Median [95% confidence interval (CI)] follow-up was 21.9 (1.4-57.0) months. Median OS and PFS were 15.5 (14.6-19) and 7 (6.1-8.1) months, respectively. Patients with HRD had improved PFS compared with no HRD when treated with first-line (1L) platinum [HR, 0.44 (95% CI 0.29-0.67); P less then 0.01], but not with 1L-non-platinum. Multivariate analysis showed HRD patients had improved OS regardless of their first-line treatment, but most had platinum exposure during their course. Biallelic HRm (11%) and core HRm (12%) had higher genomic instability, which translated to improved PFS on first-line platinum (1L-platinum) versus 1L-non-platinum. Conclusions Pathogenic HRm identifies HRD in patients with PDAC with the best outcome when treated with 1L-platinum. https://www.selleckchem.com/products/sonrotoclax.html Biallelic HRm and core HRm further enriched benefit from 1L-platinum from HRD.Background PARP inhibitors (PARPis) are standard-of-care therapy for high-grade serous ovarian cancer (HGSOC). We investigated combining cediranib (antiangiogenic) with olaparib (PARPi) at emergence of PARPi resistance. Methods The proof-of-concept EVOLVE study (NCT02681237) assessed cediranib-olaparib combination therapy after progression on a PARPi. Women with HGSOC and radiographic evidence of disease progression were enrolled into one of three cohorts platinum sensitive after PARPi; platinum resistant after PARPi; or progression on standard chemotherapy after progression on PARPi (exploratory cohort). Patients received olaparib tablets 300 mg twice daily with cediranib 20 mg once daily until progression or unacceptable toxicity. The co-primary endpoints were objective response rate (RECIST v1.1) and progression-free survival (PFS) at 16 weeks. Archival tissue (PARPi-naïve) and baseline biopsy (post-PARPi) samples were mandatory. Genomic mechanisms of resistance were assessed by whole-exome and RNA sequencing. Results Among 34 heavily pretreated patients, objective responses were observed in 0/11 (0%) platinum-sensitive patients, 2/10 (20%) platinum-resistant patients, and 1/13 (8%) in the exploratory cohort. 16-week PFS rates were 55%, 50%, and 39%, respectively. The most common grade 3 toxicities were diarrhea (12%) and anemia (9%). Acquired genomic alterations at PARPi progression were reversion mutations in BRCA1, BRCA2, or RAD51B (19%), CCNE1 amplification (16%), ABCB1 upregulation (15%), and SLFN11 downregulation (7%). Patients with reversion mutations in homologous recombination genes and/or ABCB1 upregulation had poor outcomes. Conclusion This is currently the largest post-PARPi study identifying genomic mechanisms of resistance to PARPis. In this setting, the activity of cediranib-olaparib varied according to the PARPi resistance mechanism.Objective Neutrophils contribute to the SLE pathogenesis. Neutrophil to lymphocyte ratio (NLR) is reported to correlate with disease activity in SLE. The aim of the study was to evaluate whether NLR reflects underlying immunopathogenic activity in SLE, as well as to determine the contribution of each component of NLR, neutrophil and lymphocyte count. Methods Data were obtained from a cohort of patients with SLE (n=141) recruited at Lund University, Sweden. NLR levels were compared between patients with SLE and healthy controls (n=79). The relationship between NLR and clinical and immunological markers was examined using Mann-Whitney U test and logistic regression analysis. High NLR was defined as above the 90th percentile of healthy individuals. Results Patients with SLE had elevated neutrophil count (p=0.04) and reduced lymphocyte count (p less then 0.0001), resulting in elevated NLR as compared with healthy controls (p less then 0.0001). Patients with high NLR had more active disease, and were more frequentflected different aspects of the pathogenesis of SLE. Further studies are needed to determine the causality of the associations.Objective The clinical features of rheumatic patients with coronavirus disease 2019 (COVID-19) have not been reported. This study aimed to describe the clinical features of COVID-19 in rheumatic patients and provide information for handling this situation in clinical practice. Methods This is a retrospective case series study. Deidentified data, including gender, age, laboratory and radiological results, symptoms, signs, and medication history, were collected from 2326 patients diagnosed with COVID-19, including 21 cases in combination with rheumatic disease, in Tongji Hospital between 13 January and 15 March 2020. Results Length of hospital stay and mortality rate were similar between rheumatic and non-rheumatic groups, while the presence of respiratory failure was more common in rheumatic cases (38% vs 10%, p less then 0.001). Symptoms of fever, fatigue and diarrhoea were seen in 76%, 43% and 23% of patients, respectively. There were four rheumatic patients who experienced a flare of rheumatic disease during hospital stay, with symptoms of muscle aches, back pain, joint pain or rash. While lymphocytopaenia was seen in 57% of rheumatic patients, only one patient (5%) presented with leucopenia in rheumatic cases. Rheumatic patients presented with similar radiological features of ground-glass opacity and consolidation. Patients with pre-existing interstitial lung disease showed massive fibrous stripes and crazy-paving signs at an early stage. Five rheumatic cases used hydroxychloroquine before the diagnosis of COVID-19 and none progressed to critically ill stage. Conclusions Respiratory failure was more common in rheumatic patients infected with COVID-19. Differential diagnosis between COVID-19 and a flare of rheumatic disease should be considered. Trial registration number ChiCTR2000030795.Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19, and finding high-risk patients with worse prognosis for early prevention and medical resources optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from 7 cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the DL system, making it learn lung features. Afterwards, 1266 patients (924 with COVID-19, and 471 had follow-up for 5+ days; 342 with other pneumonia) from 6 cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the 4 external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC=0.87 and 0.88) and viral pneumonia (AUC=0.