Prognostic Signature For Breast Cancer Research Articles

Identification and validation of a ferroptosis-related gene signature predictive of prognosis in breast cancer.

Ferroptosis, a novel form of regulated cell death, is closely associated with the occurrence and development of malignant tumors. Here, we utilized a bioinformatics approach to identify ferroptosis-related genes to establish a robust and reliable prognostic signature in breast cancer (BC). Univariate Cox regression and LASSO regression analyses of patient’s survival and gene expression data identified a prognostic signature consisting of 10 ferroptosis-related genes (FRGs). The signature demonstrated a favorable prediction performance, and was validated in two independent datasets, GSE21653 and GSE25066. Analyses of immune infiltrates, tumor microenvironment, immune checkpoints, mutations, drug sensitivity, and clinicopathological features revealed significant differences between low- and high-risk BC patients. A multivariate analysis revealed that the signature was an independent prognostic predictor in BC, and a nomogram combining the risk score and tumor stage intuitively displayed high accuracy and reliability with respect to predicting the survival outcomes of BC patients. These findings indicate that the identified prognostic signature is a potential indicator predictive of prognosis and immunotherapeutic responses in BC patients.

Coiled-coil domain-containing protein-124 (Ccdc124) is a novel RNA binding factor up-regulated in endometrial, ovarian, and urinary bladder cancers.

Coiled-coil domain containing protein-124 (Ccdc124) is a putative mRNA-binding factor associated with cell division, and ribosome biology. Previous reports mentioned an up-regulation of CCDC124 gene in cancer, and listed its mRNA in a molecular prognostic signature in breast cancer. Establishing RNA-binding characteristics of Ccdc124 for a better molecular functional characterization, and carrying-out retrospective studies in order to evaluate its aberrant expression in human cancer samples from various tissue origins. Bioinformatics calculations followed by RIP and RNA-seq experiments were performed to investigate mRNA targets of Ccdc124. Quantitative studies on arrays of cDNAs from different cancers and IHC assays on tissue arrays were used to assess CCDC124 expression levels in cancers. Ccdc124 was characterized as an RNA-binding protein (RBP) interacting with various mRNAs. CCDC124 mRNA levels were high in tumors, with a particular up-regulation in cancers from esophagus, adrenal gland, endometrium, liver, ovary, thyroid, and urinary bladder. IHC assays indicated strong Ccdc124 positivity in endometrial (95.4%), urinary bladder (68.4%), and ovarian cancers (86.8%). Ccdc124 is a cytokinesis related RBP interacting with various mRNAs. CCDC124 mRNA over-expression and an accompanied increase in Ccdc124 protein accumulation was reported in cancers, indicating this RBP as a novel cancer cell marker.

Identification of autophagy-related long non-coding RNA prognostic signature for breast cancer.

Autophagy‐related long non‐coding RNAs (lncRNAs) disorders are related to the occurrence and development of breast cancer. The purpose of this study is to explore whether autophagy‐related lncRNA can predict the prognosis of breast cancer patients. The autophagy‐related lncRNAs prognostic signature was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression. We identified five autophagy‐related lncRNAs (MAPT‐AS1, LINC01871, AL122010.1, AC090912.1, AC061992.1) associated with prognostic value, and they were used to construct an autophagy‐related lncRNA prognostic signature (ALPS) model. ALPS model offered an independent prognostic value (HR = 1.664, 1.381‐2.006), where this risk score of the model was significantly related to the TNM stage, ER, PR and HER2 status in breast cancer patients. Nomogram could be utilized to predict survival for patients with breast cancer. Principal component analysis and Sankey Diagram results indicated that the distribution of five lncRNAs from the ALPS model tends to be low‐risk. Gene set enrichment analysis showed that the high‐risk group was enriched in autophagy and cancer‐related pathways, and the low‐risk group was enriched in regulatory immune‐related pathways. These results indicated that the ALPS model composed of five autophagy‐related lncRNAs could predict the prognosis of breast cancer patients.

A seven-nuclear receptor-based prognostic signature in breast cancer.

Breast cancer (BRCA) is a malignant cancer that threatened the life of female with unsatisfactory prognosis. The aim of this study was to identify prognostic nuclear receptors (NRs) signature of BRCA. BRCA patient samples were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Consensus clustering analysis, univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were performed to evaluate, select NRs as prognostic factors and build Risk Score model. GSEAanalysis was explored to check signaling differences between High- and Low-Risk group. Nomogram model basing on age and Risk Score was established to predict the 1-, 3- and 5-year survival. Model performance was assessed by a time-dependent receiver operating characteristic (ROC) curve and calibration plot. CIBERSORT, ESTIMATE and TIMER algorithm were introduced to evaluate the immune landscape. NR3C1, NR4A3, THRA, RXRG, NR2F6, NR1D2 and RORB were optimized as a prognostic signature for BRCA. This seven-NR-based Risk Score could effectively predict overall survival status. The area under the curve (AUC) of 1-, 3- and 5-year overall survival are 0.702, 0.734 and 0.722 in TCGA training cohort, and 0.630, 0.721 and 0.823 in GEO validation cohort, respectively. Calibration plot demonstrated satisfactory agreement between predictive and observed outcomes. Nomogram model worked well on predicting survival probabilities. Multiple cancer-related pathways were highly enriched in High-Risk group. High- and Low-Risk groups showed significant differed immune cell infiltration. There exists an obvious connection between Risk Score and immune checkpoints LAG3, PD1 and TIM3. The seven-NR-based Risk Score represents a promising signature for estimating overall survival in patients with BRCA, and is correlated with the immune microenvironment.

Construction and validation of an immunity-related prognostic signature for breast cancer.

Breast cancer is one of the most lethal malignancies among women, and understanding the effects of host immunity on disease progression offers the potential to improve immunotherapies against it. Here, we constructed an immunity-related gene (IRG)-based prognostic signature to stratify breast cancer patients and predict their survival. We identified differentially-expressed genes by analyzing the breast cancer transcriptome data from The Cancer Genome Atlas. Univariate Cox regression revealed 179 survival-correlated IRGs, 12 of which we used to construct an immunity-based prognostic signature that stratified breast cancer patients into high- and low-risk groups. The signature was an independent predictor for survival and was validated in an independent dataset. We also investigated the correlations between our prognostic signature and immune infiltrates and found that signature-derived risk scores correlated negatively with infiltration of B cells, CD4+ T cells, CD8+ T cells, neutrophils and dendritic cells. Our results show that the proposed prognostic signature reflects the tumor immune microenvironment, which makes it a potential indicator for survival that warrants further research to assess its clinical utility.

Identification of a 9‐gene prognostic signature for breast cancer

Breast cancer (BRCA) is the most common cancer among women and is the second leading cause of cancer death in women. In this study, we developed a 9‐gene prognostic signature to predict the prognosis of patients with BRCA. GSE20685, GSE42568, GSE20711, and GSE88770 were used as training sets. The Kaplan–Meier plot was constructed to assess survival differences and log‐rank test was performed to evaluate the statistical significance. The overall survival (OS) of patients in the low‐risk group was significantly higher than that in the high‐risk group. ROC analysis indicated that this 9‐gene signature shows good diagnostic efficiency both in OS and disease‐free survival (DFS). The 9‐gene signature was further validated through GSE16446, GSE7390, and TCGA‐BRCA datasets. We also established a nomogram that integrates clinicopathological features and 9‐gene signature. The analysis of the calibration plot showed that the nomogram has good prognostic performance. More convincingly, real‐time reverse transcription‐polymerase chain reaction (RT‐PCR) results indicated that the protective prognostic factors in BRCA patients were downregulated, whereas the dangerous prognostic factors were upregulated. The innovation of this article is not only constructing a prognostic gene signature, but also combining with clinical information to further establish a nomogram to better predict the survival probability of patients. It is worth mentioning that this signature also does not depend on other clinical factors or variables.

Identification and Validation of an Immunological Expression-Based Prognostic Signature in Breast Cancer.

Background: Emerging evidence suggests that the immune system plays a crucial role in the regulation of the response to therapy and long-term outcomes of patients with breast cancer (BRCA). In this study, we aimed to identify a significant signature based on immune-related genes to predict the prognosis of BRCA patients.Methods: The expression data were downloaded from The Cancer Genome Atlas (TCGA). The immune-related gene list, the transcription factor (TF) gene list, and the immune infiltrate scores of samples in the TCGA database were acquired from the ImmPort database, the Cistrome Cancer database, and the TIMER database, respectively. Univariate Cox regression analysis was utilized to identify prognostic immune-related differentially expressed genes (DEGs) (PIRDEGs) in BRCA. A prognostic immune signature containing 15 PIRDEGs in BRCA was established using the least absolute shrinkage and selection operator (LASSO) model with 1,000 iterations followed by a stepwise Cox proportional hazards model with a training set of 508 samples in TCGA. An independent assessment of the prognostic prediction ability of the signature was conducted using Kaplan–Meier survival analysis with a testing set of 505 samples in TCGA.Results: We identified 466 PIRDEGs and 80 TFs among the DEGs. A gene signature containing 15 PIRDEGs was constructed. Risk scores of BRCA patients were calculated using this model, which showed a high accuracy of prognosis prediction in both the training set and testing set and could be an independent prognostic factor of BRCA patients.Conclusions: Our study revealed that a PIRDEG signature could be a candidate prognostic biomarker for predicting the overall survival (OS) of patients with BRCA.

Five-mRNA Signature for the Prognosis of Breast Cancer Based on the ceRNA Network.

Background The purpose of this study was to investigate the regulatory mechanisms of ceRNAs in breast cancer (BC) and construct a new five-mRNA prognostic signature. Methods The ceRNA network was constructed by different RNAs screened by the edgeR package. The BC prognostic signature was built based on the Cox regression analysis. The log-rank method was used to analyse the survival rate of BC patients with different risk scores. The expression of the 5 genes was verified by the GSE81540 dataset and CPTAC database. Results A total of 41 BC-adjacent tissues and 473 BC tissues were included in this study. A total of 2,966 differentially expressed lncRNAs, 5,370 differentially expressed mRNAs, and 359 differentially expressed miRNAs were screened. The ceRNA network was constructed using 13 lncRNAs, 267 mRNAs, and 35 miRNAs. Kaplan-Meier (K-M) methods showed that two lncRNAs (AC037487.1 and MIR22HG) are related to prognosis. Five mRNAs (VPS28, COL17A1, HSF1, PUF60, and SMOC1) in the ceRNA network were used to establish a prognostic signature. Survival analysis showed that the prognosis of patients in the low-risk group was significantly better than that in the high-risk group (p = 0.0022). ROC analysis showed that this signature has a good diagnostic ability (AUC = 0.77). Compared with clinical features, this signature was also an independent prognostic factor (HR: 1.206, 95% CI 1.108−1.311; p < 0.001). External verification results showed that the expression of the 5 mRNAs differed between the normal and tumour groups at the chip and protein levels (p < 0.001). Conclusions These ceRNAs may play a key role in the development of BC, and the new 5-mRNA prognostic signature can improve the prediction of survival for BC patients.

Identification and validation of stemness-related lncRNA prognostic signature for breast cancer

BackgroundLong noncoding RNAs (lncRNAs) are emerging as crucial contributors to the development of breast cancer and are involved in the stemness regulation of breast cancer stem cells (BCSCs). LncRNAs are closely associated with the prognosis of breast cancer patients. It is critical to identify BCSC-related lncRNAs with prognostic value in breast cancer.MethodsA co-expression network of BCSC-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA) was constructed. Univariate and multivariate Cox proportional hazards analyses were used to identify a stemness risk model with prognostic value. Kaplan–Meier analysis, univariate and multivariate Cox regression analyses and receiver operating characteristic (ROC) curve analysis were performed to validate the risk model. Principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation were conducted to analyze the risk model.ResultsIn this study, BCSC-related lncRNAs in breast cancer were identified. We evaluated the prognostic value of these BCSC-related lncRNAs and eventually obtained a prognostic risk model consisting of 12 BCSC-related lncRNAs (Z68871.1, LINC00578, AC097639.1, AP003119.3, AP001207.3, LINC00668, AL122010.1, AC245297.3, LINC01871, AP000851.2, AC022509.2 and SEMA3B-AS1). The risk model was further verified as a novel independent prognostic factor for breast cancer patients based on the calculated risk score. Moreover, based on the risk model, the low- risk and high-risk groups displayed different stemness statuses.ConclusionsThese findings suggested that the 12 BCSC-related lncRNA signature might be a promising prognostic factor for breast cancer and can promote the management of BCSC-related therapy in clinical practice.

Identification and validation of prognostic signature for breast cancer based on genes potentially involved in autophagy.

We aimed to identify prognostic signature based on autophagy-related genes (ARGs) for breast cancer patients. The datasets of breast cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to construct multiple-ARG risk signature. In total, 32 ARGs were identified as differentially expressed between tumors and adjacent normal tissues based on TCGA. Six ARGs (IFNG, TP63, PPP1R15A, PTK6, EIF4EBP1 and NKX2-3) with non-zero coefficient were selected from the 32 ARGs using LASSO regression. The 6-ARG signature divided patients into high-and low-risk group. Survival analysis indicated that low-risk group had longer survival time than high-risk group. We further validated the 6-ARG signature using dataset from GEO and found similar results. We analyzed the associations between ARGs and breast cancer survival in TCGA and nine GEO datasets, and obtained 170 ARGs with significant associations. EIF4EBP1, FOS and FAS were the top three ARGs with highest numbers of significant associations. EIF4EBP1 may be a key ARG which had a higher expression level in patients with more malignant molecular subtypes and higher grade breast cancer. In conclusion, our 6-ARG signature was of significance in predicting of overall survival of patients with breast cancer. EIF4EBP1 may be a key ARG associated with breast cancer survival.

Identification of a prognostic LncRNA signature for ER-positive, ER-negative and triple-negative breast cancers.

The development of multi-gene signatures has led to improvements in identification of breast cancer patients at high risk of recurrence. The prognostic power of commercially available gene signatures is mostly restricted to estrogen receptor (ER)-positive breast cancer. On the contrary, immune-related gene signatures predict prognosis only in ER-negative breast cancer. This study aimed to develop a better prognostic signature for breast cancer. The expressions of long non-coding RNA (lncRNA) genes from 30 independent microarray datasets with a total of 4813 samples were analyzed. A prognostic lncRNA signature was developed based on likelihood-ratio Cox regression analysis. Survival analysis was used to compare the prognostic efficiencies of our signature and 10 previously reported prognostic gene signatures. Cox regression analysis on 30 independent datasets showed that the 6-lncRNA signature identified in this study performed as well as five commercially available signatures in recurrence prediction for ER-positive breast cancer. In ER-negative breast cancer, this lncRNA signature was as prognostic as three immune-related gene signatures. Moreover, our lncRNA signature also demonstrated a good capacity to predict recurrence risk for triple-negative breast cancer. Function analysis showed that several lncRNAs in this signature were probably involved in cell proliferation and immune processes. A six-LncRNA signature was identified that is prognostic for ER-positive, ER-negative, and triple-negative breast cancers and thus deserves further validation in prospective studies.

Development and validation of a hypoxia-related prognostic signature for breast cancer.

Hypoxia, an important component of the tumor microenvironment, plays a crucial role in the occurrence and progression of cancer. However, to the best of our knowledge, a systematic analysis of a hypoxia-related prognostic signature for breast cancer is lacking and is urgently required. Therefore, in the present study, RNA-seq data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and served as a discovery cohort. Cox proportional hazards regression analysis was performed to construct a 14-gene prognostic signature (PFKL, P4HA2, GRHPR, SDC3, PPP1R15A, SIAH2, NDRG1, BTG1, TPD52, MAFF, ISG20, LALBA, ERRFI1 and VHL). The hypoxia-related signature successfully predicted survival outcomes of the discovery cohort (P<0.001 for the TCGA dataset). Three independent Gene Expression Omnibus databases (GSE10886, GSE20685 and GSE96058) were used as validation cohorts to verify the value of the predictive signature (P=0.007 for GSE10886, P=0.021 for GSE20685, P<0.001 for GSE96058). In the present study, a robust predictive signature was developed for patients with breast cancer, and the findings revealed that the 14-gene hypoxia-related signature could serve as a potential prognostic biomarker for breast cancer.

Identification and validation of immune-related lncRNA prognostic signature for breast cancer

The prognosis of patients with breast cancer is closely related to both the infiltration of immune cells and the expression of lncRNAs. In this study, we evaluated the infiltration of immune cells in 1109 breast cancer samples obtained from TCGA by applying the ssGSEA to the transcriptome of these samples, thereby generating high immune cell infiltration group and low immune cell infiltration group. On the basis of these groupings, we found 696 differentially expressed lncRNAs which were sequentially subjected to univariate Cox regression and stepwise multiple Cox regression analysis. 11 lncRNAs were identified as prognostic signature for breast cancer. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, and ROC analyses further revealed that this 11-lncRNA signature was a novel and important prognostic factor independent of multiple clinicopathological parameters. The TIMER database showed that this 11-lncRNA prognostic signature for breast cancer was associated with the infiltration of immune cell subtypes.

Abstract P3-07-08: Comparison of MammaTyper® RT-qPCR based subtyping with simulated breast cancer prognostic signatures

Abstract Background: Current prognostic signatures are based on measurements of numerous targets and require specialized equipment and complex calculations. While the overall prognostic capabilities of the tests may be comparable in ER+/HER2- breast cancer, side by side comparisons of different tests have shown that results differ on the individual patient level (Bartlett et al., JNCI, 2016). Here we determined risk scores and classifications in 160 tumors to the following commercial and academic prognostic tests using a custom gene panel for which the publically available algorithms were trained against the actual commercial test scores: Mammaprint®, Oncotype DX®, Prosigna® including PAM50 subtyping; and academic risk scores: IHC4-like, 95-gene score and the Genomic Grade Index. These trained risk classifiers were compared to the results of the MammaTyper® IVD-kit. Methods: RNA was extracted from FFPE tissue sections and analyzed on the Nanostring nCounter® using a probe set containing all targets and reference genes used in the original signatures (N=199). Nanostring measurements and calculations of simulated risk classifiers were carried out as described previously (Bayani et al., npj Breast Cancer, 2017) and retrained against actual commercial test scores (Bartlett et al., in prep.). MammaTyper® RT-qPCR of ERBB2, ESR1, PGR and MKI67 (normalized to CALM2) was carried out on a separate aliquot of the same RNA sample. St. Gallen surrogate subtypes were assigned based on binary mRNA expression according to pre-defined cutoffs. For calculating pairwise agreements between test categorizations, simulated test results with ternary classification were transformed to binary classifications (IHC4-like: low + int vs. high, Oncotype DX-trained: RS≤25 vs. RS&amp;gt;25, Prosigna-trained ROR-P: low vs. int + high). Comparison of MammaTyper® with simulated risk scores was done on the level of surrogate subtypes and on the level of continuous MKI67 and LRP score values. The MammaTyper® LRP score is a previously developed signature to predict RS ≤25 results with high specificity. Results: Marker positivity rate by RT-qPCR was 83% for ESR1, 72% for PGR, 9% for ERBB2 and 58% for MKI67 based on the pre-specified MammaTyper® cutoffs. The highest rate of low risk results was found for binary RS classification with 68.1% of RS ≤25 samples. MammaTyper® Luminal A-like result was found in 30.6% of samples a rate similar to ROR-P, Genomic Grade Index or MammaPrint-trained low risk results (35.0%, 38.1% and 38.8% respectively). The highest agreement between the simulated risk classifications was observed between binary OncotypeDX-trained RS and IHC4-like classifications with 93.1%. Prosigna-trained (ROR-P) and IHC4-like score had the lowest pairwise agreement with only 65.0% agreement. Oncotype DX-trained and the 95 gene score had the highest concordance to MammaTyper® Luminal A-like subtype with 100% and 96% of Luminal A-like samples classified as RS ≤25 and low risk also by these signatures, respectively. ROC analyses of MammaTyper®MKI67 against binary 95-gene score risk classifier resulted in an AUC of 0.949. The lowest AUC for MKI67 was achieved against simulated MammaPrint-trained classification (AUC=0.874). When applying the LRP score to ROC analysis, almost identical AUCs were achieved for IHC4-like and binary Oncotype DX-trained classifications with AUCs of 0.976 and 0.969 respectively. Conclusion: Low cost local RT-qPCR measurements by MammaTyper® show a high agreement with the scores generated by our Oncotype DX-trained scores. The academic and well validated 95-gene signature agrees well with MammaTyper®MKI67 single gene measurement. Therefore, the locally performed MammaTyper® test may serve as cost effective surrogate for complex prognostic signatures or at least enables a pre-screening for molecularly “obviously” low risk samples. Citation Format: Mark Laible, Sebastian Aulmann, Alfred Etzrodt, Marcus Schmidt, Cheryl Crozier, Ralph Wirtz, Jane Bayani, Ugur Sahin, John MS Bartlett. Comparison of MammaTyper® RT-qPCR based subtyping with simulated breast cancer prognostic signatures [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-07-08.

Development of an Immune-Related Prognostic Signature in Breast Cancer.

BackgroundAlthough increased early detection, diagnosis and treatment have improved the outcome of breast cancer patients, prognosis estimation still poses challenges due to the disease heterogeneity. Accumulating data indicated an evident correlation between tumor immune microenvironment and clinical outcomes.ObjectiveTo construct an immune-related signature that can estimate disease prognosis and patient survival in breast cancer.MethodsGene expression profiles and clinical data of breast cancer patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, which were further divided into a training set (n = 499), a testing set (n = 234) and a Meta-validation set (n = 519). In the training set, immune-related genes were recognized using combination of gene expression data and ESTIMATE algorithm-derived immune scores. An immune-related prognostic signature was generated with LASSO Cox regression analysis. The prognostic value of the signature was validated in the testing set and the Meta-validation set.ResultsA total of 991 immune-related genes were identified. Twelve genes with non-zero coefficients in LASSO analysis were used to construct an immune-related prognostic signature. The 12-gene signature significantly stratified patients into high and low immune risk groups in terms of overall survival independent of clinical and pathologic factors. The signature also significantly stratified overall survival in clinical defined groups, including stage I/II disease. Several biological processes, such as immune response, were enriched among genes in the immune-related signature. The percentage of M2 macrophage infiltration was significantly different between low and high immune risk groups. Time-dependent ROC curves indicated good performance of our signature in predicting the 1-, 3- and 5-year overall survival for patients from the full TCGA cohort. Furthermore, the composite signature derived by integrating immune-related signature with clinical factors, provided a more accurate estimation of survival relative to molecular signature alone.ConclusionWe developed a 12-gene prognostic signature, providing novel insights into the identification of breast cancer with a high risk of death and assessment of the possibility of immunotherapy incorporation in personalized breast cancer management.

Evaluation of the 12-Gene Molecular Score and the 21-Gene Recurrence Score as Predictors of Response to Neo-adjuvant Chemotherapy in Estrogen Receptor-Positive, HER2-Negative Breast Cancer

BackgroundNeo-adjuvant chemotherapy (NaCT) facilitates complete surgical resection in locally advanced breast cancer. Due to its association with improved outcome, complete pathologic response (pCR) to neo-adjuvant treatment has been accepted as a surrogate for long-term outcome in clinical trials of human epidermal growth factor receptor 2 (HER2)-positive, triple-negative, or luminal B breast cancer patients. In contrast, NaCT is effective in only ~ 7–10% of estrogen receptor (ER)-positive, HER2-negative disease. Response biomarkers would enable such patients to be selected for NaCT.MethodsTwo commercially available breast cancer prognostic signatures [12-gene molecular score (MS) and the 21-gene Recurrence Score (RS)] were compared in their ability to predict pCR to NaCT in ER-positive, HER2-negative breast cancer in six public RNA expression microarray data sets. Scores were approximated according to published algorithms and analyzed by logistic regression.ResultsExpression data were available for 764 ER-positive, HER2-negative breast cancer samples, including 59 patients with pCR. The two scores were well correlated. Either score was a significant predictor of pCR (12-gene MS p = 9.4 × 10−5; 21-gene RS p = 0.0041). However, in a model containing both scores, the 12-gene MS remained significant (p = 0.0079), while the 21-gene RS did not (p = 0.79).ConclusionsIn this microarray study, two commercial breast cancer prognostic scores were significant predictors of response to NaCT. In direct comparison, the 12-gene MS outperformed the 21-gene RS as a predictive marker for NaCT. Considering pCR as surrogate for improved survival, these results support the ability of both scores to predict chemotherapy sensitivity.

Low cleaved caspase-7 levels indicate unfavourable outcome across all breast cancers.

BCL2 levels are elevated in breast cancer where they are marker of good prognosis. BCL2 and active caspase levels correlate negatively; yet, active caspases indicate good outcome. Low BCL2 and low caspase-7 are highly prognostic of unfavourable outcome across all breast cancers. BCL2 levels indicate molecular subtype and tumour proliferation status in breast cancer.

Integrated analysis of DNA methylation, immunohistochemistry and mRNA expression, data identifies a methylation expression index (MEI) robustly associated with survival of ER-positive breast cancer patients.

Identification of prognostic gene expression signatures may enable improved decisions about management of breast cancer. To identify a prognostic signature for breast cancer, we performed DNA methylation profiling and identified methylation markers that were associated with expression of ER, PR, HER2, CK5/6, and EGFR proteins. Methylation markers that were correlated with corresponding mRNA expression levels were identified using 208 invasive tumors from a population-based case-control study conducted in Poland. Using this approach, we defined the methylation expression index (MEI) signature that was based on a weighted sum of mRNA levels of 57 genes. Classification of cases as low or high MEI scores was related to survival using Cox regression models. In the Polish study, women with ER-positive low MEI cancers had reduced survival at a median of 5.20 years of follow-up, HR = 2.85 95 % CI = 1.25-6.47. Low MEI was also related to decreased survival in four independent datasets totaling over 2500 ER-positive breast cancers. These results suggest that integrated analysis of tumor expression markers, DNA methylation, and mRNA data may be an important approach for identifying breast cancer prognostic signatures. Prospective assessment of MEI along with other prognostic signatures should be evaluated in future studies.

Autophagy‐related prognostic signature for breast cancer

Autophagy is a process that degrades intracellular constituents, such as long-lived or damaged proteins and organelles, to buffer metabolic stress under starvation conditions. Deregulation of autophagy is involved in the progression of cancer. However, the predictive value of autophagy for breast cancer prognosis remains unclear. First, based on gene expression profiling, we found that autophagy genes were implicated in breast cancer. Then, using the Cox proportional hazard regression model, we detected autophagy prognostic signature for breast cancer in a training dataset. We identified a set of eight autophagy genes (BCL2, BIRC5, EIF4EBP1, ERO1L, FOS, GAPDH, ITPR1 and VEGFA) that were significantly associated with overall survival in breast cancer. The eight autophagy genes were assigned as a autophagy-related prognostic signature for breast cancer. Based on the autophagy-related signature, the training dataset GSE21653 could be classified into high-risk and low-risk subgroups with significantly different survival times (HR = 2.72, 95% CI = (1.91, 3.87); P = 1.37 × 10(-5)). Inactivation of autophagy was associated with shortened survival of breast cancer patients. The prognostic value of the autophagy-related signature was confirmed in the testing dataset GSE3494 (HR = 2.12, 95% CI = (1.48, 3.03); P = 1.65 × 10(-3)) and GSE7390 (HR = 1.76, 95% CI = (1.22, 2.54); P = 9.95 × 10(-4)). Further analysis revealed that the prognostic value of the autophagy signature was independent of known clinical prognostic factors, including age, tumor size, grade, estrogen receptor status, progesterone receptor status, ERBB2 status, lymph node status and TP53 mutation status. Finally, we demonstrated that the autophagy signature could also predict distant metastasis-free survival for breast cancer.

An optimized five-gene multi-platform predictor of hormone receptor negative and triple negative breast cancer metastatic risk

IntroductionOutcome predictors in use today are prognostic only for hormone receptor-positive (HRpos) breast cancer. Although microarray-derived multigene predictors of hormone receptor-negative (HRneg) and/or triple negative (Tneg) breast cancer recurrence risk are emerging, to date none have been transferred to clinically suitable assay platforms (for example, RT-PCR) or validated against formalin-fixed paraffin-embedded (FFPE) HRneg/Tneg samples.MethodsMultiplexed RT-PCR was used to assay two microarray-derived HRneg/Tneg prognostic signatures IR-7 and Buck-4) in a pooled FFPE collection of 139 chemotherapy-naïve HRneg breast cancers. The prognostic value of the RT-PCR measured gene signatures were evaluated as continuous and dichotomous variables, and in conditional risk models incorporating clinical parameters. An optimized five-gene index was derived by evaluating gene combinations from both signatures.ResultsRT-PCR measured IR-7 and Buck-4 signatures proved prognostic as continuous variables; and conditional risk modeling chose nodal status, the IR-7 signature, and tumor grade as significant predictors of distant recurrence (DR). From the Buck-4 and IR-7 signatures, an optimized five-gene (TNFRSF17, CLIC5, HLA-F, CXCL13, XCL2) predictor was generated, referred to as the Integrated Cytokine Score (ICS) based on its functional pathway linkage through interferon-γ and IL-10. Across all FFPE cases, the ICS was prognostic as either a continuous or dichotomous variable, and conditional risk modeling selected nodal status and ICS as DR predictors. Further dichotomization of node-negative/ICS-low FFPE cases identified a subset of low-grade HRneg tumors with <10% 5-year DR risk. The prognostic value of ICS was reaffirmed in two previously studied microarray assayed cohorts containing 274 node-negative and chemotherapy naive HRneg breast cancers, including 95 Tneg cases where it proved prognostically independent of Tneg molecular subtyping. In additional HRneg/Tneg microarray assayed cohorts, the five-gene ICS also proved prognostic irrespective of primary tumor nodal status and adjuvant chemotherapy intervention.ConclusionWe advanced the measurement of two previously reported microarray-derived HRneg/Tneg breast cancer prognostic signatures for use in FFPE samples, and derived an optimized five-gene Integrated Cytokine Score (ICS) with multi-platform capability of predicting metastatic outcome from primary HRneg/Tneg tumors independent of nodal status, adjuvant chemotherapy use, and Tneg molecular subtype.