A prognostic model for lymph node-negative breast cancer patients based on the integration of proliferation and immunity

Development of Gene Expression-Based Random Forest Model for Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer

Simple Summary

Only 20–50% of patients with triple negative breast cancer achieve a pathological complete response from neoadjuvant chemotherapy, a strong indicator of patient survival. Therefore, there is an urgent need for a reliable predictive model of the patient’s pathological complete response prior to actual treatment. The purpose of this study was to develop such a model based on random forest recursive feature elimination and to benchmark the performance of the proposed model against existing predictive models. Our study suggests that an 86-gene-based random forest model associated to DNA repair and cell cycle mechanisms can provide reliable predictions of neoadjuvant chemotherapy response in patients with triple negative breast cancer.

Abstract

Neoadjuvant chemotherapy (NAC) response is an important indicator of patient survival in triple negative breast cancer (TNBC), but predicting chemosensitivity remains a challenge in clinical practice. We developed an 86-gene-based random forest (RF) classifier capable of predicting neoadjuvant chemotherapy response (pathological Complete Response (pCR) or Residual Disease (RD)) in TNBC patients. The performance of pCR classification of the proposed model was evaluated by Receiver Operating Characteristic (ROC) curve and Precision Recall (PR) curve. The AUROC and AUPRC of the proposed model on the test set were 0.891 and 0.829, respectively. At a predefined specificity (>90%), the proposed model shows a superior sensitivity compared to the best performing reported NAC response prediction model (69.2% vs. 36.9%). Moreover, the predicted pCR status by the model well explains the distance recurrence free survival (DRFS) of TNBC patients. In addition, the pCR probabilities of the proposed model using the expression profiles of the CCLE TNBC cell lines show a high Spearman rank correlation with cyclophosphamide sensitivity in the TNBC cell lines (SRCC =0.697, p-value =0.031). Associations between the 86 genes and DNA repair/cell cycle mechanisms were provided through function enrichment analysis. Our study suggests that the random forest-based prediction model provides a reliable prediction of the clinical response to neoadjuvant chemotherapy and may explain chemosensitivity in TNBC.

Gene Expression Profiling in Early Breast Cancer-Patient Stratification Based on Molecular and Tumor Microenvironment Features

Patients with early-stage hormone receptor-positive, human epidermal growth factor receptor 2-negative (HER2−) breast cancer (BC) are typically treated with surgery, followed by adjuvant systemic endocrine therapy with or without adjuvant chemotherapy and radiation therapy. Current guidelines regarding the use of adjuvant systemic therapy depend on clinical and pathological factors, such as the morphological assessment of tumor subtype; histological grade; tumor size; lymphovascular invasion; and lymph node status combined with estrogen receptor, progesterone receptor, and HER2 biomarker profiles assessed using immunohistochemistry and in situ hybridization. Additionally, the prognostic and predictive value of tumor-infiltrating lymphocytes and their composition is emerging as a key marker in triple negative (TNBC) and HER2-enriched molecular breast tumor subtypes. However, all these factors do not necessarily reflect the molecular heterogeneity and complexity of breast cancer. In the last two decades, gene expression signatures or profiling (GEP) tests have been developed to predict the risk of disease recurrence and estimate the potential benefit of receiving adjuvant systemic chemotherapy in patients with luminal breast cancer. GEPs have been utilized to help physicians to refine decision-making process, complementing clinicopathological parameters, and can now be used to classify the risk of recurrence and tailoring personalized treatments. Several clinical trials using GEPs validate the increasing value of such assays in different clinical settings, addressing relevant clinical endpoints. Finally, the recent approval of immune checkpoint inhibitors in TNBC and the increasing use of immunotherapy in different molecular BC populations highlight the opportunity to refine current GEPs by including a variety of immune-related genes that may help to improve predicting drug response and finetune prognosis.

Risk Factors, Prognostic Factors, and Nomogram for Distant Metastasis in Breast Cancer Patients Without Lymph Node Metastasis

Background

Lymph node negative (N0) breast cancer can be found coexisting with distant metastasis (DM), which might consequently make clinicians underestimate the risk of relapse and insufficient treatment for this subpopulation.

Methods

The clinicopathological characteristics of N0 breast cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database between January 2010 and December 2015 were retrospectively reviewed. Multivariate logistic and Cox analyses were used to identify independent risk factors in promoting DM and the 1-, 3-, and 5- year cancer-specific survival (CSS) in this subpopulation.

Result

Seven factors including age (<40 years), tumor size (>10 mm), race (Black), location (central), grade (poor differentiation), histology (invasive lobular carcinoma), and subtype (luminal B and Her-2 enriched) were associated with DM, and the area under curve (AUC) was 0.776 (95% CI: 0.763-0.790). Moreover, T1-3N0M1 patients with age >60 years at diagnosis, Black race, triple-negative breast cancer subtype, no surgery performed, and multiple DMs presented a worse 1-, 3-, and 5-year CSS. The areas under the ROC for 1-, 3-, and 5- year CSS in the training cohort were 0.772, 0.741, and 0.762, respectively, and 0.725, 0.695, and 0.699 in the validation cohort.

Conclusion

The clinicopathological characteristics associated with the risk of DM and the prognosis of female breast cancer patients without lymph node metastasis but with DM are determined. A novel nomogram for predicting 1-, 3-, 5- year CSS in T1-3N0M1 patients is also well established and validated, which could help clinicians better stratify patients who are at a high-risk level for receiving relatively aggressive management.

A novel immune prognostic index for stratification of high-risk patients with early breast cancer

The prognostic value of current multigene assays for breast cancer is limited to hormone receptor-positive, human epidermal growth factor receptor 2-negative early breast cancer. Despite the prognostic significance of immune response-related genes in breast cancer, immune gene signatures have not been incorporated into most multigene assays. Here, using public gene expression microarray datasets, we classified breast cancer patients into three risk groups according to clinical risk and proliferation risk. We then developed the immune prognostic index based on expression of five immune response-related genes (TRAT1, IL2RB, CTLA4, IGHM and IL21R) and lymph node status to predict the risk of recurrence in the clinical and proliferation high-risk (CPH) group. The 10-year probability of disease-free survival (DFS) or distant metastasis-free survival (DMFS) of patients classified as high risk according to the immune prognostic index was significantly lower than those of patients classified as intermediate or low risk. Multivariate analysis revealed that the index is an independent prognostic factor for DFS or DMFS. Moreover, the C-index revealed that it is superior to clinicopathological variables for predicting prognosis. Its prognostic significance was also validated in independent datasets. The immune prognostic index identified low-risk patients among patients classified as CPH, regardless of the molecular subtype of breast cancer, and may overcome the limitations of current multigene assays.

Differences in the Immune Response of the Nonmetastatic Axillary Lymph Nodes between Triple-Negative and Luminal A Breast Cancer Surrogate Subtypes

Breast cancer (BC) comprises four immunohistochemical surrogate subtypes of which triple-negative breast cancer (TNBC) has the highest risk of mortality. Axillary lymph nodes (ALNs) are the regions where BC cells first establish before distant metastasis, and the presence of tumor cells in the ALN causes an immune tolerance profile that contrasts with that of the nonmetastatic ALN (ALN^-). However, few studies have compared the immune components of the ALNs^- in BC subtypes. The present study aimed to determine whether differences between immune populations in the primary tumor and ALNs^- were associated with the luminal A or TNBC subtype. We evaluated a retrospective cohort of 144 patients using paraffin-embedded biopsies. The TNBC samples tended to have a higher histologic grade and proliferation index and had higher levels of immune markers compared with luminal A in primary tumors and ALNs^-. Two methods for validating the multivariate analysis found that histologic grade, intratumoral S100 dendritic cells, and CD8 T lymphocytes and CD57 natural killer cells in the ALNs^- were factors associated with TNBC, whereas CD83 dendritic cells in the ALNs^- were associated with the luminal A subtype. In conclusion, we found that intratumoral regions and ALNs^- of TNBC contained higher concentrations of markers related to immune tolerance than luminal A. This finding partially explains the worse prognosis of patients with TNBC.

A DNA Methylation-Based Panel for the Prognosis and Dagnosis of Patients With Breast Cancer and Its Mechanisms

Objective

To identify DNA methylation related biomarkers in patients with breast cancer (BC).

Materials and Methods

A total of seven BC methylation studies including 1,438 BC patients or breast tissues were included in this study. An elastic net regularized Cox proportional hazards regression (CPH) model was used to build a multi-5′-C-phosphate-G-3′ methylation panel. The diagnosis and prognosis power of the panel was evaluated and validated using a Kaplan–Meier curve, univariate and multivariable CPH, subgroup analysis. A nomogram containing the panel was developed. The relationships between the panel-based methylation risk and the immune landscape and genomic metrics were investigated.

Results

Sixty-eight CpG sites were significantly correlated with the overall survival (OS) of BC patients, and based on the result of penalized CPH, a 28-CpG site based multi CpG methylation panel was found. The prognosis and diagnosis role of the panel was validated in the discovery set, validation set, and six independent cohorts, which indicated that higher methylation risk was associated with poor OS, and the panel outperformed currently available biomarkers and remained an independent factor after adjusting for other clinical features. The methylation risk was negatively correlated with innated and adaptive immune cells, and positively correlated with total mutation load, SCNA, and MATH.

Conclusions

We validated a multi CpG methylation panel that could independently predict the OS of BC patients. The Th2-mediated tumor promotion effect—suppression of innate and adaptive immunity—participated in the progression of high-risk BC. Patients with high methylation risk were associated with tumor heterogeneity and poor survival.

UBE2C Overexpression Aggravates Patient Outcome by Promoting Estrogen-Dependent/Independent Cell Proliferation in Early Hormone Receptor-Positive and HER2-Negative Breast Cancer

We previously showed that UBE2C mRNA expression is significantly associated with poor prognosis only in patients with hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)– breast cancer. In this study, we further reanalyzed the correlation between UBE2C mRNA expression and clinical outcomes in patients with HR+/HER2– breast cancer, and we investigated the molecular mechanism underlying the role of UBE2C modulation in disease progression in this subgroup of patients. Univariate and multivariate analyses showed that high UBE2C expression was associated with significantly shorter survival of breast cancer patients with pN0 and pN1 tumors but not pN2/N3 tumors (P < 0.05). In vitro functional experiments in HR+/HER2– breast cancer cells showed that UBE2C expression is a tumorigenic factor, and that estrogen upregulated UBE2C mRNA and protein by directly binding to the UBE2C promoter region. UBE2C knockdown inhibited cell proliferation by affecting cell cycle progression, and UBE2C overexpression was associated with estrogen-independent growth. UBE2C depletion markedly increased the cytotoxicity of tamoxifen by inducing apoptosis. The present findings suggest that UBE2C overexpression is correlated with relapse and promotes estrogen-dependent/independent proliferation in early HR+/HER2– breast cancer.

Emerging immune gene signatures as prognostic or predictive biomarkers in breast cancer

Several multigene assays have been developed to predict the risk of distant recurrence and response to adjuvant therapy in early breast cancer. However, the prognostic or predictive value of current proliferation gene signature-based assays are limited to hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+/HER2-) early breast cancer. Considerable discordance between the different assays in classifying patients into risk groups has also been reported, thus raising questions about the clinical utility of these assays for individual patients. Therefore, there still remains a need for better prognostic or predictive biomarkers for breast cancer. The role of immune cells comprising tumor microenvironment in tumor progression has been recognized. Accumulating evidences have shown that immune gene signatures and tumor-infiltrating lymphocytes (TILs) can be prognostic or predictive factors in breast cancer, particularly with regard to HER2+ and triple-negative breast cancer. In this review, I summarize current multigene assays for breast cancer and discuss recent progress in identifying novel breast cancer biomarkers, focusing on the emerging importance of immune gene signatures and TILs as prognostic or predictive biomarkers.

Engrailed 1 overexpression as a potential prognostic marker in quintuple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype characterized by poor patient prognosis and for which no targeted therapies are currently available. TNBC can be further categorized as either basal-like (BLBC) or quintuple-negative breast cancer (QNBC). In the present study, we aimed to identify novel molecular therapeutic targets for TNBC by analyzing the mRNA expression of TNBC-related genes in publicly available microarray data sets. We found that Engrailed 1 (EN1) was significantly overexpressed in TNBC. Using breast cancer cell lines, we found that EN1 was more highly expressed in TNBC than in other breast cancer subtypes. EN1 expression was analyzed in 199 TNBC paraffin-embedded tissue samples by immunohistochemistry. EN1 protein expression was positively associated with reduced overall survival (OS) rate in patients with QNBC, but not those with BLBC. The importance of EN1 expression in QNBC cell viability and tumorigenicity was evaluated using the QNBC cell lines, HCC38 and HCC1395. Based on our data, EN1 may promote the proliferation, migration, and multinucleation of QNBC cells, likely via the transcriptional activation of HDAC8, UTP11L, and ZIC3. We also demonstrated that actinomycin D effectively inhibits EN1 activity in QNBC cells. The results of the present study suggest that EN1 activity is highly clinically relevant to the survival prognosis of patients with QNBC and EN1 is a promising potential therapeutic target for future QNBC treatment.

Prognostic immune-related gene models for breast cancer: a pooled analysis

Breast cancer, the most common cancer among women, is a clinically and biologically heterogeneous disease. Numerous prognostic tools have been proposed, including gene signatures. Unlike proliferation-related prognostic gene signatures, many immune-related gene signatures have emerged as principal biology-driven predictors of breast cancer. Diverse statistical methods and data sets were used for building these immune-related prognostic models, making it difficult to compare or use them in clinically meaningful ways. This study evaluated successfully published immune-related prognostic gene signatures through systematic validations of publicly available data sets. Eight prognostic models that were built upon immune-related gene signatures were evaluated. The performances of these models were compared and ranked in ten publicly available data sets, comprising a total of 2,449 breast cancer cases. Predictive accuracies were measured as concordance indices (C-indices). All tests of statistical significance were two-sided. Immune-related gene models performed better in estrogen receptor-negative (ER−) and lymph node-positive (LN+) breast cancer subtypes. The three top-ranked ER− breast cancer models achieved overall C-indices of 0.62–0.63. Two models predicted better than chance for ER+ breast cancer, with C-indices of 0.53 and 0.59, respectively. For LN+ breast cancer, four models showed predictive advantage, with C-indices between 0.56 and 0.61. Predicted prognostic values were positively correlated with ER status when evaluated using univariate analyses in most of the models under investigation. Multivariate analyses indicated that prognostic values of the three models were independent of known clinical prognostic factors. Collectively, these analyses provided a comprehensive evaluation of immune-related prognostic gene signatures. By synthesizing C-indices in multiple independent data sets, immune-related gene signatures were ranked for ER+, ER−, LN+, and LN− breast cancer subtypes. Taken together, these data showed that immune-related gene signatures have good prognostic values in breast cancer, especially for ER− and LN+ tumors.

MMP11 and CD2 as novel prognostic factors in hormone receptor-negative, HER2-positive breast cancer

Purpose

More accurate prediction of patient outcome based on molecular subtype is required to identify patients who will benefit from specific treatments.

Methods

We selected novel 16 candidate prognostic genes, including 10 proliferation-related genes (p-genes) and 6 immune response-related genes (i-genes), from the gene list identified in our previous study. We then analyzed the association between their expression, measured by quantitative real-time reverse transcription-PCR in formalin-fixed, paraffin-embedded tissues, and clinical outcome in 819 breast cancer patients according to molecular subtype.

Results

The prognostic significance of clinical and gene variables varied according to the molecular subtype. Univariate analysis showed that positive lymph node status was significantly correlated with the increased risk of distant metastasis in all subtypes except the hormone receptor-negative, HER2-positive (HR−/HER2+) subtype. Most p-genes were significantly associated with poor prognosis in patients with the HR+/HER2− subtype, whereas i-genes correlated with a favorable outcome in patients with HR−/HER2+ breast cancer. In HR−/HER2+ breast cancer, four genes (three i-genes BTN3A2, CD2, and TRBC1 and the p-gene MMP11) were significantly associated with distant metastasis-free survival (DMFS). A new prognostic model for HR−/HER2+ breast cancer based on the expression of MMP11 and CD2 was developed and the DMFS for patients in the high-risk group according to our model was significantly lower than that for those in the low-risk group. Multivariate analyses revealed that our risk score is an independent prognostic factor for DMFS. Moreover, C-index showed that our risk score has a superior prognostic performance to traditional clinicopathological factors.

Conclusions

Our new prognostic model for HR−/HER2+ breast cancer provides more accurate information on the risk of distant metastasis than traditional clinical prognostic factors and may be used to identify patients with a good prognosis in this aggressive subtype of breast cancer.

Electronic supplementary material

The online version of this article (doi:10.1007/s10549-017-4234-4) contains supplementary material, which is available to authorized users.

A new molecular prognostic score for predicting the risk of distant metastasis in patients with HR+/HER2- early breast cancer

To make an optimal treatment decision for early stage breast cancer, it is important to identify risk of recurrence. Here, we developed and validated a new prognostic model for predicting the risk of distant metastasis in patients with pN0-N1, hormone receptor-positive, HER2-negative (HR+/HER2−) breast cancer treated with hormone therapy alone. RNA was extracted from formalin-fixed, paraffin-embedded tumor tissues and gene expression was measured by quantitative real-time reverse transcription-PCR. The relative expression of six novel prognostic genes was combined with two clinical variables (nodal status and tumor size) to calculate a risk score (BCT score). In the validation cohort treated with hormone therapy alone, the 10 year rate of distant metastasis in the high-risk group (26.3%) according to BCT score was significantly higher than that in the low-risk group (3.8%) (P < 0.001). Multivariate analysis adjusted for clinical variables revealed that BCT score is an independent predictor of distant metastasis. Moreover, the C-index estimate revealed that BCT score has a prognostic power superior to that of prognostic models based on clinicopathological parameters. The BCT score outperforms prognostic models based on traditional clinicopathological factors and predicts the risk of distant metastasis in patients with HR+/HER2− early breast cancer.

The effects of lymph node status on predicting outcome in ER+ /HER2- tamoxifen treated breast cancer patients using gene signatures

BACKGROUND

Lymph node (LN) status is the most important prognostic variable used to guide ER positive (+) breast cancer treatment. While a positive nodal status is traditionally associated with a poor prognosis, a subset of these patients respond well to treatment and achieve long-term survival. Several gene signatures have been established as a means of predicting outcome of breast cancer patients, but the development and indication for use of these assays varies. Here we compare the capacity of two approved gene signatures and a third novel signature to predict outcome in distinct LN negative (-) and LN+ populations. We also examine biological differences between tumours associated with LN- and LN+ disease.

METHODS

Gene expression data from publically available data sets was used to compare the ability of Oncotype DX and Prosigna to predict Distant Metastasis Free Survival (DMFS) using an in silico platform. A novel gene signature (Ellen) was developed by including patients with both LN- and LN+ disease and using Prediction Analysis of Microarrays (PAM) software. Gene Set Enrichment Analysis (GSEA) was used to determine biological pathways associated with patient outcome in both LN- and LN+ tumors.

RESULTS

The Oncotype DX gene signature, which only used LN- patients during development, significantly predicted outcome in LN- patients, but not LN+ patients. The Prosigna gene signature, which included both LN- and LN+ patients during development, predicted outcome in both LN- and LN+ patient groups. Ellen was also able to predict outcome in both LN- and LN+ patient groups. GSEA suggested that epigenetic modification may be related to poor outcome in LN- disease, whereas immune response may be related to good outcome in LN+ disease.

CONCLUSIONS

We demonstrate the importance of incorporating lymph node status during the development of prognostic gene signatures. Ellen may be a useful tool to predict outcome of patients regardless of lymph node status, or for those with unknown lymph node status. Finally we present candidate biological processes, unique to LN- and LN+ disease, that may indicate risk of relapse.

Intratumoral CD8⁺ cytotoxic lymphocyte is a favorable prognostic marker in node-negative breast cancer

Background

The prognostic effect of tumor infiltrating CD8⁺ cytotoxic lymphocytes (CTLs) in breast cancer is controversial. We analyzed the association between CD8⁺ CTLs and survival of untreated node-negative breast cancer patients.

Material and Methods

CD8⁺ CTLs infiltrate was evaluated by immunostaining in a cohort of 332 node-negative breast cancer patients with a median follow-up of 152 months. The prognostic significance of CD8⁺ CTLs for disease-free survival (DFS) and breast cancer-specific overall survival (OS) was evaluated with Kaplan-Meier survival analysis as well as univariate analysis and multivariate Cox analysis adjusted for age at diagnosis, pT stage, histological grade, estrogen receptor (ER) status, progesterone receptor (PR) status, Ki-67 expression and human epidermal growth factor receptor 2 (HER-2) status.

Results

285 (85.8%) patients showed strong CD8⁺ CTLs infiltrate positive status. Univariate analysis showed that CD8⁺ CTLs had statistically significant association with DFS (P = 0.004, hazard ratio [HR] = 0.454, 95% confidence interval [CI] = 0.265–0.777) and OS (P = 0.014, HR = 0.430, 95% CI = 0.220–0.840) in the entire cohort. The significance of CD8⁺ CTLs was especially strong in ER negative, HER-2 negative and ER, PR, HER-2 triple-negative breast cancers. In Kaplan-Meier analysis, CD8⁺ CTLs had significant effect on prognosis of patients (Log-rank test: P = 0.003 for DFS and P = 0.011 for OS), independent of established clinical factors for DFS (P = 0.002, HR = 0.418, 95% CI = 0.242–0.724) as well as for OS (P = 0.009, HR = 0.401, 95% CI = 0.202–0.797).

Immunoglobulin kappa C predicts overall survival in node-negative breast cancer

Background

Biomarkers of the immune system are currently not used as prognostic factors in breast cancer. We analyzed the association of the B cell/plasma cell marker immunoglobulin kappa C (IGKC) and survival of untreated node-negative breast cancer patients.

Material and Methods

IGKC expression was evaluated by immunostaining in a cohort of 335 node-negative breast cancer patients with a median follow-up of 152 months. The prognostic significance of IGKC for disease-free survival (DFS) and breast cancer-specific overall survival (OS) was evaluated with Kaplan-Meier survival analysis as well as univariate and multivariate Cox analysis adjusted for age at diagnosis, pT stage, histological grade, estrogen receptor (ER) status, progesterone receptor (PR) status, Ki-67 and human epidermal growth factor receptor 2 (HER-2) status.

Results

160 patients (47.7%) showed strong expression of IGKC. Univariate analysis showed that IGKC was significantly associated with DFS (P = 0.017, hazard ratio [HR] = 0.570, 95% confidence interval [CI] = 0.360–0.903) and OS (P = 0.011, HR = 0.438, 95% CI = 0.233–0.822) in the entire cohort. The significance of IGKC was especially strong in ER negative and in luminal B carcinomas. In multivariate analysis IGKC retained its significance independent of established clinical factors for DFS (P = 0.004, HR = 0.504, 95% CI = 0.315–0.804) as well as for OS (P = 0.002, HR = 0.371, 95% CI = 0.196–0.705).

Conclusion

Expression of IGKC has an independent protective impact on DFS and OS in node-negative breast cancer.