Risk Assessment for Venous Thromboembolism in Chemotherapy-Treated Ambulatory Cancer Patients: A Machine Learning Approach

OBJECTIVE: To design a precision medicine approach aimed at exploiting significant patterns in data, in order to produce venous thromboembolism (VTE) risk predictors for cancer outpatients that might be of advantage over the currently recommended model (Khorana score). DESIGN: Multiple kernel learning (MKL) based on support vector machines and random optimization (RO) models were used to produce VTE risk predictors (referred to as machine learning [ML]-RO) yielding the best classification performance over a training (3-fold cross-validation) and testing set. RESULTS: Attributes of the patient data set ( n = 1179) were clustered into 9 groups according to clinical significance. Our analysis produced 6 ML-RO models in the training set, which yielded better likelihood ratios (LRs) than baseline models. Of interest, the most significant LRs were observed in 2 ML-RO approaches not including the Khorana score (ML-RO-2: positive likelihood ratio [+LR] = 1.68, negative likelihood ratio [-LR] = 0.24; ML-RO-3: +LR = 1.64, -LR = 0.37). The enhanced performance of ML-RO approaches over the Khorana score was further confirmed by the analysis of the areas under the Precision-Recall curve (AUCPR), and the approaches were superior in the ML-RO approaches (best performances: ML-RO-2: AUCPR = 0.212; ML-RO-3-K: AUCPR = 0.146) compared with the Khorana score (AUCPR = 0.096). Of interest, the best-fitting model was ML-RO-2, in which blood lipids and body mass index/performance status retained the strongest weights, with a weaker association with tumor site/stage and drugs. CONCLUSIONS: Although the monocentric validation of the presented predictors might represent a limitation, these results demonstrate that a model based on MKL and RO may represent a novel methodological approach to derive VTE risk classifiers. Moreover, this study highlights the advantages of optimizing the relative importance of groups of clinical attributes in the selection of VTE risk predictors

Low-density lipoprotein-lowering medication and platelet function

Elevated low-density lipoprotein (LDL) cholesterol (LDL-C) levels represent one of the most important risk factors for atherosclerosis and therefore cardiovascular morbidity and mortality. LDL-C operates at different levels and through various classic and non-classic mechanisms. In particular, increased or modified LDL enhances platelet function and increases sensitivity of platelets to several naturally occurring agonists. Agents that lower LDL-C in hypercholesterolemic patients have been shown to interfere with platelet function. Several studies, in fact, suggested that statins exert anti-thrombotic effects largely as a result of an anti-platelet activity. Among the other LDL-C-lowering agents those acting by interfering with cholesterol reabsorption from the gut (cholestyramine, colestipol) do not appear to interfere with platelet function, whereas peroxisome proliferator-activated receptor agonists (such as fibrates) can inhibit platelet function. The full potential of these drugs in vascular protection is only just being realized. Further studies are still needed to elucidate the full therapeutic benefits of these agents in plaque stabilization and thrombosis. Copyright (c) 2006 S. Karger AG, Basel

Oxidant stress and platelet activation in hypercholesterolemia

Hypercholesterolemia is the dominant risk factor associated with atherothrombotic disorders in the western world. Consequently, much attention has been devoted to defining its role in the pathogenesis of atherosclerosis. It is currently recognized that hypercholesterolemia induces phenotypic changes in the microcirculation that are consistent with oxidative and nitrosative stresses. Superoxide is generated via several cellular systems and, once formed, participates in a number of reactions, yielding various free radicals, such as hydrogen peroxide, peroxynitrite, or oxidized low-density lipoproteins. Once oxidant stress is invoked, characteristic pathophysiologic features ensue, such as platelet activation and lipid peroxidation, which are both involved in the initiation and progression of the atherosclerotic lesions. Thus, therapeutic strategies that act to maintain the normal balance in the oxidant status of the vascular bed may prove effective in reducing the deleterious consequences of hypercholesterolemia

Ensuring sample quality for biomarker discovery studies - Use of ict tools to trace biosample life-cycle

The growing demand of personalized medicine marked the transition from an empirical medicine to a molecular one, aimed at predicting safer and more effective medical treatment for every patient, while minimizing adverse effects. This passage has emphasized the importance of biomarker discovery studies, and has led sample availability to assume a crucial role in biomedical research. Accordingly, a great interest in Biological Bank science has grown concomitantly. In biobanks, biological material and its accompanying data are collected, handled and stored in accordance with standard operating procedures (SOPs) and existing legislation. Sample quality is ensured by adherence to SOPs and sample whole life-cycle can be recorded by innovative tracking systems employing information technology (IT) tools for monitoring storage conditions and characterization of vast amount of data. All the above will ensure proper sample exchangeability among research facilities and will represent the starting point of all future personalized medicine-based clinical trials

Association between serum carcinoembryonic antigen and endothelial cell adhesion molecules in colorectal cancer

OBJECTIVES: To analyse the behaviour of pre-surgical serum levels of soluble (s)E-selectin and vascular cell adhesion molecule (sVCAM) in patients with colorectal cancer, and to evaluate their possible correlation with carcinoembryonic antigen (CEA), pro-inflammatory cytokines and clinicopathological features with respect to their prognostic value in predicting metastatic disease. METHODS: Pre-surgical serum levels of sE-selectin, sVCAM, interleukin-6 (IL-6), IL-1beta, tumour necrosis factor-alpha (TNF-alpha) and CEA were measured in 194 patients with colorectal adenocarcinoma, 40 patients with benign colorectal diseases and 59 healthy subjects. RESULTS: sE-selectin, sVCAM, TNF-alpha and IL-6 levels were significantly higher in patients with colorectal cancer compared to either healthy subjects or patients with benign disease. Positive rates of sE-selectin, sVCAM and TNF-alpha levels were significantly associated with Dukes' stage D colorectal cancer, and all three variables were independently associated to the presence of distant metastases. Positive sE-selectin, sVCAM and TNF-alpha levels were significantly associated to CEA. TNF-alpha and CEA levels were independently related to the presence of positive levels of sE-selectin and/or sVCAM. CONCLUSIONS: Our findings suggest that the host inflammatory response to cancer cells, and/or their released products (i.e. CEA), might be responsible (via cytokine release) for the elevation in circulating adhesion molecules in patients with colorectal cancer

Clinical utility of plasma KRAS, NRAS and BRAF mutational analysis with real time PCR in metastatic colorectal cancer patients -The importance of tissue/plasma discordant cases

Background: Tumor tissue (T) mutational analysis represents the standard for metastatic colorectal cancer (mCRC); however, circulating tumor DNA (ctDNA) detected by liquid biopsy in plasma (PL) can better represent tumor heterogeneity. Methods: mCRC patients undergoing standard first-line chemotherapy with known T-KRAS/NRAS/BRAF status were enrolled in the present prospective study. PL mutations were assessed within 2 weeks before chemotherapy start with real time PCR and correlated with T status and Progression free survival (PFS). Clinical and biochemical variables including also total number of tumor lesions (TNL) and the sum of maximum diameter (SMD) of all lesions were assessed as potential predictors of T/PL discordance. RESULTS: Among 45 enrolled patients, all BRAF mutations were concordant between T and PL and there were 20% of patients RAS discordant: 9% wild type in T and mutated in PL and 11% mutated in T and wild type in PL. T mutations were significantly associated to median PFS (mPFS of 4.5, 8.3 and 22.9 months for T-BRAF mutated, T-RAS mutated, and T-wild type patients, respectively, p for trend 0.00014). PL mutations further refined prognosis: RAS wild type in T and mutated in PL had significantly shorter PFS than concordant RAS wild type in T and PL: mPFS 9.6 vs. 23.3 months, respectively, p = 0.02. Patients RAS mutated in T and wild type in PL had longer PFS than concordant RAS mutated in T and PL: 24.4 vs. 7.8 months, respectively, p = 0.008. At a multivariate cox regression analysis for PFS, PL mutations were independent prognostic factor superior to T analysis (HR 0.13, p = 0.0008). At multivariate logistic regression analysis TNL and SMD were significant predictors of discordant cases. Conclusions: PL mutational analysis allows a better prognostication than T analysis alone and could help in mCRC treatment management

Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis

Simple Summary Metastatic colorectal cancer (mCRC) has high incidence and mortality. Nevertheless, innovative biomarkers have been developed for predicting the response to therapy. We have examined the ability of learning methods to build prognostic and predictive models to predict response to chemotherapy, alone or combined with targeted therapy in mCRC patients, by targeting specific narrative publications. After a literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. We showed that all investigations conducted in this field provided generally promising results in predicting the response to therapy or toxic side-effects, using a meta-analytic approach. We found that radiomics and molecular biomarker signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Our study supports the use of computer science for developing personalized treatment decision processes for mCRC patients. Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in clinical decision making. In this systematic review, we have examined the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 to identify appropriate original scientific articles. After the literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. Our results show that all investigations conducted on this field have provided generally promising results in predicting the response to therapy or toxic side-effects. By a meta-analytic approach we found that the overall weighted means of the area under the receiver operating characteristic (ROC) curve (AUC) were 0.90, 95% C.I. 0.80-0.95 and 0.83, 95% C.I. 0.74-0.89 in training and validation sets, respectively, indicating a good classification performance in discriminating response vs. non-response. The calculation of overall HR indicates that learning models have strong ability to predict improved survival. Lastly, the delta-radiomics and the 74 gene signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Specifically, when we evaluated the predictive models with tests reaching 80% sensitivity (SE) and 90% specificity (SP), the delta radiomics showed an SE of 99% and an SP of 94% in the training set and an SE of 85% and SP of 92 in the test set, whereas for the 74 gene signatures the SE was 97.6% and the SP 100% in the training set

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease

Background: Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated. Objectives: To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD. Methods: From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA). Non-obstructive CAD was defined by a stenosis < 50% in one or more vessels. Baseline clinical and psycho-socio-cultural characteristics were used for computing a Rockwood and Mitnitski frailty index, and a gender score according to GENESIS-PRAXY methodology. Serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. Through an XGBoost classifier combined with an explainable artificial intelligence tool (SHAP), we identified the most influential features in discriminating obstructive versus non-obstructive CAD. Results: Among the overall EVA cohort (n = 509), 311 individuals (mean age 67 ± 11 years, 38% females; 67% obstructive CAD) with complete data were analysed. The ML-based model (83% accuracy and 87% precision) showed that while obstructive CAD was associated with higher frailty index, older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was associated with a higher gender score (i.e., social characteristics traditionally ascribed to women) and with a cytokine signature characterized by IL-18, IL-8, IL-23. Conclusions: Integrating clinical, biological, and psycho-social features, we have optimized a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of these associations. Clinical trial registration: NCT02737982