The term 'field effect' (also known as field defect, field cancerization, or field carcinogenesis) has been used to describe a field of cellular and molecular alteration, which predisposes to the development of neoplasms within that territory. We explore an expanded, integrative concept, 'etiologic field effect', which asserts that various etiologic factors (the exposome including dietary, lifestyle, environmental, microbial, hormonal, and genetic factors) and their interactions (the interactome) contribute to a tissue microenvironmental milieu that constitutes a 'field of susceptibility' to neoplasia initiation, evolution, and progression. Importantly, etiological fields predate the acquisition of molecular aberrations commonly considered to indicate presence of filed effect. Inspired by molecular pathological epidemiology (MPE) research, which examines the influence of etiologic factors on cellular and molecular alterations during disease course, an etiologically focused approach to field effect can: (1) broaden the horizons of our inquiry into cancer susceptibility and progression at molecular, cellular, and environmental levels, during all stages of tumor evolution; (2) embrace host-environment-tumor interactions (including gene-environment interactions) occurring in the tumor microenvironment; and, (3) help explain intriguing observations, such as shared molecular features between bilateral primary breast carcinomas, and between synchronous colorectal cancers, where similar molecular changes are absent from intervening normal colon. MPE research has identified a number of endogenous and environmental exposures which can influence not only molecular signatures in the genome, epigenome, transcriptome, proteome, metabolome and interactome, but also host immunity and tumor behavior. We anticipate that future technological advances will allow the development of in vivo biosensors capable of detecting and quantifying 'etiologic field effect' as abnormal network pathology patterns of cellular and microenvironmental responses to endogenous and exogenous exposures. Through an 'etiologic field effect' paradigm, and holistic systems pathology (systems biology) approaches to cancer biology, we can improve personalized prevention and treatment strategies for precision medicine.Modern Pathology advance online publication, 13 June 2014; doi:10.1038/modpathol.2014.81.
Lindström S, Thompson DJ, Paterson AD, Li J, Gierach GL, Scott C, Stone J, Douglas JA, dos-Santos-Silva I, Fernandez-Navarro P, Verghase J, Smith P, Brown J, Luben R, Wareham NJ, Loos RJF, Heit JA, Pankratz SV, Norman A, Goode EL, Cunningham JM, deAndrade M, Vierkant RA, Czene K, Fasching PA, Baglietto L, Southey MC, Giles GG, Shah KP, Chan H-P, Helvie MA, Beck AH, Knoblauch NW, Hazra A, Hunter DJ, Kraft P, Pollan M, Figueroa JD, Couch FJ, Hopper JL, Hall P, Easton DF, Boyd NF, Vachon CM, Tamimi RM. Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk. Nat Commun 2014;5:5303.Abstract
Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5 × 10(-8)) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B and SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23 and TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease-susceptibility loci.
Gene set enrichment analysis (GSEA) associates gene sets and phenotypes, its use is predicated on the choice of a pre-defined collection of sets. The defacto standard implementation of GSEA provides seven collections yet there are no guidelines for the choice of collections and the impact of such choice, if any, is unknown. Here we compare each of the standard gene set collections in the context of a large dataset of drug response in human cancer cell lines. We define and test a new collection based on gene co-expression in cancer cell lines to compare the performance of the standard collections to an externally derived cell line based collection. The results show that GSEA findings vary significantly depending on the collection chosen for analysis. Henceforth, collections should be carefully selected and reported in studies that leverage GSEA.
Stromal cells within the tumor microenvironment are essential for tumor progression and metastasis. Surprisingly little is known about the factors that drive the transcriptional reprogramming of stromal cells within tumors. We report that the transcriptional regulator heat shock factor 1 (HSF1) is frequently activated in cancer-associated fibroblasts (CAFs), where it is a potent enabler of malignancy. HSF1 drives a transcriptional program in CAFs that complements, yet is completely different from, the program it drives in adjacent cancer cells. This CAF program is uniquely structured to support malignancy in a non-cell-autonomous way. Two central stromal signaling molecules-TGF-β and SDF1-play a critical role. In early-stage breast and lung cancer, high stromal HSF1 activation is strongly associated with poor patient outcome. Thus, tumors co-opt the ancient survival functions of HSF1 to orchestrate malignancy in both cell-autonomous and non-cell-autonomous ways, with far-reaching therapeutic implications.
Triple-negative breast cancer (TNBC) is currently the only major breast tumor subtype without effective targeted therapy and, as a consequence, in general has a poor outcome. To identify new therapeutic targets in TNBC, we performed a short hairpin RNA (shRNA) screen for protein kinases commonly amplified and overexpressed in breast cancer. Using this approach, we identified AKT3 as a gene preferentially required for the growth of TNBCs. Downregulation of Akt3 significantly inhibits the growth of TNBC lines in three-dimensional (3D) spheroid cultures and in mouse xenograft models, whereas loss of Akt1 or Akt2 have more modest effects. Akt3 silencing markedly upregulates the p27 cell-cycle inhibitor and this is critical for the ability of Akt3 to inhibit spheroid growth. In contrast with Akt1, Akt3 silencing results in only a minor enhancement of migration and does not promote invasion. Depletion of Akt3 in TNBC sensitizes cells to the pan-Akt inhibitor GSK690693. These results imply that Akt3 has a specific function in TNBCs; thus, its therapeutic targeting may provide a new treatment option for this tumor subtype.
The categorization of intraductal proliferative lesions of the breast based on routine light microscopic examination of histopathologic sections is in many cases challenging, even for experienced pathologists. The development of computational tools to aid pathologists in the characterization of these lesions would have great diagnostic and clinical value. As a first step to address this issue, we evaluated the ability of computational image analysis to accurately classify DCIS and UDH and to stratify nuclear grade within DCIS. Using 116 breast biopsies diagnosed as DCIS or UDH from the Massachusetts General Hospital (MGH), we developed a computational method to extract 392 features corresponding to the mean and standard deviation in nuclear size and shape, intensity, and texture across 8 color channels. We used L1-regularized logistic regression to build classification models to discriminate DCIS from UDH. The top-performing model contained 22 active features and achieved an AUC of 0.95 in cross-validation on the MGH data-set. We applied this model to an external validation set of 51 breast biopsies diagnosed as DCIS or UDH from the Beth Israel Deaconess Medical Center, and the model achieved an AUC of 0.86. The top-performing model contained active features from all color-spaces and from the three classes of features (morphology, intensity, and texture), suggesting the value of each for prediction. We built models to stratify grade within DCIS and obtained strong performance for stratifying low nuclear grade vs. high nuclear grade DCIS (AUC = 0.98 in cross-validation) with only moderate performance for discriminating low nuclear grade vs. intermediate nuclear grade and intermediate nuclear grade vs. high nuclear grade DCIS (AUC = 0.83 and 0.69, respectively). These data show that computational pathology models can robustly discriminate benign from malignant intraductal proliferative lesions of the breast and may aid pathologists in the diagnosis and classification of these lesions.
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.
Antibodies against CD47, which block tumor cell CD47 interactions with macrophage signal regulatory protein-α, have been shown to decrease tumor size in hematological and epithelial tumor models by interfering with the protection from phagocytosis by macrophages that intact CD47 bestows upon tumor cells. Leiomyosarcoma (LMS) is a tumor of smooth muscle that can express varying levels of colony-stimulating factor-1 (CSF1), the expression of which correlates with the numbers of tumor-associated macrophages (TAMs) that are found in these tumors. We have previously shown that the presence of TAMs in LMS is associated with poor clinical outcome and the overall effect of TAMs in LMS therefore appears to be protumorigenic. However, the use of inhibitory antibodies against CD47 offers an opportunity to turn TAMs against LMS cells by allowing the phagocytic behavior of resident macrophages to predominate. Here we show that interference with CD47 increases phagocytosis of two human LMS cell lines, LMS04 and LMS05, in vitro. In addition, treatment of mice bearing subcutaneous LMS04 and LMS05 tumors with a novel, humanized anti-CD47 antibody resulted in significant reductions in tumor size. Mice bearing LMS04 tumors develop large numbers of lymph node and lung metastases. In a unique model for neoadjuvant treatment, mice were treated with anti-CD47 antibody starting 1 wk before resection of established primary tumors and subsequently showed a striking decrease in the size and number of metastases. These data suggest that treatment with anti-CD47 antibodies not only reduces primary tumor size but can also be used to inhibit the development of, or to eliminate, metastatic disease.
Leiomyosarcoma (LMS) is a malignant, soft-tissue tumor for which few effective therapies exist. Previously, we showed that there are three molecular subtypes of LMS. Here, we analyzed genes differentially expressed in each of the three LMS subtypes as compared to benign leiomyomas and then used the Connectivity Map (cmap) to calculate enrichment scores for the 1309 cmap drugs in order to identify candidate molecules with the potential to induce a benign, leiomyoma-like phenotype in LMS cells. 11 drugs were selected and tested for their ability to inhibit the growth of three human LMS cell lines. We identified two drugs with in vitro efficacy against LMS, one of which had a strongly negative enrichment score (Cantharidin) and the other of which had a strongly positive enrichment score (MG-132). Given MG-132's strong inhibitory effect on LMS cell viability, we hypothesized that LMS cells may be sensitive to treatment with other proteasome inhibitors and demonstrated that bortezomib, a clinically-approved proteasome inhibitor not included in the original cmap screen, potently inhibited the viability of the LMS cell lines. These findings suggest that systematically linking LMS subtype-specific expression signatures with drug-associated expression profiles represents a promising approach for the identification of new drugs for LMS.
We analysed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at >10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the luminal A subtype. We identified two novel protein-expression-defined subgroups, possibly produced by stromal/microenvironmental elements, and integrated analyses identified specific signalling pathways dominant in each molecular subtype including a HER2/phosphorylated HER2/EGFR/phosphorylated EGFR signature within the HER2-enriched expression subtype. Comparison of basal-like breast tumours with high-grade serous ovarian tumours showed many molecular commonalities, indicating a related aetiology and similar therapeutic opportunities. The biological finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biological subtypes of breast cancer.
ProExC expression has been shown to perform similarly to p16 as an aid in the diagnosis of cervical dysplasia but has not been well characterized in head and neck squamous cell carcinomas (SCC). The purpose of this study is to determine whether ProExC performs similarly to p16 as a prognostic marker in oropharyngeal SCC and to evaluate the threshold of ProExC and p16 staining that correlates with survival. ProExC, p16, and human papillomavirus DNA in situ hybridization were performed on tissue microarray (TMA) cores and whole sections from 62 patients with oropharyngeal SCC. Sensitivity and specificity for high-risk HPV and correlation with overall survival (OS), cancer-specific survival (CSS), and time to distant metastasis (TDM) were calculated for ProExC and p16 at different thresholds. ProExC did not prove to be a robust marker. It showed strong correlation with OS at a 66% threshold on TMA cores, but correlation with OS was lost on whole sections. It also exhibited low sensitivity (53.7%) on TMA cores and low specificity on whole sections (65%). ProExC at a 33% threshold exhibited unacceptably low specificity and did not correlate with OS, CSS, or TDM. Sensitivity and specificity of p16 varied predictably with threshold: higher sensitivity and lower specificity with lower thresholds and vice versa for higher thresholds. p16 at a 50% threshold offers a balance between sensitivity and specificity, and correlates with OS, CSS, and TDM on whole sections; correlation with TDM is lost on TMA cores. These findings indicate that ProExC does not perform well enough to be used as a prognostic marker in oropharyngeal SCC. p16 should be used and scored as positive when at least half the tumor is strongly stained.
In recent decades, epidemiology, public health, and medical sciences have been increasingly compartmentalized into narrower disciplines. The authors recognize the value of integration of divergent scientific fields in order to create new methods, concepts, paradigms, and knowledge. Herein they describe the recent emergence of molecular pathological epidemiology (MPE), which represents an integration of population and molecular biologic science to gain insights into the etiologies, pathogenesis, evolution, and outcomes of complex multifactorial diseases. Most human diseases, including common cancers (such as breast, lung, prostate, and colorectal cancers, leukemia, and lymphoma) and other chronic diseases (such as diabetes mellitus, cardiovascular diseases, hypertension, autoimmune diseases, psychiatric diseases, and some infectious diseases), are caused by alterations in the genome, epigenome, transcriptome, proteome, metabolome, microbiome, and interactome of all of the above components. In this era of personalized medicine and personalized prevention, we need integrated science (such as MPE) which can decipher diseases at the molecular, genetic, cellular, and population levels simultaneously. The authors believe that convergence and integration of multiple disciplines should be commonplace in research and education. We need to be open-minded and flexible in designing integrated education curricula and training programs for future students, clinicians, practitioners, and investigators.