Evolutionary Dynamics in Cancer

Amir Asiaee:
Disjunctive Bayesian Network Infers Cancer Progression Network
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Cancer is an evolutionary process that can be modeled as a sequence of fixation of genetic alterations throughout the tumor cell population. Each new driver alteration confers a selective growth advantage to the cell and sweeps through the population, which results in clonal expansion. But the order in which accumulating alterations fixate in tumors is not arbitrary and is restricted by the type of advantage that is required to lay the ground for later ones. Perhaps the most famous Bayesian Network model of cancer progression is Conjunctive Bayesian Network (CBN) where the assumption is that all parent alterations must be present in order for a child aberration to occur. The assumption of CBNs is restrictive because a single advantageous hit is usually enough for clonal expansion. We proposed the Disjunctive Bayesian Network (DBN) in which each alteration can occur if at least one of its parents has happened before. DBN generalizes CBN and therefore has a larger search space but we have designed a scalable algorithm to infer DBN from cross-sectional data. I present our specific findings for the order of mutations in melanoma.

David Basanta:
The Bone Ecosystem and its Influence on Cancer Evolutionary Dynamics
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Cancer evolutionary dynamics result from the interplay between a heterogeneous tumor and the ecosystem which it inhabits. While beautiful work has shed light on the role of intra tumor heterogeneity and experimental techniques have allowed us to reconstruct the genetic paths that a cancer has followed in a patient, precious little has been done in understanding eco-evolutionary dynamics in cancer. In our group we have use mathematical and computational tools, integrating experimental data and challenged with clinical data, to study the bone ecosystem and its role in explaining the growth and progression of tumors. This will allow us to understand the interplay between the tumor and the bone, how that shapes its evolutionary dynamics and how treatments could be designed that take that into account.

Kimberly Bussey:
Reversion to Single-Cell Biology in Cancer
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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.

Jasmine Foo:
Epigenetically-Driven Drug Resistance in Cancer

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.

Harsh Jain:
Glioblastoma Evolution Under Treatment with Temozolomide with/without DNA Damage-Repair Inhibitors
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The average life expectancy of patients diagnosed with glioblastomas is 14 months with treatment. Standard treatment includes the chemotherapeutic drug temozolomide, that works by inducing DNA methylation. However, the BER and MGMT repair pathways efficiently repair the damage caused by this drug, reducing the efficacy of treatment. It has been hypothesized that inhibiting these repair pathways may lead to overcoming chemotherapy resistance. In this talk, I will present a novel mathematical model that captures the effect of chemotherapy on brain cancer cells, and includes detailed mechanisms of DNA damage and repair. The model is extensively parametrized and carefully validated using a wide array of available experimental data. Issues of parameter identifiability are also investigated. A global sensitivity analysis is performed to reveal those parameters most critical in the emergence of chemotherapy resistance. The calibrated model is then applied to predict treatment strategies that are optimized with respect to specific cancer cell phenotypes. A virtual cohort of glioblastoma patients -- each with a heterogeneous tumor -- is created, and a genetic algorithm employed to identify optimal treatment strategies. Our results suggest that patients can be broadly classified into 4 types in terms of these dosage schedules, based on the overall phenotypes of their tumors. Thus, resistance to chemotherapy can be mitigated to a certain extent by using novel dosage schedules, and combining standard treatment with cell-repair enzyme inhibitors.

Mary Kuhner:
Positive Selection Does not Always Tend Towards Cancer
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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.

Carlo Maley
Insights from Comparative Oncology

Michael Metzger:
Evolution of Contagious Cancers in Clams
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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.

Luay Nakhleh:
Elucidating Intra-tumor Heterogeneity from Single-cell DNA Sequencing Data
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Intra-tumor heterogeneity, as caused by a combination of mutation and selection, poses significant challenges to the diagnosis and treatment of cancer. Resolving this heterogeneity to identify the tumor cell populations (clones) and delineate their evolutionary history is of critical importance in improving cancer diagnosis and therapy. This heterogeneity can be readily elucidated and understood through the reconstruction of the clonal genotypes and evolutionary history of the tumor cells. Recently introduced single-cell DNA sequencing (SCS) technologies promise to provide the appropriate type of data for resolving intra-tumor heterogeneity. However, inherent technical errors in SCS datasets, due to allelic dropout, cell doublets and coverage non-uniformity, significantly complicate these tasks.

In this talk, I will first describe a maximum likelihood method for inferring tumor trees from SCS genotype data with potentially erroneous and missing entries, under a finite-sites model of evolution. I will then describe a non-parametric Bayesian method that simultaneously reconstructs the clonal populations as clusters of single cells, mutations associated with each clone, and the genealogical relationships between the clonal populations. I will demonstrate the performance of the methods on both synthetic and real data sets.

This is collaborative work with Hamim Zafar, Anthony Tzen, Ken Chen, and Nicholas Navin.

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

Ben Raphael:
Copy Number Aberrations in Tumor Evolution

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

Dan Stover:
Clinical Applications for Evolutionary Dynamics in Cancer

Zachary Weber, David Tallman, Sinclair Stockard, Katharine Collier, Sarah Asad, Justin Rhoades, Samuel Freeman, Heather A. Parsons, Sara Tolaney, Gavin Ha, Viktor Adalsteinsson, Daniel G. Stover

Although nearly every tumor is presumed to derive from a single cell, tumor cells evolve over time and in response to therapy resulting in many unique sub-populations within a single tumor. The heterogeneity within a single tumor is complex, including genetic alterations but also other factors also such as epigenetic alterations, lineage diversification, stochastic variations, and influences from the tumor microenvironment. This complex heterogeneity has been hypothesized to contributed to cancer cell resistance to therapy and understanding the evolutionary dynamics of tumor cells offers the potential to more rationally develop combinations and sequencing of therapies to optimally treat patients.

In cancer, the specific features that define intratumoral heterogeneity and lead to evolution vary by cancer type and by treatment type. For example, solid tumors coexist and interact with a complex tumor microenvironment while many hematoligic malignancies primarily reside in the circulation. Heterogeneity may impact therapy differently – with targeted therapy low-frequency populations resistant to targeted therapies are detectable even prior to starting treatment, while chemotherapy appears to induce a stress response in cells leading to stochastic patterns of resistance.

Monitoring solid tumors is a significant challenge due to difficulty accessing tissue, as biopsies are associated with patient anxiety, pain, and risk of complications. Circulating tumor DNA (ctDNA) offers the ability to repeatedly interrogate tumor genomic information, providing an opportunity for real-time monitoring of tumor genomic shifts.

We use ctDNA approaches to deeply analyze multiple samples collected over narrow time frames (days-to-weeks) from patients with metastatic triple-negative breast cancer, a cancer type known to have high ctDNA content. ctDNA was extracted from multiple plasma samples per patient, collected between 6 and 42 days between samples. Each plasma sample and underwent ultra-low pass whole genome sequencing (ULP-WGS; average depth 0.1x), deep targeted panel sequencing of 402 cancer-related genes with unique molecular identifier indexing (depth 10,000x), and samples with tumor fraction >10% underwent whole exome sequencing (WES; depth 200x), with germline sequencing of both targeted panel and WES for downstream analyses. We demonstrate that tumor fraction/purity estimates were largely concordant when comparing orthogonal sequencing approaches (ULP-WGS, WES) and tumor fraction estimation algorithms. Through statistical modeling, we tracked distinct clonal populations for each patient and found diverse clonal architectures and dynamics through treatment. Additionally, several emergent somatic alterations were discovered at late time points including alterations found in coding regions, proximal regulatory sites, and introns of key drug targets, underscoring the speed at which tumors can adapt to therapeutic agents.

Tumor evolution in cancer is diverse depending on the cancer and therapy type. Understanding evolutionary dynamics offer the potential to improve therapeutic strategies to enhance efficacy of treatments. Analysis of serial ctDNA samples collected at narrow time intervals (days-to-weeks) provides unique insight into the dynamics of ctDNA as well as clonal evolution, offering a minimally invasive approach for real-time genomic monitoring.

Chi Wang:
A Probabilistic Method to Estimate the Temporal Order of Pathway Mutations During Carcinogenesis by Leveraging Intra-Tumor Phylogenies and Functional Annotations

Cancer arises through accumulation of somatically acquired genetic mutations. An important question is to delineate the temporal order of somatic mutations during carcinogenesis, which contributes to better understanding of cancer biology and facilitates identification of new therapeutic targets. We develop a probabilistic approach for estimating the temporal order of pathway mutations by leveraging intra-tumor phylogenies and functional annotations of mutations. Our method infers the order of mutations at the pathway level, wherein it uses a probabilistic method to characterize the likelihood of mutational events from different pathways occurring in a certain order. The intra-tumor phylogeny is used to suggest possible orders of mutations within each tumor sample. The functional impact of each mutation is incorporated to weigh more on a mutation that is more integral to tumor development. A maximum likelihood method is used to estimate parameters and infer the probability of one pathway being mutated prior to another. Analysis of colon cancer data from The Cancer Genome Atlas demonstrates that our method is able to infer the temporal order of pathway mutations mostly consistent with the cancer research literature.

Nancy Zhang:
Genetic Heterogeneity Profiling and Subclone Detection by Single Cell RNA Sequencing
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Detection of genetically distinct subclones and profiling the transcriptomic differences between them is needed for studying the evolutionary dynamics of tumors, as well as for accurate prognosis and effective treatment of cancer in the clinic.  For the profiling of intra-tumor transcriptional heterogeneity, single cell RNA-sequencing (scRNA-seq) is now ubiquitously adopted in ongoing and planned cancer studies. Detection of somatic DNA mutations and inference of clonal membership from scRNA-seq, however, is currently unreliable. In this talk, I will describe DENDRO, a new method for subclone detection and DNA mutation profiling using single cell transcriptomic sequencing data.  DENDRO utilizes information from single nucleotide mutations in transcribed regions, and accounts for technical noise and expression stochasticity at the single cell level. I will show accuracy evaluations based on spike-in datasets and on scRNA-seq data with known subpopulation structure.  Then, I will describe several case studies:  We applied DENDRO to delineate subclonal expansion in a mouse melanoma model in response to immunotherapy, highlighting the role of neoantigens in treatment response. We also applied DENDRO to primary and lymph-node metastasis samples in breast cancer, where the new approach allowed us to better understand the relationship between genetic and transcriptomic intratumor variation.

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

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

Single-cell sequencing provides a powerful approach for elucidating intratumor heterogeneity by resolving cell-to-cell variability. However, it also poses additional challenges including elevated error rates, allelic dropout and non-uniform coverage. A recently introduced single-cell-specific mutation detection algorithm leverages the evolutionary relationship between cells for denoising the data. However, due to its probabilistic nature, this method does not scale well with the number of cells. Here, we develop a novel combinatorial approach for utilizing the genealogical relationship of cells in detecting mutations from noisy single-cell sequencing data. Our method, called scVILP, jointly detects mutations in individual cells and reconstructs a perfect phylogeny among these cells. We employ a novel Integer Linear Program algorithm for deterministically and efficiently solving the joint inference problem. We show that scVILP achieves similar or better accuracy but significantly better runtime over existing methods on simulated data. We also applied scVILP to an empirical human cancer dataset from a high grade serous ovarian cancer patient.

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

Single-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs)  have  been implicated in the development and progression of various types of cancer. A wide array of methods for CNA detection has been either developed specifically for or adapted to single-cell DNA sequencing data. Understanding the strengths and limitations that are unique to each of these methods is very important for obtaining accurate copy number profiles from single-cell DNA sequencing data. Here we review the major steps that are followed by these methods when analyzing such data, and then review the strengths and limitations of the methods individually. In terms of segmenting the genome into regions of different copy numbers, we categorize the methods into three groups, select a representative method from each group that has been commonly used in this context, and benchmark them on simulated as well as real datasets. While single-cell DNA sequencing is very promising for elucidating and understanding CNAs, even the best existing method does not exceed 80% accuracy. New methods that significantly improve upon the accuracy of these three methods are needed. Furthermore, with the large datasets being generated, the methods must be computationally efficient.

Yuan Gao:
MO: Inferring Mutation Order During Cancer Progression

Although our understanding of cancer progression and genetics has improved tremendously, the complexity of tumor progression, cancer dynamics and the evolutionary process within a tumor are still not completely clear. An unanswered question is how mutations interact to generate a specic phenotype. For example, quite similar cancer subtypes often display different landscapes of genetic and epigenetic alterations, and even tumors that harbor the same mutations respond differently to therapy, suggesting that the order in which mutations accumulate in the cancer cells may be important.

In this work, we develop a method, called MO, to infer the temporal order of cancer-associated mutations using single-cell sequencing (SCS) data. Our method involves the application of models at two levels: the evolutionary process of mutation and the technical errors from the SCS data collection process. To evaluate the ability of MO to correctly identify the location of mutation in the phylogenetic tree and the mutation order under different conditions, we conduct simulations under a variety of conditions and we compare its performance to two similar methods (SCITE and SiFit) within 192 different simulation
settings. The results indicate that MO has better performance than SCITE and SiFit under most conditions. We also apply our method to data for two prostate cancer patients and for two colorectal cancer patients, and nd that our inferred mutation orders are consistent with past studies.

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

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

Jeungeun Park:
Traveling Waves in a Chemotaxis Model

The directed motile response of organisms to chemical cues is called chemotaxis. Chemotaxis is an important process in many medical and biological applications such as tumor growth. It also plays a critical role in the development of a coherent pattern or waveform in biological systems. In this poster we are particularly interested in traveling waves to describe propagating patterns in a chemotaxis model. We investigate the wave patterns by solving the traveling wave problem of the chemotaxis model, and focus on how cell growth affects the formation of traveling wave solutions. Furthermore we incorporate recent results and discuss other aspects of traveling wave solutions such as wave speed, shape and stability.