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Cardiovascular disease (CVD) is the most common non-cancer cause of death in cancer survivors and there is an unmet clinical need for easy, accurate, and safe CVD prognostic risk-stratification in adult cancer survivors. This study investigated whether a previously validated 27-plasma protein prognostic model for four-year cardiovascular (CV) events could have such a utility. We used the 27-plasma protein model to predict the four-year risk of a CV event (myocardial infarction, stroke, transient ischemic attack, heart failure hospitalization, death) in 906 participants with a prior history or active malignancy of any type of cancer and compared predictive results to follow-up CV outcome data. The participants were from the BASEL VIII or ARIC (visit 3) studies with a medically adjudicated prior diagnosis of cancer. BASEL VIII is an observational cohort study in patients with suspected coronary artery disease. ARIC is a multi-site cohort study funded by the NHLBI, NCI, and NPCR investigating risk factors for CV health. A subset analysis was conducted to assess model performance in participants with no prior history of CVD and those with stable CVD. The 27-plasma protein model accurately stratified participants into 4 distinct and non-overlapping (95% CI) risk bins. The median time to event for all cancer survivors who had an event in this study was 1.3 years. Observed 4-year event rates across the 4 risk bins (low, medium-low, medium-high, and high) were 11.0%, 17.3%, 31.2% and 60.2%, respectively, which were higher than stratified event rates from our previously published metacohort analyses (5.6%, 11.2%, 20.0% and 43.4%, respectively) in participants with elevated CVD risk factors (e.g., prior events, diabetes, kidney disease and suspected coronary artery disease). The plasma protein model accurately predicted 4-year CVD risk with a C-index of 0.71 (0.68, 0.74) and 4-year AUC of 0.74 (0.69, 0.79). Performance of the protein model was comparable between participants with no prior history of CVD (C-Index: 0.69; AUC: 0.71) and stable CVD (C-Index: 0.69; AUC: 0.72), demonstrating the model accurately predicts CV event risk in cancer survivors regardless of cardiovascular history. Cancer survivors in this cohort can be distinguished with 4-year CV event rates as high as 60.2%, underscoring the urgent need for an easy and accurate risk stratification tool for this population. Prognostic protein testing may provide a novel tool for CVD risk assessment in adult cancer survivors. Citation Format: Emma V. Troth, Matthew Ayala, Jessica Chadwick, Erin Hales, Michael Hinterberg, Jessica N. Kuzma, Clare Paterson, Rachel Ostroff, Joan E. Walter, Christian Mueller, Josef Coresh. The plasma proteome as a cardiovascular disease risk assessment tool in cancer survivors. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4361.

Introduction: The critical path for novel treatment and prevention strategies, both pharmacological and non-pharmacological, requires clinical outcomes trials, which are slow, expensive and retard patient access to successful therapies. For marketed drugs, unreliable residual risk stratification hinders precision-directed therapy and facilitates payor reticence. Hypothesis: Large-scale proteomics and machine learning can identify a reliable, individualized, and dynamic surrogate of cardiovascular risk, synoptic for multiple biologic mechanisms, with a resulting multimarker model that is more robust than individual biomarkers. Avoidance of immutable prognostic factors (e.g. age, sex, medical diagnoses) will enhance sensitivity to change in risk. Methods: We quantified ~5000 proteins in >31,000 archived samples from >21,000 higher risk individuals in nine clinical datasets, generating ~150M plasma protein measurements. We derived and validated a 27-protein model encompassing 10 biologic processes to predict the four-year likelihood of myocardial infarction, stroke, hospitalization for heart failure or death. We assessed the association of model-predicted change with observed outcomes in a variety of conditions in four clinical intervention trials. Results: See table for details. In validation, observed event rates were 7.8 fold higher in quintile 5 than quintile 1 for proteins vs. 2.9 for an optimized clinical factor model; the Net Reclassification Index was +0.43. AUCs were 0.73 for proteins vs. 0.63 for clinical, and c-statistics 0.71 vs. 0.62. The protein model also responded concordantly with outcomes in four intervention studies. Conclusions: A 27-protein model is more prognostic than clinical factors in higher risk populations and has directionally concordant responsiveness to all mechanistic challenges to date. These features are consistent with surrogacy, and may prove useful in clinical trials and precision medicine.

In the past decade, the introduction of molecularly targeted agents and immune-checkpoint inhibitors has led to improved survival outcomes for patients with advanced-stage lung cancer; however, this disease remains the leading cause of cancer-related mortality worldwide. Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening in high-risk populations - the US National Lung Screening Trial (NLST) and NELSON - have provided evidence of a statistically significant mortality reduction in patients. LDCT-based screening programmes for individuals at a high risk of lung cancer have already been implemented in the USA. Furthermore, implementation programmes are currently underway in the UK following the success of the UK Lung Cancer Screening (UKLS) trial, which included the Liverpool Health Lung Project, Manchester Lung Health Check, the Lung Screen Uptake Trial, the West London Lung Cancer Screening pilot and the Yorkshire Lung Screening trial. In this Review, we focus on the current evidence on LDCT-based lung cancer screening and discuss the clinical developments in high-risk populations worldwide; additionally, we address aspects such as cost-effectiveness. We present a framework to define the scope of future implementation research on lung cancer screening programmes referred to as Screening Planning and Implementation RAtionale for Lung cancer (SPIRAL).