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Early detection of ovarian cancer has the potential to impact mortality. A multimodal screening strategy where rising CA125 values over time, analyzed with the risk of ovarian cancer algorithm (ROCA), triggers transvaginal sonography and possible surgery has high sensitivity and specificity, but still fails to detect the 20% of early-stage cases that do not express CA125. Use of multiple biomarkers could detect cases missed by CA125. We have studied the sensitivity and lead time of a multi-marker panel (CA125, HE4, MMP-7, and CA 72-4) compared with CA125 alone. We used PRoBE design principles to select preclinical longitudinal specimens from 75 women (50 screen-positive, 25 screen-negative) who developed invasive epithelial ovarian cancer (3–5 serial specimens each) and 547 corresponding healthy controls (1–10 serial specimens each) from the ovarian cancer screening trial, UKCTOCS, in a blinded fashion. We measured the multi-marker concentrations in ultra-low serum volumes (16 μL) utilizing multiplexed bead-based immunoassays with low detection limits, high inter- and intra-assay precision, negligible cross-reactivity, and good correlation with standard immunoassays. While, at least one of the complementary biomarkers rose with CA125 in 44% (22/50) of screen-positive cases, there was no advantage in lead time over CA125. Therefore, we developed single-marker longitudinal algorithms (ROCA-like) to determine the presence of a change point to distinguish between the cases and controls. Using these algorithms, at 98% specificity, HE4 and CA72-4 identified 16% (4/25) of screen-negative cases, while MMP-7 identified none. Taken together, HE4 and CA72-4 show promise as complementary biomarkers to CA125 for longitudinal screening.

Supplementary Materials and Methods, Supplementary Table S1. List of 9 PRLR gene isoforms in the UCSC Genome Browser based on RNASeqV2 data. Supplementary Table S2. Protein expression profile by RPPA analysis in Ishikawa tumors treated with mannitol or G129R. Supplementary Table S3. Pathological staining results for PRLR using 2 scoring metrics in normal uterine tissue (n=3 patients) and UCEC-EEA (n=3 patients). Supplementary Figure S1 (supporting Figure 1). Expression of PRLR_SF (uc003jjl.3) in UCEC-EEA patients. Supplementary Figure S2 (supporting Figure 1). Expression of PRLR in human uterine cancer cell lines with both PTENmut and PTENWT backgrounds. Supplementary Figure S3 (supporting Figure 2). Representative images from the Ishikawa model showed the different sizes of primary tumors at the uterine horn in different groups. Supplementary Figure S4 (supporting Figure 3). In vitro efficacy of G129R in Ishikawa and Hec1-A cells under hormone-depleted condition. Supplementary Figure S5 (supporting Figure 3). G129R reduced the viabilities of Ishikawa and Hec1A cells in comparison with PRL stimulation at hormone-depleted condition. Supplementary Figure S6 (supporting Figure 5). siRNAs knock down the expression of FOXO3a or EIF-4EBP1 in Ishikawa and Hec1A cells. Supplementary Figure S7 (supporting Figure 4C & 4D, Supplementary Figure S2, and Supplementary Figure S6). Uncropped western blots produced in this study.

Systematic approaches for accurate repurposing of targeted therapies are needed. We developed and aimed to biologically validate our therapy predicting tool (TPT) for the repurposing of targeted therapies for specific tumor types by testing the role of Bromodomain and Extra-Terminal motif inhibitors (BETi) in inhibiting BRD4 function and downregulating Notch3 signaling in ovarian cancer.Utilizing established ovarian cancer preclinical models, we carried out in vitro and in vivo studies with clinically relevant BETis to determine their therapeutic effect and impact on Notch3 signaling.Treatment with BETis or siRNA-mediated BRD4 knockdown resulted in decreased cell viability, reduced cell proliferation, and increased cell apoptosis in vitro. In vivo studies with orthotopic mouse models demonstrated that treatment with BETi decreased tumor growth. In addition, knockdown of BRD4 with doxycycline-inducible shRNA increased survival up to 50% (P < 0.001). Treatment with either BETis or BRD4 siRNA decreased Notch3 expression both in vitro and in vivo. BRD4 inhibition also decreased the expression of NOTCH3 targets, including HES1. Chromatin immunoprecipitation revealed that BRD4 was present at the NOTCH3 promoter.Our findings provide biological validation for the TPT by demonstrating that BETis can be an effective therapeutic agent for ovarian cancer by downregulating Notch3 expression.The TPT could rapidly identify candidate drugs for ovarian or other cancers along with novel companion biomarkers.

Raw RPPA data for OVCAR 5.

Abnormal activity of human prolactin (PRL) and its membrane-associated receptor (PRLR) contributes to the progression of uterine carcinoma. However, the underlying mechanisms are not well understood, and current means of targeting the PRL/PRLR axis in uterine cancer are limited. Our integrated analyses using The Cancer Genome Atlas and Genotype-Tissue Expression (GTEx) databases demonstrated that a short form of PRLR (PRLR_SF) is the isoform predominantly expressed in human uterine cancers; expression of this PRLR_SF was elevated in uterine cancers in comparison with cancer-free uterine tissues. We hypothesized that the overexpression of PRLR_SF in uterine cancer cells contributes, in part, to the oncogenic activity of the PRL/PRLR axis. Next, we employed G129R, an antagonist of human PRL, to block the PRL/PRLR axis in both PTENwt and PTENmut orthotopic mouse models of uterine cancer. In comparison with control groups, treatment with G129R as monotherapy or in combination with paclitaxel resulted in a significant reduction of growth and progression of orthotopic uterine tumors. Results from protein profiling of uterine cancer cells and in vivo tumors revealed a set of new downstream targets for G129R. Our results showed that G129R induced sub-G0 population arrest, decreased nascent protein synthesis, and initiated FOXO3a/EIF-4EBP1–mediated cell death in both PTENwt and PTENmut uterine cancer cells. Collectively, our results show a unique pattern of PRLR_SF expression predominantly in uterine cancer. Moreover, FOXO3a and EIF-4EBP1 are important mediators of cell death following G129R treatment in uterine cancer models.

Tables 1-4

Supplemental Figure S1. TP53R249S mutation detection in HCC tumors by RFLP. Supplemental Figure S2. TP53R249S mutation detection in cfDNA by RFLP. Supplementary Figure 3. Prognostic value of TP53R249S mutation in the cfDNA from HCC patients as shown by overall survival. Supplementary Figure S4. Clinical and demographic features associated with TP53R249S mutation in TCGA HCCs.

Supplementary Table 1. Dataset for the reverse-phase protein array (RPPA) and processed data for Ingenuity Pathway Analysis.

Full report from Ingenuity Pathway Analysis of the RPPA results.

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.

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

Raw RPPA data for OVCAR 4.

Figures 1-3

Supplemental Methods 1. Exome sequencing pipeline Supplemental Methods 2. RNA seq pipeline Supplemental Methods 3. DNA methylation profiling pipeline Supplemental Methods 4. Fluorescence in situ hybridization Supplemental Methods 5. TCGA samples with possible copy neutral loss of heterozygosity in 3p21 and 3p25 regions

Purpose: Several different multivariate prediction models using routine clinical variables or multigene signatures have been proposed to predict pathologic complete response to combination chemotherapy in breast cancer. Our goal was to compare the performance of four conceptually different predictors in an independent cohort of patients.Experimental Design: Gene expression profiling was done on fine-needle aspirations of 100 stage I to III breast cancers before preoperative paclitaxel, 5-fluorouracil, doxorubicin, and cyclophosphamide combination chemotherapy. Pathologic response was correlated with prediction results from a clinical nomogram, a human cancer–derived genomic predictor (DLDA30), a cell line–based genomic predictor [in vitro coexpression extrapolation (COXEN)], and an optimized cell line–derived (in vivo COXEN) predictor. None of the 100 test cases were used in the development of these predictors.Results: The in vitro COXEN using a combination of four individual drug sensitivity predictions derived from cell lines was not predictive [area under the receiver operator characteristic curve (AUC), 0.5; 95% confidence interval, (95% CI), 0.41-0.59]. The clinical nomogram (AUC, 0.73; 95% CI, 0.65-0.80) and the DLDA30 (AUC, 0.73; 95% CI, 0.66-0.80) genomic predictor had similar performances. The in vivo COXEN that used informative genes from cell lines but was trained on a separate human data set also showed significant predictive value (AUC, 0.67; 95% CI, 0.60-0.74). These three different prediction scores correlated with each other and were significant in univariate but not in multivariate analysis.Conclusions: Three conceptually different predictors performed similarly in this validation study and tended to identify the same patients as responders. A genomic predictor that relied solely on a composite of individual drug sensitivity predictions from cell lines did not show any predictive value. Clin Cancer Res; 16(2); 711–8

Purpose: Sarcomatoid renal cell carcinoma (SRCC) ranks among the most aggressive clinicopathologic phenotypes of RCC. However, the paucity of high-quality, genome-wide molecular examinations of SRCC has hindered our understanding of this entity.Experimental Design: We interrogated the mutational, copy number, and transcriptional characteristics of SRCC and compared these data with those of nonsarcomatoid RCC (RCC). We evaluated whole-exome sequencing, single-nucleotide polymorphism, and RNA sequencing data from patients with SRCC (n = 65) and RCC (n = 598) across different parent RCC subtypes, including clear-cell RCC, papillary RCC, and chromophobe RCC subtypes.Results: SRCC was molecularly discrete from RCC and clustered according to its parent RCC subtype, though with upregulation of TGFβ signaling across all subtypes. The epithelioid (E-) and spindled (S-) histologic components of SRCC did not show differences in mutational load among cancer-related genes despite a higher mutational burden in S-. Notably, sarcomatoid clear-cell RCC (SccRCC) showed significantly fewer deletions at 3p21-25, a lower rate of two-hit loss for VHL and PBRM1, and more mutations in PTEN, TP53, and RELN compared with ccRCC. A two-hit loss involving VHL predicted for ccRCC and a better prognosis, whereas mutations in PTEN, TP53, or RELN predicted for SccRCC and worse prognosis.Conclusions: SRCC segregates by parent subtype, and SccRCC has a fundamentally different early molecular pathogenesis, usually lacking the classic 3p21-25 deletion and showing distinctive mutational and transcriptional profiles. These features prompt a more precise molecular classification of RCC, with diagnostic, prognostic, and therapeutic implications. Clin Cancer Res; 23(21); 6686–96. ©2017 AACR.See related commentary by Bergerot et al., p. 6381

SUPPLEMENTARY TABLE 1. AVPC ('ANAPLASTIC') ELIGIBILITY CRITERIA FOR NCT00514540, A PHASE II STUDY OF CARBOPLATIN AND DOCETAXEL IN PATIENTS WITH ANAPLASTIC PROSTATE CARCINOMA. SUPPLEMENTARY TABLE 2. CLINICOPATHOLOGICAL FEATURES OF THE PDX DONOR PATIENTS. SUPPLEMENTARY TABLE 3. PDX GROWTH RATE AND PSA PRODUCTION. SUPPLEMENTARY TABLE 4. ANTIBODIES USED FOR IMMUNOHISTOCHEMISTRY AND WESTERN BLOT. SUPPLEMENTARY TABLE 5. qRT-PCR PRIMER SEQUENCES. SUPPLEMENTARY TABLE 6. DONOR PATIENT CHARACTERISTICS. SUPPLEMENTARY TABLE 7. SERUM MARKER LEVELS AT TIME OF REGISTRATION TO NCT00514540. SUPPLEMENTARY TABLE 8. PRINCIPAL COMPONENT VARIATE WEIGHTINGS TCGA/UNSELECTED CRPC/AVPC SAMPLES. SUPPLEMENTARY TABLE 9. LINEAR DISCRIMINANT ANALYSIS WEIGHTS. SUPPLEMENTARY TABLE 10. Tp53 MUTATIONS DETECTED IN TCGA, UNSELECTED CRPC (21) AND AVPC SAMPLES.

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

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 �m. 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

Legends for Supplementary Data

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.

Supplementary Data from Prospective Comparison of Clinical and Genomic Multivariate Predictors of Response to Neoadjuvant Chemotherapy in Breast Cancer

Figure legends for Figures S1-S10

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.

Table S7a. Ingenuity Pathway Analysis of SRCC vs RCC Table S7b. Ingenuity Pathway Analysis of SccRCC vs advanced stage ccRCC Table S7c. Ingenuity Pathway Analysis of E vs S components of ccRCC

Purpose: Morphologically heterogeneous prostate cancers that behave clinically like small-cell prostate cancers (SCPC) share their chemotherapy responsiveness. We asked whether these clinically defined, morphologically diverse, “aggressive variant prostate cancer (AVPC)” also share molecular features with SCPC.Experimental Design: Fifty-nine prostate cancer samples from 40 clinical trial participants meeting AVPC criteria, and 8 patient-tumor derived xenografts (PDX) from 6 of them, were stained for markers aberrantly expressed in SCPC. DNA from 36 and 8 PDX was analyzed by Oncoscan for copy number gains (CNG) and losses (CNL). We used the AVPC PDX to expand observations and referenced publicly available datasets to arrive at a candidate molecular signature for the AVPC.Results: Irrespective of morphology, Ki67 and Tp53 stained ≥10% cells in 80% and 41% of samples, respectively. RB1 stained MYC (surrogate for 8q) CNG and RB1 CNL showed in 54% of 44 samples each and PTEN CNL in 48%. All but 1 of 8 PDX bore Tp53 missense mutations. RB1 CNL was the strongest discriminator between unselected castration-resistant prostate cancer (CRPC) and the AVPC. Combined alterations in RB1, Tp53, and/or PTEN were more frequent in the AVPC than in unselected CRPC and in The Cancer Genome Atlas samples.Conclusions: Clinically defined AVPC share molecular features with SCPC and are characterized by combined alterations in RB1, Tp53, and/or PTEN. Clin Cancer Res; 22(6); 1520–30. ©2015 AACR.

IHC, qRT-PCR and Western blot methods.

Purpose: The successful translation of laboratory research into effective therapies is dependent upon the validity of peer-reviewed publications. However, several publications in recent years suggested that published scientific findings could be reproduced only 11% to 45% of the time. Multiple surveys attempted to elucidate the fundamental causes of data irreproducibility and underscored potential solutions, more robust experimental designs, better statistics, and better mentorship. However, no prior survey has addressed the role of the review and publication process on honest reporting.Experimental Design: We developed an anonymous online survey intended for trainees involved in bench research. The survey included questions related to mentoring/career development, research practice, integrity, and transparency, and how the pressure to publish and the publication process itself influence their reporting practices.Results: Responses to questions related to mentoring and training practices were largely positive, although an average of approximately 25% did not seem to receive optimal mentoring. A total of 39.2% revealed having been pressured by a principle investigator or collaborator to produce “positive” data. About 62.8% admitted that the pressure to publish influences the way they report data. The majority of respondents did not believe that extensive revisions significantly improved the manuscript while adding to the cost and time invested.Conclusions: This survey indicates that trainees believe that the pressure to publish affects honest reporting, mostly emanating from our system of rewards and advancement. The publication process itself affects faculty and trainees and appears to influence a shift in their ethics from honest reporting (“negative data”) to selective reporting, data falsification, or even fabrication. Clin Cancer Res; 24(14); 3447–55. ©2018 AACR.

Supplemental Tables 1, 2, 3

Supplemental Figure 6A

Supplemental Figure 3

Supplemental Table 8

Supplemental Table 7

Genetically 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.

Supplementary Table 4 from Characterization of a Naturally Occurring Breast Cancer Subset Enriched in Epithelial-to-Mesenchymal Transition and Stem Cell Characteristics

Supplementary Tables 1-3 from Characterization of a Naturally Occurring Breast Cancer Subset Enriched in Epithelial-to-Mesenchymal Transition and Stem Cell Characteristics

Supplemental Figure and Table Legends

Supplemental Figure 2

Supplemental Table 6

Metaplastic breast cancers (MBC) are aggressive, chemoresistant tumors characterized by lineage plasticity. To advance understanding of their pathogenesis and relatedness to other breast cancer subtypes, 28 MBCs were compared with common breast cancers using comparative genomic hybridization, transcriptional profiling, and reverse-phase protein arrays and by sequencing for common breast cancer mutations. MBCs showed unique DNA copy number aberrations compared with common breast cancers. PIK3CA mutations were detected in 9 of 19 MBCs (47.4%) versus 80 of 232 hormone receptor–positive cancers (34.5%; P = 0.32), 17 of 75 HER-2–positive samples (22.7%; P = 0.04), 20 of 240 basal-like cancers (8.3%; P < 0.0001), and 0 of 14 claudin-low tumors (P = 0.004). Of 7 phosphatidylinositol 3-kinase/AKT pathway phosphorylation sites, 6 were more highly phosphorylated in MBCs than in other breast tumor subtypes. The majority of MBCs displayed mRNA profiles different from those of the most common, including basal-like cancers. By transcriptional profiling, MBCs and the recently identified claudin-low breast cancer subset constitute related receptor-negative subgroups characterized by low expression of GATA3-regulated genes and of genes responsible for cell-cell adhesion with enrichment for markers linked to stem cell function and epithelial-to-mesenchymal transition (EMT). In contrast to other breast cancers, claudin-low tumors and most MBCs showed a significant similarity to a “tumorigenic” signature defined using CD44+/CD24− breast tumor–initiating stem cell–like cells. MBCs and claudin-low tumors are thus enriched in EMT and stem cell–like features, and may arise from an earlier, more chemoresistant breast epithelial precursor than basal-like or luminal cancers. PIK3CA mutations, EMT, and stem cell-like characteristics likely contribute to the poor outcomes of MBC and suggest novel therapeutic targets. [Cancer Res 2009;69(10):4116–24]

Supplemental Figure 1

Supplementary Legends for Figures and Tables 1-8 from Characterization of a Naturally Occurring Breast Cancer Subset Enriched in Epithelial-to-Mesenchymal Transition and Stem Cell Characteristics