Mitochondrial DNA Copy Number Variation Across Human Cancers

Mitochondrial DNA Copy Number Variation Across Human Cancers

Ed Reznik, Martin Miller, Yasin Senbabaoglu, Nadeem Riaz, William Lee, Chris Sander

In cancer, mitochondrial dysfunction, through mutations, deletions, and changes in copy number of mitochondrial DNA (mtDNA), contributes to the malignant transformation and progression of tumors. Here, we report the first large-scale survey of mtDNA copy number variation across 21 distinct solid tumor types, examining over 13,000 tissue samples profiled with next-generation sequencing methods. We find a tendency for cancers, especially of the bladder and kidney, to be significantly depleted of mtDNA, relative to matched normal tissue. We show that mtDNA copy number is correlated to the expression of mitochondrially-localized metabolic pathways, suggesting that mtDNA copy number variation reflect gross changes in mitochondrial metabolic activity. Finally, we identify a subset of tumor-type-specific somatic alterations, including IDH1 and NF1 mutations in gliomas, whose incidence is strongly correlated to mtDNA copy number. Our findings suggest that modulation of mtDNA copy number may play a role in the pathology of cancer.

Musings on the theory that variation in cancer risk among tissues can be explained by the number of divisions of normal stem cells

Musings on the theory that variation in cancer risk among tissues can be explained by the number of divisions of normal stem cells

Cristian Tomasetti, Bert Vogelstein
(Submitted on 21 Jan 2015)

This manuscript has been written to address questions related to our recent publication (Science 347:78-81, 2015). We appreciate the many reactions to this paper that have been communicated to us, either privately or publicly. The following addresses several of the most important statistical and technical issues related to our analysis and conclusions. Our responses to non-technical questions are available at this http URL

Bridging scales in cancer progression: Mapping genotype to phenotype using neural networks

Bridging scales in cancer progression: Mapping genotype to phenotype using neural networks

Philip Gerlee, Eunjung Kim, Alexander R.A. Anderson
(Submitted on 28 Apr 2014)

In this review we summarize our recent efforts in trying to understand the role of heterogeneity in cancer progression by using neural networks to characterise different aspects of the mapping from a cancer cells genotype and environment to its phenotype. Our central premise is that cancer is an evolving system subject to mutation and selection, and the primary conduit for these processes to occur is the cancer cell whose behaviour is regulated on multiple biological scales. The selection pressure is mainly driven by the microenvironment that the tumour is growing in and this acts directly upon the cell phenotype. In turn, the phenotype is driven by the intracellular pathways that are regulated by the genotype. Integrating all of these processes is a massive undertaking and requires bridging many biological scales (i.e. genotype, pathway, phenotype and environment) that we will only scratch the surface of in this review. We will focus on models that use neural networks as a means of connecting these different biological scales, since they allow us to easily create heterogeneity for selection to act upon and importantly this heterogeneity can be implemented at different biological scales. More specifically, we consider three different neural networks that bridge different aspects of these scales and the dialogue with the micro-environment, (i) the impact of the micro-environment on evolutionary dynamics, (ii) the mapping from genotype to phenotype under drug-induced perturbations and (iii) pathway activity in both normal and cancer cells under different micro-environmental conditions.

A tug-of-war between driver and passenger mutations in cancer and other adaptive processes

A tug-of-war between driver and passenger mutations in cancer and other adaptive processes

Christopher McFarland, Leonid Mirny, Kirill S. Korolev
(Submitted on 25 Feb 2014)

Cancer progression is an example of a rapid adaptive process where evolving new traits is essential for survival and requires a high mutation rate. Precancerous cells acquire a few key mutations that drive rapid population growth and carcinogenesis. Cancer genomics demonstrates that these few ‘driver’ mutations occur alongside thousands of random ‘passenger’ mutations-a natural consequence of cancer’s elevated mutation rate. Some passengers can be deleterious to cancer cells, yet have been largely ignored in cancer research. In population genetics, however, the accumulation of mildly deleterious mutations has been shown to cause population meltdown. Here we develop a stochastic population model where beneficial drivers engage in a tug-of-war with frequent mildly deleterious passengers. These passengers present a barrier to cancer progression that is described by a critical population size, below which most lesions fail to progress, and a critical mutation rate, above which cancers meltdown. We find support for the model in cancer age-incidence and cancer genomics data that also allow us to estimate the fitness advantage of drivers and fitness costs of passengers. We identify two regimes of adaptive evolutionary dynamics and use these regimes to rationalize successes and failures of different treatment strategies. We find that a tumor’s load of deleterious passengers can explain previously paradoxical treatment outcomes and suggest that it could potentially serve as a biomarker of response to mutagenic therapies. Collective deleterious effect of passengers is currently an unexploited therapeutic target. We discuss how their effects might be exacerbated by both current and future therapies.

Accurate Computation of Survival Statistics in Genome-wide Studies

Accurate Computation of Survival Statistics in Genome-wide Studies
Fabio Vandin, Alexandra Papoutsaki, Benjamin J. Raphael, Eli Upfal
(Submitted on 17 Sep 2013)

A key challenge in genomics is to identify genetic variants that distinguish patients with different survival time following diagnosis or treatment. While the log-rank test is widely used for this purpose, nearly all implementations of the log-rank test rely on an asymptotic approximation that is not appropriate in many genomics applications. This is because: the two populations determined by a genetic variant may have very different sizes; and the evaluation of many possible variants demands highly accurate computation of very small p-values. We demonstrate this problem for cancer genomics data where the standard log-rank test leads to many false positive associations between somatic mutations and survival time. We develop and analyze a novel algorithm, Exact Log-rank Test (ExaLT), that accurately computes the p-value of the log-rank statistic under an exact distribution that is appropriate for any size populations. We demonstrate the advantages of ExaLT on data from published cancer genomics studies, finding significant differences from the reported p-values. We analyze somatic mutations in six cancer types from The Cancer Genome Atlas (TCGA), finding mutations with known association to survival as well as several novel associations. In contrast, standard implementations of the log-rank test report dozens-hundreds of likely false positive associations as more significant than these known associations.

Population genetics of neutral mutations in exponentially growing cancer cell populations

Population genetics of neutral mutations in exponentially growing cancer cell populations
Rick Durrett
(Submitted on 12 Feb 2013)

In order to analyze data from cancer genome sequencing projects, we need to be able to distinguish causative, or “driver,” mutations from “passenger” mutations that have no selective effect. Toward this end, we prove results concerning the frequency of neutural mutations in exponentially growing multitype branching processes that have been widely used in cancer modeling. Our results yield a simple new population genetics result for the site frequency spectrum of a sample from an exponentially growing population.