Breast cancer is the most common malignancy affecting women, marked by significant complexity and heterogeneity. This disease includes multiple subtypes, each with unique biological features and treatment responses. Despite significant advancements in detection and therapy, challenges remain, particularly in managing aggressive forms like triple-negative breast cancer and overcoming drug resistance. Breast cancer classification and subtype determination are typically performed by immunohistochemistry (IHC) method, which assesses four key markers (ER, PR, HER2, KI67); however, due to the recognized issues with this approach-especially regarding the evaluation of Ki67-there is a risk of misclassification. Patients who may be suitable for chemotherapy could miss possible advantages and only experience needless toxicity as a result of improper treatment decisions. Molecular profiling has improved breast cancer management, enabling the creation of multigene prognostic tests (MPTs) like Oncotype Dx, MammaPrint, Prosigna, Endopredict, and Breast Cancer Index which assess gene expression profiles to more accurately predict recurrence risks. These tools help personalize treatment, identifying patients who can avoid chemotherapy and/or extended endocrine therapy. While many MPTs are available, only Oncotype Dx and MammaPrint have prospective validation, with Prosigna providing additional prognostic insights by incorporating clinical variables. Molecular tests are especially usefull in the "gray zone," which includes tumors measuring between 1 and 3 cm with 0-3 positive lymph nodes and an intermediate proliferation index. However, their clinical utility has not been definitively established, and significant differences exist between them. This article provides an in-depth analysis of established genomic assays, including testing procedures, clinical validity, utility, diagnostic frameworks, and methodologies. Our comparison aims to improve early breast cancer management by guiding pathologists and oncologists in optimizing the use of genomic assays in clinical practice. By presenting this information, we aim to enhance understanding of the clinical utility and effectiveness of these assays, supporting the development of personalized treatment strategies for early breast cancer patients. Genomic assays offer important insights that can support treatment decisions in early-stage breast cancer, especially when used alongside other clinical evaluations, predictive tools, and management guidelines. While multiple gene expression profiling tests are available, they classify patients differently and are not interchangeable; therefore, their application should be at the clinician's discretion during the decision-making process. It is essential that these tests are not the sole factor in determining the best treatment plan: other clinical considerations and patient preferences should also play a significant role in guiding treatment decisions.

Hormone receptor-positive (HR+) and human epidermal growth factor receptor 2-negative (HER2-) tumors represent the most common types of early-stage breast cancer. However, their response to adjuvant systemic treatments varies widely due to tumor heterogeneity. Current decisions for adjuvant treatment rely heavily on clinical and pathological characteristics, which can sometimes lead to overtreatment. Accurately identifying patients who will benefit from adjuvant chemotherapy at an individual level remains a challenge. Multigene profiling assays are now widely used in clinics to better assess recurrence risk and chemotherapy response for HR+ disease. In this report, we examine the advantages and limitations of two widely used molecular profiling tests-Oncotype DX and Prosigna. Both Oncotype DX and Prosigna have been demonstrated to be effective prognostic tools in early breast cancer, with Oncotype DX also being validated as a predictive tool to guide chemotherapy decisions. We focus on studies that directly compare these molecular tests and discuss how their strengths can be leveraged to improve clinical decision-making for early-stage HR+ breast cancers. Finally, we highlight remaining knowledge gaps and propose directions for future research.

To systematically review the effectiveness of gene expression profiling tests to inform adjuvant chemotherapy decisions in people with hormone receptor-positive (HR+), lymph node-positive (LN+) breast cancer.

This systematic review assessed the effectiveness of Oncotype DX, Prosigna, EndoPredict and MammaPrint for guiding adjuvant chemotherapy decisions in HR+ early breast cancer with 1-3 positive nodes, in terms of prognostic ability, prediction of chemotherapy benefit, impact on chemotherapy decisions, quality of life and anxiety. Searches covered MEDLINE, EMBASE and Cochrane databases in April 2023.

Fifty-five articles were included. All four tests were prognostic for distant recurrence in LN+ patients. The RxPONDER trial reported no chemotherapy benefit in post-menopausal LN+ patients with low Oncotype DX (RS 0-25), whilst pre-menopausal patients had statistically significant chemotherapy benefit. An RCT reanalysis of Oncotype DX (SWOG-8814) suggested greater chemotherapy benefit with higher RS in post-menopausal LN+ patients. The MINDACT trial reported that LN+ patients with high clinical risk and low MammaPrint risk had a non-statistically significant chemotherapy benefit, but was not designed assess differential chemotherapy benefit per risk group. Decisions to undergo chemotherapy reduced by 12-75% following Oncotype DX testing in LN+ patients in the UK and Europe. No studies in LN+ populations were identified for prediction of chemotherapy benefit by Prosigna or EndoPredict; or for chemotherapy decisions for Prosigna, EndoPredict or MammaPrint; or for anxiety or quality of life impact for any test.

All four tests have prognostic ability in LN+ patients. Evidence on predictive benefit is weaker, with equivocal evidence that Oncotype DX may predict chemotherapy benefit in LN+ post-menopausal patients. Use of Oncotype DX leads to fewer patients being recommended chemotherapy.

CDK4/6 inhibitors in combination with endocrine therapy are widely used to treat HR+/HER2- metastatic breast cancer leading to improved progression-free survival (PFS) compared to single agent endocrine therapy. Over 300 patients receiving standard-of-care CDK4/6 inhibitor combination therapy for metastatic disease were enrolled at a single institution. Clinical, pathological, and gene expression data were employed to define determinants for PFS duration. Visceral disease (HR 1.55, p = 0.0013), prior endocrine therapy (HR 2.34, p < 0.001), and the type of endocrine therapy (HR 2.16, p < 0.001) were highly associated with PFS duration. Multiple pre-defined gene expression signatures were employed to determine association with response to CDK4/6 inhibitor-based therapy. Random survival forest was applied to define key gene expression and clinical features associated with PFS and develop a predictive model. The time to progression predicted by this model was related to the median PFS observed in PALOMA-2/3 and PEARL studies. Interrogating genes identified as highly significant across all studies indicated common enrichment of gene networks associated with cell cycle and estrogen receptor signaling. These findings indicate that there are common features from real-world use of CDK4/6 inhibitors that could be used to infer time to progression and better inform treatment.

Harmonized global collaborations are crucial to improving outcomes in hormone sensitive operable breast cancer.

Current assays fail to address breast cancer's complex biology and accurately predict treatment response. On a retrospective cohort of 1082 female breast tissues, we develop and validate mFISHseq, which integrates multiplexed RNA fluorescent in situ hybridization with RNA-sequencing, guided by laser capture microdissection. This technique ensures tumor purity, unbiased whole transcriptome profiling, and explicitly quantifies intratumoral heterogeneity. Here we show mFISHseq has 93% accuracy compared to immunohistochemistry. Our consensus subtyping and risk groups mitigate single sample discordance, provide early and late prognostic information, and identify high risk patients with enriched immune signatures, which predict response to neoadjuvant immunotherapy in the multicenter, phase II, prospective I-SPY2 trial. We identify putative antibody-drug conjugate (ADC)-responsive patients, as evidenced by a 19-feature T-DM1 classifier, validated on I-SPY2. Deploying mFISHseq as a research-use only test on 48 patients demonstrates clinical feasibility, revealing insights into the efficacy of targeted therapies, like CDK4/6 inhibitors, immunotherapies, and ADCs.

Adjuvant therapy decisions in hormone receptor positive, HER2 negative breast cancer are evolving. Gene panel testing has reduced the number of patients recommended for chemotherapy by up to two thirds. Identifying low risk genomic cases before testing could represent a significant economic impact. Systemic inflammatory indices have shown promise as prognostic markers in early breast cancer. We investigated the utility of four systemic inflammatory indices with the Nottingham Prognostic Index to predict the Oncotype DX® recurrence scores threshold level (low or high score), in women aged 50 and over with node negative invasive ductal carcinoma of the breast.

A retrospective review of 245 patients with Oncotype DX® Recurrence Score testing from 2007 to 2021 were identified. The Nottingham Prognostic Index and systemic inflammatory indices ratios were estimated from histology results and preoperative peripheral blood samples respectively.

22.4% of the cohort had a Recurrence Score in the higher risk group. This group had a greater percentage of grade 3 tumours, progesterone receptor negativity, higher Nottingham Prognostic Scores, and inflammatory indices ratios than the lower risk group. A decision tree incorporating the Neutrophil Lymphocyte Ratio with clinicopathological features showed potential as an indicator of a high Oncotype DX® RS score, such that further investigation is warranted to assess whether Recurrence Score testing could be triaged in certain cohorts of patients. In this study, 38% of patients might be able to avoid genomic testing based on the decision tree analysis.

Utility of inflammatory indices with clinicopathological features may help triage gene panel testing.

Several guidelines recommend the use of different classifiers to determine the risk of recurrence (ROR) and treatment decisions in patients with HR+HER2- breast cancer. However, data are still lacking for their usefulness in Latin American (LA) patients. Our aim was to evaluate the comparative prognostic and predictive performance of different ROR classifiers in a real-world LA cohort.

The Molecular Profile of Breast Cancer Study (MPBCS) is an LA case-cohort study with 5-year follow-up. Stages I and II, clinically node-negative HR+HER2- patients (n = 340) who received adjuvant hormone therapy and/or chemotherapy, were analyzed. Time-dependent receiver-operator characteristic-area under the curve, univariate and multivariate Cox proportional hazards regression (CPHR) models were used to compare the prognostic performance of several risk biomarkers. Multivariate CPHR with interaction models tested the predictive ability of selected risk classifiers.

Within this cohort, transcriptomic-based classifiers such as the recurrence score (RS), EndoPredict (EP risk and EPClin), and PAM50-risk of recurrence scores (ROR-S and ROR-PC) presented better prognostic performances for node-negative patients (univariate C-index 0.61-0.68, adjusted C-index 0.77-0.80, adjusted hazard ratios [HR] between high and low risk: 4.06-9.97) than the traditional classifiers Ki67 and Nottingham Prognostic Index (univariate C-index 0.53-0.59, adjusted C-index 0.72-0.75, and adjusted HR 1.85-2.54). RS (and to some extent, EndoPredict) also showed predictive capacity for chemotherapy benefit in node-negative patients (interaction P = .0200 and .0510, respectively).

In summary, we could prove the clinical validity of most transcriptomic-based risk classifiers and their superiority over clinical and immunohistochemical-based methods in the heterogenous, real-world node-negative HR+HER2- MPBCS cohort.

This revised consensus statement of the Spanish Society of Medical Oncology (SEOM) and the Spanish Society of Pathological Anatomy (SEAP) updates the recommendations for biomarkers use in the diagnosis and treatment of breast cancer that we first published in 2018. The expert group recommends determining in early breast cancer the estrogen receptor (ER), progesterone receptor (PR), Ki-67, and Human Epidermal growth factor Receptor 2 (HER2), as well as BReast CAncer (BRCA) genes in high-risk HER2-negative breast cancer, to assist prognosis and help in indicating the therapeutic options, including hormone therapy, chemotherapy, anti-HER2 therapy, and other targeted therapies. One of the four available genetic prognostic platforms (Oncotype DX®, MammaPrint®, Prosigna®, or EndoPredict®) may be used in ER-positive patients with early breast cancer to establish a prognostic category and help decide with the patient whether adjuvant treatment may be limited to hormonal therapy. In second-line advanced breast cancer, in addition, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) and estrogen receptor 1 (ESR1) should be tested in hormone-sensitive cases, BRCA gene mutations in HER2-negative cancers, and in triple-negative breast cancer (TNBC), programmed cell death-1 ligand (PD-L1). Newer biomarkers and technologies, including tumor-infiltrating lymphocytes (TILs), homologous recombination deficiency (HRD) testing, serine/threonine kinase (AKT) pathway activation, and next-generation sequencing (NGS), are at this point investigational.

Background Clinicians use prognostic biomarker/multi-gene-based tests for predicting recurrence in hormone receptor-positive/HER2-negative (HR+/HER2-) early-stage breast cancer (EBC). CanAssist Beast (CAB) uses the expression of five protein biomarkers in combination with tumor-specific parameters such as tumor size, histopathological grade, and lymph node status to predict the risk of distant recurrence within five years of diagnosis for patients with HR+/HER2-, EBC. The current study aimed to evaluate the impact of prognostic tests on adjuvant chemotherapy decisions by assessing the agreement between clinical and CAB risk stratification as low-risk (LR) or high-risk (HR) for distant recurrence. Methods The primary study group included 300 patients with HR+/HER2-, EBC diagnosed between 2016 and 2021. The clinical risk assessment and recommended treatment plan were captured before and after receiving the results for CAB. The risk stratification of patients into CAB LR and HR was obtained. Finally, compliance with CAB was analyzed by assessing the concordance of treatment prescribed with the CAB risk category. Results Before performing the CanAssist Breast test, patients were stratified based on clinicopathological features, with 52% of patients as LR, 21% as HR, and 27% of patients distributed as uncertain/intermediate risk (IR) category. CAB re-stratified the same cohort of patients, 67% as LR and 33% as HR, which was 15% higher than that of clinical LR assessment. The clinical IR category was re-stratified by CAB as 51% LR and 49% HR. Changes in treatment recommendations were seen in both clinical HR and clinical LR groups, which were 87% and 85%, respectively. Conclusions CAB has a significant impact on chemotherapy decisions. CAB provides definite treatment recommendations for patients with clinical intermediate risk. Overall, CAB has changed treatment recommendations in 23% of the cohort and for 88% of clinical IR patients helped physicians make a treatment decision.

Prognostication has been used to identify patient populations that could potentially benefit from treatment de-escalation. In patients with hormone receptor-positive (HRpos), human epidermal growth factor receptor 2-negative (HER2neg) early breast cancer (eBC), treatment de-escalation classically involved omitting chemotherapy. With recently developed specialized therapies that require hands-on side-effect management, the therapeutic landscape is changing and therapy decisions are no longer based only on prognosis, but also consider potential side-effects. Therefore, identification of patient groups based on prognostication has gained importance.

In this retrospective analysis, a population of 2359 node-negative HRpos/HER2neg eBC patients was selected from all patients treated at the University Breast Center of Franconia, Germany between 2002 and 2021. The prognostic value of the IHC3 score (incorporating immunohistochemical measurements of the estrogen and progesterone receptor status and Ki-67) with clinical parameters (lymph node status, tumor stage, grading) regarding invasive disease-free survival (iDFS) and overall survival (OS) was assessed.

IHC3 positively correlated with Ki-67 expression and inversely correlated with hormone receptor expression. IHC3 categorized into quartiles identified patients with a more unfavorable prognosis: 5-year and 10-year iDFS rates for patients in the highest versus the lowest quartile were 84% versus 95% and 70% versus 88%, respectively. A sensitivity analysis of distant disease-free survival showed similar results to those of iDFS. Five-year and 10-year OS rates for patients in the highest versus the lowest quartile were, respectively, 92% versus 97% and 81% versus 92%.

IHC3 is able to define prognostic groups in patients with node-negative, HRpos/HER2neg eBC. Node-negative patients with a high IHC3 score had the worst prognosis, which was comparable to that of node-positive patients described in recent trials. This simple and cost-effective tool could thus potentially aid in identifying patient groups for innovative therapeutic approaches.

Hormone-receptor positive (HR +) and human epidermal growth factor receptor 2 (HER2) negative early breast cancer (eBC) is a heterogeneous disease with several contributing factors for increased risk of recurrence, including tumor features, individual biomarkers, and genomic risk. The current standard approach in the management of HR + /HER2neg eBC includes chemotherapy and endocrine therapy (ET), and additional therapies based on risk profile, menopausal status, and genetics are sometimes appropriate. The risk of recurrence is more pronounced in patients with high-risk eBC including large tumor size, nodal involvement, high proliferative index, and genetic predisposition. In premenopausal patients with high-risk eBC, ovarian function suppression in combination with adjuvant ET improves survival. In postmenopausal patients, extended aromatase inhibitor (AI) therapy can be considered. Recent trials have identified novel treatment approaches to reduce the risk of recurrence in high-risk HR + /HER2neg eBC including the addition of cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors to adjuvant ET. For patients with germline BRCA1/BRCA2 mutations, adjuvant poly(adenosine diphosphate-ribose) polymerase (PARP) inhibitors have been shown to improve overall survival (OS). However, despite these recent advances, the risk of recurrence remains substantial, highlighting an area of unmet need. There are several ongoing clinical trials further investigating the role of CDK 4/6 inhibitors and immunotherapy in high-risk HR + /HER2neg eBC.

Breast Cancer Index (BCI) is a genomic assay that evaluates the benefit of extending endocrine therapy (ET) from 5 to 10 years and predicts recurrence risk (RR). We evaluated the association between BCI and Oncotype DX (ODX).

Women with hormone receptor (HR)-positive early-stage breast cancer (EBC) who had BCI and ODX performed were included.

We performed a retrospective review of women with HR-positive EBC. BCI was categorized as predictive of extended ET versus not and ODX recurrence score (RS) as low (0-10), intermediate (11-25), and high (26-100). Univariate and multivariable logistic and linear regression models assessed the relationship between BCI and ODX, factors associated with each, and discordance between scores.

We identified 153 women, 22% were premenopausal and 18% were lymph node positive. The univariate logistic and linear models revealed an association between BCI predictive score and ODX RS (OR 7.84, CI, 2.63-23.36, P < .001) and log of BCI RR (Beta 0.04, CI, 0.02-0.06, P < .001). Seventy-four percent of BCI predictive scores were concordant with ODX RS and 83% of BCI RR was concordant with ODX RR. In a univariate logistic regression model, BCI predictive of ET benefit was associated with discordance (OR 28.00, CI, 10.58-74.02, P < .001). Higher ODX RR was associated with discordance (OR 1.92, CI, 1.42-2.59, P < .001).

We found a significant association between ODX and BCI predictive and prognostic scores. BCI predictive of extended ET benefit was associated with discordance with ODX RS. Higher predicted RR on ODX was associated with discordance with BCI predicted RR.

In this study, we validate the use of Nottingham Prognostic x (NPx), consisting of tumour size, tumour grade, progesterone receptor (PR) and Ki67 in luminal BC.

Two large cohorts of luminal early-stage BC (n = 2864) were included. PR and Ki67 expression were assessed using full-face resection samples using immunohistochemistry. NPx was calculated and correlated with clinical variables and outcome, together with Oncotype DX recurrence score (RS), that is frequently used as a risk stratifier in luminal BC.

In the whole cohort, 38% of patients were classified as high risk using NPx which showed significant association with parameters characteristics of aggressive tumour behaviour and shorter survival (P < 0.0001). NPx classified the moderate Nottingham Prognostic Index (NPI) risk group (n = 1812) into two distinct prognostic subgroups. Of the 82% low-risk group, only 3.8% developed events. Contrasting this, 14% of the high-risk patients developed events during follow-up. A strong association was observed between NPx and Oncotype Dx RS (P < 0.0001), where 66% of patients with intermediate risk RS who had subsequent distant metastases also had a high-risk NPx.

NPx is a reliable prognostic index in patients with luminal early-stage BC, and in selected patients may be used to guide adjuvant chemotherapy recommendations.

Stratipath Breast is a CE-IVD marked artificial intelligence-based solution for prognostic risk stratification of breast cancer patients into high- and low-risk groups, using haematoxylin and eosin (H&E)-stained histopathology whole slide images (WSIs). In this validation study, we assessed the prognostic performance of Stratipath Breast in two independent breast cancer cohorts.

This retrospective multi-site validation study included 2719 patients with primary breast cancer from two Swedish hospitals. The Stratipath Breast tool was applied to stratify patients based on digitised WSIs of the diagnostic H&E-stained tissue sections from surgically resected tumours. The prognostic performance was evaluated using time-to-event analysis by multivariable Cox Proportional Hazards analysis with progression-free survival (PFS) as the primary endpoint.

In the clinically relevant oestrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative patient subgroup, the estimated hazard ratio (HR) associated with PFS between low- and high-risk groups was 2.76 (95% CI: 1.63-4.66, p-value < 0.001) after adjusting for established risk factors. In the ER+/HER2- Nottingham histological grade (NHG) 2 subgroup, the HR was 2.20 (95% CI: 1.22-3.98, p-value = 0.009) between low- and high-risk groups.

The results indicate an independent prognostic value of Stratipath Breast among all breast cancer patients, as well as in the clinically relevant ER+/HER2- subgroup and the NHG2/ER+/HER2- subgroup. Improved risk stratification of intermediate-risk ER+/HER2- breast cancers provides information relevant for treatment decisions of adjuvant chemotherapy and has the potential to reduce both under- and overtreatment. Image-based risk stratification provides the added benefit of short lead times and substantially lower cost compared to molecular diagnostics and therefore has the potential to reach broader patient groups.

Quantification of Ki67 in breast cancer is a well-established prognostic and predictive marker, but inter-laboratory variability has hampered its clinical usefulness. This study compares the prognostic value and reproducibility of Ki67 scoring using four automated, digital image analysis (DIA) methods and two manual methods.

The study cohort consisted of 367 patients diagnosed between 1990 and 2004, with hormone receptor positive, HER2 negative, lymph node negative breast cancer. Manual scoring of Ki67 was performed using predefined criteria. DIA Ki67 scoring was performed using QuPath and Visiopharm® platforms. Reproducibility was assessed by the intraclass correlation coefficient (ICC). ROC curve survival analysis identified optimal cutoff values in addition to recommendations by the International Ki67 Working Group and Norwegian Guidelines. Kaplan-Meier curves, log-rank test and Cox regression analysis assessed the association between Ki67 scoring and distant metastasis (DM) free survival.

The manual hotspot and global scoring methods showed good agreement when compared to their counterpart DIA methods (ICC > 0.780), and good to excellent agreement between different DIA hotspot scoring platforms (ICC 0.781-0.906). Different Ki67 cutoffs demonstrate significant DM-free survival (p < 0.05). DIA scoring had greater prognostic value for DM-free survival using a 14% cutoff (HR 3.054-4.077) than manual scoring (HR 2.012-2.056). The use of a single cutoff for all scoring methods affected the distribution of prediction outcomes (e.g. false positives and negatives).

This study demonstrates that DIA scoring of Ki67 is superior to manual methods, but further study is required to standardize automated, DIA scoring and definition of a clinical cut-off.

To report the prospective long-term outcome data of patients whose chemotherapy decision was guided by the EndoPredict test.

Patients with hormone receptor-positive HER2-negative early breast cancer with 0-3 positive lymph nodes were enrolled. The EndoPredict test was carried out on all tumor samples. Treatment compliance, local recurrence, distant metastases, and survival were evaluated. Associations of EPclin risk stratification with 5-year disease-free survival and distant metastasis-free survival were evaluated by time-to-event analysis.

368 consecutive patients were included in the analysis. Median follow-up was 8.2 years. EndoPredict allocated 238 (65%) in the low-risk and 130 (35%) patients in the high-risk group. Risk for disease recurrence or death in EPclin high-risk patients was twofold higher than in EPclin low-risk patients (hazard ratio [HR] 2.08; 95% CI 1.26-3.44; p = 0.004). EPclin low-risk patients had a 5-year disease-free survival of 95.3% (95% CI 92.6-98.0%). EPclin high-risk patients were at higher risk of developing distant metastases or death (HR 2.21; 95% CI 1.27-3.88; p = 0.005). EPclin high-risk patients who underwent chemotherapy had a 5-year DFS of 89.1% (95% CI 82.7-96.1%) in contrast to high-risk patients without chemotherapy (68.9%; 95% CI 56.2-84.5%; HR 0.46; 95% CI 0.23-0.95; p = 0.036). EPclin high-risk patients were at higher risk of experiencing distant metastases or death than EPclin low-risk patients regardless of menopausal status (premenopausal: HR 3.55; 95% CI 1.17-12.32; p = 0.025; postmenopausal: HR 1.92; 95% CI 0.99-3.7; p = 0.054).

EndoPredict can guide decisions on adjuvant chemotherapy in early luminal breast cancer. EndoPredict risk stratification is also applicable in premenopausal women.

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.

Adjuvant endocrine therapy (ET) represents the standard of care for almost all hormone receptor (HR)+/HER2- breast cancers, and different agents and durations are currently available. In this context, the tailoring and optimization of adjuvant endocrine treatment by reducing unnecessary toxic treatment while taking into account the biological heterogeneity of HR+/HER2- breast cancer represents a clinical priority. There is therefore a significant need for the integration of biological biomarkers in the choice of adjuvant ET beyond currently used clinicopathological characteristics. Several gene expression assays have been developed to identify patients with HR+/HER2- breast cancer who will not derive benefit from the addition of adjuvant chemotherapy. By enhancing risk stratification and predicting therapeutic response, genomic assays have also shown to be a promising tool for optimizing endocrine treatment decisions. In this study, we review evidence supporting the use of most common commercially available gene expression assays [Oncotype DX, MammaPrint, Breast Cancer Index (BCI), Prosigna, and EndoPredict] in tailoring adjuvant ET. Available data on the use of genomic tests to inform extended adjuvant treatment choice based on the risk of late relapse and on the estimated benefit of a prolonged ET are discussed. Moreover, preliminary evidence regarding the use of genomic assays to inform de-escalation of endocrine treatment, such as shorter durations or omission, for low-risk patients is reviewed. Overall, gene expression assays are emerging as potential tools to further personalize adjuvant treatment for patients with HR+/HER2- breast cancers.

Genomic tests, such as the Oncotype Dx 21-gene and Prosigna risk of recurrence (ROR-P) assay, are commonly used for breast cancer prognostication. Emerging data suggest variability between assays, but this has not been compared in diverse populations.

RNA sequencing was performed on 647 previously untreated stage I-III estrogen receptor-positive/human epidermal growth factor receptor 2-negative tumors in the Carolina Breast Cancer Study, which oversampled Black and younger women (age <50 years at diagnosis), using research versions of two common RNA-based prognostic assays: ROR-PR and the 21-gene recurrence score (RSR). Relative frequency differences and 95% CIs were estimated for associations with race and age, and hazards of 5-year local or distant recurrence were modeled with Cox regression. Proliferation and estrogen module scores from each assay, representing broad activity of genes in those pathways, were examined to guide interpretation of differences between tests.

Among both younger and older individuals, Black women had higher frequency of intermediate and high ROR-PR scores than non-Black women. Race was not significantly associated with RSR in either age group. High (hazard ratio [HR], 4.67 [95% CI, 1.73 to 12.70]) and intermediate (HR, 2.12 [95% CI, 0.98 to 4.62]) ROR-PR scores were associated with greater risk of recurrence, but RSR did not predict recurrence. RSR emphasized estrogen over proliferation modules, whereas ROR-PR emphasized proliferation. Higher proliferation scores were associated with younger age and Black race in both assays. Modifications to the RSR algorithm that increased emphasis on proliferation improved prognostication in this diverse population.

ROR-PR and the 21-gene RSR differentially emphasize estrogen-related and proliferative biology. The emphasis of 21-gene RS on estrogen-related biology and lower endocrine therapy initiation among Black women may contribute to poorer prognostic ability in heterogeneously treated populations.

To evaluate the Stratipath Breast tool for image-based risk profiling and compare it with an established prognostic multigene assay for risk profiling in a real-world case series of estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative early breast cancer patients categorized as intermediate risk based on classic clinicopathological variables and eligible for chemotherapy.

In a case series comprising 234 invasive ER-positive/HER2-negative tumors, clinicopathological data including Prosigna results and corresponding HE-stained tissue slides were retrieved. The digitized HE slides were analysed by Stratipath Breast.

Our findings showed that the Stratipath Breast analysis identified 49.6% of the clinically intermediate tumors as low risk and 50.4% as high risk. The Prosigna assay classified 32.5%, 47.0% and 20.5% tumors as low, intermediate and high risk, respectively. Among Prosigna intermediate-risk tumors, 47.3% were stratified as Stratipath low risk and 52.7% as high risk. In addition, 89.7% of Stratipath low-risk cases were classified as Prosigna low/intermediate risk. The overall agreement between the two tests for low-risk and high-risk groups (N = 124) was 71.0%, with a Cohen's kappa of 0.42. For both risk profiling tests, grade and Ki67 differed significantly between risk groups.

The results from this clinical evaluation of image-based risk stratification shows a considerable agreement to an established gene expression assay in routine breast pathology.

Intrinsic breast cancer molecular subtyping (IBCMS) provides significant prognostic information for patients with breast cancer and helps determine treatment. This study compared IBCMS methods on various gene-expression platforms in PALOMA-2 and PALLET trials. PALOMA-2 tumor samples were profiled using EdgeSeq and nanostring and subtyped with AIMS, PAM50, and research-use-only (ruo)Prosigna. PALLET tumor biopsies were profiled using mRNA sequencing and subtyped with AIMS and PAM50. In PALOMA-2 (n = 222), a 54% agreement was observed between results from AIMS and gold-standard ruoProsigna, with AIMS assigning 67% basal-like to HER2-enriched. In PALLET (n = 224), a 69% agreement was observed between results from PAM50 and AIMS. Different IBCMS methods may lead to different results and could misguide treatment selection; hence, a standardized clinical PAM50 assay and computational approach should be used.Trial number: NCT01740427.

EMIT-1 is a national, observational, single-arm trial designed to assess the value of the Prosigna, Prediction Analysis of Microarray using the 50 gene classifier (PAM50)/Risk of Recurrence (ROR), test as a routine diagnostic tool, examining its impact on adjuvant treatment decisions, clinical outcomes, side-effects and cost-effectiveness. Here we present the impact on treatment decisions.

Patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative pT1-pT2 lymph node-negative early breast cancer (EBC) were included. The Prosigna test and standard histopathology assessments were carried out. Clinicians' treatment decisions were recorded before (pre-Prosigna) and after (post-Prosigna) the Prosigna test results were disclosed.

Of 2217 patients included, 2178 had conclusive Prosigna results. The pre-Prosigna treatment decisions were: no systemic treatment (NT) in 27% of patients, endocrine treatment alone (ET) in 38% and chemotherapy (CT) followed by ET (CT + ET) in 35%. Post-Prosigna treatment decisions were 25% NT, 51% ET and 24% CT + ET, respectively. Adjuvant treatment changed in 28% of patients, including 21% change in CT use. Among patients assigned to CT + ET pre-Prosigna, 45% were de-escalated to ET post-Prosigna. Of patients assigned to ET, 12% were escalated to CT + ET and 8% were de-escalated to NT; of those assigned to NT, 18% were escalated to ET/CT + ET. CT was more frequently recommended for patients aged ≤50 years. In the subgroup with pT1c-pT2 G2 and intermediate Ki67 (0.5-1.5× local laboratory median Ki67 score), the pre-Prosigna CT treatment decision varied widely across hospitals (3%-51%). Post-Prosigna, the variability of CT use was markedly reduced (8%-24%). The correlation between Ki67 and ROR score within this subgroup was poor (r = 0.25-0.39). The median ROR score increased by increasing histological grade, but the ROR score ranges were wide (for G1 0-79, G2 0-90, G3 16-94).

The Prosigna test result changed adjuvant treatment decisions in all EBC clinical risk groups, markedly decreased the CT use for patients categorized as higher clinical risk pre-Prosigna and reduced treatment decision discrepancies between hospitals.

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.

The purpose of this study was to evaluate the prognostic value of the multigene EndoPredict test in prospectively collected data of patients screened for the randomized, double-blind, phase III UNIRAD trial, which evaluated the addition of everolimus to adjuvant endocrine therapy in high-risk, hormone receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative early breast cancer.

Patients were classified into low or high risk according to the EPclin score, consisting of a 12-gene molecular score combined with tumor size and nodal status. Association of the EPclin score with disease-free survival (DFS) and distant metastasis-free survival (DMFS) was evaluated using Kaplan-Meier estimates. The independent prognostic added value of EPclin score was tested in a multivariate Cox model after adjusting on tumor characteristics.

EndoPredict test results were available for 768 patients: 663 patients classified as EPclin high risk (EPCH) and 105 patients as EPclin low risk (EPCL). Median follow-up was 70 months (range 1-172 months). For the 429 EPCH randomized patients, there was no significant difference in DFS between treatment arms. The 60-month relapse rate for patients in the EPCL and EPCH groups was 0% and 7%, respectively. Hazard ratio (HR) supposing continuous EPclin score was 1.87 [95% confidence interval (CI) 1.4-2.5, P < 0.0001]. This prognostic effect remained significant when assessed in a Cox model adjusting on tumor size, number of positive nodes and tumor grade (HR 1.52, 95% CI 1.09-2.13, P = 0.0141). The 60-month DMFS for patients in the EPCL and EPCH groups was 100% and 94%, respectively (adjusted HR 8.10, 95% CI 1.1-59.1, P < 0.0001).

The results confirm the value of EPclin score as an independent prognostic parameter in node-positive, hormone receptor-positive, HER2-negative early breast cancer patients receiving standard adjuvant treatment. EPclin score can be used to identify patients at higher risk of recurrence who may warrant additional systemic treatments.

More than half of patients with early-stage estrogen receptor-positive (ER+) breast cancer relapse after completing 5 years of adjuvant endocrine therapy, so it is important to determine which patients are candidates for extended endocrine therapy. The clinical treatment score after 5 years (CTS5) is a prognostic tool developed based on postmenopausal ER+ breast cancer to assess the risk of late distant recurrence (LDR) after 5 years of adjuvant endocrine therapy for breast cancer. We aimed to externally validate the prognostic value of CTS5 in premenopausal and postmenopausal patients and combined with Ki-67 to develop a new model to improve the ability of prognosis prediction.

We included a total of 516 patients with early-stage ER+ breast cancer who had received 5 years of adjuvant endocrine therapy and were recurrence-free for 5 years after surgery. According to menopausal status, we divided the study population into 2 groups: premenopausal and postmenopausal women. The CTS5 of each patient was calculated using a previously published formula, and the patients were divided into low, intermediate, and high CTS5 risk groups according to their CTS5 values. Based on the results of the univariate analysis ( P <0.01), a multivariate COX proportional hazards regression analysis was conducted to establish a nomogram with significant variables ( P <0.05). The discriminative power and accuracy of the nomograms were assessed using the concordance index (C-index), calibration curve, and area under the time-dependent receiver operating characteristic curve. Discrimination and calibration were evaluated by bootstrapping 1000 times. Finally, we utilized decision curve analysis to assess the performance of our novel predictive model in comparison to the CTS5 scoring system with regard to their respective benefits and advantages.

The median follow-up time was 7 years (6 to 9 years). The 516 women were categorized by CTS5 as follows: 246(47.7%) low risk, 179(34.7%) intermediate risk, and 91(17.6%) high risk. Using the CTS5 score as a continuous variable, patients' risk score was significantly positively associated with recurrence risk in both premenopausal and postmenopausal subgroups. For HER2- premenopausal patients and HER2+ postmenopausal patients, the CTS5 score was positively correlated with LDR risk. Patients with a Ki-67≥20% had a higher risk of LDR regardless of menopausal status. Using the CTS5 score as a categorical variable, the high-risk group of HER2- premenopausal patients had a higher risk of LDR. However, the CTS5 model could not distinguish the risk of LDR in different risk groups for HER2+ postmenopausal patients. In the high-risk group, patients with Ki-67≥20% had a higher risk of LDR, regardless of menopausal status. We developed a new nomogram model by combining the CTS5 model with Ki-67 levels. The C-indexes premenopausal and postmenopausal cohorts were 0.731 and 0.713, respectively. The nomogram model was well calibrated, and the time-dependent ROC curves indicated good specificity and sensitivity. Furthermore, decision curve analysis demonstrated that the new model had a wider and practical range of threshold probabilities, resulting in an increased net benefit compared with the CTS5 model.

Our study demonstrated that the CTS5 model can effectively predict the risk of LDR in early-stage ER+ breast cancer patients in both premenopausal and postmenopausal patients. Extended endocrine therapy is recommended for patients with Ki-67≥20% in the CTS5 high-risk group, as well as premenopausal patients with HER2-. Compared with CTS5, the new nomogram model has better identification and calibration capabilities, and further research is required to validate its efficacy in large-scale, multicenter, and prospective studies.

BACKGROUNDIntrinsic molecular subtypes define distinct biological breast cancers and can be used to further improve diagnosis and risk allocation.METHODSThe Copenhagen Breast Cancer Genomics Study (CBCGS) prospectively included women diagnosed with breast cancer at Rigshospitalet from 2014 to 2021. Eligible patients were females with a primary invasive breast cancer (T1c, if N0M0; otherwise, any T, any N, or any M stage) and no prior malignancy. All patients underwent molecular profiling with the CIT256 and PAM50 molecular profile.RESULTSIn the study period, 2,816 patients were included in the CBCGS. Molecular subtyping showed an increase in nonluminal (molecular-apocrine, luminal C, and Basal-like) as compared with luminal (luminal A, luminal B, and Normal-like) subtypes with increasing stage from I to IV. Across all stages, we found a significant difference in survival among subtypes; 91% of patients with LumA were alive at 5 years compared with 91% for LumB, 84% for LumC, 82% for mApo, and 80% for Basal-like. We identified 442 tumors (16%) that were discordant in subtype between CIT256 and IHC. Discordant subtype proved to be a risk factor of death among patients with IHC luminal breast cancer (hazard ratio [HR], 2.08; 95% CI, 1.51-2.86) in a multivariable Cox regression analysis. Discordance occurred more often among patients with N3, stage IV, or grade III disease.CONCLUSIONOur findings indicate that molecular subtypes are a predominant classification for survival. Assessment is particularly crucial for patients with IHC luminal breast cancer with known high-risk factors, since they are at an increased risk of harboring an aggressive molecular subtype.

The clinical utility of gene signatures in older breast cancer patients remains unclear. We aimed to determine signature prognostic capacity in this patient subgroup.

Research versions of the genomic grade index (GGI), 70-gene, recurrence score (RS), cell cycle score (CCS), PAM50 risk-of-recurrence proliferation (ROR-P), and PAM50 signatures were applied to 39 breast cancer datasets (N = 9583). After filtering on age ≥ 70 years, and the presence of estrogen receptor (ER) and survival data, 871 patients remained. Signature prognostic capacity was tested in all (n = 871), ER-positive/lymph node-positive (ER + /LN + , n = 335) and ER-positive/lymph node-negative (ER + /LN-, n = 374) patients using Kaplan-Meier and multivariable Cox-proportional hazard (PH) modelling.

All signatures were statistically significant in Kaplan-Meier analysis of all patients (Log-rank P < 0.001). This significance remained in multivariable analysis (Cox-PH, P ≤ 0.05). In ER + /LN + patients all signatures except PAM50 were significant in Kaplan-Meier analysis (Log-rank P ≤ 0.05) and remained so in multivariable analysis (Cox-PH, P ≤ 0.05). In ER + /LN- patients all except RS were significant in Kaplan-Meier analysis (Log-rank P ≤ 0.05) but only the 70-gene, CCS, ROR-P, and PAM50 signatures remained so in multivariable analysis (Cox-PH, P ≤ 0.05).

We found that gene signatures provide prognostic information in survival analyses of all, ER + /LN + and ER + /LN- older (≥ 70 years) breast cancer patients, suggesting a potential role in aiding treatment decisions in older patients.

We investigated key signalling pathways' activity and mutational status of early-stage breast carcinomas with low and intermediate 21-gene recurrence score (RS) to identify molecular features that may predict recurrence.

This is a retrospective case-control study of 18 patients with recurrent breast carcinoma with low and intermediate 21-gene RS (<25) and control group of 15 non-recurrent breast cancer patients. DNA and mRNA were extracted from tumour tissue. mRNA expression of genes involved in oestrogen receptor (ER), androgen receptor (AR), PI3K and MAPK signalling pathways was measured by real-time quantitative reverse transcription-qPCR (OncoSIGNal G4 test, InnoSIGN). Tumour mutational landscape was assessed by targeted DNA sequencing (Oncomine Precision Assay).

There were no statistical differences between the groups' demographic and clinicopathological characteristics. PI3K pathway showed significantly higher activity in cases compared with controls (p=0.0014). Receiver operating characteristic curve analysis showed an area under the curve of 0.79 for PI3K pathway activity in the prediction of recurrent disease in low and intermediate 21-gene RS breast cancer. There was no difference in ER, AR and MAPK pathway activity. PIK3CA alterations were the most common driver mutations, but no difference was found between the groups (p=0.46) and no association with PI3K pathway activity (p=0.86). Higher Ki67 gene expression was associated with recurrences (p=0.042) CONCLUSION: Increased PI3K pathway activity, independent of PIK3CA mutations, may play a role in the recurrence of early-stage breast cancer with low and intermediate 21-gene RS. Pathway analysis can help to identify high-risk patients in this setting.

Luminal A tumours generally have a favourable prognosis but possess the highest 10-year recurrence risk among breast cancers. Additionally, a quarter of the recurrence cases occur within 5 years post-diagnosis. Identifying such patients is crucial as long-term relapsers could benefit from extended hormone therapy, while early relapsers might require more aggressive treatment.

We conducted a study to explore non-structural chromosome maintenance condensin I complex subunit H's (NCAPH) role in luminal A breast cancer pathogenesis, both in vitro and in vivo, aiming to identify an intratumoural gene expression signature, with a focus on elevated NCAPH levels, as a potential marker for unfavourable progression. Our analysis included transgenic mouse models overexpressing NCAPH and a genetically diverse mouse cohort generated by backcrossing. A least absolute shrinkage and selection operator (LASSO) multivariate regression analysis was performed on transcripts associated with elevated intratumoural NCAPH levels.

We found that NCAPH contributes to adverse luminal A breast cancer progression. The intratumoural gene expression signature associated with elevated NCAPH levels emerged as a potential risk identifier. Transgenic mice overexpressing NCAPH developed breast tumours with extended latency, and in Mouse Mammary Tumor Virus (MMTV)-NCAPHErbB2 double-transgenic mice, luminal tumours showed increased aggressiveness. High intratumoural Ncaph levels correlated with worse breast cancer outcome and subpar chemotherapy response. A 10-gene risk score, termed Gene Signature for Luminal A 10 (GSLA10), was derived from the LASSO analysis, correlating with adverse luminal A breast cancer progression.

The GSLA10 signature outperformed the Oncotype DX signature in discerning tumours with unfavourable outcomes, previously categorised as luminal A by Prediction Analysis of Microarray 50 (PAM50) across three independent human cohorts. This new signature holds promise for identifying luminal A tumour patients with adverse prognosis, aiding in the development of personalised treatment strategies to significantly improve patient outcomes.