Review ArticleA systematic review of biomarkers for the prediction of thromboembolism in lung cancer - Results, practical issues and proposed strategies for future risk prediction models

- Thrombosis burden in lung cancer is substantial but heterogeneous.
- Heterogeneity in risk favours a personalised risk-adaptive assessment model.
- Existing prediction models do not adequately stratify lung cancer patients.
- Prospective studies with correlative longitudinal biomarker assessments are needed.

IntroductionThis review aimed to identify candidate biomarkers for the prediction of thromboembolism (TE) in lung cancer.Materials and methodsSystematic review of publications indexed in PubMed or EMBASE databases in the past 5 years (01/05/2011-01/05/2016) which evaluated baseline and/or longitudinal biomarker measurements as a predictor of subsequent TE (venous and arterial) in lung cancer patients.ResultsOf 1105 studies identified, 18 fulfilled predefined inclusion criteria: 6 prospective and 12 retrospective. The 18 studies included 11,262 patients and 36 unique biomarkers. The combined TE rate was 7% (741/10,854), increasing to 11% (294/2612) within prospective studies. All biomarker measurements were baseline only, with no longitudinal assessment reported. The most frequently investigated biomarkers were tumour-related driver mutations, D-dimer, haemoglobin, white cell, and platelet count; as well as biomarker combinations previously used in risk prediction models, such as Khorana risk score. Biomarker thresholds rather than continuous variable analyses were generally applied, however thresholds were not consistent across studies. D-dimer and epidermal growth factor receptor mutation were the strongest and most reproducible predictors of TE.ConclusionAn important limitation is the lack of prospective data across specific subpopulations of cancer, with correlative, and preferably longitudinal, biomarker assessments. This would provide insight into the pathophysiology, allow patient profiling, and the development of personalised decision-making tools that can be used real-time and throughout the course of the patients' journey, for targeted, risk-adaptive preventative strategies.