Periodic Reporting for period 2 - PREVALUNG EU (Biomarkers affecting the transition from cardiovascular disease to lung cancer: towards stratified interception.)
Berichtszeitraum: 2023-12-01 bis 2025-05-31
Cardiovascular disease (CVD) and lung cancer (LC) are leading causes of deaths and intertwined chronic inflammatory processes associated with metabolism reprogramming, clonal hematopoiesis of indeterminate potential (CHIP), intestinal dysbiosis, and maladaptive immunity. CVD prone-tobacco users exhibit a 1-2% yearly incidence of LC. Low dose computed tomography screening programs reduce LC mortality by 20%. Beyond epidemiological scores, risk identification, early cancer detection and interception are mostly based on cell autonomous approaches. Understanding pathophysiological failures linking CVD to LC would allow to take steps for prevention.
We suggest to estimate LC risk of CVD patients by assessing parameters relevant to metabolism, immunity, hematopoiesis and/or intestinal barrier fitness and to feed the results of this equation into rational intervention strategies designed to suppress pro-carcinogenic inflammation.
Based on this premise, several members of this consortium brought up the first proof-of-concept of the relevance of host-derived factors (as opposed to cell autonomous parameters) to predict health to disease transition, meaning early lung carcinogenesis in the context of CVD-related chronic systemic inflammation. We conducted a prospective observational study (i.e “PREVALUNG” NCT03976804) using the system biology approach to find omics-based predictors of LC incidence in a population of CVD-affected individuals. Patients from the PREVALUNG study displayed stabilized CVD and secondary prevention, LC remaining the major avoidable cause of death in this population.
According to our PREVALUNG EU vision, two specific aims will be harnessed within 5 years:
• Validate in both retrospective biobanks and large prospective cohorts, classifiers representing four functional drivers of chronic inflammation detecting CVD individuals pre-symptomatic and at early stages of lung carcinogenesis: this will allow to implement patient stratification for preventive interventions based on dysmetabolism, innate immunosuppression, clonal hematopoiesis or gut microbiota dysbiosis (4 main drivers).
• Demonstrate the actionability of such biomarkers: develop and test specific interceptive measures for each of the 4 main drivers of inflammation using food supplements or pharmacological agents (on the top of diet and lifestyle modifications) to return to homeostasis
We suggest to estimate LC risk of CVD patients by assessing parameters relevant to metabolism, immunity, hematopoiesis and/or intestinal barrier fitness and to feed the results of this equation into rational intervention strategies designed to suppress pro-carcinogenic inflammation.
Based on this premise, several members of this consortium brought up the first proof-of-concept of the relevance of host-derived factors (as opposed to cell autonomous parameters) to predict health to disease transition, meaning early lung carcinogenesis in the context of CVD-related chronic systemic inflammation. We conducted a prospective observational study (i.e “PREVALUNG” NCT03976804) using the system biology approach to find omics-based predictors of LC incidence in a population of CVD-affected individuals. Patients from the PREVALUNG study displayed stabilized CVD and secondary prevention, LC remaining the major avoidable cause of death in this population.
According to our PREVALUNG EU vision, two specific aims will be harnessed within 5 years:
• Validate in both retrospective biobanks and large prospective cohorts, classifiers representing four functional drivers of chronic inflammation detecting CVD individuals pre-symptomatic and at early stages of lung carcinogenesis: this will allow to implement patient stratification for preventive interventions based on dysmetabolism, innate immunosuppression, clonal hematopoiesis or gut microbiota dysbiosis (4 main drivers).
• Demonstrate the actionability of such biomarkers: develop and test specific interceptive measures for each of the 4 main drivers of inflammation using food supplements or pharmacological agents (on the top of diet and lifestyle modifications) to return to homeostasis
Multiomics analysis have been performed on two independent cohorts (PREVALUNG, and FLEMENGHO). In total, more than 3000 features were measured in each patient when adequate samples were available (FLEMENGHO: 192 patients, PREVALUNG: 145 patients).
Machine learning algorithms modelling high-dimensional data might unveil mechanisms and biomarkers underlying the inflammation-to-cancer transition in tobacco users with cardiovascular disease (CVD), thereby facilitating screening programs. We performed multi-omics related to inflammation, immunity, metabolism, microbiota and clonal hematopoiesis (CHIP) on retrospective and prospective cohorts enrolling 97 and 86 smokers with CVD, respectively, to validate a plasma fingerprint of cancer risk with AUROC=0.85 (CI95% [0.82; 0.87]) in the test and 0.63 (CI95% [0.49; 0.79]) for prediction at 1 year in the validation cohorts. This cancer risk prediction was further confirmed in patients with TP53 germline mutations with AUROC=0.71 (CI95% [0.51; 0.90] at 2 years. Finally, a prospective analysis validated this cancer risk score. We found that a compendium of 27 plasma-based analytes and CHIP mutations reflecting major drivers of carcinogenesis (inflammation, immunity, metabolism and gut barrier dysfunction) clustered at-risk patients into three subgroups that are potentially amenable to distinct interceptive measures.
Machine learning algorithms modelling high-dimensional data might unveil mechanisms and biomarkers underlying the inflammation-to-cancer transition in tobacco users with cardiovascular disease (CVD), thereby facilitating screening programs. We performed multi-omics related to inflammation, immunity, metabolism, microbiota and clonal hematopoiesis (CHIP) on retrospective and prospective cohorts enrolling 97 and 86 smokers with CVD, respectively, to validate a plasma fingerprint of cancer risk with AUROC=0.85 (CI95% [0.82; 0.87]) in the test and 0.63 (CI95% [0.49; 0.79]) for prediction at 1 year in the validation cohorts. This cancer risk prediction was further confirmed in patients with TP53 germline mutations with AUROC=0.71 (CI95% [0.51; 0.90] at 2 years. Finally, a prospective analysis validated this cancer risk score. We found that a compendium of 27 plasma-based analytes and CHIP mutations reflecting major drivers of carcinogenesis (inflammation, immunity, metabolism and gut barrier dysfunction) clustered at-risk patients into three subgroups that are potentially amenable to distinct interceptive measures.
Although validated worldwide, purely clinico-epidemiological scores (usually based on age, BMI, ethnicity, education, history of COPD, smoking habits and the personal or family history of cancer) are insufficient to predict the transition from (apparent) health to cancer diagnosis at the individual level in our PREVALUNG cohort. Thus, 30% of the CVD patients enrolled in the PREVALUNG cohort who eventually developed cancer were not included in the at-risk category, based on the purely clinical NLST, NELSON and PLCOm2012 scores. Hence, risk assessment to advise LDCT LC screening programs remains imprecise. Here, we propose functional biomarkers to identify the health-to-cancer transition, based on high-dimensional unbiased multi-omics technologies.
Machine learning guided dimension reduction led to a predictive algorithm based on blood borne soluble factors measured in a first retrospective cohort (FLEMENGHO) that we applied and validated in external cohort, one with a similar risk epidemiology (ACVD and tobacco for PREVALUNG). The risk score was computed in CVD tobacco users from 27 soluble factors that were validated in two other cohorts and a prospective blinded FU of controls with high-risk scores at study entry. Interestingly, this 27-plex fingerprint was also able to detect history of cancer, related or not to tobacco consumption. Finally, this 27-plex fingerprint allowed to categorize patients into three major functional clusters (relying on cholesterol metabolism and CHIP, gut barrier permeability/IL-6/TH2, and maladaptive immunity with cumulative immune inhibitory checkpoints and CHIP), amenable to specific interceptive measures.
CHIP mutation analysis revealed the presence of multiple CHIP mutations in some tobacco users with CVD that developed LC. This accumulation of CHIP variants correlated with elevated IL-1β levels. Fueling this notion, high-dimensional spectral blood flow cytometry-based immune profiling revealed an increase of specific subsets of inflammatory monocytes in tobacco users with CVD who developed LC. These data suggest a dysregulated epigenetic control of clonal myeloid progenitors leading to systemic IL-1β-associated inflammation and LC, in accord with recent reports. Individuals combining several CHIP variants or exhibiting high variant allelic frequencies (VAFs) might benefit from recombinant IL-1R antagonist or colchicine. Indeed, IL-1R antagonist may be particularly useful for patients with cancer-associated CHIP variants, which are causatively linked to increase circulating IL-1β.
Several limitations affect our study. The value of the PREVALUNG prospective study is limited by the low number of cases. Moreover, the absence of longitudinal sampling precludes the elucidation of the temporal order of biological deviations affecting each subject. Despite the strength of machine learning-based algorithms used in this study, and its blinded confirmation in the prolonged follow up of the CVD controls, further prospective validation is needed to strengthen our conclusions. Regardless of such shortcomings, distinct biomarkers identified in this work have already been unveiled in other reports as candidate biomarkers of early diagnosis of lung cancer or other malignancies. Pending further validation, the new biological signatures we found may permit a comprehensive and personalized screening program and prepare the grounds for adapted cancer interception.
Machine learning guided dimension reduction led to a predictive algorithm based on blood borne soluble factors measured in a first retrospective cohort (FLEMENGHO) that we applied and validated in external cohort, one with a similar risk epidemiology (ACVD and tobacco for PREVALUNG). The risk score was computed in CVD tobacco users from 27 soluble factors that were validated in two other cohorts and a prospective blinded FU of controls with high-risk scores at study entry. Interestingly, this 27-plex fingerprint was also able to detect history of cancer, related or not to tobacco consumption. Finally, this 27-plex fingerprint allowed to categorize patients into three major functional clusters (relying on cholesterol metabolism and CHIP, gut barrier permeability/IL-6/TH2, and maladaptive immunity with cumulative immune inhibitory checkpoints and CHIP), amenable to specific interceptive measures.
CHIP mutation analysis revealed the presence of multiple CHIP mutations in some tobacco users with CVD that developed LC. This accumulation of CHIP variants correlated with elevated IL-1β levels. Fueling this notion, high-dimensional spectral blood flow cytometry-based immune profiling revealed an increase of specific subsets of inflammatory monocytes in tobacco users with CVD who developed LC. These data suggest a dysregulated epigenetic control of clonal myeloid progenitors leading to systemic IL-1β-associated inflammation and LC, in accord with recent reports. Individuals combining several CHIP variants or exhibiting high variant allelic frequencies (VAFs) might benefit from recombinant IL-1R antagonist or colchicine. Indeed, IL-1R antagonist may be particularly useful for patients with cancer-associated CHIP variants, which are causatively linked to increase circulating IL-1β.
Several limitations affect our study. The value of the PREVALUNG prospective study is limited by the low number of cases. Moreover, the absence of longitudinal sampling precludes the elucidation of the temporal order of biological deviations affecting each subject. Despite the strength of machine learning-based algorithms used in this study, and its blinded confirmation in the prolonged follow up of the CVD controls, further prospective validation is needed to strengthen our conclusions. Regardless of such shortcomings, distinct biomarkers identified in this work have already been unveiled in other reports as candidate biomarkers of early diagnosis of lung cancer or other malignancies. Pending further validation, the new biological signatures we found may permit a comprehensive and personalized screening program and prepare the grounds for adapted cancer interception.