A Nomogram Based on Consolidation Tumor Ratio Combined with Solid or Micropapillary Patterns for Postoperative Recurrence in Pathological Stage IA Lung Adenocarcinoma.

BACKGROUND: Patients with pathological stage IA lung adenocarcinoma (LUAD) are at risk of relapse. The value of the TNM staging system is limited in predicting recurrence. Our study aimed to develop a precise recurrence prediction model for stage IA LUAD. MATERIALS AND METHODS: Patients with pathological stage IA LUAD who received surgical treatment at Zhongshan Hospital Fudan University were retrospectively analyzed. Multivariate Cox proportional hazards regression models were used to create nomograms for recurrence-free survival (RFS). The predictive performance of the model was assessed using calibration plots and the concordance index (C-index). RESULTS: The multivariate Cox regression analysis revealed that CTR (0.75 < CTR ≤ 1; HR = 9.882, 95% CI: 2.036-47.959, p = 0.004) and solid/micropapillary-predominance (SMPP; >5% and the most dominant) (HR = 4.743, 95% CI: 1.506-14.933, p = 0.008) were independent prognostic factors of RFS. These risk factors were used to construct a nomogram to predict postoperative recurrence in these patients. The C-index of the nomogram for predicting RFS was higher than that of the eighth T-stage system (0.873 for the nomogram and 0.643 for the eighth T stage). The nomogram also achieved good predictive performance for RFS with a well-fitted calibration curve. CONCLUSIONS: We developed and validated a nomogram based on CTR and SMP patterns for predicting postoperative recurrence in pathological stage IA LUAD. This model is simple to operate and has better predictive performance than the eighth T stage system, making it suitable for selecting further adjuvant treatment and follow-up.

Machine learning to predict occult metastatic lymph nodes along the recurrent laryngeal nerves in thoracic esophageal squamous cell carcinoma.

PURPOSE: Esophageal squamous cell carcinoma (ESCC) metastasizes in an unpredictable fashion to adjacent lymph nodes, including those along the recurrent laryngeal nerves (RLNs). This study is to apply machine learning (ML) for prediction of RLN node metastasis in ESCC. METHODS: The dataset contained 3352 surgically treated ESCC patients whose RLN lymph nodes were removed and pathologically evaluated. Using their baseline and pathological features, ML models were established to predict RLN node metastasis on each side with or without the node status of the contralateral side. Models were trained to achieve at least 90% negative predictive value (NPV) in fivefold cross-validation. The importance of each feature was measured by the permutation score. RESULTS: Tumor metastases were found in 17.0% RLN lymph nodes on the right and 10.8% on the left. In both tasks, the performance of each model was comparable, with a mean area under the curve ranging from 0.731 to 0.739 (without contralateral RLN node status) and from 0.744 to 0.748 (with contralateral status). All models showed approximately 90% NPV scores, suggesting proper generalizability. The pathology status of chest paraesophgeal nodes and tumor depth had the highest impacts on the risk of RLN node metastasis in both models. CONCLUSION: This study demonstrated the feasibility of ML in predicting RLN node metastasis in ESCC. These models may potentially be used intraoperatively to spare RLN node dissection in low-risk patients, thereby minimizing adverse events associated with RLN injuries.

Targeting HSPA1A in ARID2-deficient lung adenocarcinoma.

Somatic mutations of the chromatin remodeling gene ARID2 are observed in ∼7% of human lung adenocarcinomas (LUADs). However, the role of ARID2 in the pathogenesis of LUADs remains largely unknown. Here we find that ARID2 expression is decreased during the malignant progression of both human and mice LUADs. Using two KrasG12D -based genetically engineered murine models, we demonstrate that ARID2 knockout significantly promotes lung cancer malignant progression and shortens overall survival. Consistently, ARID2 knockdown significantly promotes cell proliferation in human and mice lung cancer cells. Through integrative analyses of ChIP-Seq and RNA-Seq data, we find that Hspa1a is up-regulated by Arid2 loss. Knockdown of Hspa1a specifically inhibits malignant progression of Arid2-deficient but not Arid2-wt lung cancers in both cell lines as well as animal models. Treatment with an HSPA1A inhibitor could significantly inhibit the malignant progression of lung cancer with ARID2 deficiency. Together, our findings establish ARID2 as an important tumor suppressor in LUADs with novel mechanistic insights, and further identify HSPA1A as a potential therapeutic target in ARID2-deficient LUADs.

Loss of Scribble confers cisplatin resistance during NSCLC chemotherapy via Nox2/ROS and Nrf2/PD-L1 signaling.

BACKGROUND: Cisplatin resistance remains a major clinical obstacle to the successful treatment of non-small cell lung cancer (NSCLC). Scribble contributes to ROS-induced inflammation and cisplatin-elevated toxic reactive oxygen species (ROS) promotes cell death. However, it is unknown whether and how Scribble is involved in the cisplatin-related cell death and the underlying mechanism of Scribble in response to chemotherapies and in the process of oxidative stress in NSCLC. METHODS: We used two independent cohorts of NSCLC samples derived from patients treated with platinum-containing chemotherapy and xenograft modeling in vivo. We analyzed the correlation between Scribble and Nox2 or Nrf2/PD-L1 both in vivo and in vitro, and explored the role of Scribble in cisplatin-induced ROS and apoptosis. FINDINGS: Clinical analysis revealed that Scribble expression positively correlated with clinical outcomes and chemotherapeutic sensitivity in NSCLC patients. Scribble protected Nox2 protein from proteasomal degradation. Scribble knockdown induced cisplatin resistance by blocking Nox2/ROS and apoptosis in LRR domain-dependent manner. In addition, low levels of Scribble correlated with high levels of PD-L1 via activation of Nrf2 transcription in vivo and in vitro. INTERPRETATIONS: Our study revealed that polarity protein Scribble increased cisplatin-induced ROS generation and is beneficial to chemotherapeutic outcomes in NSCLC. Although Scribble deficiency tends to lead to cisplatin resistance by Nox2/ROS and Nrf2/PD-L1, it is still possible that Scribble deficiency-induced PD-L1 may yield benefits in immunotherapy. FUND: National Key R&D Program of China, Strategic Priority Research Program of the Chinese Academy of Sciences, National Natural Science Foundation of China, China Postdoctoral Science Foundation.

Dynamics of EGFR mutations in plasma recapitulates the clinical response to EGFR-TKIs in NSCLC patients.

OBJECTIVES: Genomic profiling using plasma cell-free DNA (cfDNA) represents a non-invasive alternative to tumor re-biopsy, which is challenging in clinical practice. The feasibility of dynamically monitoring epidermal growth factor receptor (EGFR) mutation status using serial plasma samples from non-small cell lung cancer (NSCLC) patients treated by tyrosine kinase inhibitors (TKIs) and its application in tracking clinical response and detection of resistance were investigated. PATIENTS AND METHODS: Forty-five NSCLC patients with EGFR mutation-positive pre-TKI plasma and at least two post-TKI plasma collections were recruited to this study. EGFR mutations including L858R, exon 19 deletion (19-del) and T790M were analyzed using droplet digital PCR (ddPCR) in longitudinally collected plasma samples. RESULTS: We observed a significant reduction in plasma EGFR mutation abundance during the first two-month of TKI treatment. Acquiring of secondary T790M gatekeeper mutation or completed "loss" of EGFR mutations represented two major categories of resistance profiles. Moreover, we demonstrated that levels of plasma EGFR mutations highly correlated with changes of tumor diameter as determined by radiographic imaging, or development of new lesions. In a subset of patients, we further showed that reappearance of EGFR mutations could be detected in plasma up to 5 months ahead of progressive disease (PD), suggesting an early detection of drug resistance. CONCLUSIONS: Our findings suggest that genomic analysis using plasma cfDNA may offer an effective approach to monitor clinical response and emergence of resistance.