Developing survival prediction models in colorectal cancer using epigenome-wide DNA methylation data from whole blood.

While genome-wide association studies are valuable in identifying CRC survival predictors, the benefit of adding blood DNA methylation (blood-DNAm) to clinical features, including the TNM system, remains unclear. In a multi-site population-based patient cohort study of 2116 CRC patients with baseline blood-DNAm, we analyzed survival predictions using eXtreme Gradient Boosting with a 5-fold nested leave-sites-out cross-validation across four groups: traditional and comprehensive clinical features, blood-DNAm, and their combination. Model performance was assessed using time-dependent ROC curves and calibrations. During a median follow-up of 10.3 years, 1166 patients died. Although blood-DNAm-based predictive signatures achieved moderate performances, predictive signatures based on clinical features outperformed blood-DNAm signatures. The inclusion of blood-DNAm did not improve survival prediction over clinical features. M1 stage, age at blood collection, and N2 stage were the top contributors. Despite some prognostic value, incorporating blood DNA methylation did not enhance survival prediction of CRC patients beyond clinical features.

Large-scale external validation and meta-analysis of gene methylation biomarkers in tumor tissue for colorectal cancer prognosis.

BACKGROUND: DNA methylation biomarkers in colorectal cancer (CRC) tissue hold potential as prognostic indicators. However, individual studies have yielded heterogeneous results, and external validation is largely absent. We conducted a comprehensive external validation and meta-analysis of previously suggested gene methylation biomarkers for CRC prognosis. METHODS: We performed a systematic search to identify relevant studies investigating gene methylation biomarkers for CRC prognosis until March 2024. Our external validation cohort with long-term follow-up included 2303 patients with CRC from 22 hospitals in southwest Germany. We used Cox regression analyses to assess associations between previously suggested gene methylation biomarkers and prognosis, adjusting for clinical variables. We calculated pooled hazard ratios (HRs) and their 95% confidence intervals (CIs) using random-effects models. FINDINGS: Of 151 single gene and 29 multiple gene methylation biomarkers identified from 121 studies, 37 single gene and seven multiple gene biomarkers were significantly associated with CRC prognosis after adjustment for clinical variables. Moreover, the directions of these associations with prognosis remained consistent between the original studies and our validation analyses. Seven single biomarkers and two multi-biomarker signatures were significantly associated with CRC prognosis in the meta-analysis, with a relatively strong level of evidence for CDKN2A, WNT5A, MLH1, and EVL. INTERPRETATION: In a comprehensive evaluation of the so far identified gene methylation biomarkers for CRC prognosis, we identified candidates with potential clinical relevance for further investigation. FUNDING: The German Research Council, the Interdisciplinary Research Program of the National Center for Tumor Diseases (NCT), Germany, the German Federal Ministry of Education and Research.

Differential Immunoexpression of Inhibitory Immune Checkpoint Molecules and Clinicopathological Correlates in Keratoacanthoma, Primary Cutaneous Squamous Cell Carcinoma and Metastases.

Beyond established anti-programmed cell death protein 1/programmed cell death ligand 1 immunotherapy, T-cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibition motif domain (TIGIT) and its ligand CD155 are promising novel inhibitory immune checkpoint targets in human malignancies. Yet, in cutaneous squamous cell carcinoma, evidence on the collective expression patterns of these inhibitory immune checkpoints is scarce. Complete tumour sections of 36 cutaneous squamous cell carcinoma, 5 cutaneous metastases and 9 keratoacanthomas, a highly-differentiated, squamoproliferative tumour, with disparately benign biologic behaviour, were evaluated by immunohistochemistry for expression of programmed cell death ligand 1 (Tumor Proportion Score, Immune Cell Score), TIGIT, CD155 and CD8+ immune infiltrates. Unlike keratoacanthomas, cutaneous squamous cell carcinoma displayed a strong positive correlation of programmed cell death ligand 1 Tumor Proportion Score and CD115 expression (p < 0.001) with significantly higher programmed cell death ligand 1 Tumor Proportion Score (p < 0.001) and CD155 expression (p < 0.01) in poorly differentiated G3-cutaneous squamous cell carcinoma compared with keratoacanthomas. TIGIT+ infiltrates were significantly increased in programmed cell death ligand 1 Immune Cell Score positive primary tumours (p = 0.05). Yet, a strong positive correlation of TIGIT expression with CD8+ infiltrates was only detected in cutaneous squamous cell carcinoma (p < 0.01), but not keratoacanthomas. Providing a comprehensive overview on the collective landscape of inhibitory immune checkpoint expression, this study reveals associations of novel inhibitory immune checkpoint with CD8+ immune infiltrates and tumour differentiation and highlights the TIGIT/CD155 axis as a potential new target for cutaneous squamous cell carcinoma immunotherapy.

CpG-biomarkers in tumor tissue and prediction models for the survival of colorectal cancer: A systematic review and external validation study.

The research aimed to identify previously published CpG-methylation-based prognostic biomarkers and prediction models for colorectal cancer (CRC) prognosis and validate them in a large external cohort. A systematic search was conducted, analyzing 298 unique CpGs and 12 CpG-based prognostic models from 28 studies. After adjustment for clinical variables, 48 CpGs and five prognostic models were confirmed to be associated with survival. However, the discrimination ability of the models was insufficient, with area under the receiver operating characteristic curves ranging from 0.53 to 0.62. Calibration accuracy was mostly poor, and no significant added prognostic value beyond traditional clinical variables was observed. All prognostic models were rated at high risk of bias. While a fraction of CpGs showed potential clinical utility and generalizability, the CpG-based prognostic models performed poorly and lacked clinical relevance.

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: A systematic review of epigenome-wide studies.

BACKGROUND: DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis. METHODS: We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 2 January 2023. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from 'A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST)' and from the 'Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). Different ML methods and workflows used in included studies were summarized and visualized by a sunburst chart, a bubble chart, and Sankey diagrams, respectively. RESULTS: Eighty-three studies were included in this review. Three major types of ML-based workflows were identified. 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques. CONCLUSIONS: There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. In theory, most existing workflows could not handle the high multi-collinearity and potentially non-linearity interactions in epigenome-wide DNA methylation data. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.

Phase I study evaluating the Fc-optimized FLT3 antibody FLYSYN in AML patients with measurable residual disease.

BACKGROUND: About half of AML patients achieving complete remission (CR) display measurable residual disease (MRD) and eventually relapse. FLYSYN is an Fc-optimized antibody for eradication of MRD directed to FLT3/CD135, which is abundantly expressed on AML cells. METHODS: This first-in-human, open-label, single-arm, multicenter trial included AML patients in CR with persisting or increasing MRD and evaluated safety/tolerability, pharmacokinetics and preliminary efficacy of FLYSYN at different dose levels administered intravenously (cohort 1-5: single dose of 0.5 mg/m2, 1.5 mg/m2, 5 mg/m2, 15 mg/m2, 45 mg/m2; cohort 6: 15 mg/m2 on day 1, 15 and 29). Three patients were treated per cohort except for cohorts 4 and 6, which were expanded to nine and ten patients, respectively. Primary objective was safety, and secondary efficacy objective was ≥ 1 log MRD reduction or negativity in bone marrow. RESULTS: Overall, 31 patients were treated, of whom seven patients (22.6%) experienced a transient decrease in neutrophil count (two grade 3, others ≤ grade 2). No infusion-related reaction or dose-limiting toxicity was observed. Adverse events (AEs) were mostly mild to moderate, with the most frequent AEs being hematologic events and laboratory abnormalities. Response per predefined criteria was documented in 35% of patients, and two patients maintained MRD negativity until end of study. Application of 45 mg/m2 FLYSYN as single or cumulative dose achieved objective responses in 46% of patients, whereas 28% responded at lower doses. CONCLUSIONS: FLYSYN monotherapy is safe and well-tolerated in AML patients with MRD. Early efficacy data are promising and warrant further evaluation in an up-coming phase II trial. Trial registration This clinical is registered on clinicaltrials.gov (NCT02789254).

Genome-wide study of genetic polymorphisms predictive for outcome from first-line oxaliplatin-based chemotherapy in colorectal cancer patients.

We conducted the first large genome-wide association study to identify novel genetic variants that predict better (or poorer) prognosis in colorectal cancer patients receiving standard first-line oxaliplatin-based chemotherapy vs chemotherapy without oxaliplatin. We used data from two phase III trials, NCCTG N0147 and NCCTG N9741 and a population-based patient cohort, DACHS. Multivariable Cox proportional hazards models were employed, including an interaction term between each SNP and type of treatment for overall survival (OS) and progression-free survival. The analysis was performed for studies individually, and the results were combined using fixed-effect meta-analyses separately for resected stage III colon cancer (3098 patients from NCCTG N0147 and 549 patients from DACHS) and mCRC (505 patients from NCCTG N9741 and 437 patients from DACHS). We further performed gene-based analysis as well as in silico bioinformatics analysis for CRC-relevant functional genomic annotation of identified loci. In stage III colon cancer patients, a locus on chr22 (rs11912167) was associated with significantly poorer OS after oxaliplatin-based chemotherapy vs chemotherapy without oxaliplatin (Pinteraction < 5 × 10-8 ). For mCRC patients, three loci on chr1 (rs1234556), chr12 (rs11052270) and chr15 (rs11858406) were found to be associated with differential OS (P < 5 × 10-7 ). The locus on chr1 located in the intronic region of RCSD1 was replicated in an independent cohort of 586 mCRC patients from ALGB/SWOG 80405 (Pinteraction = .04). The GWA gene-based analysis yielded for RCSD1 the most significant association with differential OS in mCRC (P = 6.6 × 10-6 ). With further investigation into its biological mechanisms, this finding could potentially be used to individualize first-line treatment and improve clinical outcomes.

Using real-world data to predict health outcomes-The prediction design: Application and sample size planning.

The gold standard for investigating the efficacy of a new therapy is a (pragmatic) randomized controlled trial (RCT). This approach is costly, time-consuming, and not always practicable. At the same time, huge quantities of available patient-level control condition data in analyzable format of (former) RCTs or real-world data (RWD) are neglected. Therefore, alternative study designs are desirable. The design presented here consists of setting up a prediction model for determining treatment effects under the control condition for future patients. When a new treatment is intended to be tested against a control treatment, a single-arm trial for the new therapy is conducted. The treatment effect is then evaluated by comparing the outcomes of the single-arm trial against the predicted outcomes under the control condition. While there are obvious advantages of this design compared to classical RCTs (increased efficiency, lower cost, alleviating participants' fear of being on control treatment), there are several sources of bias. Our aim is to investigate whether and how such a design-the prediction design-may be used to provide information on treatment effects by leveraging external data sources. For this purpose, we investigated under what assumptions linear prediction models could be used to predict the counterfactual of patients precisely enough to construct a test and an appropriate sample size formula for evaluating the average treatment effect in the population of a new study. A user-friendly R Shiny application (available at: https://web.imbi.uni-heidelberg.de/PredictionDesignR/) facilitates the application of the proposed methods, while a real-world application example illustrates them.

Prognostic Value of Post-Operative C-Reactive Protein-Based Inflammatory Biomarkers in Colorectal Cancer Patients: Systematic Review and Meta-Analysis.

Post-operative inflammation in cancer patients can be modulated by drugs and diets, but evidence on its prognostic role, which would be crucial for personalized treatment and surveillance schemes, remains rather limited. We aimed to systematically review and meta-analyse studies on the prognostic value of post-operative C-reactive protein (CRP)-based inflammatory biomarkers among patients with colorectal cancer (CRC) (PROSPERO#: CRD42022293832). PubMed, Web of Science and Cochrane databases were searched until February 2023. Studies reporting associations between post-operative CRP, Glasgow Prognostic Score (GPS) or modified Glasgow Prognostic Score (mGPS) with overall survival (OS), CRC-specific survival (CSS) and recurrence-free survival (RFS) were included. Hazard ratios (HRs) with 95% confidence intervals (CIs) for the predictor-outcome associations were pooled using R-software, version 4.2. Sixteen studies (n = 6079) were included in the meta-analyses. Elevated post-operative CRP was a predictor of poor OS, CSS and RFS compared with low CRP levels [HR (95% CI): 1.72 (1.32-2.25); 1.63 (1.30-2.05); 2.23 (1.44-3.47), respectively]. A unit increase in post-operative GPS predicted poor OS [HR (95% Cl): 1.31 (1.14-1.51)]. Moreover, a unit increase in post-operative mGPS was associated with poor OS and CSS [HR (95% Cl): 1.93 (1.37-2.72); 3.16 (1.48-6.76), respectively]. Post-operative CRP-based inflammatory biomarkers have a significant prognostic role for patients with CRC. Prognostic value of these easy-to-obtain routine measurements thereby seems to outperform most of the much more complex blood- or tissue-based predictors in the current focus of multi-omics-based research. Future studies should validate our findings, establish optimal time for biomarker assessment and determine clinically useful cut-off values of these biomarkers for post-operative risk-stratification and treatment-response monitoring.

Polygenic Risk Score for Defining Personalized Surveillance Intervals After Adenoma Detection and Removal at Colonoscopy.

BACKGROUND & AIMS: Polygenic risk scores (PRSs) could help to define personalized colorectal cancer (CRC) screening strategies. The aim of this study was to evaluate whether a PRS, along with adenoma characteristics, could help to define more personalized and risk-adapted surveillance intervals. METHODS: In a population-based, case-control study from Germany, detailed information on previous colonoscopies and a PRS based on 140 CRC-related, single-nucleotide polymorphisms was obtained from 4696 CRC cases and 3709 controls. Participants were classified as having low, medium, or high genetic risk according to tertiles of PRSs among controls. We calculated the absolute risk of CRC based on the PRS and colonoscopy history and findings. RESULTS: We observed major variations of CRC risk according to the PRS, including among individuals with detection and removal of adenomas at colonoscopy. For instance, the estimated 10-year absolute risk of CRC for 50-year-old men and women with no polyps, for whom repeat screening colonoscopy is recommended after 10 years only, was 0.2%. Equivalent absolute risks were estimated for people with low-risk adenomas and low PRS. However, the same levels of absolute risk were reached within 3 to 5 years by those with low-risk adenomas and high PRS and with high-risk adenomas irrespective of the PRS. CONCLUSIONS: Consideration of genetic predisposition to CRC risk, as determined by a PRS, could help to define personalized, risk-adapted surveillance intervals after detection and removal of adenomas at screening colonoscopy. However, whether the risk variation is strong enough to direct clinical risk stratification needs to be explored further.

Performance of phase-I dose finding designs with and without a run-in intra-patient dose escalation stage.

Dose-finding designs for phase-I trials aim to determine the recommended phase-II dose (RP2D) for further phase-II drug development. If the trial includes patients for whom several lines of standard therapy failed or if the toxicity of the investigated agent does not necessarily increase with dose, optimal dose-finding designs should limit the frequency of treatment with suboptimal doses. We propose a two-stage design strategy with a run-in intra-patient dose escalation part followed by a more traditional dose-finding design. We conduct simulation studies to compare the 3 + 3 design, the Bayesian Optimal Interval Design (BOIN) and the Continual Reassessment Method (CRM) with and without intra-patient dose escalation. The endpoints are accuracy, sample size, safety, and therapeutic efficiency. For scenarios where the correct RP2D is the highest dose, inclusion of an intra-patient dose escalation stage generally increases accuracy and therapeutic efficiency. However, for scenarios where the correct RP2D is below the highest dose, intra-patient dose escalation designs lead to increased risk of overdosing and an overestimation of RP2D. The magnitude of the change in operating characteristics after including an intra-patient stage is largest for the 3 + 3 design, decreases for the BOIN and is smallest for the CRM.

Predictive Polygenic Score for Outcome after First-Line Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients Using Supervised Principal Component Analysis.

BACKGROUND: Associations between candidate germline genetic variants and treatment outcome of oxaliplatin, a drug commonly used for patients with colorectal cancer, have been reported but not robustly established. This study aimed to construct polygenic hazard scores (PHSs) as predictive markers for oxaliplatin treatment outcome by using a supervised principal component approach (PCA). METHODS: Genome-wide association analysis for overall survival, including interaction terms (SNP*treatment type) was carried out using two phase III trials, 3,098 resected stage III colon cancer (rCC) patients of NCCTG N0147 and 506 metastatic colorectal cancer (mCRC) patients of NCCTG N9741, separately. SNPs showing interaction with genome-wide significance (P < 5 × 10-8) were selected for PCA to derive a PHS. PHS interaction with treatment was included in Cox regression models to predict outcome. Replication of prediction models was performed in an independent cohort, DACHS. RESULTS: The two PHSs based on the first two principal components of selected SNPs (15SNPs for rCC and 13SNPs for mCRC) were used to construct interaction terms with treatment type and included in models adjusted for clinical covariables. However, in the DACHS study, the addition of the two PHS terms to clinical models did not improve the prediction error in either patients with rCC or mCRC. PHS interaction was also not replicated. CONCLUSIONS: The PHSs derived using principal components efficiently combined multiple predictive SNPs for estimating likelihood of benefit from oxaliplatin versus other treatment but could not be replicated. IMPACT: These results highlight the potential but also challenges in generating evidence for a predictive polygenic score for oxaliplatin efficacy.

A consistent version of distance covariance for right-censored survival data and its application in hypothesis testing.

Distance covariance is a powerful new dependence measure that was recently introduced by Székely et al. and Székely and Rizzo. In this work, the concept of distance covariance is extended to measuring dependence between a covariate vector and a right-censored survival endpoint by establishing an estimator based on an inverse-probability-of-censoring weighted U-statistic. The consistency of the novel estimator is derived. In a large simulation study, it is shown that induced distance covariance permutation tests show a good performance in detecting various complex associations. Applying the distance covariance permutation tests on a gene expression dataset from breast cancer patients outlines its potential for biostatistical practice.

Validation of Genetic Markers Associated with Survival in Colorectal Cancer Patients Treated with Oxaliplatin-Based Chemotherapy.

BACKGROUND: Associations between candidate genetic variants and treatment outcomes of oxaliplatin, a drug commonly used for colorectal cancer patients, have been reported but not robustly established. This study aimed to validate previously reported prognostic and predictive genetic markers for oxaliplatin treatment outcomes and evaluate additional putative functional variants. METHODS: Fifty-three SNPs were selected based on previous reports (40 SNPs) or putative function in candidate genes (13 SNPs). We used data from 1,502 patients with stage II-IV colorectal cancer who received primary adjuvant chemotherapy, 37% of whom received oxaliplatin treatment. Multivariable Cox proportional hazards models for overall survival and progression-free survival were applied separately in stage II-III and stage IV patients. For predictive SNPs, differential outcomes according to the type of chemotherapy (oxaliplatin-based vs. others) were evaluated using an interaction term. For prognostic SNPs, the association was assessed solely in patients with oxaliplatin-based treatment. RESULTS: Twelve SNPs were predictive and/or prognostic at P < 0.05 with differential survival based on the type of treatment, in patients with stage II-III (GSTM5-rs11807, ERCC2-rs13181, ERCC2-rs1799793, ERCC5-rs2016073, XPC-rs2228000, P2RX7-rs208294, HMGB1-rs1360485) and in patients with stage IV (GSTM5-rs11807, MNAT1-rs3783819, MNAT1-rs4151330, CXCR1-rs2234671, VEGFA-rs833061, P2RX7-rs2234671). In addition, five novel putative functional SNPs were identified to be predictive (ATP8B3-rs7250872, P2RX7-rs2230911, RPA1-rs5030755, MGMT-rs12917, P2RX7-rs2227963). CONCLUSIONS: Some SNPs yielded prognostic and/or predictive associations significant at P < 0.05, however, none of the associations remained significant after correction for multiple testing. IMPACT: We did not robustly confirm previously reported SNPs despite some suggestive findings but identified further potential predictive SNPs, which warrant further investigation in well-powered studies.

TERT promoter mutation and chromosome 6 loss define a high-risk subtype of ependymoma evolving from posterior fossa subependymoma.

Subependymomas are benign tumors characteristically encountered in the posterior fossa of adults that show distinct epigenetic profiles assigned to the molecular group "subependymoma, posterior fossa" (PFSE) of the recently established DNA methylation-based classification of central nervous system tumors. In contrast, most posterior fossa ependymomas exhibit a more aggressive biological behavior and are allocated to the molecular subgroups PFA or PFB. A subset of ependymomas shows epigenetic similarities with subependymomas, but the precise biology of these tumors and their potential relationships remain unknown. We therefore set out to characterize epigenetic traits, mutational profiles, and clinical outcomes of 50 posterior fossa ependymal tumors of the PFSE group. On histo-morphology, these tumors comprised 12 ependymomas, 14 subependymomas and 24 tumors with mixed ependymoma-subependymoma morphology. Mixed ependymoma-subependymoma tumors varied in their extent of ependymoma differentiation (2-95%) but consistently exhibited global epigenetic profiles of the PFSE group. Selective methylome analysis of microdissected tumor components revealed CpG signatures in mixed tumors that coalesce with their pure counterparts. Loss of chr6 (20/50 cases), as well as TERT mutations (21/50 cases), were frequent events enriched in tumors with pure ependymoma morphology (p < 0.001) and confined to areas with ependymoma differentiation in mixed tumors. Clinically, pure ependymoma phenotype, chr6 loss, and TERT mutations were associated with shorter progression-free survival (each p < 0.001). In conclusion, our results suggest that subependymomas may acquire genetic and epigenetic changes throughout tumor evolution giving rise to subclones with ependymoma morphology (resulting in mixed tumors) that eventually overpopulate the subependymoma component (pure PFSE ependymomas).

Which patients to sample in clinical cohort studies when the number of events is high and measurement of additional markers is constrained by limited resources.

PURPOSE: We consider an existing clinical cohort with events but limited resources for the investigation of a further potentially expensive marker. Biological material of the patients is stored in a biobank, but only a limited number of samples can be analyzed with respect to the marker. The question arises as to which patients to sample, if the number of events preclude standard sampling designs. METHODS: Modifications of the nested case-control and the case-cohort design for the proportional hazards model are applied, that allow efficient sampling in situations where standard nested case-control and case-cohort are not feasible. These sampling designs are compared to simple random sampling and extreme group sampling, the latter including only patients with extreme outcomes, ie either with an event early in time or without an event until at least a point later in time. RESULTS: The modified nested case-control design and the modified case-cohort design provide powerful methods for sampling in a clinical cohort with many events. The simple random sampling usually is less efficient. If focus is on precise estimation of a potential effect in terms of a hazard ratio, extreme group sampling is not competitive. If focus is on screening for important biomarkers, extreme group sampling markedly outperforms the other sampling designs. CONCLUSIONS: When it is not feasible to sample all events, a modified nested case-control design or case-cohort design leads to efficient effect estimates in the proportional hazards model. If screening for important biomarkers is the primary objective, extreme group sampling is preferable.

A global test for competing risks survival analysis.

Standard tests for the Cox model, such as the likelihood ratio test or the Wald test, do not perform well in situations, where the number of covariates is substantially higher than the number of observed events. This issue is perpetuated in competing risks settings, where the number of observed occurrences for each event type is usually rather small. Yet, appropriate testing methodology for competing risks survival analysis with few events per variable is missing. In this article, we show how to extend the global test for survival by Goeman et al. to competing risks and multistate models[Per journal style, abstracts should not have reference citations. Therefore, can you kindly delete this reference citation.]. Conducting detailed simulation studies, we show that both for type I error control and for power, the novel test outperforms the likelihood ratio test and the Wald test based on the cause-specific hazards model in settings where the number of events is small compared to the number of covariates. The benefit of the global tests for competing risks survival analysis and multistate models is further demonstrated in real data examples of cancer patients from the European Society for Blood and Marrow Transplantation.