We describe a strategy that combines histologic and molecular mapping that permits interrogation of the chronology of changes associated with cancer development on a whole-organ scale. Using this approach, we present the sequence of alterations around RB1 in the development of bladder cancer. We show that RB1 is not involved in initial expansion of the preneoplastic clone. Instead, we found a set of contiguous genes that we term “forerunner” genes whose silencing is associated with the development of plaque-like field effects initiating carcinogenesis. Specifically, we identified five candidate forerunner genes (ITM2B, LPAR6, MLNR, CAB39L, and ARL11) mapping near RB1. Two of these genes, LPAR6 and CAB39L, are preferentially downregulated in the luminal and basal subtypes of bladder cancer, respectively. Their loss of function dysregulates urothelial differentiation, sensitizing the urothelium to N-butyl-N-(4-hydroxybutyl)nitrosamine-induced cancers, which recapitulate the luminal and basal subtypes of human bladder cancer.

File contains synthesis of CN210, sequences for siRNAs, supplementary tables 1 to 3, supplementary figures 1 through 8 and figure legends.

Table S2. miRNAs correlate with differentially expressed genes.

AbstractGenetically engineered mouse mammary cancer models have been used over the years as systems to study human breast cancer. However, much controversy exists on the utility of such models as valid equivalents to the human cancer condition. To perform an interspecies gene expression comparative study in breast cancer we used a mouse model that most closely resembles human breast carcinogenesis. This system relies on the transplant of p53 null mouse mammary epithelial cells into the cleared mammary fat pads of syngeneic hosts. Serial analysis of gene expression (SAGE) was used to obtain gene expression profiles of normal and tumor samples from this mouse mammary cancer model (>300,000 mouse mammary-specific tags). The resulting mouse data were compared with 25 of our human breast cancer SAGE libraries (>2.5 million human breast-specific tags). We observed significant similarities in the deregulation of specific genes and gene families when comparing mouse with human breast cancer SAGE data. A total of 72 transcripts were identified as commonly deregulated in both species. We observed a systematic and significant down-regulation in all of the tumors from both species of various cytokines, including CXCL1 (GRO1), LIF, interleukin 6, and CCL2. All of the mouse and most human mammary tumors also displayed decreased expression of genes known to inhibit cell proliferation, including NFKBIA (IKBα), GADD45B, and CDKN1A (p21); transcription-related genes such as CEBP, JUN, JUNB, and ELF1; and apoptosis-related transcripts such as IER3 and GADD34/PPP1R15A. Examples of overexpressed transcripts in tumors from both species include proliferation-related genes such as CCND1, CKS1B, and STMN1 (oncoprotein 18); and genes related to other functions such as SEPW1, SDFR1, DNCI2, and SP110. Importantly, abnormal expression of several of these genes has not been associated previously with breast cancer. The consistency of these observations was validated in independent mouse and human mammary cancer sets.This is the first interspecies comparison of mammary cancer gene expression profiles. The comparative analysis of mouse and human SAGE mammary cancer data validates this p53 null mouse tumor model as a useful system closely resembling human breast cancer development and progression. More importantly, these studies are allowing us to identify relevant biomarkers of potential use in human studies while leading to a better understanding of specific mechanisms of human breast carcinogenesis.

<p>PDF file - 65K, Microarray Analysis of Significantly Down- regulated Genes in OVCAR3 cells With or Without Notch3 Silencing.</p>

PDF file - 109KB, Effects of MK-2206 on cell growth and survival of human AML cell lines.

Supplemental Tables 1, 2, 3

PDF file 190K, Figure S6. (A, B) Glu-tubulin immunofluorescence staining (green) in RMUG-S and RMUG-L cells treated with 100 nM KX-01 for 24 hours and incubated with anti-glu-tubulin antibody followed by Alexa Fluor 488-conjugated anti-rabbit antibody. Nuclei (blue) were stained with DAPI. Left: tubulin stain only; Middle: DAPI nuclear stain; Right: tubulin and DAPI nuclear stain

PDF file 218K, Figure S5. (A) 72 proteins significantly differed between cells treated with KX-01 and control cells in RMUG-S cells. (B) 60 proteins significantly differed between cells treated with KX-01 and control cells in RMUG-L cells. (C) Western blot results showing cyclin B1 and CDC2 expression in RMUG-S and RMUG-L cells treated with 100nM or 200nM KX-01 for 24 hours. Bars represent means with standard errors. *p<0.05 compared with the control group. (D, E) Lamin B1 immunofluorescence staining (green) in RMUG-S and RMUG-L cells treated with 100 nM KX-01 for 24 hours and incubated with anti-lamin B1 antibody. Nuclei (blue) were stained with DAPI

PDF file - 357K, Supplementary Figure 1. Chemical structure of paclitaxel: (2α,4α,5β,7β,10β,13α)-4,10-bis(acetyloxy)-13-{(2R,3S)-3-(benzoylamino)-2-hydroxy-3-phenylpropanoyloxy}-1,7-dihydroxy-9-oxo-5,20-epoxytax-11-en-2-yl benzoate. Supplementary Figure 2. Overexpression of PEA-15 phosphorylated at both Ser104 and Ser116 enhanced the apoptotic effect of paclitaxel in ovarian cancer cells. Supplementary Figure 3. The addition of caspase 8 and 9 inhibitors reduced paclitaxel-induced apoptosis in SKOV3.ip1-AA and SKOV3.ip1-DD cells. Supplementary Figure 4. Quantification of SCLIP mRNA levels in SKOV3.ip1 stable cells. Supplementary Figure 5. SCLIP mediated paclitaxel resistance in ovarian cancer cells.

Supplementary Figure 1. Supervised hierarchical clustering of differentially expressed genes between typical CRPC PDX ("ADENO") and CRPC PDX with small cell carcinoma morphology ("SCPC"). Supplementary Figure 2. Morphologic spectrum of tumors of patients with the aggressive variant phenotype in the primary (A-D) and metastatic tumor sites (E-H) Supplementary Figure 3. Immunohistochemical analysis of all 59 available NCT00514540 samples and 8 PDX lines. Supplementary Figure 4. Principal component analysis of a16-serum-and-IHC-tumor-marker set in "Baseline" samples from 21 patients. Supplementary Figure 5. Total number of copy number alterations per sample. Supplementary Figure 6. mRNA levels of genes of interest. Supplementary Figure 7. Principal component analysis of a 10-CNA-marker set in TCGA, unsupervised CRPC and AVPC samples.

Supplementary Table S1. Demographic and clinical parameters of the 41 HCC Hispanic patients. Supplementary Table S2. Demographic and clinical parameters of 218 HCC patients. Supplementary Table S3. Genes differentially expressed in HCC tumors with TP53R249S mutation compared to HCC tumors without TP53R249S mutation. Supplementary Table S4. Demographic and clinical characteristic of HCC patients with TP53R249S mutation in TCGA.

Raw data from our survey. Tab 1. All survey responses, including date of response. Tab 2. Survey responses from eligible respondents only (graduate students or postdoctoral fellows performing bench research). Tab 3. Extrapolated results from those who answered “other†to certain questions. Tab 4. Responses compiled in bar graphs.

PDF file - 765K, Overall Survival (OS) Analysis in Patients With High-Grade Serous Ovarian Cancer HGS-OvCa (S1); In Vitro Functional Studies of Notch3 siRNA or GSI and combined with Paclitaxel in OvCa Cells (S2); qRT-PCR Analysis of Expression of Gamma-Secretase Complex in Ovarian Cancer Cells (S3); In vitro effects of blocking endocytosis on Jagged-1/Notch3 pathway in cancer cells (S4); In vivo study of Notch3 siRNA in orthotopic mouse models of ovarian cancer (S5).

PDF file 196K, Figure S2. Effects of KX-01 and oxaliplatin on cell proliferation (Ki-67; A), angiogenesis (CD31; B), apoptosis (Cleaved caspase-3; C) RMUG-L model. Original magnification 200x . Bars in the graphs correspond sequentially to the labeled columns of images on the left. Data are shown as mean {plus-minus} standard deviation (error bars). *p<0.05, **p<0.01 compared with the control group

Supplementary material and methods, Tables 1-3. Supplementary table 1. Changes in the expression of proteins and their phosphorylated forms with everolimus treatment analyzed by RPPA; Supplementary table 2. Results of binomial analysis showing direction of changes in the expression of proteins and their phosphorylated forms, analyzed by RPPA, for individual patients; Supplementary Table 3. Major everolimus pharmacokinetic parameters in cycles 1 and 2a.

Abstract microRNAs (miRNAs/miRs) belong to a class of small noncoding RNAs that can negatively regulate messenger RNA (mRNA) expression of target genes. miRNAs are involved in multiple aspects of ovarian cancer cell dysfunction and the phenotype of ovarian cancer cells can be modified by targeting miRNA expression. miRNA profiling has detected a number of candidate miRNAs with the potential to regulate many important biologic functions in ovarian cancer, but their role still needs to be clarified, given the remarkable heterogeneity among ovarian cancers and the context-dependent role of miRNAs. This review summarizes the data collected from The Cancer Genome Atlas (TCGA) and several other genome-wide projects to identify dysregulated miRNAs in ovarian cancers. Copy number variations (CNVs), epigenetic alterations, and oncogenic mutations are also discussed that affect miRNA levels in ovarian disease. Emphasis is given to the role of particular miRNAs in altering expression of genes in human ovarian cancers with the potential to provide diagnostic, prognostic, and therapeutic targets. Particular attention has been given to TP53, BRCA1/2, CA125 (MUC16), HE4 (WFDC2), and imprinted genes such as ARHI (DIRAS3). A better understanding of the abnormalities in miRNA expression and downstream transcriptional and biologic consequences will provide leads for more effective biomarkers and translational approaches in the management of ovarian cancer. Mol Cancer Res; 13(3); 393–401. ©2014 AACR.

Answers to Question 28. All comments provided in the “free comments†section at the end of the survey.

Table S1. Differentially expressed genes.

Figure S1. Biphasic histologic components of sarcomatoid renal cell carcinoma. The macrodissected paired epithelioid or carcinomatous (E) and spindled or sarcomatoid (S) components of clear cell RCC (upper panel), Papillary RCC (middle panel), and chromophobe RCC (lower panel). H&E stain, scale bar 200 ï­m. Figure S2. Sarcomatoid ccRCC shows fewer VHL deletions. (A) Clear cell RCC (H&E stain, scale bar 100 µm) with (B) Fluorescence in situ hybridization (FISH) image showing paired CEN3q signals (green) and a single VHL signal (red). (C) Sarcomatoid ccRCC (H&E stain, scale bar 100 µm) with (D) FISH image showing balanced CEN3q (green) and VHL (red) signals. (E) Box plot showing significantly higher VHL/3q ratios associated with sarcomatoid histology, P<0.008. Figure S3: The smooth scatter plot of signal B versus signal A. Figure S4a: 3p21 and 3p21.1 copy number versus chromosome position. Figure S4b: 3p25 copy number versus chromosome position. Figure S5: Kernel density plots. Each sample per row; left three columns: 3p21; middle three columns: 2q37; and right three columns: 1p1. Figure S6a: Example of more than one peak in the summed signal. Figure S6b: Example of 4 peaks in signals A and B. Figure S7: Density plots of TCGA samples with copy-neutral LOH. Figure S8. VHL and PBRM1 show fewer 2-hit inactivation in sarcomatoid ccRCC. Sarcomatoid ccRCC and ccRCC cases shown in terms of the inactivating "hits" on 3p21-25 genes (VHL, PBRM1, SETD2, BAP1) consisting of mutations or methylation (mutually exclusive for VHL) and deletions. Figure S9. Top activated and inhibited pathways of sarcomatoid samples. Pathways altered by differentially expressed genes between non-sarcomatoid and sarcomatoid samples. The genes selected were differentially expressed in both the TCGA and MD Anderson samples. Figure S10. S- component shows a higher mutational load in sarcomatoid RCC. The total number of non-synonymous mutations in the E- and S- components of sarcomatoid RCC, across all parent RCC subtypes (A) and in clear cell RCC (B).