Pine Biotech held a workshop and discussion of the approaches that are being developed for the T-BioInfo platform. The approaches were presented by Julia Panov, a Ph.D. student from Haifa University and data scientist working at Pine Biotech. Her presentation covered some of the projects with preliminary results showing a promising method of integrating various omics data types and applying them to noisy datasets signal processing for target discovery and biomarkers. The workshop was held at the Flower Hall at Tulane University. This was the first presentation to over 40 people that gathered to discuss interesting applications of biomedical data analysis to healthcare and pharma. Dr. Yu-Ping Wang and Dr. Lars Gilbertson from the department of Biomedical Engineering also shared their perspective on this important topic. http://pine-biotech.com/workshop-roundtable-discussion-tulane-pine-biotech/

1. Tauber Bioinformatics Research Center at the University of Haifa has a proven track
record in Bioinformatics with scientific collaborations with Hospitals, Universities,
involvement in government-funded projects, and multiple publications in leading
journals.
Pine Biotech holds an exclusive license for commercialization of tools developed at theTBRC
for research, industry applications and education.The startup is located at the BioInnovation
Center in New Orleans, LA. In collaboration withTBRC staff, Pine Biotech is completing
several pilot projects to validate our approach.

2. Dr. Leonid Brodsky
Dr. Alfred Tauber
Dr. Baruch Rinkevich
Dr. Hanoch Kaphzan
Bioinformatics
Immunology
Marine Biology
Neurobiology
Rare Genomic Diseases

3. Data Types
Specific Pipelines
Integration
Education
Machine Learning

4. Projects and Approaches

5. Noisy and Complex Heterogenous Datasets
BiAssociation:
Integration of different types of –omics data
Identifying hidden patterns in data
BiClustering:
Example of Assembly of raw transcriptomic reads
from exosomal RNAs and other non aligned reads

6. Integration and Identification of Key Features
Drug 1 Drug 2 Drug 3
Cell Line 1 IG50 IG51 IG52
Cell Line 2 IG51 IG52 IG53
Cell Line 3 IG52 IG53 IG54
Cell Line 4 IG53 IG54 IG55
Cell Line 1 Cell Line 2 Cell Line 3
Gene 1 Exp. Level Exp. Level Exp. Level
Gene 2 Exp. Level Exp. Level Exp. Level
Gene 3 Exp. Level Exp. Level Exp. Level
Gene 4 Exp. Level Exp. Level Exp. Level
Data Source 1 Data Source 2
Clustering Clustering
Many-to-Many Relationships
of clustering results
Key Feature 1
Key Feature 2
…
BiAssociation

7. Identification of predictor genes and mutations for
drug efficacy
Selection of tumor and stroma genes as biomarker
candidates

8. cell lines
mutations
cell lines
genes
cell lines
drugs
Presence/Absence
ofSNP(1/0)
Expression
Values
IC50
Values
drugs
celllines
IC50
Values
chemicaldescriptors
IC50
Values
patients
IC50
Values
drugs
drugs
Network of Integrations
Linking clinical conditions with omics data in model experiments

9. Processed Tables of Raw Expression Data
Samples
Expression levels
Variation
Association
Sequence
Pathway
Variation
Function

10. Drug-Gene BiAssociation using swRegression
cell lines
genes
Expression
Values
drugs
celllines
IC50
Values
cell line 1
cell line 2
cell line 3
cell line 4
cell line 5
cell line 6
cell line 7
gene expression IC50 value
Detection of gene activation linked to an IC50 value by cell line.
Each cell line represents a subtype of cancer, selected by
modeling that biological condition

11. Predictors:
lnc.LEKR1.3.1
ENST00000426717
ENSG00000138092
ENST00000449399
ENST00000336915
ENSG00000149136
ENST00000456986
ENSG00000115758
ENSG00000100416
ENST00000361219
ENSG00000171793
ENSG00000187741
ENST00000309276
ENSG00000141293
non-coding RNA
Isoform
Gene
Expressed Molecular Features as Predictors

12. Multivariate Mutation-Expression BiAssociation
cell lines
genes
Expression
Values cell lines
mutations
Mutation
Values(1/0)
cell line 1
cell line 2
cell line 3
cell line 4
cell line 5
cell line 6
cell line 7
gene expression
mutation islands
island abundance
neighboring gene
DOCK6
DOCK6
DOCK6
DOCK6
DOCK6
DOCK6
DOCK6
OR2
OR11
OR5A1
46 cell lines
173 genes of the
Olfactory pathway
mutation
island vs. SNP
“neighborhood”
gene

13. Applications to Clinical Studies
cell line 1
cell line 2
cell line 3
cell line 4
cell line 5
cell line 6
cell line 7
minus Log (GI50) mutation islands neighbor
ROBO1
ROBO1
ROBO1
ROBO1
ROBO1
ROBO1
ROBO1
46 cell
lines
Doxorubicin
GI50 Profile
mutation
island vs. SNP
ROBO1
receptor
Doxorubicin:
standard treatment in
eligible patients with
advanced/metastatic
soft tissue sarcoma
Who? Why?
How?
SLIT2 protein
SLIT2 protein
SLIT2 protein
SLIT2 protein
SLT2 protein
ROBO1 receptor
SLIT2 protein

14. BiAssociation for Hidden Patterns in Omics Data

15. Lymphoma possibly associated with Epstein-Barr virus Stroma-Specific Sample IdentificationSmall Cell Lung Carcinoma Samples

16. Lymphomagenesis Samples
Genes deferentially expressed in these outlier samples are enriched with immune
processes in the tumor. We hypothesize that these tumors are lymphomas.
One sample from these outlier samples is a chronic lymphocytic leukemia sample and
so the B-cell presence in this sample is not surprising. However, the other two samples
are lung bronchogenic cancer and lung squamous cancer respectively. Our hypothesis
is that these two cancers are lymphoma cancers associated with Epstein-Barr virus
References: Patient-Derived Tumor Xenografts Are Susceptible to Formation of
Human Lymphocytic Tumors (2015) and Human Solid Tumor Xenografts in
Immunodeficient Mice Are Vulnerable to Lymphomagenesis Associated with
Epstein-Barr Virus (2012)

17. Stroma-Specific Samples
Tumor up-regulated (pVal<0.0001) gene:
RBFOX1
Tumor down-regulated: ENT4 and known
lincRNA (RP11-1070N10)
Stroma up-regulated genes are enriched in the
following functional clusters: mitochondrion;
zinc-finger H2C2; ion transmembrane transport;
metal ion binding; cytoplasm; alternative splicing
and transcription factor
Outlier Samples:

18. Significantly (p-val <0.0001) up-regulated (in
Small-cell Carcinoma Lung Cancer samples)
tumor genes (491 genes) are enriched by the
following functional clusters: Zinc finger C2H2;
Kelch repeat; CUB domain; protein phosphatase
2C.
Significantly down-regulated tumor genes (p-
val<0.0001; 1056 genes) are enriched by the
following functional clusters: connecting peptide;
MHC 1; tumor necrosis factor-activated receptor
activity; calcium binding
Significantly down-regulated stroma genes
(p-val <0.0001; 323 genes) are enriched by the
following functional clusters: Interferon regulatory
factor; SOCS box; 2'-5'-oligoadenylate synthetase
activity
Small Cell Lung Carcinoma

19. The exosome consists of the following RNAs:
mRNA, RNA repeats, rRNA, small RNA (Transfer
RNA (tRNA), small interfering (siRNA), small
nucleolar RNA (snoRNA), small cytoplasmic RNA
(scRNA), small nuclear RNA (snRNA), miRNA
lncRNA, snoRNA, piwi-interacting RNA (piRNA),
rRNA, viral RNA, bacterial RNA
BiClustering for Exosomal RNA

20. Consensus
known sequences
known sequences
K-chainsRaw reads Assembly
BiClustering Procedure
Assembly of small RNA, repetitive elements and other
transcribed genomic elements via BiClustering.

21. Thank You!