Genomic characterization of human brain metastases identifies drivers of metastatic lung adenocarcinoma

Abstract

Brain metastases from lung adenocarcinoma (BM-LUAD) cause significant patient mortality. To identify genomic alterations that promote brain metastases, we performed whole-exome sequencing of 73 BM-LUAD cases. Using case-control analyses, we discovered candidate drivers of brain metastasis by identifying genes with more frequent copy-number aberrations in BM-LUAD compared to 503 primary lung adenocarcinomas. We identified three regions with significantly higher amplification frequencies in BM-LUAD, including MYC (12% vs 6%), YAP1 (7% vs 0.8%), and MMP13 (10% vs 0.6%) and significantly more frequent deletions in CDKN2A/B (27% vs 13%). We confirmed that amplification frequencies of MYC and YAP1 / MMP13 were elevated in an independent cohort of 105 patients. Functional assessment in patient-derived xenograft mouse models validated that MYC , YAP1 or MMP13 overexpression increased the brain metastasis incidence. These results demonstrate that somatic alterations contribute to brain metastases and that genomic sequencing of a large number of metastatic tumors can reveal novel metastatic drivers.

Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

“…Each Target Site is expressed from a strong promoter allowing it to be captured by single-cell RNA-sequencing. After amplifying and sequencing the 20 Target Site mRNAs, the reads were analyzed using the Cassiopeia processing pipeline (32). Briefly, this pipeline leverages unique molecular identifier (UMI) information and redundancy in sequencing reads to confidently call intBCs and indel alleles from the lineage data, which inform subsequent phylogenetic reconstruction ( Fig.…”

“…from cells' static intBCs, as described above) but rather in reconstructing subclonal lineage dynamics (i.e. from cells' 20 continuously evolving indel alleles, as in retrospective approaches). As such, we next reconstructed highresolution phylogenetic trees using the Cassiopeia suite of phylogenetic inference algorithms (32) with modified parameters tailored to this dataset's unprecedented complexity and scale (Methods).…”

“…For example, non-metastatic populations result in all clades inhabiting a single tissue ( Fig. 3A-B, left); conversely, highly metastatic populations result in 20 closely related cells residing in different tissues ( Fig. 3A-B, right).…”

“…These "retrospective" tracing approaches are particularly valuable for studying the 5 subclonal dynamics of cancer in patient-derived samples, such as elucidating which mutations contribute to metastasis and when they occur (15)(16)(17)(18). However, these conclusions can be confounded by incomplete or impure bulk tumor sampling (19), sequencing artifacts (20), varying levels of intratumor heterogeneity, and non-neutral mutations (1,5); some of these technical limitations can be mitigated using single-cell resolution measurements or whole-genome sequencing. 10 The recent development of Cas9-enabled lineage tracing techniques with single-cell RNA readouts (21)(22)(23)(24) provides the potential to explore cancer progression at vastly larger scale and finer resolution than was previously possible.…”

“…Then, as in retrospective tracing approaches, various computational approaches (27)(28)(29)(30)(31)(32) can reconstruct a phylogenetic tree that best models subclonal cellular relationships (e.g., by maximum-parsimony) from the observed alleles. Thus far, 20 Cas9-enabled tracing has been successfully applied to study important aspects of metazoan biology, like the cellular progenitor landscape in early mammalian embryogenesis (22,33) and neural development in zebrafish (21). Additionally, resources now exist for studying other phylogenetic processes in mouse (22,33), and analytical tools are available for computationally reconstructing and benchmarking trees from large lineage tracing datasets (32,34).…”