Evolutionary Dynamics in Cancer

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Image of bifurcating tree next to cancer patient
November 4 - November 6, 2019
8:00AM - 5:00PM
Location
MBI Auditorium, Jennings Hall 355

Date Range
Add to Calendar 2019-11-04 08:00:00 2019-11-06 17:00:00 Evolutionary Dynamics in Cancer

Cancer is a heterogeneous disease, with variation across individuals with regard to aspects such as rate of progression, response to treatment, and survival time. Although the polyclonal nature of many cancers and the role of evolutionary processes in tumor progression was first established in the late 1970’s, increased attention has recently been given to these processes as their potential role in clinical outcomes has become more widely recognized.  This has in part been motivated by the availability of data at a finer scale than ever before, though technological developments such as single-cell sequencing. Given the availability of such data, there is now a need for models and methods that can produce realistic pictures of the evolutionary history of a tumor. In this workshop, we explore the use of mathematical and statistical models that include an evolutionary component to study cancer at various scales, including evolution that occurs within individual tumors, across transmissible cancers, and within populations, possibly in conjunction with environmental and genetic factors.

The deadline to be considered for funding has passed. However, all MBI events are free and open to the public so if you wish to attend please register using the link below.

MBI Auditorium, Jennings Hall 355 Mathematical Biosciences Institute mbi-webmaster@osu.edu America/New_York public
Description

Cancer is a heterogeneous disease, with variation across individuals with regard to aspects such as rate of progression, response to treatment, and survival time. Although the polyclonal nature of many cancers and the role of evolutionary processes in tumor progression was first established in the late 1970’s, increased attention has recently been given to these processes as their potential role in clinical outcomes has become more widely recognized.  This has in part been motivated by the availability of data at a finer scale than ever before, though technological developments such as single-cell sequencing. Given the availability of such data, there is now a need for models and methods that can produce realistic pictures of the evolutionary history of a tumor. In this workshop, we explore the use of mathematical and statistical models that include an evolutionary component to study cancer at various scales, including evolution that occurs within individual tumors, across transmissible cancers, and within populations, possibly in conjunction with environmental and genetic factors.

The deadline to be considered for funding has passed. However, all MBI events are free and open to the public so if you wish to attend please register using the link below.

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This MBI workshop is being co-sponsored by the National Institute of Statistical Sciences

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Organizers

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Julia Chifman
Department of Mathematics & Statistics
American University
chifman@american.edu

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Kevin Coombes
Department of Biomedical Informatics
The Ohio State University
coombes.3@osu.edu

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Laura Kubatko
Mathematical Biosciences Institute
The Ohio State University
lkubatko@stat.osu.edu

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Diego Mallo
Biodesign Institute
Arizona State University
Diego.Malloadan@asu.edu

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Marc Suchard
Departments of Biomathematics, Biostatistics, and Human Genetics
UCLA
msuchard@ucla.edu

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Schedule

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Time Session
09:00 AM
09:40 AM
Harsh Jain
09:40 AM
10:20 AM
Luay Nakhleh
10:20 AM
10:50 AM
Workshop morning discussion with cookies and coffee
10:50 AM
11:30 AM
Kimberly Bussey - Reversion to Single-Cell Biology in Cancer
11:30 AM
12:00 PM
Panel discussion led by Marc Suchard
12:00 PM
02:00 PM
Lunch (on your own)
02:00 PM
02:40 PM
Amir Asiaee
02:40 PM
03:20 PM
Katharina Jahn - Dissecting Clonal Diversity Through High-Throughput Single-Cell Genomics
03:20 PM
03:50 PM
Coffee break
03:50 PM
04:30 PM
David Posada - Rapid Evolution and Biogeographic Spread in a Colorectal Cancer
04:30 PM
05:00 PM
Panel discussion led by Diego Mallo
06:00 PM
06:30 PM
Cash Bar at Crowne Plaza
06:30 PM
08:00 PM
Workshop Discussion Session and Banquet Dinner at Crowne Plaza (Pinnacle Room)
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Time Session
09:00 AM
09:40 AM
David Basanta
09:40 AM
10:20 AM
Ben Raphael
10:20 AM
10:50 AM
Workshop morning discussion with cookies and coffee
10:50 AM
11:30 AM
Jasmine Foo
11:30 AM
12:00 PM
Panel discussion led by Julia Chifman
12:00 PM
02:00 PM
Lunch (on your own)
02:00 PM
02:40 PM
Michael Metzger - Evolution of Contagious Cancers in Clams
02:40 PM
03:20 PM
Carlo Maley
03:20 PM
03:50 PM
Coffee break
03:50 PM
04:30 PM
Tianjian Zhou - Inferring Latent Tumor Cell Subpopulations with Latent Feature Allocation Models
04:30 PM
05:00 PM
Panel discussion led by Laura Kubatko
05:00 PM
07:00 PM
Poster session
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Time Session
09:00 AM
09:40 AM
Dan Stover
09:40 AM
10:20 AM
Nancy Zhang
10:20 AM
10:50 AM
Workshop morning discussion with cookies and coffee
10:50 AM
11:30 AM
Chi Wang
11:30 AM
12:00 PM
Panel discussion led by Kevin Coombes
12:00 PM
02:00 PM
Lunch (on your own)
02:00 PM
02:40 PM
Russell Schwartz - Assessing the Role of Evolutionary Diversification Versus Selection on Cancer Progression Risk
02:40 PM
03:20 PM
Mary Kuhner - Positive Selection Does not Always Tend Towards Cancer
03:20 PM
03:50 PM
Panel discussion and workshop wrap-up led by Julia Chifman
03:50 PM
04:30 PM
Wrap-up discussions
04:30 PM
05:00 PM
Departures
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Speakers and Talks

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Name Affiliation Email
Amir Asiaee T. Mathematical Biosciences Institute, The Ohio State University asiaeetaheri.1@mbi.osu.edu
David Basanta Moffitt Cancer Center david.basanta@moffitt.org
Kimberly Bussey Department of Biomedical Informatics, Arizona State University Kimberly.Bussey@asu.edu
Jasmine Foo School of Mathematics, University of Minnesota Twin Cities jyfoo@math.umn.edu
Katharina Jahn Department of Biosystems Science and Engineering, ETH Zürich katharina.jahn@bsse.ethz.ch
Harsh Jain Department of Mathematics, Florida State University jain@math.fsu.edu
Mary Kuhner Genome Sciences Department, University of Washington mkkuhner@u.washington.edu
Carlo Maley School of Life Sciences, Arizona State University maley@asu.edu
Michael Metzger Metzger Lab, Pacific Northwest Research Institute metzgerm@pnri.org
Luay Nakhleh Department of Computer Science, Rice University nakhleh@rice.edu
Ben Raphael Computer Science Department, Princeton University braphael@cs.princeton.edu
Russell Schwartz Department ofBiological Sciences, Carnegie Mellon University russells@andrew.cmu.edu
Daniel Stover Department of Internal Medicine, The Ohio State University daniel.stover@osumc.edu
Chi Wang College of Public Health, University of Kentucky chi.wang@uky.edu
Tianjian Zhou University of Chicago tjzhou95@gmail.com
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Kimberly Bussey:
Reversion to Single-Cell Biology in Cancer

What is cancer? It is a fundamental question that still lacks an adequate answer. Cancers or cancer-like phenomena are found across the tree of life in multicellular organisms. The hallmarks of cancer describe the functions a cell or group of cells must express to become a cancerous tumor, including uncontrolled growth, uninhibited mobility, and resistance to cell death. The current paradigm ascribes the acquisition of such behavior to the gradual accumulation of genomic changes. This gene-centric view has been useful up to a point, but it suffers from the problem that most oncogenic changes are neither necessary, sufficient, nor context-independent. Furthermore, such behaviors can be suppressed in a physiologically normal environment.

We propose thinking about cancer as an atavism, in this case the re-expression of single-cell biology in a multicellular context. Cancer is the result of re-deploying single-cell biology in the context of cells that have evolved to be part of a multicellular organism. Under this context, we hypothesize that we can detect evidence of single-cell stress-responses, such as stress-induced mutation (SIM), in cancer genomes. Our work shows that there is evidence of SIM in cancer genomes which has clinical ramifications for both patient survival and treatment approaches.


Katharine Jahn:
Dissecting Clonal Diversity Through High-Throughput Single-Cell Genomics

Clonal heterogeneity allows tumours to adapt and survive under the selective pressure of treatment, leading to clinical resistance and relapse. An accurate dissection of the clonal architecture and the underlying mutational history is therefore of clinical importance and may help to design more effective treatment plans. Present studies on clonal diversity are primarily based on sequencing data obtained from bulk tumour tissue which systematically underestimate a tumour's mutational heterogeneity. However through recent technological advances, high-throughput single-cell genomics has become a feasible alternative that allows to study clonal diversity at an unprecedented resolution.

In this talk, I will review our work on single-cell phylogenetics and the insights we obtained from sequencing longitudinal bone marrow samples of 77 AML patients. Using a microfluidics-based single-cell DNA sequencing platform, we genotyped 556,951 cells for a panel of 19 genes known to be recurrently mutated in AML. We observed patterns of mutual exclusivity, mutational co-occurrence, as well as instances of convergent evolution. Moreover, the longitudinal nature of the data revealed patterns of clonal dynamics in response to targeted AML therapy which correlated with clinical resistance and relapse.


Mary Kuhner:
Positive Selection Does not Always Tend Towards Cancer

Barrett's Esophagus is a neoplastic condition that usually remains stable, but progresses to esophageal adenocarcinoma (EA) in approximately 5% of cases.  We used WGS to survey 4 biopsies from 40 EA-outcome and 40 non-EA-outcome Barrett's patients.   These data showed two strongly contrasting patterns.  A constellation of genomic-damage indicators including mutations in TP53, copy-number changes, genome doubling, and chromosomal rearrangements were strongly associated with progression to cancer.  However, a separate set of features including very high point mutation and indel loads, mutation signature 17, copy-number variation at fragile sites, and positive selection on multiple loci were observed across patients regardless of outcome and showed little to no association with progression.  While features in this second group are advantageous in the hostile environment of the Barrett's segment, they are not sufficient for development of EA.  These findings show the critical importance of contrasting cancer and non-cancer outcomes, as otherwise selectively favored traits in pre-cancer tissues will be mistaken for drivers of progression.


Michael Metzger:
Evolution of Contagious Cancers in Clams

Cancer is normally an evolutionary dead-end—neoplastic cells that arise and evolve within an organism either regress or they kill their host, and the death of the host marks the death of the cancer lineage. However, in some cases, neoplastic cells develop the ability to spread from individual to individual, turning from conventional cancers into clonal contagious cancer lineages. The natural transmission of cancer cells has been observed in two mammals (Tasmanian devils and dogs), and we have found that a leukemia-like disease in soft-shell clams (Mya arenaria) is due to the horizontal spread of a clonal cancer lineage. We also analyzed mussels (Mytilus trossulus), cockles (Cerastoderma edule), and carpet shell clams (Polititapes aureus) and found that the neoplasias in all three of these species are due to independent transmissible cancer lineages. We are currently assembling a reference genome using PacBio sequencing combined with HiC data. Using draft reference genomes, we are investigating genomic changes in the evolution of this unique cancer lineage, including SNPs, structural variation, and copy number variation. In particular, we found a retrotransposon, Steamer, which is expressed and amplified in genomic DNA of the contagious cancer lineage, expanding from 2-10 copies per haploid genome in normal animals to >100 in neoplastic cells. Genomic analysis of cancer samples from isolated clam populations in Maine and Prince Edward Island confirm initial qPCR analysis, and it shows that at least 130 sites are found in cancer cells from both populations. These common sites likely integrated early in the evolution of the cancer lineage, and they have been conserved either as passenger or driver mutations. We also found many insertions that are unique to only one subgroup (494 and 144, unique insertions in Maine and PEI, respectively), showing either continued amplification or deletion after divergence of the cancer in the two populations. These new integration events and other genomic changes have likely played a role in oncogenesis and continued evolution of the cancer with its hosts.


David Posada:
Rapid Evolution and Biogeographic Spread in a Colorectal Cancer

How and when tumoral clones start spreading to surrounding and distant tissues is currently unclear. Here, we applied a sophisticated evolutionary framework to describe the evolutionary history of a colorectal cancer in time and space. In particular, we have leveraged state-of-the-art approaches from statistical phylogenetics, phylodynamics, and biogeography that allowed us to date the origin of the tumor, to quantify its demography, and to identify the different colonization events that took place. Thus, our analyses strongly support an early monoclonal metastatic colonization, followed by a rapid population expansion at both primary and secondary sites. Moreover, we infer a hematogenous metastatic spread seemingly under positive selection, plus the return of some tumoral cells from the liver back to the colon lymph nodes. This study provides unprecedented detail a picture of the tempo and mode of the tumoral clonal dynamics within a single patient. Importantly, it exemplifies how sound methods from organismal evolutionary biology can be ported to the within-individual level in order to understand complex tumoral dynamics over time and space.


Russell Schwartz:
Assessing the Role of Evolutionary Diversification Versus Selection on Cancer Progression Risk 

It has long been recognized that cancer is a process of aberrant evolution, in which clonal diversification and selection result in progression from an initially healthy cell through precancerous and successively more aggressive cancerous states.  Genomic studies have increasingly elucidated the finer details of these evolutionary processes and how they act in and vary between cancers, but there are still large gaps in our knowledge and our ability to apply it to translational directions.  One key gap is our still-limited ability to predict risk of future cancer progression, e.g., which precancerous lesions are likely to progress to cancer, or which early cancers are likely to threaten the patient, to respond or recur following treatment, or to metastasize.  Here, we explore the predictive power for progression outcomes of variations patient-to-patient in evolutionary diversification – i.e., risk arising from patient-specific variation in mechanisms of somatic hypermutability – as compared to the predictive power of selection – i.e., risk arising from patient-specific differences in the spectra of driver gene mutations.  We estimate these factors using tumor genome sequence data, combining driver mutation calls with quantitative features derived from tumor phylogeny models and overall mutation burdens to assess in a machine learning analysis how these two classes of evolutionary factors collectively predict future cancer progression.  Through application to a set of patient cohorts from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC), we show that measures of diversification and selection each contribute complementary and partially orthogonal predictive power relative to one another and to conventional clinical predictors of outcome.  The work suggests the importance of better characterizing mechanisms of somatic variation in cancers and their role in cancer risk across diverse progression outcomes.


Tianjian Zhou:
Inferring Latent Tumor Cell Subpopulations with Latent Feature Allocation Models

Tumor cell population consists of genetically heterogeneous subpopulations (subclones), with each subpopulation being characterized by overlapping sets of single nucleotide variants (SNVs). Bulk sequencing data using high-throughput sequencing technology provide short reads mapped to many nucleotide loci as a mixture of signals from different subclones. Based on such data, we infer tumor subclones using latent feature allocation models. Specifically, we estimate the number of subclones, their genotypes, cellular proportions and the phylogenetic tree spanned by the inferred subclones. Prior probabilities are assigned to these latent quantities, and posterior inference is implemented through Markov chain Monte Carlo simulations. A key innovation in our method, TreeClone, is to model short reads mapped to pairs of proximal SNVs, which we refer to as mutation pairs. The performance of our method is assessed using simulated and real datasets with single and multiple tumor samples.

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Mohammadamin Edrisi:
A Combinatorial Approach for Single-cell Variant Detection via Phylogenetic Inference


Xian Fan:
Methods for Copy Number Aberration Detection from Single-cell DNA Sequencing Data


Yuan Gao:
Inferring Mutation Order During Cancer Progression


Sophia Jang:
A Mathematical Model of Tumor-Immune Interactions with an Immune Checkpoint Inhibitor


Javad Noorbakhsh:
Pan-Cancer Classifications of Tumor Histological Images Using Deep Learning


Etienne Nzabarushimana:
Genetic Variation and Evolutionary Dynamics in Microbiota Transplantation Treatment


Jeungeun Park:
Traveling Waves in a Chemotaxis Model

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