Advanced Techniques in Biology & Medicine

Advanced Techniques in Biology & Medicine
Open Access

ISSN: 2379-1764

Research Article - (2015) Volume 3, Issue 3

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Metagenomic Analysis of Molecular Profile of Breast Cancer Using Genie a Literature Based Gene Prioritizing Tool: A Novel Approach

Amit Kumar Yadav* and Vidya Jha
Division of Molecular Pathology, Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
*Corresponding Author: Amit Kumar Yadav, Assistant Professor, Division of Molecular Pathology, Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India, Tel: 091-26707408 Email:

Abstract

Background: The biological complexity and heterogeneity of breast cancer can be explained by molecular profile. Microarray technique is the method of choice to study it. But there are certain limitations. Computational techniques are a novel method to study gene expression profile.

Materials and method: Genie, a freely available web based software was used to analyze literature, gene and homology information from MEDLINE, NCBI Gene and HomoloGene databases. Inputs given were target species (Homo sapiens) and biomedical topic (breast cancer). According to input provided genes of target species are prioritized.

Results: The ranking given to 1906 reported genes was not along expected lines. Therefore, they were manually re-ranked according to number of hits. These were narrowed to 70. The proteins encoded by these genes and their functions were obtained from NCBI database. On the basis of their function and role in carcinogenesis these genes were then grouped together into distinct categories.

Conclusion: A novel computational approach to study molecular profile of breast cancer has been demonstrated. A panel of 70 genes to study gene expression profile of breast cancer has been suggested. These genes comprehensively evaluate all aspects of molecular pathogenesis of breast cancer and recommended for future clinical studies.

Keywords: Breast cancer, Molecular profile, Genie, Gene prioritization, Metagenomic analysis

Introduction

Breast cancer is a very complex and heterogeneous disease. The vast majority of cases are morphologically infiltrating ductal carcinoma NOS. However, in these morphologically similar looking cases the biological behaviour is different. This leads to difference in response to therapy and outcome. Clinical parameters like age, tumour stage, grade and routinely used biomarkers like estrogen receptor (ER), progesterone receptor (PR) and HER2-neu cannot fully explain this heterogeneity [1,2]. This is due to the difference in molecular profile of each case.

Traditional classification of breast cancer is based on morphology. The study of gene expression profile has led to a new system of classification. In 2000, Perou and co-workers [3] in a seminal paper classified breast cancer into intrinsic subtypes based on gene expression profile. Subsequent work of numerous authors has fundamentally changed the way breast cancer is understood and classified [4-6].

Microarray technique which allows simultaneous analysis of expression of thousands of genes has been the method of choice to study gene expression profile of breast cancer [7]. The disadvantage of this technique is the limited sample and variability [8,9]. Also a comprehensive summarization of the genes is not possible due to the large number of genes and abstracts involved.

A novel approach to study molecular profile is using computational techniques to study gene function by analyzing the vast literature which is now available. Fontaine and co-workers [10] developed the Genie algorithm and web server. The input for the software is a biological topic.

It evaluates the entire MEDLINE database for relevance to that subject, and then evaluates all the genes of a user’s requested organism according to the relevance of their associated MEDLINE records. The advantage of this approach is that large amount of published data regarding genes in a particular disease can be analyzed. This kind of analysis at a multi genomic scale is not possible without computational approach. To the best of our knowledge this approach has not been used to date for studying molecular profile of breast cancer.

Materials and Methods

Analysis at a multi genomic scale is not possible without computational approach. To the best of our knowledge this approach has not been used to date for studying molecular profile of breast cancer.

The system requires two basic inputs: a target species (e.g., Homo sapiens) and a biomedical topic ideally related to a gene function (breast cancer in the present study). According to the input provided the genes of the target species are prioritized. The target species is defined by its scientific name or its taxonomic ID (e.g., Homo sapiens - 9606).

The biomedical topic is ultimately defined by a set of biomedical references represented by MEDLINE records. After giving the inputs the software initialised in 9 seconds and the analysis was complete in 73 seconds. It went through 796 abstracts on PubMed. All the relevant protein coding genes as per the input provided were analyzed. In order to ensure that only significant genes were reported the cut offs were taken as p<0.01 for abstracts and false discovery rate<0.01 for genes. A one-sided Fisher’s exact test was carried out by the algorithm to define the significance of gene-to topic relationship. It compared the number of selected abstracts to what is observed in a simulation using a set of ten thousand randomly selected abstracts.

Literature extension by orthology was not done as it was not needed. This is done when genes from poorly studied organisms are studied. The genes are ranked using, in addition to the abstracts directly associated to them, the abstracts associated to their orthologs in other species.

The genes are then presented in a list sorted by false discovery rate (FDR) with hyperlinks to the most significant abstracts, Entrez Gene and HomoloGene databases. A list of the words found to be relevant to the topic is provided to facilitate the interpretation of the results.

Results

A total number of 1906 genes were reported by the software to be associated with breast cancer. These genes were prioritized and ranked according to the abstracts directly associated with them. It was observed that the ranking given by the software was not along expected lines as it did not correlate with the data available from previous work [11-14]. For example, ZNF703 (zinc finger protein 703) was given first rank while ERBB2 (erb-b2 receptor tyrosine kinase) and ESR1 (estrogen receptor 1) were ranked third and seventh respectively.

In order to remove this discrepancy an alternative method of ranking was devised. The genes were ranked manually according to the number of hits that is the gene with the maximum number of hits was ranked first and one with minimum number of hits was ranked last. In order to find out the most important genes different cut offs for the number of hits were used-50 and 100 (Table 1). The number of cut offs was arrived at by trial method.

Rank GeneID Symbol PMID Hits
1 2099 ESR1 2339 851
2 2064 ERBB2 1756 733
3 1956 EGFR 3346 492
4 7157 TP53 6528 483
5 672 BRCA1 2027 405
6 207 AKT1 1997 263
7 2100 ESR2 875 255
8 7422 VEGFA 3104 243
9 3091 HIF1A 1901 188
10 595 CCND1 1116 178
11 5241 PGR 555 177
12 5728 PTEN 1298 177
13 6774 STAT3 1459 169
14 5743 PTGS2 1745 169
15 367 AR 1634 159
16 999 CDH1 1306 155
17 5290 PIK3CA 807 153
18 7040 TGFB1 2828 150
19 675 BRCA2 1223 148
20 3480 IGF1R 690 141
21 596 BCL2 1374 142
22 4318 MMP9 1862 141
23 7852 CXCR4 1335 137
24 4790 NFKB1 2235 134
25 332 BIRC5 888 134
26 1499 CTNNB1 1609 131
27 1026 CDKN1A 1202 121
28 1588 CYP19A1 596 111
29 4233 MET 660 111
30 7316 UBC 3431 111
31 960 CD44 751 106
32 6714 SRC 1032 104
33 1027 CDKN1B 762 100
34 3845 KRAS 1280 99
35 5594 MAPK1 1555 99
36 2475 MTOR 981 97
37 4609 MYC 1180 94
38 8202 NCOA3 278 89
39 3479 IGF1 1278 84
40 1029 CDKN2A 1654 82
41 5595 MAPK3 1074 81
42 4313 MMP2 1208 80
43 4582 MUC1 524 78
44 9429 ABCG2 517 76
45 3569 IL6 3074 76
46 7424 VEGFC 308 75
47 2146 EZH2 406 75
48 5243 ABCB1 1590 74
49 4288 MKI67 449 73
50 6387 CXCL12 885 70
51 5468 PPARG 1563 69
52 2065 ERBB3 289 69
53 8743 TNFSF10 606 64
54 6696 SPP1 657 64
55 4851 NOTCH1 714 63
56 5747 PTK2 607 61
57 6667 SP1 833 59
58 7124 TNF 4360 56
59 3065 HDAC1 803 56
60 857 CAV1 637 55
61 5970 RELA 976 55
62 5268 SERPINB5 167 54
63 100133941 CD24 180 54
64 6615 SNAI1 263 54
65 5328 PLAU 463 54
66 11186 RASSF1 334 53
67 4193 MDM2 1205 53
68 5925 RB1 899 51
69 3486 IGFBP3 535 50
70 2950 GSTP1 1248 50

Table 1: Genes reported by genie software with their rank, gene ID, symbol, PMID and number of hits.

When cut off was used as 50 a total of 70 genes were selected, whereas a cut off of 100 yielded 33 genes. The first 5 ranks are now occupied by ESR1 (estrogen receptor 1), ERBB2 (erb-b2 receptor tyrosine kinase 2), EGFR (epidermal growth factor receptor), TP53 (tumor protein p53) and BRCA1 (breast cancer 1, early onset) genes. Previously these were taken by ZNF703 (zinc finger protein 703), GREB1 (Growth regulation by estrogen in breast cancer 1), ERBB2 (erb-b2 receptor tyrosine kinase 2), CST6 (cystatin E/M) and WISP2 (WNT1 inducible signalling pathway protein 2).

The proteins encoded by these genes and their functions were obtained from NCBI database. These are summarized in Table 2. On the basis of their function and role in carcinogenesis these genes were then grouped together into following distinct categories (Figure 1)

Rank Gene Protein encoded Function
1 ESR1 Estrogen receptor 1 Localizes to nucleus and binds estrogen hormone.
2 ERBB2 erb-b2 receptor tyrosine kinase 2 A member of the epidermal growth factor (EGF) receptor family of receptor tyrosine kinases. It forms a heterodimer, stabilizing ligand binding and enhancing kinase-mediated activation of downstream signalling pathways,
3 EGFR Epidermal growth factor receptor A transmembrane glycoprotein which is a receptor for members of the epidermal growth factor family. Binding of the protein to ligand leads to cell proliferation.
4 TP53 Tumour protein p53 Tumour suppressor protein
5 BRCA1 Breast cancer 1, early onset Nuclear phosphoprotein playing role in maintaining genomic stability and acting as a tumor suppressor.
6 AKT1 V-akt murine thymoma viral oncogene homolog 1 Role in cell survival and inhibition of apoptosis.
7 ESR2 Estrogen receptor 2 Binds to estrogen hormone and interact with specific DNA sequences to activate transcription.
8 VEGFA Vascular endothelial growth factor a Acts on endothelial cells and leads to increased vascular permeability, inducing angiogenesis, vasculogenesis and endothelial cell growth, promoting cell migration, and inhibiting apoptosis.
9 HIF1A Hypoxia inducible factor 1, alpha subunit Master regulator of cellular and systemic homeostatic response to hypoxia by activating transcription of many genes.
10 CCND1 Cyclin d1 Regulation of cell cycle.
11 PGR Progesterone receptor Mediates action of progesterone hormone.
12 PTEN Phosphatase and tensin homolog Tumour suppressor protein
13 STAT3 Signal transducer and activator of transcription 3 Act as transcriptional activators to many genes.
14 PTGS2 Prostaglandin-endoperoxide synthase 2 Prostaglandin biosynthesis
15 AR Androgen receptor. Stimulates transcription of androgen responsive genes.
16 CDH1 Cadherin 1, type 1, e-cadherin (epithelial) Mediates cell-cell adhesion.
17 PIK3CA Phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha Catalytic subunit of Phosphatidylinositol 3-kinase
18 TGFB1 Transforming growth factor, beta 1 Regulates proliferation, differentiation, adhesion, migration and other functions.
19 BRCA2 Breast cancer 2, early onset Maintenance of genome stability, specifically the homologous recombination pathway for double-strand DNA repair. 
20 IGF1R Insulin-like growth factor 1 receptor Binds insulin-like growth factor and functions as an anti-apoptotic agent by enhancing cell survival. 
21 BCL2 B-cell cll/lymphoma 2  Integral outer mitochondrial membrane protein that blocks apoptosis.
22 MMP9 Matrix metallopeptidase 9 Degrades type IV and V collagens.
23 CXCR4 Chemokine (c-x-c motif) receptor 4 Encodes a CXC chemokine receptor specific for stromal cell-derived factor-1.
24 NFKB1 Nuclear factor of kappa light polypeptide gene enhancer in b-cells 1 DNA binding subunit of the NF-kappa-B (NFKB) protein complex.
25 BIRC5 Baculoviraliap repeat containing 5 Encode negative regulatory proteins that prevent apoptotic cell death.
26 CTNNB1 Catenin (cadherin-associated protein), beta 1, 88kda Part of a complex of proteins that constitute adherens junctions (AJs) and may be responsible for transmitting the contact inhibition signal that causes cells to stop dividing once the epithelial sheet is complete.
27 CDKN1A Cyclin-dependent kinase inhibitor 1a (p21, cip1)  Regulator of cell cycle progression at G1.
28 CYP19A1 Cytochrome p450, family 19, subfamily a, polypeptide 1 Member of the cytochrome P450 superfamily of enzymes. This protein localizes to the endoplasmic reticulum and catalyses the last steps of estrogen biosynthesis. 
29 MET Met proto-oncogene, receptor tyrosine kinase Encodes tyrosine-kinase activity.
30 UBC Ubiquitin c Protein degradation, DNA repairs, cell cycle regulation, kinase modification, endocytosis, and regulation of other cell signalling pathways. 
31 CD44 Cd44 molecule (Indian blood group) Participates in a wide variety of cellular functions including lymphocyte activation, recirculation and homing, hematopoiesis, and tumor metastasis.
32 SRC Src proto-oncogene, non-receptor tyrosine kinase  Regulation of embryonic development and cell growth.
33 CDKN1B Cyclin-dependent kinase inhibitor 1b Controls the cell cycle progression at G1.
34 KRAS Kirsten rat sarcoma viral oncogene homolog Member of the small GTPase superfamily.
35 MAPK1 Mitogen-activated protein kinase 1 Integration point for multiple biochemical signals, and involved in a wide variety of cellular processes such as proliferation, differentiation, transcription regulation and development.
36 MTOR Mechanistic target of rapamycin (serine/threonine kinase) Mediate cellular responses to stresses such as DNA damage and nutrient deprivation.
37 MYC V-myc avian myelocytomatosis viral oncogene homolog Role in cell cycle progression, apoptosis and cellular transformation.
38 NCOA3 Nuclear receptor coactivator 3  Nuclear receptor coactivator that interacts with nuclear hormone receptors to enhance their transcriptional activator functions.
39 IGF1 Insulin-like growth factor 1 Member of a family of proteins involved in mediating growth and development.
40 CDKN2A Cyclin-dependent kinase inhibitor 2a Involved in cell cycle G1 control and is  an important tumor suppressor gene. 
41 MAPK3 Mitogen-activated protein kinase 3 Act in a signaling cascade that regulates various cellular processes such as proliferation, differentiation, and cell cycle progression in response to a variety of extracellular signals.
42 MMP2 Matrix metallopeptidase 2 Zinc-dependent enzymes capable of cleaving components of the extracellular matrix and molecules involved in signal transduction.
43 MUC1 Mucin 1, cell surface associated Membrane-bound protein playing an essential role in forming protective mucous barriers on epithelial surfaces and intracellular signaling. 
44 ABCG2 Atp-binding cassette, sub-family g (white), member 2 (junior blood group) Transport various molecules across extra- and intra-cellular membranes. Also referred to as a breast cancer resistance protein, this protein functions as a xenobiotic transporter which may play a major role in multi-drug resistance.
45 IL6 Interleukin 6 Cytokine that functions in inflammation and the maturation of B cells.
46 VEGFC Vascular endothelial growth factor c Promotes angiogenesis and endothelial cell growth, and can also affect the permeability of blood vessels.
47 EZH2 Enhancer of zeste 2 polycomb repressive complex 2 subunit Involved in maintaining the transcriptional repressive state of genes over successive cell generations.
48 ABCB1 Atp-binding cassette, sub-family b (mdr/tap), member 1 Transport various molecules across extra- and intra-cellular membranes. Involved in multidrug resistance by acting as an ATP-dependent drug efflux pump. It often mediates the development of resistance to anticancer drugs.
49 MKI67 Marker of proliferation ki-67 Associated with and may be necessary for cellular proliferation. 
50 CXCL12 Chemokine (c-x-c motif) ligand 12 Functions as the ligand for the G-protein coupled receptor, chemokine (C-X-C motif) receptor 4, and plays a role in many diverse cellular functions, including embryogenesis, immune surveillance, inflammation response, tissue homeostasis, and tumor growth and metastasis. 
51 PPARG Peroxisome proliferator-activated receptor gamma Regulator of adipocyte differentiation.
52 ERBB3 Erb-b2 receptor tyrosine kinase 3 Forms heterodimers with other EGF receptor family members having kinase activity. This leads to the activation of pathways which lead to cell proliferation or differentiation.
53 TNFSF10 Tumor necrosis factor (ligand) superfamily, member 10 Preferentially induces apoptosis in transformed and tumor cells, but does not appear to kill normal cells although it is expressed at a significant level in most normal tissues.
54 SPP1 Secreted phosphoprotein 1 Cytokine that upregulates expression of interferon-gamma and interleukin-12. 
55 NOTCH1 Notch 1  Play a role in a variety of developmental processes by controlling cell fate decisions.
56 PTK2 Protein tyrosine kinase 2 Cell growth and intracellular signal transduction pathways triggered in response to certain neural peptides or to cell interactions with the extracellular matrix.
57 SP1 Sp1 transcription factor Zinc finger transcription factor involved in many cellular processes, including cell differentiation, cell growth, apoptosis, immune responses, response to DNA damage, and chromatin remodeling.
58 TNF Tumor necrosis factor Regulation of a wide spectrum of biological processes including cell proliferation, differentiation, apoptosis, lipid metabolism, and coagulation.
59 HDAC1 Histone deacetylase 1 Component of the histone deacetylase complex. It also interacts with retinoblastoma tumor-suppressor protein and this complex is a key element in the control of cell proliferation and differentiation.
60 CAV1 Caveolin 1, caveolae protein, 22kda Promote cell cycle progression and tumor suppressor gene
61 RELA V-rel avian reticuloendotheliosis viral oncogene homolog a Inhibitor of NF-kappa-B (NFKB1).
62 SERPINB5 Serpin peptidase inhibitor, clade b (ovalbumin), member 5 Tumor suppressor. It blocks the growth, invasion, and metastatic properties of mammary tumors.
63 CD24 CD24 molecule Modulates growth and differentiation signals to granulocytes and B cells.
64 SNAI1 Snail family zinc finger 1 Zinc finger transcriptional repressor which downregulates the expression of ectodermal genes within the mesoderm.
65 PLAU Plasminogen activator, urokinase Serine protease involved in degradation of the extracellular matrix and possibly tumor cell migration and proliferation.
66 RASSF1 Ras association (ralgds/af-6) domain family member 1 Tumour suppressor function.
67 MDM2 Mdm2 proto-oncogene, e3 ubiquitin protein ligase Promote tumor formation by targeting tumor suppressor proteins, such as p53, for proteasomal degradation.
68 RB1 Retinoblastoma 1 Negative regulator of the cell cycle and a tumor suppressor gene.
69 IGFBP3 Insulin-like growth factor binding protein 3 Protein forms a ternary complex with insulin-like growth factor acid-labile subunit (IGFALS) and either insulin-like growth factor (IGF) I or II. It circulates in the plasma, prolonging the half-life of IGFs and altering their interaction with cell surface receptors.
70 GSTP1 Glutathione s-transferase pi 1 Function in xenobiotic metabolism and play a role in susceptibility to cancer, and other diseases.

Table 2: Proteins encoded by the genes and their function.

advanced-techniqe-Categorization-genes

Figure 1: Categorization of genes according to their function and role in genesis of breast cancer.

a) Proliferation.

b) Evading apoptosis.

c) Invasion and metastasis.

d) Sustained angiogenesis.

e) Tumour suppressor genes.

f) Estrogen.

g) Her-2 neu.

h) Miscellaneous.

Discussion

Breast cancer is a leading cause of cancer related mortality in women. As per WHO statistics [15] nearly 1.7 million new cases were diagnosed in 2012 (second most common cancer overall). This represents about 12% of all new cancer cases and 25% of all cancers in women. Traditionally clinical parameters like age, tumour stage, grade and routinely used biomarkers like oestrogen receptor(ER), progesterone receptor (PR) and HER2-neu have been used to evaluate the prognosis and guide therapy [16,17].

Vast amount of molecular information in breast cancer is now available from gene expression profiling studies. These have become an extremely important tool to assess the prognosis and guide appropriate management [18]. The technique of choice for this is microarray. The data that has become available from gene expression profile studies has impacted not only the management of breast cancer but other tumours like lung [19,20] and colon cancer [21]. However, microarray technique suffers from many disadvantages as mentioned previously [8,9]. Thus there is a need to explore alternative methods to study gene expression profile of breast cancer.

The authors have used one such technique Genie algorithm and web server in the present study to evaluate molecular profile of breast cancer. The utility of computational approach to find human genes associated with a disease has been shown previously [22-24]. However, Genie goes one step ahead and besides highlighting well known genes it brings out new candidate genes [10]. This helps in better characterization of a disease.

There are alternative gene prioritizing tools that perform automatic gene name extraction and normalization [25,26]. The basic concept behind such an analysis is that when two words repeatedly occur together in an abstract they are likely to be functionally related. However, these methods suffer from a disadvantage that they wrongly identify genes in text which leads to ambiguous results [27,28]. Genie overcomes this limitation by using NCBI curated gene associations and unambiguous gene identifiers [10].

A total number of 1906 genes were found to be associated with breast cancer. These genes were manually ranked using the number of hits as a parameter. Then the number of genes was further narrowed down by using two cut offs, i.e., 50 and 100 hits. These yielded 70 and 33 genes respectively. The authors feel that 33 genes are not sufficient for a comprehensive analysis of gene expression profile. Thus it is recommended that at least 70 genes should be studied for a proper examination. On categorization of these genes on the basis of function it was observed that amongst all the categories maximum number of genes belonged to the proliferation related group (24 genes). This was followed by tumour suppressor genes (12 genes). Overall the genes reported by the software comprehensively cover all aspects of the biology of breast cancer.

The availability of new data from microarray studies led to the development of many multigene prognostic tests to improve assessment of prognosis and therapeutic response in breast cancer [29]. The most widely used amongst these are Oncotype DX [14] and Mammaprint [13].

Oncotype DX (Genomic Health, Redwood City, CA, USA) is based on high-throughput real time, reverse transcriptase polymerase chain reaction (RT-PCR) analysis of formalin fixed paraffin-embedded (FFPE) tumor tissue [30,31]. Thus, it can also be used on archival blocks. The test utilizes 16 genes which have been shown to have highest correlation with distant recurrence after 10 years along with five housekeeping genes. The test algorithm is designed to calculate recurrence score (RS) from 0 to 100. A higher RS is associated with greater probability of recurrence at 10 years and vice versa.

MammaPrint (Agilent, Amsterdam, Netherlands) is a microarraybased test. It measures the expression of 70 genes. The test is recommended as an adjunctive prognostic test for breast cancer patients who are less than 61 years of age with stage I/II disease, lymph node-negative or one to three lymph node-positive [12]. MammaPrint stratifies patients into low-risk or high-risk prognostic groups [13]. The prognostic risk discrimination is good among. In patients who are ER-positive the assay has a good prognostic risk assessment. However, almost all ER-negative cases are stratified as high risk. This makes the prognostic score of limited clinical value in this group [32].

A large multicenter retrospective study suggested that adjuvant chemotherapy was beneficial only in the high-risk ER positive patients [33]. MammaPrint as described originally needed fresh-frozen tissue. This was a major drawback and reason for its limited clinical utilization. However, recently described version of the test can be performed on FFPE tissue [34].

Using a novel computational technique to carry out molecular profiling at a multi-genomic scale the authors suggest an alternative panel of genes (Table 1) to study gene expression profile of breast cancer. It is believed that this panel includes some of the important genes which were not present in the initial panels.

A comparison was done between the panel suggested in the present study and those included in Oncotype DX [14] and MammaPrint [13]. In case of Oncotype DX out of the total 16 genes related to breast cancer 6 (38%) are also present in the new panel. While in the case of MammaPrint out of the total 70 only a single gene i.e. MMP9 is shared with our panel. Thus there was greater correlation with Oncotype DX as compared to MammaPrint.

In a recent review of clinical utility of gene-expression profiling in women with early breast cancer Marrone and his co-workers [35] have said that five systematic reviews found no direct evidence of clinical utility for either Oncotype DX or MammaPrint. Indirect evidence showed Oncotype DX was able to predict treatment effects of adjuvant chemotherapy, whereas no evidence of predictive value was found for MammaPrint. No studies provided any direct evidence that using gene-expression profiling tests to direct treatment decisions improved outcomes in women with breast cancer. The authors believe that one of the main reasons for this apparent failure of the two techniques to influence treatment outcome is probably inappropriate gene selection. On going through the list of genes in both the tests it was felt that possibly some important genes have not been included. Thus there is a need for an alternative panel.

Conclusion

The present study has demonstrated a novel computational approach to study molecular profile of breast cancer using genie, a gene prioritizing software. A novel panel of 70 genes to study gene expression profile of breast cancer has been suggested in which the majority of genes are different from currently used panels. These genes we believe comprehensively evaluate all aspects of the molecular pathogenesis of breast cancer. However, the clinical utility of the present study can only be found out by carrying out well designed clinical studies in the future.

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Citation: Yadav AK, Jha V (2015) Metagenomic Analysis of Molecular Profile of Breast Cancer Using Genie a Literature Based Gene Prioritizing Tool: A Novel Approach. Adv Tech Biol Med 3:147.

Copyright: © 2015 Yadav AK, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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