Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Effect of hydroxychloroquine with or without azithromycin on the mortality of coronavirus disease 2019 (COVID-19) patients: a systematic review and meta-analysis.

Cancers remain a major public health problem worldwide, which still require profound research in both the basic and preclinical fields. Patient-derived xenograft (PDX) models are created when cancerous cells or tissues from patients’ primary tumors are implanted into immunodeficient mice to simulate human tumor biology in vivo, which have been extensively used in cancer research. The routes of implantation appeared to affect the outcome of PDX research, and there has been increasing applications of patient-derived orthotopic xenograft (PDOX) models. In this review, we firstly summarize the methodology to establish PDX models and then go over recent application and function of PDX models in basic cancer research on the areas of cancer characterization, initiation, proliferation, metastasis, and tumor microenvironment and in preclinical explorations of anti-cancer targets, drugs, and therapeutic strategies and finally give our perspectives on the future prospects of PDX models.

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Current status and perspectives of patient-derived xenograft models in cancer research.

OBJECTIVES: We conducted a systematic literature review and meta-analysis of observational studies to investigate the association between diabetes, hypertension, body mass index (BMI) or smoking with the risk of death in patients with COVID-19 and to estimate the proportion of deaths attributable to these conditions. METHODS: Relevant observational studies were identified by searches in the PubMed, Cochrane library and Embase databases through 14 November 2020. Random-effects models were used to estimate summary relative risks (SRRs) and 95% CIs. Certainty of evidence was assessed using the Cochrane methods and the Grading of Recommendations, Assessment, Development and Evaluations framework. RESULTS: A total of 186 studies representing 210 447 deaths among 1 304 587 patients with COVID-19 were included in this analysis. The SRR for death in patients with COVID-19 was 1.54 (95% CI 1.44 to 1.64, I2=92%, n=145, low certainty) for diabetes and 1.42 (95% CI 1.30 to 1.54, I2=90%, n=127, low certainty) for hypertension compared with patients without each of these comorbidities. Regarding obesity, the SSR was 1.45 (95% CI 1.31 to 1.61, I2=91%, n=54, high certainty) for patients with BMI ≥30 kg/m2 compared with those with BMI <30 kg/m2 and 1.12 (95% CI 1.07 to 1.17, I2=68%, n=25) per 5 kg/m2 increase in BMI. There was evidence of a J-shaped non-linear dose-response relationship between BMI and mortality from COVID-19, with the nadir of the curve at a BMI of around 22-24, and a 1.5-2-fold increase in COVID-19 mortality with extreme obesity (BMI of 40-45). The SRR was 1.28 (95% CI 1.17 to 1.40, I2=74%, n=28, low certainty) for ever, 1.29 (95% CI 1.03 to 1.62, I2=84%, n=19) for current and 1.25 (95% CI 1.11 to 1.42, I2=75%, n=14) for former smokers compared with never smokers. The absolute risk of COVID-19 death was increased by 14%, 11%, 12% and 7% for diabetes, hypertension, obesity and smoking, respectively. The proportion of deaths attributable to diabetes, hypertension, obesity and smoking was 8%, 7%, 11% and 2%, respectively. CONCLUSION: Our findings suggest that diabetes, hypertension, obesity and smoking were associated with higher COVID-19 mortality, contributing to nearly 30% of COVID-19 deaths. TRIAL REGISTRATION NUMBER: CRD42020218115.

https://bmjopen.bmj.com/content/bmjopen/11/10/e052777.full.pdf

Diabetes, hypertension, body mass index, smoking and COVID-19-related mortality: a systematic review and meta-analysis of observational studies.

The finding that cancer chemotherapeutic drugs and ionizing radiation often promote autophagy has provided the foundation for clinical trials combining autophagy-blocking agents with antitumor drugs and radiation. The premise driving these trials is that therapy-induced autophagy is cytoprotective; consequently, inhibition of autophagy is anticipated to sensitize malignancies to therapy. However, it is well-established that autophagy may also mediate the toxicity of antitumor drugs while evidence also exists for a nonprotective function of autophagy. Consequently, given that it cannot be predicted what form autophagy will take upon treatment with chemotherapy or radiation, the current ongoing clinical trials are likely to generate contradictory or inconsistent results, with the potential consequence that autophagy inhibition could be dismissed as therapeutic strategy based on what are essentially false-negative outcomes. Appropriate interpretation of the outcomes of these trials would require knowledge as to whether the drugs or radiation used promote the cytoprotective form of autophagy in the tumor cells as well as whether the chloroquine or hydroxychloroquine actually inhibit the autophagy. Ultimately, it will be necessary to identify those patients for whom the strategy of autophagy inhibition would be anticipated to improve the response to therapy. However, this is currently not feasible in the absence of appropriate bioassays or predictive markers for characterization of the autophagy or the effectiveness of pharmacologic approaches for autophagy inhibition in the clinic. Cancer Res; 76(19); 5610-4. ©2016 AACR.

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The Challenge of Developing Autophagy Inhibition as a Therapeutic Strategy.

Background: To assess factors associated with one-month mortality among older inpatients with Covid-19.

Results: The mean age was 78 ± 7.8 years, 55.5% were men, CT scan lung damage was observed in 76% of the patients (mild 23%, moderate 38%, extensive 22%, and severe 7%). The mortality rate was 26%. Dependency/Activities of Daily Living (ADL) score ≤ 5/6, D-Dimers, LDH, and no anticoagulation by reference for curative were independently associated with one-month mortality. A score derived from the multivariate model showed good calibration and very good discrimination (Harrell’s C index [95%CI] = 0.83 [0.79-0.87]).

Conclusion: ADL-dependency, high serum levels of D-Dimers and LDH and the absence of anticoagulation were independently associated with one-month mortality among older inpatients with Covid-19.

Methods: 108 consecutive older inpatients aged 65 and over with Covid-19 confirmed by RT-PCR and/or typical CT chest scan were prospectively included in a French single-centre cohort study from March to April 2020. A systematic geriatric assessment was performed. Covariates were lymphocyte count, serum levels of albumin, C-Reactive Protein, D-Dimers and Lactate Dehydrogenase (LDH), anticoagulation level, and exposure to the hydroxychloroquine and azithromycin combined therapy. Cox uni- and multivariate proportional-hazard regressions were performed to identify predictors of one-month mortality.

ADL-dependency, D-Dimers, LDH and absence of anticoagulation are independently associated with one-month mortality in older inpatients with Covid-19.

The occurrence of metastases severely affects prognosis for patients with cancer, making metastatic disease a daily societal challenge. Because of resistance to drugs, the potential curability with chemotherapy at the metastatic stage remains low. Large genomic analyses to identify new targets have their limitations due to intratumor heterogeneity when they are performed on tumor samples from primary tumors and because the functional value of molecular abnormalities in a cancer is usually not known. Additional tools are thus required for the development of new anticancer agents. The use of preclinical models is a key component of translational research in oncology. For four decades, xenograft models of human cancer cell lines injected subcutaneously in immunocompromised mice have been widely used, with disappointing results for predicting the clinical benefit of a new drug. Patient-derived xenografts are preclinical models rediscovered as innovative pharmacological tools, both for the preclinical development of anticancer drugs and as individual models for personalized treatment of metastatic disease. Here, we review the recent progress reported using patient-derived xenografts for the treatment of metastatic disease, and discuss the feasibility of their implementation in daily oncological care.

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Patient-Derived Xenograft: An Adjuvant Technology for the Treatment of Metastatic Disease.

Breast cancer remains a major health issue in the world with 1.7 million new cases in 2012 worldwide. It is the second cause of death from cancer in western countries. Genomics have started to modify the treatment of breast cancer, and the developments should become more and more significant, especially in the present era of treatment personalization and with the implementation of new technologies. With molecular signatures, genomics enabled a de-escalation of chemotherapy and personalized treatments of localized forms of estrogen-dependent breast cancers. Genomics can also make a real contribution to constitutional genetics, so as to identify mutations in a panel of candidate genes. In this review, we will discuss the contributions of genomics applied to the treatment of breast cancer, whether already validated contributions or possible future applications linked to research data.

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Genomics applied to the treatment of breast cancer

COVID-19 pneumonia can be severe, with an unpredictable evolution and high mortality prevalence in older patients. The diagnosis is usually performed by RT-PCR or CT chest scan. Lung ultrasonography (LUS) has been proposed as an alternative method to monitor patients with COVID-19 pneumonia. To assess the diagnostic performance of LUS, we performed LUS using a portable device and adapting a protocol already used in Acute Respiratory Syndrome. We used the score obtained with the index we created to assess for LUS diagnostic performance as compared to lung CT chest scan and to predict for oxygen requirements. Daily bedside LUS was easy to perform and microbiologically safe. LUS was 89% sensitive and 100% specific in predicting CT chest scan abnormalities, and 95% sensitive and 67% specific in detecting oxygen requirements. This is the first report on the diagnostic performance of LUS as compared to CT chest scan for the diagnosis of COVID-19 pneumonia and assessments of oxygen requirements by LUS. LUS could help in the orientation of dyspneic patients to intensive care. It could also be proposed when there is limited access to CT scan in the context of a pandemic crisis, or to implement clinical lung examinations for outpatient follow-up.

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Lung ultrasound is a reliable diagnostic technique to predict abnormal CT chest scan and to detect oxygen requirements in COVID-19 pneumonia.

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Guilhem Bousquet

Papers

ADL-dependency, D-Dimers, LDH and absence of anticoagulation are independently associated with one-month mortality in older inpatients with Covid-19.

Patient-Derived Xenograft: An Adjuvant Technology for the Treatment of Metastatic Disease.

Genomics applied to the treatment of breast cancer

Lung ultrasound is a reliable diagnostic technique to predict abnormal CT chest scan and to detect oxygen requirements in COVID-19 pneumonia.

Increased risk of brain metastases among patients with melanoma and PROM2 expression in metastatic lymph nodes.