Lung squamous cell carcinoma (LUSC) represents a major subtype of lung cancer, and it demonstrates limited treatment options and worse survival. Identifications of a prognostic model and chemoresistance mechanism can be helpful for improving stratification and guiding therapy decisions. The integrative development of machine learning-based models reveals a random survival forest (RSF) prognostic model for LUSC. The 12-gene RSF model exhibits high prognostic power in more than 1,000 LUSC patients. High-risk LUSC patients are associated with worse survival and the activation of the epithelial-mesenchymal transition pathway. Additionally, high-risk LUSC patients are resistant to docetaxel or vinorelbine treatment. In vitro and in vivo drug sensitivity experiments indicates that high-risk HCC15/H226 tumour cells and cell line-derived xenograft models are more resistant to vinorelbine treatment. Furthermore, the combination of chemotherapy with transforming growth factor-β inhibition augments antitumour responses in LUSC tumours. Our study provides valuable insights into prognosis stratification and the development of therapeutic strategies for LUSC. A random survival forest prognostic model for the risk assessment of newly diagnosed LUSC patients may guide the application of chemotherapy in LUSC.

The efficacy and safety of induction-immunotherapy followed by surgery for unresectable Stage III non-small cell lung cancer (NSCLC) remain challenging. In this open-label, single-center, phase II clinical umbrella trial (ChiCTR2000035367), 100 unresectable Stage III NSCLC patients are enrolled. Patients with PD-L1 expression ≥ 50% but contraindications to anti-angiogenic therapy receive immuno-monotherapy. Patients with PD-L1 expression ≥ 1% and no contraindications to anti-angiogenic therapy receive immunotherapy plus anti-angiogenesis therapy. Patients with PD-L1 expression between 1% and 49%, contraindications to anti-angiogenic therapy, or negative/unknown PD-L1 expression receive chemoimmunotherapy. The primary endpoint is the major pathological response (MPR) rate. Among 47 surgically-treated patients, the MPR rate is 61.7% (95% confidence interval [CI]: 46.4%-75.5%), achieving the prespecified endpoint. For secondary endpoints, the objective response rate for all patients is 54.0% (95% CI: 43.7-64.0). The median event-free survival is 29.9 months (95% CI: 17.0-42.7). Most common adverse event is anemia (49.0%). Exploratory transcriptomic analyses reveal Bone Marrow Stromal Cell Antigen 1 (BST1) as a promising biomarker for response to chemoimmunotherapy. Generally, for unresectable stage III NSCLC patients, anti-PD1 based induction-therapy according to PD-L1 expression and contraindication to antiangiogenic therapy followed by surgery is a feasible option. Induction-immunotherapy before surgery has been suggested for patients with unresectable stage III non-small cell lung cancer (NSCLC). Here, the authors report a phase 2 umbrella clinical trial where patients with unresectable stage III NSCLC receive anti-PD1-based induction-therapy based on PD-L1 expression and contraindications to antiangiogenic therapy.

This multicentre, two-arm, phase 2 study aimed to explore the efficacy and safety of neoadjuvant camrelizumab plus chemotherapy or apatinib in patients with initially unresectable stage II-III non-small-cell lung cancer (NSCLC). Eligible patients regardless of PD-L1 expression received neoadjuvant camrelizumab 200 mg and platinum-doublet chemotherapy every 3 weeks (arm A) or those with PD-L1-positive tumors received neoadjuvant camrelizumab and apatinib 250 mg once daily (arm B), for 2-4 cycles, followed by surgery. The primary endpoint was major pathological response (MPR) rate. Thirty patients in arm A and 21 in arm B were enrolled. Surgery rates were 50.0% (15/30) in arm A and 42.9% (9/21) in arm B, with all patients achieving R0 resections. Of these patients, the MPR and pathological complete response rates were both 20.0% (95% CI 4.3-48.1) in arm A and were 55.6% (95% CI 21.2-86.3) and 11.1% (95% CI 0.3-48.2) in arm B, respectively. The corresponding objective response rates were 33.3% (95% CI 11.8-61.6) and 55.6% (95% CI 21.2-86.3). With a median follow-up of 22.4 months (95% CI 19.0-26.0), the median event-free survival was not reached (NR; 95% CI 13.6-NR) in arm A and 16.8 months (95% CI 8.6-NR) in arm B. Grade 3 or above treatment-related adverse events occurred in eight (26.7%) patients in arm A and three (14.3%) in arm B. Biomarker analysis showed baseline TYROBP expression was predictive of treatment response in arm B. Neoadjuvant camrelizumab plus chemotherapy or apatinib exhibits preliminary efficacy and manageable toxicity in patients with initially unresectable stage II-III NSCLC.

We performed comprehensive proteogenomic characterization of small cell lung cancer (SCLC) using paired tumors and adjacent lung tissues from 112 treatment-naive patients who underwent surgical resection. Integrated multi-omics analysis illustrated cancer biology downstream of genetic aberrations and highlighted oncogenic roles of FAT1 mutation, RB1 deletion, and chromosome 5q loss. Two prognostic biomarkers, HMGB3 and CASP10, were identified. Overexpression of HMGB3 promoted SCLC cell migration via transcriptional regulation of cell junction-related genes. Immune landscape characterization revealed an association between ZFHX3 mutation and high immune infiltration and underscored a potential immunosuppressive role of elevated DNA damage response activity via inhibition of the cGAS-STING pathway. Multi-omics clustering identified four subtypes with subtype-specific therapeutic vulnerabilities. Cell line and patient-derived xenograft-based drug tests validated the specific therapeutic responses predicted by multi-omics subtyping. This study provides a valuable resource as well as insights to better understand SCLC biology and improve clinical practice.

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Although immune checkpoint blockade (ICB) therapies have shifted the treatment paradigm for non-small-cell lung cancer (NSCLC), many patients remain resistant. Here we characterize the tumor cell states and spatial cellular compositions of the NSCLC tumor microenvironment (TME) by analyzing single-cell transcriptomes of 232,080 cells and spatially resolved transcriptomes of tumors from 19 patients before and after ICB–chemotherapy. We find that tumor cells and secreted phosphoprotein 1-positive macrophages interact with collagen type XI alpha 1 chain-positive cancer-associated fibroblasts to stimulate the deposition and entanglement of collagen fibers at tumor boundaries, obstructing T cell infiltration and leading to poor prognosis. We also reveal distinct states of tertiary lymphoid structures (TLSs) in the TME. Activated TLSs are associated with improved prognosis, whereas a hypoxic microenvironment appears to suppress TLS development and is associated with poor prognosis. Our study provides novel insights into different cellular and molecular components corresponding to NSCLC ICB–chemotherapeutic responsiveness, which will benefit future individualized immuno-chemotherapy. Samples from 19 patients with non-small-cell lung cancer treated with a combination of chemotherapy and immune checkpoint blockade are profiled with single-cell RNA sequencing and spatial transcriptomics to identify factors associated with treatment resistance.

The rapid accumulation of single-cell RNA-seq data has provided rich resources to characterize various human cell types. Cell type annotation is the critical step in analyzing single-cell RNA-seq data. However, accurate cell type annotation based on public references is challenging due to the inconsistent annotations, batch effects, and poor characterization of rare cell types. Here, we introduce SELINA (single cELl identity NAvigator), an integrative annotation transferring framework for automatic cell type annotation. SELINA optimizes the annotation for minority cell types by synthetic minority over-sampling, removes batch effects among reference datasets using a multiple-adversarial domain adaptation network (MADA), and fits the query data with reference data using an autoencoder. Finally, SELINA affords a comprehensive and uniform reference atlas with 1.7 million cells covering 230 major human cell types. We demonstrated the robustness and superiority of SELINA in most human tissues compared to existing methods. SELINA provided a one-stop solution for human single- cell RNA-seq data annotation with the potential to extend for other species.

Background: There is a rapidly increasing population referring themselves for lung cancer screening in China. This study sought to investigate the clinicopathological characteristics of self-referred versus symptom-driven detection of lung cancer, and the consequences of self-referred behavior in patients ineligible for LDCT screening. Methods: A total of 58,433 patients receiving surgical treatment for primary lung cancer, between 1989 and 2018 were included, 61.2% of whom were self-referred. Overdiagnosis refers to detection of adenocarcinoma in situ (AIS) by self-referred screening. Findings: Lung cancers detected via self-referral rose from 9.5% (n=30) in 1989 to 81.2% (n=10127) in 2018, contributing to a significant decrease in tumor size (AAPC, self-referred: -3.89% versus symptom-driven: -2.72%) and a wider application of wedge resection in tumor ≤1cm (self-referred: 39.9% versus symptom-driven: 30.6%, p<0.001). Self-referred screening independently improved lung cancer survival (HR: 0.74; 95%CI: 0.71-0.78). The proportion of patients eligible for LDCT screening decreased from 41.5% to 10.2% (AAPC: -5.13%), mainly caused by self-referred screening population. Shifts in histology (adenocarcinoma) and stage (stage-I) attributed to self-referred screening were more prevalent in patients ineligible for LDCT screening. Self-referral-associated overdiagnosis rate was 22.0%, dependent on tumor size (≤1cm, 1-2cm, and 2-3cm: 50.5%, 5.3%, and 0.3%, respectively). Interpretation: Increases in self-referred screening have contributed to an early detection, less extensive resection, and improved survival of lung cancer. The favorable shift in cancer stage was more pronounced among self-referred patients who were ineligible for screening, suggesting an opportunity for improved screening protocols. However, the risk of overdiagnosis should not be disregarded. Funding: This study was supported by the National Natural Science Foundation of China (Grant No. 82172848).Declaration of Interest: René Horsleben Petersen reports speaker fee from Medtronic, AMBU, AstraZeneca and Medela and Advisory Board member: AstraZeneca, Roche and MSD. Dominique Gossot reports speaker fee from Medtronic and Johnson&Johnson, and consultant fee from Delacroix- Chevalier. Other authors have no conflicts of interest to declare.Ethical Approval: The study protocol was approved by the Institutional Review Broad of Shanghai Pulmonary Hospital, Tongji University (no. K21-377), and patients informed consent was waived because of its retrospective nature.

Immunotherapy has revolutionized cancer treatment, but most patients are refractory to immunotherapy or acquire resistance, with the underlying mechanisms remaining to be explored. We characterized the transcriptomes of 92,000 single cells from 3 pre-treatment and 12 post-treatment patients with non-small cell lung cancer (NSCLC) who received neoadjuvant PD-1 blockade combined with chemotherapy. The 12 post-treatment samples were categorized into two groups based on pathologic response: major pathologic response (MPR; n = 4) and non-MPR (NMPR; n = 8). Distinct therapy-induced cancer cell transcriptomes were associated with clinical response. Cancer cells from MPR patients exhibited a signature of activated antigen presentation via major histocompatibility complex class II (MHC-II). Further, the transcriptional signatures of FCRL4+FCRL5+ memory B cells and CD16+CX3CR1+ monocytes were enriched in MPR patients and are predictors of immunotherapy response. Cancer cells from NMPR patients exhibited overexpression of estrogen metabolism enzymes and elevated serum estradiol. In all patients, therapy promoted expansion and activation of cytotoxic T cells and CD16+ NK cells, reduction of immunosuppressive Tregs, and activation of memory CD8+T cells into an effector phenotype. Tissue-resident macrophages were expanded after therapy, and tumor-associated macrophages (TAMs) were remodeled into a neutral instead of an anti-tumor phenotype. We revealed the heterogeneity of neutrophils during immunotherapy and identified an aged CCL3+ neutrophil subset was decreased in MPR patients. The aged CCL3+ neutrophils were predicted to interact with SPP1+ TAMs through a positive feedback loop to contribute to a poor therapy response. Neoadjuvant PD-1 blockade combined with chemotherapy led to distinct NSCLC tumor microenvironment transcriptomes that correlated with therapy response. Although limited by a small patient sample size subjected to combination therapy, this study provides novel biomarkers to predict therapy response and suggests potential strategies to overcome immunotherapy resistance.

Afatinib, an irreversible ErbB-family blocker, could improve the survival of advanced epidermal growth factor receptor ( EGFR )-mutant non-small cell lung cancer patients (NSCLCm+). This phase II trial (NCT04201756) aimed to assess the feasibility of neoadjuvant Afatinib treatment for stage III NSCLCm+. Forty-seven patients received neoadjuvant Afatinib treatment (40 mg daily). The primary endpoint was objective response rate (ORR). Secondary endpoints included pathological complete response (pCR) rate, pathological downstaging rate, margin-free resection (R0) rate, event-free survival, disease-free survival, progression-free survival, overall survival, treatment-related adverse events (TRAEs). The ORR was 70.2% (95% CI: 56.5% to 84.0%), meeting the pre-specified endpoint. The major pathological response (MPR), pCR, pathological downstaging, and R0 rates were 9.1%, 3.0%, 57.6%, and 87.9%, respectively. The median survivals were not reached. The most common TRAEs were diarrhea (78.7%) and rash (78.7%). Only three patients experienced grade 3/4 TRAEs. Biomarker analysis and tumor microenvironment dynamics by bulk RNA sequencing were included as predefined exploratory endpoints. CISH expression was a promising marker for Afatinib response (AUC = 0.918). In responders, compared to baseline samples, increasing T-cell- and B-cell-related features were observed in post-treatment tumor and lymph-node samples, respectively. Neoadjuvant Afatinib is feasible for stage III NSCLC+ patients and leads to dynamic changes in the tumor microenvironment.

Objective:To explore the sex-based heterogeneity in demographic and pathological trends of lung cancer during the past 30 years.Methods:Patients with primary lung cancer who received surgical treatment in the Department of thoracic surgery, Shanghai Pulmonary Hospital Tongji University from 1989 to 2018 were retrospectively analyzed. The differences between male and female patients in age, smoking history, pathological stage and type were compared. Mann- Kendall trend test was performed for trend analysis. Results:A total of 58 433 patients were included in this study, encompassing 30 729(52.6%) men and 27 , 704(47.4%) women. Compared with male patients, female patients were younger(56.0 years old vs. 59.7 years old), and had a higher proportion of non-smokers(98.3% vs. 52.3%), stage Ⅰ lung cancers(60.6% vs. 49.3%), and adenocarcinoma(93.7% vs. 56.1%, all P-values <0.001). Trend analyses revealed that the proportion of female patients increased year by year, and surpassed males in 2015, with the current ratio of male to female being 1∶1.5. After 2013, the age of onset in females was getting younger, and the average age decreased from 58.7 years old to 54.7 years old( P=0.02). The decrease in the proportion of smoking patients was mainly reflected by male patients(from 68.5% to 31.1%, P<0.01). Stage Ⅰ lung cancers in male and females outnumbered advanced stage in 2012 and 2010, respectively, with a much higher proportion in female patients. Among male patients, adenocarcinoma has replaced squamous cell carcinoma as the most common pathological type since 2012, while in female patients adenocarcinoma remained the most common pathological type of lung cancer, and its proportion continued to increase reaching over 98%. Conclusion:A dramatic change in gender distribution was noticed during the past 30 years. Female patients became the primary population in surgically-treated lung cancers, with a trend of getting younger. The proportion of smokers and squamous cell carcinoma decreased significantly in male patients, and adenocarcinoma has become the most common pathological type of lung cancer. The proportion of stage Ⅰ lung cancers was on a dramatic rise, with the popularization of CT screening for lung cancer.

Background Surgery is the primary treatment option for Lung adenosquamous carcinoma (ASC) patients. However, no study compares the benefits of lobectomy and sublobar resection in ASC patients. Methods A total of 1379 patients in the Surveillance, epidemiology, and End Results (SEER) database and 466 patients in Shanghai Pulmonary Hospital (SPH) were enrolled. Survival benefits were evaluated after possible confounders were eliminated by propensity score matching (PSM). Results After 1:3 PSM, 463 SEER database patients and 244 SPH patients were enrolled. Lobectomy was associated with better overall survival (OS) and disease-free survival (DFS) than sublobar resection for ASC patients (5-year OS of SEER: 46.9% vs. 33.3%, P =0.017; 5-year OS of SPH: 35.0% vs. 16.4%, P =0.002; 5-year DFS of SPH: 29.5% vs. 14.8%, P =0.002). Similar results were observed in stage I patients. Univariate and multivariate Cox regression analyses showed that sublobar resection was an adverse prognostic factor independently (SEER: HR: 1.40, 95%CI: 1.08-1.81, P =0.012; SPH: HR: 1.73, 95%CI: 1.11-2.70, P =0.015). Subgroup analysis showed that all of the ASC patient subtypes tended to benefit more from lobectomy than sublobar resection. Conclusions Lobectomy remains the primary option for ASC patients compared to sublobar resection, including stage I.

Background This trial aimed to analyse the safety, effectiveness and transcriptomic characteristics of neoadjuvant toripalimab plus chemotherapy in II–III non-small-cell lung cancer (NSCLC). Methods Patient eligibility mainly involved treatment-naive, clinical stage II–III and wild-type EGFR/ALK NSCLC. The patients received 2–4 cycles of toripalimab (240 mg q3w) plus carboplatin-based chemotherapy. After the second treatment cycle, all patients were re-evaluated by a multidisciplinary team. Candidates eligible for surgery underwent surgery; otherwise, patients received the remaining treatment cycles. The primary endpoints were safety and major pathological response (MPR). Secondary endpoints were R0 resection rate, progression-free survival (PFS) and overall survival (OS). RNA sequencing of baseline and post-treatment samples was conducted to explore the transcriptomic characteristics of the therapeutic response. Results In total, 50 eligible patients were enrolled, including 12 (24.0%) with resectable disease (RD) and 38 (76.0%) with potentially resectable disease (PRD). Treatment-related adverse events (TRAEs) were recorded in 48 cases (96.0%). Severe TRAEs occurred in 3 (6.0%) cases, including myelosuppression, drug-induced liver injury and death related to haemoptysis. The objective response rate (ORR) was 76.0%, with 8 (16.0%) patients having a complete response (CR), 30 (60.0%) partial response (PR), 10 (20.0%) stable disease (SD) and 2 (4.0%) progressive disease (PD). Surgery could be achieved in 12 (100%) patients with RD and 25 (65.8%) with PRD; 1 (2.0%) with PRD refused surgery. Therefore, R0 resection was performed for all 36 (100%) patients who underwent surgery; 20 (55.6%) achieved MPR, including 10 (27.8%) with a complete pathological response (pCR). The CHI3L1 (chitinase-3-like protein 1) immunohistochemistry (IHC) expression of baseline tumour samples could predict the therapeutic response (AUC=0.732), OS ( P =0.017) and PFS ( P =0.001). Increased PD-1 expression, T cell abundance and immune-related pathway enrichment were observed in post-treatment samples compared to baseline in the response group (CR+PR) but not in the non-response group (SD+PD). Conclusions Neoadjuvant toripalimab plus chemotherapy was safe and effective, with a high MPR and manageable TRAEs for II–III NSCLC, even converting initially PRD to RD. Disparate transcriptomic characteristics of therapeutic efficiency were observed, and CHI3L1 expression predicted therapeutic response and survival. Trial registration ChiCTR1900024014, June 22, 2019.

BackgroundSurvival outcomes of early-stage T1-2N0M0 small cell lung cancer (SCLC) patients differ widely, and the existing Veterans Administration Lung Study Group (VALSG) or TNM staging system is inefficient at predicting individual prognoses. In our study, we developed and validated nomograms for individually predicting overall survival (OS) and lung cancer-specific survival (LCSS) in this special subset of patients.MethodsData on patients diagnosed with T1-2N0M0 SCLC between 2000 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. All enrolled patients were split into a training cohort and a validation cohort according to the year of diagnosis. Using multivariable Cox regression, significant prognostic factors were identified and integrated to develop nomograms for 1-, 3-, and 5-year OS and LCSS prediction. The prognostic performance of our new model was measured by the concordance index (C-index) and calibration curve. We compared our latest model and the 8th AJCC staging system using decision curve analyses (DCA). Kaplan–Meier survival analyses were applied to test the application of the risk stratification system.ResultsA total of 1,147 patients diagnosed from 2000 to 2011 were assigned to the training cohort, and 498 cases that were diagnosed from 2012 to 2015 comprised the validation cohort. Age, surgery, lymph node removal (LNR), and chemotherapy were independent predictors of LCSS. The variables of sex, age, surgery, LNR, and chemotherapy were identified as independent predictors of OS. The above-mentioned prognostic factors were entered into the nomogram construction of OS and LCSS. The C-index of this model in the training cohort was 0.663, 0.702, 0.733, and 0.658, 0.702, 0.733 for predicting 1-, 3-, and 5-year OS and LCSS, respectively. Additionally, in the validation cohort, there were 0.706, 0.707, 0.718 and 0.712, 0.691, 0.692. The calibration curve showed accepted prediction accuracy between nomogram-predicted survival and actual observed survival, regardless of OS or LCSS. In addition, there were significant distinctions in the survival curves of OS and LCSS between different risk groups stratified by prognostic scores. Compared with the 8th AJCC staging system, our new model also improved net benefits.ConclusionsWe developed and validated novel nomograms for individual prediction of OS and LCSS, integrating the characteristics of patients and tumors. The model showed superior reliability and may help clinicians make treatment strategies and survival predictions for early-stage T1-2N0M0 SCLC patients.

Machine learning is an important artificial intelligence technique that is widely applied in cancer diagnosis and detection. More recently, with the rise of personalised and precision medicine, there is a growing trend towards machine learning applications for prognosis prediction. However, to date, building reliable prediction models of cancer outcomes in everyday clinical practice is still a hurdle. In this work, we integrate genomic, clinical and demographic data of lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) patients from The Cancer Genome Atlas (TCGA) and introduce copy number variation (CNV) and mutation information of 15 selected genes to generate predictive models for recurrence and survivability. We compare the accuracy and benefits of three well-established machine learning algorithms: decision tree methods, neural networks and support vector machines. Although the accuracy of predictive models using the decision tree method has no significant advantage, the tree models reveal the most important predictors among genomic information (e.g. KRAS, EGFR, TP53), clinical status (e.g. TNM stage and radiotherapy) and demographics (e.g. age and gender) and how they influence the prediction of recurrence and survivability for both early stage LUAD and LUSC. The machine learning models have the potential to help clinicians to make personalised decisions on aspects such as follow-up timeline and to assist with personalised planning of future social care needs.

Background The diversity of histologic composition reflects the inter- and intra-tumor heterogeneity of lung adenocarcinomas (LUADs) macroscopically. Insights into the oncological characteristics and tumor microenvironment (TME) of different histologic subtypes of LUAD at the single-cell level can help identify potential therapeutic vulnerabilities and combinational approaches to improve the survival of LUAD patients. Methods Through comparative profiling of cell communities defined by scRNA-seq data, we characterized the TME of LUAD samples of distinct histologic subtypes, with relevant results further confirmed in multiple bulk transcriptomic, proteomic datasets and an independent immunohistochemical validation cohort. Results We find that the hypoxic and acidic situation is the worst in the TME of solid LUADs compared to other histologic subtypes. Besides, the tumor metabolic preferences vary across histologic subtypes and may correspondingly impinge on the metabolism and function of immune cells. Remarkably, tumor cells from solid LUADs upregulate energy and substance metabolic activities, particularly the folate-mediated one-carbon metabolism and the key gene MTHFD2, which could serve as a potential therapeutic target. Additionally, ubiquitination modifications may also be involved in the progression of histologic patterns. Immunologically, solid LUADs are characterized by a predominance of exhausted T cells and immunosuppressive myeloid cells, where the hypoxic, acidified and nutrient-deprived TME has a non-negligible impact. Discrepancies in stromal cell function, evidenced by varying degrees of stromal remodeling and fibrosis, may also contribute to the specific immune phenotype of solid LUADs. Conclusions Overall, our research proposes several potential entry points to improve the immunosuppressive TME of solid LUADs, thereby synergistically potentiating their immunotherapeutic efficacy, and may provide precise therapeutic strategies for LUAD patients of distinct histologic subtype constitution.

Objective The choice of adjuvant therapy for early stage lung adenocarcinoma (LUAD) remains controversial. Identifying the metabolism characteristics leading to worse prognosis may have clinical utility in offering adjuvant therapy. Methods The gene expression profiles of LUAD were collected from 22 public datasets. The patients were divided into a meta-training cohort (n = 790), meta-testing cohort (n = 716), and three independent validation cohorts (n = 345, 358, and 321). A metabolism-related gene pair index (MRGPI) was trained and validated in the cohorts. Subgroup analyses regarding tumor stage and adjuvant chemotherapy (ACT) were performed. To explore potential therapeutic targets, we performed in silico analysis of the MRGPI. Results Through machine learning, MRGPI consisting of 12 metabolism-related gene pairs was constructed. MRGPI robustly stratified patients into high- vs low-risk groups in terms of overall survival across and within subpopulations with stage I or II disease in all cohorts. Multivariable analysis confirmed that MRGPI was an independent prognostic factor. ACT could not improve prognosis in high-risk patients with stage I disease, but could improve prognosis in the high-risk patients with stage II disease. In silico analysis indicated that B3GNT3 (overexpressed in high-risk patients) and HSD17B6 (down-expressed in high-risk patients) may make synergic reaction in immune evasion by the PD-1/PD-L1 pathway. When integrated with clinical characteristics, the composite clinical and metabolism signature showed improved prognostic accuracy. Conclusions MRGPI could effectively predict prognosis of the patients with early stage LUAD. The patients at high risk may get survival benefit from PD-1/PD-L1 blockade (stage I) or combined with chemotherapy (stage II).

BACKGROUND:Combined small cell lung cancer (CSCLC) is an uncommon and heterogeneous subtype of small cell lung cancer (SCLC). However, there is limited data concerning the different molecular changes and clinical features in CSCLC compared to pure SCLC.METHODS:The clinical and pathological characteristics of pure SCLC and CSCLC patients were analyzed. Immunohistochemistry and microdissection were performed to isolate the CSCLC components. Further molecular analysis was carried out by next-generation sequencing (NGS) in 12 CSCLC and 30 pure SCLC.RESULTS:There were no significant differences in clinical features between CSCLC and pure SCLC. Overall survival (OS) of CSCLC patients was worse than pure SCLC (P=0.005). NGS results indicated that TP53 and RB1 were the most frequently mutated genes in both CSCLC (83.33% and 66.67%) and pure SCLC (80.00% and 63.33%) groups. However, less than 10% common mutations were found in both CSCLC and pure SCLC. When analyzing the data of SCLC and non-small cell lung cancer (NSCLC) components of CSCLC, more than 50% common mutations, and identical genes with mutations were detected. Moreover, there were also common biological processes and signaling pathways identified in CSCLC and pure SCLC, in addition to SCLC and NSCLC components.CONCLUSIONS:There were no significant differences in terms of clinical features between CSCLC and pure SCLC. However, the prognosis for CSCLC was worse than pure SCLC. NGS analysis suggested that CSCLC components might derive from the same pluripotent single clone with common initial molecular alterations and subsequent acquisitions of other genetic mutations.

Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors but posed computational challenges on integrating and utilizing the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly 2 million cells from 76 high-quality tumor datasets across 27 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, clustering, cell-type annotation, malignant cell classification, differential expression analysis and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.