Development and validation of a preoperative noninvasive predictive model based on circular tumor DNA for lymph node metastasis in resectable non-small cell lung cancer

Variant allele frequency in circulating tumor DNA correlated with tumor disease burden and predicted outcomes in patients with advanced breast cancer

PURPOSE

In patients with first-line advanced breast cancer (ABC), the correlation between ctDNA variant allele frequency (VAF) and tumor disease burden, and its prognostic value remains poorly investigated.

METHODS

This study included patients with ABC diagnosed at Peking University Cancer Hospital who performed ctDNA test before receiving first-line treatment. Baseline plasma samples were collected for assessing ctDNA alterations and VAF with next-generation sequencing. The sum of tumor target lesion diameters (SLD) was measured with imaging methods according to RECIST 1.1 criteria.

RESULTS

The final cohort included 184 patients. The median age of the cohort was 49.4 (IQR: 42.3-56.8) years. The median VAF was 15.6% (IQR: 5.4%-33.7%). VAF showed positive correlation with SLD in patients with relatively large tumor lesions (r = 0.314, p = 0.003), but not in patients with small tumor lesions (p = 0.226). VAF was associated with multiple metastasis sites (p = 0.001). Multivariate Cox regression analysis showed that high VAF was associated with shorter overall survival (OS) (HR: 3.519, 95% confidence interval (CI): 2.149-5.761), and first-line progression-free survival (PFS) (HR: 2.352, 95%CI: 1.462-3.782). Combined VAF and SLD improved prediction performance, both median OS and PFS of patients in VAF(H)/SLD(H) group were significantly longer than VAF(L)/SLD(L) group (mOS: 49.3 vs. 174.1 months; mPFS: 9.6 vs. 25.3 months).

CONCLUSION

ctDNA VAF associated with tumor disease burden, and was a prognostic factor for patients with ABC. A combination of ctDNA test and radiographic imaging might enhance tumor burden evaluation, and improve prognosis stratification in patients with ABC.

Risk profiles and a concise prediction model for lymph node metastasis in patients with lung adenocarcinoma

BACKGROUND

Lung cancer is the second most commonly diagnosed cancer and ranks the first in mortality. Pathological lymph node status(pN) of lung cancer affects the treatment strategy after surgery while systematic lymph node dissection(SLND) is always unsatisfied.

METHODS

We reviewed the clinicopathological features of 2,696 patients with LUAD and one single lesion ≤ 5 cm who underwent SLND in addition to lung resection at the Sun Yat-Sen University Cancer Center. The relationship between the pN status and all other clinicopathological features was assessed. All participants were stochastically divided into development and validation cohorts; the former was used to establish a logistic regression model based on selected factors from stepwise backward algorithm to predict pN status. C-statistics, accuracy, sensitivity, and specificity were calculated for both cohorts to test the model performance.

RESULTS

Nerve tract infiltration (NTI), visceral pleural infiltration (PI), lymphovascular infiltration (LVI), right upper lobe (RUL), low differentiated component, tumor size, micropapillary component, lepidic component, and micropapillary predominance were included in the final model. Model performance in the development and validation cohorts was as follows: 0.861 (95% CI: 0.842-0.883) and 0.840 (95% CI: 0.804-0.876) for the C-statistics and 0.803 (95% CI: 0.784-0.821) and 0.785 (95% CI: 0.755-0.814) for accuracy, and 0.754 (95% CI: 0.706-0.798) and 0.686 (95% CI: 0.607-0.757) for sensitivity and 0.814 (95% CI: 0.794-0.833) and 0.811 (95% CI: 0.778-0.841) for specificity, respectively.

CONCLUSION

Our study showed an easy and credible tool with good performance in predicting pN in patients with LUAD with a single tumor ≤ 5.0 cm without SLND and it is valuable to adjust the treatment strategy.

Liquid Biopsy in Lung Cancer: Biomarkers for the Management of Recurrence and Metastasis

Liquid biopsies have emerged as a promising tool for the detection of metastases as well as local and regional recurrence in lung cancer. Liquid biopsy tests involve analyzing a patient's blood, urine, or other body fluids for the detection of biomarkers, including circulating tumor cells or tumor-derived DNA/RNA that have been shed into the bloodstream. Studies have shown that liquid biopsies can detect lung cancer metastases with high accuracy and sensitivity, even before they are visible on imaging scans. Such tests are valuable for early intervention and personalized treatment, aiming to improve patient outcomes. Liquid biopsies are also minimally invasive compared to traditional tissue biopsies, which require the removal of a sample of the tumor for further analysis. This makes liquid biopsies a more convenient and less risky option for patients, particularly those who are not good candidates for invasive procedures due to other medical conditions. While liquid biopsies for lung cancer metastases and relapse are still being developed and validated, they hold great promise for improving the detection and treatment of this deadly disease. Herein, we summarize available and novel approaches to liquid biopsy tests for lung cancer metastases and recurrence detection and describe their applications in clinical practice.

Applications of Liquid Biopsies in Non-Small-Cell Lung Cancer

The concept of liquid biopsy as an analysis tool for non-solid tissue carried out for the purpose of providing information about solid tumors was introduced approximately 20 years ago. Additional to the detection of circulating tumor cells (CTCs), the liquid biopsy approach quickly included the analysis of circulating tumor DNA (ctDNA) and other tumor-derived markers such as circulating cell-free RNA or extracellular vesicles. Liquid biopsy is a non-invasive technique for detecting multiple cancer-associated biomarkers that is easy to obtain and can reflect the characteristics of the entire tumor mass. Currently, ctDNA is the key component of the liquid biopsy approach from the point of view of the prognosis assessment, prediction, and monitoring of the treatment of non-small-cell lung cancer (NSCLC) patients. ctDNA in NSCLC patients carries variants or rearrangements that drive carcinogenesis, such as those in EGFR, KRAS, ALK, or ROS1. Due to advances in pharmacology, these variants are the subject of targeted therapy. Therefore, the detection of these variants has gained attention in clinical medicine. Recently, methods based on qPCR (ddPCR, BEAMing) and next-generation sequencing (NGS) are the most effective approaches for ctDNA analysis. This review addresses various aspects of the use of liquid biopsy with an emphasis on ctDNA as a biomarker in NSCLC patients.

Development and validation of prognostic nomograms for patients with metastatic small bowel adenocarcinoma: a retrospective cohort study

We aimed to explore factors associated with prognosis in patients with metastatic small bowel adenocarcinoma (SBA) as well as to develop and validate nomograms to predict overall survival (OS) and cancer-specific survival (CSS). Relevant information of patients diagnosed between 2004 and 2016 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms for predicting 1- and 3-year OS and CSS were established with potential risk factors screened from multivariate cox regression analysis. The discrimination and accuracy of the nomograms were assessed by concordance index (C-index), calibration plots, and the area under receiver operating characteristic curve (AUC). In total, 373 SBA patients with M1 category were enrolled. Multivariate analysis revealed that age, size and grade of primary tumor, primary tumor surgery, and chemotherapy were significant variables associated with OS and CSS. The C-index values of the nomogram for OS were 0.715 and 0.687 in the training and validation cohorts, respectively. For CSS, it was 0.711 and 0.690, respectively. Through AUC, decision curve analysis (DCA) and calibration plots, the nomograms displayed satisfactory prognostic predicted ability and clinical application both in the OS and CSS. Our models could be served as a reliable tool for prognostic evaluation of patients with metastatic SBA, which are favorable in facilitating individualized survival predictions and clinical decision-making.