Basal-like breast cancer (BLBC) is an aggressive breast cancer subtype with features similar to the basal cells surrounding the mammary ducts. Treatment of patients with BLBC has been challenging due to the lack of well-defined molecular targets. Due to the clinical and pathological similarities of BLBC with BRCA-deficient breast cancers, the effectiveness of Poly (ADP-ribose) polymerase inhibitors (PARPi) has been tested in early phase clinical trials for patients with advanced BLBC, with limited clinical responses. Recently, it was reported that HORMAD1 overexpression sensitizes BLBC to HR-targeting agents by suppressing homologous recombination. Our independent analysis suggests that HORMAD1 is aberrantly overexpressed in about 80% of BLBC, and its expression in normal tissues is restricted to testis. Our experimental data suggests that HORMAD1 overexpression correlates with focal hypomethylation in BLBC. On the other hand, investigation of the Genomics of Drug Sensitivity in Cancer dataset revealed significantly reduced sensitivity of HORMAD1-overexpressing BLBC cell lines to Rucaparib, a commonly used PARPi. To further assess the role of HORMAD1 in PARPi sensitivity, we generated three HORMAD1-overexpressing xenograft models using the HORMAD1-low BLBC cell lines HCC1954, HCC1806, and BT20; we then subjected these xenograft models to Rucaparib treatment. Ectopic expression of HORMAD1 enhances tumor formations in two of these models, and significantly reduces sensitivity to Rucaparib in the HCC1954 model. Taken together, our data suggest that epigenetic activation of HORMAD1 by hypomethylation in BLBC may endow reduced sensitivity to Rucaparib treatment in some tumor models.

Identifying new gene functions and pathways underlying diseases and biological processes are major challenges in genomics research. Particularly, most methods for interpreting the pathways characteristic of an experimental gene list defined by genomic data are limited by their dependence on assessing the overlapping genes or their interactome topology, which cannot account for the variety of functional relations. This is particularly problematic for pathway discovery from single-cell genomics with low gene coverage or interpreting complex pathway changes such as during change of cell states. Here, we exploited the comprehensive sets of molecular concepts that combine ontologies, pathways, interactions and domains to help inform the functional relations. We first developed a universal concept signature (uniConSig) analysis for genome-wide quantification of new gene functions underlying biological or pathological processes based on the signature molecular concepts computed from known functional gene lists. We then further developed a novel concept signature enrichment analysis (CSEA) for deep functional assessment of the pathways enriched in an experimental gene list. This method is grounded on the framework of shared concept signatures between gene sets at multiple functional levels, thus overcoming the limitations of the current methods. Through meta-analysis of transcriptomic data sets of cancer cell line models and single hematopoietic stem cells, we demonstrate the broad applications of CSEA on pathway discovery from gene expression and single-cell transcriptomic data sets for genetic perturbations and change of cell states, which complements the current modalities. The R modules for uniConSig analysis and CSEA are available through https://github.com/wangxlab/uniConSig.

Application of traditional somatic evolutionary theory can offer an appropriate context for studying tumor growth at the molecular level. However, high degrees of heterogeneity (especially genome-level heterogeneity) within tumors coupled with a lack of common driver mutations have posed a challenge to the generally accepted stepwise concept of cancer evolution, where clonal expansion is the key. In order to account for multiple levels of heterogeneity and better understand tumor growth and progression, a new, holistic conceptual framework must be applied in cancer research. Herein, we discuss one such framework, the genome theory of cancer evolution, with respect to tumor growth. This includes detailing the ultimate importance of chromosome aberrations in cancer, the somatic cell evolutionary pattern, and single-cell/population growth dynamics. Under this new framework, tumor growth is a highly dynamic process where emergent outlier subpopulations can greatly influence the pattern of progression and the direction of evolution. Further, genome level changes have a greater impact on cancer evolution than individual gene mutations in most cancer types, as karyotype alteration often results in altered system inheritance which defines the network structure and even can change the meaning of individual genes (representing 'parts inheritance' by changing the gene context. Based on this analysis, we call for a focus shift back on cytogenetic and cytogenomic alterations (especially on non-clonal chromosomal aberrations) in monitoring population growth, identifying the emergence of new subpopulations and studying patterns of evolutionary dynamics. This new insight has implications in understanding cancer evolution in general as well as searching for new diagnostic and treatment strategies.