Heterogeneity in the tumour size dynamics differentiates Vemurafenib, Dabrafenib and Trametinib in metastatic melanoma

Hitesh Mistry, David Orrell, Raluca Eftimie

 

Abstract

Molecular heterogeneity in tumours leads to variability in drug response both between patients and across lesions within a patient. These sources of variability could be explored through analysis of routinely collected clinical trial imaging data. We applied a mathematical model of tumour growth to analyse both within and between patient variability in tumour size dynamics to clinical data from three drugs, Vemurafenib, Dabrafenib and Trametinib, used in the treatment of metastatic melanoma. The analysis revealed: 1) existence of homogeneity in drug response and resistance development within a patient; 2) tumour shrinkage rate does not relate to rate of resistance development; 3) Vemurafenib and Dabrafenib, two BRAF inhibitors, have different variability in tumour shrinkage rates. Overall these results show how analysis of the dynamics of individual lesions can shed light on the within and between patient differences in tumour shrinkage and resistance rates, which could be used to gain a macroscopic understanding of tumour heterogeneity.

Keywords: heterogeneity, vemurafenib, dabrafenib, trametinib, melanoma, metastasis 

doi: http://biorxiv.org/content/early/2017/01/26/103366

 

 

Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution

 

Vanessa D Jonsson, Colin M Blakely, Luping Lin, Saurabh Asthana, Victor Olivas, Matthew A Gubens, Nikolai Matni, Boris C Bastian, Barry S Taylor, John C Doyle, Trever G Bivona

 

Abstract

The success of targeted cancer therapy is limited by drug resistance that can result from tumor genetic heterogeneity. The current approach to address resistance typically involves initiating a new treatment after clinical/radiographic disease progression, ultimately resulting in futility in most patients. Towards a potential alternative solution, we developed a novel computational framework that uses human cancer profiling data to systematically identify dynamic, pre-emptive, and sometimes non-intuitive treatment strategies that can better control tumors in real-time. By studying lung adenocarcinoma clinical specimens and preclinical models, our computational analyses revealed that the best anti-cancer strategies addressed existing resistant subpopulations as they emerged dynamically during treatment. In some cases, the best computed treatment strategy used unconventional therapy switching while the bulk tumor was responding, a prediction we confirmed in vitro. The new framework presented here could guide the principled implementation of dynamic molecular monitoring and treatment strategies to improve cancer control.

Link: http://dx.doi.org/10.1101/086553

 

Toxicity Management in CAR T cell therapy for B-ALL: Mathematical modelling as a new avenue for improvement.

Shalla Hanson, David Robert Grimes, Jake P. Taylor-King, Benedikt Bauer, Pravnam I. Warman, Ziv Frankenstein, Artem Kaznatcheev, Michael J. Bonassar, Vincent L. Cannataro, Zeinab Y. Motawe, Ernesto A. B. F. Lima, Sungjune Kim, Marco L. Davila, Arturo Araujo

Abstract

Advances in genetic engineering have made it possible to reprogram individual immune cells to express receptors that recognise markers on tumour cell surfaces. The process of re-engineering T cell lymphocytes to express Chimeric Antigen Receptors (CARs), and then re-infusing the CAR-modified T cells into patients to treat various cancers is referred to as CAR T cell therapy. This therapy is being explored in clinical trials - most prominently for B Cell Acute Lymphoblastic Leukaemia (B-ALL), a common B cell malignancy, for which CAR T cell therapy has led to remission in up to 90% of patients. Despite this extraordinary response rate, however, potentially fatal inflammatory side effects occur in up to 10% of patients who have positive responses. Further, approximately 50% of patients who initially respond to the therapy relapse. Significant improvement is thus necessary before the therapy can be made widely available for use in the clinic. To inform future development, we develop a mathematical model to explore interactions between CAR T cells, inflammatory toxicity, and individual patients' tumour burdens in silico. This paper outlines the underlying system of coupled ordinary differential equations designed based on well-known immunological principles and widely accepted views on the mechanism of toxicity development in CAR T cell therapy for B-ALL - and reports in silico outcomes in relationship to standard and recently conjectured predictors of toxicity in a heterogeneous, randomly generated patient population. Our initial results and analyses are consistent with and connect immunological mechanisms to the clinically observed, counterintuitive hypothesis that initial tumour burden is a stronger predictor of toxicity than is the dose of CAR T cells administered to patients. We outline how the mechanism of action in CAR T cell therapy can give rise to such non-standard trends in toxicity development, and demonstrate the utility of mathematical modelling in understanding the relationship between predictors of toxicity, mechanism of action, and patient outcomes.

Link:http://biorxiv.org/content/early/2016/04/22/049908

Stochasticity in the Genotype-Phenotype Map: Implications for the Robustness and Persistence of Bet-Hedging

Stochasticity in the Genotype-Phenotype Map: Implications for the Robustness and Persistence of Bet-Hedging

Daniel Nichol, Mark Robertson-Tessi, Peter Jeavons, Alexander RA Anderson

Abstract

For the last few decades modern biology has focused on quantifying, understanding and mapping the genetic characteristics of cells. This genotype-driven perspective has led to significant advances in our understanding and treatment of diseases such as cancer e.g. the discovery of driver mutations and the development of molecularly-targeted therapeutics. However, this perspective has largely ignored the functional outcome of genetic changes: the cellular phenotype. In part, this is simply because phenotypes are neither easy to define or measure as they critically depend on both genotype and context. Heterogeneity at the gene scale has been known for sometime, and there has been significant effort invested in trying to find patterns within it, but much less is understood about how this heterogeneity manifests itself in phenotypic change, i.e. the genotype-phenotype map (GP-map). This mapping is not one-to-one but many-to-many and is fundamentally the junction at which both genes and environment meet to produce phenotypes. Many genotypes produce similar phenotypes, and multiple phenotypes can emerge from a single genotype. To further complicate matters, genetically identical cells in uniform environments still exhibit phenotypic heterogeneity. Therefore a central open question in biology today is how can we connect the abundance of genomic data with cell phenotypic behaviour, this is especially pertinent to the issue of treatment resistance as many therapies act on cellular phenotypes. Our focus here is to tackle the GP-map question through the use of the simplest functional mapping we can define that also captures phenotypic heterogeneity: a molecular switch. Molecular switches are ubiquitous in biology, observed in many organisms and naturally map molecular components to decisions (i.e. phenotypes). Often stochastic in nature, such switches can be the difference between life or death in environments that fluctuate unpredictably, since they will ensure that at least some offspring are adapted to future environments. For convenience we use Chemical Reaction Networks (CRNs) to define the map of gene products to phenotypes, allowing us to investigate the impact of distinct mappings (CRNs) and perturbations to them. We observe that key biological properties naturally emerge, including both robustness and persistence. Robustness may explain why such bet hedging strategies are common in biology, and not readily destroyed through mutation. Whereas persistence may explain the apparent paradox of bet-hedging - why does phenotypic hedging exist in environments beneficial to only one of the phenotypes, when selection necessarily acts against it? The structure of the molecular switch, itself subject to selection, can slow the loss of hedging to ensure a survival mechanism even against environmental catastrophes which are very rare. Critically, these properties when taken together have profound and significant implications for the emergence of treatment resistance, since the timescale of extinction depends heavily on the underlying GP-map.

doi: http://dx.doi.org/10.1101/042424

Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes

Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes

Mark Robertson-Tessi, Robert J Gillies, Robert A Gatenby, Alexander RA Anderson

Abstract

A hybrid multiscale mathematical model of tumor growth is used to investigate how tumoral and microenvironmental heterogeneity affect treatment outcomes. A key component of this model is normal and tumor metabolism and its interaction with microenvironmental factors. In early stages of growth, tumors are stratified, with the most aggressive cells developing within the interior of the tumor. Simulations suggest that in some cases chemotherapy may increase the metabolic aggressiveness of a tumor due to drug-mediated selection.

Link: http://dx.doi.org/10.1101/038273

The role of contact inhibition in intratumoral heterogeneity: An off-lattice individual based model

Jill Gallaher, Alexander Anderson

Abstract

We present a model that shows how intratumoral heterogeneity, in terms of tumor cell phenotypic traits, can evolve in a tumor mass as a result of selection when space is a limited resource. This model specifically looks at the traits of proliferation rate and migration speed. The competition for space amongst individuals in the tumor mass creates a selection pressure for the cells with the fittest traits. To allow for organic movement and capture the invasive behavior, we use an off-lattice individual-based model.

DOI: http://biorxiv.org/content/early/2016/01/11/036467

Cancer evolution simulation identifies possible principles underlying intratumor heterogeneity

Atsushi Niida, Satoshi Ito, Georg Tremmel, Seiya Imoto, Ryutaro Uchi, Yusuke Takahashi, Yusuke Takahashi, Koshi Mimori, Satoru Miyano

Cancer arises from accumulation of somatic mutations and accompanying evolutionary selection for growth advantage. During the evolutionary process, an ancestor clone branches into multiple clones, yielding intratumor heterogeneity. However, principles underlying intratumor heterogeneity have been poorly understood. Here, to explore the principles, we built a cellular automaton model, termed the BEP model, which can reproduce the branching cancer evolution in silico. We then extensively searched for conditions leading to high intratumor heterogeneity by performing simulations with various parameter settings on a supercomputer. Our result suggests that multiple driver genes of moderate strength can shape subclonal structures by positive natural selection. Moreover, we found that high mutation rate and a stem cell hierarchy can contribute to extremely high intratumor heterogeneity, which is characterized by fractal patterns, through neutral evolution. Collectively, This study identified the possible principles underlying intratumor heterogeneity, which provide novel insights into the origin of cancer robustness and evolvability.

http://biorxiv.org/content/early/2015/07/17/022806

 

Spatial model predicts dispersal and cell turnover cause reduced intra-tumor heterogeneity

Spatial model predicts dispersal and cell turnover cause reduced intra-tumor heterogeneity

Bartlomiej Waclaw , Ivana Bozic , Meredith E Pittman , Ralph H Hruban , Bert Vogelstein , Martin A Nowak

 

Most cancers in humans are large, measuring centimeters in diameter, composed of many billions of cells. An equivalent mass of normal cells would be highly heterogeneous as a result of the mutations that occur during each cell division. What is remarkable about cancers is their homogeneity - virtually every neoplastic cell within a large cancer contains the same core set of genetic alterations, with heterogeneity confined to mutations that have emerged after the last clonal expansions. How such clones expand within the spatially-constrained three dimensional architecture of a tumor, and come to dominate a large, pre-existing lesion, has never been explained. We here describe a model for tumor evolution that shows how short-range migration and cell turnover can account for rapid cell mixing inside the tumor. With it, we show that even a small selective advantage of a single cell within a large tumor allows the descendants of that cell to replace the precursor mass in a clinically relevant time frame. We also demonstrate that the same mechanisms can be responsible for the rapid onset of resistance to chemotherapy. Our model not only provides novel insights into spatial and temporal aspects of tumor growth but also suggests that targeting short range cellular migratory activity could have dramatic effects on tumor growth rates.

 

BioRXiv: link 

Cancer Metastasis: Collective Invasion in Heterogeneous Multicellular Systems

Adrien Hallou, Joel Jennings, Alexandre Kabla

(Submitted on 31 Dec 2014)

Heterogeneity within tumour cell populations is associated with an increase in malignancy and appears to play an important role during cancer metastasis. Using in silico experiments, we study the interplay between collective behaviours and cell motility heterogeneities in a model system. Working with tumour spheroids that contain two non-proliferating cell populations of different motile properties, we explore the conditions required for maximal invasion into surrounding tissues. We show emerging spatial patterns of cellular organisation and invasion which are consistent with in vitro and in vivo observations. This demonstrates that mechanical interactions at the cellular level are sufficient to account for many of the observed morphologies of invasion and that heterogeneity in cell motility can be more important than average mechanical properties in controlling the fate of large cell populations.

link: http://arxiv.org/abs/1501.00065

Phylogenetic quantification of intra-tumour heterogeneity

Roland F Schwarz, Anne Trinh, Botond Sipos, James D Brenton, Nick Goldman, Florian Markowetz

(Submitted on 7 Jun 2013)

Background: Intra-tumour heterogeneity (ITH) is the result of ongoing evolutionary change within each cancer. The expansion of genetically distinct sub-clonal populations may explain the emergence of drug resistance and if so would have prognostic and predictive utility. However, methods for objectively quantifying ITH have been missing and are particularly difficult to establish in cancers where predominant copy number variation prevents accurate phylogenetic reconstruction owing to horizontal dependencies caused by long and cascading genomic rearrangements.
Results: To address these challenges we present MEDICC, a method for phylogenetic reconstruction and ITH quantification based on a Minimum Event Distance for Intra-tumour Copynumber Comparisons. Using a transducer-based pairwise comparison function we determine optimal phasing of major and minor alleles, as well as evolutionary distances between samples, and are able to reconstruct ancestral genomes. Rigorous simulations and an extensive clinical study show the power of our method, which outperforms state-of-the-art competitors in reconstruction accuracy and additionally allows unbiased numerical quantification of ITH.
Conclusions: Accurate quantification and evolutionary inference are essential to understand the functional consequences of ITH. The MEDICC algorithms are independent of the experimental techniques used and are applicable to both next-generation sequencing and array CGH data.

A new paper from +Jill Gallaher and +Alexander Anderson is out on the arXiv.  I asked Jill for a PLoS style 'author summary' in non-technical language, and here it is:

Author Summary:

A tumor can be thought of as an ecosystem, which critically means that we cannot just consider it as a collection of mutated cells. A tumor is more of a complex system of many interacting cellular and microenvironmental elements. There is variation among cells within the tumor, and with an increased proliferation capacity, there is competition for space, so evolution and selection occurs.  Because our current understanding at the genetic scale gives little information on translating to actual changes in cell behavior, we bypass the translation of genetics to behavior by focussing on the functional end result of the cell’s traits (phenotype) combined with the environmental influence of limited space, which will ultimately dictate tumor aggressiveness and treatability. 

The evolution of the population depends on the way in which traits are passed on as cells divide. We investigate trait inheritance by building a cell based simulation in which individual cells with varied trait combinations compete for space over time. Specifically, we characterize cell behavior in terms of two traits: proliferation rate and migration speed. The mode in which these traits are inherited significantly affects the evolution, composition, and fitness of a tumor population. To investigate competition for space, we initiate the population as a tight cluster, representing a growing tumor mass, and as a dispersed population, representing a cell culture experiment. We find that the dispersed population has more space, less competition, and reduced selection.  With a growing cluster of cells, there is more competition and selection. But constraining the allowable trait combinations so that several phenotypes are equally fit reduces competition and leads to the coexistence of several phenotypes. In this case, local heterogeneity may be advantageous to maximize growth.

Evolution of intratumoral phenotypic heterogeneity: the role of trait inheritance

Jill Gallaher, Alexander R. A. Anderson

(Submitted on 2 May 2013)

A tumor can be thought of as an ecosystem, which critically means that we cannot just consider it as a collection of mutated cells but more as a complex system of many interacting cellular and microenvironmental elements. At its simplest, a growing tumor with increased proliferation capacity must compete for space as a limited resource. Hypercellularity leads to a contact-inhibited core with a competitive proliferating rim. Evolution and selection occurs, and an individual cell's capacity to survive and propagate is determined by its combination of traits and interaction with the environment. With heterogeneity in phenotypes, the clone that will dominate is not always obvious as there are both local interactions and global pressures. Several combinations of phenotypes can coexist, changing the fitness of the whole.
To understand some aspects of heterogeneity in a growing tumor we build an off-lattice agent based model consisting of individual cells with assigned trait values for proliferation and migration rates. We represent heterogeneity in these traits with frequency distributions and combinations of traits with density maps. How the distributions change over time is dependent on how traits are passed on to progeny cells, which is our main inquiry. We bypass the translation of genetics to behavior by focussing on the functional end result of inheritance of the phenotype combined with the environmental influence of limited space.