EGFR associated expression profiles vary with breast tumor subtype

<p>Abstract</p> <p>Background</p> <p>The epidermal growth factor receptor (EGFR/HER1) and its downstream signaling events are important for regulating cell growth and behavior in many epithelial tumors types. In breast cancer, the role of EGFR is complex and appears to vary relative to important clinical features including estrogen receptor (ER) status. To investigate EGFR-signaling using a genomics approach, several breast basal-like and luminal epithelial cell lines were examined for sensitivity to EGFR inhibitors. An EGFR-associated gene expression signature was identified in the basal-like SUM102 cell line and was used to classify a diverse set of sporadic breast tumors.</p> <p>Results</p> <p><it>In vitro</it>, breast basal-like cell lines were more sensitive to EGFR inhibitors compared to luminal cell lines. The basal-like tumor derived lines were also the most sensitive to carboplatin, which acted synergistically with cetuximab. An EGFR-associated signature was developed <it>in vitro</it>, evaluated on 241 primary breast tumors; three distinct clusters of genes were evident <it>in vivo</it>, two of which were predictive of poor patient outcomes. These EGFR-associated poor prognostic signatures were highly expressed in almost all basal-like tumors and many of the HER2+/ER- and Luminal B tumors.</p> <p>Conclusion</p> <p>These results suggest that breast basal-like cell lines are sensitive to EGFR inhibitors and carboplatin, and this combination may also be synergistic. <it>In vivo</it>, the EGFR-signatures were of prognostic value, were associated with tumor subtype, and were uniquely associated with the high expression of distinct EGFR-RAS-MEK pathway genes.</p

The triple negative paradox: Primary tumor chemosensitivity of breast cancer subtypes

"Purpose: Gene expression analysis identifies several breast cancer subtypes. We examined the relationship of neoadjuvant chemotherapy response to outcome among these breast cancer subtypes. Experimental Design: We used immunohistochemical profiles [human epidermal growth factor receptor 2–positive (HER2+)/hormone receptor–negative for HER2+/estrogen receptor–negative (ER−), hormone receptor and HER2− for basal-like, hormone receptor–positive for luminal] to subtype a prospectively maintained data set of patients with breast cancer treated with neoadjuvant anthracycline-based (doxorubicin plus cyclophosphamide, AC) chemotherapy. We analyzed each subtype for clinical and pathologic response to neoadjuvant chemotherapy and examined the relationship of response to distant disease–free survival and overall survival. Results: Of the 107 patients tested, 34 (32%) were basal-like, 11 (10%) were HER2+/ER−, and 62 (58%) were luminal. After neoadjuvant AC, 75% received subsequent chemotherapy and all received endocrine therapy if hormone receptor–positive. The chemotherapy regimen and pretreatment stage did not differ by subtype. Clinical response to AC was higher among the HER2+/ER− (70%) and basal-like (85%) than the luminal subtypes (47%; P less than 0.0001). Pathologic complete response occurred in 36% of HER2+/ER−, 27% of basal-like, and 7% of luminal subtypes (P = 0.01). Despite initial chemosensitivity, patients with the basal-like and HER2+/ER− subtypes had worse distant disease–free survival (P = 0.04) and overall survival (P = 0.02) than those with the luminal subtypes. Regardless of subtype, only 2 of 17 patients with pathologic complete response relapsed. The worse outcome among basal-like and HER+/ER− subtypes was due to higher relapse among those with residual disease (P = 0.003). Conclusions: Basal-like and HER2+/ER− subtypes are more sensitive to anthracycline-based neoadjuvant chemotherapy than luminal breast cancers. Patients that had pathologic complete response to chemotherapy had a good prognosis regardless of subtype. The poorer prognosis of basal-like and HER2+/ER− breast cancers could be explained by a higher likelihood of relapse in those patients in whom pathologic complete response was not achieved.

The molecular portraits of breast tumors are conserved acress microarray platforms

Background Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. Results A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. Conclusion This study validates the breast tumor intrinsic subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile

The molecular portraits of breast tumors are conserved across microarray platforms

BACKGROUND: Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. RESULTS: A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. CONCLUSION: This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile

Polymorphisms in CYP1B1, GSTM1, GSTT1 and GSTP1, and susceptibility to breast cancer

Polymorphisms in the cytochrome P450 1B1 (CYP1B1) and glutathione S-transferase (GST) drug metabolic enzymes, which are responsible for metabolic activation/detoxification of estrogen and environmental carcinogens, were analyzed for their association with breast cancer risk in 541 cases and 635 controls from a North Carolina population. Each polymorphism, altering the catalytic function of their respective enzymes, was analyzed in Caucasian and African-American women. As reported in previous studies, individual polymorphisms did not significantly impact breast cancer risk in either Caucasian or African-American women. However, African-American women exhibited a trend towards a protective effect when they had at least one CYP1B1 119S allele (OR=0.53; 95% CI=0.20–1.40) and increased risk for those women harboring at least one CYP1B1 432V allele (OR=5.52; 95% CI=0.50–61.37). Stratified analyses demonstrated significant interactions in younger (age ≤60) Caucasian women with the CYP1B1 119SS genotype (OR=3.09; 95% CI=1.22–7.84) and younger African-American women with the GSTT1 null genotype (OR=4.07; 95% CI=1.12–14.80). A notable trend was also found in Caucasian women with a history of smoking and at least one valine allele at GSTP1 114 (OR=2.12; 95% CI=1.02–4.41). In Caucasian women, the combined GSTP1 105IV/VV and CYP1B1 119AA genotypes resulted in a near 2-fold increase in risk (OR=1.96; 95% CI=1.04–3.72) and the three way combination of GSTP1 105IV/VV, CYP1B1 119AS/SS and GSTT1 null genotypes resulted in an almost 4-fold increase in risk (OR=3.97; 95% CI=1.27–12.40). These results suggest the importance of estrogen/carcinogen metabolic enzymes in the etiology of breast cancer, especially in women before the age of 60, as well as preventative measures such as smoking cessation

Polymorphisms in drug metabolism genes, smoking, andp53 mutations in breast cancer

Polymorphisms in phase I and phase II enzymes may enhance the occurrence of mutations at critical tumor suppressor genes, such as p53, and increase breast cancer risk by either increasing the activation or detoxification of carcinogens and/or endogenous estrogens. We analyzed polymorphisms in CYP1B1, GSTM1, GSTT1, and GSTP1 and p53 mutations in 323 breast tumor samples. Approximately 11% of patients exhibited mutations in p53. Women with mutations had a significantly younger age of diagnosis (P = 0.01) and a greater incidence of tumors classified as stage II or higher (P = 0.002). More women with mutations had a history of smoking (55%) compared to women without mutations (39%). Although none of the genotypes alone were associated with p53 mutations, positive smoking history was associated with p53 mutations in women with the GSTM1 null allele [OR = 3.54; 95% CI = 0.97–12.90 P = 0.06] compared to women with the wild-type genotype and smoking history [OR = 0.62, 95% CI = 0.19–2.07], although this association did not reach statistical significance. To test for gene–gene interactions, our exploratory analysis in the Caucasian cases suggested that individuals with the combined GSTP1 105 VV, CYP1B1 432 LV/VV, and GSTM1 positive genotype were more likely to harbor mutations in p53 [OR = 4.94; 95% CI = 1.11–22.06]. Our results suggest that gene–smoking and gene–gene interactions may impact the prevalence of p53 mutations in breast tumors. Elucidating the etiology of breast cancer as a consequence of common genetic polymorphisms and the genotoxic effects of smoking will enable us to improve the design of prevention strategies, such as lifestyle modifications, in genetically susceptible subpopulations

Genetic insights into biological mechanisms governing human ovarian ageing.

Reproductive longevity is essential for fertility and influences healthy ageing in women &lt;sup&gt;1,2&lt;/sup&gt; , but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations &lt;sup&gt;3&lt;/sup&gt; . The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease

Large-scale genomic analyses link reproductive aging to hypothalamic signaling, breast cancer susceptibility, and BRCA1-mediated DNA repair [editorial comment]

ABSTRACT: Menopause timing has a major impact on infertility and risk of disease. Younger age at natural (nonsurgical) menopause (ANM) is associated with a higher risk of osteoporosis, cardiovascular disease, and type 2 diabetes and a lower risk of breast cancer. Late menopause is associated with a higher risk of breast cancer. It is well known that the age at which women go through menopause is partly determined by genes, but the underlying mechanisms are poorly understood. Genome-wide association studies have identified 18 common genetic variants associated with ANM. These variants explain less than 5% of the variation in ANM compared with the 21% explained by all common variants on genome-wide association study arrays. This genome-wide association study was the collaborative effort of researchers from 177 institutions worldwide. The study was designed to investigate genetic variants associated with timing of menopause among a population of approximately 70,000 women of European ancestry. A dual strategy was used to identify both common and, for the first time, low-frequency coding variants associated with ANM. The causal relationship between ANM and breast cancer was investigated using a Mendelian randomization approach. Combined analysis identified 1208 single-nucleotide polymorphisms (SNPs) of a total of approximately 2.6 million that reached the genome-wide significance threshold for association with ANM. Forty-four regions with common variants were identified; among these 44 loci were 2 rare low-frequency missense alleles of large effect. A majority of ANM SNPs were enriched in DNA damage response (DDR) genes, including the first common coding variant in BRCA1 associated with any complex trait. Mendelian randomization analyses supported a causal relationship between delayed ANM and breast cancer risk; there was approximately 6% increase in risk per year; P = 3 × 10-14); increased risk with delayed menopause appeared to be mediated primarily by prolonged sex hormone exposure in a woman’s lifetime, not DDR mechanisms. This is the first study to confirm the link between early and late menopause and breast cancer risk using genetic information. Age at natural menopause genetic variants influence breast cancer risk primarily through variation in menopause timing. Although carrying higher numbers of ANM-increasing variants and enrichment in DDR genes are associated with a modest increase in breast cancer risk, the major mechanism for increased risk appears to be prolonged estrogen and/or progesterone exposure due to delayed menopause

Genetic insights into biological mechanisms governing human ovarian ageing.

Reproductive longevity is essential for fertility and influences healthy ageing in women1,2, but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations3. The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease.Cambridge: Claudia Langenberg and Nicholas J Wareham are funded by the Medical Research Council (MC_UU_12015/1 and MC_UU_00006/1). Nicholas J Wareham is a NIHR Senior Investigator. Ken Ong, John Perry, Stasa Stankovic and Felix Day are supported by the Medical Research Council (Unit programmes: MC_UU_12015/2 and MC_UU_00006/2)

Large-scale genomic analyses link reproductive aging to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair

Copyright © 2015, Rights Managed by Nature Publishing GroupThis is the author's version of an article subsequently published in definitive form at: Nature Genetics (2015) doi:10.1038/ng.3412See supplementary documents for full affiliations and acknowledgmentsMenopause timing has a substantial impact on infertility and risk of disease, including breast cancer, but the underlying mechanisms are poorly understood. We report a dual strategy in ∼70,000 women to identify common and low-frequency protein-coding variation associated with age at natural menopause (ANM). We identified 44 regions with common variants, including two regions harboring additional rare missense alleles of large effect. We found enrichment of signals in or near genes involved in delayed puberty, highlighting the first molecular links between the onset and end of reproductive lifespan. Pathway analyses identified major association with DNA damage response (DDR) genes, including the first common coding variant in BRCA1 associated with any complex trait. Mendelian randomization analyses supported a causal effect of later ANM on breast cancer risk (∼6% increase in risk per year; P = 3 × 10(-14)), likely mediated by prolonged sex hormone exposure rather than DDR mechanisms