Background Haematological malignancies and their treatments are likely to affect SARS-CoV-2 vaccine efficacy. We aimed to evaluate serological response to BNT162b2 vaccine in patients with haematological malignancies by type of treatment. Methods Our national prospective cohort study was done in Lithuania and assessed serological response to one and two BNT162b2 (Comirnaty, Pfizer-BioNTech) vaccine doses in healthy health-care workers and in patients with haematological malignancies. Eligible participants were aged 18 years or older, had received both vaccine doses, and had available biobanked blood samples from before vaccination and after the second dose. Biobanked samples and health data were obtained from Vilnius University Hospital Santaros Klinikos Biobank. Abbott Architect SARS-CoV-2 IgG Quant II chemiluminescent microparticle assay was used to quantify serum anti-SARS-CoV-2-S1 IgG antibody (anti-S1 IgG antibody) concentrations 0–10 days before the first BNT162b2 vaccine, on the day of second immunisation (around day 21), and 7 to 21 days after the second immunisation. Adverse events were assessed by a standardised questionnaire. Breakthrough infections were characterised clinically and by SARS-CoV-2 genotyping whenever possible. This study is registered with ClinicalTrials.gov , NCT04871165 . Findings Between Jan 8 and April 21, 2021, 885 participants with haematological malignancies were included in the study. 857 patients were anti-S1 IgG seronegative at timepoint 0 and constituted the main analysis cohort. The age-matched comparison was made between 315 patients with haematological malignancies who were aged 18–60 years and 67 healthy health-care workers in the same age group. Patients aged 18–60 years with haematological malignancies had lower median anti-S1 IgG antibody responses after two BNT162b2 vaccine doses than did health-care workers of the same age group (median 6961 AU/mL [IQR 1292–20 672] vs 21 395 AU/mL [14 831–33 553]; p<0·0001). Compared with untreated patients with haematological malignancies (n=53; median 5761 AU/mL [629–16 141]), patients actively treated with Bruton tyrosine kinase inhibitors (BTKIs; n=44; 0 AU/mL [0–7]; p<0·0001), ruxolitinib (n=16; 10 AU/mL [0–45]; p<0·0001), venetoclax (n=10; 4 AU/mL [0–1218]; p=0·0005), or anti-CD20 antibody therapy (n=87; 17 AU/mL [1–2319]; p<0·0001) showed particularly poor anti-S1 IgG antibody responses following two BNT162b2 doses. Patients being treated with tyrosine kinase inhibitors (n=41; 10 537 AU/mL [IQR 2335–19 388]) or patients who received autologous haematopoietic stem-cell transplantation (HSCT; n=192; 6203 AU/mL [1451–16 834]) or allogeneic HSCT (n=122; 6304 AU/mL [1120–16 913]) were among the subgroups with the highest numerical responses. Nine SARS-CoV-2 infections and three COVID-19 deaths were observed among fully vaccinated patients with haematological malignancies. ...

For the first time in Europe hundreds of rare disease (RD) experts team up to actively share and jointly analyse existing patient’s data. Solve-RD is a Horizon 2020-supported EU flagship project bringing together >300 clinicians, scientists, and patient representatives of 51 sites from 15 countries. Solve-RD is built upon a core group of four European Reference Networks (ERNs; ERN-ITHACA, ERN-RND, ERN-Euro NMD, ERN-GENTURIS) which annually see more than 270,000 RD patients with respective pathologies. The main ambition is to solve unsolved rare diseases for which a molecular cause is not yet known. This is achieved through an innovative clinical research environment that introduces novel ways to organise expertise and data. Two major approaches are being pursued (i) massive data re-analysis of >19,000 unsolved rare disease patients and (ii) novel combined -omics approaches. The minimum requirement to be eligible for the analysis activities is an inconclusive exome that can be shared with controlled access. The first preliminary data re-analysis has already diagnosed 255 cases form 8393 exomes/genome datasets. This unprecedented degree of collaboration focused on sharing of data and expertise shall identify many new disease genes and enable diagnosis of many so far undiagnosed patients from all over Europe.

Reanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP’s Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative samples) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics.

STUDY QUESTION Are there more de novo mutations (DNMs) present in the genomes of children born through medical assisted reproduction (MAR) compared to spontaneously conceived children? SUMMARY ANSWER In this pilot study, no statistically significant difference was observed in the number of DNMs observed in the genomes of MAR children versus spontaneously conceived children. WHAT IS KNOWN ALREADY DNMs are known to play a major role in sporadic disorders with reduced fitness such as severe developmental disorders, including intellectual disability and epilepsy. Advanced paternal age is known to place offspring at increased disease risk, amongst others by increasing the number of DNMs in their genome. There are very few studies reporting on the effect of MAR on the number of DNMs in the offspring, especially when male infertility is known to be affecting the potential fathers. With delayed parenthood an ongoing epidemiological trend in the 21st century, there are more children born from fathers of advanced age and more children born through MAR every day. STUDY DESIGN, SIZE, DURATION This observational pilot study was conducted from January 2015 to March 2019 in the tertiary care centre at Radboud University Medical Center. We included a total of 53 children and their respective parents, forming 49 trios (mother, father and child) and two quartets (mother, father and two siblings). One group of children was born after spontaneous conception (n = 18); a second group of children born after IVF (n = 17) and a third group of children born after ICSI combined with testicular sperm extraction (ICSI-TESE) (n = 18). In this pilot study, we also subdivided each group by paternal age, resulting in a subgroup of children born to younger fathers (&lt;35 years of age at conception) and older fathers (&gt;45 years of age at conception). PARTICIPANTS/MATERIALS, SETTING, METHODS Whole-genome sequencing (WGS) was performed on all parent-offspring trios to identify DNMs. For 34 of 53 trios/quartets, WGS was performed twice to independently detect and validate the presence of DNMs. Quality of WGS-based DNM calling was independently assessed by targeted Sanger sequencing. MAIN RESULTS AND THE ROLE OF CHANCE No significant differences were observed in the number of DNMs per child for the different methods of conception, independent of parental age at conception (multi-factorial ANOVA, f(2) = 0.17, P-value = 0.85). As expected, a clear paternal age effect was observed after adjusting for method of conception and maternal age at conception (multiple regression model, t = 5.636, P-value = 8.97 × 10−7), with on average 71 DNMs in the genomes of children born to young fathers (&lt;35 years of age) and an average of 94 DNMs in the genomes of children born to older fathers (&gt;45 years of age). LIMITATIONS, REASONS FOR CAUTION This is a pilot study and other small-scale studies have recently reported contrasting results. Larger unbiased studies are required to confirm or falsify these results. WIDER IMPLICATIONS OF THE FINDINGS This pilot study did not show an effect for the method of conception on the number of DNMs per genome in offspring. Given the role that DNMs play in disease risk, this negative result is good news for IVF and ICSI-TESE born children, if replicated in a larger cohort. STUDY FUNDING/COMPETING INTEREST(S) This research was funded by the Netherlands Organisation for Scientific Research (918-15-667) and by an Investigator Award in Science from the Wellcome Trust (209451). The authors have no conflicts of interest to declare. TRIAL REGISTRATION NUMBER N/A.