Dear Editor, Esophageal cancer(EC)is a malignant tumor originating from esophageal epithelium and remains a leading cause of cancer incidence and mortality worldwide1,2.According to the 2020 World Health Organization statistics,there were approx-imately 604,000 new EC cases and 544,000 EC-related deaths globally with China reporting approximately 320,000 new cases and 300,000 deaths,mainly from esophageal squamous cell carcinoma(ESCC)3.

Background Malignant mesothelioma (MM) is a cancer caused mainly by asbestos exposure, and is aggressive and incurable. This study aimed to identify differential metabolites and metabolic pathways involved in the pathogenesis and diagnosis of malignant mesothelioma. Methods By using gas chromatography-mass spectrometry (GC-MS), this study examined the plasma metabolic profile of human malignant mesothelioma. We performed univariate and multivariate analyses and pathway analyses to identify differential metabolites, enriched metabolism pathways, and potential metabolic targets. The area under the receiver-operating curve (AUC) criterion was used to identify possible plasma biomarkers. Results Using samples from MM (n = 19) and healthy control (n = 22) participants, 20 metabolites were annotated. Seven metabolic pathways were disrupted, involving alanine, aspartate, and glutamate metabolism; glyoxylate and dicarboxylate metabolism; arginine and proline metabolism; butanoate and histidine metabolism; beta-alanine metabolism; and pentose phosphate metabolic pathway. The AUC was used to identify potential plasma biomarkers. Using a threshold of AUC = 0.9, five metabolites were identified, including xanthurenic acid, (s)-3,4-hydroxybutyric acid, D-arabinose, gluconic acid, and beta-d-glucopyranuronic acid. Conclusions To the best of our knowledge, this is the first report of a plasma metabolomics analysis using GC-MS analyses of Asian MM patients. Our identification of these metabolic abnormalities is critical for identifying plasma biomarkers in patients with MM. However, additional research using a larger population is needed to validate our findings.

Acquired anaplastic lymphoma kinase (ALK) mutation is the major resistant mechanism to ALK tyrosine kinase inhibitors (TKIs) in non-small cell lung cancer (NSCLC) patients. At present, treatment options after acquiring secondary ALK mutations are still limited. Here, we report on a patient with metastatic ALK-rearranged NSCLC who was sequentially treated with ALK TKIs, from crizotinib to lorlatinib, and developed rare acquired compound ALK mutations (L1196M and D1203N) that confer resistance to lorlatinib. Moreover, our report describes the clinical response of an NSCLC patient with these compound mutations to multiple anti-tumor therapies. Among them, the patient was treated with SAF-189s 120 mg daily and had a stable disease lasting 3 months. Chemotherapy (pemetrexed-carboplatin) combined with bevacizumab was then administered. She achieved a partial response, which was maintained for 7 months as the best response. Since both SAF-189s and chemotherapy have shown a clear antitumor effect, they may be viable therapeutic options for these patients. Thus, our study can provide some reference in the treatment of NSCLC patients with ALK L1196M/D1203N compound mutations.

Background Malignant mesothelioma (MM) is a rare and highly aggressive cancer. Despite advances in multidisciplinary treatments for cancer, the prognosis for MM remains poor with no effective diagnostic biomarkers currently available. The aim of this study was to identify plasma metabolic biomarkers for better MM diagnosis and prognosis by use of a MM cell line-derived xenograft (CDX) model. Methods The MM CDX model was confirmed by hematoxylin and eosin staining and immunohistochemistry. Twenty female nude mice were randomly divided into two groups, 10 for the MM CDX model and 10 controls. Plasma samples were collected two weeks after tumor cell implantation. Gas chromatography-mass spectrometry analysis was conducted. Both univariate and multivariate statistics were used to select potential metabolic biomarkers. Hierarchical clustering analysis, metabolic pathway analysis, and receiver operating characteristic (ROC) analysis were performed. Additionally, bioinformatics analysis was used to investigate differential genes between tumor and normal tissues, and survival-associated genes. Results The MM CDX model was successfully established. With VIP > 1.0 and P-value < 0.05, a total of 23 differential metabolites were annotated, in which isoleucine, 5-dihydrocortisol, and indole-3-acetamide had the highest diagnostic values based on ROC analysis. These were mainly enriched in pathways for starch and sucrose metabolism, pentose and glucuronate interconversions, galactose metabolism, steroid hormone biosynthesis, as well as phenylalanine, tyrosine and tryptophan biosynthesis. Further, down-regulation was observed for amino acids, especially isoleucine, which is consistent with up-regulation of amino acid transporter genes SLC7A5 and SLC1A3 in MM. Overall survival was also negatively associated with SLC1A5 , SLC7A5 , and SLC1A3 . Conclusion We found several altered plasma metabolites in the MM CDX model. The importance of specific metabolic pathways, for example amino acid metabolism, is herein highlighted, although further investigation is warranted.

Background:According to the latest the World Health Organization (WHO) classification in 2015, invasive mucinous adenocarcinoma (IMA) is defined as a new pathological subtype of lung adenocarcinoma (LUAD). However, whether this rare subtype of lung pathology has any difference in prognosis than conventional LUAD is debatable. Our study attempted to compare clinical characteristics and prognosis of IMA vs. noninvasive mucinous adenocarcinomas (NMA).Methods:A total of 1,857 patients with LUAD who underwent radical resection were screened from 2010 to 2015 at Zhejiang Cancer Hospital. Patients with pulmonary IMA were matched 1:1 by using propensity scores with LUAD adjusted for clinicopathological characteristics. After follow-up, overall survival (OS) and disease-free survival (DFS) were explored by Kaplan-Meier and Cox regression analyses. Forest plots were used for subgroup analyses.Results:Following screening, 499 patients with LUAD were enrolled, with 97 IMA and 402 NMA. Compared to NMA of the lung, IMA was proportionately lower in women (50.5% vs. 63.4%; P=0.026) and nonsmokers (P<0.001). IMA was also associated with earlier tumor stage I (68.0% vs. 55.5%; P=0.033) and lower frequency of upper lobe tumors compared to NMA (P=0.007). Following propensity score matching, 97 pairs were selected, among which we found that patients with pulmonary IMA had a longer OS than those with NMA (P=0.014). According to the subgroup analysis, improved OS in the IMA cohort versus the NMA cohort was observed across various factors, including the absence of lymphovascular invasion or perineural invasion.Conclusions:In this study, we found that resectable IMA patients had a better OS than NMA patients. This study contributes to the understanding of IMA in depth, but it needs to be validated through additional multicenter studies.

Epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) acquired resistance remains a major barrier in the clinical treatment of lung adenocarcinoma with epidermal growth factor receptor (EGFR) mutations. Despite extensive efforts, mechanism of acquired resistance has not yet been elucidated clearly. The subject of this study was to characterize the metabolic signatures relevant to acquired EGFR-TKI resistance in pleural effusion (PE), and identify potential biomarkers in PE of patients with acquired EGFR-TKI resistance. PE from EGFR-TKI untreated group (n = 30) and EGFR-TKI resistant group (n = 18) was analyzed using liquid chromatography-mass spectrometry (LCMS) based metabolomic. Multivariate statistical analysis revealed distinctive diff ;erences between the groups. A total of 34 significantly differential metabolites in PE were identified, among which, the acquired EGFR-TKI resistant group had higher levels of l-lysine, taurine, ornithine and citrulline, and lower levels of l-tryptophan, kynurenine, l-phenylalanine, l-leucine, N-formyl-l-methionine, 3-hydroxyphenylacetic acid and N-acetyl-d-phenylalanine in PE than that of the EGFR-TKI untreated group. These metabolites are mainly involved in six amino acid metabolic pathways. In addition, 3-hydroxyphenylacetic acid and N-acetyl-d-phenylalanine showed the highest AUC values of 0.934 and 0.929 in receiver operating characteristic analysis. Through LCMS metabolomics, our study identified potential biomarkers in PE, differentiating EGFR-TKI resistant patients from untreated patients, as well as the mechanisms underlying acquired EGFR-TKI resistance; thus, providing novel insights into acquired EGFR-TKI resistance.

BackgroundInvasive mucinous adenocarcinoma (IMA) of the lung is a rare and distinct subtype of adenocarcinoma. At present, people have no idea whether IMA patients can benefit from immunotherapy and target therapy; thus there is an urgent need to clarify the immune microenvironment and genetic characteristics of this cohort.MethodsA total of 31 IMA patients matched with 27 non-mucinous adenocarcinoma (non-IMA) patients were enrolled in this study, and clinical data was collected. The expression of PD-L1, CD8+ tumor-infiltrating lymphocytes (TILs) and ALK was determined by immunohistochemistry. Polymerase Chain Reaction was used to determine the mutations of EGFR. The Chi-square test, Kaplan–Meier method and Cox proportional hazard regression model were used to explore the correlations between these clinicopathological variables, survival and identify risk factors.ResultsOf the patients with IMA 9.7% (3/31) revealed positive PD-L1 expression and 35.5% (11/31) showed CD8+ TIL infiltration, which were markedly lower than that of non-IMA group [PD-L1: 48.1% (13/27); CD8: 81.5% (22/27)]. Moreover, five (16.1%) patients in IMA group and 10 (37.0%) patients in non-IMA group had EGFR mutations, and nine (29.0%) patients in IMA group and zero (0.0%) patient in non-IMA group had ALK rearrangements. Additionally, we observed that IMA patients with CD8+ TIL infiltration had a worse prognosis than CD8-negative group (P = 0.024). Multivariate analyses showed that CD8 was an independent prognostic factor for patient’s survival (HR = 5.60, 95% CI: 1.35–23.22, P = 0.017).ConclusionPatients with IMA have down-regulated expression of PD-L1 and less CD8+ TIL infiltration in tumor microenvironment. Besides, a lower frequency of EGFR mutations was detected in patients with IMA than non-IMA patients while a higher rate of ALK rearrangements was found. Our results provide important reference for therapy of lung IMA.

目的 探讨表皮生长因子受体(EGFR)基因突变和间变性淋巴瘤激酶(ALK)重排在肺浸润性黏液腺癌(IMA)中的表达及与临床病理特征的关系.方法 选取2010年1月至2015年12月中国科学院大学附属肿瘤医院收治的IMA患者101例,应用突变扩增系统PCR法检测EGFR基因,VENTANA ALK免疫组化法检测ALK重排.结果 101例患者中共67例患者行EGFR和(或)ALK检测,其中EGFR/ALK均行检测32例,仅行EGFR检测20例,仅行ALK检测15例;34例患者未参与检测.EGFR突变阳性率为23.1％,在女性患者中表达率更高(P<0.05).ALK重排阳性率为27.7％,其表达情况与T分期有关,T分期越高越倾向于发生ALK重排(P<0.05).EGFR/ALK检测同时阳性占3.1％.结论 IMA患者的EGFR突变多见于女性,ALK重排与T分期有关.EGFR突变和ALK重排很少共存.

Pulmonary clear cell sarcoma is a rare malignant tumor that has rarely been reported and is challenging to diagnose, especially when differentiating from malignant melanoma. Currently, EWSR1-ATF1 is the key marker for distinguishing clear cell sarcoma from melanoma, but IHC has diagnostic limitations. We report a patient diagnosed with pulmonary clear cell sarcoma, in which an NGS was used to help with the pathological diagnosis. The exposure to the immune microenvironment in pulmonary clear cell sarcoma suggests that TIGIT-related drugs may be a new and effective treatment for this rare disease. Immune microenvironment-related markers, including PD-L1, CD8, TIM3, LAG3, and CD163, were negatively expressed in pulmonary clear cell sarcoma.

Background: Malignant mesothelioma (MM) is an aggressive and incurable carcinoma that is primarily caused by asbestos exposure. However, the current diagnostic tool for MM is still under-developed. Therefore, the aim of this study is to explore the diagnostic significance of a strategy that combined plasma-based metabolomics with machine learning algorithms for MM. Methods: Plasma samples collected from 25 MM patients and 32 healthy controls (HCs) were randomly divided into train set and test set, after which analyzation was performed by liquid chromatography-mass spectrometry-based metabolomics. Differential metabolites were screened out from the samples of the train set. Subsequently, metabolite-based diagnostic models, including receiver operating characteristic (ROC) curves and Random Forest model (RF), were established, and their prediction accuracies were calculated for the test set samples. Results: Twenty differential plasma metabolites were annotated in the train set; 10 of these metabolites were validated in the test set. The seven most prevalent diagnostic metabolites were taurocholic acid), 0.7142 (uracil), 0.7142 (biliverdin), 0.8571 (histidine), 0.5000 (tauroursodeoxycholic acid), 0.8571 (pyrroline hydroxycarboxylic acid), and 0.7857 (phenylalanine). Furthermore, RF based on 20 annotated metabolites showed a prediction accuracy of 0.9286, and its optimized version achieved 1.0000 in the test set. Moreover, the comparison between the samples of peritoneal MM (n = 8) and pleural MM (n = 17) illustrated a significant increase in levels of taurocholic acid and tauroursodeoxycholic acid, as well as an evident decrease in biliverdin. Conclusions: Our results revealed the potential diagnostic value of plasma-based metabolomics combined with machine learning for MM. Further research with large sample size is worthy conducting. Moreover, our data demonstrated dysregulated metabolism pathways in MM, which aids in better understanding of molecular mechanisms related to the initiation and development of MM.

BACKGROUND:The primary aim of this study was to investigate the prognostic value of peripheral blood lymphocyte subsets in non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs).METHODS:From 2018 to 2019, 82 patients diagnosed with stage IIIB-IV NSCLC at Zhejiang Cancer Hospital were recruited for this study. Peripheral blood lymphocyte subsets of NSCLC patients were analyzed using flow cytometry before and after ICI treatment. The relationship between the percentage of peripheral blood lymphocyte subsets, clinicopathological features, progression-free survival (PFS), and overall survival (OS) was identified by correlation heat map, Kaplan-Meier curve, log-rank test, and Cox regression analysis.RESULTS:The CD4/CD8 ratio and the percentage of B cells was decreased after ICI treatment. Furthermore, the percentage of CD3+ T cells, natural killer (NK) cells, and natural killer T (NKT) cells before ICI treatment was associated with brain metastases, the proportion of CD3+CD4+ T cells before ICI treatment was related to epidermal growth factor receptor (EGFR) status, the CD4/CD8 ratio before ICI treatment was correlated to pathology, the ratio of B cells before ICI treatment was related to therapeutic regimen, and the percentage of NKT cells before ICI treatment was associated with use of radiotherapy. Furthermore, univariate survival analysis revealed that low percentage of B cells forecasted a poor OS for NSCLC patients with ICI treatment. In addition, the nomogram developed by percentages of peripheral blood lymphocyte subsets could determine survival probability and survival time of NSCLC patients with immunotherapy.CONCLUSIONS:ICI treatment induced changes in the percentage of peripheral blood lymphocyte subsets, which had prognostic value for brain metastases, radiotherapy, EGFR status, pathology, and therapeutic regimen, along with prognostic value, for NSCLC patients treated with ICIs.

Background: Esophageal squamous cell carcinoma (ESCC) is a gastrointestinal malignancy with a poor prognosis. Although studies have shown metabolic reprogramming to be linked to ESCC development, no prognostic metabolic biomarkers or potential therapeutic metabolic targets have been identified. Method: The present study investigated some circulating metabolites associated with overall survival in 276 curatively resected ESCC patients using liquid chromatography/mass spectrometry metabolomics and Kaplan-Meier analysis. Tissue metabolomic analysis of 23-paired ESCC tissue samples was performed to discover metabolic dysregulation in ESCC cancerous tissue. A method consisting of support vector machine recursive feature elimination and LIMMA differential expression analysis was utilized to select promising feature genes within transcriptomic data from 179-paired ESCC tissue samples. Joint pathway analysis with genes and metabolites identified relevant metabolic pathways and targets for ESCC. Results: Four metabolites, kynurenine, 1-myristoyl-glycero-3-phosphocholine (LPC(14:0)sn-1), 2-piperidinone, and hippuric acid, were identified as prognostic factors in the preoperative plasma from ESCC patients. A risk score consisting of kynurenine and LPC(14:0)sn-1 significantly improved the prognostic performance of the tumor-node-metastasis staging system and was able to stratify risk for ESCC. Combined tissue metabolomic analysis and support vector machine recursive feature elimination gene selection revealed dysregulated kynurenine pathway as an important metabolic feature of ESCC, including accumulation of tryptophan, formylkynurenine, and kynurenine, as well as up-regulated indoleamine 2,3-dioxygenase 1 in ESCC cancerous tissue. Conclusions: This work identified for the first time four potential prognostic circulating metabolites. In addition, kynurenine pathway metabolism was shown to be up-regulated tryptophan-kynurenine metabolism in ESCC. Results not only provide a metabolite-based risk score system for prognosis, but also improve the understanding of the molecular basis of ESCC onset and progression, and as well as novel potential therapeutic targets for ESCC.