Genomic Grade Research Articles

Abstract 3203: Oncogenic pathways analysis and survival outcome in advanced serous ovarian cancer

Abstract INTRODUCTION AND OBJECTIVES: Ovarian cancer is the leading cause of mortality among gynecological cancers. Identification of relevant pathways may contribute in individualization of treatment strategies. The objective of this study was to relate cellular pathway activation to survival outcome. Since survival is studied, a selection was made of patients with advanced stages of serous papillary carcinoma with uniform treatment to detect differences without histology-, stage- and treatment- induced biases. MATERIAL AND METHODS: Two publicly available microarray datasets of advanced stage (III and IV) ovarian cancer samples (N=165, N=125) were analyzed for oncogenic pathways (AKT, BetaCatenin, E2F1, EGFR, ER, HER2, MYC, INFa, IFNg, p53, p63, PI3K, PR, RAS, SRC, STAT3, TNFa, TGFb, VEGFA) (Gatza et al. PNAS 2010, Hu et al. BMC Med 2009). These pathway gene signatures were correlated with survival outcome and 3 prognostic gene signatures (Wound Response Signature WHR, Genomic Grade Index GGI and the Invasiveness Gene Signature IGS). RESULTS AND DISCUSSION: Although the WHR, GGI, and IGS have shown prognostic value in breast cancer and other malignancies, these three prognostic signatures showed opposite survival outcome in this selected population of advanced serous papillary carcinomas. Patients with a higher genomic grade had a better survival outcome than lower genomic grade tumors. Since proliferation genes seems to be the driving force within these prognostic signatures, this observation is concordant with earlier findings that tumors with high proliferation index are more chemosensitive. We furthermore show that an activated BetaCatenin, RAS and p63 pathway was associated with favorable survival outcome in both datasets (p<0.05) displaying the predictive value of combination carboplatinum-taxane chemotherapy. Furthermore these oncogenic pathways were significantly correlated with IGS, WHR and GGI (p<0.001), suggesting that these pathways contribute to chemosensitivity. These data are in line with findings of recent clinical findings that inhibition of farnesyltransferase (and downstream RAS pathway) in combination with carbotaxol in first line was not beneficial or even significantly inferior to carbotaxol alone. CONCLUSIONS: Microarray analysis of two independent datasets shows that activation of the BetaCatenin, RAS and p63 oncogenic pathways were consistently of predictive value for carboplatinum-taxane chemotherapy in advanced stage serous papillary ovarian cancer. Since these pathways may contribute to proliferation and consequent chemosensitivity, these findings may be of clinical importance for designing treatment strategies. Validation studies are ongoing. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3203. doi:10.1158/1538-7445.AM2011-3203

Abstract 4834: Microarray gene expression analysis on formalin-fixed, paraffin embedded material is feasible and the resulting profiles are comparable to profiles from fresh frozen matched material

Abstract Formalin Fixed Paraffin Embedded (FFPE) samples represent a valuable resource for cancer research and clinical setting. However, the discovery and development of new cancer biomarkers often requires fresh frozen (FF) samples. In this study, we profiled, in duplicate, 20 FFPE breast cancer tissues and 20 matched FF tissues and evaluated the concordance of the gene expression results from FFPE and matched FF material using the whole Genome (WG) DASL assay. We show that clinically relevant biomarkers developed on FF samples can be applied to FFPE samples. We show that after proper normalization, all FFPE and FF pairs exhibited a high level of similarity (Pearson correlation > 0.7), significantly larger than the similarity between non-paired samples. Interestingly, the probes showing the highest correlation had a higher G/C content percentage and were enriched for cell cycle genes. Predictions of gene expression signatures developed on frozen material (Intrinsic subtype, Genomic Grade Index, 70 gene prognosis signature) show a high level of concordance between FFPE and FF matched pairs. Predictions based on a 60 gene DASL list (best match with the 70 gene signature) show very high concordance with MammaPrint® results. We demonstrate that data generated from FFPE material with the DASL assay, if properly processed, are comparable to data extracted from the FF counterpart. This opens up the possibility of using both FFPE and FF material in gene expressions analyses, leading to a vast increase in the potential resources available for cancer research. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4834. doi:10.1158/1538-7445.AM2011-4834

Gene Expression Profiles from Formalin Fixed Paraffin Embedded Breast Cancer Tissue Are Largely Comparable to Fresh Frozen Matched Tissue

Background and MethodsFormalin Fixed Paraffin Embedded (FFPE) samples represent a valuable resource for cancer research. However, the discovery and development of new cancer biomarkers often requires fresh frozen (FF) samples. Recently, the Whole Genome (WG) DASL (cDNA-mediated Annealing, Selection, extension and Ligation) assay was specifically developed to profile FFPE tissue. However, a thorough comparison of data generated from FFPE RNA and Fresh Frozen (FF) RNA using this platform is lacking. To this end we profiled, in duplicate, 20 FFPE tissues and 20 matched FF tissues and evaluated the concordance of the DASL results from FFPE and matched FF material.Methodology and Principal FindingsWe show that after proper normalization, all FFPE and FF pairs exhibit a high level of similarity (Pearson correlation >0.7), significantly larger than the similarity between non-paired samples. Interestingly, the probes showing the highest correlation had a higher percentage G/C content and were enriched for cell cycle genes. Predictions of gene expression signatures developed on frozen material (Intrinsic subtype, Genomic Grade Index, 70 gene signature) showed a high level of concordance between FFPE and FF matched pairs. Interestingly, predictions based on a 60 gene DASL list (best match with the 70 gene signature) showed very high concordance with the MammaPrint® results.Conclusions and SignificanceWe demonstrate that data generated from FFPE material with the DASL assay, if properly processed, are comparable to data extracted from the FF counterpart. Specifically, gene expression profiles for a known set of prognostic genes for a specific disease are highly comparable between two conditions. This opens up the possibility of using both FFPE and FF material in gene expressions analyses, leading to a vast increase in the potential resources available for cancer research.

Abstract P3-10-09: Genomic Grade Index (GGI): Tumor Grading Performance and Prognostic Value Compared to Ki67 and Mitotic Index in Early Invasive Breast Cancer — A Reference Centre Experience

Abstract Background: Genomic grading represents a molecular approach to tumor grading. The GGI is a 97-gene assay which improves tumor grading by resolving histological grade (HG) 2 into high or low genomic grade. GGI carries added clinical value particularly in ER+/HER2-/N0 tumors where grade is a key decision factor for adjuvant treatment. The GGI algorithm has been validated for use in routine practice, including the definition of an appropriate cut-off and statistical confidence interval corresponding to a 3:1 odds-ratio. This study aimed at retrospectively testing the GGI classification performance and prognostic value in a series of 169 early breast cancers from a comprehensive cancer centre. Methods: Female breast cancers from the Institut Curie Database were selected on the following criteria: small size (pT1-2), node negative (pN0), 10-year follow-up data, availability of extracted RNA and frozen tissue. Genomic profiles were obtained using Affymetrix HGU133 Plus 2.0 gene chips. GGI was computed using Ipsogen MapQuant Dx®. Elston-Ellis (EE) grade, mitotic index (MI, mitosis/mm2), and Ki-67 IHC (continuous -% of positive cells-, or binary-median=20%-) were assessed retrospectively on the most representative tumor block in routine conditions by several pathologists. The GGI, pathological features, and proliferation markers were correlated. Survival analyses were performed using Kaplan-Meier, with comparisons using the logrank test and hazard ratios estimated using Cox proportional hazard model. Results: Profiles were obtained in 163 cases (96%).Tumor size ranged from 7 to 45 mm. 86% of tumors were ER+, and 94% were HER2-, 32.5% were EE-1, 43% EE-2 (76% mitotic score 1 and 24% mitotic score 2-3), 24.5% EE-3. Chemotherapy was given to 11 patients. Median follow-up was 12.9 yrs [0.5-15.2 yrs]. GGI classified 79% of all cases with a 95 % concordance with HG1 and HG3, and reclassified 69% of HG2 tumors (50% GGI-1 and 19% GGI-3). Concordance was 81% between Ki67 and GGI. In the ER+/HER2-/no chemotherapy subgroup (n=126), using GGI and Ki67 as continuous variables, GGI was the only significant factor in multivariate Cox regression including EE, age and size (GGI: HR=2.36 and p=0.005; Ki67: HR=1.02 and p=0.11). This was also true when GGI and Ki67 were analyzed as binary indexes (GGI: HR=5.23, p=0.02; Ki67: HR=2.44 and p=0.13). Relative risk (RR) were 4, 2.55 and 2.85 for GGI, Ki67 and MI resp. (p=0.007, p=0.052 and p=0.053). In the ER+/HG2 cohort (n=63, 10 metastasis events), RR were 3.27 and 1.78 for GGI and Ki67, none reaching statistical significance, certainly due to insufficient statistical power issue. Conclusion: In a cohort of pT1-2, pN0 early invasive breast cancers, GGI has a 95% concordance with EE grading (1 and 3) and reclassifies 69% of EE-2 tumors. In ER+/pN0/HER2- breast cancer, GGI, either continuous or binary, has a higher prognostic value than Ki67 or MI. In a reference comprehensive cancer centre setting, GGI should add clinical information in this particular breast cancer subgroup where adjuvant treatment decision remains a routine challenge. GGI and EE tumor grading: contingency table Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P3-10-09.

Abstract P3-10-08: Combining Genomic Grade and NPI Refines Risk Group Classification

Abstract Background: The Nottingham Prognostic Index (NPI) is a clinically validated prognostic model which predicts survival for patients with early primary breast cancer. The NPI combines information on tumor size, lymph node stage and tumor grade and stratifies patients into risk groups useful for treatment decisions. Classically, NPI defines 5 risk categories: Excellent, Good, Moderate I, Moderate II and Poor Prognostic Group (EPG, GPG, MPGI, MPGII and PPG). However, the value of NPI to identify EPG patients has recently being challenged. The Genomic Grade Index (GGI) is a 97-gene test which has been shown to improve tumor grading by resolving the intermediate Histological Grade (HG) 2 tumors into high or low Genomic Grades. In this study, we investigated the potential of GGI to refine the NPI classification, and especially to discriminate patients with the lowest risk. Material and methods:Breast cancer microarray profiles from node negative, untreated patients with the required clinical and pathological information were retrieved from 3 public databases. GGI was calculated using the Ipsogen MapQuant Dx® protocol which defines two classes, GG1 and GG3 (results falling in the CI are reported as Undetermined, “UD”). Standard NPI was calculated based on HG according to the usual formula: [0.2 x tumor size (cm) + HG(1-3) + LN stage (I-III)]. NPI scores were also calculated using GGI (“G-NPI”): the HG was replaced by the GG in the equation, and UD patients were given a value of 2 for tumor grade. Patients were classified into prognostic groups (cut-offs 2.4, 3.4 and 5.4) for both NPI (EPG, GPG, MPG and PPG) and G-NPI (G-EPG, G-GPG, G-MPG and G-PPG). NPI and G-NPI risk classifications were compared and prognostic performances were analyzed using Kaplan Meier survival curves (10-yr MFS). Results:Unique microarray expression profiles with adequate MapQuant Dx® quality criteria and relevant clinical information were identified for 472 patients. HG was distributed as follows: 18 % HG1, 52 % HG2 and 30% HG3 with size ranging from 0.1 to 6 cm (mean = 2.1 cm). HG2 cases (n=246) were reclassified into 47% GG1 and 28% GG3, with 25% remaining UD. There were no patients classified in the PPG, using the standard NPI or the G-NPI. Comparison of risk group classification showed that G-NPI globally shifted patients from low to better prognostic groups: 28% of patients were classified in the G-EPG as opposed to only 12% in the EPG [Table 1]. Within the HG2 subgroup, none of the patients were classified in the EPG by standard NPI, while 75 patients were classified in this group by G-NPI. In the entire cohort, EPG and G-EPG 10-yrs MFS were comparable (82 and 88% resp.). In the HG2 subgroup, 10-yrs MFS was 92% for the G-EPG, 87 % for the G-GPG and 57% for the G-MPG. Conclusion: Using GGI to calculate NPI scores provides additional information compared to standard NPI. This is particularly true in the HG2 group where G-NPI is able to identify patients matching the EPG definition with a 92% 10-yr MFS. Combining genomic information with NPI may facilitate adjuvant therapy decision making. This approach deserves further validation. Prognostic Group Classification according to classic NPI and G-NPI Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P3-10-08.

Abstract P5-13-11: Medico-Economic Assessment of the Genomic Grade Index on Adjuvant Treatment Strategy in Elston-Ellis Grade 2, Estrogen Receptor Positive, HER2 Negative, Node Negative, Small Size Breast Carcinomas

Abstract Background: Elston-Ellis grading (EE) is one of the key criteria for adjuvant therapy decision in ER+/HER2-/pN0/pT1 -2 tumors. The genomic grade index (GGI) is a 97-gene micro-array assay able to identify tumors of high or low genomic grade. GGI has been developed in order to improve the prognosis determination, especially in EE2 tumors which represent a significant proportion of breast carcinomas and where the inter-assessor variability is the highest. The aim of the present study was to model the influence of the GGI on adjuvant treatment decision in EE2 patients, using a cohort of ER+/HER2-/pN0/pT1-2 tumors. Methods: A randomly selected series of pT1-2, pN0 breast cancers from the Institut Curie 1995-1996 cohort was profiled using Affymetrix HGU133 Plus 2.0 gene chips. The GGI was calculated using Ipsogen Mapßuant Dx®. Treatment decisions were made for EE2 cases based on the Institut Curie adjuvant treatment guidelines (www.curie.fr). The treatment decision algorithm was firstly run using the grade as defined by Elston Ellis, and secondly using the GGI (undetermined cases were classified as grade2). Results: Out of 72 EE2 tumors, 35 were classified as GGI-1 (49%) and 13 (18%) as GGI-3. Based on EE, 7% of patients would have received adjuvant chemotherapy (ACT) alone (all were ER-/PR-), 50% adjuvant hormonal therapy (AHT) alone, and 43% both. Using the GGI, 7% (n=5) of patients would have been spared AHT and 10% (n=7) ACT. In the ER+/HER2- subgroup of 62 pts, 58% of patients would have received AHT alone and 42% AHT+ACT based on EE; GGI would have lead to a 14% reduction in AHT prescription and to a 26% reduction in ACT prescription. Conclusion: Applying the Genomic Grade Index instead of the Elston Ellis Grade to determine the adjuvant treatment strategy in a series of EE2/ER+/HER2-/pN0/pT1-2 breast carcinomas would lead to a 14% and 27% reduction in the administration of, respectively, adjuvant hormone-therapy and chemotherapy. Adjuvant treatment decision: models based on Elston-Ellis or Genomic Grading Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P5-13-11.

Abstract PD01-06: Activation of the Insulin-Like Growth Factor Pathway Distinguishes African American from European American Patients with Triple-Negative Breast Cancer by Gene Expression Profiling

Abstract Background: Triple-negative breast cancer (TNBC) is characterized by high histologic grade, high rates of distant recurrence and a poor overall prognosis. Treatment options are limited due to the lack of specific targets such as hormone receptors or HER2 which drive other breast cancer subtypes. Epidemiological studies show a markedly higher prevalence of TNBC in young women of African ancestry. In this study we sought to identify transcriptional modules that are differentially regulated between African American (AA) and European American (EA) women. Methods: A hospital-based cohort of 130 breast cancer patients diagnosed between 1985 and 2007 was selected by an institutional pathology database (CoPath) search for invasive, triple-negative breast cancer and enriched for patients of African American ethnicity. Racial distribution 47% AA, 33% EA, 8% Hispanic and 12% other or unknown. Clinical data was extracted from the Yale and Bridgeport Hospitals Tumor Registry following IRB approval. Invasive disease was identified on H&E sections and an average of 3 tissue cores from FFPE blocks were subjected to RNA extraction using the RecoverAll Total Nucleic Acid Isolation kit (Applied Biosystems) following the manufacturer's protocol. The extracted material was hybridized to Whole Genome-DASL assays (Illumina). Statistical analysis of gene expression data was carried out using Bioconductor/R software. A set of relevant signatures was selected by enrichment analysis of modules identified by principal component analysis. Signature scores were computed as Pearson correlation between the signature vector of gene contributions and each sample's expression profile for these genes. Results: African American patients show a significantly higher activation score for a 273-gene Insulin-like Growth Factor 1 signature compared with European American patients (stage-adjusted p=0.0006). Similarly, samples from AA patients show higher scores in a BRCA 1 mutant signature defined by van ‘t Veer and colleagues in 2002 (p=0.001) and in a luminal progenitor (CD49f+EpCAM+) signature from Lim et al. (2009) (p=0.01). The Genomic Grade Index (GGI, Sotiriou et al. 2006) in samples from AA patients was also found to be significantly elevated (p=0.0007). Conclusions: Our findings indicate significant activation of the IGF pathway in AA compared to EA patients with TNBC. The 273-gene IGF signature was associated with poor differentiation and high proliferation in an independent cohort, which is in agreement with the high GGI score observed in AA patients. BRCA1 mutant-like and luminal progenitor-like properties in AA tumor samples further support this hypothesis as they both are related to basal-like histology which constitutes an aggressive subgroup of triple-negative tumors. These data suggest that African American patients may benefit from IGF pathway inhibiting drugs. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr PD01-06.

14 Genomic versus histological grading in soft tissue sarcomas

14 Genomic versus histological grading in soft tissue sarcomas

Prognostic and Therapeutic Implications of Distinct Kinase Expression Patterns in Different Subtypes of Breast Cancer

Different kinases are expressed in different clinical subsets of breast cancer. In this study, we assessed kinase expression patterns in different clinical subtypes of breast cancer, evaluated the prognostic and predictive values of kinase metagenes, and investigated their functions in vitro. Four hundred twenty-eight protein kinases in gene expression data were examined from 684 cases of breast cancer and 51 breast cancer cell lines to identify kinase expression patterns. We tested the prognostic value of kinase metagenes in 684 node-negative patients who received no adjuvant therapy and the predictive value in 233 patients who received uniform neoadjuvant chemotherapy. Twelve kinases were overexpressed in estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative, 7 in HER2(+), and 28 in ER(-)/HER2(-) cancers, respectively. We examined the functional role of 22 kinases overexpressed in ER(-)/HER2(-) cancers using siRNA. Downregulation of these kinases caused significant subtype-specific inhibition of cell growth in vitro. Two robust kinase clusters, including an immune kinase cluster and a mitosis kinase cluster, were present in all clinical subgroups. High mitosis kinase score was associated with worse prognosis but higher pathologic complete response (pCR) in ER(+)/HER2(-) cancers, but not in ER(-)/HER2(-) or HER2(+) cancers, in univariate and multivariate analyses including other genomic predictors (MammaPrint, genomic grade index, and the 76-gene signature). Conversely, higher immune kinase score was associated with better survival in ER(+)/HER2(-) and HER2(+) tumors and also predicted higher probability of pCR in HER2(+) cancers. Taken together, our results indicate that kinases regulating mitosis and immune functions convey distinct prognostic information that varies by clinical subtype.

High genomic grade index associated with poor prognosis for lymph node‐negative and estrogen receptor‐positive breast cancers and with good response to chemotherapy

The aim of the present study was to investigate the prognostic value of the genomic grade index for lymph node-negative and estrogen receptor (ER)-positive breast cancers of Japanese women treated with adjuvant hormonal therapy alone, as well as the relation between genomic grade index and pathological complete response (CR) to neoadjuvant chemotherapy. Genomic grade index was determined by DNA microarray (U133plus2.0; Affymetrix, Santa Clara, Calif) in tumor tissues obtained from lymph node-negative and ER-positive breast cancers (n = 105) treated with adjuvant hormonal therapy alone or in breast tumor biopsy specimens (n = 84, Mammotome) obtained before neoadjuvant chemotherapy (paclitaxel followed by 5-fluorouracil/epirubicin/cyclophosphomide) to investigate the prognostic and predictive values of genomic grade index. Recurrence-free survival of patients with high genomic grade index tumors was significantly (P < .001) lower than that of patients with low genomic grade index tumors (55% vs 88%, 10 years after surgery). Multivariate analysis demonstrated that genomic grade index was the most important and significant predictive factor for disease recurrence (P = .013) independently of other prognostic factors, including tumor size, histological grade, progesterone receptor, human epidermal growth receptor 2, and Ki67. High genomic grade index tumors showed a significantly (P = .022) higher pathological CR rate for neoadjuvant chemotherapy than low genomic grade index tumors (31.9% [15 of 47] vs 10.8% [4 of 37]). Genomic grade index is a powerful prognostic factor for lymph node-negative and ER-positive tumors treated with adjuvant hormonal therapy alone, and high genomic grade index tumors are more likely to respond to chemotherapy. Genomic grade index also appears to be very useful for decision making regarding the need for adjuvant chemotherapy for lymph node-negative and ER-positive breast cancers.

Array-Based DNA Methylation Profiling for Breast Cancer Subtype Discrimination

BackgroundAbnormal DNA methylation is well established for breast cancer and contributes to its progression by silencing tumor suppressor genes. DNA methylation profiling platforms might provide an alternative approach to expression microarrays for accurate breast tumor subtyping. We sought to determine whether the distinction of the inflammatory breast cancer (IBC) phenotype from the non-IBC phenotype by transcriptomics could be sustained by methylomics.Methodology/Principal FindingsWe performed methylation profiling on a cohort of IBC (N = 19) and non-IBC (N = 43) samples using the Illumina Infinium Methylation Assay. These results were correlated with gene expression profiles. Methylation values allowed separation of breast tumor samples into high and low methylation groups. This separation was significantly related to DNMT3B mRNA levels. The high methylation group was enriched for breast tumor samples from patients with distant metastasis and poor prognosis, as predicted by the 70-gene prognostic signature. Furthermore, this tumor group tended to be enriched for IBC samples (54% vs. 24%) and samples with a high genomic grade index (67% vs. 38%). A set of 16 CpG loci (14 genes) correctly classified 97% of samples into the low or high methylation group. Differentially methylated genes appeared to be mainly related to focal adhesion, cytokine-cytokine receptor interactions, Wnt signaling pathway, chemokine signaling pathways and metabolic processes. Comparison of IBC with non-IBC led to the identification of only four differentially methylated genes (TJP3, MOGAT2, NTSR2 and AGT). A significant correlation between methylation values and gene expression was shown for 4,981 of 6,605 (75%) genes.Conclusions/SignificanceA subset of clinical samples of breast cancer was characterized by high methylation levels, which coincided with increased DNMT3B expression. Furthermore, an association was observed with molecular signatures indicative of poor patient prognosis. The results of the current study also suggest that aberrant DNA methylation is not the main force driving the molecular biology of IBC.

Development of 95-gene classifier as a powerful predictor of recurrences in node-negative and ER-positive breast cancer patients

Our aim was to develop an accurate diagnostic system using gene expression analysis by means of DNA microarray for prognosis of node-negative and estrogen receptor (ER)-positive breast cancer patients in order to identify a subset of patients who can be safely spared adjuvant chemotherapy. A diagnostic system comprising a 95-gene classifier was developed for predicting the prognosis of node-negative and ER-positive breast cancer patients by using already published DNA microarray (gene expression) data (n = 549) as the training set and the DNA microarray data (n = 105) obtained at our institute as the validation set. Performance of the 95-gene classifier was compared with that of conventional prognostic factors as well as of the genomic grade index (GGI) based on the expression of 70 genes. With the 95-gene classifier we could classify the 105 patients in the validation set into a high-risk (n = 44) and a low-risk (n = 61) group with 10-year recurrence-free survival rates of 93 and 53%, respectively (P = 8.6e-7). Multivariate analysis demonstrated that the 95-gene classifier was the most important and significant predictor of recurrence (P = 9.6e-4) independently of tumor size, histological grade, progesterone receptor, HER2, Ki67, or GGI. The 95-gene classifier developed by us can predict the prognosis of node-negative and ER-positive breast cancer patients with high accuracy. The 95-gene classifier seems to perform better than the GGI. As many as 58% of the patients classified into the low-risk group with this classifier could be safely spared adjuvant chemotherapy.

MIB1/Ki-67 labelling index can classify grade 2 breast cancer into two clinically distinct subgroups

Histological grade is recognized as one of the strongest prognostic factors in operable breast cancer (BC). Although grade 1 and grade 3 tumours are biologically and clinically distinct, grade 2 tumours bear considerable difficulty in outcome prediction and planning therapies. Several attempts such as genomic grade index have been performed to subclassify grade 2 into two subgroups with clinical relevance. Here, we present evidence that the routinely evaluable immunohistochemical MIB1/Ki67 labelling index (MIB-LI) can classify grade 2 tumours into two clinically distinct subgroups. In this study, growth fractions of 1,550 primary operable invasive breast carcinomas were immunohistochemically assayed on full-face tissue sections using the MIB1 clone of Ki-67. Growth fractions were assessed as number of MIB1 positive nuclei in 1,000 tumour nuclei at high-power magnification and expressed as MIB1-LI. Using a 10% cut-point of MIB1-LI, grade 2 BCs were classified into low (49.8%) and high (50.2%) proliferative subgroups. Univariate and multivariate survival analysis revealed statistically significant differences between these subgroups regarding patients' BC specific survival (P < 0.001), and metastasis free survival (P < 0.001) which was independent of the well-established prognostic factors (HR = 2.944, 95% CI = 1.634-5.303, P < 0.001). In conclusion, our results further demonstrate that grade 2 BCs may represent at least two biological or behaviourally different entities. Assay of growth fraction in BC using MIB1/Ki67 immunohistochemistry is a robust cost-effective diagnostic tool that subdivides grade 2 tumours into low and high risk populations providing additional prognostic information in planning therapies and outcome prediction.

629 The molecular portraits of breast cancer and their relationship to mammary stem cells

The last decade has brought a breakthrough in the knowledge of the biology of breast cancer. The technological development, and in particular the high throughput technologies, have allowed researchers to inquire more deeply into the nature of the disease through the comparative study of large numbers of samples. The classification of breast cancer by traditional parameters has been joined by rankings based on gene expression. Among the most popular platforms are MammaPrint®, Oncotype DX® the wound-response model, the rate of two genes model, the genomic grade index and the intrinsic subtype model. The latter one provides the amplest biological information and allows for the classification of breast cancer into six intrinsic subtypes: luminal A, luminal B, HER2-enriched, basal-like, normal breast and claudin-low. These new classifications are not yet fully applicable to clinical practice not only because they have not been standardized, but also because they entail a substantial economic outlay. Nevertheless, they have provided valuable information on tumor biology that has led to a better understanding of the signaling pathways governing the processes of formation, maintenance and expansion of the tumors. Researchers now know more about the HER2, estrogen receptor, IGF1R, PI3K/AKT, mTOR, AMPK and angiogenesis pathways which has allowed for the development of new targeted therapeutics now being tested in ongoing clinical trials.In general, one can say that the last decade has changed the way researchers understand, classify and study breast cancer, and it has reshaped the way doctors diagnose and treat this disease. In addition, it has undoubtedly changed the search for alternative therapies by integrating molecular studies and the selection of study populations based on their molecular markers into clinical trials. The present review summarizes the advances that have allowed researchers to both better classify the disease, as well as explore some of the most important signaling pathways.

Limited Clinical Utility of Prognostic Gene Expression Profiles in Grade 3 Node-Negative Early Stage Breast Cancer.

Abstract Background: There are a variety of commercially available prognostic gene expression profiles (GEPs) for early stage breast cancer. These have been shown to be highly concordant in their risk classification, as their prognostic performance is largely driven by an improved quantification of cellular proliferation in the ER+/HER2- (luminal) subtype. We hypothesized that the clinical utility of prognostic GEPs would be limited to the subset of tumors with an indeterminate assessment of proliferation by histological grading (grade 2).Methods: We computed five gene-expression signatures – genomic grade index (GGI), 70-gene signature (GENE70), 76-gene signature (GENE76), 21-gene recurrence score, and wound response signature – in 999 lymph node negative, untreated breast tumors (median follow-up of 7.8 years) from six publicly available datasets. We compared their performance against traditional clinicopathological risk classification using Adjuvant!Online (AOL) for each histological grading category.Results: All five GEPs demonstrated superior prognostication of 10-year distant metastasis free survival (DMFS) in grade 1 (range of area under the ROC curves (AUC)=0.63-0.75) and grade 2 (AUC=0.64-0.71) tumors than in grade 3 (AUC=0.48-0.53) tumors. For the GGI, GENE70, and GENE76 profiles, discordant risk classification with AOL was more frequent in grade 2 (53%) than grade 1 (38%) and grade 3 (26%) tumors (p&amp;lt;.001). In the event of discordant risk classification, GEP identified a low risk subgroup that might be spared chemotherapy in grade 2 (10-yr DFMS: 80% vs 66% (GGI); 81% vs 65% (GENE70); 80% vs 67% (GENE76)) and grade 1 (10-yr DFMS: 92% vs 69% (GGI); 84% vs 79% (GENE70); and 87% vs 70% (GENE76)) but not grade 3 (10-yr DFMS: 67% vs 47% (GGI); 75% vs 47% (GENE70); and 71% vs 52% (GENE76)). Even within the subset of all small (T&amp;lt;2cm), ER-positive, node-negative grade 3 tumors, low genomic risk patients did not experience a sufficiently favorable long-term outcome (10-yr DMFS: 60% (GGI), 69% (GENE70) and 64% (GENE76)) to justify the omission of adjuvant chemotherapy.Conclusion: There is limited clinical utility of prognostic GEPs in grade 3 early breast cancer. Histological grading should guide decision-making for assessment of genomic risk. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 103.

A microRNA Expression Profile Consisting of 12 microRNAs Is Associated with Inflammatory Breast Cancer.

Abstract Introduction: Inflammatory breast cancer (IBC) is an aggressive form of breast cancer with a dismal clinical outcome. In the past we showed that IBC is characterized by a specific gene expression pattern. In this study, we investigate if microRNAs (miRNAs) are responsible for the specific IBC gene signature.Materials and methods: 377 miRNAs were profiled in 20 IBC tumor samples and 50 non-stage matched nIBC tumour samples using the human MicroRNA A Array Set version 2.0 (Applied Biosystems). The IBC and nIBC study populations are matched for tumor grade and ER status. All miRNAs with Ct-values less than 35 in 25% of the samples were included for analysis. Data normalization was performed relative to the median miRNA expression level per sample and expression values were log2-transformed. Principal component analysis (PCA) was performed to identify metagenes of miRNAs, associated with the IBC phenotype. Relevant metagenes were compared with prognostic/predictive gene signature-classifications (wound healing response, invasiveness gene signature, 70-gene prognostic signature, genomic grade index, recurrence score, HOXB13/IL17RB-expression ratio and the molecular breast cancer subtypes). Data were validated by analysing relevant miRNA target gene sets on an IBC/nIBC gene expression data set.Results: Using PCA, we identified a metagene associated with the IBC phenotype (Hazard Ratio: 4.500; P=0.011). This metagene was also significantly associated with the Luminal B phenotype, the IBC signature and with an elevated HOXB13/IL17RB-expresion ratio (range Rs = 0.302 – 0.366; P&amp;lt;0.05). MiRNAs significantly (FDR&amp;lt;0.1) associated with this metagene were: miR29A, miR544, miR574-3p, miR548C-3p, miR451, miR548B-5p, miR24, miR28-5p, miR137, miR302B, miR148A and miR302A (range Rs: -0.367 – 0.307). Cluster analysis using this miRNA signature identified two sample clusters, one containing 70% of the IBC samples and a second one containing 64% of the nIBC samples (Pearson X2; P=0.021). Using gene set enrichment analysis for miR24-, miR28-, miR29A-, miR137-, miR148A-, miR302A- and miR302B target gene sets, we confirmed the differential expression for the above mentioned miRNAs (P&amp;lt;0.01) except for miR28. Using Oncomine analyses we were able to associate the identified miRNAs with NFkB activation (miR29, miR148), steroid receptor signalling (miR302A, miR302B, and miR137), cell adhesion (miR29), stem cell signalling (miR137, miR28) and regulation of the extracellular matrix composition (miR29, miR148).Discussion: Through PCA we identified a miRNA signature associated with IBC. The miRNA signature was partly validated on the gene expression level. Interestingly, most identified miRNAs regulate biological processes previously associated with IBC through gene expression analysis, confirming our results also at the biological level. The association of the IBC-specific miRNA-metagene with the Luminal B phenotype and elevated HOXB13/IL17RB-expression ratios confirms our data that ER+ IBC samples, which constitute the majority of the present IBC study population, generally belong to the Luminal B subtype and are associated with resistance to endocrine therapy. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 6119.

Early Assessment of Proliferation by the Genomic Grade Index (GGI) Predicts Response to Neo-Adjuvant Letrozole.

Abstract Background: Expression of the proliferation antigen Ki-67 after short-term pre-surgical endocrine treatment is predictive of long-term relapse free survival in post-menopausal women with estrogen-receptor (ER) positive breast cancer (Dowsett et al., JNCI 2007). There are no established predictive markers of clinical response to neo-adjuvant endocrine therapy.Methods: Whole genome expression profiles (Affymetrix HG-U133A) from paired tumor biopsies before and after 10 to 14 days of neo-adjuvant letrozole were compared to tumor volume changes over 3 months of treatment for 52 post-menopausal women with hormone receptor positive breast cancer to evaluate early predictive markers of clinical response.Results: Clinical response was observed in 80% Luminal A (24/30) and 59% Luminal B (13/22) tumors (p=0.1). Baseline tumor proliferation assessed by GGI or Ki-67 mRNA expression were not predictive of clinical response (p=0.29 and p=0.73 respectively). The absolute value of GGI at Day 10 to 14 was predictive of clinical response (p=0.001), particularly for luminal B (p=0.001) but not luminal A (p=0.35) tumors. The change in GGI from baseline to Day 10 to 14 was marginally predictive of response (p=0.063). Absolute day 10 to 14 Ki67 mRNA expression and the change in Ki67 mRNA were not associated with clinical response (p=0.54 and p=0.58 respectively). Baseline, absolute Day 10 to 14, and change in the expression of the ER gene (ESR1) or an integrative gene module reflecting ER signaling were not predictive of clinical response.Conclusion: Expression of GGI after 10 to 14 days of neo-adjuvant letrozole and the change in GGI with short-term treatment are predictive of clinical response observed after 3 months of therapy. Early assessment of GGI may be useful to determine which post-menopausal patients with highly-proliferative ER-positive disease benefit from endocrine therapy. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 2016.

A microRNA Expression Profile Consisting of 15 microRNAs, Including miR205, Is Associated with Poor Prognosis in Breast Cancer.

Abstract Introduction: MicroRNAs (miRNA) are a class of non-coding RNAs able to regulate gene expression at the post-transcriptional level. In breast cancer, levels of specific miRNAs differ between malignant and normal breast tissue and are able to classify tumors according to clinicopathologic variables. This highlights the potential of miRNAs as novel prognostic and/or predictive indicators. In this study we sought for miRNAs denoting poor prognosis in breast cancer.Materials and methods: 377 miRNAs were profiled in 70 breast tumor samples using the human MicroRNA A Array Set version 2.0 (Applied Biosystems). All miRNAs with Ct-values less than 35 in 25% of the samples were included for analysis. Data normalization was performed relative to the median miRNA expression level per sample and expression values were log2-transformed. Principal component analysis (PCA) was performed to identify metagenes of miRNAs associated with clinicopathological variables (TNM-status, tumor stage, histological grade, ER-, PR-, ErbB2- and P53-status) and prognostic/predictive gene signature-classifications (wound healing response, invasiveness gene signature, 70-gene prognostic signature, genomic grade index, recurrence score, HOXB13/IL17RB-expression ratio and the molecular breast cancer subtypes). Results were validated by investigating relationships between the expression of a relevant miRNA target gene signature and clinicopathological variables in 5 publicly available breast cancer gene expression data sets.Results: Using PCA-analysis we identified a metagene significantly associated with the Luminal B phenotype, an elevated genomic grade index, an elevated recurrence score, an activated wound healing response, the invasiveness gene signature and poor prognosis according to the 70-gene prognostic profile (range Rs: 0.325-0.372; P&amp;lt;0.05). Using multivariate regression analysis we demonstrated that this metagene was also associated with loss of PR expression (Hazard Ratio (HR): 9.00; P=0.0001) and the presence of metastases at time of diagnosis (HR: 18.9; P=0.0002). MiRNAs significantly (FDR&amp;lt;0.1) associated with this metagene were: miR205, miR548C-5p, miR548D-5p, miR187, miR671-3p, miR492, miR365, miR499-5p, miR548D-3p, miR615-3p, miR193A, miR589, miR660, miR219-1-3p, miR888 (range Rs: -0.415 – 0.456). At the gene expression level, a miR205 target gene signature, composed of 173 unique genes, was associated with relapse-free survival (range HR: 1.225 – 1.387; P&amp;lt;0.01) and distant metastasis-free survival (HR: 3.950; P=0.006) in respectively 3 and 1 independent breast cancer data set(s).Discussion: Through principal component analysis we identified a miRNA signature associated with poor prognosis in breast cancer. This signature was composed of 15 individual miRNAs, including miR205 which is known for its role in epithelial-to-mesenchymal transition and the induction of stem cell characteristics. The association of a miR205 target gene signature with relapse-free and distant metastases-free survival confirms our results on the gene expression level. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 4062.

Improvement of the clinical applicability of the Genomic Grade Index through a qRT-PCR test performed on frozen and formalin-fixed paraffin-embedded tissues

BackgroundProliferation and tumor differentiation captured by the genomic grade index (GGI) are important prognostic indicators in breast cancer (BC) especially for the estrogen receptor positive (ER+) disease. The aims of this study were to convert this microarray index to a qRT-PCR assay (PCR-GGI), which could be realized on formalin fixed paraffin embedded samples (FFPE), and to assess its prognostic performance and predictive value of clinical benefit in early and advanced ER+ BC patients treated with tamoxifen.MethodsThe accuracy and concordance of the PCR-GGI with the GGI was assessed using BC patients for which frozen and FFPE tissues as well as microarray data were available (n = 19). The evaluation of the prognostic value of the PCR-GGI was assessed on FFPE material using a consecutive series of 212 systemically treated early BC patients. The predictive performance for tamoxifen benefit was assessed using two ER+ BC populations treated either with adjuvant tamoxifen only (n = 77+139) or first-line tamoxifen for advanced disease (n = 270).ResultsThe PCR-GGI is based on the expression of 8 genes (4 representative of the GGI and 4 reference genes). A significant correlation was observed between the microarray-derived GGI and the qRT-PCR assay using frozen (ρ = 0.95, p < 10E-06) and FFPE material (ρ = 0.89, p < 10E-06). The prognostic performance of the PCR-GGI was confirmed on FFPE samples (HRunivar. = 1.89; [95CI:1.01-3.54], p = 0.05). The PCR-GGI further identified two subgroups of patients with statistically different time to distant metastasis free survival (DMFS) across the two cohorts of ER+ BC patients treated with adjuvant tamoxifen. Additionally, the PCR-GGI was associated with response to tamoxifen in the advanced setting (HRunivar. = 1.98; [95CI:1.51-2.59], p = 6.9E-07).ConclusionPCR-GGI recapitulates in an accurate and reproducible manner the performances of the GGI using frozen and FFPE samples.

Prognostic value of the genomic grade index (GGi) as compared to centrally measured Ki-67 IHC and mitotic index in early breast cancer patients

11094 Background: Genomic grading has been proposed to improve tumor grading. The genomic grade index (GGi) is a 97-gene continuous measure which resolves 80% of histological grade 2 (HG 2) tumors into HG 1 and HG 3 risk categories. GGi has higher prognostic value than HG in patients treated with and without systemic adjuvant endocrine and chemotherapy. A key issue is whether the GGi adds prognostic information to centrally-determined mitotic index and Ki-67 IHC. Methods: The control arm of the PACS 01 trial included 996 women with node-positive (N+) early breast cancer treated with six cycles of fluorouracil, epirubicin, and cyclophosphamide (FEC) and tamoxifen as required. 128 genomic profiles could be obtained from available frozen tumor samples using Affymetrix U133 Plus 2.0 gene chips through the “Carte d'Identite des Tumeurs” program of the French Ligue Nationale contre le Cancer. The Genomic Grade index (GGi) was computed using Ipsogen MapQuant Dx(R). Central Elston-Ellis grade, mitotic index (mitosis / mm2), and Ki-67 IHC (% positive cells) were available for 125 patients. The GGi and histological parameters were correlated to the 5-year metastasis status (MFS-5) by ROC analysis and to the metastasis hazard (follow-up of 6.2 ± years) by Cox regression. Results: In ER+ patients (n=93), the GGi was the only significant correlate to metastasis hazard in multivariate Cox regression with histological parameters (HR = 3.5 [1.7–7.5], p&lt;0.001). It was the best predictor of MFS-5 (ROC AUC = 0.83, p=1E-6) when compared to histological parameters (ROC AUC = 0.71, 0.72 and 0.66 resp. p=0.003 to 0.03). In HG 2 subgroup (n=43), the GGi was the only significant predictor of MFS-5 (ROC AUC = 0.81, p=0.016). Conclusions: In our sample of N+ ER+ breast cancer patients, the GGi improved prognostication compared to centrally-measured mitotic index and Ki-67 IHC used alone and in combination. Moreover, the GGi was the only prognostic factor in histological grade 2 patients. [Table: see text]