Early cancer detection by cell-free DNA faces multiple challenges: low fraction of tumor cell-free DNA, molecular heterogeneity of cancer, and sample sizes that are not sufficient to reflect diverse patient populations. Here, we develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of the cell-free DNA methylome (with > 12-fold enrichment over whole genome bisulfite sequencing in CpG islands), and a computational method to extract methylation information and diagnose patients. Applying our approach to 408 colon, liver, lung, and stomach cancer patients and controls, at 97.9% specificity we achieve 80.7% and 74.5% sensitivity in detecting all-stage and early-stage cancer, and 89.1% and 85.0% accuracy for locating tissue-of-origin of all-stage and early-stage cancer, respectively. Our approach cost-effectively retains methylome profiles of cancer abnormalities, allowing us to learn new features and expand to other cancer types as training cohorts grow. Early cancer detection by cell-free DNA (cfDNA) is challenged by the low amount of tumour DNA in cfDNA, tumour heterogeneity and the small patient cohorts. Here, the authors develop a method, cfMethyl-Seq, for cost-effective methylome profiling of cfDNA and for detecting and locating cancer.

Background : The aim of this study was to assess whether both ubiquitous and heterogeneous somatic mutations could be detected in circulating cell-free DNA (cfDNA) from patients with esophageal squamous cell carcinoma (ESCC). Methods : Paired multi-regional tumor tissues, cfDNA and white blood cells (WBCs) collected from five ESCC patients before treatment from a prospective study (NCT02395705). Of them, samples from Cohort 1 (E102 and E110) were sequenced by whole-exome sequencing (WES) and those from Cohort 2 (E104, E111 and E121) were sequenced by targeted captured sequencing with a panel of 560 cancer-related genes respectively. To call somatic single nucleotide variations (SNVs) by comparing the solid tumor or cfDNA with matched WBCs, the minimal variant allele frequency (VAF min ) as 0.1% and P value <0.05 were allowed. Results : Genomic DNA (gDNA) and plasma-derived cfDNA from 26 samples were successfully sequenced. In Cohort 1, 596 (596/712, 83%) and 562 (562/796, 71%) were heterogeneous SNVs in E102 and E110 respectively. There was a statistically significant linear relationship between the VAFs for tumor and cfDNA ( R 2 = 0.78, P <0.0001). In Cohort 2, 296 (296/323, 92%), 384 (384/423, 91%) and 331 (331/357, 93%) were heterogeneous SNVs in E104, E111 and E121respectively. cfDNA could recover an average of 60.7% (31/51; range, 35.7%-76.2%) of somatic mutations present in matched solid tumors. The correlation of VAFs between cfDNA and matched solid tumor was significantly positive (r 2 =0.92, P <0.0001). Conclusions : Both sequencing approaches revealed the highly intratumoral heterogeneity in ESCC and enabled the detection of both ubiquitous and heterogeneous mutations in cfDNA. Further validation in cfDNA is required to define its potential utility for ESCC in clinical practice. Trial registration All patients selected in this study were from the registered clinical trial from ClinicalTrials.gov (NCT02395705). Date of registration: March 24, 2015.

Supplementary Figure from cfTrack: A Method of Exome-Wide Mutation Analysis of Cell-free DNA to Simultaneously Monitor the Full Spectrum of Cancer Treatment Outcomes Including MRD, Recurrence, and Evolution

Cell-free DNA (cfDNA) is attractive for many applications, including detecting cancer, identifying the tissue of origin, and monitoring. A fundamental task underlying these applications is SNV calling from cfDNA, which is hindered by the very low tumor content. Thus sensitive and accurate detection of low-frequency mutations (<5%) remains challenging for existing SNV callers. Here we present cfSNV, a method incorporating multi-layer error suppression and hierarchical mutation calling, to address this challenge. Furthermore, by leveraging cfDNA’s comprehensive coverage of tumor clonal landscape, cfSNV can profile mutations in subclones. In both simulated and real patient data, cfSNV outperforms existing tools in sensitivity while maintaining high precision. cfSNV enhances the clinical utilities of cfDNA by improving mutation detection performance in medium-depth sequencing data, therefore making Whole-Exome Sequencing a viable option. As an example, we demonstrate that the tumor mutation profile from cfDNA WES data can provide an effective biomarker to predict immunotherapy outcomes.

Monitoring cancer patients for the early detection of minimal residual disease (MRD), cancer recurrence, and cancer progression is essential for assessing treatment response and predicting early relapse during/after treatment. Plasma cell-free DNA (cfDNA) provides unique opportunities for cancer monitoring given its non-invasive and comprehensive sampling of heterogeneous tumor clones. However, the low tumor content in cfDNA poses a major challenge for detecting tumor signals. Previous methods mostly rely on deep sequencing of small panels to capture the tumor signal. These methods usually require the labor-intensive design of customized gene panel, yet fail to identify the evolving tumor, which is essential for detecting second primary diseases or emerging subclones. To address these limitations, here we present OncoMonitor, a cancer monitoring method that comprehensively analyzes tumor mutations in cfDNA whole-exome sequencing data. Taking advantage of the availability of mutation information across the whole exome, our method (1) integrates all clonal tumor mutations identified from pre-treatment cfDNA samples (or tumor samples) to compensate for low tumor fraction in cfDNA, (2) suppresses sequencing errors at read level with an accurate random forest classifier to model the observed features from cfDNA fragments and further enhance the tumor signal, (3) builds sample-specific background noise distributions to predict recurrence and MRD to avoid interference from inter-individual variations and inter-experimental biases, and (4) detects tumor changes, especially second primary diseases, by identifying newly emerging tumor mutations de novo. Combining these techniques, for the first time, we show that OncoMonitor can sensitively and specifically detect both cancer recurrence/MRD and secondary primary cancers from plasma samples with low tumor fraction. Using simulated cfDNA sequence data with artificial mutation spike-ins, our method can detect recurrence at 0.025% tumor fraction with > 95% sensitivity and 95% specificity, and the second primary disease at 0.1% tumor fraction with around 75% sensitivity and 100% specificity. In a cohort of 9 non-small-cell lung cancer patients, we show that OncoMonitor can provide comprehensive tumor changes for treatment response prediction and capture emerging tumor clones; this cannot be achieved by previous methods that are based only on mutations in the pre-treatment surgery samples. In summary, with broad genomic sequencing coverage and comprehensive mutation analysis, our method can identify patients suffering from MRD or cancer recurrence/progression, provide a thorough view of their tumor status, and enable early intervention and personalized treatment. Citation Format: Shuo Li, Weihua Zeng, Xiaohui Ni, Mary L. Stackpole, Yonggang Zhou, Zorawar Noor, Zuyang Yuan, Edward B. Garon, Steven M. Dubinett, Wenyuan Li, Xianghong Zhou. cMonitor: Comprehensive and sensitive monitoring of diverse cancer treatment outcomes by exome-wide mutation analysis in plasma cfDNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 567.

Abstract Early cancer detection by cell-free DNA (cfDNA) faces multiple challenges: the low fraction of tumor DNA in cfDNA, the molecular heterogeneity of cancer, and sample sizes that are too small to reflect the heterogeneous patient population. We have developed an integrated cancer detection system, CancerRadar, that addresses all three challenges. It consists of (1) a cost-effective experimental assay, cfMethyl-Seq, for genome-wide methylation profiling of cfDNA, which provides >12-fold enrichment over Whole Genome Bisulfite Sequencing (WGBS) in CpG islands; and (2) a computational platform to extract information from cfMethyl-Seq data and diagnose the patient. The platform derives cfDNA methylations, cfDNA fragment sizes, copy number variations (CNV), and microbial composition from the raw cfMethyl-Seq data, and performs multi-feature ensemble learning. We demonstrate the power of CancerRadar by detecting and locating cancer in a cohort of 275 colon, liver, lung, and stomach cancer patients and 204 non-cancer individuals. For cancer detection, we achieve a sensitivity of 85.6%± 6.7% across all stages and 80.6%±9.1% for early stages (I and II), with a specificity of 99% in both cases. These metrics are derived using leave-one-out cross-validation. During independent validation on a reserved subsample, it achieves a sensitivity of 89.1%±11.3% across all stages and 85.7%±14.2% for early stages, with a specificity of 97% (one false positive). For locating a tumor's tissue of origin (TOO), CancerRadar achieved an accuracy of 91.5%±5.0% for all stages and 89.1%±7.3% for early stages, on an independent subsample. This study is the first to integrate cfDNA methylation, cfDNA fragment size, CNV, and microbial composition analyses for cancer detection on the same patient cohort. cfDNA methylation was the most useful for detecting cancer, but including features from other categories significantly increased the performance, especially for early-stage cancer. In contrast, with respect to TOO prediction, methylation-derived features were overwhelmingly important while including other features did not further improve performance. To fully exploit the power of cfDNA methylation, we identified four types of methylation markers with different characteristics. We have also improved our previous read-level deconvolution algorithm to more accurately identify trace tumor signals. Finally, our data show that as training sample sizes increase, the detection power of CancerRadar continues to increase. Although all existing cancer detection studies are limited by training sample sizes, the CancerRadar system uniquely and cost-effectively retains the genome-wide epigenetic and genetic profiles of cancer abnormalities, thereby permitting the classification models to learn and exploit newly significant features as training cohorts grow, as well as expanding their scope to other cancer types. Citation Format: Mary Stackpole, Weihua Zeng, Shuo Li, Chun-Chi Liu, Yonggang Zhou, Shanshan He, Angela Yeh, Ziye Wang, Fengzhu Sun, Qingjiao Li, Zuyang Yuan, Asli Yildirim, Pin Jung Chen, Paul Winograd, Shize Li, Zorawar Noor, Edward Garon, Samuel French, Clara Magyar, Sarah Dry, Clara Lajonchere, Daniel Geschwind, Gina Choi, Sammy Saab, Frank Alber, Wing Hung Wong, Steven Dubinett, Denise Aberle, Vatche Agopian, Steven-Huy Han, Xiaohui Ni, Wenyuan Li, Xianghong Jasmine Zhou. Multi-feature ensemble learning on cell-free dna for accurately detecting and locating cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 24.

食管癌(esophageal cancer,EC)是常见的癌症之一,组织病理类型分为食管鳞状细胞癌和食管腺癌.因确诊晚,并缺乏有效的治疗手段,EC成为世界范围的公共健康问题之一.与常规的肿瘤活检不同,液态活检因损伤性小,可作为传统活检的补充甚至替代方法.尤其是细胞游离DNA(cell-free DNA,cf DNA),在癌症临床管理中已表现出应用前景.cf DNA已成为非侵入性癌症诊断和监测的有效循环分子标志物.虽然很多研究报道了cf DNA在各种癌症中的临床应用,但对其在EC中的作用尚缺乏了解.因此,我们就这一主题进行了综述,并讨论了其在EC诊断和监测中的优势及局限性.

AbstractLiquid biopsy using cell-free DNA (cfDNA) is attractive for a wide range of clinical applications, including cancer detection, locating, and monitoring. However, developing these applications requires precise and sensitive calling of somatic single nucleotide variations (SNVs) from cfDNA sequencing data. To date, no SNV caller addresses all the special challenges of cfDNA to provide reliable results. Here we present cfSNV, a revolutionary somatic SNV caller with five innovative techniques to overcome and exploit the unique properties of cfDNA. cfSNV provides hierarchical mutation profiling, thanks to cfDNA’s complete coverage of the clonal landscape, and multi-layer error suppression. In both simulated datasets and real patient data, we demonstrate that cfSNV is superior to existing tools, especially for low-frequency somatic SNVs. We also show how the five novel techniques contribute to its performance. Further, we demonstrate a clinical application using cfSNV to select non-small-cell lung cancer patients for immunotherapy treatment.

Esophageal cancer (EC) is a common cancer and is histopathologically classified into esophageal squamous cell carcinoma and esophageal adenocarcinoma. EC is a worldwide public health issue because of late diagnosis and lack of effective therapy. In contrast to standard tumor biopsies, liquid biopsies are emerging as a tool which is minimally invasive that can complement or even substitute more classical approaches. Specifically, cell-free DNA (cfDNA) has shown promise in cancer-related clinical applications. Indeed, cfDNA has been shown to be an effective circulating biomarker for non-invasive cancer diagnosis and monitoring of cancer patients. Although the clinical application of cfDNA has been reported on other cancers, few studies have evaluated its use in EC. Here, we review this relevant literature and discuss limitations and advantages of its application in the diagnosis and monitoring of EC.

Background : The aim of this study was to assess whether both ubiquitous and heterogeneous somatic mutations could be detected in circulating cell-free DNA (cfDNA) from patients with esophageal squamous cell carcinoma (ESCC). Methods : Paired multi-regional tumor tissues, cfDNA and white blood cells (WBCs) collected from five ESCC patients before treatment from a prospective study (NCT02395705). Of them, samples from Cohort 1 (E102 and E110) were sequenced by whole-exome sequencing (WES) and those from Cohort 2 (E104, E111 and E121) were sequenced by targeted captured sequencing with a panel of 560 cancer-related genes respectively. To call somatic single nucleotide variations (SNVs) by comparing the solid tumor or cfDNA with matched WBCs, the minimal variant allele frequency (VAF min ) as 0.1% and P value <0.05 were allowed. Results : Genomic DNA (gDNA) and plasma-derived cfDNA from 26 samples were successfully sequenced. In Cohort 1, 596 (596/712, 83%) and 562 (562/796, 71%) were heterogeneous SNVs in E102 and E110 respectively. There was a statistically significant linear relationship between the VAFs for tumor and cfDNA ( R 2 = 0.78, P <0.0001). In Cohort 2, 296 (296/323, 92%), 384 (384/423, 91%) and 331 (331/357, 93%) were heterogeneous SNVs in E104, E111 and E121respectively. cfDNA could recover an average of 60.7% (31/51; range, 35.7%-76.2%) of somatic mutations present in matched solid tumors. The correlation of VAFs between cfDNA and matched solid tumor was significantly positive (r 2 =0.92, P <0.0001). Conclusions : Both sequencing approaches revealed the highly intratumoral heterogeneity in ESCC and enabled the detection of both ubiquitous and heterogeneous mutations in cfDNA. Further validation in cfDNA is required to define its potential utility for ESCC in clinical practice. Trial registration All patients selected in this study were from the registered clinical trial from ClinicalTrials.gov (NCT02395705). Date of registration: March 24, 2015.

Abstract Liquid biopsy using cell-free DNA (cfDNA) is attractive for a wide range of clinical applications, including cancer detection, locating, and monitoring. However, developing these applications requires precise and sensitive calling of somatic single nucleotide variations (SNVs) from cfDNA sequencing data. To date, no SNV caller comprehensively addresses the unique challenges of cfDNA to provide reliable results. Here we present cfSNV, a revolutionary somatic SNV caller with five innovative techniques to overcome and exploit the unique properties of cfDNA: (1) a joint-genotype statistical model, to modeling the mixed nature of cfDNA and thus probabilistically deconvolute the mixture of tumor- and normal-derived reads; (2) hierarchical mutation profiling to detect clusters of mutations with different variant allele frequencies; (3) a data pre-processing step that exploits the overlapping read mates caused by short cfDNA fragments to improve data quality; (4) a new filtering procedure to remove misalignment errors that account for the non-random fragmentation pattern of cfDNA; and (5) a machine learning approach that incorporates the sequencing context to filter errors at the level of individual reads. These five techniques permit cfSNV to far outperform existing methods in sensitively and reliably detecting mutations in cfDNA, even with medium-coverage WES data. On both simulated data and real patient data, we demonstrate that cfSNV is superior to existing tools, especially for low-frequency somatic SNVs. On simulated data, without sacrificing precision, cfSNV showed 3.0- and 3.7-fold increase in sensitivity of all somatic SNVs, and specifically 11.1- and 138.5-fold increase in sensitivity of somatic SNVs with allele frequency < 1% comparing to MuTect and Strelka2 respectively. The superior performance of cfSNV has been further confirmed on real patient data from 18 metastatic breast cancer and prostate cancer patients. Besides comparison with existing tools, we also showed how the five novel techniques contribute to its performance. The applicability of cfSNV to WES data enables cfDNA to be used in a wide variety of clinical applications, and we present one example of such an application: a novel and effective immunotherapy response measure that we name truncal-bTMB. This measure exploits cfDNA's ability to provide comprehensive coverage of the mutation landscape and hence describe the clonal structure. We demonstrated this new marker allows a better selection of non-small-cell lung cancer patients for immunotherapy treatment. Citation Format: Shuo Li, Zorawar Noor, Weihua Zeng, Xiaohui Ni, Zuyang Yuan, Frank Alber, Wenyuan Li, Edward B. Garon, Xianghong Zhou, Wing Hung Wong, Vatche G. Agopian, Steven M. Dubinett M. Dubinett. Sensitive detection of tumor mutations from blood and its application to immunotherapy prognosis [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-247.

We aimed to verify the expression status and diagnostic significance of isocitrate dehydrogenase 1 (IDH1) in non-small-cell lung cancer (NSCLC), especially during early stages. Serum IDH1 levels were measured by ELISA. A total of 1223 participants (660 patients with NSCLC, 276 healthy controls [HCs], 95 patients with benign pulmonary conditions [BPCs], 135 patients with other cancers [OCs], and 57 samples with interfering factors) were divided into a training cohort and a validation cohort according to 3 testing centers. The IDH1 concentrations in the NSCLC group were obviously higher than those in the control groups (P < .001). Area under the receiver operating characteristic curves (AUCs) for discriminating NSCLC patients from controls (HC, BPC, and OC) were 0.870 and 0.745 (sensitivity, 63.3% and 55.0%; specificity, 86.8% and 86.3%) in the training cohort and validation cohort, respectively. The AUCs for discriminating stage 0-IA lung cancer patients from HCs were 0.907 and 0.788 (sensitivity, 58.6% and 59.1%; specificity, 92.9% and 89.3%) in 2 cohorts, respectively. Isocitrate dehydrogenase 1 showed specificity for NSCLC and had no diagnostic value for other common cancers. Furthermore, IDH1 was significantly reduced in postoperative serum. Isocitrate dehydrogenase 1 shows clinical utility as a serum protein biomarker for the early diagnosis of NSCLC.

Natural products derived from herbal medicines have become a major focus of anti-cancer drug discovery studies. Acetyl-macrocalin B (A-macB) is an ent-diterpenoid isolated from Isodon silvatica. This study aimed to examine the effect and molecular action of A-macB in esophageal squamous cell carcinoma (ESCC) and explore possible drug synergistic modalities. A-macB induced cellular reactive oxygen species (ROS) generation, initiated the p38 mitogen-activated protein kinase (MAPK) signaling pathway, and triggered the caspase-9-dependent apoptosis cascade in ESCC cells. The ROS scavenger N-acetylcysteine (NAC) and the specific p38 inhibitor SB203580 reversed the effects of A-macB on the p38 network and thus rescued ESCC cells from apoptosis. The cellular ROS increase was at least partially due to the suppression of glutathione-S-transferase P1 (GSTP1) by A-macB. A-macB also upregulated the Chk1/Chk2-Cdc25C/Cdc2/Cyclin B1 axis to induce G2/M phase arrest. The cell growth inhibition induced by A-macB was further enhanced by AZD7762, a specific Chk1/Chk2 inhibitor, with a combination index (CI) of <1. Moreover, A-macB efficiently suppressed xenograft growth without inducing significant toxicity, and AZD7762 potentiated the effects of A-macB in the suppression of tumor growth in vivo. Taken together, A-macB is a promising lead compound for ESCC and exerts synergistic anti-cancer effects with AZD7762.

PURPOSE:Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive squamous cell carcinomas and is generally resistant to chemotherapy. In the present study, the cytotoxic activity of Rabdocoestin B (Rabd-B) against ESCC and the underlying mechanisms were investigated.METHODS:The inhibitory effect of Rabd-B on KYSE30 and KYSE450 was evaluated by Cell Counting Kit-8 (CCK8) and colony formation assays in vitro. The cell cycle distribution and apoptosis of cells treated with Rabd-B were determined by flow cytometry. The mechanisms underlying the effects of Rabd-B were systematically examined by Western blot. The in vivo anti-tumor ability of Rabd-B was measured in mouse xenograft models and cisplatin (DDP) was used as positive control.RESULTS:Rabd-B efficiently induced G2/M phase arrest in ESCC cells by upregulating the Chk1/Chk2-Cdc25C axis to inhibit the G2→M transition facilitated by Cdc2/Cyclin B1. Furthermore, Rabd-B suppressed ATM/ATR phosphorylation, thereby inhibiting BRCA1-mediated DNA repair, which resulted in mitotic catastrophe and induced cell apoptosis. Rabd-B also decreased the activity of the Akt and NF-κB survival signaling pathways and ultimately initiated the caspase-9-dependent intrinsic apoptotic pathway in ESCC cells. The apoptosis induced by Rabd-B could be partially reversed by a caspase-9-specific inhibitor (Z-LEHD-FMK) and a pan-caspase inhibitor (Z-VAD-FMK). Moreover, Rabd-B effectively suppressed tumor growth in mouse xenografts which was comparable to that of DDP without significant injuries to the mice.CONCLUSION:Taken together, these findings indicate that Rabd-B is a promising precursor compound that may be useful as a treatment for ESCC and thus warrants further investigation.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide, and novel effective drugs against NSCLC are urgently needed. Isodon species are rich in ent-kaurane diterpenoids that have been reported to have antitumor bioactivity. Acetyl-macrocalin B (A-macB) is a novel ent-kaurane diterpenoid isolated from Isodon silvatica, and its antitumor efficacy against NSCLC and the underlying mechanisms were scrutinized in depth. The viability of cells treated with A-macB was detected by CCK-8 and colony formation assays. Apoptosis and cell cycle distribution were analyzed by flow cytometry. The mechanisms were investigated by detecting ROS and performing western blotting and verification experiments with specific inhibitors. The in vivo effect of A-macB was explored in a nude mouse xenograft model. A-macB effectively inhibited H1299 and A549 cell viability, triggered apoptosis and delayed cells in the G2/M phase. A-macB induced cellular ROS production and then activated the p38 MAPK-mediated, caspase 9-dependent apoptotic pathway. Both the ROS scavenger NAC and the specific p38 inhibitor SB203580 inactivated the function of p38 induced by A-macB, thus preventing cells from apoptosis. A-macB activated the Chk1/2-Cdc25C-Cdc2/cyclin B1 axis to induce G2/M phase arrest. AZD7762 abrogated the function of Chk1/2, abolished the G2/M delay and enhanced the cytotoxicity of A-macB. Moreover, A-macB efficiently suppressed tumor growth in a mouse xenograft model without noticeable toxicity to normal tissues. Having both efficacy and relative safety, A-macB is a potential lead compound that is worthy of further exploration for development as an anticancer agent.

Effective treatments for esophageal squamous cell carcinoma (ESCC), one of the most common cancers in China, are lacking. Longikaurin A (LK-A), an ent-kauranoid diterpenoid isolated from Isodon ternifolius, has been shown to have potent cytotoxic effects on ESCC cells both in vivo and in vitro, mainly by inducing apoptosis. In this study, LK-A inhibited ESCC cells viability and induced G2/M cell cycle arrest. Moreover, LK-A was also highly effective in a KYSE-30 xenograft nude mouse model. Treatment with Z-VAD(OMe)-FMK partially attenuated LK-A-induced apoptosis. LK-A significantly induced reactive oxygen species (ROS) production in ESCC cells, and LK-A-induced apoptosis was attenuated by the ROS scavenger N-acetyl cysteine (NAC). Furthermore, we found that treatment with LK-A activated both the JNK and p38 MAPK signaling pathways, resulting in increases in ROS levels and apoptosis induction. Taken together, these findings indicate that LK-A exerts novel anti-tumor effects in ESCC cells by activating the JNK and p38 MAPK pathways and inducing increases in ROS production, which suggest that the compound may have potential as a clinical therapeutic agent.

Thymoma is an uncommon tumor without a widely accepted standard care to date. We aimed to investigate the clinicopathologic variables of patients with thymoma and identify possible predictors of survival and recurrence after initial resection.

BACKGROUND Preoperative neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) have prognostic value in patients with various operable tumors. The aim of our study was to determine whether NLR and PLR are predictive of survival in thymic carcinoma patients after complete resection. METHODS A total of seventy-nine patients who underwent complete resection of thymic carcinoma at our hospital between January 2005 and December 2015 were retrospectively enrolled. Differential leukocyte counts were collected before surgery, and the relationships of NLR, PLR, and other patient clinical variables with survival were estimated by Cox regression analysis and Kaplan-Meier survival analysis. RESULTS Univariate analysis found that a high level of NLR was associated with lower disease-free survival (DFS) (HR: 3.385, 95% CI: 1.073-10.678, P=0.037) and lower overall survival (OS) (HR: 12.836, 95% CI: 1.615-101.990, P=0.016). The optimal NLR threshold of 4.1 could stratify the patients with high risk of recurrence or metastasis (P=0.026) and death (P=0.006). Meanwhile, the NLR value of >4.1 in those patients was associated with bigger tumor size (P=0.035) and more advanced Masaoka stages (P=0.040) compared with NLR ≤4.1. However, the PLR and other variables were not significantly associated with survival in thymic carcinoma patients. CONCLUSIONS The preoperative NLR of >4.1 was significantly associated with larger tumor size, more advanced Masaoka stages and reduced DFS and OS, but was not an independent predictor of survival in thymic carcinoma patients after complete resection.