Dynamic Risk Prediction of 30-Day Mortality in Patients With Advanced Lung Cancer:Comparing Five Machine Learning Approaches

International audiencePURPOSE Administering systemic anticancer treatment (SACT) to patients near death can negatively affect their health-related quality of life. Late SACT administrations should be avoided in these cases. Machine learning techniques could be used to build decision support tools leveraging registry data for clinicians to limit late SACT administration. MATERIALS AND METHODS Patients with advanced lung cancer who were treated at the Department of Oncology, Aalborg University Hospital and died between 2010 and 2019 were included (N = 2,368). Diagnoses, treatments, biochemical data, and histopathologic results were used to train predictive models of 30-day mortality using logistic regression with elastic net penalty, random forest, gradient tree boosting, multilayer perceptron, and long short-term memory network. The importance of the variables and the clinical utility of the models were evaluated. RESULTS The random forest and gradient tree boosting models outperformed other models, whereas the artificial neural network–based models underperformed. Adding summary variables had a modest effect on performance with an increase in average precision from 0.500 to 0.505 and from 0.498 to 0.509 for the gradient tree boosting and random forest models, respectively. Biochemical results alone contained most of the information with a limited degradation of the performances when fitting models with only these variables. The utility analysis showed that by applying a simple threshold to the predicted risk of 30-day mortality, 40% of late SACT administrations could have been prevented at the cost of 2% of patients stopping their treatment 90 days before death. CONCLUSION This study demonstrates the potential of a decision support tool to limit late SACT administration in patients with cancer. Further work is warranted to refine the model, build an easy-to-use prototype, and conduct a prospective validation study

Fractional exhaled nitric oxide as a potential biomarker for radiation pneumonitis in patients with non-small cell lung cancer:A pilot study

Introduction The aim of the study was to investigate repetitive fractional exhaled nitric oxide (FeNO) measurements during high-dose radiation therapy (HDRT) and to evaluate the use of FeNO to predict symptomatic radiation pneumonitis (RP) in patients being treated for non-small cell lung cancer (NSCLC). Materials and methods A total of 50 patients with NSCLC referred for HDRT were enrolled. FeNO was measured at baseline, weekly during HDRT, one month- and every third month after HDRT for a one-year follow-up period. The mean FeNO(visit 0-6) was calculated using the arithmetic mean of the baseline and weekly measurements during HDRT. Patients with grade ≥ 2 of RP according to the Common Terminology Criteria for Adverse Events (CTCAE) were considered symptomatic. Results A total of 42 patients completed HDRT and weekly FeNO measurements. Grade ≥ 2 of RP was diagnosed in 24 (57%) patients. The mean FeNO(visit 0-6) ± standard deviation in patients with and without RP was 15.0 ± 7.1 ppb (95%CI: 12.0–18.0) and 10.3 ± 3.4 ppb (95%CI: 8.6–11.9) respectively with significant differences between the groups (p = 0.0169, 95%CI: 2.3–2.6). The leave-one-out cross-validated cut-off value of the mean FeNO(visit 0-6) ≥ 14.8 ppb was predictive of grade ≥ 2 RP with a specificity of 71% and a positive predictive value of 78%. Conclusions The mean FeNO(visit 0-6) in patients with symptomatic RP after HDRT for NSCLC was significantly higher than in patients without RP and may serve as a potential biomarker for RP

Development of a Precision Medicine Workflow in Hematological Cancers, Aalborg University Hospital, Denmark

Within recent years, many precision cancer medicine initiatives have been developed. Most of these have focused on solid cancers, while the potential of precision medicine for patients with hematological malignancies, especially in the relapse situation, are less elucidated. Here, we present a demographic unbiased and observational prospective study at Aalborg University Hospital Denmark, referral site for 10% of the Danish population. We developed a hematological precision medicine workflow based on sequencing analysis of whole exome tumor DNA and RNA. All steps involved are outlined in detail, illustrating how the developed workflow can provide relevant molecular information to multidisciplinary teams. A group of 174 hematological patients with progressive disease or relapse was included in a non-interventional and population-based study, of which 92 patient samples were sequenced. Based on analysis of small nucleotide variants, copy number variants, and fusion transcripts, we found variants with potential and strong clinical relevance in 62% and 9.5% of the patients, respectively. The most frequently mutated genes in individual disease entities were in concordance with previous studies. We did not find tumor mutational burden or micro satellite instability to be informative in our hematologic patient cohort

Dynamic Intracellular Metabolic Cell Signaling Profiles During Ag-Dependent B-Cell Differentiation

© 2021 Díez, Pérez-Andrés, Bøgsted, Azkargorta, García-Valiente, Dégano, Blanco, Mateos-Gomez, Bárcena, Santa Cruz, Góngora, Elortza, Landeira-Viñuela, Juanes-Velasco, Segura, Manzano-Román, Almeida, Dybkaer, Orfao and Fuentes.Human B-cell differentiation has been extensively investigated on genomic and transcriptomic grounds; however, no studies have accomplished so far detailed analysis of antigen-dependent maturation-associated human B-cell populations from a proteomic perspective. Here, we investigate for the first time the quantitative proteomic profiles of B-cells undergoing antigen-dependent maturation using a label-free LC-MS/MS approach applied on 5 purified B-cell subpopulations (naive, centroblasts, centrocytes, memory and plasma B-cells) from human tonsils (data are available via ProteomeXchange with identifier PXD006191). Our results revealed that the actual differences among these B-cell subpopulations are a combination of expression of a few maturation stage-specific proteins within each B-cell subset and maturation-associated changes in relative protein expression levels, which are related with metabolic regulation. The considerable overlap of the proteome of the 5 studied B-cell subsets strengthens the key role of the regulation of the stoichiometry of molecules associated with metabolic regulation and programming, among other signaling cascades (such as antigen recognition and presentation and cell survival) crucial for the transition between each B-cell maturation stage.We gratefully acknowledge financial support from the Spanish Health Institute Carlos III (ISCIII) for the grants: FIS PI14/01538, FIS PI17/01930 and CB16/12/00400. We also acknowledge Fondos FEDER (EU) and Junta Castilla-León (COVID19 grant COV20EDU/00187). Fundación Solórzano FS/38-2017.The Proteomics Unit belongs to ProteoRed, PRB3-ISCIII, supported by grant PT17/0019/0023, of the PE I + D + I 2017-2020, funded by ISCIII and FEDER. AL-V is supported by VIII Centenario-USAL PhD Program. PJ-V is supported by JCYL PhD Program and scholarship JCYL-EDU/601/2020. PD and EB are supported by a JCYL-EDU/346/2013 Ph.D. scholarship

A RT-qPCR system using a degenerate probe for specific identification and differentiation of SARS-CoV-2 Omicron (B.1.1.529) variants of concern

Fast surveillance strategies are needed to control the spread of new emerging SARS-CoV-2 variants and gain time for evaluation of their pathogenic potential. This was essential for the Omicron variant (B.1.1.529) that replaced the Delta variant (B.1.617.2) and is currently the dominant SARS-CoV-2 variant circulating worldwide. RT-qPCR strategies complement whole genome sequencing, especially in resource lean countries, but mutations in the targeting primer and probe sequences of new emerging variants can lead to a failure of the existing RT-qPCRs. Here, we introduced an RT-qPCR platform for detecting the Delta- and the Omicron variant simultaneously using a degenerate probe targeting the key ΔH69/V70 mutation in the spike protein. By inclusion of the L452R mutation into the RT-qPCR platform, we could detect not only the Delta and the Omicron variants, but also the Omicron sub-lineages BA.1, BA.2 and BA.4/BA.5. The RT-qPCR platform was validated in small- and large-scale. It can easily be incorporated for continued monitoring of Omicron sub-lineages, and offers a fast adaption strategy of existing RT-qPCRs to detect new emerging SARS-CoV-2 variants using degenerate probes.</p