Using the DNA Integrity Number to Analyze DNA Quality in Specimens Collected from Liquid-Based Cytology after Fine-Needle Aspiration of Breast Tumors and Lesions.

INTRODUCTION: Cancer genome analysis using next-generation sequencing requires adequate and high-quality DNA samples. Genomic analyses were conventionally performed using formalin-fixed paraffin-embedded sections rather than cytology samples such as cell block or smear specimens. Specimens collected from liquid-based cytology (LBC) have the potential to be sources of high-quality DNA suitable for genetic analysis even after long-term storage. METHODS: We collected breast tumor/lesion fractions from 92 residual LBC specimens using fine-needle aspiration (FNA) biopsy, including breast carcinoma (1 invasive carcinoma and 4 ductal carcinomas in situ), papillomatous lesion (5 intraductal papillomas), and fibroepithelial lesion (19 phyllodes tumors and 53 fibroadenomas) samples, and others (1 ductal adenoma, 1 hamartoma, 1 fibrocystic disease, and 7 unknown). DNA was extracted from all samples and subjected to DNA integrity number (DIN) score analysis. RESULTS: Average DIN score collected from 92 LBC specimens was significantly higher score. In addition, high-quality DNA with high DIN values (7.39 ± 0.80) was successfully extracted more than 12 months after storage of residual LBC specimens. CONCLUSION: Residual LBC specimens collected from FNA of the breast were verified to carry high-quality DNA and could serve as an alternate source for genetic analysis.

Semibulk RNA-seq analysis as a convenient method for measuring gene expression statuses in a local cellular environment.

When biologically interpretation of the data obtained from the single-cell RNA sequencing (scRNA-seq) analysis is attempted, additional information on the location of the single cells, behavior of the surrounding cells, and the microenvironment they generate, would be very important. We developed an inexpensive, high throughput application while preserving spatial organization, named "semibulk RNA-seq" (sbRNA-seq). We utilized a microfluidic device specifically designed for the experiments to encapsulate both a barcoded bead and a cell aggregate (a semibulk) into a single droplet. Using sbRNA-seq, we firstly analyzed mouse kidney specimens. In the mouse model, we could associate the pathological information with the gene expression information. We validated the results using spatial transcriptome analysis and found them highly consistent. When we applied the sbRNA-seq analysis to the human breast cancer specimens, we identified spatial interactions between a particular population of immune cells and that of cancer-associated fibroblast cells, which were not precisely represented solely by the single-cell analysis. Semibulk analysis may provide a convenient and versatile method, compared to a standard spatial transcriptome sequencing platform, to associate spatial information with transcriptome information.

Mitochonic Acid 5 Improves Duchenne Muscular Dystrophy and Parkinson's Disease Model of Caenorhabditis elegans.

Mitochonic Acid 5 (MA-5) enhances mitochondrial ATP production, restores fibroblasts from mitochondrial disease patients and extends the lifespan of the disease model "Mitomouse". Additionally, MA-5 interacts with mitofilin and modulates the mitochondrial inner membrane organizing system (MINOS) in mammalian cultured cells. Here, we used the nematode Caenorhabditis elegans to investigate whether MA-5 improves the Duchenne muscular dystrophy (DMD) model. Firstly, we confirmed the efficient penetration of MA-5 in the mitochondria of C. elegans. MA-5 also alleviated symptoms such as movement decline, muscular tone, mitochondrial fragmentation and Ca2+ accumulation of the DMD model. To assess the effect of MA-5 on mitochondria perturbation, we employed a low concentration of rotenone with or without MA-5. MA-5 significantly suppressed rotenone-induced mitochondria reactive oxygen species (ROS) increase, mitochondrial network fragmentation and nuclear destruction in body wall muscles as well as endogenous ATP levels decline. In addition, MA-5 suppressed rotenone-induced degeneration of dopaminergic cephalic (CEP) neurons seen in the Parkinson's disease (PD) model. Furthermore, the application of MA-5 reduced mitochondrial swelling due to the immt-1 null mutation. These results indicate that MA-5 has broad mitochondrial homing and MINOS stabilizing activity in metazoans and may be a therapeutic agent for these by ameliorating mitochondrial dysfunction in DMD and PD.

Phasing analysis of lung cancer genomes using a long read sequencer.

Chromosomal backgrounds of cancerous mutations still remain elusive. Here, we conduct the phasing analysis of non-small cell lung cancer specimens of 20 Japanese patients. By the combinatory use of short and long read sequencing data, we obtain long phased blocks of 834 kb in N50 length with >99% concordance rate. By analyzing the obtained phasing information, we reveal that several cancer genomes harbor regions in which mutations are unevenly distributed to either of two haplotypes. Large-scale chromosomal rearrangement events, which resemble chromothripsis events but have smaller scales, occur on only one chromosome, and these events account for the observed biased distributions. Interestingly, the events are characteristic of EGFR mutation-positive lung adenocarcinomas. Further integration of long read epigenomic and transcriptomic data reveal that haploid chromosomes are not always at equivalent transcriptomic/epigenomic conditions. Distinct chromosomal backgrounds are responsible for later cancerous aberrations in a haplotype-specific manner.

Whole-genome Methylation Analysis of APOBEC Enzyme-converted DNA (~5 kb) by Nanopore Sequencing.

In recent years, DNA methylation research has been accelerated by the advent of nanopore sequencers. However, read length has been limited by the constraints of base conversion using the bisulfite method, making analysis of chromatin content difficult. The read length of the previous method combining bisulfite conversion and long-read sequencing was ~1.5 kb, even using targeted PCR. In this study, we have improved read length (~5 kb), by converting unmethylated cytosines to uracils with APOBEC enzymes, to reduce DNA fragmentation. The converted DNA was then sequenced using a PromethION nanopore sequencer. We have also developed a new analysis pipeline that accounts for base conversions, which are not present in conventional nanopore sequencing, as well as errors produced by nanopore sequencing.

Intensive single-cell analysis reveals immune-cell diversity among healthy individuals.

Immune responses are different between individuals and personal health histories and unique environmental conditions should collectively determine the present state of immune cells. However, the molecular systems underlying such heterogeneity remain elusive. Here, we conducted a systematic time-lapse single-cell analysis, using 171 single-cell libraries and 30 mass cytometry datasets intensively for seven healthy individuals. We found substantial diversity in immune-cell profiles between different individuals. These patterns showed daily fluctuations even within the same individual. Similar diversities were also observed for the T-cell and B-cell receptor repertoires. Detailed immune-cell profiles at healthy statuses should give essential background information to understand their immune responses, when the individual is exposed to various environmental conditions. To demonstrate this idea, we conducted the similar analysis for the same individuals on the vaccination of influenza and SARS-CoV-2. In fact, we detected distinct responses to vaccines between individuals, although key responses are common. Single-cell immune-cell profile data should make fundamental data resource to understand variable immune responses, which are unique to each individual.

Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation.

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.

Single-cell and spatial analyses of cancer cells: toward elucidating the molecular mechanisms of clonal evolution and drug resistance acquisition.

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.

Long-read whole-genome methylation patterning using enzymatic base conversion and nanopore sequencing.

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.

Genomic profiling reveals heterogeneous populations of ductal carcinoma in situ of the breast.

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.

Fbxo22-mediated KDM4B degradation determines selective estrogen receptor modulator activity in breast cancer.

The agonistic/antagonistic biocharacter of selective estrogen receptor modulators (SERMs) can have therapeutic advantages, particularly in the case of premenopausal breast cancers. Although the contradictory effects of these modulators have been studied in terms of crosstalk between the estrogen receptor α (ER) and coactivator dynamics and growth factor signaling, the molecular basis of these mechanisms is still obscure. We identify a series of regulatory mechanisms controlling cofactor dynamics on ER and SERM function, whose activities require F-box protein 22 (Fbxo22). Skp1, Cullin1, F-box-containing complex (SCFFbxo22) ubiquitylated lysine demethylase 4B (KDM4B) complexed with tamoxifen-bound (TAM-bound) ER, whose degradation released steroid receptor coactivator (SRC) from ER. Depletion of Fbxo22 resulted in ER-dependent transcriptional activation via transactivation function 1 (AF1) function, even in the presence of SERMs. In living cells, TAM released SRC and KDM4B from ER in a Fbxo22-dependent manner. SRC release by TAM required Fbxo22 on almost all ER-SRC-bound enhancers and promoters. TAM failed to prevent the growth of Fbxo22-depleted, ER-positive breast cancers both in vitro and in vivo. Clinically, a low level of Fbxo22 in tumor tissues predicted a poorer outcome in ER-positive/human epidermal growth factor receptor type 2-negative (HER2-negative) breast cancers with high hazard ratios, independently of other markers such as Ki-67 and node status. We propose that the level of Fbxo22 in tumor tissues defines a new subclass of ER-positive breast cancers for which SCFFbxo22-mediated KDM4B degradation in patients can be a therapeutic target for the next generation of SERMs.

MED12 exon 2 mutations in phyllodes tumors of the breast.

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.

LSD1 overexpression is associated with poor prognosis in basal-like breast cancer, and sensitivity to PARP inhibition.

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.

NF-κB signaling mediates acquired resistance after PARP inhibition.

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.