TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasis in non-small cell lung cancer

Hideaki Umeyama, Department of Biological Science, Chuo University

MItsuo Iwadate, Department of Biological Science, Chuo University

＊Y-h. Taguchi, Department of Physics, Chuo University

Introduction

Purpose of the present research:Purpose of the present research:Applying recently proposed “PCA based unsupervised FE” to drug discovery, by combining with FAMS/choseLD pipeline.

Target in this presentation:Target in this presentation:Metastasis of non-small cell lung cancer (NSCLC)

MaterialsMaterials::(GSE52144)Two NSCLC cell lines (HTB56/A549), each with/without metastasis (in total, four types (classes)).

MeasurementsMeasurements:(GSE52144)mRNA expression + promoter methylation(microarray) (microarray+NGS)

Plan:Plan:Target gene identification using integrated analysis of mRNA/expression and promoter methylation, i.e.,

11. aberrant mRNA expression associated with metastasis22. aberrant promoter methylation associated with metastasis33. Negative correlation between mRNA expression and promoter methylation

→ → 1&2&3 : target genes 1&2&3 : target genes

ProblemsProblems::11. Difficulty of detection of negative correlation.Simple application Pearson correlation analysis + adjusted P-values considering multiple comparison

→ P<0.05 : only one gene

22. Difficulty of detection of aberrant mRNA expression / promoter methylation

Simple t test: with vs without metastasis + adjusted P-values considering multiple comparison

mRNA:(P<0.05)A549 No genes / HTB56 434 genes

Promoter methylation:(P<0.05)A549 No genes / HTB56 No genes

Using PCA based unsupervised FE

Embeddings of Probes to 2D surfacePC1

PC2 mRNA methylation

PC1:no sample dependence

A549 without metastasisA549 with metastasisHTB56 without metastasisHTB56 with metastasis

mRNA 97% Methylation 87%

PC2: distinction between two cell lines

mRNA 1.7% Methylation 9.6%

mRNA 0.2% Methylation 1.5%

PC3: distinction between with and without metastasis for HTB56 only

Top 100 outliers Top 100 outliers

Common seven genes (P=1x10-4)

mRNA 0.6% Methylation 0.09%

PC5 VS PC4: distinction between with and without metastasis for A549 only

Common four genes (P=1x10-8)

mRNA

meth

ylation

PC1 PC2

PC3 PC4 vs PC5

PCA based unsupervised FE could successfully selected genes ….

1 aberrant mRNA expression2 aberrant promoter methylation3 negative correlation between mRNA expression and promoter methylation

11 genes:HOXB2, CCDC8, ZNF114, DIO2, LAPTM5, RGS1, B3GALNT1, TINAGL1, PMEPA1, CX3CL1, ICAM1

11 genes:HOXB2, CCDC8, ZNF114, DIO2, LAPTM5, RGS1, B3GALNT1, TINAGL1, PMEPA1, CX3CL1, ICAM1

Gendoo Server:Gendoo Server:Many cancer related diseases association

Target of TF AHRTarget of TF AHR:AHR: reported to be cancer causing TF, including lung cancer

As a result, it was successful also from biological point of views

Since we could seem to identify metastasis causing genes in NSCLC, next we try to investigate if these are therapy (drug) targets.

We downloaded corresponding amino acid sequence from UniProt, and unloaded to profile base protein structure inference server, FAMS.FAMS: “Full Automatic Modeling System” developed by Profs. Umeyama and Iwadate.

Predicted structures were also compared with the predictions by public domain prediction server Phyre2 and was confirmed that there were no significant differences.

ExampleExample:

HOXB2: Structure predicted partially by FAMSHOXB2 has Homeobox structure (see below) that binds to DNA/RNA region.

If we can find some compounds that “blcok” DNA/RNA binding of HOXB2, the compound can be a drug.

We have investigated eleven genes from this point views, and proposed two possible drug target genes.

TINAGL1 B3GALNT1

TINAGL1 is usually classified as tumor supressor, but was also reported to be upregulated in metastasis.

Amino Acid Sequence

PDB

Tertiary Structure Template Ligands

ChEMBL Homologous Protein

Ligand (Drug) Candidate

BLAST

BLAST

FAMSChooseLD*

* profile based ligand docking prediction software

Drug candidate Compounds identified from ChEMBL

Binding modes of two compounds inferred by chooseLD

Independent Binding affinity evaluation by Cyscore→ Not very good, only acceptable

Known ligand (PDB)Drug candidate (ChEMBL + chooseLD)

ConclusionConclusion

Using integrated analysis of mRNA expression and promoter methylation with PCA based unsupervised FE, we successfully identified eleven possible metastasis causing genes.

Using FAMS/chooseLD, we identified possible drug candidate compounds for two of eleven genes.

This research was supported by both This research was supported by both

Chuo University Joint Research Project Grant“Drug discovery based on protein functional inference by FAMS”

and

Grant-in-Aid for Scientific Research on Innovative Area “Initiative for high-dimensional data-driven science based on sparse modeling”

It is also accepted as Oral presentation at InCob2014 (not yet paper acceptance)