1. GraDe-SVM: Graph-Diffused Classification for the Analysis of Somatic Mutations in Cancer Morteza H.Chalabi, Fabio Vandin
Hello everyone
My name is Morteza Chalabi
from the university of southern Denmark
I am going to talk about cancer types classification using a new form of support vector machines which we named it as GraDe-SVM

2. Challenge Motivation Problem
Given a feature (=gene mutations) vector, how to predict cancer type?
Make these data applicable to clinical and therapeutic purposes
What would be the cancer type, if we knew mutations in cancer genome?
Promising for circulating tumor cells/DNAs in liquid biopsies
Recent advances in next generation sequencing data have allowed the collection of somatic mutations
from a large number of patients
from several cancer types
<Motivation>
-recent advances
-TCGA & ICGC
<challenge>
-make data applicable clinical and therapeutic
-the natural question is then if mutations in cancer genome were known, what would be the cancer type?
_answering this question would be useful for CTCs in liquid biopsies
_I suggest reading this MIT review article ...

3. Related Work
Similar projects mainly have used gene expression and/or for a limited number of cancer types
Asgharzadeh S. et al, J. Natl. Cancer Inst., 2006; Herschkowitz J. et al, Genome Biol., 2007; Hwang T. et al, Proc 8th IEEE Int. Conf. Data Mining, 2008; Lee E.S. et al, Cancer Res., 2008; Paik S., J. Clin. Oncol., 2006; Pawitan Y. et al, Breast Cancer Res., 2005, etc.
Lavi, O. et al., 2012, Journal of Computational Biology
Network-Induced Classification Kernels for Gene Expression Profile Analysis
-say and many more
-Lavi et al use heuristic ways to incorporate network into SVM
-this work is worth mentioning
-say why net interactions improve classification, concept of network, SVM

4. Our Contribution
New method integrating network local topology into classification (SVM)
local network topology: captured by diffusion process
We tested GraDe-SVM on
somatic mutation sequence data
copy number variation (CNV) & single nucleotide variation (SNV)
from 3450 samples
11 cancer types from The Cancer Genome Atlas

5. GraDe-SVM Taking network topology into account
idea: genes with similar function should have similar weights in SVM
similar function (= interactions on a network) is captured by diffusion process (used in HotNet21) not only by direct interactions
strategy I
transforming input feature/attribute vectors using
diffusion process (random walk)
strategy II
regularizing SVM optimization problem using
diffusion process capturing interactions (random walk matrix)
NICK2: a similar approach capturing immediate interactions (adjacency matrix)
_ strategy I: say feature vector is mapped on the network and gets diffused over it
_the blinking red rectangle is the regularization part
A typical feature vector (FV): 𝑔 0 𝑔 1 𝑔 2 𝑔 3 … 𝑔 𝑛−1 𝑔 𝑛 …
FV: 𝑔 𝑔 𝑔 𝑔 3 … 𝑔 𝑛 …1
Diffusion Process
Map on Network
1: Leiserson, M. D. M., Vandin, F., et al, 2015, Nature Genetics
2: Lavi, O., et al, 2012, Journal of Computational Biology

6. Future Work & Conclusion
Results
Future Work & Conclusion
Future:
There are many directions esp. considering non-coding region variations: Intron, IGR
how to find a small set of genes returning acceptable performance
Conclusion
we introduced GraDe-SVM to capture local network topology
tested on real data, we achieved higher accuracy
GraDe-SVM was evaluated and tested on
a cohort of 3424 cancer samples
from 11 cancer types
from The Cancer Genome Atlas (TCGA)
using both single nucleotide variants (SNVs) and copy number variants (CNVs)
9786 genes in the network (HINT+HI20121)
10-fold cross validation
Results
Improved classification of cancer types vs. no network or network but no diffusion process
finds a number of known driver genes & genes with mutations distinguishing cancer types
<Results>
_talk about accuracy reduction using 269 and genesets
<Future>
_currently may not be possible to measure all mutations in gnome, it’s important to find a small set of genes returning good performance
1: Leiserson, M. D. M., Vandin, F., et al, 2015, Nature Genetics