Gene expression profiling to dissect the complexity of cancer biology: Pitfalls and promise

SSBP1 is a novel prognostic marker and promotes disease progression via p38MAPK signaling pathway in multiple myeloma

Multiple myeloma (MM) remains an incurable disease. Identification of meaningful co-expressed gene clusters or representative biomarkers of MM may help to identify new pathological mechanisms and promote the development of new therapies. Here, we performed weighted sgene co-expression network analysis and a series of bioinformatics analysis to identify single stranded DNA binding protein 1 (SSBP1) as novel hub gene associated with MM development and prognosis. In vitro, CRISPR/cas9 mediated knockdown of SSBP1 can significantly inhibit the proliferation of MM cells through inducing apoptosis and cell cycle arrest in G0/G1 phase. We also found that decreased SSBP1 expression significantly increased mitochondrial reactive oxygen species (mtROS) generation and the level of phosphorylated p38MAPK. Furthermore, it was further verified that disruption of SSBP1 expression could inhibit the tumor growth via p38MAPK pathway in a human myeloma xenograft model. In summary, our study is the first to demonstrate that SSBP1 promotes MM development by regulating the p38MAPK pathway.

Identification of Potential Therapeutic Targets for Plasmablastic Lymphoma Through Gene Expression Analysis: Insights into RAS and Wnt Signaling Pathways

Plasmablastic lymphoma (PBL) is a rare and aggressive B-cell lymphoma with overlapping characteristics with diffuse large B-cell lymphoma (DLBCL) and multiple myeloma. Hyperactive Wnt signaling derails homeostasis and promotes oncogenesis and chemoresistance in DLBCL and multiple myeloma. Evidence suggests active cross-talk between the Wnt and RAS pathways impacting metastasis in solid cancers in which combined targeted therapies show effective results. Recent genomic studies in PBL demonstrated a high frequency of mutations linked with the RAS signaling pathway. However, the role of RAS and Wnt signaling pathway molecule expression in PBL remained unknown. We examined the expression of Wnt and RAS pathway-related genes in a well-curated cohort of PBL. Because activated B cells are considered immediate precursors of plasmablasts in B cell development, we compared this data with activated B-cell type DLBCL (ABC-DLBCL) patients, employing NanoString transcriptome analysis (770 genes). Hierarchical clustering revealed distinctive differential gene expression between PBL and ABC-DLBCL. Gene set enrichment analysis labeled the RAS signaling pathway as the most enriched (37 genes) in PBL, including upregulating critical genes, such as NRAS, RAF1, SHC1, and SOS1. Wnt pathway genes were also enriched (n = 22) by gene set enrichment analysis. Molecules linked with Wnt signaling activation, such as ligands or targets (FZD3, FZD7, c-MYC, WNT5A, WNT5B, and WNT10B), were elevated in PBL. Our data also showed that, unlike ABC-DLBCL, the deranged Wnt signaling activity in PBL was not linked with hyperactive nuclear factor κB and B-cell receptor signaling. In divergence, Wnt signaling inhibitors (CXXC4, SFRP2, and DKK1) also showed overexpression in PBL. The high expression of RAS signaling molecules reported may indicate linkage with gain-in-function RAS mutations. In addition, high expression of Wnt and RAS signaling molecules may pave pathways to explore benefiting from combined targeted therapies, as reported in solid cancer, to improve prognosis in PBL patients.

Development of bioinformatics and multi-omics analyses in organoids

Pre-clinical models are critical in gaining mechanistic and biological insights into disease progression. Recently, patient-derived organoid models have been developed to facilitate our understanding of disease development and to improve the discovery of therapeutic options by faithfully recapitulating in vivo tissues or organs. As technological developments of organoid models are rapidly growing, computational methods are gaining attention in organoid researchers to improve the ability to systematically analyze experimental results. In this review, we summarize the recent advances in organoid models to recapitulate human diseases and computational advancements to analyze experimental results from organoids. [BMB Reports 2023; 56(1): 43-48].

Systematic Evaluation of the Diagnostic and Prognostic Significance of Competitive Endogenous RNA Networks in Prostate Cancer

Long non-coding RNA (lncRNA)-mediated competitive endogenous RNA (ceRNA) networks act as essential mechanisms in tumor initiation and progression, but their diagnostic and prognostic significance in prostate cancer (PCa) remains poorly understood. Presently, using the RNA expression data derived from multiple independent PCa-related studies, we constructed a high confidence and PCa-specific core ceRNA network by employing three lncRNA-gene inference approaches and key node filter strategies and then established a logistic model and risk score formula to evaluate its diagnostic and prognostic values, respectively. The core ceRNA network consists of 10 nodes, all of which are significantly associated with clinical outcomes. Combination of expression of the 10 ceRNAs with a logistic model achieved AUC of ROC and PR curve up to ∼96 and 99% in excluding normal prostate samples, respectively. Additionally, a risk score formula constructed with the ceRNAs exhibited significant association with disease-free survival. More importantly, utilizing the expression of RNAs in the core ceRNA network as a molecular signature, the TCGA-PRAD cohort was divided into four novel clinically relevant subgroups with distinct expression patterns, highlighting a feasible way for improving patient stratification in the future. Overall, we constructed a PCa-specific core ceRNA network, which provides diagnostic and prognostic value.

Etiology-Specific Analysis of Hepatocellular Carcinoma Transcriptome Reveals Genetic Dysregulation in Pathways Implicated in Immunotherapy Efficacy

Immunotherapy has emerged in recent years as arguably the most effective treatment for advanced hepatocellular carcinoma (HCC), but the failure of a large percentage of patients to respond to immunotherapy remains as the ultimate obstacle to successful treatment. Etiology-associated dysregulation of immune-associated (IA) genes may be central to the development of this differential clinical response. We identified immune-associated genes potentially dysregulated by alcohol or viral hepatitis B in HCC and validated alcohol-induced dysregulations in vitro while using large-scale RNA-sequencing data from The Cancer Genome Atlas (TCGA). Thirty-four clinically relevant dysregulated IA genes were identified. We profiled the correlation of all genomic alterations in HCC patients to IA gene expression while using the information theory-based algorithm REVEALER to investigate the molecular mechanism for their dysregulation and explore the possibility of genome-based patient stratification. We also studied gene expression regulators and identified multiple microRNAs that were implicated in HCC pathogenesis that can potentially regulate these IA genes' expression. Our study identified potential key pathways, including the IL-7 signaling pathway and TNFRSF4 (OX40)- NF-κB pathway, to target in immunotherapy treatments and presents microRNAs as promising therapeutic targets for dysregulated IA genes because of their extensive regulatory roles in the cancer immune landscape.

Identification of gene expression levels in primary melanoma associated with clinically meaningful characteristics

Factors influencing melanoma survival include sex, age, clinical stage, lymph node involvement, as well as Breslow thickness, presence of tumor-infiltrating lymphocytes based on histological analysis of primary melanoma, mitotic rate, and ulceration. Identification of genes whose expression in primary tumors is associated with these key tumor/patient characteristics can shed light on molecular mechanisms of melanoma survival. Here, we show results from a gene expression analysis of formalin-fixed paraffin-embedded primary melanomas with extensive clinical annotation. The Cancer Genome Atlas data on primary melanomas were used for validation of nominally significant associations. We identified five genes that were significantly associated with the presence of tumor-infiltrating lymphocytes in the joint analysis after adjustment for multiple testing: IL1R2, PPL, PLA2G3, RASAL1, and SGK2. We also identified two genes significantly associated with melanoma metastasis to the regional lymph nodes (PIK3CG and IL2RA), and two genes significantly associated with sex (KDM5C and KDM6A). We found that LEF1 was significantly associated with Breslow thickness and CCNA2 and UBE2T with mitosis. RAD50 was the gene most significantly associated with survival, with a higher level of expression associated with worse survival.

Emerging paradigms in the treatment of liver metastases in colorectal cancer

Efforts to combat colorectal cancer have benefited from improved screening and surveillance, which facilitates early detection. The survival rate associated with diagnosis at stage I is approximately 90%. However, progress in improving survival in metastatic colorectal cancer (mCRC) has been minimal. This review focuses on mCRC with special emphasis on the molecular aspects of liver metastases, which is one of the most frequently involved organ site. Better molecular evidence is required to guide the decisions for surgical and other interventions used in the clinical management of mCRC. Results from different treatment modalities have exposed significant gaps in the existing paradigms of the mCRC management. Indeed there is a critical need to better understand molecular events and pathways that lead to colorectal cancer liver metastasis. Such a focused approach may help identify biomarkers and drug targets that can be useful in the clinical applications. With this focus, we provide an account of the molecular pathways involved in the spread of CRC to the liver. Specifically, the molecular changes at the DNA and RNA levels that are associated with liver metastases are discussed. Similarly, we describe relevant microRNAs that are identified as regulators of gene expression and can also serve as biomarkers. Conventionally applied biomarkers are not yet specific and sensitive enough to be relied in routine clinical decision making. Hence search for novel biomarkers is critically needed especially if these can be utilized using liquid biopsies. This review provides a comprehensive analysis of current molecular evidence along with potential future directions that could reshape the diagnostic and management paradigms and thus mitigate the devastating impact of colorectal cancer metastasis to the liver.

DynSig: Modelling Dynamic Signaling Alterations along Gene Pathways for Identifying Differential Pathways

Although a number of methods have been proposed for identifying differentially expressed pathways (DEPs), few efforts consider the dynamic components of pathway networks, i.e., gene links. We here propose a signaling dynamics detection method for identification of DEPs, DynSig, which detects the molecular signaling changes in cancerous cells along pathway topology. Specifically, DynSig relies on gene links, instead of gene nodes, in pathways, and models the dynamic behavior of pathways based on Markov chain model (MCM). By incorporating the dynamics of molecular signaling, DynSig allows for an in-depth characterization of pathway activity. To identify DEPs, a novel statistic of activity alteration of pathways was formulated as an overall signaling perturbation score between sample classes. Experimental results on both simulation and real-world datasets demonstrate the effectiveness and efficiency of the proposed method in identifying differential pathways.

A data mining approach for identifying pathway-gene biomarkers for predicting clinical outcome: A case study of erlotinib and sorafenib

A novel data mining procedure is proposed for identifying potential pathway-gene biomarkers from preclinical drug sensitivity data for predicting clinical responses to erlotinib or sorafenib. The analysis applies linear ridge regression modeling to generate a small (N~1000) set of baseline gene expressions that jointly yield quality predictions of preclinical drug sensitivity data and clinical responses. Standard clustering of the pathway-gene combinations from gene set enrichment analysis of this initial gene set, according to their shared appearance in molecular function pathways, yields a reduced (N~300) set of potential pathway-gene biomarkers. A modified method for quantifying pathway fitness is used to determine smaller numbers of over and under expressed genes that correspond with favorable and unfavorable clinical responses. Detailed literature-based evidence is provided in support of the roles of these under and over expressed genes in compound efficacy. RandomForest analysis of potential pathway-gene biomarkers finds average treatment prediction errors of 10% and 22%, respectively, for patients receiving erlotinib or sorafenib that had a favorable clinical response. Higher errors were found for both compounds when predicting an unfavorable clinical response. Collectively these results suggest complementary roles for biomarker genes and biomarker pathways when predicting clinical responses from preclinical data.

Discovery of genes from feces correlated with colorectal cancer progression

Colorectal cancer (CRC) is considered to develop slowly via a progressive accumulation of genetic mutations. Markers of CRC may serve to provide the basis for decision-making, and may assist in cancer prevention, detection and prognostic prediction. DNA and messenger (m)RNA molecules that are present in human feces faithfully represent CRC manifestations. In the present study, exogenous mouse cells verified the feasibility of total fecal RNA as a marker of CRC. Furthermore, five significant genes encoding solute carrier family 15, member 4 (SLC15A4), cluster of differentiation (CD)44, 3-oxoacid CoA-transferase 1 (OXCT1), placenta-specific 8 (PLAC8) and growth arrest-specific 2 (GAS2), which are differentially expressed in the feces of CRC patients, were verified in different CRC cell lines using quantitative polymerase chain reaction. The present study demonstrated that the mRNA level of SLC15A4 was increased in the majority of CRC cell lines evaluated (SW1116, LS123, Caco-2 and T84). An increased level of CD44 mRNA was only detected in an early-stage CRC cell line, SW1116, whereas OXCT1 was expressed at higher levels in the metastatic CRC cell line CC-M3. In addition, two genes, PLAC8 and GAS2, were highly expressed in the recurrent CRC cell line SW620. Genes identified in the feces of CRC patients differed according to their clinical characteristics, and this differential expression was also detected in the corresponding CRC cell lines. In conclusion, feces represent a good marker of CRC and can be interpreted through the appropriate CRC cell lines.

NETBAGs: a network-based clustering approach with gene signatures for cancer subtyping analysis

AIM

To evaluate gene signature and network-based approach for cancer subtyping and classification.

MATERIALS & METHODS

Here we introduced NETwork Based clustering Approach with Gene signatures (NETBAGs) algorithm, which clustered samples based on gene signatures and identified molecular markers based on their significantly expressed gene network profiles.

RESULTS

Applying NETBAGs to multiple independent breast cancer datasets, we demonstrated that the clustering results were highly associated with the clinical subtypes and clearly revealed the genomic diversity of breast cancer samples.

CONCLUSION

NETBAGs algorithm is able to classify samples by their genomic signatures into clinically significant phenotypes so that potential biomarkers can be identified. The approach may contribute to cancer research and clinical study of complex diseases.

Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles

Characterization of tumors at the molecular level has improved our knowledge of cancer causation and progression. Proteomic analysis of their signaling pathways promises to enhance our understanding of cancer aberrations at the functional level, but this requires accurate and robust tools. Here, we develop a state of the art quantitative mass spectrometric pipeline to characterize formalin-fixed paraffin-embedded tissues of patients with closely related subtypes of diffuse large B-cell lymphoma. We combined a super-SILAC approach with label-free quantification (hybrid LFQ) to address situations where the protein is absent in the super-SILAC standard but present in the patient samples. Shotgun proteomic analysis on a quadrupole Orbitrap quantified almost 9,000 tumor proteins in 20 patients. The quantitative accuracy of our approach allowed the segregation of diffuse large B-cell lymphoma patients according to their cell of origin using both their global protein expression patterns and the 55-protein signature obtained previously from patient-derived cell lines (Deeb, S. J., D'Souza, R. C., Cox, J., Schmidt-Supprian, M., and Mann, M. (2012) Mol. Cell. Proteomics 11, 77–89). Expression levels of individual segregation-driving proteins as well as categories such as extracellular matrix proteins behaved consistently with known trends between the subtypes. We used machine learning (support vector machines) to extract candidate proteins with the highest segregating power. A panel of four proteins (PALD1, MME, TNFAIP8, and TBC1D4) is predicted to classify patients with low error rates. Highly ranked proteins from the support vector analysis revealed differential expression of core signaling molecules between the subtypes, elucidating aspects of their pathobiology.

Transferring genomics to the clinic: distinguishing Burkitt and diffuse large B cell lymphomas

BACKGROUND

Classifiers based on molecular criteria such as gene expression signatures have been developed to distinguish Burkitt lymphoma and diffuse large B cell lymphoma, which help to explore the intermediate cases where traditional diagnosis is difficult. Transfer of these research classifiers into a clinical setting is challenging because there are competing classifiers in the literature based on different methodology and gene sets with no clear best choice; classifiers based on one expression measurement platform may not transfer effectively to another; and, classifiers developed using fresh frozen samples may not work effectively with the commonly used and more convenient formalin fixed paraffin-embedded samples used in routine diagnosis.

METHODS

Here we thoroughly compared two published high profile classifiers developed on data from different Affymetrix array platforms and fresh-frozen tissue, examining their transferability and concordance. Based on this analysis, a new Burkitt and diffuse large B cell lymphoma classifier (BDC) was developed and employed on Illumina DASL data from our own paraffin-embedded samples, allowing comparison with the diagnosis made in a central haematopathology laboratory and evaluation of clinical relevance.

RESULTS

We show that both previous classifiers can be recapitulated using very much smaller gene sets than originally employed, and that the classification result is closely dependent on the Burkitt lymphoma criteria applied in the training set. The BDC classification on our data exhibits high agreement (~95 %) with the original diagnosis. A simple outcome comparison in the patients presenting intermediate features on conventional criteria suggests that the cases classified as Burkitt lymphoma by BDC have worse response to standard diffuse large B cell lymphoma treatment than those classified as diffuse large B cell lymphoma.

CONCLUSIONS

In this study, we comprehensively investigate two previous Burkitt lymphoma molecular classifiers, and implement a new gene expression classifier, BDC, that works effectively on paraffin-embedded samples and provides useful information for treatment decisions. The classifier is available as a free software package under the GNU public licence within the R statistical software environment through the link http://www.bioinformatics.leeds.ac.uk/labpages/softwares/ or on github https://github.com/Sharlene/BDC.

Just caring: assessing the ethical and economic costs of personalized medicine

Personalized medicine has been touted as a revolutionary form of cancer care. It has been portrayed as precision medicine, targeting with deadly accuracy cancer cells and sparing patients the debilitating broad-spectrum side effects of more traditional forms of cancer therapy. But personalized medicine still has its costs to patients and society, both moral and economic costs. How to recognize and address those issues will be the focus of this essay. We start with these questions: Does everyone faced with cancer have a moral right to the most effective cancer care available, no matter what the cost, no matter whether a particular individual has the personal ability to pay for that care or not? Or are there limits to the cancer care that anyone has a right to at social expense? If so, what are those limits and how are those limits to be determined? Are those limits a matter of both morality and economics? I will answer this last question in the affirmative. This is what I refer to as the "Just Caring" problem in health care.

Cancer stem cells, a fuzzy evolving concept: a cell population or a cell property?

The cancer stem cells (CSC) hypothesis represents a pathological extrapolation of the physiological concept of embryonic and somatic stem cells. In its initial definition, it encompassed the hypothesis of a qualitatively distinct population of immortal cancer cells originating from somatic stem cells, which generate in xenotransplants by a deterministic irreversible process, the hierarchy of more differentiated finite lifespan derived cells, which constitute, themselves, the bulk of the cancer. These CSC would express specific biomarkers and gene expressions related to chemo- and radioresistance, stemness, epithelial–mesenchymal transition, etc.

No convincing congruence of several of these properties in one cell population has been demonstrated. The concept has greatly evolved with time and with different authors (“the plasticity of cancer stem cells”), leading to a minimal definition of cells generating a hierarchy of derived cells. In this article these concepts are analyzed. It is proposed that stemness is a property, more or less reversible, a hallmark of some cells at some time in a cancer cell population, as immortality, dormancy, chemo- or radioresistance, epithelial–mesenchymal transition etc. These phenotypic properties represent the result of independent, linked, or more or less congruent, genetic, epigenetic, or signaling programs.

Network-based stratification of tumor mutations

Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence.

A transcriptional and metabolic signature of primary aneuploidy is present in chromosomally unstable cancer cells and informs clinical prognosis

Aneuploidy is invariably associated with poor proliferation of primary cells, but the specific contributions of abnormal karyotypes to cancer, a disease characterized by aneuploidy and dysregulated proliferation, remain unclear. In this study, I demonstrate that the transcriptional alterations caused by aneuploidy in primary cells are also present in chromosomally unstable cancer cell lines, but the same alterations are not common to all aneuploid cancers. Chromosomally unstable cancer lines and aneuploid primary cells also share an increase in glycolytic and TCA cycle flux. The biological response to aneuploidy is associated with cellular stress and slow proliferation, and a 70-gene signature derived from primary aneuploid cells was defined as a strong predictor of increased survival in several cancers. Inversely, a transcriptional signature derived from clonal aneuploidy in tumors correlated with high mitotic activity and poor prognosis. Together, these findings suggested that there are two types of aneuploidy in cancer: one is clonal aneuploidy, which is selected during tumor evolution and associated with robust growth, and the other is subclonal aneuploidy caused by chromosomal instability (CIN). Subclonal aneuploidy more closely resembles the stressed state of primary aneuploid cells, yet CIN is not benign; a subset of genes upregulated in high-CIN cancers predict aggressive disease in human patients in a proliferation-independent manner.

"Just caring": can we afford the ethical and economic costs of circumventing cancer drug resistance?

Personalized medicine has been presented in public and professional contexts in excessively optimistic tones. In the area of cancer what has become clear is the extraordinary heterogeneity and resilience of tumors in the face of numerous targeted therapies. This is the problem of cancer drug resistance. I summarize this problem in the first part of this essay. I then place this problem in the context of the larger political economic problem of escalating health care costs in both the EU and the US. In turn, that needs to be placed within an ethical context: How should we fairly distribute access to needed health care for an enormous range of health care needs when we have only limited resources (money) to meet virtually unlimited health care needs (cancer and everything else)? This is the problem of health care rationing. It is inescapable as a moral problem and requires a just resolution. Ultimately that resolution must be forged through a process of rational democratic deliberation.

Clinical value of prognosis gene expression signatures in colorectal cancer: a systematic review

Introduction

The traditional staging system is inadequate to identify those patients with stage II colorectal cancer (CRC) at high risk of recurrence or with stage III CRC at low risk. A number of gene expression signatures to predict CRC prognosis have been proposed, but none is routinely used in the clinic. The aim of this work was to assess the prediction ability and potential clinical usefulness of these signatures in a series of independent datasets.

Methods

A literature review identified 31 gene expression signatures that used gene expression data to predict prognosis in CRC tissue. The search was based on the PubMed database and was restricted to papers published from January 2004 to December 2011. Eleven CRC gene expression datasets with outcome information were identified and downloaded from public repositories. Random Forest classifier was used to build predictors from the gene lists. Matthews correlation coefficient was chosen as a measure of classification accuracy and its associated p-value was used to assess association with prognosis. For clinical usefulness evaluation, positive and negative post-tests probabilities were computed in stage II and III samples.

Results

Five gene signatures showed significant association with prognosis and provided reasonable prediction accuracy in their own training datasets. Nevertheless, all signatures showed low reproducibility in independent data. Stratified analyses by stage or microsatellite instability status showed significant association but limited discrimination ability, especially in stage II tumors. From a clinical perspective, the most predictive signatures showed a minor but significant improvement over the classical staging system.

Conclusions

The published signatures show low prediction accuracy but moderate clinical usefulness. Although gene expression data may inform prognosis, better strategies for signature validation are needed to encourage their widespread use in the clinic.