Does the risk of hospitalization among elderly patients receiving chemotherapy for metastatic, nonsquamous, non–small cell lung cancer vary by chemotherapy regimen?In a cohort study of 2182 propensity-matched Medicare beneficiaries with non–small cell lung cancer, the 30-day hospitalization risk was 5% lower for patients receiving carboplatin with pemetrexed (21%) than for those receiving carboplatin with paclitaxel (26%), a statistically significant difference.When multiple standard-of-care chemotherapy regimens are available in a specific treatment setting, measures of regimen-specific hospitalization risk may be relevant for treatment selection. Chemotherapy is a mainstay treatment of metastatic non–small cell lung cancer. However, little is known about the comparative risk of hospitalization associated with commonly used chemotherapy regimens.To evaluate the real-world association of specific lung cancer chemotherapy regimens with measures of acute hospital care (primary objective) and survival (secondary objective).Retrospective, propensity-matched, cohort study using the Surveillance, Epidemiology, and End Results–Medicare linked data for cancer diagnoses between 2008 and 2013, with follow-up through 2014. Patients were Medicare beneficiaries 66 years of age or older receiving initial chemotherapy for treatment of stage IV, nonsquamous, non–small cell lung cancer. Analyses were performed between September 2017 and April 2018.Receipt of chemotherapy with carboplatin-pemetrexed or carboplatin-paclitaxel, with or without concurrent bevacizumab.The primary outcome was risk of hospitalization within 30 days of chemotherapy initiation. Secondary measures included cumulative 90-day hospitalizations, 90-day mean hospital-free survival time, and overall survival.P = .003). The 90-day cumulative hospitalizations did not differ significantly between the 2 treatment groups; however, the 90-day mean hospital-free survival time was improved for patients receiving carboplatin-pemetrexed . The median survival time was 9.0 months  with carboplatin-pemetrexed therapy vs 7.6 months  with carboplatin-paclitaxel therapy (P = .005). Stratified analyses showed no association of bevacizumab use with hospitalization risk or survival when combined with either chemotherapy regimen.Of the 3310 eligible patients, 1823 received carboplatin-pemetrexed, and 1487 received carboplatin-paclitaxel. The median age at diagnosis was 73 years , 1784 patients (53.9%) were men, and 2909 patients (87.9%) were non-Hispanic white. In total, 2182 patients were included in the propensity-matched analysis. The 30-day hospitalization risk was 20.7%  among patients receiving carboplatin-pemetrexed vs 26.0%  among patients receiving carboplatin-paclitaxel  program data to assess hospitalization risk and survival among US patients receiving carboplatin-paclitaxel or carboplatin-pemetrexed for treatment of metastatic, nonsquamous, non–small cell lung cancer. The outcomes of palliative chemotherapy for cancer are commonly assessed in terms of overall survival. The use of survival as a summary outcome measure is necessary but not sufficient for evaluating and describing the effectiveness of palliative-intent cancer therapy because patients value not only survival but also quality of life and avoiding adverse effects. Because clinical trials are typically conducted in populations that are younger and healthier than those encountered in practice, observational data sources—including medical records, tumor registries, administrative claims, and, in particular, the Surveillance, Epidemiology, and End Results (SEER)–Medicare linked database—are an important source of information about the relative benefits and harms of alternative chemotherapy regimens.1 and there is growing acknowledgment that hospitalization itself can be an adverse effect of cancer treatment. Hospitalization of patients with cancer is common,3 variable across regions and practices,5 and potentially avoidable.6 Furthermore, prior studies have shown that chemotherapy regimen selection can be associated with hospitalization risk.8However, claims-based measures of drug toxicity and adverse events are underdeveloped. “Hospitalization indicated” is a general criterion for classifying chemotherapy-related adverse events as grade 3 (severe) using the Common Terminology Criteria for Adverse Events of the National Cancer Institute,The present study compares the hospitalization and survival rates among Medicare beneficiaries receiving the 2 most common cytotoxic chemotherapy regimens for metastatic, nonsquamous, non–small cell lung cancer: carboplatin with paclitaxel or carboplatin with pemetrexed. This analysis evaluates whether the choice of chemotherapy regimen was associated with hospitalization as captured by 3 alternative metrics. We secondarily evaluated survival among these patients.9 The SEER program data cover approximately 28% of the US population. Since 1991, the National Cancer Institute has linked SEER data with Medicare administrative data for more than 94% of SEER registry patients 65 years of age or older.10 The linked Medicare data used in this analysis included deidentified fee-for-service claims and event records from inpatient, outpatient, carrier, durable medical equipment, and Part D event files. The research question, study design, and research outcomes (primary and secondary) were all prespecified in the research protocol, as recommended in the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) task force report on good research practices.11 The study was approved by the Dartmouth College institutional review board, which also waived the need for obtaining informed patient consent because the data were deidentified, and was performed under a data use agreement with the National Cancer Institute.We performed a retrospective cohort study of Medicare beneficiaries receiving first-line chemotherapy for metastatic, nonsquamous, non–small cell lung cancer with either carboplatin-paclitaxel or carboplatin-pemetrexed (either regimen with or without bevacizumab). The data source for this study was the SEER-Medicare linked data for patients who received a diagnosis of lung cancer between 2008 and 2013 . The SEER program of the National Cancer Institute collects uniformly reported data from population-based cancer registries, including cancer site, stage, diagnosis date, and other clinical variables.AJCC Cancer Staging Manual, 6th edition). We subsequently excluded patients with a squamous histologic subtype to focus our analysis on patients who were candidates to receive the chemotherapy regimens of interest. We further restricted our sample to patients who were 66 years of age or older at the time of diagnosis and who had continuous enrollment in fee-for-service Medicare (Parts A and B) in the 12 months before and 6 months after cancer diagnosis (or until death) to ensure adequate measurement of baseline comorbidities and posttreatment outcomes. Additional exclusions included prior cancer diagnosis  or concurrent radiation therapy (defined as receipt of radiation therapy on the same day as chemotherapy initiation). The final analytic cohort was restricted to patients receiving first-line treatment with either carboplatin-pemetrexed or carboplatin-paclitaxel (with or without concurrent bevacizumab).We identified all patients in the SEER-Medicare linked data who had received a new diagnosis of metastatic non–small cell lung cancer between 2008 and 2013  as captured in Medicare claims from the outpatient, carrier, durable medical equipment, or Part D event files within 90 days of the first day of the diagnosis month. We then identified all chemotherapy claims from within 7 days of the index date and assigned the chemotherapy regimen based on all chemotherapy agents received within this period.12; incorporating hospitalizations into the restricted mean survival time provides a framework for evaluating both death and the semicompeting risk of hospitalization within a single measure. We assessed overall survival at 90 days and at the median survival time.The prespecified primary outcome measure was 30-day hospitalization risk, defined as the risk of acute inpatient hospital admission within 30 days of the first chemotherapy treatment. Additional hospitalization measures were 90-day cumulative hospitalizations  and 90-day mean hospital-free survival time . The restricted mean survival time has been endorsed as a nonparametric (model-free) measure that is relatively intuitivet tests for continuous variables and χ2 tests for categorical variables. Given observed imbalances in some of these characteristics, we then used propensity score matching to balance the observable baseline characteristics across the cohorts.We performed descriptive analyses of patient clinical and sociodemographic characteristics, comparing the 2 patient cohorts (patients receiving carboplatin-pemetrexed vs carboplatin-paclitaxel) using 2-sided 14International Classification of Diseases for Oncology, Third Revision (ICD-O-3) site and histologic codes, receipt of radiation treatment within the prior 30 days, and hospitalization within the prior 180 days. Additional variables included in the propensity model were area-level measures taken from the SEER data, including measures of census tract poverty, urban-rural residence classification, and US geographic region of residence as well as a claims-based indicator for chemotherapy treatment at hospital-based vs office-based practice sites. Further details of the propensity model are presented in 17 The postmatch covariate balance was verified by calculating the standardized difference of mean values for each factor in the 2 groups.The propensity model evaluated the probability that a patient would receive carboplatin-pemetrexed (vs carboplatin-paclitaxel). Demographic and clinicopathologic variables included in the propensity model were age group, sex, race/ethnicity, marital status, year of cancer diagnosis, Charlson Comorbidity Index ,P values. We also report descriptive characteristics of hospitalizations occurring within 90 days of chemotherapy initiation stratified by chemotherapy regimen, including information about common admission diagnoses and hospital length of stay. We assessed for differences in the mean length of stay with a 2-sample Wilcoxon test. Admission diagnoses were identified using International Classification of Diseases, Ninth Revision, Clinical Modification codes on inpatient hospital claims from the inpatient file, and diagnosis codes were grouped into descriptive categories by one of us (G.A.B).After propensity score matching, we assessed the study outcomes of 30-day hospitalization risk (primary outcome), 90-day cumulative hospitalizations, and 90-day mean hospital-free survival time. For overall survival, we report both the 90-day overall survival probability and the median overall survival time . Main outcome measures are reported with 95% CIs and Finally, we performed additional analyses to evaluate the association of bevacizumab with 30-day hospitalization risk and overall survival. These stratified analyses were performed within the prematch patient cohort and are reported separately for each of the studied chemotherapy regimens. For the analysis of 30-day hospitalization risk, we fitted multivariable logistic regression models in which the main exposure of interest was receipt of bevacizumab. For the analysis of overall survival, we used Cox proportional hazards models to evaluate the association of bevacizumab use with mortality. Each of these analyses was adjusted for the same covariates included in the previously described propensity model.P ≤ .05 was considered statistically significant. Analyses were conducted between September 2017 and April 2018 using Stata, version 15 (StataCorp); R, version 3.1.1 (packages survRM218 and surv2sampleComp19); and SAS, version 9.4 (SAS Institute Inc).A 2-sided In total, 3310 patients with stage IV, nonsquamous, non–small cell lung cancer met study inclusion criteria, including 1487 patients receiving carboplatin-paclitaxel and 1823 patients receiving carboplatin-pemetrexed. Details of the cohort selection are shown in ICD-O-3 site code (P < .001]). Use of bevacizumab was considered to be nonindependent from the choice of chemotherapy regimen , and bevacizumab use was not included in the propensity model.Prior to propensity score matching, patients receiving carboplatin-pemetrexed and patients receiving carboplatin-paclitaxel were similar by age, sex, race/ethnicity, marital status, Charlson Comorbidity Index, and ite code . Howeverite code . BevacizP = .003). There were also fewer cumulative hospitalizations at 90 days among patients receiving carboplatin-pemetrexed vs carboplatin-paclitaxel ; however, the difference was not statistically significant (P = .11). The 90-day mean hospital-free survival time was significantly longer among patients receiving carboplatin-pemetrexed than among patients receiving carboplatin-paclitaxel . Findings are summarized in The 30-day hospitalization risk was 20.7%  among patients receiving carboplatin-pemetrexed and 26.0%  among patients receiving carboplatin-paclitaxel, corresponding to a 5.3% lower risk of hospitalization with carboplatin-pemetrexed (P = .13). The most common reasons for admission were respiratory tract complications, fever or infection, and cancer (lung cancer or metastasis); admission diagnosis codes from these categories accounted for 40% of all hospitalizations. Category distributions of admission diagnosis codes were similar for both chemotherapy regimens, including similar numbers of hospitalizations for fever or infection  and for neutropenia .The mean hospital length of stay did not differ between patients receiving carboplatin-pemetrexed and patients receiving carboplatin-paclitaxel ; Both the 90-day survival probability and the median overall survival were modestly but significantly improved for patients receiving carboplatin-pemetrexed compared with patients receiving carboplatin-paclitaxel . The medThere was no evidence of an association between bevacizumab use and hospitalization among patients receiving either carboplatin-pemetrexed  or carboplatin-paclitaxel . In addition, bevacizumab use was not associated with differences in overall survival among patients receiving either carboplatin-pemetrexed  or carboplatin-paclitaxel .P = .005). These findings are notable because carboplatin-pemetrexed and carboplatin-paclitaxel are the 2 most commonly used cytotoxic chemotherapy regimens for advanced lung cancer, and, to our knowledge, there are no large prospective studies directly comparing these regimens. Our main analysis includes 2182 patients with nonsquamous non–small cell lung cancer with a median age of 73 years, whereas existing prospective studies of each of these regimens are skewed toward younger, more pathologically heterogeneous patient populations.20In this propensity-matched analysis of patients with stage IV, nonsquamous, non–small cell lung cancer, we found that first-line use of carboplatin-pemetrexed compared with carboplatin-paclitaxel was associated with a lower risk of hospitalization and improved survival. We reported a 5.3% reduction in the 30-day hospitalization risk among patients receiving carboplatin-pemetrexed, corresponding to a 20% reduction in hospitalizations in the 30 days following chemotherapy initiation. Treatment with carboplatin-pemetrexed was also associated with significant improvement in the median survival, by 1.4 months –positive metastatic lung cancer; however, more than half of patients in this relatively favorable subgroup experience progression within a year or less.21 Platinum-based doublets are the recommended second-line therapy after progression following first-line immunotherapy. For patients with PD-L1–negative lung cancer, combination chemoimmunotherapy with a platinum-based doublet is an emerging standard of care.22 In either setting, platinum-based doublet chemotherapy will continue to play an important role for most patients with metastatic lung cancer, highlighting the ongoing relevance of the findings reported here.Although the first-line treatment of advanced non–small cell lung cancer is in rapid evolution today,23 showed apparent equivalence of 4 chemotherapy doublets in the treatment of advanced non–small cell lung cancer, with a median survival of approximately 8 months for all regimens. Carboplatin-paclitaxel was the only noncisplatin combination evaluated, and subsequent widespread adoption of carboplatin-paclitaxel was based in part on its favorable toxicity profile. Pemetrexed was not approved for treatment of lung cancer until 2008, when Scagliotti and colleagues reported that the combination of cisplatin and pemetrexed appeared to offer improved survival in comparison with cisplatin and gemcitabine, although only for the nonsquamous histologic subgroup.25 Cisplatin remains infrequently used for Medicare patients26 owing to toxicity and patient comorbidity, and a subsequent study27 supported the use of pemetrexed with carboplatin instead of cisplatin. In this way, carboplatin-paclitaxel and carboplatin-pemetrexed became the 2 preferred chemotherapy regimens for nonsquamous non–small cell lung cancer without having been directly compared with each other in a large prospective study. The PointBreak study28 (n = 939) compared these regimens in the context of universal bevacizumab use, and the PRONOUNCE trial29 (n = 361) compared carboplatin-pemetrexed with carboplatin-paclitaxel-bevacizumab, with both studies reporting null results for their primary outcomes. In addition, the various maintenance strategies within these studies and the differences in the use of bevacizumab have left a number of unresolved questions about the comparative efficacy of these regimens.To place our findings in context, it is helpful to review how carboplatin-pemetrexed and carboplatin-paclitaxel became the predominant cytotoxic chemotherapy regimens for treatment of metastatic, nonsquamous, non–small cell lung cancer. In 2002, a study from the Eastern Cooperative Oncology GroupA key objective of the present study was to investigate new outcome measures of acute hospitalization applicable to patients receiving chemotherapy. We chose 30-day hospitalization risk as the primary outcome because the period immediately after chemotherapy initiation is known to be high risk for hospital admission and because short-term outcome measures are less likely to be affected by the competing risk of mortality. Our study confirms a startlingly high 30-day hospitalization risk among patients with lung cancer initiating chemotherapy . Another measure evaluated here, the 90-day mean hospital-free survival time, provides insight into the competing risks of hospitalization and death. We contend that these measures, readily extractable from claims data, can serve as valuable proxies for adverse events during chemotherapy. A third hospitalization measure, 90-day cumulative hospitalizations, showed no difference between patients receiving either of the 2 chemotherapy regimens. This is likely because improved survival among patients receiving carboplatin-pemetrexed translated into greater time at risk of hospitalization, leading to a similar number of cumulative hospitalizations with improved hospital-free survival time.30 We did not perform a formal cost-effectiveness analysis; however, any anticipated reduction in hospitalizations is very unlikely to offset the incremental cost of substituting pemetrexed for paclitaxel. Still, patients may derive substantial quality-of-life benefit from avoiding hospitalization.Overall, we found that carboplatin-pemetrexed therapy was associated with a lower risk of hospitalization and superior survival outcomes compared with carboplatin-paclitaxel therapy—a clear win for carboplatin-pemetrexed therapy from the effectiveness perspective. The relative economic value of these treatments is less clear-cut. Using the average sales price from the fourth quarter of 2017, we found that the drug cost is approximately $6000 for a 3-week cycle of carboplatin-pemetrexed and less than $50 for a 3-week cycle of carboplatin-paclitaxel. Use of bevacizumab at standard doses adds more than $8000 per treatment cycle with either regimen.31 and retrospective analyses have confirmed improved survival with bevacizumab use.33 However, our finding of no survival advantage with the addition of bevacizumab to chemotherapy is consistent with observational studies that have challenged the utility of bevacizumab for elderly patients.34 As immunotherapies become first-line therapies for many patients with advanced non–small cell lung cancer, it may be appropriate to abandon costly and underwhelming vascular endothelial growth factor inhibitor therapies for most elderly patients with lung cancer.We did not find an association of bevacizumab use with either hospitalization or survival. The addition of bevacizumab to carboplatin-paclitaxel therapy led to a 2-month survival improvement in a prospective clinical trial,The strengths of our analysis included the use of the linked SEER-Medicare data, which are richly annotated with cancer diagnosis information, including month of diagnosis, cancer stage, and histologic findings. Comprehensive eligibility and claims data provided high confidence that outcomes assessments were comprehensive, and the use of a propensity-matched analytic design provided strong support for the assumption of comparable patient groups. A key limitation of our analysis was that we could not fully adjust the analysis for the use of bevacizumab, an active cancer therapy that was imbalanced across the patient groups. However, our exploratory analysis provided reassurance that bevacizumab use did not appear to be an important determinant of either hospitalization or survival outcomes. Also, we could not exclude the possibility that unmeasured factors were associated with both the choice of chemotherapy regimen and the risk of hospitalization.Treatment with carboplatin-pemetrexed for elderly patients with advanced lung cancer was associated with a 5% absolute reduction in 30-day hospitalization risk compared with that for treatment with carboplatin-paclitaxel. Carboplatin-pemetrexed therapy was also associated with more favorable survival. As immunotherapy supplements and replaces cytotoxic chemotherapy in the first-line treatment of metastatic non–small cell lung cancer, these results remain relevant to first- and second-line treatment selection and are useful for benchmarking important treatment outcomes in the Medicare population.


KRAS alterations. In silico deconvolution, that is the inferring of tumor microenvironment composition by gene expression data, through TIMER algorithm has been performed to explore immune microenvironment. Estimation performed on our gene expression matrix showed that B cell infiltration is lower in the KRAS-mutated enriched cluster, as in the TCGA-LUAD dataset. Such a finding has been validated in situ through immunohistochemistry in an independent cohort. Moreover, cases in LUAD-TCGA with low B cell infiltration have a significantly worse overall survival than those with higher levels. In the real-life cohort we observed that cases belonging to cluster enriched in KRAS-mutated patients have a poor outcome. LUAD driven by KRAS mutation represents an unmet clinical need, being refractory to pharmacological inhibition. Our results link KRAS mutations to B cell infiltration. Thus, the present findings could be helpful in a better definition of immunotherapeutic approaches for KRAS mutated patients.Non-small-cell lung cancer, histologically classified into adenocarcinoma (AD) and squamous cell carcinoma, is one of the most deadly malignancies worldwide. Lung AD (LUAD) could benefit of a plethora of target therapies and, in the last few years, also of immunotherapies. Here we focused on a real-life cohort of LUAD and The Cancer Genome Atlas (TCGA)-LUAD dataset aiming to gain insights into the immune contexture of such a malignancy. We explored the mutational status of 41 genes and the expression of 94 genes, related to immune-checkpoint, inflammation, and stromal microenvironment. Surprisingly, we found that our cohort has a very low mutational burden if we consider our panel as its surrogate. Regarding gene expression data, we identified 31 genes significantly deregulated in tumor tissues compared with a pool of normal samples. Unsupervised hierarchical clustering of the deregulated genes is able to identify two clusters of tumor samples, differently enriched in alterations in actionable genes. In particular, we identified a cluster enriched in patients carrying  Lung cancer remains one of the most deadly malignancies worldwide. The majority of patients have non-small-cell lung cancer (NSCLC) and in an advanced stage . They caEGFR (growth factor receptor) mutations, ALK/ROS1 translocations) made it possible to treat patients in a targeted way, even if only a small percentage of them benefit from these approaches. In a different percentage of LUAD (20%–25%) the discovery of KRAS mutations has been associated with poorer overall survival (OS) than with KRAS wild-type tumors, particularly in the advanced-stage setting [Historically, these patients were treated with platinum chemotherapy regardless of histological subtype. Despite an improvement in overall survival (OS) when compared with best supportive care, a therapeutic plateau has been reached with a response rate of about 20% and a median survival of 8–10 months [ setting ,4.EGFR/KRAS  mutations and immune checkpoint inhibitors response have been evidenced. KRAS, which is considered as undruggable, has been demonstrated by several groups to be able to modulate the immune response, as in colorectal and pancreatic cancers [In order to improve the clinical outcome of these patients, a novel emerging approach is based on the immunotherapy that acts targeting specific immune regulatory checkpoints. In particular, Cytotoxic T-Lymphocyte Antigen 4, CTLA-4, and Programmed cell death protein 1/Programmed death-ligand1, PD-1/PD-L1, are the main checkpoints to be targeted . Briefly cancers ,7,8.Thus, to study how the mutational status of cancer cells could contribute to the enhancement of immunogenicity, should be able to indicate the sensitivity to immune checkpoint blockade . It is wIn this study we explored the biological heterogeneity of the immune landscape of a real-life cohort of 20 patients affected by LUAD and of a cohort affected by LUAD and reported as TGCA dataset with the aim to identify potential biomarkers useful for a possible immune-therapy approach in these patients.EGFR, KRAS, and TP53 alterations, respectively.In the present study, we analyzed a cohort including 20 LUAD patients. The series comprises patients affected by advanced stage LUAD (stage IIIB-IV), carrying 7/20 (35%), 8/20 (40%), 8/20 (40%) To explore the mutational status of genes involved in immunological/inflammatory response, we designed a targeted sequencing panel including 41 genes. After filtering, as described in the Materials and Methods section, variants were annotated and their deleteriousness was predicted through 16 tools, as shown in The oncoprint plot in targeted panel including 95 genes. To explore deregulation, we compared our tumor samples with nonpathological samples through the DeSeq2 algorithm. We detected 31/95 genes significantly deregulated (n = 574) through the web-browser of TIMER  was drawn. It could be seen that almost all KRAS mutated patients are grouped in cluster 2 (7/8 mutated cases in cluster 2)  . In partKRAS mutation carrying patients and the identification of pathway/GO terms of the deregulated genes are not sufficient to infer the composition of microenvironment.The identification of two clusters differently enriched in KRAS mutational status, B cell infiltration is lower in both cluster 2 subsets and KRAS mutated patients than in cases belonging to cluster 1and in KRAS WT (p = 0.06) . Estimation performed on our gene expression matrix showed that, after stratification based both on cluster and  = 0.06) a,b. Notapatients c. DendriKRAS WT cases have B cell infiltration ; whilst 5/9 KRAS (55%) mutated showed no B cell infiltration  of ltration .Of note, immune infiltration estimation on LUAD-TCGA data has been performed on RNA-Seq data whilst we performed estimation in our cohort using a matrix including 95 genes. Thus, our custom RNA panel has a good performance in TIMER estimation, being able to confirm the result related to B cell infiltration.KRAS-mutant samples. KRAS mutations are detected almost exclusively in LUAD [KRAS mutant is biologically heterogeneous in terms of type of alteration and concomitant mutations. The type of KRAS mutation has been dissected with respect to patient survival. No specific distribution has been observed of the nine types of mutations at codons 12, 13 e 61 in NSCLC in the three co-occurring mutation clusters [KRAS-G12C or KRAS-G12V [KRAS-G12C has been correlated to poorer overall survival than KRAS-G12D mutants or KRAS-G12A [KRAS mutations to survival needs more data and appropriate study design. The cohort of the present study carries only alterations at codon 12 and 13. In our cohort, patients in KRAS-mutant enriched cluster, 33% of samples carry a mutation in TP53, but only the half of them are KRAS/TP53 co-mutant. Of note, TP53 mutants in the other cluster are all KRAS wild-type. Thus, cluster 2 is enriched both in KRAS mutation carriers and KRAS/TP53 double mutants. Skoulidis et al. identified three subgroups of KRAS-driven LUAD based on co-occurring mutations. In particular, the so-called KP cluster, including KRAS/TP53 mutants, has been demonstrated to be featured by immunoediting, T-cell inflammation, and resistance to adaptive immune response and also JAK-STAT pathway activation and interferon signaling [The advent of immunotherapies posed a step forward in the treatment of many solid tumors, in particular LUAD. In the present study, both the analysis of real-life cohorts and TCGA-LUAD data allowed to gain insights into the biology of such a disease. Through the set-up of two NGS custom panels, both for DNA and RNA, including genes related to major immunity checkpoints and pathways, it was possible to dissect different aspects of LUAD. 41-gene DNA panel evidenced no mutational enrichment. This is an important finding because mutational status of genes involved in immunological/inflammatory pathways could be less important than their fine regulation. Tumor mutational burden (TMB) is a potential biomarker, also in NSCLC, for response to immunotherapy targeting PD-1/PD-L1 ,17. If o in LUAD ,21 and t in LUAD ,23. Moreclusters . HoweverRAS-G12V . In NSCLRAS-G12A . Neverthignaling .KRAS mutants. B-cell result has been confirmed using the 95-gene panel expression matrix both stratifying by KRAS mutational status and clustering information and by IHC on an independent cohort. The function in anti-tumor immunity of B cells has emerged in lung cancer in several studies. Indeed, it has been shown that B lymphocytes are able to promote T cell activation and expansion. B cells, which are responsible for humoral immunity, are able in lung to differentiate into plasma cells producing antibodies against tumor antigens  and the high concentration of tumor-antigen antibodies has been associated to a high proportion of follicular B cells [KRAS alteration is the most interesting finding of the present study. The role of KRAS in the modulation of the tumor microenvironment has been demonstrated not only for the immune component but also the stromal one. GO/KEGG analysis evidenced a significant enrichment of terms related to Th17 lymphocytes and in the KRAS-mutants enriched cluster the upregulation of IL6 and MMP12 has been detected, confirming the crosstalk between tumor and Th17 cells [KRAS and B cell. The role of KRAS in the B lymphopoiesis has been shown by Chen et al. [KRAS in murine model impairs B cell development and maturation. Nevertheless, models to study the crosstalk between KRAS mutant cells and B lymphocytes are mandatory because this relationship needs to be studied to improve the (immune)therapeutic setting of LUAD patients. Indeed, recently Meng et al. [KRAS mutants, eliciting the humoral immune response through infiltrating B cells in pancreatic cancer. Such a mechanism is promising because it is similar to the NY-ESO-1 approach in melanoma [To gain a deeper knowledge of the microenvironment, TCGA-LUAD cohort has been analyzed through TIMER, a deconvolution algorithm, which highlighted a significant low infiltration level of B-cells and dentritic cells in  B cells . Plasma  B cells , and co- B cells ,30. Such17 cells ,32. To dn et al.  that demg et al.  demonstrmelanoma ,36. Regamelanoma . It is aKRAS mutation/low B cell infiltration association with respect to overall survival highlighted the possibility to set-up novel immunotherapies, such as the B lymphocytes-based approach. Indeed, recent clinical studies testing allogeneic B cells from PBMCs  of donors in renal cell tumor and melanoma [In particular, melanoma  and adopmelanoma  showed pIn conclusion, the present study, exploiting through genomic data the biology of LUAD, confirmed the importance of the mutational status regarding the relationship between the tumor cell and microenvironment. Intriguingly, the association of KRAS alterations to B cell infiltration and survival shed a light to the set-up of new therapeutic approaches. Moreover, the results coming from deconvolution on the expression matrix of the custom targeted panel demonstrated that it could be a useful tool to explore the TME composition, as demonstrated through the TCGA-LUAD dataset molecular data, and in situ validations.KRAS mutational status was known, was used to validate deconvolution results by the in situ approach.Twenty advanced-stage (IIIB and IV) NSCLC patients were enrolled by the Department of Thoracic Oncology of the Giovanni Paolo II Institute of Bari between November 2016 and May 2017.Evaluation of survival was performed according to RECIST 1.1 criteria with a median time OS time of 9.67 months. Moreover 8 patients who underwent surgery with lung tumor free diagnosis were enrolled by the Department of Surgery of San Paolo Hospital, Bari. All cases were diagnosed according to the current WHO guidelines. A cohort including 15 LUAD cases, whose The study was approved by the local Ethics Committee of the IRCCS “Giovanni Paolo II” of Bari (CE prot. n. 540/2015) and was performed in accordance with the international standards of good clinical practice. All patients signed informed consent.Blood samples from healthy patients were collected and stored as germline biological material.Between three to six FFPE tissue sections (6 μm thick) with adequate tumor cellularity selected by a pathologist (>50%), were macro dissected and subjected to DNA and RNA isolation using QIAamp DNA FFPE Tissue Kit  and RNeasy FFPE Kit(Qiagen), respectively, according to manufacturers’ protocols. DNA was also isolated from blood samples using QIAamp DNA Blood Midi Kit (Qiagen). DNA and RNA concentration was determined using Qubit 2.0 fluorometer .Sequencing was performed as previously reported in . BrieflyAll generated reads were aligned to human genome hg19 using the Torrent Suite Server.https://cancer.sanger.ac.uk/cosmic).Variant calling and filtering was described in . BrieflyThe OncoPrint plot was designed with the ComplexHeatmap R package .https://cistrome.shinyapps.io/timer/) both to the analyzed TCGA-LUAD dataset and real-life cohort [Gene expression was evaluated, as stated above, through a custom panel including 95 genes. Ten 10)ng of input RNA were reverse transcribed for target enrichment by the Ion AmpliSeq Library Kit 2.0 (Thermo Fisher Scientific). Libraries were then quantified and pooled in an equimolar ratio. Then libraries were pooled and templated using the Ion OneTouch 2 system. Sequencing of the Ion 318 chip was carried out on the Personal Genome Machine  according to the Ion PGM Sequencing 200 Kit (Thermo Fisher Scientific). Read count matrix was downloaded from the Torrent Server and analyzed with DeSeq2 R package . Deconvong of inpParaffin-embedded tissue sections of patients fixed in formalin were cut into 4µm, the slides were processed and stained by the indirect immunoperoxidase method using the Dako Omnis Platform . The slides underwent deparaffinization, followed by antigen retrieval with EnVision FLEX Target Retrieval Solution, Low pH for PD-L1, and High pH for CD20. The sections were incubated with primary antibodies Monoclonal Mouse Anti-Human PD-L1  and Monoclonal Mouse Anti-Human CD20cy . After endogenous peroxidase activity blocking with EnVision FLEX Peroxidase-Blocking Reagent, the products of the antigen–antibody reactions were visualized by EnVision FLEX DAB + Chromogen and EnVsion FLEX Substrate Buffer. Cell nuclei were stained with Hematoxylin (8 min). Tonsil FFPE sections were used as positive controls. For negative control, the primary antibody was omitted.KRAS mutational status, which is responsible for a poor survival in LUAD patients, and B cell infiltration, which, in turn, has been indicated as a good prognostic marker. Notwithstanding the small size of the real-life cohort, the in silico exploration of the TCGA-LUAD dataset and the immunohistochemistry validation of B cell infiltration on an independent cohort indicate that the results could be further explored in a more extensive way. The aforementioned relationship between KRAS and B lymphocytes, moreover, will be explored in a larger cohort, including also LUAD patients treated with standard immunotherapies. To gain functional insights into the response to immune checkpoint inhibitors and the link between KRAS and B cells,anin vistrostudy will be planned.The present paper, for the first time, links


Phytocystatins (PhyCys) act as endogenous regulators of cysteine proteases (CysProt) involved in various physiological processes. Besides, PhyCys are involved in plant reactions to abiotic stresses like drought or darkness and have been used as effective molecules against different pests and pathogens. The barley PhyCys-CysProt system is considered a model of protease-inhibitor regulation of protein turnover. Thirteen barley cystatins (HvCPI-1 to HvCPI-13) have been previously identified and characterized. Among them HvCPI-2 has been shown to have a relevant role in plant responses to pathogens and pests, as well as in the plant response to drought.HvIcy-2 silencing lines behave differentially against the phytopathogenic fungus Magnaporthe oryzae and a light deprivation treatment. The induced expression of HvIcy-2 by the fungal stress correlated to a higher susceptibility of silencing HvIcy-2 plants. In contrast, a reduction in the expression of HvIcy-2 and in the cathepsin-L and -B like activities in the silencing HvIcy-2 plants was not accompanied by apparent phenotypical differences with control plants in response to light deprivation.The present work explores the multiple role of this barley PhyCys in response to both, biotic and abiotic stresses, focusing on the impact of silencing this gene. These results highlight the specificity of PhyCys in the responses to diverse external prompts as well as the complexity of the regulatory events leading to the response to a particular stress. The mechanism of regulation of these stress responses seems to be focused in maintaining the balance of CysProt and PhyCys levels, which is crucial for the modulation of physiological processes induced by biotic or abiotic stresses.The online version of this article (10.1186/s12870-018-1560-6) contains supplementary material, which is available to authorized users. Arabidopsis thaliana, Glycine max and Malus domestica plants has been reported owing to the over-expression of PhyCys [Among plant protease inhibitors, the cysteine protease (CysProt) inhibitors called phytocystatins (PhyCys) have been largely studied since they participate in numerous physiological processes. The complexity of PhyCys at genomic and structural levels, with multiple members and specific expression patterns and affinity to CysProt, indicates a great diversity of functions –4. PhyCyf PhyCys , 22. Phyf PhyCys , 24. Likf PhyCys .Ustilago maydis, indicating that this PhyCys suppresses host immunity by inhibition of apoplastic cysteine proteases [Alternaria alternata and elevated tolerance against Botrytis cinerea [PhyCys superfamily is one of the most studied inducible defenses of the plant, and PhyCys has been used as effective molecules against different pests and pathogens –31. Recoroteases . Convers cinerea . Althoug cinerea –39 but i cinerea . In addi cinerea –43.Hordeum vulgare L.) represents a good model to study the implication of cystatins in responses to biotic and abiotic stresses, given the comprehensive knowledge of its CysProt and PhyCys families. In previous works, thirteen cystatins (HvCPI-1 to HvCPI-13) have been identified and characterized [Magnaporthe oryzae, but Arabidopsis plants over-expressing this PhyCys showed no differences in fungal and bacteria resistance levels in comparison to non-transformed plants [Barley  have been used to carry out in vivo experiments to test the response of modified plants towards drought [HvIcy-2 varies in response to different abiotic or biotic stresses, the same KD Icy2 lines were used to measure the messenger HvIcy-2 levels upon M. oryzae or darkness treatments.Homozygous barley plants silencing the  drought . To knowHvIcy-2 gene after M. oryzae infection in HvIcy-2 silencing barley lines, as well as wild type plants, when compared with control conditions. However, after light deprivation, the HvIcy-2 gene in the transgenic lines rather than increase is down-regulated comparing to control conditions and comparing to WT, where no significant differences were observed , in order to analyze the effect of the modified barley plants on the fungus behavior  [HvIcy-2 gene has been also analyzed under abiotic stresses. While HvIcy-2 gene is notably up-regulated under drought conditions [HvIcy-2 plants are a key tool. Interestingly, the expression of HvIcy-2 decreased in silencing lines after dark treatment, but this lower HvIcy-2 expression was accompanied to a decrease in cathepsin activities. Besides, these changes were not translated to biochemical or phenotypical differences. This striking behavior may be due to a transcriptional reprogramming of PhyCys expression to compensate HvIcy-2 silencing. Most of the responses to abiotic stresses are a consequence of the direct inhibition of plant protease targets [HvIcy-3-6 and 8–9 was induced by darkness [HvIcy-2 together to HvIcy-4 showed the highest level of expression after water deprivation in barley leaves [HvIcy-2 and HvIcy-4 genes in silencing lines after water deprivation supports a cooperative role of these two cystatins [HvPap-12 and -19 between control and silenced lines was found. This effect could partially explain the lower cathepsin activity showed by silencing HvIcy-2 lines after darkness treatment and supports that responses to different stresses are accompanied to molecular alterations with striking compensation effects at the transcriptional level.The participation of cystatin family members in biotic and abiotic stresses responses has been widely described . Their p. oryzae . The ind. oryzae , 52, and. oryzae , 40, higose from  that shomycelium –39. In cmycelium . The secresponse . Besidesr (PIN2) . In any nditions  no diffenditions . However targets , and difdarkness  and HvIcy leaves . Mutual ystatins . LikewisHvIcy-2 silencing lines perform different under biotic or abiotic stresses. Results highlight the specificity of PhyCys in the responses to diverse external prompts as well as the complexity of the regulatory events leading to the response to a particular stress. HvIcy-2 silencing lines not only behave differentially against biotic or abiotic stresses, but also against two abiotic stresses like drought and light deprivation. The mechanism of regulation of these stress responses seems to be focused in maintaining the balance of CysProt and cystatins accumulation levels, which is crucial for the regulation of the physiological processes induced by biotic or abiotic stresses. Thus, HvCPI-2 may be participating directly on the stress, which would explain the highest susceptibility of KD Icy2 barley plants to the fungal attack, or a transcriptional reprogramming of the expression of different PhyCys and CysProt may be occurring to compensate HvIcy-2 silencing, which would explain the low cysteine protease activity showed by silencing HvIcy-2 lines after darkness treatment. Further research needs to be done to decipher the putative in vivo roles of cystatins and CysProt, and to deepen on the existing knowledge about the proteolytic events underlying responses to different stresses.In this study, it has been shown how transgenic barley H. vulgare cv. Golden Promise were used. Barley transgenic lines silencing the HvIcy-2 gene (KD Icy2) were generated as described in [Source of Plants: Barley plants of ribed in . Grains ribed in , and theHordeum vulgare spring type cv. Golden Promise provided by the IPK Gatersleben, Plant Reproductive Biology Group. Barley transgenic lines silencing the barley Icy-2 gene (KD Icy2) were generated also in collaboration with the IPK Gatersleben, Plant Reproductive Biology Group.The plant specimens were taking from seeds of barley plants of Magnaporthe oryzae wild-type strain Guy11 [The fungal isolate used in this study for infection assays was the in Guy11 , kindly M. oryzae small subunit of ribosomal RNA (Mo28S-rRNA) were quantified as described in [HvCycl gene) mRNA levels following [Total RNA was extracted from frozen barley leaves by the phenol/chloroform method and digested with DNase as described in . cDNAs wribed in . Quantifollowing . The priTotal protein was extracted from treated and control leaves as described in , and quaa and b, and total carotenoids (xanthophylls and carotenes) were extracted from leaves and pigment content was calculated as described in [Chlorophyll ribed in . To deteribed in .The flagellin peptide (flg22) with amino acid sequence QRLSTGSRINSAKDDAAGLQIA (AnaSpec laboratories) and chitosan purified from crab shells (Sigma Aldrich) were used for elicitor treatments as described in . ElicitoHvIcy-2 gene were infected with the fungus M. oryzae as described in [M. oryzae wild type strain Guy11 kindly provided by Dr. Sesma  was the fungal isolate used. The infection assays were performed as described in [Seven days old barley plants of wild type and transgenic lines silencing the ribed in . The M. ribed in  by sprayM. oryzae damage observed on barley leaves were scanned using a HP Scanjet 5590 Digital Flatbed Scanner and foliar damage was analyzed as described in [2) mean ± SE of seven measurements.ribed in  using thStatistical differences among treatments or lines were analyzed by one-way ANOVA followed by studentized range distribution of Student Newman-Keuls (SNK) multiple comparison test performed using the soft R Project (v.3.1.2) package. At least three biological replicates as well as three technical replicates were performed for all the experiments.Additional file 1:Table S1. List of primers. (DOCX 17 kb)


Recent advances of neuroimaging methodology and artificial intelligence have resulted in renewed interest in board games like chess and Baduk  and have provided clues as to the mechanisms behind the games. However, an interesting question that remains to be answered is whether the board game expertise as one of cognitive skills goes beyond just being good at the trained game and how it maps on networks associated with cognitive abilities that are not directly trained. To address this issue, we examined functional activity and connectivity in Baduk experts, compared to novices, while performing a visual n-back working memory (WM) task. We found that experts, compared to novices, had greater activation in superior parietal cortex during face WM, though there were no group differences in behavioral performances. Using a data-driven, whole-brain multivariate approach, we also found significant group differences in the multivariate pattern of connectivity in frontal pole and inferior parietal cortex, further showing greater connectivity between frontal and parietal regions and between frontal and temporal regions in experts. Our findings suggest that long-term trained Baduk experts have the reorganization of functional interactions between brain regions even for untrained cognitive ability. People have very different levels of cognitive ability, from profound impairments to superior skills. However, while our understanding of neural mechanisms of cognitive impairments has been greatly enhanced via neuroimaging studies for general and cognitive-impaired individuals, those of superior skills still remain poorly understood.1), is an abstract strategy board game like chess; it is played on a square board with a 19 by 19 grid of lines. Its rule is simple; two players, one playing with white stones and the other playing with black ones, take turns placing a stone to capture more territory on the board than the opponent by surrounding the opponent’s stones . Despite such simple rule, Baduk is considered more complex than chess owing to its enormous branching factors   .Board games like Baduk and chess have been commonly used to investigate the mechanism underlying cognitive expertise, as playing involves diverse high-level cognitive functions such as visuospatial processing, decision making, and attention . For exaDespite the efforts mentioned above, an interesting question that remains to be answered is whether the board game expertise goes beyond just being good at the trained game. In other words, do behavioral and neural differences between BEs and novices exist in untrained cognitive abilities? Although there are some studies investigating the effects of chess instructions on untrained tasks , it is sWorking memory (WM) is involved in the frontoparietal network . ParticuHere we examined the functional activity and connectivity of Baduk experts, compared to novices, while performing a visual n-back WM task with both object and spatial WM conditions. Especially, we chose to use neutral face stimuli for the task because like the board games, face discrimination and recognition rely on at least partly on holistic processing and reliably activate the FFA , where t2. Seventeen novices who were age, sex, and IQ-matched with those in the BEs, also recruited through online advertisements for purpose of comparison. Based on the simple screening questionnaire, all participants were not experts in any games, except Baduk for BEs. All participants were also right-handed and had no history of psychiatric or neurological disease. This dataset included resting-state fMRI, n-back WM task-based fMRI, T1-weighted anatomical MRI, and diffusion tensor imaging. Previous reports from this dataset have concerned differences in anatomical connectivity  out of 34 were excluded from analyses due to excessive missing trials during the task. The final sample consisted of 15 BEs  and 15 novices . Our sample size corresponded to samples used in previous behavioral , 2009 anFigure 1B). The task had four WM loading conditions: 0-back, 1-back, 2-back, and 3-back. The 0-back served as a control condition, in which participants responded with a button press to a predetermined target stimulus. For the n-back face matching WM conditions, participants responded if the current face was identical to the previous one, two, and three faces ago, respectively. For the n-back spatial location matching WM conditions, participants responded if the current face was in the same place n faces ago regardless of the face identity. During the task, participants responded by pressing a button with their right index finger. The face stimuli consisted of 20 gray-scale pictures of neutral faces (10 Korean men and 10 Korean women), selected from stimuli used in our previous studies , resulting in six blocks for each of four loading conditions on face and spatial WM. Blocks were presented pseudo-randomly in order of increasing (or decreasing) memory load. Each block included 10 trials (23.4 s) and was split up with the resting blocks of 4 TRs (9.36 s). Visual instructions (2.34 s) preceded each block to indicate the upcoming condition. A face stimulus for each trial was presented for 1500 ms followed by 840 ms of fixation. Before scanning, the participant was given practice to learn the task rules.All image data were acquired on a 1.5T Siemens Avanto MRI scanner. While performing the task, fMRI data were obtained via a gradient echo-planar image pulse sequence (repetition time (TR)/echo time (TE) = 2340/52 ms, flip angle (FA) = 90°, field of view (FOV) = 220 mm, voxel size = 3.44 mm × 3.44 mm × 5 mm). High-resolution anatomical images were acquired with T1-weighted 3-D MPRAGE sequence . Other image parameters that are not related to the present study are not described herein.3. For each participant, after discarding the first three images in each run, images were then corrected for slice timing, realigned to the first volume, and co-registered with each participant’s structural image. There were no significant group differences in the head motion parameters. All images were then normalized to the MNI space using the normalization parameters estimated from the structural MRI. The normalized images were smoothed with 6 mm FWHM Gaussian kernel. First level-analyses were performed using the general linear model (GLM) with regressors for the cue, 0-back, l-back, 2-back, and 3-back conditions for each type of face and spatial WM in addition to six head motion parameters and a constant term. The regressors were modeled as boxcar functions convolved with a canonical hemodynamic response function for the length of each condition. To delineate brain regions activated during WM for each group and determine regions showing group differences, one-sample t-tests and two-sample t-tests were, respectively, performed for the contrast images of the 1-, 2-, 3-back versus 0-back conditions for each type of face and spatial WM. To further explore a group by WM load interaction for each type of face and spatial WM, we also performed a two (group) by three (WM load) analysis of variance (ANOVA) using the following contrast images: 1-back > 0-back, 2-back > 0-back, and 3-back > 0-back. The results were corrected for multiple comparisons to a significance level of p < 0.05 .Image analysis was performed with SPM124. First, we computed FC (Pearson correlation coefficient) between time series of a given voxel and those of every other voxel within the gray matter mask including cortical and subcortical areas. Second, we evaluated the overall multivariate pattern of FC for a given voxel by calculating a distance metric between every pair of FC calculated above for a given voxel . To calculate the distance metric, we used p-value) was assessed using 5,000 iterations of a permutation test. All these steps were repeated for every gray matter voxel to produce a whole-brain p-value map. The p-value map was converted to z-value for multiple comparisons corrections. As in z > 2.326 and a corrected probability of p < 0.05 using 10,000 Monte Carlo simulations.The MDMR-based CWAS method has been described in detail elsewhere . For MDMlacement . For eacpost hoc seed-based FC analyses for each cluster identified by MDMR-based CWAS. Seed-based FC maps were generated by Pearson correlation between time series of each seed cluster and those of every other voxel and then Fisher r-to-z transformed. Two-sample t-tests were conducted to examine group differences in z-transformed seed-based FC maps. Statistical significance was set at a voxel-level FWE-corrected p < 0.05.Although MDMR-based CWAS identifies clusters where group differences are present based on multivariate patterns of FC, it does not provide specific connections and direction of observed clusters . Accordi5 or the strength of FC) in the identified regions as functional ROI and behavioral performances during the task and training duration in BEs.Once regions showing significant effects from the aforementioned analyses were detected, we further conducted partial correlation analyses (controlling for age and sex) between brain measures . The two groups did not differ significantly in the accuracy and the reaction time (RT) during the face and spatial n-back WM .Figure 2A and Table 2). However, between-group comparisons revealed greater activation in the left superior parietal cortex (SPC) in BEs than novices during face WM at cluster-level family-wise error (FWE)-corrected p < 0.05 . The post hoc ROI analysis to further characterize the group difference in the region showed less SPC activation in BEs for the face 0-back control condition , albeit only marginally significant, than novices but not for the face WM loading condition . No significant correlations were found between neural activities in the SPC and behavioral performances (accuracy and RT) during the task and training durations in BEs . There were no significant group differences in any other regions and other task conditions and no group by WM load interactions .Both BEs and novices showed similar activation patterns in the frontal and parietal regions during both face and spatial n-back WM  for face WM condition and the other is the left inferior parietal cortex  for spatial WM condition .Multivariate distance-based matrix regression-based CWAS analyses revealed two regions where the multivariate patterns of FC differ between BEs and novices at cluster-level corrected post hoc seed-based FC analysis to each of these identified clusters . For face WM condition, BEs compared to novices had greater FC between the left FP seed and right FFA , right supramarginal cortex , left middle temporal cortex  adjacent to superior temporal sulcus (STS). For spatial WM condition, BEs compared to novices had greater FC between the left IPC seed and left lateral frontal cortex . However, the strengths of FC between these regions had no significant correlations with behavioral performances during the task and training durations in BEs.To further characterize the FC of the regions identified by MDMR-based CWAS, we performed To address the question as to whether BEs, individuals having cognitive expertise including the highest level of domain-specific pattern recognition, differ from novices in untrained cognitive functions in terms of behavioral performance and brain function, here we explored the brain function of the Baduk (the game of Go) experts while performing n-back WM tasks. Despite no behavioral differences on task performance, BEs compared to novices showed greater SPC activation during face n-back task. Significant differences between BEs and novices were also found in the multifocal patterns of FC in the left FP and IPC for the face and spatial WM conditions, respectively, further showing greater functional couplings between frontal and parietal and temporal regions in BEs compared to novices.The present study demonstrates that BEs with long-term training do not show an increase in WM ability but have disparate functional neural patterns. Consistent with our results, the same pattern of the absence of far transfer occurs in different types of training, including chess, music, and video game training -rule” . Howeverpost hoc analysis revealed that this activation difference was caused by less SPC activation in BEs, compared to novices, for the 0-back condition. The SPC is known to be involved in the top-down allocation of visual spatial attention (In the present study, both groups showed similar activation patterns in the frontoparietal areas and matched task performances for both face and spatial WM conditions. However, when comparing neural activity between groups, BEs compared to novice had greater SPC activation during face WM, particularly in response of the contrast of the face 1-, 2-, and 3-back versus 0-back control conditions. The ttention . Given tttention  and in tttention , 2013. Ottention . Anotherttention . As mentpost hoc seed-based FC analyses for these identified clusters further revealed group differences in FC between specific brain regions; BE compared to novices had greater FC between the left FP seed and several temporal and parietal areas, including the left MTC and right SMC and FFA, during face WM and greater FC between the left IPC seed and left LFC during spatial WM. Neuroimaging studies have consistently reported the co-activation of multiple frontal and parietal regions during spatial attention (Expertise in board game playing may be associated with the change of FC between brain regions, rather than regional neural activity, to efficiently solve domain-specific problems  and thisttention , WM (LaBttention , and fluttention , suggestttention . The IPCttention . Especiattention , 2000, wttention . Recent ttention  and thatttention . Based oThe present study had some limitations to be addressed in future research. First, a relatively small sample size and scanning on a 1.5 T magnet may lead to resultant low statistical power and to limit the spatial resolution, respectively. However, our sample size corresponded to samples used in previous studies examining the differences in brain function between BEs and novices . Given tTo our knowledge, this is the first study to examine whether there were differences in the functional activity and connectivity between BEs and novices while performing standard n-back WM task with both the face and spatial WM conditions, associated with the frontoparietal network, unlike previous studies to test domain-specific pattern recognition. Despite no behavioral differences, greater SPC activation in BEs compared to novices was observed during face WM. We also found altered connectivity in the FP and IPC in BEs in terms of multivariate patterns of FC using a new data-driven multivariate FC approach and further observed greater FC between frontal and parietal and temporal regions in BEs during WM. Our results provide novel insights into the mechanism behind Baduk expertise beyond domain-specific cognitive ability and provide evidence for differences in brain circuits associated with WM ability between experts and novices.WJ and JK designed and supervised the research. WJ, YY, and C-HC performed the experiments and analyzed data. WJ, YY, and TL wrote the manuscript. All authors reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer VT and handling Editor declared their shared affiliation.


We cross-validate the scale in three independent samples  and in a fourth sample , we demonstrated the importance of work-nonwork boundary management fit for employee well-being . In particular, we confirmed its unique role in predicting employee well-being, above and beyond workload and work interrupting nonwork behaviors. Hence, we argue for considering work-nonwork boundary management fit when studying how work-family policies and organizational culture affect employees in the workplace.Recently, work-family scholars have empirically demonstrated the importance of congruence between employees' boundary management preferences and boundary management supplies provided by the work environment in relation to employee attitudes and behavior. However, a theoretically grounded construct that captures this congruence is lacking. The present study addresses this gap by developing the construct and measure of  Research devoted to the interplay of work and nonwork  life continues to flourish, as “balancing” both roles becomes a vital concern to an increasing proportion of employees that engages in multiple roles  demonstrate its relevance by establishing the relation with employee well-being.To that end, we (1) introduce the new construct and corresponding measure of In doing so, this study contributes to the work-family literature by introducing a sound new theoretical construct that offers insights into the perception of congruence between an employee and his/her work environment with regard to boundary management. This is relevant since work-family studies have consistently shown that family-friendly policies and a culture of integration or segmentation are not universally beneficial to all employees; their effectiveness seems to depend on various individual differences  and perceived boundary management supplies as provided by the workplace. In particular, employees who feel that their boundary management preference—regardless of whether it leans toward segmentation or integration—is met by the culture and policies in the workplace are expected to experience high levels of work-nonwork boundary management fit. Conversely, when employees feel that their work environment does not meet their boundary management needs, they may perceive low levels of work-nonwork boundary management fit. Hence, employees in the same work environment may therefore experience a different level of fit. In this respect, Ammons  that allow them to create boundaries according to their preference. A fitting work environment allows employees to manage their work and nonwork demands within their preferred boundaries and establish their desired level of work-nonwork interference. This facilitates the reconciliation of work and nonwork demands and empowers employees to create their ideal work-nonwork interface, thereby preventing work-life conflicts to occur Kreiner, . FurtherTo develop and evaluate the work-nonwork boundary management fit scale, we followed a multiphase process  to strongly agree (7).We constructed the work-nonwork boundary management fit scale based upon two existing scales measuring perceived needs-supplies fit with both the job and the organization  and 35% were male. Seventy-three percent of the respondents worked fulltime. In terms of occupation, 73% of the respondents were white-collar workers, 12% were self-employed, 10% were civil servants, and 5% were blue-collar workers. Eighty-four percent of the respondents had a cohabiting partner and our respondents had one child on average (SD = 1.38). The sample was diverse, yet dominated by white-collar employees.Participants were recruited through an online social network using snowball sampling and participation was voluntary and anonymous. Inclusion criteria were (1) being at least 18 years and (2) having a paid job. A total of 188 employees participated in the study. Respondents' mean age was 41 years , and their average tenure was 11 years (SD = 11.03). Sixty percent of the respondents were male and 75% worked fulltime. Seventy-nine percent of the respondents had a cohabiting partner and respondents had one child on average (SD = 1.02).Participants in this study were hospital employees, who were invited to participate by the head of their department. Seventy-five employees completed the survey, resulting in a response rate of 37.5%. Respondents' mean age was 41 years (2(6) = 289.57, p < 0.05, CFI = 1.000, TLI = 1.008, RMSEA = 0.000, SRMR = 0.009. SRMR values close to 0.08 or below and RMSEA values close to 0.06 or below indicate acceptable model fit, with smaller values indicating a better model fit, whereas CFI and TLI values larger than 0.90 indicate good fit, and values larger than 0.95 indicate excellent fit  and dissimilar measures . Seventy-six percent of the employees had a cohabiting partner and had 2 children on average (SD = 1.14) living in their household. Ninety-one percent of the respondents worked fulltime and their average tenure was 12 years (SD = 10.02).This sample included employees from one local Belgian branch of a multinational car company. Data were collected through a call for participation of the HR department. One hundred and eighty employees received an invitation to participate in a work-life survey. A total of 81 employees returned the survey, which translated into an effective response rate of 45%. Most of the employees were male (65%), with a mean age of 42 years  and 7 (strongly agree). All items were translated into Dutch and we performed a back-translation to acquire accurate translations.We used eight items of Kreiner  to captuWe measured task variety by using four items of Morgeson and Humphrey . Sample We used six items developed by Carlson et al.  to measuWork-life balance was measured using six items of Carlson et al. . Sample Job satisfaction was evaluated using Camman et al.'s  three-itOrganizational commitment was measured with the six-item subscale of affective organizational commitment developed by Meyer et al. . ParticiTurnover intention was measured by Camman et al.  three-itr = 0.37 (p < 0.01), showing convergent validity while underlining that both scales are not measuring the same concept. These findings are in line with prior studies examining the relation between measures of perceived and calculated fit, showing similar correlations ranging from 0.08 to 0.41  to task variety, a theoretically distinct variable. Overall, work-nonwork boundary management fit is related to but distinct from theoretically relevant variables, providing general support for convergent and discriminant validity. All correlation coefficients can be found in Table Preliminary support for convergent validity was found given that all items loaded highly and significantly on the latent variable, with an average of 0.79 , organizational commitment , and work-life balance , and the negative relation with turnover intention  and work-life conflict . According to Cohen (r > 0.30) to strong (r > 0.50). Therefore, the assumed relationships attain statistical significance and thus provide evidence for criterion-related validity . The majority of the respondents (92%) had a cohabiting partner and respondents had two children on average (SD = 0.80) living in their household. The majority of our participants are white-collar workers (94%) who represent a variety of job levels including entry level workers (48%), middle management and professionals (35%), and managers (11%). Fifty-eight percent of the respondents worked fulltime.This sample included working parents with at least one child under the age of 12. The pool of participants was obtained through Belgian childcare institutions, kindergartens and primary schools which invited parents to participate. A total of 458 individuals returned self-report surveys, resulting in a response rate of 9%. Eighty-five percent of the respondents were female, with an average age of 37 years  and strongly agree (7). All items were translated into Dutch and we performed a back-translation to acquire accurate translations. The Cronbach's alpha coefficient of work-nonwork boundary management fit indicated good internal scale reliability and consistency (α = 0.85). The scale evaluating work-life conflict (α = 0.88) was identical to that in Study 3.Stress was measured by Anderson et al.'s  seven-itWorkload was assessed with three items from the Dutch version Furda,  of KarasWork interrupting nonwork was measured using Kossek et al.'s  cross-rop < 0.001) and work-life conflict  compared to male employees. Working hours also significantly predicted work-life conflict . At step 2, workload and work interrupting nonwork behaviors were entered as predictors. Both workload and work interrupting nonwork behaviors were significant predictors of stress  and work-life conflict . Workload tended to be a more important predictor of stress and work-life conflict, compared to work interrupting nonwork behaviors. Finally, we entered work-nonwork boundary management fit as predictor in step 3. Work-nonwork boundary management fit significantly predicted stress  and work-life conflict , beyond workload and work interrupting nonwork behaviors. Support for our hypothesis was found given that work-nonwork boundary management fit accounted for unique variance associated with stress  and work-life conflict  above and beyond the variance already explained by workload and work interrupting nonwork behaviors.Separate three-step, hierarchical regression analyses were performed for each outcome variable in order to test the unique role of work-nonwork boundary management fit in predicting stress and work-life conflict. Correlations can be found in Table The current study introduces the construct of work-nonwork boundary management fit and a psychometrically sound measure for it. The perception of work-nonwork boundary management fit is just above average in all four samples . This distinction is important because fit may affect attitudes and well-being only if employees consciously experience it  and work-life conflict , the interaction between work-nonwork boundary management fit and segmentation preference was not significant in predicting stress  and work-life conflict  . Therefore, we argue that work-nonwork boundary management fit is important for employee well-being, independent of the employee's boundary management preference. Also the employee's supervisor may play an important role. Allen , creating concerns in terms of common method variance. In addition, we were not able to make any definitive claims of causality. To address the limitations associated with cross-sectional data, we designed the questionnaire in a way to minimize the effects of common method bias (e.g., use of various response formats and protecting respondent anonymity; Podsakoff et al., e. Allen  states te. Allen , p. 417.Changes in the nature of work, technology, and work demographics have yielded unprecedented potential for organizations to move toward a culture of work-nonwork integration (Rothbard et al., employees' psychological experience of congruence between their personal boundary management preference and the work environment's boundary management supplies. We empirically confirm that work-nonwork boundary management fit is a relevant construct that has the potential to significantly progress the contribution work-family scholars make toward improving employee well-being and organizational effectiveness. Work-family researchers should therefore pay closer attention to employees' perceived work-nonwork boundary management fit when studying how human resource policies, organizational culture, and supervisor behaviors affect employees' wellbeing, attitudes, and behaviors in the workplace.Although various studies have empirically confirmed the importance of person-environment congruence with regard to boundary management (e.g., Rothbard et al., This study was carried out in accordance with the recommendations of the social and societal ethics committee of the University of Leuven. The protocol was approved by the social and societal ethics committee. All subjects gave written informed consent in accordance with the Declaration of Helsinki.YB, RD, and SD developed the research idea and conceptual framework of the article. YB and SD collected the data. YB performed the data analysis and interpreted the results. YB drafted the manuscript. RD and SD provided critical revisions and edited the manuscript. All authors made substantial contributions to the work reported in the article and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To develop and internally validate the PROgnosis of functional recovery after Trauma (PRO-Trauma) prediction model.A prospective single-center longitudinal cohort study. Patients were assessed at 6 weeks and 12 months post-injury.functional recovery, was defined as a Short Musculoskeletal Function Assessment (SMFA-NL) Problems with Daily Activities (PDA) subscale ≤ 12.2 points at 12 months post-injury (Dutch population norm). Predictors were: gender, age, living with partner, number of chronic health conditions, SMFA-NL PDA score 6 weeks post-injury, ICU admission, length of stay in hospital, injury severity score, occurrence of complications and treatment type. All predictors were obtained before 6 weeks post-injury. Missing data were multiply imputed. Predictor variables were selected using backward stepwise multivariable logistic regression. Hosmer-Lemeshow tests were used to evaluate calibration. Bootstrap resampling was used to internally validate the final model.Patients that presented at the emergency department with an acute traumatic injury, were prompted for participation. Patients that completed the assessments at 6 weeks and 12 months post injury were included. Exclusion criteria: age < 18, age > 65, pathologic fractures, injuries that resulted in severe neurologic deficits. The predicted outcome, 2 was 0.33 , the c-statistic was 0.79 . Hosmer-Lemeshow test indicated good calibration (p = 0.92). Optimism-corrected R2 was 0.28  and the optimism-corrected Area Under the Curve was 0.77 .A total of 246 patients were included, of which 104 (44%) showed functional recovery. The predictors in the final PRO-Trauma model were: living with partner, the number of chronic health conditions, SMFA-NL PDA subscale score at 6 weeks post-injury and length of stay in hospital. The apparent RThe PRO-Trauma prediction model can be used to obtain valid predictions of attaining functional recovery after trauma at 12 months post-injury. The PRO-Trauma prediction model showed acceptable calibration and discrimination. Ex. Ex18]. The required sample size was derived from the number of predictors in the model. Previous studies have suggested that about 10 events (patients that recovered) are required per predictor variable, in order to avoid over-fitting ,20. We aMissing data was handled using multiple imputation . MissingThe 6 weeks and 12 months post-injury SMFA-NL Problems with Daily Activities subscale scores were calculated. The outcome functional recovery was defined by dichotomizing the 12-months post-injury SMFA-NL Problems with Daily Activities subscale score by 12.2 points .A multivariable logistic regression analysis was used to evaluate the relation of the predictors with the outcome functional recovery in each imputed dataset. All predictors were considered in the full model and were eliminated using backward stepwise selection. To decide which predictors should be kept in the final model, predictors were removed from the pooled model, using the Pooled Sampling Variance (D1) method ,27. A p-1; Q3] p-value > 0.05 was considered evidence of good calibration  Nagelkerke’s R2 and the c-statistic across the imputed datasets  optimism-corrected Nagelkerke’s R2 and c-statistic were calculated . The median c-statistic was 0.79 , indicating acceptable discrimination. The Hosmer-Lemeshow test showed a median p-value of 0.93 , indicating good calibration. This was supported by the calibration curve of the PRO-Trauma model in each imputed dataset , shown in Clinical example: A 25 year-old man, was admitted due to motor vehicle injury. Had no chronic health conditions and lived without a partner. The patient was diagnosed with a left-sided femoral shaft fracture, for which he was treated with an antegrade femoral nail. The patient was dismissed after 5 days. At 6 weeks post-injury the SMFA-NL Problems with Daily Activities subscale score was 44 points. The Risk Score (linear predictor) was -0.147, which corresponds to a 54% chance on functional recovery after 1 year. In the nomogram, each variable was rewarded points; the variables summed to 48 points in total, corresponding with a probability on functional recovery of about 54% .2 was 0.41  and the median C-statistic was 0.83 .The selected model consisted of the SMFA-NL Problems with Daily Activities scale 6 weeks post-injury, BMI , smoking status , living with partner, number of chronic health conditions, length of stay in hospital. The median Hosmer-Lemeshow test p-value was 0.81 , indicating good calibration. The median Nagelkerke’s RThe PRO-Trauma prediction model provided estimates of the overall chance on being functionally recovered at 12 months post-injury. The model consisted of four variables: SMFA-NL Problems with Daily Activities, length of stay in hospital, living with a partner and the number of chronic health conditions. The PRO-Trauma prediction model showed acceptable calibration and discrimination. Predictions can be made in an early stage of the recovery trajectory: at 6 weeks post-injury.The predictors of the final model, were clinically relevant and sustained by previous literature ,5–8. ThePROMs such as the SMFA-NL, are mostly used as an end-point, to evaluate functional outcome. However, PROMS such as the SMFA-NL, may also serve as a quality-control instrument for benchmarking performance of healthcare institutions, or as clinical follow-up instrument that measures change in physical functioning over time ,37. The Patients without a partner were at risk of not reaching functional recovery. We think this may reflect the strength of patients’ social network and available social support. Partners may provide support in coping, as well as providing aid to overcome a patient’s own activity limitations and participation restrictions. Having a partner and a strong social network have been associated with better functional outcome and higher return to work rates ,39. AlthLength of stay in hospital was an independent predictor of functional recovery. The injury severity score (ISS), was not. The ISS is an established score to rate the overall severity of injury in patients with one or more injuries. The ISS takes into account anatomic injury region, injury type and the number of injuries. However, the ISS did not provide additional significant prognostic value and was removed from the model in the backward-stepwise selection procedure. This may be related to the construct that the ISS intends to measure. Namely, the ISS was developed to predict trauma related mortality, and not morbidity or functional outcome ,18. ThouSmoking and BMI were both identified as independent predictors of functional recovery in the sensitivity analysis. The negative effects of smoking and sub-optimal BMI on general health are widely known. These negative effects also appeared to apply to the chance of being functionally recovered after 12 months. Both parameters could not be included in the model, since both were assessed at 12 months post-injury. However, it is likely that the c-statistic of the PRO-Trauma model would have been higher, if these variables could have been included.In trauma surgery, evaluation of treatment outcome, traditionally focused on mortality. As a consequence, multiple prognostic models were developed to predict mortality after trauma, including TRISS, ISS and the Revised Trauma Score . HoweverIn a related clinical field, two prognostic models have been developed that predict clinical outcome in patients that sustained traumatic brain injury. Steyerberg et al. developed a model using physiological, biochemical and radiographic predictors, on the Glasgow Outcome Scale (GOS) at 6 months post-injury . The AUCThe PRO-Trauma prediction model was developed for patients of the working-age without traumatic brain injury or severe neurologic deficits. Traumatic brain injury and severe neurologic deficits due to trauma have vastly different recovery patterns in terms of physical functioning compared to patients with musculoskeletal injuries. Both the SMFA questionnaire and PRO-Trauma model were not developed and validated for patients with such conditions. Without a specific validation procedure in these patients, the model may not be used in patients with such injuries. In addition the PRO-Trauma prediction model cannot be directly applied to geriatric trauma patients. These patients were not part of the study sample. There is accumulating evidence that injury types, severity and recovery patterns after trauma in elderly follows distinct patterns compared to younger patients ,46. EldeEarly prediction of functional recovery, allows clinicians to systematically identify patients that are at risk of developing a poor functional outcome. In addition, clinicians may inform patients with a quantified estimate of their chance on functional recovery, which may aid in shared decision making. Though, it should be noted that clinical prediction models are an augmentation to clinical appreciation of patients, and should not replace clinical judgment of patients that sustained an injury. Clinicians may prefer the nomogram for a quick and comprehensive assessment. Exact probabilities may be calculated by hand or using the provided Excel sheet (electronic supplement).The PRO-Trauma prediction model may be useful in clinical research. Being able to identify which patients are at risk of a poor outcome, may have relevant consequences for the design and analysis of clinical trials. For example, patients at risk of poor functional outcome may be specifically included in an RCT, or risk scores may be used to adjust for treatment effects .Fitting a large number of predictors carries the risk of overfitting, thereby making the findings less robust ,20. The Missing data can seriously decrease the effective sample size and statistical power in regression models, even with a limited number of missing values . In the The validity and generalizability of clinical prediction models should be evaluated thoroughly before they can be used in clinical practice and decision making. One of the limitations of the present study was that only internal validity of the model was assessed. Internal validity is especially useful to evaluate the stability of the selected predictors and to appraise the quality of the predictions . HoweverNormative data of the SMFA-NL were used as a reference to indicate functional recovery . GeneralEven though the ISS was shown not to be a relevant predictor in the model, we think that the model does account for injury characteristics and injury severity, however not with traditional variables that directly reflect injury type or anatomic region. We think that the 6-weeks post-injury SMFA Problems with Daily Activities (PDA) subscale score and the Length of Hospital Stay predictors are reflective of injury severity and injury type. Patients with more severe injuries are likely to report a worse SMFA PDA score 6 weeks post-injury, which predicts a lower chance on functional recovery. In addition, more severely injured patients, are likely to have a longer length of hospital stay, which both predict a lower chance on functional recovery. Specification of injury type and anatomic region  may improve the accuracy of the model, but would require creation of many predictor variables and/or categories. In the present study this was not feasible. Due to the sample size, additional variables could not be added to the model since this would violate the EPV ratio of 10:1 which was used to avoid overfitting and false-positive predictor selection. A secondary drawback of adding many injury type and region-specific variables is that it would complicate the model, which would negatively impact clinical usability. In future studies additional injury-specific variables may be evaluated to expand the PRO-Trauma prediction model, as well as other variables that may be predictive of functional recovery that were not available in the present study. Such predictors may be additional chronic health conditions such as kidney disease, radiographic signs such as (early) fracture consolidation, psychological factors, health literacy or a wider analysis of patients’ social support ,49–52.Prognosis of physical functioning after trauma is a complex context that is important to patients, health-care professionals and decision makers. The present study may be regarded as an initial exploring step in the direction of the development of prognostic models that predict outcome after trauma, rather than mortality. The external validation of the PRO-Trauma prediction model is required to evaluate the performance of the model in other populations and may be an important step towards clinical adoption. In addition, the PRO-Trauma prediction model may be expanded with additional predictors of functional recovery that improve the accuracy of the model. Though multiple longitudinal cohort studies have evaluated well-known predictors, a better understanding of factors that determine functional outcome after trauma is needed . Due to In conclusion, a prognostic model that predicts functional recovery at 12 months post-injury is now available. The PRO-Trauma prediction model showed acceptable calibration and discrimination. Physical functioning shortly assessed after the injury, was predictive for functional recovery. The PRO-Trauma prediction model may be useful for quantifying the chance of reaching functional recovery at 12 months post-injury, identifying and informing patients that are at risk of developing a poor outcome and for adjusting treatment effects in clinical trials.S1 FigOriginal data = 0; Imputed datasets are 1 to 5. Blue circles: original observed data. Red dots: imputed data. The imputed data are within the observed data, indicating realistic imputations in each dataset.(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 FigThis nomogram may be used and re-printed for use in clinical settings.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.