Prognostic Index Model Research Articles

Identify truly high-risk TP53-mutated diffuse large B cell lymphoma patients and explore the underlying biological mechanisms

TP53 mutation (TP53-mut) correlates with inferior survival in many cancers, whereas its prognostic role in diffuse large B-cell lymphoma (DLBCL) is still in controversy. Therefore, more precise risk stratification needs to be further explored for TP53-mut DLBCL patients. A set of 2637 DLBCL cases from multiple cohorts, was enrolled in our analysis. Among the 2637 DLBCL patients, 14.0% patients (370/2637) had TP53-mut. Since missense mutations account for the vast majority of TP53-mut DLBCL patients, and most non-missense mutations affect the function of the P53 protein, leading to worse survival rates, we distinguished patients with missense mutations. A TP53 missense mutation risk model was constructed based on a 150-combination machine learning computational framework, demonstrating excellent performance in predicting prognosis. Further analysis revealed that patients with high-risk missense mutations are significantly associated with early progression and exhibit dysregulation of multiple immune and metabolic pathways at the transcriptional level. Additionally, the high-risk group showed an absolutely suppressed immune microenvironment. To stratify the entire cohort of TP53-mut DLBCL, we combined clinical characteristics and ultimately constructed the TP53 Prognostic Index (TP53PI) model. In summary, we identified the truly high-risk TP53-mut DLBCL patients and explained this difference at the mutation and transcriptional levels.

The predictive value of next generation sequencing for matching advanced hepatocellular carcinoma patients to targeted and immunotherapy.

Targeted and Immunotherapy has emerged as a new first-line treatment for advanced hepatocellular carcinoma (aHCC). To identify the appropriate targeted and immunotherapy, we implemented next generation sequencing (NGS) to provide predictive and prognostic values for aHCC patients. Pretreatment samples from 127 HCC patients were examined for genomic changes using 680-gene NGS, and PD-L1 expression was detected by immunohistochemistry. Demographic and treatment data were included for analyses of links among treatment outcomes, drug responses, and genetic profiles. A prognostic index model for predicting benefit from treatment was constructed, taking into account of biomarkers, including TP53, TERT, PD-L1, and tumor mutation burden (TMB) as possible independent prognostic factors. The multivariate Cox regression analyses showed that PD-L1≥1% (HR 25.07, 95%CI 1.56 - 403.29, p=0.023), TMB≥5Mb (HR 86.67, 95% CI 4.00 - 1876.48, p=0.004), TERT MU (HR 84.09, 95% CI 5.23 - 1352.70, p=0.002) and TP53 WT (HR 0.01, 95%CI 0.00 - 0.47, p=0.022) were independent risk factors for overall survival (OS), even after adjusting for various confounders. A prognostic nomogram for OS was developed, with an area under the ROC curve of 0.91, 0.85, and 0.98 at 1-, 2-, and 3- year, respectively, and a prognostic index cutoff of 1.2. According to the cutoff value, the patients were divided into the high-risk group (n=29) and low-risk group (n=98). The benefit of targeted and immunotherapy in the low-risk group was not distinguishable according to types of agents. However, treatment of Atezolizumab and Bevacizumab appeared to provide longer OS in the high-risk group (12 months vs 9.2, 9, or 5 months for other treatments, p<0.001). The prognostic model constructed by PD-L1, TMB, TERT, and TP53 can identify aHCC patients who would benefit from targeted and immunotherapy, providing insights for the personalized treatment of HCC.

Using combination of albumin to fibrinogen ratio and prognostic nutritional index model for predicting disease activity in patients with systemic lupus erythematosus.

Background: Systemic lupus erythematosus (SLE) is chronic autoimmune disease with multiple organ damage and is associated with poor prognosis and high mortality. Identification of universal biomarkers to predict SLE activity is challenging due to the heterogeneity of the disease. This study aimed to identify the indicators that are sensitive and specific to predict activity of SLE.Methods: We retrospectively analyzed 108 patients with SLE. Patients were categorized into SLE with activity and without activity groups on the basis of SLE disease activity index. We analyzed the potential of routine and novel indicators in predicting the SLE activity using receiver operating characteristic curves and multivariate logistic regression. The Spearman method was used to understand the correlation between albumin to fibrinogen ratio (AFR), prognostic nutritional index (PNI), AFR-PNI model and disease activity.Results: SLE with activity group had higher ESR, CRP, D-dimer, fibrinogen, CRP to albumin ratio, positive rate of anti-dsDNA and ANUA, and lower C3, total bilirubin, total protein, albumin, albumin/globulin, creatinine, high density liptein cholesterol, hemoglobin, hematocrit, lymphocyte count, positive rate of anti-SSA, AFR, PNI than SLE without activity. A further established model based on combination of AFR and PNI (AFR-PNI model) showed prominent value in distinguishing SLE with activity patients from SLE without activity patients. In addition, the sensitivity and specificity of AFR-PNI model + anti-dsDNA combination model were superior to AFR-PNI model. AFR and PNI were risk factors for SLE activity. Moreover, AFR+PNI model correlated with disease activity and AFR-PNI model was associated with fever, pleurisy, pericarditis, renal involvement.Conclusion: These findings suggest that predictive model based on combination of AFR and PNI may be useful markers to identify active SLE in clinical practice.

Primary Central Nervous System Lymphoma and Contemporary Clinical Prognostication

Background: Primary central nervous system lymphoma (PCNSL) is a rare form of non-Hodgkin lymphoma with two-year overall survival (OS) rates of 66-70% reported in prospective clinical trials. Induction therapy with high-dose methotrexate (HD-MTX) followed by consolidation has become the mainstay of treatment, however, there are few contemporary and robust prognostication indices widely available for clinical use. Some of the most used indices were developed prior to the routine use of rituximab and include the International Extranodal Lymphoma Study Group (IELSG; 1980-1999), Memorial Sloan-Kettering Cancer Center (MSKCC; 1983-2003), Nottingham/Barcelona (NB; 1986-2001), and Taipei Score (TS; 2003-2015). Herein, we describe a contemporary comparison of clinical prognostication indices using a single-center cohort of PCNSL patients treated at the Mayo Clinic. Methods: Patients with a diagnosis of PCNSL who received HD-MTX as part of induction therapy at Mayo Clinic between October 2010 and June 2022 were identified and retrospectively reviewed. Patients with prior or concurrent systemic lymphoma were excluded. Primary endpoints were progression-free survival (PFS) defined as time from diagnosis to relapse, progression, or death due to any cause; and OS defined as time from diagnosis to death due to any cause. PFS and OS were evaluated using Kaplan-Meier curves and compared by risk scores using a log-rank test. The association between baseline characteristics and survival outcome were assessed in univariate Cox regression models. Any characteristics showing significant (p-value &amp;lt;0.05) association with outcome were combined using a multivariate Cox regression model. All characteristics that maintained significant association with outcome were combined into a new prognostic model. We compared the performance of each prognostic index model using Harrell's C (HC). Results: A total of 148 patients were identified (50.7% female) with median age at diagnosis of 66 years (range 29-85), with 48 patients (32.4%) age &amp;gt;70 (Table 1). Most had multifocal disease (60.1%) and deep brain involvement (67.6%) at diagnosis with elevated CSF protein (58.1%) but no documented CSF involvement (93.9%). In total, 117 patients (79.1%) received methotrexate, rituximab, and temozolomide (MRT) induction therapy, and the remaining 31 patients (20.9%) received methotrexate and rituximab (MR) induction therapy with various consolidation strategies. The median follow-up for all patients was 4.5 years and the two-year OS was 79% (95% CI: 73-86%). This cohort was stratified using previously published prognostication indices (Table 1). Upon analysis, PFS was not accurately predicted using the IELSG (score 4-5: HR 0.7 (CI 0.3-2.0); HC 0.6), MSKCC (3: 1.3 (0.4-4.0); 0.5), NB (3: 0.9 (0.4-2.5); 0.6), or TS (3: 0.9 (0.1-7.2); 0.5) prognostication indices. Furthermore, OS was not accurately predicted using the IELSG (4-5: 1.5 (0.6-4.0); 0.6), MSKCC (3: 1.2 (0.5-3.2); 0.6), NB (3: 1.2 (0.5-3.2); 0.6), or TS (3: 1.3 (0.2-9.8); 0.6) prognostication indices. Upon multivariate analysis, no variables listed in Table 1 were associated with PFS within this cohort. The two variables predicting a worsened OS were an ECOG performance status of 2 or more (1.9 (1.01-3.6)) and an MR induction regimen instead of MRT (2.1 (1.05-4.3)) (Figure 1). The two-year OS with no risk factors was 86.1% (95% CI: 78.8-94.1%), one risk factor was 75.7% (95% CI: 63.6-90.1%), and two risk factors was 61% (95% CI: 43-86%) (Figure 1). Conclusions: This study reports overall improved outcomes in patients with PCNSL compared to prior prospective and retrospective cohorts, likely in part due to treatment advances and supportive care strategies. This single-center, retrospective cohort of rituximab-exposed patients with PCNSL were not well prognosticated using previously published indices, indicating that further study and more sophisticated approaches to prognostication are warranted. Although multivariate analysis did not demonstrate any variables associated with PFS, it did demonstrate an inferior OS with a performance score of 2 or more and in patients treated with MR rather than MRT.

EP12.01-18 Validation of the ALK-Brain Prognostic Index (ALK-BPI) Model in Patients with ALK-rearranged Non-small Cell Lung Cancer and Brain Metastases

EP12.01-18 Validation of the ALK-Brain Prognostic Index (ALK-BPI) Model in Patients with ALK-rearranged Non-small Cell Lung Cancer and Brain Metastases

Survival Prediction of Esophageal Squamous Cell Carcinoma Based on the Prognostic Index and Sparrow Search Algorithm-Support Vector Machine

Aim: Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world, and recent studies show that the incidence of ESCC is on the rise, and the mortality rate remains high. An effective survival prediction model can assist physicians in treatment decisions and improve the quality of patient survival. Introduction: In this study, ESCC prognostic index and survival prediction model based on blood indicators and TNM staging information are developed, and their effectiveness is analyzed. Methods: Kaplan-Meier survival analysis and COX regression analysis are used to find influencing factors that are significantly associated with patient survival. The binary logistic regression method is utilized to construct a prognostic index (PI) for esophageal squamous cell carcinoma (ESCC). Based on the sparrow search algorithm (SSA) and support vector machine (SVM), a survival prediction model for patients with ESCC is established. Results: Eight factors significantly associated with patient survival are selected by Kaplan-Meier survival analysis and COX regression analysis. PI is divided into four stages, and the stages can reasonably reflect the survival condition of diverse patients. Compared with the other four existing models, the sparrow search algorithm-support vector machine (SSA-SVM) proposed in this paper has higher prediction accuracy. Conclusion: In order to accurately and effectively predict the five-year survival rate of patients with ESCC, a survival prediction model based on Kaplan-Meier survival analysis, COX regression analysis, binary logistic regression and support vector machine is proposed in this paper. The results show that the method proposed in this paper can accurately predict the five-year survival rate of ESCC patients.

17th International Conference on Malignant Lymphoma, Palazzo dei Congressi, Lugano, Switzerland, 13 - 17 June, 2023.

Introduction: TP53 mutation (TP53-mut) correlates with inferior survivals in many cancers, whereas its prognostic role in diffuse large B-cell lymphoma (DLBCL) is still in controversy. TP53-mut is frequently enriched in A53 subtype and also can be found in other gene subtypes. However, A53 subtype which was associated with fatal prognosis cannot be identified using the next-generation sequencing (NGS) which used mostly in clinical practice. Therefore, more precise risk stratification is need to be further explored for TP53-mut DLBCL patients. Methods: The available clinical information and corresponding mutation data of DLBCL were retrieved and obtained from published articles. Ultimately, 2637 DLBCL patients in six cohorts were enrolled in the final analysis. Among the 109 DLBCL patients in the Jiangsu Province Hospital (JSPH) study cohort, all tumor tissue samples were collected to perform NGS while 104 samples were analyzed the gene expression levels using RNA-seq. Results: Among the 2637 DLBCL patients from the integrated cohort, 14.0% patients (370/2637) had TP53-mut. The distribution of mutation events was mainly located in the DNA binding domain (N = 333, 83.3%), containing 34 at Arg248, corresponding to the TP53 hotspots in non-Hodgkin lymphoma described in previous studies (Figure 1A). Compared with TP53 wild type (TP53-wt) patients, significant p value was generated by Breslow test (p = 0.0014), whereas Log Rank test uncovered border-line p value for overall survival (OS) (p = 0.0840). Such a result indicated that adverse survival of TP53-mut patients just occurred during early survival while the 10-year OS was even slightly better than the TP53-wrt patients (Figure 1B). Accordingly, we sought to construct a model to identify the truly high-risk patients. As shown in Figure 1C, three variables (age, international prognostic index score and MCD subtype) retained independent prognostic factors for progression free survival (PFS) and OS in TP53-mut patients. The TP53 prognostic index (TP53-PI) model could significantly distinguish the prognosis of TP53-mut DLBCL patients (p < 0.0001, Figure 1D). To calculate the weight of each selected factor, a nomogram was generated, which was used to predict the survival rates. Differential expression analysis by RNA-seq analysis in JSPH cohort offered insights into the underlying biological mechanisms of poor survivals for high TP53-PI risk DLBCL patients. The immune-associated biological processes occupied a large proportion in the result of Gene Ontology functional enrichment analysis between low and high TP53-PI risk DLBCL patients (Figure 1E). Of note, the TP53-mut group had a unique immune microenvironment. Keywords: aggressive B-cell non-Hodgkin lymphoma, diagnostic and prognostic biomarkers, microenvironment

A model to predict the prognosis of diffuse large B-cell lymphoma based on ultrasound images

The purpose of this paper was to assess the value of ultrasonography in the prognosis of diffuse large b-cell lymphoma (DLBCL) by developing a new prognostic model. One hundred and eleven DLBCL patients with complete clinical information and ultrasound findings were enrolled in our study. Univariate and multivariate regression analyses were used to identify independent risk factors for progression-free survival (PFS) and overall survival (OS). Receiver operator characteristic (ROC) curves were plotted and the corresponding area under the curve (AUC) was calculated to assess the accuracy of the international prognostic index (IPI) and new model in DLBCL risk stratification. The results suggested that hilum loss and ineffective treatment were independent risk variables for both PFS and OS in DLBCL patients. Additionally, the new model that added hilum loss and ineffective treatment to IPI had a better AUC for PFS and OS than IPI alone (AUC: 0.90, 0.88, and 0.82 vs. 0.71, 0.74, and 0.68 for 1-, 3-, and 5-year PFS, respectively; AUC: 0.92, 0.85 and 0.86 vs. 0.71, 0.75 and 0.76, for 1-, 3-, and 5-year OS, respectively). The model based on ultrasound images could better suggest PFS and OS of DLBCL, allowing for better risk stratification.

The Novel Prognostic Index Model of Combining Circulating Tumor DNA and PINK-E Predicts the Clinical Outcomes for Newly Diagnosed Extranodal NK/T-cell Lymphoma.

Extranodal NK/T-cell lymphoma (ENKTL) is a highly aggressive and heterogeneous disease with poor clinical outcome. Our previous work had demonstrated that circulating tumor DNA (ctDNA) analyses were feasible in ENKTL, and dynamic tracing of ctDNA could be used to monitor the disease status. However, the prognostic value of ctDNA in ENKTL has not been fully investigated. Patients with newly diagnosed ENKTL from February 2017 to December 2021 (n = 70) were enrolled. The pretreatment ctDNA concentration (hGE/mL) was measured. The prognostic value of ctDNA, international prognostic index (IPI), Korean prognostic index (KPI), PINK-E, and the combination of PINK-E and ctDNA (PINK-EC) were investigated in our cohort. The IPI and PINK-E risk categories had a significant difference in progression-free survival (PFS) and overall survival (OS) between the low-risk and intermediate-risk groups. The KPI risk category had a difference in PFS and OS between the intermediate-risk and high-risk groups. Furthermore, integrating ctDNA into the PINK-E model could overcome the shortcomings of other prognostic models, which could significantly distinguish the different-risk groups. Overall, our results demonstrated that PINK-EC showed a superior prognostic prediction value and stability compared with IPI, KPI, and PINK-E. The integration of molecular features of the tumor into classic risk categories might better characterize a high-risk group where novel treatment approaches are most needed.

Factors Associated With Survival in Patients With End-of-Life Hematological Malignancies Who Received Chemotherapy: A Retrospective Single-Institution Study.

Clinicians can appropriately terminate treatment or reduce treatment intensity by determining prognostic factors of end-of-life chemotherapy. In particular, it provides important information for patients with hematological malignancies who receive chemotherapy until near-the-end of life compared with patients with solid tumors. This study aimed to clarify whether existing prognostic tools are associated with the survival in patients with end-of-life hematological malignancies who received chemotherapy. We retrospectively reviewed the records of 247 patients diagnosed with hematological malignancies and died at our university hospital hematology ward between May 2015 and May 2021. We performed multivariate analysis in 82 (33.2%) patients who received end-of-life chemotherapy using the Palliative Prognostic Index (PPI) and inflammation-based prognostic models, such as the Glasgow Prognostic Score (GPS), Prognostic Nutritional Index (PNI), and Controlling Nutrition Status (CONUT). On comparing 82 patients who received end-of-life chemotherapy with 165 patients who did not, the proportion of patients with PPI group A, GPS score = 0, and CONUT normal/mild was significantly higher among patients who received chemotherapy. In multivariate analysis, we identified PPI groups B (2.0 < PPI ≤ 4.0) and C (PPI > 4.0) [hazard ratio (HR) 2.1290, 95% CI 1.1830-3.828, P = .01166, respectively] and age ≥ 65years (HR 2.0170, 95% CI 1.1280-3.607, P = .01805) were associated with overall survival. PPI use and age were independent associating factors for patients with hematological malignancies receiving end-of-life chemotherapy. PPI, a popular prognostic tool may be helpful for patients and hematologists to make decisions about end-of-life care.

Development of a Conditional Event-Free Survival Tool in Diffuse Large B-Cell Lymphoma

Background: The International Prognostic Index (IPI) and related prognostic models for diffuse large B-cell lymphoma (DLBCL) use baseline clinical factors to predict outcomes at the time of initial therapy. These models are static and do not update over the disease course. Although most patients respond to initial therapy, patients and their treating physicians need better tools to understand recurrence risk, which decreases over time. For example, a young woman who completed treatment two years ago and is now contemplating pregnancy may wish to know about her recurrence risk in the next year. Similarly, physicians and patients alike may want to design a surveillance strategy that is commensurate with recurrence risk over time. Conditional event-free survival (cEFS) accounts for both patient risk factors plus the declining probability of event recurrence the longer a patient has been in remission. We report the development and validation of a dynamic risk prediction tool for DLBCL patients following completion of front-line immunochemotherapy (IC). Methods: Data from the LEO/MER cohort study and several prospective NCTN clinical trials (Alliance/CALGB 50303 [NCT00118209], S1001 [NCT01359592], E1412 [NCT01856192], and E4494 [NCT00003150]) were used to construct a dynamic prognostic model. Patients who had completed initial therapy and achieved a complete response (CR) (when response data were available; i.e., trial data) or did not experience an event within 12 weeks of end of therapy (when response data were not available; i.e., LEO/MER cohorts) were selected. Baseline variables included age, sex, stage, Eastern Cooperative Oncology Group (ECOG) performance status, pathology, and labs. Cox proportional-hazards regression modeling with backwards stepwise selection was utilized for model development. Model performance was assessed at fixed timepoints, using time-dependent areas under the receiver operating characteristic curve (tAUC of ROC) and Brier scores, and over the entire span of time in the data, using C-statistics. Model performance was compared to the performance of the IPI. Internal validation was performed using bootstrapping. External validation was separately performed using the Danish National Lymphoma Registry (LYFO). To develop the dynamic prediction tool, a landmark method was used, in which time since completion of therapy was also included as a key model input. An R Shiny application was developed to implement this dynamic prediction and facilitate point-of-care utilization, with the output being probability of remaining event-free during a user-specified time frame. Results: 2897 patients were included in model development, with the final model consisting of age (transformed to account for the non-linear association with outcome), sex, ECOG performance status, stage, and lactate dehydrogenase (LDH). Model parameters are shown in the Table. The final model outperformed a model containing only IPI in terms of C-statistic, tAUC, and Brier scores. The C-statistic was 0.66 (95% CI: 0.64, 0.68). Brier scores varied from 0.07 (0.06, 0.08) at 12 months to 0.19 (0.17, 0.20) at 84 months. The tAUCs varied from 0.65 (0.62, 0.69) at 12 months to 0.71 (0.68, 0.74) at 84 months. In contrast, in the IPI-only model, the C-statistic was 0.63, while the Brier scores ranged from 0.07 to 0.21 and tAUCs ranged from 0.64 to 0.65 between the same time intervals. After applying the model to 3250 patients from LYFO, the Brier scores and tAUCs were similar, ranging from 0.11 to 0.22 and 0.69 to 0.71, respectively, also between the same time intervals. In the Figure, we show that the 2-year cEFS of a 38-year-old female with stage III disease, a performance score of 1, and a LDH ratio of 4 having survived one year post-treatment is 0.908, compared to 0.921 for a female with the same model parameters but having survived 2 years. Conclusions: We present a dynamic risk prediction tool for patients with DLBCL having achieved a CR following front-line IC that allows for personalized prognostication of EFS both at the time of therapy completion and during follow-up. We are actively exploring prospective clinical trials of rational post-treatment surveillance based on individual cEFS, as a step towards evidence-based follow-up strategies. Support: U10CA180821 (CALGB 50303), https://acknowledgments.alliancefound.org, U10CA180820, UG1CA233339, and UG1CA232760 (ECOG-ACRIN), and U10CA180888 (SWOG). Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

FLIPI24: An Improved International Prognostic Model Developed on Early Events in Follicular Lymphoma

Background: Early events are associated with inferior outcomes in follicular lymphoma (FL) and increased risk of death due to refractory FL. Risk prediction of early events prior to the start of therapy may enhance clinical management and research strategies. We report the FL International Prognostic Index model developed specifically to predict the risk of event within 24 months (EFS24) of starting first-line immunochemotherapy (IC) (FLIPI24). Methods: Modeling was performed using individual patient data from 10 observational cohorts from Europe, North America, and Australia. Eligible patients were diagnosed with grades 1-3A FL and initiated frontline R-CHOP, R-CVP, R-bendamustine (B-R), or like therapies. Event-free survival (EFS) was defined as time from start of IC to progression, retreatment, transformation, or death due to any cause. Model development utilized a truncated Cox model stratified on IC type and cohort with a time-dependent adjustment for maintenance therapy. Multiple imputation was implemented to address missing data. Functional forms of variables were assessed using splines. Model selection was based on model performance, clinical relevance, and parsimony. Gradient boosting machines were evaluated as a complementary approach and used to inform model selection. Model building was performed on an 80% split sample with 20% held for internal testing. External validation occurred in an independent cohort. FLIPI24 risk scores assumed B-R or R-CHOP based chemotherapy and 50% likelihood of maintenance. Results: 4485 patients initiating frontline IC between 2002 and 2018 were utilized in the analysis; 3577 patients were utilized for model building and 908 for internal model testing. Median age at diagnosis in the model build dataset was 61 years (IQR 53-69) and 51% were male. FLIPI was 21%, 32%, and 48% for low, intermediate, and high risk, respectively. IC type was 1874 R-CHOP or like (52%), 730 B-R or like (20%), and 973 R-CVP (27%); 2164 received CD20 antibody maintenance (61%). At median follow-up of 77 months (IQR 48-115), EFS24 estimate was 81% (95% CI: 79-82) and 835 patients (23%) died. 5-year survival after an early (non-death) event was 46% (95% CI: 42-50). The FLIPI24 model utilizes 5 continuous variables: age (linear 60-90 years with inflection at age 75), hemoglobin (linear 8-17 g/dL), white blood cell count (linear 4-11 109/L), normalized lactate dehydrogenase (linear 0.5-5), and beta-2-microglobulin (linear 1-10 mg/L). Linear variables and valid ranges were utilized based on close examination of functional forms across potential models. Continuous FLIPI24 risk scores were grouped for visualization in K-M curves as follows: very low risk (0-0.10), low risk (0.10-0.15), average risk (0.15-0.20), high risk (0.20-0.40), very high risk (>0.40), Figure 1. Internal validation was performed on the 20% holdout set (N=908). The FLIPI24 had superior concordance (c-stat) for EFS24 (c-stat=0.666) compared to FLIPI (c-stat=0.630) and PRIMA-PI (c-stat=0.590) models, Table 1. Importantly, FLIPI24 also improved prognostic ability for 10-year OS following IC (c-stat = 0.672) vs FLIPI (c-stat=0.629) or PRIMA-PI (c-stat=0.595). External validation was performed in a prospective cohort of 1438 newly diagnosed patients with FL (558 treated with IC) enrolled in the multicenter US LEO Cohort between 2015 and 2020. The FLIPI24 had superior c-stats compared to the FLIPI and PRIMA-PI for EFS24 (0.671 vs 0.600 vs 0.602) and OS (0.732 vs. 0.652 vs 0.635) in IC treated patients and for OS (0.785 vs 0.703 vs 0.662) when assessed in all patients. Conclusion: The FLIPI24 model provides an individual risk score at diagnosis for the likelihood of experiencing an event within 24 months from starting IC. The FLIPI24 was rigorously developed, tested, and externally validated using large harmonized and pooled international observational cohorts from the IC era. The FLIPI24 utilizes objective variables easily and reliably measured at diagnosis. A novel feature of the model is the significance of established blood-based measurements in the model, which appears to capture underlying tumor and host biology. Model performance for FLIPI24 was superior to standard clinical models for prediction of both EFS24 and OS in internal and external validation sets. Elevated FLIPI24 risk represents a patient population for further biologic studies and who should be considered for novel frontline therapies. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Construction and validation of an immune-related genes prognostic index (IRGPI) model in colon cancer.

Though immunotherapy has become one of the standard therapies for colon cancer, the overall effective rate of immunotherapy is very low. Constructing an immune-related genes prognostic index (IRGPI) model may help to predict the response to immunotherapy and clinical outcomes. Differentially expressed immune-related genes (DEIRGs) between normal tissues and colon cancer tissues were identified and used to construct the co-expression network. Genes in the module with the most significant differences were further analyzed. Independent prognostic immune-related genes (IRGs) were identified by univariate and multivariate cox regression analysis. Independent prognostic IRGs were used to construct the IRGPI model using the multivariate cox proportional hazards regression model, and the IRGPI model was validated by independent dataset. ROC curves were plotted and AUCs were calculated to estimate the predictive power of the IRGPI model to prognosis. Gene set enrichment analysis (GSEA) was performed to screen the enriched KEGG pathways in the high-risk and low-risk phenotype. Correlations between IRGPI and clinical characteristic, immune checkpoint expression, TMB, immune cell infiltration, immune function, immune dysfunction, immune exclusion, immune subtype were analyzed. Totally 680 DEIRGs were identified. Three independent IRGs,NR5A2, PPARGC1A and LGALS4, were independently related to survival. NR5A2, PPARGC1A and LGALS4 were used to establish the IRGPI model. Survival analysis showed that patients with high-risk showed worse survival than patients in the low-risk group. The AUC of the IRGPI model for 1-year, 3-year and 5-year were 0.584, 0.608 and 0.697, respectively. Univariate analysis and multivariate cox regression analysis indicated that IRGPI were independent prognostic factors for survival. Stratified survival analysis showed that patients with IRGPI low-risk and low TMB had the best survival, which suggested that combination of TMB and IRGPI can better predict clinical outcome. Immune cell infiltration, immune function, immune checkpoint expression and immune exclusion were different between IRGPI high-risk and low-risk patients. An immune-related genes prognostic index (IRGPI) was constructed and validated in the current study and the IRGPI maybe a potential biomarker for evaluating response to immunotherapy and clinical outcome for colon cancer patients.

Visceral fat area and albumin based nutrition-related prognostic index model could better stratify the prognosis of diffuse large B-cell lymphoma in rituximab era

BackgroundDiffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease and the existing prognosis systems based on clinical variables are difficult to stratify patients accurately. Nutritional indices play a meaningful role in prognosis of solid tumors, whereas the effect on DLBCL is still equivocal. This retrospective study aimed to develop a novel model based on nutritional indices and other clinical variables to accurately differentiate the prognosis of DLBCL.MethodsA total of 129 patients pathologically diagnosed with DLBCL in Affiliated Hospital of Xuzhou Medical University from 2014 to 2018 were retrospectively recruited. The total fat area (TFA), visceral fat area (VFA) and subcutaneous fat area (SFA) at the third lumbar vertebra level spine were obtained by computed tomography (CT) to assess the effect of nutritional status on the prognosis of DLBCL. Principal component analysis was used to reduce the dimension of nutritional indices, and continuous variables were evaluated according to X-Tile and Restricted cubic spline. Univariable and multivariable Cox proportional hazard analyses were performed on potential variables. Kaplan-Meier method was utilized to evaluate survival probabilities and the differences between groups were assessed by log-rank test.ResultsX-Tile analysis divided VFA and albumin into two and three groups when applying 114.7 cm2 of VFA, 38.3 and 42.4 g/L of albumin as the optimal cut-off points, respectively. The final scoring model of nutrition-related prognostic index (NPI) comprised four independent prognostic variables. The C-index of the final model was 0.823 [95% CI (0.749~0.897)] by bootstrap resampling. Finally, a maximum score of 6 points was obtained. Compared with IPI, NCCN-IPI and GELTAMO-IPI, NPI showed better accuracy in discerning the prognostic risk of patients.ConclusionVFA and albumin were associated with the prognosis of DLBCL, and the NPI model based on nutritional indices could better stratify the prognosis of DLBCL.

Classification of Muscle Invasive Bladder Cancer to Predict Prognosis of Patients Treated with Immunotherapy.

Background Recently, immunotherapies have been approved for advanced muscle invasive bladder cancer (MIBC) treatment, but only a small fraction of MIBC patients could achieve a durable drug response. Our study is aimed at identifying tumor microenvironment (TME) subtypes that have different immunotherapy response rates. Methods The mRNA expression profiles of MIBC samples from seven discovery datasets (GSE13507, GSE31684, GSE32548, GSE32894, GSE48075, GSE48276, and GSE69795) were analyzed to identify TME subtypes. The identified TME subtypes were then validated by an independent dataset (TCGA-MIBC). The subtype-related biomarkers were discovered using computational analyses and then utilized to establish a random forest predictive model. The associations of TME subtypes with immunotherapy therapeutic responses were investigated in a group of patients who had been treated with immunotherapy. A prognostic index model was constructed using the subtype-related biomarkers. Two nomograms were built by the subtype-related biomarkers or the clinical parameters. Results Two TME subtypes, including ECM-enriched class (EC) and immune-enriched class (IC), were found. EC was associated with greater extracellular matrix (ECM) pathways, and IC was correlated with immune pathways, respectively. Overall survival was significantly greater for tumors classified as IC, whereas the EC subtype had a worse prognosis. A total of nine genes (AKAP12, APOL3, CXCL13, CXCL9, GBP4, LRIG1, PEG3, PODN, and PTPRD) were selected by computational analyses to construct the random forest model. The area under the curve (AUC) values for this model were 0.827 and 0.767 in the testing and external validation datasets, respectively. Therapeutic response rates were greater in IC patients than in EC patients (28 percent vs. 18 percent). Patients with a high prognostic index had a poorer prognosis than those with a low prognostic index. The nomogram constructed from nine genes and stage achieved a C-index of 0.71. Conclusion The present investigation defined two distinct TME subtypes and developed models to assess immunotherapeutic treatment outcomes.

A prognostic index model for assessing the prognosis of ccRCC patients by using the mRNA expression profiles of AIF1L, SERPINC1 and CES1

Background: Kidney carcinoma is a major cause of carcinoma-related death, with the prognosis for advanced or metastatic renal cell carcinoma still very poor. The aim of this study was to investigate feasible prognostic biomarkers that can be used to construct a prognostic index model for clear cell renal cell carcinoma (ccRCC) patients. Methods: The mRNA expression profiles of ccRCC samples were downloaded from the The Cancer Genome Atlas (TCGA) dataset and the correlation of AIF1L with malignancy, tumor stage and prognosis were evaluated. Differentially expressed genes (DEGs) between AIF1L-low and AIF1L-high expression groups were selected. Those with prognostic value as determined by univariate and multivariate Cox regression analysis were then used to construct a prognostic index model capable of predicting the outcome of ccRCC patients. Results: The expression level of AIF1L was lower in ccRCC samples than in normal kidney samples. AIF1L expression showed an inverse correlation with tumor stage and a positive association with better prognosis. ccRCC samples were divided into high- and low-expression groups according to the median value of AIF1L expression. In the AIF1L-high expression group, 165 up-regulated DEGs and 601 down-regulated DEGs were identified. Three genes (AIF1L, SERPINC1 and CES1) were selected following univariate and multivariate Cox regression analysis. The hazard ratio (HR) and 95% confidence intervals (CI) for these genes were: AIF1L (HR = 0.83, 95% CI: 0.76–0.91), SERPINC1 (HR = 1.33, 95% CI: 1.12–1.58), and CES1 (HR = 0.87, 95% CI: 0.78–0.97). A prognostic index model based on the expression level of the three genes showed good performance in predicting ccRCC patient outcome, with an area under the ROC curve (AUC) of 0.671. Conclusion: This research provides a better understanding of the correlation between AIF1L expression and ccRCC. We propose a novel prognostic index model comprising AIF1L, SERPINC1 and CES1 expression that may assist physicians in determining the prognosis of ccRCC patients.

Risk factors and prognostic index model for pancreatic cancer.

Pancreatic cancer is a highly malignant tumor with poor prognosis. Chronic inflammation contributes to the progression of pancreatic cancer. However, few studies have examined the prognostic role of inflammatory markers in this cancer. Our study sought to analyze the prognostic risk factors of and construct a prognostic index (PI) model using inflammatory markers for pancreatic cancer. Forty-eight patients diagnosed with pancreatic cancer at our hospital were selected for this retrospective analysis. Data on the general clinical characteristics, tumor-related features, blood index factors, and treatment methods were collected. The Cox proportional-hazards model was used to analyze the factors affecting the prognosis, and the Kaplan-Meier analysis was used to draw the survival curve. The median overall survival time was 14.5 months, and the 1-, 2-, and 3-year survival rates were 20.83% (10/48), 6.25% (3/48), and 4.17% (2/48), respectively. The univariate analysis showed that tumor grade, vascular invasion, adjacent tissue invasion, lymph node metastasis, tumor-node-metastasis (TNM) stage, the neutrophil-lymphocyte ratio (NLR), the platelet-lymphocyte ratio (PLR), and the lymphocyte-monocyte ratio (LMR) were significantly correlated with the median survival of pancreatic cancer patients (P<0.05). The Cox regression equation showed that tumor grade III-IV (X1), vascular invasion (X2), TNM stage III-IV (X3), a NLR >3.8 (X4), and a PLR >182.1 (X5) were independent risk factors affecting the prognosis of patients with pancreatic cancer (all P<0.05). The prognostic model for pancreatic cancer can be expressed as: PI =3.521X1+4.157X2+1.282X3+2.441X4+6.015X5. Patients with tumor grade I-II, non-vascular invasion, TNM stage I-II, a NLR ≤3.8, and a PLR ≤182.1 exhibited a higher 1-year survival rate. The areas under the receiver operating characteristic (ROC) curves for the NLR >3.8 and the PLR >182.1 were 0.778 and 0.713, respectively. Tumor grade, vascular invasion, TNM staging, the NLR, and the PLR are independent risk factors affecting the prognosis of pancreatic cancer patients. The NLR and PLR have good clinical value in predicting the survival outcomes of pancreatic cancer patients.

A Laboratory Prognostic Index Model for Predicting Survival in Patients with Malignant Pleural Mesothelioma

A Laboratory Prognostic Index Model for Predicting Survival in Patients with Malignant Pleural Mesothelioma

Low BACH2 Expression Predicts Adverse Outcome in Chronic Lymphocytic Leukaemia.

Simple SummaryChronic lymphocytic leukaemia (CLL) is the most frequent type of leukaemia with a highly variable presentation, clinical course, and outcome. The overall aim of our study was to determine the clinical and functional significance of two B-cell regulators, BACH2 and BCL6, in CLL. The results showed that CLL patients expressing low levels of BACH2 and BCL6 RNA had a shorter overall survival (OS). Their low RNA expression was associated with a shorter overall survival of immunoglobulin heavy chain variable region-mutated (IGHV-M) CLL patients, as well as those with 11q and 13q deletions. Further, co-immunoprecipitation and mass spectrometry studies from MEC-1 CLL cells showed no direct interaction between BACH2 and BCL6, but shared protein networks that are involved in ubiquitination mediated B-cell receptor functions, nucleic acid metabolism, protein degradation, and homeostasis in CLL biology. Taken together, this study reports BACH2 as a potential prognosticator and indicates the protein networks influenced by BCL6 and BACH2 in CLL.Chronic lymphocytic leukaemia (CLL) is a heterogeneous disease with a highly variable clinical outcome. There are well-established CLL prognostic biomarkers that have transformed treatment and improved the understanding of CLL biology. Here, we have studied the clinical significance of two crucial B cell regulators, BACH2 (BTB and CNC homology 1, basic leucine zipper transcription factor 2) and BCL6 (B-cell CLL/lymphoma 6), in a cohort of 102 CLL patients and determined the protein interaction networks that they participate in using MEC-1 CLL cells. We observed that CLL patients expressing low levels of BCL6 and BACH2 RNA had significantly shorter overall survival (OS) than high BCL6- and BACH2-expressing cases. Notably, their low expression specifically decreased the OS of immunoglobulin heavy chain variable region-mutated (IGHV-M) CLL patients, as well as those with 11q and 13q deletions. Similar to the RNA data, a low BACH2 protein expression was associated with a significantly shorter OS than a high expression. There was no direct interaction observed between BACH2 and BCL6 in MEC-1 CLL cells, but they shared protein networks that included fifty different proteins. Interestingly, a prognostic index (PI) model that we generated, using integrative risk score values of BACH2 RNA expression, age, and 17p deletion status, predicted patient outcomes in our cohort. Taken together, these data have shown for the first time a possible prognostic role for BACH2 in CLL and have revealed protein interaction networks shared by BCL6 and BACH2, indicating a significant role for BACH2 and BCL6 in key cellular processes, including ubiquitination mediated B-cell receptor functions, nucleic acid metabolism, protein degradation, and homeostasis in CLL biology.

Prognostic biomarkers for predicting papillary thyroid carcinoma patients at high risk using nine genes of apoptotic pathway.

Aberrant expressions of apoptotic genes have been associated with papillary thyroid carcinoma (PTC) in the past, however, their prognostic role and utility as biomarkers remains poorly understood. In this study, we analysed 505 PTC patients by employing Cox-PH regression techniques, prognostic index models and machine learning methods to elucidate the relationship between overall survival (OS) of PTC patients and 165 apoptosis related genes. It was observed that nine genes (ANXA1, TGFBR3, CLU, PSEN1, TNFRSF12A, GPX4, TIMP3, LEF1, BNIP3L) showed significant association with OS of PTC patients. Five out of nine genes were found to be positively correlated with OS of the patients, while the remaining four genes were negatively correlated. These genes were used for developing risk prediction models, which can be utilized to classify patients with a higher risk of death from the patients which have a good prognosis. Our voting-based model achieved highest performance (HR = 41.59, p = 3.36x10-4, C = 0.84, logrank-p = 3.8x10-8). The performance of voting-based model improved significantly when we used the age of patients with prognostic biomarker genes and achieved HR = 57.04 with p = 10-4 (C = 0.88, logrank-p = 1.44x10-9). We also developed classification models that can classify high risk patients (survival ≤ 6 years) and low risk patients (survival > 6 years). Our best model achieved AUROC of 0.92. Further, the expression pattern of the prognostic genes was verified at mRNA level, which showed their differential expression between normal and PTC samples. Also, the immunostaining results from HPA validated these findings. Since these genes can also be used as potential therapeutic targets in PTC, we also identified potential drug molecules which could modulate their expression profile. The study briefly revealed the key prognostic biomarker genes in the apoptotic pathway whose altered expression is associated with PTC progression and aggressiveness. In addition to this, risk assessment models proposed here can help in efficient management of PTC patients.