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

ABSTRACT: 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.

SUBMITTER: Curigliano G

PROVIDER: S-EPMC10307886 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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

Curigliano Giuseppe G Dent Rebecca R Llombart-Cussac Antonio A Pegram Mark M Pusztai Lajos L Turner Nicholas N Viale Giuseppe G

NPJ breast cancer 20230628 1

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 po ...[more]

PMID: 37380659

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Project description:Background Breast cancer has the highest incidence and mortality rates among women worldwide. Hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)− breast cancer is the most common molecular subtype, accounting for 50–79% of breast cancers. Deep learning has been widely used in cancer image analysis, especially for predicting targets related to precise treatment and patient prognosis. However, studies focusing on therapeutic target and prognosis predicting in HR+/HER2− breast cancer are lacking. Methods This study retrospectively collected hematoxylin and eosin (H&E)-stained slides of HR+/HER2− breast cancer patients between January 2013 and December 2014 at Fudan University Shanghai Cancer Center (FUSCC) and scanned to generate whole-slide images (WSIs). Then, we built a deep-learning-based workflow to train and validate model to predict clinicopathological features, multi-omics molecular features and prognosis; the area under the curve (AUC) of the receiver operating characteristic (ROC) and the concordance index (C-index) of the test set were used to assess model effectiveness. Results A total of 421 HR+/HER2− breast cancer patients were included in our study. Regarding clinicopathological features, grade III could be predicted with an AUC of 0.90 [95% confidence interval (CI): 0.84–0.97]. Regarding somatic mutations, TP53 and GATA3 mutation could be predicted with AUCs of 0.68 (95% CI: 0.56–0.81) and 0.68 (95% CI: 0.47–0.89), respectively. Regarding gene set enrichment analysis (GSEA) pathways, the G2-M checkpoint pathway was predicted with an AUC of 0.79 (95% CI: 0.69–0.90). Regarding markers of immunotherapy response, intratumoral tumor-infiltrating lymphocytes (iTILs), stromal tumor-infiltrating lymphocytes (sTILs), CD8A, and PDCD1 were predicted with AUCs of 0.78 (95% CI: 0.55–1.00), 0.76 (95% CI: 0.65–0.87), 0.71 (95% CI: 0.60–0.82), and 0.74 (95% CI: 0.63–0.85), respectively. In addition, we found that the integration of clinical prognostic variables and deep features of images can improve the stratification of patient prognosis. Conclusions Using a deep-learning-based workflow, we developed models to predict the clinicopathological features, multi-omics features and prognosis of patients with HR+/HER2− breast cancer using pathological WSIs. This work may contribute to efficient patient stratification to promote the personalized management of HR+/HER2− breast cancer.

Project description:IntroductionReal-world data on patients with early breast cancer (EBC) with high-risk features remains limited. This population-based study determined the incidence, outcomes and characteristics of patients with hormone receptor (HR)-positive, human epidermal growth factor 2 receptor (HER2)-negative EBC with high-risk features treated in everyday clinical care in two Finnish hospital districts which represent approximately 40% (2.5 million) of the total Finnish population (5.5 million).MethodsAdult female patients with BC (ICD-10 C50*) diagnosed between January 2012-June 2019 were indexed at the first BC diagnosis and followed until December 2019 or death. EBC was defined as having no records of metastasis within 90 days of index. High-risk status was defined as ≥ 4 positive axillary lymph nodes (ALNs) or 1-3 ALNs with either grade 3 tumor or tumor size ≥ 5 cm. Outcomes included invasive disease-free survival (IDFS), distant relapse-free survival (DRFS) and overall survival (OS) and were assessed using Kaplan-Meier methods and Cox regression models.ResultsAmong the 8678 patients with HR+/HER2- EBC, risk classification was feasible in 8081 (93.1%) individuals. Of these, 1407 (17.4%) were defined as high-risk and the remaining 6674 (82.6%) as low-risk patients. The average annual incidence of high-risk HR+/HER2- EBC in 2012-2018 was 21.8/100,000 women. Five-year invasive disease-free survival (IDFS) and distant recurrence-free survival (DRFS) showed higher risk of recurrence for the high-risk group: IDFS 79.7% (95% CI 77.0-82.2) vs 89.3% (88.3-90.2) in the low-risk group; DRFS 82.4% (79.7-84.7) vs 92.9% (92.1-93.7) in the low-risk. Five-year overall survival (OS) in the high-risk group was 89.5% (87.3-91.4) and was 95.4% (94.7-96.0) in the low-risk group.ConclusionsThis study showed that high-risk patients account for 17% of newly diagnosed HR+/HER2- EBC in Finland. The high-risk profile was associated with increased risk of recurrence, distant relapse and death compared to low-risk patients. The poorer outcomes of high-risk HR+/HER2- EBC emphasizes a clear unmet need in improving the identification and treatment of these patients.

Dataset Information

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

Publications

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

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