Overall Survival In Non-small Cell Lung Cancer Patients Research Articles

Pretreatment lymphocyte-to-monocyte ratio as a prognostic factor and influence on dose-effect in fractionated stereotactic radiotherapy for oligometastatic brain metastases in non-small cell lung cancer patients.

Studies on the prognostic factors for patients with brain oligo-metastasis treated with fractionated stereotactic radiotherapy (FSRT) usually focus on the size of metastatic tumor and radiation dose. Some inflammatory indicators have predictive value in non-small cell lung cancer (NSCLC) with brain metastasis receiving stereotactic radiotherapy. However, the prognostic value of inflammatory indicators in NSCLC patients with brain oligo-metastasis treated with FSRT, and their effect on radiotherapy dose is unknown. A total of 95 advanced NSCLC patients with brain oligo-metastasis who had undergone FSRT treatment at Ningbo Medical Center Lihuili Hospital between January 2015 and April 2022 were enrolled into the study. Neutrophil to lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), tumor diameter and biologically effective dose (BED10) were analyzed using Chi-square test. Univariate and multivariate Cox regressions were used to identify predictors of survival. Tumor diameter (< 2 cm), BED10 (≥ 48Gy) and LMR (≥ 4) were found to be independently associated with good intracranial local control survival (i-LCS) through multivariate analysis. The median i-LCS was longer in patients with 2 independent risk factors (tumor diameter ≥ 2 and LMR < 4) administered with BED10 > 53.6Gy compared with patients administered with BED10 ≤ 53.6Gy (20.7 months vs 12.0 months, P = 0.042). LMR ≥ 4 (P = 0.019) and positivity for driver gene mutations (P = 0.011) were independently associated with better overall survival (OS). LMR is an independent prognostic factor of i-LCS and OS in NSCLC patients with brain oligo-metastasis treated with FSRT. Patients with tumor diameter ≥ 2 and LMR < 4 should be treated with BED10 greater than 53.6Gy.

Update on real-world efficacy of anlotinib in non-small cell lung cancer.

e21173 Background: Anlotinib is a novel oral small molecule TKI with anti-tumor vascular generation and growth inhibition effects, targeting VEGFR, PDGFR, FGFR, and c-kit. It has been shown to be highly effective and low toxicity in the ALTER 0303 study and was approved by NMPA in 2018 for the treatment of NSCLC(non-small cell lung cancer)as third-line and above. Previous studies have shown that anlotinib can benefit patients with advanced NSCLC, especially those in the front-line(first-/second-line). This real-world study aims to update the efficacy of anlotinib in treating NSCLC and to screen the advantage population for anlotinib treatment from front-line/back-line (third-/above-line) treatment, pathological subtype, metastasis site, gene mutation, to provide more effective anlotinib treatment strategies. Methods: This is a multicenter, retrospective, real-world study. Patients with NSCLC who have received anlotinib are included, and subgroup analysis is performed based on pathological subtypes, clinical stages, gene mutations (EGFR mutant/wildtype), front-line/back-line treatments, exploring the impact of different factors on the PFS and OS of NSCLC patients using anlotinib. Results: As of September 2022, a total of 478 patients diagnosed with NSCLC were enrolled in the study, of which 434 were evaluable, with a median age of 63 years, 263 (60.6%) were male, adenocarcinoma in 292 (67.3%), squamous cell carcinoma in 75 (17.3%). At baseline, 59 patients (13.6%) had brain metastasis, 39 (9.0%) had liver metastasis, 127 (29.3%) had bone metastasis, 194 cases (44.7%) had genetic mutations, including 135 cases (31.1%) carrying EGFR mutations. 198 patients (45.6%) received anlotinib as front-line treatment, and 236 patients (54.4%) as back-line treatment. The median follow-up time was 5.09 months (95% Cl, 2.86-7.16), with a mPFS of 4.37 months (95% Cl: 3.78-5.45) in the overall population and a mOS of 20.34 months (95% Cl: 14.88-NA). Among the 138 patients who were evaluable for efficacy, 0 cases achieved complete response (CR), 6 cases partial response (PR), and 57 cases stable disease (SD). ORR was 4.3%, and DCR was 44.9%. The mPFS of front-line was longer than the back-line (5.45 vs 3.52 months), and front-line group had a longer mOS than back-line (NR vs 16.03 months). Subgroup analysis did not find any correlations between pathological subtype or EGFR mutations and patient PFS and OS. However, it was found that the presence of liver and bone metastasis at baseline was related to a shorter OS with anlotinib in the back-line (8.28 vs 16.23 months, p = 0.0253; 7.89 months vs NR, p &lt; 0.001). Conclusions: Anlotinib shows well efficacy in NSCLC patients, prolonging progression-free survival (PFS) and overall survival (OS), especially for front-line patients, suggesting that these patients may be a preferred population for using anlotinib, providing effective evidence for the use of anlotinib treatment for NSCLC patients.

Newly diagnosed non-small cell lung cancer with interstitial lung abnormality: Prevalence, characteristics, and prognosis.

Along with the improvement of lung cancer screening implementation, the identification of interstitial lung abnormality (ILA) is increasing. Currently, there is a limited description of the oncogenic status and ILA subtypes among newly diagnosed non-small cell lung cancer (NSCLC) patients with ILA in the Chinese population. This study aimed to investigate the prevalence, characteristics, oncogenic status and factors associated with overall survival (OS) among NSCLC patients with ILA. A total of 765 newly diagnosed NSCLC cases at our hospital were reviewed and ILA was diagnosed according to the criteria of the Fleischner Society. The characteristics, clinical pathological features and OS of NSCLC patients with ILA were retrospectively analyzed. Of the 765 patients included in the study, 101 (13.2%) cases experienced ILA at the time of NSCLC diagnosis. Multivariate analysis revealed that ILA was more likely to be detected in NSCLC patients who were age ≥60 (OR 2.404, p = 0.001), male gender (OR 2.476, p = 0.004), and EGFR wild-type (OR 2.035, p = 0.007). Additionally, according to the multivariate Cox model, the presence of ILA in NSCLC patients was significantly associated with a shorter OS period than those without ILA (751 days vs. 445 days, HR 0.6, p = 0.001). Following analysis, it was determined that OS in patients with usual interstitial pneumonia (UIP) was shorter than in those without UIP (HR 1.82, p = 0.037). ILA is a common comorbidity among newly diagnosed NSCLC patients. We found that patients with EGFR wild-type NSCLC were more likely to develop ILA. The presence of ILA, especially UIP, was significantly associated with poor NSCLC prognosis.

Clinical benefit of pembrolizumab in treatment of first line non-small cell lung cancer: a systematic review and meta-analysis of clinical characteristics

ObjectivePembrolizumab has become an integral first line therapeutic agent for non-small cell lung cancer (NSCLC), but its potential predictive role in clinical and molecular characteristics remains to be clarified. Accordingly, we performed a systematic review and meta-analysis to evaluate the clinical benefit of pembrolizumab in treatment of first line NSCLC and to select individuals with the greatest potential benefit from pembrolizumab therapy, in order to obtain a more accurate treatment of NSCLC in immunotherapy.MethodsMainstream oncology datasets and conferences were searched for randomized clinical trials (RCTs) published before August 2022. RCTs involved individuals with first line NSCLC treated with pembrolizumab monotherapy or in combination with chemotherapy. Two authors independently selected the studies, extracted data, and assessed the risk of bias. The basic characteristics of the included studies were recorded, along with 95 percent confidence intervals (CI) and hazard ratios (HR) for all patients and subgroups. The primary endpoint was overall survival (OS), and secondary endpoints was progression-free survival (PFS). Pooled treatment data were estimated using the inverse variance-weighted method.ResultsFive RCTs involving 2,877 individuals were included in the study. Pembrolizumab-based therapy significantly improved OS (HR 0.66; CI 95%, 0.55–0.79; p < 0.00001) and PFS (HR 0.60; CI 95%, 0.40–0.91; p = 0.02) compared with chemotherapy. OS was substantially enhanced in individuals aged < 65 years (HR 0.59; CI 95%, 0.42–0.82; p = 0.002), males (HR 0.74; CI 95%, 0.65–0.83; p < 0.00001), with a smoking history (HR 0.65; CI 95%, 0.52–0.82; p = 0.0003), with PD-L1 tumor proportion score (TPS) < 1% (HR 0.55; CI 95%, 0.41–0.73; p < 0.0001) and TPS ≥ 50% (HR 0.66; CI 95%, 0.56–0.76; p < 0.00001), but not in individuals aged ≥ 75 years (HR 0.82; CI 95%, 0.56–1.21; p = 0.32), females (HR 0.57; CI 95%, 0.31–1.06; p = 0.08), never smokers (HR 0.57; CI 95%, 0.18–1.80; p = 0.34), or with TPS 1–49% (HR 0.72; CI 95%, 0.52–1.01; p = 0.06). Pembrolizumab significantly prolonged OS in NSCLC patients, regardless of histology type (squamous or non-squamous NSCLC), performance status (PS) (0 or 1), and brain metastatic status (all p < 0.05). Subgroup analysis revealed that pembrolizumab combined with chemotherapy had more favorable HR values than pembrolizumab monotherapy in improving the OS of individuals with different clinical and molecular features.ConclusionPembrolizumab-based therapy is a valuable option for first line treating advanced or metastatic NSCLC. Age, sex, smoking history and PD-L1 expression status can be used to predict the clinical benefit of pembrolizumab. Cautiousness was needed when using pembrolizumab in NSCLC patients aged ≥ 75 years, females, never smokers, or in patients with TPS 1–49%. Furthermore, pembrolizumab in combination with chemotherapy may be a more effective treatment regimen.

Sodium-glucose cotransporter 2 (SGLT2) inhibitors and non-small cell lung cancer survival.

Sodium-glucose cotransporter 2 (SGLT2) inhibitors are a relatively new class of antidiabetic drugs with demonstrated renal and cardiovascular disease benefit. This study evaluates the role of SGLT2 inhibitors on the survival of non-small cell lung cancer (NSCLC) patients. We used National Surveillance, Epidemiology and End Results (SEER)-Medicare linked data. Twenty four thousand nine hundred fifteen NSCLC patients newly diagnosed between 2014 and 2017 with pre-exiting diabetes and aged 66 years or older were included and followed to the end of 2019. Information on SGLT2 inhibitors use was extracted from the Medicare Part D file. SGLT2 inhibitor use was associated with significantly reduced mortality risk after adjusting for potential confounders (HR = 0.68, 95% CI = 0.60-0.77) with stronger association for longer duration of use (HR = 0.54, 85% CI = 0.44-0.68). Further, we found that SGLT2 inhibitor use was associated with a significant reduced risk of mortality regardless of patients' demographic, tumour characteristics and cancer treatments. Our large SEER-Medicare linked data study indicates that SGLT2 inhibitors use was associated with improved overall survival of NSCLC patients with pre-existing diabetes. Further studies are needed to confirm our findings and elucidate the possible mechanisms behind the association.

The association of BTLA gene polymorphisms with non-small lung cancer risk in smokers and never-smokers.

Lung cancer is the predominant cause of death among cancer patients and non-small cell lung cancer (NSCLC) is the most common type. Cigarette smoking is the prevailing risk factor for NSCLC, nevertheless, this cancer is also diagnosed in never-smokers. B and T lymphocyte attenuator (BTLA) belongs to immunological checkpoints which are key regulatory molecules of the immune response. A growing body of evidence highlights the important role of BTLA in cancer. In our previous studies, we showed a significant association between BTLA gene variants and susceptibility to chronic lymphoblastic leukemia and renal cell carcinoma in the Polish population. The present study aimed to analyze the impact of BTLA polymorphic variants on the susceptibility to NSCLC and NSCLC patients' overall survival (OS). Using TaqMan probes we genotyped seven BTLA single-nucleotide polymorphisms (SNPs): rs2705511, rs1982809, rs9288952, rs9288953, rs1844089, rs11921669 and rs2633582 with the use of ViiA 7 Real-Time PCR System. We found that rs1982809 within BTLA is associated with NSCLC risk, where carriers of rs1982809G allele (AG+GG genotypes) were more frequent in patients compared to controls. In subgroup analyses, we also noticed that rs1982809G carriers are significantly overrepresented in never-smokers, but not in smokers compared to controls. Additionally, the global distribution of the haplotypes differed between the never-smokers and smokers, where haplotypes A G G C A, C G A C G, and C G A T G were more frequent in never-smoking patients. Furthermore, the presence rs1982809G (AG+GG genotypes) allele as well as the presence of rs9288953T allele (CT+TT genotypes) increased NSCLC risk in females' patients. After stratification by histological type, we noticed that rs1982809G and rs2705511C carriers were more frequent among adenocarcinoma patients. Moreover, rs1982809G and rs2705511C correlated with the more advanced stages of NSCLC (stage II and III), but not with stage IV. Furthermore, we showed that rs2705511 and rs1982809 significantly modified OS, while rs9288952 tend to be associated with patients' survival. Our results indicate that BTLA polymorphic variants may be considered low penetrating risk factors for NSCLC especially in never-smokers, and in females, and are associated with OS of NSCLC patients.

Abstract B010: Association between temporal muscle thickness and overall survival in non-small cell lung cancer patients with brain metastasis

Abstract Temporal muscle thickness (TMT) has recently been suggested as a novel biomarker of sarcopenia in head and neck malignancies. However, few studies have evaluated TMT as a prognostic marker in patients with brain metastasis. This study investigated the association of TMT with overall survival (OS) in non-small cell lung cancer (NSCLC) patients with brain metastasis. The records of all NSCLC patients with brain metastasis between 2009 and 2018 at St. Vincent’s Hospital were reviewed retrospectively. A total of 221 patients met our eligibility criteria. In the group with TMT thicker than the median, OS was longer than the group with TMT thinner than the median (240 days versus 139 days, p = 0.014). In multivariate analysis, the thicker TMT group had longer survival (HR 0.73 CI 0.56–0.96, p = 0.024). Male (HR 1.58 CI 1.19–2.09, p = 0.002) and older age (≥ 65 years) (HR 2.05 CI 1.53–2.74, p &amp;lt; 0.001) also showed statistical significance. We also performed subgroup analysis in older patients (≥ 65 years). In this subgroup of 107 patients, the thicker TMT group also showed longer OS than the thinner TMT group (209 days versus 82 days, p = 0.009). Our findings suggest that TMT can be a useful biomarker for OS in NSCLC patients with brain metastasis. Citation Format: Seung Ho Yang, Young Il Kim, Hyun Ho Kim, Byoung Yong Shim. Association between temporal muscle thickness and overall survival in non-small cell lung cancer patients with brain metastasis [abstract]. In: Proceedings of the AACR Special Conference: Aging and Cancer; 2022 Nov 17-20; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_1):Abstract nr B010.

Overweight and Obesity are Associated with Poorer Survival Among Patients with Advanced Non-Small Cell Lung Cancer Receiving Platinum-Based Chemotherapy

Most patients with non-small cell lung cancer (NSCLC) are diagnosed in advanced-stage disease and therefore have poor overall survival. It remains unclear whether nutritional status affects response rate and overall survival in NSCLC patients. This study aimed to evaluate the association of nutritional status with treatment response and overall survival in patients with advanced stage of NSCLC. Patients aged ≥18 years with stage II-IV NSCLC (January-June 2018) in a national cancer center in Indonesia were enrolled in this study. The patients were followed up for 2 years since NSCLC diagnosis was established. Clinical data including age, sex, histology of cancer, disease stage, cachexia, and weight status before chemotherapy were reviewed and analyzed. Logistic regression and Cox regression analyses were performed. A total of 174 patients (71% males, mean age = 58±9.4 years) was included. Complete response was found in <1% patients, partial response 41%, stable disease 33%, and progressive disease 25%. Median survival was 12 months (95% CI: 11-13 months). Mortality rate was 5.7 per 100 person-months. Poor survival was associated with being males (HR: 1.77, 95% CI: 1.15-2.72, P = 0.009), and overweight or obesity (HR 1.67, 95% CI: 1.04-2.69, P = 0.034). These associations were independent of sex, age, staging, histopathology, performance status and D-dimer level at baseline. Cachexia and BMI at baseline were not associated with treatment response. Males and having overweight or obesity are independently associated with lower survival in patients with advanced stage of NSCLC undergoing platinum-based chemotherapy.

The clinicopathological and prognostic significance of mTOR and p-mTOR expression in patients with non-small cell lung cancer: A meta-analysis.

The mammalian target of rapamycin (mTOR) has a crucial role in carcinogenesis, angiogenesis, cellular proliferation, and metastasis; however, its significance in non-small cell lung cancer (NSCLC) remains contentious. Consequently, this study aims to assess the clinicopathological and prognostic importance of mTOR/p-mTOR expression in NSCLC. Literature retrieval was undertaken by searching English databases PubMed, EMBASE, Web of Science, and Cochrane Library as well as Chinese databases CNKI, Wan Fang, and VIP for full-text publications that satisfied our eligibility criteria up to November 2021. STATA 12.0 was used to conduct statistical analysis (STATA Corporation, College Station, TX). This meta-analysis includes a total of 4683 patients from 28 primary publications. mTOR/p-mTOR expression was associated with sex (OR = 0.608, 95% CI: 0.442-0.836), lymph node metastasis (OR = 2.084, 95% CI: 1.437-3.182), and CEA (OR = 1.584, 95% CI: 1.135-2.209), but not with age, histological type, depth of tumor invasion, distant metastasis, TNM stage, differentiation degree, tumor size, or smoking. In addition, the expression of mTOR/p-mTOR is related to shorter overall survival in NSCLC patients (HR = 1.415, 95% CI: 1.051-1.905). Positive mTOR/p-mTOR expression was substantially correlated with unfavorable conditions on the sex, lymph node metastases, and CEA levels. mTOR/p-mTOR may indicate a bad prognosis for NSCLC. The current findings must be confirmed and changed by other high-quality research employing a multivariate analysis on bigger sample size.

COL1A1 as a Potential Prognostic Marker and Therapeutic Target in Non-small Cell Lung Cancer

Background: Nowadays, non-small cell lung cancer (NSCLC) is a common and highly fatal malignancy worldwide. Therefore, identifying the potential prognostic markers and therapeutic targets is urgent for patients. Objective: This study aimed at finding hub targets associated with NSCLC using multiple databases. Methods: Differentially expressed genes (DEGs) from Genome Expression Omnibus (GEO) cohorts were employed for the enrichment analyses of Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genome (KEGG) pathways. Candidate key genes, filtered from the topological parameter 'Degree' and validated using the Cancer Genome Atlas (TCGA) cohort, were analyzed for their association with clinicopathological features and prognosis of NSCLC. Meanwhile, immunohistochemical cohort analyses and biological verification were further evaluated. Results: A total of 146 DEGs were identified following data preprocessing, and a protein-protein interaction (PPI) systematic network was constructed based on them. The top ten candidate core genes were further extracted from the above PPI network by using 'Degree' value, among which COL1A1 was shown to associate with overall survival (OS) of NSCLC as determined by using the Kaplan-Meier analysis (p=0.028), and could serve as an independent prognostic factor for OS in NSCLC patients (HR, 0.814; 95% CI, 0.665-0.996; p=0.046). We then analyzed the clinical stages, PPI, mutations, potential biological functions, and immune regulations of COL1A1 in NSCLC patients using multiple bioinformatics tools, including GEPIA, GeneMANIA, cBioPortal, GESA, and TISIDB. Finally, we further experimentally validated the overexpression of COL1A1 in NSCLC samples and found that inhibition of COL1A1 expression moderately sensitized NSCLC cells to cisplatin. Conclusion: Thus, our results showed that COL1A1 may serve as a potential prognostic marker and therapeutic target in NSCLC.

Prognostic value of 18F-FDG PET/CT radiomic model based on primary tumor in patients with non-small cell lung cancer: A large single-center cohort study.

In the present study, we aimed to determine the prognostic value of the 18F-FDG PET/CT-based radiomics model when predicting progression-free survival (PFS) and overall survival (OS) in patients with non-small cell lung cancer (NSCLC). A total of 368 NSCLC patients who underwent 18F-FDG PET/CT before treatment were randomly assigned to the training (n = 257) and validation (n = 111) cohorts. Radiomics signatures from PET and CT images were obtained using LIFEx software, and then clinical and complex models were constructed and validated by selecting optimal parameters based on PFS and OS to construct radiomics signatures. In the training cohort, the C-index of the clinical model for predicting PFS and OS in NSCLC patients was 0.748 and 0.834, respectively, and the AUC values ​​were 0.758 and 0.846, respectively. The C-index of the complex model for predicting PFS and OS was 0.775 and 0.881, respectively, and the AUC values ​​were 0.780 and 0.891, respectively. The C-index of the clinical model for predicting PFS and OS in the validation group was 0.729 and 0.832, respectively, and the AUC values ​​were 0.776 and 0.850, respectively. The C-index of the complex model for predicting PFS and OS was 0.755 and 0.867, respectively, and the AUC values ​​were 0.791 and 0.874, respectively. Moreover, decision curve analysis showed that the complex model had a higher net benefit than the clinical model. 18F-FDG PET/CT radiomics before treatment could predict PFS and OS in NSCLC patients, and the predictive power was higher when combined with clinical factors.

Bioinformatics identification of miR-514b-5p promotes NSCLC progression and induces PI3K/AKT and p38 pathways by targeting small glutamine-rich tetratricopeptide repeat-containing protein beta.

Lung cancer is the most aggressive cancer with the highest mortality and incidence rates worldwide. MicroRNAs have been identified as potential targets for non-small cell lung cancer (NSCLC) treatment. However, the modulatory role of miR-514b-5p in NSCLC progression is little known. In the present study, miRNA expression datasets for NSCLC were downloaded from the Cancer Genome Atlas and Gene Ontology Omnibus databases. Gene expression was assessed using a quantitative real-time PCR, and western blot analysis and immunohistochemical staining was used to determine protein expression. Gain and loss of function experiments were performed to investigate the impact of miR-514b-5p and small glutamine-rich tetratricopeptide repeat-containing protein beta (SGTB) on cell proliferation and apoptosis. RNA immunoprecipitation and dual-luciferase assays were performed to analyse the target gene of miR-514b-5p. The biological roles of miR-514b-5p were lastly evaluated using nude mouse tumorigenicity assays in vivo. We found that miR-514b-5p was dramatically increased in NSCLC tissues and higher miR-514b-5p expression was associated with poorer overall survival in NSCLC patients. Furthermore, overexpression of miR-514b-5p promoted NSCLC cell growth and suppressed apoptosis by inducing the activation of the phosphatidylinositol-3-kinase (PI3K)/AKT and p38 signalling pathways. Mechanistically, dual-luciferase and the RNA immunoprecipitation results highlighted that SGTB was a target gene of miR-514b-5p. Moreover, overexpression of SGTB reduced cell division and promoted apoptosis in vitro through blocking the PI3K/AKT and p38 signalling pathways. Our findings indicated that miR-514b-5p contributes to carcinoma progression in NSCLC via the PI3K/AKT and p38 signalling pathways by targeting SGTB and this could be a promising diagnostic and therapeutic target for the treatment of NSCLC.

Use of Advanced Flexible Modeling Approaches for Survival Extrapolation from Early Follow-up Data in two Nivolumab Trials in Advanced NSCLC with Extended Follow-up.

Immuno-oncology (IO) therapies are often associated with delayed responses that are deep and durable, manifesting as long-term survival benefits in patients with metastatic cancer. Complex hazard functions arising from IO treatments may limit the accuracy of extrapolations from standard parametric models (SPMs). We evaluated the ability of flexible parametric models (FPMs) to improve survival extrapolations using data from 2 trials involving patients with non-small-cell lung cancer (NSCLC). Our analyses used consecutive database locks (DBLs) at 2-, 3-, and 5-y minimum follow-up from trials evaluating nivolumab versus docetaxel in patients with pretreated metastatic squamous (CheckMate-017) and nonsquamous (CheckMate-057) NSCLC. For each DBL, SPMs, as well as 3 FPMs-landmark response models (LRMs), mixture cure models (MCMs), and Bayesian multiparameter evidence synthesis (B-MPES)-were estimated on nivolumab overall survival (OS). The performance of each parametric model was assessed by comparing milestone restricted mean survival times (RMSTs) and survival probabilities with results obtained from externally validated SPMs. For the 2- and 3-y DBLs of both trials, all models tended to underestimate 5-y OS. Predictions from nonvalidated SPMs fitted to the 2-y DBLs were highly unreliable, whereas extrapolations from FPMs were much more consistent between models fitted to successive DBLs. For CheckMate-017, in which an apparent survival plateau emerges in the 3-y DBL, MCMs fitted to this DBL estimated 5-y OS most accurately (11.6% v. 12.3% observed), and long-term predictions were similar to those from the 5-y validated SPM (20-y RMST: 30.2 v. 30.5 mo). For CheckMate-057, where there is no clear evidence of a survival plateau in the early DBLs, only B-MPES was able to accurately predict 5-y OS (14.1% v. 14.0% observed [3-y DBL]). We demonstrate that the use of FPMs for modeling OS in NSCLC patients from early follow-up data can yield accurate estimates for RMST observed with longer follow-up and provide similar long-term extrapolations to externally validated SPMs based on later data cuts. B-MPES generated reasonable predictions even when fitted to the 2-y DBLs of the studies, whereas MCMs were more reliant on longer-term data to estimate a plateau and therefore performed better from 3 y. Generally, LRM extrapolations were less reliable than those from alternative FPMs and validated SPMs but remained superior to nonvalidated SPMs. Our work demonstrates the potential benefits of using advanced parametric models that incorporate external data sources, such as B-MPES and MCMs, to allow for accurate evaluation of treatment clinical and cost-effectiveness from trial data with limited follow-up. Flexible advanced parametric modeling methods can provide improved survival extrapolations for immuno-oncology cost-effectiveness in health technology assessments from early clinical trial data that better anticipate extended follow-up.Advantages include leveraging additional observable trial data, the systematic integration of external data, and more detailed modeling of underlying processes.Bayesian multiparameter evidence synthesis performed particularly well, with well-matched external data.Mixture cure models also performed well but may require relatively longer follow-up to identify an emergent plateau, depending on the specific setting.Landmark response models offered marginal benefits in this scenario and may require greater numbers in each response group and/or increased follow-up to support improved extrapolation within each subgroup.

Prognostic value of the modified systemic inflammation score in non-small-cell lung cancer with brain metastasis

BackgroundBrain metastases (BM) from non-small-cell lung cancer (NSCLC) is the most common brain malignancy. Systemic inflammation biomarkers have recently been evaluated as prognosis indicators in several tumors. The combination of these markers has not been evaluated in NSCLC with BM yet. Here, we explored the predictive value of pretreatment inflammatory biomarkers and established a novel, clinically applicable prognostic index for NSCLC patients with BM.MethodsA retrospective investigation of 951 NSCLC patients newly diagnosed with BM at Sun Yat-sen University Cancer Center was conducted. We randomly divided patients into a training cohort (n = 674) or validation cohort (n = 277). Receiver operating characteristic (ROC) curve analysis was carried out to obtain the optimal cut-off values of pretreatment systemic inflammatory indexes. The associations between serum biomarkers and overall survival (OS) were analyzed by Kaplan–Meier curves and Cox proportional models. The resulting prediction model has been externally verified through the validation cohort.ResultsThe optimal cut-off value of the neutrophil–lymphocyte ratio (NLR) in predicting OS was 4.71, while the clinical standard of 40 mg/L was chosen as the optimal cut-off value of albumin. Univariate and multivariate analyses revealed that patients receiving local treatment, chemotherapy, a NLR < 4.71 and albumin ≥ 40 mg/l independently predicted improved survival. We combined the two inflammatory indexes (NLR and albumin level) to establish the modified systemic inflammation score (mSIS) which divides patients into low risk, medium risk or high-risk groups. The 1-year OS rates of three groups were 59.7%, 40.5% and 29.4%, respectively in the training cohort. The same result was verified in the validation cohort with the 1-year OS rates 69.7%, 47.0% and 7.7%, respectively. The mSIS exhibited better discrimination power than the American Joint Committee on Cancer’s (AJCC) 7th T + N staging system in the training cohort (Harrell’s concordance index (C-index): 0.744 vs 0.502, P < 0.05), and the discrimination was also superior to that of AJCC’s 7th T + N staging system in the validation cohort (C-index: 0.724 vs 0.527, P < 0.05). The 1-year and 2-year OS rates of the AUC also exhibited superior survival predictive ability to that of the AJCC’s 7th T + N staging system in NSCLC patients with BM.ConclusionThe pretreatment mSIS may be an independent prognostic factor for OS in NSCLC patients with BM and warrants further research.

Lymph node dissections and survival in sublobar resection of non-small cell lung cancer ≤ 20mm.

A randomized trial of lobectomy versus segmentectomy for small-sized (≤ 20mm) non-small cell lung cancer (NSCLC) showed that patients who had undergone segmentectomy had a significantly longer overall survival (OS) than those who had lobectomy. More attention is needed regarding the required extent of thoracic lymphadenectomy in patients with small-sized NSCLC who undergo sublobar resection. The National Cancer Database was queried for patients with clinically node-negative NSCLC ≤ 20mm who had undergone sublobar resection between 2004 and 2017. OS of NSCLC patients by the number of lymph node dissections (LNDs) was analyzed using log-rank tests and Cox proportional hazards model. The cutoff value of the LNDs was set to 10 according to the Commission on Cancer's recommendation. This study included 4379 segmentectomy and 23,138 wedge resection cases. The sequential improvement in the HRs by the number of LNDs was evident, and the HR was the lowest if the number of LNDs exceeded 10. Patients with ≤ 9 LNDs had a significantly shorter OS than those with ≥ 10 LNDs (hazard ratio [HR] 1.50, 95% confidence interval [CI] 1.40-1.61, P < 0.0001). Multivariable analysis revealed that performing ≤ 9 LNDs was an independent factor for predicting OS (HR for death: 1.34, 95% CI 1.24-1.44, P < 0.0001). These results remained significant in subgroup analyses by the type of sublobar resection (segmentectomy, wedge resection). Performing ≥ 10 LNDs has a prognostic role in patients with small-sized NSCLC even if the resection is sublobar.

Association between Temporal Muscle Thickness and Overall Survival in Non-Small Cell Lung Cancer Patients with Brain Metastasis.

Temporal muscle thickness (TMT) has recently been suggested as a novel biomarker of sarcopenia in head and neck malignancies. However, few studies have evaluated TMT as a prognostic marker in patients with brain metastasis. This study investigated the association of TMT with overall survival (OS) in non-small cell lung cancer (NSCLC) patients with brain metastasis. The records of all NSCLC patients with brain metastasis between 2009 and 2018 at St. Vincent’s Hospital were reviewed retrospectively. A total of 221 patients met our eligibility criteria. In the group with TMT thicker than the median, OS was longer than the group with TMT thinner than the median (240 days versus 139 days, p = 0.014). In multivariate analysis, the thicker TMT group had longer survival (HR 0.73 CI 0.56–0.96, p = 0.024). Male (HR 1.58 CI 1.19–2.09, p = 0.002) and older age (≥65 years) (HR 2.05 CI 1.53–2.74, p < 0.001) also showed statistical significance. We also performed subgroup analysis in older patients (≥65 years). In this subgroup of 107 patients, the thicker TMT group also showed longer OS than the thinner TMT group (209 days versus 82 days, p = 0.009). Our findings suggest that TMT can be a useful biomarker for OS in NSCLC patients with brain metastasis.

Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer

BackgroundLung cancer is a high-incidence cancer, and it is also the most common cause of cancer death worldwide. 80–85% of lung cancer cases can be classified as non-small cell lung cancer (NSCLC).MethodsNSCLC transcriptome data and clinical information were downloaded from the TCGA database and GEO database. Firstly, we analyzed and identified the differentially expressed genes (DEGs) between non-metastasis group and metastasis group of NSCLC in the TCGA database, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were consulted to explore the functions of the DEGs. Thereafter, univariate Cox regression and LASSO Cox regression algorithms were applied to identify prognostic metastasis-related signature, followed by the construction of the risk score model and nomogram for predicting the survival of NSCLC patients. GSEA analyzed that differentially expressed gene-related signaling pathways in the high-risk group and the low-risk group. The survival of NSCLC patients was analyzed by the Kaplan–Meier method. ROC curve was plotted to evaluate the accuracy of the model. Finally, the GEO database was further applied to verify the metastasis‑related prognostic signature.ResultsIn total, 2058 DEGs were identified. GO functions and KEGG pathways analysis results showed that the DEGs mainly concentrated in epidermis development, skin development, and the pathway of Neuro active ligand -receptor interaction in cancer. A six-gene metastasis-related risk signature including C1QL2, FLNC, LUZP2, PRSS3, SPIC, and GRAMD1B was constructed to predict the overall survival of NSCLC patients. The reliability of the gene signature was verified in GSE13213. The NSCLC patients were grouped into low-risk and high-risk groups based on the median value of risk scores. And low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for NSCLC.ConclusionOur study identified 6 metastasis biomarkers in the NSCLC. The biomarkers may contribute to individual risk estimation, survival prognosis.

HMGA1 gene expression level in cancer tissue and blood samples of non-small cell lung cancer (NSCLC) patients: preliminary report

The study aimed to assess the HMGA1 gene expression level in NSCLC patients and to evaluate its association with selected clinicopathological features and overall survival of patients. The expression of the HMGA1, coding non-histone transcription regulator HMGA1, was previously proved to correlate with the ability of cancer cells to metastasize the advancement of the disease. The prognostic value of the HMGA1 expression level was demonstrated in some neoplasms, e.g., pancreatic, gastric, endometrial, hepatocellular cancer, but the knowledge about its role in non-small cell lung cancer (NSCLC) is still limited. Thus, the HMGA1 expression level was evaluated by real-time PCR method in postoperative tumor tissue and blood samples collected at the time of diagnosis, 100 days and 1 year after surgery from 47 NSCLC patients. Mean HMGA1 expression level in blood decreased systematically from the time of cancer diagnosis to 1 year after surgery. The blood HMGA1 expression level 1 year after surgery was associated with the tobacco smoking status of patients (p= 0.0230). Patients with high blood HMGA1 expression levels measured 100 days after surgery tend to have worse overall survival than those with low expression levels (p= 0.1197). Tumor HMGA1 expression level was associated with neither features nor the overall survival of NSCLC patients. Moreover, no correlation between HMGA1 expression level measured in tumor tissue and blood samples was stated. Blood HMGA1 mRNA level could be a promising factor in the prognostication of non-small cell lung cancer patients.

Comparison of nomogram and machine‐learning methods for predicting the survival of non‐small cell lung cancer patients

AbstractBackgroundMost patients with advanced non‐small cell lung cancer (NSCLC) have a poor prognosis. Predicting overall survival using clinical data would benefit cancer patients by allowing providers to design an optimum treatment plan. We compared the performance of nomograms with machine‐learning models at predicting the overall survival of NSCLC patients. This comparison benefits the development and selection of models during the clinical decision‐making process for NSCLC patients.MethodsMultiple machine‐learning models were used in a retrospective cohort of 6586 patients. First, we modeled and validated a nomogram to predict the overall survival of NSCLC patients. Subsequently, five machine‐learning models (logistic regression, random forest, XGBoost, decision tree, and light gradient boosting machine) were used to predict survival status. Next, we evaluated the performance of the models. Finally, the machine‐learning model with the highest accuracy was chosen for comparison with the nomogram at predicting survival status by observing a novel performance measure: time‐dependent prediction accuracy.ResultsAmong the five machine‐learning models, the accuracy of random forest model outperformed the others. Compared with the nomogram for time‐dependent prediction accuracy with a follow‐up time ranging from 12 to 60 months, the prediction accuracies of both the nomogram and machine‐learning models changed as time varied. The nomogram reached a maximum prediction accuracy of 0.85 in the 60th month, and the random forest algorithm reached a maximum prediction accuracy of 0.74 in the 13th month.ConclusionsOverall, the nomogram provided more reliable prognostic assessments of NSCLC patients than machine‐learning models over our observation period. Although machine‐learning methods have been widely adopted for predicting clinical prognoses in recent studies, the conventional nomogram was competitive. In real clinical applications, a comprehensive model that combines these two methods may demonstrate superior capabilities.

The identification of hub biomarkers and pathways in lung cancer and prognostic evaluation.

BackgroundLung cancer is the most frequently diagnosed malignant tumor and the highest mortality worldwide, and can be divided into two differential histologic subtypes, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). However, there are significant differences in diagnosis and prognosis between NSCLC and SCLC. We aimed to identify hub differentially expressed genes (DEGs) and pathways for diagnostic and prognostic prediction in NSCLC and SCLC.MethodsThree expression profiles (GSE43346, GSE40275 and GSE18842) were obtained through GEO2R tools from Gene Expression Omnibus (GEO) database. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to investigate functional enrichment of the DEGs. The protein–protein interaction network was constructed by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. Kaplan-Meier analysis was performed using Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis (GEPIA).ResultsWe have identified 84 overlap DEGs that may play an important role in SCLC & NSCLC. However, we also found some genes were only significantly differential expressed in SCLC or NSCLC. There were 87 DEGs unique to SCLC tissues and 28 DEGs unique to NSCLC ones. Functional analysis results indicated that these DEGs had different biological functions and were significantly enriched in different pathways. Hub DEGs were identified via protein-protein interaction network and cross-validated using Kaplan-Meier plotter and GEPIA. The 14 hub DEGs were highly correlated with the overall survival of NSCLC. Kyoto Encyclopedia of Genes and Genome (KEGG) re-analysis of 14 hub DEGs showed that RRM2, CHEK1 and SERPINB5 enriched in the p53 signaling pathway, RRM2 and TYMS enriched in pyrimidine metabolism pathway maybe play a key role in SCLC&NSCLC and were significantly related to overall survival in patients with NSCLC.ConclusionsRRM2, CHEK1, TYMS and SERPINB5, which are mainly enriched in the p53 signaling pathway and pyrimidine metabolism pathway, were significantly associated with the overall survival of NSCLC patients. These genes could serve as potential prognostic markers in NSCLC and therapeutic target in lung cancer for personalized oncology.