Summary of head-to-head comparisons of patient risk classifications by the 21-gene Recurrence Score® (RS) assay and other genomic assays for early breast cancer

Selecting postoperative adjuvant systemic therapy for early-stage breast cancer: An updated assessment and systematic review of leading commercially available gene expression assays

Gene expression assays (GEAs) can guide treatment for early-stage breast cancer. Several large prospective randomized clinical trials, and numerous additional studies, now provide new information for selecting an appropriate GEA. This systematic review builds upon prior reviews, with a focus on five widely commercialized GEAs (Breast Cancer Index®, EndoPredict®, MammaPrint®, Oncotype DX®, and Prosigna®). The comprehensive dataset available provides a contemporary opportunity to assess each GEA's utility as a prognosticator and/or predictor of adjuvant therapy benefit.

The Evolving Role of Genomic Testing in Early Breast Cancer: Implications for Diagnosis, Prognosis, and Therapy

Multigene prognostic genomic assays have become indispensable in managing early breast cancer (EBC), offering crucial information for risk stratification and guiding adjuvant treatment strategies in conjunction with traditional clinicopathological parameters. The American Society of Clinical Oncology (ASCO) guidelines endorse these assays, though some clinical contexts still lack definitive recommendations. The dynamic landscape of EBC management demands further refinement and optimization of genomic assays to streamline their incorporation into clinical practice. The breast cancer community is poised at the brink of transformative advances in enhancing the clinical utility of genomic assays, aiming to significantly improve the precision and effectiveness of both diagnosis and treatment for women with EBC. This article methodically examines the testing methodologies, clinical validity and utility, costs, diagnostic frameworks, and methodologies of the established genomic tests, including the Oncotype Dx Breast Recurrence Score®, MammaPrint, Prosigna®, EndoPredict®, and Breast Cancer Index (BCI). Among these tests, Prosigna and EndoPredict® have at present been validated only on a prognostic level, while Oncotype Dx, MammaPrint, and BCI hold both a prognostic and predictive role. Oncologists and pathologists engaged in the management of EBC will find in this review a thorough comparison of available genomic assays, as well as strategies to optimize the utilization of the information derived from them.

Molecular pathology in breast disease: diagnostic, prognostic, and therapeutic tools

Molecular testing in breast cancer gained increasing attention and importance as specific molecular results can tailor not only oncological decisions on systemic adjuvant or neoadjuvant or in metastatic setting, but increasingly serve in diagnostic routine histopathological services to differentiate between morphologically overlapping or ambiguous histological pictures. Diagnostic tools involve in most cases a broad spectrum of immunohistochemical panels, followed by entity-specific in situ hybridization probes and in given cases NGS-based sequencing. Workflow of which methodology is applied and in which order depends on the specific entity resp. on the given differential diagnosis in question. Regarding prognostic/predictive molecular testing, the choice of assay and the workflow are based on clinical algorithms and on the evidence of targeted therapies following the molecular alterations. In this review paper, we aim to address the use of molecular technics in [1] the histological diagnostic setting (such as subtyping of invasive carcinomas/malignant spindle cell tumors and sarcomas and some B3 lesions) and [2] in the context of adjuvant or neoadjuvant or other clinical settings with special focus of targeted therapies.

Incorporating clinicopathological and molecular risk prediction tools to improve outcomes in early HR+/HER2- breast cancer

Stratification of recurrence risk is a cornerstone of early breast cancer diagnosis that informs a patient's optimal treatment pathway. Several tools exist that combine clinicopathological and molecular information, including multigene assays, which can estimate risk of recurrence and quantify the potential benefit of different adjuvant treatment modalities. While the tools endorsed by treatment guidelines are supported by level I and II evidence and provide similar prognostic accuracy at the population level, they can yield discordant risk prediction at the individual patient level. This review examines the evidence for these tools in clinical practice and offers a perspective of potential future risk stratification strategies. Experience from clinical trials with cyclin D kinase 4/6 (CDK4/6) inhibitors in the setting of hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) early breast cancer is provided as an illustrative example of risk stratification.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

PROCURE European consensus on breast cancer multigene signatures in early breast cancer management

Breast cancer multigene signatures (BCMS) have changed how patients with early-stage breast cancer (eBC) are managed, as they provide prognostic information and can be used to select patients who may avoid adjuvant chemotherapy. Clinical guidelines make recommendations on the use of BCMS; however, little is known on the current use of BCMS in clinical practice. We conduct a two-round Delphi survey to enquire about current use and perceived utility for specific patient profiles, and unmet needs of BCMS. Overall, 133 panellists experienced in breast cancer across 11 European countries have participated, most using BCMS either routinely (66.2%) or in selected cases (27.1%). Our results show that BCMS are mainly used to assess the risk of recurrence and to select patients for adjuvant chemotherapy; notably, no consensus has been reached on the lack of utility of BCMS for selecting the type of chemotherapy to administer. Also, there are discrepancies between the recommended and current use of BCMS in clinical practice, with use in certain patient profiles for which there is no supporting evidence. Our study suggests that physician education initiatives are needed to ensure the correct use and interpretation of BCMS to, ultimately, improve management of patients with eBC.

Carbonic Anhydrase IX in Tumor Tissue and Plasma of Breast Cancer Patients: Reliable Biomarker of Hypoxia and Prognosis

Carbonic anhydrase IX (CA IX) is recognized as an excellent marker of hypoxia and an adverse prognostic factor in solid tumors, including breast cancer (BC). Clinical studies confirm that soluble CA IX (sCA IX), shed into body fluids, predicts the response to some therapeutics. However, CA IX is not included in clinical practice guidelines, possibly due to a lack of validated diagnostic tools. Here, we present two novel diagnostic tools-a monoclonal antibody for CA IX detection by immunohistochemistry and an ELISA kit for the detection of sCA IX in the plasma-validated on a cohort of 100 patients with early BC. We confirm that tissue CA IX positivity (24%) correlates with tumor grading, necrosis, negative hormone receptor status, and the TNBC molecular subtype. We show that antibody IV/18 can specifically detect all subcellular forms of CA IX. Our ELISA test provides 70% sensitivity and 90% specificity. Although we showed that this test could detect exosomes in addition to shed CA IX ectodomain, we could not demonstrate a clear association of sCA IX with prognosis. Our results indicate that the amount of sCA IX depends on subcellular CA IX localization, but more strictly on the molecular composition of individual molecular subtypes of BC, particularly on metalloproteinases inhibitor expression.

Eight gene mutation-based polygenic hazard score as a potential predictor for immune checkpoint inhibitor therapy outcome in metastatic melanoma

Background: Immune checkpoint inhibitor (ICI) therapies have revolutionized the treatment of metastatic cutaneous melanoma, but have only benefitted a subset of them. Gene mutations were reported to impact the ICI therapy outcomes in metastatic melanoma but have not been fully investigated. Hence, we systematically analyzed the impact of cancer-related gene mutations on the clinical outcome in metastatic melanoma patients who underwent ICI therapies.

Methods: Publicly available discovery and validation cohorts (312 patients and 110 patients respectively, all the patients received ICI therapies) were included in this study. Cox proportional hazards regression analysis was used to assess the association of 468 cancer-related gene mutations with overall survival (OS) in the discovery cohort, and the polygenic hazard score (PHS) was constructed subsequently, and validated in the validation cohort. The Tumor Immune Estimation Resource (TIMER) online tools, which are based on The Cancer Genome Atlas database, were used to analyze the impact of gene mutations on tumor-infiltrated immune cells in melanoma samples.

Results: We found eight gene mutations that were significantly associated with the overall survival (BAP1, CARD11, IGF1R, KMT2D, PTPRD, PTPRT, ROS1, and TERT, p < 0.05, mutation frequency >0.05). The PHS, which was based on these genes, was found to effectively discriminate the subset which benefited most from ICI therapies (HR = 1·54, 95%CI, 1.25–1.95; p < 0.001). After adjusting with age, sex, ICI regimes, and tumor mutation burden (TMB), we found that PHS was an independent predictor for the outcome of ICI therapies (adjusted HR = 1.84, 95%CI, 1.22–2.79; p = 0.004). The PHS was validated in the validation cohort (log-Rank p = 0.038). Further research found that CARD11 and PTPRD mutations were significantly associated with more tumor-infiltrated immune cells in melanoma samples.

Conclusion: For the first time, we have shown that PHS can independently and effectively predict the ICI therapy outcome in metastatic melanoma, which once validated by larger research, may help the decision-making process in melanoma.

Understanding Breast Cancers through Spatial and High-Resolution Visualization Using Imaging Technologies

Breast cancer is the most common cancer affecting women worldwide. Although many analyses and treatments have traditionally targeted the breast cancer cells themselves, recent studies have focused on investigating entire cancer tissues, including breast cancer cells. To understand the structure of breast cancer tissues, including breast cancer cells, it is necessary to investigate the three-dimensional location of the cells and/or proteins comprising the tissues and to clarify the relationship between the three-dimensional structure and malignant transformation or metastasis of breast cancers. In this review, we aim to summarize the methods for analyzing the three-dimensional structure of breast cancer tissue, paying particular attention to the recent technological advances in the combination of the tissue-clearing method and optical three-dimensional imaging. We also aimed to identify the latest methods for exploring the relationship between the three-dimensional cell arrangement in breast cancer tissues and the gene expression of each cell. Finally, we aimed to describe the three-dimensional imaging features of breast cancer tissues using noninvasive photoacoustic imaging methods.

Predictive biomarkers for molecularly targeted therapies and immunotherapies in breast cancer

Globally, breast cancer is the most common malignancy in women. Substantial efforts have been made to develop novel therapies, including targeted therapies and immunotherapies, for patients with breast cancer who do not respond to standard therapies. Consequently, new targeted therapies, such as cyclin-dependent kinase 4 and 6 inhibitors, poly (ADP-ribose) polymerase inhibitors, phosphoinositide 3-kinase inhibitor, and antibody-drug conjugates targeting human epidermal growth factor receptor 2 or trophoblast cell surface antigen-2, and immune checkpoint inhibitor targeting programmed cell death-1, have been developed and are now in clinical use. However, only some patients have benefited from these novel therapies; therefore, the identification and validation of reliable or more accurate biomarkers for predicting responses to these agents remain a major challenge. This review summarizes the currently available predictive biomarkers for breast cancer and describes recent efforts undertaken to identify potential predictive markers for molecularly targeted therapies and immune checkpoint inhibitors.

Clinical Utility of Genomic Assay in Node-Positive Early-Stage Breast Cancer

Breast cancer (BC) is the most common malignancy among women in Canada. Adjuvant treatment in early BC can reduce the risk of BC recurrence. Historically, the decision for adjuvant chemotherapy for early BC was made only based on clinical and tumour characteristics. In recent years, there has been an effort toward developing genomic assays as a predictive and prognostic tool to improve precision in estimating disease recurrence, sensitivity to systemic treatment and ultimately with clinical utility for guidance regarding adjuvant systemic treatment(s). There are various commercial genomic tests available for early-stage ER+/HER-2 negative BC. This paper will review the Oncotype DX 21-gene Recurrence Score (RS), MammaPrint, EndoPredict, Prosigna®, and Breast Cancer Index (BCI) genomic assays. We will also focus on these genomic assays’ clinical application and utility in node-positive early-stage BC based on the most recent evidence and guidance recommendations.

Using Breast Cancer Gene Expression Signatures in Clinical Practice: Unsolved Issues, Ongoing Trials and Future Perspectives

The development of gene expression signatures since the early 2000's has offered standardized assays to evaluate the prognosis of early breast cancer. Five signatures are currently commercially available and recommended by several international guidelines to individualize adjuvant chemotherapy decisions in hormone receptors-positive/HER2-negative early breast cancer. However, many questions remain unanswered about their predictive ability, reproducibility and external validity in specific populations. They also represent a new hope to tailor (neo)adjuvant systemic treatment, adjuvant radiation therapy, hormone therapy duration and to identify a subset of patients who might benefit from CDK4/6 inhibitor adjuvant treatment. This review will highlight these particular issues, address the remaining questions and discuss the ongoing and future trials.

The usefulness of CanAssist breast in the assessment of recurrence risk in patients of ethnic Indian origin

Accurate recurrence risk assessment in hormone receptor positive, HER2/neu negative breast cancer is critical to plan precise therapy. CanAssist Breast (CAB) assesses recurrence risk based on tumor biology using artificial intelligence-based approach. We report CAB risk assessment correlating with disease outcomes in multiple clinically high- and low-risk subgroups. In this retrospective cohort of 925 patients [median age-54 (22–86)] CAB had hazard ratio (HR) of 3 (1.83–5.21) and 2.5 (1.45–4.29), P = 0.0009) in univariate and multivariate analysis. CAB's HR in sub-groups with the other determinants of outcome, T2 (HR: 2.79 (1.49–5.25), P = 0.0001); age [< 50 (HR: 3.14 (1.39–7), P = 0.0008)]. Besides application in node-negative patients, CAB's HR was 2.45 (1.34–4.47), P = 0.0023) in node-positive patients. In clinically low-risk patients (N0 tumors up to 5 cms) (HR: 2.48 (0.79–7.8), P = 0.03) and with luminal-A characteristics (HR: 4.54 (1–19.75), P = 0.004), CAB identified >16% as high-risk with recurrence rates of up to 12%. In clinically high-risk patients (T2N1 tumors (HR: 2.65 (1.31–5.36), P = 0.003; low-risk DMFS: 92.66 ± 1.88) and in women with luminal-B characteristics (HR: 3.24; (1.69–6.22), P < 0.0001; low-risk DMFS: 93.34 ± 1.34)), CAB identified >64% as low-risk. Thus, CAB prognostication was significant in women with clinically low- and high-risk disease. The data imply the use of CAB for providing helpful information to stratify tumors based on biology incorporated with clinical features for Indian patients, which can be extrapolated to regions with similarly characterized patients, South-East Asia.

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Prospective analysis of CAB prognostication in a retrospective cohort of ethnic Indian origin.

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CAB based prognostication was significant in women aged above and below 50 years.

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In clinically high-risk and women with luminal B features greater numbers were low-risk.

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In clinically low-risk and women with luminal A features greater numbers were high-risk.

A novel metabolic gene signature-based nomogram to predict overall survival in breast cancer

Background

Breast cancer risk prediction is often based on clinicopathological characteristics despite the high heterogeneity derived from gene expression. Metabolic alteration is a hallmark of cancer, and thus, the integration of a metabolic signature with clinical parameters is necessary to predict disease outcomes in breast cancers.

Methods

Metabolic genes were downloaded from the Gene Set Enrichment Analysis (GSEA) dataset. Genes with statistical significance in the univariate analysis were applied in the least absolute shrinkage and selection operator (LASSO) analysis to build a gene signature in the GSE20685 dataset. Clinicopathological characteristics and risk scores with prognostic significance were incorporated into the nomogram to predict the overall survival (OS) of patients. The Cancer Genome Atlas (TCGA) and GSE866166 datasets were used as the validation datasets. Time-dependent receiver operating characteristic (tROC) curves and calibration plots were used to assess the accuracy and discrimination of the model.

Results

A 55-gene metabolic gene signature (MGS) was constructed, and was significantly related to OS both in the discovery (P<0.001) and validation (P<0.001) datasets. The MGS was an independent prognostic factor and could divide patients into high- and low-risk groups regardless of their different prediction analysis of microarray 50 (PAM50) subtypes. Time-dependent ROC curves indicated that the risk scores based on the MGS [area under the ROC curve (AUC): 0.931] were superior to the those based on the American Joint Committee on Cancer (AJCC) stage (AUC: 0.781) and PAM50 (AUC: 0.675). A nomogram based on the AJCC stage and risk score could predict OS, and the calibration curves showed good agreement to the actual outcome, indicating that the nomogram may have practical utility. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis indicated that this MGS was primarily enriched in amino acid pathways.

Conclusions

Our results demonstrated that the MGS was superior to existing risk predictors such as PAM50 and AJCC stage. By combining clinical factors (AJCC stage) and the MGS, a nomogram was constructed and showed good predictive ability for OS in breast cancer.

Validation of the GenesWell BCT Score in Young Asian Women With HR+/HER2- Early Breast Cancer

Background

The prognostic or predictive value of commonly used multigene assays in young patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) early breast cancer is unclear. In this study, we assessed the prognostic value of the GenesWell BCT assay according to age group.

Methods

We identified patients with pN0-1, HR+/HER2- breast cancer in a prospective cohort of women who underwent surgery between 2005 and 2017. The GenesWell BCT assay was performed on tissue samples from selected patients. Distant metastasis-free survival (DMFS) and disease-free survival (DFS) were compared between the risk groups assigned by the BCT score.

Results

A total of 712 patients were eligible for analysis. The median follow-up time was 7.47 years. The BCT score was prognostic in patients aged ≤50 years (n = 404) and those aged >50 years (n = 308). In both age groups, the 10-year DMFS and DFS rates for patients classified as high risk by the BCT score were significantly lower than those for patients classified as low risk. A multivariate analysis revealed that the BCT score was an independent prognostic factor for DFS in patients aged ≤50 years (hazard ratio, 1.28; 95% CI, 1.05-1.56; P = 0.015), as well as those aged >50 years.

Conclusion

The BCT score could be used to identify low-risk patients who will not benefit from adjuvant chemotherapy to treat HR+/HER2- early breast cancer regardless of age. A further prospective study to assess the prognostic and predictive value of the BCT score is required.

Recent Discoveries of Macromolecule- and Cell-Based Biomarkers and Therapeutic Implications in Breast Cancer

Breast cancer is the most commonly diagnosed cancer type and the leading cause of cancer-related mortality in women worldwide. Breast cancer is fairly heterogeneous and reveals six molecular subtypes: luminal A, luminal B, HER2+, basal-like subtype (ER−, PR−, and HER2−), normal breast-like, and claudin-low. Breast cancer screening and early diagnosis play critical roles in improving therapeutic outcomes and prognosis. Mammography is currently the main commercially available detection method for breast cancer; however, it has numerous limitations. Therefore, reliable noninvasive diagnostic and prognostic biomarkers are required. Biomarkers used in cancer range from macromolecules, such as DNA, RNA, and proteins, to whole cells. Biomarkers for cancer risk, diagnosis, proliferation, metastasis, drug resistance, and prognosis have been identified in breast cancer. In addition, there is currently a greater demand for personalized or precise treatments; moreover, the identification of novel biomarkers to further the development of new drugs is urgently needed. In this review, we summarize and focus on the recent discoveries of promising macromolecules and cell-based biomarkers for the diagnosis and prognosis of breast cancer and provide implications for therapeutic strategies.

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.

Oncotype DX and Prosigna in breast cancer patients: A comparison study

BACKGROUND

Oncotype Dx® (ODX) is the most used prognostic and predictive assay for ER + breast cancer (BCa) and is categorized into low (< 18), intermediate (18 to 30), or high (≥31) risk of recurrence. Prosigna® is a prognostic signature to estimate distant recurrence-free survival for stage I/II, ER+ cancer in postmenopausal women treated with adjuvant therapy. The goal of the study is to assess the agreement between ODX and Prosigna®.

MATERIALS AND METHODS

100 previously ODX classified peri and postmenopausal, early-stage (I or II) BCa patients were retrieved and Prosigna assay was performed on archived tumor blocks on a NanoString nCounter® DX Analysis System.

RESULTS

ODX assay was assigned as follows: 57% low, 39% intermediate, and 4% high. There were 8% two-step disagreements (high to low or vice versa) between ODX and Prosigna®; and 42% one-step disagreement (low to intermediate or vice versa). 78% were classified by Prosigna as luminal A and 22% as luminal B. The majority of luminal A cases (67/78; 85.9%) had low ROR score whereas ODX classified almost two-thirds (50/78~ 64%) as low RS. An insignificant percentage of luminal B cases (1/22 - 4.5%) were classified as high RS by ODX, and a modest percentage were classified as high ROR by Prosigna (15/22 ~68%). According to our follow up results, recurrence was detected in three cases. In all three cases; Prosigna was a better indicator of recurrence.

CONCLUSIONS

The agreement between ODX and Prosigna® is low, and this has management implications, especially when chemotherapy is needed.

21-Gene Recurrence Assay Associated With Favorable Metabolic Profiles in HR-Positive, HER2-Negative Early-Stage Breast Cancer Patients

BACKGROUND

Comprehensive investigations of the associations between 21-gene recurrence assay and metabolic profiles in Chinese breast cancer patients are limited.

METHODS

We evaluated the relations of the 21-gene recurrence risk score (RS) and the expression of cancer-related genes with metabolic factors and biomarkers of insulin and the insulin-like growth factor (IGF) axis, and examined the interactions between the 21-gene RS and these metabolic profiles on breast cancer recurrence in Chinese women with HR-positive, HER2-negative early-stage breast cancer.

RESULTS

The 21-gene RS was inversely associated with body mass index ([BMI]β: -0.178 kg/m²; P=0.040), the homeostasis model assessment of insulin resistance index ([HOMA-IR] β: -0.031; P=0.042), insulin (β: -0.036 uIU/ml; P=0.009), and C-peptide (β: -0.021 ug/L; P=0.014) and was positively associated with high-density lipoprotein cholesterol (β: 0.025 mmol/L; P=0.004), which were driven by the relation patterns between specific cancer-related genes and these metabolic profiles. Each 10-unit increase in the 21-gene RS was associated with 28% (95% CI: 5-47%) higher risk of breast cancer recurrence; this association was also observed in patients with favorable metabolic profiles in relevant to an absence of obesity, insulin resistance, hyperglycemia, hypertension, or dyslipidemia (28-44% higher risk) and among women with a low level of insulin, C-peptide, or the IGF1/IGFBP3 ratio (41-155% higher risk).

CONCLUSIONS

The 21-gene RS was related to favorable metabolic profiles including lower BMI, HOMA-IR, insulin, and C-peptide, and higher HDL in Chinese breast cancer patients, and its prognostic impact on breast cancer recurrence was more likely to present among patients with relatively favorable metabolic profiles.

Comparison of breast cancer prognostic tests CanAssist Breast and Oncotype DX

BACKGROUND

CanAssist Breast (CAB) is a prognostic test for early stage hormone receptor-positive (HR+), human epidermal growth factor receptor 2 negative (HER2-) breast cancer patients, validated on Indian and Caucasian patients. The 21-gene signature Oncotype DX (ODX) is the most widely used commercially available breast cancer prognostic test. In the current study, risk stratification of CAB is compared with that done with ODX along with the respective outcomes of these patients.

METHODS

A cohort of 109 early stage breast cancer patients who had previously taken the ODX test were retested with CAB, and the results respectively compared with old cut-offs of ODX as well as cut-offs suggested by TAILORx, a prospective randomized trial of ODX. Distant metastasis-free survival after 5 years was taken as the end point.

RESULTS

CanAssist Breast stratified 83.5% of the cohort into low-risk and 16.5% into high-risk. With the TAILORx cut-offs, ODX stratified the cohort into 89.9% low-risk and 10.1% into high-risk. The low, intermediate, and high-risk groups with ODX old cut-offs were 62.4%, 31.2%, and 6.4%, respectively. The overall concordance of CAB with ODX using both cut-offs is 75%-76%, with ~82%-83% concordance in the low-risk category of these tests. The NPV of the low-risk category of CAB was 93.4%, and of ODX with TAILORx cut-offs was 91.8% and 89.7% with old cut-offs.

CONCLUSIONS

Compared to the concordance reported for other tests, CAB shows high concordance with ODX, and in addition shows comparable performance in the patient outcomes in this cohort. CAB is thus an excellent and cost-effective alternative to ODX.

Gene Expression Signatures of BRCAness and Tumor Inflammation Define Subgroups of Early-Stage Hormone Receptor-Positive Breast Cancer Patients

PURPOSE

Patients with estrogen receptor- and/or progesterone receptor-positive, early breast cancer benefit from hormonal treatment, yet high global death burdens due to high prevalence and long-term recurrence risk call for biomarkers to guide additional treatment approaches.

EXPERIMENTAL DESIGN

From a prospective, observational study of postmenopausal early breast cancer patients treated with tamoxifen or aromatase inhibitors, gene expression analyses of 612 tumors was performed using the NanoString Breast Cancer 360 panel to interrogate 23 breast cancer pathways. Candidate signatures associated with disease subtype and event-free survival (EFS) were obtained by cluster analysis, Cox modeling, and conditional inference trees, and were independently tested in 613 patients from BreastMark. Tumor-infiltrating lymphocytes (TIL) were assessed on tissue sections, and mutational burden was assessed in 36 tumors by whole-exome sequencing.

RESULTS

PAM50-derived classification distinguished lower-risk (Luminal A) from higher-risk subtypes (Luminal B, P = 0.04; HER2, P = 0.006; Basal, P = 0.008). In higher-risk patients, shorter EFS was associated with low androgen receptor [HR = 3.61; 95% confidence interval (CI), 1.72-7.56; P = 0.001] or high BRCAness signature expression (HR = 3.58; 95% CI, 1.19-10.7; P = 0.023). BRCAness was independently confirmed as a predictor of shorter EFS (HR = 2.64; 95% CI, 1.31-5.34; P = 0.007). About 13%-15% of patients, enriched for high-grade, higher-risk subtypes (P ≤ 0.0001), had strong expression of the Tumor Inflammation Signature (TIS) suggestive of an inhibited antitumor immune response. TIS scores were strongly associated with TIL numbers (P < 1e-30) but not with tumor mutation status.

CONCLUSIONS

BRCA-related DNA repair deficiency and suppressed tumor immune responses may be clinically relevant predictors of endocrine therapy complementing treatment options in subgroups of hormone-sensitive early 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.

Comparison of 21-gene assay and St.Gallen International Expert Consensus in the treatment decision for patients with early invasive breast cancers

This study aimed to evaluate the impacts of 21-gene recurrence score (RS) and St. Gallen International Expert Consensus on treatment decision and prognosis of patients with invasive breast cancer. We retrospectively analyzed the therapy protocol and outcome of 134 cases based on age, body mass index (BMI), menopause, pathological types, tumor-node-metastasis (TNM) stages, percentage of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 (HER2), Ki-67, molecular subtype, and tumor biomarkers. RS was calculated based on 21-gene assay following traditional (old RS cutoff) and updated (new RS cutoff) National Comprehensive Cancer Network (NCCN) guideline. In addition, we also compared treatment protocol of NCCN guidelines with St. Gallen International Expert Consensus. The results showed that BMI, PR, Ki-67, and molecular subtype are critical for the evaluation of risk factors. Based on the new cutoff, low, middle, and high RS were 18%, 66%, and 16%, respectively. In contrast, based on the old cutoff, low, middle, and high RS were 60%, 29%, and 11%, respectively. The agreement rate of NCCN guidelines and St. Gallen International Expert Consensus for adjuvant treatment was 50. However, there is minimal agreement (0.151, 0.071) in kappa coefficient of old and new cutoff. This study revealed that the combination of NCCN guidelines and St. Gallen International Expert Consensus might improve the benefits of adjuvant treatment in patients with early invasive breast cancer.

Breast Cancer Prognostic Tests: Helping Patients Understand Testing Results and Their Implications

Breast cancer prognostic tests have become an essential component of breast cancer care. Oncology nurses play an important role in assisting patients in their understanding of and decision making regarding the use of prognostic tests in treatment planning. This article outlines the most commonly used breast cancer prognostic tests, including the individual assay's purpose, its genomic makeup, the targeted patient population, and its prognostic and predictive abilities. Key nursing implications are discussed, highlighting how nurses can best apply knowledge of breast cancer prognostic tests to nursing practice.