Bladder cancer is one of the most common malignancies. The prognosis is particularly poor for advanced cancer. There is a need for new prognostic markers to guide treatment decisions and promote the development of novel therapeutic strategies to increase the success rate of current treatment regimens. The tumor microenvironment is characterized by an increase in extracellular ATP levels. ATP-recognizing P2X receptors have been implicated in the growth and metastasis of various malignant cancers. Here, we analyzed the potential of different P2X receptor subtypes as prognostic markers in muscle-invasive bladder cancer. In vitro experiments confirmed a growth-promoting effect of extracellular ATP and P2X receptors on bladder cancer cells. In agreement, our analyses of cancer tissue samples from 173 patients showed that expression of P2X1 and P2X7 receptors is an independent negative predictor of survival and a potential therapeutic target in muscle-invasive bladder cancer.Bladder cancer is amongst the most common causes of cancer death worldwide. Muscle-invasive bladder cancer (MIBC) bears a particularly poor prognosis. Overexpression of purinergic P2X receptors (P2XRs) has been associated with worse outcome in several malignant tumors. Here, we investigated the role of P2XRs in bladder cancer cell proliferation in vitro and the prognostic value of P2XR expression in MIBC patients. Cell culture experiments with T24, RT4, and non-transformed TRT-HU-1 cells revealed a link between high ATP concentrations in the cell culture supernatants of bladder cell lines and a higher grade of malignancy. Furthermore, proliferation of highly malignant T24 bladder cancer cells depended on autocrine signaling through P2X receptors. P2X1R, P2X4R, and P2X7R expression was immunohistochemically analyzed in tumor specimens from 173 patients with MIBC. High P2X1R expression was associated with pathological parameters of disease progression and reduced survival time. High combined expression of P2X1R and P2X7R increased the risk of distant metastasis and was an independent negative predictor of overall and tumor-specific survival in multivariate analyses. Our results suggest that P2X1R/P2X7R expression scores are powerful negative prognostic markers in MIBC patients and that P2XR-mediated pathways are potential targets for novel therapeutic strategies in bladder cancer. Urinary bladder cancer accounts for about 573,000 newly diagnosed cancer cases and 212,000 deaths worldwide each year .While cisplatin-based chemotherapy remains the standard therapy perioperatively and in patients with metastatic disease, several new agents have been introduced for the treatment of bladder cancer over the last decade, including immune checkpoint inhibitors and targeted therapies with antibody–drug conjugates ,5. NeverThe tumor microenvironment (TME) plays a critical role in tumor growth and progression . The purThe accumulation of ATP in the TME is often paralleled by altered P2 receptor expression patterns in tumor tissues . ChangesThe purpose of this study was to investigate the role of P2XRs in bladder cancer cell proliferation and the prognostic value of P2XR expression in MIBC. We focused on P2X1R, P2X4R, and P2X7R as these receptors have been repeatedly associated with tumor growth in different cancers ,19,26 anThis retrospective study was approved by the Medical Ethics Committee of Ludwig Maximilian University (LMU) Munich (reference number 20–179). The study group consisted of 173 patients who were diagnosed with MIBC and underwent radical cystectomy at the Department of Urology (LMU Munich) between 2004 and 2014. Surgery was performed with curative or palliative intent using standardized procedures for urinary diversion by ileal conduit or ileal neobladder formation. All histological specimens were systematically reviewed to confirm tumor type, grade, and stage by experienced pathologists. Staging was performed according to the AJCC/UICC TNM staging guidelines (8th edition) and the latest WHO classification of genitourinary tumors (5th edition) ,29. PatiExpression of P2X1R, P2X4R, and P2X7R was examined in tissue microarrays (TMAs) using formalin-fixed, paraffin-embedded (FFPE) tissue blocks from 173 MIBCs. Punched-out 1 mm tumor cores from three different tumor areas per patient were arrayed into new TMA blocks. Triplicates of each sample were used to minimize tissue loss and to overcome tumor heterogeneity. The embedded tissue was cut into 4 µm thick slices and used for immunohistochemical analyses.Antibody staining was established with appropriate isotype and system controls. Tonsil tissue was used as a positive control and was included in each staining run. All antibodies had been validated by the manufacturers against Western blot and RNA-seq data on a wide range of normal and cancer tissue.For P2X1R staining, antigen retrieval was carried out by heat treatment using the antigen retrieval AR-10 kit . Slides were incubated with the polyclonal rabbit anti-human P2X1R primary antibody  for 60 min at room temperature. Bound antibodies were detected by the use of the ImmPRESS Anti-Rabbit IgG Polymer Kit .For P2X4R staining, slides were subjected to heat pre-treatment with ProTaqs IV Antigen Enhancer . After incubation with the rabbit polyclonal anti-human P2X4R primary antibody  for 60 min at room temperature, the MACH 3 Rabbit HRP Polymer Detection kit  was used as a detection system.For P2X7R staining, slides were subjected to heat retrieval with Novocastra Epitope Retrieval Solution pH 8.0 . After incubation with the monoclonal mouse anti-human P2X7R antibody  for 60 min at room temperature, bound antibodies were detected with the MACH 3 Mouse HRP Polymer Detection system .All reactions were visualized using DAB+  as a chromogen. Sections were then counterstained with hematoxylin , dehydrated, and mounted.Immunoreactivity of P2XRs was scored using the histochemical scoring system (H-score) as previously described . The H-s2 and 95% air.T24, RT4, and TRT-HU-1 cell lines were kindly provided by Dr. Rosalyn M. Adams . T24 and RT4 cells were cultured in McCoy’s 5A medium  supplemented with 10% heat-inactivated fetal bovine serum , 100 U/mL penicillin, and 100 µg/mL streptomycin (Gibco). The hTERT-immortalized human urothelial cell line TRT-HU-1 was maintained in Dulbecco’s modified Eagle medium  supplemented with 15% non-heat-inactivated FBS (Invitrogen), MEM non-essential amino acids (Gibco), and 0.01% 1-thioglycerol . Cells were cultured at 37 °C in a humidified atmosphere containing 5% CO6 cells/mL in CFSE staining solution (5 µM in PBS). After 15 min incubation at 37 °C, ten volumes of cell culture medium were added, and the cell mixture was incubated for another 5 min. Then, cells were pelleted by centrifugation, washed once with culture medium, and seeded at 15,000 cells per well in a 24-well cell culture plate. If indicated, antagonists of P2X1R , P2X4R , or P2X7R , the P2 receptor antagonist suramin , the ATP-degrading enzyme apyrase , the ATP release blocker carbenoxolone , or the natural P2XR ligand ATP  were added, and the cells were maintained in a final volume of 500 µL in 5% CO2/95% air at 37 °C. Final drug concentrations were chosen based on previous studies by us and others [kt was fitted to the data by non-linear regression  to determine the growth rate k and the doubling time, DT (DT = ln2/k).Proliferation of T24, RT4, and TRT-HU-1 cells was assessed using the CellTrace™ CFSE Cell Proliferation Kit (Invitrogen) as recommended by the manufacturer with a few modifications. Briefly, cells were detached by trypsin, washed with phosphate-buffered saline (PBS), and resuspended at 5 × 10d others ,19. At td others . The fun5 cells per well in a final volume of 500 µL and allowed to attach for 3 h in an incubator adjusted to 5% CO2/95% air at 37 °C. Cell culture supernatants were collected, cooled in an ice bath for 10 min, and centrifuged twice to ensure cell-free samples. Perchloric acid (400 mM) was added to the supernatants to stop any enzymatic activity. Then, samples were processed, and ATP concentrations were determined by high-performance liquid chromatography (HPLC) as previously described [T24, RT4, and TRT-HU-1 cells were seeded in 24-well plates at a density of 3.75 × 10escribed .p   <  0.05 were considered statistically significant.Statistical analyses were performed with SigmaPlot 12.5 software. OS was defined as the time between primary surgery and death from any cause. For TSS, death caused by bladder cancer was defined as the clinical endpoint. Patients who were alive at the end of follow-up were censored. DFS refers to the time between primary surgery and relapse. Patients were censored for DFS if recurrent cancer and metastasis were absent at the end of follow-up or at the time of death. Survival curves were calculated using the Kaplan–Meier method and compared by the log-rank test. Associations between P2XR expression, demographic characteristics , and clinicopathological parameters , lymphovascular invasion (L), blood vessel invasion (V), perineural invasion (Pn), resection margin (R), UICC stage, receipt of adjuvant therapy) were examined using the Chi-square test or unpaired t-test for categorical and numerical variables, respectively. To assess the prognostic value of low or high P2XR expression, multivariable hazard ratios (HRs) were determined by Cox proportional hazard regression and adjusted for the parameters that correlated with P2XR expression . One-way ANOVA and the Holm–Sidak test were used to analyze results from cell culture experiments. Values of p < 0.001; While numerous studies in recent years have established pro-tumorigenic roles of extracellular ATP and P2X receptors in various cancers ,23,24,25p < 0.001; We and others have previously reported that P2X1R, P2X4R, and P2X7R have growth-promoting effects in different cancer cells in vitro and in vivo ,36,37,38p = 0.02), lymph node metastasis  and UICC stage (p = 0.01). In addition, high combined expression of P2X1R and P2X7R was significantly associated with lymphovascular invasion , lymph node metastasis , distant metastasis , and UICC stage (p = 0.001). Patient demographics, tumor characteristics, and their associations with P2XR expression are summarized in The results of the in vitro experiments suggested that P2X1R, P2X4R, and P2X7R could be markers of bladder cancer growth and progression. To test this possibility, we next assessed the expression profiles of these receptors in MIBC and analyzed possible associations between expression intensity and clinicopathological characteristics. A total of 173 MIBC patients were included in the study. The mean age at the time of surgery was 67.1 (±9.0) years. Consistent with previous studies, the majority of patients were male (73.4%) [Median OS of all patients after initial surgery was 17.0 months. Median OS in patients with low P2X1R expression was 23.5 ± 45.6 months and significantly longer than in patients with high P2X1R expression  and median TSS was 15.07 months. TSS in patients with high P2X1R expression (11.8 ± 10.0 months) was significantly lower than in patients with low P2X1R expression  and TSS  in MIBC patients. For P2X1R, P2X4R, or P2X7R expression, no statistically independent influence on survival was found. The results of the multivariate analysis are summarized in Multivariable survival analyses were calculated for OS, TSS, and DFS and adjusted for clinicopathological covariates that correlated with P2XR expression  or non-transformed immortalized TRT-HU-1 cells. This is consistent with reduced expression of ATP-converting ectonucleotidases and a reduction in the ATP-hydrolyzing capacity described in bladder cancer cells of high malignancy ,60. On tOnly a few studies have investigated the role of P2X4R in cancer so far with sometimes contradictory results. Anti-proliferative effects of P2X4R were described in gastric and breast cancer cells in vitro ,63. On tWhile we restricted our study to MIBC patients, who bear the poorest prognosis, most newly diagnosed bladder cancers are non-muscle-invasive bladder cancers (NMIBCs) ,68. NMIBWhile we demonstrated that P2X1R/P2X7R expression is linked to lymph node and distant metastasis, the detailed underlying molecular mechanisms remain unclear. In addition, other purinergic receptor subtypes might affect malignancy factors such as tumor growth, tumor cell migration, invasion, and metastasis in different types of bladder cancer. Of note, high P2X6R expression was recently associated with prolonged survival in a mixed population of low- and high-grade bladder cancer patients . Thus, fThe prognosis of advanced bladder cancer is poor and current therapies are often associated with severe side effects and significant costs. To avoid overtreatment, there is a high demand for new markers that can accurately assess the prognosis of patients with MIBC . Our res


In the whole study cohort, FABP3 was independently associated with all-cause death, and both FABP3 and FABP4 concentrations were associated with CV mortality. The predictive values of these two molecules for all-cause  and CV mortality  were only statistically significant in the subgroup of subjects with T2D. Notably, FABP4 , but not FABP3, also predicted the occurrence of the composite endpoint  only in subjects with T2D. All these associations were not found in CHF subjects without T2D. Our findings support the usefulness of serum FABP3 and FABP4 concentrations as independent predictors for the occurrence of all-cause and CV mortality in ambulatory subjects with CHF with T2D.Subjects with type 2 diabetes mellitus (T2D) are at increased risk for heart failure (HF). The cardiac-specific (FABP3) and adipose-tissue-specific (FABP4) types of the fatty acid binding proteins have been associated with both all-cause and cardiovascular (CV) mortality. The aim of this study was to explore the prognosis value of FABP3 and FABP4 in ambulatory subjects with chronic HF (CHF), with and without T2D. A prospective study involving 240 ambulatory CHF subjects was performed. Patients were followed-up for a mean of 5.78 ± 3.30 years and cause of death (if any) was recorded. Primary endpoints were defined as all-cause and CV death, and a composite endpoint that included CV death or hospitalization for HF was included as a secondary endpoint. Baseline serum samples were obtained and the serum FABP3 and FABP4 concentrations were assessed by sandwich enzyme-linked immunosorbent assay. Survival analysis was performed with multivariable Cox regressions, using Fine and Gray competing risks models when needed, to explore the prognostic value of FABP3 and FABP4 concentrations, adjusting for potential confounders. Type 2 diabetes mellitus was highly prevalent, accounting for 47.5% for total subjects with CHF. Subjects with T2D showed higher mortality rates (T2D: 69.30%; non-T2D: 50.79%,  Increasing evidence has shown a greater risk for heart failure (HF) associated with the presence of type 2 diabetes mellitus (T2D) . Indeed,Metabolic disturbances, including impaired glucose and fatty acid metabolism, have been related to increased risk for HF independently of coronary artery disease . EnhanceBoth FABP3 and FABP4 have been linked to oxidative stress. For instance, circulating FABP3 has been positively related to oxidative stress biomarkers, including malondialdehyde (MDA) and asymmetric dimethylarginine (ADMA), and inversely associated with the total antioxidant capacity (TAC) in patients with carbon-monoxide-induced cardiotoxicity . On the An increasing body of evidence supports the notion that both FABP3 and FABP4 serum concentrations can predict both all-cause ,40,41,42The current research was performed in a subset of a well-characterized ambulatory cohort of subjects with CHF, prospectively admitted in a structured ambulatory multidisciplinary HF unit ,60. SpecAll participants provided written informed consent. The study was approved by Local Ethics Committee of the Hospital Universitari Germans Trias i Pujol (code: EO 10-076) and was performed according to the ethical standards outlined in the Declaration of Helsinki .Anthropometric and clinical data were obtained at the point of study inclusion and were described elsewhere .Serum concentrations of FABP3 and FABP4  were determined in duplicate using commercial sandwich enzyme-linked immunosorbent assay kits (intra- and inter-assay coefficients of variation were estimated <5%).A diagnosis of T2D was made when one of the following criteria were met: (1) a diagnosis of T2D was previously established and recorded in the patient’s electronic history, (2) fasting plasma glucose ≥ 126 mg/dL or HbA1c ≥ 6.5% identified by laboratory testing  or (3) tt test and Mann–Whitney U test, as required.The Kolmogorov–Smirnov test was used to determine the normality of the continuous variables. Continuous variables were expressed as median and interquartile range, unless otherwise indicated. Categorical variables are expressed as numbers with percentages. Differences between patients were analyzed by the Chi-squared test, Student’s p < 0.05.The association of FABP3 and FABP4 with all-cause and CV mortality, or the composite endpoint , was evaluated using a multivariable Cox regression analysis approach. The primary endpoints were considered as the dependent variables and the selected relevant clinical variables  functional class, time of evolution, FEECO (ejection fraction on echocardiography) NT-ProBNP and obesity) plus FABP3 or FABP4 as independent covariables. Competing risks models using the Fine and Gray method were realized with CV mortality and the composite endpoint . FABP3 and FABP4 analyses were performed for each 1 ng/mL or 1 ng/dL increase, respectively. Statistical analyses were performed using STATA V.16.0 . Differences were considered statistically significant with a two-sided p-value = 0.002; 80.7% vs. 49.2%, p-value < 0.001, respectively). Upon inclusion, 73.7% of subjects with CHF and T2D were receiving oral antidiabetic drugs and 62.3% were under insulin treatment. The percentage of subjects with NYHA functional classes III-IV was higher in subjects with T2D compared with those without T2D . Subjects with T2D showed lower serum concentrations of total-, HDL- and LDL-cholesterol and increased serum concentrations of creatinine and NT-proBNP, compared with subjects without T2D. Additionally, subjects with T2D also showed increased serum concentrations of FABP3  and FABP4  compared with subjects without T2D. No significant correlations were found between lipid parameters  with FABP3 and FABP4 in T2D patients , non-significant correlations were found in T2D patients  . During a mean follow-up period of 5.78 ± 3.30 years, 143 patients died. The average years of follow-up until death (5.22 (2.02–8.17) vs. 7.13 (3.27–8.90), p-value = 0.009) or the composite endpoint (death or readmission) (2.57 (0.61–6.81) vs. 5.72 (0.90–8.31), p-value = 0.006) were lower in T2D than in non-T2D subjects.The baseline characteristics of the study population and a comparison of the clinical and biochemical parameters of patients with CHF with and without T2D are shown in patients . Urate w< 0.692) . The morThe rate of all-cause mortality increased along with FABP3 A and FABp-value = 0.002; FABP4: HR 2.21, 95% CI 1.12–4.36, p-value = 0.023, respectively)  , but not FABP3, predicted the occurrence of the composite endpoint  in subjects with CHF and T2D   and CV dctively) . Neverthctively)  and CV (ctively)  mortalit and T2D , but not and T2D . Indeed, and T2D A and FAB and T2D B tertile and T2D  death we and T2D  was pred and T2D .Multivariable models were adjusted for clinically relevant variables. AUC for FABP3 = 0.8644. AUC for FABP4 = 0.8657. FABP3: fatty acid binding protein 3; FABP4: fatty acid binding protein 4; HF: heart failure; NYHA: New York heart association; LVEF: left ventricular ejection fraction; NTproBNP: pro-B-type natriuretic peptide; eGFR estimated glomerular filtration rate (CKD-EPI equation).Multivariable models were adjusted for clinically relevant variables. AUC for FABP3 model = 0.7532. AUC for FABP4 model = 0.7428. FABP3: fatty acid binding protein 3; FABP4: fatty acid binding protein 4; HF: heart failure; NYHA: New York heart association; LVEF: left ventricular ejection fraction; NTproBNP: pro-B-type natriuretic peptide; eGFR estimated glomerular filtration rate (CKD-EPI equation); SHR: Subdistribution Hazard Ratio.Multivariable models were adjusted for clinically relevant variables. AUC for FABP3 model = 0.7131. AUC for FABP4 model = 0.7188. FABP3: fatty acid binding protein 3; FABP4: fatty acid binding protein 4; HF: heart failure; NYHA: New York heart association; LVEF: left ventricular ejection fraction; NTproBNP: pro-B-type natriuretic peptide; eGFR estimated glomerular filtration rate (CKD-EPI equation); SHR: Subdistribution Hazard Ratio.Subjects with CHF and T2D frequently display a poor prognosis . In thesThe role of FABP3 and FABP4 as independent predictors of mortality has been reported in subjects with pulmonary embolism ,47 and aIn the present study, a comprehensive Cox regression model was built in order to further analyze the potential role of both FABP3 and FABP4 as all-cause mortality predictors in a cohort of ambulatory patients with CHF. Serum concentrations of both molecules were higher in patients with CHF and T2D compared with patients with CHF without T2D. In our models, FABP3, but not FABP4, was identified as an independent predictor of the all-cause death in the whole study cohort. Remarkably, when only CHF subjects with T2D were considered, both FABP3 and FABP4 predicted the occurrence of all-cause mortality but were unable to predict the all-cause mortality in the subgroup of subjects without T2D.Focusing on CV mortality prediction, previous studies identified that low concentrations of FABP3 may predict CV death in combination with high BNP concentrations in patients with non-ischemic dilated cardiomyopathy . MoreoveIn the metabolic context of T2D, the energy substrates of cardiomyocytes to produce metabolic energy switch from glucose to fatty acid. The increased use of fatty acids for energy production in mitochondria is frequently associated with increased reactive oxygen species (ROS) production, which leads to enhanced oxidative stress in diabetic cardiomyocytes. The accumulation of ROS profoundly affects normal cardiomyocyte physiology and function, leading to reduced cardiac contractibility and maladaptive cardiac response . In thisOur study has some limitations. First, it was performed in a subset of the general population attending a single-center HF unit in a tertiary hospital, and it is not possible to rule out the possibility of bias due to selection. The relatively small sample size attenuated the impact of the results. Unfortunately, data on insulin, as well as oxidative parameters other than urate were unavailable in our cohort data sets. Additionally, the retrospective nature of our study precludes the extrapolation of causal relationships from our data. Nevertheless, our data are in line with increasing evidence suggesting a prognosis value of both FABP3 and FABP4 in a wide range of pathologies. Finally, although several adjusted multivariate models were performed, additional confounders may have had an impact on the results.Overall, our findings strongly support the role of both serum FABP3 and FABP4 as independent predictors for the occurrence of all-cause and CV mortality in ambulatory subjects with T2D and CHF.


Mutations in the KEAP1-NFE2L2 signaling pathway were linked to increased tumorigenesis and aggressiveness. Interestingly, not all hotspot mutations on NFE2L2 were damaging; some even were activating. However, there was conflicting evidence about the association between NFE2L2 mutation and Nrf2-activating mutation and responsiveness to immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) and other multiple cancers.The study with the largest sample size  explored the landscape of NFE2L2 mutations and their impact response/resistance to ICIs using public cohorts. In addition, the in-house WXPH cohort was used to validate the efficacy of immunotherapy in the NFE2L2 mutated patients with NSCLC.In two pan-cancer cohorts, Nrf2-activating mutation was associated with higher TMB value compared to wild-type. We identified a significant association between Nrf2-activating mutation and shorter overall survival in pan-cancer patients and NSCLC patients but not in those undergoing ICIs treatment. Similar findings were obtained in cancer patients carrying the NFE2L2 mutation. Furthermore, in NSCLC and other cancer cohorts, patients with NFE2L2 mutation demonstrated more objective responses to ICIs than patients with wild type. Our in-house WXPH cohort further confirmed the efficacy of immunotherapy in the NFE2L2 mutated patients with NSCLC. Lastly, decreased inflammatory signaling pathways and immune-depleted immunological microenvironments were enriched in Nrf2-activating mutation patients with NSCLC.Our study found that patients with Nrf2-activating mutation had improved immunotherapy outcomes than patients with wild type in NSCLC and other tumor cohorts, implying that Nrf2-activating mutation defined a distinct subset of pan-cancers and might have implications as a biomarker for guiding ICI treatment, especially NSCLC.The online version contains supplementary material available at 10.1186/s12935-023-03056-9. Currently, immune-checkpoint blocking (ICB) therapy, such as CTLA-4 or PD-1/PD-L1 inhibitors, has a therapeutic application on the majority of human tumors, and identifying individuals who are susceptible to ICB is the focus of non-surgical cancer treatment –6. FurthThe KEAP1/NFE2L2 signaling pathway is considered one of the most crucial system components for cell defense against oxidative stress injury . Once thNotedly, any two subtypes of the catenin-yaps127a-l30p/R34P mutants of NFE2L2 in hepatoblastoma of children are tumorigenic without relying on the activity of KEAP1, providing direct proof of NFE2L2 being an oncoprotein . The FooHerein, we sought to examine the landscape of NFE2L2 mutations and Nrf2-activating mutations, in 49,533 patients in a pan-cancer cohort (OrigiMed cohort) and the other two pan-cancer cohorts  and their association with clinical outcomes . MoreoveWe used ten public cohorts and one in-house cohort to explore the landscape of NFE2L2 mutations and Nrf2-activating mutations and their impact on response/resistance to ICIs in NSCLC and other solid tumors  of the genome under investigation . The TMBThe OrigiMed cohort was comprised of 10,194 patients across 25 tumor types who underwent a next-generation sequencing (NGS) assay in the Clinical Laboratory Improvement Amendments (CLIA)-certified and College of American Pathologists (CAP)-accredited laboratory, Shanghai, China. Comprehensive genomic profiling for the Chinese cohort was performed using customized panels of 450 genes .https://www.cbioportal.org/) [https://portal.gdc.cancer.gov/).Somatic mutation data of the MSK MetTropism cohort  and the TCGA cohort  data were downloaded from cBioPortal platform   The tranhttps://www.cbioportal.org/). The DFCI cohort included 176 patients who were being treated with ICIs for bladder cancer, lung cancer, or melanoma. [The efficacy of ICIs was assessed in seven publicly ICB cohorts including DFCI, MSK, MSK 1661, Pender, OAK, POPLAR and PUCH cohorts –37. The elanoma. , 6. The elanoma. . The Penelanoma. . In the elanoma. . The MSKelanoma. , 43.The WXPH cohort consisted of 65 NSCLC patients who were treated with anti-PD-1/PD-L1 agents and NGS tests and were recruited by the Affiliated Wuxi People's Hospital of Nanjing Medical University between 2018 and 2022. Burning Rock Dx performed the PD-L1 and NGS testing. The Clinical Research Ethics Committee of the Affiliated Wuxi People's Hospital of Nanjing Medical University granted ethical approval (Number: KY21126) for the collection of these participants.The t-test or Mann–Whitney test were used to compare differences between the two groups. To compare categorical variables, the Chi-square tests or Fish exact probabilities were utilized. Pearson or Spearman correlation analysis was used to directly measure the relationship between the two continuous variables. The legend provides the aforementioned statistical details, such as the statistical tests performed, the number of samples, dispersion, data type, and how the level of significance was calculated. The Kaplan–Meier method was implemented to make survival curves, and the Log-rank test was performed to compare differences. The Cox regression model was used to compare survival outcomes across several groups, and multiple testing correction was performed using the Bonferroni technique, with p < 0.05 considered significant. To account for confounding effects, multivariate Cox regression models from the survminer package were in use . For diferal NFE2L2 MU-subtypes, such as R34 (n = 24), E79 (n = 23), D77 (n = 9), D29 (n = 17), G31 (n = 16), L30 (n = 14), D27 (n = 11), and E82 (n = 23) were clustered in identified hotspots  in the OrigiMed cohort with esophageal carcinoma  ranking first followed by thyroid carcinoma (6.3%), gallbladder carcinoma (5.4%) and NSCLC (4.7%)  had at least one NFE2L2 MU Fig. B.Fig. 3SP < 0.001; Additional file Interestingly, there was a significant association between the frequency of NFE2L2 MU and median TMB across multiple tumor types  and LUAD  had Nrf2-activating MU in the LUAD subtype and 88 (18.0%) did so in the LUSC subtype Fig. C. NSCLC To investigate the predictive efficacy of NFE2L2 MU for ICI treatment in pan-cancer, we evaluated the association between NFE2L2 MU and clinical outcomes in multiple types tumors of a pooled cohort n = 2611) composed of DFCI, MSK, MSK1661, OAK, Pender, POPLAR and PUCH cohorts. Clinical characteristics of the pooled cohort, including cancer types, NFE2L2 MU, response to ICI, gender, ICIs treatment, age and TMB were showed in Table 1 composeP = 0.63, Fig. Furthermore, there was not significant difference of over survival among four groups including Nrf2-activating MU, Nrf2-inactivating MU, unknown NFE2L2 MU and WT groups  having NFE2L2 MU. Notably, NSCLC patients with NFE2L2 MU had greater PD-L1 and TMB levels as compared to patients without NFE2L2 MU  the low frequency of NFE2L2 MUs (< 4%) was observed in the two pan-cancer cohorts with more common mutation cancers types including NSCLC, ESCA and head and neck cancer; (ii) The NFE2L2 MU and Nrf2-activating MU were associated with higher TMB value compared with wild-type in the two pan-cancer cohorts; (iii) OS were shorter in patients with NFE2L2 MU and Nrf2-activating MU compared with the WT group from NSCLC patients and pan-cancer patients in the MSK MetTropism cohort, LUAD patients in TCGA cohort; (iv) NFE2L2 MU and Nrf2-activating MU were not associated with OS in NSCLC patients and pan-cancer patients from several cohorts with ICIs treatment; (v) More objective responses to ICIs were observed in the patients with NFE2L2 MU than in those with NFE2L2 WT from several cohorts with ICIs treatment; and (vi) decreased inflammatory signaling pathways and immune-depleted immunological microenvironments were enriched in NSCLC patients with NFE2L2 MU and Nrf2-activating MU.The NFE2L2 pathway plays a crucial role in cellular defense mechanisms against oxidative stress and the activation of antioxidant responses . TypicalA previous study shows that high expression of nuclear NFE2L2 predicts poor prognosis in patients with esophageal squamous cell carcinoma . FurtherIn particular, when classifying Keap1/NFE2L2 mutations into different groups: Nrf2-activating MU, Nrf2-inactivating MU, unknown NFE2L2 MU and WT groups, it was observed that most immunotherapy-blocked genes were lower expressed, and there was a low enrichment of tumor-related immune cells, TCR richness, and Shannon diversity in Nrf2-activating MU group. However, such trends were not observed in the Nrf2-inactivating mutation group. These findings suggest that Nrf2-activating MU cancers, based on KEAP1/NFE2L2 mutations, may be more suitable for immunotherapy, might be more amenable to immunotherapy. While the presence of NFE2L2 mutations may result in a lower number of immune cells, there is potential for adoptive T cell therapy to enhance the efficacy of immunotherapy in NFE2L2 mutated cancers . HoweverInspired by several previous studies , 66–69, Activating mTORC1 signaling pathway accelerates the synthesis of proteins in cells to provide a material basis for tumor cell growth . Thus, mThere are several limitations to our study. First, despite NFE2L2-mutated NSCLC being marked by changes in signaling pathways, such as in interferon response, inflammatory, JAK STAT3, p53, mTORC1, and ROS, the molecular basis of NFE2L2 MUs associated with ICI response remained unclear and required experimental research. Second, the low frequency of NFE2L2 MUs (1.9–3.6% in pan-cancers) might restrict the therapeutic use of detecting NFE2L2 MUs and explaining the mechanism of disease progression in NFE2L2 mutated tumors. Third, the large study included several cohorts with ICIs treatments, which might have sometimes caused in bias for data analysis. To a certain goal, we investigated the main clinical outcomes subtyped by study and tumor types, while a small sample size may lead to inadequate explanations for NEF2L2 MU effect.In summary, our study highlights the landscape of NFE2L2 MUs and Nrf2-activating MUs and their association with various aspects of cancer, including TMB, patient survival, response to ICIs, and the tumor microenvironment. These findings provide valuable insights into the complexity of Nrf2-activating MUs and their potential implications for cancer treatment, especially NSCLC. Further research and clinical trials are warranted to fully understand the underlying mechanisms and therapeutic strategies associated with NFE2L2 and Nrf2-activating mutations in NSCLC.Additional file 1: Figure S1. The flowchart of this study. The flowchart outlines the primary objective of the selected cohorts and the analytical procedure. Figure S2. Comparison of clinical characters among Nrf2-activating mutations, Nrf2-inactivating mutations, unknown NFE2L2 mutations and WT groups in the OrigiMed cohort. (A) The proportion of Nrf2-activating mutations, Nrf2-inactivating mutations and unknown NFE2L2 mutations. Distribution of tumor stage (B), metastasis status (C), sex (D) and treatment methods (E) among Nrf2-activating mutations, Nrf2-inactivating mutations, unknown NFE2L2 mutations and WT groups. (F) The correlation between the frequency of NFE2L2 mutation and objective response rates to ICIs stratified by cancer type. WT: wild type. Figure S3. Summary of NFE2L2 mutations, Nrf2-activating mutations and clinical characters in the MSK MetTropism cohort. (A) OncoPrint plot showing NFE2L2 MU across pan-cancers. (B) The proportion of Nrf2-activating mutations, Nrf2-inactivating mutations and unknown NFE2L2 mutations. (C) The correlation between the frequency of NFE2L2 mutation and median tumor mutation burden stratified by cancer types. Comparison of TMB (D), mutation count (E), MSI score (F), FGA (G) and metastasis status (H) among Nrf2-activating mutations, Nrf2-inactivating mutations, unknown NFE2L2 mutations and WT groups. (I) The correlation between the frequency of NFE2L2 mutation and objective response rates to ICIs stratified by cancer type. (J) Kaplan–Meier survival curves of OS between NFE2L2 MU and NFE2L2 WT groups. (L) Association of NFE2L2 mutation with OS stratified by cancer type. (M) Association of Nrf2-inactivating MU with OS stratified by cancer type. Figure S4. Summary of NFE2L2 mutations and survival outcomes in TCGA cohort and patients with NSCLC. (A) Association of NFE2L2 mutation with OS stratified by cancer type. (B) Kaplan–Meier survival curves of OS between NFE2L2 MU and NFE2L2 WT patients with NSCLC in TCGA cohort. (C) The multivariate Cox regression analysis for OS in patients with LUSC. Figure S5. (A) Meta-analysis for each cancer and melanoma to summarize association of NFE2L2 mutation with OS after ICIs treatment. Kaplan–Meier survival curves of OS between NFE2L2 MU and NFE2L2 WT NSCLC groups from the MSK1661 , OAK  and POPLAR  cohorts. (E) Comparison of TMB between NFE2L2 MU and NFE2L2 WT NSCLC groups from the MSK1661, and POPLAR cohorts. (F) Kaplan–Meier survival curves of OS between HTMB and LTMB groups from the MSK1661, OAK and POPLAR cohorts. HTMB: high tumor mutation burden, L TMB: Low tumor mutation burden. (G) Kaplan–Meier survival curves of OS among HTMB NFE2L2 MU, HTMB NFE2L2 WT, LTMB NFE2L2 MU and LTMB NFE2L2 WT patients with NSCLC from the MSK1661, OAK and POPLAR cohorts. OS: overall survival; MU: mutation; WT: wild type. Figure S6. Several immune checkpoints block genes expression among Nrf2-activating mutations, Nrf2-inactivating mutations, unknown NFE2L2 mutations and WT groups with NSCLC in TCGA cohort. Figure S7. Violin plots of TCR Shannon among Nrf2-activating mutations, Nrf2-inactivating mutations, unknown NFE2L2 mutations and WT groups. Table S1. Summary of gene mutations concluded from NGS analysis.


There is still a therapeutic challenge in treating gastric cancer (GC) due to its high incidence and poor prognosis. Collagen type V alpha 2 (COL5A2) is increased in various cancers, yet it remains unclear how it contributes to the prognosis and immunity of GC.The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to download transcriptome profiling , single-cell RNA sequencing (scRNA-seq) data (GSE167297) and clinical information. COL5A2 expression and its relationship with clinicopathological factors were analyzed. We conducted survival analysis and Cox regression analysis to evaluate the prognosis and independent factors of GC. Co-expressed analysis was also performed. To identify the underlying mechanism, we conducted analyses of differentially expressed genes (DEGs) and functional enrichment. The correlations between COL5A2 expression and immune cell infiltration levels and immune infiltrate gene marker sets were further explored. Additionally, we analyzed the association of COL5A2 expression with immunological checkpoint molecules. Furthermore, the relationship between COL5A2 expression and immunotherapy sensitivity was also investigated.COL5A2 expression was elevated in GC. More than this, the scRNA-seq analysis revealed that COL5A2 expression had a spatial gradient. The upregulated COL5A2 was associated with worse overall survival. A significant correlation was found between COL5A2 overexpression and age, T classification and clinical stage in GC. COL5A2 was found to be an independent factor for the unfortunate outcome in Cox regression analysis. The co-expressed genes of COL5A2 were associated with tumor stage or poor survival. Enrichment analysis revealed that the DEGs were mainly associated with extracellular matrix (ECM)-related processes, PI3K-AKT signaling pathway, and focal adhesion. GSEA analyses revealed that COL5A2 was associated with tumor progression-related pathways. Meanwhile, COL5A2 expression was correlated with tumor-infiltrating immune cells. Moreover, immunophenoscore (IPS) analysis and PRJEB25780 cohorts showed that patients with low COL5A2 expression were highly sensitive to immunotherapy.COL5A2 might act as a prognostic biomarker of GC prognosis and immune infiltration and may provide a therapeutic intervention strategy.The online version contains supplementary material available at 10.1186/s12920-023-01659-9. GC is one of the most common malignancies that has a poor prognosis. The incidence and mortality are fifth and fourth globally . AccordiCollagen is the most plentiful protein in human tissue and is the basal component of the extracellular matrix (ECM) architecture . ResearcCOL5A2 was tested for its prognostic value in GC in this study. The TCGA and GEO databases were used to acquire RNA-seq and microarray data. We examined COL5A2 expression in human stomach adenocarcinoma (STAD) samples. In addition, the scRNA-seq analysis was also conducted to detect the expression of COL5A2. COL5A2 expression level and clinicopathological characteristics, along with prognosis, were comprehensively analyzed. We conducted differential expression analysis based on the expression of COL5A2, along with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of these DEGs, as well as Gene Set Enrichment Analysis (GSEA) to fully study the biological functions of COL5A2 in GC. Moreover, the further investigation suggested that COL5A2 expression was associated with tumor-infiltrating immune cells, immune marker sets and immune checkpoint molecules, and may affect the immunotherapy response. These findings may provide an underlying mechanism by which COL5A2 contributes to GC progression and immune tolerance. Hence, for patients with GC, COL5A2 can be a promising biomarker and therapeutic target.https://portal.gdc.cancer.gov). The Perl (http://www.perl.org/) script was used to merge the RNA-seq data files into a matrix file. Then 375 samples of GC samples and 32 adjacent nontumor samples were obtained. A Perl script was used to convert Ensemble IDs (http://ensembl.org/index.html) into gene symbols. We searched the GEO database (https://www.ncbi.nlm.nih.gov/geo/) with the keywords “gastric cancer survival” and “homo sapiens”, and downloaded “GSE84437” which had the maximum dataset.Transcriptome profiling and clinical data of STAD patients were downloaded from the TCGA website  in R was used to perform the quality control (QC) process. The raw count matrix was converted into a Seurat object. With the “PercentageFeatureSet” function, the percentage of mitochondrial genes, ribosomal genes and haemoglobin genes were calculated. We excluded cells expressing less than 300 genes, and genes expressed in fewer than three cells, as well as noncoding genes. Mitochondrial gene content > 15%, ribosomal gene content > 3% and haemoglobin gene content > 1% were also excluded. We normalized the filtered data using “LogNormalize” with 10,000 scales. Then, based on the top 2000 highly variable genes, an analysis of principal components (PCA) was conducted. A batch effect was corrected using the harmony R package. To identify cell types, Uniform Manifold Approximation and Projection (UMAP) and marker gene analyses were carried out . The UMAhttp://timer.cistrome.org/) [Based on TCGA expression profile data, 33 cancers were evaluated for their COL5A2 mRNA levels via Tumor Immune Estimation Resource (TIMER) 2.0 (me.org/) . We analTo investigate whether the mRNA expression of COL5A2 correlates with the clinicopathological factors of GC patients, we explored clinical information of 375 GC samples extracted from TCGA and verified the results with 433 GC samples using GSE84437 via limma package in R software.A total of 30 GC and 10 normal gastric tissues as controls were selected from The Second Hospital of Hebei Medical University, Hebei Province, China, between June 2022 and June 2023. Ethics approval for the study was obtained from the institution. Pathological examinations confirmed the diagnosis of gastric cancer in all patients. No prior treatment was given to any of the patients before undergoing surgical resection. Samples from patients were prepared as formalin-fixed paraffin-embedded (FFPE) sections and the IHC staining against COL5A2 was performed as previously described . Anti-COP-value < 0.05 was considered significant.The samples were classified as high-expression and low-expression groups according to COL5A2 expression levels above or below the median value. In this study, based on the TCGA datasets and GSE84437, we conducted survival analyses to evaluate the impact of COL5A2 expression on GC survival. Univariate and multivariate Cox regression analyses were performed to determine independent prognostic factors. The prognostic characteristics of COL5A2 and clinical characteristics were assessed by calculating the area under the receiver operating characteristic (ROC) curve (AUC) for GC patients in TCGA-STAD. The statistical analyses were conducted using the R software. The limma, survival, survminer and timeROC packages in R software were used appropriately to conduct the statistical analyses. A https://www.cbioportal.org/). The top six significant COL5A2 co-expressed genes were used for further analysis, and the TIMER2.0 database was used to verify their correlation with COL5A2 expression. In addition, by using Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/index.html), we explored COL5A2 co-expressed genes in GC and the relationship between these genes and tumor stages. Moreover, we analyzed the prognosis significance of the six co-expression genes via Kaplan–Meier plotter online database .For further exploration of the COL5A2-associated molecular mechanism, COL5A2 co-expressed genes were identified with the cBioPortal database  [To investigate the correlation between COL5A2 and 28 tumor-infiltrating lymphocytes (TILs), we employed the TISIDB online platform (TISIDB/) . AdditioTISIDB/) –23.http://gepia.cancer-pku.cn/index.html) [The Gene Expression Profiling Interactive Analysis (GEPIA) (ex.html)  was usedMoreover, the association between COL5A2 expression and the 47 common immune checkpoint molecules was examined via R software with limma, reshape2, ggplot2, ggpubr, and corrplot packages.https://tcia.at/home). The limma and ggpubr packages in R software were used for analysis. Additionally, as a part of our analysis of the predictive value of COL5A2 expression in immunotherapy response, we downloaded RNA-Seq data of the immunotherapy cohort PRJEB25780 from European Nucleotide Archive (ENA) (https://www.ebi.ac.uk/ena/browser/home). In this cohort, researchers examined 45 patients with metastatic or recurrent gastric cancer who received anti-programmed cell death protein 1 (PD-1) therapy [According to previous research, IPS can predict the efficacy of immunotherapy . IPS for therapy . The immP < 0.05 to be statistically significant.In this study, scRNA-seq analysis was conducted with R software (version 4.1.3), and the rest statistical analyses were conducted using R software (version 4.1.2) and Perl (version 5.30.0). Throughout the study, the appropriate R packages and statistical methods were described. Statistical analyses involved Chi-square tests with SPSS version 26.0 . We considered p < 0.001, Fig. p < 0.001, Fig. p values were calculated . Overall, the findings revealed significantly higher levels of COL5A2 expression in GC tissues than in normal tissues.Firstly, to fully evaluate the significance of COL5A2 in various solid tumors, pan-cancer analysis was conducted to compare COL5A2 mRNA expression levels between tumor samples and normal samples of the TCGA-STAD through the TIMER2.0 database. According to the analysis, COL5A2 was significantly upregulated in cancerous tissues, such as STAD, cholangiocarcinoma, thyroid cancer and colon adenocarcinoma. It suggested that COL5A2 in these cancers could be an oncogene Fig. a. Secondn = 8947), B cells (n = 4733), myeloid cells (n = 2594), plasma cells (n = 2591), epithelial cells (n = 1498), endothelial cells (n = 861) and fibroblasts (n = 685) , N classification (P = 0.03), and TNM stage (P = 0.001).The association between COL5A2 expression and clinicopathological features in the TCGA clinical data was analyzed. The clinicopathological characteristics included age, gender, histologic grade, clinical stage, tumor, node and metastasis (TNM) classification, survival status and survival time  was employed to verify the result. Similarly, the high COL5A2 expression group had a worse survival rate (p = 0.025)  for further analysis, as shown in Fig. r = 0.94, P = 1.74e-194), COL3A1 , COL5A1 , COL1A1 , SPARC , COL12A1  were strongly positively correlated with COL5A2 as shown in Fig. To gain insight into COL5A2’s biological meaning in GC, we identified genes that are positively co-expressed with COL5A2 via cBioPortal. We selected a total of six genes with high significance correlations with COL5A2  analysis was employed to study COL5A2 and its potential pathogenicity in GC. We identified 521 DEGs between high and low COL5A2 expression datasets. There were 125 down-regulated genes and 396 upregulated genes among these DEGs. The 50 DEGs were shown in the heatmap Fig. a, b. Thep = 6.03e-14), central memory CD4 T cell , Th1 , Treg , NK , plasmacytoid dendritic cell (pDC) , macrophage , mast cell . In addition, we employed EPIC, QUANTISEQ, COUNTER, CIBERSORT-ABS, TIMER and XCELL tools from TIMER2.0 to confirm the relationship between immune cell infiltration and COL5A2 expression , CD8 + T cells, monocytes, M1 and M2 macrophages, B cells, TAMs, NK cells, DCs and neutrophils. Moreover, we examined functional T cells, such as Th1, Th2, Treg, Th17, Tfh and exhausted T cells. Using purity-adjusted correlations, a link between COL5A2 expression and immune cells can only be established when COL5A2 was associated with all immune cells’ markers. we found that COL5A2 expression was significantly related to M1 and M2 macrophages, TAMs, monocytes, Th1, Th2, Treg, and T cell exhaustion  the expression of COL1A2, COL3A1, COL5A1, COL1A1, SPARC and COL12A1 mRNA levels in GC tissues and normal gastric tissues. *, P<0.05.Additional file 2: Figure S2. Analysis of the correlations between COL5A2 co-expressed genes and gastric cancer stage using GEPIA. (a-f) The association between COL1A2, COL3A1, COL5A1, COL1A1, SPARC and COL12A1 expression and gastric cancer stage.Additional file 3: Figure S3. Analysis of the correlations between COL5A2 co-expressed genes and the survival of gastric cancer patients using Kaplan-Meier plotter online database. (a-f) The association between COL1A2, COL3A1, COL5A1, COL1A1, SPARC and COL12A1 expression and gastric cancer survival.Additional file 4: Table S1. Analysis of the correlations between COL5A2 expression and immune marker sets through GEPIA.


Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997—2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection dynamics, presumably via heterosubtypic cross-immunity. Influenza viruses continually accumulate genetic changes in epitopes of two major surface proteins, hemagglutinin (HA) and neuraminidase (NA), in a process known as “antigenic drift.” Though individual hosts develop long-lasting immunity to specific influenza virus strains after infection, antigenic drift helps the virus to escape immune recognition, leaving previously exposed hosts susceptible to reinfection and necessitating the regular updates to the antigens included in the influenza vaccine . While aAmong the influenza virus types that routinely co-circulate in humans (A and B), type A viruses, particularly subtype A(H3N2), experience the fastest rates of antigenic evolution and cause the most substantial morbidity and mortality –7. SeasoBecause mutations to the HA1 region of the HA protein are considered to drive the majority of antigenic drift ,21, inflThere has been recent recognition of NA’s role in virus inhibiting antibodies and its potential as a vaccine target –40. ThouHere, we link A(H3N2) virus evolutionary dynamics to epidemiologic surveillance data in the United States over the course of 22 influenza seasons prior to the coronavirus disease (COVID-19) pandemic, considering the full diversity of viruses circulating in this period. We analyze a variety of antigenic and genetic markers of HA and NA evolution against multiple indicators characterizing the epidemiology and disease burden of annual outbreaks. We find a signature of both HA and NA antigenic drift in surveillance data, with a more pronounced relationship in epitope change rather than the serology-based indicator, along with a major effect of subtype interference. Our study has implications for surveillance of evolutionary indicators that are most relevant for population impact and for prediction of influenza burden on inter-annual timeframes.Our study focuses on the impact of A(H3N2) virus evolution on seasonal epidemics from seasons 1997–1998 to 2018–2019 in the US; whenever possible, we make use of regionally disaggregated indicators and analyses. We start by identifying multiple indicators of influenza evolution each season based on changes in HA and NA. Next, we compile influenza virus subtype-specific incidence time series for US Department of Health and Human Service (HHS) regions and estimate multiple indicators characterizing influenza A(H3N2) epidemic dynamics each season, including epidemic burden, severity, intensity, type/subtype dominance, timing, and the age distribution of cases. We then assess univariate relationships between indicators of evolution and epidemic characteristics. Lastly, we measure the relative importance of viral evolution, heterosubtypic interference, and prior immunity in predicting regional A(H3N2) epidemic dynamics, using multivariable regression models and random forest models.We characterized seasonal patterns of genetic and antigenic evolution among A(H3N2) viruses circulating from 1997 to 2019, using HA and NA sequence data shared via the Global Initiative on Sharing Avian Influenza Data (GISAID) EpiFlu database  and ferrOur choice of evolutionary indicators builds on earlier studies that found hemagglutination inhibition (HI) phenotype or HA sequence data beneficial in forecasting seasonal influenza virus evolution ,61–63 orWe included other indicators of viral fitness for HA and NA, including the number of substitutions at non-epitope sites  ,61 and t2 titer distances from HI titer measurements (t and the prior season t-1 year (one season lag) or two prior seasons ago t-2 years (two season lag). These time windows generated seasonal antigenic distances consistent with empirical and theoretical studies characterizing transitions between H3 or N2 antigenic clusters [2 titer distance, at two-season lags, and N2 epitope distance, at one-season lags, capturing expected “jumps” in antigenic drift during key seasons that have been previously associated with major antigenic transitions [To measure antigenic distances between consecutive seasons, we calculated mean genetic distances at epitope sites or mean logurements , betweenclusters ,55,62,74nsitions , such asnsitions ,27. Give2 titer distance and HA RBS distance and HI log2 titer distance  epidemic dynamics from seasons 1997–1998 to 2018–2019, excluding the 2009 A(H1N1) pandemic, using syndromic and virologic surveillance data collected by the US CDC and WHO.We estimated weekly incidences of influenza A(H3N2), A(H1N1), and B in 10 HHS regions by multiplying the influenza-like illness (ILI) rate – the proportion of outpatient encounters for ILI, weighted by regional population size – by the regional proportion of respiratory samples testing positive for each influenza type/subtype (percent positive) ,77. We c2 titer distance. Both H3 epitope distance (t – 2) and N2 epitope distance (t – 1) correlated with increased epidemic size  and peak incidence  and higher effective Rt  (regression results: 2 titer distance (t – 2) exhibited positive but non-significant associations with different measures of epidemic impact (t or prior season (t – 1) had strong negative correlations with effective Rt : R2 = 0.49, P = 0.009; N2, t: R2 = 0.46, P = 0.006) and epidemic intensity : R2 = 0.45, P = 0.003; N2, t: R2 = 0.51, P = 0.001)  –S8. Seas= 0.001) . The rem= 0.001) .t – 2) and N2 epitope distance (t – 1)  may predispose this subtype to dominate influenza virus circulation in a given season. A(H3N2) subtype dominance – the proportion of influenza positive samples typed as A(H3N2) – increased with H3 epitope distance (t or prior season (t – 1)   evolution and epidemic timing, including onset week, defined as the winter changepoint in incidence , and pea2 = 0.29, P = 0.1; two-season lag: R2 = 0.59, P = 0.003) and individuals aged 5–24 years  and negatively correlated with the fraction of cases in adults aged 25–64 years  and ≥65 years  (t – 2) had negative but non-significant associations with the fraction of cases in children and positive but non-significant associations with the fraction of cases in adult age groups  –S16. As e groups –S16.2 = 0.65, P = 0.01), peak incidence , and excess mortality   . Influenza B epidemic size was not significantly correlated with any A(H3N2) epidemic metrics (We investigated the effects of influenza type/subtype interference – proxied by influenza A(H1N1) and B epidemic size – on A(H3N2) incidence during annual outbreaks. Across the entire study period, we observed moderate-to-strong, non-linear relationships between A(H1N1) epidemic size and A(H3N2) epidemic size  , wherein = 0.01) . A(H3N2) metrics .The internal gene segments NS, M, NP, PA, and PB2 of A(H3N2) viruses and pre-2009 seasonal A(H1N1) viruses share a common ancestor  whereas We compared A(H3N2) epidemic timing across A(H3N2) and A(H1N1) dominant seasons, which we defined as when ≥70% of influenza A positive samples are typed as A(H3N2) or A(H1N1)/A(H1N1)pdm09, respectively. We applied a strict threshold for subtype dominance because seasons with < 70% samples of one IAV subtype tended to have greater geographic heterogeneity in circulation, resulting in regions with dominant subtypes that were not nationally dominant. A(H3N2) epidemic onsets and peaks occurred, on average, three weeks earlier in A(H3N2) dominant seasons . In A(H1N1) dominant seasons, regional A(H3N2) epidemics exhibited greater heterogeneity in epidemic timing  and were significantly shorter in duration compared to A(H3N2) dominant seasons .2 = 0.23, P = 0.1; 2 = 0.05, P = 0.5; We applied a wavelet approach  to weeklt – 2), N2 epitope distance (t – 1), HI log2 titer distance (t – 2), and H3 and N2 LBI diversity in the current and prior season, due to weaker or non-significant correlations between the other evolutionary metrics and epidemic burden  epidemic metrics . We limic burden . To accoc burden . Lastly,2 titer distance) were retained in the best-tuned LASSO models of effective Rt and epidemic intensity  epidemic size in the current season was the most informative predictor of A(H3N2) epidemic size and peak incidence, followed by H3 epitope distance, and the dominant IAV subtype in the previous season or N2 epitope distance . For AH. We notentensity .ρ = 0.94, regional range = 0.8 – 0.98), peak incidence , and epidemic size , while predictions of effective Rt and epidemic intensity were less accurate   subtype dominance (ctively) . Random ctively) .ρ = 0.51, P = 0.02; peak, ρ = 0.61, P = 0.007) and N2 epitope distance . For models of epidemic intensity, seasonal RMSE increased with N2 epitope distance  but not H3 epitope distance   across all regional predictions in a season – increased with H3 epitope distance  –S22. TheP = 0.8) –22.2 = 0.69; peak incidence, adj. R2 = 0.63; effective Rt, adj. R2 = 0.65; epidemic intensity, adj. R2 = 0.75), except for subtype dominance (adj. R2 = 0.48)  to select the best fit model for each epidemic metric, allowing each metric’s regression model to include up to three independent variables. This additional step of variable selection demonstrated that models with few predictors fit the observed data relatively well  . The setAntigenic drift between currently circulating influenza viruses and the previous season’s viruses is expected to confer increased viral fitness, leading to earlier, larger, or more severe epidemics. However, prior evidence for the impact of antigenic drift on seasonal influenza outbreaks is mixed. Here, we systematically compare experimental and sequence-based measures of A(H3N2) evolution in predicting regional epidemic dynamics in the United States across 22 seasons, from 1997 to 2019. We also consider the effects of other co-circulating influenza viruses, prior immunity, and vaccine-related parameters, such as coverage and effectiveness, on A(H3N2) incidence. Our findings indicate that evolution in both major surface proteins – hemagglutinin (HA) and neuraminidase (NA) – contributes to variability in epidemic magnitude across seasons, though viral fitness appears to be secondary to subtype interference in shaping annual outbreaks.The first question of this study sought to determine which metrics of viral fitness have the strongest relationships with A(H3N2) epidemic burden and timing. Among our set of candidate evolutionary predictors, genetic distances based on broad sets of epitope sites  had the strongest, most consistent associations with A(H3N2) epidemic size, transmission rate, severity, subtype dominance, and age-specific patterns. Increased epitope distance in both H3 and N2 correlated with larger epidemics and increased transmissibility, with univariate analyses finding H3 distance more strongly correlated with epidemic size, peak incidence, transmissibility, and excess mortality, and N2 distance more strongly correlated with epidemic intensity  and subtype dominance patterns. However, we note that minor differences in correlative strength between H3 and N2 epitope distance are not necessarily biologically relevant and could be attributed to noise in epidemiological or virological data or the limited number of influenza seasons in our study. The fraction of ILI cases in children relative to adults was negatively correlated with N2 epitope distance, consistent with the expectation that cases are more restricted to immunologically naïve children in seasons with low antigenic novelty ,78. RegaThe local branching index (LBI) is traditionally used to predict the success of individual clades, with a high LBI value indicating high viral fitness ,62. In oPositive associations between H3 antigenic drift and population-level epidemic burden are consistent with previous observations from theoretical models ,26,83. FWe did not observe a clear relationship between H3 receptor binding site (RBS) distance and epidemic burden, even though single substitutions at these seven amino acid positions are implicated in major antigenic transitions ,84. The A key result from our study is the direct link between NA antigenic drift and A(H3N2) incidence patterns. Although HA and NA both contribute to antigenicity ,89 and u2 titer distance was positively correlated with different measures of epidemic impact yet underperformed in comparison to H3 and N2 epitope distances. This outcome was surprising given that we expected our method for generating titer distances to produce more realistic estimates of immune cross-protection between viruses than epitope-based measures. Our computational approach for inferring HI phenotype dynamically incorporates newer titer measurements and assigns antigenic weight to phylogenetic branches rather than fixed sequence positions [HI logositions ,63, whilositions ,35,62. Hositions , with thHI titer measurements may be more immunologically relevant than epitope-based measures, yet several factors could explain why substitutions at epitope sites outperformed HI titer distances in epidemiological predictions. First, epitope distances may capture properties that affect viral fitness (and in turn outbreak intensity) but are unrelated to immune escape, such as intrinsic transmissibility, ability to replicate, or epistatic interactions. A second set of factors concern methodological issues associated with HI assays. The reference anti-sera for HI assays are routinely produced in ferrets recovering from their first influenza virus infection. Most humans are infected by different influenza virus strains over the course of their lifetimes, and one’s immune history influences the specificity of antibodies generated against drifted influenza virus strains –95. ThusA related methodological issue is that HI assays disproportionately measure anti-HA antibodies that bind near the receptor binding site and, similar to the RBS distance metric, may capture only a partial view of the antigenic change occurring in the HA protein ,78,96,97Novel antigenic variants are expected to have higher infectivity in immune populations, leading to earlier epidemics and more rapid geographic spread , but fewAfter exploring individual correlations between evolutionary indicators and annual epidemics, we considered the effects of influenza A(H1N1) incidence and B incidence on A(H3N2) virus circulation within a season. We detected strong negative associations between A(H1N1) incidence and A(H3N2) epidemic size, peak incidence, transmissibility, and excess mortality, consistent with previous animal, epidemiological, phylodynamic, and theoretical studies that found evidence for cross-immunity between IAV subtypes ,59,102. In our study, univariate correlations between A(H1N1) and A(H3N2) incidence were more pronounced than those observed between A(H3N2) incidence and evolutionary indicators, and A(H1N1) epidemic size was the highest ranked feature by random forest models predicting epidemic size and peak incidence. Consequently, interference between the two influenza A subtypes may be more impactful than viral evolution in determining the size of annual A(H3N2) outbreaks. Concerning epidemic timing, we did not detect a relationship between A(H3N2) antigenic change and the relative timing of A(H3N2) and A(H1N1) cases; specifically, A(H3N2) incidence did not consistently lead A(H1N1) incidence in seasons with greater H3 or N2 antigenic change. Overall, we did not find any indication that influenza B incidence affects A(H3N2) epidemic burden or timing, which is not unexpected, given that few T and B cell epitopes are shared between the two virus types .Lastly, we used random forest models and multivariable linear regression models to assess the relative importance of viral evolution, prior population immunity, co-circulation of other influenza viruses, and vaccine-related parameters in predicting regional A(H3N2) epidemic dynamics. We chose conditional inference random forest models as our primary method of variable selection because several covariates were collinear, relationships between some predictors and target variables were nonlinear, and our goal was inferential rather than predictive. We performed leave-one-season-out cross-validation to tune each model, but, due to the limited number of seasons in our dataset, we were not able to test predictive performance on an independent test set. With the caveat that models were likely overfit to historical data, random forest models produced accurate predictions of regional epidemic size, peak incidence, and subtype dominance patterns, while predictions of epidemic intensity and transmission rates were less exact. The latter two measures could be more closely tied to climatic factors, the timing of influenza case importations from abroad, or mobility patterns ,13,14,16Our study is subject to several limitations, specifically regarding geographic resolution and data availability. First, our analysis is limited to one country with a temperate climate and its findings concerning interactions between A(H3N2), A(H1N1), and type B viruses may not be applicable to tropical or subtropical countries, which experience sporadic epidemics of all three viruses throughout the year . Second,Beginning in early 2020, non-pharmaceutical interventions (NPIs), including lockdowns, school closures, physical distancing, and masking, were implemented in the United States and globally to slow the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the COVID-19 pandemic. These mitigation measures disrupted the transmission of seasonal influenza viruses and other directly-transmitted respiratory viruses throughout 2020 and 2021 –113, andIn conclusion, relationships between A(H3N2) antigenic drift, epidemic impact, and age dynamics are moderate, with genetic distances based on broad sets of H3 and N2 epitope sites having greater predictive power than serology-based antigenic distances for the timeframe analyzed. Influenza epidemiological patterns are consistent with increased population susceptibility in seasons with high antigenic novelty, and our study is the first to link NA antigenic drift to epidemic burden, timing, and the age distribution of cases. It is well established that anti-HA and anti-NA antibodies are independent correlates of immunity ,116–118,Unless otherwise noted, data processing and statistical analyses were performed using R version 4.3.0.We obtained weekly epidemiological and virological data for influenza seasons 1997–1998 to 2018–2019, at the U.S. HHS region level . We defiWe extracted syndromic surveillance data for the ten HHS regions from the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet) . ILINet Data on weekly influenza virus type and subtype circulation were obtained from the US CDC’s World Health Organization (WHO) Collaborating Center for Surveillance, Epidemiology and Control of Influenza . We esti+) is considered a robust estimate of influenza activity and has been used in multiple prior modeling studies [For each HHS region, we estimated weekly incidences of influenza A(H3N2), A(H1N1), and B by multiplying the percentage of influenza-like illness among outpatient visits, weighted by regional population, with the percentage of respiratory samples testing positive for a particular type/subtype ,77. We c studies ,77,122. +  in the post-pandemic period to that of the pre-pandemic period. Incidences for HHS Region 10 were not adjusted for pre- and post-pandemic reporting because surveillance data for this region were not available before 2009. To account for differences in reporting rates across HHS regions, we next scaled each region’s type/subtype incidences by its mean weekly ILI+ for the entire study period. Scaled incidences were used in all downstream analyses of epidemic burden and timing.The emergence of A(H1N1)pdm09 in 2009 altered influenza testing and reporting patterns. We adjusted weekly incidences for differences in reporting rates between the pre-2009 pandemic period – defined as 1997 week 40 to 2009 week 17 – and the post-pandemic period – defined as the weeks after 2010 week 33. For each region, we scaled pre-pandemic incidences by the ratio of mean weekly ILIpeak incidence as the maximum weekly scaled incidence and epidemic size as the cumulative weekly scaled incidence. We also estimated epidemic intensity based on a method previously developed to study variation in the shape  of influenza epidemics across US cities [We considered three complementary indicators of epidemic burden, separately for each influenza type/subtype, HHS region, and season. We defined S cities . EpidemiSpecifically, we defined the incidence distribution Epidemic intensity values were normalized to fall between 0 and 1.For each region, we used the Epidemia R package to model annual A(H3N2) epidemics and to estimate time-varying (instantaneous) reproduction numbers, effective Rt ,125 and averaged the two highest Rt estimates (maximum Rt). Initial Rt and maximum Rt produced qualitatively similar results in downstream analyses; we opted to report results for maximum Rt.To generate seasonal indicators of transmission intensity, we extracted posterior draws of daily Rt estimates for each region and season, calculated the median value for each day, and averaged daily median values by epidemic week. For each region and season, we averaged Rt estimates from the weeks spanning epidemic onset to epidemic peak  from Hansen et al., 2022 . Excess We estimated the regional onsets of A(H1N1), A(H1N1)pdm09, A(H3N2), and B epidemics each season by fitting piecewise linear models to subtype-specific incidence curves from week 30 to the first week of maximum incidence. We did not estimate epidemic onsets for regions with insufficient signal, which we defined as fewer than three weeks of consecutive incidence and/or greater than 30% of weeks with missing data in a particular season. The timing of the changepoint in incidence represents epidemic establishment  rather the timing of influenza introduction or arrival . We wereWe calculated the seasonal proportion of ILI encounters in each age group . Data for more narrow age groups are available after 2009, but we chose these four categories to increase the number of seasons in our analysis.Influenza vaccination coverage and effectiveness vary between years and would be expected to affect the population impact of seasonal outbreaks, and in turn our epidemiologic indicators. We obtained seasonal estimates of national vaccination coverage for adults 18–49 years and adults ≥65 years from studies utilizing vaccination questionnaire data collected by the National Health Interview Survey –135. We We obtained seasonal estimates of adjusted A(H3N2) vaccine effectiveness (VE) from 32 observational studies –167. MosWherein  for VEi .The 95% CI for the weighted mean VE was calculated as:We used Spearman’s correlation coefficients to measure pairwise relationships between A(H3N2) epidemiological indictors. We adjusted P-values for multiple testing using the Benjamini and Hochberg method .We considered multiple indicators of influenza evolution based on genetic and phenotypic (serologic) data, separately for HA and NA.We downloaded all H3 sequences and associated metadata from the Global Initiative on Sharing Avian Influenza Data (GISAID) EpiFlu database . We focuAs with the H3 analysis, we downloaded all N2 sequences and associated metadata from GISAID and selected complete N2 sequences that were sampled between January 1, 1997, and October 1, 2019. We excluded all sequences with ambiguous year, month, and day annotations, forced the inclusion of reference strains collected no earlier than 5 years prior to January 1, 1997, and compiled five replicate subsampled datasets with preferential sampling for North America .log2 titer drops normalized by the corresponding log2 autologous measurements [Hemagglutination inhibition (HI) measurements from ferret sera were provided by WHO GISRS Collaborating Centers in London, Melbourne, Atlanta, and Tokyo. We converted these raw two-fold dilution measurements to urements ,63.Although a phenotypic assay exists for NA, NA inhibiting antibody titers are not routinely measured for influenza surveillance. Therefore, we could not include a phenotypic marker of NA evolution in our study.For each set of H3 and N2 sequences, we aligned sequences with the augur align command  and MAFFFollowing Huddleston et al., 2020 , we defiHA epitope distance, HA RBS distance, and HA stalk footprint distance. To estimate antigenic drift with hemagglutination inhibition (HI) titer data, hereon HI log2titer distance, we applied the phylogenetic tree model from Neher et al., 2016 [2 titer change.We estimated antigenic drift for each H3 strain using either genetic or serologic data. We implemented three sequence-based metrics based on substitutions at putative epitope sites: 129 sites in HA1 ,67,174, l., 2016  to the HNA epitope distance.To estimate N2 antigenic drift, we implemented two sequence-based metrics that count substitutions at putative epitope sites in the NA head: 223 sites  or 53 siHA non-epitope distance and NA non-epitope distance. Mutational load metrics produce higher values for strains that are less fit compared to previously circulating strains.To estimate mutational load for each H3 and N2 strain, an inverse proxy of viral fitness , we implith bin.We estimated the seasonal growth of H3 clades and N2 clades with the local branching index (LBI) . To calc and viruses circulating during the prior season  viral fitness and regional A(H3N2) epidemic parameters – peak incidence, epidemic size, effective Rt, epidemic intensity, subtype dominance, excess P&I deaths, onset timing, peak timing, spatiotemporal synchrony, the number of weeks from onset to peak, and seasonal duration. We first measured Spearman correlation coefficients between pairs of scaled fitness indicators and epidemic metrics using 1000 bootstrap replicates of the original dataset (1000 samples with replacement).Next, we fit regression models with different distribution families (Gaussian or Gamma) and link functions  to observed data and used Bayesian information criterion (BIC) to select the best fit model, with lower BIC values indicating a better fit to the data. For subtype dominance, epidemic intensity, and age-specific proportions of ILI cases, we fit Beta regression models with logit links. For each epidemic metric, we fit the best-performing regression model to the resampled dataset. To measure the effects of sub(type) interference on A(H3N2) epidemics, the same approach was applied to measure the univariate relationships between A(H1N1) or B epidemic size and A(H3N2) peak incidence, epidemic size, effective Rt, epidemic intensity, and excess mortality. As a sensitivity analysis, we tested univariate relationships between A(H3N2) epidemic metrics and A(H1N1) epidemic size during pre-2009 seasons  viruses) and post-2009 seasons (A(H1N1)pdm09 viruses) separately.All predictors were centered and scaled prior to measuring Spearman’s correlations or fitting regression models.Next, we explored multivariable approaches that would shed light on the potential mechanisms driving annual epidemic impact. Considering that we had many predictors and relatively few observations (22 seasons × 9–10 HHS regions), several covariates were collinear, and our goal was explicative rather than predictive, we settled on methods that tend to select few covariates.We first used conditional inference random forest models to select relevant predictors of A(H3N2) epidemic size, peak incidence, effective Rt, epidemic intensity, and subtype dominance (party and caret R packages) –179. Can2 values from the best tuned model. We used permutation importance (N = 50 permutations) to estimate the relative importance of each predictor in determining model outcomes. Permutation importance is the decrease in prediction accuracy when a single feature (predictor) is randomly permuted, with larger values indicating more important variables. Because our features were collinear, we used conditional permutation importance to compute feature importance scores, rather than the standard marginal procedure [We created each forest by generating 3,000 regression trees from 10 repeats of a leave-one-season-out (jackknife) cross-validated sample of the data. Due to the small size of our dataset, evaluating the predictive accuracy of random forest models on a quasi-independent test set produced unstable estimates. Consequently, we included all data in the training set and report root mean squared error (RMSE) and Rrocedure ,180,181.2 values from the best tuned model (glmnet and caret R packages)[As an alternative method for variable selection, we performed LASSO regression on the same cross-validated dataset and report RMSE and Rpackages),182. UnlTo further reduce the set of predictors for each epidemic metric, we performed model selection with linear regression models that considered all combinations of the top 10 ranked predictors from conditional inference random forest models. Candidate models were limited to three independent variables, and models were compared using BIC. We did not include HHS region or season as fixed or random effects in these models because these variables either did not improve model fit (region) or caused convergence issues (season).All predictors were centered and scaled prior to fitting random forest or regression models.Supplement 1Supplement 2Supplement 3


IKZF1 mutation in AML specifically is extremely limited. In a previous work, we described the distribution pattern of IKZF1 mutation in AML, but its clinical impact has remained undefined due to the limited number of cases. Herein, we attempt to answer this question in one relatively large cohort covering 522 newly diagnosed AML patients. A total of 26 IKZF1 mutations were found in 20 AML patients . This condition has a young median age of onset of morbidity (P = 0.032). The baseline characteristics of IKZF1-mutated and wild-type patients were comparable. IKZF1 mutation showed significant co-occurrences with CEBPA (P < 0.001), SF3B1 (P < 0.001), and CSF3R (P = 0.005) mutations, and it was mutually exclusive with NPM1 mutation (P = 0.033). Although IKZF1-mutated AML was more preferably classified into the intermediate-risk group (P = 0.004), it showed one inferior complete remission rate (P = 0.032). AML with high burden of IKZF1 mutation  showed relatively short overall survival period (P = 0.012), and it was an independent factor for the increased risk of death . In subgroup analysis, our results showed that IKZF1 mutation conferred poor therapeutic response and prognosis for SF3B1-mutated AML (P = 0.0017). We believe this work improves our knowledge of IKZF1 mutation.Genetic heterogeneity poses a great challenge to the understanding and management of acute myeloid leukemia (AML). Knowledge of the The online version contains supplementary material available at 10.1186/s40164-023-00398-y. IKZF1 mutation is one rare but recurrent alteration in AML. In a previous work, we described its distribution pattern in AML [IKZF1 mutation on AML remains undefined. We here address this issue in a cohort of 522 newly diagnosed AML patients . Missense mutation preferred to localize at the exon 5 (91.67%), which mainly influences the DNA binding of IKZF1. A total of 35.7% of nonsense and frame-shift mutations were found to disrupt the DNA-binding domain and caused loss of the dimerization domain, while 64.3% of them only disrupted the dimerization domain  showed significantly poorer OS than those with low VAF or IKZF1WT, but the RFS did show any statistically significant difference   was more common than non-CEBPAbZIP−inf mutation (16.7%). IKZF1 mutation conferred a relatively low CR in the CEBPAWT/non-CEBPAbZIP−inf−MUT group, but not in the CEBPAbZIP−inf−MUT group . This may be because patients were of different races or it may be because of differences in sequencing depth. IKZF1 deletion, caused by -7/monosomy 7, was detected in 3.20% of our patients. Unlike in ALL [IKZF1 mutation and deletion were equally dominant in AML [IKZF1 mutations, and it almost affected the DNA-binding domain in AML, while its DNA-binding domain and dimerization domain involvement was relatively balanced in ALL [IKZF1 aberration conferred poor prognosis in ALL [IKZF1 mutation predicted poor OS in AML because IKZF1 mutation with VAF < 10% accounted for 35% of all IKZF1MUT patients, and IKZF1 mutation contributed less to disease than other mutations did in this group of patients. CEBPA mutation was the most common co-mutation that occurred alongside IKZF1 mutation in AML [Compared with foreign cohorts  Frequent mutations with more than 10 counts in our cohort were showed. (B) The relationship between mutations was analyzed, concurrent and mutually-exclusive mutations were indicated. (C) The concurrent or mutually-exclusive mutations for common rearrangements in AML were exhibited.Additional file 2: Fig S2. The prognostic role of CSF3R, CEBPAbZIP-inf, or SF3B1 mutation in AML. (A-B) The prognostic role of CSF3R mutation in AML, and OS (A) as well as PFS (B) were showed. (C-D) The OS (C) and PFS (D) of patients with CEBPAWT plus non-CEBPAbZIP-inf mutation or CEBPAbZIP-inf mutation in AML. (E-F) The OS (E) and RFS (F) of SF3B1WT and SF3B1MUT groups in our AML cohort.Additional file 3: Fig S3. The prognostic role of IKZF1 mutation in the specific genetic AML subtype. (A-B) The influence of IKZF1 mutation on the OS (A) and PFS (B) of CSF3R-mutated AML. (C-D) The influence of IKZF1 mutation on the prognosis of the CEBPA-mutated AML was studied, and the OS (A) as well as RFS (B) of CEBPAWT plus non-CEBPAbZIP-inf-MUT and CEBPAbZIP-inf-MUT groups with or without IKZF1 mutation were showed.Additional file 4: Table S1. IKZF1-mutated AML patients in our cohort.Additional file 5: Table S2. The CR rate of AML with different burdens of IKZF1 mutation.Additional file 6: Table S3. Univariate and multivariate analysis for overall survival duration.Additional file 7: Table S4. The influence of IKZF1 mutation on AML with different CSF3R-mutated status.Additional file 8: Table S5. The influence of IKZF1 mutation on AML with different CEBPA-mutated status.Additional file 9: Table S6. The influence of IKZF1 mutation on AML with different SF3B1-mutated status.