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PubMed2026

Oncotype DX: Clinical Utility, Evidence, and Future Trends in Personalized Breast Cancer Management.

The Oncotype DX assay has revolutionized the management of early-stage, hormone receptor-positive, HER2-negative breast cancer. Developed in 2004, it quantifies 21 genes to generate a recurrence score that predicts distant recurrence risk and guides adjuvant chemotherapy. Multiple studies have validated its reliability and clinical utility in enabling more precise risk stratification and individualized treatment planning, thereby minimizing unnecessary chemotherapy exposure and improving patient outcomes. Leading oncology organizations such as the American Society of Clinical Oncology and National Comprehensive Cancer Network have incorporated it into their clinical guidelines. Beyond its well-established role in adjuvant chemotherapy decision-making, Oncotype DX is increasingly being investigated in broader clinical contexts, including lymph node-positive breast cancer, neoadjuvant therapy, radiotherapy, and ductal carcinoma in situ. Ongoing research and technological advancements, such as artificial intelligence-based predictive models and novel biomarker identification, hold significant promise for further enhancing its predictive accuracy and expanding its applications. This review synthesizes current evidence supporting the clinical utility of Oncotype DX, discusses evolving applications, and highlights future directions for integrating this genomic tool into precision oncology practice.

Humans↗

PubMed2026

Selective Omission of Oncotype Dx Genomic Testing in Ultra-Low-Risk Luminal A Breast Cancer.

INTRODUCTION: Older women with early-stage luminal A breast cancer have an excellent prognosis with a 5-year relative survival >99%. Research on curtailing overtreatment has challenged historical standards of care by reducing radiotherapy dose and volume, and selectively omitting sentinel lymph node biopsy. This study assessed the utility of Oncotype Dx genomic testing in ultra-low-risk luminal A breast cancer. PATIENTS AND METHODS: A community hospital cancer registry was queried to identify consecutive breast cancer patients from 2014 to 2022. An ultra-low-risk population was defined as age ≥60 years, hormone-receptor positive, HER2-negative, pathologic stage T1b-T2N0 without lymphovascular invasion, and Ki-67 ≤13.25%. Analyses examined the distribution of Oncotype Dx scores categorized as low risk (0-18), intermediate risk (19-25), or high risk (26-100), as well as recurrence and overall survival. RESULTS: Of 1711 patients, 91 met ultra-low-risk eligibility criteria with available Oncotype Dx results. The median age was 70 years with a median follow-up of 5.1 years, and no patients received chemotherapy. Only 1 patient (1.1%) had a high-risk Oncotype Dx score of ≥26, while 12 (13.2%) had intermediate scores and 78 (85.7%) were low risk. Five-year local control and overall survival were 100% and 93%, respectively. Ten non-breast cancer deaths occurred, and no patients developed distant metastases. CONCLUSION: We describe a pragmatic method using common clinical and pathological features to define an ultra-low-risk cohort. Older luminal A patients with favorable pathology have a very low probability of receiving a high-risk Oncotype Dx score and demonstrate an excellent prognosis with standard adjuvant therapy.

Chemotherapy↗

PubMed2026

Oncotype DX-guided vs physician-directed chemotherapy and survival in HR+/HER2- breast cancer.

BACKGROUND: Oncotype DX testing guides adjuvant chemotherapy decisions in early-stage hormone receptor-positive/HER2-negative breast cancer, but testing is not universally performed, and outcomes associated with genomic-informed versus clinicopathologic-based chemotherapy decision pathways remain unclear. METHODS: Using the 2022 National Cancer Database Breast Participant User File, we identified women diagnosed from 2010 to 2022 with pathologic T1b-T2, node-negative, hormone receptor-positive/HER2-negative invasive breast cancer who received adjuvant chemotherapy and endocrine therapy. Patients were classified into an Oncotype-guided group, defined by Oncotype DX testing with a recurrence score of 26 or higher, and a physician-directed group, defined by receipt of chemotherapy without genomic testing. The primary outcome was overall survival. Analyses used multivariable Cox models, logistic-IPTW and MLP-IPTW, restricted mean survival time analysis, and a Bayesian latent confounding survival model. RESULTS: Among 56,625 women, 27,278 were in the Oncotype-guided group and 29,347 in the physician-directed group. Median ages were 59 and 56 years, respectively. The Oncotype-guided group had more favorable overall survival than the physician-directed group in multivariable Cox analysis (HR, 0.906; 95% CI, 0.856-0.959; P < 0.001), with similar findings in IPTW analyses. The association was concentrated among patients aged 56 years or older (HR, 0.866; 95% CI, 0.809-0.927; P < 0.001). The Bayesian model showed no strong residual confounding signal. CONCLUSIONS: Among chemotherapy-treated women, an Oncotype-guided pathway was associated with more favorable overall survival than a physician-directed pathway, particularly among older patients, which indicating prognostic heterogeneity selected using genomic versus conventional clinicopathologic information.

Humans↗

PubMed2026

The 21-gene recurrence score assay as a tool for predicting recurrence risk and guiding adjuvant treatment selection in early breast cancer.

INTRODUCTION: Estrogen receptor-positive (ER+), HER2-negative breast cancer is the most common breast cancer subtype. While adjuvant endocrine therapy reduces recurrence risk, identifying which patients benefit from the addition of chemotherapy remains a key clinical challenge. The Oncotype DX® 21-gene Recurrence Score assay (Exact Sciences, via Genomic Health, Inc.) was developed to address this by quantifying distant recurrence risk and informing chemotherapy decisions in early-stage ER+/HER2- disease. AREAS COVERED: This diagnostic profile reviews the development, validation, and clinical evidence for Oncotype DX, including findings from the TAILORx and RxPONDER prospective trials and the subsequent development of hybrid tools integrating genomic and clinicopathological data. Alternative multiparameter molecular tests (MammaPrint, Prosigna, EndoPredict, Breast Cancer Index) are summarized and compared. We review international guideline recommendations, decision impact studies, cost-effectiveness evidence, and ongoing trials. EXPERT OPINION: Oncotype DX has strong prognostic evidence and has meaningfully reduced chemotherapy use, though its case as a biomarker predictive of therapeutic effect from chemotherapy rests on trial designs with important limitations. Its independent prognostic contribution beyond comprehensive clinicopathological assessment requires further clarification, and cost-effectiveness varies substantially by indication and healthcare setting.

Humans↗

PubMed2026

H&E to recurrence score: A step forward, but not yet a substitute for genomic testing.

Shamai and colleagues developed a multimodal deep-learning model that predicts Oncotype DX recurrence scores from routine H&E slides and clinicopathological variables in hormone receptor‑positive, HER2‑negative early breast cancer. Validated across the TAILORx trial and six external cohorts (over 5000 patients), the model achieved an AUC of 0.898 for identifying recurrence score ≥26 and recapitulated genomic assay patterns of chemotherapy benefit. Notably, 31% of clinically high-risk postmenopausal women were downgraded to low risk by AI, suggesting potential to reduce overtreatment. However, several limitations preclude immediate clinical substitution for genomic testing. First, intratumoural heterogeneity leads to discordant predictions with unclear management guidance. Second, the model's chemotherapy benefit estimates rely on TAILORx's age-based menopausal surrogates, which may not reflect real-world hormonal status or LHRH agonist use. Third, predictive value in node-positive disease remains untested in randomised datasets such as RxPONDER. Additionally, calibration uncertainty near risk thresholds and global scalability issues (including IHC requirements and digital pathology infrastructure) persist. While this represents a landmark step toward democratising precision oncology, the AI tool should currently serve as a complementary decision aid, with genomic testing remaining the gold standard for intermediate, borderline, or discordant cases.

Breast cancer↗

PubMed2025

Computational Pathology for Accurate Prediction of Breast Cancer Recurrence: Development and Validation of a Deep Learning-Based Tool.

Accurate recurrence risk stratification is crucial for optimizing treatment plans for breast cancer patients. Current prognostic tools like Oncotype DX offer valuable genomic insights into hormone receptor-positive and human epidermal growth factor receptor-negative patients but are limited by cost and accessibility, particularly in underserved populations. In this study, we present Deep-Breast-Cancer-Recurrence (BCR)-Auto, a deep learning-based computational pathology approach that predicts breast cancer recurrence risk from routine hematoxylin and eosin-stained whole slide images. Our methodology was validated on 2 independent cohorts: The Cancer Genome Atlas Program breast cancer data set and an in-house data set from The Ohio State University. Deep-BCR-Auto demonstrated robust performance in stratifying patients into low- and high-recurrence risk categories. On The Cancer Genome Atlas Program breast cancer data set, the model achieved an area under the receiver operating characteristic curve of 0.827, significantly outperforming the existing weakly supervised models (P = .041). In the independent The Ohio State University data set, Deep-BCR-Auto maintained strong generalizability, achieving an area under the receiver operating characteristic curve of 0.832, along with 82.0% accuracy, 85.0% specificity, and 67.7% sensitivity. These findings highlight the potential of computational pathology as a cost-effective alternative for recurrence risk assessment, broadening access to personalized treatment strategies. This study underscores the clinical utility of integrating deep learning-based computational pathology into routine pathological assessment for breast cancer prognosis across diverse clinical settings.

Humans↗

PubMed2025

Partial Breast Irradiation for High Molecular Risk Early-Stage Breast Cancer.

PURPOSE: Partial breast irradiation (PBI) is a suitable and well-tolerated alternative to whole breast irradiation (WBI) following lumpectomy for many forms of low-risk, early-stage breast cancer. Molecular risk scores, such as the Oncotype DX recurrence score (ODX RS), are increasingly guiding systemic treatment decisions. However, molecular/genomic profiling for radiation therapy (RT) decision-making remains investigational, and it is unclear whether a high ODX RS should preclude the use of PBI. We compared oncologic outcomes among patients with high ODX RS (>25) treated with PBI versus WBI. METHODS AND MATERIALS: Patients who underwent breast conservation followed by PBI or WBI with ODX RS > 25 were ascertained from a prospectively maintained institutional database. Comparable PBI and WBI cohorts were generated in 1:5 fashion using propensity score matching based on salient clinicopathologic features. We evaluated the incidence of local recurrence (LR) as a function of RT approach. RESULTS: We identified 968 patients with an ODX RS > 25 who were treated with adjuvant RT, with a median age of 59 years (range, 25-86) and a median 5.3 years of follow-up. In a propensity matched cohort analysis that included 28 patients who received PBI matched to 140 who received WBI, we observed 3 LR events among those receiving PBI (2 of which were in different quadrants from the primary lesion) and 5 events among those receiving WBI. Among this cohort with ODX RS > 25, the 72-month cumulative incidence of LR following PBI was 7.9% (95% CI, 1.3%-23%) compared to 4.8% (95% CI, 1.6%-11%) following WBI (P = .6). CONCLUSIONS: In this cohort of patients with high ODX RS, few LRs were observed, and no statistically significant difference in LR was identified between PBI and WBI. Although these findings suggest that PBI may be considered in carefully-selected high-genomic-risk patients, larger studies with longer follow-up are needed to definitively establish the safety of this approach.

Humans↗

PubMed2026

Development and Validation of a Multimodal Clinical, Pathologic, and Genomic Model for Breast Cancer Recurrence.

PURPOSE: To develop and validate a multimodal recurrence-risk model integrating histology, genomic testing, and clinical variables. METHODS: We developed AI-Path, a whole-slide image biomarker for recurrence prediction trained in CALGB 9344, and validated it in three independent cohorts: TAILORx, a multi-site Chicago cohort, and the MDX-BRCA cohort. We then integrated AI-Path with Oncotype DX Recurrence Score (RS), tumor size, and nodal status into a Cox model, PathClinRS, fit using 60% of cases from TAILORx, with the remaining 40% held out for validation. The primary end point was distant recurrence-free interval. Performance was assessed using Harrell's concordance index (C-index) and Kaplan-Meier analyses. RESULTS: A total of 12,418 patients were included. In TAILORx, AI-Path outperformed RS for distant recurrence (C-index, 0.682 vs 0.647; P = .038), driven by superior prediction of late recurrence (0.656 vs 0.567; P < .001). In node-negative disease, PathClinRS outperformed RSClin in the TAILORx fitting (0.72 vs 0.70; P = .016) and validation sets (0.74 vs 0.70; P = .004). In node-positive disease, PathClinRS outperformed RSClinN+ in Chicago (0.94 vs 0.74; P < .001) and MDX-BRCA (0.71 vs 0.66; P = .004) cohorts. Compared with NATALEE eligibility, PathClinRS identified nearly twice as many high-risk node-negative patients while maintaining a comparable 10-year distant recurrence risk (16.7% vs 16.6% per NATALEE eligibility in TAILORx fitting; 21.0% vs 19.4% in TAILORx validation). PathClinRS identified 68% of intermediate risk premenopausal patients as low-risk with no evidence of chemotherapy benefit, compared to only 36% identified as low risk by standard clinicopathologic criteria. CONCLUSION: Digital histopathology provides prognostic information complementary to genomic assays and has the potential to personalize therapy beyond existing clinicogenomic tools.

Journal Article↗