KTH

Current status

Jens Lagergren

Royal Institute of Technology

THE CORE VIEW

★ Differentiation

★ Space

★ Time

★ Phenotype etcetera

improve cancer treatment depend critically on progress with these computational and statisticaltasks. In the following, we briefly summarize the current state of the art in the field and highlightmajor challenges that lie ahead, including (i) reconstruction of evolutionary history based ondifferent types of genomic alterations, (ii) functional interpretation of mutations, and (iii) pre-dictive modeling of the evolutionary dynamics of cancer. We argue that an interdisciplinaryapproach, including statistical and computational data analysis as well as evolutionary modelingof cancer, will be essential for translating technological advances into clinical benefits.

Cancer As an Evolutionary ProcessCancer is a genetic disease that arises when normal cellular functions are disrupted by muta-tions arising in DNA. These changes occur at the level of single cells, which are then propa-gated into subpopulations as cells divide and pass mutations through cell lineages (Fig 1).Differences in growth rates between clones produce a complex tumor microenvironment con-sisting of many different interacting and evolving cells, including normal stromal and immunecells. These differences can manifest on various spatial, organizational, and functional levels.Furthermore, although mutations are thought to primarily arise during the development ofcancerous tissue, there is a growing body of evidence, including theoretical [6], histological [7],and genetic [8,9] approaches, supporting the idea that somatic mutations occur throughout theentire lifetime of the host organism. Such mutations can be detected at low levels in circulatingcells [8], as well as directly from tissue. In eyelid epidermal cells, for example, it has recentlybeen shown that perfectly functional cells harbor a plethora of mutations that are also found inknown cancer genes [9].

The resultant intratumor genetic diversity is a huge problem for correctly diagnosing andsuccessfully treating tumors [10,11]. For example, the biopsy obtained from a heterogeneoustumor may not be representative of the entire tumor because of insufficient resolution or spa-tial heterogeneity. The treatment decision is then based on an incomplete or biased sample andtherefore is at risk of failing to target existing but undetected tumor subclones. This problem isparticularly pronounced for targeted drug therapy, in which small tumor subpopulations resis-tant to targeted treatment are likely to preexist in the tumor prior to therapy [12].

Epistatic interactions among mutations are abundant. For example, many different cancermutations can result in deregulation of the same signaling pathways, and distinct mutational

Fig 1. Schematic representation of neoplastic transformation. (A) The left-hand side represents regular homeostatic tissue. The middle regionrepresents a mutation undergoing a selective sweep across a population of phenotypically normal tissue. The right-hand side indicates a period of clonalgrowth, during which different mutations combine across subclones. (B) A phylogenetic tree on the right mirrors the subclonal structure in (A); the circlesrepresent mutations, and their sizes indicate the size of the corresponding subpopulation. The green subclone contains a branching process of mutationaccumulation, indicating the continual stochastic processes that underlie the approximation that is a clonal evolution tree.

doi:10.1371/journal.pcbi.1004717.g001

PLOS Computational Biology | DOI:10.1371/journal.pcbi.1004717 February 4, 2016 2 / 12

PERSPECTIVE

Computational Cancer Biology: AnEvolutionary PerspectiveNiko Beerenwinkel1,2*, Chris D. Greenman3*, Jens Lagergren4*

1 Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland, 2 SIB SwissInstitute of Bioinformatics, Basel, Switzerland, 3 School of Computing Sciences, University of East Anglia,Norwich, United Kingdom, 4 Science for Life Laboratory, School of Computer Science and Communication,Swedish E-Science Research Center, KTH Royal Institute of Technology, Solna, Sweden

* niko.beerenwinkel@bsse.ethz.ch (NB); c.greenman@uea.ac.uk (CDG); jensl@csc.kth.se (JL)

Cancer is a leading cause of death worldwide and represents one of the biggest biomedicalresearch challenges of our time. Tumor progression is caused by somatic evolution of cell popu-lations. Cancer cells expand because of the accumulation of selectively advantageous mutations,and expanding clones give rise to new cell subpopulations with increasingly higher somatic fit-ness (Fig 1). In the 1970s, Nowell and others established this somatic evolutionary view of can-cer [1]. Today, computational biologists have the opportunity to take advantage of large-scalemolecular profiling data in order to carve out the principles of tumor evolution and to elucidatehow it manifests across cancer types. Analogous to other evolutionary studies, mathematicalmodeling will be key to the success of understanding the somatic evolution of cancer [2].

In general, cancer research involves a range of clinical, epidemiological, and molecularapproaches, as well as mathematical and computational modeling. An early and very successfulexample of mathematical modeling was the work of Nordling [3] and of Armitage and Doll[4]. In the 1950s, long before cancer genome data was available, they analyzed cancer incidencedata and postulated, based on the observed age-incidence curves, that cancer is a multistep pro-cess. In search of these rate-limiting events, cancer progression was then linked to the accumu-lation of genomic alterations. Since then, the evolutionary perspective on cancer has provenuseful in many instances, and the mathematical theory of cancer evolution has been developedmuch further. However, little clinical benefit could be gained from this approach so far. Muchof evolutionary modeling in general, and of cancer in particular, has remained conceptual orqualitative, either because of strong simplifications in the interest of mathematical tractabilityor lack of informative data.

Next-generation sequencing (NGS) technologies and their various applications havechanged this situation fundamentally [5]. Today, cancer cells can be analyzed in great detail atthe molecular level, and tumor cell populations can be sampled extensively. Driven by thistechnological revolution, large numbers of high-dimensional molecular profiles of tumors, andeven of individual cancer cells, are collected by cancer genome consortia, as well as by manyindividual labs. Large catalogs of cancer genomes, epigenomes, transcriptomes, proteomes, andother molecular profiles are generated to assess variation among tumors from different patients(intertumor heterogeneity) as well as among individual cells of single tumors (intratumor het-erogeneity). These data hold the promise not only of new cancer biology discoveries but also ofprogress in cancer diagnostics and treatment.

Analyzing these complex data and interpreting them in the context of ongoing somatic evo-lution, disease progression, and treatment response is a major challenge, and the prospects to

PLOS Computational Biology | DOI:10.1371/journal.pcbi.1004717 February 4, 2016 1 / 12

OPEN ACCESS

Citation: Beerenwinkel N, Greenman CD, LagergrenJ (2016) Computational Cancer Biology: AnEvolutionary Perspective. PLoS Comput Biol 12(2):e1004717. doi:10.1371/journal.pcbi.1004717

Editor: Ruth Nussinov, National Cancer Institute,United States of America and Tel Aviv University,Israel, UNITED STATES

Published: February 4, 2016

Copyright: © 2016 Beerenwinkel et al. This is anopen access article distributed under the terms of theCreative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in anymedium, provided the original author and source arecredited.

Funding: NB was partially supported by ERCSynergy Grant 609883 (http://erc.europa.eu/),SystemsX.ch RTD Grant 2013/150 (http://www.systemsx.ch/), and Swiss Cancer League Grant KLS-2892-02-2012 (www.krebsliga.ch). JL was supportedby the Swedish Research Council Grant 2013-4993(www.vr.se). The funders had no role in study design,data collection and analysis, decision to publish, orpreparation of the manuscript.

Competing Interests: The authors have declaredthat no competing interests exist.

APPLICANTS

★ Jens Lagergren, KTH

★ Lukas Käll, KTH

★ Johan Hartman, KS

★ Jonas Frisén, KI

RECRUITMENT2/7/2016 Academic Records

http://registration.boun.edu.tr/scripts/stuinfar.asp 1/1

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Photo Name: HAZAL KOPTAGELStudent#: 2014700096 GPA: 3,94  Semester: 4   English Test / Result: ELTS / P  

Faculty: INST.FOR GRADUATE STUDIES INSCIENCE&ENGINEERING  

Department: COMPUTER ENGINEERING  Status /

Term Status: MASTER / NORMAL  

High School: SİVAS FEN LİSESİ  E­Mail: koptagelhazal@gmail.com  

Advisor(s): ALİ TAYLAN CEMGİL(taylan.cemgil@boun.edu.tr)  

The student is registered for the semester 2015/2016­1.

Term Information 

Semester :  2015/2016­1 GPA :  3,94 Overall Attempted : 25+0

Overall Completed : 25+0 Overall Points :  98,5

Status :  SPA :  4 Sem. Attempted : 6 +0 Sem. Completed : 6 +0 Sem. Points :  24CMPE540 .01 PRINCIPLES OF ARTIFICIAL INTELLIGENCE AA   3CMPE585 .01 SP.TP.WEARABLE COMPUTING AA   3CMPE690 .07 M.S.THESIS TP   0

Semester :  2014/2015­2 GPA :  3,92 Overall Attempted : 19+0

Overall Completed : 19+0 Overall Points :  74,5

Status :  SPA :  3,83 Sem. Attempted : 9 +0 Sem. Completed : 9 +0 Sem. Points :  34,5CMPE545 .01 ARTIFICIAL NEURAL NETWORKS BA   3CMPE548 .01 MONTE CARLO METHODS AA   3CMPE58P .01 SP.TOP.IN CMPE:MACHINE LISTENING AA   3

Semester :  2014/2015­1 GPA :  4 Overall Attempted : 10+0

Overall Completed : 10+0 Overall Points :  40

Status :  SPA :  4 Sem. Attempted : 10 +0 Sem. Completed : 10 +0 Sem. Points :  40CMPE544 .01 PATTERN RECOGNITION AA   3CMPE547 .01 BAYESIAN STATISTICS&MACHINE LEARNING AA   3CMPE579 .01 GRADUATE SEMINAR P   0CMPE599 .05 GUIDED RESEARCH IN MS­I AA   4

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Olivier Bilenne

Gustavo Stolf JeukenDoktorand i beräkningsbiologi och maskininlärningRef nr: B-2017-0087-141 Datum för ansökan: 2017-05-13 23:39:35

Födelsedatum 1986-05-19Adress Marconigatan 11, Läg 065

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Frågor1. Var såg du annonsen för första gången?

Lediga jobb på KTH:s webbsida

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Gustavo Jeuken

PhDs

Postdocs

Sept.

Ayan Chaudhury

Jan.

Jonas Maaskola

12/07/2017 KTH | Jonas Maaskola

https://www.kth.se/profile/maaskola 1/2

KTH ProfilerProfiles in English

KTH / Profiler / Jonas Maaskola

Jonas Maaskola

RESEARCHER

Kungliga Tekniska Högskolan https://www.kth.se/profile/maaskola

Detaljer

Arbetar vidDIVISION OF GENE TECHNOLOGYAdressTomtebodavägen 23A, SolnaE-postjonas.maaskola@scilifelab.seArbetar på Science for Life Laboratory

Publikationer

Publikationslista

Seong-Hwan Jun

Oct.

HAZAL KOPTAGEL

★ Cell lineage trees

✴ Proper model

✴ Use (early) SNVs for detecting allelic drop-out

2/7/2016 Academic Records

http://registration.boun.edu.tr/scripts/stuinfar.asp 1/1

Academic RecordsClose   Print 

To jump to commitee records follow this link ­­> Commitee Rec.

Photo Name: HAZAL KOPTAGELStudent#: 2014700096 GPA: 3,94  Semester: 4   English Test / Result: ELTS / P  

Faculty: INST.FOR GRADUATE STUDIES INSCIENCE&ENGINEERING  

Department: COMPUTER ENGINEERING  Status /

Term Status: MASTER / NORMAL  

High School: SİVAS FEN LİSESİ  E­Mail: koptagelhazal@gmail.com  

Advisor(s): ALİ TAYLAN CEMGİL(taylan.cemgil@boun.edu.tr)  

The student is registered for the semester 2015/2016­1.

Term Information 

Semester :  2015/2016­1 GPA :  3,94 Overall Attempted : 25+0

Overall Completed : 25+0 Overall Points :  98,5

Status :  SPA :  4 Sem. Attempted : 6 +0 Sem. Completed : 6 +0 Sem. Points :  24CMPE540 .01 PRINCIPLES OF ARTIFICIAL INTELLIGENCE AA   3CMPE585 .01 SP.TP.WEARABLE COMPUTING AA   3CMPE690 .07 M.S.THESIS TP   0

Semester :  2014/2015­2 GPA :  3,92 Overall Attempted : 19+0

Overall Completed : 19+0 Overall Points :  74,5

Status :  SPA :  3,83 Sem. Attempted : 9 +0 Sem. Completed : 9 +0 Sem. Points :  34,5CMPE545 .01 ARTIFICIAL NEURAL NETWORKS BA   3CMPE548 .01 MONTE CARLO METHODS AA   3CMPE58P .01 SP.TOP.IN CMPE:MACHINE LISTENING AA   3

Semester :  2014/2015­1 GPA :  4 Overall Attempted : 10+0

Overall Completed : 10+0 Overall Points :  40

Status :  SPA :  4 Sem. Attempted : 10 +0 Sem. Completed : 10 +0 Sem. Points :  40CMPE544 .01 PATTERN RECOGNITION AA   3CMPE547 .01 BAYESIAN STATISTICS&MACHINE LEARNING AA   3CMPE579 .01 GRADUATE SEMINAR P   0CMPE599 .05 GUIDED RESEARCH IN MS­I AA   4

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JONAS MAASKOLA

★ Spatial Transcriptomics not yet single-cell resolution

✴ Deconvolute and differentiate between cell types

µg t s

σs

xg t s

xg s

θt sφg t

γg t

ρg t

rt

pt

c

d

e

f

c ′

d ′

e ′

f ′

a

b

Gene g

Cell type t

Spot s

Figure 2: Graphical model representation of the Poisson factor analysis model. Circled nodes are randomvariables, those without circles are parameters. Doubly-circled nodes are deterministic random variables. Theshaded node is observed.

C Probabilistic model for Poisson factor analysis

We propose the following Poisson factor analysis model here:

xg s =T!

t=1xg t s (24)

xg t s ∼ Pois"µg t s

#(25)

µg t s =φg tθt sσs (26)

φg t ∼ Gamma$γg t ,

1−ρg t

ρg t

%(27)

θt s ∼ Gamma$rt ,

1−pt

pt

%(28)

σs ∼ Gamma(a,b) (29)

γg t ∼ Gamma(c,d) (30)

ρg t ∼ Beta"e, f

#(31)

rt ∼ Gamma"c ′,d ′# (32)

pt ∼ Beta"e ′, f ′# (33)

The corresponding graphical model is depicted in fig. 2. Default values of the hyper parameters are givenin table 3.

10 draft-65-g9b78970-dirty

OLIVIER BILENNE

2017-07-12, 14)19Viever

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★ Pathway-based path model for Cancer Progression

★ Extend - tree, single-cell, frequencies

EU ITN - CONTRAConsortium

MemberCountry Dept./

Division /Scientist-in-Charge #PhDs

Beneficiaries

Royal Institute of Technology Sweden CST, CSC Jens Lagergren 2

University of Cambridge United Kingdom Cancer Research UK Cambridge institute

Florian Markowetz 2

University of Warsaw Poland The Faculty of Mathematics, In format ics and Mechanics, Institute of Informatics

Ewa Szczurek 2

ETH Zurich Switzerland Dept. of Biosystems Science and Engineering

Niko Beerenwinkel 2

University of Vigo Spain Dept. Biochemistry, Genetics and Immunology

David Posada 2

King’s College London United Kingdom Cancer Studies Francesca Ciccarelli 2

Institute for Research in Biomedicine

Spain Biomedical Genomics Group, Structural and Bioinformatics and Cancer Programs

Nuria Lopez-Bigas 2

The Institute of Cancer Research United Kingdom Molecular Pathology, Centre for Evolution and Cancer

Yinyin Yuan 1

Partner Organisations

Ardigen SA Poland Stefan Loska

Merck KGaA Germany Eike Staub

SBG UK Dr. Julia F. Li

Spatial Transcriptomics AB Sweden Biotech Florian Baumgartner

Galician Research and Development Centre in Advanced

Telecommunications

Spain eHealth Helena Fernández

British Columbia Cancer Agency Branch

Canada D e p a r t m e n t o f M o l e c u l a r Oncology

Dr. Sohrab Shah

Torus Software Solutions SL Spain HPC and Big Data Guillermo López

TRAINING EVENTSMain Training Events & Conferences ECTS

(if any)Lead

InstitutionAction Month (estimated)

1 1st workshop: “kick-off and background” 2 KTH 7

2 1st summer school: “Handling single cell data” 5 UNIWARSAW 10

3 2nd summer school: “Cancer biology and treatment” 5 UCAM 21

4 3rd summer school: “Promoting your career” 5 Gradiant 33

5 2nd workshop: “Software development” 5 Ardigen 13

6 3rd workshop: “Models of evolution” 3 IRP 18

7 4th workshop: “Spatial models of tumours” 3 ICR 29

8 International conference 2 ETH 36

THANKS