Accurate Prediction and Validation of Response to Endocrine Therapy in Breast Cancer

Purpose Aromatase inhibitors (AIs) have an established role in the treatment of breast cancer. Response rates are only 50% to 70% in the neoadjuvant setting and lower in advanced disease. Accurate biomarkers are urgently needed to predict response in these settings and to determine which individuals will benefit from adjuvant AI therapy. Patients and Methods Pretreatment and on-treatment (after 2 weeks and 3 months) biopsies were obtained from 89 postmenopausal women who had estrogen receptor–alpha positive breast cancer and were receiving neoadjuvant letrozole for transcript profiling. Dynamic clinical response was assessed with use of three-dimensional ultrasound measurements. Results The molecular response to letrozole was characterized and a four-gene classifier of clinical response was established (accuracy of 96%) on the basis of the level of two genes before treatment (one gene [IL6ST] was associated with immune signaling, and the other [NGFRAP1] was associated with apoptosis) and the level of two proliferation genes (ASPM, MCM4) after 2 weeks of therapy. The four-gene signature was found to be 91% accurate in a blinded, completely independent validation data set of patients treated with anastrozole. Matched 2-week on-treatment biopsies were associated with improved predictive power as compared with pretreatment biopsies alone. This signature also significantly predicted recurrence-free survival (P = .029) and breast cancer –specific survival (P = .009). We demonstrate that the test can also be performed with use of quantitative polymerase chain reaction or immunohistochemistry. Conclusion A four-gene predictive model of clinical response to AIs by 2 weeks has been generated and validated. Deregulated immune and apoptotic responses before treatment and cell proliferation that is not reduced 2 weeks after initiation of treatment are functional characteristics of breast tumors that do not respond to AIs.

Download Full-text

A novel ferroptosis related gene signature is associated with prognosis in patients with ovarian serous cystadenocarcinoma

Scientific Reports ◽

10.1038/s41598-021-90126-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zhixiang Yu ◽

Haiyan He ◽

Yanan Chen ◽

Qiuhe Ji ◽

Min Sun

Keyword(s):

Cox Regression ◽

Expression Profiles ◽

Critical Role ◽

Gene Signature ◽

Cancer Genome ◽

Training Data ◽

Hub Genes ◽

Validation Data ◽

Data Set ◽

Cox Regression Analysis

AbstractOvarian cancer (OV) is a common type of carcinoma in females. Many studies have reported that ferroptosis is associated with the prognosis of OV patients. However, the mechanism by which this occurs is not well understood. We utilized Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) to identify ferroptosis-related genes in OV. In the present study, we applied Cox regression analysis to select hub genes and used the least absolute shrinkage and selection operator to construct a prognosis prediction model with mRNA expression profiles and clinical data from TCGA. A series of analyses for this signature was performed in TCGA. We then verified the identified signature using International Cancer Genome Consortium (ICGC) data. After a series of analyses, we identified six hub genes (DNAJB6, RB1, VIMP/ SELENOS, STEAP3, BACH1, and ALOX12) that were then used to construct a model using a training data set. The model was then tested using a validation data set and was found to have high sensitivity and specificity. The identified ferroptosis-related hub genes might play a critical role in the mechanism of OV development. The gene signature we identified may be useful for future clinical applications.

Download Full-text

Estrogen receptor alpha (ESR1) gene amplification status and clinical outcome in tamoxifen-treated postmenopausal patients with endocrine-responsive early breast cancer: An analysis of the prospective ABCSG-6 trial.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.10501 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. 10501-10501 ◽

Cited By ~ 2

Author(s):

Christian F. Singer ◽

Frederik Holst ◽

Stefan Steurer ◽

E C Burandt ◽

Hellmut Samonigg ◽

...

Keyword(s):

Breast Cancer ◽

Estrogen Receptor ◽

Clinical Outcome ◽

Copy Number ◽

Estrogen Receptor Alpha ◽

Distant Recurrence ◽

Breast Cancer Specific Survival ◽

Cancer Specific Survival ◽

Esr1 Gene

10501 Background: Estrogen receptor alpha (ERα) expression is a prognostic parameter in breast cancer and predicts response to endocrine therapy. One of the factors important for protein expression is amplification of its encoding gene ESR1. We have investigated the value of ESR1 amplification in predicting the long-term clinical outcome in tamoxifen-treated postmenopausal women with endocrine-responsive breast cancer. Methods: 394 patients who had been randomized into the tamoxifen-only arm of the prospectively designed endocrine ABCSG-06 trial and in whom FFPE tumor tissue was available were included in this analysis. Immunohistochemical ERα expression was evaluated both locally and centrally using the Allred score, while ESR1 gene amplification status was evaluated by FISH analysis using the ESR1/CEN6 ratio. Results: ESR1 copy number gains were detected in 187 of 394 (47%) tumor specimen and was associated with favorable clinical outcome. At a median follow-up of 10 years, women with intratumoral ESR1 copy number gains had a significantly longer distant recurrence-free survival (adjusted HR for relapse 0.48; 95% CI 0.28-0.83; p=0.009) and breast cancer-specific survival (adjusted HR for death 0.46; CI 0.46-0.71; p=0.006) when compared to women with normal ESR1 copy numbers. Immunohistochemical ERα protein expression, evaluated by Allred score, was significantly correlated with ESR1 copy number alterations (p<0.0001; Chi-Square test), but did itself not allow to discriminate between patients with poor and good prognosis. Conclusions: ESR1 amplification status is an independent and powerful predictor for long-term distant recurrence-free and breast cancer-specific survival in postmenopausal women with endocrine-responsive early-stage breast cancer who received 5 years of tamoxifen.

Download Full-text

Outcome of patients with an ultralow risk 70-gene signature in the MINDACT trial.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.500 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 500-500

Author(s):

Josephine Lopes Cardozo ◽

Caroline Drukker ◽

Marjanka Schmidt ◽

Laura van 't Veer ◽

Annuska Glas ◽

...

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Gene Signature ◽

Distant Recurrence ◽

Low Risk ◽

Risk Category ◽

Breast Cancer Specific Survival ◽

Cancer Specific Survival ◽

Clinical Risk ◽

Risk Patients

500 Background: Gene signatures have proven successful in identifying patients with a low risk of distant recurrence who could forego chemotherapy (CT) and are currently included in international treatment guidelines for breast cancer. For the 70-gene signature (MammaPrint) an additional threshold was established within the low risk category to identify patients with an ultralow risk of distant recurrence. In independent cohorts, these patients had excellent breast cancer specific survival at 15 years, suggesting that ultralow risk cancers represent indolent disease (Esserman, JAMA Oncol 2017, Delahaye, BC Res Treat 2017). Here we evaluate survival of patients with an ultralow risk 70-gene signature who participated in the randomized phase 3 MINDACT trial (Piccart, Lancet Oncol 2021). Methods: Of the 6,693 patients enrolled in the MINDACT trial (EORTC 10041/BIG 3-04) between 2007-2011, profiling revealed an ultralow risk 70-gene signature in 1,000 patients (15%). We assessed 5- and 8-year distant metastasis free interval (DMFI) and breast cancer specific survival (BCSS) in patients stratified by 70-gene signature result (high, low, ultralow), and within the ultralow risk group stratified by clinical risk. For these exploratory analyses, we used Kaplan-Meier estimates for time to event endpoints and Cox-regression models to calculate hazard ratio’s (HR). Results: Median follow-up was 8.7 years. Among the ultralow risk patients (n = 1,000), 67% were ≥50 years, 81% had tumors < 2cm, 80% were lymph node negative, 96% had grade 1 or 2 tumors and 99% were ER-positive. Systemic therapy was received by 83% of patients (69% endocrine therapy (ET), 14% ET + CT) and 16% received no adjuvant systemic treatment (AST). Survival estimates for all endpoints are shown in the table; 8-year DMFI was 97.0% (95% CI 95.8-98.1) for ultralow risk. The 8-year DMFI in ultralow risk patients who received no AST or ET only was 97.8% (95% CI 95.3-100) and 97.4% (95% CI 96.1-98.7), respectively. The HR for DMFI was 0.66 (95% CI 0.46-0.95) for ultralow vs low risk, after adjusting for tumor and treatment characteristics (preliminary results). Conclusions: In this prospective study, patients with an ultralow risk 70-gene signature have an excellent prognosis with 8-year BCSS above 99% regardless of clinical risk status, and with an 8-year DMFI of 95-98%.[Table: see text]

Download Full-text

Genomic Prediction of Locoregional Recurrence After Mastectomy in Breast Cancer

Journal of Clinical Oncology ◽

10.1200/jco.2005.02.5676 ◽

2006 ◽

Vol 24 (28) ◽

pp. 4594-4602 ◽

Cited By ~ 73

Author(s):

Skye H. Cheng ◽

Cheng-Fang Horng ◽

Mike West ◽

Erich Huang ◽

Jennifer Pittman ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cancer Patients ◽

Expression Profiles ◽

Eligible Patient ◽

Gene Expression Profiles ◽

Breast Cancer Patients ◽

Predictive Index ◽

Validation Data ◽

Data Set

Purpose This study aims to explore gene expression profiles that are associated with locoregional (LR) recurrence in breast cancer after mastectomy. Patients and Methods A total of 94 breast cancer patients who underwent mastectomy between 1990 and 2001 and had DNA microarray study on the primary tumor tissues were chosen for this study. Eligible patient should have no evidence of LR recurrence without postmastectomy radiotherapy (PMRT) after a minimum of 3-year follow-up (n = 67) and any LR recurrence (n = 27). They were randomly split into training and validation sets. Statistical classification tree analysis and proportional hazards models were developed to identify and validate gene expression profiles that relate to LR recurrence. Results Our study demonstrates two sets of gene expression profiles (one with 258 genes and the other 34 genes) to be of predictive value with respect to LR recurrence. The overall accuracy of the prediction tree model in validation sets is estimated 75% to 78%. Of patients in validation data set, the 3-year LR control rate with predictive index more than 0.8 derived from 34-gene prediction models is 91%, and predictive index 0.8 or less is 40% (P = .008). Multivariate analysis of all patients reveals that estrogen receptor and genomic predictive index are independent prognostic factors that affect LR control. Conclusion Using gene expression profiles to develop prediction tree models effectively identifies breast cancer patients who are at higher risk for LR recurrence. This gene expression–based predictive index can be used to select patients for PMRT.

Download Full-text

Reclassification of BRCA1 and BRCA2 variants of uncertain significance: a multifactorial analysis of multicentre prospective cohort

Journal of Medical Genetics ◽

10.1136/jmedgenet-2018-105565 ◽

2018 ◽

Vol 55 (12) ◽

pp. 794-802 ◽

Cited By ~ 10

Author(s):

Jee-Soo Lee ◽

Sohee Oh ◽

Sue Kyung Park ◽

Min-Hyuk Lee ◽

Jong Won Lee ◽

...

Keyword(s):

Breast Cancer ◽

External Validation ◽

Brca1 And Brca2 ◽

Clinical Genetic ◽

Validation Data ◽

Data Set ◽

Variants Of Uncertain Significance ◽

Pathogenic Variants ◽

Uncertain Significance ◽

Highly Correlated

BackgroundBRCA1 and BRCA2 (BRCA1/2) variants classified ambiguously as variants of uncertain significance (VUS) are a major challenge for clinical genetic testing in breast cancer; their relevance to the cancer risk is unclear and the association with the response to specific BRCA1/2-targeted agents is uncertain. To minimise the proportion of VUS in BRCA1/2, we performed the multifactorial likelihood analysis and validated this method using an independent cohort of patients with breast cancer.MethodsWe used a data set of 2115 patients with breast cancer from the nationwide multicentre prospective Korean Hereditary Breast Cancer study. In total, 83 BRCA1/2 VUSs (BRCA1, n=26; BRCA2, n=57) were analysed. The multifactorial probability was estimated by combining the prior probability with the overall likelihood ratio derived from co-occurrence of each VUS with pathogenic variants, personal and family history, and tumour characteristics. The classification was compared with the interpretation according to the American College of Medical Genetics and Genomics–Association for Molecular Pathology (ACMG/AMP) guidelines. An external validation was conducted using independent data set of 810 patients.ResultsWe were able to redefine 38 VUSs (BRCA1, n=10; BRCA2, n=28). The revised classification was highly correlated with the ACMG/AMP guideline-based interpretation (BRCA1, p for trend=0.015; BRCA2, p=0.001). Our approach reduced the proportion of VUS from 19% (154/810) to 8.9% (72/810) in the retrospective validation data set.ConclusionThe classification in this study would minimise the ‘uncertainty’ in clinical interpretation, and this validated multifactorial model can be used for the reliable annotation of BRCA1/2 VUSs.

Download Full-text

Expression signature based on TP53 target genes doesn't predict response to TP53-MDM2 inhibitor in wild type TP53 tumors

eLife ◽

10.7554/elife.10279 ◽

2015 ◽

Vol 4 ◽

Cited By ~ 11

Author(s):

Dmitriy Sonkin

Keyword(s):

Cell Lines ◽

Target Genes ◽

Gene Signature ◽

Wild Type ◽

Validation Data ◽

Data Set ◽

Expression Signature ◽

Selection Of Patients ◽

Mdm2 Inhibitor ◽

Selection Of

A number of TP53-MDM2 inhibitors are currently under investigation as therapeutic agents in a variety of clinical trials in patients with TP53 wild type tumors. Not all wild type TP53 tumors are sensitive to such inhibitors. In an attempt to improve selection of patients with TP53 wild type tumors, an mRNA expression signature based on 13 TP53 transcriptional target genes was recently developed (Jeay et al. 2015). Careful reanalysis of TP53 status in the study validation data set of cancer cell lines considered to be TP53 wild type detected TP53 inactivating alterations in 23% of cell lines. The subsequent reanalysis of the remaining TP53 wild type cell lines clearly demonstrated that unfortunately the 13-gene signature cannot predict response to TP53-MDM2 inhibitor in TP53 wild type tumors.

Download Full-text

Predictive Gene Signature in MAGE-A3 Antigen-Specific Cancer Immunotherapy

Journal of Clinical Oncology ◽

10.1200/jco.2012.44.3762 ◽

2013 ◽

Vol 31 (19) ◽

pp. 2388-2395 ◽

Cited By ~ 206

Author(s):

Fernando Ulloa-Montoya ◽

Jamila Louahed ◽

Benjamin Dizier ◽

Olivier Gruselle ◽

Bart Spiessens ◽

...

Keyword(s):

Clinical Response ◽

Metastatic Melanoma ◽

Early Stage ◽

Quantitative Polymerase Chain Reaction ◽

Gene Expression Signature ◽

Gene Signature ◽

Two Phase ◽

Phase Ii Studies ◽

Resected Nsclc ◽

Cancer Setting

Purpose To detect a pretreatment gene expression signature (GS) predictive of response to MAGE-A3 immunotherapeutic in patients with metastatic melanoma and to investigate its applicability in a different cancer setting (adjuvant therapy of resected early-stage non–small-cell lung cancer [NSCLC]). Patients and Methods Patients were participants in two phase II studies of the recombinant MAGE-A3 antigen combined with an immunostimulant (AS15 or AS02B). mRNA from melanoma biopsies was analyzed by microarray analysis and quantitative polymerase chain reaction. These results were used to identify and cross-validate the GS, which was then applied to the NSCLC data. Results In the patients with melanoma, 84 genes were identified whose expression was potentially associated with clinical benefit. This effect was strongest when the immunostimulant AS15 was included in the immunotherapy (hazard ratio [HR] for overall survival, 0.37; 95% CI, 0.13 to 1.05; P = .06) and was less strong with the other immunostimulant AS02B (HR, 0.84; 95% CI, 0.36 to 1.97; P = .70). The same GS was then used to predict the outcome for patients with resected NSCLC treated with MAGE-A3 plus AS02B; actively treated GS-positive patients showed a favorable disease-free interval compared with placebo-treated GS-positive patients (HR, 0.42; 95% CI, 0.17 to 1.03; P = .06), whereas among GS-negative patients, no such difference was found (HR, 1.17; 95% CI, 0.59 to 2.31; P = .65). The genes identified were mainly immune related, involving interferon gamma pathways and specific chemokines, suggesting that their pretreatment expression influences the tumor's immune microenvironment and the patient's clinical response. Conclusion An 84-gene GS associated with clinical response for MAGE-A3 immunotherapeutic was identified in metastatic melanoma and confirmed in resected NSCLC.

Download Full-text

Personalized analysis of breast cancer using sample-specific networks

PeerJ ◽

10.7717/peerj.9161 ◽

2020 ◽

Vol 8 ◽

pp. e9161

Author(s):

Ke Zhu ◽

Cong Pian ◽

Qiong Xiang ◽

Xin Liu ◽

Yuanyuan Chen

Keyword(s):

Breast Cancer ◽

Expression Profiles ◽

Single Gene ◽

Gene Interaction ◽

Interaction Networks ◽

Cancer Prognosis ◽

Specific Gene ◽

Validation Data ◽

Data Set ◽

Gene Interaction Networks

Breast cancer is a disease with high heterogeneity. Cancer is not usually caused by a single gene, but by multiple genes and their interactions with others and surroundings. Estimating breast cancer-specific gene–gene interaction networks is critical to elucidate the mechanisms of breast cancer from a biological network perspective. In this study, sample-specific gene–gene interaction networks of breast cancer samples were established by using a sample-specific network analysis method based on gene expression profiles. Then, gene–gene interaction networks and pathways related to breast cancer and its subtypes and stages were further identified. The similarity and difference among these subtype-related (and stage-related) networks and pathways were studied, which showed highly specific for subtype Basal-like and Stages IV and V. Finally, gene pairwise interactions associated with breast cancer prognosis were identified by a Cox proportional hazards regression model, and a risk prediction model based on the gene pairs was established, which also performed very well on an independent validation data set. This work will help us to better understand the mechanism underlying the occurrence of breast cancer from the sample-specific network perspective.

Download Full-text

Established and Validated a Novel Nomogram for Predicting Prognosis of Post-mastectomy pN0-1 Breast Cancer Without Adjuvant Radiotherapy

10.21203/rs.3.rs-84772/v1 ◽

2020 ◽

Author(s):

Wei-Xiang Qi ◽

Cao Lu ◽

Cheng Xu ◽

shengguang zhao ◽

Jiayi Chen

Keyword(s):

Breast Cancer ◽

Adjuvant Radiotherapy ◽

Early Stage ◽

External Validation ◽

Training Data ◽

Phase Iii ◽

Lasso Regression ◽

Cancer Specific Survival ◽

Data Set ◽

Free Survival

Abstract BackgroundThe present study aims to establish and validate a nomogram for predicting prognosis of breast cancer with pN0-1 who are treated with mastectomy and without adjuvant radiotherapy. Material and methods Between Jan 2009 and Dec 2015, a total of 1879 breast cancer without adjuvant radiotherapy were used for nomogram development. The model was externally validated in an independent cohort of 1356 patients from one phase III trial (NCT00041119). The least absolute shrinkage and selection operator (LASSO) regression were performed to identify predictors of breast cancer specific survival (BCSS), local regional recurrence (LRR), and distant metastasis (DM). Results The 5-year BCSS, LRR and DM rates for the entire cohort was 98% ,2% and 4%, respectively. LASSO regression analysis found that pathological T stage, number of positive LN, grade and Ki-67 were significant predictors for both BCSS and DM-free survival in post-mastectomy breast cancer with pN0-1, while number of resected LN and PR status were predictors for DM-free survival. In addition, number of positive LN was the only significant predictor for developing LRR. The C-indexes for the 5-year BCSS and DM nomograms were 0.81 and 0.78 in the training data set, 0.65 and 0.70 in the testing set, and 0.72 and 0.69 in the external validation set, respectively. Calibration plots illustrated excellent agreement between the nomogram predictions and actual observations for 5-year BCSS and DM-free survival.Conclusion Our prognostic nomograms could accurately predict 5-year BCSS and DM-free survival in post-mastectomy early stage breast cancer without adjuvant radiotherapy, which provide a useful tool to identify high-risk patients who could benefit from additional adjuvant therapy.

Download Full-text