Despite the current standard of care, breast cancer remains one of the leading causes of mortality in women worldwide, thus emphasizing the need for better predictive and therapeutic targets. ABI1 is associated with poor survival and an aggressive breast cancer phenotype, although its role in tumorigenesis, metastasis, and the disease outcome remains to be elucidated. Here, we define the ABI1‐based seven‐gene prognostic signature that predicts survival of metastatic breast cancer patients; ABI1 is an essential component of the signature. Genetic disruption of Abi1 in primary breast cancer tumors of PyMT mice led to significant reduction of the number and size of lung metastases in a gene dose‐dependent manner. The disruption of Abi1 resulted in deregulation of the WAVE complex at the mRNA and protein levels in mouse tumors. In conclusion, ABI1 is a prognostic metastatic biomarker in breast cancer. We demonstrate, for the first time, that lung metastasis is associated with an Abi1 gene dose and specific gene expression aberrations in primary breast cancer tumors. These results indicate that targeting ABI1 may provide a therapeutic advantage in breast cancer patients.

An estimated 35,000 patients with breast cancer (BC) have pathogenic BRCA1/2 variants. Unfortunately, in the current state of medical practice, 50-80% of pathogenic mutation carriers remain undetected and undiagnosed. These are largely women with moderate penetrance mutations, including women with BRCA1 mutations, which may not have been identified. There is an unmet challenge to improve the identification and risks of patients who have an inherited increased lifetime risk of BC. BRCA1 mutation carriers (BRCA1-mc) consist ~8% of woman suffering from BCs. A transcriptional R-loop is a guanine-rich 3-stranded nucleic acid structure that plays key roles in (dis)regulation of transcription events, DNA damage, and genome (in)stability. R-loops can be involved in cancer cell precursor development in BRCA1-mc, but it is not clear how they affect luminal precursor differentiation to mature basal-like BCs associated with BRCA1-mc. The role of R-loops in basal-like cells in normal-like tissue of BRCA1-mc have not been studied, limiting the biological interpretation of previous observations. At a genome wide scale, accuracy and resolution of experimentally defined R-loop boundaries is still a challenge. Integrative statistical and genomic approaches have demonstrated that it is possible to computationally predict R-loop forming sequences (RLFSs) in the human genome at an accuracy of 84-92%. In these regards, integrative analysis of available datasets could be useful. We hypothesize that such analyses of RLFS-positive RNA:DNA hybrids/R-loops and gene expression profiles of BRCA1 wild-type and BRCA1 mutation non-cancer epithelial cells could distinguish normal individuals and pre-malignant risks in BRCA1-mc. To test this hypothesis, we used publicly available datasets and carried out a comprehensive genome-wide analysis for BRCA1-mc and non-carriers without BC malignancy. We identified differentially expressed genes (DEGs) for non-cancer breast epithelium cells from publicly available 10 non-cancer individuals with and 10 without BRCA1 mutations. Our QmRLFS finder, DRIP-seq dataset (GEO:GSE96672) and our R-loop database (http://rloop.bii.a-star.edu.sg/) were used to identify RLFS-positive R-loops genome wide. A majority of DEGs, 1813, in non-cancer epithelial cells of BRCA1-mc are downregulated; Gene Ontology terms include metabolism, immune system, cell signaling, cell adhesion, transcription, and stress response. But, 597 DEGs were upregulated; splicing events. 662 downregulated and 185 upregulated DEGs contained RLFS-positive R-loops. In BRCA1 mutation carrier and non-carrier genomes, RLFS and R-loops were commonly located upstream and downstream promoter regions of many genes regardless of breast cell types. Interestingly, RLFS-positive R-loops were observed most often in luminal cells, however they were also found in mature basal-like cells. In particular, RLFS-positive R-loops were found only in BRCA1 locus (in 1st intron) of mature basal-like cells of non-BRCA1 mutation breast tissue samples, but not in luminal cells. However, for BRCA1-mc, we observed R-loops in BRCA1 locus of luminal precursors and mature luminal cells, but not in basal-like cells. Also, in BRCA1 pathway BACH1 promoter region, the RLFS-positive R-loops were observed in all breast cell types for both BRCA1-mc and non-carriers. This study suggests strong association of RLFS-positive R-loop formation and its enrichment variations within mature basal-like breast cells. We highlight the potential clinical significance of early detection of RLFS-positive R-loops in non-cancer breast Citation Format: Andre Grageda, Debasree Sarkar, Vladimir A Kuznetsov. Genome wide cell specification of R-loops in BRCA1 mutation carriers without malignancy [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-05-19.

Transcriptional R-loops are triple-stranded RNA:DNA hybrid genome structures which are defined in more than 75% of genes of the human genome, functionally versatile in cells, and mechanistically involved in tumorigenesis. In cancer cells, excessive/aberrant R-loop structures promote mutagenesis and genome instability due to their key regulatory roles in transcription and genotoxic stress. Thus, a balance in the regulation of R-loop initiation, stabilization, and suppression is required for proper function. Recent studies have shown that R-loop formation increases in luminal but not in basal-like cells of the tumor-free mammary epithelium of BRCA1 mutation carriers. R-loop forming sequences (RLFSs) are strand-specific G-rich regions of ssDNA that initiate and stabilize the formation of R-loops. The roles of the RLFSs in functional R-loops associated with premalignant states of BRCA1-mc have not been studied. Using our QmRLFS model and R-loopDB (http://rloop.bii.a-star.edu.sg/), we identified RLFS-associated (RLFS(+)) and non-associated (RLFS(-)) RNA:DNA hybrids detected by published DRIP-seq data in gene regions in luminal precursors, mature luminal, and basal-like epithelial cell type populations, extracted from BRCA1 wild-type and mutated non-cancer breast tissue samples (GSE96672). We found that genome regions of RLFS(+) RNA:DNA hybrid signals are strongly associated with G4-quadruplex, ssDNA regions, histone markers (acetylation, demethylation), and other open chromatin markers while the RLFS(-) RNA:DNA hybrid signal regions were not significantly associated with the positions of these regulatory markers. Our statistical modeling and cell-type-specific gene analysis of normal and BRCA1-deficient non-cancer epithelial cells samples shown that RLFS(+) R-loop frequency in luminal cells was mostly increased; in contrast, RLFS(+) R-loops were essentially decreased in basal-like breast cells. Analyzing published gene expression profiles of mammary epithelial cells obtained from disease-free prophylactic mastectomy tissues of BRCA1-mutation carriers and reduction mammoplasty tissues from non-mutation carriers (GSE25835 & GSE19383), we found the differential expressed genes (DEGs) associated with RLFS(+) RNA-DNA hybrid (DRIP-seq) signals. Integrating these findings with our RLFS RNA-DNA hybrid characterizations, we dichotomized the DEGs referring to the luminal and basal-like cell types. According to pathway analysis, a majority of BRCA1-deficient altered transcripts referring to luminal cell’s genes are involved metabolism and cell cycle while basal-like cells involved immune functions, cell signaling, and epithelial-mesenchymal transition pathways. We also found that the genes associated with RLFS clusters present on the sense strand are more associated with nucleic acid binding and transcription regulation, while the genes with an RLFS present on the anti-sense strand are more associated with immune system functions depleted in BRCA1 deficient non-cancer carriers. Thus, for the first time, we demonstrate the roles of RLFS mediated R-looping in the luminal and basal-like breast cells from BRCA1-mutation carrier tissues that specify aberrantly transcribed genes involving in tumorigenesis and immune function pathways. This study suggests novel regulatory mechanics of BRCA1-driven pre-malignant state development and prospective biomarker discovery. Citation Format: Andre Grageda, Vladimir A Kuznetsov. R-loop forming sequences determine early response genes and tumorigenic pathways driven in BRCA1-deficient carriers [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-09-07.

Modern high-throughput biological systems detection methods generate empirical frequency distributions (EFD) which exhibit complex forms and have long right-side tails Such EFD are often observed in normal and pathological processes, of which the probabilistic properties are essential, but the underlying probability mechanisms are poorly understood. To better understand the probability mechanisms driving biological complexity and the pathological role of extreme values, we propose that the observed skewed discrete distributions are generated by non-linear transition rates of birth and death processes (BDPs). We introduce a (3d+1)-parameter Generalized Gaussian Hypergeometric Probability ((3d+1)-GHP) model with the probabilities defined by a stationary solution of generalized BDP (g-BDP) and represented by generalized hypergeometric series with regularly varying function properties. We study the Regularly Varying 3d-Parameter Generalized Gaussian Hypergeometric Probability (3d-RGHP) function's regular variation properties, asymptotically constant slow varying component, unimodality and upward/downward convexity which allows us to specify a family of 3d-RGHP models and study their analytical and numerical characteristics. The frequency distribution of unique mutations occurring in the human genome of patients with melanoma have been analyzed as an example application of our theory in bioinformatics. The results show that the parameterized model not only fits the 'heavy tail' well, but also the entire EFD taken on the complete experimental outcome space. Our model provides a rigorous and flexible mathematical framework for analysis and application of skewed distributions generated by BDPs which often occur in bioinformatics and big data science.