Neoadjuvant camrelizumab (an anti-PD-1 antibody) plus chemotherapy or apatinib (a VEGFR-2 inhibitor) for initially unresectable stage II-III non-small-cell lung cancer: a multicentre, two-arm, phase 2 exploratory study.

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.

Proteogenomic characterization of small cell lung cancer identifies biological insights and subtype-specific therapeutic strategies.

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.

Single-cell assignment using multiple-adversarial domain adaptation network with large-scale references.

The rapid accumulation of single-cell RNA-seq data has provided rich resources to characterize various human cell populations. However, achieving accurate cell-type annotation using public references presents challenges due to inconsistent annotations, batch effects, and rare cell types. Here, we introduce SELINA (single-cell identity navigator), an integrative and automatic cell-type annotation framework based on a pre-curated reference atlas spanning various tissues. SELINA employs a multiple-adversarial domain adaptation network to remove batch effects within the reference dataset. Additionally, it enhances the annotation of less frequent cell types by synthetic minority oversampling and fits query data with the reference data using an autoencoder. SELINA culminates in the creation of a comprehensive and uniform reference atlas, encompassing 1.7 million cells covering 230 distinct human cell types. We substantiate its robustness and superiority across a multitude of human tissues. Notably, SELINA could accurately annotate cells within diverse disease contexts. SELINA provides a complete solution for human single-cell RNA-seq data annotation with both python and R packages.

Neoadjuvant Afatinib for stage III EGFR-mutant non-small cell lung cancer: a phase II study.

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.

Tumor microenvironment remodeling after neoadjuvant immunotherapy in non-small cell lung cancer revealed by single-cell RNA sequencing.

BACKGROUND: Immunotherapy has revolutionized cancer treatment, but most patients are refractory to immunotherapy or acquire resistance, with the underlying mechanisms remaining to be explored. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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.

Development and validation of nomogram prognostic model for early-stage T1-2N0M0 small cell lung cancer: A population-based analysis.

Background: Survival 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. Methods: Data 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. Results: A 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. Conclusions: We 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.

Safety and effectiveness of neoadjuvant PD-1 inhibitor (toripalimab) plus chemotherapy in stage II-III NSCLC (LungMate 002): an open-label, single-arm, phase 2 trial.

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.

Comparative profiling of single-cell transcriptome reveals heterogeneity of tumor microenvironment between solid and acinar lung adenocarcinoma.

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.

Surgical Options for Resectable Lung Adenosquamous Carcinoma: A Propensity Score-Matched Analysis.

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.

Machine learning application in personalised lung cancer recurrence and survivability prediction.

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.

Identification of an Individualized Metabolism Prognostic Signature and Related Therapy Regimens in Early Stage Lung Adenocarcinoma.

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).

Comprehensive genomic profiling of combined small cell lung cancer.

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.

TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment.

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.

3D-printed cellular tips for tuning fork atomic force microscopy in shear mode.

Conventional atomic force microscopy (AFM) tips have remained largely unchanged in nanomachining processes, constituent materials, and microstructural constructions for decades, which limits the measurement performance based on force-sensing feedbacks. In order to save the scanning images from distortions due to excessive mechanical interactions in the intermittent shear-mode contact between scanning tips and sample, we propose the application of controlled microstructural architectured material to construct AFM tips by exploiting material-related energy-absorbing behavior in response to the tip-sample impact, leading to visual promotions of imaging quality. Evidenced by numerical analysis of compressive responses and practical scanning tests on various samples, the essential scanning functionality and the unique contribution of the cellular buffer layer to imaging optimization are strongly proved. This approach opens new avenues towards the specific applications of cellular solids in the energy-absorption field and sheds light on novel AFM studies based on 3D-printed tips possessing exotic properties.

An individualized immune prognostic signature in lung adenocarcinoma.

BACKGROUND: Tumor immune infiltration is closely associated with clinical outcome in lung cancer. We aimed to develop an immune signature to improve the prognostic predictions of lung adenocarcinoma (LUAD). METHODS: We applied "Cell type Identification by Estimating Relative Subsets of RNA Transcripts" method to quantify the fraction of 22 leukocyte cells from six public microarray datasets. Four datasets from GPL570 were treated as the training cohort and two datasets from GPL96 and GPL10379 as the validation cohorts. An immune risk score (IRS) based on leukocyte cell fraction was established by least absolute shrinkage and selection operator cox regression model. RESULTS: IRS consisting of 6 types of leukocytes was constructed in the training dataset. In the training cohort (520 patients), the IRS stratified patients into high-IRS group (215 patients) and low-IRS group (305 patients) with significant differences in overall survival (OS) (HR: 2.77, 95% CI 2.08-3.06). Multivariate analysis including age, gender, stage, IRS and tumor purity revealed the IRS to be an independent prognostic factor in all datasets (training: HR: 10.71, 95% CI 5.72-20.07; validation-1: HR 2.68, 95% CI 1.15-6.27; validation-2: HR 3.71, 95% CI 1.33-10.33); all p < 0.05). IRS was significantly positively correlated to the expression levels of PD1, PDL1, CTLA and LAG3 (all p < 0.001). When integrated with clinical characteristics including stage and age, the composite immune and clinical signature presented with improved prognostic accuracy than IRS (mean C-index 0.66 vs. 0.60). CONCLUSIONS: The proposed immune-clinical signature could predict OS in patients with LUAD effectively.

Pulmonary Sarcomatoid Carcinoma: Experience From SEER Database and Shanghai Pulmonary Hospital.

BACKGROUND: Pulmonary sarcomatoid carcinoma (PSC) is a rare type of lung cancer. This study aimed to explore the appropriate treatment for PSC. METHODS: Two cohorts were used: patients from the Surveillance, Epidemiology, and End Results (SEER) database (1988 to 2014) and Shanghai resident patients at Shanghai Pulmonary Hospital (2009 to 2019) in China. Cox regression analysis was applied to identify prognostic factors for progression-free survival and overall survival (OS). Interaction assessments were performed using likelihood ratio tests to examine relationships between adjuvant chemotherapy and other baseline characteristics. RESULTS: In the SEER cohort, 1640 patients with PSC were identified, with a median survival and a 5-year OS rate of 7 months (95% confidence interval (CI), 6 to 8 months) and 19.5%, respectively. Multivariable Cox analysis of surgically treated patients revealed that adjuvant chemotherapy was significantly associated with better survival (hazard ratio, 0.78; 95% CI, 0.62 to 0.98), and the benefit was more pronounced in T3 to T4 stage (P = .04) and N-positive patients (P < .01). In the Shanghai Pulmonary Hospital cohort (n = 175), the median progression-free survival and OS were 8 months (95% CI, 7 to 12 months) and 12 months (95% CI, 10 to 18 months), respectively, with a 5-year OS rate of 25.1%. Similarly, the survival benefit of adjuvant chemotherapy was confirmed in patients with surgical resection (hazard ratio, 0.50; 95% CI, 0.31 to 0.81), but this benefit was restricted to patients who were younger (age <63 years; P = .02) and had a higher body mass index (>25 kg/m2; P < .01) by interaction assessments. The disease control rate after chemotherapy was 58.62%, and the disease control rate after targeted therapy was 57.14%. CONCLUSIONS: Adjuvant chemotherapy should be recommended for patients with surgically treated PSC, especially for patients with advanced-stage cancer, younger age, or higher body mass index.

Pulmonary carcinosarcoma: analysis from the Surveillance, Epidemiology and End Results database.

OBJECTIVES: Pulmonary carcinosarcoma (PCS) is a rare neoplasm. This study explored the clinicopathological characteristics and survival outcomes of PCS. METHODS: The Surveillance, Epidemiology and End Results (SEER) database (1988-2014) was queried for PCS. Overall survival (OS) was evaluated by multivariable Cox regression and nomograms were constructed to predict 3-year OS for PCS. Prognostic performance was evaluated using concordance index and area under the curve analysis. In M0 surgically treated patients, interaction assessments were performed using likelihood ratio tests. Subgroup analysis was performed according to patient age. The clinical features of PCSs were further compared to other non-small-cell lung cancers (NSCLCs). RESULTS: Multivariable analysis identified age [hazard ratio (HR) 1.03, 95% confidence interval (CI) 1.01-1.04], surgery (HR 0.53, 95% CI 0.36-0.77) and chemotherapy (HR 0.51, 95% CI 0.36-0.73) as significantly associated with OS. The nomogram had a concordance index of 0.747 and an area under the curve of 0.803. The association between age and OS was stronger in those receiving pneumonectomy (P = 0.04 for interactions) compared to those that did not (HR 5.14, 95% CI 1.64-16.07), and was associated with a poorer outcome compared to lobectomy amongst the elderly (age ≥ 70 years). Patients with PCS were more likely to receive surgical treatment and had lower lymphatic metastasis compared to adenocarcinoma, squamous cell carcinoma and large cell carcinoma (all P < 0.05). CONCLUSIONS: PCS had unique clinical features compared to common types of NSCLCs in terms of lymphatic invasion and surgical treatment. Pneumonectomy was associated with poorer survival in elderly patients.

Programmable Liquid Adhesion on Bio-Inspired Re-Entrant Structures.

Surfaces combining antispreading and high adhesion can find wide applications in the manipulation of liquid droplets, generation of micropatterns and liquid enrichment. To fabricate such surfaces, almost all the traditional methods demand multi-step processes and chemical modification. And even so, most of them cannot be applied for some liquids with extremely low surface energy. In the past decade, multiply re-entrant structures have aroused much attention because of their universal and modification-independent antiadhesion or antipenetration ability. Unfortunately, theories and applications about their liquid adhesion behavior are still rare. In this work, inspired by the springtail skin and gecko feet in the adhered state, it is demonstrated that programmable liquid adhesion is realized on the 3D-printed micro doubly re-entrant arrays. By arranging the arrays reasonably, three different Cassie adhesion behaviors can be obtained: I) no residue adhesion, II) tunable adhesion, and III) absolute adhesion. Furthermore, various arrays are designed to tune macro/micro liquid droplet manipulation, which can find applications in the transportation of liquid droplets, liquid enrichment, generation of tiny droplets, and micropatterns.

3D Printing of Bioinspired Liquid Superrepellent Structures.

Bioinspired re-entrant structures have been proved to be effective in achieving liquid superrepellence (including anti-penetration, anti-adhesion, and anti-spreading). However, except for a few reports relying on isotropic etching of silicon wafers, most fluorination-dependent surfaces are still unable to repel liquids with extreme low surface energy (i.e., γ < 15 mN m-1 ), especially those fluorinated solvents. Herein, triply re-entrant structures, possessing superrepellence to water (with surface tension γ of 72.8 mN m-1 ) and various organic liquids (γ = 12.0-27.1 mN m-1 ), are fabricated via two-photon polymerization based 3D printing technology. Such structures can be constructed both on rigid and flexible substrates, and the liquid superrepellent properties can be kept even after oxygen plasma treatment. Based on the prepared triply re-entrant structures, micro open capillaries are constructed on them to realize directional liquid spreading, which may be applied in microfluidic platforms and lab-on-a-chip applications. The fabricated arrays can also find potential applications in electronic devices, gas sensors, microchemical/physical reactors, high-throughput biological sensors, and optical displays.