Improving the quality of patient care in lung cancer: key factors for successful multidisciplinary team working.

International Guidelines as well as Cancer Associations recommend a multidisciplinary approach to lung cancer care. A multidisciplinary team (MDT) can significantly improve treatment decision-making and patient coordination by putting different physicians and other health professionals "in the same room", who collectively decide upon the best possible treatment. However, this is not a panacea for cancer treatment. The impact of multidisciplinary care (MDC) on patient outcomes is not univocal, while the effective functioning of the MDT depends on many factors. This review presents the available MDT literature with an emphasis on the key factors that characterize high-quality patient care in lung cancer. The study was conducted with a bibliographic search using different electronic databases (PubMed Central, Scopus, Google Scholar, and Google) referring to multidisciplinary cancer care settings. Many key elements appear consolidated, while others emerge as prevalent and actual, especially those related to visible barriers which work across geographic, organizational, and disciplinary boundaries. MDTs must be sustained by strategic management, structured within the entity, and cannot be managed as a separate care process. Furthermore, they need to coordinate with other teams (within and outside the organization) and join with the broad range of services delivered by multiple providers at various points of the cancer journey or within the system, with the vision of integrated care.

The Impact of Inadequate Exposure to Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitors on the Development of Resistance in Non-Small-Cell Lung Cancer Cells.

Epidermal growth factor receptor (EGFR)-mutant non-small-cell lung cancer (NSCLC) patients treated with EGFR-tyrosine kinase inhibitors (TKIs) inevitably develop resistance through several biological mechanisms. However, little is known on the molecular mechanisms underlying acquired resistance to suboptimal EGFR-TKI doses, due to pharmacodynamics leading to inadequate drug exposure. To evaluate the effects of suboptimal EGFR-TKI exposure on resistance in NSCLC, we obtained HCC827 and PC9 cell lines resistant to suboptimal fixed and intermittent doses of gefitinib and compared them to cells exposed to higher doses of the drug. We analyzed the differences in terms of EGFR signaling activation and the expression of epithelial-mesenchymal transition (EMT) markers, whole transcriptomes byRNA sequencing, and cell motility. We observed that the exposure to low doses of gefitinib more frequently induced a partial EMT associated with an induced migratory ability, and an enhanced transcription of cancer stem cell markers, particularly in the HCC827 gefitinib-resistant cells. Finally, the HCC827 gefitinib-resistant cells showed increased secretion of the EMT inducer transforming growth factor (TGF)-ß1, whose inhibition was able to partially restore gefitinib sensitivity. These data provide evidence that different levels of exposure to EGFR-TKIs in tumor masses might promote different mechanisms of acquired resistance.

Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.

PURPOSE: To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases. METHODS: Patient selection in a retrospective study was carried out from January 2018 to May 2021 considering the following inclusion criteria: patients subjected to surgical resection for liver metastases; proven pathological liver metastases; patients subjected to enhanced CT examination in the presurgical setting with a good quality of images; and RAS assessment as standard reference. A total of 851 radiomics features were extracted using the PyRadiomics Python package from the Slicer 3D image computing platform after slice-by-slice segmentation on CT portal phase by two expert radiologists of each individual liver metastasis performed first independently by the individual reader and then in consensus. Balancing technique was performed, and inter- and intraclass correlation coefficients were calculated to assess the between-observer and within-observer reproducibility of features. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers were considered. Moreover, features selection was performed before and after a normalized procedure using two different methods (3-sigma and z-score). RESULTS: Seventy-seven liver metastases in 28 patients with a mean age of 60 years (range 40-80 years) were analyzed. The best predictors, at univariate analysis for both normalized procedures, were original_shape_Maximum2DDiameter and wavelet_HLL_glcm_InverseVariance that reached an accuracy of 80%, an AUC ≥ 0.75, a sensitivity ≥ 80% and a specificity ≥ 70% (p value < < 0.01). However, a multivariate analysis significantly increased the accuracy in RAS prediction when a linear regression model (LRM) was used. The best performance was obtained using a LRM combining linearly 12 robust features after a z-score normalization procedure: AUC of 0.953, accuracy 98%, sensitivity 96%, specificity of 100%, PPV 100% and NPV 96% (p value < < 0.01). No statistically significant increase was obtained considering the tested machine learning both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in CT radiomics analysis allows to predict RAS mutational status in colorectal liver metastases patients.

Harmonization of homologous recombination deficiency testing in ovarian cancer: Results from the MITO16A/MaNGO-OV2 trial.

BACKGROUND: Homologous Recombination Deficiency (HRD) status predicts response to treatment with poly(ADP-ribose) polymerase inhibitors in Ovarian Cancer (OC) patients. The Myriad myChoiceCDx Assay is approved by Food and Drug Agency for the HRD assessment. Here we compared the HRD status obtained by three commercial panels with the results from Myriad reference test. METHODS: The HRD analysis was performed on DNA from formalin-fixed and paraffin-embedded tumor samples of 100 untreated OC patients for which Myriad assay results were available, using TruSight Oncology 500 HRD assay (Illumina), Oncomine Comprehensive Assay Plus (Thermo Fisher Scientific) and SOPHiA DDM HRD solution panel (SOPHiA Genetics). RESULTS: A good overall concordance with the reference method was demonstrated at three different levels: BRCA mutational status (from 94.4 % to 97.7 %), the genomic instability value (from 88.2 % to 95.3 %) and for the HRD status (from 90.4 % to 97.6 %). Moreover, a trend in favour of HRD positive patients for response rate, progression-free survival and overall survival similar to Myriad was observed for all three tests. DISCUSSION: Our data suggest the feasibility of commercial testing for assessing HRD status, with a good concordance with the reference method and association with clinical outcome.

Demographic Analysis of Cancer Research Priorities and Treatment Correlations.

Understanding the diversity in cancer research priorities and the correlations among different treatment modalities is essential to address the evolving landscape of oncology. This study, conducted in collaboration with the European Cancer Patient Coalition (ECPC) and Childhood Cancer International-Europe (CCI-E) as part of the "UNCAN.eu" initiative, analyzed data from a comprehensive survey to explore the complex interplay of demographics, time since cancer diagnosis, and types of treatments received. Demographic analysis revealed intriguing trends, highlighting the importance of tailoring cancer research efforts to specific age groups and genders. Individuals aged 45-69 exhibited highly aligned research priorities, emphasizing the need to address the unique concerns of middle-aged and older populations. In contrast, patients over 70 years demonstrated a divergence in research priorities, underscoring the importance of recognising the distinct needs of older individuals in cancer research. The analysis of correlations among different types of cancer treatments underscored the multidisciplinary approach to cancer care, with surgery, radiotherapy, chemotherapy, precision therapy, and biological therapies playing integral roles. These findings support the need for personalized and combined treatment strategies to achieve optimal outcomes. In conclusion, this study provides valuable insights into the complexity of cancer research priorities and treatment correlations in a European context. It emphasizes the importance of a multifaceted, patient-centred approach to cancer research and treatment, highlighting the need for ongoing support, adaptation, and collaboration to address the ever-changing landscape of oncology.

Harmonization of tumor mutation burden testing with comprehensive genomic profiling assays: an IQN Path initiative.

BACKGROUND: Although conflicting results emerged from different studies, the tumor mutational burden (TMB) appears as one of most reliable biomarkers of sensitivity to immune checkpoint inhibitors. Several laboratories are reporting TMB values when performing comprehensive genomic profiling (CGP) without providing a clinical interpretation, due to the lack of validated cut-off values. The International Quality Network for Pathology launched an initiative to harmonize TMB testing with CGP assay and favor the clinical implementation of this biomarker. METHODS: TMB evaluation was performed with three commercially available CGP panels, TruSight Oncology 500 (TSO500), Oncomine Comprehensive Plus Assay (OCA) and QIAseq Multimodal Panel (QIA), versus the reference assay FoundationOne CDx (F1CDx). Archived clinical samples derived from 60 patients with non-small cell lung cancer were used for TMB assessment. Adjusted cut-off values for each panel were calculated. RESULTS: Testing was successful for 91.7%, 100%, 96.7% and 100% of cases using F1CDx, TSO500, OCA and QIA, respectively. The matrix comparison analysis, between the F1CDx and CGP assays, showed a linear correlation for all three panels, with a higher correlation between F1CDx and TSO500 (rho=0.88) than in the other two comparisons (rho=0.77 for QIA; 0.72 for OCA). The TSO500 showed the best area under the curve (AUC, value 0.96), with a statistically significant difference when compared with the AUC of OCA (0.83, p value=0.01) and QIA (0.88, p value=0.028). The Youden Index calculation allowed us to extrapolate TMB cut-offs of the different panels corresponding to the 10 mutations/megabase (muts/Mb) cut-off of F1CDx: 10.19, 10.4 and 12.37 muts/Mb for TSO500, OCA and QIA, respectively. Using these values, we calculated the relative accuracy measures for the three panels. TSO500 showed 86% specificity and 96% sensitivity, while OCA and QIA had lower yet similar values of specificity and sensitivity (73% and 88%, respectively). CONCLUSION: This study estimated TMB cut-off values for commercially available CGP panels. The results showed a good performance of all panels on clinical samples and the calculated cut-offs support better accuracy measures for TSO500. The validated cut-off values can drive clinical interpretation of TMB testing in clinical research and clinical practice.

Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging.

PURPOSE: To assess the efficacy of radiomics features, obtained by magnetic resonance imaging (MRI) with hepatospecific contrast agent, in pre-surgical setting, to predict RAS mutational status in liver metastases. METHODS: Patients with MRI in pre-surgical setting were enrolled in a retrospective study. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. The features were extracted considering the agreement with the Imaging Biomarker Standardization Initiative (IBSI). Balancing was performed through synthesis of samples for the underrepresented classes using the self-adaptive synthetic oversampling (SASYNO) approach. Inter- and intraclass correlation coefficients (ICC) were calculated to assess the between-observer and within-observer reproducibility of all radiomics characteristics. For continuous variables, nonparametric Wilcoxon-Mann-Whitney test was utilized. Benjamini and Hochberg's false discovery rate (FDR) adjustment for multiple testing was used. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Moreover, features selection were performed before and after a normalized procedure using two different methods (3-sigma and z-score). McNemar test was used to assess differences statistically significant between dichotomic tables. All statistical procedures were done using MATLAB R2021b Statistics and Machine Toolbox (MathWorks, Natick, MA, USA). RESULTS: Seven normalized radiomics features, extracted from arterial phase, 11 normalized radiomics features, from portal phase, 12 normalized radiomics features from hepatobiliary phase and 12 normalized features from T2-W SPACE sequence were robust predictors of RAS mutational status. The multivariate analysis increased significantly the accuracy in RAS prediction when a LRM was used, combining 12 robust normalized features extracted by VIBE hepatobiliary phase reaching an accuracy of 99%, a sensitivity 97%, a specificity of 100%, a PPV of 100% and a NPV of 98%. No statistically significant increase was obtained, considering the tested classifiers DT, KNN and SVM, both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in MRI radiomics analysis allows to predict RAS mutational status.

Electrochemical detection of miRNA using commercial and hand-made screen-printed electrodes: liquid biopsy for cancer management as case of study.

The growth of liquid biopsy, i. e., the possibility of obtaining health information by analysing circulating species (nucleic acids, cells, proteins, and vesicles) in peripheric biofluids, is pushing the field of sensors and biosensors beyond the limit to provide decentralised solutions for nonspecialists. In particular, among all the circulating species that can be adopted in managing cancer evolution, both for diagnostic and prognostic applications, microRNAs have been highly studied and detected. The development of electrochemical devices is particularly relevant for liquid biopsy purposes, and the screen-printed electrodes (SPEs) represent one of the building blocks for producing novel portable devices. In this work, we have taken miR-2115-3p as model target (it is related to lung cancer), and we have developed a biosensor by exploiting the use of a complementary DNA probe modified with methylene blue as redox mediator. In particular, the chosen sensing architecture was applied to serum measurements of the selected miRNA, obtaining a detection limit within the low nanomolar range; in addition, various platforms were interrogated, namely commercial and hand-made SPEs, with the aim of providing the reader with some insights about the optimal platform to be used by considering both the cost and the analytical performance.

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.

PURPOSE: We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases. METHODS: Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon-Mann-Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. RESULTS: The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%. CONCLUSIONS: Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.

Aligning Cancer Research Priorities in Europe with Recommendations for Conquering Cancer: A Comprehensive Analysis.

Improvements in cancer care require a new degree of collaboration beyond the purely medical sphere, extending deeply into the world of other stakeholders-preeminently patients but also the other stakeholders in the hardware and software of care. Cancer remains a global health challenge, necessitating collaborative efforts to understand, prevent, and treat this complex disease. To achieve this goal, a comprehensive analysis was conducted, aligning the prioritization of cancer research measures in 13 European countries with 13 key recommendations for conquering cancer in the region. The study utilized a survey involving both patients and citizens, alongside data from IQVIA, a global healthcare data provider, to assess the availability and access to single-biomarker tests in multiple European countries. The results revealed a focused approach toward understanding, preventing, and treating cancer, with each country emphasizing specific research measures tailored to its strengths and healthcare objectives. This analysis highlights the intricate relationship between research priorities, access to biomarker tests, and financial support. Timely access to tests and increased availability positively influence research areas such as cancer prevention, early detection, ageing, and data utilization. The alignment of these country-specific measures with 13 recommendations for conquering cancer in Europe underscores the importance of tailored strategies for understanding, preventing, and treating cancer.

Interpreting and integrating genomic tests results in clinical cancer care: Overview and practical guidance.

The last decade has seen rapid progress in the use of genomic tests, including gene panels, whole-exome sequencing, and whole-genome sequencing, in research and clinical cancer care. These advances have created expansive opportunities to characterize the molecular attributes of cancer, revealing a subset of cancer-associated aberrations called driver mutations. The identification of these driver mutations can unearth vulnerabilities of cancer cells to targeted therapeutics, which has led to the development and approval of novel diagnostics and personalized interventions in various malignancies. The applications of this modern approach, often referred to as precision oncology or precision cancer medicine, are already becoming a staple in cancer care and will expand exponentially over the coming years. Although genomic tests can lead to better outcomes by informing cancer risk, prognosis, and therapeutic selection, they remain underutilized in routine cancer care. A contributing factor is a lack of understanding of their clinical utility and the difficulty of results interpretation by the broad oncology community. Practical guidelines on how to interpret and integrate genomic information in the clinical setting, addressed to clinicians without expertise in cancer genomics, are currently limited. Building upon the genomic foundations of cancer and the concept of precision oncology, the authors have developed practical guidance to aid the interpretation of genomic test results that help inform clinical decision making for patients with cancer. They also discuss the challenges that prevent the wider implementation of precision oncology.

Fluorouracil, Leucovorin, and Irinotecan Plus Cetuximab Versus Cetuximab as Maintenance Therapy in First-Line Therapy for RAS and BRAF Wild-Type Metastatic Colorectal Cancer: Phase III ERMES Study.

PURPOSE: The intensity of anti-EGFR-based first-line therapy for RAS/BRAF wild-type (wt) metastatic colorectal cancer (mCRC), once disease control is achieved, is controversial. A de-escalation strategy with anti-EGFR monotherapy represents a potential option to maintain efficacy while reducing cytotoxicity. METHODS: In this multicenter, open-label, phase III trial, patients with untreated RAS/BRAF wt mCRC were randomly assigned to receive either fluorouracil, leucovorin, and irinotecan/cetuximab (FOLFIRI/Cet) until disease progression (arm A) or FOLFIRI/Cet for eight cycles followed by Cet alone (arm B). The coprimary end points were a noninferior progression-free survival (PFS) in the modified per-protocol (mPP) population (>eight cycles) and a lower incidence of grade (G) 3-4 adverse events (AEs) for arm B compared with arm A. RESULTS: Overall, 606 patients were randomly assigned, with 300 assigned to arm A and 306 to arm B. The median follow-up was 22.3 months. In the mPP population, 291 events occurred with a PFS of 10 versus 12.2 months for arms B and A, respectively (P of noninferiority = .43). In the intention-to-treatment (ITT, ≥one cycle) population, 503 events occurred with a PFS of 9 versus 10.7 months (P = .39). The overall survival was 35.7 versus 30.7 months (P = .119) and 31.0 versus 25.2 months (P = .32) in the mPP and ITT population, respectively. Arm B had lower G3-4 AEs during the maintenance period than arm A (20.2% v 35.1%). CONCLUSION: The ERMES study did not demonstrate noninferiority of maintenance with Cet alone. Despite a more favorable safety profile, maintenance with single-agent Cet after induction with FOLFIRI/Cet cannot be recommended for all patients but could represent an option in selected cases.

Tackling the implementation gap for the uptake of NGS and advanced molecular diagnostics into healthcare systems.

Next-generation sequencing (NGS) and liquid biopsy (LB) showed positive results in the fight against different cancer types. This paper aims to assess the uptake of advanced molecular diagnostics/NGS for quick and efficient genetic profiles of tumour cells. For that purpose, the European Alliance for Personalised Medicine conducted a series of expert interviews to ascertain the current status across member states. One stakeholder meeting was additionally conducted to prioritize relevant factors by stakeholders. Seven common pillars were identified, and twenty-five measures were defined based on these pillars. Results showed that a multi-faceted approach is necessary for successful NGS implementation and that regional differences may be influenced by healthcare policies, resources, and infrastructure. It is important to consider different correlations when interpreting the results and to use them as a starting point for further discussion.

Practical guidelines for molecular testing of cholangiocarcinoma in clinical practice: Italian experts' position paper.

Biliary tract cancers (BTCs) represent a spectrum of malignancies associated with a dismal prognosis. Recent genomic profiling studies have provided a deeper understanding of the complex and heterogenous molecular landscape of BTCs, identifying several actionable genetic alterations, and expanding treatment options. Due to the high number and complexity of genetic alterations which require testing, next-generation sequencing (NGS) is currently the preferred approach over conventional methods (i.e., immunohistochemistry, fluorescence in-situ hybridization and PCR) for molecular profiling of BTCs and should be performed upfront in all BTC patients. However, BTC sampling often yields low tumor cellularity tissue, hampering NGS analysis. Future perspectives to overcome this obstacle include liquid biopsy and optimization of biopsy protocols. In this position paper, the authors discuss the current histopathologic, molecular, and therapeutic landscape of BTCs, provide a critical overview of the available testing methods for molecular diagnostics, and propose a practical diagnostic algorithm for molecular testing of BTC samples.

Molecular profiling in cholangiocarcinoma: A practical guide to next-generation sequencing.

Cholangiocarcinomas (CCA) are a heterogeneous group of tumors that are classified as intrahepatic, perihilar, or distal according to the anatomic location within the biliary tract. Each CCA subtype is associated with distinct genomic alterations, including single nucleotide variants, copy number variants, and chromosomal rearrangements or gene fusions, each of which can influence disease prognosis and/or treatment outcomes. Molecular profiling using next-generation sequencing (NGS) is a powerful technique for identifying unique gene variants carried by an individual tumor, which can facilitate their accurate diagnosis as well as promote the optimal selection of gene variant-matched targeted treatments. NGS is particularly useful in patients with CCA because between one-third and one-half of these patients have genomic alterations that can be targeted by drugs that are either approved or in clinical development. NGS can also provide information about disease evolution and secondary resistance alterations that can develop during targeted therapy, and thus facilitate assessment of prognosis and choice of alternative targeted treatments. Pathologists play a critical role in assessing the viability of biopsy samples for NGS, and advising treating clinicians whether NGS can be performed and which of the available platforms should be used to optimize testing outcomes. This review aims to provide clinical pathologists and other healthcare professionals with practical step-by-step guidance on the use of NGS for molecular profiling of patients with CCA, with respect to tumor biopsy techniques, pre-analytic sample preparation, selecting the appropriate NGS panel, and understanding and interpreting results of the NGS test.

GIInger predicts homologous recombination deficiency and patient response to PARPi treatment from shallow genomic profiles.

Homologous recombination deficiency (HRD) is a predictive biomarker for poly(ADP-ribose) polymerase 1 inhibitor (PARPi) sensitivity. Routine HRD testing relies on identifying BRCA mutations, but additional HRD-positive patients can be identified by measuring genomic instability (GI), a consequence of HRD. However, the cost and complexity of available solutions hamper GI testing. We introduce a deep learning framework, GIInger, that identifies GI from HRD-induced scarring observed in low-pass whole-genome sequencing data. GIInger seamlessly integrates into standard BRCA testing workflows and yields reproducible results concordant with a reference method in a multisite study of 327 ovarian cancer samples. Applied to a BRCA wild-type enriched subgroup of 195 PAOLA-1 clinical trial patients, GIInger identified HRD-positive patients who experienced significantly extended progression-free survival when treated with PARPi. GIInger is, therefore, a cost-effective and easy-to-implement method for accurately stratifying patients with ovarian cancer for first-line PARPi treatment.

Oligo-metastatic neoPlasms from the gastro-intestinal tract: iDentIfiCaTIon of cliNical and molecular drivers: the PREDICTION study.

BACKGROUND: Metastatic disease in tumors originating from the gastrointestinal tract can exhibit varying degrees of tumor burden at presentation. Some patients follow a less aggressive disease course, characterized by a limited number of metastatic sites, referred to as "oligo-metastatic disease" (OMD). The precise biological characteristics that define the oligometastatic behavior remain uncertain. In this study, we present a protocol designed to prospectively identify OMD, with the aim of proposing novel therapeutic approaches and monitoring strategies. METHODS: The PREDICTION study is a monocentric, prospective, observational investigation. Enrolled patients will receive standard treatment, while translational activities will involve analysis of the tumor microenvironment and genomic profiling using immunohistochemistry and next-generation sequencing, respectively. The first primary objective (descriptive) is to determine the prevalence of biological characteristics in OMD derived from gastrointestinal tract neoplasms, including high genetic concordance between primary tumors and metastases, a significant infiltration of T lymphocytes, and the absence of clonal evolution favoring specific driver genes (KRAS and PIK3CA). The second co-primary objective (analytic) is to identify a prognostic score for true OMD, with a primary focus on metastatic colorectal cancer. The score will comprise genetic concordance (> 80%), high T-lymphocyte infiltration, and the absence of clonal evolution favoring driver genes. It is hypothesized that patients with true OMD (score 3+) will have a lower rate of progression/recurrence within one year (20%) compared to those with false OMD (80%). The endpoint of the co-primary objective is the rate of recurrence/progression at one year. Considering a reasonable probability (60%) of the three factors occurring simultaneously in true OMD (score 3+), using a significance level of α = 0.05 and a test power of 90%, the study requires a minimum enrollment of 32 patients. DISCUSSION: Few studies have explored the precise genetic and biological features of OMD thus far. In clinical settings, the diagnosis of OMD is typically made retrospectively, as some patients who undergo intensive treatment for oligometastases develop polymetastatic diseases within a year, while others do not experience disease progression (true OMD). In the coming years, the identification of true OMD will allow us to employ more personalized and comprehensive strategies in cancer treatment. TRIAL REGISTRATION: ClinicalTrials.gov ID NCT05806151.

Association between exposure to wind turbines and sleep disorders: A systematic review and meta-analysis.

To date, there is scarce evidence on the association between sleep disorders and noise generated by wind turbines. We searched six relevant electronic databases from the inception to May 2023 for relevant articles. The methodological quality of the included articles was evaluated using the US National Institutes of Health tool. Fifteen articles met the inclusion criteria. The overall prevalence of sleep disorders among residents close to wind turbines was 34% (95% Confidence Interval, 0.22-0.47). Univariate meta-regressions for distance and sound power level showed that at higher distance the prevalence of sleep disorders decreases (p = 0.010) and with a higher sound power level the prevalence increases (p = 0.037). Furthermore, this systematic review and meta-analysis highlighted that the overall quality of current research on this topic is poor, and the methods to measure the results are often based on subjective assessments and not validated questionnaires. In conclusion, our preliminary findings suggest that there may be a possible relation between exposure to wind turbines and sleep disorders, although no conclusions can be drawn in terms of causality due to the nature of the retrieved data and the poor quality of current evidence. Future studies should adopt a longitudinal design and focus on objective measurements, supported by validated subjective methods such as questionnaires.

Point-of-Care Testing for the Detection of MicroRNAs: Towards Liquid Biopsy on a Chip.

Point-of-care (PoC) testing is revolutionizing the healthcare sector improving patient care in daily hospital practice and allowing reaching even remote geographical areas. In the frame of cancer management, the design and validation of PoC enabling the non-invasive, rapid detection of cancer markers is urgently required to implement liquid biopsy in clinical practice. Therefore, focusing on stable blood-based markers with high-specificity, such as microRNAs, is of crucial importance. In this work, we highlight the potential impact of circulating microRNAs detection on cancer management and the crucial role of PoC testing devices, especially for low-income countries. A detailed discussion about the challenges that should be faced to promote the technological transfer and clinical use of these tools has been added, to provide the readers with a complete overview of potentialities and current limitations.