Valentina Russo

The ITALUNG trial (Italian Lung Cancer Screening Trial) is a randomised controlled trial that evaluated the efficacy of Low-Dose Computed Tomography (LDCT) for lung cancer screening of over 3100 high-risk individuals, smokers and ex-smokers, aged 55–69. ITALUNG was coordinated by the Institute for Cancer Prevention Research in Florence and carried out across Tuscany in three main screening centres: Florence, Pisa, and Pistoia. The ITALUNG study demonstrated lower long-term lung cancer (LC) and cardiovascular mortality in the screened groups, with notable benefits observed in women. When the performance of the ITALUNG biomarker panel, i.e., loss of heterozygosity and microsatellite instability (LOH/MSI) and circulating free DNA (cfDNA), was analysed in samples of blood and sputum from asymptomatic high-risk subjects screened for lung cancer, sensitivity (SE) was 90% at baseline screening. However, the respective roles of LDCT screening and fluid biomarkers in screening for lung cancer are not established, highlighting the need for biomarkers to be able to detect tumours not only at the time of diagnosis but also when tested in pre-diagnostic samples. Therefore, we evaluated the longitudinal changes in the performance of multiple biomarkers in the ITALUNG biomarker panel and/or its single components to identify LC patients in ITALUNG. The cohort consisted of 55 LC patients whose biological specimens were collected over several years before diagnosis. Good results were found in test specimens obtained close to the time of diagnosis; out of 21 LC cases, 20 were positive in the ITALUNG biomarker panel [95% SE; 95% C.I. 0.86–1.04], 18 for LOH/MSI (86% SE; 95% C.I. 0.71–1.01) and 17 for cfDNA (85% SE; 95% C.I. 0.70–1.00). The panel was able to identify over 80% of LC cases by analysing the pre-diagnostic samples collected within 3 years prior to diagnosis: out of six LC patients, five were positive in the ITALUNG biomarker panel (83% SE; 95% C.I. 0.53–1.13). In addition, over 67% of the early and resected cancer cases were detected by the multiple-biomarker panel when biomarker testing samples were obtained within 11 years prior to diagnosis. The positivity of the ITALUNG biomarker panel might be associated with both carcinogenic field effects and early tumour presence when using pre-diagnostic samples obtained several years before diagnosis.

Metastatic colorectal cancer (mCRC) is a severe condition with high rates of illness and death. Current treatments are limited and not always effective because the cancer responds differently to drugs in different patients. This research aims to use artificial intelligence (AI) to improve treatment by predicting which therapies will work best for individual patients. By analyzing large sets of patient data and using machine learning, we hope to create a model that can identify which patients will respond to chemotherapy, either alone or combined with other targeted treatments. The study will involve dividing patients into training and validation sets to develop and test the models, avoiding overfitting. Various machine learning algorithms, like random survival forest and neural networks, will be integrated to develop a highly accurate and stable predictive model. The model’s performance will be evaluated using statistical measures such as sensitivity, specificity, and the area under the curve (AUC). The aim is to personalize treatments, improve patient outcomes, reduce healthcare costs, and make the treatment process more efficient. If successful, this research could significantly impact the medical community by providing a new tool for better managing and treating mCRC, leading to more personalized and effective cancer care. In addition, we examine the applicability of learning methods to biomarker discovery and therapy prediction by considering recent narrative publications.

Studies have indicated that air pollution, including surface-level ozone (O₃), can significantly influence the risk of chronic diseases. To better understand the carcinogenic mechanisms of air pollutants and identify predictive disease biomarkers, we examined the association between traffic-related pollutants with DNA methylation alterations and bulky DNA adducts, two biomarkers of carcinogen exposure and cancer risk, in the peripheral blood of 140 volunteers—95 traffic police officers, and 45 unexposed subjects. The DNA methylation and adduct measurements were performed by bisulfite-PCR and pyrosequencing and ³²P-postlabeling assay. Airborne levels of benzo(a)pyrene [B(a)P], carbon monoxide, and tropospheric O₃ were determined by personal exposure biomonitoring or by fixed monitoring stations. Overall, air pollution exposure was associated with a significant reduction (1.41 units) in global DNA methylation (95% C.I. −2.65–0.04, p = 0.026). The decrement in ALU repetitive elements was greatest in the policemen working downtown (95% C.I. −3.23–−0.49, p = 0.008). The DNA adducts were found to be significantly increased (0.45 units) in the municipal officers with respect to unexposed subjects (95% C.I. 0.02–0.88, p = 0.039), mainly in those who were controlling traffic in downtown areas (95% C.I. 0.39–1.29, p < 0.001). Regression models indicated an increment of ALU methylation at higher B(a)P concentrations (95% C.I. 0.03–0.60, p = 0.032). Moreover, statistical models showed a decrement in ALU methylation and an increment of DNA damage only above the cut-off value of 30 µg/m³ O₃. A significant increment of 0.73 units of IL-6 gene methylation was also found in smokers with respect to non-smokers. Our results highlighted the role of air pollution on epigenetic alterations and genotoxic effects, especially above the target value of 30 µg/m³ surface-level O₃, supporting the necessity for developing public health strategies aimed to reduce traffic-related air pollution molecular alterations.

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.

Epidemiologic studies have indicated that cruciferous vegetables can influence the cancer risk; therefore, we examined with a cross-sectional approach the correlation between the frequent consumption of the total cruciferous vegetables and the formation of bulky DNA damage, a biomarker of carcinogen exposure and cancer risk, in the Gen-Air study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. DNA damage measurements were performed in the peripheral blood of 696 of those apparently healthy without cancer controls, including 379 never-smokers and 317 former smokers from seven European countries by the ³²P-postlabeling assay. In the Gen-Air controls, the median intake of cruciferous vegetables was 6.16 (IQR 1.16–13.66) g/day, ranging from 0.37 (IQR 0–6.00) g/day in Spain to 11.34 (IQR 6.02–16.07) g/day in the UK. Based on this information, participants were grouped into: (a) high consumers (>20 g/day), (b) medium consumers (3–20 g/day) and (c) low consumers (p = 0.032); this was particularly evident in former smokers (up to a 40% change, p = 0.008). The Generalized Linear Regression models indicated an overall Mean Ratio between the high and the low consumers of 0.78 (95% confidence interval, 0.64–0.97). The current study suggests that a higher intake of cruciferous vegetables is associated with a lower level of bulky DNA adducts and supports the potential for cancer prevention strategies through dietary habit changes aimed at increasing the consumption of cruciferous vegetables.