Transcriptome profiling of archived sectioned formalin-fixed paraffin-embedded (AS-FFPE) tissue for disease classification

PON3::LCN1 and HTN3::MSANTD3 Gene Fusions With NR4A3/NR4A2 Expression in Salivary Acinic Cell Carcinoma

Acinic cell carcinoma of the salivary gland (AciCC) is a low-grade carcinoma characterized by the overexpression of the transcription factor nuclear receptor subfamily 4 group A member 3 (NR4A3). AciCC has been the subject of a few molecular research projects. This study delves into AciCC's molecular landscape to identify additional alterations and explore their clinical implications. RNA sequencing and immunohistochemical staining for markers NR4A3/NR4A2, DOG-1, S100, and mammaglobin were utilized on 41 AciCCs and 11 secretory carcinoma (SC) samples. NR4A3 was evident in 35 AciCCs, while the residual 6 were NR4A3-negative and NR4A2-positive; SC samples were consistently NR4A3-negative. A novel fusion, PON3 exon 1- LCN1 exon 5, was detected in 9/41 (21.9%) AciCCs, exhibiting a classical histologic pattern with serous cell components growing in solid sheets alongside the intercalated duct-like component. Clinical follow-up of 39 patients over a median of 59 months revealed diverse prognostic outcomes: 34 patients exhibited no disease evidence, whereas the remaining 5 experienced poorer prognosis, involving local recurrence, lymph node, and distant metastasis, and disease-associated death, 4 of which harbored the PON3::LCN1 fusion. In addition, the HTN3::MSANTD3 fusion was recurrently identified in 7/41 AciCC cases. SC patients lacked both fusions. Immunohistochemistry uncovered differential expression of DOG-1, S100, and mammaglobin across samples, providing nuanced insights into their roles in AciCC. This study accentuates PON3::LCN1 and HTN3::MSANTD3 fusions as recurrent molecular events in AciCC, offering potential diagnostic and prognostic utility and propelling further research into targeted therapeutic strategies.

Comprehensive analysis and validation of SNX7 as a novel biomarker for the diagnosis, prognosis, and prediction of chemotherapy and immunotherapy response in hepatocellular carcinoma

BACKGROUND

Studies have demonstrated that Sorting nexin 7 (SNX7) functions as an anti-apoptotic protein in liver tissue and plays a crucial role in the survival of hepatocytes during early embryonic development. However, its diagnostic and prognostic value as well as the predictive value of chemotherapy and immunotherapy have not been reported in hepatocellular carcinoma (HCC).

METHODS

SNX7 mRNA expression and its diagnostic efficacy were examined in GEO datasets, and the findings were further confirmed in TCGA, ICGC cohorts, and cell lines. The protein level of SNX7 was determined using CPTAC and HPA databases, and the results were validated through immunohistochemistry (IHC). Survival analyses were performed in TCGA and ICGC cohorts, and the results were subsequently validated via Kaplan-Meier Plotter. The response to chemotherapy and immunotherapy was predicted via GDSC dataset and TIDE algorithm, respectively. R packages were employed to explore the relationship between SNX7 expression and immune infiltration, m6A modification, as well as the functional enrichment of differentially expressed genes (DEGs).

RESULTS

The expression of SNX7 at both mRNA and protein levels was significantly upregulated in HCC tissues. SNX7 exhibited superior diagnostic efficacy compared to AFP alone for HCC detection, and combining it with AFP improved the diagnostic accuracy for HCC. High SNX7 was associated with unfavorable outcomes, including poor overall survival, disease-specific survival, progression-free survival, and advanced pathological stage, in patients with HCC, and SNX7 was identified as an independent risk factor for HCC. Moreover, elevated SNX7 expression was positively correlated with increased sensitivity to various chemotherapy drugs, including sorafenib, while it was associated with resistance to immunotherapy in HCC patients. Correlation analysis revealed a relationship between SNX7 and multiple m6A-related genes and various immune cells. Finally, enrichment analysis demonstrated strong associations of SNX7 with critical biological processes, such as cell cycle regulation, cellular senescence, cell adhesion, DNA replication, and mismatch repair pathway in HCC.

CONCLUSIONS

Our study highlights the association of SNX7 with the immune microenvironment and its potential influence on HCC progression. SNX7 emerges as a promising novel biomarker for the diagnosis, prognosis, and prediction of response to chemotherapy and immunotherapy in patients with HCC.

MALT1 paracaspase is overexpressed in hepatocellular carcinoma and promotes cancer cell survival and growth

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the third leading cause of cancer-related deaths worldwide. Despite recent advances in treatment options, therapeutic management of HCC remains a challenge, emphasizing the importance of exploring novel targets. MALT1 paracaspase is a druggable signaling molecule whose dysregulation has been linked to hematological and solid tumors. However, the role of MALT1 in HCC remains poorly understood, leaving its molecular functions and oncogenic implications unclear. Here we provide evidence that MALT1 expression is elevated in human HCC tumors and cell lines, and that correlates with tumor grade and differentiation state, respectively. Our results indicate that ectopic expression of MALT1 confers increased cell proliferation, 2D clonogenic growth, and 3D spheroid formation in well differentiated HCC cell lines with relatively low MALT1 levels. In contrast, stable silencing of endogenous MALT1 through RNA interference attenuates these aggressive cancer cell phenotypes, as well as migration, invasion, and tumor-forming ability, in poorly differentiated HCC cell lines with higher paracaspase expression. Consistently, we find that pharmacological inhibition of MALT1 proteolytic activity with MI-2 recapitulates MALT1 depletion phenotypes. Finally, we show that MALT1 expression is positively correlated with NF-kB activation in human HCC tissues and cell lines, suggesting that its tumor promoting functions may involve functional interaction with the NF-kB signaling pathway. This work unveils new insights into the molecular implications of MALT1 in hepatocarcinogenesis and places this paracaspase as a potential marker and druggable liability in HCC.

Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology

In recent years there has been increased interest in using the immune contexture of the primary tumors to predict the patient's prognosis. The tumor microenvironment of patients with cancers consists of different types of lymphocytes, tumor-infiltrating leukocytes, dendritic cells, and others. Different technologies can be used for the evaluation of the tumor microenvironment, all of which require a tissue or cell sample. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Therefore, interventional radiologists (IRs) play an essential role in the evaluation of patients treated with systemically administered immunotherapies. This review provides a detailed description of different technologies used for immune assessment and analysis of the data collected from the use of these technologies. The detailed approach provided herein is intended to provide the reader with the knowledge necessary to not only interpret studies containing such data but also design and apply these tools for clinical practice and future research studies.

Hepatocellular carcinoma chemoprevention by targeting the angiotensin-converting enzyme and EGFR transactivation

Hepatocellular carcinoma (HCC) is a leading cause of death among cirrhotic patients, for which chemopreventive strategies are lacking. Recently, we developed a simple human cell-based system modeling a clinical prognostic liver signature (PLS) predicting liver disease progression and HCC risk. In a previous study, we applied our cell-based system for drug discovery and identified captopril, an approved angiotensin converting enzyme (ACE) inhibitor, as a candidate compound for HCC chemoprevention. Here, we explored ACE as a therapeutic target for HCC chemoprevention. Captopril reduced liver fibrosis and effectively prevented liver disease progression toward HCC development in a diethylnitrosamine (DEN) rat cirrhosis model and a diet-based rat model for nonalcoholic steatohepatitis–induced (NASH-induced) hepatocarcinogenesis. RNA-Seq analysis of cirrhotic rat liver tissues uncovered that captopril suppressed the expression of pathways mediating fibrogenesis, inflammation, and carcinogenesis, including epidermal growth factor receptor (EGFR) signaling. Mechanistic data in liver disease models uncovered a cross-activation of the EGFR pathway by angiotensin. Corroborating the clinical translatability of the approach, captopril significantly reversed the HCC high-risk status of the PLS in liver tissues of patients with advanced fibrosis. Captopril effectively prevents fibrotic liver disease progression toward HCC development in preclinical models and is a generic and safe candidate drug for HCC chemoprevention.

Gene expression profiling of HPV-associated cervical carcinogenesis in formalin-fixed paraffin-embedded (FFPE) tissues using the NanoString nCounterTM platform

Infection by high-risk human papillomavirus (HPV) causes genetic alterations in host cervical cells with consequent changes in gene expression affecting downstream molecular pathways, leading to the development of cervical cancer. In this exploratory study, we aimed to identify the perturbed cellular pathways during the various stages of cervical carcinogenesis. Total RNA was extracted from three formalin-fixed paraffin-embedded (FFPE) samples each of normal cervix, HPV-infected low-grade squamous intraepithelial lesion (LSIL), high-grade SIL (HSIL) and squamous cell carcinoma (SCC). Gene expression profiling was performed using the 770-gene panel from NanoString nCounter® PanCancer Pathways Panel to identify differentially expressed genes (DEGs) and significantly associated pathways in each stage of cervical cancer development. We identified 121 DEGs involved in cervical carcinogenesis. In the transformation from normal cells to LSIL, the MAPK, transcriptional misregulation and JAK-STAT pathways are implicated, while IL1B may promote inflammation and indirectly activates MMP9, resulting in collagen breakdown and cell migration. The cell cycle - apoptosis pathway with upregulation of E2F1 and MCM2, and DNA repair genes BRCA2-BRIP1 and FANCA are crucial during the progression from LSIL to HSIL. In the final stage of progression to SCC, the cell cycle and signaling pathways, as well as upregulation of c-MYC appear essential. In conclusion, archived FFPE-derived tissue samples are a valuable resource for gene expression profiling. The postulated dysregulated pathways and genes provide a guide of the molecular mechanisms that may be involved in the development of HPV-associated cervical cancer, for further investigation and validation studies.

Hormone and receptor activator of NF-κB (RANK) pathway gene expression in plasma and mammographic breast density in postmenopausal women

Background

Hormones impact breast tissue proliferation. Studies investigating the associations of circulating hormone levels with mammographic breast density have reported conflicting results. Due to the limited number of studies, we investigated the associations of hormone gene expression as well as their downstream mediators within the plasma with mammographic breast density in postmenopausal women.

Methods

We recruited postmenopausal women at their annual screening mammogram at Washington University School of Medicine, St. Louis. We used the NanoString nCounter platform to quantify gene expression of hormones (prolactin, progesterone receptor (PGR), estrogen receptor 1 (ESR1), signal transducer and activator of transcription (STAT1 and STAT5), and receptor activator of nuclear factor-kB (RANK) pathway markers (RANK, RANKL, osteoprotegerin, TNFRSF18, and TNFRSF13B) in plasma. We used Volpara to measure volumetric percent density, dense volume, and non-dense volume. Linear regression models, adjusted for confounders, were used to evaluate associations between gene expression (linear fold change) and mammographic breast density.

Results

One unit increase in ESR1, RANK, and TNFRSF18 gene expression was associated with 8% (95% CI 0–15%, p value = 0.05), 10% (95% CI 0–20%, p value = 0.04) and % (95% CI 0–9%, p value = 0.04) higher volumetric percent density, respectively. There were no associations between gene expression of other markers and volumetric percent density. One unit increase in osteoprotegerin and PGR gene expression was associated with 12% (95% CI 4–19%, p value = 0.003) and 7% (95% CI 0–13%, p value = 0.04) lower non-dense volume, respectively.

Conclusion

These findings provide new insight on the associations of plasma hormonal and RANK pathway gene expression with mammographic breast density in postmenopausal women and require confirmation in other studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-022-01522-2.

Gene Expression Profiling of FFPE Samples: A Titration Test

The gene expression analysis of formalin-fixed paraffin-embedded (FFPE) tissues is often hampered by poor RNA quality, which results from the oxidation, cross-linking and other chemical modifications induced by the inclusion in paraffin. Yet, FFPE samples are a valuable source for molecular studies and can provide great insights into disease progression and prognosis. With the advancement of genomic technologies, new methods have been established that offer reliable and accurate gene expression workflows on samples of poor quality. NanoString is a probe-based technology that allows the direct counting of the mRNA transcripts and can be applied to degraded samples. Here, we have tested 2 RNA extraction methods for FFPE samples, and we have performed a titration experiment to evaluate the impact of RNA degradation and RNA input on the gene expression profiles assessed using the NanoString IO360 panel. We have selected FFPE samples of different DV200 values and assessed them on the nCounter platform with 2 different amounts of input RNA. This study concludes that the nCounter is a robust and reliable platform to assess the gene expression of RNA samples with DV200 > 30%; its robustness and ease of use could be of particular benefit to clinical settings.

GJA1 promotes hepatocellular carcinoma progression by mediating TGF-β-induced activation and the epithelial-mesenchymal transition of hepatic stellate cells

Introduction

Gap junction protein, alpha 1 (GJA1), which is correlated with recurrences and unfavorable prognoses in hepatocellular carcinomas (HCCs), is one of the specific proteins expressed by activated hepatic stellate cells (HSCs).

Methods

Expression of GJA1 was compared between HCCs and nontumor tissues (NTs), between hepatic cirrhosis and NTs, and between primary and metastatic HCCs using transcriptomic datasets from the Gene Expression Omnibus and the Integrative Molecular Database of Hepatocellular Carcinoma. The in vitro activities of GJA1 were investigated in cultured HSCs and HCC cells. The underlying mechanism was characterized using Gene Set Enrichment Analysis and validated by western blotting.

Results

The expression of GJA1 was significantly increased in HCCs and hepatic cirrhosis compared to that in NTs. GJA1 was also overexpressed in pulmonary metastases from HCCs when compared with HCCs without metastasis. Overexpression of GJA1 promoted while knockdown of GJA1 inhibited proliferation and transforming growth factor (TGF)-β-mediated activation and migration of cultured HSCs. Overexpression of GJA1 by lentivirus infection promoted proliferation and migration, while conditioned medium from HSCs overexpressing GJA1 promoted migration but inhibited proliferation of Hep3B and PLC-PRF-5 cells. Lentivirus infection with shGJA1 or conditioned medium from shGJA1-infected HSCs inhibited the proliferation and migration of HCCLM3 cells that had a high propensity toward lung metastasis. Mechanistically, GJA1 induced the epithelial–mesenchymal transition (EMT) in HSCs and HCCLM3 cells.

Conclusion

GJA1 promoted HCC progression by inducing HSC activation and the EMT in HSCs. GJA1 is potentially regulated by TGF-β and thus may be a therapeutic target to inhibit HCC progression.

A Germinal Center-Associated Microenvironmental Signature Reflects Malignant Phenotype and Outcome of DLBCL

The DLBCL microenvironment signature scoring system was established using nCounter-based profiling of GC-related microenvironmental genes.

DMS scores stratified DLBCL patients with different prognosis independently of existing prognostic models.

Diffuse large B-cell lymphoma (DLBCL) is the most common B-cell malignancy, with varying prognosis after the gold standard rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). Several prognostic models have been established by focusing primarily on characteristics of lymphoma cells themselves, including cell-of-origin (COO), genomic alterations, and gene/protein expressions. However, the prognostic impact of the lymphoma microenvironment and its association with characteristics of lymphoma cells are not fully understood. Using the nCounter-based gene expression profiling of untreated DLBCL tissues, we assess the clinical impact of lymphoma microenvironment on the clinical outcomes and pathophysiological, molecular signatures in DLBCL. The presence of normal germinal center (GC)-microenvironmental cells, including follicular T cells, macrophage/dendritic cells, and stromal cells in lymphoma tissue indicates a positive therapeutic response. Our prognostic model, based on quantitation of transcripts from distinct GC-microenvironmental cell markers, clearly identified patients with graded prognosis independently of existing prognostic models. We observed increased incidences of genomic alterations and aberrant gene expression associated with poor prognosis in DLBCL tissues lacking GC-microenvironmental cells relative to those containing these cells. These data suggest that the loss of GC-associated microenvironmental signature dictates clinical outcomes of DLBCL patients reflecting the accumulation of “unfavorable” molecular signatures.

A human liver cell-based system modeling a clinical prognostic liver signature for therapeutic discovery

Chronic liver disease and hepatocellular carcinoma (HCC) are life-threatening diseases with limited treatment options. The lack of clinically relevant/tractable experimental models hampers therapeutic discovery. Here, we develop a simple and robust human liver cell-based system modeling a clinical prognostic liver signature (PLS) predicting long-term liver disease progression toward HCC. Using the PLS as a readout, followed by validation in nonalcoholic steatohepatitis/fibrosis/HCC animal models and patient-derived liver spheroids, we identify nizatidine, a histamine receptor H2 (HRH2) blocker, for treatment of advanced liver disease and HCC chemoprevention. Moreover, perturbation studies combined with single cell RNA-Seq analyses of patient liver tissues uncover hepatocytes and HRH2+, CLEC5Ahigh, MARCOlow liver macrophages as potential nizatidine targets. The PLS model combined with single cell RNA-Seq of patient tissues enables discovery of urgently needed targets and therapeutics for treatment of advanced liver disease and cancer prevention.

Transcription factors linked to the molecular signatures in the development of hepatocellular carcinoma on a cirrhotic background

Mechanisms underlying the regulation of gene expression in cancer have been surveyed for decades to find novel prognostic factors and new targets for molecular targeted therapies in cancer. Because most cases of liver cancer are associated with liver cirrhosis, we aimed to analyze the gene expression signatures and the gene regulatory mechanism in hepatocellular carcinoma (HCC) on a cirrhotic background using high-throughput data analysis. In the present study, three valid array-based datasets containing HCC and liver cirrhosis samples were obtained to identify common differentially expressed genes (DEGs). Moreover, a comprehensive data analysis was conducted based on RNA-Seq data and using Kaplan-Meier curve analysis to find molecular signatures that reduce patients' survival rate. Furthermore, we proposed a gene regulatory network (GRN) to explore the possible regulatory mechanism of these molecular signatures by transcription factors in HCC progression from cirrhosis. Besides, we analyzed protein-protein interactions, gene ontology (GO), and pathway enrichment to elucidate the cellular and molecular function of the GRN elements in HCC. In this way, we found a list of 231 molecular signatures in HCC derived from cirrhosis. We also found the importance of TCF4, RUNX1, HINFP, KDM2B, MAF, JUN, NR5A2, NFYA, and AR as key differentially expressed transcription factors (DETFs) in the progression of HCC from cirrhosis. In conclusion, the identified molecular signatures and their transcription factors propose candidate prognostic markers and possible molecular targets in the progression of HCC.

PRELP has prognostic value and regulates cell proliferation and migration in hepatocellular carcinoma

Purpose: Hepatocellular carcinoma (HCC) is an aggressive and prevalent tumor threatening human health. A previous study suggested low PRELP (proline/arginine-rich end leucine-rich repeat protein) expression was associated with poor patient survival in pancreatic ductal adenocarcinoma (PDAC). However, the role of PRELP in HCC has not yet been illuminated. Methods: PRELP expression analyses were carried out using transcriptomic datasets from the Integrative Molecular Database of Hepatocellular Carcinoma (HCCDB). The correlations between PRELP expression and clinicopathological features, and prognostic analyses were performed with a tissue microarray (TMA) and immunohistochemistry (IHC). The endogenous expression and in vitro roles of PRELP were investigated in cultured HCC cell lines. The potential mechanisms were characterized by a Gene Set Enrichment Analysis (GSEA) and gene-gene correlation analyses. Results: We found that PRELP mRNA expression was dramatically decreased in HCCs in comparison with that in adjacent normal tissues (NTs) or hepatic cirrhosis. IHC staining showed that PRELP was down-regulated in HCCs, which mainly located in cytoplasm, and was also found in nuclei. The correlation analyses revealed that PRELP expression was relevant to later p-stages (p= 0.028) and tumor size (p= 0.001). The overall survival (OS) and relapse free survival (RFS) time was shorter in HCC patients with lower PRELP expression levels than that with higher PRELP expression levels. Overexpression of PRELP inhibited, while knockdown of PRELP promoted proliferation and migration of HCC cells. For potential mechanisms, PRELP may inhibit progression of HCCs by interacting with integrin family members and the extracellular microenvironment. Conclusion: Our findings demonstrated that overexpression of PRELP correlates with better patient survival and inhibits both cell proliferation and migration in HCC. Therefore, PRELP can serve as a potential prognostic biomarker and therapeutic target which deserves further investigation.

Molecular profiling of radical prostatectomy tissue from patients with no sign of progression identifies ERG as the strongest independent predictor of recurrence

BACKGROUND

As a major cause of morbidity and mortality among men, prostate cancer is a heterogenous disease, with a vast heterogeneity in the biology of the disease and in clinical outcome. While it often runs an indolent course, local progression or metastasis may eventually develop, even among patients considered "low risk" at diagnosis. Therefore, biomarkers that can discriminate aggressive from indolent disease at an early stage would greatly benefit patients. We hypothesized that tissue specimens from early stage prostate cancers may harbor predictive signatures for disease progression.

METHODS

We used a cohort of radical prostatectomy patients with longitudinal follow-up, who had tumors with low grade and stage that revealed no signs of future disease progression at surgery. During the follow-up period, some patients either remained indolent (non-BCR) or progressed to biochemical recurrence (BCR). Total RNA was extracted from tumor, and adjacent normal epithelium of formalin-fixed-paraffin-embedded (FFPE) specimens. Differential gene expression in tumors, and in tumor versus normal tissues between BCR and non-BCR patients were analyzed by NanoString using a customized CodeSet of 151 probes.

RESULTS

After controlling for false discovery rates, we identified a panel of eight genes (ERG, GGT1, HDAC1, KLK2, MYO6, PLA2G7, BICD1 and CACNAID) that distinguished BCR from non-BCR patients. We found a clear association of ERG expression with non-BCR, which was further corroborated by quantitative RT-PCR and immunohistochemistry assays.

CONCLUSIONS

Our results identified ERG as the strongest predictor for BCR and showed that potential prognostic prostate cancer biomarkers can be identified from FFPE tumor specimens.

Postnatal human enteric neurospheres show a remarkable molecular complexity

BACKGROUND

The enteric nervous system (ENS), a complex network of neurons and glial cells, coordinates major gastrointestinal functions. Impaired development or secondary aberrations cause severe enteric neuropathies. Neural crest-derived stem cells as well as enteric neuronal progenitor cells, which form enteric neurospheres, represent a promising tool to unravel molecular pathomechanisms and to develop novel therapy options. However, so far little is known about the detailed cellular composition and the proportional distribution of enteric neurospheres. Comprehensive knowledge will not only be essential for basic research but also for prospective cell replacement therapies to restore or to improve enteric neuronal dysfunction.

METHODS

Human enteric neurospheres were generated from three individuals with varying age. For detailed molecular characterization, nCounter target gene expression analyses focusing on stem, progenitor, neuronal, glial, muscular, and epithelial cell markers were performed. Corresponding archived paraffin-embedded individuals' specimens were analyzed accordingly.

KEY RESULTS

Our data revealed a remarkable molecular complexity of enteric neurospheres and archived specimens. Amongst the expression of multipotent stem cell, progenitor cell, neuronal, glial, muscle and epithelial cell markers, moderate levels for the pluripotency marker POU5F1 were observed. Furthermore, besides the interindividual variability, we identified highly distinct intraindividual expression profiles.

CONCLUSIONS & INFERENCES

Our results emphasize the assessment of molecular signatures to be essential for standardized use, optimization of experimental approaches, and elimination of potential risk factors, as the formation of tumors. Our study pipeline may serve as a blueprint implemented into the characterization procedure of enteric neurospheres for various future applications.

Data mining of the expression and regulatory role of BCAT1 in hepatocellular carcinoma

Branched chain amino acid transaminase 1 (BCAT1) catalyzes the production of glutamates and branched-chain α-ketoacids from branched chain amino acids, and a normal BCAT1 expression is associated with tumorigenesis. Sequencing data from public databases, including The Cancer Genome Atlas, was used to analyze BCAT1 expression and regulation networks for hepatocellular carcinoma (HCC). Expression and methylation were assessed using UALCAN analysis, and data from multiple datasets concerning the BCAT1 expression level and associated survival rates were further analyzed using HCCDB; interaction networks of biological function were constructed using GeneMANIA. LinkedOmics was used to indicate correlations between BCAT1 and any identified differentially expressed genes. Gene enrichment analysis of BCAT-associated genes was conducted using the Web-based Gene SeT AnaLysis Toolkit. The expression levels of BCAT1 were increased in patients with HCC and in most cases, the level of BCAT1 promoter methylation was reduced. Interaction network analysis suggested that BCAT1 was involved in ‘metabolism’, ‘carcinogenesis’ and the ‘immune response’ via numerous cancer-associated pathways. The present study revealed the expression patterns and potential function networks of BCAT1 in HCC, providing insights for future research into the role of BCAT1 in hepatocarcinogenesis. In addition, the study provided researchers with a way to analyze the genes of interest so they can continue their research in the right direction.

Microenvironmental immune cell signatures dictate clinical outcomes for PTCL-NOS

Peripheral T-cell lymphoma (PTCL), not otherwise specified (PTCL-NOS) is among the most common disease subtypes of PTCL, one that exhibits heterogeneous clinicopathological features. Although multiple disease-stratification models, including the cell-of-origin or gene-expression profiling methods, have been proposed for this condition, their clinical significance remains unclear. To establish a clinically meaningful stratification model, we analyzed gene-expression signatures of tumors and tumor-infiltrating immune cells using the nCounter system, which enables accurate quantification of low abundance and/or highly fragmented transcripts. To do so, we assessed transcripts of 120 genes related to cancer or immune cells using tumor samples from 68 newly diagnosed PTCL-NOS patients and validated findings by immunofluorescence in tumor sections. We show that gene-expression signatures representing tumor-infiltrating immune cells, but not those of cancerous T cells, dictate patient clinical outcomes. Cases exhibiting both B-cell and dendritic cell (DC) signatures (BD subgroup) showed favorable clinical outcomes, whereas those exhibiting neither B-cell nor DC signatures (non-BD subgroup) showed extremely poor prognosis. Notably, half of the non-BD cases exhibited a macrophage signature, and macrophage infiltration was evident in those cases, as revealed by immunofluorescence. Importantly, tumor-infiltrating macrophages expressed the immune-checkpoint molecules programmed death ligand 1/2 and indoleamine 2, 3-dioxygenase 1 at high levels, suggesting that checkpoint inhibitors could serve as therapeutic options for patients in this subgroup. Our study identifies clinically distinct subgroups of PTCL-NOS and suggests a novel therapeutic strategy for 1 subgroup associated with a poor prognosis. Our data also suggest functional interactions between cancerous T cells and tumor-infiltrating immune cells potentially relevant to PTCL-NOS pathogenesis.

A Multigene Model for Predicting Tumor Responsiveness After Preoperative Chemoradiotherapy for Rectal Cancer

PURPOSE

Although preoperative chemoradiotherapy (PCRT) is regarded as a standard treatment for locally advanced rectal cancer, there is no reliable biomarker for predicting responsiveness to PCRT. We aimed to develop a biomarker model for predicting response to PCRT.

METHODS AND MATERIALS

We included 184 patients who received PCRT followed by surgical resection and categorized them as good responders (complete or near-complete regression) or poor responders (all other patients). Candidate gene mRNAs were isolated from formalin-fixed paraffin-embedded tumor specimens and analyzed using the NanoString nCounter gene expression assay. Stepwise logistic regression analysis was used to select genes in discovery and training phases. A quantitative radio-responsiveness prediction model was developed and validated using internal cross-validation groups, and the model's predictive value was assessed based on the area under the receiver operating characteristic curve (AUC).

RESULTS

By comparing the gene expressions between good and poor responders, we created a multigene mRNA model using FZD9, HRAS, ITGA7, MECOM, MMP3, NKD1, PIK3CD, and PRKCB. This panel showed good ability to predict treatment response (AUC: 0.846 for the whole data set). Internal cross-validation was performed to evaluate the model's predictive stability among 3 cohorts, which provided AUC values of 0.808-0.909. The satisfactory diagnostic performance of the radio-response prediction index persisted regardless of other clinicopathologic features such as clinical T or N stage, interval between radiation and surgery, and pretreatment carcinoembryonic antigen levels (P = .001, 95% CI, 0.686-0.905).

CONCLUSIONS

We developed a multigene mRNA-based biomarker model that allows prediction of rectal cancer response to PCRT, which may help identify patients who will benefit most from PCRT.

Diagnosis of T-cell-mediated kidney rejection in formalin-fixed, paraffin-embedded tissues using RNA-Seq-based machine learning algorithms

Molecular diagnosis is being increasingly used in transplant pathology to render more objective and quantitative determinations that also provide mechanistic and prognostic insights. This study performed RNA-Seq on biopsies from kidneys with stable function (STA) and biopsies with classical findings of T-cell-mediated rejection (TCMR). Machine learning tools were used to develop prediction models for distinguishing TCMR and STA samples using the top genes identified by DSeq2. The prediction models were tested on 703 biopsies with Affymetrix chip gene expression profiles available in the public domain. Linear discriminant analysis predicted TCMR in 55 of 67 biopsies labeled TCMR, and 65 of 105 biopsies designated as antibody-mediated rejection. The random forest and support vector machine models showed comparable performance. These data illustrate the feasibility of using RNA-Seq for molecular diagnosis of TCMR in formalin-fixed tissue. Application of the derived diagnostic algorithms to publicly available data sets demonstrates frequent coexistence of TCMR in biopsies designated as antibody-mediated rejection. This underrecognition of TCMR in renal allograft biopsies has significant implications with respect to patient care.

A novel method for RNA extraction from FFPE samples reveals significant differences in biomarker expression between orthotopic and subcutaneous pancreatic cancer patient-derived xenografts

Next-generation sequencing (NGS) can identify and validate new biomarkers of cancer onset, progression and therapy resistance. Substantial archives of formalin-fixed, paraffin-embedded (FFPE) cancer samples from patients represent a rich resource for linking molecular signatures to clinical data. However, performing NGS on FFPE samples is limited by poor RNA purification methods. To address this hurdle, we developed an improved methodology for extracting high-quality RNA from FFPE samples. By briefly integrating a newly-designed micro-homogenizing (mH) tool with commercially available FFPE RNA extraction protocols, RNA recovery is increased by approximately 3-fold while maintaining standard A260/A280 ratios and RNA quality index (RQI) values. Furthermore, we demonstrate that the mH-purified FFPE RNAs are longer and of higher integrity. Previous studies have suggested that pancreatic ductal adenocarcinoma (PDAC) gene expression signatures vary significantly under in vitro versus in vivo and in vivo subcutaneous versus orthotopic conditions. By using our improved mH-based method, we were able to preserve established expression patterns of KRas-dependency genes within these three unique microenvironments. Finally, expression analysis of novel biomarkers in KRas mutant PDAC samples revealed that PEAK1 decreases and MST1R increases by over 100-fold in orthotopic versus subcutaneous microenvironments. Interestingly, however, only PEAK1 levels remain elevated in orthotopically grown KRas wild-type PDAC cells. These results demonstrate the critical nature of the orthotopic tumor microenvironment when evaluating the clinical relevance of new biomarkers in cells or patient-derived samples. Furthermore, this new mH-based FFPE RNA extraction method has the potential to enhance and expand future FFPE-RNA-NGS cancer biomarker studies.

Evaluation of the quality of RNA extracted from archival FFPE glioblastoma and epilepsy surgical samples for gene expression assays

Aims

Histopathological tissue samples are being increasingly used as sources of nucleic acids in molecular pathology translational research. This study investigated the suitability of glioblastoma and control central nervous system (CNS) formalin-fixed paraffin embedded (FFPE) tissue-derived RNA for gene expression analyses.

Methods

Total RNA was extracted from control (temporal lobe resection tissue) and glioblastoma FFPE tissue samples. RNA purity (260/280 ratios) was determined and RNA integrity number (RIN) analysis was performed. RNA was subsequently used for RT-qPCR for two reference genes,18SandGAPDH.

Results

Reference gene expression was equivalent between control and glioblastoma tissue when using RNA extracted from FFPE tissue, which has key implications for biological normalisation for CNS gene expression studies. There was a significant difference between the mean RIN values of control and glioblastoma FFPE tissue. There was no significant correlation between 260/280 or RIN values versus total RNA yield. The age of the tissue blocks did not influence RNA yield, fragmentation or purity. There was no significant correlation between RIN or 260/280 ratios and mean qPCR cycle threshold for either reference gene.

Conclusions

This study showed that routinely available CNS FFPE tissue is suitable for RNA extraction and downstream gene expression studies, even after 60 months of storage. Substantial RNA fragmentation associated with glioblastoma and control FFPE tissue blocks did not preclude downstream RT-qPCR gene expression analyses. Cross validation with both archival and prospectively collated FFPE specimens is required to further demonstrate that CNS tissue blocks can be used in novel translational molecular biomarker studies.

A Comparison of RNA-Seq Results from Paired Formalin-Fixed Paraffin-Embedded and Fresh-Frozen Glioblastoma Tissue Samples

The molecular classification of glioblastoma (GBM) based on gene expression might better explain outcome and response to treatment than clinical factors. Whole transcriptome sequencing using next-generation sequencing platforms is rapidly becoming accepted as a tool for measuring gene expression for both research and clinical use. Fresh frozen (FF) tissue specimens of GBM are difficult to obtain since tumor tissue obtained at surgery is often scarce and necrotic and diagnosis is prioritized over freezing. After diagnosis, leftover tissue is usually stored as formalin-fixed paraffin-embedded (FFPE) tissue. However, RNA from FFPE tissues is usually degraded, which could hamper gene expression analysis. We compared RNA-Seq data obtained from matched pairs of FF and FFPE GBM specimens. Only three FFPE out of eleven FFPE-FF matched samples yielded informative results. Several quality-control measurements showed that RNA from FFPE samples was highly degraded but maintained transcriptomic similarities to RNA from FF samples. Certain issues regarding mutation analysis and subtype prediction were detected. Nevertheless, our results suggest that RNA-Seq of FFPE GBM specimens provides reliable gene expression data that can be used in molecular studies of GBM if the RNA is sufficiently preserved.

Expression microdissection isolation of enriched cell populations from archival brain tissue

BACKGROUND

Laser capture microdissection (LCM) is an established technique for the procurement of enriched cell populations that can undergo further downstream analysis, although it does have limitations. Expression microdissection (xMD) is a new technique that begins to address these pitfalls, such as operator dependence and contamination.

NEW METHOD

xMD utilises immunohistochemistry in conjunction with a chromogen to isolate specific cell types by extending the fundamental principles of LCM to create an operator-independent method for the procurement of specific CNS cell types.

RESULTS

We report how xMD enables the isolation of specific cell populations, namely neurones and astrocytes, from rat formalin fixed-paraffin embedded (FFPE) tissue. Subsequent reverse transcriptase-polymerase chain reaction (RT-PCR) analysis confirms the enrichment of these specific populations. RIN values after xMD indicate samples are sufficient to carry out further analysis.

COMPARISON WITH EXISTING METHOD

xMD offers a rapid method of isolating specific CNS cell types without the need for identification by an operator, reducing the amount of unintentional contamination caused by operator error, whilst also significantly reducing the time required by the current basic LCM technique.

CONCLUSIONS

xMD is a superior method for the procurement of enriched cell populations from post-mortem tissue, which can be utilised to create transcriptome profiles, aiding our understanding of the contribution of these cells to a range of neurological diseases. xMD also addresses the issues associated with LCM, such as reliance on an operator to identify target cells, which can cause contamination, as well as addressing the time consuming nature of LCM.

Comparison of Nanostring nCounter® Data on FFPE Colon Cancer Samples and Affymetrix Microarray Data on Matched Frozen Tissues

The prognosis of colorectal cancer (CRC) stage II and III patients remains a challenge due to the difficulties of finding robust biomarkers suitable for testing clinical samples. The majority of published gene signatures of CRC have been generated on fresh frozen colorectal tissues. Because collection of frozen tissue is not practical for routine surgical pathology practice, a clinical test that improves prognostic capabilities beyond standard pathological staging of colon cancer will need to be designed for formalin-fixed paraffin-embedded (FFPE) tissues. The NanoString nCounter^® platform is a gene expression analysis tool developed for use with FFPE-derived samples. We designed a custom nCounter^® codeset based on elements from multiple published fresh frozen tissue microarray-based prognostic gene signatures for colon cancer, and we used this platform to systematically compare gene expression data from FFPE with matched microarray array data from frozen tissues. Our results show moderate correlation of gene expression between two platforms and discovery of a small subset of genes as candidate biomarkers for colon cancer prognosis that are detectable and quantifiable in FFPE tissue sections.

Vasorin is a potential serum biomarker and drug target of hepatocarcinoma screened by subtractive-EMSA-SELEX to clinic patient serum

We report a new biomarker of hepatocarcinoma, vasorin (VASN), screened by a subtractive EMSA-SELEX strategy from AFP negative serum of hepatocellular carcinoma (HCC) patients with extrahepatic metastases. VASN was verified to be highly expressed in sera of 100 cases of HCC patients compared with 97 cases of normal persons and 129 cases of hepatitis patients. Further validation by Q-PCR, IFA and Western blot showed higher expression of VASN at mRNA and protein levels in HCC cell lines and HCC tissues than in normal controls. RNA interference and forced overexpression assays verified that VASN promotes cell proliferation and migration and inhibits apoptosis. Down-regulation of microRNA miR145 and miR146a is an important mechanism leading to high expression of VASN. Conclusion: As a membrane protein and/or as free protein, VASN may be an effective target for biological treatment of liver cancer and is a potential biomarker for HCC diagnosis. Small molecular nucleotides targeting VASN are promising biological therapies to HCC.

Advances in clinical next-generation sequencing: target enrichment and sequencing technologies

The huge parallel sequencing capabilities of next generation sequencing technologies have made them the tools of choice to characterize genomic aberrations for research and diagnostic purposes. For clinical applications, screening the whole genome or exome is challenging owing to the large genomic area to be sequenced, associated costs, complexity of data, and lack of known clinical significance of all genes. Consequently, routine screening involves limited markers with established clinical relevance. This process, referred to as targeted genome sequencing, requires selective enrichment of the genomic areas comprising these markers via one of several primer or probe-based enrichment strategies, followed by sequencing of the enriched genomic areas. Here, the authors review current target enrichment approaches and next generation sequencing platforms, focusing on the underlying principles, capabilities, and limitations of each technology along with validation and implementation for clinical testing.

Clinicopathological indices to predict hepatocellular carcinoma molecular classification

BACKGROUND & AIMS

Hepatocellular carcinoma (HCC) is the second most lethal cancer caused by lack of effective therapies. Although promising, HCC molecular classification, which enriches potential responders to specific therapies, has not yet been assessed in clinical trials of anti-HCC drugs. We aimed to overcome these challenges by developing clinicopathological surrogate indices of HCC molecular classification.

METHODS

Hepatocellular carcinoma classification defined in our previous transcriptome meta-analysis (S1, S2 and S3 subclasses) was implemented in an FDA-approved diagnostic platform (Elements assay, NanoString). Ninety-six HCC tumours (training set) were assayed to develop molecular subclass-predictive indices based on clinicopathological features, which were independently validated in 99 HCC tumours (validation set). Molecular deregulations associated with the histopathological features were determined by pathway analysis. Sample sizes for HCC clinical trials enriched with specific molecular subclasses were determined.

RESULTS

Hepatocellular carcinoma subclass-predictive indices were steatohepatitic (SH)-HCC variant and immune cell infiltrate for S1 subclass, macrotrabecular/compact pattern, lack of pseudoglandular pattern, and high serum alpha-foetoprotein (>400 ng/ml) for S2 subclass, and microtrabecular pattern, lack of SH-HCC and clear cell variants, and lower histological grade for S3 subclass. Macrotrabecular/compact pattern, a predictor of S2 subclass, was associated with the activation of therapeutically targetable oncogene YAP and stemness markers EPCAM/KRT19. BMP4 was associated with pseudoglandular pattern. Subclass-predictive indices-based patient enrichment reduced clinical trial sample sizes from 121, 184 and 53 to 30, 43 and 22 for S1, S2 and S3 subclass-targeting therapies respectively.

CONCLUSIONS

Hepatocellular carcinoma molecular subclasses can be enriched by clinicopathological indices tightly associated with deregulation of therapeutically targetable molecular pathways.

A Method to Correlate mRNA Expression Datasets Obtained from Fresh Frozen and Formalin-Fixed, Paraffin-Embedded Tissue Samples: A Matter of Thresholds

Background

Gene expression profiling of tumors is a successful tool for the discovery of new cancer biomarkers and potential targets for the development of new therapeutic strategies. Reliable profiling is preferably performed on fresh frozen (FF) tissues in which the quality of nucleic acids is better preserved than in formalin-fixed paraffin-embedded (FFPE) material. However, since snap-freezing of biopsy materials is often not part of daily routine in pathology laboratories, one may have to rely on archival FFPE material. Procedures to retrieve the RNAs from FFPE materials have been developed and therefore, datasets obtained from FFPE and FF materials need to be made compatible to ensure reliable comparisons are possible.

Aim

To develop an efficient method to compare gene expression profiles obtained from FFPE and FF samples using the same platform.

Methods

Twenty-six FFPE-FF sample pairs of the same tumors representing various cancer types, and two FFPE-FF sample pairs of breast cancer cell lines, were included. Total RNA was extracted and gene expression profiling was carried out using Illumina’s Whole-Genome cDNA-mediated Annealing, Selection, extension and Ligation (WG-DASL) V3 arrays, enabling the simultaneous detection of 24,526 mRNA transcripts. A sample exclusion criterion was created based on the expression of 11 stably expressed reference genes. Pearson correlation at the probe level was calculated for paired FFPE-FF, and three cut-off values were chosen. Spearman correlation coefficients between the matched FFPE and FF samples were calculated for three probe lists with varying levels of significance and compared to the correlation based on all measured probes. Unsupervised hierarchical cluster analysis was performed to verify performance of the included probe lists to compare matched FPPE-FF samples.

Results

Twenty-seven FFPE-FF pairs passed the sample exclusion criterion. From the profiles of 27 FFPE and FF matched samples, the best correlating probes were identified for various levels of significance (Pearson P<0.01, n = 1,432; P<0.05, n = 2,530; and P<0.10, n = 3,351 probes). Unsupervised hierarchical clustering of the 27 pairs using the resulting probes yielded 25, 21, and 19 correctly clustered pairs, respectively, compared to 1 pair when all probes were used.

Conclusion

The proposed method enables comparison of gene expression profiles of FFPE and/or FF origin measured on the same platform.

Molecular prognostic prediction in liver cirrhosis

The natural history of cirrhosis varies and therefore prognostic prediction is critical given the sizable patient population. A variety of clinical prognostic indicators have been developed and enable patient risk stratification although their performance is somewhat limited especially within relatively earlier stage of disease. Molecular prognostic indicators are expected to refine the prediction, and potentially link a subset of patients with molecular targeted interventions that counteract poor prognosis. Here we overview clinical and molecular prognostic indicators in the literature, and discuss critical issues to successfully define, evaluate, and deploy prognostic indicators as clinical scores or tests. The use of liver biopsy has been diminishing due to sampling variability on fibrosis assessment and emergence of imaging- or lab test-based fibrosis assessment methods. However, recent rapid developments of genomics technologies and selective molecular targeted agents has highlighted the need for biopsy tissue specimen to explore and establish molecular information-guided personalized/stratified clinical care, and eventually achieve “precision medicine”.

A genomic and clinical prognostic index for hepatitis C-related early-stage cirrhosis that predicts clinical deterioration

OBJECTIVE

The number of patients with HCV-related cirrhosis is increasing, leading to a rising risk of complications and death. Prognostic stratification in patients with early-stage cirrhosis is still challenging. We aimed to develop and validate a clinically useful prognostic index based on genomic and clinical variables to identify patients at high risk of disease progression.

DESIGN

We developed a prognostic index, comprised of a 186-gene signature validated in our previous genome-wide profiling study, bilirubin (>1 mg/dL) and platelet count (<100,000/mm(3)), in an Italian HCV cirrhosis cohort (training cohort, n=216, median follow-up 10 years). The gene signature test was implemented using a digital transcript counting (nCounter) assay specifically developed for clinical use and the prognostic index was evaluated using archived specimens from an independent cohort of HCV-related cirrhosis in the USA (validation cohort, n=145, median follow-up 8 years).

RESULTS

In the training cohort, the prognostic index was associated with hepatic decompensation (HR=2.71, p=0.003), overall death (HR=6.00, p<0.001), hepatocellular carcinoma (HR=3.31, p=0.001) and progression of Child-Turcotte-Pugh class (HR=6.70, p<0.001). The patients in the validation cohort were stratified into high-risk (16%), intermediate-risk (42%) or low-risk (42%) groups by the prognostic index. The high-risk group had a significantly increased risk of hepatic decompensation (HR=7.36, p<0.001), overall death (HR=3.57, p=0.002), liver-related death (HR=6.49, p<0.001) and all liver-related adverse events (HR=4.98, p<0.001).

CONCLUSIONS

A genomic and clinical prognostic index readily available for clinical use was successfully validated, warranting further clinical evaluation for prognostic prediction and clinical trial stratification and enrichment for preventive interventions.

Cancer biomarker discovery and validation

With the emergence of genomic profiling technologies and selective molecular targeted therapies, biomarkers play an increasingly important role in the clinical management of cancer patients. Single gene/protein or multi-gene "signature"-based assays have been introduced to measure specific molecular pathway deregulations that guide therapeutic decision-making as predictive biomarkers. Genome-based prognostic biomarkers are also available for several cancer types for potential incorporation into clinical prognostic staging systems or practice guidelines. However, there is still a large gap between initial biomarker discovery studies and their clinical translation due to the challenges in the process of cancer biomarker development. In this review we summarize the steps of biomarker development, highlight key issues in successful validation and implementation, and overview representative examples in the oncology field. We also discuss regulatory issues and future perspectives in the era of big data analysis and precision medicine.

Gene expression profiling in non-Hodgkin lymphomas

Although the current WHO classification (Swerdlow et al. WHO classification of tumours of haematopoietic and lymphoid tissues. International Agency for Research on Cancer, Lyon, 2008 [1]) for hematolymphoid neoplasms has delineated lymphomas based on the combined morphologic, immunophenotypic, and genotypic findings, further refinement is necessary especially in regard to therapeutics and prognostic implications. High-throughput gene expression profiling (GEP) using microarray technology (Schena et al. Science 270:467-470, 1995 [2]; Augenlicht et al. Proc Natl Acad Sci USA 88:3286-3289, 1991 [3]) was developed about 20 years ago, and further refinement of the technology and analytical approaches has enabled us to routinely evaluate practically the entire transcriptome at a time. GEP has helped to improve the classification and prognostication of non-Hodgkin lymphomas (NHL) as well as improved our understanding of their pathophysiology and response to new therapeutics. In this paper, we will briefly review how this revolutionary tool has transformed our understanding of lymphomas and given us insight into targeted therapeutics. We will also discuss the current efforts in adapting the findings to routine clinical practice, the evolution of the research technology and directions in the future.

Prognostic gene signature profiles of hepatitis C-related early-stage liver cirrhosis

The rate of hepatitis C virus (HCV) related liver cirrhosis and subsequent cancer development is increasing and raising the risk of related mortality and morbidity. To address this issue, we aimed to develop a prognostic index that can be used to stratify patients for risk of disease progression. This index was developed in part by using a gene signature test implemented in a clinically applicable digital transcript counting platform (NanoString nCounter system). A cohort of 145 U.S. patients with HCV-related early-stage cirrhosis was analyzed by using the assay. This dataset (GEO accession number GPL17230) provides information of expression levels of the prognostic genes in the cohort.

Efficient utilization of EBUS-TBNA samples for both diagnosis and molecular analyses

BACKGROUND

The assessment of an increasing number of molecular markers is becoming a standard requirement from endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) specimens. However, it is unclear how many needle passes should be performed and the amount of lung cancer cells that should be sent for molecular analyses. The objective of this study was to determine if it is feasible to divide the material obtained by EBUS-TBNA to allow for molecular analysis without compromising the accuracy of mediastinal staging.

OBJECTIVE

We aimed to determine if dividing EBUS-TBNA specimens has a negative impact on either histopathological diagnosis or molecular analysis.

METHODS

EBUS-TBNA was performed in 249 enlarged lymph nodes. Negative or ambiguous histopathological results were confirmed by surgical means and clinical follow-up over 6 months. The tissue obtained by EBUS-TBNA was placed onto a glass slide and divided for histopathological workup and molecular analysis. The number of passes was recorded. Both the accuracy of the mediastinal lymph node staging and the applicability of the sample division for molecular analysis were assessed.

RESULTS

Each lymph node was punctured an average of 3.18 times and division of the obtained material for diagnosis and molecular analysis was feasible in all cases. The sensitivity and accuracy of the mediastinal lymph node staging were 96.6% and 97.6%, respectively. A cytokeratin (CK)-19-mRNA concentration-based molecular test was feasible in 74.1% of cases.

CONCLUSION

Dividing EBUS-TBNA samples for both histopathological diagnosis and molecular testing is feasible and does not compromise the accuracy of mediastinal staging. This method may be an alternative to taking additional needle passes for molecular analyses.

Integration of genomic information in the clinical management of HCC

Molecular profiling of hepatocellular carcinoma (HCC) is enabling the advancement of novel approaches to disease diagnosis and management. Accurate prognosis prediction in HCC is specially critical. Clinical staging systems for HCC support clinical decision-making (e.g., BCLC algorithm) might be complemented by molecular-based information in the near future. Molecular signatures derived from tumour and non-tumour samples are associated with patient recurrence an outcome. Single nucleotide polymorphisms have been linked with HCC development. Next generation sequencing studies have brought to light the genomic diversity of this disease. Gens recurrently altered in HCC and susceptible to be targeted belong to signalling pathways including telomere maintenance, cell cycle, chromatin remodelling, Wnt/beta-catenin, RAS/RAF/MAPK and PI3K/AKT/mTOR pathways. Oncogenic loops are unknown but might include some of the already discovered aberrations. Despite the intratumoral heterogeneity observed in HCC tumours, studies including large number of samples can identify key genetic drivers and contribute to the development of novel treatments and a personalized medicine.