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BMC Medical Genomics

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Case-oriented pathways analysis in pancreatic adenocarcinoma using data from a sleeping beauty transposon mutagenesis screen

  • Yen-Yi Ho1, 2Email author,
  • Timothy K. Starr2, 4,
  • Rebecca S. LaRue5 and
  • David A. Largaespada2, 3
BMC Medical GenomicsBMC series – open, inclusive and trusted20169:16

https://doi.org/10.1186/s12920-016-0176-7

Received: 20 January 2015

Accepted: 9 March 2016

Published: 1 April 2016

Abstract

Background

Mutation studies of pancreatic ductal adenocarcinoma (PDA) have revealed complicated heterogeneous genomic landscapes of the disease. These studies cataloged a number of genes mutated at high frequencies, but also report a very large number of genes mutated in lower percentages of tumors. Taking advantage of a well-established forward genetic screening technique, with the Sleeping Beauty (SB) transposon, several studies produced PDA and discovered a number of common insertion sites (CIS) and associated genes that are recurrently mutated at high frequencies. As with human mutation studies, a very large number of genes were found to be altered by transposon insertion at low frequencies. These low frequency CIS associated genes may be very valuable to consider for their roles in cancer, since collectively they might emerge from a core group of genetic pathways.

Result

In this paper, we determined whether the genetic mutations in SB-accelerated PDA occur within a collated group of biological processes defined as gene sets. The approach considered both genes mutated in high and lower frequencies. We implemented a case-oriented, gene set enrichment analysis (CO-GSEA) on SB altered genes in PDA. Compared to traditional GSEA, CO-GSEA enables us to consider individual characteristics of mutation profiles of each PDA tumor. We identified genetic pathways with higher numbers of genetic mutations than expected by chance. We also present the correlations between these significant enriched genetic pathways, and their associations with CIS genes.

Conclusion

These data suggest that certain pathway alterations cooperate in PDA development.

Keywords

Forward genetic screen Sleeping Beauty transposon Case-oriented gene set analysisPathways correlationsCISCommon insertion sites

Background

The molecular analysis of human cancer cells has revealed a startling amount of genetic and epigenetic heterogeneity. In recent years, forward genetic screens have taken place in mice using DNA transposons, primarily Sleeping Beauty (SB) [1]. The SB-based approach has been successfully employed to induce many different forms of cancer such as brain tumors, sarcomas, hematopoietic malignancies, and carcinomas [2, 3] via insertional mutagenesis. A large number of of loci recurrently mutated by insertion of SB transposons called common insertion sites (CIS) have been identified [4]. The general impression from these studies is one of tremendous genetic complexity.

Recent large-scale analyses of human cancer genomes mirrors these results in general. Most types of human cancer harbor a small number of genes that are altered in a high percentage of cases, so called “mountains”, and a large number of genes altered in a low percentage of cases, so called “hills”. In addition, two patients diagnosed with the same type of cancer often show distinct genetic alternations, however, the disrupted pathways tend to be similar among patients [5].

Conventional pathway analysis approaches usually obtain gene-based scores by summarizing data across tumor cases, then calculating pathway statistics using the scores of the genes in the pathway. However, these approaches could potentially lose information regarding whether multiple mutations in a pathway are from a single patient or multiple cases with a single mutation at various genes in the pathway. In contrast, case-oriented gene set analysis (CO-GSEA) can consider the two situations differently and hence can incorporate heterogeneity of each tumor case into the analysis. This approach provides a case-based score for each pathway and further enhances the study of correlation of mutation events between pathways, as well as between genes and pathways. It has recently been applied to the analysis of human tumors [6].

Pancreatic ductal adenocarcinoma (PDA) is the fourth leading cause of death due to cancer, with over a 98 % case-fatality rate. The crucial molecular events, required for progression from a pre-invasive and non-life threatening state to an invasive and metastatic lethal condition, are not well-understood. We previously reported the results of a SB transposon-based forward genetic screen for drivers of PDA in mice expressing the KrasG12D oncogene in epithelial cells of the pancreas [7]. Our screen revealed new candidate genes for PDA and confirmed the importance of many genes and pathways previously implicated in human PDA. The most commonly mutated gene was the X chromosome-linked deubiquitinase Usp9x, which was inactivated in over 50 % of the tumors. In addition, several hundred candidate PDA genes were identified as CIS in this screen.

In this paper, we report analyses intended to determine whether a core group biological processes or pathways are populated by genes from CIS. We applied a less stringent criterion to consider CIS associated genes that mutated both at high frequencies (mountains) and at lower frequencies (hills). Secondly, we determined whether non-random associations between alteration of genes in certain pathways or biological pathways exist by analysis of CIS from individual tumors.

Results and discussion

Certain pathways are enriched in CIS-associated genes

We collected insertional mutatgenesis data of tumor samples from 146 Kras LSL-G12D ; Pdx1-cre; T2/Onc; Rosa26-LSL-SB13 mice. To determine whether a core group of pathways were enriched with CIS-associated genes than reported previously [7], we analyszed 968 CIS with uncorrected p value <10−4 from TapDance. Among these, 239 genes were mapped an grouped into 281 KEGG curated pathways categories. After excluding pathways with less than 6 genes, 272 KEGG pathways remain in the following analysis.

Using the CO-GSEA described in the Method Section, we found 95 KEGG pathways that are enriched with CIS-associated genes with permutated p value <10−7 listed in Table 1 (more details about the disrupted genes in each pathway can be found in Additional file 1). In Table 1, “# of genes” records the number of genes defined in the pathway from KEGG; “# of CIS” (third column) reports the number of CIS genes in the pathway; and “# of mutated cases” (fourth column) records the number of cases that the pathway was disrupted. A histogram of the sizes of each of the KEGG pathways is shown in Additional file 2. In Figs. 1 and 2, we plotted the KEGG diagrams of two pathways that are enriched CIS-associated genes.
Fig. 1

Frequently mutated genes in ubiquitin mediated proteolysis pathway. Darker red color indicates higher mutation frequencies in mice

Fig. 2

Frequently mutated genes in ErbB signaling pathway. Darker color indicates higher mutation frequencies in mice

Table 1

Pathways that are enriched with CIS-associated genes (permuted p value <10−7)

 

KEGG id

Pathway name

# of genes

# of CIS

# of mutated cases

  

Cellular Processes

   

1

4110

Cell cycle

126

9

101

2

4530

Tight junction

136

10

127

3

4810

Regulation of actin cytoskeleton

217

13

121

4

4510

Focal adhesion

207

13

123

5

4540

Gap junction

87

3

67

6

4520

Adherens junction

74

11

122

  

Human Diseases

   

7

5164

Influenza A

170

11

117

8

5034

Alcoholism

199

5

85

9

5169

Epstein-Barr virus infection

212

17

120

10

5203

Viral carcinogenesis

229

14

116

11

5160

Hepatitis C

136

7

97

12

5010

Alzheimer’s disease

173

12

121

13

5016

Huntington’s disease

182

14

125

14

5200

Pathways in cancer

323

23

134

15

5211

Renal cell carcinoma

67

6

84

16

5206

MicroRNAs in cancer

270

10

108

17

5152

Tuberculosis

176

11

114

18

5166

HTLV-I infection

277

11

106

19

5100

Bacterial invasion of epithelial cells

77

7

103

20

5412

Arrhythmogenic right ventricular cardiomyopathy (ARVC)

74

7

96

21

5202

Transcriptional misregulation in cancer

178

12

121

22

5133

Pertussis

74

6

84

23

5142

Chagas disease (American trypanosomiasis)

103

7

87

24

5161

Hepatitis B

145

10

107

25

5205

Proteoglycans in cancer

226

11

115

26

5214

Glioma

65

6

95

27

5216

Thyroid cancer

29

3

66

28

5210

Colorectal cancer

64

8

105

29

5212

Pancreatic cancer

66

6

74

30

5213

Endometrial cancer

52

8

111

31

5215

Prostate cancer

89

10

117

32

5218

Melanoma

71

4

86

33

5219

Bladder cancer

38

2

46

34

5220

Chronic myeloid leukemia

73

9

95

35

5221

Acute myeloid leukemia

57

6

82

36

5223

Non-small cell lung cancer

56

5

77

37

5222

Small cell lung cancer

85

7

95

38

5217

Basal cell carcinoma

55

3

65

  

Environmental Information Processing

   

39

4151

PI3K-Akt signaling pathway

351

22

130

40

4390

Hippo signaling pathway

154

14

123

41

4066

HIF-1 signaling pathway

111

7

91

42

4012

ErbB signaling pathway

87

7

100

43

4014

Ras signaling pathway

228

13

119

44

4310

Wnt signaling pathway

143

16

126

45

4350

TGF-beta signaling pathway

82

11

119

46

4010

MAPK signaling pathway

253

14

118

47

4015

Rap1 signaling pathway

216

14

134

48

4370

VEGF signaling pathway

60

4

68

49

4340

Hedgehog signaling pathway

49

3

61

50

4330

Notch signaling pathway

49

4

68

51

4070

Phosphatidylinositol signaling system

81

4

78

52

4068

FoxO signaling pathway

135

7

101

53

4150

mTOR signaling pathway

61

4

86

  

Metabolism

   

54

4141

Protein processing in endoplasmic reticulum

169

8

111

55

670

One carbon pool by folate

19

2

45

56

3015

mRNA surveillance pathway

96

8

95

57

4120

Ubiquitin mediated proteolysis

139

14

118

58

00250

Alanine, aspartate and glutamate metabolism

34

2

53

59

3020

RNA polymerase

29

2

50

60

510

N-Glycan biosynthesis

50

3

69

61

3018

RNA degradation

77

5

83

62

310

Lysine degradation

51

6

95

63

512

Mucin type O-Glycan biosynthesis

28

2

46

64

900

Terpenoid backbone biosynthesis

21

2

54

65

563

Glycosylphosphatidylinositol(GPI)-anchor biosynthesis

25

2

46

66

4122

Sulfur relay system

10

3

59

67

4062

Chemokine signaling pathway

196

13

124

68

4722

Neurotrophin signaling pathway

123

12

118

69

4670

Leukocyte transendothelial migration

121

9

106

70

4728

Dopaminergic synapse

133

8

105

71

4270

Vascular smooth muscle contraction

137

8

104

72

4713

Circadian entrainment

98

5

93

73

4723

Retrograde endocannabinoid signaling

103

4

88

74

4724

Glutamatergic synapse

114

5

93

75

4725

Cholinergic synapse

113

5

93

76

4726

Serotonergic synapse

133

5

94

77

4910

Insulin signaling pathway

142

8

95

78

4650

Natural killer cell mediated cytotoxicity

146

5

73

79

4917

Prolactin signaling pathway

74

7

98

80

4611

Platelet activation

131

9

111

81

4912

GnRH signaling pathway

89

5

80

82

4914

Progesterone-mediated oocyte maturation

87

4

68

83

4915

Estrogen signaling pathway

98

6

92

84

4916

Melanogenesis

100

9

111

85

4921

Oxytocin signaling pathway

158

9

108

86

4360

Axon guidance

129

10

117

87

4919

Thyroid hormone signaling pathway

118

11

114

88

4960

Aldosterone-regulated sodium reabsorption

40

2

49

89

4720

Long-term potentiation

66

6

91

90

4660

T cell receptor signaling pathway

105

9

113

91

4662

B cell receptor signaling pathway

73

6

91

92

4730

Long-term depression

61

3

67

93

4664

Fc epsilon RI signaling pathway

70

4

68

94

4666

Fc gamma R-mediated phagocytosis

88

6

86

95

4320

Dorso-ventral axis formation

22

2

60

The genetic screen was designed to discover genes that when altered would cause acceleration of PDA in pancreatic ductal epithelial cells expressing an activated form of the Kras oncogene, Kras G12D . As such it was not surprising that KEGG pathways with the strongest statistical support for CIS associated gene enrichment were many cancer associated pathways. As expected, we found some of the same pathways previously reported and which were expected [7, 8]. An informal prior analysis [7] suggested that TGF β signaling was enriched in CIS-associated genes and indeed we found that this KEGG pathway is enriched. Similarly, Rb1/p16Inka4a pathway was suggested to be recurrently altered by CIS-associated genes [7]. Indeed, we found that the KEGG pathway CELL CYCLE was enriched in CIS-associated genes. Many other cancer-associated pathways were enriched in CIS-associated genes including the RAS, PI3K-AKT, HIPPO, VEGF, HEDGEHOG, MAPK, FOXO1, and MTOR pathways. Moreover, the human disease KEGG pathway PANCREATIC CANCER and several other human cancer pathways were enriched in CIS-associated genes.

In addition to these expected KEGG pathways, many involving metabolism have not been strongly linked to pancreatic cancer development or cancer development in general. However, recent studies revealed evidence of metabolic reprogramming to sustain tumor survival in KRAS-mutated PDA tumors [9]. For example, KRAS-dependent tumor cells compensated the energy loss through increasing glycolysis, amino acid and lipid biosynthesis [10]. In particular, TERPENOID BIOSYNTHESIS, LYSINE DEGRADATION and the SULFUR RELAY SYSTEM are significantly altered in the SB-accelerated tumor models. To date KRAS remains a poorly druggable target, hence, targeting the downstream metabolic regulation could be effective alternatives in inhibiting tumor growth.

Several organismal systems KEGG pathways were also enriched in CIS-associated genes despite not being strongly linked to pancreatic cancer development. These include OXYTOCIN SIGNALING, CHOLINERGIC SYNAPSE, and MELANOGENESIS. Our recent work helped show that the AXON GUIDANCE pathway is enriched for CIS-associated genes, a result which led to the discovery that these genes and the pathways they participate in are altered in human PDA [8]. This result was reproduced in this current analysis. Thus, it is clear that the broadened definition of CIS allows for the identification of many known and novel candidate cancer pathways. These data suggest many new hypotheses to be tested in PDA development.

Analysis of individual tumor reveals significant co-altered pathways

We and others have published results of SB screens in which we found that individual CIS tended to be co-mutated by transposon insertion more than expected by chance (e.g. [11]). We wondered whether an analysis of individual tumors would reveal that specific pathways would be co-altered in this same manner. Figure 3 shows a heat map of adjusted correlation between pair of pathways, which are co-altered by transposon insertions within/near genes in those pathways. We observed that there are two major clusters of strongly co-altered pathways. Within these clusters certain specific pathways show strong associations, being altered by transposon insertion in the same tumors more often than would be expected by chance. These data provide the basis for developing specific hypotheses about pathways that interact to cause cancer. Thus, alterations of one pathway may allow the other pathway to exert its full oncogenic effects.
Fig. 3

Heat map of correlation between pair of pathways. Legend indicates strength of correlation coefficient (red: high correlation; black: weak correlation). a Heat map of correlation between all pairs of pathways. b Zoom in of block 1 shown in panel a. c Zoom in of block 2 shown in panel a. Pathway names from left to right (bottom to top): 1: Ubiquitin mediated proteolysis, 2: One carbon pool by folate, 3: Wnt signaling pathway, 4: Cell cycle, 5: Hippo signaling pathway, 6: Protein processing in endoplasmic reticulum, 7: MAPK signaling pathway, 8: Lysine degradation, 9: RNA polymerase, 10: N-Glycan biosynthesis, 11: mRNA surveillance pathway, 12: Hedgehog signaling pathway, 13: Dopaminergic synapse, 14: GPI-anchor, 15: RNA degradation, 16: Terpenoid backbone biosynthesis, 17: Mucin type O-Glycan biosynthesis, 18: Rap1 signaling pathway, 19: Adherens junction, 20: Leukocyte transendothelial migration, 21: TGF-beta signaling pathway, 22: Axon guidance, 23: MicroRNAs in cancer, 24: Tight junction, 25: PI3K-Akt signaling pathway, 26: Ras signaling pathway, 27: Chemokine signaling pathway, 28: Serotonergic synapse, 29: Glutamatergic synapse, 30: Cholinergic synapse, 31: Retrograde endocannabinoid signaling, 32: Circadian entrainment, 33: Sulfur relay system, 34: Notch signaling pathway, 35: Alanine, aspartate and glutamate metabolism, 36: Aldosterone-regulated sodium reabsorption, 37: Natural killer cell mediated cytotoxicity, 38: VEGF signaling pathway, 39: Fc epsilon RI signaling pathway, 40: Gap junction, 41: Melanogenesis, 42: FoxO signaling pathway, 43: HIF-1 signaling pathway, 44: Fc gamma R-mediated phagocytosis, 45: Estrogen signaling pathway, 46: Platelet activation, 47: Oxytocin signaling pathway, 48: Vascular smooth muscle contraction, 49: PIP, 50: mTOR signaling pathway, 51: Focal adhesion, 52: Regulation of actin cytoskeleton, 53: Insulin signaling pathway, 54: Thyroid hormone signaling pathway, 55: ErbB signaling pathway, 56: T cell receptor signaling pathway, 57: Neurotrophin signaling pathway, 58: B cell receptor signaling pathway, 59: Prolactin signaling pathway

A careful analysis of some of the associations reveals pairs of pathways that might be predicted to interact based on what is known about their functions and regulation already. For example, block 1, labeled in Fig. 3, contains strong associations between the ubiquitin processing pathway and several pathways including ErbB, Insulin and mTOR signaling. It is known that cell signaling pathways that transmit signals from the extracellular space into the cell cytoplasm and nucleus are regulated by the abundance and stability of certain proteins. In many cases, the stability of these proteins is regulated by ubiquitination and degradation by the proteosome. Well known examples, include NF κB and Wnt/ β-catenin signaling pathways. Work shows that members of the ErbB family of receptors are downregulated by ubiquitination involving the E3 ubiquitin ligase Cbl [12]. Ubiquitination also regulates Akt-mTOR signaling in multiple myeloma [13] and Akt-mTOR is activated by insulinsignaling [14].

Block 2, labeled in Fig. 3 contains several other intriguing pathway-pathway associations. For example, we see a strong association between cell cycle control and miRNAs known to be involved in cancer. Indeed, there are several well studied examples of miRNAs that regulate the mRNA transcripts of cell cycle regulators such as MYC [15], RB1 [16] and CCND1 [17]. Also in block 2, we see evidence for TGF β pathway and MAPK pathway co-dysregulation. Abundant evidence for crosstalk between these pathways has been published [18, 19]. Thus, it is entirely plausible that co-alteration between these pathways is specifically selected for during PDA progression. Specific hypotheses can, or have been, tested in the laboratory. For example, MAPK activation, via expression of the KrasG12D oncogene, cooperates strongly with Smad4 inactivation, which alters/inactivates TGF β signaling, in a mouse model of PDA [20]. This functionally confirms the observation from the analyses done here. We can thus predict, that many other pathway-pathway associations observed in Fig. 3 can be functionally validated. More speculative, but of tremendous therapeutic significance, is the idea that targeting one pathway of a pathway-pathway pair observed in Fig. 3 would alter the ability of the second pathway to exert its oncogenic effects. Indeed, co-targeting both of such pairs of altered pathways may be the most effective way to treat individual cases of PDA. These ideas wait functional testing in the laboratory using model systems.

Association of CIS-associated genes and enriched pathways

Several of the most commonly altered genes in the PDA screen (i.e. the top ranked CIS-associated genes) have little published functional data. We speculate that by finding which pathways they most often interact with, something could be learned about their function in general and in PDA development. The associations between the top ranked CIS and enriched pathways are shown in Fig. 4. In Fig. 4, several CIS-associated genes such as Stag2, Arhgap5, Usp9x, Magi1, Arid1a have few connections to enriched pathways then other CIS-associated genes. In Additional file 3, we listed these connections and corresponding estimates from regression model, p values and FDR. For example, in Additional file 1, Usp9x is associated with PI3K-AKT signaling pathway, DOPAMINERGICSYNAPSE, HIPPO signaling, and TIGHT JUNCTION pathways. Thus, it seems likely that Usp9x mutation or down regulation has to cooperate with alterations in these other pathways in order for PDA to develop. The CIS-associated genes that also demonstrated association with the these Usp9x-associated pathways are Gsk3b, Ctnna1, Mll5, Pten, Arfip1, Magil.
Fig. 4

Associations between CIS and enriched pathways. Red nodes represent CIS associated genes, and blue nodes indicate pathways. Abbreviations: miRNAs: MicroRNAs in cancer, GPI: Glycosylphosphatidylinositol-anchor biosynthesis, T.J.: Tight junction, Hedgehog: Hedgehog signaling pathway, PIP: Phosphatidylinositol signaling system, B cell: B cell receptor signaling pathway, Leuk. M.: Leukocyte transendothelial migration, DA: Dopaminergic synapse, Axon: Axon guidance, Ubiquitin: Ubiquitin mediated proteolysis, F.A: Focal adhesion, M.G.: Melanogenesis, Lysine D.: Lysine degradation, Protein: Protein processing in endoplasmic reticulum, T cell: T cell receptor signaling pathway, T.H.: Thyroid hormone signaling pathway, N.T: Neurotrophin signaling pathway, A.J.: Adherens junction, Actin: Regulation of actin cytoskeleton, Ck: Chemokine signaling pathway

Conclusion

In this work, we demonstrate the non-random enrichment of CIS-associated genes from a transposon-based screen for PDA into certain KEGG signaling pathways, disease states and biological processes.

Methods

To assess whether a pathway harbors more CIS-associated genes than expected by chance, we use CO-GSEA approach. For each tumor sample, we considered a pathway is altered (coded: 1) if at least 1 gene in the pathway was mutated; coded zero if it’s not. A score for each pathway was calculated to be the number of tumors in which the pathway is altered. We assessed whether the score of a pathway was statistically significant through random permutation. For example, if a mouse tumor contains 100 mutations, we randomly assigned the 100 mutations to 100 different genes. A score for each pathway can be obtained by counting the number of altered tumor samples after the permutation. We repeated the permutation 107 times to obtain the distribution of score under the null for each pathway and calculated a p value based on the permuted null distribution. A similar approach was also applied in mutation analysis of human tumor samples in [6].

The ability to detect a significant pathway using the CO-GSEA approach depends on the background mutation rate and the size of the pathway under consideration. The relationship between the number of total cases, and the expected score of a given pathway under random permutation can be described as: \(N- \Sigma _{i=1}^{N}\tiny {\frac {\left (\! \begin {array}{c} G-n_{i} \\ P_{s} \end {array} \!\right)} {\left (\! \begin {array}{c} G\\ P_{s} \end {array} \!\right)}}\), where N is the total number of cases; G is the number of genes considered in the pathway analysis; n i is the number of events in sample i and P s is the number of genes in the pathway [6].

Analysis of co-altered pathways

To investigate whether a pair of pathways was co-altered in a significant manner, we remove the CIS-associated genes that are present in both pathways, and for each sample, we calculated the mutation frequency in each pathway using the remaining non-overlapping CIS as: \(\frac {\text {\# of mutations in sample } i\text { in the pathway}}{\text {\# of non-overlapping CIS in the pathway}}\). For each pair of pathways, Pearson correlations were calculated to present the correlation between pathways characterized by non-overlapping CIS using the mutation counts.

Association between top CIS-associated genes and enriched pathways

Among the top 20 CIS-associated genes previously reported [7], 12 of them listed do not map to any KEGG pathways. We conducted association analysis between the top 20 CIS-associated genes and the enriched KEGG pathway using quasi-Poisson regression models with over-dispersion. For each CIS-associated gene, we examined whether mutation status of the CIS-associated gene (code 1 if mutated; 0 otherwise) is associated with higher mutation counts (the number of altered CIS-associated genes) for a pathway under consideration. We reported the CIS and pathways associations with FDR < 0.001.

Declarations

Acknowledgements

Y-YH is partially supported by NIH/NCI grants: P30-CA077598, P30-DK050456, P50-CA101955, U19-CA157345, R01 CA179246 and American Cancer Society grant 125627-RSG-14-074-01-TBG. TKS is supported by funding from the NIH NCI (5R00CA151672-04) and the Masonic Cancer Center NIH support grant (P30-CA77598). DL is partially supported by P50-CA101955.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Division of Biostatistics, School of Public Health, University of Minnesota
(2)
Masonic Cancer Center, University of Minnesota
(3)
Department of Pediatrics
(4)
Department of Obstetrics, Gynecology & Women’s Health
(5)
Minnesota Super Computing Institute

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