Consensus statement on mandatory measurements in pancreatic cancer trials (COMM-PACT) for systemic treatment of unresectable disease

Veer, Emil ter; Rijssen, L.B. van; Besselink, Marc G.; Mali, Rosa M A; Berlin, Jordan; Boeck, Stefan; Bonnetain, Franck; Chau, Ian; Conroy, Thierry; Cutsem, Éric Van; Deplanque, Gaël; Frieß, Helmut; Glimelius, Bengt; Goldstein, David; Herrmann, Richard; Labianca, Roberto; Laethem, Jean‐Luc Van; Macarulla, Teresa; Meer, Jonathan H M van der; Neoptolemos, John P.; Okusaka, Takuji; O’Reilly, Eileen M.; Pelzer, Uwe; Philip, Philip A.; Poel, Marcel J. van der; Reni, Michele; Scheithauer, Werner; Siveke, Jens T.; Verslype, Chris; Busch, Olivier R.; Wilmink, Johanna W.; Oijen, Martijn G.H. van; Laarhoven, Hanneke W. M. van

doi:10.1016/s1470-2045(18)30098-6

Cited by 56 publications

(37 citation statements)

References 79 publications

(27 reference statements)

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“…A modified two-round Delphi consensus, similar to the COMM-PACT study in metastatic pancreatic cancer, was performed to extend this set with possibly important predictors that were missed [23]. A systematic review on prognostic factors in advanced oesophagogastric cancer served as a basis for the Delphi consensus procedure [14].…”

Section: Methodsmentioning

confidence: 99%

SOURCE: A Registry-Based Prediction Model for Overall Survival in Patients with Metastatic Oesophageal or Gastric Cancer

Boorn

Abu‐Hanna

Veer

et al. 2019

Cancers

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Prediction models are only sparsely available for metastatic oesophagogastric cancer. Because treatment in this setting is often preference-based, decision-making with the aid of a prediction model is wanted. The aim of this study is to construct a prediction model, called SOURCE, for the overall survival in patients with metastatic oesophagogastric cancer. Data from patients with metastatic oesophageal (n = 8010) or gastric (n = 4763) cancer diagnosed during 2005–2015 were retrieved from the nationwide Netherlands cancer registry. A multivariate Cox regression model was created to predict overall survival for various treatments. Predictor selection was performed via the Akaike Information Criterion and a Delphi consensus among experts in palliative oesophagogastric cancer. Validation was performed according to a temporal internal-external scheme. The predictive quality was assessed with the concordance-index (c-index) and calibration. The model c-indices showed consistent discriminative ability during validation: 0.71 for oesophageal cancer and 0.68 for gastric cancer. The calibration showed an average slope of 1.0 and intercept of 0.0 for both tumour locations, indicating a close agreement between predicted and observed survival. With a fair c-index and good calibration, SOURCE provides a solid foundation for further investigation in clinical practice to determine its added value in shared decision making.

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Section: Methodsmentioning

confidence: 99%

SOURCE: A Registry-Based Prediction Model for Overall Survival in Patients with Metastatic Oesophageal or Gastric Cancer

Boorn

Abu‐Hanna

Veer

et al. 2019

Cancers

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“…1 The body of evidence supporting the routine assessment of indices of the systemic inflammatory response (SIR) as prognostic markers is such that recent consensus statements have proposed their mandatory inclusion in future oncology trials of patients with advanced colorectal and pancreatic cancer. 2,3 Similarly, markers of the SIR may also inform prognosis of patients undergoing potentially curative treatment. In this regard, two recent meta-analyses have confirmed the independent prognostic value of the modified Glasgow Prognostic Score (mGPS), a cumulative score based on circulating serum C-reactive protein (CRP) and albumin concentrations, in patients with primary operable colorectal cancer.…”

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confidence: 99%

Systemic Inflammation and Outcome in 2295 Patients with Stage I–III Colorectal Cancer from Scotland and Norway: First Results from the ScotScan Colorectal Cancer Group

et al. 2020

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Background. Systemic inflammatory response (SIR) is an adverse prognostic marker in colorectal cancer (CRC) patients. The ScotScan Colorectal Cancer Group was established to examine how markers of the SIR differ between populations and may be utilised to guide prognosis. Patients and Methods. Patients undergoing resection of stage I-III CRC from two prospective datasets in Scotland and Norway were included. The relationship between the modified Glasgow Prognostic Score (mGPS; combination of C-reactive protein and albumin) and overall survival (OS) was examined. The relationship between OS, adjuvant chemotherapy regime and mGPS was examined in patients with stage III colon cancer. Results. A total of 2295 patients were included. Patients from Scotland were more inflamed despite controlling for associated characteristics using multivariate logistic regression or propensity score matching (OR 2.82, 95% CI 1.98-4.01, p \ 0.001). mGPS had similar independent prognostic value in both cohorts (Scotland: HR 1.27, 95% CI 1.12-1.45; Norway: HR 1.23, 95% CI 1.01-1.49) and stratified survival independent of TNM group in the whole cohort. In patients with stage III colon cancer receiving adjuvant therapy, there appeared to be a survival benefit in systemically inflamed patients receiving oxaliplatin but not single-agent 5-fluorouracil or capecitabine. Conclusions. The SIR differs between populations from different countries; however prognostic value remains similar. The present study strongly supports the routine reporting of the mGPS in patients with CRC.

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“…Preferably, the HR of the prognostic factor of interest should be controlled for exactly the same type and number of other factors in multivariable regression analysis in each individual study (72). However, often the number and type of factors that are controlled for are not reported, which was the case in multiple RCTs investigating systemic treatment for advanced pancreatic cancer (33). Therefore, the pooled result of multivariate HRs should be interpreted with caution, but might give an indication of which prognostic factors are most important to select in large real-world databases.…”

Section: Discussion and Future Perspectivesmentioning

confidence: 99%

“…For example, recently a prognostic model for the survival outcome of patients with advanced esophagogastric cancer was based on data from the Dutch national cancer registry (100). The factors were pre-selected by Delphi consensus with medical oncologists according to the methodology used in a previous Delphi study in advanced pancreatic cancer (33). The evidence on certain potential prognostic factors that was shown to the experts was obtained from a previously published systematic review and meta-analysis on advanced esophagogastric cancer (31).…”

Section: Discussion and Future Perspectivesmentioning

confidence: 99%

“…Inconsistent reporting of the baseline characteristics hampers adequate between-study comparison (32), and calls for standard sets of characteristics to report in clinical studies. This will allow more adequate comparison of patient populations between studies, such as the COMM-PACT statement for advanced pancreatic cancer trials (33).…”

Section: Assumptions In the Use Of Meta-analysismentioning

confidence: 99%

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The Use of (Network) Meta-Analysis in Clinical Oncology

2019

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Meta-analysis is important in oncological research to provide a more reliable answer to a clinical research question that was assessed in multiple studies but with inconsistent results. Pair-wise meta-analysis can be applied when comparing two treatments at once, whereas it is possible to compare multiple treatments at once with network meta-analysis (NMA). After careful systematic review of the literature and quality assessment of the identified studies, there are several assumptions in the use of meta-analysis. First, the added value of meta-analysis should be evaluated by examining the comparability of study populations. Second, the appropriate comparator in meta-analysis should be chosen according to the types of comparisons made in individual studies: (1) Experimental and comparator arms are different treatments (A vs. B); (2) Substitution of a conventional treatment by an experimental treatment (A+B vs. A+C); or (3) Addition of an experimental treatment (A+B vs. B). Ideally there is one common comparator treatment, but when there are multiple common comparators, the most efficacious comparator is preferable. Third, treatments can only be adequately pooled in meta-analysis or merged into one treatment node in NMA when considering likewise mechanism of action and similar setting in which treatment is indicated. Fourth, for both pair-wise meta-analysis and NMA, adequate assessment of heterogeneity should be performed and sub-analysis and sensitivity analysis can be applied to objectify a possible confounding factor. Network inconsistency, as statistical manifestation of violating the transitivity assumption, can best be evaluated by node-split modeling. NMA has advantages over pair-wise meta-analysis, such as clarification of inconsistent outcomes from multiple studies including multiple common comparators and indirect effect calculation of missing direct comparisons between important treatments. Also, NMA can provide increased statistical power and cross-validation of the observed treatment effect of weak connections with reasonable network connectivity and sufficient sample-sizes. However, inappropriate use of NMA can cause misleading results, and may emerge when there is low network connectivity, and therefore low statistical power. Furthermore, indirect evidence is still observational and should be interpreted with caution. NMA should therefore preferably be conducted and interpreted by both expert clinicians in the field and an experienced statistician. Finally, the use of meta-analysis can be extended to other areas, for example the identification of prognostic and predictive factors. Also, the integration of evidence from both meta-analysis and expert opinion can improve the construction of prognostic models in real-world databases.

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Consensus statement on mandatory measurements in pancreatic cancer trials (COMM-PACT) for systemic treatment of unresectable disease

Cited by 56 publications

References 79 publications

SOURCE: A Registry-Based Prediction Model for Overall Survival in Patients with Metastatic Oesophageal or Gastric Cancer

SOURCE: A Registry-Based Prediction Model for Overall Survival in Patients with Metastatic Oesophageal or Gastric Cancer

Systemic Inflammation and Outcome in 2295 Patients with Stage I–III Colorectal Cancer from Scotland and Norway: First Results from the ScotScan Colorectal Cancer Group

The Use of (Network) Meta-Analysis in Clinical Oncology

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