A GHEP-ISFG collaborative study on the genetic variation of 38 autosomal indels for human identification in different continental populations

A collaborative effort was carried out by the Spanish and Portuguese Speaking Working Group of the International Society for Forensic Genetics (GHEP-ISFG) to promote knowledge exchange between associate laboratories interested in the implementation of indel-based methodologies and build allele frequency databases of 38 indels for forensic applications. These databases include populations from different countries that are relevant for identification and kinship investigations undertaken by the participating laboratories. Before compiling population data, participants were asked to type the 38 indels in blind samples from annual GHEP-ISFG proficiency tests, using an amplification protocol previously described. Only laboratories that reported correct results contributed with population data to this study. A total of 5839 samples were genotyped from 45 different populations from Africa, America, East Asia, Europe and Middle East. Population differentiation analysis showed significant differences between most populations studied from Africa and America, as well as between two Asian populations from China and East Timor. Low FST values were detected among most European populations. Overall diversities and parameters of forensic efficiency were high in populations from all continents.RP is supported by a postdoctoral fellowship (SFRH/BPD/81986/2011) awarded by the Portuguese Foundation for Science and Technology (FCT) and co-financed by the European Social Fund (Human Potential Thematic Operational Programme – POPH

Time-Dependent COVID-19 Mortality in Patients with Cancer: An Updated Analysis of the OnCovid Registry

Importance: Whether the severity and mortality of COVID-19 in patients with cancer have improved in terms of disease management and capacity is yet to be defined. Objective: To test whether severity and mortality from COVID-19 among patients with cancer have improved during the course of the pandemic. Design, Setting, and Participants: OnCovid is a European registry that collects data on consecutive patients with solid or hematologic cancer and COVID-19. This multicenter case series study included real-world data from 35 institutions across 6 countries (UK, Italy, Spain, France, Belgium, and Germany). This update included patients diagnosed between February 27, 2020, and February, 14, 2021. Inclusion criteria were confirmed diagnosis of SARS-CoV-2 infection and a history of solid or hematologic cancer. Exposures: SARS-CoV-2 infection. Main Outcomes and Measures: Deaths were differentiated at 14 days and 3 months as the 2 landmark end points. Patient characteristics and outcomes were compared by stratifying patients across 5 phases (February to March 2020, April to June 2020, July to September 2020, October to December 2020, and January to February 2021) and across 2 major outbreaks (February to June 2020 and July 2020 to February 2021). Results: At data cutoff, 2795 consecutive patients were included, with 2634 patients eligible for analysis (median [IQR] age, 68 [18-77] years; 52.8% men). Eligible patients demonstrated significant time-dependent improvement in 14-day case-fatality rate (CFR) with estimates of 29.8% (95% CI, 0.26-0.33) for February to March 2020; 20.3% (95% CI, 0.17-0.23) for April to June 2020; 12.5% (95% CI, 0.06-22.90) for July to September 2020; 17.2% (95% CI, 0.15-0.21) for October to December 2020; and 14.5% (95% CI, 0.09-0.21) for January to February 2021 (all P <.001) across the predefined phases. Compared with the second major outbreak, patients diagnosed in the first outbreak were more likely to be 65 years or older (974 of 1626 [60.3%] vs 564 of 1008 [56.1%]; P =.03), have at least 2 comorbidities (793 of 1626 [48.8%] vs 427 of 1008 [42.4%]; P =.001), and have advanced tumors (708 of 1626 [46.4%] vs 536 of 1008 [56.1%]; P <.001). Complications of COVID-19 were more likely to be seen (738 of 1626 [45.4%] vs 342 of 1008 [33.9%]; P <.001) and require hospitalization (969 of 1626 [59.8%] vs 418 of 1008 [42.1%]; P <.001) and anti-COVID-19 therapy (1004 of 1626 [61.7%] vs 501 of 1008 [49.7%]; P <.001) during the first major outbreak. The 14-day CFRs for the first and second major outbreaks were 25.6% (95% CI, 0.23-0.28) vs 16.2% (95% CI, 0.13-0.19; P <.001), respectively. After adjusting for country, sex, age, comorbidities, tumor stage and status, anti-COVID-19 and anticancer therapy, and COVID-19 complications, patients diagnosed in the first outbreak had an increased risk of death at 14 days (hazard ratio [HR], 1.85; 95% CI, 1.47-2.32) and 3 months (HR, 1.28; 95% CI, 1.08-1.51) compared with those diagnosed in the second outbreak. Conclusions and Relevance: The findings of this registry-based study suggest that mortality in patients with cancer diagnosed with COVID-19 has improved in Europe; this improvement may be associated with earlier diagnosis, improved management, and dynamic changes in community transmission over time.

Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma

BackgroundPathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy response score (CRS) for omental tumor deposits. The main limitation of CRS is that it requires surgical sampling after initial neoadjuvant chemotherapy (NACT) treatment. Earlier and non-invasive response predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures to predict neoadjuvant response before NACT using CRS as a gold standard. MethodsOmental CT-based radiomics models, yielding a simplified fully interpretable radiomic signature, were developed using Elastic Net logistic regression and compared to predictions based on omental tumor volume alone. Models were developed on a single institution cohort of neoadjuvant-treated HGSOC (n = 61; 41% complete response to NCT) and tested on an external test cohort (n = 48; 21% complete response). ResultsThe performance of the comprehensive radiomics models and the fully interpretable radiomics model was significantly higher than volume-based predictions of response in both the discovery and external test sets when assessed using G-mean (geometric mean of sensitivity and specificity) and NPV, indicating high generalizability and reliability in identifying non-responders when using radiomics. The performance of a fully interpretable model was similar to that of comprehensive radiomics models. ConclusionsCT-based radiomics allows for predicting response to NACT in a timely manner and without the need for abdominal surgery. Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing. A radiomic signature based on five robust predictive features provides improved clinical interpretability and may thus facilitate clinical acceptance and application

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

Acknowledgements: We dedicate this work to the memory of Prof. Naveena Singh, whose invaluable contributions and enduring passion for the field have left an indelible mark, and she will be greatly missed. We acknowledge funding and support from Cancer Research UK and the Cancer Research UK Cambridge Centre [A22905, C9545/A29580, A25117, A17197, A19274, A20240], the Wellcome Trust Innovator Award [RG98755], The Mark Foundation for Cancer Research and the CRUK National Cancer Imaging Translational Accelerator (NCITA) [A27066]. Additional support was also provided by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre [RC-1215-20014] and EPSRC Tier-2 capital grant [EP/P020259/1]. This research was also supported by Target Ovarian Cancer and the Medical Research Council through their Joint Clinical Research Training Fellowship for Dr Moore. Work in the Cancer Molecular Diagnostics Laboratory/Blood Processing Laboratory was supported by the NIHR Cambridge Biomedical Research Centre [BRC-1215-20014], Cancer Research UK Cambridge Centre and the Mark Foundation Institute for Integrated Cancer Medicine. Microsoft Radiomics was provided to Addenbrooke’s Hospital (Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK) by the Microsoft InnerEye project. We thank all patients who participated in and donated samples and data to this study. We thank Karen Hosking and the OV04 study team for their help with clinical samples and data. We thank Oscar Rueda for useful discussions and support with data analysis. We thank staff from the Cancer Molecular Diagnostics Laboratory for performing blood collections. We thank the Cancer Research UK Cambridge Institute Compliance & Biobanking, Research Instrumentation and Cell Services, Genomics and Bioinformatics core facilities for their support with various aspects of this study. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

Acknowledgements: We dedicate this work to the memory of Prof. Naveena Singh, whose invaluable contributions and enduring passion for the field have left an indelible mark, and she will be greatly missed. We acknowledge funding and support from Cancer Research UK and the Cancer Research UK Cambridge Centre [A22905, C9545/A29580, A25117, A17197, A19274, A20240], the Wellcome Trust Innovator Award [RG98755], The Mark Foundation for Cancer Research and the CRUK National Cancer Imaging Translational Accelerator (NCITA) [A27066]. Additional support was also provided by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre [RC-1215-20014] and EPSRC Tier-2 capital grant [EP/P020259/1]. This research was also supported by Target Ovarian Cancer and the Medical Research Council through their Joint Clinical Research Training Fellowship for Dr Moore. Work in the Cancer Molecular Diagnostics Laboratory/Blood Processing Laboratory was supported by the NIHR Cambridge Biomedical Research Centre [BRC-1215-20014], Cancer Research UK Cambridge Centre and the Mark Foundation Institute for Integrated Cancer Medicine. Microsoft Radiomics was provided to Addenbrooke’s Hospital (Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK) by the Microsoft InnerEye project. We thank all patients who participated in and donated samples and data to this study. We thank Karen Hosking and the OV04 study team for their help with clinical samples and data. We thank Oscar Rueda for useful discussions and support with data analysis. We thank staff from the Cancer Molecular Diagnostics Laboratory for performing blood collections. We thank the Cancer Research UK Cambridge Institute Compliance & Biobanking, Research Instrumentation and Cell Services, Genomics and Bioinformatics core facilities for their support with various aspects of this study. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC

Identification of Novel Anti-amoebic Pharmacophores From Kinase Inhibitor Chemotypes

Acanthamoeba species, Naegleria fowleri, and Balamuthia mandrillaris are opportunistic pathogens that cause a range of brain, skin, eye, and disseminated diseases in humans and animals. These pathogenic free-living amoebae (pFLA) are commonly misdiagnosed and have sub-optimal treatment regimens which contribute to the extremely high mortality rates (\u3e90%) when they infect the central nervous system. To address the unmet medical need for effective therapeutics, we screened kinase inhibitor chemotypes against three pFLA using phenotypic drug assays involving CellTiter-Glo 2.0. Herein, we report the activity of the compounds against the trophozoite stage of each of the three amoebae, ranging from nanomolar to low micromolar potency. The most potent compounds that were identified from this screening effort were: 2d (A. castellanii EC50: 0.92 ± 0.3 μM; and N. fowleri EC50: 0.43 ± 0.13 μM), 1c and 2b (N. fowleri EC50s: \u3c0.63 μM, and 0.3 ± 0.21 μM), and 4b and 7b (B. mandrillaris EC50s: 1.0 ± 0.12 μM, and 1.4 ± 0.17 μM, respectively). With several of these pharmacophores already possessing blood–brain barrier (BBB) permeability properties, or are predicted to penetrate the BBB, these hits present novel starting points for optimization as future treatments for pFLA-caused diseases

Systemic pro-inflammatory response identifies patients with cancer with adverse outcomes from SARS-CoV-2 infection: The OnCovid Inflammatory Score

Background Patients with cancer are particularly susceptible to SARS-CoV-2 infection. The systemic inflammatory response is a pathogenic mechanism shared by cancer progression and COVID-19. We investigated systemic inflammation as a driver of severity and mortality from COVID-19, evaluating the prognostic role of commonly used inflammatory indices in SARS-CoV-2-infected patients with cancer accrued to the OnCovid study. Methods In a multicenter cohort of SARS-CoV-2-infected patients with cancer in Europe, we evaluated dynamic changes in neutrophil:lymphocyte ratio (NLR); platelet:lymphocyte ratio (PLR); Prognostic Nutritional Index (PNI), renamed the OnCovid Inflammatory Score (OIS); modified Glasgow Prognostic Score (mGPS); and Prognostic Index (PI) in relation to oncological and COVID-19 infection features, testing their prognostic potential in independent training (n=529) and validation (n=542) sets. Results We evaluated 1071 eligible patients, of which 625 (58.3%) were men, and 420 were patients with malignancy in advanced stage (39.2%), most commonly genitourinary (n=216, 20.2%). 844 (78.8%) had ≥1 comorbidity and 754 (70.4%) had ≥1 COVID-19 complication. NLR, OIS, and mGPS worsened at COVID-19 diagnosis compared with pre-COVID-19 measurement (p<0.01), recovering in survivors to pre-COVID-19 levels. Patients in poorer risk categories for each index except the PLR exhibited higher mortality rates (p<0.001) and shorter median overall survival in the training and validation sets (p<0.01). Multivariable analyses revealed the OIS to be most independently predictive of survival (validation set HR 2.48, 95% CI 1.47 to 4.20, p=0.001; adjusted concordance index score 0.611). Conclusions Systemic inflammation is a validated prognostic domain in SARS-CoV-2-infected patients with cancer and can be used as a bedside predictor of adverse outcome. Lymphocytopenia and hypoalbuminemia as computed by the OIS are independently predictive of severe COVID-19, supporting their use for risk stratification. Reversal of the COVID-19-induced proinflammatory state is a putative therapeutic strategy in patients with cancer

Time-Dependent COVID-19 Mortality in Patients with Cancer: An Updated Analysis of the OnCovid Registry

Importance: Whether the severity and mortality of COVID-19 in patients with cancer have improved in terms of disease management and capacity is yet to be defined. Objective: To test whether severity and mortality from COVID-19 among patients with cancer have improved during the course of the pandemic. Design, Setting, and Participants: OnCovid is a European registry that collects data on consecutive patients with solid or hematologic cancer and COVID-19. This multicenter case series study included real-world data from 35 institutions across 6 countries (UK, Italy, Spain, France, Belgium, and Germany). This update included patients diagnosed between February 27, 2020, and February, 14, 2021. Inclusion criteria were confirmed diagnosis of SARS-CoV-2 infection and a history of solid or hematologic cancer. Exposures: SARS-CoV-2 infection. Main Outcomes and Measures: Deaths were differentiated at 14 days and 3 months as the 2 landmark end points. Patient characteristics and outcomes were compared by stratifying patients across 5 phases (February to March 2020, April to June 2020, July to September 2020, October to December 2020, and January to February 2021) and across 2 major outbreaks (February to June 2020 and July 2020 to February 2021). Results: At data cutoff, 2795 consecutive patients were included, with 2634 patients eligible for analysis (median [IQR] age, 68 [18-77] years; 52.8% men). Eligible patients demonstrated significant time-dependent improvement in 14-day case-fatality rate (CFR) with estimates of 29.8% (95% CI, 0.26-0.33) for February to March 2020; 20.3% (95% CI, 0.17-0.23) for April to June 2020; 12.5% (95% CI, 0.06-22.90) for July to September 2020; 17.2% (95% CI, 0.15-0.21) for October to December 2020; and 14.5% (95% CI, 0.09-0.21) for January to February 2021 (all P <.001) across the predefined phases. Compared with the second major outbreak, patients diagnosed in the first outbreak were more likely to be 65 years or older (974 of 1626 [60.3%] vs 564 of 1008 [56.1%]; P =.03), have at least 2 comorbidities (793 of 1626 [48.8%] vs 427 of 1008 [42.4%]; P =.001), and have advanced tumors (708 of 1626 [46.4%] vs 536 of 1008 [56.1%]; P <.001). Complications of COVID-19 were more likely to be seen (738 of 1626 [45.4%] vs 342 of 1008 [33.9%]; P <.001) and require hospitalization (969 of 1626 [59.8%] vs 418 of 1008 [42.1%]; P <.001) and anti-COVID-19 therapy (1004 of 1626 [61.7%] vs 501 of 1008 [49.7%]; P <.001) during the first major outbreak. The 14-day CFRs for the first and second major outbreaks were 25.6% (95% CI, 0.23-0.28) vs 16.2% (95% CI, 0.13-0.19; P <.001), respectively. After adjusting for country, sex, age, comorbidities, tumor stage and status, anti-COVID-19 and anticancer therapy, and COVID-19 complications, patients diagnosed in the first outbreak had an increased risk of death at 14 days (hazard ratio [HR], 1.85; 95% CI, 1.47-2.32) and 3 months (HR, 1.28; 95% CI, 1.08-1.51) compared with those diagnosed in the second outbreak. Conclusions and Relevance: The findings of this registry-based study suggest that mortality in patients with cancer diagnosed with COVID-19 has improved in Europe; this improvement may be associated with earlier diagnosis, improved management, and dynamic changes in community transmission over time.