The gene expression signature of genomic instability in breast cancer is an independent predictor of clinical outcome

A genomic instability-associated lncRNA signature for predicting prognosis and biomarkers in lung adenocarcinoma

Genomic instability (GI) was associated with tumorigenesis. However, GI-related lncRNA signature (GILncSig) in lung adenocarcinoma (LUAD) is still unknown. In this study, the lncRNA expression data, somatic mutation information and clinical survival information of LUAD were downloaded from The Cancer Genome Atlas (TCGA) and performed differential analysis. Functional and prognosis analysis revealed that multiple GI-related pathways were enriched. By using univariate and multivariate Cox regression analysis, 5 GI-associated lncRNAs (AC012085.2, FAM83A-AS1, MIR223HG, MIR193BHG, LINC01116) were identified and used to construct a GILncSig model. Mutation burden analysis indicated that the high-risk GI group had much higher somatic mutation count and the risk score constructed by the 5 GI-associated lncRNAs was an independent predictor for overall survival (OS) (P < 0.05). Overall, our study provides valuable insights into the involvement of GI-associated lncRNAs in LUAD and highlights their potential as therapeutic targets.

TPX2 overexpression promotes sensitivity to dasatinib in breast cancer by activating YAP transcriptional signaling

Chromosomal instability (CIN) is a hallmark of cancer aggressiveness, providing genetic plasticity and tumor heterogeneity that allows the tumor to evolve and adapt to stress conditions. CIN is considered a cancer therapeutic biomarker because healthy cells do not exhibit CIN. Despite recent efforts to identify therapeutic strategies related to CIN, the results obtained have been very limited. CIN is characterized by a genetic signature where a collection of genes, mostly mitotic regulators, are overexpressed in CIN-positive tumors, providing aggressiveness and poor prognosis. We attempted to identify new therapeutic strategies related to CIN genes by performing a drug screen, using cells that individually express CIN-associated genes in an inducible manner. We find that the overexpression of targeting protein for Xklp2 (TPX2) enhances sensitivity to the proto-oncogene c-Src (SRC) inhibitor dasatinib due to activation of the Yes-associated protein 1 (YAP) pathway. Furthermore, using breast cancer data from The Cancer Genome Atlas (TCGA) and a cohort of cancer-derived patient samples, we find that both TPX2 overexpression and YAP activation are present in a significant percentage of cancer tumor samples and are associated with poor prognosis; therefore, they are putative biomarkers for selection for dasatinib therapy.

Exploring Novel Genome Instability-associated lncRNAs and their Potential Function in Pan-Renal Cell Carcinoma

OBJECTIVE

Genomic instability can drive clonal evolution, continuous modification of tumor genomes, and tumor genomic heterogeneity. The molecular mechanism of genomic instability still needs further investigation. This study aims to identify novel genome instabilityassociated lncRNAs (GI-lncRNAs) and investigate the role of genome instability in pan-Renal cell carcinoma (RCC).

MATERIALS AND METHODS

A mutator hypothesis was employed, combining the TCGA database of somatic mutation (SM) information, to identify GI-lncRNAs. Subsequently, a training cohort (n = 442) and a testing cohort (n = 439) were formed by randomly dividing all RCC patients. Based on the training cohort dataset, a multivariate Cox regression analysis lncRNAs risk model was created. Further validations were performed in the testing cohort, TCGA cohort, and different RCC subtypes. To confirm the relative expression levels of lncRNAs in HK-2, 786-O, and 769-P cells, qPCR was carried out. Functional pathway enrichment analyses were performed for further investigation.

RESULTS

A total of 170 novel GI-lncRNAs were identified. The lncRNA prognostic risk model was constructed based on LINC00460, AC073218.1, AC010789.1, and COLCA1. This risk model successfully differentiated patients into distinct risk groups with significantly different clinical outcomes. The model was further validated in multiple independent patient cohorts. Additionally, functional and pathway enrichment analyses revealed that GI-lncRNAs play a crucial role in GI. Furthermore, the assessments of immune response, drug sensitivity, and cancer stemness revealed a significant relationship between GI-lncRNAs and tumor microenvironment infiltration, mutational burden, microsatellite instability, and drug resistance.

CONCLUSIONS

In this study, we discovered four novel GI-lncRNAs and developed a novel signature that effectively predicted clinical outcomes in pan-RCC. The findings provide valuable insights for pan-RCC immunotherapy and shed light on potential underlying mechanisms.

Characterization of genomic instability-related genes predicts survival and therapeutic response in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC) and is the leading cause of cancer death worldwide. Its progression is characterized by genomic instability. In turn, the level of genomic instability affects the prognosis and immune status of patients with LUAD. However, the impact of molecular features associated with genomic instability on the tumor microenvironment (TME) has not been well characterized. In addition, the effect of the genes related to genomic instability in LUAD on individualized treatment of LUAD is unknown.

METHODS

The RNA-Sequencing, somatic mutation, and clinical data of LUAD patients were downloaded from publicly available databases. A genetic signature associated with genomic instability (GSAGI) was constructed by univariate Cox regression, Lasso regression, and multivariate Cox regression analysis. Bioinformatics analysis investigated the differences in prognosis, immune characteristics, and the most appropriate treatment strategy among different subtypes of LUAD patients. CCK-8 and colony formation verified the various effects of Etoposide on different subtypes of LUAD cell lines. Cell-to-cell communication analysis was performed using the "CellChat" R package. The expression of the risk factors in the GSAGI was verified using real-time quantitative PCR (qRT-PCR) and Immunohistochemistry (IHC).

RESULTS

We constructed and validated the GSAGI, consisting of five genes: ANLN, RHOV, KRT6A, SIGLEC6, and KLRG2. The GSAGI was an independent prognostic factor for LUAD patients. Patients in the high-risk group distinguished by the GSAGI are more suitable for chemotherapy. More immune cells are infiltrating the tumor microenvironment of patients in the low-risk group, especially B cells. Low-risk group patients are more suitable for receiving immunotherapy. The single-cell level analysis confirmed the influence of the GSAGI on TME and revealed the Mode of action between tumor cells and other types of cells. qRT-PCR and IHC showed increased ANLN, RHOV, and KRT6A expression in the LUAD cells and tumor tissues.

CONCLUSION

This study confirms that genes related to genomic instability can affect the prognosis and immune status of LUAD patients. The GSAGI we identified has the potential to guide clinicians in predicting clinical outcomes, assessing immunological status, and even developing personalized treatment plans for LUAD patients.

The Anti-Cancer Activity of the Naturally Occurring Dipeptide Carnosine: Potential for Breast Cancer

Carnosine is an endogenous dipeptide composed of β-alanine and L-histidine, possessing a multimodal pharmacodynamic profile that includes anti-inflammatory and anti-oxidant activities. Carnosine has also shown its ability to modulate cell proliferation, cell cycle arrest, apoptosis, and even glycolytic energy metabolism, all processes playing a key role in the context of cancer. Cancer is one of the most dreaded diseases of the 20th and 21st centuries. Among the different types of cancer, breast cancer represents the most common non-skin cancer among women, accounting for an estimated 15% of all cancer-related deaths in women. The main aim of the present review was to provide an overview of studies on the anti-cancer activity of carnosine, and in particular its activity against breast cancer. We also highlighted the possible advantages and limitations involved in the use of this dipeptide. The first part of the review entailed a brief description of carnosine's biological activities and the pathophysiology of cancer, with a focus on breast cancer. The second part of the review described the anti-tumoral activity of carnosine, for which numerous studies have been carried out, especially at the preclinical level, showing promising results. However, only a few studies have investigated the therapeutic potential of this dipeptide for breast cancer prevention or treatment. In this context, carnosine has shown to be able to decrease the size of cancer cells and their viability. It also reduces the levels of vascular endothelial growth factor (VEGF), cyclin D1, NAD+, and ATP, as well as cytochrome c oxidase activity in vitro. When tested in mice with induced breast cancer, carnosine proved to be non-toxic to healthy cells and exhibited chemopreventive activity by reducing tumor growth. Some evidence has also been reported at the clinical level. A randomized phase III prospective placebo-controlled trial showed the ability of Zn-carnosine to prevent dysphagia in breast cancer patients undergoing adjuvant radiotherapy. Despite this evidence, more preclinical and clinical studies are needed to better understand carnosine's anti-tumoral activity, especially in the context of breast cancer.

Genome instability-related LINC02577, LINC01133 and AC107464.2 are lncRNA prognostic markers correlated with immune microenvironment in pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PAAD) is a leading cause of malignancy-related deaths worldwide, and the efficacy of immunotherapy on PAAD is limited. Studies report that long non-coding RNAs (lncRNAs) play an important role in modulating genomic instability and immunotherapy. However, the identification of genome instability-related lncRNAs and their clinical significance has not been investigated in PAAD.

METHODS

The current study developed a computational framework for mutation hypothesis based on lncRNA expression profile and somatic mutation spectrum in pancreatic adenocarcinoma genome. We explored the potential of GInLncRNAs(genome instability-related lncRNAs) through co-expression analysis and function enrichment analysis. We further analyzed GInLncRNAs by Cox regression and used the results to construct a prognostic lncRNA signature. Finally, we analyzed the relationship between GILncSig (genomic instability derived 3-lncRNA signature) and immunotherapy.

RESULTS

A GILncSig was developed using bioinformatics analyses. It could divide patients into high-risk and low-risk groups, and there was a significant difference in OS between the two groups. In addition, GILncSig was associated with genome mutation rate in pancreatic adenocarcinoma, indicating its potential value as a marker for genomic instability. The GILncSig accurately grouped wild type patients of KRAS into two risk groups. The prognosis of the low-risk group was significantly improved. GILncSig was significantly correlated with the level of immune cell infiltration and immune checkpoint.

CONCLUSIONS

In summary, the current study provides a basis for further studies on the role of lncRNA in genomic instability and immunotherapy. The study provides a novel method for identification of cancer biomarkers related to genomic instability and immunotherapy.

Feasibility study of expressing epcam + /vimentin + CTC in prostate cancer diagnosis

PURPOSE

Prostate cancer (PCa) is one of the most common malignancies in men and one of the leading causes of cancer-related deaths; circulating tumor cells (CTC) are malignant cells that have broken off from original tumor or metastatic sites and extravasated into the blood vessels either naturally or maybe as a consequence of surgical procedures. This study aims to explore the feasibility of liquid biopsy technique to diagnose prostate cancer.

METHOD

We constructed an assay platform integrating magnetic separation and fluorescence in situ hybridization (FISH) to effectively capture prostate cancer CTCs and evaluate the distribution between healthy volunteers and prostate cancer patients, respectively.

RESULTS

There was a significant difference in the number of CTCs between the healthy population and prostate cancer patients (P < 0.001). The results of the study showed that the CTCs capture identification system has good sensitivity and specificity in identifying prostate cancer patients.

CONCLUSION

The CTCs test allows us to accurately identify patients who are at high risk for prostate cancer, allowing for early intervention and treating patients effectively.

Chromosome instability-associated prognostic signature and cluster investigation for cutaneous melanoma cases

Chromosomal instability (CIN) is closely associated to the early detection of several clinical tumours. In this study, the authors first established a novel prognostic model of melanoma using the hub genes of CIN, based on the datasets of The cancer genome atlas-skin cutaneous melanoma (TCGA-SKCM) and GSE65904 cohorts. Based on the risk scores of our model, the disease-specific survival (DSS) prognosis was worse in the high-risk group. Combining risk score, stage, age, ulceration, and clark factors, a Nomogram was generated to predict 1, 3, 5-year survival rates, which indicated a good clinical validity. Our finding also showed a correlation between high/low risk and tumour infiltration levels of 'activated CD8 T cells' and 'effector memory CD8 T cells'. Moreover, the authors first performed a CIN-based tumour clustering analysis using TCGA-SKCM cases, and identified two melanoma clusters, which exhibit the distinct DSS prognosis and the tumour-infiltrating levels of CD8 T cells. Taken together, a promising CIN-related prognostic signature and clustering for melanoma cases were first established in our study.

Development of genomic instability-associated long non-coding RNA signature: A prognostic risk model of clear cell renal cell carcinoma

Purpose

Renal clear cell carcinoma (ccRCC) is the most lethal of all pathological subtypes of renal cell carcinoma (RCC). Genomic instability was recently reported to be related to the occurrence and development of kidney cancer. The biological roles of long non-coding RNAs (lncRNAs) in tumorigenesis have been increasingly valued, and various lncRNAs were found to be oncogenes or cancer suppressors. Herein, we identified a novel genomic instability-associated lncRNA (GILncs) model for ccRCC patients to predict the overall survival (OS).

Methods

The Cancer Genome Atlas (TCGA) database was utilized to obtain full transcriptome data, somatic mutation profiles, and clinical characteristics. The differentially expressed lncRNAs between the genome-unstable-like group (GU) and the genome-stable-like group (GS) were defined as GILncs, with |logFC| > 1 and an adjusted p-value< 0.05 for a false discovery rate. All samples were allocated into GU-like or GS-like types based on the expression of GILncs observed using hierarchical cluster analyses. A genomic instability-associated lncRNA signature (GILncSig) was constructed using parameters of the included lncRNAs. Quantitative real-time PCR analysis was used to detect the in vitro expression of the included lncRNAs. Validation of the risk model was performed by the log-rank test, time-dependent receiver operating characteristic (ROC) curves analysis, and multivariate Cox regression analysis.

Results

Forty-six lncRNAs were identified as GILncs. LINC00460, AL139351.1, and AC156455.1 were employed for GILncSig calculation based on the results of Cox analysis. GILncSig was confirmed as an independent predictor for OS of ccRCC patients. Additionally, it presented a higher efficiency and accuracy than other RCC prognostic models reported before.

Conclusion

GILncSig score was qualified as a critical indicator, independent of other clinical factors, for prognostic prediction of ccRCC patients.

A novel genomic instability-derived lncRNA signature to predict prognosis and immune characteristics of pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignant tumor of the digestive system. Its grim prognosis is mainly attributed to the lack of means for early diagnosis and poor response to treatments. Genomic instability is shown to be an important cancer feature and prognostic factor, and its pattern and extent may be associated with poor treatment outcomes in PDAC. Recently, it has been reported that long non-coding RNAs (lncRNAs) play a key role in maintaining genomic instability. However, the identification and clinical significance of genomic instability-related lncRNAs in PDAC have not been fully elucidated.

Methods

Genomic instability-derived lncRNA signature (GILncSig) was constructed based on the results of multiple regression analysis combined with genomic instability-associated lncRNAs and its predictive power was verified by the Kaplan-Meier method. And real-time quantitative polymerase chain reaction (qRT-PCR) was used for simple validation in human cancers and their adjacent non-cancerous tissues. In addition, the correlation between GILncSig and tumor microenvironment (TME) and epithelial-mesenchymal transition (EMT) was investigated by Pearson correlation analysis.

Results

The computational framework identified 206 lncRNAs associated with genomic instability in PDAC and was subsequently used to construct a genome instability-derived five lncRNA-based gene signature. Afterwards, we successfully validated its prognostic capacity in The Cancer Genome Atlas (TCGA) cohort. In addition, via careful examination of the transcriptome expression profile of PDAC patients, we discovered that GILncSig is associated with EMT and an adaptive immunity deficient immune profile within TME.

Conclusions

Our study established a genomic instability-associated lncRNAs-derived model (GILncSig) for prognosis prediction in patients with PDAC, and revealed the potential functional regulatory role of GILncSig.

A Novel Necroptosis-Related Prognostic Signature of Glioblastoma Based on Transcriptomics Analysis and Single Cell Sequencing Analysis

Background: Glioblastoma (GBM) is the most common and deadly brain tumor. The clinical significance of necroptosis (NCPS) genes in GBM is unclear. The goal of this study is to reveal the potential prognostic NCPS genes associated with GBM, elucidate their functions, and establish an effective prognostic model for GBM patients. Methods: Firstly, the NCPS genes in GBM were identified by single-cell analysis of the GSE182109 dataset in the GEO database and weighted co-expression network analysis (WGCNA) of The Cancer Genome Atlas (TCGA) data. Three machine learning algorithms (Lasso, SVM-RFE, Boruta) combined with COX regression were used to build prognostic models. The subsequent analysis included survival, immune microenvironments, and mutations. Finally, the clinical significance of NCPS in GBM was explored by constructing nomograms. Results: We constructed a GBM prognostic model composed of NCPS-related genes, including CTSD, AP1S1, YWHAG, and IER3, which were validated to have good performance. According to the above prognostic model, GBM patients in the TCGA and CGGA groups could be divided into two groups according to NCPS, with significant differences in survival analysis between the two groups and a markedly worse prognostic status in the high NCPS group (p < 0.001). In addition, the high NCPS group had higher levels of immune checkpoint-related gene expression, suggesting that they may be more likely to benefit from immunotherapy. Conclusions: Four genes (CTSD, AP1S1, YWHAG, and IER3) were screened through three machine learning algorithms to construct a prognostic model for GBM. These key and novel diagnostic markers may become new targets for diagnosing and treating patients with GBM.

Development of a Genomic Instability-Derived lncRNAs-Based Risk Signature as a Predictor of Prognosis for Endometrial Cancer

Endometrial cancer (EC) ranks fourth in the incidence rate among the most frequent gynaecological malignancies reported in the developed countries. Approximately 280,000 endometrial cancer cases are reported worldwide every year. Genomic instability and mutation are some of the favourable characteristics of human malignancies such as endometrial cancer. Studies have established that the majority of genomic mutations in human malignancies are found in the chromosomal regions that do not code for proteins. In addition, the majority of transcriptional products of these mutations are long non-coding RNAs (lncRNAs). In this study, 78 lncRNA genes were found on the basis of their mutation counts. Then, these lncRNAs were investigated to determine their relationship with genomic instability through hierarchical cluster analysis, mutation analysis, and differential analysis of driving genes responsible for genomic instability. The prognostic value of these lncRNAs was also assessed in patients with EC, and a risk factor score formula composed of 15 lncRNAs was constructed. We then identified this formula as genome instability-derived lncRNA-based gene signature (GILncSig), which stratified patients into high- and low-risk groups with significantly different outcome. And GILncSig was further validated in multiple independent patient cohorts as a prognostic factor of other clinicopathological features, such as stage, grade, overall survival rate. We observed that a high-risk score is often associated with an unfavourable prognosis in patients with EC.

Identification of a Gene-Expression-Based Surrogate of Genomic Instability during Oral Carcinogenesis

BACKGROUND

Our goal was to identify a gene-expression-based surrogate of genomic instability (GI) associated with the transformation of oral potentially malignant disorder (OPMD) into oral squamous cell carcinoma (OSCC).

METHODS

GI was defined as the fraction of genome altered (FGA). Training sets included the CCLE and TCGA databases. The relevance of the enrichment score of the top correlated genes, referred to as the GIN score, was evaluated in eight independent public datasets from the GEO repository, including a cohort of patients with OPMD with available outcome.

RESULTS

A set of 20 genes correlated with FGA in head and neck SCC were identified. A significant correlation was found between the 20-gene based GIN score and FGA in 95 esophagus SCC (r = 0.59) and 501 lung SCC (r = 0.63), and in 33 OPMD/OSCC (r = 0.38). A significantly increased GIN score was observed at different stages of oral carcinogenesis (normal-dysplasia -OSCC) in five independent datasets. The GIN score was higher in 10 OPMD that transformed into oral cancer compared to 10 nontransforming OPMD (p = 0.0288), and was associated with oral-cancer-free survival in 86 patients with OPMD (p = 0.0081).

CONCLUSIONS

The GIN score is a gene-expression surrogate of GI, and is associated with oral carcinogenesis and OPMD malignant transformation.

Identification and Validation of a Novel Genomic Instability-Associated Long Non-Coding RNA Prognostic Signature in Head and Neck Squamous Cell Carcinoma

Background: Head and neck squamous cell carcinoma (HNSCC) is one of the most aggressive malignant cancers worldwide, and accurate prognostic models are urgently needed. Emerging evidence revealed that long non-coding RNAs (lncRNAs) are related to genomic instability. We sought to identify and validate a genomic instability-associated lncRNA prognostic signature to assess HNSCC patient survival outcomes.

Methods: RNA-sequencing data, somatic mutation files, and patient clinical data were downloaded from The Cancer Genome Atlas database. A total of 491 patients with completely clinical files were randomly divided into training and testing sets. In the training set, genomic instability-associated lncRNAs were screened through univariate Cox regression analyses and least absolute shrinkage and selection operator regression analyses to build a genomic instability-associated lncRNA signature (GILncSig). In addition, time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier survival curve, and clinical stratification analyses were used to evaluate the signature’s reliability. Finally, in situ hybridization experiments were performed to validate GILncSig expression levels between adjacent non-tumor tissues and tumor tissues from HNSCC patients.

Results: Four genomic instability-associated lncRNAs (AC023310.4, AC091729.1, LINC01564, and MIR3142HG) were selected for the prognostic signature. The model was successfully validated using the testing cohort. ROC analysis demonstrated its strong predictive ability for HNSCC prognosis. Univariate and multivariate Cox analyses revealed that the GILncSig was an independent predictor of prognosis. HNSCC patients with a low-risk score showed a substantially better prognosis than the high-risk groups. The in situ hybridization experiments using human HNSCC tissue revealed high GILncSig expression in HNSCC tissues compared with adjacent non-tumor tissues.

Conclusion: We developed a novel GILncSig for prognosis prediction in HNSCC patients, and the components of that signature might be therapeutic targets for HNSCC.

Polo-Like Kinase 1 Regulates Chromosomal Instability and Paclitaxel Resistance in Breast Cancer Cells

Purpose

Chromosomal instability (CIN) contributes to intercellular genetic heterogeneity and has been implicated in paclitaxel (PTX) resistance in breast cancer. In this study, we explored polo-like kinase 1 (PLK1) as an important regulator of mitotic integrity and as a useful predictive biomarker for PTX resistance in breast cancer.

Methods

We performed PTX resistance screening using the human kinome CRISPR/Cas9 library in breast cancer cells. In vitro cell proliferation and apoptosis assays and in vivo xenograft experiments were performed to determine the effects of PLK1 on breast cancer cells. Immunofluorescence microscopy was used to measure the degree of multipolar cell division.

Results

Kinome-wide CRISPR/Cas9 screening identified various kinases involved in PTX resistance in breast cancer cells; among these, PLK1 was chosen for further experiments. PLK1 knockdown inhibited the proliferation of MDA-MB-231 and MDA-MB-468 cells in vitro and in vivo. Moreover, PLK1 silencing sensitized breast cancer cells and mouse xenograft tumor models to PTX cytotoxicity. Silencing of PLK1 induced the formation of multipolar spindles and increased the percentage of multipolar cells. In addition, PLK1 silencing resulted in the downregulation of BubR1 and Mad2 in breast cancer cells. Furthermore, PLK1 upregulation in primary breast cancer was associated with decreased overall patient survival based on the analysis of The Cancer Genome Atlas and Molecular Taxonomy of Breast Cancer International Consortium databases.

Conclusion

PLK1 plays an important role in PTX resistance by regulating CIN in breast cancer cells. Targeting PLK1 may be an effective treatment strategy for PTX-resistant breast cancers.

Six mutator-derived lncRNA signature of genome instability for predicting the clinical outcome of colon cancer

Background

Colon adenocarcinoma (COAD) is one of the most common malignancies worldwide. Genomic instability is one of the hallmarks of colon cancer and is associated with prognosis. Nevertheless, the impact of genome instability-associated long non-coding RNAs (lncRNAs) along with their clinical significance in cancers has remained mostly unexplored.

Methods

In this study, a mutator hypothesis-derived computational frame integrating the somatic mutation profiles and lncRNA expression profiles in a tumor genome was developed, which enabled the identification of 137 novel genomic instability-associated lncRNAs in colon cancer. Subsequently, a genome instability-derived lncRNA signature (GILncSig) segregated the patients into low- and high-risk groups with prominent differences in outcomes.

Results

Combined with the overall survival data, we established 6 six lncRNA-based signature to predict prognosis, which were LINC00896, AC007996.1, NKILA, AP003555.2, MIRLET7BHG, and AC009237.14. We found that the expression level of PD-L1 (CD274) and somatic mutations in the high-risk group were higher than those in the low-risk group. This suggests that high-risk patients may be sensitive to immunotherapy. We further found that the prognosis of patients in the high-risk group was significantly lower than that of patients in the low-risk group, and that patients' prognosis was likely to be worse as the patient's risk score increased.

Conclusions

In conclusion, this study explores the role of lncRNAs in genomic instability and cancer prognosis and provides a new idea for the prognostic prediction of colon cancer.

Genomic Instability Promotes the Progression of Clear Cell Renal Cell Carcinoma Through Influencing the Immune Microenvironment

Background: Long non-coding RNAs (lncRNAs) are now under discussion as novel promising biomarkers for clear cell renal cell carcinoma (ccRCC). However, the role of genomic instability-associated lncRNA signatures in tumors has not been thoroughly uncovered. The purpose of our study is to probe the role of genomic instability-derived lncRNA signature (GILncSig) and to further investigate the mechanism of genomic instability-mediated ccRCC progression. Methods: The transcriptome data and somatic mutation profiles of ccRCC as well as clinical characteristics used in this study were obtained from The Cancer Genome Atlas database and Gene Expression Omnibus database. Lasso regression analysis was performed to construct the GILncSig. Gene set enrichment analysis (GSEA) was performed to elucidate the biological functions and relative pathways. CIBERSORT and EPIC algorithm were applied to calculate the proportion of immune cells in ccRCC. ESTIMATE algorithm was utilized to compute the immune microenvironment scores. Results: In total, 148 novel genomic instability-derived lncRNAs in ccRCC were identified. Immediately, on the basis of univariate cox analysis and lasso analysis, a GILncSig was appraised, through which the patients were allocated into High-Risk and Low-Risk groups with significantly different characteristics and prognoses. In addition, we confirmed that the somatic mutation count, tumor mutation burden, and the expression of UBQLN4, which were ascertainably associated with genomic instability, were significantly correlated with the GILncSig, indicating its reliability as a measurement of the genomic instability. Furthermore, the efficiency of GILncSig in prognostic aspects was better than the single mutation gene in ccRCC. In addition, MNX1-AS1 was defined to be a potential biomarker characterized by strong correlation with clinical features. Moreover, GSEA results indicated that the IL6/JAK/STAT3/SIGNALING pathway could be considered as a potential mechanism of genomic instability to influence tumor progression. Besides, the immune microenvironment showed significant differences between the GS-like group and the GU-like group, which was specifically manifested as high expression of CTLA4, GITR, TNFSF14, and regulatory T cells (Tregs) as well as low expression of endothelial cells (ECs) in the GU-like group. Finally, the prognostic value and clinical relevance of GILncSig were verified in GEO datasets and other urinary tumors in TCGA dataset. Conclusion: In conclusion, our study provided a new perspective for the role of lncRNAs in genomic instability and revealed that genomic instability may mediate tumor progression by affecting immunity. Besides, MNX1-AS1 played critical roles in promoting the progression of ccRCC, which may be a potential therapeutic target. What is more, the immune atlas of genomic instability was characterized by high expression of CTLA4, GITR, TNFSF14, and Tregs, and low expression of ECs.

Genomic Instability of Mutation-Derived Gene Prognostic Signatures for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the major cancer-related deaths worldwide. Genomic instability is correlated with the prognosis of cancers. A biomarker associated with genomic instability might be effective to predict the prognosis of HCC. In the present study, data of HCC patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases were used. A total of 370 HCC patients from the TCGA database were randomly classified into a training set and a test set. A prognostic signature of the training set based on nine overall survival (OS)–related genomic instability–derived genes (SLCO2A1, RPS6KA2, EPHB6, SLC2A5, PDZD4, CST2, MARVELD1, MAGEA6, and SEMA6A) was constructed, which was validated in the test and TCGA and ICGC sets. This prognostic signature showed more accurate prediction for prognosis of HCC compared with tumor grade, pathological stage, and four published signatures. Cox multivariate analysis revealed that the risk score could be an independent prognostic factor of HCC. A nomogram that combines pathological stage and risk score performed well compared with an ideal model. Ultimately, paired differential expression profiles of genes in the prognostic signature were validated at mRNA and protein level using HCC and paratumor tissues obtained from our institute. Taken together, we constructed and validated a genomic instability–derived gene prognostic signature, which can help to predict the OS of HCC and help us to explore the potential therapeutic targets of HCC.

Genomic instability in lower-grade glioma: Prediction of prognosis based on lncRNA and immune infiltration

Glioma is the most common type of primary malignant tumor in the central nervous system. Tumor recurrence and progression are common in lower-grade glioma (LGG). Immune checkpoint blockade (ICB), as an emerging immunotherapy, is expected to improve the prognosis of patients undergoing conventional treatment, but it currently performs poorly in glioma. We divided patients into genome-stable and -unstable groups according to the somatic mutation count and then found that the expression of CDC20 was positively correlated with genomic instability. We compared the differences in long non-coding RNA (lncRNA) expression and immune infiltration between the two groups. Five lncRNAs and three immune cell types were identified to construct risk models and a nomogram combing clinical features. Through internal and external validation, the models exhibited sufficient ability to predict the prognosis and the possible response to ICB therapy of patients. This study provided a potential predictive approach for the precise application of ICB and support for improving the prognosis of LGG patients.

Calculated identification of mutator-derived lncRNA signatures of genomic instability to predict the clinical outcome of muscle-invasive bladder cancer

BACKGROUND

Muscle-invasive bladder cancer (MIBC) is one of the most important type of bladder cancer, with a high morbidity and mortality rate. Studies have found that long non-coding RNA (lncRNA) plays a key role in maintaining genomic instability. However, Identification of lncRNAs related to genomic instability (GIlncRNAs) and their clinical significance in cancers have not been extensively studied yet.

METHODS

Here, we downloaded the lncRNA expression profiles, somatic mutation profiles and clinical related data in MIBC patients from The Cancer Genome Atlas (TCGA) database. A lncRNA computational framework was used to find differentially expressed GIlncRNAs. Multivariate Cox regression analysis was used to construct a genomic instability-related lncRNA signature (GIlncSig). Univariate and multivariate Cox analyses were used to assess the independent prognostic for the GIlncSig and other key clinical factors.

RESULTS

We found 43 differentially expressed GIlncRNAs and constructed the GIlncSig with 6 GIlncRNAs in the training cohort. The patients were divided into two risk groups. The overall survival of patients in the high-risk group was lower than that in the low-risk group (P < 0.001), which were further verified in the testing cohort and the entire TCGA cohort. Univariate and multivariate Cox regression showed that the GIlncSig was an independent prognostic factor. In addition, the GIlncSig correlated with the genomic mutation rate of MIBC, indicating its potential as a measure of the degree of genomic instability. The GIlncSig was able to divide FGFR3 wild- and mutant-type patients into two risk groups, and effectively enhanced the prediction effect.

CONCLUSION

Our study introduced an important reference for further research on the role of GIlncRNAs, and provided prognostic indicators and potential biological therapy targets for MIBC.

Genome Instability-Derived Genes Are Novel Prognostic Biomarkers for Triple-Negative Breast Cancer

Background

Triple-negative breast cancer (TNBC) is an aggressive disease. Recent studies have identified genome instability-derived genes for patient outcomes. However, most of the studies mainly focused on only one or a few genome instability-related genes. Prognostic potential and clinical significance of genome instability-associated genes in TNBC have not been well explored.

Methods

In this study, we developed a computational approach to identify TNBC prognostic signature. It consisted of (1) using somatic mutations and copy number variations (CNVs) in TNBC to build a binary matrix and identifying the top and bottom 25% mutated samples, (2) comparing the gene expression between the top and bottom 25% samples to identify genome instability-related genes, and (3) performing univariate Cox proportional hazards regression analysis to identify survival-associated gene signature, and Kaplan–Meier, log-rank test, and multivariate Cox regression analyses to obtain overall survival (OS) information for TNBC outcome prediction.

Results

From the identified 111 genome instability-related genes, we extracted a genome instability-derived gene signature (GIGenSig) of 11 genes. Through survival analysis, we were able to classify TNBC cases into high- and low-risk groups by the signature in the training dataset (log-rank test p = 2.66e−04), validated its prognostic performance in the testing (log-rank test p = 2.45e−02) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (log-rank test p = 2.57e−05) datasets, and further validated the predictive power of the signature in five independent datasets.

Conclusion

The identified novel signature provides a better understanding of genome instability in TNBC and can be applied as prognostic markers for clinical TNBC management.

Single Cell Genetic Profiling of Tumors of Breast Cancer Patients Aged 50 Years and Older Reveals Enormous Intratumor Heterogeneity Independent of Individual Prognosis

PURPOSE

Older breast cancer patients are underrepresented in cancer research even though the majority (81.4%) of women dying of breast cancer are 55 years and older. Here we study a common phenomenon observed in breast cancer which is a large inter- and intratumor heterogeneity; this poses a tremendous clinical challenge, for example with respect to treatment stratification. To further elucidate genomic instability and tumor heterogeneity in older patients, we analyzed the genetic aberration profiles of 39 breast cancer patients aged 50 years and older (median 67 years) with either short (median 2.4 years) or long survival (median 19 years). The analysis was based on copy number enumeration of eight breast cancer-associated genes using multiplex interphase fluorescence in situ hybridization (miFISH) of single cells, and by targeted next-generation sequencing of 563 cancer-related genes.

RESULTS

We detected enormous inter- and intratumor heterogeneity, yet maintenance of common cancer gene mutations and breast cancer specific chromosomal gains and losses. The gain of COX2 was most common (72%), followed by MYC (69%); losses were most prevalent for CDH1 (74%) and TP53 (69%). The degree of intratumor heterogeneity did not correlate with disease outcome. Comparing the miFISH results of diploid with aneuploid tumor samples significant differences were found: aneuploid tumors showed significantly higher average signal numbers, copy number alterations (CNAs) and instability indices. Mutations in PIKC3A were mostly restricted to luminal A tumors. Furthermore, a significant co-occurrence of CNAs of DBC2/MYC, HER2/DBC2 and HER2/TP53 and mutual exclusivity of CNAs of HER2 and PIK3CA mutations and CNAs of CCND1 and PIK3CA mutations were revealed.

CONCLUSION

Our results provide a comprehensive picture of genome instability profiles with a large variety of inter- and intratumor heterogeneity in breast cancer patients aged 50 years and older. In most cases, the distribution of chromosomal aneuploidies was consistent with previous results; however, striking exceptions, such as tumors driven by exclusive loss of chromosomes, were identified.

Genome instability-related long non-coding RNA in clear renal cell carcinoma determined using computational biology

Background

There is evidence that long non-coding RNA (lncRNA) is related to genetic stability. However, the complex biological functions of these lncRNAs are unclear.

Method

TCGA - KIRC lncRNAs expression matrix and somatic mutation information data were obtained from TCGA database. “GSVA” package was applied to evaluate the genomic related pathway in each samples. GO and KEGG analysis were performed to show the biological function of lncRNAs-mRNAs. “Survival” package was applied to determine the prognostic significance of lncRNAs. Multivariate Cox proportional hazard regression analysis was applied to conduct lncRNA prognosis model.

Results

In the present study, we applied computational biology to identify genome-related long noncoding RNA and identified 26 novel genomic instability-associated lncRNAs in clear cell renal cell carcinoma. We identified a genome instability-derived six lncRNA-based gene signature that significantly divided clear renal cell samples into high- and low-risk groups. We validated it in test cohorts. To further elucidate the role of the six lncRNAs in the model’s genome stability, we performed a gene set variation analysis (GSVA) on the matrix. We performed Pearson correlation analysis between the GSVA scores of genomic stability-related pathways and lncRNA. It was determined that LINC00460 and LINC01234 could be used as critical factors in this study. They may influence the genome stability of clear cell carcinoma by participating in mediating critical targets in the base excision repair pathway, the DNA replication pathway, homologous recombination, mismatch repair pathway, and the P53 signaling pathway.

Conclusion subsections

These data suggest that LINC00460 and LINC01234 are crucial for the stability of the clear cell renal cell carcinoma genome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08356-9.

Harnessing Genomic Stress for Antitumor Immunity

Significance: Genomic instability, a hallmark of cancer, renders cancer cells susceptible to genomic stress from both endogenous and exogenous origins, resulting in the increased tendency to accrue DNA damage, chromosomal instability, or aberrant DNA localization. Apart from the cell autonomous tumor-promoting effects, genomic stress in cancer cells could have a profound impact on the tumor microenvironment. Recent Advances: Recently, it is increasingly appreciated that harnessing genomic stress could provide a promising strategy to revive antitumor immunity, and thereby offer new therapeutic opportunities in cancer treatment. Critical Issues: Genomic stress is closely intertwined with antitumor immunity via mechanisms involving the direct crosstalk with DNA damage response components, upregulation of immune-stimulatory/inhibitory ligands, release of damage-associated molecular patterns, increase of neoantigen repertoire, and activation of DNA sensing pathways. A better understanding of these mechanisms will provide molecular basis for exploiting the genomic stress to boost antitumor immunity. Future Directions: Future research should pay attention to the heterogeneity between individual cancers in the genomic instability and the associated immune response, and how to balance the toxicity and benefit by specifying the types, potency, and treatment sequence of genomic stress inducer in therapeutic practice. Antioxid. Redox Signal. 34, 1128-1150.

Karyotype Aberrations in Action: The Evolution of Cancer Genomes and the Tumor Microenvironment

Cancer is a disease of cellular evolution. For this cellular evolution to take place, a population of cells must contain functional heterogeneity and an assessment of this heterogeneity in the form of natural selection. Cancer cells from advanced malignancies are genomically and functionally very different compared to the healthy cells from which they evolved. Genomic alterations include aneuploidy (numerical and structural changes in chromosome content) and polyploidy (e.g., whole genome doubling), which can have considerable effects on cell physiology and phenotype. Likewise, conditions in the tumor microenvironment are spatially heterogeneous and vastly different than in healthy tissues, resulting in a number of environmental niches that play important roles in driving the evolution of tumor cells. While a number of studies have documented abnormal conditions of the tumor microenvironment and the cellular consequences of aneuploidy and polyploidy, a thorough overview of the interplay between karyotypically abnormal cells and the tissue and tumor microenvironments is not available. Here, we examine the evidence for how this interaction may unfold during tumor evolution. We describe a bidirectional interplay in which aneuploid and polyploid cells alter and shape the microenvironment in which they and their progeny reside; in turn, this microenvironment modulates the rate of genesis for new karyotype aberrations and selects for cells that are most fit under a given condition. We conclude by discussing the importance of this interaction for tumor evolution and the possibility of leveraging our understanding of this interplay for cancer therapy.

Identification of the Prognostic Significance of Somatic Mutation-Derived LncRNA Signatures of Genomic Instability in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is a highly heterogeneous tumor with substantial somatic mutations and genome instability, which are emerging hallmarks of cancer. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in genomic instability. However, the identification of genome instability-related lncRNAs (GInLncRNAs) and their clinical significance has not been investigated in LUAD.

Methods: We determined GInLncRNAs by combining somatic mutation and transcriptome data of 457 patients with LUAD and probed their potential function using co-expression network and Gene Ontology (GO) enrichment analyses. We then filtered GInLncRNAs by Cox regression and LASSO regression to construct a genome instability-related lncRNA signature (GInLncSig). We subsequently evaluated GInLncSig using correlation analyses with mutations, external validation, model comparisons, independent prognostic significance analyses, and clinical stratification analyses. Finally, we established a nomogram for prognosis prediction in patients with LUAD and validated it in the testing set and the entire TCGA dataset.

Results: We identified 161 GInLncRNAs, of which seven were screened to develop a prognostic GInLncSig model (LINC01133, LINC01116, LINC01671, FAM83A-AS1, PLAC4, MIR223HG, and AL590226.1). GInLncSig independently predicted the overall survival of patients with LUAD and displayed an improved performance compared to other similar signatures. Furthermore, GInLncSig was related to somatic mutation patterns, suggesting its ability to reflect genome instability in LUAD. Finally, a nomogram comprising the GInLncSig and tumor stage exhibited improved robustness and clinical practicability for predicting patient prognosis.

Conclusion: Our study identified a signature for prognostic prediction in LUAD comprising seven lncRNAs associated with genome instability, which may provide a useful indicator for clinical stratification management and treatment decisions for patients with LUAD.

Pancancer survival analysis of cancer hallmark genes

Cancer hallmark genes are responsible for the most essential phenotypic characteristics of malignant transformation and progression. In this study, our aim was to estimate the prognostic effect of the established cancer hallmark genes in multiple distinct cancer types. RNA-seq HTSeq counts and survival data from 26 different tumor types were acquired from the TCGA repository. DESeq was used for normalization. Correlations between gene expression and survival were computed using the Cox proportional hazards regression and by plotting Kaplan–Meier survival plots. The false discovery rate was calculated to correct for multiple hypothesis testing. Signatures based on genes involved in genome instability and invasion reached significance in most individual cancer types. Thyroid and glioblastoma were independent of hallmark genes (61 and 54 genes significant, respectively), while renal clear cell cancer and low grade gliomas harbored the most prognostic changes (403 and 419 genes significant, respectively). The eight genes with the highest significance included BRCA1 (genome instability, HR 4.26, p < 1E−16), RUNX1 (sustaining proliferative signaling, HR 2.96, p = 3.1E−10) and SERPINE1 (inducing angiogenesis, HR 3.36, p = 1.5E−12) in low grade glioma, CDK1 (cell death resistance, HR = 5.67, p = 2.1E−10) in kidney papillary carcinoma, E2F1 (tumor suppressor, HR 0.38, p = 2.4E−05) and EREG (enabling replicative immortality, HR 3.23, p = 2.1E−07) in cervical cancer, FBP1 (deregulation of cellular energetics, HR 0.45, p = 2.8E−07) in kidney renal clear cell carcinoma and MYC (invasion and metastasis, HR 1.81, p = 5.8E−05) in bladder cancer. We observed unexpected heterogeneity and tissue specificity when correlating cancer hallmark genes and survival. These results will help to prioritize future targeted therapy development in different types of solid tumors.

Genome Instability Profiles Predict Disease Outcome in a Cohort of 4,003 Patients with Breast Cancer

PURPOSE

The choice of therapy for patients with breast cancer is often based on clinicopathologic parameters, hormone receptor status, and HER2 amplification. To improve individual prognostication and tailored treatment decisions, we combined clinicopathologic prognostic data with genome instabilty profiles established by quantitative measurements of the DNA content.

EXPERIMENTAL DESIGN

We retrospectively assessed clinical data of 4,003 patients with breast cancer with a minimum postoperative follow-up period of 10 years. For the entire cohort, we established genome instability profiles. We applied statistical methods, including correlation matrices, Kaplan-Meier curves, and multivariable Cox proportional hazard models, to ascertain the potential of standard clinicopathologic data and genome instability profiles as independent predictors of disease-specific survival in distinct subgroups, defined clinically or with respect to treatment.

RESULTS

In Cox regression analyses, two parameters of the genome instability profiles, the S-phase fraction and the stemline scatter index, emerged as independent predictors in premenopausal women, outperforming all clinicopathologic parameters. In postmenopausal women, age and hormone receptor status were the predominant prognostic factors. However, by including S-phase fraction and 2.5c exceeding rate, we could improve disease outcome prediction in pT1 tumors irrespective of the lymph node status. In pT3-pT4 tumors, a higher S-phase fraction led to poorer prognosis. In patients who received adjuvant endocrine therapy, chemotherapy or radiotherapy, or a combination, the ploidy profiles improved prognostication.

CONCLUSIONS

Genome instability profiles predict disease outcome in patients with breast cancer independent of clinicopathologic parameters. This applies especially to premenopausal patients. In patients receiving adjuvant therapy, the profiles improve identification of high-risk patients.

Hepatitis B Virus-X Downregulates Expression of Selenium Binding Protein 1

Selenium binding protein 1 (SELENBP1) has been known to be reduced in various types cancer, and epigenetic change is shown to be likely to account for the reduction of SELNEBP1 expression. With cDNA microarray comparative analysis, we found that SELENBP1 is markedly decreased in hepatitis B virus-X (HBx)-expressing cells. To clarify the effect of HBx on SELENBP1 expression, we compared the expression levels of SELENBP1 mRNA and protein by semi-quantitative RT-PCR, Northern blot, and Western blot. As expected, SELENBP1 expression was shown to be reduced in cells expressing HBx, and reporter gene analysis showed that the SELENBP1 promoter is repressed by HBx. In addition, the stepwise deletion of 5′ flanking promoter sequences resulted in a gradual decrease in basal promoter activity and inhibition of SELENBP1 expression by HBx. Moreover, immunohistochemistry on tissue microarrays containing 60 pairs of human liver tissue showed decreased intensity of SELENBP1 in tumor tissues as compared with their matched non-tumor liver tissues. Taken together, our findings suggest that inhibition of SELENBP1 expression by HBx might act as one of the causes in the development of hepatocellular carcinoma caused by HBV infection.

High Levels of Chromosomal Copy Number Alterations and TP53 Mutations Correlate with Poor Outcome in Younger Breast Cancer Patients

Prognosis in young patients with breast cancer is generally poor, yet considerable differences in clinical outcomes between individual patients exist. To understand the genetic basis of the disparate clinical courses, tumors were collected from 34 younger women, 17 with good and 17 with poor outcomes, as determined by disease-specific survival during a follow-up period of 17 years. The clinicopathologic parameters of the tumors were complemented with DNA image cytometry profiles, enumeration of copy numbers of eight breast cancer genes by multicolor fluorescence in situ hybridization, and targeted sequence analysis of 563 cancer genes. Both groups included diploid and aneuploid tumors. The degree of intratumor heterogeneity was significantly higher in aneuploid versus diploid cases, and so were gains of the oncogenes MYC and ZNF217. Significantly more copy number alterations were observed in the group with poor outcome. Almost all tumors in the group with long survival were classified as luminal A, whereas triple-negative tumors predominantly occurred in the short survival group. Mutations in PIK3CA were more common in the group with good outcome, whereas TP53 mutations were more frequent in patients with poor outcomes. This study shows that TP53 mutations and the extent of genomic imbalances are associated with poor outcome in younger breast cancer patients and thus emphasize the central role of genomic instability vis-a-vis tumor aggressiveness.

Clinical Implications of Chromosomal Instability (CIN) and Kinetochore Abnormalities in Breast Cancers

Genetic instability is a defining property of cancer cells and is the basis of various lesions including point mutations, copy number alterations and translocations. Chromosomal instability (CIN) is part of the genetic instability of cancer and consists of copy number alterations in whole or parts of cancer cell chromosomes. CIN is observed in differing degrees in most cancers. In breast cancer, CIN is commonly part of the genomic landscape of the disease and has a higher incidence in aggressive sub-types. Tumor suppressors that are commonly mutated or disabled in cancer, such as p53 and pRB, play roles in protection against CIN, and as a result, their dysfunction contributes to the establishment or tolerance of CIN. Several structural and regulatory proteins of the centromeres and kinetochore, the complex structure that is responsible for the correct distribution of genetic material in the daughter cells during mitosis, are direct or, mostly, indirect transcription targets of p53 and pRB. Thus, despite the absence of structural defects in genes encoding for centromere and kinetochore components, dysfunction of these tumor suppressors may have profound implications for the correct function of the mitotic apparatus contributing to CIN. CIN and its prognostic and therapeutic implications in breast cancer are discussed in this article.

Chromosome Instability; Implications in Cancer Development, Progression, and Clinical Outcomes

Chromosome instability (CIN) refers to an ongoing rate of chromosomal changes and is a driver of genetic, cell-to-cell heterogeneity. It is an aberrant phenotype that is intimately associated with cancer development and progression. The presence, extent, and level of CIN has tremendous implications for the clinical management and outcomes of those living with cancer. Despite its relevance in cancer, there is still extensive misuse of the term CIN, and this has adversely impacted our ability to identify and characterize the molecular determinants of CIN. Though several decades of genetic research have provided insight into CIN, the molecular determinants remain largely unknown, which severely limits its clinical potential. In this review, we provide a definition of CIN, describe the two main types, and discuss how it differs from aneuploidy. We subsequently detail its impact on cancer development and progression, and describe how it influences metastatic potential with reference to cancer prognosis and outcomes. Finally, we end with a discussion of how CIN induces genetic heterogeneity to influence the use and efficacy of several precision medicine strategies, including patient and risk stratification, as well as its impact on the acquisition of drug resistance and disease recurrence.

Targeting Metabolic Deregulation Landscapes in Breast Cancer Subtypes

Metabolic deregulation is an emergent hallmark of cancer. Altered patterns of metabolic pathways result in exacerbated synthesis of macromolecules, increased proliferation, and resistance to treatment via alteration of drug processing. In addition, molecular heterogeneity creates a barrier to therapeutic options. In breast cancer, this broad variation in molecular metabolism constitutes, simultaneously, a source of prognostic and therapeutic challenges and a doorway to novel interventions. In this work, we investigated the metabolic deregulation landscapes in breast cancer molecular subtypes. Such landscapes are the regulatory signatures behind subtype-specific metabolic features. n = 735 breast cancer samples of the Luminal A, Luminal B, Her2+, and Basal subtypes, as well as n = 113 healthy breast tissue samples were analyzed. By means of a single-sample-based algorithm, deregulation for all metabolic pathways in every sample was determined. Deregulation levels match almost perfectly with the molecular classification, indicating that metabolic anomalies are closely associated with gene-expression signatures. Luminal B tumors are the most deregulated but are also the ones with higher within-subtype variance. We argued that this variation may underlie the fact that Luminal B tumors usually present the worst prognosis, a high rate of recurrence, and the lowest response to treatment in the long term. Finally, we designed a therapeutic scheme to regulate purine metabolism in breast cancer, independently of the molecular subtype. This scheme is founded on a computational tool that provides a set of FDA-approved drugs to target pathway-specific differentially expressed genes. By providing metabolic deregulation patterns at the single-sample level in breast cancer subtypes, we have been able to further characterize tumor behavior. This approach, together with targeted therapy, may open novel avenues for the design of personalized diagnostic, prognostic, and therapeutic strategies.

The evolution of single cell-derived colorectal cancer cell lines is dominated by the continued selection of tumor-specific genomic imbalances, despite random chromosomal instability

Intratumor heterogeneity is a major challenge in cancer treatment. To decipher patterns of chromosomal heterogeneity, we analyzed six colorectal cancer cell lines by multiplex interphase FISH (miFISH). The mismatch-repair-deficient cell lines DLD-1 and HCT116 had the most stable copy numbers, whereas aneuploid cell lines (HT-29, SW480, SW620 and H508) displayed a higher degree of instability. We subsequently assessed the clonal evolution of single cells in two colorectal carcinoma cell lines, SW480 and HT-29, which both have aneuploid karyotypes but different degrees of chromosomal instability. The clonal compositions of the single cell-derived daughter lines, as assessed by miFISH, differed for HT-29 and SW480. Daughters of HT-29 were stable, clonal, with little heterogeneity. Daughters of SW480 were more heterogeneous, with the single cell-derived daughter lines separating into two distinct populations with different ploidy (hyper-diploid and near-triploid), morphology, gene expression and tumorigenicity. To better understand the evolutionary trajectory for the two SW480 populations, we constructed phylogenetic trees which showed ongoing instability in the daughter lines. When analyzing the evolutionary development over time, most single cell-derived daughter lines maintained their major clonal pattern, with the exception of one daughter line that showed a switch involving a loss of APC. Our meticulous analysis of the clonal evolution and composition of these colorectal cancer models shows that all chromosomes are subject to segregation errors, however, specific net genomic imbalances are maintained. Karyotype evolution is driven by the necessity to arrive at and maintain a specific plateau of chromosomal copy numbers as the drivers of carcinogenesis.

Computational identification of mutator-derived lncRNA signatures of genome instability for improving the clinical outcome of cancers: a case study in breast cancer

Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, identification of genome instability-associated lncRNAs and their clinical significance in cancers remain largely unexplored. Here, we developed a mutator hypothesis-derived computational frame combining lncRNA expression profiles and somatic mutation profiles in a tumor genome and identified 128 novel genomic instability-associated lncRNAs in breast cancer as a case study. We then identified a genome instability-derived two lncRNA-based gene signature (GILncSig) that stratified patients into high- and low-risk groups with significantly different outcome and was further validated in multiple independent patient cohorts. Furthermore, the GILncSig correlated with genomic mutation rate in both ovarian cancer and breast cancer, indicating its potential as a measurement of the degree of genome instability. The GILncSig was able to divide TP53 wide-type patients into two risk groups, with the low-risk group showing significantly improved outcome and the high-risk group showing no significant difference compared with those with TP53 mutation. In summary, this study provided a critical approach and resource for further studies examining the role of lncRNAs in genome instability and introduced a potential new avenue for identifying genomic instability-associated cancer biomarkers.

Metastatic heterogeneity of breast cancer: Molecular mechanism and potential therapeutic targets

Breast cancer is one of the most common malignancies among women throughout the world and is the major cause of most cancer-related deaths. Several explanations account for the high rate of mortality of breast cancer, and metastasis to vital organs is identified as the principal cause. Over the past few years, intensive efforts have demonstrated that breast cancer exhibits metastatic heterogeneity with distinct metastatic precedence to various organs, giving rise to differences in prognoses and responses to therapy in breast cancer patients. Bone, lung, liver, and brain are generally accepted as the primary target sites of breast cancer metastasis. However, the underlying molecular mechanism of metastatic heterogeneity of breast cancer remains to be further elucidated. Recently, the advent of novel genomic and pathologic approaches as well as technological breakthroughs in imaging analysis and animal modelling have yielded an unprecedented change in our understanding of the heterogeneity of breast cancer metastasis and provided novel insight for establishing more effective therapeutics. This review summarizes recent molecular mechanisms and emerging concepts on the metastatic heterogeneity of breast cancer and discusses the potential of identifying specific molecules against tumor cells or tumor microenvironments to thwart the development of metastatic disease and improve the prognosis of breast cancer patients.

A 17-marker panel for global genomic instability in breast cancer

Genomic instability is a hallmark of cancer that plays a pivotal role in breast cancer development and evolution. A number of existing prognostic gene expression signatures for breast cancer are based on proliferation-related genes. Here, we identified a 17-marker panel associated with genome stability. A total of 136 primary breast carcinomas were stratified by genome stability. Matched gene expression profiles showed an innate segregation based on genome stability. We identified a 17-marker panel stratifying the training and validation cohorts into high- and low-risk patients. The 17 genes associated with genomic instability strongly impacted clinical outcome in breast cancer. Pathway analyses determined chromosome organisation, cell cycle regulation, and RNA processing as the underlying biological processes, thereby offering options for drug development and treatment tailoring. Our work supports the applicability of the 17-marker panel to improve clinical outcome prediction for breast cancer patients based on a signature accounting for genomic instability.

Evaluation of variable selection methods for random forests and omics data sets

Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction performance is selected. However, if the objective is the identification of involved variables to find active networks and pathways, approaches that aim to select all relevant variables should be preferred. We evaluated several variable selection procedures based on simulated data as well as publicly available experimental methylation and gene expression data. Our comparison included the Boruta algorithm, the Vita method, recurrent relative variable importance, a permutation approach and its parametric variant (Altmann) as well as recursive feature elimination (RFE).  In our simulation studies, Boruta was the most powerful approach, followed closely by the Vita method. Both approaches demonstrated similar stability in variable selection, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome. In the analysis of the different experimental data sets, Vita demonstrated slightly better stability in variable selection and was less computationally intensive than Boruta. In conclusion, we recommend the Boruta and Vita approaches for the analysis of high-dimensional data sets. Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings.

TPX2/Aurora kinase A signaling as a potential therapeutic target in genomically unstable cancer cells

Genomic instability is a hallmark feature of cancer cells, and can be caused by defective DNA repair, for instance due to inactivation of BRCA2. Paradoxically, loss of Brca2 in mice results in embryonic lethality, whereas cancer cells can tolerate BRCA2 loss. This holds true for multiple DNA repair genes, and suggests that cancer cells are molecularly "rewired" to cope with defective DNA repair and the resulting high levels of genomic instability. In this study, we aim to identify genes that genomically unstable cancer cells rely on for their survival. Using functional genomic mRNA (FGmRNA) profiling, 16,172 cancer samples were previously ranked based on their degree of genomic instability. We analyzed the top 250 genes that showed a positive correlation between FGmRNA levels and the degree of genomic instability, in a co-functionality network. Within this co-functionality network, a strong cluster of 11 cell cycle-related genes was identified, including TPX2. We then assessed the dependency on these 11 genes in the context of survival of genomically unstable cancer cells, induced by BRCA2 inactivation. Depletion of TPX2 or its associated kinase Aurora-A preferentially reduced cell viability in a panel of BRCA2-deficient cancer cells. In line with these findings, BRCA2-depleted and BRCA2-mutant human cell lines, or tumor cell lines derived from Brca2-/-;p53-/- mice showed increased sensitivity to the Aurora-A kinase inhibitor alisertib, with delayed mitotic progression and frequent mitotic failure. Our findings reveal that BRCA2-deficient cancer cells show enhanced sensitivity to inactivation of TPX2 or its partner Aurora-A, which points at an actionable dependency of genomically unstable cancers.

Mitotic slippage: an old tale with a new twist

Targeting the mitotic machinery using anti-mitotic drugs for elimination of cancer cells is a century-old concept, which continues to be routinely used as a first line of treatment in the clinic. However, patient response remains unpredictable and drug resistance limits effectiveness of these drugs. Cancer cells exit from drug-induced mitotic arrest (mitotic slippage) to avoid subsequent cell death which is thought to be a major mechanism contributing to this resistance. The tumor cells that acquire resistance to anti-mitotic drugs have chromosomal instability (CIN) and are often aneuploid. In this review, we outline the key mechanisms involved in dictating the cell fate during perturbed mitosis and how these processes impede the efficacy of anti-mitotic therapies. Further, we emphasize the recent work from our laboratory, which highlights the functional role of CEP55 in protecting aneuploid cells from death. We also discuss the rationale of targeting CEP55 in vivo, which could prove to be a novel and effective therapeutic strategy for sensitizing cells to microtubule inhibitors and might offer significantly improved patient outcome. Abbreviations: APC/C: Anaphase-Promoting Complex/Cyclosome; BAD: BCL2-Associated agonist of cell Death; BAK1: BCL2 Antagonist Kinase1; BAX: BCL2-Associated X; BCL2: B-cell Chronic Lymphocytic Leukaemia (CLL)/Lymphoma 2; BH: BCL2 Homology Domain; BID: BH3-Interacting domain Death agonist; BIM: BCL2-Interacting Mediator of cell death; BUB: Budding Uninhibited by Benzimidazoles; CDC: Cell Division Cycle; CDH1: Cadherin-1; CDK1: Cyclin-Dependent Kinase 1; CEP55: Centrosomal Protein (55 KDa): CIN: Chromosomal Instability; CTA: Cancer Testis Antigen; EGR1: Early Growth Response protein 1; ERK: Extracellular Signal-Regulated Kinase; ESCRT: Endosomal Sorting Complexes Required for Transport; GIN: Genomic Instability; MAD2: Mitotic Arrest Deficient 2; MCL1: Myeloid Cell Leukemia sequence 1; MPS1: Monopolar Spindle 1 Kinase; MYT1: MYelin Transcription factor 1; PLK1: Polo Like Kinase 1; PUMA: p53-Upregulated Mediator of Apoptosis; SAC: Spindle Assembly Checkpoint; TAA: Tumor-Associated Antigen.

Contradictory mRNA and protein misexpression of EEF1A1 in ductal breast carcinoma due to cell cycle regulation and cellular stress

Encoded by EEF1A1, the eukaryotic translation elongation factor eEF1α1 strongly promotes the heat shock response, which protects cancer cells from proteotoxic stress, following for instance oxidative stress, hypoxia or aneuploidy. Unexpectedly, therefore, we find that EEF1A1 mRNA levels are reduced in virtually all breast cancers, in particular in ductal carcinomas. Univariate and multivariate analyses indicate that EEF1A1 mRNA underexpression independently predicts poor patient prognosis for estrogen receptor-positive (ER+) cancers. EEF1A1 mRNA levels are lowest in the most invasive, lymph node-positive, advanced stage and postmenopausal tumors. In sharp contrast, immunohistochemistry on 100 ductal breast carcinomas revealed that at the protein level eEF1α1 is ubiquitously overexpressed, especially in ER+ , progesterone receptor-positive and lymph node-negative tumors. Explaining this paradox, we find that EEF1A1 mRNA levels in breast carcinomas are low due to EEF1A1 allelic copy number loss, found in 27% of tumors, and cell cycle-specific expression, because mRNA levels are high in G1 and low in proliferating cells. This also links estrogen-induced cell proliferation to clinical observations. In contrast, high eEF1α1 protein levels protect tumor cells from stress-induced cell death. These observations suggest that, by obviating EEF1A1 transcription, cancer cells can rapidly induce the heat shock response following proteotoxic stress, and survive.

Aneuploidy, TP53 mutation, and amplification of MYC correlate with increased intratumor heterogeneity and poor prognosis of breast cancer patients

The clinical course of breast cancer varies from one patient to another. Currently, the choice of therapy relies on clinical parameters and histological and molecular tumor features. Alas, these markers are informative in only a subset of patients. Therefore, additional predictors of disease outcome would be valuable for treatment stratification. Extensive studies showed that the degree of variation of the nuclear DNA content, i.e., aneuploidy, determines prognosis. Our aim was to further elucidate the molecular basis of aneuploidy. We analyzed five diploid and six aneuploid tumors with more than 20 years of follow-up. By performing FISH with a multiplexed panel of 10 probes to enumerate copy numbers in individual cells, and by sequencing 563 cancer-related genes, we analyzed how aneuploidy is linked to intratumor heterogeneity. In our cohort, none of the patients with diploid tumors died of breast cancer during follow-up in contrast to four of six patients with aneuploid tumors (mean survival 86.4 months). The FISH analysis showed markedly increased genomic instability and intratumor heterogeneity in aneuploid tumors. MYC gain was observed in only 20% of the diploid cancers, while all aneuploid cases showed a gain. The mutation burden was similar in diploid and aneuploid tumors, however, TP53 mutations were not observed in diploid tumors, but in all aneuploid tumors in our collective. We conclude that quantitative measurements of intratumor heterogeneity by multiplex FISH, detection of MYC amplification and TP53 mutation could augment prognostication in breast cancer patients.

Translocation Breakpoints Preferentially Occur in Euchromatin and Acrocentric Chromosomes

Chromosomal translocations drive the development of many hematological and some solid cancers. Several factors have been identified to explain the non-random occurrence of translocation breakpoints in the genome. These include chromatin density, gene density and CCCTC-binding factor (CTCF)/cohesin binding site density. However, such factors are at least partially interdependent. Using 13,844 and 1563 karyotypes from human blood and solid cancers, respectively, our multiple regression analysis only identified chromatin density as the primary statistically significant predictor. Specifically, translocation breakpoints preferentially occur in open chromatin. Also, blood and solid tumors show markedly distinct translocation signatures. Strikingly, translocation breakpoints occur significantly more frequently in acrocentric chromosomes than in non-acrocentric chromosomes. Thus, translocations are probably often generated around nucleoli in the inner nucleoplasm, away from the nuclear envelope. Importantly, our findings remain true both in multivariate analyses and after removal of highly recurrent translocations. Finally, we applied pairwise probabilistic co-occurrence modeling. In addition to well-known highly prevalent translocations, such as those resulting in BCR-ABL1 (BCR-ABL) and RUNX1-RUNX1T1 (AML1-ETO) fusion genes, we identified significantly underrepresented translocations with putative fusion genes, which are probably subject to strong negative selection during tumor evolution. Taken together, our findings provide novel insights into the generation and selection of translocations during cancer development.

Chromosomal Instability in Gastric Cancer Biology

Gastric cancer (GC) is the fifth most common cancer in the world and accounts for 7% of the total cancer incidence. The prognosis of GC is dismal in Western countries due to late diagnosis: approximately 70% of the patients die within 5 years following initial diagnosis. Recently, integrative genomic analyses led to the proposal of a molecular classification of GC into four subtypes, i.e.,microsatellite-instable, Epstein-Barr virus–positive, chromosomal-instable (CIN), and genomically stable GCs. Molecular classification of GC advances our knowledge of the biology of GC and may have implications for diagnostics and patient treatment. Diagnosis of microsatellite-instable GC and Epstein-Barr virus–positive GC is more or less straightforward. Microsatellite instability can be tested by immunohistochemistry (MLH1, PMS2, MSH2, and MSH6) and/or molecular-biological analysis. Epstein-Barr virus–positive GC can be tested by in situ hybridization (Epstein-Barr virus encoded small RNA). However, with regard to CIN, testing may be more complicated and may require a more in-depth knowledge of the underlying mechanism leading to CIN. In addition, CIN GC may not constitute a distinct subgroup but may rather be a compilation of a more heterogeneous group of tumors. In this review, we aim to clarify the definition of CIN and to point out the molecular mechanisms leading to this molecular phenotype and the challenges faced in characterizing this type of cancer.

Detection of Circulating Tumor Cells Using Negative Enrichment Immunofluorescence and an In Situ Hybridization System in Pancreatic Cancer

Pancreatic cancer (PC) is the most lethal type of gastrointestinal cancer, and early detection and monitoring is an urgent problem. Circulating tumor cells (CTCs) are emerging as a non-invasive biomarker for tumor detection. However, the low sensitivity is a main problem in the traditional CellSearch System for detecting CTCs, especially in patients with PC. In this study, we used negative enrichment (NE), immunofluorescence and in situ hybridization (FISH) of chromosome 8 (NE-iFISH) to capture and identify CTCs in PC patients. We showed that the NE-iFISH system exhibited a dramatically high detection rate of CTCs in PC patients (90%). The diagnostic rate of PC reached 97.5% when combining CTCs ≥ 2 and carbohydrate antigen 19-9 (CA19-9) > 37 µmol/L. The 1-year survival in the group of CTCs < 3 was significantly higher than that of CTCs ≥ 3 (p = 0.043). In addition, we analyzed the role of chromosomal instability in CTCs detection. The group of triploid (three hybridization signals of chromosome 8) CTCs ≥ 3 showed a shorter 1-year survival (p = 0.0279) and overall survival (p = 0.0188) than the group with triploid CTCs < 3. Importantly, the triploid CTC number but not the overall CTC counts could be a predictor of chemo-sensitivity. Moreover, circulating tumor microembolus (CTMs) were found in stage IV patients, and were positively related to the poor response to chemotherapy. In conclusion, the NE-iFISH system significantly improved the positive detection rate of CTCs and triploid CTC could be used to predict prognosis or the response to the chemotherapy of PC patients. CTM is a potential indicator of the chemotherapeutic effect in advanced PC patients.

Guidelines for the selection of functional assays to evaluate the hallmarks of cancer

The hallmarks of cancer capture the most essential phenotypic characteristics of malignant transformation and progression. Although numerous factors involved in this multi-step process are still unknown to date, an ever-increasing number of mutated/altered candidate genes are being identified within large-scale cancer genomic projects. Therefore, investigators need to be aware of available and appropriate techniques capable of determining characteristic features of each hallmark. We review the methods tailored to experimental cancer researchers to evaluate cell proliferation, programmed cell death, replicative immortality, induction of angiogenesis, invasion and metastasis, genome instability, and reprogramming of energy metabolism. Selecting the ideal method is based on the investigator's goals, available equipment and also on financial constraints. Multiplexing strategies enable a more in-depth data collection from a single experiment - obtaining several results from a single procedure reduces variability and saves time and relative cost, leading to more robust conclusions compared to a single end point measurement. Each hallmark possesses characteristics that can be analyzed by immunoblot, RT-PCR, immunocytochemistry, immunoprecipitation, RNA microarray or RNA-seq. In general, flow cytometry, fluorescence microscopy, and multiwell readers are extremely versatile tools and, with proper sample preparation, allow the detection of a vast number of hallmark features. Finally, we also provide a list of hallmark-specific genes to be measured in transcriptome-level studies. Although our list is not exhaustive, we provide a snapshot of the most widely used methods, with an emphasis on methods enabling the simultaneous evaluation of multiple hallmark features.

A four gene signature predicts benefit from anthracyclines: evidence from the BR9601 and MA.5 clinical trials

Chromosome instability (CIN) in solid tumours results in multiple numerical and structural chromosomal aberrations and is associated with poor prognosis in multiple tumour types. Recent evidence demonstrated CEP17 duplication, a CIN marker, is a predictive marker of anthracycline benefit. An analysis of the BR9601 and MA.5 clinical trials was performed to test the role of existing CIN gene expression signatures as predictive markers of anthracycline sensitivity in breast cancer. Univariate analysis demonstrated, high CIN25 expression score was associated with improved distant relapse free survival (DRFS) (HR: 0.74, 95% CI 0.54-0.99, p = 0.046). High tumour CIN70 and CIN25 scores were associated with aggressive clinicopathological phenotype and increased sensitivity to anthracycline therapy compared to low CIN scores. However, in a prospectively planned multivariate analysis only pathological grade, nodal status and tumour size were significant predictors of outcome for CIN25/CIN70. A limited gene signature was generated, patients with low tumour CIN4 scores benefited from anthracycline treatment significantly more than those with high CIN4 scores (HR 0.37, 95% CI 0.20-0.56, p = 0.001). In multivariate analyses the treatment by marker interaction for CIN4/anthracyclines demonstrated hazard ratio of 0.35 (95% CI 0.15-0.80, p = 0.012) for DRFS. This data shows CIN4 is independent predictor of anthracycline benefit for DRFS in breast cancer.

A four gene signature of chromosome instability (CIN4) predicts for benefit from taxanes in the NCIC-CTG MA21 clinical trial

Recent evidence demonstrated CIN4 as a predictive marker of anthracycline benefit in early breast cancer. An analysis of the NCIC CTG MA.21 clinical trial was performed to test the role of existing CIN gene expression signatures as prognostic and predictive markers in the context of taxane based chemotherapy.

RNA was extracted from patients in cyclophosphamide, epirubicin and fluorouracil (CEF) and epirubicin, cyclophosphamide and paclitaxel (EC/T) arms of the NCIC CTG MA.21 trial and analysed using NanoString technology.

After multivariate analysis both high CIN25 and CIN70 score was significantly associated with an increased in RFS (HR 1.76, 95%CI 1.07-2.86, p=0.0018 and HR 1.59, 95%CI 1.12-2.25, p=0.0096 respectively). Patients whose tumours had low CIN4 gene expression scores were associated with an increase in RFS (HR: 0.64, 95% CI 0.39-1.03, p=0.06) when treated with EC/T compared to patients treated with CEF.

In conclusion we have demonstrated CIN25 and CIN70 as prognostic markers in breast cancer and that CIN4 is a potential predictive maker of benefit from taxane treatment.

Negative Selection and Chromosome Instability Induced by Mad2 Overexpression Delay Breast Cancer but Facilitate Oncogene-Independent Outgrowth

Chromosome instability (CIN) is associated with poor survival and therapeutic outcome in a number of malignancies. Despite this correlation, CIN can also lead to growth disadvantages. Here, we show that simultaneous overexpression of the mitotic checkpoint protein Mad2 with Kras^G12D or Her2 in mammary glands of adult mice results in mitotic checkpoint overactivation and a delay in tumor onset. Time-lapse imaging of organotypic cultures and pathologic analysis prior to tumor establishment reveals error-prone mitosis, mitotic arrest, and cell death. Nonetheless, Mad2 expression persists and increases karyotype complexity in Kras tumors. Faced with the selective pressure of oncogene withdrawal, Mad2-positive tumors have a higher frequency of developing persistent subclones that avoid remission and continue to grow.

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Mad2 overexpression leads to mitotic arrest, cell delamination, and cell death

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High Mad2 levels delay oncogene-induced mammary tumorigenesis

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Mad2 overexpression increases chromosome instability prior to and during tumor growth

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Elevated Mad2 levels facilitate the development of oncogene-independent subclones

Patterns of Chromosomal Aberrations in Solid Tumors

Chromosomal abnormalities are a defining feature of solid tumors. Such cytogenetic alterations are mainly classified into structural chromosomal aberrations and copy number alterations, giving rise to aneuploid karyotypes. The increasing detection of these genetic changes allowed the description of specific tumor entities and the associated patterns of gene expression. In fact, tumor-specific landscapes of gross genomic copy number changes, including aneuploidies of entire chromosome arms and chromosomes result in a global deregulation of the transcriptome of cancer cells. Furthermore, the molecular characterization of cytogenetic abnormalities has provided insights into the mechanisms of tumorigenesis and has, in a few instances, led to the clinical implementation of effective diagnostic and prognostic tools, as well as treatment strategies that target a specific genetic abnormality.