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Colorectal cancer is caused by a sequence of somatic genomic alterations affecting driver genes in core cancer pathways1. Here, to understand the functional and prognostic impact of cancer-causing somatic mutations, we analysed the whole genomes and transcriptomes of 1,063 primary colorectal cancers in a population-based cohort with long-term follow-up. From the 96 mutated driver genes, 9 were not previously implicated in colorectal cancer and 24 had not been linked to any cancer. Two distinct patterns of pathway co-mutations were observed, timing analyses identified nine early and three late driver gene mutations, and several signatures of colorectal-cancer-specific mutational processes were identified. Mutations in WNT, EGFR and TGFβ pathway genes, the mitochondrial CYB gene and 3 regulatory elements along with 21 copy-number variations and the COSMIC SBS44 signature correlated with survival. Gene expression classification yielded five prognostic subtypes with distinct molecular features, in part explained by underlying genomic alterations. Microsatellite-instable tumours divided into two classes with different levels of hypoxia and infiltration of immune and stromal cells. To our knowledge, this study constitutes the largest integrated genome and transcriptome analysis of colorectal cancer, and interlinks mutations, gene expression and patient outcomes. The identification of prognostic mutations and expression subtypes can guide future efforts to individualize colorectal cancer therapy.

BackgroundDifferences in the prognosis after colorectal cancer (CRC) by socioeconomic position (SEP) have been reported previously; however, most studies focused on survival differences at a particular time since diagnosis. We quantified the lifetime impact of CRC and its variation by SEP, using individualised income to conceptualise SEP.MethodsData included all adults with a first-time diagnosis of colon or rectal cancers in Sweden between 2008 and 2021. The analysis was done separately for colon and rectal cancers using flexible parametric models. For each cancer and income group, we estimated the life expectancy in the absence of cancer, the life expectancy in the presence of cancer and the loss in life expectancy (LLE).ResultsWe found large income disparities in life expectancy after a cancer diagnosis, with larger differences among the youngest patients. Higher income resulted in more years lost following a cancer diagnosis. For example, 40-year-old females with colon cancer lost 17.64 years if in the highest-income group and 13.68 years if in the lowest-income group. Rectal cancer resulted in higher LLE compared with colon cancer. Males lost a larger proportion of their lives. All patients, including the oldest, lost more than 30% of their remaining life expectancy. Based on the number of colon and rectal cancer diagnoses in 2021, colon cancer results in almost double the number of years lost compared with rectal cancer (24 669 and 12 105 years, respectively).ConclusionWhile our results should be interpreted in line with what individualised income represents, they highlight the need to address inequalities.

Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 1061-megapixel images in ~5.5?h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console; a persistent deployment is available at https://deepcell.org/.

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A Correction to this paper has been published: https://doi.org/10.1038/s41592-021-01059-w