Yuxi Wang, Abigail Godec, Xiaochun Zhang, Patrik Pipkorn, Christopher A. Miller, Carina Dehner, Sonika Dahiya, and Angela C. Hirbe. 2020. "Unmasking Intra-Tumoral Heterogeneity and Clonal Evolution in NF1-MPNST" Genes 11, no. 5: 499. https://doi.org/10.3390/genes11050499

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop