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A Suppl. Figure 1 A 1.0 p = 0.0192 0.8 0.6 0.4 Proportion Surviving 0.2 Gain Normal 0.0 0122436486072 Months After Esophagectomy B p = 0.034 Below Median Proportion Surviving Above Median Months After Esophagectomy Suppl. Figure 2 Suppl. Figure 3 Suppl. Figure 4 Suppl. Figure 5 A Score 0 Score 1 Score 2 Score 3 N=25, (22.5% of patients) N=53, (47.7% of patients) N=23, (20.7% of patients) N=10, (9.0% of patients) B 60 50 40 30 % of Patients 20 10 0 1 23 Score SUPPLEMENTARY MATERIAL Supplementary methods: Real-time PCR analysis. Real-time PCR analysis for CDK4 expression was performed in 108 tumors using Assays-on-Demand (AOD, Life technologies Corp, Carlsbad, CA). 88ng of cDNA (RNA equivalents) template was used in each qPCR reaction and assays were performed in triplicate. Quantification of CDK4 expression was measured relative to the geometric mean of five endogenous control genes (HMBS, POLRA, TBP, PGK, UCBH). Network-based analysis. Genes that were differently expressed between CDK6-positive and CDK6- negative samples with a p-value < 0.05 and fold-change >0.5 were identified using limma package from bioconductor (1). Network-based analysis was performed using the functional interaction (FI) network (2). Briefly, this consists of 10956 proteins and more than 200,000 curated functional interactions. We calculated pairwise shortest paths among genes of interest in the FI network, hierarchically clustered them based on the average linkage method, and then selected a cluster containing more than 80% altered genes. To calculate a p-value for average shortest path we performed 1000-fold permutation test by randomly selecting the same number of genes from the biggest connected network component. A minimum spanning tree algorithm was used to find linkers and connected all genes of interest in one subnetwork (3). For network clustering we used Markov Cluster Algorithm (MCL) (4) with inflation 1.8. Only clusters with 10 or more genes were taken into account. We used the hypergeometric test to evaluate whether up (or down)-regulated genes were represented more than expected by chance within each cluster. All network diagrams were drawn with Cytoscape (5). The functional enrichment analysis for pathways was based on binominal test. False discovery rate was calculated based on 1000 permutations on all genes in the FI network. siRNA sequences: CDK6, si1: Sense - GUUUGUAACAGAUAUCGAUtt, Antisense - AUCGAUAUCUGUUACAAACtt CDK6, si2: Sense - GCAGAAAUGUUUCGUAGAAtt, Antisense - UUCUACGAAACAUUUCUGCaa CDK4, si1: Sense – UGCUGACUUUUAACCCACAtt, Antisense - UGUGGGUUAAAAGUCAGCAtt CDK4, si2: Sense – CACCCGUGGUUGUUACACUtt, Antisense - AGUGUAACAACCACGGGUGta Construction of Tissue Microarray (TMA): TMAs, containing 38 cases of Barrett’s esophagus (BE), 81 cases of columnar cell metaplasia (CCM), 86 cases of squamous epithelium (SE), 18 cases of low grade dysplasia (LGD), 15 cases of high grade dysplasia (HGD), and 116 cases of EAC, were constructed from the representative areas of formalin-fixed specimens collected between 1997-2005 in the Department of Pathology and Laboratory Medicine, University of Rochester Medical Center/Strong Memorial Hospital, Rochester, New York. Five-micron sections were cut from tissue microarrays and were stained with H&E to confirm the presence of the expected tissue histology within each tissue core. Additional sections were cut for IHC analysis. Patients for Tissue Microarrays: All the 116 patients with esophageal adenocarcinoma used for the tissue microarray construction were treated with esophagectomy in Strong Memorial Hospital/University of Rochester between 1997-2005. These patients included 104 males (89.6%) and 12 females (10.4%). The patient age ranged from 34 to 85 years with a mean of 65 years. Immunohistochemistry. Tissue sections were deparaffinized, rehydrated and incubated with anti-CDK6 antibody (Santa Cruz Biotechnology, Santa Cruz, CA). Sections were then incubated with the secondary antibody (Flex mouse-link, Dako North America, Inc., Carpinteria, CA) followed by Flex-HRP then Flex DAB chromogen and counterstained with hematoxylin. The intensity of the CDK6 immunostaining was scored as follows: score 0, no stain, score 1, weak stain in cytoplasm and/or nucleus (>30% of cells), score 2, moderate stain in cytoplasm and/or nucleus (>30% of cells) and score 3, strong stain in cytoplasm and/or nucleus (>30% of cells). Anchorage-independent growth assay. For anchorage-independence, two layer agarose-containg media were plated in 24-well plates. The first layer (300μl) was McCoy growth medium (Invitrogen) containing 0.8% sea plaque agarose (a gift from Dr. Andrew Bateman, McGill University, Montreal, Canada) and 5% FBS.The second layer contained 3000 cells in McCoy medium containing 0.4% agarose and 7.5% FBS. Colonies were left to grow in 5% CO2 at 37ºC in a humidified incubator for about three weeks. Colonies were counted using an inverted microscope at 4X amplification. Supplementary Table 1: Clinical characteristics of esophageal adenocarcinoma patients. No. of Variable patients Sex Male 95 Female 21 N-Stage N0 48 N1 65 NX 3 Stage T1 36 T2 17 T3 59 T4 2 TX 2 Overall Stage I 28 II 31 III 49 IV 7 Unknown 1 Follow Up (months) Median 26.8 Range 2.3-76.5 Supplementary Table 2: Pathway annotation for two major network clusters identified by MCL algorithm. P-values in the table are calculated based on binominal test and FDR values based on 1000 permutation tests. Only outputs with p-value < 0.05 and FDR<0.05 are listed. In the brackets after pathways we marked the data resource where the pathway was curated: “R” for Reactome, “K” for KEGG, “B” for BioCarta, “P” for Panther, and “N” for NCI-Nature. Cluster Pathway P-Value FDR IDs [HLA-DQB1, ITK, HLA-DRB1, HLA-DMA, <1.000e- 2 TCR signaling (R) 3.42E-14 HLA-DQA1, TRAC, HLA-DPA1, INPP5D, 03 HLA-DRA] [HLA-DQB1, HLA-DRB1, CTSS, HLA- Antigen processing and <5.000e- 2 1.07E-09 DQA1, HLA-DRB4, HLA-DPA1, HLA- presentation (K) 04 DRA] Cell adhesion molecules [HLA-DQB1, HLA-DRB1, HLA-DQA1, 2 8.17E-07 3.33E-04 (K) HLA-DRB4, HLA-DPA1, HLA-DRA] LCK and FYN tyrosine 2 kinases in initiation of 6.43E-06 2.50E-04 [HLA-DRB1, TRAC, HLA-DRA] TCR activation (B) [TRA@, HLA-DMA, HLA-DQA1, HLA- 2 T cell activation (P) 9.14E-06 2.00E-04 DPA1, HLA-DRA] 2 IL 4 signaling pathway (B) 1.25E-05 1.67E-04 [HLA-DRB1, IL2RG, HLA-DRA] IL12-mediated signaling [HLA-DRB1, TRAC, IL2RG, JAK2, HLA- 2 1.58E-05 2.86E-04 events (N) DRA] The co-stimulatory signal 2 3.39E-05 3.75E-04 [HLA-DRB1, TRAC, HLA-DRA] during T-cell activation(B) TCR signaling in naive [ITK, HLA-DRB1, TRAC, INPP5D, HLA- 2 3.58E-05 3.33E-04 CD4+ T cells (N) DRA] Role of mef2d in T-cell 2 6.37E-05 6.00E-04 [HLA-DRB1, TRAC, HLA-DRA] apoptosis (B) Activation of csk by camp- dependent protein kinase 2 2.61E-04 2.82E-03 [HLA-DRB1, TRAC, HLA-DRA] inhibits signaling through the T cell receptor (B) T cell receptor signaling 2 4.59E-04 5.50E-03 [HLA-DRB1, TRAC, HLA-DRA] pathway (B) IL4-mediated signaling 2 7.71E-04 1.05E-02 [IL2RG, JAK2, INPP5D] events (N) Jak-STAT signaling 2 3.07E-03 3.50E-02 [IL7R, IL2RG, JAK2] pathway (K) IL12 signaling mediated 2 3.66E-03 3.66E-02 [HLA-DRB1, HLA-DRA] by STAT4 (N) EPO signaling pathway 2 4.40E-03 3.81E-02 [JAK2, INPP5D] (N) Receptor-ligand <1.000e- [GNGT1, ADRB1, CCR6, GNB2, CXCL9, 3 complexes bind G 3.67E-08 03 CXCL11] proteins(R) Class A/1 (Rhodopsin-like 3 1.23E-04 9.50E-03 [ADRB1, CCR6, CXCL9, CXCL11] receptors)(R) Cytokine-cytokine 3 3.55E-04 1.80E-02 [CCR6, CXCL13, CXCL9, CXCL11] receptor interaction(K) Heterotrimeric G-protein signaling pathway-Gq 3 8.50E-04 2.68E-02 [GNGT1, GNB2, RGS5] alpha and Go alpha mediated pathway (P) 5HT2 type receptor 3 mediated signaling 9.32E-04 2.30E-02 [GNGT1, GNB2] pathway (P) Heterotrimeric G-protein signaling pathway-Gi 3 1.95E-03 3.33E-02 [GNGT1, ADRB1, RGS5] alpha and Gs alpha mediated pathway (P) Supplementary results: Network-analysis of CDK6 amplified versus non-amplified tumors. While the association of amplification and overexpression of CDK6 with poor outcome in patients seems to be readily explained by its important role in regulating the cell cycle, a network-based analysis of genes that are differentially expressed between CDK6 amplified and non-amplified tumors raises another intriguing possibility. It is well known that solid human tumors are often infiltrated by lymphocytes (tumor- infiltrating lymphocytes [TILs]) and T cells have been shown to be mediators of anti-tumor immunity (6). The presence of TILs in esophageal carcinoma is often correlated with improved patient survival (7). In our analysis we found one cluster of genes associated with T-cell signaling and another cluster associated with chemokines and chemokine receptors, several of which are involved in attraction of T and B lymphocytes. Thus it is possible that tumors with increased CDK6 copy number produce less T-cell chemo-attractants, have fewer TIL’s and subsequently, these patients have worse survival. The nature of this observation and correlation with CDK6 amplification is not clear and needs to be experimentally verified. Although there is some evidence that chemokine signaling can influence CDK6 activity via pRB (8), we are not aware of any data suggesting that CDK6 activity can influence chemokine expression. We identified 213 genes (including CDK6) that were differently expressed between CDK6-amplified and CDK6-non-amplified tumor samples. Of these, 111 (52.11%) were in the FI network and hierarchical clustering reduced this to a set of 88 of the most interconnected candidates.
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  • Chr CNV Start CNV Stop Gene Gene Feature 1 37261312 37269719

    Chr CNV Start CNV Stop Gene Gene Feature 1 37261312 37269719

    chr CNV start CNV stop Gene Gene feature 1 37261312 37269719 Tmem131 closest upstream gene 1 37261312 37269719 Cnga3 closest downstream gene 1 41160869 41180390 Tmem182 closest upstream gene 1 41160869 41180390 2610017I09Rik closest downstream gene 1 66835123 66839616 1110028C15Rik in region 2 88714200 88719211 Olfr1206 closest upstream gene 2 88714200 88719211 Olfr1208 closest downstream gene 2 154840037 154846228 a in region 3 30065831 30417157 Mecom closest upstream gene 3 30065831 30417157 Arpm1 closest downstream gene 3 35476875 35495913 Sox2ot closest upstream gene 3 35476875 35495913 Atp11b closest downstream gene 3 39563408 39598697 Fat4 closest upstream gene 3 39563408 39598697 Intu closest downstream gene 3 94246481 94410611 Celf3 in region 3 94246481 94410611 Mrpl9 in region 3 94246481 94410611 Riiad1 in region 3 94246481 94410611 Snx27 in region 3 104311901 104319916 Lrig2 in region 3 144613709 144619149 Clca6 in region 3 144613709 144619149 Clca6 in region 4 108673 137301 Vmn1r2 closest downstream gene 4 3353037 5882883 6330407A03Rik in region 4 3353037 5882883 Chchd7 in region 4 3353037 5882883 Fam110b in region 4 3353037 5882883 Impad1 in region 4 3353037 5882883 Lyn in region 4 3353037 5882883 Mos in region 4 3353037 5882883 Penk in region 4 3353037 5882883 Plag1 in region 4 3353037 5882883 Rps20 in region 4 3353037 5882883 Sdr16c5 in region 4 3353037 5882883 Sdr16c6 in region 4 3353037 5882883 Tgs1 in region 4 3353037 5882883 Tmem68 in region 4 5919294 6304249 Cyp7a1 in region 4 5919294 6304249 Sdcbp in region 4 5919294