Pan-cancer whole-genome analyses of metastatic solid tumours

Contains fulltext : 215492.pdf (publisher's version ) (Open Access)Metastatic cancer is a major cause of death and is associated with poor treatment efficacy. A better understanding of the characteristics of late-stage cancer is required to help adapt personalized treatments, reduce overtreatment and improve outcomes. Here we describe the largest, to our knowledge, pan-cancer study of metastatic solid tumour genomes, including whole-genome sequencing data for 2,520 pairs of tumour and normal tissue, analysed at median depths of 106x and 38x, respectively, and surveying more than 70 million somatic variants. The characteristic mutations of metastatic lesions varied widely, with mutations that reflect those of the primary tumour types, and with high rates of whole-genome duplication events (56%). Individual metastatic lesions were relatively homogeneous, with the vast majority (96%) of driver mutations being clonal and up to 80% of tumour-suppressor genes being inactivated bi-allelically by different mutational mechanisms. Although metastatic tumour genomes showed similar mutational landscape and driver genes to primary tumours, we find characteristics that could contribute to responsiveness to therapy or resistance in individual patients. We implement an approach for the review of clinically relevant associations and their potential for actionability. For 62% of patients, we identify genetic variants that may be used to stratify patients towards therapies that either have been approved or are in clinical trials. This demonstrates the importance of comprehensive genomic tumour profiling for precision medicine in cancer

A blood atlas of COVID-19 defines hallmarks of disease severity and specificity.

Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism, and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems-based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design, and personalized medicine approaches for COVID-19

Predicative Justification and Development of a Second Order Theory of Finite Sets

We will predicatively justify the induction axioms for arithmetical sets, which are the induction axioms of (the first-order) Peano Arithmetic, in the predicative second order theory of hereditarily finite sets FSS. The comprehension axioms of FSS permit the predicative formation of infinite classes of hereditarily finite sets. The predicative formation means that the infinite classes are comprehended without quantification over infinite classes. We interprete into FSS the theory FSI which includes also the axiom of induction usable with the classes of FSS. This will show that the induction is consistent with FSS. We also show FSI equivalent to ACA0. The second order theory FSI was developed for its use in computer programming. For the motivation and more details see [8]. The predicative justification of FSI within FSS was inspired by S. Feferman and G. Hellman's paper Challenges to Predicative Foundations of Arithmetic [2]