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Key points• Pan-cancer computational histopathology analysis with deep learning extracts histopathological patterns and accurately discriminates 28 cancer and 14 normal tissue types • Computational histopathology predicts whole genome duplications, focal amplifications and deletions, as well as driver gene mutations • Wide-spread correlations with gene expression indicative of immune infiltration and proliferation • Prognostic information augments conventional grading and histopathology subtyping in the majority of cancers AbstractHere we use deep transfer learning to quantify histopathological patterns across 17,396 H&E stained histopathology image slides from 28 cancer types and correlate these with underlying genomic and transcriptomic data. Pan-cancer computational histopathology (PC-CHiP) classifies the tissue origin across organ sites and provides highly accurate, spatially resolved tumor and normal distinction within a given slide. The learned computational histopathological features correlate with a large range of recurrent genetic aberrations, including whole genome duplications (WGDs), arm-level copy number gains and losses, focal amplifications and deletions as well as driver gene mutations within a range of cancer types. WGDs can be predicted in 25/27 cancer types (mean AUC=0.79) including those that were not part of model training. Similarly, we observe associations with 25% of mRNA transcript levels, which enables to learn and localise histopathological patterns of molecularly defined cell types on each slide. Lastly, we find that computational histopathology provides prognostic information augmenting histopathological subtyping and grading in the majority of cancers assessed, which pinpoints prognostically relevant areas such as necrosis or infiltrating lymphocytes on each tumour section. Taken together, these findings highlight the large potential of PC-CHiP to discover new molecular and prognostic associations, which can augment diagnostic workflows and lay out a rationale for integrating molecular and histopathological data.

Despite regional successes in controlling the SARS-CoV-2 pandemic, global cases have reached an all time high in April 2021 in part due to the evolution of more transmissible variants. Here we use the dense genomic surveillance generated by the COVID-19 Genomics UK Consortium to reconstruct the dynamics of 62 different lineages in each of 315 English local authorities between September 2020 and April 2021. This analysis reveals a series of sub-epidemics that peaked in the early autumn of 2020, followed by a singular jump in transmissibility of the B.1.1.7 lineage. B.1.1.7 grew when other lineages declined during the second national lockdown and regionally tiered restrictions between November and December 2020. A third more stringent national lockdown eventually suppressed B.1.1.7 and eliminated nearly all other lineages in early 2021. However, a series of variants (mostly containing the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. Accounting for sustained introductions, however, indicates that their transmissibility is unlikely to exceed that of B.1.1.7. Finally, B.1.617.2 was repeatedly introduced to England and grew rapidly in April 2021, constituting approximately 40% of sampled COVID-19 genomes on May 15.

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