Integrative Molecular Analyses of an Individual Transcription Factor-Based Genomic Model for Lung Cancer Prognosis

Objective. Precision medicine with molecular profiles has revolutionized the management of lung cancer contributing to improved prognosis. Herein, we aimed to uncover the gene expression profiling of transcription factors (TFs) in lung cancer as well as to develop a TF-based genomic model. Methods. We retrospectively curated lung cancer patients from public databases. Through comparing mRNA expression profiling between lung cancer and normal specimens, specific TFs were determined. Thereafter, a TF genomic model was developed with univariate Cox regression and stepwise multivariable Cox analyses, which was verified through the GSE72094 dataset. Gene set enrichment analyses (GSEA) were presented. Downstream targets of TFs were predicted with ChEA, JASPAR, and MotifMap projects, and their biological significance was investigated through the clusterProfiler algorithm. Results. In the TCGA cohort, we proposed a TF-based genomic model, comprised of SATB2, HLF, and NPAS2. Lung cancer individuals were remarkably stratified into high- and low-risk groups. Survival analyses uncovered that high-risk populations presented unfavorable survival outcomes. ROCs confirmed the excellent predictive potency in patients’ prognosis. Additionally, this model was an independent prognostic indicator in accordance with multivariate analyses. The clinical implication of the model was well verified in an independent dataset. High risk score was in relation to carcinogenic pathways. Downstream targets were characterized by immune and carcinogenic activation. Conclusion. The proposed TF genomic model acts as a promising marker for estimation of lung cancer patients’ outcomes. Prospective research is required for testing the clinical utility of the model in individualized management of lung cancer.

Accurate Prognosis Prediction of Pancreatic Ductal Adenocarcinoma Using Integrated Clinico-Genomic Data of Endoscopic Ultrasound-Guided Fine Needle Biopsy

The aim of this study was to investigate the clinical utility of minimal specimens acquired from endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) and perform targeted deep sequencing as a prognosis prediction tool for pancreatic ductal adenocarcinoma (PDAC). A total of 116 specimens with pathologically confirmed PDAC via EUS-FNB were tested using CancerSCAN® panel for a customized targeted deep sequencing. Clinical prognostic factors significantly associated with survival in PDACs were as follows: stage, tumor mass size, tumor location, metastasis, chemotherapy, and initial CA19-9 level. A total of 114 patients (98.3%) had at least a single genetic alteration, and no mutations were detected in two patients, although they were qualified for the targeted deep sequencing. The frequencies of major gene mutations responsible for PDACs were KRAS 90%, CDKN2A 31%, TP53 77%, and SMAD4 29%. A somatic point mutation of NF1, copy number alteration of SMAD4, and loss-of-function of CDKN2A were significantly associated genetic factors for overall survival. Moreover, BRCA2 point mutation was related to liver metastasis. Finally, a clinico-genomic model was developed to estimate the prognosis of patients with PDAC based on clinical parameters and genetic alterations affecting survival in patients; 20 single nucleotide variants and three copy number variations were selected. Targeted deep sequencing on minimal specimens of PDACs was performed, and it was applied to establish a clinico-genomic model for prognosis prediction.

Development of a genomic predictive model for cholangiocarcinoma using copy number alteration data

AimsCholangiocarcinoma (CC) is a rare tumour arising from the biliary tract epithelium. The aim of this study was to perform a genomic characterisation of CC tumours and to implement a model to differentiate extrahepatic (ECC) and intrahepatic (ICC) cholangiocarcinoma.MethodsDNA extracted from tumour samples of 23 patients with CC, namely 10 patients with ECC and 13 patients with ICC, was analysed by array comparative genomic hybridisation. A support vector machine algorithm for classification was applied to the genomic data to distinguish between ICC and ECC. A survival analysis comparing both groups of patients was also performed.ResultsWith these whole genome results, we observed several common alterations between tumour samples of the same CC anatomical type, namely gain of Xp and loss of 3p, 11q11, 14q, 16q, Yp and Yq in ICC tumours, and gain of 16p25.3 and loss of 3q26.1, 6p25.3–22.3, 12p13.31, 17p, 18q and Yp in ECC tumours. Gain of 2q37.3 was observed in the samples of both tumour subtypes, ICC and ECC. The developed genomic model comprised four chromosomal regions that seem to enable the distinction between ICC and ECC, with an accuracy of 71.43% (95% CI 43% to 100%). Survival analysis revealed that in our cohort, patients with ECC survived on average 8 months less than patients with ICC.ConclusionsThis genomic characterisation and the introduction of genomic models to clinical practice could be important for patient management and for the development of targeted therapies. The power of this genomic model should be evaluated in other CC populations.

Estimating dominance genetic variances for growth traits in American Angus males using genomic models

Estimates of dominance variance for growth traits in beef cattle based on pedigree data vary considerably across studies, and the proportion of genetic variance explained by dominance deviations remains largely unknown. The potential benefits of including nonadditive genetic effects in the genomic model combined with the increasing availability of large genomic data sets have recently renewed the interest in including nonadditive genetic effects in genomic evaluation models. The availability of genomic information enables the computation of covariance matrices of dominant genomic relationships among animals, similar to matrices of additive genomic relationships, and in a more straightforward manner than the pedigree-based dominance relationship matrix. Data from 19,357 genotyped American Angus males were used to estimate additive and dominant variance components for 3 growth traits: birth weight, weaning weight, and postweaning gain, and to evaluate the benefit of including dominance effects in beef cattle genomic evaluations. Variance components were estimated using 2 models: the first one included only additive effects (MG) and the second one included both additive and dominance effects (MGD). The dominance deviation variance ranged from 3% to 8% of the additive variance for all 3 traits. Gibbs sampling and REML estimates showed good concordance. Goodness of fit of the models was assessed by a likelihood ratio test. For all traits, MG fitted the data as well as MGD as assessed either by the likelihood ratio test or by the Akaike information criterion. Predictive ability of both models was assessed by cross-validation and did not improve when including dominance effects in the model. There was little evidence of nonadditive genetic variation for growth traits in the American Angus male population as only a small proportion of genetic variation was explained by nonadditive effects. A genomic model including the dominance effect did not improve the model fit. Consequently, including nonadditive effects in the genomic evaluation model is not beneficial for growth traits in the American Angus male population.

Estimation of Genetic Parameters by Single-Trait and Multi-Trait Models for Carcass Traits in Hanwoo Cattle

Hanwoo breed is preferred in South Korea because of the high standards in marbling and the palatability of its meat. Numerous studies have been conducted and are ongoing to increase the meat production and quality in this beef population. The aim of this study was to estimate and compare genetic parameters for carcass traits using BLUPF90 software. Four models were constructed, single trait pedigree model (STPM), single-trait genomic model (STGM), multi-trait pedigree model (MTPM), and multi-trait genomic model (MTGM), using the pedigree, phenotype, and genomic information of 7991 Hanwoo cattle. Four carcass traits were evaluated: Back fat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). Heritability estimates of 0.40 and 0.41 for BFT, 0.33 and 0.34 for CWT, 0.36 and 0.37 for EMA, and 0.35 and 0.38 for MS were obtained for the single-trait pedigree model and the multi-trait pedigree model, respectively, in Hanwoo. Further, the genomic model showed more improved results compared to the pedigree model, with heritability of 0.39 (CWT), 0.39 (EMA), and 0.46 (MS), except for 0.39 (BFT), which may be due to random events. Utilization of genomic information in the form of single nucleotide polymorphisms (SNPs) has allowed more capturing of the variance from the traits improving the variance components.

Dissecting the Transcriptomic Basis of Phenotypic Evolution in an Aquatic Keystone Grazer

Knowledge of the molecular basis of phenotypic responses to environmental cues is key to understanding the process of adaptation. Insights to adaptation at an evolutionary time scale can be gained by observing organismal responses before and after a shift in environmental conditions, but such observations can rarely be made. Using the ecological and genomic model Daphnia, we linked transcriptomic responses and phosphorus (P)-related phenotypic traits under high and low P availability. We mapped weighted gene coexpression networks to traits previously assessed in resurrected ancient (600 years old) and modern Daphnia pulicaria from a lake with a historic shift in P-enrichment. Subsequently, we assessed evolutionary conservation or divergence in transcriptional networks of the same isolates. We discovered highly preserved gene networks shared between ancient genotypes and their modern descendants, but also detected clear evidence of transcriptional divergence between these evolutionarily separated genotypes. Our study highlights that phenotypic evolution is a result of molecular fine-tuning on different layers ranging from basic cellular responses to higher order phenotypes. In a broader context, these findings advance our understanding how populations are able to persist throughout major environmental shifts.