Garcia-Closas M, Couch FJ, Lindstrom S, Michailidou K, Schmidt MK, Brook MN, Orr N, Rhie SK, Riboli E, Feigelson HS, Le Marchand L, Buring JE, Eccles D, Miron P, Fasching PA, Brauch H, Chang-Claude J, Carpenter J, Godwin AK, Nevanlinna H, Giles GG, Cox A, Hopper JL, Bolla MK, Wang Q, Dennis J, Dicks E, Howat WJ, Schoof N, Bojesen SE, Lambrechts D, Broeks A, Andrulis IL, Guénel P, Burwinkel B, Sawyer EJ, Hollestelle A, Fletcher O, Winqvist R, Brenner H, Mannermaa A, Hamann U, Meindl A, Lindblom A, Zheng W, Devillee P, Goldberg MS, Lubinski J, Kristensen V, Swerdlow A, Anton-Culver H, Dörk T, Muir K, Matsuo K, Wu AH, Radice P, Teo SH, Shu X-O, Blot W, Kang D, Hartman M, Sangrajrang S, Shen C-Y, Southey MC, Park DJ, Hammet F, Stone J, Veer LV'tJ, Rutgers EJ, Lophatananon A, Stewart-Brown S, Siriwanarangsan P, Peto J, Schrauder MG, Ekici AB, Beckmann MW, dos Santos Silva I, Johnson N, Warren H, Tomlinson I, Kerin MJ, Miller N, Marme F, Schneeweiss A, Sohn C, Truong T, Laurent-Puig P, Kerbrat P, Nordestgaard BG, Nielsen SF, Flyger H, Milne RL, Arias Perez JI, Menéndez P, Müller H, Arndt V, Stegmaier C, Lichtner P, Lochmann M, Justenhoven C, Ko Y-D, Muranen TA, Aittomäki K, Blomqvist C, Greco D, Heikkinen T, Ito H, Iwata H, Yatabe Y, Antonenkova NN, Margolin S, Kataja V, Kosma V-M, Hartikainen JM, Balleine R, Tseng C-chen, Van Den Berg D, Stram DO, Neven P, Dieudonné A-S, Leunen K, Rudolph A, Nickels S, Flesch-Janys D, Peterlongo P, Peissel B, Bernard L, Olson JE, Wang X, Stevens K, Severi G, Baglietto L, McLean C, Coetzee GA, Feng Y, Henderson BE, Schumacher F, Bogdanova NV, Labrèche F, Dumont M, Yip CH, Taib NAM, Cheng C-Y, Shrubsole M, Long J, Pylkäs K, Jukkola-Vuorinen A, Kauppila S, Knight JA, Glendon G, Mulligan AM, Tollenaar RAEM, Seynaeve CM, Kriege M, Hooning MJ, van den Ouweland AMW, van Deurzen CHM, Lu W, Gao Y-T, Cai H, Balasubramanian SP, Cross SS, Reed MWR, Signorello L, Cai Q, Shah M, Miao H, Chan CW, Chia KS, Jakubowska A, Jaworska K, Durda K, Hsiung C-N, Wu P-E, Yu J-C, Ashworth A, Jones M, Tessier DC, González-Neira A, Pita G, Alonso RM, Vincent D, Bacot F, Ambrosone CB, Bandera EV, John EM, Chen GK, Hu JJ, Rodriguez-Gil JL, Bernstein L, Press MF, Ziegler RG, Millikan RM, Deming-Halverson SL, Nyante S, Ingles SA, Waisfisz Q, Tsimiklis H, Makalic E, Schmidt D, Bui M, Gibson L, Müller-Myhsok B, Schmutzler RK, Hein R, Dahmen N, Beckmann L, Aaltonen K, Czene K, Irwanto A, Liu J, Turnbull C, Rahman N, Meijers-Heijboer H, Uitterlinden AG, Rivadeneira F, Olswold C, Slager S, Pilarski R, Ademuyiwa F, Konstantopoulou I, Martin NG, Montgomery GW, Slamon DJ, Rauh C, Lux MP, Jud SM, Bruning T, Weaver J, Sharma P, Pathak H, Tapper W, Gerty S, Durcan L, Trichopoulos D, Tumino R, Peeters PH, Kaaks R, Campa D, Canzian F, Weiderpass E, Johansson M, Khaw K-T, Travis R, Clavel-Chapelon F, Kolonel LN, Chen C, Beck A, Hankinson SE, Berg CD, Hoover RN, Lissowska J, Figueroa JD, Chasman DI, Gaudet MM, Diver RW, Willett WC, Hunter DJ, Simard J, Benitez J, Dunning AM, Sherman ME, Chenevix-Trench G, Chanock SJ, Hall P, Pharoah PDP, Vachon C, Easton DF, Haiman CA, Kraft P. Genome-wide association studies identify four ER negative-specific breast cancer risk loci. Nat Genet 2013;45(4):392-8, 398e1-2.Abstract
Estrogen receptor (ER)-negative tumors represent 20-30% of all breast cancers, with a higher proportion occurring in younger women and women of African ancestry. The etiology and clinical behavior of ER-negative tumors are different from those of tumors expressing ER (ER positive), including differences in genetic predisposition. To identify susceptibility loci specific to ER-negative disease, we combined in a meta-analysis 3 genome-wide association studies of 4,193 ER-negative breast cancer cases and 35,194 controls with a series of 40 follow-up studies (6,514 cases and 41,455 controls), genotyped using a custom Illumina array, iCOGS, developed by the Collaborative Oncological Gene-environment Study (COGS). SNPs at four loci, 1q32.1 (MDM4, P = 2.1 × 10(-12) and LGR6, P = 1.4 × 10(-8)), 2p24.1 (P = 4.6 × 10(-8)) and 16q12.2 (FTO, P = 4.0 × 10(-8)), were associated with ER-negative but not ER-positive breast cancer (P > 0.05). These findings provide further evidence for distinct etiological pathways associated with invasive ER-positive and ER-negative breast cancers.
Increasing evidence points to an important role for the ribosome in the regulation of biological processes and as a target for deregulation in disease. Here, we describe a SILAC (stable isotope labeling by amino acids in cell culture)-based mass spectrometry approach to probing mammalian riboproteomes. Using a panel of cell lines, as well as genetic and pharmacological perturbations, we obtained a comparative characterization of the cellular riboproteome. This analysis identified a set of riboproteome components, consisting of a diverse array of proteins with a strong enrichment for RNA-binding proteins. Importantly, this global analysis uncovers a high incidence of genetic alterations to riboproteome components in cancer, with a distinct bias toward genetic amplification. We further validated association with polyribosomes for several riboproteome components and demonstrate that enrichment at the riboproteome can depend on cell type, genetics, or cellular stimulus. Our results have important implications for the understanding of how ribosomes function and provide a platform for uncovering regulators of translation.
Advancements in molecular biology have unveiled multiple breast cancer promoting pathways and potential therapeutic targets. Large randomized clinical trials remain the ultimate means of validating therapeutic efficacy, but they require large cohorts of patients and are lengthy and costly. A useful approach is to conduct a window of opportunity study in which patients are exposed to a drug pre-surgically during the interval between the core needle biopsy and the definitive surgery. These are non-therapeutic studies and the end point is not clinical or pathological response but rather evaluation of molecular changes in the tumor specimens that can predict response. However, since the end points of the non-therapeutic studies are biologic, it is critical to first define the biologic changes that occur in the absence of treatment. In this study, we compared the molecular profiles of breast cancer tumors at the time of the diagnostic biopsy versus the definitive surgery in the absence of any intervention using the Nanostring nCounter platform. We found that while the majority of the transcripts did not vary between the two biopsies, there was evidence of activation of immune related genes in response to the first biopsy and further investigations of the immune changes after a biopsy in early breast cancer seem warranted.
INTRODUCTION: Estrogen receptor (ER) and progesterone receptor (PR) testing are performed in the evaluation of breast cancer. While the clinical utility of ER as a predictive biomarker to identify patients likely to benefit from hormonal therapy is well-established, the added value of PR is less well-defined. The primary goals of our study were to assess the distribution, inter-assay reproducibility, and prognostic significance of breast cancer subtypes defined by patterns of ER and PR expression.
METHODS: We integrated gene expression microarray (GEM) and clinico-pathologic data from 20 published studies to determine the frequency (n = 4,111) and inter-assay reproducibility (n = 1,752) of ER/PR subtypes (ER+/PR+, ER+/PR-, ER-/PR-, ER-/PR+). To extend our findings, we utilized a cohort of patients from the Nurses' Health Study (NHS) with ER/PR data recorded in the medical record and assessed on tissue microarrays (n = 2,011). In both datasets, we assessed the association of ER and PR expression with survival.
RESULTS: In a genome-wide analysis, progesterone receptor was among the least variable genes in ER- breast cancer. The ER-/PR+ subtype was rare (approximately 1 to 4%) and showed no significant reproducibility (Kappa = 0.02 and 0.06, in the GEM and NHS datasets, respectively). The vast majority of patients classified as ER-/PR+ in the medical record (97% and 94%, in the GEM and NHS datasets) were re-classified by a second method. In the GEM dataset (n = 2,731), progesterone receptor mRNA expression was associated with prognosis in ER+ breast cancer (adjusted P <0.001), but not in ER- breast cancer (adjusted P = 0.21). PR protein expression did not contribute significant prognostic information to multivariate models considering ER and other standard clinico-pathologic features in the GEM or NHS datasets.
CONCLUSION: ER-/PR+ breast cancer is not a reproducible subtype. PR expression is not associated with prognosis in ER- breast cancer, and PR does not contribute significant independent prognostic information to multivariate models considering ER and other standard clinico-pathologic factors. Given that PR provides no clinically actionable information in ER+ breast cancer, these findings question the utility of routine PR testing in breast cancer.
French JD, Ghoussaini M, Edwards SL, Meyer KB, Michailidou K, Ahmed S, Khan S, Maranian MJ, O'Reilly M, Hillman KM, Betts JA, Carroll T, Bailey PJ, Dicks E, Beesley J, Tyrer J, Maia A-T, Beck A, Knoblauch NW, Chen C, Kraft P, Barnes D, González-Neira A, Alonso RM, Herrero D, Tessier DC, Vincent D, Bacot F, Luccarini C, Baynes C, Conroy D, Dennis J, Bolla MK, Wang Q, Hopper JL, Southey MC, Schmidt MK, Broeks A, Verhoef S, Cornelissen S, Muir K, Lophatananon A, Stewart-Brown S, Siriwanarangsan P, Fasching PA, Loehberg CR, Ekici AB, Beckmann MW, Peto J, dos Santos Silva I, Johnson N, Aitken Z, Sawyer EJ, Tomlinson I, Kerin MJ, Miller N, Marme F, Schneeweiss A, Sohn C, Burwinkel B, Guénel P, Truong T, Laurent-Puig P, Menegaux F, Bojesen SE, Nordestgaard BG, Nielsen SF, Flyger H, Milne RL, Zamora PM, Arias Perez JI, Benitez J, Anton-Culver H, Brenner H, Müller H, Arndt V, Stegmaier C, Meindl A, Lichtner P, Schmutzler RK, Engel C, Brauch H, Hamann U, Justenhoven C, Aaltonen K, Heikkilä P, Aittomäki K, Blomqvist C, Matsuo K, Ito H, Iwata H, Sueta A, Bogdanova NV, Antonenkova NN, Dörk T, Lindblom A, Margolin S, Mannermaa A, Kataja V, Kosma V-M, Hartikainen JM, Wu AH, Tseng C-chen, Van Den Berg D, Stram DO, Lambrechts D, Peeters S, Smeets A, Floris G, Chang-Claude J, Rudolph A, Nickels S, Flesch-Janys D, Radice P, Peterlongo P, Bonanni B, Sardella D, Couch FJ, Wang X, Pankratz VS, Lee A, Giles GG, Severi G, Baglietto L, Haiman CA, Henderson BE, Schumacher F, Le Marchand L, Simard J, Goldberg MS, Labrèche F, Dumont M, Teo SH, Yip CH, Ng C-H, Vithana EN, Kristensen V, Zheng W, Deming-Halverson S, Shrubsole M, Long J, Winqvist R, Pylkäs K, Jukkola-Vuorinen A, Grip M, Andrulis IL, Knight JA, Glendon G, Mulligan AM, Devilee P, Seynaeve C, García-Closas M, Figueroa J, Chanock SJ, Lissowska J, Czene K, Klevebring D, Schoof N, Hooning MJ, Martens JWM, Collée MJ, Tilanus-Linthorst M, Hall P, Li J, Liu J, Humphreys K, Shu X-O, Lu W, Gao Y-T, Cai H, Cox A, Balasubramanian SP, Blot W, Signorello LB, Cai Q, Pharoah PDP, Healey CS, Shah M, Pooley KA, Kang D, Yoo K-Y, Noh D-Y, Hartman M, Miao H, Sng J-H, Sim X, Jakubowska A, Lubinski J, Jaworska-Bieniek K, Durda K, Sangrajrang S, Gaborieau V, McKay J, Toland AE, Ambrosone CB, Yannoukakos D, Godwin AK, Shen C-Y, Hsiung C-N, Wu P-E, Chen S-T, Swerdlow A, Ashworth A, Orr N, Schoemaker MJ, Ponder BAJ, Nevanlinna H, Brown MA, Chenevix-Trench G, Easton DF, Dunning AM. Functional variants at the 11q13 risk locus for breast cancer regulate cyclin D1 expression through long-range enhancers. Am J Hum Genet 2013;92(4):489-503.Abstract
Analysis of 4,405 variants in 89,050 European subjects from 41 case-control studies identified three independent association signals for estrogen-receptor-positive tumors at 11q13. The strongest signal maps to a transcriptional enhancer element in which the G allele of the best candidate causative variant rs554219 increases risk of breast cancer, reduces both binding of ELK4 transcription factor and luciferase activity in reporter assays, and may be associated with low cyclin D1 protein levels in tumors. Another candidate variant, rs78540526, lies in the same enhancer element. Risk association signal 2, rs75915166, creates a GATA3 binding site within a silencer element. Chromatin conformation studies demonstrate that these enhancer and silencer elements interact with each other and with their likely target gene, CCND1.
Two large-scale pharmacogenomic studies were published recently in this journal. Genomic data are well correlated between studies; however, the measured drug response data are highly discordant. Although the source of inconsistencies remains uncertain, it has potential implications for using these outcome measures to assess gene-drug associations or select potential anticancer drugs on the basis of their reported results.
INTRODUCTION: Understanding whether mammographic density (MD) is associated with all breast tumor subtypes and whether the strength of association varies by age is important for utilizing MD in risk models.
METHODS: Data were pooled from six studies including 3414 women with breast cancer and 7199 without who underwent screening mammography. Percent MD was assessed from digitized film-screen mammograms using a computer-assisted threshold technique. We used polytomous logistic regression to calculate breast cancer odds according to tumor type, histopathological characteristics, and receptor (estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor (HER2)) status by age (<55, 55-64, and ≥ 65 years).
RESULTS: MD was positively associated with risk of invasive tumors across all ages, with a two-fold increased risk for high (>51%) versus average density (11-25%). Women ages <55 years with high MD had stronger increased risk of ductal carcinoma in situ (DCIS) compared to women ages 55-64 and ≥ 65 years (P(age-interaction) = 0.02). Among all ages, MD had a stronger association with large (>2.1 cm) versus small tumors and positive versus negative lymph node status (P's < 0.01). For women ages <55 years, there was a stronger association of MD with ER-negative breast cancer than ER-positive tumors compared to women ages 55-64 and ≥ 65 years (P(age-interaction) = 0.04). MD was positively associated with both HER2-negative and HER2-positive tumors within each age group.
CONCLUSION: MD is strongly associated with all breast cancer subtypes, but particularly tumors of large size and positive lymph nodes across all ages, and ER-negative status among women ages <55 years, suggesting high MD may play an important role in tumor aggressiveness, especially in younger women.
Although the incidence of human papillomavirus (HPV)-associated anal neoplasia is increasing, interobserver and intraobserver reproducibility in the grading of biopsy specimens from this area remains unacceptably low. Attempts to produce a more reproducible grading scheme have led to the use of biomarkers for the detection of high-risk HPV (HR-HPV). We evaluated the performance of standard morphology and biomarkers p16, ProEx C, and Ki-67 in a set of 75 lesions [17 nondysplastic lesions, 23 low-grade squamous intraepithelial lesions (LSIL)/condyloma, 20 high-grade squamous intraepithelial lesions (HSIL), 15 invasive squamous cell carcinomas] from the anal and perianal region in 65 patients and correlated these findings with HPV subtype on the basis of a type-specific multiplex real-time polymerase chain reaction assay designed to detect HR-HPV. A subset of cases with amplifiable HPV DNA was also sequenced. HSIL was typically flat (15/20), and only a minority (4/20) had koilocytes. In contrast, only 1 LSIL was flat (1/23), and the remainder were exophytic. The majority of LSIL had areas of koilocytic change (20/23). HR-HPV DNA was detected in the majority (89%) of invasive carcinomas and HSIL biopsies, 86% and 97% of which were accurately labeled by strong and diffuse block-positive p16 and ProEx C, respectively. LSIL cases, however, only infrequently harbored HR-HPV (13%); most harbored low-risk HPV (LR-HPV) types 6 and 11. Within the LSIL group, p16 outperformed ProEx C, resulting in fewer false-positive cases (5% vs. 75%). Ki-67 was also increased in HR-HPV-positive lesions, although biopsies with increased inflammation and reactive changes also showed higher Ki-67 indices. These data suggest that strong and diffuse block-positive nuclear and cytoplasmic labeling with p16 is a highly specific biomarker for the presence of HR-HPV in anal biopsies and that this finding correlates with high-grade lesions.
A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression data from a candidate prognostic gene set, and if the resulting clusters show statistically significant outcome stratification, to associate the gene set with prognosis, suggesting its biological and clinical importance. Recent work has questioned the validity of this approach by showing in several breast cancer data sets that "random" gene sets tend to cluster patients into prognostically variable subgroups. This work suggests that new rigorous statistical methods are needed to identify biologically informative prognostic gene sets. To address this problem, we developed Significance Analysis of Prognostic Signatures (SAPS) which integrates standard prognostic tests with a new prognostic significance test based on stratifying patients into prognostic subtypes with random gene sets. SAPS ensures that a significant gene set is not only able to stratify patients into prognostically variable groups, but is also enriched for genes showing strong univariate associations with patient prognosis, and performs significantly better than random gene sets. We use SAPS to perform a large meta-analysis (the largest completed to date) of prognostic pathways in breast and ovarian cancer and their molecular subtypes. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we show that prognostic signatures in ER negative breast cancer are more similar to prognostic signatures in ovarian cancer than to prognostic signatures in ER positive breast cancer. SAPS is a powerful new method for deriving robust prognostic biological signatures from clinically annotated genomic datasets.