Using high-throughput sequencing transcriptome data for INDEL detection: challenges for cancer drug discovery

Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies

Efficacy of cancer immunotherapies relies on correct recognition of tumor antigens by lymphocytes, eliciting thus functional responses capable of eliminating tumor cells. Therefore, important efforts have been carried out in antigen identification, with the aim of understanding mechanisms of response to immunotherapy and to design safer and more efficient strategies. In addition to classical tumor-associated antigens identified during the last decades, implementation of next-generation sequencing methodologies is enabling the identification of neoantigens (neoAgs) arising from mutations, leading to the development of new neoAg-directed therapies. Moreover, there are numerous non-classical tumor antigens originated from other sources and identified by new methodologies. Here, we review the relevance of neoAgs in different immunotherapies and the results obtained by applying neoAg-based strategies. In addition, the different types of non-classical tumor antigens and the best approaches for their identification are described. This will help to increase the spectrum of targetable molecules useful in cancer immunotherapies.

Exploration and validation of key genes associated with early lymph node metastasis in thyroid carcinoma using weighted gene co-expression network analysis and machine learning

Background

Thyroid carcinoma (THCA), the most common endocrine neoplasm, typically exhibits an indolent behavior. However, in some instances, lymph node metastasis (LNM) may occur in the early stages, with the underlying mechanisms not yet fully understood.

Materials and methods

LNM potential was defined as the tumor's capability to metastasize to lymph nodes at an early stage, even when the tumor volume is small. We performed differential expression analysis using the 'Limma' R package and conducted enrichment analyses using the Metascape tool. Co-expression networks were established using the 'WGCNA' R package, with the soft threshold power determined by the 'pickSoftThreshold' algorithm. For unsupervised clustering, we utilized the 'ConsensusCluster Plus' R package. To determine the topological features and degree centralities of each node (protein) within the Protein-Protein Interaction (PPI) network, we used the CytoNCA plugin integrated with the Cytoscape tool. Immune cell infiltration was assessed using the Immune Cell Abundance Identifier (ImmuCellAI) database. We applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF) algorithms individually, with the 'glmnet,' 'e1071,' and 'randomForest' R packages, respectively. Ridge regression was performed using the 'oncoPredict' algorithm, and all the predictions were based on data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. To ascertain the protein expression levels and subcellular localization of genes, we consulted the Human Protein Atlas (HPA) database. Molecular docking was carried out using the mcule 1-click Docking server online. Experimental validation of gene and protein expression levels was conducted through Real-Time Quantitative PCR (RT-qPCR) and immunohistochemistry (IHC) assays.

Results

Through WGCNA and PPI network analysis, we identified twelve hub genes as the most relevant to LNM potential from these two modules. These 12 hub genes displayed differential expression in THCA and exhibited significant correlations with the downregulation of neutrophil infiltration, as well as the upregulation of dendritic cell and macrophage infiltration, along with activation of the EMT pathway in THCA. We propose a novel molecular classification approach and provide an online web-based nomogram for evaluating the LNM potential of THCA (http://www.empowerstats.net/pmodel/?m=17617_LNM). Machine learning algorithms have identified ERBB3 as the most critical gene associated with LNM potential in THCA. ERBB3 exhibits high expression in patients with THCA who have experienced LNM or have advanced-stage disease. The differential methylation levels partially explain this differential expression of ERBB3. ROC analysis has identified ERBB3 as a diagnostic marker for THCA (AUC=0.89), THCA with high LNM potential (AUC=0.75), and lymph nodes with tumor metastasis (AUC=0.86). We have presented a comprehensive review of endocrine disruptor chemical (EDC) exposures, environmental toxins, and pharmacological agents that may potentially impact LNM potential. Molecular docking revealed a docking score of -10.1 kcal/mol for Lapatinib and ERBB3, indicating a strong binding affinity.

Conclusion

In conclusion, our study, utilizing bioinformatics analysis techniques, identified gene modules and hub genes influencing LNM potential in THCA patients. ERBB3 was identified as a key gene with therapeutic implications. We have also developed a novel molecular classification approach and a user-friendly web-based nomogram tool for assessing LNM potential. These findings pave the way for investigations into the mechanisms underlying differences in LNM potential and provide guidance for personalized clinical treatment plans.

Rapid genotyping of 32 insertion/deletion panel for human identification using fluorogenic probes-based multiplex real-time PCR

BACKGROUND

Insertion and deletion polymorphisms (InDels) have considerable potential in the field of forensic genetics because of their low mutation rate and small amplicons. At present, InDel polymorphisms detection based on the technique of capillary electrophoresis is the main technique used in forensic DNA laboratory. However, this method is complicated and time-consuming, and is not suitable for rapid on-site paternity and personal identification. Next-generation sequencing analysis of InDels polymorphisms requires expensive instruments, large upfront reagent and supply costs, computational requirements and complex bioinformatics, increased the time to obtain results. Thus, there is an urgent need to establish a method to provide reliable, rapid, sensitive and economical genotyping for InDels.

METHOD

A rapid InDels (32 InDels) panel was established using fluorogenic probes-based multiplex real-time PCR with microfluidic test cartridge and portable real-time PCR instrument. Then, we performed several validation studies including concordance, accuracy, sensitivity, stability, species specificity.

RESULTS

It showed that the complete genotypes could be obtained from ≥100 pg of input DNA and from a series of challenging samples with high accuracy and specificity within 90 min.

CONCLUSION

This method provides a rapid and cost-effective solution for InDels genotyping and personal identification in portable format.

Detecting Medium and Large Insertions and Deletions with transIndel

Insertions and deletions (indels) are primarily detected from DNA sequencing (DNA-seq) data, but their transcriptional consequences remain unexplored due to challenges in distinguishing medium- and large-sized indels from RNA splicing events in RNA-seq data. We introduce transIndel, a splice-aware algorithm that parses the chimeric alignments predicted by a short read aligner and reconstructs the mid-sized insertions and large deletions based on the linear alignments of split reads from DNA-seq or RNA-seq data. Here, we describe the method and provide a tutorial on the installation and application of transIndel.

Hypoxia-regulated lncRNA CRPAT4 promotes cell migration via regulating AVL9 in clear cell renal cell carcinomas

Introduction

Long noncoding RNAs (lncRNAs) are proven to be key regulators in cancer biology. Our screening effort for clear cell renal cell carcinoma (ccRCC) prognosis-associated lncRNAs identified a novel lncRNA, ccRCC prognosis-associated transcript 4 (CRPAT4), as one of the top candidates that was previously uncharacterized. The aim of this study was to verify the clinical significance of CRPAT4 in ccRCC patients and to explore its biological role as well as the underlying mechanisms, in ccRCC cell lines.

Materials and methods

Quantitative real-time polymerase chain reaction (PCR) was performed to demonstrate that CRPAT4 was differentially expressed between ccRCC and the normal controls and that high CRPAT4 expression significantly associated with advanced Fuhrman nuclear grades.

Results

Kaplan–Meier survival analysis with The Cancer Genome Atlas KIRC RNA sequencing data indicated that high CRPAT4 expression was significantly associated with poor overall survival and progression-free survival. Functional studies indicated that CRPAT4 was an HIF-1α regulated gene, and CRPAT4 knockdown significantly inhibited cell migration and proliferation in the absence of HIF-1α. In addition, a mechanistic study revealed that CRPAT4 could regulate the expression of the migration-associated protein AVL9.

Conclusion

Collectively, our study first identified CRPAT4 as a hypoxia-regulated lncRNA, acting as an oncogene in ccRCC progression via regulating AVL9 protein, thus expanding our knowledge on the hypoxia pathway in ccRCC biology from a noncoding perspective. Moreover, CRPAT4 has the potential to be a prognostic marker in ccRCC patients.

Indel detection from DNA and RNA sequencing data with transIndel

Background

Insertions and deletions (indels) are a major class of genomic variation associated with human disease. Indels are primarily detected from DNA sequencing (DNA-seq) data but their transcriptional consequences remain unexplored due to challenges in discriminating medium-sized and large indels from splicing events in RNA-seq data.

Results

Here, we developed transIndel, a splice-aware algorithm that parses the chimeric alignments predicted by a short read aligner and reconstructs the mid-sized insertions and large deletions based on the linear alignments of split reads from DNA-seq or RNA-seq data. TransIndel exhibits competitive or superior performance over eight state-of-the-art indel detection tools on benchmarks using both synthetic and real DNA-seq data. Additionally, we applied transIndel to DNA-seq and RNA-seq datasets from 333 primary prostate cancer patients from The Cancer Genome Atlas (TCGA) and 59 metastatic prostate cancer patients from AACR-PCF Stand-Up- To-Cancer (SU2C) studies. TransIndel enhanced the taxonomy of DNA- and RNA-level alterations in prostate cancer by identifying recurrent FOXA1 indels as well as exitron splicing in genes implicated in disease progression.

Conclusions

Our study demonstrates that transIndel is a robust tool for elucidation of medium- and large-sized indels from DNA-seq and RNA-seq data. Including RNA-seq in indel discovery efforts leads to significant improvements in sensitivity for identification of med-sized and large indels missed by DNA-seq, and reveals non-canonical RNA-splicing events in genes associated with disease pathology.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4671-4) contains supplementary material, which is available to authorized users.