Clinical mutation assay of tumors: new developments

Investigating the clinical role and prognostic value of genes related to insulin-like growth factor signaling pathway in thyroid cancer

BACKGROUND

Thyroid cancer (THCA) is the most common endocrine malignancy having a female predominance. The insulin-like growth factor (IGF) pathway contributed to the unregulated cell proliferation in multiple malignancies. We aimed to explore the IGF-related signature for THCA prognosis.

METHOD

The TCGA-THCA dataset was collected from the Cancer Genome Atlas (TCGA) for screening of key prognostic genes. The limma R package was applied for differentially expressed genes (DEGs) and the clusterProfiler R package was used for the Gene Ontology (GO) and KEGG analysis of DEGs. Then, the un/multivariate and least absolute shrinkage and selection operator (Lasso) Cox regression analysis was used for the establishment of RiskScore model. Receiver Operating Characteristic (ROC) analysis was used to verify the model's predictive performance. CIBERSORT and MCP-counter algorithms were applied for immune infiltration analysis. Finally, we analyzed the mutation features and the correlation between the RiskScore and cancer hallmark pathway by using the GSEA.

RESULT

We obtained 5 key RiskScore model genes for patient's risk stratification from the 721 DEGs. ROC analysis indicated that our model is an ideal classifier, the high-risk patients are associated with the poor prognosis, immune infiltration, high tumor mutation burden (TMB), stronger cancer stemness and stronger correlation with the typical cancer-activation pathways. A nomogram combined with multiple clinical features was developed and exhibited excellent performance upon long-term survival quantitative prediction.

CONCLUSIONS

We constructed an excellent prognostic model RiskScore based on IGF-related signature and concluded that the IGF signal pathway may become a reliable prognostic phenotype in THCA intervention.

Oncogenic role of copper‑induced cell death‑associated protein DLD in human cancer: A pan‑cancer analysis and experimental verification

Copper ions can bind directly to lipoylated components of the tricarboxylic acid (TCA) cycle, triggering the aggregation of mitochondrial lipoylated proteins and the destabilization of Fe-S cluster proteins, resulting in copper-dependent cell death. Dihydrolipoamide dehydrogenase (DLD) is a key protein of the TCA cycle and constitutes the E3 component of the α-ketoglutarate dehydrogenase complex, which is deeply interconnected with the mitochondrial electron transfer chain in the TCA cycle. Tumor cells demonstrate dependency on glutaminolysis fuelling to carry out the TCA cycle and essential biosynthetic processes supporting tumor growth. Therefore, DLD plays an important role in the tumor biological process. However, to the best of our knowledge, no pan-cancer analysis is currently available for DLD. Therefore, the present study first explored the DLD expression profile in 33 tumors in publicly available datasets, including TIMER2, GEPIA2, UALCAN, cBioPortal and STRING. TIMER2, GEPIA2 and UALCAN were used for exploring gene expression; survival prognosis was detected by GEPIA2; genetic alteration was analysed by cBioPortal; immune infiltration data was obtained from TIMER2; interacting proteins of DLD were detected by STRING. DLD was found to be highly expressed in colon, liver, lung, stomach, renal, corpus uteri endometrial and ovarian cancers compared with normal tissues, and its high expression was associated with poorer prognosis in ovarian cancer. To the best of our knowledge, the present study provided the first comprehensive pan-cancer analysis of the oncogenic role of DLD across different tumors types. As the expression of DLD in ovarian cancer was high, and high expression is associated with poor prognosis, experimental verification of DLD in ovarian cancer was conducted. In the present study, DLD expression was found to be high in the ovarian cancer OC3 cell line, compared with the normal ovarian epithelial IOSE80 cell line by reverse transcription-quantitative PCR analysis. After knockdown of DLD expression, it was found that DLD regulated metabolic pathways by suppressing the intracellular NAD⁺/NADH ratio, which then in turn suppressed tumor cell proliferation detected by MTT assay. In conclusion, the present pan-cancer analysis of DLD demonstrated that DLD expression was associated with the clinical prognosis, immune infiltration and tumor mutational burden in 33 tumor types, and experimental verification in ovarian cancer was conducted. These results may contribute to the understanding of the role of DLD in tumorigenesis.

Modified Taq polymerase for allele-specific ultra-sensitive detection of genetic variants

Allele-specific polymerase chain reaction (AS-PCR) has been used as a simple, cost-effective method for genotyping and gene mapping in research and clinical settings. AS-PCR permits the detection of single nucleotide variants (SNVs) and indels owing to the selective extension of a perfectly matched primer (to the template DNA) over a mismatched primer. Thus, the mismatch discrimination power of the DNA polymerase is critical. Unfortunately, currently available polymerases often amplify some mismatched primer-template complexes as well as matched ones, obscuring allele-specific detection. To increase mismatch discrimination, we have generated mutations in the Thermus aquaticus (Taq) DNA polymerase, selected the most efficient variant, and evaluated its performance in SNP and cancer mutation genotyping. In addition, the primer design and reaction buffer conditions were optimized for allele-specific amplification. Our highly selective AS-PCR, which is based on an allele-discriminating priming system (ADPS) that leverages a Taq polymerase variant with optimized primers and reaction buffer, can detect mutations with mutant allele frequency as low as 0.01% in genomic DNA and 0.0001% in plasmid DNA. This method serves as a simple, fast, cost-effective, and ultra-sensitive way to detect SNVs and indels with low abundance.

Omics analyses in peritoneal metastasis-utility in the management of peritoneal metastases from colorectal cancer and pseudomyxoma peritonei: a narrative review

High-throughput "-omics" analysis may provide a broader and deeper understanding of cancer biology to define prognostic and predictive biomarkers and identify novel therapy targets. In this review we provide an overview of studies where the peritoneal tumor component of peritoneal metastases from colorectal cancer (PM-CRC) and pseudomyxoma peritonei (PMP) were analyzed. Most of the available data was derived from DNA mutation analysis, but a brief review of findings from transcriptomic and protein expression analysis was also performed. Studies reporting genomic analysis of peritoneal tumor samples from 1,779 PM-CRC and 623 PMP cases were identified. The most frequently mutated genes in PM-CRC were KRAS, APC, SMAD4, BRAF, and PIK3CA, while in PMP KRAS, GNAS, FAT4, TGFBR1, TP53 and SMAD3/4 mutations were most commonly identified. Analyses were performed by single-gene analyses and to some extent targeted next-generation sequencing, and a very limited amount of broad explorative data exists. The investigated cohorts were typically small and heterogeneous with respect to the methods used and to the reporting of clinical data. This was even more apparent regarding transcriptomic and protein data, as the low number of cases examined and quality of clinical data would not support firm conclusions. Even for the most frequently mutated genes, the results varied greatly; for instance, KRAS mutations were reported at frequencies between 20-57% in PM-CRC and 38-100% in PMP. Such variation could be caused by random effects in small cohorts, heterogeneity in patient selection, or sensitivity of applied technology. Although a large number of samples have been subjected to analysis, cross-study comparisons are difficult to perform, and combined with small cohorts and varying quality and detail of clinical information, the observed variation precludes useful interpretation in a clinical context. Although omics data in theory could answer questions to aid management decisions in PM-CRC and PMP, the existing data does not presently support clinical implementation. With the necessary technologies being generally available, the main challenge will be to obtain sufficiently large, representative cohorts with adequate clinical data and standardized reporting of results. Importantly, studies where the focus is specifically on peritoneal disease are needed, where the study designs are aligned with clearly defined research questions to allow robust conclusions. Such studies are highly warranted if patients with PM-CRC and PMP are to derive benefit from recent advances in precision cancer medicine.

Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling

The complexity of diagnostic (surgical) pathology has increased substantially over the last decades with respect to histomorphological and molecular profiling. Pathology has steadily expanded its role in tumor diagnostics and beyond from disease entity identification via prognosis estimation to precision therapy prediction. It is therefore not surprising that pathology is among the disciplines in medicine with high expectations in the application of artificial intelligence (AI) or machine learning approaches given their capabilities to analyze complex data in a quantitative and standardized manner to further enhance scope and precision of diagnostics. While an obvious application is the analysis of histological images, recent applications for the analysis of molecular profiling data from different sources and clinical data support the notion that AI will enhance both histopathology and molecular pathology in the future. At the same time, current literature should not be misunderstood in a way that pathologists will likely be replaced by AI applications in the foreseeable future. Although AI will transform pathology in the coming years, recent studies reporting AI algorithms to diagnose cancer or predict certain molecular properties deal with relatively simple diagnostic problems that fall short of the diagnostic complexity pathologists face in clinical routine. Here, we review the pertinent literature of AI methods and their applications to pathology, and put the current achievements and what can be expected in the future in the context of the requirements for research and routine diagnostics.

Genetic Markers in Lung Cancer Diagnosis: A Review

Lung cancer is the most often diagnosed cancer in the world and the most frequent cause of cancer death. The prognosis for lung cancer is relatively poor and 75% of patients are diagnosed at its advanced stage. The currently used diagnostic tools are not sensitive enough and do not enable diagnosis at the early stage of the disease. Therefore, searching for new methods of early and accurate diagnosis of lung cancer is crucial for its effective treatment. Lung cancer is the result of multistage carcinogenesis with gradually increasing genetic and epigenetic changes. Screening for the characteristic genetic markers could enable the diagnosis of lung cancer at its early stage. The aim of this review was the summarization of both the preclinical and clinical approaches in the genetic diagnostics of lung cancer. The advancement of molecular strategies and analytic platforms makes it possible to analyze the genome changes leading to cancer development-i.e., the potential biomarkers of lung cancer. In the reviewed studies, the diagnostic values of microsatellite changes, DNA hypermethylation, and p53 and KRAS gene mutations, as well as microRNAs expression, have been analyzed as potential genetic markers. It seems that microRNAs and their expression profiles have the greatest diagnostic potential value in lung cancer diagnosis, but their quantification requires standardization.