Predict Breast Cancer Outcome Research Articles

The effect of BMI on survival outcome of breast cancer patients: a systematic review and meta-analysis.

The main goal of the present research is to explore the potential link of body mass index (BMI) with different survival metrics in breast cancer patients. Our aim is to offer the latest and most thorough meta-analysis, assessing the strength and reliability of the connection that BMI has with prognostic indicators in this disease. As of January 2024, we conducted a systematic literature search across PubMed, Embase, Web of Science, and the Cochrane Library databases. Our search aimed to identify studies examining BMI as an exposure factor, with breast cancer patients constituting the study population, and utilizing adjusted hazard ratio (HR) as the data type of interest. The evidence synthesis incorporated a total of 61 eligible articles involving 201,006 patients. Being underweight posed a risk factor for overall survival (OS) in breast cancer patients compared to normal weight (HR 1.15, 95% CI 0.98-1.35; P = 0.08). Overweight or obesity, in comparison to normal weight, was a risk factor for OS (HR 1.18, 95% CI 1.14-1.23; P < 0.00001), disease-free survival (DFS) (HR 1.11, 95% CI 1.08-1.13; P < 0.00001), relapse-free survival (RFS) (HR 1.14, 95% CI 1.06-1.22; P = 0.03), and breast cancer-specific survival (BCSS) (HR 1.18, 95% CI 1.11-1.26; P < 0.00001), but not for progression-free survival (PFS) (HR 0.91, 95% CI 0.76-1.10; P = 0.33). Notably, in subgroup analyses, overweight patients achieved prolonged PFS (HR 0.80, 95% CI 0.64-0.99; P = 0.04), and compared to the obese population, the overweight cohort exhibited a significant difference in OS (HR 1.11, 95% CI 1.05-1.16; P < 0.00001) and DFS (HR 1.06, 95% CI 1.03-1.10; P = 0.0004), with a considerably stronger association. Furthermore, compared to HER- patients, HER + patients exhibited a greater predictive value for OS (HR 1.23, 95% CI 1.10-1.37; P = 0.0004), RFS (HR 1.30, 95% CI 1.03-1.64; P < 0.00001), and DFS (HR 1.10, 95% CI 1.03-1.17; P = 0.003). The results of our meta-analysis reveal a notable association between BMI and various survival measures in breast cancer prognosis. These findings provide a solid basis for predicting breast cancer outcomes and implementing more effective therapeutic approaches.

An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome

BackgroundPredicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome prediction. However, the application of these methods is still challenged by interpretability. In this study, we proposed a novel multitask deep neural network called UISNet to predict the outcome of breast cancer. The UISNet is able to interpret the importance of features for the prediction model via an uncertainty-based integrated gradients algorithm. UISNet improved the prediction by introducing prior biological pathway knowledge and utilizing patient heterogeneity information.ResultsThe model was tested in seven public datasets of breast cancer, and showed better performance (average C-index = 0.691) than the state-of-the-art methods (average C-index = 0.650, ranged from 0.619 to 0.677). Importantly, the UISNet identified 20 genes as associated with breast cancer, among which 11 have been proven to be associated with breast cancer by previous studies, and others are novel findings of this study.ConclusionsOur proposed method is accurate and robust in predicting breast cancer outcomes, and it is an effective way to identify breast cancer-associated genes. The method codes are available at: https://github.com/chh171/UISNet.

Elucidating Axillary Soft Tissue Involvement in Breast Cancer: Unveiling Metastatic Mechanisms and Therapeutic Perspectives

This perspective focuses on axillary soft tissue (AXT) involvement in breast cancer, revealing diverse pathological entities beyond traditional axillary lymph node metastasis. AXT involvement is linked to increased risks of distant metastasis and locoregional failure, emphasizing its significance in predicting breast cancer outcomes. We posit that AXT involvement could signify a retrograde metastatic event stemming from reactivated circulating tumor cells navigating towards the axillary soft tissue guided by chemokines. Therefore, AXT disease warrants aggressive systemic therapy. Axillary radiation therapy could be a potentially preferable alternative to axillary lymph node dissection. Routine reporting of axillary soft tissue involvement is crucial for accurate treatment planning.

Pathologic Exploration of the Axillary Soft Tissue Microenvironment and Its Impact on Axillary Management and Breast Cancer Outcomes.

Axillary soft tissue (AXT) involvement with tumor cells extending beyond the positive lymph node (LN+) and extracapsular extension (ECE) has been overlooked in breast pathology specimen analysis. We analyzed 2,162 LN+ patients, dividing them into four groups on the basis of axillary pathology: (1) LN+ only, (2) LN+ and ECE only, (3) LN+ and AXT without ECE, and (4) LN+ with both AXT and ECE. The primary end points were 10-year locoregional failure (LRF), the 10-year axillary failure, and 10-year distant metastasis rates. Multivariable Cox models, accounting for clinical factors, were fitted using the entire cohort, and subgroups analyses were conducted. The median follow-up was 9.4 years. The 10-year distant metastasis incidence was 42% for LN + AXT + ECE, 23% for both LN + AXT and LN + ECE only, and 13% for LN+ only. The 10-year axillary failure rates were 4.5% for LN + AXT + ECE, 4.6% for LN + AXT, 0.8% for LN + ECE only, and 1.6% for LN+ only. The 10-year LRF rates were 14% for LN + AXT + ECE, 10% for LN + AXT, 5.7% for LN + ECE only, and 6.2% for LN+ only. Multivariable analysis revealed that AXT was significantly associated with distant metastasis (hazard ratio [HR], 1.6; P < .001), locoregional failure (HR, 2.3; P < .001), and axillary failure (HR, 3.3; P = .003). Subgroup analyses showed that regional LN radiation (RLNR) improved locoregional tumor outcomes with AXT, ECE, or both (HR, 0.5; P = .03). Delivering ≤50 Gy to the axilla in the presence of AXT/ECE increased axillary failure (HR, 3.0; P = .04). Moreover, when delivering RLNR, axillary LN dissection could be de-escalated to sentinel node biopsy even in the presence of features such as AXT or ECE without significantly increasing any failure outcome: (HR, 1.0; P = .92) for LRF, (HR, 1.1; P = .94) axillary failure, and (HR, 0.4; P = .01) distant metastasis. Routine reporting of axillary tissue involvement, beyond LNs and ECE, is crucial in predicting breast cancer outcomes. Ruling out the presence of AXT is imperative before any form of axillary de-escalation, especially RLNR omission.

The Effect of Clinical (Anatomical) and Prognostic Stage Groups on Survival in Patients Diagnosed with Breast Cancer: MULTI-Center Study Results, TROD Breast Cancer Study Group

The American Joint Committee on Cancer (AJCC) anatomic stage groups have arguably been the most powerful method in predicting breast cancer outcomes. In the present study, we aimed to determine the differences between anatomical stage and clinical prognostic stage groups, which were obtained by adding biological markers such as histologic tumor grade, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 status to patients diagnosed with breast cancer, the rate of change in stage, and the effect of stage change on survival. The study protocol was sent to all radiation oncology centers in Türkiye. Four centers enrolled their patients to the study. A total of 1470 stage I-III breast cancer patients with complete information on biological markers (histologic tumor grade, hormone receptor, and c-erb B2 receptor status), haven't been treated with neoadjuvant therapy were included to the study and evaluated retrospectively. The 8th edition of AJCC consolidated biological markers in to clinical prognostic stage groups. Kaplan-Meier curves were used to estimate survival. The log-rank test was used to compare the difference between groups. The Cox proportional-hazards regression model was used to determine the association between anatomic-prognostic stage, disease-free survival (DFS), and overall survival (OS). The median follow-up time was 82 (6-237) months. Median age of the patients was 52 years (20-88) and 865 cases (58.8%) were in the postmenopausal period. The axillary lymph node status was negative (N0) in 765 patients (52.1%). Tumor grade was grade I in 200 patients (13.6%), grade II in 812 patients (55.2%) and grade III in 452 patients (30.7%). Estrogen receptor status was positive in 1247 patients (84.8%), PR status was positive in 1178 patients and Her2-neu status was positive in 207 patients (14.1%). A stage change has been identified in a total of 777 patients (52.9%). Compared with the anatomic stage groups, application of the clinical prognostic stage groups assigned 46.4% cases lower and 6.5% cases higher stage. Five- and ten-year OS and DFS rates of the patients are 73.7%, 44.3% and 91.9%, 86.3% respectively. Age (p<0.001), tumor grade (p<0.001), ER status (p<0.001), PR status (p<0.001), cerbB2 receptor status (p = 0.025) were found to be statistically significant variables in multivariable analysis for OS. For DFS, multivariable analysis showed that age (p = 0.027), tumor grade (p = 0.005), anatomical stage (p<0.001) and assigned to higher stage (p = .001) were statistically significant variables. Hormone receptors and c-erb B2 receptor status are independent variables which impact OS and DFS in our patient group which is mostly consisted of early-stage cases according to anatomical stage. In prognostic staging, upstaging stands out as an independent prognostic factor for DFS. The 8th edition of AJCC prognostic stage groups determines the prognosis much better in our patient cohort.

Clonal expansion of intra-epithelial T cells in breast cancer revealed by spatial transcriptomics.

The spatial distribution of tumor-infiltrating lymphocytes (TIL) predicts breast cancer outcome and response to systemic therapy, highlighting the importance of an intact tissue structure for characterizing tumors. Here, we present ST-FFPE, a spatial transcriptomics method for the analysis of formalin-fixed paraffin-embedded samples, which opens the possibility of interrogating archival tissue. The method involves extraction, exome capture and sequencing of RNA from different tumor compartments microdissected by laser-capture, and can be used to study the cellular composition of tumor microenvironment. Focusing on triple-negative breast cancer (TNBC), we characterized T cells, B cells, dendritic cells, fibroblasts and endothelial cells in both stromal and intra-epithelial compartments. We found a highly variable spatial distribution of immune cell subsets among tumors. This analysis revealed that the immune repertoires of intra-epithelial T and B cells were consistently less diverse and more clonal than those of stromal T and B cells. T-cell receptor (TCR) sequencing confirmed a reduced diversity and higher clonality of intra-epithelial T cells relative to the corresponding stromal T cells. Analysis of the top 10 dominant clonotypes in the two compartments showed a majority of shared but also some unique clonotypes both in stromal and intra-epithelial T cells. Hyperexpanded clonotypes were more abundant among intra-epithelial than stromal T cells. These findings validate the ST-FFPE method and suggest an accumulation of antigen-specific T cells within tumor core. Because ST-FFPE is applicable for analysis of previously collected tissue samples, it could be useful for rapid assessment of intratumoral cellular heterogeneity in multiple disease and treatment settings.

A Metabolic Gene Signature to Predict Breast Cancer Prognosis.

Background: The existing metabolic gene signatures for predicting breast cancer outcomes only focus on gene expression data without considering clinical characteristics. Therefore, this study aimed to establish a predictive risk model combining metabolic enzyme genes and clinicopathological characteristics to predict the overall survival in patients with breast cancer. Methods: Transcriptomics and corresponding clinical data for patients with breast cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed metabolic genes between tumors and normal tissues were identified in the TCGA dataset (training dataset). A prognostic model was then built using univariate and multifactorial Cox proportional hazards regression analyses in the training dataset. The capability of the predictive model was then assessed using the receiver operating characteristic in both datasets. Pathway enrichment analysis and immune cell infiltration were performed using Kyoto Encyclopedia of Genes and Genomes (KEGG)/Gene Ontology (GO) enrichment and CIBERSORT algorithm, respectively. Results: In breast cancer and normal tissues, 212 metabolic enzyme genes were differentially expressed. The predictive model included four factors: age, stage, and expression of SLC35A2 and PLA2G10. Patients with breast cancer were classified into high- and low-risk groups based on the model; the high-risk group had a significantly poorer overall survival rate than the low-risk group. Furthermore, the two risk groups showed different activation of pathways and alterations in the properties of tumor microenvironment-infiltrating immune cells. Conclusion: We developed a powerful model to predict prognosis in patients with breast cancer by combining the gene expression of metabolic enzymes with clinicopathological characteristics.

Oncogene-mediated metabolic gene signature predicts breast cancer outcome

Breast cancer remains the second most lethal cancer among women in the United States and triple-negative breast cancer is the most aggressive subtype with limited treatment options. Trop2, a cell membrane glycoprotein, is overexpressed in almost all epithelial cancers. In this study, we demonstrate that Trop2 is overexpressed in triple-negative breast cancer (TNBC), and downregulation of Trop2 delays TNBC cell and tumor growth supporting the oncogenic role of Trop2 in breast cancer. Through proteomic profiling, we discovered a metabolic signature comprised of TALDO1, GPI, LDHA, SHMT2, and ADK proteins that were downregulated in Trop2-depleted breast cancer tumors. The identified oncogene-mediated metabolic gene signature is significantly upregulated in TNBC patients across multiple RNA-expression clinical datasets. Our study further reveals that the metabolic gene signature reliably predicts poor survival of breast cancer patients with early stages of the disease. Taken together, our study identified a new five-gene metabolic signature as an accurate predictor of breast cancer outcome.

Abstract 2324: A novel oncogene mediated metabolic gene signature predicts breast cancer outcome

Abstract Introduction and Objectives: Breast cancer remains the second most lethal cancer among women in the US, with triple negative breast cancer (TNBC) as the most aggressive subtype with limited treatment options. Trop2, a cell membrane glycoprotein, is overexpressed nearly all epithelial cancers including triple negative breast cancer. In this study, we demonstrate that loss of Trop2 derives downregulation of metabolic genes. Trop2 oncogene-driven metabolic gene signature is highly elevated in breast cancer patients and predicts poor outcome. Methods: Trop2 expression levels in two independent patient cohorts were analyzed with immunohistochemistry. Trop2 was knockdown in TNBC cell line HCC1806 with shRNA and knock-out in HCC1806 with CRISPR Cas9 system. Trop2 downregulation or gene deletion effect was tested in vitro with colony formation and 3D Matrigel invasion assays. The effect of Trop2 downregulation in tumorigenesis was evaluated in vivo by subcutaneous implantation of control and Trop2 knockdown xenografts. Proteomic changes were analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS). Trop2 led to downregulation of TALDO1 associated metabolic cluster genes which expression levels were further investigated in four independent breast cancer cohort. Finally, the correlation of Trop2 oncogene-driven metabolic gene signature with breast cancer outcome was analyzed in 13 independent breast cancer patient datasets. Results: We demonstrate Trop2 is overexpressed in TNBC, and downregulation of Trop2 disrupts TNBC cell and tumor growth. Through whole proteomic profiling, we discovered a novel metabolic cluster that was downregulated in Trop2-depleted breast cancer tumors. The identified oncogene-driven metabolic gene signature is significantly upregulated in TNBC patients across multiple clinical datasets. Our study further reveals that the metabolic gene signature reliably predicts poor survival of breast cancer patients with early stage of the disease. Conclusion: Our study identifies a new five gene metabolic oncogene-driven signature as a prognostic predictor of breast cancer. Citation Format: Merve Aslan, En-Chi Hsu, Fernando J. Garcia Marques, Abel Bermudez, Michelle Shen, Meghan A. Rice, Shiqin Liu, Robert West, Sharon J. Pitteri, Balázs Győrffy, Tanya Stoyanova. A novel oncogene mediated metabolic gene signature predicts breast cancer outcome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2324.

A gp78/AMFR protein-driven gene signature that predicts breast cancer outcome.

553 Background: gp78, also known as the autocrine motility factor receptor (AMFR) or RNF45, is a polytopic RING-type E3 ubiquitin ligase resident to the endoplasmic reticulum (ER) that plays major role in the cellular response to stress by regulating ER homeostasis and signaling through its participation in the unfolded protein response (UPR) and ER associated degradation. We used machine learning (ML) and statistical modeling (SM) to assess gp78 as a protein biomarker that is an independent predictor of breast cancer (bc) survival exclusively in women of self-reported African descent as opposed to European ancestry. Methods: We examined a cohort of racially diverse 555 BC bc patients who underwent surgery for their primary BC in Greenville, NC using ML and SM approach. We leveraged the availability of RNA-seq gene expression data on a portion of our bc cohort (N=136 of 555) to construct gene expression signatures. Results: Using antibodies developed in the Weissman lab and established methods for quantitative IHC, we have found that gp78 expression is significantly increased in the tumors of bc patients compared to normal breast epithelia. In addition, we found that gp78 is expressed at significantly higher levels in bc of non-Hispanic black women (NHB) compared to non-Hispanic white women (NHW) (p=0.0038), and that bc subtypes known to be more aggressive and associated with higher grades like, Basal (p=1.6e-12), Luminal B (p=2.3e-4) and HER2(8.3e-4), display significantly higher levels of gp78 compare to Luminal A. Moreover, Kaplan-Meier survival curve analyses show that gp78 protein expression is more significantly associated with poor survival in NHB women (HR:1.65, p=0.073) compared to NHW women (HR:2.01, p=0.004). Finally, multivariate analysis reveals that gp78 protein expression, based on quantitative IHC, is an independent predictor of poor bc survival exclusively in women of African (NHB) ancestry (HR:1.99, p=0.017). We leveraged the availability of RNA-seq gene expression data on a portion of our bc cohort to construct gene expression signatures or gene modules. An analysis of pooled publicly available data from 845 patients that underwent neoadjuvant chemotherapy for bc (primarily taxane and anthracycline based), reveals that gp78 gene modules are highly predictive of patient response to therapy. gp78-derived gene modules show both high fold difference and significance in predicting response to therapy (AUC:0.72) which is very similar to other multi-gene panels that are currently in clinical use including Prosigna, MammaPrint, and Oncotype Dx. Conclusions: Our results show that gp78/AMFR is an independent predictor of bc survival and response to therapy, based on race, thus implicating a role for this protein, and potentially the UPR, as underlying biological differences in tumor properties linked to genetic ancestry.

377 Poster - Circulating tumour DNA as a prognostic biomarker in predicting breast cancer outcomes: Systematic review and meta-analysis

377 Poster - Circulating tumour DNA as a prognostic biomarker in predicting breast cancer outcomes: Systematic review and meta-analysis

Elevated levels of tumour apolipoprotein D independently predict poor outcome in breast cancer patients.

Apolipoprotein D (ApoD) is a protein that is regulated by androgen and oestrogen, and is a major constituent of breast cysts. Although ApoD has been reported to be a marker of breast cancer, its prognostic importance in invasive breast cancer is unclear. The aim of this study was to investigate the relationship between ApoD protein expression, oestrogen receptor-α (ERα) expression and androgen receptor (AR) expression in predicting breast cancer outcome. ApoD levels were measured by the use of immunohistochemistry and video image analysis on tissue sections from a breast cancer cohort (n=214). We assessed the associations of ApoD expression with disease-free survival (DFS), metastasis-free survival (MFS), and overall survival (OS). We also assessed the relationship between ApoD expression, AR expression and ERα expression in predicting OS. ApoD expression (>1% ApoD positivity) was found in 72% (154/214) of tissues. High ApoD positivity (≥20.7%, fourth quartile) was an independent predictor of MFS and OS, and conferred a 2.2-fold increased risk of developing metastatic disease and a 2.1-fold increased risk of breast cancer-related death. ApoD positivity was not associated with AR or ERα nuclear positivity. However, patients with (≥1%) ERα-positive cancers with low (<20.7%) ApoD positivity, or those showing high (≥78%) AR positivity and low (<20.7%) ApoD positivity had better OS than other patient groups. ApoD expression could be used to predict breast cancer prognosis independently of ERα and AR expression.

AB022. Circulating tumour DNA as a prognostic biomarker in predicting breast cancer outcomes: systematic review and meta-analysis

Background: Fragmented deoxyribonucleic acid (DNA) is constantly released into the circulation by apoptosis and necrosis of both cancerous and non-cancerous cell. When it is released by cancer cells, it is specifically known as circulating tumour DNA (ctDNA). We performed a systematic review and meta-analysis to determine the clinical utility of ctDNA as a prognostic biomarker in predicting breast cancer outcomes.

Integral characteristic of the immune system state predicts breast cancer outcome

We have evaluated 17parameters cha-racterizing key innate and adaptive arms of immune system in 146patients with primary diagnosed local breast cancer(BC) before cancer treatment. Using the original integrative approach of NovoSpark Corporation (Canada), we have presented the state of the immune system by a single visual image for each patient. We classified all BC patients in two groups: with favorable and unfavorable immune system states according to our previous data demonstrating dramatic differences of the visual images of immune system states in patients with good or poor disease outcomes. Then, we have examined the relationship between integral characteristic of the immune system state and the clinical outcome. The 3-year disease-free survival (DFS) of BC patients with favorable immune system state was more than 96.0% vs 65.4% in patients with unfavorable status (p=0.00006). Univariate Cox proportional hazards regression analysis showed that the integral characteristic of immune system state assessed prior to cancer treatment as unfavorable, was predictive of the poorer DFS (HR = 15.70 [2.15-114.90], p=0.0007). The integral characteristic of the immune system state is a significant prognostic factor in BC patients. The BC patients with favorable integral immune system state can be considered as a target group for immunotherapeutic approach.

Comparison of AJCC Anatomic and Clinical Prognostic Stage Groups in Breast Cancer: Analysis of 3322 Cases From a Single Institution

BackgroundThe American Joint Committee on Cancer anatomic stage/prognostic group template arguably is the most powerful in predicting breast cancer outcomes because it considers the primary tumor, regional lymph node involvement, and presence of distant metastasis. However, other tumor and host characteristics have also been proved to be of prognostic value, including histologic grade and estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 status. Thus, the 8th edition of the American Joint Committee on Cancer consolidated these factors into clinical prognostic stage groups. Materials and MethodsWe validated the clinical prognostic stage groups compared with the anatomic stage groups in a cohort of 3322 breast cancer patients. ResultsCompared with the anatomic stage, application of the clinical prognostic stage assigned 27.7% and 24.7% of cases to higher and lower stage groups, respectively. In 14% and 2.8% of cases, the assignment of clinical prognostic stage varied by 2 and 3 anatomic stages up or down, respectively. The Cox proportional hazard model demonstrated superior discriminatory power for clinical prognostic stage (overall χ2, 464.5; P < .0001 vs. χ2, 363.9; P < .0001). A pairwise comparison revealed that significant improvement was especially observed for patients with clinically prognostic stage I and III disease compared with that of the anatomic stage groups. ConclusionThe new clinical prognostic stage provides a more powerful, yet imperfect, tool for predicting breast cancer outcomes. Further refinement of this system might be necessary in the pursuit of precision medicine.

Cyclin D1-mediated microRNA expression signature predicts breast cancer outcome.

Background: Genetic classification of breast cancer based on the coding mRNA suggests the evolution of distinct subtypes. Whether the non-coding genome is altered concordantly with the coding genome and the mechanism by which the cell cycle directly controls the non-coding genome is poorly understood.Methods: Herein, the miRNA signature maintained by endogenous cyclin D1 in human breast cancer cells was defined. In order to determine the clinical significance of the cyclin D1-mediated miRNA signature, we defined a miRNA expression superset from 459 breast cancer samples. We compared the coding and non-coding genome of breast cancer subtypes.Results: Hierarchical clustering of human breast cancers defined four distinct miRNA clusters (G1-G4) associated with distinguishable relapse-free survival by Kaplan-Meier analysis. The cyclin D1-regulated miRNA signature included several oncomirs, was conserved in multiple breast cancer cell lines, was associated with the G2 tumor miRNA cluster, ERα+ status, better outcome and activation of the Wnt pathway. The coding and non-coding genome were discordant within breast cancer subtypes. Seed elements for cyclin D1-regulated miRNA were identified in 63 genes of the Wnt signaling pathway including DKK. Cyclin D1 restrained DKK1 via the 3'UTR. In vivo studies using inducible transgenics confirmed cyclin D1 induces Wnt-dependent gene expression.Conclusion: The non-coding genome defines breast cancer subtypes that are discordant with their coding genome subtype suggesting distinct evolutionary drivers within the tumors. Cyclin D1 orchestrates expression of a miRNA signature that induces Wnt/β-catenin signaling, therefore cyclin D1 serves both upstream and downstream of Wnt/β-catenin signaling.

Serum concentrations of active tamoxifen metabolites predict long-term survival in adjuvantly treated breast cancer patients

BackgroundControversies exist as to whether the genetic polymorphisms of the enzymes responsible for the metabolism of tamoxifen can predict breast cancer outcome in patients using adjuvant tamoxifen. Direct measurement of concentrations of active tamoxifen metabolites in serum may be a more biological plausible and robust approach. We have investigated the association between CYP2D6 genotypes, serum concentrations of active tamoxifen metabolites, and long-term outcome in tamoxifen treated breast cancer patients.MethodsFrom an original observational study comprising 817 breast cancer patients, 99 women with operable breast cancer were retrospectively included in the present study. This cohort of patients were adjuvantly treated with tamoxifen, had provided serum samples suitable for measuring tamoxifen metabolites, and were relapse-free at 3 years after the primary treatment commenced. The median follow-up time from this entry point to breast cancer death was 13.9 years. Patients were CYP2D6 genotyped and grouped into four CYP2D6 phenotype groups (Ultra rapid, extensive, intermediate, and poor metabolizers). Tamoxifen and nine metabolites were quantified in serum (n = 86) and compared with CYP2D6 phenotype groups and outcome.ResultsBreast cancer patients with low concentrations of Z-4-hydroxy-tamoxifen (Z-4OHtam; ≤ 3.26 nM) had a breast cancer-specific survival (BCSS) of 60% compared to 84% in patients with Z-4OHtam concentrations > 3.26 nM (p = 0.020, log-rank hazard ratio (HR) = 3.56, 95% confidence interval (CI) = 1.14–11.07). For patients with Z-4-hydroxy-N-desmethyl-tamoxifen (Z-endoxifen) levels ≤ 9.00 nM BCSS was 57% compared to 84% for patients with concentrations > 9.00 nM (p = 0.029, HR = 3.73, 95% CI = 1.05–13.22). Low concentrations of Z-4OHtam and Z-endoxifen were associated with poorer survival also after adjusting for clinically relevant variables (HR = 4.27, 95% CI = 1.35–13.58, and HR = 3.70, 95% CI = 1.03–13.25, respectively). Overall survival analysis showed similar survival differences for both active metabolites. The Antiestrogen Activity Score showed comparable effects, but did not improve the prognostic information.ConclusionsPatients with Z-4OHtam and Z-endoxifen concentrations lower than 3.26 nM or 9.00 nM, respectively, showed an adverse outcome. Our results suggest that direct measurement of active tamoxifen metabolite concentrations could be of clinical value. Validation in larger study cohorts is warranted.

Tumor and serum DNA methylation in women receiving preoperative chemotherapy with or without vorinostat in TBCRC008

Methylated gene markers have shown promise in predicting breast cancer outcomes and treatment response. We evaluated whether baseline and changes in tissue and serum methylation levels would predict pathological complete response (pCR) in patients with HER2-negative early breast cancer undergoing preoperative chemotherapy. The TBCRC008 trial investigated pCR following 12weeks of preoperative carboplatin and albumin-bound paclitaxel+vorinostat/placebo (n=62). We measured methylation of a 10-gene panel by quantitative multiplex methylation-specific polymerase chain reaction and expressed results as cumulative methylation index (CMI). We evaluated association between CMI level [baseline, day 15 (D15), and change] and pCR using univariate and multivariable logistic regression models controlling for treatment and hormone receptor (HR) status, and performed exploratory subgroup analyses. In univariate analysis, one log unit increase in tissue CMI levels at D15 was associated with 40% lower chance of obtaining pCR (odds ratio, OR0.60, 95% CI 0.37-0.97; p=0.037). Subgroup analyses suggested a significant association between tissue D15 CMI levels and pCR in vorinostat-treated [OR 0.44 (0.20, 0.93), p=0.03], but not placebo-treated patients. In this study investigating the predictive roles of tissue and serum CMI levels in patients with early breast cancer for the first time, we demonstrate that high D15 tissue CMI levels may predict poor response. Larger studies and improved analytical procedures to detect methylated gene markers in early stage breast cancer are needed. TBCRC008 is registered on ClinicalTrials.gov (NCT00616967).

Gene expression profiling of the 8q22-24 position in human breast cancer: TSPYL5, MTDH, ATAD2 and CCNE2 genes are implicated in oncogenesis, while WISP1 and EXT1 genes may predict a risk of metastasis.

Gene expression profiling has been suggested to predict breast cancer outcome. The prognostic value of the 8q22-24 position in breast cancer remains to be elucidated. The present study evaluated expression patterns of the genes located at this position in metastatic and non-metastatic breast cancer. A total of 85 patients with recurrent/metastatic (n=15) and non-metastatic (n=70) early-stage, estrogen receptor-positive and lymph node-negative breast tumors were included. In addition, 15 normal breast tissue samples were used as controls. Demographic and clinical features were recorded. Subsequently, mRNA copy numbers of exostosin glycosyltransferase 1 (EXT1), WNT1 inducible signaling pathway protein 1 (WISP1), ATPase family, AAA domain containing 2 (ATAD2), TSP-like 5 (TSPYL5), metadherin (MTDH) and cyclin E2 (CCNE2) genes were measured by reverse transcription-quantitative polymerase chain reaction assay. The expression of EXT1 and WISP1 exhibited a significant decline in the metastatic breast cancer group compared to the control (P=0.015 and P=0.012, respectively). The expression of TSPYL5, MTDH and ATAD2 was significantly decreased in the metastatic (P=0.002, P=0.018 and P=0.016, respectively) and non-metastatic (P=0.038, P=0.045 and P=0.000, respectively) breast cancer groups compared with the control. The expression of CCNE2 in the metastatic and non-metastatic breast cancer groups was significantly increased compared with the control (P=0.002 and P=0.001, respectively). WISP1 expression demonstrated a correlation with patient age and tumor size, and TSPYL5 expression was correlated with lymphovascular invasion. None of the genes investigated exhibited any correlation with stage and grade of disease. The TSPYL5, MTDH, ATAD2 and CCNE2 genes may be implicated in the pathogenesis of human breast cancer, while the WISP1 and EXT1 genes may have the potential to serve as promising indicators of the risk of metastasis. However, further studies are required to validate these results.

Validity of the online PREDICT tool in older patients with breast cancer: a population-based study.

Background:Predicting breast cancer outcome in older patients is challenging, as it has been shown that the available tools are not accurate in older patients. The PREDICT tool may serve as an alternative tool, as it was developed in a cohort that included almost 1800 women aged 65 years or over. The aim of this study was to assess the validity of the online PREDICT tool in a population-based cohort of unselected older patients with breast cancer.Methods:Patients were included from the population-based FOCUS-cohort. Observed 5- and 10-year overall survival were estimated using the Kaplan–Meier method, and compared with predicted outcomes. Calibration was tested by composing calibration plots and Poisson Regression. Discriminatory accuracy was assessed by composing receiver-operator-curves and corresponding c-indices.Results:In all 2012 included patients, observed and predicted overall survival differed by 1.7%, 95% confidence interval (CI)=−0.3–3.7, for 5-year overall survival, and 4.5%, 95% CI=2.3–6.6, for 10-year overall survival. Poisson regression showed that 5-year overall survival did not significantly differ from the ideal line (standardised mortality ratio (SMR)=1.07, 95% CI=0.98–1.16, P=0.133), but 10-year overall survival was significantly different from the perfect calibration (SMR=1.12, 95% CI=1.05–1.20, P=0.0004). The c-index for 5-year overall survival was 0.73, 95% CI=0.70–0.75, and 0.74, 95% CI=0.72–0.76, for 10-year overall survival.Conclusions:PREDICT can accurately predict 5-year overall survival in older patients with breast cancer. Ten-year predicted overall survival was, however, slightly overestimated.