AI/ML advances in non-small cell lung cancer biomarker discovery

Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers

The Effect of Freeze-Thaw Cycles on Gene Expression Levels in Lymphoblastoid Cell Lines

Epstein-Barr virus (EBV) transformed lymphoblastoid cell lines (LCLs) are a widely used renewable resource for functional genomic studies in humans. The ability to accumulate multidimensional data pertaining to the same individual cell lines, from complete genomic sequences to detailed gene regulatory profiles, further enhances the utility of LCLs as a model system. However, the extent to which LCLs are a faithful model system is relatively unknown. We have previously shown that gene expression profiles of newly established LCLs maintain a strong individual component. Here, we extend our study to investigate the effect of freeze-thaw cycles on gene expression patterns in mature LCLs, especially in the context of inter-individual variation in gene expression. We report a profound difference in the gene expression profiles of newly established and mature LCLs. Once newly established LCLs undergo a freeze-thaw cycle, the individual specific gene expression signatures become much less pronounced as the gene expression levels in LCLs from different individuals converge to a more uniform profile, which reflects a mature transformed B cell phenotype. We found that previously identified eQTLs are enriched among the relatively few genes whose regulations in mature LCLs maintain marked individual signatures. We thus conclude that while insight drawn from gene regulatory studies in mature LCLs may generally not be affected by the artificial nature of the LCL model system, many aspects of primary B cell biology cannot be observed and studied in mature LCL cultures.</p

Host Genetic Variation Influences Gene Expression Response to Rhinovirus Infection

Rhinovirus (RV) is the most prevalent human respiratory virus and is responsible for at least half of all common colds. RV infections may result in a broad spectrum of effects that range from asymptomatic infections to severe lower respiratory illnesses. The basis for inter-individual variation in the response to RV infection is not well understood. In this study, we explored whether host genetic variation is associated with variation in gene expression response to RV infections between individuals. To do so, we obtained genome-wide genotype and gene expression data in uninfected and RV-infected peripheral blood mononuclear cells (PBMCs) from 98 individuals. We mapped local and distant genetic variation that is associated with inter-individual differences in gene expression levels (eQTLs) in both uninfected and RV-infected cells. We focused specifically on response eQTLs (reQTLs), namely, genetic associations with inter-individual variation in gene expression response to RV infection. We identified local reQTLs for 38 genes, including genes with known functions in viral response (UBA7, OAS1, IRF5) and genes that have been associated with immune and RV-related diseases (e.g., ITGA2, MSR1, GSTM3). The putative regulatory regions of genes with reQTLs were enriched for binding sites of virus-activated STAT2, highlighting the role of condition-specific transcription factors in genotype-by-environment interactions. Overall, we suggest that the 38 loci associated with inter-individual variation in gene expression response to RV-infection represent promising candidates for affecting immune and RV-related respiratory diseases.</p

The Effect of Freeze-Thaw Cycles on Gene Expression Levels in Lymphoblastoid Cell Lines

<div><p>Epstein-Barr virus (EBV) transformed lymphoblastoid cell lines (LCLs) are a widely used renewable resource for functional genomic studies in humans. The ability to accumulate multidimensional data pertaining to the same individual cell lines, from complete genomic sequences to detailed gene regulatory profiles, further enhances the utility of LCLs as a model system. However, the extent to which LCLs are a faithful model system is relatively unknown. We have previously shown that gene expression profiles of newly established LCLs maintain a strong individual component. Here, we extend our study to investigate the effect of freeze-thaw cycles on gene expression patterns in mature LCLs, especially in the context of inter-individual variation in gene expression. We report a profound difference in the gene expression profiles of newly established and mature LCLs. Once newly established LCLs undergo a freeze-thaw cycle, the individual specific gene expression signatures become much less pronounced as the gene expression levels in LCLs from different individuals converge to a more uniform profile, which reflects a mature transformed B cell phenotype. We found that previously identified eQTLs are enriched among the relatively few genes whose regulations in mature LCLs maintain marked individual signatures. We thus conclude that while insight drawn from gene regulatory studies in mature LCLs may generally not be affected by the artificial nature of the LCL model system, many aspects of primary B cell biology cannot be observed and studied in mature LCL cultures.</p></div

Host Genetic Variation Influences Gene Expression Response to Rhinovirus Infection

<div><p>Rhinovirus (RV) is the most prevalent human respiratory virus and is responsible for at least half of all common colds. RV infections may result in a broad spectrum of effects that range from asymptomatic infections to severe lower respiratory illnesses. The basis for inter-individual variation in the response to RV infection is not well understood. In this study, we explored whether host genetic variation is associated with variation in gene expression response to RV infections between individuals. To do so, we obtained genome-wide genotype and gene expression data in uninfected and RV-infected peripheral blood mononuclear cells (PBMCs) from 98 individuals. We mapped local and distant genetic variation that is associated with inter-individual differences in gene expression levels (eQTLs) in both uninfected and RV-infected cells. We focused specifically on response eQTLs (reQTLs), namely, genetic associations with inter-individual variation in gene expression response to RV infection. We identified local reQTLs for 38 genes, including genes with known functions in viral response (<i>UBA7</i>, <i>OAS1</i>, <i>IRF5</i>) and genes that have been associated with immune and RV-related diseases (e.g., <i>ITGA2</i>, <i>MSR1</i>, <i>GSTM3</i>). The putative regulatory regions of genes with reQTLs were enriched for binding sites of virus-activated STAT2, highlighting the role of condition-specific transcription factors in genotype-by-environment interactions. Overall, we suggest that the 38 loci associated with inter-individual variation in gene expression response to RV-infection represent promising candidates for affecting immune and RV-related respiratory diseases.</p></div

Gene expression variation in mature LCLs can observe only a fraction of the inter-individual variation in gene expression levels that exists in the primary B cells.

<p><b>A</b>) Density distributions of the coefficient of variation (CV) of gene expression between-individuals within primary B cells and within LCLs of each freeze-thaw cycle. Black vertical line designates the arbitrarily chosen threshold of CV of 0.025. <b>B</b>) Bar plots showing the numbers of genes classified as having ‘highly variable’ expression patterns in the primary B cells, cycle 0, cycle 2, cycle 4, and cycle 6 LCLs. <b>C</b>) Venn diagram of the overlaps in genes with ‘highly variable’ expression patterns across primary B cells, cycle 0, cycle 2, cycle 4, and cycle 6 LCLs. We also plotted an equivalent figure based on residual data after regressing out mtDNA and EBV copy numbers; see Figure S10 in in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107166#pone.0107166.s001" target="_blank">Appendix S1</a>.</p

Identification and functional characterization of RV-responsive genes in PBMCs.

<p><b>(A)</b> Volcano plot showing the Log2 Fold change (x-axis) and the—Log10 P value (y-axis) of gene expression between uninfected and RV-infected PBMCs. Genes that were not classified as differentially expressed are shown in black. Genes that were significantly differentially expressed (<i>P</i><4.6x10^-6) but displayed <2-fold change are shown in blue; genes that were both significantly differentially expressed and had ≥2-fold change are shown in red. (<b>B)</b> Gene ontology (GO) enrichment results of the genes that were both significantly differentially expressed and had ≥2-fold change (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005111#pgen.1005111.s010" target="_blank">S3 Table</a> for results including all GO terms). <b>(C)</b> Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment results of the genes that were both significantly differentially expressed and had ≥2-fold change (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005111#pgen.1005111.s011" target="_blank">S4 Table</a> for results including all KEGG terms).</p

Previously identified eQTLs are enriched among the genes whose regulations in mature LCLs maintain marked inter-individual variation.

<p><b>A</b>) Bar plots showing the percent of genes with eQTLs (as identified in LCL-Study 5) among all genes detected as expressed and among genes with ‘highly variable’ expression patterns in B cells, cycle 0, cycle 2, cycle 4, and cycle 6 LCLs. Asterisks indicate that the null hypothesis of no difference in proportion of genes with eQTLs relative to that in all expressed genes was rejected at <i>P</i> values <8.3×10⁻⁴ (Bonferroni corrected significance threshold). Results pertaining to all twelve eQTL studies are included in Figure S8 and Table S8 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107166#pone.0107166.s001" target="_blank">Appendix S1</a>. <b>B</b>) Mean coefficient of variation (CV) of gene expression within each cell type/freeze-thaw cycle for all the genes detected as expressed and for genes with eQTLs (as identified in LCL Study-5). Results for all twelve eQTL studies are included in Figure S9 and Table S9 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107166#pone.0107166.s001" target="_blank">Appendix S1</a>.</p

Identification of local eQTLs and reQTLs.

<p><b>(A)</b> Manhattan plots of local eQTLs in uninfected cells (in blue), local eQTLs in RV-infected cells (in red), and local reQTLs (in green). Most significant local eQTL or local reQTL P value for each gene (y-axis) is displayed in the order of chromosomal positions of the genes detected as expressed in our study (x-axis). <b>(B)</b> Examples of genes with reQTLs. In each plot, genotype at the reQTL is shown on the x-axis and expression level (Log2 Expression) or response in gene expression (Log2 Fold Change) is shown on the y-axis. Sample sizes for each genotype group are shown in parentheses under the x-axis.</p

STAT2 binding sites are enriched among reQTL loci.

<p><b>(A)</b> Observed and expected numbers of variants that lie within STAT2 binding sites i) across all human cell types/conditions available in ENCODE ChIP-Seq data, ii) in IFNα-30 minute treated human K562 cell line, iii) in IFNα-6 hour treated human K562 cell line. <b>(B)</b> Regional eQTL association plots of <i>SLFN5</i> in uninfected and RV-infected cells. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005111#pgen.1005111.s006" target="_blank">S6 Fig</a> for regional eQTL association plots of the remaining four genes with significant reQTL loci SNPs that reside in STAT2 binding sites. In all five cases, the eQTL association was significant only in RV-infected cells.</p