Intratumor microbiome-derived butyrate promotes lung cancer metastasis.

Most recurrences of lung cancer (LC) occur within 3 years after surgery, but the underlying mechanism remains unclear. Here, we collect LC tissues with shorter (<3 years, recurrence group) and longer (>3 years, non-recurrence group) recurrence-free survival. By using 16S sequencing, we find that intratumor microbiome diversity is lower in the recurrence group and butyrate-producing bacteria are enriched in the recurrence group. The intratumor microbiome signature and circulating microbiome DNA can accurately predict LC recurrence. We prove that intratumor injection of butyrate-producing bacteria Roseburia can promote subcutaneous tumor growth. Mechanistically, bacteria-derived butyrate promotes LC metastasis by increasing expression of H19 in tumor cells through inhibiting HDAC2 and increasing H3K27 acetylation at the H19 promoter and inducing M2 macrophage polarization. Depletion of macrophages partially abolishes the metastasis-promoting effect of butyrate. Our results provide evidence for the cross-talk between the intratumor microbiome and LC metastasis and suggest the potential prognostic and therapeutic value of the intratumor microbiome.

Circulating microbiome DNA as biomarkers for early diagnosis and recurrence of lung cancer.

Lung cancer mortality is exacerbated by late-stage diagnosis. Emerging evidence indicates the potential clinical significance of distinct microbial signatures as diagnostic and prognostic biomarkers across various cancers. However, circulating microbiome DNA (cmDNA) profiles are underexplored in lung cancer (LC). Here, whole-genome sequencing is performed on plasma of LC patients and healthy controls (HCs). Differentially enriched microbial species are identified between LC and HC. A diagnostic model is developed, which has a high sensitivity of 87.7% and achieves an AUC of 93.2% in the independent validation dataset. Crucially, this model demonstrates the capability to detect early-stage LC, achieving a sensitivity of 86.5% for stage I and 87.1% for tumors <1 cm. In addition, we construct a cmDNA model for recurrence, which precisely predicts LC recurrence after surgery. Overall, this study highlights the significant alterations of cmDNA profiles in LC, indicating its potential as biomarkers for early diagnosis and recurrence.

TREM1/DAP12 based novel multiple chain CAR-T cells targeting DLL3 show robust anti-tumour efficacy for small cell lung cancer.

Small cell lung cancer (SCLC), recognized as the most aggressive subtype of lung cancer, presents an extremely poor prognosis. Currently, patients with small cell lung cancer face a significant dearth of effective alternative treatment options once they experience recurrence and progression after first-line therapy. Despite the promising efficacy of immunotherapy, particularly immune checkpoint inhibitors in non-small cell lung cancer (NSCLC) and various other tumours, its impact on significantly enhancing the prognosis of SCLC patients remains elusive. DLL3 has emerged as a compelling target for targeted therapy in SCLC due to its high expression on the membranes of SCLC and other neuroendocrine carcinoma cells, with minimal to no expression in normal cells. Our previous work led to the development of a novel multiple chain chimeric antigen receptor (CAR) leveraging the TREM1 receptor and DAP12, which efficiently activated T cells and conferred potent cell cytotoxicity. In this study, we have developed a DLL3-TREM1/DAP12 CAR-T (DLL3-DT CAR-T) therapy, demonstrating comparable anti-tumour efficacy against SCLC cells in vitro. In murine xenograft and patient-derived xenograft models, DLL3-DT CAR-T cells exhibited a more robust tumour eradication efficiency than second-generation DLL3-BBZ CAR-T cells. Furthermore, we observed elevated memory phenotypes, induced durable responses, and activation under antigen-presenting cells in DLL3-DT CAR-T cells. Collectively, these findings suggest that DLL3-DT CAR-T cells may offer a novel and potentially effective therapeutic strategy for treating DLL3-expressing SCLC and other solid tumours.

Multi-omics analysis reveals NNMT as a master metabolic regulator of metastasis in esophageal squamous cell carcinoma.

Metabolic reprogramming has been observed in cancer metastasis, whereas metabolic changes required for malignant cells during lymph node metastasis of esophageal squamous cell carcinoma (ESCC) are still poorly understood. Here, we performed single-cell RNA sequencing (scRNA-seq) of paired ESCC tumor tissues and lymph nodes to uncover the reprogramming of tumor microenvironment (TME) and metabolic pathways. By integrating analyses of scRNA-seq data with metabolomics of ESCC tumor tissues and plasma samples, we found nicotinate and nicotinamide metabolism pathway was dysregulated in ESCC patients with lymph node metastasis (LN+), exhibiting as significantly increased 1-methylnicotinamide (MNA) in both tumors and plasma. Further data indicated high expression of N-methyltransferase (NNMT), which converts active methyl groups from the universal methyl donor, S-adenosylmethionine (SAM), to stable MNA, contributed to the increased MNA in LN+ ESCC. NNMT promotes epithelial-mesenchymal transition (EMT) and metastasis of ESCC in vitro and in vivo by inhibiting E-cadherin expression. Mechanically, high NNMT expression consumed too much active methyl group and decreased H3K4me3 modification at E-cadherin promoter and inhibited m6A modification of E-cadherin mRNA, therefore inhibiting E-cadherin expression at both transcriptional and post-transcriptional level. Finally, a detection method of lymph node metastasis was build based on the dysregulated metabolites, which showed good performance among ESCC patients. For lymph node metastasis of ESCC, this work supports NNMT is a master regulator of the cross-talk between cellular metabolism and epigenetic modifications, which may be a therapeutic target.

Plasma cell-free DNA as a sensitive biomarker for multi-cancer detection and immunotherapy outcomes prediction.

BACKGROUND: Cell-free DNA (cfDNA) has shown promise in detecting various cancers, but the diagnostic performance of cfDNA end motifs for multiple cancer types requires verification. This study aimed to assess the utility of cfDNA end motifs for multi-cancer detection. METHODS: This study included 206 participants: 106 individuals with cancer, representing 20 cancer types, and 100 healthy individuals. The participants were divided into training and testing cohorts. All plasma cfDNA samples were profiled by whole-genome sequencing. A random forest model was constructed using cfDNA 4 bp-end-motif profiles to predict cancer in the training cohort, and its performance was evaluated in the testing cohort. Additionally, a separate random forest model was developed to predict immunotherapy responses. RESULTS: In the training cohort, the model based on 4 bp-end-motif profiles achieved an AUC of 0.962 (95% CI 0.936-0.987). The AUC in the testing cohort was 0.983 (95% CI 0.960-1.000). The model also maintained excellent predictive ability in different tumor sub-cohorts, including lung cancer (AUC 0.918, 95% CI 0.862-0.974), gastrointestinal cancer (AUC 0.966, 95% CI 0.938-0.993), and other cancer cohort (AUC 0.859, 95% CI 0.776-0.942). Moreover, the model utilizing 4 bp-end-motif profiles exhibited sensitivity in identifying responders to immunotherapy (AUC 0.784, 95% CI 0.609-0.960). CONCLUSION: The model based on 4 bp-end-motif profiles demonstrates superior sensitivity in multi-cancer detection. Detection of 4 bp-end-motif profiles may serve as potential predictive biomarkers for cancer immunotherapy.

Prediction of response to neoadjuvant chemo-immunotherapy in patients with esophageal squamous cell carcinoma by a rapid breath test.

BACKGROUND: Neoadjuvant chemo-immunotherapy combination has shown remarkable advances in the management of esophageal squamous cell carcinoma (ESCC). However, the identification of a reliable biomarker for predicting the response to this chemo-immunotherapy regimen remains elusive. While computed tomography (CT) is widely utilized for response evaluation, its inherent limitations in terms of accuracy are well recognized. Therefore, in this study, we present a novel technique to predict the response of ESCC patients before receiving chemo-immunotherapy by testing volatile organic compounds (VOCs) in exhaled breath. METHODS: This study employed a prospective-specimen-collection, retrospective-blinded-evaluation design. Patients' baseline breath samples were collected and analyzed using high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). Subsequently, patients were categorized as responders or non-responders based on the evaluation of therapeutic response using pathology (for patients who underwent surgery) or CT images (for patients who did not receive surgery). RESULTS: A total of 133 patients were included in this study, with 91 responders who achieved either a complete response (CR) or a partial response (PR), and 42 non-responders who had stable disease (SD) or progressive disease (PD). Among 83 participants who underwent both evaluations with CT and pathology, the paired t-test revealed significant differences between the two methods (p < 0.05). For the breath test prediction model using breath test data from all participants, the validation set demonstrated mean area under the curve (AUC) of 0.86 ± 0.06. For 83 patients with pathological reports, the breath test achieved mean AUC of 0.845 ± 0.123. CONCLUSIONS: Since CT has inherent weakness in hollow organ assessment and no other ideal biomarker has been found, our study provided a noninvasive, feasible, and inexpensive tool that could precisely predict ESCC patients' response to neoadjuvant chemo-immunotherapy combination using breath test based on HPPI-TOFMS.

Circular RNA P4HB promotes glycolysis and tumor progression by binding with PKM2 in lung adenocarcinoma.

BACKGROUND: Emerging evidence indicates that circular RNAs (circRNAs) play vital roles in tumor progression, including lung adenocarcinomas (LUAD). However, the mechanisms by which circRNAs promote the progression of LUAD still require further investigation. METHODS: Quantitative real-time PCR was performed to detect the expression of circP4HB in LUAD tissues and cells. Then, Kaplan-Meier analysis was used to determine the prognostic value of circP4HB expression. We employed RNA pull-down, RNA immunoprecipitation, mass spectrometry, cells fraction, glucose consumption, lactate production, pyruvate kinase M2 (PKM2) activity, and macrophage polarization assays to uncover the underlying mechanisms of circP4HB in LUAD. RESULTS: We found that circP4HB is upregulated in LUAD tissues and correlated with advanced TNM stages and lymph node metastasis. LUAD patients with high circP4HB expression had poor prognoses. Functionally, circP4HB promoted LUAD progression in vivo and in vitro. Upregulated circP4HB increased glucose consumption, lactate production and accelerated aerobic glycolysis in LUAD cells. Mechanically, circP4HB mainly accumulated in the cytoplasm of LUAD cells and bound with PKM2 and subsequently upregulating PKM2 enzymatic activity by increasing its tetramer formation. Additionally, circP4HB promoted M2 macrophage phenotype shift via targeting PKM2. Finally, rescue assays further confirmed that circP4HB could promote LUAD cell progression through its interaction with PKM2. CONCLUSION: These results demonstrate that circP4HB could promote LUAD progression, indicating circP4HB might be a potential therapeutic target of LUAD.

Identification of Plasma Metabolites Associated with Lung Cancer Survival.

Limited knowledge has been reported regarding the performance of plasma metabolomics for predicting lung cancer prognosis. In this chapter, we compared the plasma metabolomics of lung cancer patients with differential disease-free survival (DFS, <3 years vs. >4 years) using liquid chromatography-mass spectrometry. We identified 29 survival-related aqueous metabolites but no lipid metabolites. Amino acids and organic acids constitute the majority of these metabolites. The metabolic pathways of these metabolites were cysteine and methionine metabolism and arginine biosynthesis. The Cox proportional hazards regression models confirmed the predictive values of 18 metabolites for DFS, while the phosphocholine and xanthine showed independent predictive values. Regarding cancer phenotypes, thelephoric acid, phosphocholine, inosine, 3-hydroxyanthranilic acid, hypoxanthine, xanthine, and 4-hydroxybenzoic acid showed good correction with lymph node metastasis. Taken together, plasma metabolomics is a powerful tool for identifying prognostic metabolites of lung cancer.

Cancer metabolites: promising biomarkers for cancer liquid biopsy.

Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.

Diagnosis of primary lung cancer and benign pulmonary nodules: a comparison of the breath test and 18F-FDG PET-CT.

With the application of low-dose computed tomography in lung cancer screening, pulmonary nodules have become increasingly detected. Accurate discrimination between primary lung cancer and benign nodules poses a significant clinical challenge. This study aimed to investigate the viability of exhaled breath as a diagnostic tool for pulmonary nodules and compare the breath test with 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT). Exhaled breath was collected by Tedlar bags and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). A retrospective cohort (n = 100) and a prospective cohort (n = 63) of patients with pulmonary nodules were established. In the validation cohort, the breath test achieved an area under the receiver operating characteristic curve (AUC) of 0.872 (95% CI 0.760-0.983) and a combination of 16 volatile organic compounds achieved an AUC of 0.744 (95% CI 0.7586-0.901). For PET-CT, the SUVmax alone had an AUC of 0.608 (95% CI 0.433-0.784) while after combining with CT image features, 18F-FDG PET-CT had an AUC of 0.821 (95% CI 0.662-0.979). Overall, the study demonstrated the efficacy of a breath test utilizing HPPI-TOFMS for discriminating lung cancer from benign pulmonary nodules. Furthermore, the accuracy achieved by the exhaled breath test was comparable with 18F-FDG PET-CT.

Systemic inflammation influences the prognosis of patients with radically resected non-small cell lung cancer and correlates with the immunosuppressive microenvironment.

The impact of host condition on prognosis of non-small cell lung cancer (NSCLC) and the interaction between host and NSCLC remain unclear. This study investigated the association between systemic inflammation and prognosis and characteristics of radically resected NSCLC. This study consisted of a cohort study and an exploratory study of institutional prospective databases. All participants underwent video-assisted thoracoscopic lobectomy as the primary treatment. Systemic inflammation was assessed before surgery using the advanced lung cancer inflammation index and the systemic inflammation response index. Next-generation sequencing and multiplex immunofluorescence analysis were conducted to delineate tumor characteristics. In the cohort study including 1507 participants, high inflammation was associated with poor disease-free survival and overall survival before and after propensity score matching and in multivariable analysis. Systemic inflammation showed good prognostic value for stage IA-IB NSCLC, and the prognostic value diminished with upstaging of NSCLC. In the exploratory study including 217 adenocarcinomas, tumor microenvironment of high inflammation group showed a greater abundance of PDL1+ tumor cells and immune cells, which were independent from driver gene mutations and clinicopathological characteristics. Spatial analysis demonstrated a higher frequency of immune-suppressed cellular neighborhood, increased avoidance between immune cells and PDL1- tumor cells and compromised immune killing and presentation in tumor microenvironment of high inflammation group. Systemic inflammation showed limited association with genomic mutations. Systemic inflammation may influence the prognosis of NSCLC at both the systematic level and the local immune response. The correlation between high inflammation and immunosuppressive microenvironment indicates a novel thread for anticancer treatment.

E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image.

Efficient and accurate distinction of histopathological subtype of lung cancer is quite critical for the individualized treatment. So far, artificial intelligence techniques have been developed, whose performance yet remained debatable on more heterogenous data, hindering their clinical deployment. Here, we propose an end-to-end, well-generalized and data-efficient weakly supervised deep learning-based method. The method, end-to-end feature pyramid deep multi-instance learning model (E2EFP-MIL), contains an iterative sampling module, a trainable feature pyramid module and a robust feature aggregation module. E2EFP-MIL uses end-to-end learning to extract generalized morphological features automatically and identify discriminative histomorphological patterns. This method is trained with 1007 whole slide images (WSIs) of lung cancer from TCGA, with AUCs of 0.95-0.97 in test sets. We validated E2EFP-MIL in 5 real-world external heterogenous cohorts including nearly 1600 WSIs from both United States and China with AUCs of 0.94-0.97, and found that 100-200 training images are enough to achieve an AUC of >0.9. E2EFP-MIL overperforms multiple state-of-the-art MIL-based methods with high accuracy and low hardware requirements. Excellent and robust results prove generalizability and effectiveness of E2EFP-MIL in clinical practice. Our code is available at https://github.com/raycaohmu/E2EFP-MIL.

Chemical fingerprints and implicated cancer risks of Polycyclic aromatic hydrocarbons (PAHs) from fine particulate matter deposited in human lungs.

Exposure to fine particles (PM2.5) and associated PAHs are frequently linked with lung cancer, which makes the understanding of their occurrence and health risk in human lungs urgently important. Using the ultrasonic treatment and sequencing centrifugation (USC) extraction method coupled with gas chromatography-tandem mass spectrometry (GC - MS/MS) analysis, we revealed the molecular fingerprints of PM-accumulated PAHs in human lungs from a cohort of 68 patients with lung cancer in a typical air-polluted region, China. Sixteen priority PAHs can be grouped by concentrations as âˆ¼ 1 × 104 ng/g (ANT/BkF/ACE/DBA/BgP/PHN/PYR), 2-5 × 103 ng/g (BaP/FLE/NaP/BbF), and âˆ¼ 1 × 103 ng/g (IND/Acy/CHR/FLT/BaA). The sum concentration of 16 PAHs was approximately equaled to 13% of those in atmospheric PM2.5, suggesting significant pulmonary leaching of PAHs deposited in lungs. Low- and high-molecular weight PAHs accounted for âˆ¼ 41.8% and âˆ¼ 45.1% of the total PAHs, respectively, which indicated that atmospheric PM2.5, tobacco and cooking smoke were likely to be important sources of pulmonary PAHs. The evident increasing concentrations of NaP and FLE in pulmonary PM were significantly correlated with smoking history among smokers. The implicated carcinogenic potency of PM-accumulated PAHs among the participants aged 70-80 was 17 times that among participants aged 40-50 on the basis of BaP equivalent concentration (BaPeq) evaluation. The particulate enrichment factor (EFP), the PAH content in pulmonary PM relative to the bulk lung tissue, was equaled to 54 âˆ¼ 835 and averaged at 436. The high value of EFP suggested that PAHs were essentially accumulated in pulmonary PM and exhibited a pattern of "hotspot" distribution in the lungs, which would likely increase the risk of monoclonal tumorigenesis. The chemical characteristics of PM-accumulated PAHs in human lungs together with their implicated lung cancer risks could provide significant information for understanding health effects of particulate pollution in the human body.

Circular RNA, circular RARS, promotes aerobic glycolysis of non-small-cell lung cancer by binding with LDHA.

PURPOSE: Accumulating evidence has highlighted the critical roles of circular RNAs (circRNAs) in non-small-cell lung cancer (NSCLC). This study aims to unveil the roles of circRARS (circular RARS) (hsa_circ_0001551) in NSCLC. METHODS: Quantitative real-time PCR was used to determine the expression of circRARS in NSCLC tissues and cells. Kaplan-Meier analysis was used to determine the prognostic value of circRARS expression. CCK8, transwell, and wound healing assays were used to assess the proliferation, invasion, and migration abilities of NSCLC cells. RNA pull-down, cell fraction, glucose consumption, lactate production, and lactate dehydrogenase activity assays were conducted to explore the potential mechanisms of circRARS in NSCLC. RESULTS: circRARS is upregulated in NSCLC tissues and positively correlated with smoking status, lymph node metastasis, and higher tumor stages. NSCLC patients with high expression of circRARS have poor overall survival. Functional assays demonstrated that circRARS accelerated the proliferation, invasion, and migration of NSCLC cells in vitro. The cell fraction suggested that circRARS mainly accumulated in cytoplasm and the RNA pull-down assay showed lactate dehydrogenase (LDHA) could bind with circRARS. Furthermore, circRARS positively regulates LDHA activity and LDHA expression at the transcription level. Moreover, downregulated circRARS decreases glucose consumption and lactate production and compromises aerobic glycolysis in NSCLC cells. Finally, rescue assays showed circRARS could promote NSCLC cell proliferation by regulating LDHA activity. CONCLUSION: This study shows that circRARS can promote glycolysis and tumor progression in NSCLC by regulating LDHA.

Lung adenocarcinoma manifesting as subsolid nodule potentially represents tumour in the equilibrium phase of immunoediting.

Lung adenocarcinomas manifesting as subsolid nodules (SSN-LUADs) possess distinct dormant behaviour. This study was designed to compare the immune landscapes of normal lungs (nLungs), SSN-LUADs and LUADs manifesting as solid nodules (SN-LUADs) so as to better understand the status of anti-tumour immunity in SSN-LUADs. Mass cytometry by time-of-flight analysis was performed on 299, 570 single cells from nLung, SSN-LUAD and SN-LUAD tissues. The immune cells were identified by phenotype, and the percentages of different immune cell subclusters were compared between SSN-LUADs, SN-LUADs and nLungs. Elevated percentage of CD8+ T cells were identified in SSN-LUADs compared with in nLungs and SN-LUADs. Elevated CD56bright NK cells and decreased CD56dim NK cells were identified in both SSN-LUADs and SN-LUADs compared with in nLungs. The immune landscape of SSN-LUAD fits the theory of equilibrium phase of immunoediting, thus functional adaptive anti-tumour immunity but impaired innate anti-tumour immunity potentially contributes to the maintaining of its dormant behaviour.

Stage IV non-small cell lung cancer among young individuals: Incidence, presentations, and survival outcomes of conventional therapies.

Background: There is a paucity of data published on the clinicopathological features and prognosis of stage IV non-small cell lung cancer (NSCLC) patients aged ≤45 years. Herein, we evaluated a large clinical series in an effort to provide a clearer picture of this population. Methods: The least absolute shrinkage and selection operator (LASSO)-penalized Cox regression model was performed to identify prognostic factors for NSCLC among individuals aged ≤45 years. The Kaplan-Meier method with log-rank test was used to compare overall survival (OS) differences between groups. Competing risk analysis with the Fine-Gray test was used to analyze cancer-specific survival (CSS) differences. Propensity score matching (PSM) was used to minimize selection bias. Results: Incidence-rate analyses, including 588,680 NSCLC cases (stage IV, 233,881; age ≤ 45 years stage IV, 5,483; and age > 45 years stage IV, 228,398) from 2004 to 2015, showed that the incidence of stage IV NSCLC among young individuals decreased over the years. In comparative analyses of clinical features and survival outcomes, a total of 48,607 eligible stage IV cases (age ≤ 45 years stage IV, 1,390; age > 45 years stage IV, 47,217) were included. The results showed that although patients in the young cohort were more likely to be diagnosed at advanced stages, they were also more likely to receive aggressive treatments. In addition, the survival rates of the young patients were superior to those of the older patients both before and after PSM. Conclusions: Stage IV NSCLC patients aged ≤45 years comprise a relatively small but special NSCLC subgroup. Although this population had better survival outcomes than older patients, these patients deserve more attention due to their young age and the significant socioeconomic implications.

Cancer-associated fibroblast-specific lncRNA LINC01614 enhances glutamine uptake in lung adenocarcinoma.

BACKGROUND: Besides featured glucose consumption, recent studies reveal that cancer cells might prefer "addicting" specific energy substrates from the tumor microenvironment (TME); however, the underlying mechanisms remain unclear. METHODS: Fibroblast-specific long noncoding RNAs were screened using RNA-seq data of our NJLCC cohort, TCGA, and CCLE datasets. The expression and package of LINC01614 into exosomes were identified using flow cytometric sorting, fluorescence in situ hybridization (FISH), and quantitative reverse transcription polymerase chain reaction (RT-PCR). The transfer and functional role of LINC01614 in lung adenocarcinoma (LUAD) and CAFs were investigated using 4-thiouracil-labeled RNA transfer and gain- and loss-of-function approaches. RNA pull-down, RNA immunoprecipitation, dual-luciferase assay, gene expression microarray, and bioinformatics analysis were performed to investigate the underlying mechanisms involved. RESULTS: We demonstrate that cancer-associated fibroblasts (CAFs) in LUAD primarily enhance the glutamine metabolism of cancer cells. A CAF-specific long noncoding RNA, LINC01614, packaged by CAF-derived exosomes, mediates the enhancement of glutamine uptake in LUAD cells. Mechanistically, LINC01614 directly interacts with ANXA2 and p65 to facilitate the activation of NF-κB, which leads to the upregulation of the glutamine transporters SLC38A2 and SLC7A5 and eventually enhances the glutamine influx of cancer cells. Reciprocally, tumor-derived proinflammatory cytokines upregulate LINC01614 in CAFs, constituting a feedforward loop between CAFs and cancer cells. Blocking exosome-transmitted LINC01614 inhibits glutamine addiction and LUAD growth in vivo. Clinically, LINC01614 expression in CAFs is associated with the glutamine influx and poor prognosis of patients with LUAD. CONCLUSION: Our study highlights the therapeutic potential of targeting a CAF-specific lncRNA to inhibit glutamine utilization and cancer progression in LUAD.

Circulating tumor DNA integrating tissue clonality detects minimal residual disease in resectable non-small-cell lung cancer.

BACKGROUND: Circulating tumor DNA (ctDNA) has been proven as a marker for detecting minimal residual diseases following systemic therapies in mid-to-late-stage non-small-cell lung cancers (NSCLCs) by multiple studies. However, fewer studies cast light on ctDNA-based MRD monitoring in early-to-mid-stage NSCLCs that received surgical resection as the standard of care. METHODS: We prospectively recruited 128 patients with stage I-III NSCLCs who received curative surgical resections in our Lung Cancer Tempo-spatial Heterogeneity prospective cohort. Plasma samples were collected before the surgery, 7 days after the surgery, and every 3 months thereafter. Targeted sequencing was performed on a total of 628 plasma samples and 645 matched tumor samples using a panel covering 425 cancer-associated genes. Tissue clonal phylogeny of each patient was reconstructed and used to guide ctDNA detection. RESULTS: The results demonstrated that ctDNA was more frequently detected in patients with higher stage diseases pre- and postsurgery. Positive ctDNA detection at as early as 7 days postsurgery identified high-risk patients with recurrence (HR = 3.90, P < 0.001). Our results also show that longitudinal ctDNA monitoring of at least two postsurgical time points indicated a significantly higher risk (HR = 7.59, P < 0.001), preceding radiographic relapse in 73.5% of patients by a median of 145 days. Further, clonal ctDNA mutations indicated a high-level specificity, and subclonal mutations informed the origin of tumor recurrence. CONCLUSIONS: Longitudinal ctDNA surveillance integrating clonality information may stratify high-risk patients with disease recurrence and infer the evolutionary origin of ctDNA mutations.

MCluster-VAEs: An end-to-end variational deep learning-based clustering method for subtype discovery using multi-omics data.

The discovery of cancer subtypes based on unsupervised clustering helps in providing a precise diagnosis, guide treatment, and improve patients' prognoses. Instead of single-omics data, multi-omics data can improve the clustering performance because it obtains a comprehensive landscape for understanding biological systems and mechanisms. However, heterogeneous data from multiple sources raises high complexity and different kinds of noise, which are detrimental to the extraction of clustering information. We propose an end-to-end deep learning based method, called Multi-omics Clustering Variational Autoencoders (MCluster-VAEs), that can extract cluster-friendly representations on multi-omics data. First, a unified network architecture with an attention mechanism was developed for accurately modeling multi-omics data. Then, using a novel objective function built from the Variational Bayes technique, the model was trained to effectively obtain the posterior estimation of the clustering assignments. Compared with 12 other state-of-the-art multi-omics clustering methods, MCluster-VAEs achieved an outstanding performance on benchmark datasets from the TCGA database. On the Pan Cancer dataset, MCluster-VAEs achieved an adjusted Rand index of approximately 0.78 for cancer category recognition, an increase of more than 18% compared with other methods. Furthermore, a survival analysis and clinical parameter enrichment tests conducted on 10 cancer datasets demonstrated that MCluster-VAEs provides comparable and even better results than many common integrative approaches. These results demonstrate that MCluster-VAEs are a powerful new tool for dissecting complex multi-omics relationships and providing new insights for cancer subtype discovery.

Sarcopenic obesity and therapeutic outcomes in gastrointestinal surgical oncology: A meta-analysis.

Background: Sarcopenic obesity (SO) has been indicated as a scientific and clinical priority in oncology. This meta-analysis aimed to investigate the impacts of preoperative SO on therapeutic outcomes in gastrointestinal surgical oncology. Methods: We searched the PubMed, EMBASE, and Cochrane Library databases through March 4th 2022 to identify cohort studies. Endpoints included postoperative complications and survival outcomes. Newcastle Ottawa Scale was used for quality assessment. Heterogeneity and publication bias were assessed. Subgroup analyses and sensitivity analyses were performed. Results: Twenty-six studies (8,729 participants) with moderate to good quality were included. The pooled average age was 65.6 [95% confidence interval (CI) 63.7-67.6] years. The significant heterogeneity in SO definition and diagnosis among studies was observed. Patients with SO showed increased incidences of total complications (odds ratio 1.30, 95% CI: 1.03-1.64, P = 0.030) and major complications (Clavien-Dindo grade ≥ IIIa, odds ratio 2.15, 95% CI: 1.39-3.32, P = 0.001). SO was particularly associated with the incidence of cardiac complications, leak complications, and organ/space infection. SO was also predictive of poor overall survival (hazard ratio 1.73, 95% CI: 1.46-2.06, P < 0.001) and disease-free survival (hazard ratio 1.41, 95% CI: 1.20-1.66, P < 0.001). SO defined as sarcopenia in combination with obesity showed greater association with adverse outcomes than that defined as an increased ratio of fat mass to muscle mass. A low prevalence rate of SO (< 10%) was associated with increased significance for adverse outcomes compared to the high prevalence rate of SO (> 20%). Conclusion: The SO was associated with increased complications and poor survival in gastrointestinal surgical oncology. Interventions aiming at SO have potentials to promote surgery benefits for patients with gastrointestinal cancers. The heterogeneity in SO definition and diagnosis among studies should be considered when interpreting these findings. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=255286], identifier [CRD42021255286].