Dataset Information

Preoperative clinical and tumor genomic features associated with pathologic lymph node metastasis in clinical stage I and II lung adenocarcinoma.

ABSTRACT: While next-generation sequencing (NGS) is used to guide therapy in patients with metastatic lung adenocarcinoma (LUAD), use of NGS to determine pathologic LN metastasis prior to surgery has not been assessed. To bridge this knowledge gap, we performed NGS using MSK-IMPACT in 426 treatment-naive patients with clinical N2-negative LUAD. A multivariable logistic regression model that considered preoperative clinical and genomic variables was constructed. Most patients had cN0 disease (85%) with pN0, pN1, and pN2 rates of 80%, 11%, and 9%, respectively. Genes altered at higher rates in pN-positive than in pN-negative tumors were STK11 (p = 0.024), SMARCA4 (p = 0.006), and SMAD4 (p = 0.011). Fraction of genome altered (p = 0.037), copy number amplifications (p = 0.001), and whole-genome doubling (p = 0.028) were higher in pN-positive tumors. Multivariable analysis revealed solid tumor morphology, tumor SUVmax, clinical stage, SMARCA4 and SMAD4 alterations were independently associated with pathologic LN metastasis. Incorporation of clinical and tumor genomic features can identify patients at risk of pathologic LN metastasis; this may guide therapy decisions before surgical resection.

SUBMITTER: Caso R

PROVIDER: S-EPMC8295366 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Preoperative clinical and tumor genomic features associated with pathologic lymph node metastasis in clinical stage I and II lung adenocarcinoma.

Caso Raul R Connolly James G JG Zhou Jian J Tan Kay See KS Choi James J JJ Jones Gregory D GD Mastrogiacomo Brooke B Sanchez-Vega Francisco F Nguyen Bastien B Rocco Gaetano G Molena Daniela D Sihag Smita S Adusumilli Prasad S PS Bott Matthew J MJ Jones David R DR

NPJ precision oncology 20210721 1

While next-generation sequencing (NGS) is used to guide therapy in patients with metastatic lung adenocarcinoma (LUAD), use of NGS to determine pathologic LN metastasis prior to surgery has not been assessed. To bridge this knowledge gap, we performed NGS using MSK-IMPACT in 426 treatment-naive patients with clinical N2-negative LUAD. A multivariable logistic regression model that considered preoperative clinical and genomic variables was constructed. Most patients had cN0 disease (85%) with pN0 ...[more]

PMID: 34290393

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Project description:Background & aimsIt is important to identify superficial (T1) gastroesophageal adenocarcinomas (EAC) that are most or least likely to metastasize to lymph nodes, to select appropriate therapy. We aimed to develop a risk stratification model for metastasis of superficial EAC to lymph nodes using pathologic features of the primary tumor.MethodsWe collected pathology data from 210 patients with T1 EAC who underwent esophagectomy from 1996 through 2012 on factors associated with metastasis to lymph nodes (tumor size, grade, angiolymphatic invasion, and submucosal invasion). Using these variables, we developed a multivariable logistic model to generate 4 categories for estimated risk of metastasis (<5% risk, 5%-10% risk, 15%-20% risk, or >20% risk). The model was validated in a separate cohort of 39 patients who underwent endoscopic resection of superficial EAC and subsequent esophagectomy, with node stage analysis.ResultsWe developed a model based on 4 pathologic factors that determined risk of metastasis to range from 2.9% to 60% for patients in the first cohort. In the endoscopic resection validation cohort, higher risk scores were associated with increased detection of lymph node metastases at esophagectomy (P = .021). Among patients in the first cohort who did not have lymph node metastases detected before surgery (cN0), those with high risk scores (>20% risk) had 11-fold greater odds for having lymph node metastases at esophagectomy compared with patients with low risk scores (95% confidence interval, 2.3-52 fold). Increasing risk scores were associated with reduced patient survival time (P < .001) and shorter time to tumor recurrence (P < .001). Patients without lymph node metastases (pT1N0) but high risk scores had reduced times of survival (P < .001) and time to tumor recurrence (P = .001) after esophagectomy than patients with pT1N0 tumors and lower risk scores.ConclusionsPathologic features of primary superficial EACs can be used, along with the conventional node staging system, to identify patients at low risk for metastasis, who can undergo endoscopic resection, or at high risk, who may benefit from induction or adjuvant therapy.

Project description:Lymph node involvement increases the risk of breast cancer recurrence. An accurate non-invasive assessment of nodal involvement is valuable in cancer staging, surgical risk, and cost savings. Radiomics has been proposed to pre-operatively predict sentinel lymph node (SLN) status; however, radiomic models are known to be sensitive to acquisition parameters. The purpose of this study was to develop a prediction model for preoperative prediction of SLN metastasis using deep learning-based (DLB) features and compare its predictive performance to state-of-the-art radiomics. Specifically, this study aimed to compare the generalizability of radiomics vs DLB features in an independent test set with dissimilar resolution. Dynamic contrast-enhancement images from 198 patients (67 positive SLNs) were used in this study. Of these subjects, 163 had an in-plane resolution of 0.7 × 0.7 mm2, which were randomly divided into a training set (approximately 67%) and a validation set (approximately 33%). The remaining 35 subjects with a different in-plane resolution (0.78 × 0.78 mm2) were treated as independent testing set for generalizability. Two methods were employed: (1) conventional radiomics (CR), and (2) DLB features which replaced hand-curated features with pre-trained VGG-16 features. The threshold determined using the training set was applied to the independent validation and testing dataset. Same feature reduction, feature selection, model creation procedures were used for both approaches. In the validation set (same resolution as training), the DLB model outperformed the CR model (accuracy 83% vs 80%). Furthermore, in the independent testing set of the dissimilar resolution, the DLB model performed markedly better than the CR model (accuracy 77% vs 71%). The predictive performance of the DLB model outperformed the CR model for this task. More interestingly, these improvements were seen particularly in the independent testing set of dissimilar resolution. This could indicate that DLB features can ultimately result in a more generalizable model.

Project description:BackgroundPreoperative evaluation of lymph node (LN) state is of pivotal significance for informing therapeutic decisions in gastric cancer (GC) patients. However, there are no non-invasive methods that can be used to preoperatively identify such status. We aimed at developing a genomic biosignature based model to predict the possibility of LN metastasis in GC patients.MethodsWe used the RNA profile retrieving strategy and performed RNA expression profiling in a large GC cohort (GSE62254, n = 300) from Gene Expression Ominus (GEO). In the exploratory stage, 300 GC patients from GSE62254 were involved and the differentially expressed RNAs (DERs) for LN-status were determined using the R software. GC samples in GSE62254 were randomly allocated into a learning set (n = 210) and a verification set (n = 90). By using the Least absolute shrinkage and selection operator (LASSO) regression approach, a set of 23-RNA signatures were established and the signature based nomogram was subsequently built for distinguishing LN condition. The diagnostic efficiency, as well as the clinical performance of this model were assessed using the decision curve analysis (DCA). Metascape was used for bioinformatic analysis of the DERs.ResultsBased on the genomic signature, we established a nomogram that robustly distinguished LN status in the learning (AUC = 0.916, 95% CI 0.833-0.999) and verification sets (AUC = 0.775, 95% CI 0.647-0.903). DCA demonstrated the clinical value of this nomogram. Functional enrichment analysis of the DERs was performed using bioinformatics methods which revealed that these DERs were involved in several lymphangiogenesis-correlated cascades.ConclusionsIn this study, we present a genomic signature based nomogram that integrates the 23-RNA biosignature based scores and Lauren classification. This model can be utilized to estimate the probability of LN metastasis with good performance in GC. The functional analysis of the DERs reveals the prospective biogenesis of LN metastasis in GC.

Dataset Information

Preoperative clinical and tumor genomic features associated with pathologic lymph node metastasis in clinical stage I and II lung adenocarcinoma.

Publications

Preoperative clinical and tumor genomic features associated with pathologic lymph node metastasis in clinical stage I and II lung adenocarcinoma.

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