Cancer profiles by Affinity Propagation

The Affinity Propagation algorithm is applied to various problems of breast and cutaneous tumours subtyping using traditional biologic markers. The algorithm provides a procedure to determine the number of profiles to be considered. Well know breast cancer case series and cutaneous melanoma were used to compare the results of the Affinity Propagation with the results obtained with standard algorithms and indexes for the optimal choice of the number of clusters.Results from Affinity Propagation are consistent with the results already obtained having the advantage of providing an indication about the number of clusters

Conditional independence relations among biological markers may improve clinical decision as in the case of triple negative breast cancers

The associations existing among different biomarkers are important in clinical settings because they contribute to the characterisation of specific pathways related to the natural history of the disease, genetic and environmental determinants. Despite the availability of binary/linear (or at least monotonic) correlation indices, the full exploitation of molecular information depends on the knowledge of direct/indirect conditional independence (and eventually causal) relationships among biomarkers, and with target variables in the population of interest. In other words, that depends on inferences which are performed on the joint multivariate distribution of markers and target variables. Graphical models, such as Bayesian Networks, are well suited to this purpose. Therefore, we reconsidered a previously published case study on classical biomarkers in breast cancer, namely estrogen receptor (ER), progesterone receptor (PR), a proliferative index (Ki67/MIB-1) and to protein HER2/neu (NEU) and p53, to infer conditional independence relations existing in the joint distribution by inferring (learning) the structure of graphs entailing those relations of independence. We also examined the conditional distribution of a special molecular phenotype, called triple-negative, in which ER, PR and NEU were absent. We confirmed that ER is a key marker and we found that it was able to define subpopulations of patients characterized by different conditional independence relations among biomarkers. We also found a preliminary evidence that, given a triple-negative profile, the distribution of p53 protein is mostly supported in 'zero' and 'high' states providing useful information in selecting patients that could benefit from an adjuvant anthracyclines/alkylating agent-based chemotherapy

Heterogeneity of covid-19 outbreak in italy

An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) started in December 2019 in China and was declared a pandemic on 11.03.2020 by WHO. Italy is one of the most afflicted Country by this epidemic with 136,110 confirmed cases and 16,654 deaths on 9.4.2020 (at the same date, the Ministry of Health was reporting 143,626 cases). During these few months the National Health Service have made a great effort to cope with the increasing request of intensive care beds and all the elective activities in hospital have been suspended. Data from the different Italian regions shows different patterns of positive and dead for this syndrome. Moreover, striking differences of the observed lethality of the infections among different areas were immediately evident from the epidemic reports. It will be of critical relevance to understand the expected evolution of the first lock-down phase, driving the exhaustion of the Covid-19 outbreak

Contribution of vascular endothelial growth factor to the Nottingham prognostic index in node-negative breast cancer

The prognostic contribution of intratumour VEGF, the most important factor in tumour-induced angiogenesis, to NPI was evaluated by using flexible modelling in a series of 226 N-primary breast cancer patients in which steroid receptors and cell proliferation were also accounted for. VEGF provided an additional prognostic contribution to NPI mainly within ER-poor tumours. © 2001 Cancer Research Campaignhttp://www.bjcancer.co

Modeling the covariates effects on the hazard function by piecewise exponential artificial neural networks : an application to a controlled clinical trial on renal carcinoma

BACKGROUND: In exploring the time course of a disease to support or generate biological hypotheses, the shape of the hazard function provides relevant information. For long follow-ups the shape of hazard function may be complex, with the presence of multiple peaks. In this paper we present the use of a neural network extension of the piecewise exponential model to study the shape of the hazard function in time in dependence of covariates. The technique is applied to a dataset of 247 renal cell carcinoma patients from a randomized clinical trial. RESULTS: An interaction effect of treatment with number of metastatic lymph nodes but not with pathologic T-stage is highlighted. CONCLUSIONS: Piecewise Exponential Artificial Neural Networks demonstrate a clinically useful and flexible tool in assessing interaction or time-dependent effects of the prognostic factors on the hazard function

Potential benefit of intra-operative administration of ketorolac on breast cancer recurrence according to the patient's body mass index

Background: Nonsteroidal anti-inflammatory drugs (NSAIDs) are currently used in some countries as analgesics in primary cancer surgery. Retrospective studies suggest that NSAIDs could reduce breast cancer recurrences. Because NSAIDs also act on biological mechanisms present in patients with increased adiposity, we aimed at assessing whether the intra-operative administration of ketorolac or diclofenac would be associated with a reduction of recurrence in patients with elevated body mass index (BMI). Methods: We considered two institutional retrospective series of 827 and 1007 patients evaluating the administration of ketorolac (n = 529 with, n = 298 without) or diclofenac (n = 787 with, n = 220 without). The BMI subgroups were defined as less than 25 kg/m(2) (lean) and 25 or more kg/m(2) (overweight and obese). Cumulative incidence estimation of distant metastases as well as Fine-Gray and Dixon-Simon models was used. These analyses were adjusted for clinico-pathological variables. All statistical tests were two-sided. Results: The administration of ketorolac was statistically significantly associated with decreased incidence of distant recurrences (adjusted hazard ratio [aHR] = 0.59, 95% confidence interval [CI] = 0.37 to 0.96, P = .03). In particular, the association was evident in the high-body mass index (BMI) group of patients (aHR = 0.55, 95% CI = 0.31 to 0.96, P = .04). The administration of diclofenac was not statistically significantly associated with decreased incidence of distant recurrences, either in the global population or in the BMI subgroups. Conclusions: These results show that the intra-operative administration of ketorolac, but not diclofenac, is statistically significantly associated with a reduction of distant recurrences in patients with increased BMI. Altogether, this study points to a potentially important repositioning of ketorolac in the intra-operative treatment of patients with elevated BMI that, if prospectively validated, might be as impactful as and cheaper than adjuvant systemic anticancer therapies

CoViD-19, learning from the past: A wavelet and cross-correlation analysis of the epidemic dynamics looking to emergency calls and Twitter trends in Italian Lombardy region

The first case of Coronavirus Disease 2019 in Italy was detected on February the 20th in Lombardy region. Since that date, Lombardy has been the most affected Italian region by the epidemic, and its healthcare system underwent a severe overload during the outbreak. From a public health point of view, therefore, it is fundamental to provide healthcare services with tools that can reveal possible new health system stress periods with a certain time anticipation, which is the main aim of the present study. Moreover, the sequence of law decrees to face the epidemic and the large amount of news generated in the population feelings of anxiety and suspicion. Considering this whole complex context, it is easily understandable how people "overcrowded"social media with messages dealing with the pandemic, and emergency numbers were overwhelmed by the calls. Thus, in order to find potential predictors of possible new health system overloads, we analysed data both from Twitter and emergency services comparing them to the daily infected time series at a regional level. Particularly, we performed a wavelet analysis in the time-frequency plane, to finely discriminate over time the anticipation capability of the considered potential predictors. In addition, a cross-correlation analysis has been performed to find a synthetic indicator of the time delay between the predictor and the infected time series. Our results show that Twitter data are more related to social and political dynamics, while the emergency calls trends can be further evaluated as a powerful tool to potentially forecast new stress periods. Since we analysed aggregated regional data, and taking into account also the huge geographical heterogeneity of the epidemic spread, a future perspective would be to conduct the same analysis on a more local basis. Copyright

New insights in the pathophysiology of acute myocardial infarction detectable by a contemporary troponin assay

Objectives: ST-elevation and non-ST-elevation myocardial infarction (STEMI, NSTEMI) are considered two distinct pathophysiologic entities. We evaluated cardiac troponin I (cTnI) release in STEMI and NSTEMI using a \u201ccontemporary\u201d (CV > 10 to 20% at the 99th percentile concentration) cTnI assay for patients undergoing early percutaneous coronary intervention (PCI). Design andmethods: 856 patients with suspected acute coronary syndrome consecutively admitted to the Emergency Department of the Maggiore Hospital of Novara (225 STEMI and 135 NSTEMI) were selected according to: 1) early ( 644 h from admission) and successful PCI; and 2) cTnI measurements at ED presentation and within 24 h. The influence of the MI type on cTnI concentrations at baseline and after PCI as well as the velocity of cTnI [cTnI V = absolute increase (after log conversion of cTnI measurements) / delay between the two measurements] was studied by multiple regression analysis, adjusting for patient parameters. Results: A statistically significant interaction between MI type and time from symptoms was reported on cTnI concentrations (p b 0.0001): STEMI and NSTEMI differed for cTnI releases at admission and after revascularization. Higher cTnI V in STEMI was detectable in patients admitted within 6 h from symptoms. Baseline cTnI concentrations were lower in patients with a history of coronary artery disease (CAD) and increased with aging (p b 0.0001). In the elderly (>75 years), the cTnI V was significantly increased. Conclusion: STEMI and NSTEMI patients have different patterns and dynamics of cTnI release influenced by the interaction with time from symptoms, by aging and history of CAD