Gene signatures of drug resistance predict patient survival in colorectal cancer

PPARG activation promotes the proliferation of colorectal cancer cell lines and enhances the antiproliferative effect of 5-fluorouracil

BACKGROUND

Peroxisome proliferator-activated receptor gamma (PPARG) is a member of the nuclear receptor family. It is involved in the regulation of adipogenesis, lipid metabolism, insulin sensitivity, vascular homeostasis and inflammation. In addition, PPARG agonists, known as thiazolidinediones, are well established in the treatment of type 2 diabetes mellitus. PPARGs role in cancer is a matter of debate, as pro- and anti-tumour properties have been described in various tumour entities. Currently, the specific role of PPARG in patients with colorectal cancer (CRC) is not fully understood.

MATERIAL AND METHODS

The prognostic impact of PPARG expression was investigated by immunohistochemistry in a case-control study using a matched pair selection of CRC tumours (n = 246) with either distant metastases to the liver (n = 82), lung (n = 82) or without distant metastases (n = 82). Its effect on proliferation as well as the sensitivity to the chemotherapeutic drug 5-fluorouracil (5-FU) was examined after activation, inhibition, and transient gene knockdown of PPARG in the CRC cell lines SW403 and HT29.

RESULTS

High PPARG expression was significantly associated with pulmonary metastasis (p = 0.019). Patients without distant metastases had a significantly longer overall survival with low PPARG expression in their tumours compared to patients with high PPARG expression (p = 0.045). In the pulmonary metastasis cohort instead, a trend towards longer survival was observed for patients with high PPARG expression in their tumour (p = 0.059). Activation of PPARG by pioglitazone and rosiglitazone resulted in a significant dose-dependent increase in proliferation of CRC cell lines. Inhibition of PPARG by its specific inhibitor GW9662 and siRNA-mediated knockdown of PPARG significantly decreased proliferation. Activating PPARG significantly increased the CRC cell lines sensitivity to 5-FU while its inhibition decreased it.

CONCLUSION

The prognostic effect of PPARG expression depends on the metastasis localization in advanced CRC patients. Activation of PPARG increased malignancy associated traits such as proliferation in CRC cell lines but also increases sensitivity towards the chemotherapeutic agent 5-FU. Based on this finding, a combination therapy of PPARG agonists and 5-FU-based chemotherapy constitutes a promising strategy which should be further investigated.

RAB31 drives extracellular vesicle fusion and cancer-associated fibroblast formation leading to oxaliplatin resistance in colorectal cancer

Epithelial-mesenchymal transition (EMT) is associated with tumorigenesis and drug resistance. The Rab superfamily of small G-proteins plays a role in regulating cell cytoskeleton and vesicle transport. However, it is not yet clear how the Rab family contributes to cancer progression by participating in EMT. By analysing various in silico datasets, we identified a statistically significant increase in RAB31 expression in the oxaliplatin-resistant group compared to that in the parental or other chemotherapy drug groups. Our findings highlight RAB31's powerful effect on colorectal cancer cell lines when compared with other family members. In a study that analysed multiple online meta-databases, RAB31 RNA levels were continually detected in colorectal tissue arrays. Additionally, RAB31 protein levels were correlated with various clinical parameters in clinical databases and were associated with negative prognoses for patients. RAB31 expression levels in all three probes were calculated using a computer algorithm and were found to be positively correlated with EMT scores. The expression of the epithelial-type marker CDH1 was suppressed in RAB31 overexpression models, whereas the expression of the mesenchymal-type markers SNAI1 and SNAI2 increased. Notably, RAB31-induced EMT and drug resistance are dependent on extracellular vesicle (EV) secretion. Interactome analysis confirmed that RAB31/AGR2 axis-mediated exocytosis was responsible for maintaining colorectal cell resistance to oxaliplatin. Our study concluded that RAB31 alters the sensitivity of oxaliplatin, a supplementary chemotherapy approach, and is an independent prognostic factor that can be used in the treatment of colorectal cancer.

CircPOFUT1 fosters colorectal cancer metastasis and chemoresistance via decoying miR-653-5p/E2F7/WDR66 axis and stabilizing BMI1

CircRNAs are implicated in colorectal cancer (CRC) development and progression. Protein O-fucosyltransferase 1 (POFUT1) plays an oncogenic role via activating Notch1 signaling in CRC. However, the roles of circPOFUT1, which is originated from POFUT1, have not been investigated. Our study showed circPOFUT1 was highly expressed in CRC tissues and cells. CircPOFUT1 enhanced the proliferation, migration and invasion of CRC cells, and promoted tumor growth and liver metastasis in vivo. It also reinforced stemness and chemoresistance of CRC cells. Mechanistically, circPOFUT1 regulated the function of E2F7 via sponging miR-653-5p, thereby transcriptionally inducing WDR66 expression and further promoting metastasis in CRC. On the other hand, circPOFUT1 promoted stemness and chemoresistance of CRC cells via stabilizing BMI1 in an IGF2BP1-dependent manner. In conclusion, circPOFUT1 fosters CRC metastasis and chemoresistance via decoying miR-653-5p/E2F7/WDR66 axis and stabilizing BMI1.

Identification of ferroptosis and drug resistance related hub genes to predict the prognosis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Currently, overcoming the drug resistance in HCC is a critical challenge and ferroptosis has emerged as a promising therapeutic option for cancer. We aim to construct a new gene signature related to ferroptosis and drug resistance to predict the prognosis in HCC. The RNA-seq data of HCC patients was obtained from the Cancer Genome Atlas database. Using least absolute shrinkage and selection operator cox regression, Kaplan-Meier analysis, and differential analysis, we constructed a prognostic model consisting of six hub genes (TOP2A, BIRC5, VEGFA, HIF1A, FTH1, ACSL3) related to ferroptosis and drug resistance in HCC. Functional enrichment, pathway enrichment and GSEA analysis were performed to investigate the potential molecular mechanism, and construction of PPI, mRNA-miRNA, mRNA-RBP, mRNA-TF and mRNA-drugs interaction networks to predict its interaction with different molecules. Clinical prognostic characteristics were revealed by univariate, multivariate cox regression analysis and nomogram. We also analyzed the relationship between the signature, immune checkpoints, and drug sensitivity. The expression of the gene signature was detected in HCC cell lines and HPA database. Our prognostic model classified patients into high and low-risk groups based on the risk scores and found the expression level of the genes was higher in the high-risk group than the low-risk group, demonstrating that high expression of the hub genes was associated with poor prognosis in HCC. ROC analysis revealed its high diagnostic efficacy in both HCC and normal tissues. The proportional hazards model and calibration analysis confirmed that the model's prediction was most accurate for 1- and 3-years survival. QRT-PCR showed the high expression level of the gene signature in HCC. Our study built a novel gene signature with good potential to predict the prognosis of HCC, which may provide new therapeutic targets and molecular mechanism for HCC diagnosis and treatment.

Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents

BACKGROUND

Detection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches.

METHODS

To identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein-protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins.

RESULTS

We identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC.

CONCLUSION

The findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.

Gene Regulatory Network-Classifier: Gene Regulatory Network-Based Classifier and Its Applications to Gastric Cancer Drug (5-Fluorouracil) Marker Identification

The complex mechanisms of diseases involve the disturbance of the molecular network, rather than disorder in a single gene, implying that single gene-based analysis is insufficient to understand these mechanisms. Gene regulatory networks (GRNs) have attracted a lot of interest and various approaches have been developed for their statistical inference and gene network-based analysis. Although various computational methods have been developed, relatively little attention has been paid to incorporation of biological knowledge into the computational approaches. Furthermore, existing studies on network-based analysis perform prediction/classification of status of cell lines based on preconstructed GRNs, implying that we cannot extract prediction/classification-specific gene networks, leading to difficulty in interpretation of biological mechanisms and marker identification related to the status of cancer cell lines. We developed a novel strategy to build a GRN-based classifier, called a GRN-classifier. The proposed GRN-classifier estimates GRNs and classifies cell lines simultaneously, where the gene network is estimated to minimize error in gene network estimation and the negative log-likelihood for classifying cell lines. Thus, we can identify biological status-specific gene regulatory systems, enabling us to achieve biologically reliable interpretation of the classification. We also propose an algorithm to implement the GRN-classifier based on coordinate descent update. Monte Carlo simulations were conducted to examine performance of the GRN-classifier. Results: Our strategy provides effective results in feature selection in the classification model and edge selection in gene network estimation. The GRN-classifier also shows outstanding classification accuracy. We apply the GRN-classifier to classify cancer cell lines into anticancer drug-related status, that is, 5-fluorouracil (5-FU)-sensitive/resistant and 5-FU target/nontarget cancer cell lines. We then identified 5-FU markers based on 5-FU-related status classification-specific gene networks. The mechanisms of the identified markers were verified through literature survey. Our results suggest that the molecular interplay between MYOF and AHNAK2 may play a crucial role in drug resistance and can provide information on the chemotherapy efficiency of 5-FU. It is also suggested that suppression of the identified 5-FU markers, including MYOF/AHNAK2 and AKR1C1/AKR1C3 may improve 5-FU resistance of cancer cell lines.

Molecular Biomarker of Drug Resistance Developed From Patient-Derived Organoids Predicts Survival of Colorectal Cancer Patients

The drug 5-fluorouracil (5-Fu) is the critical composition of colorectal cancer (CRC) treatments. Prognostic and predictive molecular biomarkers for CRC patients (CRCpts) treated with 5-Fu-based chemotherapy can provide assistance for tailoring treatment approach. Here, we established a molecular biomarker of 5-Fu resistance derived from colorectal cancer organoids (CRCOs) for predicting the survival of CRCpts. Forty-one CRCO cultures were generated from 50 CRC tumor tissues after surgery (82%). The following experiments revealed a great diversity in drug sensitivity for 10 μM 5-Fu treatment tested by using organoid size change. Fourteen cases (34.1%) were 5-Fu sensitive and the other 27 (65.9%) were resistant. Then, differentially expressed genes (DEGs) associated with 5-Fu resistance were outputted by transcriptome sequencing. In particular, DEGs were generated in two comparison groups: 1) 5-Fu sensitive and resistant untreated CRCOs; 2) CRCOs before 5-Fu treatment and surviving CRCOs after 5-Fu treatment. Some molecules and most of the pathways that have been reported to be involved in 5-Fu resistance were identified in the current research. By using DEGs correlated with 5-Fu resistance and survival of CRCpts, the gene signature and drug-resistant score model (DRSM) containing five molecules were established in The Cancer Genome Atlas (TCGA)-CRC cohort by least absolute shrinkage and selection operator (LASSO) regression analysis and 5-fold cross-validation. Multivariate analysis revealed that drug-resistant score (DRS) was an independent prognostic factor for overall survival (OS) in CRCpts in TCGA-CRC cohort (P < 0.001). Further validation results from four Gene Expression Omnibus (GEO) cohorts elucidated that the DRSM based on five genes related to 5-Fu chemosensitivity and developed from patient-derived organoids can predict survival of CRCpts. Meanwhile, our model could predict the survival of CRCpts in different subgroups. Besides, the difference of molecular pathways, tumor mutational burden (TMB), immune response-related pathways, immune score, stromal score, and immune cell proportion were dissected between DRS-high and DRS-low patients in TCGA-CRC cohort.

A Colorectal Cancer 3D Bioprinting Workflow as a Platform for Disease Modeling and Chemotherapeutic Screening

Colorectal cancer (CRC) is the third most common malignancy and has recently moved up to the second leading cause of death among carcinomas. Prognosis, especially for advanced diseases or certain molecular subtypes of CRC, remains poor, which highlights the urgent need for better therapeutic strategies. However, currently, as little as 0.1% of all drugs make it from bench to bedside because of the inherently high false-positive and false-negative rates of current preclinical and clinical drug testing data. Therefore, the success of developing novel treatment agents lies in the introduction of improved preclinical disease models which resemble in vivo carcinomas closer, possess higher predictive properties, and offer opportunities for individualized therapies. Aiming to address these needs, we have established an affordable, flexible, and highly reproducible 3D bioprinted CRC model. The histological assessment of Caco-2 cells in 3D bioprints revealed the formation of glandular-like structures which show greater pathomorphological resemblance to tumors than monolayer cultures do. RNA expression profiles in 3D bioprinted cells were marked by upregulation of genes involved in cell adhesion, hypoxia, EGFR/KRAS signaling, and downregulation of cell cycle programs. Testing this 3D experimental platform with three of the most commonly used chemotherapeutics in CRC (5-fluoruracil, oxaliplatin, and irinotecan) revealed overall increased resistance compared to 2D cell cultures. Last, we demonstrate that our workflow can be successfully extended to primary CRC samples. Thereby, we describe a novel accessible platform for disease modeling and drug testing, which may present an innovative opportunity for personalized therapeutic screening.

A multiparametric extraction method for Vn96-isolated plasma extracellular vesicles and cell-free DNA that enables multi-omic profiling

Extracellular vesicles (EVs) have been recognized as a rich material for the analysis of DNA, RNA, and protein biomarkers. A remaining challenge for the deployment of EV-based diagnostic and prognostic assays in liquid biopsy testing is the development of an EV isolation method that is amenable to a clinical diagnostic lab setting and is compatible with multiple types of biomarker analyses. We have previously designed a synthetic peptide, known as Vn96 (ME kit), which efficiently isolates EVs from multiple biofluids in a short timeframe without the use of specialized lab equipment. Moreover, it has recently been shown that Vn96 also facilitates the co-isolation of cell-free DNA (cfDNA) along with EVs. Herein we describe an optimized method for Vn96 affinity-based EV and cfDNA isolation from plasma samples and have developed a multiparametric extraction protocol for the sequential isolation of DNA, RNA, and protein from the same plasma EV and cfDNA sample. We are able to isolate sufficient material by the multiparametric extraction protocol for use in downstream analyses, including ddPCR (DNA) and ‘omic profiling by both small RNA sequencing (RNA) and mass spectrometry (protein), from a minimum volume (4 mL) of plasma. This multiparametric extraction protocol should improve the ability to analyse multiple biomarker materials (DNA, RNA and protein) from the same limited starting material, which may improve the sensitivity and specificity of liquid biopsy tests that exploit EV-based and cfDNA biomarkers for disease detection and monitoring.

A promising naphthoquinone [8-hydroxy-2-(2-thienylcarbonyl)naphtho[2,3-b]thiophene-4,9-dione] exerts anti-colorectal cancer activity through ferroptosis and inhibition of MAPK signaling pathway based on RNA sequencing

Naphthoquinones are naturally occurring metabolites with recognized anti-cancer potential but limited clinical application. This study investigated the molecular mechanism of 8-hydroxy-2-(2-thenoyl)naphtho[2,3-b]thiophene-4,9-dione (1), a new candidate for colorectal cancer (CRC) treatment, using different experimental settings: MTT, clonogenic, wound healing, and cell cycle assays; as well as RNA sequencing. Naphthoquinone 1 selectively reduced the viability and migration of HT-29 cells by G2/M arrest and changes in their transcriptome signature with significant effect on cellular survival, proliferation, angiogenesis, response to interferon, oxidative stress, and immune response. Impact analysis identified ferroptosis and MAPK pathways as significantly affected. In summary, our results suggest that 1 induces the selective death of CRC cells by inducing oxidative stress, ferroptosis, and MAPK inhibition.

Exploration of gastric neuroendocrine carcinoma (GNEC) specific signaling pathways involved in chemoresistance via transcriptome and in vitro analysis

Gastric neuroendocrine carcinoma (GNEC) is rare cancer detected in the stomach. Previously, we demonstrated that the poorer prognosis of GNEC patients compared with gastric adenocarcinoma (GAC) patients was probably due to the lack of response to chemotherapy. Thus, it is crucial to study the specific GNEC gene expression pattern and investigate chemoresistance mechanism of GNEC. The transcriptome of GNEC patients was compared with that of GAC patients using RNA-seq. The KEGG analysis was employed to explore the specific differential expression gene function enrichment pattern. In addition, the transcriptomes of two GNEC cell lines, ECC10 and ECC12, were also compared with those of two GAC cell lines, MGC-803 and AGS, using RNA-seq. Comparing patient samples and cell lines transcriptome data, we try to uncover the potential targets and pathways which may affect the chemoresistance of GNEC. By combing all transcriptome data, we identified 22 key genes that were specifically up-regulated in GNEC. This panel of genes probably involves in the chemoresistance of GNEC. From our current experimental data, NeuroD1, one of the 22 genes, is associated with the prognosis of GNEC patients. Knockdown of NeuroD1 enhanced the sensitivity to irinotecan of GNEC cell lines. Our research sheds light in identifying a panel of novel therapeutic target specifically for GNEC clinical treatment which has not been reported before.

Identification and Characterization of the Copy Number Dosage-Sensitive Genes in Colorectal Cancer

Dosage effect is one of the common mechanisms of somatic copy number alteration in the development of colorectal cancer, yet the roles of dosage-sensitive genes (DSGs) in colorectal cancer (CRC) remain to be characterized more deeply. In this study, we developed a five-step pipeline to identify DSGs and analyzed their characterization in CRC. Results showed that our pipeline performed better than existing methods, and the result was significantly overlapped between solid tumor and cell line. We also found that the top five DSGs (PSMF1, RAF1, PTPRA, MKRN2, and ELP3) were associated with the progression of CRC. By analyzing the characterization, DSGs were enriched in driver genes and they drove sub-pathways of CRC. In addition, immune-related DSGs are associated with CRC progression. Our results also showed that the CRC samples affected by high microsatellites have fewer DSGs, but a higher overlap with DSGs in microsatellite low instability and microsatellite stable samples. In addition, we applied DSGs to identify potential drug targets, with the results showing that 22 amplified DSGs were more sensitive to four drugs. In conclusion, DSGs play an important role in CRC, and our pipeline is effective to identify them.

Identification of the Effects of Aspirin and Sulindac Sulfide on the Inhibition of HMGA2-Mediated Oncogenic Capacities in Colorectal Cancer

Distant metastatic colorectal cancer (CRC) is present in approximately 25% of patients at initial diagnosis, and eventually half of CRC patients will develop metastatic disease. The 5-year survival rate for patients with metastatic CRC is a mere 12.5%; thus, there is an urgent need to investigate the molecular mechanisms of cancer progression in CRC. High expression of human high-mobility group A2 (HMGA2) is related to tumor progression, a poor prognosis, and a poor response to therapy for CRC. Therefore, HMGA2 is an attractive target for cancer therapy. In this study, we identified aspirin and sulindac sulfide as novel potential inhibitors of HMGA2 using a genome-wide mRNA signature-based approach. In addition, aspirin and sulindac sulfide induced cytotoxicity of CRC cells stably expressing HMGA2 by inhibiting cell proliferation and migration. Moreover, a gene set enrichment analysis (GSEA) revealed that gene sets related to inflammation were positively correlated with HMGA2 and that the main molecular function of these genes was categorized as a G-protein-coupled receptor (GPCR) activity event. Collectively, this is the first study to report that aspirin and sulindac sulfide are novel potential inhibitors of HMGA2, which can induce cytotoxicity of CRC cells stably expressing HMGA2 by inhibiting cell proliferation and migration through influencing inflammatory-response genes, the majority of which are involved in GPCR signaling.

Rac1 Signaling: From Intestinal Homeostasis to Colorectal Cancer Metastasis

The small GTPase Rac1 has been implicated in a variety of dynamic cell biological processes, including cell proliferation, cell survival, cell-cell contacts, epithelial mesenchymal transition (EMT), cell motility, and invasiveness. These processes are orchestrated through the fine tuning of Rac1 activity by upstream cell surface receptors and effectors that regulate the cycling Rac1-GDP (off state)/Rac1-GTP (on state), but also through the tuning of Rac1 accumulation, activity, and subcellular localization by post translational modifications or recruitment into molecular scaffolds. Another level of regulation involves Rac1 transcripts stability and splicing. Downstream, Rac1 initiates a series of signaling networks, including regulatory complex of actin cytoskeleton remodeling, activation of protein kinases (PAKs, MAPKs) and transcription factors (NFkB, Wnt/β-catenin/TCF, STAT3, Snail), production of reactive oxygen species (NADPH oxidase holoenzymes, mitochondrial ROS). Thus, this GTPase, its regulators, and effector systems might be involved at different steps of the neoplastic progression from dysplasia to the metastatic cascade. After briefly placing Rac1 and its effector systems in the more general context of intestinal homeostasis and in wound healing after intestinal injury, the present review mainly focuses on the several levels of Rac1 signaling pathway dysregulation in colorectal carcinogenesis, their biological significance, and their clinical impact.

Predicting the effect of 5-fluorouracil-based adjuvant chemotherapy on colorectal cancer recurrence: A model using gene expression profiles

It is critical to identify patients with stage II and III colorectal cancer (CRC) who will benefit from adjuvant chemotherapy (ACT) after curative surgery, while the only use of clinical factors is insufficient to predict this beneficial effect. In this study, we performed genetic algorithm (GA) to select ACT candidate genes, and built a predictive model of support vector machine (SVM) using gene expression profiles from the Gene Expression Omnibus database. The model contained four ACT candidate genes (EDEM1, MVD, SEMA5B, and WWP2) and TNM stage (stage II or III). After using Subpopulation Treatment Effect Pattern Plot to determine the optimal cutoff value of predictive scores, the validated patients from The Cancer Genome Atlas database can be divided into the predictive ACT-benefit/-futile groups. Patients in the predictive ACT-benefit group with 5-fluorouracil (5-Fu)-based ACT had significantly longer relapse-free survival (RFS) compared to those without ACT (P = .015); However, the difference in RFS in the predictive ACT-futile group was insignificant (P = .596). The multivariable analysis found that the predictive groups were significantly associated with the effect of ACT (P_interaction  = .011). Consequently, we developed a predictive model based on the SVM and GA algorithm which was further validated to define patients who benefit from ACT on recurrence.

Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning

Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absent. Herein, we report an analytical approach that combines single cell mass spectrometry (SCMS) based metabolomics with machine learning (ML) models to address the existing challenges. Metabolomic profiles of live cancer cells (HCT-116) with different levels (i.e., no, low, and high) of chemotherapy-induced drug resistance were measured using the Single-probe SCMS technique. A series of ML models, including random forest (RF), artificial neural network (ANN), and penalized logistic regression (LR), were constructed to predict the degrees of drug resistance of individual cells. A systematic comparison of performance was conducted among multiple models, and the method validation was carried out experimentally. Our results indicate that these ML models, especially the RF model constructed on the obtained SCMS datasets, can rapidly and accurately predict different degrees of drug resistance of live single cells. With such rapid and reliable assessment of drug resistance demonstrated at the single cell level, our method can be potentially employed to evaluate chemotherapeutic efficacy in the clinic.

Construction of prognostic risk prediction model of oral squamous cell carcinoma based on co-methylated genes

This study aimed to identify DNA methylation markers in oral squamous cell carcinoma (OSCC) and to construct a prognostic prediction model of OSCC. For this purpose, the methylation data of patients with OSCC downloaded from The Cancer Genome Atlas were considered as a training dataset. The methylation profiles of GSE37745 for OSCC samples were downloaded from Gene Expression Omnibus and considered as validation dataset. Differentially methylated genes (DMGs) were screened from the TCGA training dataset, followed by co-methylation analysis using weighted correlation network analysis (WGCNA). Subsequently, the methylation and gene expression levels of DMGs involved in key modules were extracted for correlation analysis. Prognosis-related methylated genes were screened using the univariate Cox regression analysis. Finally, the risk prediction model was constructed and validated through GSE52793. The results revealed that a total of 948 DMGs with CpGs were screened out. Co-methylation gene analysis obtained 2 (brown and turquoise) modules involving 380 DMGs. Correlation analysis revealed that the methylation levels of 132 genes negatively correlated with the gene expression levels. By combining with the clinical survival prognosis of samples, 5 optimized prognostic genes [centromere protein V (CENPV), Tubby bipartite transcription factor (TUB), synaptotagmin like 2 (SYTL2), occludin (OCLN) and CAS1 domain containing 1 (CASD1)] were selected for constructing a risk prediction model. It was consistent in the training dataset and GSE52793 that low-risk samples had a better survival prognosis. On the whole, this study indicates that the constructed risk prediction model based on CENPV, SYTL2, OCLN, CASD1, and TUB may have the potential to be used for predicting the survival prognosis of patients with OSCC.

The Phenolic compound Kaempferol overcomes 5-fluorouracil resistance in human resistant LS174 colon cancer cells

Resistance to 5-Fluorouracil chemotherapy is a major cause of therapeutic failure in colon cancer cure. Development of combined therapies constitutes an effective strategy to inhibit cancer cells and prevent the emergence of drug resistance. For this purpose, we investigated the anti-tumoral effect of thirteen phenolic compounds, from the Tunisian quince Cydonia oblonga Miller, alone or combined to 5-FU, on the human 5-FU-resistant LS174-R colon cancer cells in comparison to parental cells. Our results showed that only Kaempferol was able to chemo-sensitize 5-FU-resistant LS174-R cells. This phenolic compound combined with 5-FU exerted synergistic inhibitory effect on cell viability. This combination enhanced the apoptosis and induced cell cycle arrest of both chemo-resistant and sensitive cells through impacting the expression levels of different cellular effectors. Kaempferol also blocked the production of reactive oxygen species (ROS) and modulated the expression of JAK/STAT3, MAPK, PI3K/AKT and NF-κB. In silico docking analysis suggested that the potent anti-tumoral effect of Kaempferol, compared to its two analogs (Kaempferol 3-O-glucoside and Kampferol 3-O-rutinoside), can be explained by the absence of glucosyl groups. Overall, our data propose Kaempferol as a potential chemotherapeutic agent to be used alone or in combination with 5-FU to overcome colon cancer drug resistance.

MiR-30-5p suppresses cell chemoresistance and stemness in colorectal cancer through USP22/Wnt/β-catenin signaling axis

Colorectal cancer (CRC) remains both common and fatal, and its successful treatment is greatly limited by the development of stem cell‐like characteristics (stemness) and chemoresistance. MiR‐30‐5p has been shown to function as a tumor suppressor by targeting the Wnt/β‐catenin signaling pathway, but its activity in CRC has never been assessed. We hypothesized that miR‐30‐5p exerts anti‐oncogenic effects in CRC by regulating the USP22/Wnt/β‐catenin signaling axis. In the present study, we demonstrate that tissues from CRC patients and human CRC cell lines show significantly decreased miR‐30‐5p family expression. After identifying the 3’UTR of USP22 as a potential binding site of miR‐30‐5p, we constructed a luciferase reporter containing the potential miR‐30‐5p binding site and measured the effects on USP22 expression. Western blot assays showed that miR‐30‐5p decreased USP22 protein expression in HEK293 and Caco2 CRC cells. To evaluate the effects of miR‐30‐5p on CRC cell stemness, we isolated CD133 + CRC cells (Caco2 and HCT15). We then determined that, while miR‐30‐5p is normally decreased in CD133 + CRC cells, miR‐30‐5p overexpression significantly reduces expression of stem cell markers CD133 and Sox2, sphere formation, and cell proliferation. Similarly, we found that miR‐30‐5p expression is normally reduced in 5‐fluorouracil (5‐FU) resistant CRC cells, whereas miR‐30‐5p overexpression in 5‐FU resistant cells reduces sphere formation and cell viability. Inhibition of miR‐30‐5p reversed the process. Finally, we determined that miR‐30‐5p attenuates the expression of Wnt/β‐catenin signaling target genes (Axin2 and MYC), Wnt luciferase activity, and β‐catenin protein levels in CRC stem cells.

Chemoresistance in H-Ferritin Silenced Cells: The Role of NF-κB

Nuclear Factor-κB (NF-κB) is frequently activated in tumor cells contributing to aggressive tumor growth and resistance to chemotherapy. Here we demonstrate that Ferritin Heavy Chain (FHC) protein expression inversely correlates with NF-κB activation in cancer cell lines. In fact, FHC silencing in K562 and SKOV3 cancer cell lines induced p65 nuclear accumulation, whereas FHC overexpression correlated with p65 nuclear depletion in the same cell lines. In FHC-silenced cells, the p65 nuclear accumulation was reverted by treatment with the reactive oxygen species (ROS) scavenger, indicating that NF-κB activation was an indirect effect of FHC on redox metabolism. Finally, FHC knock-down in K562 and SKOV3 cancer cell lines resulted in an improved cell viability following doxorubicin or cisplatin treatment, being counteracted by the transient expression of inhibitory of NF-κB, IκBα. Our results provide an additional layer of information on the complex interplay of FHC with cellular metabolism, and highlight a novel scenario of NF-κB-mediated chemoresistance triggered by the downregulation of FHC with potential therapeutic implications.

Inferences of individual drug responses across diverse cancer types using a novel competing endogenous RNA network

Differences in individual drug responses are an obstacle to progression in cancer treatment, and predicting responses would help to plan treatment. The accumulation of cancer molecular profiling and drug response data provides opportunities and challenges to identify novel molecular signatures and mechanisms of tumor responsiveness to drugs. This study evaluated drug responses with a competing endogenous RNA (ceRNA) system that depended on competition between diverse RNA species. We identified drug response‐related ceRNA (DRCEs) by combining the sequence and expression data of long noncoding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA), and the survival data of cancer patients treated with drugs. We constructed a patient–drug two‐layer integrated network and used a linear weighting method to predict individual drug responses. DRCEs were found to be significantly enriched in known cancer and drug‐associated data resources, involved in biological processes known to mediate drug responses, and correlated to drug activity in cancer cell lines. The dysregulation of DRCE expression influenced drug response‐associated functions and pathways, suggesting DRCEs as potential therapeutic targets affecting drug responses. A further case study in breast invasive carcinoma (BRCA) found that DRCE expression was consistent with the drug response pattern and the aberrant expression of the two NEAT1‐related DRCEs may lead to poor response to tamoxifen therapy for patients with TP53 mutations. In summary, this study provides a framework for ceRNA‐based evaluation of clinical drug responses across multiple cancer types. Understanding the underlying molecular mechanisms of drug responses will allow improved response to chemotherapy and outcomes of cancer treatment.

Current Molecular-Targeted Therapies in NSCLC and Their Mechanism of Resistance

Lung cancer is treated with many conventional therapies, such as surgery, radiation, and chemotherapy. However, these therapies have multiple undesirable side effects. To bypass the side effects elicited by these conventional treatments, molecularly-targeted therapies are currently in use or under development. Current molecularly-targeted therapies effectively target specific biomarkers, which are commonly overexpressed in lung cancers and can cause increased tumorigenicity. Unfortunately, several molecularly-targeted therapies are associated with initial dramatic responses followed by acquired resistance due to spontaneous mutations or activation of signaling pathways. Acquired resistance to molecularly targeted therapies presents a major clinical challenge in the treatment of lung cancer. Therefore, to address this clinical challenge and to improve lung cancer patient prognosis, we need to understand the mechanism of acquired resistance to current therapies and develop additional novel therapies. This review concentrates on various lung cancer biomarkers, including EGFR, ALK, and BRAF, as well as their potential mechanisms of drug resistance.

Computational models for predicting drug responses in cancer research

The computational prediction of drug responses based on the analysis of multiple types of genome-wide molecular data is vital for accomplishing the promise of precision medicine in oncology. This will benefit cancer patients by matching their tumor characteristics to the most effective therapy available. As larger and more diverse layers of patient-related data become available, further demands for new bioinformatics approaches and expertise will arise. This article reviews key strategies, resources and techniques for the prediction of drug sensitivity in cell lines and patient-derived samples. It discusses major advances and challenges associated with the different model development steps. This review highlights major trends in this area, and will assist researchers in the assessment of recent progress and in the selection of approaches to emerging applications in oncology.

The toxin BjussuLAAO-II induces oxidative stress and DNA damage, upregulates the inflammatory cytokine genes TNF and IL6, and downregulates the apoptotic-related genes BAX, BCL2 and RELA in human Caco-2 cells

Colorectal carcinoma is one of the most common cancers in adults. As chemotherapy, the first-choice treatment for colorectal carcinoma, is often infeasible due to acquired tumor resistance and several adverse effects, it is important to discover and explore new molecules with better therapeutic action. Snake venom toxins have shown promising results with high cytotoxicity against tumor cells, but their mechanisms of action remain unclear. Here we examined how BjussuLAAO-II, an L-amino acid oxidase isolated from Bothrops jararacussu snake venom, exerts cytotoxicity towards colorectal adenocarcinoma human cells (Caco-2) and human umbilical vein endothelial cell line (HUVEC). A 24-h treatment with BjussuLAAO-II at 0.25 - 5.00 μg/mL diminished cell viability by decreasing (i) mitochondrial activity, assessed by reduction of 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide and resazurin; (ii) the activity of acid phosphatases; and (iii) lysosomal function, assessed by neutral red uptake. BjussuLAAO-II also increased intracellular levels of reactive oxygen species and DNA damage, as assessed by fluorescence and the comet assay, respectively. BjussuLAAO-II altered the expression of cell proliferation-related genes, as determined by RT-qPCR: it elevated the expression of the inflammatory cytokine genes TNF and IL6, and lowered the expression of the apoptotic-related genes BAX, BCL2, and RELA. Therefore, BjussuLAAO-II induces Caco-2 cells death by acting on multiple intracellular targets, providing important data for further studies to assess whether these effects are seen in both tumor and normal cells, with the aim of selecting this drug for possible therapeutic purposes in the future.

Proteomic approach toward determining the molecular background of pazopanib resistance in synovial sarcoma

Pazopanib, a multitarget tyrosine kinase (TK) inhibitor, has been approved for treatment of soft tissue sarcoma. Elucidation of the molecular background of pazopanib resistance should lead to improved clinical outcomes in sarcomas; accordingly, we investigated this in synovial sarcoma using a proteomic approach. Pazopanib sensitivity was examined in four synovial sarcoma cell lines: SYO-1, HS-SYII, 1273/99, and YaFuSS. The 1273/99 cell line showed significantly higher IC₅₀ values than the others for pazopanib. Expression levels of 90 TKs in the cell lines were examined by western blotting. Among these, the levels of PDGFRB, DDR1, AXL, MET, and PYK2 were higher, and those of FGFR1 and VEGFR3 were lower in the 1273/99 cell line than the other cell lines. Gene silencing analysis of the TKs upregulated in 1273/99 cells showed differing effects on cell growth: PDGFRB, MET, and PYK2 knockdown induced cell growth inhibition, whereas DDR1 and AXL knockdown did not influence cell growth. Using the PamChip peptide microarray, we found that 18 peptide substrates were highly phosphorylated in the 1273/99 cell line compared with other cell lines. Using the PhosphoNet database, we found that kinases FGFR3, RET, VEGFR1, EPHA2, EPHA4, TRKA, and SRC phosphorylated these 18 peptide substrates. Moreover, the results for overexpressed and aberrantly activated TKs in pazopanib-resistant cells showed no overlap. Taken together, our study indicates that identification of comprehensive TK profiles represents an essential approach to determining the molecular background of pazopanib resistance in synovial sarcoma.

Epigenetic changes in histone acetylation underpin resistance to the topoisomerase I inhibitor irinotecan

The topoisomerase I (TOP1) inhibitor irinotecan triggers cell death by trapping TOP1 on DNA, generating cytotoxic protein-linked DNA breaks (PDBs). Despite its wide application in a variety of solid tumors, the mechanisms of cancer cell resistance to irinotecan remains poorly understood. Here, we generated colorectal cancer (CRC) cell models for irinotecan resistance and report that resistance is neither due to downregulation of the main cellular target of irinotecan TOP1 nor upregulation of the key TOP1 PDB repair factor TDP1. Instead, the faster repair of PDBs underlies resistance, which is associated with perturbed histone H4K16 acetylation. Subsequent treatment of irinotecan-resistant, but not parental, CRC cells with histone deacetylase (HDAC) inhibitors can effectively overcome resistance. Immunohistochemical analyses of CRC tissues further corroborate the importance of histone H4K16 acetylation in CRC. Finally, the resistant clones exhibit cross-resistance with oxaliplatin but not with ionising radiation or 5-fluoruracil, suggesting that the latter two could be employed following loss of irinotecan response. These findings identify perturbed chromatin acetylation in irinotecan resistance and establish HDAC inhibitors as potential therapeutic means to overcome resistance.

Identifying consistent disease subnetworks using DNet

It is critical to identify disease-specific subnetworks from the vastly available genome-wide gene expression data for elucidating how genes perform high-level biological functions together. Various algorithms have been developed for disease gene identification. However, the topological structure of the disease networks (or even the fraction of the networks) has been left largely unexplored. In this article, we present DNet, a method for the identification of significant disease subnetworks by integrating both the network structure and gene expression information. Our work will lead to the identification of missing key disease genes, which are be highly expressed in a disease-specific gene expression dataset. The experimental evaluation of our method on both the Leukemia and the Duchenne Muscular Dystrophy gene expression datasets show that DNet performs better than the existing state-of-the-art methods. In addition, literature supports were found for the discovered disease subnetworks in a case study.

Aldose reductase inhibitor increases doxorubicin-sensitivity of colon cancer cells and decreases cardiotoxicity

Anthracycline drugs such as doxorubicin (DOX) and daunorubicin remain some of the most active wide-spectrum and cost-effective drugs in cancer therapy. However, colorectal cancer (CRC) cells are inherently resistant to anthracyclines which at higher doses cause cardiotoxicity. Our recent studies indicate that aldose reductase (AR) inhibitors such as fidarestat inhibit CRC growth in vitro and in vivo. Here, we show that treatment of CRC cells with fidarestat increases the efficacy of DOX-induced death in HT-29 and SW480 cells and in nude mice xenografts. AR inhibition also results in higher intracellular accumulation of DOX and decreases the expression of drug transporter proteins MDR1, MRP1, and ABCG2. Further, fidarestat also inhibits DOX-induced increase in troponin-I and various inflammatory markers in the serum and heart and restores cardiac function in mice. These results suggest that fidarestat could be used as adjuvant therapy to enhance DOX sensitivity of CRC cells and to reduce DOX-associated cardiotoxicity.

Translationally controlled tumour protein TCTP is induced early in human colorectal tumours and contributes to the resistance of HCT116 colon cancer cells to 5-FU and oxaliplatin

Background

Translationally controlled tumour protein TCTP is an anti-apoptotic protein frequently overexpressed in cancers, where high levels are often associated with poor patient outcome. TCTP may be involved in protecting cancer cells against the cytotoxic action of anti-cancer drugs. Here we study the early increase of TCTP levels in human colorectal cancer (CRC) and the regulation of TCTP expression in HCT116 colon cancer cells, in response to treatment with the anti-cancer drugs 5-FU and oxaliplatin.

Methods

Using immunohistochemistry, we assessed TCTP levels in surgical samples from adenomas and adenocarcinomas of the colon, compared to normal colon tissue. We also studied the regulation of TCTP in HCT116 colon cancer cells in response to 5-FU and oxaliplatin by western blotting. TCTP mRNA levels were assessed by RT-qPCR. We used mTOR kinase inhibitors to demonstrate mTOR-dependent translational regulation of TCTP under these conditions. Employing the Real-Time Cell Analysis (RTCA) System and the MTS assay, we investigated the effect of TCTP-knockdown on the sensitivity of HCT116 cells to the anti-cancer drugs 5-FU and oxaliplatin.

Results

1. TCTP levels are significantly increased in colon adenomas and adenocarcinomas, compared to normal colon tissue. 2. TCTP protein levels are about 4-fold upregulated in HCT116 colon cancer cells, in response to 5-FU and oxaliplatin treatment, whereas TCTP mRNA levels are down regulated. 3. mTOR kinase inhibitors prevented the up-regulation of TCTP protein, indicating that TCTP is translationally regulated through the mTOR complex 1 signalling pathway under these conditions. 4. Using two cellular assay systems, we demonstrated that TCTP-knockdown sensitises HCT116 cells to the cytotoxicity caused by 5-FU and oxaliplatin.

Conclusions

Our results demonstrate that TCTP levels increase significantly in the early stages of CRC development. In colon cancer cells, expression of this protein is largely upregulated during treatment with the DNA-damaging anti-cancer drugs 5-FU and oxaliplatin, as part of the cellular stress response. TCTP may thus contribute to the development of anti-cancer drug resistance. These findings indicate that TCTP might be suitable as a biomarker and that combinatorial treatment using 5-FU/oxaliplatin, together with mTOR kinase inhibitors, could be a route to preventing the development of resistance to these drugs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12964-017-0164-3) contains supplementary material, which is available to authorized users.

A rank-based transcriptional signature for predicting relapse risk of stage II colorectal cancer identified with proper data sources

The irreproducibility problem seriously hinders the studies on transcriptional signatures for predicting relapse risk of early stage colorectal cancer (CRC) patients. Through reviewing recently published 34 literatures for the development of CRC prognostic signatures based on gene expression profiles, we revealed a surprising phenomenon that 33 of these studies analyzed CRC samples with and without adjuvant chemotherapy together in the training and/or validation datasets. This data misuse problem could be partially attributed to the unclear and incomplete data annotation in public data sources. Furthermore, all the signatures proposed by these studies were based on risk scores summarized from gene expression levels, which are sensitive to experimental batch effects and risk compositions of the samples analyzed together. To avoid the above-mentioned problems, we carefully selected three qualified large datasets to develop and validate a signature consisting of three pairs of genes. The within-sample relative expression orderings of these gene pairs could robustly predict relapse risk of stage II CRC samples assessed in different laboratories. The transcriptional and functional analyses provided clear evidence that the high risk patients predicted by the proposed signature represent patients with micro-metastases.

Identifying clinically relevant drug resistance genes in drug-induced resistant cancer cell lines and post-chemotherapy tissues

Until recently, few molecular signatures of drug resistance identified in drug-induced resistant cancer cell models can be translated into clinical practice. Here, we defined differentially expressed genes (DEGs) between pre-chemotherapy colorectal cancer (CRC) tissue samples of non-responders and responders for 5-fluorouracil and oxaliplatin-based therapy as clinically relevant drug resistance genes (CRG_5-FU/L-OHP). Taking CRG_5-FU/L-OHP as reference, we evaluated the clinical relevance of several types of genes derived from HCT116 CRC cells with resistance to 5-fluorouracil and oxaliplatin, respectively. The results revealed that DEGs between parental and resistant cells, when both were treated with the corresponding drug for a certain time, were significantly consistent with the CRG_5-FU/L-OHP as well as the DEGs between the post-chemotherapy CRC specimens of responders and non-responders. This study suggests a novel strategy to extract clinically relevant drug resistance genes from both drug-induced resistant cell models and post-chemotherapy cancer tissue specimens.

Multi-omics landscapes of colorectal cancer subtypes discriminated by an individualized prognostic signature for 5-fluorouracil-based chemotherapy

Until recently, few prognostic signatures for colorectal cancer (CRC) patients receiving 5-fluorouracil (5-FU)-based chemotherapy could be used in clinical practice. Here, using transcriptional profiles for a panel of cancer cell lines and three cohorts of CRC patients, we developed a prognostic signature based on within-sample relative expression orderings (REOs) of six gene pairs for stage II-III CRC patients receiving 5-FU-based chemotherapy. This REO-based signature had the unique advantage of being insensitive to experimental batch effects and free of the impractical data normalization requirement. After stratifying 184 CRC samples with multi-omics data from The Cancer Genome Atlas into two prognostic groups using the REO-based signature, we further revealed that patients with high recurrence risk were characterized by frequent gene copy number aberrations reducing 5-FU efficacy and DNA methylation aberrations inducing distinct transcriptional alternations to confer 5-FU resistance. In contrast, patients with low recurrence risk exhibited deficient mismatch repair and carried frequent gene mutations suppressing cell adhesion. These results reveal the multi-omics landscapes determining prognoses of stage II-III CRC patients receiving 5-FU-based chemotherapy.

Peritoneal expression of Matrilysin helps identify early post-operative recurrence of colorectal cancer

Recurrence of colorectal cancer (CRC) following a potentially curative resection is a challenging clinical problem. Matrix metalloproteinase-7 (MMP-7) is over-expressed by CRC cells and supposed to play a major role in CRC cell diffusion and metastasis. MMP-7 RNA expression was assessed by real-time PCR using specific primers in peritoneal washing fluid obtained during surgical procedure. After surgery, patients underwent a regular follow up for assessing recurrence. transcripts for MMP-7 were detected in 31/57 samples (54%). Patients were followed-up (range 20-48 months) for recurrence prevention. Recurrence was diagnosed in 6 out of 55 patients (11%) and two patients eventually died because of this. Notably, all the six patients who had relapsed were positive for MMP-7. Sensitivity and specificity of the test were 100% and 49% respectively. Data from patients have also been corroborated by computational approaches. Public available coloncarcinoma datasets have been employed to confirm MMP7 clinical impact on the disease. Interestingly, MMP-7 expression appeared correlated to Tgfb-1, and correlation of the two factors represented a poor prognostic factor. This study proposes positivity of MMP-7 in peritoneal cavity as a novel biomarker for predicting disease recurrence in patients with CRC.

SNRFCB: sub-network based random forest classifier for predicting chemotherapy benefit on survival for cancer treatment

Adjuvant chemotherapy (CTX) should be individualized to provide potential survival benefit and avoid potential harm to cancer patients. Our goal was to establish a computational approach for making personalized estimates of the survival benefit from adjuvant CTX. We developed Sub-Network based Random Forest classifier for predicting Chemotherapy Benefit (SNRFCB) based gene expression datasets of lung cancer. The SNRFCB approach was then validated in independent test cohorts for identifying chemotherapy responder cohorts and chemotherapy non-responder cohorts. SNRFCB involved the pre-selection of gene sub-network signatures based on the mutations and on protein-protein interaction data as well as the application of the random forest algorithm to gene expression datasets. Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer patients in the chemotherapy responder group (P = 0.008), but it was not beneficial to patients in the chemotherapy non-responder group (P = 0.657). Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer squamous cell carcinoma (SQCC) subtype patients in the chemotherapy responder cohorts (P = 0.024), but it was not beneficial to patients in the chemotherapy non-responder cohorts (P = 0.383). SNRFCB improved prediction performance as compared to the machine learning method, support vector machine (SVM). To test the general applicability of the predictive model, we further applied the SNRFCB approach to human breast cancer datasets and also observed superior performance. SNRFCB could provide recurrent probability for individual patients and identify which patients may benefit from adjuvant CTX in clinical trials.

Chemotherapy outcome predictive effectiveness by the Oncogramme: pilot trial on stage-IV colorectal cancer

Background

Colorectal cancer (CRC) remains a major public concern. While conventional chemotherapeutic regimens have proved useful against advanced/metastatic diseases, progresses are to be made to effectively cure the large portion of patients not benefiting from these treatments. One direction to improve response rates is to develop chemosensitivity and resistance assays (CSRAs) efficiently assisting clinicians in treatment selection process, an already long preoccupation of oncologists and researchers. Several methods have been described to this day, none achieving yet sufficient reliability for recommended use in the clinical routine.

Methods

We led a pilot study on 19 metastatic CRC patients evaluating capacity of the Oncogramme, a standardized process using tumor ex vivo models, to provide chemosensitivity profiles and predict clinical outcome of patients receiving standard CRC chemotherapeutics. Oncogramme responses were categorized according to the method of percentiles to assess sensitivity, specificity and concordance.

Results

We report from a primary analysis a success rate of 97.4 %, a very good sensitivity (84.6 %), a below-average specificity (33.3 %), along with a global agreement of 63.6 % and a concordance between Oncogramme results and patients’ responses (Kappa coefficient) of 0.193. A supplementary analysis, focusing on CRC patients with no treatment switch over a longer time course, demonstrated improvement in specificity and concordance.

Conclusions

Results establish feasibility and usefulness of the Oncogramme, prelude to a larger-scale trial. Advantages and drawbacks of the procedure are discussed, as well as the place of CSRAs within the future arsenal of methods available to clinicians to individualize treatments and improve patient prognosis.

Trial registration: ClinicalTrials.gov database, registration number: NCT02305368

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-016-0765-4) contains supplementary material, which is available to authorized users.

Novel Holistic Approaches for Overcoming Therapy Resistance in Pancreatic and Colon Cancers

Gastrointestinal (GI) cancers, such as of the colon and pancreas, are highly resistant to both standard and targeted therapeutics. Therapy-resistant and heterogeneous GI cancers harbor highly complex signaling networks (the resistome) that resist apoptotic programming. Commonly used gemcitabine or platinum-based regimens fail to induce meaningful (i.e. disease-reversing) perturbations in the resistome, resulting in high rates of treatment failure. The GI cancer resistance networks are, in part, due to interactions between parallel signaling and aberrantly expressed microRNAs (miRNAs) that collectively promote the development and survival of drug-resistant cancer stem cells with epithelial-to-mesenchymal transition (EMT) characteristics. The lack of understanding of the resistance networks associated with this subpopulation of cells as well as reductionist, single protein-/pathway-targeted approaches have made 'effective drug design' a difficult task. We propose that the successful design of novel therapeutic regimens to target drug-resistant GI tumors is only possible if network-based drug avenues and agents, in particular 'natural agents' with no known toxicity, are correctly identified. Natural agents (dietary agents or their synthetic derivatives) can individually alter miRNA profiles, suppress EMT pathways and eliminate cancer stem-like cells that derive from pancreatic cancer and colon cancer, by partially targeting multiple yet meaningful networks within the GI cancer resistome. However, the efficacy of these agents as combinations (e.g. consumed in the diet) against this resistome has never been studied. This short review article provides an overview of the different challenges involved in the understanding of the GI resistome, and how novel computational biology can help in the design of effective therapies to overcome resistance.

Hepatic-directed Therapies in Patients with Neuroendocrine Tumors

Neuroendocrine tumors (NETs) of the gastrointestinal (GI) tract have a propensity for producing hepatic metastases. Most GI NETs arise from the foregut or midgut, are malignant, and can cause severe debilitating symptoms adversely affecting quality of life. Aggressive treatments to reduce symptoms have an important role in therapy. Patients with GI NETs usually present with inoperable metastatic disease and severe symptoms from a variety of hormones and biogenic amines. This article describes intra-arterial hepatic-directed therapies for metastases from NETs, a group of treatments in which the therapeutic and/or embolic agents are released intra-arterially in specific hepatic vessels to target tumors.

Long Noncoding RNAs in Digestive System Malignancies: A Novel Class of Cancer Biomarkers and Therapeutic Targets?

High throughput methodologies have revealed the existence of an unexpectedly large number of long noncoding RNAs (lncRNAs). The unconventional role of lncRNAs in gene expression regulation and their broad implication in oncogenic and tumor suppressive pathways have introduced lncRNAs as novel biological tumor markers. The most prominent example of lncRNAs application in routine clinical practice is PCA3, a FDA-approved biomarker for prostate cancer. Regarding digestive system malignancies, the oncogenic HOTAIR is one of the most widely studied lncRNAs in the preclinical level and has already been identified as a potent prognostic marker for major malignancies of the gastrointestinal tract. Here, we provide an overview of recent findings regarding the emerging role of lncRNAs not only as key regulators of cancer initiation and progression in colon, stomach, pancreatic, liver, and esophageal cancers, but also as reliable tumor markers and therapeutic tools. lncRNAs can be easily, rapidly, and cost-effectively determined in tissues, serum, and gastric juice, making them highly versatile analytes. Taking also into consideration the largely unmet clinical need for early diagnosis and more accurate prognostic/predictive markers for gastrointestinal cancer patients, we comment upon the perspectives of lncRNAs as efficient molecular tools that could aid in the clinical management.