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

Curigliano, Giuseppe; Dent, Rebecca; Llombart–Cussac, Antonio; Pegram, Mark D.; Pusztai, Lajos; Turner, Nicholas C.; Viale, Giuseppe

doi:10.1038/s41523-023-00560-z

Cited by 8 publications

(5 citation statements)

References 76 publications

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“…The assay yields results in the form of a recurrence score (RS), which ranges from 0 to 100. The RS elucidates the probability of distant recurrence over a nine-year period (independently of clinicopathological characteristics) and prognosticates the potential benefits of adjuvant chemotherapy [ 15 ]. Cut-off points for RS were defined to categorize patients into groups with low (RS < 18), intermediate (18 ≤ RS ≤ 30), and high risk (RS > 30) [ 13 , 16 ].…”

Section: Testing Methodologiesmentioning

confidence: 99%

“…Prosigna analyses a gene signature (PAM50) composed of 50 genes in order to classify surgically resected BC into four intrinsic subtypes (Luminal A, Luminal B, HER2-enriched, and basal-like). The PAM50 signature also includes eight housekeeping genes for normalization, along with six positive control genes and eight negative control genes [ 15 , 24 ]. The integrations of PAM50 gene expression profiling with proliferative information, and tumor size offer a risk-of-recurrence (ROR) score [ 24 , 26 ].…”

Section: Testing Methodologiesmentioning

confidence: 99%

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

Venetis,

Pescia,

Cursano

et al. 2024

IJMS

Self Cite

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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.

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Section: Testing Methodologiesmentioning

confidence: 99%

Section: Testing Methodologiesmentioning

confidence: 99%

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

Venetis,

Pescia,

Cursano

et al. 2024

IJMS

Self Cite

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show abstract

“…Among the established prognostic multiparameter diagnostic assays based on gene expression [ 4 ], the Prosigna assay (Prosigna Breast Cancer Prognostic Gene Signature Assay, Veracyte, South San Francisco, USA) is widely used and endorsed by national and international guidelines [ 5 – 8 ]. The Prosigna assay identifies intrinsic molecular subtypes (i.e., luminal A, luminal B, HER2-enriched and basal-like) and provides an individual risk of recurrence (ROR) score between 0 and 100 along with a three-tier risk category (low, intermediate, high) based on ROR score and nodal status.…”

Section: Introductionmentioning

confidence: 99%

Clinical evaluation of deep learning-based risk profiling in breast cancer histopathology and comparison to an established multigene assay

Wang,

Sun,

Karlsson

et al. 2024

Breast Cancer Res Treat

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Purpose 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. Methods 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. Results 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. Conclusion 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.

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“…Accurately evaluating the risk of both early and late recurrence is extremely important for informing clinical management strategies [7,8]. In this respect, histopathological analysis, combined with biomarker testing, plays a pivotal role in improving patient outcomes [9][10][11][12][13][14][15]. Traditional histology criteria, including tumor size, lymph node involvement, histological grade, lymphovascular invasion, and immune cell infiltration, have long been acknowledged as significant prognostic indicators in early BC [14,16].…”

Section: Introductionmentioning

confidence: 99%

Advances in Early Breast Cancer Risk Profiling: From Histopathology to Molecular Technologies

Pescia,

Guerini-Rocco,

Viale

et al. 2023

Cancers

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Early breast cancer (BC) is the definition applied to breast-confined tumors with or without limited involvement of locoregional lymph nodes. While risk stratification is essential for guiding clinical decisions, it can be a complex endeavor in these patients due to the absence of comprehensive guidelines. Histopathological analysis and biomarker assessment play a pivotal role in defining patient outcomes. Traditional histological criteria such as tumor size, lymph node involvement, histological type and grade, lymphovascular invasion, and immune cell infiltration are significant prognostic indicators. In addition to the hormone receptor, HER2, and—in specific scenarios—BRCA1/2 testing, molecular subtyping through gene expression profiling provides valuable insights to tailor clinical decision-making. The emergence of “omics” technologies, applicable to both tissue and liquid biopsy samples, has broadened our arsenal for evaluating the risk of early BC. However, a pressing need remains for standardized methodologies and integrated pathological models that encompass multiple analytical dimensions. In this study, we provide a detailed examination of the existing strategies for early BC risk stratification, intending to serve as a practical guide for histopathologists and molecular pathologists.

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

Cited by 8 publications

References 76 publications

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

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

Clinical evaluation of deep learning-based risk profiling in breast cancer histopathology and comparison to an established multigene assay

Advances in Early Breast Cancer Risk Profiling: From Histopathology to Molecular Technologies

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