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WO2013098797A2 - Diagnostic tests for predicting prognosis, recurrence, resistance or sensitivity to therapy and metastatic status in cancer - Google Patents

Diagnostic tests for predicting prognosis, recurrence, resistance or sensitivity to therapy and metastatic status in cancer Download PDF

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WO2013098797A2
WO2013098797A2 PCT/IB2012/057844 IB2012057844W WO2013098797A2 WO 2013098797 A2 WO2013098797 A2 WO 2013098797A2 IB 2012057844 W IB2012057844 W IB 2012057844W WO 2013098797 A2 WO2013098797 A2 WO 2013098797A2
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cancer
genes
recurrent
molecular signature
tumor
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PCT/IB2012/057844
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French (fr)
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WO2013098797A3 (en
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Moni Abraham KURIAKOSE
Amritha SURESH
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Kuriakose Moni Abraham
Suresh Amritha
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Priority to IN4935CHN2014 priority Critical patent/IN2014CN04935A/en
Priority to US14/368,801 priority patent/US20140342946A1/en
Publication of WO2013098797A2 publication Critical patent/WO2013098797A2/en
Publication of WO2013098797A3 publication Critical patent/WO2013098797A3/en

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the present invention relates to a process for personalization of cancer treatment involving the use of specific genes and/or their proteins in diagnostic tests for predicting prognosis, recurrence, resistance or sensitivity to therapy and metastasis status in cancer.
  • cancer and its progression in an individual is guided by the expression and/or altered status of many genes and gene products (molecular markers). Correlation of the changes in these molecular markers can help to predict if a particular patients cancer would (a) recur in time after treatment or (b) be sensitive or resistant to therapies or (c) have metastasized at the time of initial discovery of the tumor, consequently leading to improved ability to manage cancer.
  • Molecular signature refers to the expression of two or more genes described in Tables I- V, or more specifically Table X, in a tumor tissue or in tumor cells derived from tongue or other head-and-neck cancers; the said gene expression level being determined by one or more techniques that is commonly employed for measuring gene expression levels in tissues or cells which includes microarrays and real-time quantitative polymerase chain reaction. Levels of gene expression could also be determined by measuring the level of proteins encoded by the said genes using immunohistochemistry, enzyme-linked-immunosorbent assay or other methods like proteomic techniques for mapping expression of multiple proteins.
  • Molecularly-targeted therapies shall mean a treatment modality against cancer cells targeting specific molecules involved in tumorigenesis and tumor growth.
  • Immuno modulation therapy shall mean the use of modulators that inhibit/stimulate the immune system to elicit anti-tumor effects.
  • tongue cancer is used as an example of head and neck cancer and other carcinomas to describe a method utilizing a set of genes or gene products whose altered expression, in head and neck tumor in general including tongue cancer, predicts (a) probability of recurrence in time after treatment (b) sensitivity or resistance to therapies or (c) probability of metastasis at the time of initial discovery of the tumor,
  • the novel molecular signature comprises of a combination of genes selected from the list of genes given in Tables I-V or a narrower set of more differentially expressed genes from a preferred list of genes drawn from Tables I-V and listed in Table X.
  • the molecular signature is identified in pre- treatment and post-treatment head and neck cancer and is used to determine the probability of recurrence of cancer after surgery and anti-cancer therapy. Absence of the molecular signature in the primary tumor sample would imply a far less probability of recurrence ; hence one could avoid further therapy after surgery, thus decreasing the cost of treatment as well as the morbidity associated with chemotherapy. Presence of the molecular signature in the tumor at the time of surgery would reveal a higher probability of recurrence and therefore would aid in determining if adjuvant chemotherapy is warranted or not.
  • the molecular signature is used to identify sensitivity or resistance to anti-cancer agents, in particular chemotherapy agents, but not limited to the same, and would include radiation therapy or new generation molecularly- targeted drugs or immune-modulating drugs or cell therapy like dendritic cell therapy.
  • the present invention also identifies a molecular signature, listed in Table V, which is differentially expressed in the adjacent histologically normal mucosa of the recurrent and non -recurrent patients.
  • This molecular signature describes groups of cells in the adjacent mucosa of the recurrent patients that show the over expression of stem cell markers and transcription factors. The presence of these cells, as identified by the molecular signature, in the adjacent mucosa could also be predictive of recurrence in patients with head and neck cancer.
  • the molecular signature is used to determine the probability that a tumor would has metastasized to a secondary location at the time of diagnosis of the disease, which will allow one to determine if surgery alone is sufficient or adjuvant chemotherapy or other anti-cancer drugs or therapies are required.
  • the molecular signature in Table I-V, and more specifically Table X describes characteristics of the tumor that can be also used to predict if the cancer has metastasized to a secondary location by virtue of (a) the fact that the molecular signature identifies aggressive cells in the tumor that by definition has a higher invasive potential (b) the immune repressive genes that are over- expressed would allow the tumor to escape its primary site and metastasize.
  • the non-recurrent set primarily showed Interferon signaling; Cytotoxic T- lymphocyte mediated apoptosis of target cells, protein ubiquitination and Myc mediated apoptosis as significant pathways.
  • the tumor tissue that is used for analysis include tissue biopsies - either frozen, fixed in RNA stabilizing solutions or in paraffin-embedded- formalin fixed tissues (FFPE), or saliva which is used as the source RNA or protein for determination of the molecular signature
  • the assays used for determining the molecular signature includes microarray, quantitative real-time PCR, immunohisotochemisitry, enzyme-linked immunosorbent assay, proteomic analysis or other standard methods of measuring gene expression of multiple directly or through proteins encoded by the genes.
  • Table VII List of top 10 significant genes in Non-Recurrent/recurrent tongue cancer
  • Fig 3 Significant pathways between Non-recurrent and recurrent tongue cancer Pathway analysis was carried out by Ingenuity Pathway Analysis (IP A) and the top 10 significant pathways are represented in the figure. The pathways are sorted according to significance in recurrent sub set (A) and non-recurrent samples (B).
  • IP A Ingenuity Pathway Analysis
  • FIG 4 Interaction networks identified by Ingenuity Pathway Analysis Interaction network of genes that are differentially expressed between Non-recurrent and recurrent tumors (A & B).
  • A Activation
  • E Expression
  • PP protein- Protein Interaction
  • I Inhibition
  • L Proteolysis
  • P Phosphorylation
  • T Transcription
  • PD Protein-DNA interaction.
  • HBB and HBA1 The binding partners HBB and HBA1 are both higher in expression in non-recurrent tumors.
  • Fig 5 Validation in tissues and saliva samples.
  • the expression profile of a select subset of markers was validated in tongue cancer specimens (A).
  • a distinct difference in expression profile of 4 genes (COL5A1, IGLA, HBB and CTSC) was observed in the primary tissue of patients that were non-recurrent (Group I) and recurrent (Group II).
  • the pattern of expression obtained in the patients of the latter group was similar to that obtained in the recurrent tissue of patients (Group III).
  • ROC analysis revealed these markers as most significant according to the AUC (B).
  • the profile of 6 genes in saliva samples from normal (N) and tumor (T) samples is shown (C).
  • the normal samples primarily show the expression of IL1B while at least one of the carcinogenesis related genes are expressed in the patients.
  • ROC analysis of the combination of markers shows sensitivity of 0.65 and specificity of 0.87 (D).
  • Fig 6 Irnmunohistochemical analysis of candidate markers IHC was carried out on tongue cancer samples (A) with antibodies to HBB (a, b, c, d) and COL5A1 (e, f, g, h). The expression was analyzed in normal controls (a, e), in non-recurrent tumor samples (b & f) and in recurrent samples (c & g). d & h represent negative controls.
  • the non-recurrent tumor sample showed a high expression of HBB as observed in the normal control; while an over expression of COL5A1 was observed in the recurrent tumor sample.
  • the magnifications (100 or 200 times the original magnification) are mentioned on each panel.
  • ROC analysis showed HBB as a better candidate marker as compared to COL5A1 (B & C).
  • the present invention describes a molecular signature comprising of a set of genes or gene products whose altered expression in head and neck tumor in general including tongue cancer predicts (a) resistance to chemotherapy, which would help avoid chemotherapy or use other modalities of treatment (c) probability of recurrence of the disease post treatment (d) determining probability of metastasis at the time of surgery thereby allowing one to determine if adjuvant therapy is required or not.
  • Example 1 Patient details and sample collection
  • tissue samples are collected from patients undergoing surgical treatment after obtaining mandatory approvals (Table VI).
  • the samples that were subjected to microarray analysis were collected in RNA later (Ambion, Austin, USA), while the samples for validation were either snap frozen or collected in RNA later and archived at -80°C if required to be stored.
  • the clinical characteristics of the patients are obtained from the electronic medical records maintained at the tertiary care cancer center.
  • the sample sets were grouped into three categories: Group I (Pre-treatment, non-recurrent), which included pre-treatment tissues from patients who remained disease-free after standard treatment (surgery and adjuvant chemo radiation); Group II (Pre-treatment resistant/recurrent) included pre-treatment tissues from those who recurred during a 2-year follow up period; Group III (post-treatment recurrent; standard treatment) included recurrent tissue from patients with the recurrent disease. Group I & III were analyzed by micro array, while the validation was carried out in all the three groups. The adjacent mucosal tissue was collected 2 cm away from the tumor and confirmed as histologically negative for malignancy.
  • Example 2 RNA isolation, labeling of cRNA and hybridization
  • the preliminary analysis to ascertain the internal controls and the hybridization efficiency was carried out using the Gene Chip Operating Software (GCOS) and Microarray Suite (MAS5,Affymetrix, CA, USA).
  • the CEL files were extracted and imported into GeneSpring 7.2 (Agilent Technologies, CA, USA) software package for analysis.
  • Raw image data were background corrected, normalized and summarized into probe set expression values using Robust Microarray Analysis (RMA) algorithm.
  • RMA Robust Microarray Analysis
  • data from each chip was normalized to 50% of the measurements taken from that chip (measurements of ⁇ 0.01 were set to 0.01). Probe sets that were not reliably detected were removed, by filtering out those whose expression level was not >50 and confidence -values ⁇ 0.05, in at least 20% of the samples.
  • Ingenuity Pathway Analysis was carried out to identify significant functions, signaling pathways and networks (Ingenuity Systems Inc. CA, USA) at the default core analysis and core comparison platforms. Fishers exact test was used to identify the statistically significant functions/pathways.
  • the differentially expressed genes were hierarchically clustered using Multi Experiment Viewer, v 4.5 (MeV) (TM4 Microarray Software Suite, The Institute of Genomic Research (TIGR) with the Euclidean distance measurement and p values were calculated after application of the non-parametric Wilcoxon-Mann Whitney test (p ⁇ 0.5).
  • Example 4 Validation of the microarray data in tissue and saliva samples by Quantitative PCR
  • the expression levels of the genes selected for validation (MMP1, EMP1, ABCG1, COL5A1, IgLA, HBB, CTSC and CCL18) (Table I) was assessed by QRT PCR using the relative quantification ( ⁇ method). Expression was normalized using the endogenous control (GAPDH) and normal oral mucosal tissues were used as the calibrator. Melting curve analysis was done to ensure the specificity of the product obtained.
  • GPDH endogenous control
  • RT-PCR Reverse Transcription PCR
  • a subset of 10 candidate markers (MMP1, FN1, FAPA, SERPINH2, IL8, IL1B, IgLA, ABCG1, COL5A1, HBB), were tested for their expression in saliva by QRT PCR as above. Saliva samples from healthy volunteers as the calibrator.
  • the combined test result in the binary input format was used for the statistical analysis.
  • the expression patterns were correlated to the disease status of the patients to ascertain their clinical relevance.
  • the protein expression of two representative genes (COL5A1 and HBB), validated by QRT PCR was profiled in the tissue sections of a different cohort of patients with tongue cancer.
  • the sections were deparaffinized and IHC carried out according to standard protocols.
  • the antibodies were used in dilutions of 1:50 for both COL5A1 (scl33162; Santacruz Biotechnology, Santacruz, CA, USA) and HBB (H4890; Sigma Aldrich, USA).
  • the sections were microwaved for antigen retrieval and the staining detected by Dako REALTM En Vision kit (Dako Corporation, Carpenteria, CA, USA).
  • the sections were counterstained using haematoxylin and scanned at low and high power to identify areas of even staining and percentage of positive cells.
  • the grades of positivity were scored as follows; negative ( ⁇ 1%), grade I (1-10%), grade II (10-30%), III (30-60%) and IV (>60%).
  • the intensity of staining was also graded as mild, moderate and intense.
  • the expression in the normal oral mucosal tissues was used as control.
  • Receiver Operating Characteristic (ROC) curve analyses were carried out by SPSS 19 (IBM) and MedCalc® v 11.6.0.0 for the QPCR and IHC results. Area under the curve was computed via numerical integration of the ROC curves. The biomarkers, individually or in combination, with the largest Area under Curve (AUC) were identified to have the maximum predictive power for disease recurrence. Multiple regression analysis was also carried out by the stepwise method to identify the predictive value of the marker combinations.
  • AUC Area under Curve
  • Example 6 Determination of molecular signature from FFPE samples
  • Formalin-fixed paraffin embedded (FFPE) samples of tumor and adjacent tissue is a convenient source for obtaining RNA for identification of the molecular signature described in this invention.
  • 10 ⁇ curl sections is cut from FFPE blocks of cancer or adjacent tissue, placed in a 1.5 ml microcentrifuge tube and heated at 70°C in a heating block for 20 min to allow excess paraffin wax to be removed.
  • Pre-warmed xylene (1 ml) is added to the tube and heated at 50°C for 10 min.
  • the microfuge tube is then centrifuged at 12000g for 2 min in a microcentrifuge. Waste xylene is removed by pipette and the xylene wash repeated twice more. Residual xylene is removed by the addition of 1.0 ml of 100% ethanol to the dewaxed tissue sections, which will be allowed to stand for 10 min at room temperature.
  • the tissue is centrifuged 12,000g for 5 min and the ethanol removed by pipette, and the process repeated once more with 100% ethanol.
  • the tissue is rehydrated with 1.0 ml 90% ethanol for 5 min and finally washed in 1.0 ml 70% ethanol for 5 min.
  • the sample is air dried to allow the ethanol to evaporate completely prior to protease digestion.
  • Protease digestion is performed by use of a Recoverall kitTM (Applied Biosystems, AM 1975) as per the manufacturer's protocol following which 480 ⁇ 1 of the Ambion RecoverAllTM Isolation Additive is added to the microfuge tube, and vortex mixed for 20 seconds and allowed to stand for 15 min at room temperature.
  • the tubes are pulse spun in a microfuge at 12000g for 30 seconds. Two 240 ⁇ aliquots of the resulting lysate is then stored at -20°C for RNA extraction.
  • RNA extraction is performed using the Recoverall kitTM as per manufacturer's instructions. RNA is eluted finally in a volume of 60 ⁇ . Purity and quantity are checked spectrophotometry at 260 nm and 280 nm by placing 1.3 ⁇ of eluate on the sampling pedestal of a scanning spectrophotometer. Aliquots of each sample are stored at -80°C or reverse transcribed to produce cDNA in a two step RT-PCR reaction. RNA from fresh- frozen samples will be obtained using the RNeasy kit from Qiagen, according to the manufacturer's protocol.
  • RNAs The amount and quality of RNAs is assessed by UV spectrophotometry and considered adequate for further analysis if the optical density 260/280 ratio is > 1.8 and the total RNA yield > 500ng.
  • RNA-cDNA from FFPE tissues
  • PCRs of a housekeeper gene e.g. PGK
  • amplicons of increasing length from 50 to 200
  • Quantitative Real Time PCR is carried out by the SYBR Green or Fluorescent dual labeled probe method on a real-time PCR machine, in this case- an ABI 7300 Cycler (Applied Biosystems, CA, USA).
  • the expression levels of the genes selected from Table X are assessed by QRT PCR using either the relative quantification method (AACT method) [Livak and Schmittgen, Methods 25 (2001), 402-408] using normalizer genes such as GAPDH, which is used in the present study. Normal oral mucosal tissue or other standard RNA samples could be used as Calibrator, if required. Melting curve analysis is done to ensure the specificity of the product obtained, when using SYBR green method.
  • Molecular signature can be identified by determining the expression of the individual genes represented in the signature or through determination of the proteins that these genes encode. While several methods can be used to determine the molecular signature identified in this invention, the following method is used to draw inferences from the molecular signature based on values in Table X as follows

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Abstract

The present invention describes a method utilizing a set of genes or gene products whose altered expression in cancer tissue, particularly head and neck cancer and other carcinomas, or its adjacent normal tissues predicts (a) probability of recurrence in time after treatment (b) sensitivity or resistance to therapies or (c) probability of metastasis at the time of initial discovery of the tumor. Furthermore, the invention describes methods of determining the molecular signature in tumor tissues, tissues adjacent to the tumor, or in saliva by using DNA microarray techniques, quantitative real-time PCR, immunohistochemistry or other methods that are used for determining gene or gene product expression levels.

Description

DIAGNOSTIC TESTS FOR PREDICTING PROGNOSIS, RECURRENCE, RESISTANCE OR SENSITIVITY TO THERAPY AND METASTATIC STATUS IN
CANCER
TECHNICAL FIELD OF INVENTION
The present invention relates to a process for personalization of cancer treatment involving the use of specific genes and/or their proteins in diagnostic tests for predicting prognosis, recurrence, resistance or sensitivity to therapy and metastasis status in cancer.
BACKGROUND OF THE INVENTION
Cancer and its progression in an individual is guided by the expression and/or altered status of many genes and gene products (molecular markers). Correlation of the changes in these molecular markers can help to predict if a particular patients cancer would (a) recur in time after treatment or (b) be sensitive or resistant to therapies or (c) have metastasized at the time of initial discovery of the tumor, consequently leading to improved ability to manage cancer.
More recently, molecular signatures have been described as a more robust tool for determining prognosis or metastatic status. Companies such as Genomic Health Inc. and Agendia have introduced similar diagnostic tools (Oncotype DX and Mammaprint respectively) in the market for Breast cancer and colorectal cancer (Patent No. US7930104, WO2009/114836, WO2009/002175A1). However, analogues molecular signature for head and neck cancers are limited. Patent no. US7588895 looks at an eight gene signature in oral squamous cell carcinoma predicting metastasis and extra capsular spread, while patent no. WO2007/015935A2 uses a twelve gene signature for predicting therapeutic success, recurrence free and overall survival. The set of genes in the present invention is exclusive of the genes in above mentioned inventions.
Development of efficient assays determining the probability that a tumor is likely to recur in a short time or a tumor would be resistant to cytotoxic therapy or radiation, would help the physician to seek other choices for therapy rather than subject the patient to treatments that would have no benefit. Identification of a set of markers that would predict cancers resistant to treatment modalities and hence susceptible for recurrent behavior or that can predict whether a tumor has metastasized or not will have significant clinical benefit. Further, their detection in accessible body fluids such as in saliva would be a significant advantage.
DETAILED DESCRIPTION OF THE INVENTION
In order to more clearly and concisely describe and point out the subject matter of the claimed invention, the following definitions are provided for specific terms which are used in the following written description.
"Molecular signature" refers to the expression of two or more genes described in Tables I- V, or more specifically Table X, in a tumor tissue or in tumor cells derived from tongue or other head-and-neck cancers; the said gene expression level being determined by one or more techniques that is commonly employed for measuring gene expression levels in tissues or cells which includes microarrays and real-time quantitative polymerase chain reaction. Levels of gene expression could also be determined by measuring the level of proteins encoded by the said genes using immunohistochemistry, enzyme-linked-immunosorbent assay or other methods like proteomic techniques for mapping expression of multiple proteins.
"Molecularly-targeted therapies" shall mean a treatment modality against cancer cells targeting specific molecules involved in tumorigenesis and tumor growth.
"Immune modulation therapy" shall mean the use of modulators that inhibit/stimulate the immune system to elicit anti-tumor effects. In the present invention tongue cancer is used as an example of head and neck cancer and other carcinomas to describe a method utilizing a set of genes or gene products whose altered expression, in head and neck tumor in general including tongue cancer, predicts (a) probability of recurrence in time after treatment (b) sensitivity or resistance to therapies or (c) probability of metastasis at the time of initial discovery of the tumor,
The novel molecular signature comprises of a combination of genes selected from the list of genes given in Tables I-V or a narrower set of more differentially expressed genes from a preferred list of genes drawn from Tables I-V and listed in Table X. In accordance with preferred embodiments, the molecular signature is identified in pre- treatment and post-treatment head and neck cancer and is used to determine the probability of recurrence of cancer after surgery and anti-cancer therapy. Absence of the molecular signature in the primary tumor sample would imply a far less probability of recurrence ; hence one could avoid further therapy after surgery, thus decreasing the cost of treatment as well as the morbidity associated with chemotherapy. Presence of the molecular signature in the tumor at the time of surgery would reveal a higher probability of recurrence and therefore would aid in determining if adjuvant chemotherapy is warranted or not.
In another embodiment of the invention, the molecular signature is used to identify sensitivity or resistance to anti-cancer agents, in particular chemotherapy agents, but not limited to the same, and would include radiation therapy or new generation molecularly- targeted drugs or immune-modulating drugs or cell therapy like dendritic cell therapy.
The present invention also identifies a molecular signature, listed in Table V, which is differentially expressed in the adjacent histologically normal mucosa of the recurrent and non -recurrent patients. This molecular signature describes groups of cells in the adjacent mucosa of the recurrent patients that show the over expression of stem cell markers and transcription factors. The presence of these cells, as identified by the molecular signature, in the adjacent mucosa could also be predictive of recurrence in patients with head and neck cancer.
In yet another embodiment of the invention, the molecular signature is used to determine the probability that a tumor would has metastasized to a secondary location at the time of diagnosis of the disease, which will allow one to determine if surgery alone is sufficient or adjuvant chemotherapy or other anti-cancer drugs or therapies are required. The molecular signature in Table I-V, and more specifically Table X describes characteristics of the tumor that can be also used to predict if the cancer has metastasized to a secondary location by virtue of (a) the fact that the molecular signature identifies aggressive cells in the tumor that by definition has a higher invasive potential (b) the immune repressive genes that are over- expressed would allow the tumor to escape its primary site and metastasize.
The same is indicated through the pathway enrichments seen using Ingenuity pathway analysis between the groups; Group I (Pre-treatment, non -recurrent and from Group III (post-treatment, recurrent); with /x0.05 and Fishers exact test applied as a test of significance. The top 10 canonical pathways identified in the recurrent and the non-recurrent groups after core comparison analysis are represented in Figure 3 A and 3B. The most significant pathways include Glioma invasiveness signaling, bladder cancer signaling, LXR/RXPv activation and colorectal cancer metastasis signaling in the recurrent group. In comparison, the non-recurrent set primarily showed Interferon signaling; Cytotoxic T- lymphocyte mediated apoptosis of target cells, protein ubiquitination and Myc mediated apoptosis as significant pathways. Genes differentiating between recurrent and non-recurrent tumors, listed in tables III- IV, therefore are enriched in candidates that can predict invasiveness and metastasis.
The individual genes and gene -products of the molecular signature discussed in this invention, and listed in Tables I to V, have been identified as serving key functions in disease recurrence and resistance or sensitivity to chemotherapy as well as metastasis in a large array of cancers like lung, pancreas, colorectal, hepatocellular carcinoma, breast, ovarian, melanoma, glioma, neuroblastoma, endometrial, prostate, lymphoma and a variety of other cancers. In other words the invention described herein is broadly applicable to most cancers and all carcinomas and not just tongue or head and neck cancer.
In another embodiment of the invention, the tumor tissue that is used for analysis include tissue biopsies - either frozen, fixed in RNA stabilizing solutions or in paraffin-embedded- formalin fixed tissues (FFPE), or saliva which is used as the source RNA or protein for determination of the molecular signature
In another embodiment of the invention, the assays used for determining the molecular signature includes microarray, quantitative real-time PCR, immunohisotochemisitry, enzyme-linked immunosorbent assay, proteomic analysis or other standard methods of measuring gene expression of multiple directly or through proteins encoded by the genes. BRIEF DESCRIPTION OF TABLES AND DRAWINGS
In order that the invention be readily understood and put into practical effect, reference will now be made to exemplary embodiments as illustrated with reference to the accompanying figures. The figures together with a detailed description below, are incorporated in and form part of the specification, and serve to further illustrate the embodiments and explain various principles and advantages, in accordance with the present invention.
Table I: Differentially expressed genes in the oral tongue tumors (p<0.05)
Table II: Differentially expressed genes in the non- recurrent oral tongue tumors (p<0.05)
Table III: Differentially expressed genes in the recurrent oral tongue tumors (p<0.05)
Table IV: Differentially expressed genes between Non-recurrent Tumor and Recurrent Tumor (p<0.05)
Table V: Differentially expressed genes in the adjacent mucosa (Non Recurrent versus Recurrent) (p<0.05)
Table VI: Clinical Characteristics of patients
Table VII: List of top 10 significant genes in Non-Recurrent/recurrent tongue cancer
Table VIII: List of significant genes in Recurrent tongue cancer
Table IX: Receiver Operating Curve and Regression analysis of the markers
Table X: Consolidated List of genes with high differential expression
Fig 1 Hierarchical and K-means clustering of differentially expressed genes in recurrent tongue cancer Clustering analysis was done using MeV (TIGR) after application of Wilcoxon Mann Whitney test using the Euclidean distance measurement. The clustering analysis revealed classifiers for recurrent tumors (A) and all tumors (B). K-means clustering (K=10; Euclidean distance) was also carried out with the distinct clusters of immune response genes up regulated in non recurrent tumors (C) and HBA/HBB clusters down regulated in recurrent tumors (D). Fig 2 Differential expression in the adjacent mucosal tissue Hierarchical clustering between adjacent mucosal tissue revealed extensive differences in expression profiling (A), K-means clustering showed the up regulation of a sub-set of genes including stem cell genes such as ATR, ARHGAP5 (B) and down regulation HBB/HBA1 cluster in the recurrent patients (C). Statistical analysis (ANOVA) also revealed a sub set of genes overlapping between the adjacent mucosal tissue and tumor samples of the recurrent patients (D).
Fig 3 Significant pathways between Non-recurrent and recurrent tongue cancer Pathway analysis was carried out by Ingenuity Pathway Analysis (IP A) and the top 10 significant pathways are represented in the figure. The pathways are sorted according to significance in recurrent sub set (A) and non-recurrent samples (B).
Fig 4 Interaction networks identified by Ingenuity Pathway Analysis Interaction network of genes that are differentially expressed between Non-recurrent and recurrent tumors (A & B). The symbols in the figure denote the following A: Activation, E: Expression, PP: protein- Protein Interaction, I: Inhibition, L: Proteolysis; P: Phosphorylation, T: Transcription, PD: Protein-DNA interaction. Note the group of genes, the expression of which is dependent upon XBP1 and E2F. The binding partners HBB and HBA1 are both higher in expression in non-recurrent tumors.
Fig 5 Validation in tissues and saliva samples. The expression profile of a select subset of markers was validated in tongue cancer specimens (A). A distinct difference in expression profile of 4 genes (COL5A1, IGLA, HBB and CTSC) was observed in the primary tissue of patients that were non-recurrent (Group I) and recurrent (Group II). The pattern of expression obtained in the patients of the latter group was similar to that obtained in the recurrent tissue of patients (Group III). ROC analysis revealed these markers as most significant according to the AUC (B). The profile of 6 genes in saliva samples from normal (N) and tumor (T) samples is shown (C). The normal samples primarily show the expression of IL1B while at least one of the carcinogenesis related genes are expressed in the patients. ROC analysis of the combination of markers (ABCG1, IL8, COL5A1, FN1 and MMP1) shows sensitivity of 0.65 and specificity of 0.87 (D). Fig 6 Irnmunohistochemical analysis of candidate markers IHC was carried out on tongue cancer samples (A) with antibodies to HBB (a, b, c, d) and COL5A1 (e, f, g, h). The expression was analyzed in normal controls (a, e), in non-recurrent tumor samples (b & f) and in recurrent samples (c & g). d & h represent negative controls. The non-recurrent tumor sample showed a high expression of HBB as observed in the normal control; while an over expression of COL5A1 was observed in the recurrent tumor sample. The magnifications (100 or 200 times the original magnification) are mentioned on each panel. ROC analysis showed HBB as a better candidate marker as compared to COL5A1 (B & C).
The present invention describes a molecular signature comprising of a set of genes or gene products whose altered expression in head and neck tumor in general including tongue cancer predicts (a) resistance to chemotherapy, which would help avoid chemotherapy or use other modalities of treatment (c) probability of recurrence of the disease post treatment (d) determining probability of metastasis at the time of surgery thereby allowing one to determine if adjuvant therapy is required or not.
The general molecular and cell biology methods used in this invention are known to those skilled in the art.
EXAMPLES
In order that this invention be more fully understood the following preparative and testing examples are set forth. These examples are for the purpose of illustration only and are not to be construed as limiting the scope of the invention in any way. The examples described in this invention uses squamous cell carcinoma (tongue) as an example of head and neck cancer and other cancers, particularly carcinomas, and the invention and examples are generally applicable to all head and neck cancers as well other cancers, in particular carcinomas, as the genes and proteins involved in the molecular signature are common to cancer, hence would be generally applicable to most or all of these cancers.
Example 1: Patient details and sample collection
The tissue samples are collected from patients undergoing surgical treatment after obtaining mandatory approvals (Table VI). The samples that were subjected to microarray analysis were collected in RNA later (Ambion, Austin, USA), while the samples for validation were either snap frozen or collected in RNA later and archived at -80°C if required to be stored. The clinical characteristics of the patients are obtained from the electronic medical records maintained at the tertiary care cancer center. The sample sets were grouped into three categories: Group I (Pre-treatment, non-recurrent), which included pre-treatment tissues from patients who remained disease-free after standard treatment (surgery and adjuvant chemo radiation); Group II (Pre-treatment resistant/recurrent) included pre-treatment tissues from those who recurred during a 2-year follow up period; Group III (post-treatment recurrent; standard treatment) included recurrent tissue from patients with the recurrent disease. Group I & III were analyzed by micro array, while the validation was carried out in all the three groups. The adjacent mucosal tissue was collected 2 cm away from the tumor and confirmed as histologically negative for malignancy. Normal oral mucosa was also collected from non-diseased controls (age and risk factor matched) after written informed consent. Saliva samples were collected from healthy volunteers and previously untreated patients diagnosed with oral cancer (Stage I II), after informed written consent. Unstimulated saliva was collected and mixed with RNAlater (Ambion, Austin USA) and stored at -80°C.
Example 2: RNA isolation, labeling of cRNA and hybridization
Total RNA was isolated using the Qiagen RNeasy Kit (Qiagen, CA, US) and the samples that qualified through standard quality control criteria were selected for microarray. 100- 200ng of RNA was taken and biotinylated cRNA was prepared using the Two-cycle labeling Kit protocol (Affymetrix, CA, USA). The labeled cRNA was purified by the Genechip sample cleanup module (Qiagen, CA, US), fragmented and 20μg hybridized to HGU133 plus 2 arrays (54,675 probes) using standard Affymetrix protocols. The hybridized chips were washed, stained and scanned by the Affymetrix Fluidics Station and Genechip Scanner 3000 using prescribed protocols.
Example 3: Microarray analysis
The preliminary analysis to ascertain the internal controls and the hybridization efficiency was carried out using the Gene Chip Operating Software (GCOS) and Microarray Suite (MAS5,Affymetrix, CA, USA). The CEL files were extracted and imported into GeneSpring 7.2 (Agilent Technologies, CA, USA) software package for analysis. Raw image data were background corrected, normalized and summarized into probe set expression values using Robust Microarray Analysis (RMA) algorithm. For inter-array comparisons, data from each chip was normalized to 50% of the measurements taken from that chip (measurements of <0.01 were set to 0.01). Probe sets that were not reliably detected were removed, by filtering out those whose expression level was not >50 and confidence -values <0.05, in at least 20% of the samples. To identify genes differentially expressed, both in the non -recurrent and recurrent tongue cancers as compared to adjacent mucosal samples, the remaining genes were subjected to Welch' s t-test, not assuming variances equal, at /x0.05 and furthered filtered for fold change >1.5. Expression levels for individual genes are inferred as A) Differentially expressed genes identified in case of comparison with normal sample by measuring fold change (Fold change >2) or B) When only tumor samples are being analysed, expression levels along with associated statistical significance values (p>0.01) are considered and these values are further normalized to a set of standard housekeeping genes. To determine differential Gene expression, samples were grouped into Normal/Tumor, recurrent and non -recurrent. 110 genes were differentially regulated in all the tumor samples (p<0.05), 212 in non-recurrent tumors (p<0.005) and 112 in recurrent tumors (p<0.01) (Tables I, II & III respectively).
Ingenuity Pathway Analysis was carried out to identify significant functions, signaling pathways and networks (Ingenuity Systems Inc. CA, USA) at the default core analysis and core comparison platforms. Fishers exact test was used to identify the statistically significant functions/pathways. The differentially expressed genes were hierarchically clustered using Multi Experiment Viewer, v 4.5 (MeV) (TM4 Microarray Software Suite, The Institute of Genomic Research (TIGR) with the Euclidean distance measurement and p values were calculated after application of the non-parametric Wilcoxon-Mann Whitney test (p<0.5). Furthermore, K- means clustering (K=10; Euclidean distance) was carried out to identify a sub-set of genes that would clearly differentiate the groups under study.
Example 4: Validation of the microarray data in tissue and saliva samples by Quantitative PCR
RNA was isolated from tissues using Tri Reagent (Sigma Aldrich, MO, USA), first strand synthesis was done using MMLV Reverse transcriptase (Ambion, Austin, USA) and Quantitative Real Time PCR (QRT PCR) by the Power Syber Green kit (Applied Biosystems, CA, USA) in an ABI 7300 Cycler (Applied Biosystems, CA, USA). The expression levels of the genes selected for validation (MMP1, EMP1, ABCG1, COL5A1, IgLA, HBB, CTSC and CCL18) (Table I) was assessed by QRT PCR using the relative quantification (ΔΔΟΤ method). Expression was normalized using the endogenous control (GAPDH) and normal oral mucosal tissues were used as the calibrator. Melting curve analysis was done to ensure the specificity of the product obtained.
Unstimulated saliva collected from patients/controls was mixed with RNAlater; subsequently the samples were centrifuged at 14, 000 rpm for 20 minutes at 4°C. RNA was isolated from the salivary supernatant using the Qiagen Viral RNA Kit (Qiagen, CA, US). The samples were assessed for their integrity using the expression of the endogenous control (GAPDH) by Reverse Transcription PCR (RT-PCR) as a criterion. A subset of 10 candidate markers (MMP1, FN1, FAPA, SERPINH2, IL8, IL1B, IgLA, ABCG1, COL5A1, HBB), were tested for their expression in saliva by QRT PCR as above. Saliva samples from healthy volunteers as the calibrator. The detection of one or more markers in the samples was considered as 'test positive=l' while absence of any of the markers was considered 'test negative=0' . The combined test result in the binary input format was used for the statistical analysis. The expression patterns were correlated to the disease status of the patients to ascertain their clinical relevance.
Example 5: Immunohistochemical Analysis
The protein expression of two representative genes (COL5A1 and HBB), validated by QRT PCR was profiled in the tissue sections of a different cohort of patients with tongue cancer. The sections were deparaffinized and IHC carried out according to standard protocols. The antibodies were used in dilutions of 1:50 for both COL5A1 (scl33162; Santacruz Biotechnology, Santacruz, CA, USA) and HBB (H4890; Sigma Aldrich, USA). The sections were microwaved for antigen retrieval and the staining detected by Dako REAL™ En Vision kit (Dako Corporation, Carpenteria, CA, USA). The sections were counterstained using haematoxylin and scanned at low and high power to identify areas of even staining and percentage of positive cells. The grades of positivity were scored as follows; negative (<1%), grade I (1-10%), grade II (10-30%), III (30-60%) and IV (>60%). The intensity of staining was also graded as mild, moderate and intense. The expression in the normal oral mucosal tissues was used as control.
Receiver Operating Characteristic (ROC) curve analyses were carried out by SPSS 19 (IBM) and MedCalc® v 11.6.0.0 for the QPCR and IHC results. Area under the curve was computed via numerical integration of the ROC curves. The biomarkers, individually or in combination, with the largest Area under Curve (AUC) were identified to have the maximum predictive power for disease recurrence. Multiple regression analysis was also carried out by the stepwise method to identify the predictive value of the marker combinations.
Example 6: Determination of molecular signature from FFPE samples
Formalin-fixed paraffin embedded (FFPE) samples of tumor and adjacent tissue is a convenient source for obtaining RNA for identification of the molecular signature described in this invention.
10 μηι curl sections is cut from FFPE blocks of cancer or adjacent tissue, placed in a 1.5 ml microcentrifuge tube and heated at 70°C in a heating block for 20 min to allow excess paraffin wax to be removed. Pre-warmed xylene (1 ml) is added to the tube and heated at 50°C for 10 min. The microfuge tube is then centrifuged at 12000g for 2 min in a microcentrifuge. Waste xylene is removed by pipette and the xylene wash repeated twice more. Residual xylene is removed by the addition of 1.0 ml of 100% ethanol to the dewaxed tissue sections, which will be allowed to stand for 10 min at room temperature. The tissue is centrifuged 12,000g for 5 min and the ethanol removed by pipette, and the process repeated once more with 100% ethanol. The tissue is rehydrated with 1.0 ml 90% ethanol for 5 min and finally washed in 1.0 ml 70% ethanol for 5 min. The sample is air dried to allow the ethanol to evaporate completely prior to protease digestion. Protease digestion is performed by use of a Recoverall kit™ (Applied Biosystems, AM 1975) as per the manufacturer's protocol following which 480μ1 of the Ambion RecoverAll™ Isolation Additive is added to the microfuge tube, and vortex mixed for 20 seconds and allowed to stand for 15 min at room temperature. The tubes are pulse spun in a microfuge at 12000g for 30 seconds. Two 240 μΐ aliquots of the resulting lysate is then stored at -20°C for RNA extraction.
RNA extraction is performed using the Recoverall kit™ as per manufacturer's instructions. RNA is eluted finally in a volume of 60 μΐ. Purity and quantity are checked spectrophotometry at 260 nm and 280 nm by placing 1.3 μΐ of eluate on the sampling pedestal of a scanning spectrophotometer. Aliquots of each sample are stored at -80°C or reverse transcribed to produce cDNA in a two step RT-PCR reaction. RNA from fresh- frozen samples will be obtained using the RNeasy kit from Qiagen, according to the manufacturer's protocol.
The amount and quality of RNAs is assessed by UV spectrophotometry and considered adequate for further analysis if the optical density 260/280 ratio is > 1.8 and the total RNA yield > 500ng.
Preparation of cDNA
Reverse transcription is performed using an ABI High- Capacity cDNA Archive Kit according to the manufacturer's instructions. cDNA content is measured using a spectrophotometer. In the case of RNA-cDNA from FFPE tissues, PCRs of a housekeeper gene (e.g. PGK) with amplicons of increasing length (from 50 to 200) is run on a 3% agarose gel to check the distribution of fragment lengths..
Polymerase chain reaction
Quantitative Real Time PCR (QRT PCR) is carried out by the SYBR Green or Fluorescent dual labeled probe method on a real-time PCR machine, in this case- an ABI 7300 Cycler (Applied Biosystems, CA, USA). The expression levels of the genes selected from Table X are assessed by QRT PCR using either the relative quantification method (AACT method) [Livak and Schmittgen, Methods 25 (2001), 402-408] using normalizer genes such as GAPDH, which is used in the present study. Normal oral mucosal tissue or other standard RNA samples could be used as Calibrator, if required. Melting curve analysis is done to ensure the specificity of the product obtained, when using SYBR green method.
Example 7: Interpretation of molecular signature
Molecular signature can be identified by determining the expression of the individual genes represented in the signature or through determination of the proteins that these genes encode. While several methods can be used to determine the molecular signature identified in this invention, the following method is used to draw inferences from the molecular signature based on values in Table X as follows
1. A poor prognosis indicating recurrence/ metastasis/ failure of chemotherapy, radiation therapy or other therapies are indicated if high expression levels are seen for majority of genes listed at no.1-19 and 47-50. At the same time absence/ low expression for majority of genes listed at no. 20-29; 30-46 and 51-108 will corroborate the inference
2. A good prognosis indicating non-recurrence/ absence of metastasis/ response to chemotherapy radiation therapy or other therapies are indicated if high expression levels are seen for majority of genes listed at no. 30-46 and 51-108. At the same time absence/ low expression for majority of genes listed at no.1-19; 47-50 and 109 will corroborate the inference
TABLES
Table I: Differentially expressed genes in the oral tongue tumors (/?<0.05)
Fold Fold
(R/N (NR/N
s Affymetrix Gene p(R/No orm p(NR/No ormal Fold
NO ID Symbol rmal)* al) rmal)# ) Diff$
3.83E-
1 204567_s_at ABCG1 05 6.71 0.00166 3.78 2.93 204169_at IMPDH1 0.00234 1.95 0.0351 2.11 -0.16
205479_s_at PLAU 0.00409 7.66 0.00268 4.95 2.70
204475_at MMP1 0.00519 74.50 0.00012 255.50 -181.0
202897_at SIRPA 0.00538 3.31 0.02 3.11 0.21
203417_at MFAP2 0.00596 5.40 0.00102 5.44 -0.04
225898_at WDR54 0.00674 3.13 0.00106 3.19 -0.06
227484_at — 0.00692 2.17 0.00872 2.87 -0.70
221538_s_at PLXNA1 0.00821 3.56 0.0117 2.59 0.98
203562_at FEZ1 0.00837 6.14 0.036 3.20 2.94
224472_x_at SDF4 0.00962 1.69 0.0459 1.79 -0.11
221523_s_at RRAGD 0.0102 -4.00 0.0103 -5.03 1.03
SERPINH
207714_s_at 1 0.0109 3.18 0.00855 3.97 -0.80
204924_at TLR2 0.0118 3.32 0.00355 3.05 0.26
205828_at MMP3 0.0141 26.15 0.000288 35.40 -9.25
218089_at C20orf4 0.0142 1.60 0.00069 1.58 0.02
221898_at PDPN 0.0148 5.97 0.0022 5.74 0.23
205680_at MMP10 0.0151 23.70 0.00102 29.51 -5.81
204214_s_at RAB32 0.0158 2.37 0.00044 2.27 0.10
218847_at IGF2BP2 0.0159 3.56 0.00146 3.32 0.24
212740_at PIK3R4 0.0171 1.76 0.0041 1.61 0.14
CAMS API
217196_s_at LI 0.0172 1.61 0.0196 3.56 -1.95
221730_at COL5A2 0.0179 7.79 0.014 7.00 0.78
204140_at TPST1 0.0182 3.33 0.0112 3.17 0.16
MARVEL
223095_at Dl 0.0186 2.10 0.0324 1.52 0.58
55093_at CSGlcA-T 0.0191 2.18 0.0091 2.47 -0.29
225285_at BCAT1 0.0196 6.16 0.0265 3.98 2.18 212488_at COL5A1 0.0197 7.18 0.0117 5.88 1.30
225401_at Clorf85 0.0202 2.21 0.0048 2.56 -0.35
205959_at MMP13 0.0205 25.45 0.0313 10.91 14.54
202458_at PRSS23 0.0205 4.53 0.000186 8.77 -4.24
202998_s_at LOXL2 0.0206 5.31 0.0452 3.69 1.62
203936_s_at MMP9 0.0206 8.39 0.00438 13.60 -5.22
225205_at KIF3B 0.0208 1.55 0.0101 1.92 -0.36
227846_at GPR176 0.0209 5.00 0.00381 4.00 1.00
201954_at ARPC1B 0.0209 2.65 0.00383 2.71 -0.05
202369_s_at TRAM2 0.0209 2.39 0.0254 3.50 -1.11
204041_at MAOB 0.0217 -5.08 0.00502 -4.39 -0.69
20239 l_at BASP1 0.0219 3.41 0.0265 6.22 -2.81
213139_at SNAI2 0.0222 2.81 0.00014 5.81 -3.00
200618_at LASP1 0.0223 1.84 0.015 1.87 -0.03
GALNAC
203066_at 4S-6ST 0.0224 2.66 0.0234 2.77 -0.11
204137_at GPR137B 0.0227 2.16 0.0142 3.82 -1.66
228273_at — 0.0235 2.54 0.0296 7.10 -4.55
226609_at DCBLD1 0.0239 3.60 0.0222 4.12 -0.52
209166_s_at MAN2B1 0.024 1.87 0.00842 2.54 -0.67
222108_at AMIGO2 0.024 3.27 0.00224 5.25 -1.99
223507_at CLPX 0.0246 -1.55 0.0143 -1.86 0.32
218196_at OSTM1 0.0246 2.36 0.0113 2.35 0.01
214297_at CSPG4 0.0249 5.44 0.0144 4.18 1.26
202727_s_at IFNGR1 0.0253 1.98 0.00783 2.17 -0.20
209934_s_at ATP2C1 0.0256 2.39 0.00824 2.27 0.12
203879_at PIK3CD 0.0256 2.28 0.00574 2.69 -0.42
203038_at PTPRK 0.026 2.39 0.0473 1.74 0.65
218224_at PNMA1 0.0267 2.66 0.0201 2.37 0.29 241353_s_at — 0.0271 1.93 0.0143 1.72 0.21
203505_at ABCA1 0.0273 2.21 0.00302 2.58 -0.37
203650_at PROCR 0.0275 2.83 0.0097 2.65 0.19
224735_at CYBASC3 0.028 1.91 0.0292 1.79 0.12
214853_s_at SHC1 0.0283 2.72 0.00195 2.47 0.24
TNFRSF1
207643_s_at A 0.0283 1.66 0.0366 1.61 0.04
223107_s_at ZCCHC17 0.0288 1.74 0.0165 1.59 0.15
219684_at RTP4 0.0292 3.25 0.0034 3.53 -0.28
218130_at C17orf62 0.0294 2.56 0.00845 2.94 -0.38
218404_at SNX10 0.0297 3.31 0.00437 4.40 -1.09
32069_at N4BP1 0.03 1.76 0.0356 2.66 -0.89
214329_x_at TNFSF10 0.0303 4.09 0.0312 2.96 1.13
223463_at RAB23 0.0305 2.22 0.0464 2.18 0.04
208012_x_at SP110 0.0307 2.10 0.00774 2.50 -0.40
218968_s_at ZFP64 0.031 1.61 0.0101 1.69 -0.08
LOC28366
226682_at 6 0.031 -2.85 0.0128 -2.48 -0.37
205324_s_at FTSJ1 0.0312 1.78 0.0206 2.03 -0.25
225646_at CTSC 0.0319 4.66 0.0058 7.17 -2.51
203764_at DLG7 0.0321 2.04 0.0496 7.82 -5.78
209684_at RIN2 0.0327 1.77 0.00513 2.27 -0.51
225076_s_at ZNFX1 0.0328 1.78 0.0279 1.84 -0.06
229450_at IFIT3 0.0331 4.07 0.0172 5.58 -1.52
201976_s_at MYO10 0.0333 2.21 0.00396 3.96 -1.75
219522_at FJX1 0.0342 2.60 0.0333 3.91 -1.31
225636_at STAT2 0.0345 2.02 0.0311 2.01 0.01
202859_x_at IL8 0.0352 7.67 0.0129 13.10 -5.43
204000_at GNB5 0.0356 2.17 0.0495 1.64 0.53 GSDMDC
218154_at 1 0.037 1.79 0.0278 1.70 0.09
20338 l_s_at APOE 0.0371 2.51 0.0105 2.16 0.35
209545_s_at RIPK2 0.0372 2.02 0.00761 2.31 -0.29
200734_s_at ARF3 0.0376 1.97 0.0379 3.10 -1.13
22568 l_at CTHRC1 0.0378 9.96 0.00454 16.01 -6.05
212110_at SLC39A14 0.0392 3.23 0.0072 3.39 -0.16
222692_s_at FNDC3B 0.0397 3.65 0.00576 3.52 0.13
222449_at TMEPAI 0.0401 7.70 0.0368 6.49 1.21
228885_at MAMDC2 0.0401 -7.30 0.0159 -4.85 -2.44
201710_at MYBL2 0.0406 1.88 0.00164 2.21 -0.33
221737_at GNA12 0.0408 1.95 0.0296 1.63 0.32
212012_at PXDN 0.0416 5.45 0.0483 5.11 0.34
203695_s_at DFNA5 0.0418 3.99 0.00524 3.35 0.64
223158_s_at NEK6 0.0419 2.52 0.000975 3.62 -1.11
202066_at PPFIA1 0.0427 1.61 0.029 2.09 -0.49
204092_s_at AURKA 0.0428 1.88 0.00318 3.06 -1.18
221059_s_at COTL1 0.0442 3.69 0.00174 5.15 -1.47
218552_at ECHDC2 0.0443 -3.33 0.028 -3.58 0.25
217763_s_at RAB31 0.0446 3.09 0.0407 3.08 0.01
212510_at GPD1L 0.0453 -3.48 0.0304 -7.75 4.27
218684_at LRRC8D 0.0456 1.81 0.0181 4.09 -2.28
211725_s_at BID 0.0462 2.65 0.0102 2.94 -0.29
219863_at HERC5 0.0463 1.99 0.0359 3.01 -1.02
203182_s_at SRPK2 0.0466 1.84 0.0335 2.81 -0.97
204948_s_at FST 0.0473 2.83 0.0183 4.54 -1.72
235276_at EPSTI1 0.0491 3.27 0.00967 4.48 -1.21
227143_s_at BID 0.0492 2.11 0.0341 1.97 0.14
226311_at — 0.0493 4.34 0.00957 4.98 -0.64 * Recurrent Tumor vs Normal
# Non Recurrent Tumor vs Normal
$ Fold level difference in the recurrent samples as compared to the non recurrent
samples
Table II: Differentially expressed genes in the non- recurrent oral tongue tumors 0?<0.05)
SI Affymetrix ID P-value Fold Gene Symbol
No
1 204475_at 0.00012 255.50 MMP1
2 204580_at 0.000692 64.16 MMP12
3 214677_x_at 0.00367 50.26 MEF2A
4 211430_s_at 0.00483 41.16 IGH@ /// IGHG1 ///
IGHG2 /// IGHG3 ///
IGHM /// IGHV4-31
5 209138_x_at 0.00186 36.17 IGL@
6 205828_at 0.000288 35.40 MMP3
7 205680_at 0.00102 29.51 MMP10
8 201645_at 0.000184 28.77 TNC
9 211756_at 0.000497 28.52 PPIA
10 215121_x_at 0.00254 27.28 PABPC1
11 209395_at 0.00282 24.94 CHI3L1
12 215379_x_at 0.00111 24.04 LOX
13 209924_at 0.000224 21.57 CCL18
14 202267_at 0.00441 16.25 LAMC2
15 22568 l_at 0.00454 16.01 FAM33A
16 1556773_at 0.00041 15.18 —
17 218468_s_at 0.000843 14.26 GREM1
18 32128_at 0.000984 13.70 TREX1
19 203936_s_at 0.00438 13.60 MMP9 210355_at 0.000551 13.36 PTHLH
221671_x_at 0.00132 13.29 CLEC7A
221651_x_at 0.00283 13.19 ARHGEFIOL
204533_at 0.00187 11.34 CXCL10
2l5446_s_at 0.000434 10.80 SEC16A
204415_at 0.00471 9.75 IFI6
225647_s_at 7.29E-05 9.66 UHRF1
203915_at 0.00128 9.54 CXCL9
227609_at 0.00216 9.10 LOC493869
202458_at 0.000186 8.77 PRSS23
206513_at 0.000704 8.65 AIM2
206026_s_at 0.000441 7.44 TNFAIP6
205159_at 0.00094 6.79 CSF2RB
212314_at 0.00475 6.61 TMED10
201422_at 0.000631 6.50 IFI30
212364_at 7.84E-05 6.38 MYO1B
201579_at 0.000503 6.37 FAT
207039_at 0.0043 6.30 CDKN2A
225639_at 0.00148 5.83 C14orf32
213139_at 0.00014 5.81 SP3
226368_at 0.000587 5.74 CHST11
221898_at 0.0022 5.74 CYLD
226279_at 0.00366 5.65 FAM91A1
209360_s_at 0.000443 5.55 RUNX1
203417_at 0.00102 5.44 MFAP2
229400_at 0.0015 5.44 IFIT3
222108_at 0.00224 5.25 GPR172A
203423_at 0.00155 5.25 RBP1
212588_at 0.00348 5.19 RRAS2 221059_s_at 0.00174 5.15 TXNDC5
204972_at 0.00295 5.15 OAS2
204337_at 0.00454 5.13 RGS4
203313_s_at 0.0036 5.05 TGIF1
218400_at 0.003 5.05 SNX10
202953_at 0.000743 5.01 C1QB
205479_s_at 0.00268 4.95 PLAU
212365_at 0.00127 4.77 GART
204222_s_at 0.000446 4.65 GLIPR1
201487_at 0.00284 4.52 CTSC
202558_s_at 0.000662 4.50 STCH
201564_s_at 0.00094 4.45 FSCN1
206584_at 0.000407 4.44 LY96
218404_at 0.00437 4.40 NDE1
201853_s_at 0.00253 4.35 CDC25B
203083_at 0.00134 4.34 THBS2
201818_at 0.000494 4.34 LPCAT1
226621_at 0.000208 4.30 LOC401504
204362_at 0.000204 4.29 SKAP2
201417_at 0.00397 4.20 SOX4
221881_s_at 0.000439 4.19 PDPN
226372_at 0.000739 4.18 ERGIC2
200644_at 0.00221 4.10 MARCKSLl
208966_x_at 0.00496 4.05 IFI16
227846_at 0.00381 4.00 FAM125A
210164_at 0.00115 3.96 GZMB
201976_s_at 0.00396 3.96 MYO10
202357_s_at 0.00261 3.92 CFB
209476_at 0.0024 3.87 TXNDC1 203476_at 0.000953 3.86 TPBG
200698_at 0.00427 3.84 KDELR2
AFFX- 0.000732 3.83 bioB
HUMISGF3A/M97935
_3_at
204567_s_at 0.00166 3.78 ABCG1
223343_at 0.000679 3.73 C6orfl l5
218699_at 0.00111 3.72 NXT1
201720_s_at 0.00279 3.68 LAPTM5
217892_s_at 0.000594 3.68 Clorfl08
225258_at 0.000254 3.64 RBMS1
223158_s_at 0.000975 3.62 RHOU
229860_x_at 0.00412 3.59 CLCC1
202820_at 0.00129 3.55 AHR
201669_s_at 0.00326 3.54 MARCKS
219684_at 0.0034 3.53 APOL6
200989_at 0.00393 3.50 HIF1A
201088_at 0.00443 3.50 KPNA2
208103_s_at 0.00302 3.46 ANP32E
200599_s_at 0.00342 3.46 HSP90B 1
218847_at 0.00146 3.32 NETO2
219434_at 0.00169 3.29 EGFL6
238725_at 0.00333 3.26 —
200755_s_at 0.000661 3.23 CALU
202666_s_at 0.00185 3.22 ACTL6A
226756_at 0.00146 3.22 —
214456_x_at 0.00163 3.21 BCLAF1
225415_at 0.00125 3.20 GTF2A1
202088_at 0.00429 3.20 SLC39A6 225898_at 0.00106 3.19 TP53INP1
222690_s_at 0.00484 3.18 FNDC3B
202720_at 0.00465 3.14 TES
213287_s_at 0.00339 3.13 TRIM22
224793_s_at 0.00127 3.12 IGK@ /// IGKC ///
IGKV1-5 /// IGKV2-
24
218595_s_at 0.00258 3.12 DRAM
221020_s_at 0.00484 3.11 CKLF
218368_s_at 0.00232 3.09 AKTIP
222457_s_at 0.00315 3.08 EFHD2
204092_s_at 0.00318 3.06 AURKA
208637_x_at 0.00183 3.06 ACTN1
53720_at 0.00176 3.05 MIC ALL 1
204924_at 0.00355 3.05 TLR2
201656_at 0.00316 3.05 ITGA6
231823_s_at 0.000674 3.05 ODZ2
200887_s_at 0.00168 3.02 STAT1
219161_s_at 0.00298 3.00 RHBDF2
20238 l_at 0.00474 2.99 ADAM9
205443_at 0.00148 2.97 SNAPC1
201091_s_at 0.000484 2.96 CBX3 /// LOC653972
201667_at 0.00179 2.96 GJA1
225439_at 0.00278 2.92 MIER1
207181_s_at 1.73E-06 2.91 CASP7
211676_s_at 0.00101 2.88 BID
22573 l_at 0.00479 2.84 ETV6
225853_at 0.00427 2.82 TRIM47
1558693_s_at 0.00298 2.77 Clorf85 201649_at 0.000545 2.77 UBE2L6
203693_s_at 0.004 2.76 E2F3
1558080_s_at 0.00489 2.76 LOCI 44871
217776_at 0.00275 2.73 YKT6
209852_x_at 0.00468 2.72 PSME3
208689_s_at 0.00287 2.71 RPN2
201954_at 0.00383 2.71 ARPC1B ///
LOC653888
200839_s_at 0.00208 2.70 CTSB
201128_s_at 0.00174 2.69 ACLY
208918_s_at 0.00329 2.67 NADK
201300_s_at 0.00039 2.64 PRNP
208703_s_at 0.00258 2.62 APLP2
203505_at 0.00302 2.58 ABCA1
225401_at 0.0048 2.56 —
201776_s_at 0.00145 2.54 KIAA0494
212063_at 0.00481 2.51 GPR56
213399_x_at 0.00386 2.49 MFHAS1
214853_s_at 0.00195 2.47 SFRS2
217813_s_at 0.00385 2.47 ENAH
213491_x_at 0.00484 2.46 ADAM 17
219540_at 0.00386 2.44 EAF2
224753_at 0.00453 2.41 PAFAH1B2
202603_at 0.00146 2.41 —
201944_at 0.00128 2.41 HEXB
208674_x_at 0.00369 2.40 DDOST
206976_s_at 0.00422 2.36 HSPH1
201761_at 0.00094 2.35 MTHFD2
22345 l_s_at 0.00488 2.33 CXCL16 225479_at 0.004 2.31 FRMD6
226893_at 0.00487 2.30 LRIG3
204214_s_at 0.00044 2.27 RAB32
200902_at 0.0033 2.26 Sep 15
202059_s_at 0.00194 2.24 KPNA1
224847_at 0.00292 2.23 CDK6
201710_at 0.00164 2.21 MYBL2
207396_s_at 0.000756 2.20 ALG3
201786_s_at 0.00378 2.20 ADAR
212297_at 0.0022 2.19 KIAA0746
212644_s_at 0.00282 2.19 LHFPL2
202874_s_at 0.00361 2.17 ATP6V1C1
201462_at 0.000757 2.17 SCRN1
223003_at 0.00421 2.16 TXNDC12
201762_s_at 0.00473 2.13 PSME2
200875_s_at 0.00376 2.13 NOL5A
20277 l_at 0.00219 2.13 FAM38A
20925 l_x_at 0.00243 2.11 TUBA1C
225435_at 0.00298 2.09 NUDCD1
203552_at 0.00445 2.09 MAP4K5
201587_s_at 0.00302 2.05 IRAKI
221058_s_at 0.00491 2.02 COTL1
202180_s_at 0.00149 1.98 MVP
200959_at 0.00344 1.97 FUS
200833_s_at 0.00416 1.96 hCG_1757335 ///
RAP IB
224726_at 0.00388 1.95 WDR68
225890_at 0.00499 1.94 MARCKS
22245 l_s_at 0.000506 1.92 LIMA1 225234_at 0.00108 1.91 FBLIM1
224777_s_at 0.000574 1.88 RBM17
203181_x_at 0.00365 1.88 SRPK2
209906_at 0.00258 1.87 C3AR1
1559822_s_at 0.00333 1.83 LOC644215
225475_at 0.00447 1.82 MFHAS1
215696_s_at 0.00257 1.81 SLC6A2
203396_at 0.00175 1.80 PSMA4
218768_at 0.00172 1.75 TMEM39B
202306_at 0.00282 1.73 POLR2G
213119_at 0.00144 1.72 PTPN2
221555_x_at 0.00343 1.67 MIS 12
203114_at 0.00408 1.63 SSSCA1
215222_x_at 0.00456 1.63 IGL@ /// IGLJ3 ///
IGLV2-14 /// IGLV3-
25
212740_at 0.0041 1.61 NFATC2IP
218089_at 0.00069 1.58 HRB
226054_at 0.00472 1.58 RNF145
200096_s_at 0.00381 1.58 ATP6V0E1
203677_s_at 0.00273 1.54 TARBP2
224804_s_at 0.0031 -1.72 SORT1
221527_s_at 0.0031 -1.93 LSG1
2H474_s_at 0.00235 -2.29 BAG1
20357 l_s_at 0.00247 -2.79 C10orfl l6
223183_at 0.00416 -2.94 TMEM189
219298_at 0.00438 -5.75 DERL1 Table III : Differentially expressed genes in the recurrent oral tongue tumors (#<0.05)
SI NO Affymetrix ID P-value Fold Gene Symbol
1 204475_at 0.00519 74.50 MMP1
2 205828_at 0.0141 26.15 MMP3
3 205680_at 0.0151 23.70 MMP10
4 211964_at 0.00664 11.14 COL4A2
5 211980_at 0.0103 8.53 COL4A1
6 221730_at 0.0179 7.79 COL5A2
7 205479_s_at 0.00409 7.66 PLAU
8 212488_at 0.0197 7.18 COL5A1
9 204567_s_at 3.83E-05 6.71 ABCG1
10 225285_at 0.0196 6.16 BCAT1
11 203562_at 0.00837 6.14 FEZ1
12 221898_at 0.0148 5.97 PDPN
13 210986_s_at 0.014 5.86 TPM1
14 20965 l_at 0.0105 5.46 TGFB1I1
15 226876_at 0.013 5.45 FAM101B
16 203417_at 0.00596 5.40 MFAP2
17 203065_s_at 0.0194 5.31 CAV1
18 236565_s_at 0.0145 5.12 LARP6
MAGED4 ///
19 221261_x_at 0.0183 5.10 MAGED4B
20 20809 l_s_at 0.0188 4.85 ECOP
21 201185_at 0.0118 4.48 HTRA1
22 204992_s_at 0.0164 3.94 PFN2
23 230563_at 0.0173 3.91 RASGEF1A
24 209014_at 0.00713 3.89 MAGED1 204359_at 0.0168 3.81 FLRT2
225685_at 0.00933 3.77 —
202185_at 0.0129 3.72 PLOD3
211071_s_at 0.00101 3.67 MLLT11
221538_s_at 0.00821 3.56 PLXNA1
218847_at 0.0159 3.56 IGF2BP2
221641_s_at 0.00499 3.37 ACOT9
204140_at 0.0182 3.33 TPST1
224374_s_at 0.0174 3.33 EMILIN2
204924_at 0.0118 3.32 TLR2
202897_at 0.00538 3.31 SIRPA
218618_s_at 0.0165 3.22 FNDC3B
204589_at 0.00788 3.19 NUAK1
207714_s_at 0.0109 3.18 SERPINH1
209682_at 0.0163 3.16 CBLB
225898_at 0.00674 3.13 WDR54
204030_s_at 0.0191 3.11 SCHIP1
201272_at 0.0012 3.09 AKR1B1
203823_at 0.015 2.96 RGS3
214953_s_at 0.0198 2.95 APP
204083_s_at 0.0154 2.91 TPM2
219477_s_at 0.01 2.89 THSDl /// THSDIP
218718_at 0.00781 2.77 PDGFC
203217_s_at 0.0172 2.73 ST3GAL5
208178_x_at 0.0181 2.71 TRIO
220941_s_at 0.0179 2.71 C21orf91
225303_at 0.0185 2.68 KIRREL
212169_at 0.0157 2.67 FKBP9
225841_at 0.0127 2.67 Clorf59 212117_at 0.0107 2.63 RHOQ
202570_s_at 0.00666 2.46 DLGAP4
202027_at 0.00911 2.40 TMEM184B
204214_s_at 0.0158 2.37 RAB32
230275_at 0.0198 2.29 ARSI
208079_s_at 0.0159 2.23 AURKA
222622_at 0.0191 2.22 LOC283871
209784_s_at 0.00359 2.21 JAG2
203580_s_at 0.00843 2.18 SLC7A6
55093_at 0.0191 2.18 CSGlcA-T
203140_at 0.0122 2.18 BCL6
227484_at 0.00692 2.17 —
223095_at 0.0186 2.10 MARVELDl
205449_at 0.0124 1.98 SAC3D1
224995_at 0.0169 1.96 SPIRE 1
219394_at 0.00269 1.95 PGS1
204169_at 0.00234 1.95 IMPDH1
212457_at 0.0189 1.90 TFE3
226373_at 0.00563 1.86 SFXN5
212663_at 0.00975 1.85 FKBP15
220974_x_at 0.00877 1.84 SFXN3
217855_x_at 0.00633 1.78 SDF4
212740_at 0.0171 1.76 PIK3R4
226738_at 0.000415 1.74 WDR81
219224_x_at 0.00635 1.68 ZNF408
49329_at 0.0174 1.66 KLHL22
236275_at 0.0156 1.64 KRBA1
204826_at 0.0187 1.64 CCNF
38069_at 0.0179 1.64 CLCN7 217196_s_at 0.0172 1.61 CAMSAP1L1
218089_at 0.0142 1.60 C20orf4
218991_at 0.00386 1.56 HEATR6
40093_at 0.0189 1.54 BCAM
PCDHGA1 ///
PCDHGAIO ///
PCDHGAl l ///
PCDHGA12 ///
PCDHGA2 ///
PCDHGA3 ///
PCDHGA4 ///
PCDHGA5 ///
PCDHGA6 ///
PCDHGA7 ///
PCDHGA8 ///
PCDHGA9 ///
PCDHGB 1 ///
PCDHGB2 ///
PCDHGB 3 ///
PCDHGB4 ///
PCDHGB 5 ///
PCDHGB 6 ///
PCDHGB7 ///
PCDHGC3 ///
PCDHGC4 ///
211066_x_at 0.00365 1.52 PCDHGC5
21335 l_s_at 0.019 1.50 TMCC1
228852_at 0.0102 -1.72 ENSA
223245_at 0.0159 -1.86 STRBP 91 214106_s_at 0.0123 -1.87 GMDS
92 223497_at 0.0158 -1.90 FAM135A
93 228013_at 0.00988 -2.01 —
94 230083_at 0.00189 -2.07 USP53
95 204485_s_at 0.0154 -2.10 TOM1L1
96 239069_s_at 0.0149 -2.22 —
97 229498_at 0.00893 -2.27 —
98 225508_at 0.0195 -2.75 KIAA1468
99 20371 l_s_at 0.0163 -3.16 HIBCH
100 231270_at 0.00951 -3.27 CAB
101 213572_s_at 0.0165 -3.39 SERPINB1
102 213050_at 0.0114 -3.76 COBL
103 221523_s_at 0.0102 -4.00 RRAGD
104 223822_at 0.00826 -4.18 SUSD4
105 213895_at 0.0158 -4.50 EMP1
106 218858_at 0.0199 -4.59 DEPDC6
107 231929_at 0.00177 -6.58 IKZF2
108 214063_s_at 0.0116 -6.67 TF
109 231145_at 0.0184 -7.19 —
110 209498_at 0.0174 -7.69 CEACAM1
111 1559606_at 0.0192 -11.51 GBP6
112 220026_at 0.00299 -16.26 CLCA4
Table IV: Nonrecurrent Tumor versus Recurrent Tumor
SI Fold
NO Affymetrix ID p-value (NR R) Gene Symbol
1 220690_s_at 6.62E-06 1.83 DHRS7B
2 208614_s_at 0.000933 -2.04 FLNB 226012_at 0.00115 -1.80 ANKRD11
222768_s_at 0.00217 1.76 TRMT6
211959_at 0.00223 5.81 IGFBP5
242989_at 0.0034 -1.53 —
218281_at 0.00363 1.70 MRPL48
223413_s_at 0.00389 1.92 LYAR
201582_at 0.00414 1.58 SEC23B
200805_at 0.00421 1.77 LMAN2
222437_s_at 0.00608 1.72 VPS24
218235_s_at 0.00647 1.64 UTP11L
218841_at 0.00659 1.66 ASB8
203424_s_at 0.0077 2.47 IGFBP5
218225_at 0.00791 1.52 ECSIT
209054_s_at 0.00797 -1.51 WHSC1
226426_at 0.00811 -1.58 —
225192_at 0.00834 -1.79 C10orf46
226605_at 0.00888 -1.52 DGKQ
209283_at 0.00949 3.76 CRYAB
220201_at 0.00996 -1.83 RC3H2
217973_at 0.0102 2.31 DCXR
213189_at 0.0102 1.79 MINA
20247 l_s_at 0.0103 1.60 IDH3G
208906_at 0.0124 1.72 BSCL2 /// HNRPUL2
201052_s_at 0.0128 1.68 PSMF1
208675_s_at 0.0138 1.68 DDOST
204868_at 0.0139 1.93 ICT1
209355_s_at 0.014 3.68 PPAP2B
208003_s_at 0.014 -2.44 NFAT5
202357_s_at 0.0146 2.28 CFB 228159_at 0.015 -1.74 —
202433_at 0.0154 1.58 SLC35B1
210125_s_at 0.016 2.32 BANF1
218462_at 0.0163 1.56 BXDC5
212135_s_at 0.0167 -1.56 ATP2B4
200917_s_at 0.017 2.30 SRPR
200846_s_at 0.0173 1.79 PPP1CA
221667_s_at 0.0174 2.49 HSPB8
201583_s_at 0.0175 1.89 SEC23B
209575_at 0.0177 1.87 IL10RB
209742_s_at 0.0179 6.82 MYL2
225868_at 0.0184 1.60 TRIM47
217884_at 0.0194 -1.59 NAT 10
208800_at 0.0203 1.51 SRP72
219348_at 0.0206 1.68 USE1
208238_x_at 0.0208 -1.55 —
21241 l_at 0.0212 1.62 IMP4
219217_at 0.023 1.52 NARS2
202412_s_at 0.0236 1.90 USP1
226043_at 0.0246 -1.83 GPSM1
228310_at 0.0249 -1.92 ENAH
20339 l_at 0.0261 1.66 FKBP2
233814_at 0.0265 2.26 —
203734_at 0.0265 -1.69 FOXJ2
203022_at 0.0276 1.87 RNASEH2A
209030_s_at 0.0277 2.02 CADM1
20899 l_at 0.0283 1.53 STAT3
213523_at 0.0285 -1.83 CCNE1
216032_s_at 0.029 1.52 ERGIC3 227547_at 0.0291 -1.56 —
207621_s_at 0.0293 1.77 PEMT
204839_at 0.0295 1.78 POP5
223203_at 0.0298 -1.58 TMEM29 /// TMEM29B
202905_x_at 0.0299 1.65 NBN
1553709_a_at 0.0299 1.63 PRPF38A
204074_s_at 0.0302 1.53 KIAA0562
224646_x_at 0.0311 5.66 H19
201145_at 0.0311 1.70 HAX1
201532_at 0.0321 1.58 PSMA3
212861_at 0.0328 1.82 MFSD5
218400_at 0.0334 2.76 OAS3
224609_at 0.0337 1.93 SLC44A2
208887_at 0.0338 1.56 EIF3G
155355 l_s_at 0.0343 1.95 —
218258_at 0.0345 1.54 POLR1D
228123_s_at 0.035 1.86 ABHD12
223210_at 0.0351 2.29 CHURC1
221188_s_at 0.0352 1.59 CIDEB
237563_s_at 0.0361 3.01 LOC440731
1555653_at 0.0362 2.01 HNRPA3
229322_at 0.0362 1.58 PPP2R5E
202109_at 0.0366 1.63 ARFIP2
203872_at 0.0368 9.79 ACTA1
203082_at 0.0371 -1.59 BMS1
201659_s_at 0.0373 1.73 ARL1
211745_x_at 0.0376 6.52 HBAl
211600_at 0.0376 2.17 —
209458_x_at 0.0378 5.23 HBAl /// HBA2 213201_s_at 0.0378 2.51 TNNT1
227864_s_at 0.0384 2.00 FAM125A
222527_s_at 0.0385 1.79 RBM22
209904_at 0.0386 5.12 TNNC1
228261_at 0.0386 2.43 MIB2
201534_s_at 0.0387 1.92 UBL3
212922_s_at 0.039 1.58 SMYD2
243720_at 0.0395 -1.91 CMIP
235674_at 0.0396 -1.52 KIAA0922
227276_at 0.0399 1.98 PLXDC2
225058_at 0.0399 1.56 GPR108
228408_s_at 0.04 1.70 SDAD1
203090_at 0.0401 1.51 SDF2
208717_at 0.0404 1.53 OXA1L
221998_s_at 0.0405 1.89 VRK3
221486_at 0.0406 1.62 ENSA
201264_at 0.0408 2.64 COPE
202036_s_at 0.0409 3.43 SFRP1
209852_x_at 0.0412 1.83 PSME3
242844_at 0.0413 1.69 PGGT1B
226316_at 0.0416 -1.87 —
211699_x_at 0.0422 4.50 HBAl /// HBA2
205374_at 0.0427 6.84 SLN
203882_at 0.0428 2.10 IRF9
212654_at 0.043 3.51 TPM2
208705_s_at 0.043 1.98 EIF5
219428_s_at 0.0431 1.66 PXMP4
204018_x_at 0.0441 4.61 HBAl /// HBA2
228843_at 0.0443 -2.03 — 119 222233_s_at 0.0447 1.91 DCLRE1C
120 220952_s_at 0.0453 -1.65 PLEKHA5
121 219772_s_at 0.0458 4.95 SMPX
122 209116_x_at 0.0462 11.92 HBB
123 228222_at 0.0463 2.07 PPP1CB
124 204179_at 0.0466 9.45 MB
125 204810_s_at 0.047 7.64 CKM
126 200820_at 0.0473 1.65 PSMD8
127 202296_s_at 0.0474 1.63 RER1
128 208627_s_at 0.0476 1.65 YBX1
129 201161_s_at 0.0477 1.52 CSDA
130 225294_s_at 0.0478 1.92 TRAPPC1
131 225978_at 0.0482 -1.85 FAM80B
132 217192_s_at 0.0487 2.09 PRDM1
133 217232_x_at 0.0489 8.07 HBB
134 202037_s_at 0.0491 4.64 SFRP1
135 239057_at 0.0492 2.87 LMOD2
136 214141_x_at 0.0493 1.60 SFRS7
137 201263_at 0.0494 1.65 TARS
138 209888_s_at 0.0497 6.38 MYL1
139 214102_at 0.0497 -1.63 CENTD1
140 220248_x_at 0.0499 1.59 NSFL1C
Table V: Normal : NonRecurrent versus Normal Recurrent (adiacent mucosa)
SI Fold
No Affymetrix ID P-value (NR/R) Gene Symbol
1 238035_at 0.00212 -1.96 SP3
2 217232_x_at 0.00256 23.81 HBB 225633_at 0.00267 -2.30 DPY19L3
209116_x_at 0.00287 33.93 HBB
211696_x_at 0.00288 21.34 HBB
228238_at 0.00379 -4.15 GAS5
225997_at 0.00491 -1.80 MOBKL1A
237646_x_at 0.00492 1.76 PLEKHG5
210873_x_at 0.00523 -20.12 APOBEC3A
209405_s_at 0.00539 1.99 FAM3A
34689_at 0.00569 1.94 TREX1
223415_at 0.00617 1.72 RPP25
212476_at 0.00619 -1.99 CENTB2
200069_at 0.0072 -1.83 SART3
205236_x_at 0.00742 1.94 SOD3
205784_x_at 0.00748 1.88 ARVCF
212134_at 0.00771 2.07 PHLDB1
238066_at 0.00798 2.57 RBP7
203045_at 0.00848 2.93 NINJ1
211967_at 0.00856 -2.67 TMEM123
242039_at 0.00881 1.78 CENTD2
217040_x_at 0.00915 1.97 SOX15
212474_at 0.00981 -2.37 KIAA0241
209420_s_at 0.00983 1.94 SMPD1
224726_at 0.0101 -1.79 MIB1
212782_x_at 0.0102 3.03 POLR2J
212910_at 0.0103 1.94 THAP11
213111_at 0.0106 -1.67 PIP5K3
242989_at 0.0108 -2.79 —
209849_s_at 0.0112 1.91 RAD51C
226109_at 0.0112 -2.27 C21orf91 155752 l_a_at 0.0116 -4.12 —
225433_at 0.0118 -1.69 GTF2A1
228980_at 0.0119 -2.41 RFFL
212064_x_at 0.0122 1.63 MAZ
218050_at 0.0123 -2.10 UFM1
211745_x_at 0.0124 22.72 HBA1
221274_s_at 0.0124 1.76 LMAN2L
201928_at 0.0124 -1.76 PKP4
203552_at 0.0124 -2.40 MAP4K5
230046_at 0.0125 1.63 —
212900_at 0.0125 -2.17 SEC24A
215778_x_at 0.0126 1.96 HAB1
209398_at 0.0129 3.89 HIST1H1C
209798_at 0.0131 -1.89 NPAT
218896_s_at 0.0132 -2.49 C17orf85
225479_at 0.0136 -1.90 LRRC58
212037_at 0.0136 -2.02 PNN
238563_at 0.0136 -3.91 —
222627_at 0.0144 -1.83 VPS54
227679_at 0.0146 1.97 —
203569_s_at 0.0147 -1.67 OFD1
201088_at 0.015 -3.09 KPNA2
212771_at 0.0151 1.70 C10orf38
238326_at 0.0152 1.97 LOC440836
212705_x_at 0.0153 2.13 PNPLA2
201468_s_at 0.0156 -2.34 NQO1
202933_s_at 0.0162 -2.21 YES1
211240_x_at 0.0162 -2.53 CTNND1
228487_s_at 0.0163 -1.57 — 218750_at 0.0163 -3.79 JOSD3
217414_x_at 0.0166 15.81 HBAl /// HBA2
221600_s_at 0.0166 2.00 Cl lorf67
223141_at 0.0166 1.57 UCK1
208809_s_at 0.0168 -2.72 C6orf62
225318_at 0.0169 -1.98 —
218330_s_at 0.0169 -2.15 NAV2
203421_at 0.017 1.89 TP53I11
234918_at 0.0171 1.66 GLTSCR2
226217_at 0.0171 -2.45 SLC30A7
238402_s_at 0.0172 1.79 FLJ35220
214414_x_at 0.0173 12.63 HBA2
216180_s_at 0.0174 1.66 SYNJ2
202210_x_at 0.0174 1.62 GSK3A
201845_s_at 0.0174 -1.91 RYBP
225310_at 0.0174 -2.48 RBMX
203055_s_at 0.0176 2.07 ARHGEF1
203044_at 0.0176 -1.72 CHSY1
225428_s_at 0.0179 1.64 DDX54
226208_at 0.018 -3.64 ZSWIM6
212047_s_at 0.0181 1.79 RNF167
208918_s_at 0.0182 -1.79 NADK
1566140_at 0.0182 -4.67 HOPX
209458_x_at 0.0183 23.19 HBAl /// HBA2
209903_s_at 0.0185 -1.99 ATR
226302_at 0.0186 -3.13 ATP8B1
233849_s_at 0.0186 -3.15 ARHGAP5
201458_s_at 0.019 -2.16 BUB3
217696_at 0.0192 1.70 FUT7 217986_s_at 0.0193 -3.48 BAZ1A
228603_at 0.0194 -2.30 —
237046_x_at 0.0195 1.66 C16orf77
208798_x_at 0.0199 -2.94 GOLGA8A
225343_at 0.0201 -1.84 TMED8
227642_at 0.0202 -3.28 TFCP2L1
203342_at 0.0204 2.01 TIMM17B
203693_s_at 0.0204 -3.00 E2F3
223405_at 0.0208 -2.17 NPL
224935_at 0.021 -1.94 EIF2S3
22573 l_at 0.0212 -2.57 ANKRD50
225912_at 0.0216 -2.05 TP53INP1
202883_s_at 0.0216 -2.87 PPP2R1B
200698_at 0.0217 -2.63 KDELR2
222603_at 0.0219 -2.68 ERMP1
203083_at 0.0219 -3.42 THBS2
217776_at 0.0221 -2.03 RDH11
212307_s_at 0.0222 -3.07 OGT
225773_at 0.0224 -1.96 RSPRY1
230097_at 0.0224 -3.45 GART
209739_s_at 0.0226 1.62 PNPLA4
204018_x_at 0.0232 16.41 HBAl /// HBA2
225447_at 0.0233 -2.04 GPD2
225761_at 0.0233 -2.07 PAPD4
21203 l_at 0.0236 -2.83 RBM25
1556006_s_at 0.0236 -5.59 CSNK1A1
232706_s_at 0.0237 1.58 TRABD
200729_s_at 0.0237 -4.17 ACTR2
218762_at 0.0238 1.68 ZNF574 227415_at 0.0239 -2.34 LOC283508
208785_s_at 0.024 2.01 MAP1LC3B
212377_s_at 0.0242 -1.88 NOTCH2
227517_s_at 0.0244 -5.29 GAS5 /// SNORD79
20295 l_at 0.0245 -2.21 STK38
209135_at 0.0249 -2.67 ASPH
218423_x_at 0.0251 -1.90 VPS54
222543_at 0.0251 -1.98 DERL1
227038_at 0.0252 -4.35 SGMS2
208862_s_at 0.0253 -2.78 CTNND1
224464_s_at 0.0255 2.57 NUDT22
210249_s_at 0.0256 2.04 NCOA1
212267_at 0.0257 -1.78 WAPAL
229874_x_at 0.026 1.93 LOC729604
212663_at 0.0261 1.54 FKBP15
215460_x_at 0.0261 -1.97 BRD1
202200_s_at 0.0265 -2.50 SRPK1
223092_at 0.0267 2.36 ANKH
221503_s_at 0.0267 1.74 KPNA3
227366_at 0.0268 2.30 RILP
200947_s_at 0.0268 -2.56 GLUD1
227861_at 0.027 -1.59 TMEM161B
241650_x_at 0.0272 1.56 HMCN2
202633_at 0.0274 -1.79 TOPBP1
209107_x_at 0.0276 2.12 NCOA1
203743_s_at 0.0276 -3.85 TDG
218247_s_at 0.0278 -2.34 MEX3C
218255_s_at 0.0282 1.70 FBRS
225188_at 0.0282 -2.26 RAPH1 211699_x_at 0.0284 18.70 HBAl /// HBA2
224903_at 0.0284 -1.76 CIRH1A
229758_at 0.0289 1.65 TIGD5
212834_at 0.029 -2.20 DDX52
240452_at 0.029 -4.26 GSPT1
214333_x_at 0.0293 1.87 IDH3G
221069_s_at 0.0295 1.62 CCDC44
218657_at 0.0295 -2.72 RAPGEFL1
210613_s_at 0.0296 1.71 SYNGR1
217516_x_at 0.03 1.71 ARVCF
211074_at 0.0301 4.71 FOLR1
217691_x_at 0.0302 1.83 SLC16A3
201437_s_at 0.0302 -1.99 EIF4E
203842_s_at 0.0305 1.69 MAPRE3
200626_s_at 0.0305 -1.61 MATR3
224998_at 0.0306 -2.46 CMTM4
207483_s_at 0.0307 -1.77 CAND1
221840_at 0.031 -3.76 PTPRE
235457_at 0.0314 -2.29 MAML2
227110_at 0.0319 -1.93 HNRNPC
224974_at 0.032 -2.48 SUDS3
201916_s_at 0.0321 -1.97 SEC63
218738_s_at 0.0321 -1.98 RNF138
210371_s_at 0.0321 -2.24 RBBP4
218940_at 0.0323 -2.09 C14orfl38
AFFX-r2-Bs-lys- 3_at 0.0325 6.16 ___
219037_at 0.0325 -2.58 RRP15
204829_s_at 0.0328 2.31 FOLR2 224467_s_at 0.0328 1.96 PDCD2L
200599_s_at 0.0329 -1.89 HSP90B1
225480_at 0.0332 1.71 Clorfl22
227765_at 0.0332 1.60 —
23301 l_at 0.0332 -18.73 ANXA1
226965_at 0.0334 -2.11 FAM116A
233955_x_at 0.0339 2.41 CXXC5
1553979_at 0.034 -2.00 —
217879_at 0.0342 -1.61 CDC27
225416_at 0.0342 -2.08 RNF12
208101_s_at 0.0343 1.66 URM1
209217_s_at 0.035 2.04 WDR45
201197_at 0.0351 -4.27 AMD1
220417_s_at 0.0352 1.94 LOC728944 /// THAP4
218104_at 0.0352 -1.97 TEX10
212484_at 0.0353 2.61 FAM89B
222742_s_at 0.0353 2.42 RABL5
89476_r_at 0.0353 1.60 NPEPL1
202009_at 0.0357 2.11 TWF2
216862_s_at 0.0358 2.28 MTCP1
203080_s_at 0.0358 -1.63 BAZ2B
203905_at 0.0358 -2.15 PARN
219983_at 0.0359 4.53 HRASLS
218515_at 0.0359 -1.82 C21orf66
233656_s_at 0.0359 -2.11 VPS54
211692_s_at 0.0361 1.62 BBC3
226604_at 0.0362 -2.21 TMTC3
209332_s_at 0.0363 -1.59 MAX
201456_s_at 0.0363 -1.77 BUB3 225415_at 0.0363 -1.77 DTX3L
241799_x_at 0.0366 1.57 —
209476_at 0.0369 -2.27 TXNDC1
212628_at 0.037 -2.13 PKN2
210212_x_at 0.0373 2.13 MTCP1
203567_s_at 0.0375 -1.71 TRIM38
225284_at 0.0376 -1.71 LOC 144871
208152_s_at 0.0376 -2.46 DDX21
213168_at 0.0378 -1.66 SP3
218230_at 0.0379 -2.31 ARFIP1
218595_s_at 0.0379 -2.62 HEATR1
228222_at 0.0382 3.04 PPP1CB
202396_at 0.0382 -2.48 TCERG1
220973_s_at 0.0383 2.10 SHARPIN
218743_at 0.0383 1.82 CHMP6
227586_at 0.0385 -1.95 TMEM170
224959_at 0.0385 -3.02 SLC26A2
218956_s_at 0.0386 1.70 PTCD1
203575_at 0.0386 1.68 CSNK2A2
226200_at 0.0386 1.65 VARS2
202603_at 0.0386 -1.85 —
22175 l_at 0.0386 -1.95 SLC2A3P1
223297_at 0.0387 -2.34 AMMECRIL
240038_at 0.0389 -5.46 —
222996_s_at 0.0391 2.72 CXXC5
239392_s_at 0.0392 -2.49 —
202688_at 0.0393 2.41 TNFSF10
209034_at 0.0393 2.02 PNRC1
226146_at 0.0393 1.81 — 225107_at 0.0393 -3.04 HNRNPA2B 1
202948_at 0.0395 -1.54 ILIRI
204300_at 0.0396 2.01 PET112L
212066_s_at 0.0396 -1.60 USP34
209666_s_at 0.0396 -2.00 CHUK
208003_s_at 0.0397 -2.35 NFAT5
AFFX-PheX-3_at 0.0399 4.60 —
221918_at 0.0399 -1.51 PCTK2
218803_at 0.0399 -2.30 CHFR
225973_at 0.0399 -3.51 TAP2
218533_s_at 0.0402 3.47 UCKL1
200783_s_at 0.0402 -2.07 STMN1
231513_at 0.0404 5.16 —
221802_s_at 0.0405 -4.95 KIAA1598
203775_at 0.0406 -3.10 SLC25A13
227878_s_at 0.0407 2.38 ALKBH7
202135_s_at 0.0407 1.88 ACTR1B
201795_at 0.0407 -1.93 LBR
212293_at 0.0408 -1.98 HIPK1
212378_at 0.0408 -2.42 GART
212228_s_at 0.041 4.02 COQ9
203719_at 0.041 2.09 ERCC1
225361_x_at 0.0412 -1.87 FAM122B
225643_at 0.0413 -2.21 C14orf32
223497_at 0.0413 -2.74 FAM135A
212033_at 0.0418 -2.03 RBM25
212721_at 0.042 -1.90 SFRS12
220734_s_at 0.0421 2.21 GLTPD1 /// LOC727825
206453_s_at 0.0422 2.52 NDRG2 201704_at 0.0423 -1.52 ENTPD6
1554480_a_at 0.0426 -1.56 ARMC10
223398_at 0.0427 1.75 C9orf89
228677_s_at 0.0428 1.86 FLJ21438
224887_at 0.0428 1.55 GNPTG
215696_s_at 0.0428 -1.99 SEC16A
202778_s_at 0.043 -1.91 ZMYM2
224866_at 0.0431 -4.00 MLSTD2
1553955_at 0.0432 -2.16 CCDC128
213056_at 0.0433 -4.44 FRMD4B
224436_s_at 0.0435 -1.75 NIPSNAP3A
225785_at 0.0435 -1.83 REEP3
201873_s_at 0.0437 -2.16 ABCE1
208907_s_at 0.0439 2.28 MRPS18B
224415_s_at 0.044 2.59 HINT2
22328 l_s_at 0.0443 1.69 COX 15
218647_s_at 0.0443 -2.80 YRDC
218499_at 0.0443 -5.59 RP6-213H19.1
225534_at 0.0445 2.62 C8orf40
212163_at 0.0445 -1.72 KIDINS220
204469_at 0.0445 -10.31 PTPRZ1
201586_s_at 0.0446 -3.00 SFPQ
218227_at 0.0447 1.67 NUBP2
221903_s_at 0.0447 -2.26 CYLD
23357 l_x_at 0.0449 1.94 C20orfl49
212160_at 0.0449 -2.09 XPOT
219922_s_at 0.045 2.17 LTBP3
202996_at 0.0451 1.55 POLD4
223072_s_at 0.0452 1.65 WBP1 201091_s_at 0.0452 -1.81 CBX3 /// LOC653972
227624_at 0.0453 -2.35 KIAA1546
226538_at 0.0457 -1.53 MAN2A1
220934_s_at 0.0459 2.16 MGC3196
228135_at 0.0459 -1.59 Clorf52
227422_at 0.046 -2.17 —
218984_at 0.0461 -2.15 PUS7
226003_at 0.0463 -4.05 KIF21A
229009_at 0.0466 1.96 SIX5
1554149_at 0.0469 -1.75 CLDND1
223050_s_at 0.0471 2.34 FBXW5
202314_at 0.0471 -3.31 CYP51A1
212533_at 0.0471 -4.31 WEE1
221163_s_at 0.0475 2.36 MLXIPL
205968_at 0.0477 2.44 KCNS3
200055_at 0.0477 1.82 TAF10
218841_at 0.048 3.72 ASB8
202399_s_at 0.048 1.62 AP3S2
203020_at 0.0482 -1.81 RABGAP1L
222673_x_at 0.0483 -1.88 FAM122B /// TMEM57
201939_at 0.0483 -3.32 PLK2
205436_s_at 0.0484 1.78 H2AFX
204565_at 0.0486 2.97 THEM2
211368_s_at 0.0486 -2.79 CASP1
223454_at 0.0486 -2.95 CXCL16
223312_at 0.0487 2.72 C2orf7
214213_x_at 0.0488 1.54 LMNA
202799_at 0.0489 2.14 CLPP
203739_at 0.0493 -3.72 ZNF217 321 220952_s_at 0.0495 -2.29 PLEKHA5
322 203358_s_at 0.0498 -4.76 EZH2
323 212540_at 0.05 1.99 CDC34
Table VI Clinical Characteristics of patients
Study Sample Med Risk habits# Med Follow Med DFS size Age up (months) (Years) With Without (months)
Risk Risk
Microarrav 12 54.5 6 6 47
Set
Study
Groups
Group 1 6T, 43 3 3 48 - 4N*
Group III 6T, 4N 58 3 3 46 5.5
Validation 65 55.5 31 22 23.5
Set
Study
Groups
Group 1 34 60 19 9 27
Group II 19 56 6 10 23.5 12
Group III 12 48 6 3 20.5 4
QRT 30 57 14 9 23
Group 1 14 58 7 3 22 -
Group II 8 58 3 4 15 1 1
Group III 8 50 4 2 9.5 3.5
IHC 35 56 20 13 30
Group 1 20 60 13 6 35 -
Group II 1 1 49 4 6 28 16.5
Group III 4 48 3 1 16.5 13
Saliva 37 51 1 1 14
Normal 12 52 4 6 -
T1/T2 25 50 7 8 18 Table VII List of top 10 significant genes in Non-Recurrent/recurrent tongue cancer
Non Recurrent T vs N
SI Affymetrix Gene Fold Fold
No ID Symbol (NR/Normal) p(NR Normal) (R/Normal) p(R/Normal)
1 204475_at MMP1 255.50 0.00012 74.50 0.00519
2 213139_at SNAI2 5.81 0.00014 2.81 0.0222
3 202458_at PRSS23 8.77 0.000186 4.53 0.0205
4 205828_at MMP3 35.40 0.000288 26.15 0.0141
5 205680_at MMP10 29.51 0.00102 23.70 0.0151
6 222108_at AMIG02 5.25 0.00224 3.27 0.024
7 201976_s_at MYO10 3.96 0.00396 2.21 0.0333
8 203936_s_at MMP9 13.60 0.00438 8.39 0.0206
9 22568 l_at CTHRC1 16.01 0.00454 9.96 0.0378
10 225646_at CTSC 7.17 0.0058 4.66 0.0319
Recurrent T vs N
SI Affymetrix Gene Fold Fold
No ID Symbol p(R/Normal) (R Normal) p(NR Normal) (NR/Normal)
1 204567_s_at ABCG1 3.83E-05 6.71 0.00166 3.78
2 205479_s_at PLAU 0.00409 7.66 0.00268 4.95
3 203562_at FEZ1 0.00837 6.14 0.036 3.20
4 225285_at BCAT1 0.0196 6.16 0.0265 3.98
5 212488_at COL5A1 0.0197 7.18 0.0117 5.88
6 205959_at MMP13 0.0205 25.45 0.0313 10.91
7 202998_s_at LOXL2 0.0206 5.31 0.0452 3.69
8 214297_at CSPG4 0.0249 5.44 0.0144 4.18 9 214329_x_at TNFSF10 0.0303 4.09 0.0312 2.96
10 202688_at TNFSF10 0.036 3.96 0.0141 2.31
Table VIII List of significant genes in Recurrent tongue cancer
SI Affymetrix Gene Symbol Normal p- value Tumor p- value
No ID (NR/R) (NR R)
Fold Fold
1 209116_x_at HBB 33.93 0.00287 11.92 0.0462
2 217232_x_at HBB 23.81 0.00256 8.07 0.0489
3 209458_x_at HBA1 /// 23.19 5.23
HBA2 0.0183 0.0378
4 211745_x_at HBA1 22.72 0.0124 6.52 0.0376
5 211699_x_at HBA1 /// 18.70 4.50
HBA2 0.0284 0.0422
6 204018_x_at HBA1 /// 16.41 4.61
HBA2 0.0232 0.0441
7 218841_at ASB8 3.72 0.048 1.66 0.00659
8 228222_at PPP1CB 3.04 0.0382 2.07 0.0463
9 220952_s_at PLEKHA5 -2.29 0.0495 -1.65 0.0453
10 208003_s_at NFAT5 -2.35 0.0397 -2.44 0.014
11 242989_at — -2.79 0.0108 -1.53 0.0034
Table IX: Reciever Operating Curve and Regression analysis of the markers
ROC Analysis
Test Result Variable Area Std Error Asymptotic 95 % p value
Confidence Interval
Lower Upper
bound bound
COL5A1 0.806 0.0793 0.65 0.961 0.0001
IGLA 0.824 0.0822 0.622 0.985 0.0001
HBB 0.975 0.0201 0.936 1.000 <0.0001
CTSC 0.746 0.0914 0.566 0.925 0.0072
ABCG1 0.661 0.101 0.462 0.859 0.112
MMP1 0.533 0.109 0.319 0.748 0.759
EMP1 0.464 0.11 0.249 0.679 0.745
CCL18 0.605 0.109 0.392 0.818 0.334
Regression Analysis
Independent variables Coefficient Std. Error t P
(Constant) -0.02586
COL5A1 0.3341 0.108 3.092 0.0046
HBB 0.6724 0.1088 6.182 <0.0001
Table X: Consolidated List of genes with high differential expression
Affymetrix
SI NO ID P-value Fold R/N Gene Symbol
1 204475_at 0.00519 74.50 MMP1
2 205959_at 0.0205 25.45 MMP13
3 211964_at 0.00664 11.14 COL4A2
4 211980_at 0.0103 8.53 COL4A1
5 221730_at 0.0179 7.79 COL5A2
6 205479_s_at 0.00409 7.66 PLAU
7 212488_at 0.0197 7.18 COL5A1
8 204567_s_at 3.83E-05 6.71 ABCG1
9 225285_at 0.0196 6.16 BCAT1
10 203562_at 0.00837 6.14 FEZ1
11 210986_s_at 0.014 5.86 TPM1
12 20965 l_at 0.0105 5.46 TGFB1I1
13 203065_s_at 0.0194 5.31 CAV1
14 202998_s_at 0.0206 5.31 LOXL2
15 236565_s_at 0.0145 5.12 LARP6
MAGED4 ///
16 221261_x_at 0.0183 5.10 MAGED4B
17 20809 l_s_at 0.0188 4.85 ECOP
18 201185_at 0.0118 4.48 HTRA1
19 214329_x_at 0.0303 4.09 TNFSF10
20 221523_s_at 0.0102 -4.00 RRAGD
21 223822_at 0.00826 -4.18 SUSD4
22 213895_at 0.0158 -4.50 EMP1
23 218858_at 0.0199 -4.59 DEPDC6
24 231929_at 0.00177 -6.58 IKZF2 214063_s_at 0.0116 -6.67 TF
231145_at 0.0184 -7.19 —
209498_at 0.0174 -7.69 CEACAM1
1559606_at 0.0192 -11.51 GBP6
220026_at 0.00299 -16.26 CLCA4
Affymetrix
ID P-value Fold NR/R Gene Symbol
209116_x_at 0.0462 11.92 HBB
203872_at 0.0368 9.79 ACTA1
204179_at 0.0466 9.45 MB
204810_s_at 0.047 7.64 CKM
205374_at 0.0427 6.84 SLN
209742_s_at 0.0179 6.82 MYL2
211745_x_at 0.0376 6.52 HBA1
209888_s_at 0.0497 6.38 MYL1
211959_at 0.00223 5.81 IGFBP5
224646_x_at 0.0311 5.66 H19
209904_at 0.0386 5.12 TNNC1
219772_s_at 0.0458 4.95 SMPX
202037_s_at 0.0491 4.64 SFRP1
209283_at 0.00949 3.76 CRYAB
209355_s_at 0.014 3.68 PPAP2B
212654_at 0.043 3.51 TPM2
202036_s_at 0.0409 3.43 SFRP1
243720_at 0.0395 -1.91 CMIP
228310_at 0.0249 -1.92 ENAH
208614_s_at 0.000933 -2.04 FLNB
208003_s_at 0.014 -2.44 NFAT5 204475_at 0.00012 255.50 MMP1
211430_s_at 0.00483 41.16 IGH@ /// IGHGl ///
IGHG2 /// IGHG3 /// IGHM /// IGHV4-31
209138_x_at 0.00186 36.17 IGL@
205828_at 0.000288 35.40 MMP3
205680_at 0.00102 29.51 MMP10
201645_at 0.000184 28.77 TNC
211756_at 0.000497 28.52 PPIA
215121_x_at 0.00254 27.28 PABPC1
209395_at 0.00282 24.94 CHI3L1
215379_x_at 0.00111 24.04 LOX
209924_at 0.000224 21.57 CCL18
202267_at 0.00441 16.25 LAMC2
22568 l_at 0.00454 16.01 CTHRC1
218468_s_at 0.000843 14.26 GREM1
32128_at 0.000984 13.70 TREX1
203936_s_at 0.00438 13.60 MMP9
210355_at 0.000551 13.36 PTHLH
221671_x_at 0.00132 13.29 CLEC7A
221651_x_at 0.00283 13.19 ARHGEF10L
204533_at 0.00187 11.34 CXCL10
2l5446_s_at 0.000434 10.80 SEC16A
225647_s_at 7.29E-05 9.66 UHRF1
203915_at 0.00128 9.54 CXCL9
202458_at 0.000186 8.77 PRSS23
206513_at 0.000704 8.65 AIM2
206026_s_at 0.000441 7.44 FSCN1 205159_at 0.00094 6.79 CSF2RB
201422_at 0.000631 6.50 IFI30
212364_at 7.84E-05 6.38 MYO1B
201579_at 0.000503 6.37 FAT
213139_at 0.00014 5.81 SP3
213139_at 0.00014 5.81 SNAI2
226368_at 0.000587 5.74 CHST11
221898_at 0.0022 5.74 CYLD
209360_s_at 0.000443 5.55 RUNX1
203417_at 0.00102 5.44 MFAP2
229400_at 0.0015 5.44 IFIT3
222108_at 0.00224 5.25 GPR172A
222108_at 0.00224 5.25 AMIGO2
203423_at 0.00155 5.25 RBP1
212588_at 0.00348 5.19 RRAS2
221059_s_at 0.00174 5.15 TXNDC5
204972_at 0.00295 5.15 OAS2
218400_at 0.003 5.05 SNX10
202953_at 0.000743 5.01 C1QB
212365_at 0.00127 4.77 GART
204222_s_at 0.000446 4.65 GLIPR1
201487_at 0.00284 4.52 CTSC
202558_s_at 0.000662 4.50 STCH
201564_s_at 0.00094 4.45 FSCN1
206584_at 0.000407 4.44 LY96
201853_s_at 0.00253 4.35 CDC25B
203083_at 0.00134 4.34 THBS2
201818_at 0.000494 4.34 LPCAT1 204362_at 0.000204 4.29 SKAP2
201417_at 0.00397 4.20 SOX4
226372_at 0.000739 4.18 ERGIC2
200644_at 0.00221 4.10 MARCKSLl
219298_at 0.00438 -5.75 DERL1

Claims

1. A novel molecular signature comprising of gene expression profile of a combination of two or more genes from the set ABCA1, ABCE1, ABCG1, ABHD12, ACLY, ACOT9, ACTA1, ACTL6A, ACTN1, ACTR1B, ACTR2, ADAM 17, ADAM9, ADAR, AHR, AIM2, AKR1B1, AKTIP, ALG3, ALKBH7, AMD1, AMIGO2, AMMECR1L, ANKH, ANKRD11, ANKRD50, ANP32E, ANXA1, AP3S2, APLP2, APOBEC3A, APOE, APOL6, APP, ARF3, ARFIP1, ARFIP2, ARHGAP5, ARHGEF1, ARHGEF10L, ARL1, ARMC10, ARPC1B, ARSI, ARVCF, ASB8, ASPH, ATP2B4, ATP2C1, ATP6V0E1, ATP6V1C1, ATP8B1, ATR, AURKA, BAG1, BANF1, BASP1, BAZ1A, BAZ2B, BBC3, BCAM, BCATl, BCL6, BCLAFl, BID, BMSl, BRDl, BSCL2, BUB3, BXDC5, CAB, CADM1, CALU, CAMSAP1L1, CAND1, CASP1, CASP7, CAV1, CBLB, CBX3, CCDC128, CCDC44, CCL18, CCNE1, CCNF, CDC25B, CDC27, CDC34, CDK6, CDKN2A, CEACAM1, CENTB2, CENTD1, CENTD2, CFB, CHFR, CHI3L1, CHMP6, CHST11, CHSY1, CHUK, CHURC1, CIDEB, CIRH1A, CKLF, CKM, CLCA4, CLCC1, CLCN7, CLDND1, CLEC7A, CLPP, CLPX, CMIP, CMTM4, COBL, COL4A1, COL4A2, COL5A1, COL5A2, COPE, COQ9, COTL1, COX15, CRYAB, CSDA, CSF2RB, CSGlcA-T, CSNK1A1, CSNK2A2, CSPG4, CTHRC1, CTNND1, CTSB, CTSC, CXCL10, CXCL16, CXCL9, CXXC5, CYBASC3, CYLD, CYP51A1, DCBLD1, DCLRE1C, DCXR, DDOST, DDX21, DDX, DDX54, DEPDC6, DERL1, DFNA5, DGKQ, DHRS7B, DLG7, DLGAP4, DPY19L3, DRAM, DTX3L, E2F3, EAF2, ECHDC2, ECOP, ECSIT, EFHD2, EGFL6, EIF2S3, EIF3G, EIF4E, EIF5, EMILIN2, EMP1, ENAH, ENSA, ENTPD6, EPSTI1, ERCC1, ERGIC2, ERGIC3, ERMP1, ETV6, EZH2, FAM101B, FAM116A, FAM122B, FAM122B, TMEM57, FAM125A, FAM135A, FAM33A, FAM38A, FAM3A, FAM80B, FAM89B, FAM91A1, FAT, FBLIMl, FBRS, FBXW5, FEZl, FJXl, FKBP15, FKBP2, FKBP9, FLJ21438, FLJ35220, FLNB, FLRT2, FNDC3B, FOLR1, FOLR2, FOXJ2, FRMD4B, FRMD6, FSCN1, FST, FTSJ1, FUS, FUT7, GALNAC4S-6ST, GART, GAS5, SNORD79, GBP6, GJA1, GLIPR, GLTP, GLTSCR2, GLUD1, GMD, GNA12, GNB5, GNPTG, GOLGA8A, GPD1L, GPD2, GPR108, GPR137B, GPR172A, GPR176, GPR5, GPSM1, GREM1, GSDMDC1, GSK3A, GSPT1, GTF2A1, GZMB, H19, H2AFX, HAB1, HAX1, HBA1, HBA1, HBA2, HBA2, HBB, RAP1B, HEATR1, HEATR6, HERC5, HEXB, HIBCH, HIF1A, HINT2, HIPK1, HIST1H1C, HMCN2, HNRNPA2B1, HNRNPC, HNRPA3, HOPX, HRASLS, HRB, HSP90B 1, HSPB8, HSPH1, HTRA1, ICT1, IDH3G, IFI16, IFI30, IFI6, IFIT3, IFNGR1, IGF2BP2, IGFBP5, IGH@, IGHG1, IGHG2, IGHG3, IGHM, IGHV4- 31, IGK@, IGKC, IGKV1-5, IGKV2-24, IGLJ3, IGLV2-14, IGL@, IGLV325, IKZF2, ILIORB, IL1R1, IL8, IMP4, IMPDHl, IRAKI, IRF9, ITGA6, JAG2, JOSD3, KCNS3, KDELR2, KIAA0241, KIAA0494, KIAA0562, KIAA0746, KIAA0922, KIAA1468, KIAA1546, KIAA1598, KIDINS220, KIF21A, KIF3B, KIRREL, KLHL22, KPNA1, KPNA2, KPN A3, KRBA1, LAMC2, LAPTM5, LARP6, LASP1, LBR, LHFPL2, LIMA1, LMAN2, LMAN2L, LMNA, LMOD2, THAP4, LOC729604, LOX, LOXL2, LPCAT1, LRIG3, LRRC58, LRRC8D, LSG1, LTBP3, LY96, LYAR, MAGED1, MAGED4, MAGED4B, MAMDC2, MAML2, MAN2A1, MAN2B 1, MAOB, MAP1LC3B, MAP4K5, MAPRE3, MARCKS, MARCKSLl, MARVELDl, MATR3, MAX, MAZ, MB, MEF2A, MEX3C, MFAP2, MFHAS1, MFSD5, MGC3196, MIB 1, MIB2, MIER1, MINA, MIS12, MLLT11, MLSTD2, MLXIPL, MMP1, MMP10, MMP12, MMP13, MMP3, MMP9, MOBKLIA, MRPL48, MRPS18B, MTCPl, MTHFD2, MVP, MYBL2, MYLl, MYL2, MYOIO, MYOIB, N4BP1, NADK, NARS2, NAT 10, NAV2, NBN, NCOA1, NDE1, NDRG2, NEK6, NETO2, NFAT5, NFATC2IP, NINJ1, NIPSNAP3A, NOL5A, NOTCH2, NPAT, NPEPL1, NPL, NQO1, NSFL1C, NUAK1, NUBP2, NUDCD1, NUDT22, NXT1, OAS2, OAS3, ODZ2, OFD1, OGT, OSTM1, OXA1L, PABPC1, PAFAH1B2, PAPD4, PARN, PCDHGA1, PCDHGA10, PCDHGA11, PCDHGA12, PCDHGA2, PCDHGA3, PCDHGA4, PCDHGA5, PCDHGA6, PCDHGA7, PCDHGA8, PCDHGA9, PCDHGB 1, PCDHGB2, PCDHGB, PCDHGB4, PCDHGB 5, PCDHGB 6, PCDHGB7, PCDHGC3, PCDHGC4, PCDHGC5, PCTK2, PDCD2L, PDGFC, PDPN, PEMT, PET112L, PFN2, PGGT1B, PGS1, PHLDB1, PIK3CD, PIK3R4, PIP5K3, PKN2, PKP4, PLAU, PLEKHA5, PLEKHG5, PLK2, PLOD3, PLXDC2, PLXNA1, PNMA1, PNN, PNPLA2, PNPLA4, PNRC1, POLD4, POLR1D, POLR2G, POLR2J, POP5, PPAP2B, PPFIA1, PPIA, PPP1CA, PPP1CB, PPP2R1B, PRDM1, PRNP, PROCR, PRPF38A, PRSS23, PSMA3, PSMA4, PSMD8, PSME2, PSME3, PSMFl, PTCDl, PTHLH, PTPN2, PTPRE, PTPRK, PTPRZl, PUS7, PXDN, PXMP4, RAB23, RAB31, RAB32, RABGAP1L, RABL5, RAD51C, RAPGEFL1, RAPH1, RASGEF1A, RBBP4, RBM17, RBM22, RBM25, RBMS1, RBMX, RBPl, RBP7, RC3H2, RDHl l, REEP3, RERl, RFFL, RGS3, RGS4, RHBDF2, RHOQ, RHOU, RILP, RIN2, RIPK2, RNASEH2A, RNF12, RNF138, RNF145, RNF167, RP6, 213H19.1, RPN2, RPP25, RRAGD, RRAS2, RRP15, RSPRY1, RTP4, RUNX1, RYBP, SAC3D1, SART3, SCHIP1, SCRN1, SDAD1, SDF2, SDF4, SEC 16 A, SEC23B, SEC24A, SEC63, SEP15, SERPINB1, SERPINH1, SFPQ, SFRP1, SFRS12, SFRS2, SFRS7, SFXN3, SFXN5, SGMS2, SHARPIN, SHC1, SIRPA, SIX5, SKAP2, SLC16A3, SLC25A13, SLC26A2, SLC2A3P1, SLC30A7, SLC35B1, SLC39A14, SLC39A6, SLC44A2, SLC6A2, SLC7A6, SLN, SMPD1, SMPX, SMYD2, SNAI2, SNAPC1, SNX10, SOD3, SORT1, SOX15, SOX4, SP110, SP3, SPIRE1, SRP72, SRPK1, SRPK2, SRPR, SSSCA1, ST3GAL5, STAT1, STAT2, STAT3, STCH, STK38, STMN1, STRBP, SUDS3, SUSD4, SYNGR1, SYNJ2, TAF10, TAP2, TARBP2, TARS, TCERG1, TDG, TES, TEX10, TFTFCP2L1, TFE3, TGFB1I1, TGIF1, THAP11, THBS2, THEM2, THSD1, TIGD5, TIMM17B, TLR2, TMCC1, TMED10, TMED8, TMEM123, TMEM161B, TMEM170, TMEM184B, TMEM189, TMEM29, TMEM39B, TMEPAI, TMTC3, TNC, TNFAIP6, TNFRSF1A, TNFSF10, TNNC1, TNNT1, TOM1L1, TOPBP1, TP53I11, TP53INP1, TPBG, TPM1, TPM2, TPST1, TRABD, TRAM2, TRAPPC1, TREX1, TRIM22, TRIM38, TRIM47, TRIO, TRMT6, TUBA1C, TWF2, TXNDC1, TXNDC12, TXNDC5, UBE2L6, UBL3, UCK1, UCKL1, UFM1, UHRF1, URM1, USE1, USP1, USP34, USP53, UTP11L, VARS2, VPS24, VPS54, VRK3, WAPAL, WBP1, WDR45, WDR54, WDR68, WDR81, WEE1, WHSC1, XPOT, YBX1, YES1, YKT6, YRDC, ZCCHC17, ZFP64, ZMYM2, ZNF217, ZNF408, ZNF574, ZNFX1, ZSWIM6 or expression of proteins encoded by these genes in carcinoma tissues or tissue adjacent to the carcinoma tissue that is useful for personalizing cancer treatment.
2. The molecular signature as claimed in claim 1 wherein the said molecular signature is used for predicting recurrence of cancer after surgery or treatment with anti-cancer agents or anti cancer therapy.
3. The molecular signature as claimed in claim 1 wherein the molecular signature is used for predicting sensitivity or resistance to anti-cancer agents or anti-cancer therapy
4. The molecular signature as claimed in claim 1 wherein the molecular signature is used for predicting cancer metastasis at the time of cancer diagnosis to enable appropriate treatment, surgical or non-surgical.
5. The molecular signature as claimed in claim 1 wherein the cancer type includes but is not limited to oral cancer, other head and neck cancers, pancreatic cancer, breast cancer, glioma, melanoma, neuroblastoma, cancers of the gastro-intestinal tract, lung cancer, endometrial cancer, prostate cancer, renal cancer, bone cancer, hepatocellular carcinoma, endocrine cancer, ovarian cancer, and other solid cancers.
6. The molecular signature as claimed in claim 1 wherein molecular signature is derived from cancer tissue samples or tissue adjacent to the cancer tissue samples or saliva, which are either collected in RNA stabilizing solutions, or are frozen samples, fresh samples or formalin fixed paraffin embedded samples.
7. The molecular signature as claimed in claim 1 wherein the molecular signature is identified by techniques including, but not limited to, DNA microarray, quantitative real-time PCR, immunohistochemistry, proteomic analysis, or enzyme linked immunosorbent assay.
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RU2823470C2 (en) * 2024-02-08 2024-07-23 Федеральное государственное бюджетное учреждение "Национальный медицинский исследовательский центр радиологии" Министерства здравоохранения Российской Федерации (ФГБУ "НМИЦ радиологии" Минздрава России) Method for prediction of five-year survival rate of patients in surgical management of recurrent non-generalized resectable oral cancer

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