A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers

PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer

Identifying personalized cancer driver genes and further revealing their oncogenic mechanisms is critical for understanding the mechanisms of cell transformation and aiding clinical diagnosis. Almost all existing methods primarily focus on identifying driver genes at the cohort or individual level but fail to further uncover their underlying oncogenic mechanisms. To fill this gap, we present an interpretable framework, PhenoDriver, to identify personalized cancer driver genes, elucidate their roles in cancer development and uncover the association between driver genes and clinical phenotypic alterations. By analyzing 988 breast cancer patients, we demonstrate the outstanding performance of PhenoDriver in identifying breast cancer driver genes at the cohort level compared to other state-of-the-art methods. Otherwise, our PhenoDriver can also effectively identify driver genes with both recurrent and rare mutations in individual patients. We further explore and reveal the oncogenic mechanisms of some known and unknown breast cancer driver genes (e.g. TP53, MAP3K1, HTT, etc.) identified by PhenoDriver, and construct their subnetworks for regulating clinical abnormal phenotypes. Notably, most of our findings are consistent with existing biological knowledge. Based on the personalized driver profiles, we discover two existing and one unreported breast cancer subtypes and uncover their molecular mechanisms. These results intensify our understanding for breast cancer mechanisms, guide therapeutic decisions and assist in the development of targeted anticancer therapies.

A workflow to study mechanistic indicators for driver gene prediction with Moonlight

Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2, SF3B4, KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4, KIF26B, ARL15, ARHGAP25, EMCN, GMFG, TPK1, NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R.

Estrogens and the risk of breast cancer: A narrative review of literature

In female mammals, the development and regulation of the reproductive system and non-reproductive system are significantly influenced by estrogens (oestrogens). In addition, lipid metabolism is another physiological role of estrogens. Estrogens act through different types of receptors to introduce signals to the target cell by affecting many estrogen response elements. Breast cancer is considered mostly a hormone-dependent disease. Approximately 70% of breast cancers express progesterone receptors and/or estrogen receptors, and they are a good marker for cancer prognosis. This review will discuss estrogen metabolism and the interaction of estrogen metabolites with breast cancer. The carcinogenic role of estrogen is discussed in light of both conventional and atypical cancers susceptible to hormones, such as prostate, endometrial, and lung cancer, as we examine how estrogen contributes to the formation and activation of breast cancer. In addition, this review will discuss other factors that can be associated with estrogen-driven breast cancer.

Associating somatic mutation with clinical outcomes through kernel regression and optimal transport

Somatic mutations in cancer patients are inherently sparse and potentially high dimensional. Cancer patients may share the same set of deregulated biological processes perturbed by different sets of somatically mutated genes. Therefore, when assessing the associations between somatic mutations and clinical outcomes, gene-by-gene analysis is often under-powered because it does not capture the complex disease mechanisms shared across cancer patients. Rather than testing genes one by one, an intuitive approach is to aggregate somatic mutation data of multiple genes to assess their joint association with clinical outcomes. The challenge is how to aggregate such information. Building on the optimal transport method, we propose a principled approach to estimate the similarity of somatic mutation profiles of multiple genes between tumor samples, while accounting for gene-gene similarities defined by gene annotations or empirical mutational patterns. Using such similarities, we can assess the associations between somatic mutations and clinical outcomes by kernel regression. We have applied our method to analyze somatic mutation data of 17 cancer types and identified at least five cancer types, where somatic mutations are associated with overall survival, progression-free interval, or cytolytic activity.

Comprehensive analysis predicting effects of deleterious SNPs of human progesterone receptor gene on its structure and functions: a computational approach

Progesterone receptor plays a crucial role in the development of the mammary gland and breast cancer. Single nucleotide polymorphisms (SNPs) within its gene, PGR, are associated with the risk of miscarriages and preterm birth as well as many cancers across different populations. The main aim of this work is to investigate the most deleterious SNPs in the PGR gene to identify potential biomarkers for various disease susceptibility and treatments. Both sequence and structure-based computational approaches were adopted and in total 11 nsSNPs have been filtered out of 674 nsSNPs along with seven non-coding SNPs. R740Q, I744T and D746E belonged to a mutation cluster. R740Q, D746E along with S865L altered H-bond interactions within the receptor. The same mutations have been found to be associated with several cancers including uterine and breast cancer among others. It is, therefore, possible that the high-risk SNPs associated with cancers may exert their effect by causing changes in the protein structure, particularly in its bonding patterns, and thus affecting its function. In addition, seven non-coding SNPs that were located in the UTR region created a new miRNA site while three SNPs disrupted a conserved miRNA site. These high-risk SNPs can play an instrumental role in generating a dataset of the PGR gene's SNPs. Thus, the present study may pave the way to design and develop novel therapeutics for overcoming the challenges associated with certain cancers and pregnancy that result from a change in the protein structure and function due to the SNP mutations in the PGR gene.Communicated by Ramaswamy H. Sarma.

Insights into the Mechanisms and Structure of Breakage-Fusion-Bridge Cycles in Cervical Cancer using Long-Read Sequencing

Cervical cancer is caused by human papillomavirus (HPV) infection, has few approved targeted therapeutics, and is the most common cause of cancer death in low-resource countries. We characterized 19 cervical and four head and neck cell lines using long-read DNA and RNA sequencing and identified the HPV types, HPV integration sites, chromosomal alterations, and cancer driver mutations. Structural variation analysis revealed telomeric deletions associated with DNA inversions resulting from breakage-fusion-bridge (BFB) cycles. BFB is a common mechanism of chromosomal alterations in cancer, and this is one of the first analyses of these events using long-read sequencing. Analysis of the inversion sites revealed staggered ends consistent with exonuclease digestion of the DNA after breakage. Some BFB events are complex, involving inter- or intra-chromosomal insertions or rearrangements. None of the BFB breakpoints had telomere sequences added to resolve the dicentric chromosomes and only one BFB breakpoint showed chromothripsis. Five cell lines have a Chr11q BFB event, with YAP1/BIRC2/BIRC3 gene amplification. Indeed, YAP1 amplification is associated with a 10-year earlier age of diagnosis of cervical cancer and is three times more common in African American women. This suggests that cervical cancer patients with YAP1/BIRC2/BIRC3-amplification, especially those of African American ancestry, might benefit from targeted therapy. In summary, we uncovered new insights into the mechanisms and consequences of BFB cycles in cervical cancer using long-read sequencing.

Deregulated miRNA Expression in Triple-Negative Breast Cancer of Ancestral Genomic-Characterized Latina Patients

Among patients with triple-negative breast cancer (TNBC), several studies have suggested that deregulated microRNA (miRNA) expression may be associated with a more aggressive phenotype. Although tumor molecular signatures may be race- and/or ethnicity-specific, there is limited information on the molecular profiles in women with TNBC of Hispanic and Latin American ancestry. We simultaneously profiled TNBC biopsies for the genome-wide copy number and miRNA global expression from 28 Latina women and identified a panel of 28 miRNAs associated with copy number alterations (CNAs). Four selected miRNAs (miR-141-3p, miR-150-5p, miR-182-5p, and miR-661) were validated in a subset of tumor and adjacent non-tumor tissue samples, with miR-182-5p being the most discriminatory among tissue groups (AUC value > 0.8). MiR-141-3p up-regulation was associated with increased cancer recurrence; miR-661 down-regulation with larger tumor size; and down-regulation of miR-150-5p with larger tumor size, high p53 expression, increased cancer recurrence, presence of distant metastasis, and deceased status. This study reinforces the importance of integration analysis of CNAs and miRNAs in TNBC, allowing for the identification of interactions among molecular mechanisms. Additionally, this study emphasizes the significance of considering the patients ancestral background when examining TNBC, as it can influence the relationship between intrinsic tumor molecular characteristics and clinical manifestations of the disease.

Mutational landscape of cancer-driver genes across human cancers

The genetic mutations that contribute to the transformation of healthy cells into cancerous cells have been the subject of extensive research. The molecular aberrations that lead to cancer development are often characterised by gain-of-function or loss-of-function mutations in a variety of oncogenes and tumour suppressor genes. In this study, we investigate the genomic sequences of 20,331 primary tumours representing 41 distinct human cancer types to identify and catalogue the driver mutations present in 727 known cancer genes. Our findings reveal significant variations in the frequency of cancer gene mutations across different cancer types and highlight the frequent involvement of tumour suppressor genes (94%), oncogenes (93%), transcription factors (72%), kinases (64%), cell surface receptors (63%), and phosphatases (22%), in cancer. Additionally, our analysis reveals that cancer gene mutations are predominantly co-occurring rather than exclusive in all types of cancer. Notably, we discover that patients with tumours displaying different combinations of gene mutation patterns tend to exhibit variable survival outcomes. These findings provide new insights into the genetic landscape of cancer and bring us closer to a comprehensive understanding of the underlying mechanisms driving the development of various forms of cancer.

Genomic analyses of germline and somatic variation in high-grade serous ovarian cancer

BACKGROUND

High-grade serous ovarian cancers (HGSCs) display a high degree of complex genetic alterations. In this study, we identified germline and somatic genetic alterations in HGSC and their association with relapse-free and overall survival. Using a targeted capture of 557 genes involved in DNA damage response and PI3K/AKT/mTOR pathways, we conducted next-generation sequencing of DNA from matched blood and tumor tissue from 71 HGSC participants. In addition, we performed the OncoScan assay on tumor DNA from 61 participants to examine somatic copy number alterations (SCNA).

RESULTS

Approximately one-third of tumors had loss-of-function (LOF) germline (18/71, 25.4%) or somatic (7/71, 9.9%) variants in the DNA homologous recombination repair pathway genes BRCA1, BRCA2, CHEK2, MRE11A, BLM, and PALB2. LOF germline variants also were identified in other Fanconi anemia genes and in MAPK and PI3K/AKT/mTOR pathway genes. Most tumors harbored somatic TP53 variants (65/71, 91.5%). Using the OncoScan assay on tumor DNA from 61 participants, we identified focal homozygous deletions in BRCA1, BRCA2, MAP2K4, PTEN, RB1, SLX4, STK11, CREBBP, and NF1. In total, 38% (27/71) of HGSC patients harbored pathogenic variants in DNA homologous recombination repair genes. For patients with multiple tissues from the primary debulking or from multiple surgeries, the somatic mutations were maintained with few newly acquired point mutations suggesting that tumor evolution was not through somatic mutations. There was a significant association of LOF variants in homologous recombination repair pathway genes and high-amplitude somatic copy number alterations. Using GISTIC analysis, we identified NOTCH3, ZNF536, and PIK3R2 in these regions that were significantly associated with an increase in cancer recurrence and a reduction in overall survival.

CONCLUSIONS

From 71 patients with HGCS, we performed targeted germline and tumor sequencing and provided a comprehensive analysis of these 557 genes. We identified germline and somatic genetic alterations including somatic copy number alterations and analyzed their associations with relapse-free and overall survival. This single-site long-term follow-up study provides additional information on genetic alterations related to occurrence and outcome of HGSC. Our findings suggest that targeted treatments based on both variant and SCNA profile potentially could improve relapse-free and overall survival.

Multifaceted role of NF-κB in hepatocellular carcinoma therapy: Molecular landscape, therapeutic compounds and nanomaterial approaches

The predominant kind of liver cancer is hepatocellular carcinoma (HCC) that its treatment have been troublesome difficulties for physicians due to aggressive behavior of tumor cells in proliferation and metastasis. Moreover, stemness of HCC cells can result in tumor recurrence and angiogenesis occurs. Another problem is development of resistance to chemotherapy and radiotherapy in HCC cells. Genomic mutations participate in malignant behavior of HCC and nuclear factor-kappaB (NF-κB) has been one of the oncogenic factors in different human cancers that after nuclear translocation, it binds to promoter of genes in regulating their expression. Overexpression of NF-κB has been well-documented in increasing proliferation and invasion of tumor cells and notably, when its expression enhances, it induces chemoresistance and radio-resistance. Highlighting function of NF-κB in HCC can shed some light on the pathways regulating progression of tumor cells. The first aspect is proliferation acceleration and apoptosis inhibition in HCC cells mediated by enhancement in expression level of NF-κB. Moreover, NF-κB is able to enhance invasion of HCC cells via upregulation of MMPs and EMT, and it triggers angiogenesis as another step for increasing spread of tumor cells in tissues and organs. When NF-κB expression enhances, it stimulates chemoresistance and radio-resistance in HCC cells and by increasing stemness and population of cancer-stem cells, it can provide the way for recurrence of tumor. Overexpression of NF-κB mediates therapy resistance in HCC cells and it can be regulated by non-coding RNAs in HCC. Moreover, inhibition of NF-κB by anti-cancer and epigenetic drugs suppresses HCC tumorigenesis. More importantly, nanoparticles are considered for suppressing NF-κB axis in cancer and their prospectives and results can also be utilized for treatment of HCC. Nanomaterials are promising factors in treatment of HCC and by delivery of genes and drugs, they suppress HCC progression. Furthermore, nanomaterials provide phototherapy in HCC ablation.

CXC ELR-Positive Chemokines as Diagnostic and Prognostic Markers for Breast Cancer Patients

As the most common type of malignant lesison, breast cancer is a leading challenge for clinicians. Currently, diagnosis is based on self-examination and imaging studies that require confirmation by tissue biopsy. However, there are no easily accessible diagnostic tools that can serve as diagnostic and prognostic markers for breast cancer patients. One of the possible candidates for such markers is a group of chemokines that are closely implicated in each stage of tumorigenesis. Many researchers have noted the potential of this molecule group to become tumor markers and have tried to establish their clinical utility. In this work, we summarize the results obtained by scientists on the usefulness of the ELR-positive CXC group of chemokines in ancillary diagnosis of breast cancer.

Exposure to p-dichlorobenzene and prevalent endocrine-related reproductive cancers among US women

P-dichlorobenzene (p-DCB) is a pest repellent and air deodorant that is commonly found in the household and public buildings. Exposure to p-DCB has been suggested to have potential metabolic and endocrine effects. Little is known about its association with endocrine-related female cancers. In this cross-sectional study, a nationally representative subsample of 4459 women, aged 20 years or older, in the 2003-2016 National Health and Nutrition Examination Survey was analyzed for the association between p-DCB exposure, measured as urinary concentrations of 2,5-dichlorophenol (2,5-DCP), the primary metabolite of p-DCB, and prevalent endocrine-related female cancers (defined as breast, ovarian, and uterine cancers) using multivariate logistic regression models, adjusting for potential confounders. Of the study participants, 202 women (weighted prevalence, 4.20%) reported being diagnosed with any of these endocrine-related reproductive cancers. Women with reproductive cancers showed a statistically significant increase in urinary 2,5-DCP concentrations (weighted geometric mean, 7.97 vs. 5.84 µg/g creatinine; p < 0.0001), compared to women without these cancers. After adjusting for potential confounders, we found that women in the moderate (1.94- < 28.10 µg/g creatinine) and high level (≥ 28.10 µg/g creatinine) of 2,5-DCP had significantly increased odds of endocrine-related reproductive cancers (odds ratio of 1.66 (95% CI: 1.02, 2.71) and 1.89 (1.08, 3.29), respectively), as compared with those in the low exposure group (< 1.94 µg/g creatinine). This study demonstrates a potential relation between p-DCB exposure and prevalent endocrine-related reproductive cancers in US women. Prospective and mechanistic studies would further explore these interactions and elucidate the pathogenesis of endocrine-related female cancers potentially associated with p-DCB exposure.

Learning vector quantized representation for cancer subtypes identification

BACKGROUND AND OBJECTIVE

Defining and separating cancer subtypes is essential for facilitating personalized therapy modality and prognosis of patients. The definition of subtypes has been constantly recalibrated as a result of our deepened understanding. During this recalibration, researchers often rely on clustering of cancer data to provide an intuitive visual reference that could reveal the intrinsic characteristics of subtypes. The data being clustered are often omics data such as transcriptomics that have strong correlations to the underlying biological mechanism. However, while existing studies have shown promising results, they suffer from issues associated with omics data: sample scarcity and high dimensionality while they impose unrealistic assumptions to extract useful features from the data while avoiding overfitting to spurious correlations.

METHODS

This paper proposes to leverage a recent strong generative model, Vector-Quantized Variational AutoEncoder, to tackle the data issues and extract discrete representations that are crucial to the quality of subsequent clustering by retaining only information relevant to reconstructing the input.

RESULTS

Extensive experiments and medical analysis on multiple datasets comprising 10 distinct cancers demonstrate the proposed clustering results can significantly and robustly improve prognosis over prevalent subtyping systems.

CONCLUSION

Our proposal does not impose strict assumptions on data distribution; while, its latent features are better representations of the transcriptomic data in different cancer subtypes, capable of yielding superior clustering performance with any mainstream clustering method.

In-silico analysis of TCGA data showing multiple POLE-like favourable subgroups overlapping with TP53 mutated endometrial cancer: Implications for clinical practice in low and middle-income countries

Introduction

The Cancer Genome Atlas cohort of endometrial carcinoma (TCGA-UCEC) includes almost 40% TP53-mutants encompassing missense and truncated variants. TCGA revealed 'POLE', characterized by POLE gene bearing exonuclease domain mutation (EDM), as the prognostically best molecular profile. The worst profile was characterized by TP53-mutated Type 2 cancer requiring adjuvant therapy having cost implications in low-resource settings. We aimed to find more 'POLE-like' favourable subgroups by searching TCGA cohort, especially within TP53 mutated risk group, that could eventually avoid adjuvant treatment in resource-poor settings.

Method

Our study was an in-silico survival analysis performed on the TCGA-UCEC dataset using SPSS statistical package. TP53 and POLE mutations, microsatellite instability (MSI), time-to-event and clinicopathological parameters were compared among 512 endometrial cancer cases. Deleterious POLE-mutations were identified by Polyphen2. Progression free survival was studied using Kaplan-Meier plots keeping original 'POLE' as comparator.

Result

In presence of wild type (WT)-TP53, other deleterious POLE-mutations behaved like POLE-EDM. Only truncated and not missense TP53 benefitted from POLE/MSI overlap. However, TP53 missense mutation, Y220C, was found to be as favourable as 'POLE'. Overlapping POLE, MSI and WT-TP53 also performed favourably. Truncated TP53 overlapped with POLE and/or MSI, TP53 Y220C alone and, WT-TP53 overlapped with POLE and MSI both, were named 'POLE-like' for prognostically behaving like the comparator 'POLE'.

Conclusion

Obesity being a lesser frequent event in low and middle-income countries (LMICs), relative proportion of women with lower BMI and Type 2 endometrial cancers may be high. Identification of 'POLE-like' groups may facilitate therapeutic de-escalation in some TP53-mutated cases - a novel option. Instead of 5% (POLE-EDM), potential beneficiary would then comprise 10% (POLE-like) of TCGA-UCEC.

Single-cell profiling of low-stage endometrial cancers identifies low epithelial vimentin expression as a marker of recurrent disease

BACKGROUND

Identification of aggressive low-stage endometrial cancers is challenging. So far, studies have failed to pinpoint robust features or biomarkers associated with risk of recurrence for these patients.

METHODS

Imaging mass cytometry was used to examine single-cell expression of 23 proteins in 36 primary FIGO IB endometrial cancers, of which 17 recurred. Single-cell information was extracted for each tumor and unsupervised clustering was used to identify cellular phenotypes. Distinct phenotypes and cellular neighborhoods were compared in relation to recurrence. Cellular differences were validated in a separate gene expression dataset and the TCGA EC dataset. Vimentin protein expression was evaluated by IHC in pre-operative samples from 518 patients to validate its robustness as a prognostic marker.

FINDINGS

The abundance of epithelial, immune or stromal cell types did not associate with recurrence. Clustering of patients based on tumor single cell marker expression revealed distinct patient clusters associated with outcome. A cell population neighboring CD8+ T cells, defined by vimentin, ER, and PR expressing epithelial cells, was more prevalent in non-recurrent tumors. Importantly, lower epithelial vimentin expression and lower gene expression of VIM associated with worse recurrence-free survival. Loss and low expression of vimentin was validated by IHC as a robust marker for recurrence in FIGO I stage disease and predicted poor prognosis also when including all patients and in endometrioid patients only.

INTERPRETATION

This study reveals distinct characteristics in low-stage tumors and points to vimentin as a clinically relevant marker that may aid in identifying a here to unidentified subgroup of high-risk patients.

FUNDING

A full list of funding that contributed to this study can be found in the Acknowledgements section.

Membrane trafficking in breast cancer progression: protein kinase D comes into play

Protein kinase D (PKD) is a serine/threonine kinase family that controls important cellular functions, most notably playing a key role in the secretory pathway at the trans-Golgi network. Aberrant expression of PKD isoforms has been found mainly in breast cancer, where it promotes various cellular processes such as growth, invasion, survival and stem cell maintenance. In this review, we discuss the isoform-specific functions of PKD in breast cancer progression, with a particular focus on how the PKD controlled cellular processes might be linked to deregulated membrane trafficking and secretion. We further highlight the challenges of a therapeutic approach targeting PKD to prevent breast cancer progression.

Structure Learning for Gene Regulatory Networks

Inference of biological network structures is often performed on high-dimensional data, yet is hindered by the limited sample size of high throughput "omics" data typically available. To overcome this challenge, often referred to as the "small n, large p problem," we exploit known organizing principles of biological networks that are sparse, modular, and likely share a large portion of their underlying architecture. We present SHINE-Structure Learning for Hierarchical Networks-a framework for defining data-driven structural constraints and incorporating a shared learning paradigm for efficiently learning multiple Markov networks from high-dimensional data at large p/n ratios not previously feasible. We evaluated SHINE on Pan-Cancer data comprising 23 tumor types, and found that learned tumor-specific networks exhibit expected graph properties of real biological networks, recapture previously validated interactions, and recapitulate findings in literature. Application of SHINE to the analysis of subtype-specific breast cancer networks identified key genes and biological processes for tumor maintenance and survival as well as potential therapeutic targets for modulating known breast cancer disease genes.

KK-LC-1 as a therapeutic target to eliminate ALDH+ stem cells in triple negative breast cancer

Failure to achieve complete elimination of triple negative breast cancer (TNBC) stem cells after adjuvant therapy is associated with poor outcomes. Aldehyde dehydrogenase 1 (ALDH1) is a marker of breast cancer stem cells (BCSCs), and its enzymatic activity regulates tumor stemness. Identifying upstream targets to control ALDH⁺ cells may facilitate TNBC tumor suppression. Here, we show that KK-LC-1 determines the stemness of TNBC ALDH⁺ cells via binding with FAT1 and subsequently promoting its ubiquitination and degradation. This compromises the Hippo pathway and leads to nuclear translocation of YAP1 and ALDH1A1 transcription. These findings identify the KK-LC-1-FAT1-Hippo-ALDH1A1 pathway in TNBC ALDH⁺ cells as a therapeutic target. To reverse the malignancy due to KK-LC-1 expression, we employ a computational approach and discover Z839878730 (Z8) as an small-molecule inhibitor which may disrupt KK-LC-1 and FAT1 binding. We demonstrate that Z8 suppresses TNBC tumor growth via a mechanism that reactivates the Hippo pathway and decreases TNBC ALDH⁺ cell stemness and viability.

The Clinical Utility of lncRNAs and Their Application as Molecular Biomarkers in Breast Cancer

Given their tumor-specific and stage-specific gene expression, long non-coding RNAs (lncRNAs) have demonstrated to be potential molecular biomarkers for diagnosis, prognosis, and treatment response. Particularly, the lncRNAs DSCAM-AS1 and GATA3-AS1 serve as examples of this because of their high subtype-specific expression profile in luminal B-like breast cancer. This makes them candidates to use as molecular biomarkers in clinical practice. However, lncRNA studies in breast cancer are limited in sample size and are restricted to the determination of their biological function, which represents an obstacle for its inclusion as molecular biomarkers of clinical utility. Nevertheless, due to their expression specificity among diseases, such as cancer, and their stability in body fluids, lncRNAs are promising molecular biomarkers that could improve the reliability, sensitivity, and specificity of molecular techniques used in clinical diagnosis. The development of lncRNA-based diagnostics and lncRNA-based therapeutics will be useful in routine medical practice to improve patient clinical management and quality of life.

Tumor-restrictive type III collagen in the breast cancer microenvironment: prognostic and therapeutic implications

Collagen plays a critical role in regulating breast cancer progression and therapeutic resistance. An improved understanding of both the features and drivers of tumor-permissive and -restrictive collagen matrices are critical to improve prognostication and develop more effective therapeutic strategies. In this study, using a combination of in vitro, in vivo and in silico experiments, we show that type III collagen (Col3) plays a tumor-restrictive role in human breast cancer. We demonstrate that Col3-deficient, human fibroblasts produce tumor-permissive collagen matrices that drive cell proliferation and suppress apoptosis in noninvasive and invasive breast cancer cell lines. In human TNBC biopsy samples, we demonstrate elevated deposition of Col3 relative to type I collagen (Col1) in noninvasive compared to invasive regions. Similarly, in silico analyses of over 1000 breast cancer patient biopsies from The Cancer Genome Atlas BRCA cohort revealed that patients with higher Col3:Col1 bulk tumor expression had improved overall, disease-free and progression-free survival relative to those with higher Col1:Col3 expression. Using an established 3D culture model, we show that Col3 increases spheroid formation and induces formation of lumen-like structures that resemble non-neoplastic mammary acini. Finally, our in vivo study shows co-injection of murine breast cancer cells (4T1) with rhCol3-supplemented hydrogels limits tumor growth and decreases pulmonary metastatic burden compared to controls. Taken together, these data collectively support a tumor-suppressive role for Col3 in human breast cancer and suggest that strategies that increase Col3 may provide a safe and effective modality to limit recurrence in breast cancer patients.

Multi-omic molecular profiling and network biology for precision anaesthesiology: a narrative review

Technological advancement, data democratisation, and decreasing costs have led to a revolution in molecular biology in which the entire set of DNA, RNA, proteins, and various other molecules - the 'multi-omic' profile - can be measured in humans. Sequencing 1 million bases of human DNA now costs US$0.01, and emerging technologies soon promise to reduce the cost of sequencing the whole genome to US$100. These trends have made it feasible to sample the multi-omic profile of millions of people, much of which is publicly available for medical research. Can anaesthesiologists use these data to improve patient care? This narrative review brings together a rapidly growing literature in multi-omic profiling across numerous fields that points to the future of precision anaesthesiology. Here, we discuss how DNA, RNA, proteins, and other molecules interact in molecular networks that can be used for preoperative risk stratification, intraoperative optimisation, and postoperative monitoring. This literature provides evidence for four fundamental insights: (1) Clinically similar patients have different molecular profiles and, as a consequence, different outcomes. (2) Vast, publicly available, and rapidly growing molecular datasets have been generated in chronic disease patients and can be repurposed to estimate perioperative risk. (3) Multi-omic networks are altered in the perioperative period and influence postoperative outcomes. (4) Multi-omic networks can serve as empirical, molecular measurements of a successful postoperative course. With this burgeoning universe of molecular data, the anaesthesiologist-of-the-future will tailor their clinical management to an individual's multi-omic profile to optimise postoperative outcomes and long-term health.

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.

LINC00365 functions as a tumor suppressor by inhibiting HIF-1α-mediated glucose metabolism reprogramming in breast cancer

Long non-coding RNAs (lncRNAs) play an important role in regulating several physiological processes and have been implicated in several pathologies including cancer. LncRNAs have been found to regulate key cellular pathways involved in cancer development, and their aberrant expression plays critical roles in the onset or progression of disease. The role of lncRNAs in breast cancer (BC) has become a hot topic of research in recent years. We previously showed that LINC00365 inhibits BC survival. In the current study, based on the important role of energy metabolism and HIF-1α for tumor cell proliferation, we investigated the role and mechanism of the LINC00365/HIF-1α axis in affecting tumor growth through glycolysis using the breast cancer cell lines MCF-7 and HCC-1937. We found that LINC00365 inhibited BC cell proliferation. Furthermore, LINC00365 overexpression suppressed aerobic glycolysis in BC cells. RNA-sequencing identified hypoxia-inducible factor-1α (HIF-1α), which has been linked with glycolysis and upregulates glycolysis-related genes, as a potential target gene of LINC00365. Accordingly, we found that LINC00365 overexpression resulted in decreased expression of key glycolytic enzymes such as downstream hexokinase 2 (HK2), recombinant pyruvate kinase isozymes M2 (PKM2) and lactate dehydrogenase A (LDHA). Our results suggest that targeting LINC00365 may reverse the glucose metabolism pattern of BC and effectively inhibit BC survival both in vitro and in vivo.

Systematic pan-cancer analysis identifies gasdermin B as an immunological and prognostic biomarker for kidney renal clear cell carcinoma

Gasdermin (GSDM)-mediated cell lytic death plays an essential role in immunity and tumorigenesis. Despite the association of gasdermin B (GSDMB) with the tumorigenesis of various cancers, whether GSDMB functions as a prognostic biomarker in renal cell carcinoma remains poorly understood. Here, we explored the potential immunological functions and the prognostic value of GSDMB across multiple tumors with The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, including analyzing the relationship between GSDMB expression and prognosis, tumor–immune system interactions, immunomodulators, and immune cell infiltration of different tumors. Importantly, elevated expression of GSDMB is an essential factor for the poor prognosis of kidney renal clear cell carcinoma (KIRC) patients, suggesting that it might be helpful to predict a survival benefit from a clinical therapy regimen. Furthermore, GSDMB expression promoted the level of CD4+ T-cell infiltration of the tumors but is significantly negatively associated with immature dendritic cells (iDCs) in KIRC. Additionally, we identified TNFRSF25 and TNFSF14 as immunostimulators highly correlated with GSDMB expression. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses showed that GSDMB and its interacting proteins might affect tumor growth through the serine metabolism pathway. Our current results demonstrate a promising therapeutic strategy targeting GSDMB and provide new insights into GSDMB as an immunological and prognostic biomarker for KIRC.

PIK3CA mutations and their impact on survival outcomes of patients with endometrial cancer: A systematic review and meta-analysis

Several studies have highlighted the frequent alterations of the PI3K pathway in endometrial cancer leading to increased signaling activation with potential for targeted treatment. The objective of this meta-study was to evaluate how PIK3CA exon 9/20 mutations affect survival in endometrial cancer patients, based on available literature. Topic-based search strategies were applied to databases including CENTRAL, MEDLINE, Embase, Web of Science and COSMIC. All studies assessing the impact of mutations in exon 9 and exon 20 of PIK3CA on survival rates of endometrial cancer patients were selected for inclusion. Statistical meta-analysis was performed with the 'meta' package in RStudio. Overall, 7 of 612 screened articles were included in the present study, comprising 1098 women with endometrial cancer. Meta-analysis revealed a tendency of impaired survival for patients with PIK3CA exon 9 and/or exon 20 mutations (RR 1.28; 95% CI 0.84, 1.94; p = 0.25). This tendency was consistent in subgroup analyses stratified by histologic type or -grade, with the most prominent effect in low-grade endometrial cancers (RR 2.04; 95% CI 0.90, 4.62; p = 0.09). In summary, these results suggest that PIK3CA mutations negatively influence survival outcomes of patients with endometrial cancer, including those with low-grade tumors.

LncRNA PVT1 influences breast cancer cells glycolysis through sponging miR-145-5p

BACKGROUND

Long-non-coding RNA PVT1 (lncRNA PVT1) can be used as an oncogenic regulatory non-coding RNA (ncRNA) for many cancers. However, its function and mechanism in breast cancer (BRCA) are still not clearly elucidated.

OBJECTIVE

We attempt to explain the mechanism of PVT1's role in breast cancer from different perspectives.

METHODS

We analyzed the expression of PVT1 and its correlation with the breast cancer related clinical data in the The Cancer Genome Atlas (TCGA) database. We used PVT1 overexpression and knockdown lentivirus to infect breast cancer MDA-MB-231 cell line for cell function verification, in vitro using CCK-8 to measure proliferation, flow cytometry to measure apoptosis, transwell test to measure invasion and migration ability, detecting cell extracellular acidification rate (ECAR) to assess glycolysis metabolism and explore the biological functions of PVT1 in breast cancer cells. Transcriptome sequencing was used to analyze the changes of related genes in cells after overexpression of PVT1. In vivo we used a xenograft model to study the effect of PVT1 on breast cancer.

RESULTS

PVT1 was up-regulated in breast cancer tissues and was positively correlated with the clinical stage of breast cancer patients. Overexpression of PVT1 in vitro promoted cell proliferation, migration and invasion, and promoted tumor growth in vivo. Knockdown of PVT1 led to the opposite biological consequence. Further bioinformatics analysis showed that PVT1 changes the glycolysis metabolism of tumors through regulation of glycolysis-related genes. In addition, the expression of miR-145-5p is negatively correlated with PVT1. We consider the possibility of PVT1 promoting cell proliferation and metastasis by regulating the aerobic glucose metabolism in breast cancer cells through sponging the miR-145-5p.

CONCLUSION

Our results reveal a potential pathway for competing endogenous RNA to regulate breast cancer glucose metabolism. PVT1 regulates glycolysis related genes expression by competitively binding to endogenous miR-145-5p in breast cancer cells to change the metabolic phenotype. This may Provide new ideas for precise molecular therapy targets for breast cancer.

Genomic Analyses of Germline and Somatic Variation in High-Grade Serous Ovarian Cancer

Background High-grade serous ovarian cancers (HGSCs) display a high degree of complex genetic alterations. In this study, we identified germline and somatic genetic alterations in HGSC and their association with relapse-free and overall survival. Using a targeted capture of 577 genes involved in DNA damage response and PI3K/AKT/mTOR pathways, we conducted next-generation sequencing of DNA from matched blood and tumor tissue from 71 HGSC participants. In addition, we performed the OncoScan assay on tumor DNA from 61 participants to examine somatic copy number alterations. Results Approximately one-third of tumors had loss-of-function germline (18/71, 25.4%) or somatic (7/71, 9.9%) variants in the DNA homologous recombination repair pathway genes BRCA1, BRCA2, CHEK2, MRE11A, BLM , and PALB2 . Loss-of-function germline variants also were identified in other Fanconi anemia genes and in MAPK and PI3K/AKT/mTOR pathway genes. Most tumors harbored somatic TP53 variants (65/71, 91.5%). Using the OncoScan assay on tumor DNA from 61 participants, we identified focal homozygous deletions in BRCA1, BRCA2, MAP2K4, PTEN, RB1, SLX4, STK11, CREBBP , and NF1 . In total, 38% (27/71) of HGSC patients harbored pathogenic variants in DNA homologous recombination repair genes. For patients with multiple tissues from the primary debulking or from multiple surgeries, the somatic mutations were maintained with few newly acquired point mutations suggesting that tumor evolution was not through somatic mutations. There was a significant association of loss-of-function variants in homologous recombination repair pathway genes and high-amplitude somatic copy number alterations. Using GISTIC analysis, we identified NOTCH3, ZNF536 , and PIK3R2 in these regions that were significantly associated with an increase in cancer recurrence and a reduction in overall survival. Conclusions From 71 patients with HGCS, we performed targeted germline and tumor sequencing and provided a comprehensive analysis of these 577 genes. We identified germline and somatic genetic alterations including somatic copy number alterations and analyzed their associations with relapse-free and overall survival. This single-site long-term follow-up study provides additional information on genetic alterations related to occurrence and outcome of HGSC. Our findings suggest that targeted treatments based on both variant and SCNA profile potentially could improve relapse-free and overall survival.

Landscape of Genetic Alterations Underlying Hallmark Signature Changes in Cancer Reveals TP53 Aneuploidy-driven Metabolic Reprogramming

The hallmark signatures based on gene expression capture core cancer processes. Through a pan-cancer analysis, we describe the overview of hallmark signatures across tumor types/subtypes and reveal significant relationships between these signatures and genetic alterations. TP53 mutation exerts diverse changes, including increased proliferation and glycolysis, which are closely mimicked by widespread copy-number alterations. Hallmark signature and copy-number clustering identify a cluster of squamous tumors and basal-like breast and bladder cancers with elevated proliferation signatures, frequent TP53 mutation, and high aneuploidy. In these basal-like/squamous TP53-mutated tumors, a specific and consistent spectrum of copy-number alterations is preferentially selected prior to whole-genome duplication. Within Trp53-null breast cancer mouse models, these copy-number alterations spontaneously occur and recapitulate the hallmark signature changes observed in the human condition. Together, our analysis reveals intertumor and intratumor heterogeneity of the hallmark signatures, uncovering an oncogenic program induced by TP53 mutation and select aneuploidy events to drive a worsened prognosis.

Significance

Our data demonstrate that TP53 mutation and a resultant selected pattern of aneuploidies cause an aggressive transcriptional program including upregulation of glycolysis signature with prognostic implications. Importantly, basal-like breast cancer demonstrates genetic and/or phenotypic changes closely related to squamous tumors including 5q deletion that reveal alterations that could offer therapeutic options across tumor types regardless of tissue of origin.

SOX17: A Highly Sensitive and Specific Immunomarker for Ovarian and Endometrial Carcinomas

PAX8 is the most commonly used immunomarker to link a carcinoma to the gynecologic tract; however, it lacks specificity. Through mining The Cancer Genome Atlas mRNA expression profile data, we identified SOX17 as a potential specific marker at the mRNA level for gynecologic tumors. To evaluate the utility of this marker in the identification of the gynecologic origin of a given carcinoma, we performed immunochemical staining in a large cohort of ovarian and endometrial cancer cases (n = 416), together with a large cohort of solid tumors from other organs (n = 1544) in tissue microarrays. Similar to PAX8, SOX17 was highly expressed in different subtypes of ovarian carcinoma (97.5% for SOX17 vs 97% for PAX8 in serous carcinoma, 90% vs 90% in endometrioid carcinoma, and 100% vs 100% in clear cell carcinoma), except for mucinous carcinoma (0% vs 27%), and was also highly expressed in different subtypes of endometrial carcinoma (88% vs 84% in endometrioid carcinoma, 100% vs 100% in serous and clear cell carcinoma). SOX17 was not expressed in thyroid and renal cell carcinomas, whereas PAX8 expression was high (86% and 85%, respectively). In addition, SOX17 was expressed at low levels in cervical adenocarcinoma (20%) and had no expression in cervical squamous carcinoma, mesothelioma, and carcinomas from the breast, lung, pancreas, colon, stomach, liver, bladder, and salivary gland. Our data indicate that SOX17 is not only a sensitive but also a specific marker for the origin of ovarian and endometrial carcinomas.

Lower expression of NINJ1 (Ninjurin 1), a mediator of plasma membrane rupture, is associated with advanced disease and worse prognosis in serous ovarian cancer

Gasdermin proteins (GSDMs) form pores in cell membranes upon various stimuli, leading to the release of certain proinflammatory molecules such as IL-1β and IL-18, and this ultimately results in pyroptotic cell death. NINJ1 (Ninjurin 1) has recently been identified as a cell membrane protein responsible for the final complete plasma membrane rupture following lytic cell death mechanisms including pyroptosis, causing the release of relatively larger molecules such as HMGB1 and LDH. In this study, we reported the presence of higher GSDMD and lower GSDME protein levels in ovarian tumors compared to surrounding non-malignant stroma in the tumor microenvironment. GSDME protein levels are also lower in the tumors of the omentum compared to adjacent stromal cells. We found that NINJ1 expression decreases from early to late stage in serous ovarian cancer, and the percentage of NINJ1 copy number loss events is the highest in ovarian cancer among other cancers. Moreover, we showed that low expression of NINJ1 is associated with shorter overall survival of patients with ovarian cancer. In support of the findings showing that low NINJ1 expression contributes to worse prognosis in this most lethal gynecological malignancy, NINJ1 expression was found to be lower in cisplatin-resistant ovarian cancer cells compared to cisplatin-sensitive counterparts in vitro. We suggest that the members of gasdermin family might have distinct functions in serous ovarian cancer, and low levels of NINJ1 might contribute, at least in part, to the progression and poorer prognosis of ovarian cancer. A complete picture of how pyroptosis and subsequent plasma membrane rupture are involved in ovarian cancer will be of high importance in order to identify actionable therapeutic vulnerabilities within this newly identified group of proteins.

Integrative multi-omics networks identify PKCδ and DNA-PK as master kinases of glioblastoma subtypes and guide targeted cancer therapy

Despite producing a panoply of potential cancer-specific targets, the proteogenomic characterization of human tumors has yet to demonstrate value for precision cancer medicine. Integrative multi-omics using a machine-learning network identified master kinases responsible for effecting phenotypic hallmarks of functional glioblastoma subtypes. In subtype-matched patient-derived models, we validated PKCδ and DNA-PK as master kinases of glycolytic/plurimetabolic and proliferative/progenitor subtypes, respectively, and qualified the kinases as potent and actionable glioblastoma subtype-specific therapeutic targets. Glioblastoma subtypes were associated with clinical and radiomics features, orthogonally validated by proteomics, phospho-proteomics, metabolomics, lipidomics and acetylomics analyses, and recapitulated in pediatric glioma, breast and lung squamous cell carcinoma, including subtype specificity of PKCδ and DNA-PK activity. We developed a probabilistic classification tool that performs optimally with RNA from frozen and paraffin-embedded tissues, which can be used to evaluate the association of therapeutic response with glioblastoma subtypes and to inform patient selection in prospective clinical trials.

Chromatin profile-based identification of a novel ER-positive breast cancer subgroup with reduced ER-responsive element accessibility

BACKGROUND

Oestrogen receptor (ER) signalling-dependent cancer cell growth is one of the major features of ER-positive breast cancer (BC). Inhibition of ER function is a standard and effective treatment for ER-positive tumours; however, ~20% of patients with ER-positive BC experience early or late recurrence. In this study, we examined intertumour heterogeneity from an epigenetic perspective based on the hypothesis that the intrinsic difference in epigenetic states around ER signalling pathway underlies endocrine therapy resistance.

METHODS

We performed transposase-accessible chromatin sequencing (ATAC-seq) analysis of 42 BC samples, including 35 ER-positive(+) human epidermal growth factor receptor 2 (HER2)-negative(-) and 7 triple-negative tumours. We also reanalysed ATAC-seq data of 45 ER + /HER2 - tumours in the Cancer Genome Atlas (TCGA) BC cohort to validate our observations.

RESULTS

We conducted a comprehensive analysis of cis-regulatory elements (CREs) using ATAC-seq, identifying three subgroups based on chromatin accessibility profiles. We identified a subgroup of ER-positive BCs with a distinctive chromatin accessibility pattern including reduced accessibility to ER-responsive elements (EREs). The same subgroup was also observed in TCGA BC cohort. Despite the reduced accessibility to EREs, the expression of ER and potential ER target genes were not decreased in these tumours.

CONCLUSION

Our findings highlight the existence of a subset of ER-positive BCs with unchanged ER expression but reduced EREs accessibility that cannot be distinguished by conventional immunostaining for ER. Future studies should determine whether these tumours are associated with resistance to endocrine therapy.

Single-cell dissection of cellular and molecular features underlying human cervical squamous cell carcinoma initiation and progression

Cervical squamous cell carcinoma (CESC) is a prototypical human cancer with well-characterized pathological stages of initiation and progression. However, high-resolution knowledge of the transcriptional programs underlying each stage of CESC is lacking, and important questions remain. We performed single-cell RNA sequencing of 76,911 individual cells from 13 samples of human cervical tissues at various stages of malignancy, illuminating the transcriptional tumorigenic trajectory of cervical epithelial cells and revealing key factors involved in CESC initiation and progression. In addition, we found significant correlations between the abundance of specific myeloid, lymphoid, and endothelial cell populations and the progression of CESC, which were also associated with patients' prognosis. Last, we demonstrated the tumor-promoting function of matrix cancer-associated fibroblasts via the NRG1-ERBB3 pathway in CESC. This study provides a valuable resource and deeper insights into CESC initiation and progression, which is helpful in refining CESC diagnosis and for the design of optimal treatment strategies.

Germline rare deleterious variant load alters cancer risk, age of onset and tumor characteristics

Recent studies show that rare, deleterious variants (RDVs) in certain genes are critical determinants of heritable cancer risk. To more comprehensively understand RDVs, we performed the largest-to-date germline variant calling analysis in a case-control setting for a multi-cancer association study from whole-exome sequencing data of 20,789 participants, split into discovery and validation cohorts. We confirm and extend known associations between cancer risk and germline RDVs in specific gene-sets, including DNA repair (OR = 1.50; p-value = 8.30e-07; 95% CI: 1.28-1.77), cancer predisposition (OR = 1.51; p-value = 4.58e-08; 95% CI: 1.30-1.75), and somatic cancer drivers (OR = 1.46; p-value = 4.04e-06; 95% CI: 1.24-1.72). Furthermore, personal RDV load in these gene-sets associated with increased risk, younger age of onset, increased M1 macrophages in tumor and, increased tumor mutational burden in specific cancers. Our findings can be used towards identifying high-risk individuals, who can then benefit from increased surveillance, earlier screening, and treatments that exploit their tumor characteristics, improving prognosis.

High-Sensitivity Mutation Analysis of Cell-Free DNA for Disease Monitoring in Endometrial Cancer

PURPOSE

We sought to determine whether sequencing analysis of circulating cell-free DNA (cfDNA) in patients with prospectively accrued endometrial cancer captures the mutational repertoire of the primary lesion and allows for disease monitoring.

EXPERIMENTAL DESIGN

Peripheral blood was prospectively collected from 44 newly diagnosed patients with endometrial cancer over a 24-month period (i.e., baseline, postsurgery, every 6 months after). DNA from the primary endometrial cancers was subjected to targeted next-generation sequencing (NGS) of 468 cancer-related genes, and cfDNA to a high-depth NGS assay of 129 genes with molecular barcoding. Sequencing data were analyzed using validated bioinformatics methods.

RESULTS

cfDNA levels correlated with surgical stage in endometrial cancers, with higher levels of cfDNA being present in advanced-stage disease. Mutations in cfDNA at baseline were detected preoperatively in 8 of 36 (22%) patients with sequencing data, all of whom were diagnosed with advanced-stage disease, high tumor volume, and/or aggressive histologic type. Of the 38 somatic mutations identified in the primary tumors also present in the cfDNA assay, 35 (92%) and 38 (100%) were detected at baseline and follow-up, respectively. In 6 patients with recurrent disease, changes in circulating tumor DNA (ctDNA) fraction/variant allele fractions in cfDNA during follow-up closely mirrored disease progression and therapy response, with a lead time over clinically detected recurrence in two cases. The presence of ctDNA at baseline (P < 0.001) or postsurgery (P = 0.014) was significantly associated with reduced progression-free survival.

CONCLUSIONS

cfDNA sequencing analysis in patients with endometrial cancer at diagnosis has prognostic value, and serial postsurgery cfDNA analysis enables disease and treatment response monitoring. See related commentary by Grant et al., p. 305.

Multi-omics profiling of PC-3 cells reveals bufadienolides-induced lipid metabolic remodeling by regulating long-chain lipids synthesis and hydrolysis

INTRODUCTION

Lipid metabolism participates in various biological processes such as proliferation, apoptosis, migration, invasion, and maintenance of membrane homeostasis of prostate tumor cells. Bufadienolides, the active ingredients of Chansu, show a robust anti-proliferative effect against prostate cancer cells in vitro, but whether bufadienolides could regulate the lipid metabolism in prostate cancer has not been evaluated.

OBJECTIVES

Our study explored the regulatory effects of bufadienolides on lipid metabolism in human prostate carcinoma cells (PC-3).

METHODS

Untargeted lipidomics and transcriptomics were combined to study the effect of different bufadienolides interventions on lipid and gene changes of PC-3 cells. The key genes related to lipid metabolism and prostate cancer development were verified by qPCR and western blotting.

RESULTS

Lipidomic analysis showed that the active bufadienolides significantly downregulated the content of long-chain lipids of PC-3 cells. Based on transcriptomic and qPCR analyses, many genes related to lipid metabolism were significantly regulated by active bufadienolides, such as ELOVL6, CYP2E1, GAL3ST1, CERS1, PLA2G10, PLD1, SPTLC3, and GPX2. Bioinformatics analysis of the Cancer Genome Atlas database and literature retrieval showed that elongation of very long-chain fatty acids protein 6 (ELOVL6) and phospholipase D1 (PLD1) might be important regulatory genes. Western blot analysis revealed that active bufadienolides could downregulate PLD1 protein levels which might promote anti-prostate cancer effect.

CONCLUSIONS

All these findings support that bufadienolides might induce lipid metabolic remodeling by regulating long-chain lipids synthesis and phospholipid hydrolysis to achieve an anti-prostate cancer effect, and PLD1 would probably be the key protein.

SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition

Integrating multi-omics data for cancer subtype recognition is an important task in bioinformatics. Recently, deep learning has been applied to recognize the subtype of cancers. However, existing studies almost integrate the multi-omics data simply by concatenation as the single data and then learn a latent low-dimensional representation through a deep learning model, which did not consider the distribution differently of omics data. Moreover, these methods ignore the relationship of samples. To tackle these problems, we proposed SADLN: A self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition. SADLN combined encoder, self-attention, decoder, and discriminator into a unified framework, which can not only integrate multi-omics data but also adaptively model the sample's relationship for learning an accurately latent low-dimensional representation. With the integrated representation learned from the network, SADLN used Gaussian Mixture Model to identify cancer subtypes. Experiments on ten cancer datasets of TCGA demonstrated the advantages of SADLN compared to ten methods. The Self-Attention Based Deep Learning Network (SADLN) is an effective method of integrating multi-omics data for cancer subtype recognition.

Epigenetic regulator KDM4A activates Notch1-NICD-dependent signaling to drive tumorigenesis and metastasis in breast cancer

BACKGROUND

Altered epigenetic reprogramming and events contribute to breast cancer (Bca) progression and metastasis. How the epigenetic histone demethylases modulate breast cancer progression remains poorly defined. We aimed to elucidate the biological roles of KDM4A in driving Notch1 activation and Bca progression.

METHODS

The KDM4A expression in Bca specimens was analyzed using quantitative PCR and immunohistochemical assays. The biological roles of KDM4A were evaluated using wound-healing assays and an in vivo metastasis model. The Chromatin Immunoprecipitation (ChIP)-qPCR assay was used to determine the role of KDM4A in Notch1 regulation.

RESULTS

Here, we screened that targeting KDM4A could induce notable cell growth suppression. KDM4A is required for the growth and progression of Bca cells. High KDM4A enhances tumor migration abilities and in vivo lung metastasis. Bioinformatic analysis suggested that KDM4A was highly expressed in tumors and high KDM4A correlates with poor survival outcomes. KDM4A activates Notch1 expressions via directly binding to the promoters and demethylating H3K9me3 modifications. KDM4A inhibition reduces expressions of a list of Notch1 downstream targets, and ectopic expressions of ICN1 could restore the corresponding levels. KDM4A relies on Notch1 signaling to maintain cell growth, migration and self-renewal capacities. Lastly, we divided a panel of cell lines into KDM4A^high and KDM4A^low groups. Targeting Notch1 using specific LY3039478 could efficiently suppress cell growth and colony formation abilities of KDM4A^high Bca.

CONCLUSION

Taken together, KDM4A could drive Bca progression via triggering the activation of Notch1 pathway by decreasing H3K9me3 levels, highlighting a promising therapeutic target for Bca.

Transcriptomic pan-cancer analysis using rank-based Bayesian inference

The analysis of whole genomes of pan-cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this unsupervised problem via a novel rank-based Bayesian clustering method. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model, the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions, and the transparent biological interpretation of the identified clusters since each cluster is characterized by its top-ranked genomic features. We applied our method to RNA-seq data from cancer samples from 12 tumor types from the Cancer Genome Atlas. We identified a robust clustering that mostly reflects tissue of origin but also includes pan-cancer clusters. Importantly, we identified three pan-squamous clusters composed of a mix of lung squamous cell carcinoma, head and neck squamous carcinoma, and bladder cancer, with different biological functions over-represented in the top genes that characterize the three clusters. We also found two novel subtypes of kidney cancer that show different prognosis, and we reproduced known subtypes of breast cancer. Taken together, our method allows the identification of robust and biologically meaningful clusters of pan-cancer samples.

NcPath: a novel platform for visualization and enrichment analysis of human non-coding RNA and KEGG signaling pathways

SUMMARY

Non-coding RNAs play important roles in transcriptional processes and participate in the regulation of various biological functions, in particular miRNAs and lncRNAs. Despite their importance for several biological functions, the existing signaling pathway databases do not include information on miRNA and lncRNA. Here, we redesigned a novel pathway database named NcPath by integrating and visualizing a total of 178 308 human experimentally validated miRNA-target interactions (MTIs), 32 282 experimentally verified lncRNA-target interactions (LTIs) and 4837 experimentally validated human ceRNA networks across 222 KEGG pathways (including 27 sub-categories). To expand the application potential of the redesigned NcPath database, we identified 556 798 reliable lncRNA-protein-coding genes (PCG) interaction pairs by integrating co-expression relations, ceRNA relations, co-TF-binding interactions, co-histone-modification interactions, cis-regulation relations and lncPro Tool predictions between lncRNAs and PCG. In addition, to determine the pathways in which miRNA/lncRNA targets are involved, we performed a KEGG enrichment analysis using a hypergeometric test. The NcPath database also provides information on MTIs/LTIs/ceRNA networks, PubMed IDs, gene annotations and the experimental verification method used. In summary, the NcPath database will serve as an important and continually updated platform that provides annotation and visualization of the pathways on which non-coding RNAs (miRNA and lncRNA) are involved, and provide support to multimodal non-coding RNAs enrichment analysis. The NcPath database is freely accessible at http://ncpath.pianlab.cn/.

AVAILABILITY AND IMPLEMENTATION

NcPath database is freely available at http://ncpath.pianlab.cn/. The code and manual to use NcPath can be found at https://github.com/Marscolono/NcPath/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Obesity as a Risk Factor for Breast Cancer-The Role of miRNA

Breast cancer (BC) is the most common cancer diagnosed among women in the world, with an ever-increasing incidence rate. Due to the dynamic increase in the occurrence of risk factors, including obesity and related metabolic disorders, the search for new regulatory mechanisms is necessary. This will help a complete understanding of the pathogenesis of breast cancer. The review presents the mechanisms of obesity as a factor that increases the risk of developing breast cancer and that even initiates the cancer process in the female population. The mechanisms presented in the paper relate to the inflammatory process resulting from current or progressive obesity leading to cell metabolism disorders and disturbed hormonal metabolism. All these processes are widely regulated by the action of microRNAs (miRNAs), which may constitute potential biomarkers influencing the pathogenesis of breast cancer and may be a promising target of anti-cancer therapies.

Transcriptomics indicate nuclear division and cell adhesion not recapitulated in MCF7 and MCF10A compared to luminal A breast tumours

Breast cancer (BC) cell lines are useful experimental models to understand cancer biology. Yet, their relevance to modelling cancer remains unclear. To better understand the tumour-modelling efficacy of cell lines, we performed RNA-seq analyses on a combined dataset of 2D and 3D cultures of tumourigenic MCF7 and non-tumourigenic MCF10A. To our knowledge, this was the first RNA-seq dataset comprising of 2D and 3D cultures of MCF7 and MCF10A within the same experiment, which facilitates the elucidation of differences between MCF7 and MCF10A across culture types. We compared the genes and gene sets distinguishing MCF7 from MCF10A against separate RNA-seq analyses of clinical luminal A (LumA) and normal samples from the TCGA-BRCA dataset. Among the 1031 cancer-related genes distinguishing LumA from normal samples, only 5.1% and 15.7% of these genes also distinguished MCF7 from MCF10A in 2D and 3D cultures respectively, suggesting that different genes drive cancer-related differences in cell lines compared to clinical BC. Unlike LumA tumours which showed increased nuclear division-related gene expression compared to normal tissue, nuclear division-related gene expression in MCF7 was similar to MCF10A. Moreover, although LumA tumours had similar cell adhesion-related gene expression compared to normal tissues, MCF7 showed reduced cell adhesion-related gene expression compared to MCF10A. These findings suggest that MCF7 and MCF10A cell lines were limited in their ability to model cancer-related processes in clinical LumA tumours.

Extracellular matrix profiles determine risk and prognosis of the squamous cell carcinoma subtype of non-small cell lung carcinoma

BACKGROUND

Squamous cell carcinoma (SqCC) is a subtype of non-small cell lung cancer for which patient prognosis remains poor. The extracellular matrix (ECM) is critical in regulating cell behavior; however, its importance in tumor aggressiveness remains to be comprehensively characterized.

METHODS

Multi-omics data of SqCC human tumor specimens was combined to characterize ECM features associated with initiation and recurrence. Penalized logistic regression was used to define a matrix risk signature for SqCC tumors and its performance across a panel of tumor types and in SqCC premalignant lesions was evaluated. Consensus clustering was used to define prognostic matreotypes for SqCC tumors. Matreotype-specific tumor biology was defined by integration of bulk RNAseq with scRNAseq data, cell type deconvolution, analysis of ligand-receptor interactions and enriched biological pathways, and through cross comparison of matreotype expression profiles with aging and idiopathic pulmonary fibrosis lung profiles.

RESULTS

This analysis revealed subtype-specific ECM signatures associated with tumor initiation that were predictive of premalignant progression. We identified an ECM-enriched tumor subtype associated with the poorest prognosis. In silico analysis indicates that matrix remodeling programs differentially activate intracellular signaling in tumor and stromal cells to reinforce matrix remodeling associated with resistance and progression. The matrix subtype with the poorest prognosis resembles ECM remodeling in idiopathic pulmonary fibrosis and may represent a field of cancerization associated with elevated cancer risk.

CONCLUSIONS

Collectively, this analysis defines matrix-driven features of poor prognosis to inform precision medicine prevention and treatment strategies towards improving SqCC patient outcome.

MCluster-VAEs: An end-to-end variational deep learning-based clustering method for subtype discovery using multi-omics data

The discovery of cancer subtypes based on unsupervised clustering helps in providing a precise diagnosis, guide treatment, and improve patients' prognoses. Instead of single-omics data, multi-omics data can improve the clustering performance because it obtains a comprehensive landscape for understanding biological systems and mechanisms. However, heterogeneous data from multiple sources raises high complexity and different kinds of noise, which are detrimental to the extraction of clustering information. We propose an end-to-end deep learning based method, called Multi-omics Clustering Variational Autoencoders (MCluster-VAEs), that can extract cluster-friendly representations on multi-omics data. First, a unified network architecture with an attention mechanism was developed for accurately modeling multi-omics data. Then, using a novel objective function built from the Variational Bayes technique, the model was trained to effectively obtain the posterior estimation of the clustering assignments. Compared with 12 other state-of-the-art multi-omics clustering methods, MCluster-VAEs achieved an outstanding performance on benchmark datasets from the TCGA database. On the Pan Cancer dataset, MCluster-VAEs achieved an adjusted Rand index of approximately 0.78 for cancer category recognition, an increase of more than 18% compared with other methods. Furthermore, a survival analysis and clinical parameter enrichment tests conducted on 10 cancer datasets demonstrated that MCluster-VAEs provides comparable and even better results than many common integrative approaches. These results demonstrate that MCluster-VAEs are a powerful new tool for dissecting complex multi-omics relationships and providing new insights for cancer subtype discovery.

Age-associated differences in the cancer molecular landscape

Cancer is an age-related disease, as incidence and mortality for most types of cancer increase with age. However, how molecular alterations in tumors differ among patients of different ages remains poorly understood. Recent studies have shed light on the age-associated molecular landscapes in cancer. Here, we summarize the main findings of these current studies, highlighting major differences in the genomic, transcriptomic, epigenetic, and immunological landscapes between cancer in younger and older patients. Importantly, some cancer driver genes are mutated more frequently in younger or older patients. We discuss the potential roles of aging-related processes in shaping these age-related differences in cancer. We further emphasize the remaining unsolved questions that could provide important insights that will have implications in personalized medicine.

Analyses of Transcriptomics Cell Signalling for Pre-Screening Applications in the Integrated Approach for Testing and Assessment of Non-Genotoxic Carcinogens

With recent rapid advancement of methodological tools, mechanistic understanding of biological processes leading to carcinogenesis is expanding. New approach methodologies such as transcriptomics can inform on non-genotoxic mechanisms of chemical carcinogens and can be developed for regulatory applications. The Organisation for the Economic Cooperation and Development (OECD) expert group developing an Integrated Approach to the Testing and Assessment (IATA) of Non-Genotoxic Carcinogens (NGTxC) is reviewing the possible assays to be integrated therein. In this context, we review the application of transcriptomics approaches suitable for pre-screening gene expression changes associated with phenotypic alterations that underlie the carcinogenic processes for subsequent prioritisation of downstream test methods appropriate to specific key events of non-genotoxic carcinogenesis. Using case studies, we evaluate the potential of gene expression analyses especially in relation to breast cancer, to identify the most relevant approaches that could be utilised as (pre-) screening tools, for example Gene Set Enrichment Analysis (GSEA). We also consider how to address the challenges to integrate gene panels and transcriptomic assays into the IATA, highlighting the pivotal omics markers identified for assay measurement in the IATA key events of inflammation, immune response, mitogenic signalling and cell injury.

Identifying enhancer-driven subtype-specific prognostic markers in breast cancer based on multi-omics data

Breast cancer is a cancer of high complexity and heterogeneity, with differences in prognosis and survival among patients of different subtypes. Copy number variations (CNVs) within enhancers are crucial drivers of tumorigenesis by influencing expression of their targets. In this study, we performed an integrative approach to identify CNA-driven enhancers and their effect on expression of target genes in four breast cancer subtypes by integrating expression data, copy number data and H3K27ac data. We identified 672, 555, 531, 361 CNA-driven enhancer-gene pairs and 280, 189, 113 and 98 CNA-driven enhancer-lncRNA pairs in the Basal-like, Her2, LumA and LumB subtypes, respectively. We then reconstructed a CNV-driven enhancer-lncRNA-mRNA regulatory network in each subtype. Functional analysis showed CNA-driven enhancers play an important role in the progression of breast cancer subtypes by influencing P53 signaling pathway, PPAR signaling pathway, systemic lupus erythematosus and MAPK signaling pathway in the Basal-like, Her2, LumA and LumB subtypes, respectively. We characterized the potentially prognostic value of target genes of CNV-driven enhancer and lncRNA-mRNA pairs in the subtype-specific network. We identified MUM1 and AC016876.1 as prognostic biomarkers in LumA and Basal-like subtypes, respectively. Higher expression of MUM1 with an amplified enhancer exhibited poorer prognosis in LumA patients. Lower expression of AC016876.1 with a deleted enhancer exhibited poorer survival outcomes of Basal-like patients. We also identified enhancer-related lncRNA-mRNA pairs as prognostic biomarkers, including AC012313.2-MUM1 in the LumA, AC026471.4-PLK5 in the LumB, AC027307.2-OAZ1 in the Basal-like and AC022431.1-HCN2 in the Her2 subtypes. Finally, our results highlighted target genes of CNA-driven enhancers and enhancer-related lncRNA-mRNA pairs could act as prognostic markers and potential therapeutic targets in breast cancer subtypes.

ARID1A-dependent maintenance of H3.3 is required for repressive CHD4-ZMYND8 chromatin interactions at super-enhancers

BACKGROUND

SWI/SNF (BAF) chromatin remodeling complexes regulate lineage-specific enhancer activity by promoting accessibility for diverse DNA-binding factors and chromatin regulators. Additionally, they are known to modulate the function of the epigenome through regulation of histone post-translational modifications and nucleosome composition, although the way SWI/SNF complexes govern the epigenome remains poorly understood. Here, we investigate the function of ARID1A, a subunit of certain mammalian SWI/SNF chromatin remodeling complexes associated with malignancies and benign diseases originating from the uterine endometrium.

RESULTS

Through genome-wide analysis of human endometriotic epithelial cells, we show that more than half of ARID1A binding sites are marked by the variant histone H3.3, including active regulatory elements such as super-enhancers. ARID1A knockdown leads to H3.3 depletion and gain of canonical H3.1/3.2 at ARID1A-bound active regulatory elements, and a concomitant redistribution of H3.3 toward genic elements. ARID1A interactions with the repressive chromatin remodeler CHD4 (NuRD) are associated with H3.3, and ARID1A is required for CHD4 recruitment to H3.3. ZMYND8 interacts with CHD4 to suppress a subset of ARID1A, CHD4, and ZMYND8 co-bound, H3.3+ H4K16ac+ super-enhancers near genes governing extracellular matrix, motility, adhesion, and epithelial-to-mesenchymal transition. Moreover, these gene expression alterations are observed in human endometriomas.

CONCLUSIONS

These studies demonstrate that ARID1A-containing BAF complexes are required for maintenance of the histone variant H3.3 at active regulatory elements, such as super-enhancers, and this function is required for the physiologically relevant activities of alternative chromatin remodelers.

BET inhibition induces vulnerability to MCL1 targeting through upregulation of fatty acid synthesis pathway in breast cancer

Therapeutic options for treatment of basal-like breast cancers remain limited. Here, we demonstrate that bromodomain and extra-terminal (BET) inhibition induces an adaptive response leading to MCL1 protein-driven evasion of apoptosis in breast cancer cells. Consequently, co-targeting MCL1 and BET is highly synergistic in breast cancer models. The mechanism of adaptive response to BET inhibition involves the upregulation of lipid synthesis enzymes including the rate-limiting stearoyl-coenzyme A (CoA) desaturase. Changes in lipid synthesis pathway are associated with increases in cell motility and membrane fluidity as well as re-localization and activation of HER2/EGFR. In turn, the HER2/EGFR signaling results in the accumulation of and vulnerability to the inhibition of MCL1. Drug response and genomics analyses reveal that MCL1 copy-number alterations are associated with effective BET and MCL1 co-targeting. The high frequency of MCL1 chromosomal amplifications (>30%) in basal-like breast cancers suggests that BET and MCL1 co-targeting may have therapeutic utility in this aggressive subtype of breast cancer.

A-to-I nonsynonymous RNA editing was significantly enriched in the ubiquitination site and correlated with clinical features and immune response

RNA editing is a post-transcriptional process that alters RNA sequence in a site-specific manner. A-to-I editing is the most abundant as well as the most well-studied type of RNA editing. About 0.5% of A-to-I editing sites were located in the coding regions. Despite of thousands of identified A-to-I nonsynonymous editing sites, the function of nonsynonymous editing was poorly studied. Here, we found that the nonsynonymous editing was significantly enriched in the ubiquitination site, compared to the synonymous editing. This enrichment was also in a modification type dependent manner, since it was not significantly enriched in other modification types. This observation was consistent with previous study that the codons for lysine (AAG and AAA) were enriched in the preferred deamination site for RNA editing. The peptides from proteomic data in CPTAC supported that mRNAs harboring edited ubiquitination sites can be translated into protein in cells. We identified the editing sites on ubiquitination site were significantly differential edited between tumor and para-tumor samples as well as among different subtypes in TCGA datasets and also correlated with clinical outcome, especially for the nonsynonymous editing sites on GSTM5, WDR1, SSR4 and PSMC4. Finally, the enrichment analysis revealed that the function of these above genes was specifically enriched in the immune response pathway. Our study shed a light on understanding the functions of nonsynonymous editing in tumorigenesis and provided nonsynonymous editing targets for potential cancer diagnosis and therapy.