LSD1, a lysine-specific histone demethylase, is overexpressed in several types of cancers and linked to poor outcomes. In breast cancer, the significance of LSD1 overexpression is not clear. We have performed an in silico analysis to assess the relationship of LSD1 expression to clinical outcome. We demonstrate that LSD1 overexpression is a poor prognostic factor in breast cancer, especially in basal-like breast cancer, a subtype of breast cancer with aggressive clinical features. This link is also observed in samples of triple negative breast cancer. Interestingly, we note that overexpression of LSD1 correlates with down-regulation of BRCA1 in triple negative breast cancer. This phenomenon is also observed in in vitro models of basal-like breast cancer, and is associated with an increased sensitivity to PARP inhibitors. We propose therefore that high expression levels of the demethylase LSD1 is a potential prognostic factor of poor outcome in basal-like breast cancer, and that PARP inhibition may be a therapeutic strategy of interest in this poor prognostic subtype with overexpression of LSD1.

PARP inhibitors are a class of promising anti-cancer drugs, with proven activity in BRCA mutant cancers. However, as with other targeted agents, treatment with PARP inhibitors generates acquired resistance within these tumors. The mechanism of this acquired resistance is poorly understood. We established cell lines that are resistant to PARP inhibitor by continuous treatment with the drug, and then used RNA sequencing to compare gene expression. Pathway analysis on the RNA sequencing data indicates that NF-κB signaling is preferentially up-regulated in PARP inhibitor-resistant cells, and that knockdown of core components in NF-κB signaling reverses the sensitivity to PARP inhibitor in resistant cells. Of therapeutic relevance, we show that PARP inhibitor-resistant cells are sensitive to an NF-κB inhibitor in comparison to their parental controls. Malignancies with up-regulation of NF-κB are sensitive to bortezomib, a proteasome inhibitor that is currently used in the clinic. We also show that treatment with bortezomib results in cell death in the PARP inhibitor-resistant cells, but not in parental cells. Therefore we propose that up-regulation of NF-κB signaling is a key mechanism underlying acquired resistance to PARP inhibition, and that NF-κB inhibition, or bortezomib are potentially effective anti-cancer agents after the acquisition of resistance to PARP inhibitors.

No abstract

Exon 2 of MED12, a subunit of the transcriptional mediator complex, has been frequently mutated in uterine leiomyomas and breast fibroadenomas; however, it has been rarely mutated in other tumors. Although the mutations were also found in uterine leiomyosarcomas, the frequency was significantly lower than in uterine leiomyomas. Here, we examined the MED12 mutation in phyllodes tumors, another biphasic tumor with epithelial and stromal components related to breast fibroadenomas. Mutations in MED12 exon 2 were analyzed in nine fibroadenomas and eleven phyllodes tumors via Sanger sequencing. A panel of cancer- and sarcoma-related genes was also analyzed using Ion Torrent next-generation sequencing. Six mutations in fibroadenomas, including those previously reported (6/9, 67%), and five mutations in phyllodes tumors (5/11, 45%) were observed. Three mutations in the phyllodes tumors were missense mutations at Gly44, which is common in uterine leiomyomas and breast fibroadenomas. In addition, two deletion mutations (in-frame c.133_144del12 and loss of splice acceptor c.100-68_137del106) were observed in the phyllodes tumors. No other recurrent mutation was observed with next-generation sequencing. Frequent mutations in MED12 exon 2 in the phyllodes tumors suggest that it may share genetic etiology with uterine leiomyoma, a subgroup of uterine leiomyosarcomas and breast fibroadenoma.

Spatial transcriptomics is an emerging technology requiring costly reagents and considerable skills, limiting the identification of transcriptional markers related to histology. Here, we show that predicted spatial gene-expression in unmeasured regions and tissues can enhance biologists’ histological interpretations. We developed the Deep learning model for Spatial gene Clusters and Expression, DeepSpaCE, and confirmed its performance using the spatial-transcriptome profiles and immunohistochemistry images of consecutive human breast cancer tissue sections. For example, the predicted expression patterns of SPARC, an invasion marker, highlighted a small tumor-invasion region difficult to identify using raw spatial transcriptome data alone because of a lack of measurements. We further developed semi-supervised DeepSpaCE using unlabeled histology images and increased the imputation accuracy of consecutive sections, enhancing applicability for a small sample size. Our method enables users to derive hidden histological characters via spatial transcriptome and gene annotations, leading to accelerated biological discoveries without additional experiments.

In a substantial number of patients, ductal carcinoma in situ (DCIS) of the breast will never progress to invasive ductal carcinoma, and these patients are often overtreated under the current clinical criteria. Although various candidate markers are available, relevant markers for delineating risk categories have not yet been established. In this study, we analyzed the clinical characteristics of 431 patients with DCIS and performed whole-exome sequencing analysis in a 21-patient discovery cohort and targeted deep sequencing analysis in a 72-patient validation cohort. We determined that age <45 years, HER2 amplification, and GATA3 mutation are possible indicators of relapse. PIK3CA mutation negativity and PgR negativity were also suggested to be risk factors. Spatial transcriptome analysis further revealed that GATA3 dysfunction upregulates epithelial-to-mesenchymal transition and angiogenesis, followed by PgR downregulation. These results reveal the existence of heterogeneous cell populations in DCIS and provide predictive markers for classifying DCIS and optimizing treatment.

Long-read whole-genome sequencing analysis of DNA methylation would provide useful information on the chromosomal context of gene expression regulation. Here we describe the development of a method that improves the read length generated by using the bisulfite-sequencing-based approach. In this method, we combined recently developed enzymatic base conversion, where an unmethylated cytosine (C) should be converted to thymine (T), with nanopore sequencing. After methylation-sensitive base conversion, the sequencing library was constructed using long-range polymerase chain reaction. This type of analysis is possible using a minimum of 1 ng genomic DNA, and an N50 read length of 3.4–7.6 kb is achieved. To analyze the produced data, which contained a substantial number of base mismatches due to sequence conversion and an inaccurate base read of the nanopore sequencing, a new analytical pipeline was constructed. To demonstrate the performance of long-read methylation sequencing, breast cancer cell lines and clinical specimens were subjected to analysis, which revealed the chromosomal methylation context of key cancer-related genes, allele-specific methylated genes, and repetitive or deletion regions. This method should convert the intractable specimens for which the amount of available genomic DNA is limited to the tractable targets.

Even within a single type of cancer, cells of various types exist and play interrelated roles. Each of the individual cells resides in a distinct microenvironment and behaves differently. Such heterogeneity is the most cumbersome nature of cancers, which is occasionally uncountable when effective prevention or total elimination of cancers is attempted. To understand the heterogeneous nature of each cell, the use of conventional methods for the analysis of “bulk” cells is insufficient. Although some methods are high-throughput and compressive regarding the genes being detected, the obtained data would be from the cell mass, and the average of a large number of the component cells would no longer be measured. Single-cell analysis, which has developed rapidly in recent years, is causing a drastic change. Genome, transcriptome, and epigenome analyses at single-cell resolution currently target cancer cells, cancer-associated fibroblasts, endothelial cells of vessels, and circulating and infiltrating immune cells. In fact, surprisingly diverse features of clonal evolution of cancer cells, during the development of cancer or acquisition of drug resistance, accompanied by corresponding gene expression changes in the circumstantial stromal cells, appeared in recent single-cell analyses. Based on the obtained novel insights, better optimal drug selection and new drug administration sequences were started. Even a remaining concern of the single cell analyses is being addressed. Until very recently, it was impossible to obtain positional information of cells in cancer via single-cell analysis because such information is lost during preparation of single-cell suspensions. A new method, collectively called spatial transcriptome (ST) analysis, has been developed and rapidly applied to various clinical specimens. In this review, we first outline the recent achievements of single-cell cancer analysis in analyzing the molecular basis underlying the acquisition of drug resistance, particularly focusing on the latest anti-epidermal growth factor receptor tyrosine kinase inhibitor, osimertinib. Further, we review the currently available ST analysis methods and introduce our recent attempts regarding the respective topics.