Effect of nodal status on clinical outcomes of triple-negative breast cancer: a population-based study using the SEER 18 database

Wang, Xiaoxiao; Jiang, Yi‐Zhou; Li, Junjing; Song, Chuan-Gui; Shao, Zhi‐Ming

doi:10.18632/oncotarget.9432

Cited by 21 publications

(20 citation statements)

References 25 publications

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“…In line with reports on clinicopathologic variables associated with nodal metastasis (18,21,46,47), tumor size and LVI were key variables in our CLINICAL and MIXED models for the ER þ HER2 À and HER2 þ groups, but not for TNBC tumors. Interestingly, TNBC is characterized by a low risk of nodal metastasis and displays a non-linear association between tumor size and the risk of nodal engagement (48,49). We observed low expression of basal-like markers for TNBC tumors with predicted nodal metastasis by the GEX and MIXED models, but not for the CLINICAL model.…”

Section: Discussionmentioning

confidence: 59%

Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort

Dihge

Vallon‐Christersson

Hegardt

et al. 2019

Clinical Cancer Research

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Purpose: More than 70% of patients with breast cancer present with node-negative disease, yet all undergo surgical axillary staging. We aimed to define predictors of nodal metastasis using clinicopathological characteristics (CLINICAL), gene expression data (GEX), and mixed features (MIXED) and to identify patients at low risk of metastasis who might be spared sentinel lymph node biopsy (SLNB).Experimental Design: Breast tumors (n ¼ 3,023) from the population-based Sweden Cancerome Analysis Network-Breast initiative were profiled by RNA sequencing and linked to clinicopathologic characteristics. Seven machine-learning models present the discriminative ability of N0/Nþ in development (n ¼ 2,278) and independent validation cohorts (n ¼ 745) stratified as ER þ HER2 À , HER2 þ , and TNBC. Possible SLNB reduction rates are proposed by applying CLINICAL and MIXED predictors.Results: In the validation cohort, the MIXED predictor showed the highest area under ROC curves to assess nodal metastasis; AUC ¼ 0.72. For the subgroups, the AUCs for MIXED, CLINICAL, and GEX predictors ranged from 0.66 to 0.72, 0.65 to 0.73, and 0.58 to 0.67, respectively. Enriched proliferation metagene and luminal B features were noticed in node-positive ER þ HER2 À and HER2 þ tumors, while upregulated basal-like features were observed in node-negative TNBC tumors. The SLNB reduction rates in patients with ER þ HER2 À tumors were 6% to 7% higher for the MIXED predictor compared with the CLINICAL predictor accepting false negative rates of 5% to 10%.Conclusions: Although CLINICAL and MIXED predictors of nodal metastasis had comparable accuracy, the MIXED predictor identified more node-negative patients. This translational approach holds promise for development of classifiers to reduce the rates of SLNB for patients at low risk of nodal involvement. a Values are median. b According to the TNM classification for breast cancer, seventh edition. c Percentages are calculated from the total number of patients who received adjuvant treatment. d Percentages are calculated from the total number of patients within the age range (40-74 years) for mammography within the National Breast Cancer Screening Programme.Dihge et al.Abbreviations: FN, false negative; FP, false positive; Max NPV, maximum negative predictive value; Nþ, axillary lymph node metastasis; No, no regional lymph node metastasis; SLNB, sentinel lymph node biopsy; TN, true negative; TP, true positive. Dihge et al.

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Section: Discussionmentioning

confidence: 59%

Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort

Dihge

Vallon‐Christersson

Hegardt

et al. 2019

Clinical Cancer Research

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“…3g). Here, lymph node status was determined by lymph node count: L0 (no positive nodes), L1 (1–3 positive nodes), L2 (4–9 positive nodes), and L3 (>10 nodes) [44]. …”

Section: Resultsmentioning

confidence: 99%

Application of RNAi-induced gene expression profiles for prognostic prediction in breast cancer

et al. 2016

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Homologous recombination (HR) is the primary pathway for repairing double-strand DNA breaks implicating in the development of cancer. RNAi-based knockdowns of BRCA1 and RAD51 in this pathway have been performed to investigate the resulting transcriptomic profiles. Here we propose a computational framework to utilize these profiles to calculate a score, named RNA-Interference derived Proliferation Score (RIPS), which reflects cell proliferation ability in individual breast tumors. RIPS is predictive of breast cancer classes, prognosis, genome instability, and neoadjuvant chemosensitivity. This framework directly translates the readout of knockdown experiments into potential clinical applications and generates a robust biomarker in breast cancer.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0363-3) contains supplementary material, which is available to authorized users.

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“…Nodal status is one of the most important clinicopathological factors with prognostic significance in breast cancer. 21 Nodal status has also been shown to be a prognostic factor for survival in TNBC in some [22][23][24][25] but not all studies. 26,27 In our study, lymph node negative TNBC was associated with better survival (both OS and DFS) than lymph node positive patients in univariate analysis, but the association did not apply for OS in subsequent multivariate analysis.…”

Section: Discussionmentioning

confidence: 99%

<p>Effect of Clinical and Treatment Factors on Survival Outcomes of Triple Negative Breast Cancer Patients</p>

Wen¹,

Manuel²,

Doolan³

et al. 2020

BCTT

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Purpose: Triple negative breast cancer (TNBC) accounts for approximately 15% of breast cancer cases and is associated with a poor prognosis. In this retrospective study of patients undergoing radiation therapy as part of their treatment, disease-free survival (DFS) and overall survival (OS) of TNBC patients were examined in relation to clinical and treatmentrelated factors. Patients and Methods: The electronic records of 214 consecutive TNBC patients treated with surgery followed by radiotherapy at the Mid North Coast Cancer Institute between 2006 and 2016 were reviewed. Overall survival and DFS times were analyzed using the Kaplan-Meier method; multivariate Cox proportional hazard regression modelling was used to assess the significance of prognostic factors. Results: The majority of tumors were T1 (51.9%), followed by T2 (39.2%) and T3 (6.1%). For the whole group, mean DFS was 106.4 (SD 48.7) months; OS 109.4 (SD 52.1) months. Radiotherapy technique, fractionation protocol and laterality were not significant factors for DFS or OS (p>0.05). However, compared to breast conservation, mastectomy was associated with poorer DFS (mean 114.2 vs 65.2 months; p<0.0001) and poorer OS (mean 115.5 vs 80.5 months; p=0.0015). The mastectomy group had fewer patients with tumor size T1 (p=0.001) and higher proportions of T3 (p=0.001) and T4 (p=0.02). On multivariate analysis, tumor size T3/T4 and nodal status N2/N3 were significant factors for reduced DFS (p=0.023 and p=0.0003 respectively). Tumor size T3/T4 was the only significant prognostic factor for reduced OS (p=0.019). Conclusion: Advanced disease exhibited by tumor size > 5cm and positive nodal status is associated with poorer DFS in TNBC patients. Radiotherapy technique or fractionation protocol were not associated with differences in DFS or OS in our patient cohort.

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Effect of nodal status on clinical outcomes of triple-negative breast cancer: a population-based study using the SEER 18 database

Cited by 21 publications

References 25 publications

Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort

Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort

Application of RNAi-induced gene expression profiles for prognostic prediction in breast cancer

<p>Effect of Clinical and Treatment Factors on Survival Outcomes of Triple Negative Breast Cancer Patients</p>

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