Background: Cancer driver genes are characterized by frequently recurrent mutations, substantial functional impact on in vitro and in vivo cancer growth, and several of the represent clinically important therapeutic targets. Whole exome and genome sequencing studies identified mutations in at least one cancer driver gene in about 70% breast cancers. These studies also revealed that each cancer also harbors hundreds to thousands of additional non-recurrent mutations which follow a long-tail distribution. Historically, these long tail “passenger” mutations are considered random due to genomic instability and not expected to contribute to the biology of the disease. We hypothesize that “long-tail” mutations could also have functional importance and contribute to the unique biology of a given cancer. The goal of the current analysis was to identify the long-tail mutations in breast cancer, and estimate their overall functional importance. Methods: We obtained somatic mutations from the breast cancer TCGA cohort (N=1076) and calculated the somatic mutation frequency for each gene. The dNdScv algorithm was used to estimate significantly mutated genes. A gene was considered to be in the long tail of mutations if its somatic mutation frequency was 1-3%, and the dNdScv p-value was < 0.05. We performed pathway enrichment analysis with 21 cancer pathways assembled by NanoString Technologies (Seattle, WA) for the long-tail genes using Fisher’s exact test and obtained gene dependency scores (DS) from The Cancer Dependency Map (DepMap) project which performed genome-wide pooled loss of function screening for 17,634 human genes using and CRISPR-Cas9-mediated (CRISPR) gene editing to estimate tumor cell viability after gene silencing in 563 cell lines. The more negative (i.e. lower) the dependency score the more important the gene is to sustain cell viability. We compared the DS of the long tail genes with known cancer driver genes and other human genes excluding known breast cancer driver genes. We also compared the dependency score of genes with long-tail mutations between breast cancer and other cancer types using the Mann-Whitney U test. We estimated the trend of average dependency score across genesets using the Jonckheere Terpstra test and using Kendall's tau (τ) coefficient to show the increasing (positive value) or decreasing (negative value) trend. Results: Seventy percent of breast cancers (n=763) carried long-tail mutations in 115 different genes. The average number of long tail mutations was N=3 (range from 1 to 118). Genes with long-tail mutations were enriched in epithelial-mesenchymal transition, extracellular matrix, angiogenesis, adaptive immunity, MAPK, innate immunity, inflammation, and RAS pathways (FDR<0.035). Genes with long-tail mutations showed the lowest average dependency score in breast cancer cell lines (n=28) with median DS= -0.080, the range of median DS was -0.079 to -0.056 in other cancer cell lines (P=0.70). In the breast cancer cell lines, known breast cancer driver genes (N=11) had the lowest DS (median=-0.352), followed by the long-tail genes (median=-0.0801), followed by all other human genes (median DS=-0.0528). An increasing trend (τ=1.78, P=0.024) of dependency score was observed across known cancer driver genes, long-tail genes, and other human genes which indicate that long-tail genes are important in cancer cell viability. Conclusions: Long-tail mutations are seen in most breast cancers in unique combinations. They primarily affect genes involved in key cancer pathways. Long tail genes have negative dependency scores across 563 cancer cell lines indicating functional importance in sustaining cancer viability. These results suggest that long-tail genes with mutations could contribute to the biology of breast cancers and might explain the broad range of differences in clinical behavior or morphologically similar cancers.
Citation Format: Tao Qing, Hussein Mohsen, Mariya Rozenblit, Michal Marczyk, Julia Foldi, Kim Blenman, Vignesh Gunasekharan, Lajos Pusztai. Functional importance of long-tail mutations in breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS19-05.