Translating cancer genomes and transcriptomes for precision oncology

Unraveling the complexities of colorectal cancer and its promising therapies - An updated review

Colorectal cancer (CRC) continues to be a global health concern, necessitating further research into its complex biology and innovative treatment approaches. The etiology, pathogenesis, diagnosis, and treatment of colorectal cancer are summarized in this thorough review along with recent developments. The multifactorial nature of colorectal cancer is examined, including genetic predispositions, environmental factors, and lifestyle decisions. The focus is on deciphering the complex interactions between signaling pathways such as Wnt/β-catenin, MAPK, TGF-β as well as PI3K/AKT that participate in the onset, growth, and metastasis of CRC. There is a discussion of various diagnostic modalities that span from traditional colonoscopy to sophisticated molecular techniques like liquid biopsy and radiomics, emphasizing their functions in early identification, prognostication, and treatment stratification. The potential of artificial intelligence as well as machine learning algorithms in improving accuracy as well as efficiency in colorectal cancer diagnosis and management is also explored. Regarding therapy, the review provides a thorough overview of well-known treatments like radiation, chemotherapy, and surgery as well as delves into the newly-emerging areas of targeted therapies as well as immunotherapies. Immune checkpoint inhibitors as well as other molecularly targeted treatments, such as anti-epidermal growth factor receptor (anti-EGFR) as well as anti-vascular endothelial growth factor (anti-VEGF) monoclonal antibodies, show promise in improving the prognosis of colorectal cancer patients, in particular, those suffering from metastatic disease. This review focuses on giving readers a thorough understanding of colorectal cancer by considering its complexities, the present status of treatment, and potential future paths for therapeutic interventions. Through unraveling the intricate web of this disease, we can develop a more tailored and effective approach to treating CRC.

Cathepsin L in Lung Adenocarcinoma: Prognostic Significance and Immunotherapy Response Through a Multi Omics Perspective

Objectives

Lung adenocarcinoma (LUAD), a predominant form of lung cancer, is characterized by a high rate of metastasis and recurrence, leading to a poor prognosis for LUAD patients. This study aimed to identify and rigorously validate a highly precise biomarker, Cathepsin L (CTSL), for the prognostic prediction of lung adenocarcinoma.

Methods

We employed a multicenter and omics-based approach, analyzing RNA sequencing data and mutation information from public databases such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The DepMap portal with Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR/Cas9) technology was used to assess the functional impact of CTSL. Immunohistochemistry (IHC) was conducted on a local cohort to validate the prognostic significance of CTSL at the protein expression level.

Results

Our findings revealed a significant correlation between elevated CTSL expression and advanced disease stage in LUAD patients. Kaplan-Meier survival analysis and Cox regression modeling revealed that high CTSL expression is associated with poor overall survival. The in vitro studies corroborated these findings, revealing notable suppression of tumor proliferation following CTSL knockout in cell lines, particularly in LUAD. Functional enrichment revealed that CTSL activated pathways associated with tumor progression, such as angiogenesis and Transforming growth factor beta (TGF-beta) signaling, and inhibited pathways such as apoptosis and DNA repair. Mutation analysis revealed distinct variations in the CTSL expression groups.

Conclusion

This study highlights the crucial role of CTSL as a prognostic biomarker in LUAD. This combined multicenter and omics-based analysis provides comprehensive insights into the biological role of CTSL, supporting its potential as a target for therapeutic intervention and a marker for prognosis in patients with LUAD.

Chordoma Genetic Aberrations and Targeted Therapies Panorama: A Systematic Literature Review

Background: Chordomas pose a challenge in treatment due to their local invasiveness, high recurrence, and potential lethality. Despite being slow-growing and rarely metastasizing, these tumors often resist conventional chemotherapies (CTs) and radiotherapies (RTs), making surgical resection a crucial intervention. However, achieving radical resection for chordomas is seldom possible, presenting therapeutic challenges. The accurate diagnosis of these tumors is vital for their distinct prognoses, yet differentiation is hindered by overlapping radiological and histopathological features. Fortunately, recent molecular and genetic studies, including extracranial location analysis, offer valuable insights for precise diagnosis. This literature review delves into the genetic aberrations and molecular biology of chordomas, aiming to provide an overview of more successful therapeutic strategies. Methods: A systematic search was conducted across major medical databases (PubMed, Embase, and Cochrane Library) up to 28 January 2023. The search strategy utilized relevant Medical Subject Heading (MeSH) terms and keywords related to "chordomas", "molecular biology", "gene aberrations", and "target therapies". The studies included in this review consist of preclinical cell studies, case reports, case series, randomized controlled trials, non-randomized controlled trials, and cohort studies reporting on genetic and biological aberrations in chordomas. Results: Of the initial 297 articles identified, 40 articles were included in the article. Two tables highlighted clinical studies and ongoing clinical trials, encompassing 18 and 22 studies, respectively. The clinical studies involved 185 patients diagnosed with chordomas. The tumor sites were predominantly sacral (n = 8, 44.4%), followed by clivus (n = 7, 38.9%) and lumbar spine (n = 3, 16.7%). Primary treatments preceding targeted therapies included surgery (n = 10, 55.6%), RT (n = 9, 50.0%), and systemic treatments (n = 7, 38.9%). Various agents targeting specific molecular pathways were analyzed in the studies, such as imatinib (a tyrosine kinase inhibitor), erlotinib, and bevacizumab, which target EGFR/VEGFR. Common adverse events included fatigue (47.1%), skin reactions (32.4%), hypertension (23.5%), diarrhea (17.6%), and thyroid abnormalities (5.9%). Clinical outcomes were systematically assessed based on progression-free survival (PFS), overall survival (OS), and tumor response evaluated using RECIST or CHOI criteria. Notably, stable disease (SD) occurred in 58.1% of cases, and partial responses (PRs) were observed in 28.2% of patients, while 13.7% experienced disease progression (PD) despite targeted therapy. Among the 22 clinical trials included in the analysis, Phase II trials were the most prevalent (40.9%), followed by I-II trials (31.8%) and Phase I trials (27.3%). PD-1 inhibitors were the most frequently utilized, appearing in 50% of the trials, followed by PD-L1 inhibitors (36.4%), CTLA-4 inhibitors (22.7%), and mTOR inhibitors (13.6%). Conclusions: This systematic review provides an extensive overview of the state of targeted therapy for chordomas, highlighting their potential to stabilize the illness and enhance clinical outcomes.

Genome-wide DNA methylation analysis identifies potent CpG signature for temzolomide response in non-G-CIMP glioblastomas with unmethylated MGMT promoter: MGMT-dependent roles of GPR81

PURPOSES

To identify potent DNA methylation candidates that could predict response to temozolomide (TMZ) in glioblastomas (GBMs) that do not have glioma-CpGs island methylator phenotype (G-CIMP) but have an unmethylated promoter of O-6-methylguanine-DNA methyltransferase (unMGMT).

METHODS

The discovery-validation approach was planned incorporating a series of G-CIMP-/unMGMT GBM cohorts with DNA methylation microarray data and clinical information, to construct multi-CpG prediction models. Different bioinformatic and experimental analyses were performed for biological exploration.

RESULTS

By analyzing discovery sets with radiotherapy (RT) plus TMZ versus RT alone, we identified a panel of 64 TMZ efficacy-related CpGs, from which a 10-CpG risk signature was further constructed. Both the 64-CpG panel and the 10-CpG risk signature were validated showing significant correlations with overall survival of G-CIMP-/unMGMT GBMs when treated with RT/TMZ, rather than RT alone. The 10-CpG risk signature was further observed for aiding TMZ choice by distinguishing differential outcomes to RT/TMZ versus RT within each risk subgroup. Functional studies on GPR81, the gene harboring one of the 10 CpGs, indicated its distinct impacts on TMZ resistance in GBM cells, which may be dependent on the status of MGMT expression.

CONCLUSIONS

The 64 TMZ efficacy-related CpGs and in particular the 10-CpG risk signature may serve as promising predictive biomarker candidates for guiding optimal usage of TMZ in G-CIMP-/unMGMT GBMs.

Advancements in technology for characterizing the tumor immune microenvironment

Immunotherapy plays a key role in cancer treatment, however, responses are limited to a small number of patients. The biological basis for the success of immunotherapy is the complex interaction between tumor cells and tumor immune microenvironment (TIME). Historically, research on tumor immune constitution was limited to the analysis of one or two markers, more novel technologies are needed to interpret the complex interactions between tumor cells and TIME. In recent years, major advances have already been made in depicting TIME at a considerably elevated degree of throughput, dimensionality and resolution, allowing dozens of markers to be labeled simultaneously, and analyzing the heterogeneity of tumour-immune infiltrates in detail at the single cell level, depicting the spatial landscape of the entire microenvironment, as well as applying artificial intelligence (AI) to interpret a large amount of complex data from TIME. In this review, we summarized emerging technologies that have made contributions to the field of TIME, and provided prospects for future research.

Purine salvage-associated metabolites as biomarkers for early diagnosis of esophageal squamous cell carcinoma: a diagnostic model-based study

Esophageal squamous cell carcinoma (ESCC) remains an important health concern in developing countries. Patients with advanced ESCC have a poor prognosis and survival rate, and achieving early diagnosis remains a challenge. Metabolic biomarkers are gradually gaining attention as early diagnostic biomarkers. Hence, this multicenter study comprehensively evaluated metabolism dysregulation in ESCC through an integrated research strategy to identify key metabolite biomarkers of ESCC. First, the metabolic profiles were examined in tissue and serum samples from the discovery cohort (n = 162; ESCC patients, n = 81; healthy volunteers, n = 81), and ESCC tissue-induced metabolite alterations were observed in the serum. Afterward, RNA sequencing of tissue samples (n = 46) was performed, followed by an integrated analysis of metabolomics and transcriptomics. The potential biomarkers for ESCC were further identified by censoring gene-metabolite regulatory networks. The diagnostic value of the identified biomarkers was validated in a validation cohort (n = 220), and the biological function was verified. A total of 457 dysregulated metabolites were identified in the serum, of which 36 were induced by tumor tissues. The integrated analyses revealed significant alterations in the purine salvage pathway, wherein the abundance of hypoxanthine/xanthine exhibited a positive correlation with HPRT1 expression and tumor size. A diagnostic model was developed using two purine salvage-associated metabolites. This model could accurately discriminate patients with ESCC from normal individuals, with an area under the curve (AUC) (95% confidence interval (CI): 0.680-0.843) of 0.765 in the external cohort. Hypoxanthine and HPRT1 exerted a synergistic effect in terms of promoting ESCC progression. These findings are anticipated to provide valuable support in developing novel diagnostic approaches for early ESCC and enhance our comprehension of the metabolic mechanisms underlying this disease.

Research progress on the multi-omics and survival status of circulating tumor cells

In the dynamic process of metastasis, circulating tumor cells (CTCs) emanate from the primary solid tumor and subsequently acquire the capacity to disengage from the basement membrane, facilitating their infiltration into the vascular system via the interstitial tissue. Given the pivotal role of CTCs in the intricate hematogenous metastasis, they have emerged as an essential resource for a deeper comprehension of cancer metastasis while also serving as a cornerstone for the development of new indicators for early cancer screening and new therapeutic targets. In the epoch of precision medicine, as CTC enrichment and separation technologies continually advance and reach full fruition, the domain of CTC research has transcended the mere straightforward detection and quantification. The rapid advancement of CTC analysis platforms has presented a compelling opportunity for in-depth exploration of CTCs within the bloodstream. Here, we provide an overview of the current status and research significance of multi-omics studies on CTCs, including genomics, transcriptomics, proteomics, and metabolomics. These studies have contributed to uncovering the unique heterogeneity of CTCs and identifying potential metastatic targets as well as specific recognition sites. We also review the impact of various states of CTCs in the bloodstream on their metastatic potential, such as clustered CTCs, interactions with other blood components, and the phenotypic states of CTCs after undergoing epithelial-mesenchymal transition (EMT). Within this context, we also discuss the therapeutic implications and potential of CTCs.

Oral tumor heterogeneity, its implications for patient monitoring and designing anti-cancer strategies

Oral cancer tumors occur in the mouth and are mainly derived from oral mucosa linings. It is one of the most common and fatal malignant diseases worldwide. The intratumor heterogeneity (ITH) of oral cancerous tumor is vast, so it is challenging to study and interpret. Due to environmental selection pressures, ITH arises through diverse genetic, epigenetic, and metabolic alterations. The ITH also talks about peri-tumoral vascular/ lymphatic growth, perineural permeation, tumor necrosis, invasion, and clonal expansion/ the coexistence of multiple subclones in a single tumor. The heterogeneity offers tumors the adaptability to survive, induce growth/ metastasis, and, most importantly, escape antitumor therapy. Unfortunately, the ITH is prioritized less in determining disease pathology than the traditional TNM classifications or tumor grade. Understanding ITH is challenging, but with the advancement of technology, this ITH can be decoded. Tumor genomics, proteomics, metabolomics, and other modern analyses can provide vast information. This information in clinics can assist in understanding a tumor's severity and be used for diagnostic, prognostic, and therapeutic decision-making. Lastly, the oral tumor ITH can lead to individualized, targeted therapy strategies fighting against OC.

OSppc: A web server for online survival analysis using proteome of pan-cancers

Prognostic biomarker, as a feasible and objective indicator, is valuable in the assessment of cancer risk. With the development of high-throughput sequencing technology, the screening of prognostic biomarkers has become easy, but it is difficult to screen prognostic markers based on proteomic data. In this study we developed a tool named Online consensus Survival analysis web server based on Proteome of Pan-cancers, abbreviated as OSppc, to evaluate the prognostic values of protein biomarkers. >8000 cancer cases with proteomic data, transcriptomic data and clinical follow-up information were collected from TCGA and CPTAC. 14,038 proteins (including proteins and their phosphorylated forms) analyzed by reverse-phase protein arrays and mass spectrometry in 33 types of cancers were collected. In OSppc, three analysis modules are provided, including Survival Analysis, Differential Analysis and Correlation Analysis. Survival analysis module exhibits HR with 95% CI and KM curves with log-rank p value of protein and mRNA levels of input genes. Differential analysis module shows the box plots of protein expression levels in different tissues. Correlation analysis module provides scatter plot with pearson's and spearman's correlation coefficient of the protein and its corresponding mRNA. OSppc can be accessed at http://bioinfo.henu.edu.cn/Protein/OSppc.html. SIGNIFICANCE: OSppc can analyze the association between protein, mRNA and prognosis, the correlation between proteome data and gene expression profiles, the differential expression of proteome data between subgroups such as normal and cancer as well. OSppc is registration-free and very valuable to evaluate the prognostic potency of protein of interests. OSppc is very valuable for researchers and clinicians to screen, develop and validate potential protein prognostic biomarkers in pan-cancers, and offers the opportunities to investigate the clinical important functional genes and therapeutic targets of cancers.

Stromal Interaction Molecule 1 (STIM1) is a Potential Prognostic Biomarker and Correlates with Immune Infiltrates in Solid Tumors

Increasing evidence has shown that stromal interaction molecule 1 (STIM1), a key subunit of store-operated Ca2+ entry (SOCE), is closely associated with tumor growth, development, and metastasis. However, there is no report of a comprehensive assessment of STIM1 in pan-cancer. This study aimed to perform a general analysis of STIM1 in human tumors, including its molecular characteristics, functional mechanisms, clinical significance, and immune infiltrates correlation based on pan-cancer data from The Cancer Genome Atlas (TCGA). Gene expression analysis was investigated using TCGA RNA-seq data, the Tumor Immune Estimation Resource (TIMER). Phosphorylation analysis was undertaken using the Clinical Proteomic Tumor Analysis Consortium (CP-TAC) and the PhosphoNET database. Genetic alterations of STIM1 were analyzed using cBioPortal. Prognostic analysis was via the R package "survival" function and the Kaplan-Meier plotter. Functional enrichment analysis was via by the R package "cluster Profiler" function. The association between STIM1 and tumor-infiltrating immune cells and immune markers was by the R package "GSVA" function and TIMER. STIM1 was differentially expressed and associated with distinct clinical stages in multiple tumors. The phosphorylation of STIM1 at S673 is highly expressed in clear cell renal carcinoma and lung adenocarcinoma tumors compared to normal tissues. STIM1 genetic alterations correlate with poor prognosis in several tumors, including ovarian cancer and lung squamous cell carcinomas. High STIM1 expression is associated with good or poor prognosis across diverse tumors. Overall survival (OS) analysis indicated that STIM1 is a favorable prognostic factor for patients with BRCA, KIRC, LIHC, LUAD, OV, SARC, and UCEC, and is a risk prognostic factor for BLCA, KIRP, STAD, and UVM. There is a close correlation between STIM1 expression and immune cell infiltration, immune-regulated genes, chemokines, and immune checkpoints in a variety of tumors. STIM1 functions differently in diverse tumors, playing an oncogenic or antitumor role. Moreover, It may serve as a prognostic biomarker and an immunotherapy target across multiple tumors.

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

BACKGROUND

Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers.

METHODS

We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort.

FINDINGS

Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy.

CONCLUSIONS

ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome.

FUNDING

This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

O-GlcNAcylation: an important post-translational modification and a potential therapeutic target for cancer therapy

O-linked β-d-N-acetylglucosamine (O-GlcNAc) is an important post-translational modification of serine or threonine residues on thousands of proteins in the nucleus and cytoplasm of all animals and plants. In eukaryotes, only two conserved enzymes are involved in this process. O-GlcNAc transferase is responsible for adding O-GlcNAc to proteins, while O-GlcNAcase is responsible for removing it. Aberrant O-GlcNAcylation is associated with a variety of human diseases, such as diabetes, cancer, neurodegenerative diseases, and cardiovascular diseases. Numerous studies have confirmed that O-GlcNAcylation is involved in the occurrence and progression of cancers in multiple systems throughout the body. It is also involved in regulating multiple cancer hallmarks, such as metabolic reprogramming, proliferation, invasion, metastasis, and angiogenesis. In this review, we first describe the process of O-GlcNAcylation and the structure and function of O-GlcNAc cycling enzymes. In addition, we detail the occurrence of O-GlcNAc in various cancers and the role it plays. Finally, we discuss the potential of O-GlcNAc as a promising biomarker and novel therapeutic target for cancer diagnosis, treatment, and prognosis.

Advances in liquid biopsy in neuroblastoma

Even with intensive treatment of high-risk neuroblastoma (NB) patients, half of high-risk NB patients still relapse. New therapies targeting the biological characteristics of NB have important clinical value for the personalized treatment of NB. However, the current biological markers for NB are mainly analyzed by tissue biopsy. In recent years, circulating biomarkers of NB based on liquid biopsy have attracted more and more attention. This review summarizes the analytes and methods for liquid biopsy of NB. We focus on the application of liquid biopsy in the diagnosis, prognosis assessment, and monitoring of NB. Finally, we discuss the prospects and challenges of liquid biopsy in NB.

Next-generation sequencing identifies potential novel therapeutic targets in Chinese HGSOC patients

BACKGROUND

Targeted therapy, especially the use of poly (adenosine diphosphate ribose) polymerase (PARP) inhibitors (PARPis), has improved the outcome of patients with ovarian cancer. However, most high-grade serous ovarian cancer (HGSOC) patients have wild-type BRCA1/2, and it is necessary to disclose more potential novel targets for other available targeted drugs. So, detection of genetic alterations beyond BRCA1/2 is critical to screen HGSOC patients for personalized therapy. In this study, a broad, hybrid capture-based next-generation sequencing (NGS) assay was used to identify actionable genetic alterations from HGSOC cancer tissues.

METHODS

Sixty-eight patients with HGSOC were enrolled, including 6 International Federation of Gynecology and Obstetrics (FIGO) stage I, 15 stage II, 37 stage III and 10 stage IV patients. All patients signed informed consent forms. Potentially actionable genetic alterations, including base substitutions, indels, copy number alterations, and gene fusions, were identified using targeted NGS.

RESULTS

In our study, 14.7% (10/68) of the tumors harbored actionable genetic alterations in patients with BRCA1. A total of 25.0% (17/68) of patients without BRCA1 mutations harbored other actionable genetic alterations, such as homologous recombination repair (HRR) pathway-related genes (ATM, CDK12, FANCA, and FANCD2), PI3K/AKT/mTOR pathway genes (NF1, FBXW7, PIK3CA, PTEN, TSC1, and TSC2), and some other genes (ARID1A, FGFR1, KRAS, and NRAS). Furthermore, some patients harboring ARID1A or NF1 actionable genetic alterations showed good clinical efficacy to immune checkpoint inhibitors (ICIs) and everolimus, respectively.

CONCLUSIONS

Our research indicates that 39.7% (27/68) of patients with HGSOC harbored at least one actionable genetic alteration. 25.0% (17/68) of patients had somatic mutations or copy number variations beyond BRCA1 mutations and might be treated with off-label therapy or to be allocated into clinical trial. NGS assays of HGSOC patients are necessary to screen actionable genetic alterations to guide personalized and precise treatment.

Higher intra-tumoral expression of pro-coagulation genes is a predictor of angiogenesis, epithelial mesenchymal transition and worse patient survival in gastric cancer

Coagulation regulates angiogenesis in cancer, and is associated with tumor development and metastasis. To date, there have been no studies quantifying the state of intra-tumoral coagulation. We measured intra-tumoral coagulation gene expression using the "Hallmark-COAGULATION" gene set in the MSigDB, performing gene set variation analysis and then assigning a "coagulation score" to quantify gene expression. Clinical, histologic, and genetic data were analyzed in 807 gastric cancer patients from the TCGA_STAD and GSE84437 databases. Tumors with increased expression of pro-coagulation genes were consistently associated with higher AJCC T-categories (p = 0.018), lymph node metastasis (p = 0.036), and stage (p = 0.006) in both cohorts. Patients with high coagulation scores were found to have worse disease-specific survival and overall survival (OS) (p = 0.019 and 0.011, respectively) in TCGA, and worse OS in GSE84437 cohort (p = 0.012). Higher expression of pro-coagulation genes correlated with increased intra-tumoral angiogenesis, as well as increased proportions of lymphatic and microvascular endothelial cells, endothelial cells, and pericytes, calculated by xCell algorithm. High coagulation scores were significantly associated with low tumor mutation burden, but not with intratumor heterogeneity and homologous recombination deficiency. Gastric cancers with high coagulation scores contained higher amounts of M1 macrophages and dendritic cells, and low numbers of Th1 cells (all P<0.001). Genes for epithelial mesenchymal transition (EMT), myogenesis, apical junction, transforming growth factor (TGF)-β signaling, and angiogenesis were enriched in high coagulation score-gastric cancers (all false discovery rate <0.25). In conclusion, gastric cancers expressing higher levels of pro-coagulation genes demonstrate increased angiogenesis, EMT, TGF-β signaling and worse patient prognosis.

Development of a Whole-urine, Multiplexed, Next-generation RNA-sequencing Assay for Early Detection of Aggressive Prostate Cancer

BACKGROUND

Despite biomarker development advances, early detection of aggressive prostate cancer (PCa) remains challenging. We previously developed a clinical-grade urine test (Michigan Prostate Score [MiPS]) for individualized aggressive PCa risk prediction. MiPS combines serum prostate-specific antigen (PSA), the TMPRSS2:ERG (T2:ERG) gene fusion, and PCA3 lncRNA in whole urine after digital rectal examination (DRE).

OBJECTIVE

To improve on MiPS with a novel next-generation sequencing (NGS) multibiomarker urine assay for early detection of aggressive PCa.

DESIGN, SETTING, AND PARTICIPANTS

Preclinical development and validation of a post-DRE urine RNA NGS assay (Urine Prostate Seq [UPSeq]) assessing 84 PCa transcriptomic biomarkers, including T2:ERG, PCA3, additional PCa fusions/isoforms, mRNAs, lncRNAs, and expressed mutations. Our UPSeq model was trained on 73 patients and validated on a held-out set of 36 patients representing the spectrum of disease (benign to grade group [GG] 5 PCa).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The area under the receiver operating characteristic curve (AUC) of UPSeq was compared with PSA, MiPS, and other existing models/biomarkers for predicting GG ≥3 PCa.

RESULTS AND LIMITATIONS

UPSeq demonstrated high analytical accuracy and concordance with MiPS, and was able to detect expressed germline HOXB13 and somatic SPOP mutations. In an extreme design cohort (n = 109; benign/GG 1 vs GG ≥3 PCa, stratified to exclude GG 2 cancer in order to capture signal difference between extreme ends of disease), UPSeq showed differential expression for T2:ERG.T1E4 (1.2 vs 78.8 median normalized reads, p < 0.00001) and PCA3 (1024 vs 2521, p = 0.02), additional T2:ERG splice isoforms, and other candidate biomarkers. Using machine learning, we developed a 15-transcript model on the training set (n = 73) that outperformed serum PSA and sequencing-derived MiPS in predicting GG ≥3 PCa in the held-out validation set (n = 36; AUC 0.82 vs 0.69 and 0.69, respectively).

CONCLUSIONS

These results support the potential utility of our novel urine-based RNA NGS assay to supplement PSA for improved early detection of aggressive PCa.

PATIENT SUMMARY

We have developed a new urine-based test for the detection of aggressive prostate cancer, which promises improvement upon current biomarker tests.

Comprehensive analysis of gene expression profiles to identify differential prognostic factors of primary and metastatic breast cancer

Breast cancer accounts for nearly half of all cancer-related deaths in women worldwide. However, the molecular mechanisms that lead to tumour development and progression remain poorly understood and there is a need to identify candidate genes associated with primary and metastatic breast cancer progression and prognosis. In this study, candidate genes associated with prognosis of primary and metastatic breast cancer were explored through a novel bioinformatics approach. Primary and metastatic breast cancer tissues and adjacent normal breast tissues were evaluated to identify biomarkers characteristic of primary and metastatic breast cancer. The Cancer Genome Atlas-breast invasive carcinoma (TCGA-BRCA) dataset (ID: HS-01619) was downloaded using the mRNASeq platform. Genevestigator 8.3.2 was used to analyse TCGA-BRCA gene expression profiles between the sample groups and identify the differentially-expressed genes (DEGs) in each group. For each group, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were used to determine the function of DEGs. Networks of protein-protein interactions were constructed to identify the top hub genes with the highest degree of interaction. Additionally, the top hub genes were validated based on overall survival and immunohistochemistry using The Human Protein Atlas. Of the top 20 hub genes identified, four (KRT14, KIT, RAD51, and TTK) were considered as prognostic risk factors based on overall survival. KRT14 and KIT expression levels were upregulated while those of RAD51 and TTK were downregulated in patients with breast cancer. The four proposed candidate hub genes might aid in further understanding the molecular changes that distinguish primary breast tumours from metastatic tumours as well as help in developing novel therapeutics. Furthermore, they may serve as effective prognostic risk markers based on the strong correlation between their expression and patient overall survival.

Deeper insights into long-term survival heterogeneity of pancreatic ductal adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses

Pancreatic ductal adenocarcinoma (PDAC) is categorized as the leading cause of cancer mortality worldwide. However, its predictive markers for long-term survival are not well known. It is interesting to delineate individual-specific perturbed genes when comparing long-term (LT) and short-term (ST) PDAC survivors and integrate individual- and group-based transcriptome profiling. Using a discovery cohort of 19 PDAC patients from CHU-Liège (Belgium), we first performed differential gene expression analysis comparing LT to ST survivor. Second, we adopted systems biology approaches to obtain clinically relevant gene modules. Third, we created individual-specific perturbation profiles. Furthermore, we used Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics Data (NetICS) to integrate group-based and individual-specific perturbed genes in relation to PDAC LT survival. We identified 173 differentially expressed genes (DEGs) in ST and LT survivors and five modules (including 38 DEGs) showing associations to clinical traits. Validation of DEGs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Via NetICS and DADA, we identified various known oncogenes such as CUL1 and TGFB1. Our proposed analytic workflow shows the advantages of combining clinical and omics data as well as individual- and group-level transcriptome profiling.

A Novel Pseudogene Methylation Signature to Predict Temozolomide Outcome in Non-G-CIMP Glioblastomas

Objective

Alterations in the methylation state of pseudogenes may serve as clinically useful biomarkers of glioblastomas (GBMs) that do not have glioma-CpG island methylator phenotype (G-CIMP).

Methods

Non-G-CIMP GBM datasets were included for evaluation, and a RISK-score signature was determined from the methylation state of pseudogene loci. Both bioinformatic and experimental analyses were performed for biological validation.

Results

By integrating clinical information with DNA methylation microarray data, we screened a panel of eight CpGs from discovery cohorts of non-G-CIMP GBMs. Each CpG could accurately and independently predict the prognosis of patients under a treatment regime that combined radiotherapy (RT) and temozolomide (TMZ). The 8-CpG signature appeared to show opposite prognostic correlations between patients treated with RT/TMZ and those treated with RT monotherapy. The analyses further indicated that this signature had predictive value for TMZ efficacy because different survival benefits between RT/TMZ and RT therapies were observed in each risk subgroup. The incorporation of other risk factors, such as age and O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, with our pseudogene methylation signature could provide precise risk classification. In vitro experimental data revealed that two locus-specific pseudogenes (ZNF767P and CLEC4GP1) may modulate TMZ resistance via distinct mechanisms in GBM cells.

Conclusion

The biologically and clinically relevant RISK-score signature, based on pseudogene methylation loci, may offer information for predicting TMZ responses of non-G-CIMP GBMs, that is independent from, but complementary to, MGMT-based approaches.

Comprehensive Analysis of RELL2 as a Potential Biomarker Associated with Tumor Immune Infiltrating Cells in a Pan-Cancer Analysis

Background

Receptor expressed in lymphoid tissues-like 2 (RELL2), which is a member of RELT family, is closely associated with the plasma membrane and acts as a modulator for RELT signaling. Overexpression of RELL2 induces the activation of MAPK14/p38 cascade and apoptosis. However, whether RELL2 contributes to cancers remains unclear. Here, we examined its role in cancer patient prognosis and various tumors.

Methods

We used several bioinformatics methods, specifically gene set enrichment analysis (GSEA), ScanNeo, and ESTIMATE, to analyze the CCLE dataset, GTEx dataset, and TCGA dataset. We investigated the possible association of RELL2 with the microsatellite instability (MSI) of various tumors, tumor mutational burden (TMB), immune checkpoint, immune neoantigens, immune microenvironment, and patient prognosis.

Result

RELL2 is highly expressed in cancer compared with normal tissues. RELL2 expression is linked with worse progression-free interval and overall survival in numerous cancers. In most cancers, high RELL2 expression was related to a poor prognosis. RELL2 expression was significantly associated with the tumor microenvironment, MSI, and TMB. RELL2 expression is strongly associated with phenotypes that are of major clinical significance, particularly those associated with immune neoantigens and the expression profiles of immune checkpoint genes in pan-cancer. RELL2 expression strongly linked with the expressions of methyltransferases and DNA repair genes. It also significantly correlated with multiple signaling pathways through gene set enrichment analysis.

Conclusion

RELL2 may be a prognostic biomarker in pan-cancer and may have an important function in tumorigenesis and progression.

Comparative characterization of 3D chromatin organization in triple-negative breast cancers

Triple-negative breast cancer (TNBC) is a malignant cancer subtype with a high risk of recurrence and an aggressive phenotype compared to other breast cancer subtypes. Although many breast cancer studies conducted to date have investigated genetic variations and differential target gene expression, how 3D chromatin architectures are reorganized in TNBC has been poorly elucidated. Here, using in situ Hi-C technology, we characterized the 3D chromatin organization in cells representing five distinct subtypes of breast cancer (including TNBC) compared to that in normal cells. We found that the global and local 3D architectures were severely disrupted in breast cancer. TNBC cell lines (especially BT549 cells) showed the most dramatic changes relative to normal cells. Importantly, we detected CTCF-dependent TNBC-susceptible losses/gains of 3D chromatin organization and found that these changes were strongly associated with perturbed chromatin accessibility and transcriptional dysregulation. In TNBC tissue, 3D chromatin disorganization was also observed relative to the 3D chromatin organization in normal tissues. We observed that the perturbed local 3D architectures found in TNBC cells were partially conserved in TNBC tissues. Finally, we discovered distinct tissue-specific chromatin loops by comparing normal and TNBC tissues. In this study, we elucidated the characteristics of the 3D chromatin organization in breast cancer relative to normal cells/tissues at multiple scales and identified associations between disrupted structures and various epigenetic features and transcriptomes. Collectively, our findings reveal important 3D chromatin structural features for future diagnostic and therapeutic studies of TNBC.

The 3D architecture of the genome is dramatically altered in an aggressive form of breast cancer, leading to changes in the regulation of gene expression that can fuel tumor growth. A team from South Korea, led by Hyeong-Gon Moon of Seoul National University College of Medicine and Daeyoup Lee of the Korea Advanced Institute of Science and Technology, Daejeon, detailed how chromosomes are positioned and folded within the nucleus of cell liness from five different subtypes of breast cancer. They found that triple-negative breast cancers displayed the most extreme reorganization of their genomes, a pattern also observed in biopsy tissues taken from patients with this subtype of cancer. Knowledge of these conformational changes could inform future efforts to develop therapies and diagnostics for patients with triple-negative breast tumors.

ERBB2 S310F mutation independently activates PI3K/AKT and MAPK pathways through homodimers to contribute gallbladder carcinoma growth

Genomic instability and mutability are a prominent character of tumor. The whole-exosome sequence reveals that ERBB2 mutations are the representative mutations of gallbladder carcinoma, which takes potential targets for gallbladder carcinoma therapy. However, the roles of ERBB2 mutations are unclear in gallbladder carcinoma. We identified S310F mutation is the hottest mutation of ERBB2 mutations from TCGA PanCancer Altas data with 10,967 samples and our previous study with 157 gallbladder carcinoma samples. S310F mutation located in ERBB2 extracellular domain, promoted ERBB2 homodimerization and consequent auto-phosphorylation to activate the downstream PI3K/AKT and MAPK pathways, which was independent on ERBB1, ERBB3, and ERBB4. ERBB2 S310F mutation up-regulated aerobic glycolysis and promoted gallbladder carcinoma growth. Our study reveals the roles of ERBB2 S310F mutation, which is beneficial to ERBB2 S310F mutant gallbladder carcinoma therapy.

Strategies to overcome the main challenges of the use of CRISPR/Cas9 as a replacement for cancer therapy

CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats-associated protein 9) shows the opportunity to treat a diverse array of untreated various genetic and complicated disorders. Therapeutic genome editing processes that target disease-causing genes or mutant genes have been greatly accelerated in recent years as a consequence of improvements in sequence-specific nuclease technology. However, the therapeutic promise of genome editing has yet to be explored entirely, many challenges persist that increase the risk of further mutations. Here, we highlighted the main challenges facing CRISPR/Cas9-based treatments and proposed strategies to overcome these limitations, for further enhancing this revolutionary novel therapeutics to improve long-term treatment outcome human health.

5-Methylcytosine RNA Methyltransferases-Related Long Non-coding RNA to Develop and Validate Biochemical Recurrence Signature in Prostate Cancer

The effects of 5-methylcytosine in RNA (m5C) in various human cancers have been increasingly studied recently; however, the m5C regulator signature in prostate cancer (PCa) has not been well established yet. In this study, we identified and characterized a series of m5C-related long non-coding RNAs (lncRNAs) in PCa. Univariate Cox regression analysis and least absolute shrinkage and selector operation (LASSO) regression analysis were implemented to construct a m5C-related lncRNA prognostic signature. Consequently, a prognostic m5C-lnc model was established, including 17 lncRNAs: MAFG-AS1, AC012510.1, AC012065.3, AL117332.1, AC132192.2, AP001160.2, AC129510.1, AC084018.2, UBXN10-AS1, AC138956.2, ZNF32-AS2, AC017100.1, AC004943.2, SP2-AS1, Z93930.2, AP001486.2, and LINC01135. The high m5C-lnc score calculated by the model significantly relates to poor biochemical recurrence (BCR)-free survival (p < 0.0001). Receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) further validated the accuracy of the prognostic model. Subsequently, a predictive nomogram combining the prognostic model with clinical features was created, and it exhibited promising predictive efficacy for BCR risk stratification. Next, the competing endogenous RNA (ceRNA) network and lncRNA–protein interaction network were established to explore the potential functions of these 17 lncRNAs mechanically. In addition, functional enrichment analysis revealed that these lncRNAs are involved in many cellular metabolic pathways. Lastly, MAFG-AS1 was selected for experimental validation; it was upregulated in PCa and probably promoted PCa proliferation and invasion in vitro. These results offer some insights into the m5C's effects on PCa and reveal a predictive model with the potential clinical value to improve the prognosis of patients with PCa.

Reliable Detection of Somatic Mutations for Pancreatic Cancer in Endoscopic Ultrasonography-Guided Fine Needle Aspirates with Next-Generation Sequencing: Implications from a Prospective Cohort Study

BACKGROUND OR PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is commonly diagnosed by endoscopic ultrasound-guided fine needle aspiration (EUS-FNA). However, the diagnostic adequacy of EUS-FNA is often limited by low cellularity leading to inconclusive results. We aimed to investigate the feasibility and added utility of targeted next-generation sequencing (NGS) on PDAC EUS-FNAs.

METHODS

EUS-FNAs were prospectively performed on 59 patients with suspected PDAC (2014-2017) at a high-volume center. FNAs were analyzed for the presence of somatic mutations using NGS to supplement cytopathologic evaluations and were compared to surgical specimens and circulating tumor DNA (ctDNA).

RESULTS

Fifty-nine patients with suspected PDAC were evaluated, and 52 were diagnosed with PDAC on EUS-FNA. Four of the remaining seven patients had inconclusive EUS-FNAs and were ultimately diagnosed with PDAC after surgical resection. Of these 56 cases of PDAC, 48 (85.7%) and 18 (32.1%) harbored a KRAS and/or TP53 mutation on FNA NGS, respectively. Particularly, in the four inconclusive FNA PDAC diagnoses (false negatives), half harbored KRAS mutations on FNA. No KRAS/TP53 mutation was found in remaining three non-PDAC cases. All EUS-FNA detected KRAS mutations were detected in 16 patients that underwent primary tumor NGS (100% concordance), while 75% KRAS concordance was found between FNA and ctDNA NGS.

CONCLUSION

Targeted NGS can reliably detect KRAS mutations from EUS-FNA samples and exhibits high KRAS mutational concordance with primary tumor and ctDNA. This suggests targeted NGS of EUS-FNA samples may enable preoperative ctDNA prognostication using digital droplet PCR and supplement diagnoses in patients with inconclusive EUS-FNA.

Impact of Endoscopic Ultrasound-Guided Tissue Acquisition on Decision-Making in Precision Medicine for Pancreatic Cancer: Beyond Diagnosis

Precision medicine in cancer treatment refers to targeted therapy based on the evaluation of biomarkers. Although precision medicine for pancreatic cancer (PC) remains challenging, novel biomarker-based therapies, such as pembrolizumab, olaparib, and entrectinib, have been emerging. Most commonly, endoscopic ultrasound-guided tissue acquisition (EUS-TA) had been used for the diagnosis of PC until now. However, advances in EUS-TA devices and biomarker testing, especially next-generation sequencing, have opened up the possibility of sequencing of various genes even in limited amounts of tissue samples obtained by EUS-TA, and identifying potential genetic alterations as therapeutic targets. Precision medicine benefits only a small population of patients with PC, but biomarker-based therapy has shown promising results in patients who once had no treatment options. Now, the role of EUS-TA has extended beyond diagnosis into decision-making regarding the treatment of PC. In this review, we mainly discuss tissue sampling by EUS-TA for biomarker testing and the current status of precision medicine for PC.

PTEN status determines chemosensitivity to proteasome inhibition in cholangiocarcinoma

Patient-derived xenografts (PDXs) and PDX-derived cells (PDCs) are useful in preclinical research. We performed a drug screening assay using PDCs and identified proteasome inhibitors as promising drugs for cholangiocarcinoma (CCA) treatment. Furthermore, we determined that phosphate and tensin homology deleted on chromosome ten (PTEN) deficiency promotes protein synthesis and proteasome subunit expression and proteolytic activity, creating a dependency on the proteasome for cancer cell growth and survival. Thus, targeting the proteasome machinery with the inhibitor bortezomib inhibited the proliferation and survival of CCA cells lacking functional PTEN. Therapeutic evaluation of PDXs, autochthonous mouse models, and patients confirmed this dependency on the proteasome. Mechanistically, we found that PTEN promoted the nuclear translocation of FOXO1, resulting in the increased expression of BACH1 and MAFF BACH1 and MAFF are transcriptional regulators that recognize the antioxidant response element, which is present in genes encoding proteasome subunits. PTEN induced the accumulation and nuclear translocation of these proteins, which directly repressed the transcription of genes encoding proteasome subunits. We revealed that the PTEN-proteasome axis is a potential target for therapy in PTEN-deficient CCA and other PTEN-deficient cancers.

Robustness of differential gene expression analysis of RNA-seq

RNA-sequencing (RNA-seq) is a relatively new technology that lacks standardisation. RNA-seq can be used for Differential Gene Expression (DGE) analysis, however, no consensus exists as to which methodology ensures robust and reproducible results. Indeed, it is broadly acknowledged that DGE methods provide disparate results. Despite obstacles, RNA-seq assays are in advanced development for clinical use but further optimisation will be needed. Herein, five DGE models (DESeq2, voom + limma, edgeR, EBSeq, NOISeq) for gene-level detection were investigated for robustness to sequencing alterations using a controlled analysis of fixed count matrices. Two breast cancer datasets were analysed with full and reduced sample sizes. DGE model robustness was compared between filtering regimes and for different expression levels (high, low) using unbiased metrics. Test sensitivity estimated as relative False Discovery Rate (FDR), concordance between model outputs and comparisons of a ’population’ of slopes of relative FDRs across different library sizes, generated using linear regressions, were examined. Patterns of relative DGE model robustness proved dataset-agnostic and reliable for drawing conclusions when sample sizes were sufficiently large. Overall, the non-parametric method NOISeq was the most robust followed by edgeR, voom, EBSeq and DESeq2. Our rigorous appraisal provides information for method selection for molecular diagnostics. Metrics may prove useful towards improving the standardisation of RNA-seq for precision medicine.

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.

Characterizing the Expression Patterns of Parkinson's Disease Associated Genes

Background

The expression pattern represents a quantitative phenotype that provides an in-depth view of the molecular mechanism in Parkinson’s disease (PD); however, the expression patterns of PD-associated genes (PAGs) and their relation to age at onset (AAO) remain unclear.

Methods

The known PD-causing genes and PD-risk genes, which were collected from latest published authoritative meta-analysis, were integrated as PAGs. The expression data from Genotype-Tissue Expression database, Allen Brian Map database, and BrainSpan database, were extracted to characterize the tissue specificity, inhibitory-excitatory neuron expression profile, and spatio-temporal expression pattern of PAGs, respectively. The AAO information of PD-causing gene was download from Gene4PD and MDSgene database.

Results

We prioritized 107 PAGs and found that the PAGs were more likely to be expressed in brain-related tissues than non-brain tissues and that more PAGs had higher expression levels in excitatory neurons than inhibitory neurons. In addition, we identified two spatio-temporal expression modules of PAGs in human brain: the first module showed a higher expression level in the adult period than in the prenatal period, and the second module showed the opposite features. It showed that more PAGs belong to the first module that the second module. Furthermore, we found that the median AAO of patients with mutations in PD-causing genes of the first module was lower than that of the second module.

Conclusion

In conclusion, this study provided comprehensive landscape of expression patterns, AAO features and their relationship for the first time, improving the understanding of pathogenesis, and precision medicine in PD.

Integrative analysis identifies an immune-relevant epigenetic signature for prognostication of non-G-CIMP glioblastomas

The clinical and molecular implications of DNA methylation alterations remain unclear among the majority of glioblastomas (GBMs) without glioma-CpGs island methylator phenotype (G-CIMP); integrative multi-level molecular profiling may provide useful information. Independent cohorts of non-G-CIMP GBMs or IDH wild type (wt) lower-grade gliomas (LGGs) from local and public databases with DNA methylation and gene expression microarray data were included for discovery and validation of a multimarker signature, combined using a RISK score model. Bioinformatic and in vitro functional analyses were employed for biological validation. Using a strict multistep selection approach, we identified eight CpGs, each of which was significantly correlated with overall survival (OS) of non-G-CIMP GBMs, independent of age, the O-6-methylguanine-DNA methyltransferase (MGMT) methylation status, treatments and other identified CpGs. An epigenetic RISK signature of the 8 CpGs was developed and validated to robustly and independently prognosticate prognosis in different cohorts of not only non-G-GIMP GBMs, but also IDHwt LGGs. It also showed good discriminating value in stratified cohorts by current clinical and molecular factors. Bioinformatic analysis revealed consistent correlation of the epigenetic signature to distinct immune-relevant transcriptional profiles of GBM bulks. Functional experiments showed that S100A2 appeared to be epigenetically regulated by one identified CpG and was associated with GBM cell proliferation, apoptosis, invasion, migration and immunosuppression. The prognostic 8-CpGs RISK score signature may be of promising value for refining current glioma risk classification, and its potential links to distinct immune phenotypes make it a promising biomarker candidate for predicting response to anti-glioma immunotherapy.

Blueprint for cancer research: Critical gaps and opportunities

We are experiencing a revolution in cancer. Advances in screening, targeted and immune therapies, big data, computational methodologies, and significant new knowledge of cancer biology are transforming the ways in which we prevent, detect, diagnose, treat, and survive cancer. These advances are enabling durable progress in the goal to achieve personalized cancer care. Despite these gains, more work is needed to develop better tools and strategies to limit cancer as a major health concern. One persistent gap is the inconsistent coordination among researchers and caregivers to implement evidence-based programs that rely on a fuller understanding of the molecular, cellular, and systems biology mechanisms underpinning different types of cancer. Here, the authors integrate conversations with over 90 leading cancer experts to highlight current challenges, encourage a robust and diverse national research portfolio, and capture timely opportunities to advance evidence-based approaches for all patients with cancer and for all communities.

Pan-cancer Transcriptomic Predictors of Perineural Invasion Improve Occult Histopathologic Detection

PURPOSE

Perineural invasion (PNI) is associated with aggressive tumor behavior, recurrence, and metastasis, and can influence the administration of adjuvant treatment. However, standard histopathologic examination has limited sensitivity in detecting PNI and does not provide insights into its mechanistic underpinnings.

EXPERIMENTAL DESIGN

A multivariate Cox regression was performed to validate associations between PNI and survival in 2,029 patients across 12 cancer types. Differential expression and gene set enrichment analysis were used to learn PNI-associated programs. Machine learning models were applied to build a PNI gene expression classifier. A blinded re-review of hematoxylin and eosin (H&E) slides by a board-certified pathologist helped determine whether the classifier could improve occult histopathologic detection of PNI.

RESULTS

PNI associated with both poor overall survival [HR, 1.73; 95% confidence interval (CI), 1.27-2.36; P < 0.001] and disease-free survival (HR, 1.79; 95% CI, 1.38-2.32; P < 0.001). Neural-like, prosurvival, and invasive programs were enriched in PNI-positive tumors (P _adj < 0.001). Although PNI-associated features likely reflect in part the increased presence of nerves, many differentially expressed genes mapped specifically to malignant cells from single-cell atlases. A PNI gene expression classifier was derived using random forest and evaluated as a tool for occult histopathologic detection. On a blinded H&E re-review of sections initially described as PNI negative, more specimens were reannotated as PNI positive in the high classifier score cohort compared with the low-scoring cohort (P = 0.03, Fisher exact test).

CONCLUSIONS

This study provides salient biological insights regarding PNI and demonstrates a role for gene expression classifiers to augment detection of histopathologic features.

Current Achievements and Applications of Transcriptomics in Personalized Cancer Medicine

Over the last decades, transcriptome profiling emerged as one of the most powerful approaches in oncology, providing prognostic and predictive utility for cancer management. The development of novel technologies, such as revolutionary next-generation sequencing, enables the identification of cancer biomarkers, gene signatures, and their aberrant expression affecting oncogenesis, as well as the discovery of molecular targets for anticancer therapies. Transcriptomics contribute to a change in the holistic understanding of cancer, from histopathological and organic to molecular classifications, opening a more personalized perspective for tumor diagnostics and therapy. The further advancement on transcriptome profiling may allow standardization and cost reduction of its analysis, which will be the next step for transcriptomics to become a canon of contemporary cancer medicine.

Machine Learning Models to Predict Primary Sites of Metastatic Cervical Carcinoma From Unknown Primary

Metastatic cervical carcinoma from unknown primary (MCCUP) accounts for 1–4% of all head and neck tumors, and identifying the primary site in MCCUP is challenging. The most common histopathological type of MCCUP is squamous cell carcinoma (SCC), and it remains difficult to identify the primary site pathologically. Therefore, it seems necessary and urgent to develop novel and effective methods to determine the primary site in MCCUP. In the present study, the RNA sequencing data of four types of SCC and Pan-Cancer from the cancer genome atlas (TCGA) were obtained. And after data pre-processing, their differentially expressed genes (DEGs) were identified, respectively. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that these significantly changed genes of four types of SCC share lots of similar molecular functions and histological features. Then three machine learning models, [Random Forest (RF), support vector machine (SVM), and neural network (NN)] which consisted of ten genes to distinguish these four types of SCC were developed. Among the three models with prediction tests, the RF model worked best in the external validation set, with an overall predictive accuracy of 88.2%, sensitivity of 88.71%, and specificity of 95.42%. The NN model is the second in efficacy, with an overall accuracy of 82.02%, sensitivity of 81.23%, and specificity of 93.04%. The SVM model is the last, with an overall accuracy of 76.69%, sensitivity of 74.81%, and specificity of 90.84%. The present analysis of similarities and differences among the four types of SCC, and novel models developments for distinguishing four types of SCC with informatics methods shed lights on precision MCCUP diagnosis in the future.

Deep learning of pharmacogenomics resources: moving towards precision oncology

The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor's genomic characteristics can affect its responses to drugs. This field, called pharmacogenomics, is a key area in the development of precision oncology. Deep learning (DL) methodology has emerged as a powerful technique to characterize and learn from rapidly accumulating pharmacogenomics data. We introduce the fundamentals and typical model architectures of DL. We review the use of DL in classification of cancers and cancer subtypes (diagnosis and treatment stratification of patients), prediction of drug response and drug synergy for individual tumors (treatment prioritization for a patient), drug repositioning and discovery and the study of mechanism/mode of action of treatments. For each topic, we summarize current genomics and pharmacogenomics data resources such as pan-cancer genomics data for cancer cell lines (CCLs) and tumors, and systematic pharmacologic screens of CCLs. By revisiting the published literature, including our in-house analyses, we demonstrate the unprecedented capability of DL enabled by rapid accumulation of data resources to decipher complex drug response patterns, thus potentially improving cancer medicine. Overall, this review provides an in-depth summary of state-of-the-art DL methods and up-to-date pharmacogenomics resources and future opportunities and challenges to realize the goal of precision oncology.

Characterization of the genomic landscape and actionable mutations in Chinese breast cancers by clinical sequencing

The remarkable advances in next-generation sequencing technology have enabled the wide usage of sequencing as a clinical tool. To promote the advance of precision oncology for breast cancer in China, here we report a large-scale prospective clinical sequencing program using the Fudan-BC panel, and comprehensively analyze the clinical and genomic characteristics of Chinese breast cancer. The mutational landscape of 1,134 breast cancers reveals that the most significant differences between Chinese and Western patients occurred in the hormone receptor positive, human epidermal growth factor receptor 2 negative breast cancer subtype. Mutations in p53 and Hippo signaling pathways are more prevalent, and 2 mutually exclusive and 9 co-occurring patterns exist among 9 oncogenic pathways in our cohort. Further preclinical investigation partially suggests that NF2 loss-of-function mutations can be sensitive to a Hippo-targeted strategy. We establish a public database (Fudan Portal) and a precision medicine knowledge base for data exchange and interpretation. Collectively, our study presents a leading approach to Chinese precision oncology treatment and reveals potentially actionable mutations in breast cancer.

Chinese breast cancer patients have not been well represented in clinical sequencing studies. Here the authors analyse the mutational landscape of 1,134 Chinese breast cancer patients, finding actionable targets and a higher prevalence of p53 and Hippo pathway mutations compared to Western cohorts.

A gene-based survival score for lung adenocarcinoma by multiple transcriptional datasets analysis

Background

Lung adenocarcinoma (LUAD) remains a crucial factor endangering human health. Gene-based clinical predictions could be of great help for cancer intervention strategies. Here, we tried to build a gene-based survival score (SS) for LUAD via analyzing multiple transcriptional datasets.

Methods

We first acquired differentially expressed genes between tumors and normal tissues from intersections of four LUAD datasets. Next, survival-related genes were preliminarily unscrambled by univariate Cox regression and further filtrated by LASSO regression. Then, we applied PCA to establish a comprehensive SS based on survival-related genes. Subsequently, we applied four independent LUAD datasets to evaluate prognostic prediction of SS. Moreover, we explored associations between SS and clinicopathological features. Furthermore, we assessed independent predictive value of SS by multivariate Cox analysis and then built prognostic models based on clinical stage and SS. Finally, we performed pathway enrichments analysis and investigated immune checkpoints expression underlying SS in four datasets.

Results

We established a 13 gene-based SS, which could precisely predict OS and PFS of LUAD. Close relations were elicited between SS and canonical malignant indictors. Furthermore, SS could serve as an independent risk factor for OS and PFS. Besides, the predictive efficacies of prognostic models were also reasonable (C-indexes: OS, 0.7; PFS, 0.7). Finally, we demonstrated enhanced cell proliferation and immune escape might account for high clinical risk of SS.

Conclusions

We built a 13 gene-based SS for prognostic prediction of LUAD, which exhibited wide applicability and could contribute to LUAD management.

Gene expression-based clinical predictions in lung adenocarcinoma

Mining disease-related genes contributes momentously to handling lung adenocarcinoma (LUAD). But genetic complexity and tumor heterogeneity severely get in the way. Fortunately, new light has been shed by dramatic progress of bioinformatic technology in the past decades. In this research, we investigated relationships between gene expression and clinical features of LUAD via integrative bioinformatic analysis. First, we applied limma and DESeq2 packages to analyze differentially expressed genes (DEGs) of LUAD from GEO database and TCGA project (tumor tissues versus normal tissues), and acquired 180 down-regulated DEGs and 52 up-regulated DEGs. Then, we investigated genetic and biological assignment of theses DEGs by Bioconductor packages and STRING database. We found these DEGs were distributed dispersedly among chromosomes, enriched observably in extracellular matrix-related processes, and weighted hierarchically in interaction network. Finally, we established DEGs-based statistical models for evaluating TNM stage and survival status of LUAD. And these models (logistic regression models for TNM parameter and Cox regression models for survival probability) all possessed fine predictive efficacy (C-indexes: T, 0.740; N, 0.687; M, 0.823; overall survival, 0.678; progression-free survival, 0.611). In summary, we have successfully established gene expression-based models for assessing clinical characteristics of LUAD, which will assist its pathogenesis investigation and clinical intervention.

VISTA: an immune regulatory protein checking tumor and immune cells in cancer immunotherapy

VISTA (V-domain immunoglobulin suppressor of T cell activation) is a well-established immune regulatory receptor. However, pre-clinical investigations indicated more complicated influences of VISTA on cancer immunity than previously recognized. Here, we review the current knowledge on the therapeutic phenotypes and molecular mechanisms that underlie the contradictory roles of VISTA in checking anti-cancer immune responses. Furthermore, we highlight the potential indeterminacy of VISTA-targeted strategies in cancer immunotherapy, with in silico analyses. In fact, VISTA functions like a homeostatic regulator that actively normalizes immune responses. Thus, the regulatory role of VISTA in anti-cancer immunity remains to be fully elucidated.

Gene Expression: the Key to Understanding HIV-1 Infection?

Gene expression profiling of the host response to HIV infection has promised to fill the gaps in our knowledge and provide new insights toward vaccine and cure. However, despite 20 years of research, the biggest questions remained unanswered. A literature review identified 62 studies examining gene expression dysregulation in samples from individuals living with HIV. Changes in gene expression were dependent on cell/tissue type, stage of infection, viremia, and treatment status. Some cell types, notably CD4+ T cells, exhibit upregulation of cell cycle, interferon-related, and apoptosis genes consistent with depletion. Others, including CD8+ T cells and natural killer cells, exhibit perturbed function in the absence of direct infection with HIV. Dysregulation is greatest during acute infection. Differences in study design and data reporting limit comparability of existing research and do not as yet provide a coherent overview of gene expression in HIV. This review outlines the extraordinarily complex host response to HIV and offers recommendations to realize the full potential of HIV host transcriptomics.

Epigenetics of Circulating Tumor Cells in Breast Cancer

Liquid biopsy based on the analysis of circulating tumor cells (CTCs) has emerged as an important field of research. Molecular characterization of CTCs can provide insights into cancer biology and biomarkers for the clinic, representing a non-invasive powerful tool for monitoring breast cancer metastasis and predict the therapeutic response. Epigenetic mechanisms play a key role in the control of gene expression and their alteration contributes to cancer development and progression. These epigenetic modifications in CTCs have been described mainly related to modifications of the DNA methylation pattern and changes in the expression profile of noncoding RNAs. Here we summarize the recent findings on the epigenetic characterization of CTCs in breast cancer and their clinical value as tumor biomarkers, and discuss challenges and opportunities in this field.

Changing Technologies of RNA Sequencing and Their Applications in Clinical Oncology

RNA sequencing (RNAseq) is one of the most commonly used techniques in life sciences, and has been widely used in cancer research, drug development, and cancer diagnosis and prognosis. Driven by various biological and technical questions, the techniques of RNAseq have progressed rapidly from bulk RNAseq, laser-captured micro-dissected RNAseq, and single-cell RNAseq to digital spatial RNA profiling, spatial transcriptomics, and direct in situ sequencing. These different technologies have their unique strengths, weaknesses, and suitable applications in the field of clinical oncology. To guide cancer researchers to select the most appropriate RNAseq technique for their biological questions, we will discuss each of these technologies, technical features, and clinical applications in cancer. We will help cancer researchers to understand the key differences of these RNAseq technologies and their optimal applications.

Next-Generation Sequencing of Advanced GI Tumors Reveals Individual Treatment Options

PURPOSE

Precision oncology connects highly complex diagnostic procedures with patient histories to identify individualized treatment options in interdisciplinary molecular tumor boards (MTBs). Detailed data on MTB-guided treatments and outcome with a focus on advanced GI cancers have not been reported yet.

PATIENTS AND METHODS

Next-generation sequencing of tumor and normal tissue pairs was performed between April 2016 and February 2018. After identification of relevant molecular alterations, available clinical studies or in-label, off-label, or matched experimental treatment options were recommended. Follow-up data and a response assessment that was based on radiologic imaging were recorded.

RESULTS

Ninety-six patients were presented to the MTB of Tuebingen University Hospital. Sixteen (17%) showed "pathogenic" or "likely pathogenic" germline variants. Recommendations on the basis of molecular alterations or tumor mutational burden were given for 41 patients (43%). Twenty-five received the suggested drug, and 20 were evaluable for best response assessment. Three patients (15%) reached a partial response (PR), and 6 (30%), stable disease (SD), whereas 11 (55%) had tumor progression (progressive disease). Median progression-free survival (PFS) for all treated and evaluable patients was 2.8 months (range, 1.0-9.0 months), and median overall survival (OS) of all treated patients was 5.2 months (range, 0.1 months to not reached). Patients with SD for ≥ 3 months or PR compared with progressive disease showed both a statistically significant longer median PFS (7.8 months [95% CI, 4.2 to 11.4 months] v 2.2 months [95% CI, 1.5 to 2.8 months], P < .0001) and median OS (18.0 months [95% CI, 10.4 to 25.6 months] v 3.8 months [95% CI, 2.3 to 5.4 months], P < .0001).

CONCLUSION

Next-generation sequencing diagnostics of advanced GI cancers identified a substantial number of pathogenic or likely pathogenic germline variants and unique individual treatment options. Patients with PR or SD in the course of MTB-recommended treatments seemed to benefit with respect to PFS and OS.

Development of a prognostic index of colon adenocarcinoma based on immunogenomic landscape analysis

Background

Colon adenocarcinoma (COAD) is one of the most commonly diagnosed cancers, and it is closely related to the immune microenvironment. Considering that immunotherapy is not effective for all COAD patients, it is necessary to identify the effective population before administering treatment. In this study, we established an independent prognostic index based on immune-related genes (IRGs), in order to evaluate the clinical outcome of COAD.

Methods

The gene expression profiles and IRGs taken from The Cancer Genome Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort), respectively, were integrated in order to identify the differentially expressed IRGs. Functional enrichment analysis was conducted and the prognostic value of survival-related IRGs was determined. Based on Cox regression analysis, the IRG-based prognostic index (IRGPI) was established, and the model was evaluated and applied.

Results

A total of 51 differentially expressed survival-related IRGs were identified. The most significant signaling pathway was "cytokine-cytokine receptor interaction". The index established herein was based on 12 survival-related IRGs, and it was highly accurate in monitoring prognosis. Moreover, the IRGPI was significantly correlated with multiple clinicopathologic factors, as well as with the infiltration of immune cells.

Conclusions

An independent IRGPI was established in order to assess the immune status and tumor prognosis in COAD patients. This index can serve as a robust biomarker in clinical prognosis applications, including cancer immunotherapy.

Clinical implications of intratumor heterogeneity: challenges and opportunities

In this review, we highlight the role of intratumoral heterogeneity, focusing on the clinical and biological ramifications this phenomenon poses. Intratumoral heterogeneity arises through complex genetic, epigenetic, and protein modifications that drive phenotypic selection in response to environmental pressures. Functionally, heterogeneity provides tumors with significant adaptability. This ranges from mutual beneficial cooperation between cells, which nurture features such as growth and metastasis, to the narrow escape and survival of clonal cell populations that have adapted to thrive under specific conditions such as hypoxia or chemotherapy. These dynamic intercellular interplays are guided by a Darwinian selection landscape between clonal tumor cell populations and the tumor microenvironment. Understanding the involved drivers and functional consequences of such tumor heterogeneity is challenging but also promises to provide novel insight needed to confront the problem of therapeutic resistance in tumors.

Tumor-Specific Transcripts Are Frequently Expressed in Hepatocellular Carcinoma With Clinical Implication and Potential Function

Hepatocellular carcinoma (HCC) is a highly lethal cancer and its underlying etiology remains understudied. The immense diversity and complexity of the cancer transcriptome hold the potential to yield tumor-specific transcripts (TSTs). Here, we showed that hundreds of TSTs are frequently expressed in HCC by an assembling spliced junction analysis of RNA sequencing raw data from approximately 1,000 normal and HCC tissues. Many of the TSTs were found to be unannotated and noncoding RNAs. We observed that intergenic TSTs are generated from transcription initiation sites frequently harboring long terminal repeat (LTR) elements. The strong presence of TSTs indicates significantly poor prognoses in HCC. Functional screening revealed a noncoding TST (termed TST1), which acted as a regulator of HCC cell proliferation and tumorigenesis. TST1 is generated from an LTR12C promoter regulated by DNA methylation and retinoic-acid-related drugs. Additionally, we observed that TSTs may be detected in the blood extracellular vesicles of patients with HCC. Conclusion: Our findings suggest an abundance of TSTs in HCC and their potential in clinical settings. The identification and characterization of TSTs may help toward the development of strategies for cancer diagnosis and treatment.

Precision medicine and its implementation in patients with NTRK fusion genes: perspective from developing countries

Precision oncology is the field that places emphasis on the diagnosis and treatment of tumors that harbor specific genomic alterations susceptible to inhibition or modulation. Although most alterations are only present in a minority of patients, a substantial effect on survival can be observed in this subgroup. Mass genome sequencing has led to the identification of a specific driver in the translocations of the tropomyosin receptor kinase family (NTRK) in a subset of rare tumors both in children and in adults, and to the development and investigation of Larotrectinib. This medication was granted approval by the US Food and Drug Administration for NTRK-positive tumors, regardless of histology or age group, as such, larotrectinib was the first in its kind to be approved under the premise that molecular pattern is more important than histology in terms of therapeutic approach. It yielded significant results in disease control with good tolerability across a wide range of diseases including rare pediatric tumors, salivary gland tumors, gliomas, soft-tissue sarcomas, and thyroid carcinomas. In addition, and by taking different approaches in clinical trial design and conducting allocation based on biomarkers, the effects of target therapies can be isolated and quantified. Moreover, and considering developing nations and resource-limited settings, precision oncology could offer a tool to reduce cancer-related disability and hospital costs. In addition, developing nations also present patients with rare tumors that lack a chance of treatment, outside of clinical trials. This, in turn, offers the possibility for international collaboration, and contributes to employment, education, and health service provisions. The reviews of this paper are available via the supplemental material section.

PROMO: an interactive tool for analyzing clinically-labeled multi-omic cancer datasets

BACKGROUND

Analysis of large genomic datasets along with their accompanying clinical information has shown great promise in cancer research over the last decade. Such datasets typically include thousands of samples, each measured by one or several high-throughput technologies ('omics') and annotated with extensive clinical information. While instrumental for fulfilling the promise of personalized medicine, the analysis and visualization of such large datasets is challenging and necessitates programming skills and familiarity with a large array of software tools to be used for the various steps of the analysis.

RESULTS

We developed PROMO (Profiler of Multi-Omic data), a friendly, fully interactive stand-alone software for analyzing large genomic cancer datasets together with their associated clinical information. The tool provides an array of built-in methods and algorithms for importing, preprocessing, visualizing, clustering, clinical label enrichment testing, and survival analysis that can be performed on a single or multi-omic dataset. The tool can be used for quick exploration and stratification of tumor samples taken from patients into clinically significant molecular subtypes. Identification of prognostic biomarkers and generation of simple subtype classifiers are additional important features. We review PROMO's main features and demonstrate its analysis capabilities on a breast cancer cohort from TCGA.

CONCLUSIONS

PROMO provides a single integrated solution for swiftly performing a complete analysis of cancer genomic data for subtype discovery and biomarker identification without writing a single line of code, and can, therefore, make the analysis of these data much easier for cancer biologists and biomedical researchers. PROMO is freely available for download at http://acgt.cs.tau.ac.il/promo/.

Epigenetic loss of RNA-methyltransferase NSUN5 in glioma targets ribosomes to drive a stress adaptive translational program

Tumors have aberrant proteomes that often do not match their corresponding transcriptome profiles. One possible cause of this discrepancy is the existence of aberrant RNA modification landscapes in the so-called epitranscriptome. Here, we report that human glioma cells undergo DNA methylation-associated epigenetic silencing of NSUN5, a candidate RNA methyltransferase for 5-methylcytosine. In this setting, NSUN5 exhibits tumor-suppressor characteristics in vivo glioma models. We also found that NSUN5 loss generates an unmethylated status at the C3782 position of 28S rRNA that drives an overall depletion of protein synthesis, and leads to the emergence of an adaptive translational program for survival under conditions of cellular stress. Interestingly, NSUN5 epigenetic inactivation also renders these gliomas sensitive to bioactivatable substrates of the stress-related enzyme NQO1. Most importantly, NSUN5 epigenetic inactivation is a hallmark of glioma patients with long-term survival for this otherwise devastating disease.