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Abstract. It has been demonstrated that erlotinib is effective in treating patients with brain metastasis from non-small-cell lung cancer. However, the number of studies determining the erlotinib concentration in these patients is limited. The purpose of this study was to measure the concentration of erlotinib in the cerebrospinal fluid of patients with brain metastasis from non-small-cell lung carcinoma. Six patients were treated with the standard recommended daily dose of erlotinib (150 mg) for 4 weeks. All the patients had previously received chemotherapy, but no brain radiotherapy. At the end of the treatment period, blood plasma and cerebrospinal fluid samples were collected and the erlotinib concentration was determined by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS̸MS). The average erlotinib concentration in the blood plasma and the cerebrospinal fluid was 717.7±459.7 and 23.7±13.4 ng/ml, respectively. The blood-brain barrier permeation rate of erlotinib was found to be 4.4±3.2%. In patients with partial response (PR), stable disease (SD) and progressive disease (PD), the average concentrations of erlotinib in the cerebrospinal fluid were 35.5±19.0, 19.1±8.7 and 16.4±5.9 ng/ml, respectively. In addition, the efficacy rate of erlotinib for metastatic brain lesions was 33.3%, increasing to 50% in patients with EGFR mutations. However, erlotinib appeared to be ineffective in cases with wild-type EGFR. In conclusion, a relatively high concentration of erlotinib was detected in the cerebrospinal fluid of patients with brain metastases from non-small-cell lung cancer. Thus, erlotinib may be considered as a treatment option for this patient population.

NCT00891579.

To understand the molecular mechanism of tumorigenesis of pulmonary lymphoepithelioma-like carcinoma and explore potential therapeutic strategies, we investigated the genomic profiles and PD-L1 expression of 29 Chinese pulmonary lymphoepithelioma-like carcinoma patients at various stages. We performed capture-based targeted sequencing on tissue samples collected from 27 patients with sufficient samples using a panel consisting of 520 cancer-related genes, spanning 1.64 Mb of the human genome. We identified 184 somatic mutations in 109 genes from 26 patients. One patient had no mutations detected by this panel. Copy number variations were detected in 52% (14/27) of the patients, with a majority having advancedstage disease (10/14). Except for the detection of ERBB2 amplification and KRAS mutation in two patients, no other classic lung cancer driver mutations were detected. Interestingly, 78% (21/27) of the patients had mutations in epigenetic regulators. Of the 184 mutations identified, 51 occurred in 29 epigenetics-related genes. Furthermore, we performed PD-L1 immunohistochemistry staining using the Dako 22C3 assay and demonstrated that 69% (20/29) of the cohort had positive PD-L1 expression, of which three patients received and benefited from a PD-1 inhibitor. In conclusion, we elucidated a distinct genomic landscape associated with pulmonary lymphoepithelioma-like carcinoma with no classic lung cancer driver mutation but an enrichment of mutations in epigenetic regulators. The detection of high PD-L1 expression and lack of any canonical druggable driver mutations raises the potential of checkpoint immunotherapy for pulmonary lymphoepithelioma-like carcinoma.

BackgroundThis study aimed to evaluate the antitumor activity of camrelizumab, an antiprogrammed cell death-1 antibody, in pretreated recurrent or metastatic nasopharyngeal carcinoma (NPC) and to explore predictive biomarkers.MethodsPatients with recurrent (not amenable to locally curative treatment) or metastatic NPC who had failed at least two lines of chemotherapy were eligible to receive camrelizumab (200 mg intravenously every 2 weeks) for 2 years or until disease progression, intolerable adverse events, withdrawal of consents, or investigator decision. The primary endpoint was objective response rate (ORR) assessed by an independent review committee (IRC). Programmed cell death-ligand 1 (PD-L1) expression was assessed by immunohistochemistry. Other immune-related biomarkers including major histocompatibility complex class I and major histocompatibility complex class II (MHC-II) were assessed by multiplex immunofluorescence staining.ResultsBetween August 14, 2018, and December 30, 2019, a total of 156 patients were enrolled. The IRC-assessed ORR was 28.2% (95% CI 21.3% to 36.0%). The median progression-free survival was 3.7 months (95% CI 2.0 to 4.1) per IRC, and the median overall survival was 17.4 months (95% CI 15.2 to 21.9). The ORRs were 35.2% (95% CI 25.3% to 46.1%) vs 19.4% (95% CI 10.4% to 31.4%) in patients with tumor PD-L1 expression of ≥10% and<10%, respectively. Patients with durable clinical benefit (DCB), which was defined as complete response, partial response or stable disease of ≥18 weeks, had higher density of MHC-II+ cell in stroma than patients without DCB (median 868.1 (IQR 413.4–2854.0) cells/mm2 vs median 552.4 (IQR 258.4 to 1242.1) cells/mm2). MHC-II+ cell density did not correlate with PD-L1 expression, and a composite of high stromal MHC-II+ cell density and tumor PD-L1 expression further enriched patients who could benefit from camrelizumab.ConclusionsCamrelizumab had clinically meaningful antitumor activity in patients with recurrent or metastatic NPC. The composition of both MHC-II+ cell density and PD-L1 expression could result in better patient selection.

Background Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical fields, we attempt to integrate a well‐trained deep learning algorithm to detect and classify pulmonary nodules derived from clinical CT images. Materials and Methods Open‐source data sets and multicenter data sets have been used in this study. A three‐dimensional convolutional neural network (CNN) was designed to detect pulmonary nodules and classify them into malignant or benign diseases based on pathologically and laboratory proven results. Results The sensitivity and specificity of this well‐trained model were found to be 84.4% (95% confidence interval [CI], 80.5%–88.3%) and 83.0% (95% CI, 79.5%–86.5%), respectively. Subgroup analysis of smaller nodules (<10 mm) have demonstrated remarkable sensitivity and specificity, similar to that of larger nodules (10–30 mm). Additional model validation was implemented by comparing manual assessments done by different ranks of doctors with those performed by three‐dimensional CNN. The results show that the performance of the CNN model was superior to manual assessment. Conclusion Under the companion diagnostics, the three‐dimensional CNN with a deep learning algorithm may assist radiologists in the future by providing accurate and timely information for diagnosing pulmonary nodules in regular clinical practices. Implications for Practice The three‐dimensional convolutional neural network described in this article demonstrated both high sensitivity and high specificity in classifying pulmonary nodules regardless of diameters as well as superiority compared with manual assessment. Although it still warrants further improvement and validation in larger screening cohorts, its clinical application could definitely facilitate and assist doctors in clinical practice.

BACKGROUND: Liquid biopsy is emerging as an important approach for tumor genotyping in non-small cell lung cancer, ddPCR and SuperARMS are both methods with high sensitivity and specificity for detecting EGFR mutation in plasma. We aimed to compare ddPCR and SuperARMS to detect plasma EGFR status in a cohort of advanced NSCLC patients. METHOD: A total of 79 tumor tissues and paired plasma samples were collected. The EGFR mutation status in tissue was tested by ADx-ARMS, matched plasma was detected by ddPCR and SuperARMS, respectively. RESULTS: The EGFR mutation rates were identified as 64.6% (tissue, ARMS), 55.7% (plasma, ddPCR), and 49.4% (plasma, Super ARMS), respectively. The sensitivity of ddPCR was similar with Super-ARMS in plasma EGFR detection (80.4% vs 76.5%), as well as the specificity (89.3% vs 100%). And the McNemar’s test showed there was no significant difference (P = .125). The concordance rate between SuperARMS and ddPCR was 91.1%. A significant interaction was observed between cfDNA EGFR mutation status and EGFR-TKIs treatment tested by both methods. CONCLUSION: Super-ARMS and ddPCR share the similar accuracy for EGFR mutation detection in plasma biopsy; both methods predicted well the efficacy of EGFR-TKIs by detecting plasma EGFR status.