1. Impact of Formal Methods in Biology and Medicine Final Review
Debashis Sahoo
Department of Computer Science
CSE291 – H00 – Lecture 18

2. Outline Introduction History of Bioinformatics
Introduction to computing
Data collection
Experiment design
Data analysis

3. Biological Systems What I would like to cover are these points:
More formal
Basic intro to boolean logic
Describe with example
Show how these are applied
Markers vs cell types
Normal vs cancer

4. Tissue What I would like to cover are these points: More formal
Basic intro to boolean logic
Describe with example
Show how these are applied
Markers vs cell types
Normal vs cancer

5. Boolean Analysis George Boole (1815-1864) Two values
High, Low
1, 0
Boolean operations
AND, OR, NOT
Implication
x  y
Add High/Low – maybe remove True/False
Remove figures:
Simple Boolean logic that can simplify incredbly complex continuous data
Infer biological meaning.

6. Boolean Implication Pair of genes. Four quadrants. Sparse quadrants.
ACPP
GABRB1
45,000 Affymetrix microarrays
Pair of genes.
Four quadrants.
Sparse quadrants.
Boolean relationships.
If ACPP high, then GABRB1 low
If GABRB1 high, then ACPP low
Put the introductory slides
How many microarrays
Seems like a fundamental…
If -> then
Describe x and y axis.
Describe a point.
Statistical tests for identifying sparse quadrant.

7. Threshold Calculation
Threshold for each gene
Sort expression values
StepMiner
High
CDH expression
Intermediate
Threshold
Low
Say about linear shape.
Labels in the graph bigger.
Put forbidden zone
threshold. Labels.
Sorted arrays
[Sahoo et al. 07]

8. StepMiner Statistics

9. BooleanNet Statistics
nAlow = (a00+ a01), nBlow = (a00+ a10)
total = a00+ a01+ a10+ a11, observed = a00
expected = (nAlow/ total * nBlow/ total) * total
statistic =
(expected – observed)
expected √
a00
(a00+ a01)
(a00+ a10) + ( ) 1 2
error rate =
Put the introductory slides
How many microarrays
Seems like a fundamental…
If -> then
Describe x and y axis.
Describe a point.
Statistical tests for identifying sparse quadrant.
Boolean Implication = (statistic > 3, error rate < 0.1)
[Sahoo et al. Genome Biology 08]

10. Modeling Colon Tissue What I would like to cover are these points:
More formal
Basic intro to boolean logic
Describe with example
Show how these are applied
Markers vs cell types
Normal vs cancer

11. Normal Colon Tissue http://www.siumed.edu/~dking2/erg/GI125b.htm
Make a case before

12. Simple Patterns in Cancer Dataset
Gene CA1, Gene KRT20
Dalerba*, Kalisky* and Sahoo* et al. Nat Biotechnol Nov 13;29(12):

13. Search for the Stem Cell GenesX Y
Expression
KRT20
ALCAM
Colon Cell Differentiation
Criteria:
1. KRT20 high => X high
2. Y high => ALCAM high
3. KRT20 high => Y low
4. X low => ALCAM high

15. Search for High Risk Colon Cancer Patients
List of genes fulfilling the pattern X low => ALCAM high
GPX2
CDX2
EPS8L3
GPR35
LAD1
DTX4
CDX1
USH1C
VIL1
PPP1R14D
MUC3B
PLEKHG6
IHH
ACOT11
NHEJ1
Change this
Dalerba P et al. N Engl J Med 2016;374:

16. CDX2 mRNA Expression and Disease-free Survival
Discovery Dataset (JSTO, n=466)
Figure 2. Relationship between CDX2 Expression and Disease-free Survival in the NCBI-GEO Discovery Data Set. Analysis of CDX2 messenger RNA (mRNA) expression in the NCBI-GEO discovery data set revealed the presence of a minority subgroup of CDX2-negative colon cancers that were characterized by high ALCAM mRNA expression levels (Panel A) and that were associated with a lower rate of 5-year disease-free survival than CDX2-positive colon cancers (Panel B). In Panel A, each circle in the scatter plot represents one patient sample. The association between CDX2-negative cancers and a lower rate of disease-free survival remained significant in a multivariate analysis that excluded tumor stage, tumor grade, age, and sex as confounding variables (Panel C).
Dalerba P et al. N Engl J Med 2016;374:

17. CDX2 Protein Expression and Disease-free Survival
Validation Dataset (NCI CDP, n=466)
Change colors
Dalerba P et al. N Engl J Med 2016;374:

18. Dalerba P et al. N Engl J Med 2016;374:211-222.
CDX2 Expression and Benefit from Adjuvant Chemotherapy.
Stage II Colon Cancer (Pooled dataset, n=669)
Figure 5. Relationship between CDX2 Expression and Benefit from Adjuvant Chemotherapy. The relationship between CDX2 expression and benefit from adjuvant chemotherapy was evaluated in a pooled database of 669 patients with stage II disease (Panel A) and 1228 patients with stage III disease (Panel B) from four independent data sets (NCBI-GEO, NCI-CDP, NSABP C-07, and Stanford TMAD). Among all patients with stage II disease in the entire database, treatment with adjuvant chemotherapy was not associated with a higher rate of 5-year disease-free survival. However, treatment with adjuvant chemotherapy was strongly associated with a higher rate of 5-year disease-free survival in the CDX2-negative subgroup, but it was not associated with a higher rate of 5-year disease-free survival in the CDX2-positive subgroup. Among patients with stage III disease, treatment with adjuvant chemotherapy was associated with a higher rate of 5-year disease-free survival in the entire database and in both the CDX2-negative and CDX2-positive subgroups. A test for an interaction between the biomarker and the treatment indicated that in both stage II and stage III disease, the benefit associated with adjuvant chemotherapy was superior among CDX2-negative patients than among CDX2-positive patients.
Dalerba P et al. N Engl J Med 2016;374: