Search

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.

Introduction: While the nature of somatic mutations in cancer exomes has been described, mutations in non-coding regions are less well-characterized. Compared to the rest of the genome, promoter or promoter-proximal (PPP) regions are more susceptible to mutagenesis and distinct mutational processes [e.g., transcription-replication conflicts (TRCs)]. We sought to investigate the nature of mutations in PPP regions across human cancer types and to define a novel TRC mutational signature. Methods: Whole genome and RNA sequencing data for untreated primary cancers were downloaded from the International Cancer Genome Consortium repository. PPP regions were defined as 1kb upstream of transcription start sites. We quantified the number of mutations in 1) PPP regions, 2) PPP regions of highly transcribed genes (HTGs; ≥75th percentile), and 3) common fragile sites (CFS). Results: A total of 835 patients across 12 tumor types were included in our analysis. Our data show significant variation in PPP mutations across cancer types with liver, pancreatic, and ovarian cancers demonstrating the highest burden of PPP mutations (Table 1). These same cancer types also possessed high numbers of PPP mutations specifically in HTGs. Since PPP mutations may be caused by TRCs, we proposed that liver, pancreatic, and ovarian cancers exhibited a strong TRC mutational signature. Therefore, we examined the correlation between the signature and CFS mutations—known to be caused by TRCs—to validate our results. The data show a positive correlation across tumor types; this correlation was strongest for cancer types where a TRC signature was more prevalent [correlation coefficient (R): liver: 0.985, pancreas: 0.991, ovary: 0.964]. Conclusions: Our analysis of PPP and CFS mutations suggests that transcription-dependent genome instability is more prevalent in liver, pancreatic, and ovarian cancers. Further, PPP in HTGs may be a novel TRC mutational signature. Medians and interquartile ranges of mutations by disease type. PPP: promoter or promoter-proximal Disease type Number of patients Mutations in PPP regions Mutations in PPP regions of highly transcribed genes (≥75%ile) Mutations in common fragile sites Bladder 23 4 (2-8) 2 (1-5) 12 (7-17) Bone 49 5 (2-12) 2 (0-3) 14 (7-43) Breast 89 1 (0-2) 0 (0-2) 5 (2-7) Cervix 20 1 (0-2) 1 (0-1) 3 (2-5) Colorectal 43 2 (1-14) 1 (0-5) 11 (6-81) Liver 227 54 (37-75) 13 (9-18) 264 (180-368) Lung 38 3 (1-6) 1 (0-2) 9 (4-24) Ovary 102 41 (3-68) 36 (16-52) 148 (7-213) Pancreas 140 31 (15-45) 25 (19-37) 113 (66-161) Prostate 19 0 (0-0) 0 (0-0) 1 (0-1) Stomach 34 2 (1-3) 1 (0-2) 8 (3-17) Uterus 51 1 (0-4) 1 (0-3) 6 (3-21) Citation Format: Marc A. Attiyeh, Fan Meng, Yilun Liu, Nicholas Banovich, Mustafa Raoof. Pan-cancer analysis of promoter or promoter-proximal somatic mutations defines transcription-replication conflict mutational signature [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6342.