Precision)Medicine)on)Cancer)Treatment:)AJoint)Matrix ...tua87106/ping_7.pdf · ©"2014"IBMCorporaon " Precision)Medicine)on)Cancer)Treatment:)AJoint)Matrix) Factoriza7on)Approach)

© 2014 IBM Corpora/on

Precision Medicine on Cancer Treatment: A Joint Matrix Factoriza7on Approach

Ping Zhang, Filippo Utro, Fei Wang, Jianying Hu IBM T.J. Watson Research Center


Outline

§ Introduc/on § Methodology

§ Experimental Results

§ Future Works

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Moving towards Precision Medicine (PM)

§ PM: the right pa/ent with the right drug at the right dose at the right /me. – In his 2015 State of the Union address, President Obama stated his inten/on to infuse funds into a United States na/onal "precision medicine ini7a7ve” – For pa/ents, safer and more effec/ve treatments – For doctors, reduce wasted /me and resources with fu/le treatments – For pharms, lower cost marke/ng due to targeted pa/ents, faster clinical trials, less focus on animal trials

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Pa/ent similarity and drug similarity analy/cs

§ Pa/ent Similarity analy/cs: Find pa/ents who display similar clinical characteris/c to the pa/ent of interest

§ Resul/ng insights: medical prognosis, risk stra/fica/on, care planning (especially for pa/ents has mul/ple diseases)

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§ Drug Similarity analy/cs: Find drugs which display similar pharmacological characteris/c to the drug of interest

§ Resul/ng insights: drug reposi/oning, side-‐effect predic/on, drug-‐drug interac/on predic/on

How to leverage both pa/ent similarity and drug similarity for personalized medicine?


Outline



§ Future Works

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Mul/ple data sources of drug and pa/ent informa/on

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Chemical Structure Target Proteins Side-‐effect Keywords

Drug

Calculate drug/pa/ent similari/es

Demographic SNP Copy-‐number varia/ons

Pa/ent


A graphical illustra/on of the main idea

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Algorithm Flowchart (Input and Output)

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drug chemical structure similarity network

drug target protein similarity network

drug side effect similarity network

pa/ent demographic similarity

pa/ent SNPs similarity

pa/ent CNV similarity

A unified computa/onal framework for PM treatment recommenda/on

Outputs: 1.  predicted addi/onal drug-‐pa/ent associa/ons 2.  interpretable importance of different informa/on sources 3.  latent drug and pa/ent groups as by-‐products

...... ...... known drug-‐pa/ent associa/ons


Solu/on: A Joint Matrix Factoriza/on Approach

§ We aim to analyze the drug-‐pa/ent network by minimizing the following objec/ve:

§  The reconstruc/on loss of observed drug-‐pa/ent associa/ons:

§  The reconstruc/on loss of drug similari/es:

§  The reconstruc/on loss of pa/ent similari/es:

§  Pubng everything together, we obtained the op/miza/on problem to be resolved:

minU,V,Λ,Θ,ω,πJ, subject to U≥0, V≥0, Λ≥0, ω≥0, ωT1=1, π≥0, πT1=1, PΩ(Θ)= PΩ(R)

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J = J0 + λ1J1 + λ2J2

J0 =||Θ−UΛVT ||F2

J1 = ω k ||Dk −Uk=1

Kd∑ UT ||F2 +δ1 ||ω ||2

2

J2 = π ll=1

Ks∑ || Sl −VVT ||F

2 +δ 2 ||π ||22

Nota/ons and symbols of the methodology

Similar drugs/pa/ent (latent groups) have similar behaviors

Reconstruct integrated drug/pa/ent networks


BCD approach for solving the non-‐convex op/miza/on problem

§ Block Coordinate Descent (BCD) strategy: The BCD approach works by solving the different groups of variables alterna/vely un/l convergence. At each itera/on, it solves the op/miza/on problem with respect to one group of variables with all other groups of variables fixed.

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Solved by Projected Gradient Descent (PGD) method

Computa/onal complexity is O(Rrmn) , where R is the number of BCD itera/ons, and r is the average PGD itera/ons when upda/ng Λ, U, and V.

closed-‐form solu/on


Outline



§ Future Works

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Problem descrip/on

§ Background: Glioblastoma mul/forme (GBM) is the most common and most aggressive malignant primary brain tumor in humans.

§ Raw data: GBM data from The Cancer Genome Atlas (TCGA) website.

§ How to define an “effec/ve” treatment? – Median survival without treatment is 4½ months. Median survival with standard-‐of-‐care radia/on and chemotherapy is 15 months. – We define treatments for pa/ents who live for more than 15 months (i.e., 450 days) are effec/ve.

§ Final data: 118 pa/ents, 41 dis/nct drugs and 261 known effec/ve pa/ent-‐drug associa/ons. – by matching pa/ents who have clinical informa/on (e.g., age, race, gender), treatment informa/on, genomic informa/on (i.e., SNPs), and a posi/ve response to treatment (i.e., live for more than 15 months), we got 118 pa/ents.

§ Task: For a given pa/ent, predict a personalized treatment of GBM. 12


Measurements of pa/ent similari/es and drug similari/es

§ Drug similarity evalua/on – Chemical structure: each drug was represented by an 881-‐dimen/onal PubChem fingerprint. Tanimoto coefficient (TC) of two fingerprints as chemical structure similarity. TC(A,B) = |A ∩ B|/|A ∪ B|.

§ Pa/ent similarity evalua/on – Demographics: based on pa/ent’s risk factors of GBM, derive demographic vectors based on gender (male, female), race (Asian, African American, Caucasian), and age (<41, 41~50, 51~60, 61~70, >70). – SNPs: 9507 mutated genes in the data, each pa/ent was represented by a 9507-‐dimensional binary profile -‐ elements encode for the presence or absence of each gene by 1 or 0 respec/vely. – Use Jaccard similarity to measure both pa/ent demographics and SNPs similari/es.

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Alterna/ve Method: Label Propaga/on (LP) methods

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1* (1 )( )F Yµ µ −= − −I WLabel Propaga/on on pa/ent similarity

F, Y W

Label Propaga/on on drug similarity

F, Y W

Pa/ent similarity

pa/ents propagate their known effec/ve treatments to other pa/ents based on the pa/ent network.

Drug Similarity

drugs propagate their target effec/ve pa/ents to other drugs based on the drug network.

LP method doesn’t take the considera/on of both drug and pa/ent at the same /me.


LOOCV comparison of four treatment recommenda/on methods

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Outline



§ Future Works

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Conclusions and future works

§ To support precision medicine, we apply joint matrix factoriza7on methodology by leveraging pa7ent similarity and drug similarity analy/cs.

§ For further inves/ga/on – Include more genomic data (e.g. CNV, RNA in TCGA) and drug data (e.g., target informa/on) – Explore more sophis/cated drug and pa/ent similarity measures – Consider drug combina/ons

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Thank you! | Ques/ons?

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Ping Zhang: [email protected]

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Precision)Medicine)on)Cancer)Treatment:)AJoint)Matrix ...tua87106/ping_7.pdf · ©"2014"IBMCorporaon " Precision)Medicine)on)Cancer)Treatment:)AJoint)Matrix) Factoriza7on)Approach)