Dataset Information

Validation of a Nomogram Predicting Non-Sentinel Lymph Node Metastases among Patients with Breast Cancer after Primary Systemic Therapy - a transSENTINA Substudy.

ABSTRACT:

Background

Prediction of non-sentinel lymph node (SLN) status after primary systemic therapy (PST) may allow tailored axillary staging. The aim of this analysis was to compare established nomograms from i) the primary operative (n = 6) and ii) the neoadjuvant (n = 1) setting with an optimized nomogram to predict non-SLN status in patients after PST.

Methods

181 patients converting from cN1 prior to PST to ycN0 but found to have a histologically positive SLN in the SENTINA trial were analyzed. Established models were applied. An optimized model was compiled using univariate and subsequent multivariable logistic regression (backward selection, likelihood ratio test).

Results

Area-under-the-curve (AUC) values from the primary operative models showed sufficient performance (0.82-0.71). For the neoadjuvant model, the AUC was found to be inferior to prior analyses (0.66) but within published confidence intervals. The SENTINA nomogram comprised the diameter of the largest lymph node (p = 0.006, odds ratio (OR) = 1.19), tumor size prior to PST (p = 0.085, OR = 1.31), and number of all positive SLN (p = 0.083, OR = 2.04). This model was validated using a separate cohort of arm C (n = 168, AUC 0.79, 95% confidence interval 0.74-0.85).

Conclusion

We validated 7 models of prediction of non-SLN among patients showing axillary conversion through PST. Our own 'SENTINA nomogram' yielded AUC values comparable to previous nomograms.

SUBMITTER: Liedtke C

PROVIDER: S-EPMC6381911 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Publications

Validation of a Nomogram Predicting Non-Sentinel Lymph Node Metastases among Patients with Breast Cancer after Primary Systemic Therapy - a transSENTINA Substudy.

Liedtke Cornelia C Görlich Dennis D Bauerfeind Ingo I Fehm Tanja T Fleige Barbara B Helms Gisela G Lebeau Annette A Staebler Annette A Ataseven Beyhan B Denkert Carsten C Gerber Bernd B Heil Jörg J Krug David D Kümmel Sherko S Schwentner Lukas L von Minckwitz Gunter G Loibl Sibylle S Untch Michael M Kühn Thorsten T

Breast care (Basel, Switzerland) 20181106 6

<h4>Background</h4>Prediction of non-sentinel lymph node (SLN) status after primary systemic therapy (PST) may allow tailored axillary staging. The aim of this analysis was to compare established nomograms from i) the primary operative (n = 6) and ii) the neoadjuvant (n = 1) setting with an optimized nomogram to predict non-SLN status in patients after PST.<h4>Methods</h4>181 patients converting from cN1 prior to PST to ycN0 but found to have a histologically positive SLN in the SENTINA trial we ...[more]

PMID: 30800039

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Project description:BackgroundPatients with breast cancer presenting with single lymph node metastasis (from a sentinel node) experience prolonged survival compared to patients with multiple lymph node metastases (≥3). However, little information is available on the genetic and immunological characteristics of breast cancer metastases within the regional lymph nodes as they progress from the sentinel lymph node (SLN) downstream to multiple regional lymph nodes (MLNs).MethodsGenomic profiling was performed using a next-generation sequencing panel covering 520 cancer-related genes in the primary tumour and metastatic lymph nodes of 157 female patients with breast cancer. We included primary tumours, metastatic lymph nodes and adjacent clinically normal lymph nodes (20 patients from the SLN group and 28 patients from the MLNs group) in the whole transcriptome analysis.FindingsThe downstream metastatic lymph nodes (P = 0.029) and the primary breast tumours (P = 0.011) had a higher frequency of PIK3CA mutations compared to the SLN metastasis. We identified a distinct group of 14 mutations from single sentinel node metastasis and a different group of 15 mutations from multiple nodal metastases. Only 4 distinct mutations (PIK3CA, CDK4, NFKBIA and CDKN1B) were conserved in metastases from both lymph node settings. The tumour mutational burden (TMB) was significantly lower in single nodal metastasis compared to the paired primary breast cancer (P = 0.0021), while the decline in TMB did not reach statistical significance in the MLNs group (P = 0.083). In the gene set enrichment analysis, we identified 4 upregulated signatures in both primary tumour and nodal metastases from the MLNs group, including 3 Epithelial-mesenchymal transition(EMT) signatures and 1 angiogenesis signature. Both the CD8/Treg ratio and the CD8/EMT ratio were significantly higher in adjacent normal lymph nodes from patients with a single metastasis in the SLN compared with samples from the MLNs group (P = 0.045 and P = 0.023, respectively). This suggests that the immune defence from the MLNs patients might have a less favourable microenvironment, thus permitting multiple lymph nodes metastasis.InterpretationSingle lymph node metastases and multiple lymph node metastases have significant differences in their molecular profiles and immune profiles. The findings are associated with more aggressive tumour characteristics and less favourable immune charactoristics in patients with multiple nodal metastases compared to those with a single metastasis in the sentinel node.FundingThis work was supported by funds from High-level Hospital Construction Project (DFJH201921), the National Natural Science Foundation of China (81902828 and 82002928), the Fundamental Research Funds for the Central Universities (y2syD2192230), and the Medical Scientific Research Foundation of Guangdong Province (B2019039).

Project description:Background The rate of breast-conserving surgery is very low in China, compared with that in developed countries; most breast cancer patients receive mastectomy. It is great important to explore the possibility of omitting axillary lymph node dissection (ALND) in early-stage breast cancer patients with 1 or 2 positive sentinel lymph nodes (SLNs) in China. The aim of this study was to develop a nomogram based on elastography for the prediction of the risk of non-SLN (NSLN) metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. Methods A total of 601 breast cancer patients were initially recruited. According to the inclusion and exclusion criteria, 118 early-stage breast cancer patients with 1 or 2 positive SLNs were finally enrolled and were assigned to the training cohort (n=82) and the validation cohort (n=36), respectively. In the training cohort, the independent predictors were screened by logistic regression analysis and then were used to conducted the nomogram for the prediction of NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The calibration curves, concordance index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and Decision curve analysis (DCA) were used to verified the performance of the nomogram. Results The multivariable analysis showed that the enrolled patients with positive HER2 expression (OR=6.179, P=0.013), Ki67≥14% (OR=8.976, P=0.015), larger lesion size (OR=1.038, P=0.045), and higher Emean (OR=2.237, P=0.006) were observed to be the independent factors of NSLN metastasis. Based on the above four independent predictors, a nomogram was conducted to predict the risk of the NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The nomogram showed good discrimination in the prediction of NSLN metastasis, with bias-corrected C-index of 0.855 (95% CI, 0.754-0.956) and 0.853 (95% CI, 0.724-0.983) in the training and validation cohorts, respectively. Furthermore, the AUC was 0.877 (95%CI: 0.776- 0.978) and 0.861 (95%CI: 0.732-0.991), respectively, indicating a good performance of the nomogram. The calibration curve suggested a satisfactory agreement between the predictive and actual risk in both the training (χ2 = 11.484, P=0.176, HL test) and validation (χ2 = 6.247, p = 0.620, HL test) cohorts, and the obvious clinical nets were revealed by DCA. Conclusions We conducted a satisfactory nomogram model to evaluate the risk of NSLN metastasis in early-stage breast cancer patients with 1 or 2 SLN metastases. This model could be considered as an ancillary tool to help such patients to be selectively exempted from ALND.

Project description:BackgroundCurrent practice is to perform a completion axillary lymph node dissection (ALND) for breast cancer patients with tumor-involved sentinel lymph nodes (SLNs), although fewer than half will have non-sentinel node (NSLN) metastasis. Our goal was to develop new models to quantify the risk of NSLN metastasis in SLN-positive patients and to compare predictive capabilities to another widely used model.MethodsWe constructed three models to predict NSLN status: recursive partitioning with receiver operating characteristic curves (RP-ROC), boosted Classification and Regression Trees (CART), and multivariate logistic regression (MLR) informed by CART. Data were compiled from a multicenter Northern California and Oregon database of 784 patients who prospectively underwent SLN biopsy and completion ALND. We compared the predictive abilities of our best model and the Memorial Sloan-Kettering Breast Cancer Nomogram (Nomogram) in our dataset and an independent dataset from Northwestern University.Results285 patients had positive SLNs, of which 213 had known angiolymphatic invasion status and 171 had complete pathologic data including hormone receptor status. 264 (93%) patients had limited SLN disease (micrometastasis, 70%, or isolated tumor cells, 23%). 101 (35%) of all SLN-positive patients had tumor-involved NSLNs. Three variables (tumor size, angiolymphatic invasion, and SLN metastasis size) predicted risk in all our models. RP-ROC and boosted CART stratified patients into four risk levels. MLR informed by CART was most accurate. Using two composite predictors calculated from three variables, MLR informed by CART was more accurate than the Nomogram computed using eight predictors. In our dataset, area under ROC curve (AUC) was 0.83/0.85 for MLR (n = 213/n = 171) and 0.77 for Nomogram (n = 171). When applied to an independent dataset (n = 77), AUC was 0.74 for our model and 0.62 for Nomogram. The composite predictors in our model were the product of angiolymphatic invasion and size of SLN metastasis, and the product of tumor size and square of SLN metastasis size.ConclusionWe present a new model developed from a community-based SLN database that uses only three rather than eight variables to achieve higher accuracy than the Nomogram for predicting NSLN status in two different datasets.

Dataset Information

Validation of a Nomogram Predicting Non-Sentinel Lymph Node Metastases among Patients with Breast Cancer after Primary Systemic Therapy - a transSENTINA Substudy.

Background

Methods

Results

Conclusion

Publications

Validation of a Nomogram Predicting Non-Sentinel Lymph Node Metastases among Patients with Breast Cancer after Primary Systemic Therapy - a transSENTINA Substudy.

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