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Establishing a human adrenocortical carcinoma (ACC)-specific gene mutation signature
Chinmay Satish Rahanea, Arne Kutznerb, Klaus Heesea,*
a Graduate School of Biomedical Science and Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Republic of Korea; b Department of Information Systems, College of Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Republic of Korea
Abstract
Adrenocortical carcinoma (ACC) is a rare and aggressive tumor whose molecular signaling pathways are not fully understood. Using an in-silico clinical data analysis approach we retrieved human gene mutation data from the highly reputed Cancer Genome Atlas (TCGA). ACC-specific gene mutations were correlated with proliferation marker FAM72 expression and Mutsig along with the algorithmic implementation of the 20/20 rule were used to validate their oncogenic potential. The newly identified oncogenic driver gene set (ZFPM1, LRIG1, CRIPAK, ZNF517, GARS and DGKZ), specifically and most repeatedly mutated in ACC, is involved in tumor suppression and cellular proliferation and thus could be useful for the prognosis and development of therapeutic approaches for the treatment of ACC.
Keywords Adrenal gland, CRIPAK, DGKZ, LRIG1, Oncogene, ZFPM1, Zinc finger. @ 2018 Elsevier Inc. All rights reserved.
Introduction
Adrenocortical carcinoma (ACC) is a rare cancer affecting the adrenal cortex. ACC is a malignancy of the steroid hormone (i.e., mineralocorticoids, glucocorticoids, and androgens)- producing layer of the adrenal gland. ACC occurs mostly in women and has an incidence rate of 0.7-2 cases per million people per year [1]. ACC occurs primarily between 40 and 50 years of age and is often aggressive [2,3]. ACC has a high risk of metastatization even after early diagnosis and surgery [4]. Metastasis is the ability of tumor cells to leave the pri- mary site and migrate to other tissues, leading to the spread of cancer throughout the body [5]. The molecular mecha- nisms of metastasis have been well-studied in carcinomas, in which epithelial to mesenchymal transition (EMT) is con- sidered a critical process [5,6]. Invasion could either occur through the lymphatic system, through the venous system or through the extra-adrenal tissues [7]. Tissue biopsy is unhelp- ful and tumor protein p53 (TP53) expression appeared to be
inadequate as prognostic marker in ACC; instead, quantifica- tion of cell proliferation marker MKI67 is critical for prognosis also in ACC [1,5,8,9]. Several genes have been proposed as cancer drivers in ACC, amongst them: TP53 and catenin B 1 (CTNNB1) [10-12]. TP53 mutations have been observed in more than 50% of child patients and 4% of adult patients of ACC [11,12]. Alterations in the components of the WNT/B- catenin pathway are a prominent marker in ACC. CTNNB1 ac- cumulation has been reported from cases of ACC [13,14] and somatic mutations in CTNNB1 have been observed in large cohort studies [13,15,16]. Activating mutations in CTNNB1 have been observed in approximately 25% of adrenocorti- cal cancers [14]. CTNNB1 and TP53 mutations have been reported to be mutually exclusive in aggressive adrenal can- cers [17]. CTNNB1 knockdown was reported to inhibit EMT, however, that may not apply to adrenal cells due to their mesodermal origins [18]. Other genes associated with ma- lignant ACC include insulin growth factor 2 (IGF2) and splic- ing factor 1 (SF1). IGF2 overexpression is observed in more than 80% cases of ACC and was among the earliest abnor- malities described in ACC [19]. SF1 overexpression is asso- ciated with poor prognosis in childhood and adult sporadic ACC [20].
Despite the importance of the IGF- and WNT/B-catenin signaling pathways, the pivotal driving factors behind ACC
Received August 10, 2018; received in revised form October 20, 2018; accepted October 22, 2018
* Corresponding author.
E-mail addresses: klaus@hanyang.ac.kr, klaus.heese@rub.de
are still unknown and the treatment of ACC remains an unresolved challenge [10,13,19,21,22]. Menin 1 (MEN1), protein kinase cAMP dependent type-I regulatory subunit alpha (PRKAR1A), ribosomal protein L22 (RPL22), telomeric repeat binding factor 2 (TERF2), cyclin E1 (CCNE1) and neurofibromin 1 (NF1) were recently identified as possible cancer drivers in ACC [16]. However, it is highly likely that other genes are involved in ACC tumorigenesis, which have not been reported yet [16].
In our study, we conducted a complete analysis of the mu- tational data of ACC using the comprehensive public cBio- Portal human cancer study database in order to determine new genes involved in ACC oncogenesis. We identified a new ACC-specific gene mutation signature, comprised of the six genes ZFPM1, LRIG1, CRIPAK, GARS, ZNF517 and DGKZ. We validated our findings using the Mutsig (for “Mutation Significance”) [23] data as well as the 20/20 rule proposed by Vogelstein et al. [24]. Our new ACC-specific gene sig- nature ties in with the WNT/B-catenin and MAPK signaling pathways and offers new targets for the effective treatment of ACC.
Materials and methods
Human cancer patient data sources
A publicly available human ACC study (http://www.cbioportal. org/) [25,26] from The Cancer Genome Atlas (TCGA) was an- alyzed for mutations and mRNA expression data (provisional data set). cBioPortal is a human cancer genomics database that contains 169 studies with 40,408 samples (as of Jan- uary 2018)) covering 29 different tissues. cBioPortal com- bines data from TCGA (http://cancergenome.nih.gov/), the In- ternational Cancer Genome Consortium (ICGC; https://icgc. org/), the Wellcome Trust Sanger Institute’s (WTSI) Cancer Genome Project (http://www.sanger.ac.uk/research/projects/ cancergenome/) and the Cancer Genomics Hub (CGHub; https://cghub.ucsc.edu/). TCGA is a collaborative effort be- tween the National Cancer Institute (NCI; http://www.cancer. gov/) and the National Human Genome Research Institute (NHGRI; https://www.genome.gov/). The TCGA ACC dataset on cBioPortal consisted of mutation data from 90 patients. mRNA expression data was available from 79 of those 90 pa- tients. The mutation data consisted of somatic mutations in 6946 genes.
ACC-specific gene signature identification: ACC-specific gene mutation - FAM72 (A-D) paralogs mRNA expression correlation analysis
Complete mutation data for all genes was retrieved from the ACC TCGA study. The data was sorted by the frequency of mutations in each gene across the ACC study. The five genes demonstrating highest number of mutations were selected for display. Mutations in non-oncogenic genes, as described by Lawrence et al. and Greenman et al. [27,28], were not considered for the analysis. The mutations in the five genes demonstrating highest number of mutations in the study were
compared to the mRNA expression of the proliferation marker FAM72 (A-D) paralogs in the ACC study and visualized with the Xena Functional Genomics Explorer [29]. FAM72 (A-D) is a set of four human-specific paralogs associated with neural stem cells [30-32] and is involved with cellular proliferation in cancerous cells where FAM72 expression is triggered by oncogenic mutations and thus they can be useful as cell proliferation markers [33]. Clinical data from the TCGA ACC study was retrieved from cBioPortal for patient-gene-specific analyses.
ACC-specific mRNA expression analysis of the proliferation marker FAM72 (A-D) paralogs
mRNA expression z-scores (RNA sequencing (RNASeq V1/V2) from the 79 patients in the TCGA ACC study were locally computed on the foundation of raw expression data available on cBioPortal. A z-score is a statistical measure- ment indicating how many standard deviations the element is from the mean. The formula is z= (X- m)/o, where zis the z- score, X is the value of the element, m is the numerical mean of the population, and o is the standard deviation [34]. The z- score was normalized for all samples so that they sum to zero. Linear regression was determined between the FAM72A, the proliferation marker MKI67 [35] and genes expressed in the M-phase of the cell cycle, for all available samples across the TCGA ACC study. The regression curve analysis was visualized with the Python-based Bokeh online visualization tool [36].
ACC-specific gene mutation - FAM72 (A-D) paralogs mRNA expression correlation analysis visualized by the bucket method
The mRNA expression z-scores for FAM72 (A-D) paralogs were separately grouped in buckets with a bucket size of 0.7 Z-score units and consequently correlated with genes show- ing high numbers of ACC-tissue-specific gene mutations. The Y-axis denotes the z-score buckets for the selected FAM72 gene. The genes whose mutation numbers are to be visualized, lie on the X-axis. The data was then visualized with the Python-based Bokeh interactive visualization tool [36]. Number of mutations in a gene in the samples within a bucket are denoted by a color code. The color intensity of the buckets is directly proportional to the number of samples, while the colors visualize the relation of samples with a mutation to the total number of samples. Brighter colors indicate more samples in the bucket while paler colors indi- cate fewer samples in the bucket. Colors tending to the red side of the spectrum indicate increasing number of samples with a mutation in relation to the total number of samples in the bucket. Colors tending to the blue side of the spectrum indicate decreasing number of samples with a mutation in the bucket. Black bands denote absence of mutations or lack of expression data in the gene, while bright grey bands indicate absence of samples within the group. Bright pink boxes indicate that only one sample is present in the bucket that contains one mutation in the gene of interest.
ARTICLE IN PRESS
Novel ACC-specific gene signature
| A | ACC | FAM72A | FAM72B | FAM72D | ZFPM1 | LRIG1 | CRIPAK | ZNF517 | GARS | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5' 3' 5' | 3ª 5' | 3' | 5' 3' 5' | 3' | |||||||||
| Maximum | for each count + 1) - | ||||||||||||
| No change Minimum No data | mean, calculated log2 (normalized sample in column | null | null | null | |||||||||
| B | 7.3 | 100% mutation | C 8 | MKI67 | |||||||||
| 6.7 | |||||||||||||
| 6.0 | 80% one | 6 | ASPM | ||||||||||
| FAM72A | 5.3 4.7 | least | M-phase | BUB1 | |||||||||
| 4.0 | at | and 4 | CENPE | ||||||||||
| of | 3.3 | showing 60% | MKI67 | CENPF | |||||||||
| 2.7 | |||||||||||||
| expression [z-score] | 2.0 | bucket 40% | of [z-score] 2 | CEP55 | |||||||||
| 1.3 | in | KIF14 | |||||||||||
| mRNA | 0.7 0.0 | GARS | LRIG1 | ZNF517 | Percentage of samples 20% >0 =0 S % 30 1111 1 1 | mRNA expression cell cycle genes 0 -2 | KIF23 NEK2 NUF2 SGO1 | ||||||
| -0.7 -1.3 -2.0 | CRIPAK | ZFPM1 | |||||||||||
| Five genes with highest number of mutations in dataset, arranged alphabetically | Population of bucket | -2 0 2 4 6 mRNA expression of FAM72A [z-score] | 8 | ||||||||||
| D Highly mutated genes in all 90 patients | E Highly mutated genes in 57 surviving patients | ||||||||||||
| Gene ID | Total number of mutations in gene | Total number of mutations in samples containing | gene in gene Frequency | across all 90 of mutations samples | Gene ID | Total number of mutations in gene | Total number of samples containing in mutations in gene | Frequency of mutations gene across all 57 samples | |||||
| ZFPM1 | 112 | 47 | 52.22% | ZFPM1 | 64 | 28 | 49.12% | ||||||
| LRIG1 | 54 | 31 | 34.44% | LRIG1 | 41 | 22 | 38.60% | ||||||
| CRIPAK | 37 | 24 | 26.67% | CRIPAK | 29 | 17 | 29.82% | ||||||
| GARS | 35 | 34 | 37.78% | GARS | 25 | 24 | 42.11% | ||||||
| ZNF517 | 34 | 33 | 36.67% | ZNF517 | 22 | 21 | 36.84% | ||||||
| TP53 | 20 | 18 | 20% | TP53 | 8 | 7 | 12.28% | ||||||
| F Highly mutated genes | in 33 deceased patients with mutation | data | G Somatic mutations unique to 33 deceased patients with mutation data | ||||||||||
| Gene ID | Total number of mutations in gene | Total number of samples containing mutations in | gene Frequency of mutations in gene across all 33 samples | Gene ID | Total number of mutations in gene | Total number of samples containing in mutations in gene | Frequency of mutations gene across all 33 samples | ||||||
| ZFPM1 | 48 | 19 | 57.58% | DGKZ | 6 | 5 | 5.56% | ||||||
| ZNF517 | 12 | 12 | 36.36% | GOLGA4 | 5 | 5 | 5.56% | ||||||
| ZNF787 | 12 | 12 | 36.36% | KCNH7 | 4 | 4 | 4.44% | ||||||
| PODXL | 12 | 11 | 33.33% | NOS3 | 4 | 4 | 4.44% | ||||||
| TP53 | 12 | 11 | 33.33% | ADAMTSL4 | 5 | 4 | 4.44% | ||||||
| SOWAHA | 11 | 11 | 33.33% | ZNRF3 | 4 | 4 | 4.44% | ||||||
| H OVERALL SURVIVAL STATUS | |||||||||||||
| DGKZ | 6% | ||||||||||||
| GOLGA4 | 6% | ||||||||||||
| KCNH7 | 4% | ||||||||||||
| NOS3 | 4% | ||||||||||||
| ADAMTSL4 | 4% | ||||||||||||
| ZNRF3 | 4% | ||||||||||||
| TP53 | 20% | ||||||||||||
| MEN1 | 10% | ||||||||||||
| GENETIC ALTERATION INFRAME MUTATION | MISSENSE MUTATION MISSENSE MUTATION (UNKNOWN SIGNIFICANCE) | ||||||||||||
| TRUNCATING MUTATION TRUNCATING MUTATION (UNKNOWN SIGNIFICANCE) DECEASED LIVING | NO ALTERATIONS | ||||||||||||
| OVERALL SURVIVAL STATUS | |||||||||||||
Please cite this article as: C.S. Rahane, A. Kutzner and K. Heese, Establishing a human adrenocortical carcinoma (ACC)-specific gene mutation signature, Cancer Genetics, https://doi.org/10.1016/j.cancergen.2018.10.005
Gene-specific survival analysis
The prognostic significance of selected genes from ACC was analyzed using available Kaplan-Meier curves from the cBio- Portal database and compared by the log-rank test [37].
Application of the ‘20/20’ rule and Mutsig to validate the oncogenic potential of selected genes in ACC
We applied the 20/20 rule as described by Vogelstein et al. [24] to our findings from the mutation data. In brief, the 20/20 rule classifies genes into oncogenes (ONG) or tumor sup- pressors (TSG). If the frequency of gain of function mutations in the gene is greater than 20%, then the gene is an ONG. If the frequency of recurrent loss of function mutations in a gene is greater than 20% and the frequency of gain is func- tion mutations in the gene is less than 20%, then the gene is a TSG [24]. We used this approach on the somatic mu- tation data from the ACC study on cBioPortal and checked whether selected genes were sorted as ONG or TSG. Mutsig data information for each of the highest mutated genes was also retrieved from cBioPortal. Mutsig is a package of tools for analyzing mutation data. It operates on a cohort of patients and identifies mutations, genes, and other genomic elements predicted to be driver candidates [27]. The Mutsig package develops a background mutation model of the tumor and then analyzes mutations in each gene to identify genes that have been mutated at a higher frequency than background. Con- sequently, Mutsig assigns a Q-value to each mutated gene, based on a false discovery rate of mutations, and genes are then sorted based on their Q-value. Lower Q-value indicates a lower false discovery rate of mutational drivers, therefore
the lower the Q-value, the higher the significance of the muta- tion in that gene. Here, our identified genes were validated as potential oncogenes by combining these two (‘20/20’ rule and Mutsig) methods. CTNNB1, PRKR1A, RB1, MEN1 and NF1 were used as positive controls, as they had been described as confirmed oncogenes in ACC from previous studies [16].
Results
Human gene mutation analysis of the TCGA ACC study
We found five genes prominently mutated in ACC: the leucine- rich repeats and immunoglobulin like domains 1 (LRIG1), the cysteine-rich PAK1 inhibitor (CRIPAK), the zinc finger protein 517 (ZNF517), the zinc finger protein FOG family member 1 (ZFPM1) and the glycyl-tRNA synthetase (GARS) (Fig. 1(A), Supplementary File 1). Although mucin 5B (MUC5B) had a higher number of mutations than CRIPAK, gene mutations in mucins have been reported to be passengers and not onco- genic [27]. We thus disregarded the mucins and focused on the remaining top five most mutated genes, namely ZFPM1, LRIG1, CRIPAK, GARS, and ZNF517.
Highest number of mutations were noted for the gene ZFPM1, followed by LRIG1 and GARS. The mRNA expres- sion level of FAM72 paralogs is not linearly correlated with these mutations (Fig. 1(A)). Fig. 1(B) shows that mutations in ZFPM1, LRIG1 and CRIPAK are spread through the samples and no single driver oncogene could probably cause cellular proliferation, but an accumulation of mutations across a set of genes may be responsible for the cause of ACC. The linear MKI67-FAM72 mRNA expression correlation graph clearly demonstrates that FAM72 is highly expressed in proliferat-
Fig. 1 ACC-specific gene-mutation signature. Correlation between mRNA expression of human proliferation marker FAM72 (A- D) paralogs and proto-oncogenes / tumor suppressor genes frequently mutated in the TCGA ACC study comprised of 79 samples that contained both mRNA expression and mutation data. FAM72 paralogs were used as a comparison as they are known to be expressed in proliferative cells [38]. Comparison between the tumor samples sorted by sample in descending order of FAM72A expression (on the left hand), and the ACC-specific gene-mutation signature represented by the five genes demonstrating highest number of mutations (ZFPM1, LRIG1, CRIPAK, ZNF517 and GARS) in the same ACC samples sorted by number of mutations (on the right hand; sorted from left to right) (A). Red bands indicate increased expression, green bands indicate decreased expression and black bands indicate no change in expression. Blue dots (A, right hand) represent missense or in-frame mutations in the indicated gene in a sample, while red dots represent nonsense or frameshift indel mutations in that gene in the sample. ZFPM1 was the most frequently mutated gene in ACC and mutations occurred predominantly at the E444/L446 region. LRIG1 showed the second highest mutation frequency mutated at the same site L24V in its protein sequences. Bucket-wise distribution of mutations in the top five genes demonstrating highest number of mutations in ACC, sorted by FAM72A expression (B). The grey area in the heat maps and bucketed diagrams indicate lack of data. High gene expression correlation between FAM72A and the proliferative marker MKI67 as well as other M-phase-specific cell cycle genes indicates that FAM72A is highly expressed in proliferating ACC cells (C). The sample size was 79, standard error was 0.09, slope was 0.62, and two sided p-value was 0.0 for the linear regression plot. Mutations in selected genes across all 90 patients in the ACC study (D). Mutations in selected genes across 57 survivors (E). Frequency of mutations in ZFPM1 was slightly lower in survivors than overall patients, but mutation frequency in TP53 was 8% lower in samples from survivors. Most frequently mutated genes in 33 deceased patients with mutation data (F). Somatic mutations observed uniquely in deceased patients highlight a novel ACC-specific gene-mutation signature (DGKZ, GOLGA4, KCNH7, NOS3, ADAMTSL4, and ZNRF3) (G). OncoPrint data from the ACC study on cBioPortal for visualization of the relationship between somatic mutations in genes from (G) and survival of patient (H). Mutations in the genes stated in (D), (E), and (F) are well-established oncogenic drivers. However, the genes mentioned in (G) are - thus far - not reported to be oncogenic drivers, but may assist in metastasis. OncoPrint data in (H) clearly shows that mutations occurred in separate patients, all of whom are deceased, and did not overlap, implying that all genes from this novel gene-mutation signature from (G) could have each played a pivotal lethal role as primary driver oncogene in the oncogenic pathway of ACC or in metastasis in conjunction with other driver oncogenes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article).
Novel ACC-specific gene signature
JID: CGEN
Alteration frequency
y
4
3
2
2
Alteration frequency
Adrenocortical carcinoma
Neuroblastoma
2
2
Ampullary carcinoma
1
Small cell lung cancer
2
3
ON
Colorectal cancer
Alteration frequency
Mixed cancer types
Cutaneous melanoma
30%
Adrenocortical carcinoma
Head and neck cancer
2
Melanoma
15
CNS cancer
Bladder cancer
16
Small cell lung cancer
Melanoma
2
9
ARTICLE IN PRESS
Mature B-cell neoplasm
Prostate cancer
O
Head and neck cancer
Germ cell tumor
Alteration frequency
Adrenocortical carcinoma
Pheochromocytoma Renal cell carcinoma -
Esophagogastric cancer
Small cell lung cancer
1
Cervical cancer
ZFPM1
Esophagogastric cancer
2
2
CNS cancer
Skin cancer, non-melanoma -
Hepatobiliary cancer
1
Cutaneous melanoma
Non-Hodgkins’s lymphoma
Non-Hodgkins’s lymphoma
2
Melanoma
Bladder cancer
Pancreatic cancer
Endometrial cancer
Alteration frequency
Adrenocortical carcinoma
Endometrial cancer-
Breast cancer
Melanoma
Esophagogastric cancer
Bone cancer
Ovarian cancer
Cutaneous melanoma
Pliocytic astrocytoma- Colorectal cancer
CRIPAK
Cutaneous melanoma
Monoclonal B-cell lymphocytosis
Colorectal cancer
Endometrial cancer
3
EL
Skin canceer, non-melanoma
Multiple myeloma
Thyroid cancer
Fig. 2
?
Endometrial cancer
Skin cancer, non-melanoma
Breast cancer Mesothelioma
Renal cell carcinoma
Cervical cancer
Bladder cancer
Soft tissue sarcoma
Please cite this article as: C.S. Rahane, A. Kutzner and K. Heese, Establishing a human adrenocortical carcinoma (ACC)-specific gene
Alteration frequency
Esophagal stomach cancer, NOS
Non-small cell lung cancer
Leukemia
mutation signature, Cancer Genetics, https://doi.org/10.1016/j.cancergen.2018.10.005
Cutaneous melanoma
Ampullary carcinoma
Cervical cancer
Alteration frequency
Small cell lung cancer
Colorectal cancer
GARS
Pancreatic cancer
2
2
Small cell lung cancer
Non-small cell lung cancer
Non-small cell lung cancer Salivary gland cancer
2
Melanoma
Esophagogastric cancer Multiple myeloma-
Non-Hodgkins’s lymphoma
Glioma
20%
25%
2
7
Skin canceer, non-melanoma Adrenocortical carcinoma
Hepatobiliary cancer
Pancreatic cancer Embryonal tumor
Leukemia
₹
Je
Non-Hodgkins’s lymphoma Head and neck cancer
Cutaneous melanoma
Non-small cell lung cancer
Thyroid cancer
2
Skin canceer, non-melanoma
Ampullary carcinoma
Breast cancer
Soft tissue sarcoma
Cancer of unknown primary
Endometrial cancer
Germ cell tumor
DGKZ
Mature B-cell neoplasm
Hepatobiliary cancer Ovarian cancer
Prostate cancer
Esophagogastric cancer
Wilm’s tumor
Alteration frequency
Adrenocortical carcinoma
Adrenocortical carcinoma
Cutaneous melanoma
Melanoma
Head and neck cancer
Thymic tumor
Peripheral nervous system
30
75
Melanoma
Small cell lung cancer CNS cancer
High grade glioma K27Mut
Non-small cell lung cancer
Renal cell carcinoma
2
Bladder cancer
CNS cancer
Esophagogastric cancer Cervical cancer
Colorectal cancer
2
1
Endometrial cancer
High grade glioma K27Mwt
Prostate cancer
Peripheral nervous system
Endometrial cancer
Bladder cancer
Bone cancer
Prostate cancer
Esophagogastric cancer
Colorectal cancer
Pancreatic cancer
Adrenocortical carcinoma
Colorectal cancer
Prostate cancer, NOS Hepatobiliary cancer
Mature B-cell neoplasm Multiple myeloma
KCNH7
Breast cancer
Hepatobiliary cancer
Embryonal tumor wth rosettes
Non-Hodgkins’s lymphoma
Bladder cancer
Renal cell carcinoma
Embryonal tumor
Prostate cancer, NOS
Bone cancer
LRIG1
Non-small cell lung cancer
Prostate cancer
Mature B-cell neoplasm
Hepatobiliary cancer Cervical cancer
Renal cell carcinoma
CNS cancer
Ampullary carcinoma
Thymic tumor Bone cancer
Ovarian cancer
Bladder cancer
Non-Hodgkins’s lymphoma
Non-Hodgkins’s lymphoma
Leukemia
High grade glioma
Cervical cancer
Multiple myeloma
Head and neck cancer Prostate cancer, NOS Breast cancer
NOS3
Breast cancer
Glioma
Small cell lung cancer
Ovarian cancer
Salivary gland cancer
Alteration frequency
Pancreatic cancer
100%
Esophagogastric cancer
Pancreatic cancer
Mesothelioma-
Glioma
Skin cancer, non-melanoma
Pancreatic cancer Germ cell tumor
80%
ZNF517
61
Hepatobiliary cancer
20%
Mature B-cell neoplasm
Prostate cancer
Glioma
Ovarian cancer Soft tissue sarcoma
2
Cutaneous melanoma
Non-small cell lung cancer
Lymphoid cancer
Colorectal cancer
Skin cancer, non-melanoma
Renal cell carcinoma
Germ cell tumor Leukemia
Alteration frequency
Adrenocortical carcinoma
Endometrial carcinoma
Small cell lung cancer
2
Ependymoma supratentorial Adrenocortical carcinoma Ampullary carcinoma.
Non-small cell lung cancer
Head and neck cancer
3
1
2
Endometrial cancer Cervical cancer
Pancreatic cancer
Renal cell carcinoma
2
Embryonal tumor
Breast cancer
Mixed cancer types
Cutaneous melanoma
Bladder cancer
Glioma
Esophagogastric cancer
Ovarian cancer
Alteration frequency
Small cell lung cancer
Cutaneous melanoma
TOLLE
Head and neck cancer
GOLGA4
Leukemia
Adrenocortical carcinoma
Medulloblastoma Ependymoma infratentorial Mature B-cell neoplasm
Melanoma
3
Skin canceer, non-melanoma
Salivary gland cancer
High grade glioma K27Mwt
Prostate cancer
2
Ampullary carcinoma
7
Osteosarcoma
High grade glioma, K27Mut Bladder cancer
Breast cancer
Esophagogastric cancer
Renal cell carcinoma
Adrenocortical carcinoma
High grade glioma, K27Mwt
Cutaneous melanoma
Melanoma CNS cancer
Melanoma
Leukemia
High grade glioma K27Mwt
Esophagogastric cancer Endometrial cancer
Monoclonal B-cell lymphocytosis
ADAMTSL4
Prostate cancer, NOS-
Glioma
Prostate cancer, NOS
Colorectal cancer Hepatobiliary cancer
Colorectal cancer
Hepatobiliary cancer
Endometrial cancer
Bone cancer
Colorectal cancer
Cervical cancer
Head and neck cancer Ampullary carcinoma
Medulloblastoma
Skin cancer non-melanoma Head and neck cancer
Head and neck cancer
Germ cell tumor
Pheochromocytoma Pancreatic cancer Bladder cancer
Non-small cell lung cancer
Skin canceer, non-melanoma
Small cell lung cancer
Small cell lung cancer
ZNRF3
Pheochromocytoma Renal cell carcinoma
Melanoma
Mature B-cell neoplasm
Multiple myeloma
Thymic tumor
Breast cancer
Mesothelioma
Bone cancer
Non-Hodgkins’s lymphoma Ovarian cancer
Prostate cancer
Hepatobiliary cancer
Non-small cell lung cancer Bladder cancer
Non-small cell lung cancer
Prostate cancer
Renal cell carcinoma
Glioma
Renal cell carcinoma
Prostate cancer Germ cell tumor
Pancreatic cancer
Cervical cancer
Non-Hodgkins’s lymphoma
Breast cancer
Thyroid cancer Breast cancer
Non-Hodgkins’s lymphoma
Ovarian cancer
Glioma
Pancreatic cancer
Cancer of unknown primary-
Glioma
Ovarian cancer
Thyroid cancer Leukemia
Soft tissue sarcoma
Leukemia
Salivary gland cancer
ing ACC cells (Fig. 1(C)). Frequency of mutations in LRIG1, CRIPAK and GARS were slightly higher in survivors as com- pared to total number of patients, while the frequency of mu- tations in ZNF517 remained similar both in total number of patients as well as survivors. However, the frequency of mu- tations in TP53 was lower in survivors than in overall patients (Fig. 1(D)-(E)). We compared the list of frequently mutated genes overall with genes which were frequently mutated only in deceased patients. We found six genes, diacylglycerol ki- nase zeta (DGKZ), golgin A4 (GOLGA4), potassium voltage- gated channel subfamily H member 7 (KCNH7), ADAMTS like 4 (ADAMTSL4), nitric oxide synthase 3 (NOS3), and zinc and ring finger 3 (ZNRF3), which showed a significant num- ber of mutations and adding to the novel ACC-specific gene mutation signature (Fig. 1(G)). We also observed a potential gender effect: mutations in DGKZ, GOLGA4 and NOS3 were observed mainly in women, with female to male occurrence ratios ranging from 3:1 to 4:0 (Supplementary File 1). This hints at a gender-specific role of these genes in ACC. Patient data and list of genes mutated solely in deceased patients is provided in Supplementary File 1.
We analyzed whether mutations in these top-five mutated genes, as well as genes observed only in deceased patients, were specific to ACC by checking alteration frequency of these genes across all human cancer tissue types available on the cBioPortal database. We found that alteration fre- quency of the ZFPM1, LRIG1, CRIPAK, GARS, and ZNF517 was significantly higher in ACC as compared to all other cancer types. This indicates that these five genes were specifically altered in ACC but not in other cancer tissues (Fig. 2). Moreover, the mutations in DGKZ and ZNRF3 were most frequently observed in ACC too, while GOLGA4, KCNH7, ADAMTSL4, and NOS3 did not show increased ACC-specific alteration frequencies (Fig. 2).
Moreover, we found that mutations in the five highest mu- tated genes were localized only to one or two positions in their amino acid (AA) sequences - particularly observed in ZFPM1, LRIG1, GARS and ZNF517; however, this AA loca- tion specificity was not observed in CRIPAK. These locations were specific to ACC and were not observed in any other can- cer tissue (Fig. 3). Recurrent mutations at the same site pro- vided further evidence of the ACC-specific oncogenic poten- tial of these mutations for this specific gene set. The deceased patient-specific gene set did not show such a site-specificity for mutations (Supplementary File 1).
Gene-specific survival analysis in ACC
Kaplan Meier survival curves for ZFPM1, LRIG1, CRIPAK, GARS, and ZNF517 show no change between patients with and without alterations in ACC (Supplementary File
1). However, survival curves for DGKZ, GOLGA4, KCNH7, ADAMTSL4, NOS3 and ZNRF3 show extensive differences between patients with and without mutations in them (Fig. 4). The low number of samples containing mutations in selected genes could be one reason for the large differences between curves.
Application of the ‘20/20’ rule to validate the oncogenic potential of selected genes
Applying the ‘20/20’ test to the mutation data from ACC also confirmed our hypothesis that the genes of interest are po- tential oncogenes. This was additionally verified by the Mut- sig Q-value data from cBioPortal (Supplementary File 1). We used genes reported previously as potential drivers in ACC [14,16] as positive controls and compared their 20/20 re- sults and Mutsig Q-values with our gene set of interest. We found that ZFPM1, LRIG1, GARS, and ZNF517 show lower Q-values than the reported oncogenes. DGKZ qualifies as a potential oncogene by the 20/20 test, however, Q-values for DGKZ were unavailable. CRIPAK had a higher Q-value, even though it fits the parameters for an oncogene by the 20/20 test, while GOLGA4, KCNH7, ADAMTSL4, NOS3 and ZNRF3 were neither oncogenes nor tumor suppressors nor had any Q-value data. This could be due to the low number of muta- tions in the genes. Low Q-values for the genes of interest indi- cate that the genes are significantly mutated in ACC, lending credence to their status as potential ACC-specific oncogenes.
Discussion
Although our understanding of the molecular mechanism driv- ing ACC has advanced, the oncogenic mutations leading to ACC are poorly understood [1]. Thus, we aimed at unravelling novel ACC-specific targets and determined a novel gene set of six genes that were significantly and specifically mutated in ACC.
Kaplan-Meier curves for the gene set from deceased pa- tients show dramatic differences between patients with and without mutations. This indicates that mutations in DGKZ, GOLGA4, KCNH7, ADAMTSL4, NOS3 or ZNRF3 dramati- cally worsen prognosis in ACC and are thus genes of special interest. Although the number of samples containing muta- tions in these genes is very low and could be a possible reason for differences in survival curves, the data correlate very well with the control gene TP53. In addition, GOLGA4, KCNH7, ADAMTSL4, NOS3 or ZNRF3 are not specifically mutated in ACC, nor do they fit 20/20 and Mutsig parameters. Hence we included DGKZ, but not the others, as part of our ACC-specific gene mutation signature. The specific integrated role of this
Fig. 2 Mutation frequency of the selected genes in ACC compared across all available (at cBioPortal) human cancer tissue types (adapted from cBioPortal). Cancer types lacking mutations in the specified genes have not been shown. Bars in red color indicate ACC. Mutation frequency in the five genes with highest number of mutations in ACC (A). Almost 40% of samples in ACC show mutations in ZFPM1, which is significantly higher than in any other cancer type. Similarly, mutations in LRIG1, CRIPAK, ZNF517 and GARS are significantly higher in ACC as compared to all other human cancer tissue types. This indicates that alterations in this particular five-gene set may be important for ACC oncogenesis. Mutation frequency in the top six genes observed only in deceased ACC patients (B). Mutation frequencies of these genes is relatively low in ACC (compared with genes shown in (A)), however, DGKZ and ZNRF3 are altered more in ACC as compared to other cancer types. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article).
Novel ACC-specific gene signature
Mutations in amino acid sequences of selected genes across all cancer tissues
Mutations in amino acid sequences of selected genes in ACC
ZFPM1
ZFPM1
# of mutations
66
E444Afs*227/D/_L446del
# of mutations
66
E444Afs#227/D/_L446del
0
0
0
200
400
600
800
1000
0
200
400
600
800
1000
Amino acid position
Amino acid position
L24V
LRIG1
31
L24V
LRIG1
# of mutations
# of mutations
31
0
1.
0
0
200
400
600
800
1000 1093
0
200
400
600
800
1000 1093
Amino acid position
Amino acid position
C143R
CRIPAK
CRIPAK
# of mutations
10
# of mutations
8
C143R
0
01
0
100
200
300
400 446
0
100
200
300
400 446
Amino acid position
Amino acid position
ZNF517
ZNF517
# of mutations
33
V349A
# of mutations
33
V349A
7
0
01
0
100
200
300
400
492
0
100
200
300
400
492
Amino acid position
Amino acid position
# of mutations
P42A
GARS
GARS
33
# of mutations
33
P42A
0
0
0
200
400
600
739
0
200
400
600
739
Amino acid position
Amino acid position
zinc finger motif
histidyl, glycyl, threonyl and prolyl anticodon binding domain
leucine-rich repeat
Krüppel associated box
leucine rich repeat C-terminal domain
immunoglobulin I-set domain
helix-turn-helix domain
tRNA synthetase class II core domain
ACC-specific gene mutation signature in a convergent ACC cancer cell signaling mechanism is as yet unclear. The sig- naling pathways involving our identified gene set as well as those from Zheng et al. [16] can be interlinked (Fig. 5).
Downstream signaling by tyrosine kinase receptors like EGFR activate multiple pathways including the mitogen- activated protein kinase (MAPK), phosphatidylinositol-4, 5- bisphosphate 3-kinase (PI3K) and WNT signaling path- ways, and there is extensive cross-regulation between these pathways [48]. PAK1 belongs to the p21-activated ser- ine/threonine kinase family and plays a critical role in link- ing multiple signaling pathways, including cell polarity, actin cytoskeleton reorganization and cellular proliferation [49,50]. PAK1 interacts directly with Rac family small GTPase 1 (RAC1) and PI3K, effecting downstream signaling by phos- phorylating other kinases [51]. Increase in PAK1 expres-
sion has been observed in various cancers [49,51]. PAK1 is a promising target for therapy with both ATP-competitive as well as allosteric inhibitors being screened for treatment [52]. The functional role of CRIPAK is inhibition of the PAK1 kinase [43] and disruption in the function of CRIPAK via mu- tations would lead to a dysregulation of PAK1, thereby en- hancing cellular proliferation (Fig. 5). Activation of the PI3K pathway leads to suppression of TP53 via MDM2 activation. AKT also phosphorylates and inhibits glycogen synthase ki- nase 3 (GSK3), thereby stabilizing downstream targets of GSK3 such as the G1 cyclins and TFs such as MYC proto- oncogene (MYC) and Jun proto-oncogene (JUN) [53]. AKT also phosphorylates CDKN1B, inactivating its cell-cycle pre- vention effect [53]. GSK3 phosphorylates CTNNB1, marking it for degradation. Mutation in CTNNB1 makes degradation im- possible; thereby cause constitutive activation of target genes
ARTICLE IN PRESS
100%
DGKZ
100%
GOLGA4
90%
90%
P-value: 6.316e-5
P-value: 0.00158
80%
80%
Overall survival
Overall survival
70%
70%
60%
60%
50%
50%-
40%
40%
30%
30%
20%
20%
10%
10%
0%
0
10
20
30
40
50
60
70
80
90
100
110
120
130
) 140
150
0%
0
10
20
30
40
50
60
70
80
90
0 100 110 120 130 140 150
Months survival
Months survival
100%
KCNH7
100%
ADAMTSL4
90%
90%
P-value: 2.783e-4
P-value: 4.601e-6
80%
80%
Overall survival
Overall survival
70%
70%
60%
60%
50%
50%
40%
40%
30%
30%
20%
20%
10%
10%
0%
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
0%
0
10
20
30
40
50
60
70
80
90
100
110
120 1
130
140
150
Months survival
Months survival
100%
NOS3
100%
ZNRF3
90%
90%
P-value: 0.00420
P-value: 4.975e-6
80%
80%
Overall survival
Overall survival
70%
70%
60%
60%
50%
50%
40%
40%
30%
30%
20%
20%
10%
10%
0%
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
0%
0
10
20
30
40
50
60
70
80
90 100
110
120
130
140
150
Months survival
Months survival
100%
TP53
100%
MEN1
90%
P-value: 0.000682
90%
P-value: 0.0564
80%
80%
Overall survival
Overall survival
70%
70%
60%
60%
50%
50%
40%
40%-
30%
30%
20%
20%-
10%
10%
0%
0
10
20
30
40
50
60
70
80
90
100
110
120
130
0 140
150
0%
0
10
20
30
40
50
60
70
80
90
100
120
130
140
150
Months survival
Months survival
Please cite this article as: C.S. Rahane, A. Kutzner and K. Heese, Establishing a human adrenocortical carcinoma (ACC)-specific gene mutation signature, Cancer Genetics, https://doi.org/10.1016/j.cancergen.2018.10.005
Novel ACC-specific gene signature
such as MYC and cyclin D1 (CCND1). RPL22 is a 60S riboso- mal subunit protein and activates TP53 expression by binding and inactivating MDM2, thereby suppressing cancer cell sur- vival [46]. Inactivation of RPL22 and mutation in TP53 thus acts as a ‘double whammy’ in enhancing proliferation of can- cer cells. The role of TP53 in oncogenesis is well established. TP53 can induce both cell cycle arrest and apoptosis, and mutations in TP53 are the most common ones observed in tumors [42]. PRKAR1A is the regulatory subunit of cAMP de- pendent protein kinase A (PKA) and mutations in PRKAR1A have been known to cause an autosomal dominant neopla- sia syndrome called Carney complex [41]. The downstream signaling of EGFR also regulates CTNNB1, via the mucin-1 (MUC1) protein. Decrease in MUC1 expression suppresses EGFR-dependent tumorigenesis and CCND1 protein expres- sion [54]. The menin (MEN1) protein has both tumor sup- pressive and proliferative functions. Mutations in MEN1 lead to multiple endocrine neoplasia, which causes tumors in en- docrine glands, inactivating mutations in MEN1 are linked to inhibition of CDKN1B, and MEN1 has been reported to sup- press AKT kinase activity in murine cells [55].
LRIG1 is a transmembrane protein which inhibits EGFR kinase receptor signaling by binding to it via its ectodomain [56]. LRIG1 functions as a tumor suppressor in multiple can- cers [57-59] and increased expression of the (non-mutated) proto-oncogene LRIG1 is associated with good prognosis in breast, bladder, lung cancer, melanoma and glioma [60]. Inac- tivating mutations in LRIG1 lead to a ‘switch on’ of the EGFR kinase-signaling pathway, resulting in high cellular prolifer- ation. LRIG1 has already been tested as a therapeutic for glioma. LRIG1 acts as a sensitizing agent for chemotherapeu- tics and injection of soluble LRIG1 has been reported to in- hibit glioblastoma [61-64]. Therefore, LRIG1 could be a highly promising target for immunotherapy for ACC, given that it acts as an inhibitor of EGFR kinase. Ex-vivo sensitized dendritic cells to mutant LRIG1 could be targeted specifically to ACC cells expressing mutant LRIG1 to mediate an endogenous immune response to fight ACC. Antibodies targeting mutant LRIG1 could be generated, thereby killing the ACC cells via a cell-mediated cytotoxic processes. Another approach would be to target other genes on combination with LRIG1. FAM72 is a novel proliferation marker, which is overexpressed in non- neural cancer tissues [65,66]. Singling out FAM72 would me- diate mitotic damage in all growing cells, however, target- ing mutant LRIG1 and FAM72 together will ensure that only cancerous cells expressing mutated LRIG1 and overexpress- ing FAM72 will be targeted while leaving the other non- cancerous cells unharmed.
The zinc finger protein ZFPM1, also known as FOG1, acts as a cofactor with the transcription factor GATA1 and regu- lates differentiation and proliferation of blood cells at various stages [67,68]. ZFPM1 has been reported to be upregulated in myeloid leukemia [69] but no evidence of mutational effects have been reported. Both GATA1 and ZFPM1 interact with
the nucleosome remodeling and deacetylase (NuRD) com- plex, which is comprised of retinoblastoma binding protein 4 (RBBP4), ATPases and histone deacetylases. This protein complex is critical in transcriptional repression of ZFPM1- GATA1 target genes [44,70]. Loss of function mutations in ZFPM1 could prevent differentiation, thereby activating pro- liferative mechanisms in the adrenocortical cells. RBBP4, in turn, forms a complex with RB1 and regulates the function of E2F1 [71]. This directly affects the cell cycle, as E2F1 is a transcriptional activator for G1 and S-phase genes.
GARS mutations are typically associated with the neuro- pathic Charcot-Marie-Tooth disorder [72]. However, there is some evidence to suggest that it also plays a role in the adap- tive immune system by suppressing MAPK signaling in tumor cells [73]. GARS dysregulation has also been reported in a va- riety of cancers [74,75] though oncogenic mutations have not yet been reported. Recent evidence suggests that GARS acts as a chaperone for the proteins directly involved in the ned- dylation pathway [45]. Neddylation is the conjugation of the NEDD8 protein, via GARS, to its targets and is critical in cell cycle regulation: the NEDD8-cullin complex activates cullin- RING ubiquitin ligases, which degrade the p27 cell-cycle in- hibitor and continues cell cycle progression [76]. Inhibition of GARS thus leads to reduction of metastasis [45]. This is of high interest as gain of function mutations in GARS would lead to higher levels of neddylation, thereby increasing cellu- lar proliferation. This makes GARS a very attractive target for therapeutic applications.
There is little evidence about the function of the other zinc finger protein ZNF517. It forms part of the interactome of heat shock 90 kDa proteins (HSP90) [77] so it may be involved in protein folding or intracellular transport, but there is no other report of its cellular function. ZNF517 is a zinc-finger protein, which are the largest family of transcription factors. ZNFs work as transcriptional activators or repressors and play signifi- cant roles in cellular growth, maturation and metabolism [78]. ZNF517 contains Kruppel associated box (KRAB) domains, indicating its potential function as a transcriptional repressor. The KRAB domain binds to other corepressors via direct pro- tein interactions leading to transcriptional repression of genes [79]. KRAB-ZNFs play important roles in cellular differentia- tion, apoptosis and cancer by acting as tumor suppressors and promoters of apoptosis [80]. Mutations in ZNF517 could lead to constitutive expression of downstream signaling path- ways leading to proliferation. DGKZ is a lipid kinase involved in phosphatidic acid synthesis. DGKZ knockdown has been re- ported to inhibit proliferation in glioma cells [81]. DGKZ inhibits RAS guanyl-releasing protein (RASGRP1), thereby modulat- ing RAS activity [47]. Inactivating mutations in DGKZ could lead to increased phosphorylation of RAS, thereby causing continuous activation of the MAPK or PI3K signaling path- ways, ending in cellular proliferation. Mutations in these genes drastically worsen the prognosis, and therefore must act in
Fig. 4 Survival plots showing the prognosis of ACC patients with somatic mutations in DGKZ, GOLGA4, KCNH7, NOS3, ADAMTSL4 and ZNRF3, respectively (adapted from cBioPortal). DGKZ, GOLGA4, KCNH7, NOS3, ADAMTSL4 and ZNRF3 are the genes with highest number of somatic mutations observed only in deceased ACC patients. All patients with mutations in these genes show worse prognosis compared to all other ACC patients. MEN1 and TP53 survival plots are shown as control comparison. Red line: cases with alterations in query gene, blue line: cases without alterations in query gene. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article).
Please cite this article as: C.S. Rahane, A. Kutzner and K. Heese, Establishing a human adrenocortical carcinoma (ACC)-specific gene mutation signature, Cancer Genetics, https://doi.org/10.1016/j.cancergen.2018.10.005
WNT 1/3a/8
cell membrane
LRIG1
EGFR
LRP5/6
Frizzled receptor
DGKZ
polypeptide
PI3K
CAMP
PTEN
MUC1
RAS
GSK3B
GARS
PIP3
RPL22
PRKARIA
mRNA
RACI
BMP7
AKT
CTNNB1
chaperone
ribosome
GARS
NEDD8
RAF
PAK1
CRIPAK
MAP2K6
MAP3K7
NEDD8
MAP2K1/2
MAP3K1
cullin
MDM2
MAPK8
MAPK14
ring ligase
MAPK1/3
cullin
NEDD8
nucleus
MEN1
TP53
CDC25
S
CDKNIB
CDKNIA
Interphase
tumor
+ RB1
CTNNB1
suppression
G2
G1
ZNF517
regulation?
RBBP4
ZFPM1)GATAI
JUN FOS
Mitosis M
CDK4
GO
CCND
phosphatase
protein
multistep inhibition
GTPase
- direct inhibition
translocation
transcription factor
- direct activation
proto-oncogene from Zheng et al
kinase
----- + multistep activation
proto-oncogene from our study
conjunction with our novel gene set as well as other proto oncogenes such as MEN1, CTNNB1 and TP53.
EGFR signaling is the primary node through which the proliferative pathways can be initiated and most of the proto- oncogenes in ACC act downstream of EGFR. Inhibition of EGFR via LRIG1 is thus a key step in regulating, either par- tially or fully, the consequent signaling cascades. LRIG1 mu- tations would cause a continuous expression of the EGFR signaling cascade, thereby causing cellular proliferation. Sim- ilarly, mutations in GARS also serve to increase proliferation via a cascade that is independent of the PI3K/MAPK/WNT
signaling pathways. Mutations in our selected genes thus ap- pear to be more influential in ACC tumorigenesis than those described in earlier studies are.
Our study thus sheds new light on potential driver genes in this rare ACC cancer. Our findings describe a novel ACC- specific gene signature providing a gene set (ZFPM1, LRIG1, CRIPAK, GARS, ZNF517, and DGKZ) and validate its onco- genic potential, which could serve as a powerful therapeutic target.
Novel ACC-specific gene signature
Acknowledgments
This study was supported by Hanyang University by pro- viding a scholarship to C.S.R. and by the Basic Science Research Program through the National Research Founda- tion of Korea (NRF) funded by the Ministry of Education (2015R1D1A1A01057243 and 2016R1D1A1B03932599).
Competing interests
The authors declare no conflicts of interest
Supplementary materials
Supplementary material associated with this article can be found, in the online version, at doi: 10.1016/j.cancergen.2018. 10.005.
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