Amit G Deshwar

Inferring clonal evolution of tumors from single nucleotide somatic mutations

BackgroundHigh-throughput sequencing allows the detection and quantification of frequencies of somatic single nucleotide variants (SNV) in heterogeneous tumor cell populations. In some cases, the evolutionary history and population frequency of the subclonal lineages of tumor cells present in the sample can be reconstructed from these SNV frequency measurements. But automated methods to do this reconstruction are not available and the conditions under which reconstruction is possible have not been described.ResultsWe describe the conditions under which the evolutionary history can be uniquely reconstructed from SNV frequencies from single or multiple samples from the tumor population and we introduce a new statistical model, PhyloSub, that infers the phylogeny and genotype of the major subclonal lineages represented in the population of cancer cells. It uses a Bayesian nonparametric prior over trees that groups SNVs into major subclonal lineages and automatically estimates the number of lineages and their ancestry. We sample from the joint posterior distribution over trees to identify evolutionary histories and cell population frequencies that have the highest probability of generating the observed SNV frequency data. When multiple phylogenies are consistent with a given set of SNV frequencies, PhyloSub represents the uncertainty in the tumor phylogeny using a “partial order plot”. Experiments on a simulated dataset and two real datasets comprising tumor samples from acute myeloid leukemia and chronic lymphocytic leukemia patients demonstrate that PhyloSub can infer both linear (or chain) and branching lineages and its inferences are in good agreement with ground truth, where it is available.ConclusionsPhyloSub can be applied to frequencies of any “binary” somatic mutation, including SNVs as well as small insertions and deletions. The PhyloSub and partial order plot software is available from https://github.com/morrislab/phylosub/.

PhyloWGS: Reconstructing subclonal composition and evolution from whole-genome sequencing of tumors

Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations. We describe a new method, PhyloWGS, which can be applied to whole-genome sequencing data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutations and population frequencies of structural variations. We introduce a principled phylogenic correction for VAFs in loci affected by copy number alterations and we show that this correction greatly improves subclonal reconstruction compared to existing methods. PhyloWGS is free, open-source software, available at https://github.com/morrislab/phylowgs.

Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction

Tumor heterogeneity is a limiting factor in cancer treatment and in the discovery of biomarkers to personalize it. We describe a computational purification tool, ISOpure, which directly addresses the effects of variable contamination by normal tissue in clinical tumor specimens. ISOpure uses a set of tumor expression profiles and a panel of healthy tissue expression profiles to generate a purified cancer profile for each tumor sample, and an estimate of the proportion of RNA originating from cancerous cells. Applying ISOpure before identifying gene signatures leads to significant improvements in the prediction of prognosis and other clinical variables in lung and prostate cancer.

Correction of a splicing defect in a mouse model of congenital muscular dystrophy type 1A using a homology-directed-repair-independent mechanism

Splice-site defects account for about 10% of pathogenic mutations that cause Mendelian diseases. Prevalence is higher in neuromuscular disorders (NMDs), owing to the unusually large size and multi-exonic nature of genes encoding muscle structural proteins. Therapeutic genome editing to correct disease-causing splice-site mutations has been accomplished only through the homology-directed repair pathway, which is extremely inefficient in postmitotic tissues such as skeletal muscle. Here we describe a strategy using nonhomologous end-joining (NHEJ) to correct a pathogenic splice-site mutation. As a proof of principle, we focus on congenital muscular dystrophy type 1A (MDC1A), which is characterized by severe muscle wasting and paralysis. Specifically, we correct a splice-site mutation that causes the exclusion of exon 2 from Lama2 mRNA and the truncation of Lama2 protein in the dy2J/dy2J mouse model of MDC1A. Through systemic delivery of adeno-associated virus (AAV) carrying clustered regularly interspaced short palindromic repeats (CRISPR)–Cas9 genome-editing components, we simultaneously excise an intronic region containing the mutation and create a functional donor splice site through NHEJ. This strategy leads to the inclusion of exon 2 in the Lama2 transcript and restoration of full-length Lama2 protein. Treated dy2J/dy2J mice display substantial improvement in muscle histopathology and function without signs of paralysis.

PLIDA: cross-platform gene expression normalization using perturbed topic models

MOTIVATION Gene expression data are currently collected on a wide range of platforms. Differences between platforms make it challenging to combine and compare data collected on different platforms. We propose a new method of cross-platform normalization that uses topic models to summarize the expression patterns in each dataset before normalizing the topics learned from each dataset using per-gene multiplicative weights. RESULTS This method allows for cross-platform normalization even when samples profiled on different platforms have systematic differences, allows the simultaneous normalization of data from an arbitrary number of platforms and, after suitable training, allows for online normalization of expression data collected individually or in small batches. In addition, our method outperforms existing state-of-the-art platform normalization tools. AVAILABILITY AND IMPLEMENTATION MATLAB code is available at http://morrislab.med.utoronto.ca/plida/.

Relapsing-remitting multiple sclerosis classification using elastic net logistic regression on gene expression data

As part of the first Industrial Methodology for Process Verification in Research Challenge, the aim of the MS Diagnostic sub-challenge was to identify a robust diagnostic signature for relapsing-remitting multiple sclerosis from gene expression data. In this regard, we built a classifier that discriminates samples into two phenotype groups, either RRMS or controls, using the transcriptome of peripheral blood mononuclear cells. For our classifier, we used logistic regression with elastic net regression as implemented in the glmnet package in R. We selected the values of the regularization hyper-parameters using cross-validation performance on the provided training data, number of non-zero parameters in our model, and based on the distribution of output values when the input vector for the test data were used with our classifier. We analyzed our classifier performance with two different strategies for feature extraction, using either only genes or including additional constructed features from gene pathways data. The two different strategies produced little differences in performance when comparing the 10-fold cross-validation of the training data and prediction on the test data. Our final submission for the sub-challenge used only genes as features, and identified a diagnostic signature consisting of 58 genes, that was ranked second out of a total of 39 submissions.

Identifying Cancer Specific Functionally Relevant miRNAs from Gene Expression and miRNA-to-Gene Networks Using Regularized Regression

Identifying microRNA signatures for the different types and subtypes of cancer can result in improved detection, characterization and understanding of cancer and move us towards more personalized treatment strategies. However, using microRNAs differential expression (tumour versus normal) to determine these signatures may lead to inaccurate predictions and low interpretability because of the noisy nature of miRNA expression data. We present a method for the selection of biologically active microRNAs using gene expression data and microRNA-to-gene interaction network. Our method is based on a linear regression with an elastic net regularization. Our simulations show that, with our method, the active miRNAs can be detected with high accuracy and our approach is robust to high levels of noise and missing information. Furthermore, our results on real datasets for glioblastoma and prostate cancer are confirmed by microRNA expression measurements. Our method leads to the selection of potentially functionally important microRNAs. The associations of some of our identified miRNAs with cancer mechanisms are already confirmed in other studies (hypoxia related hsa-mir-210 and apoptosis-related hsa-mir-296-5p). We have also identified additional miRNAs that were not previously studied in the context of cancer but are coherently predicted as active by our method and may warrant further investigation. The code is available in Matlab and R and can be downloaded on http://www.cs.toronto.edu/goldenberg/Anna_Goldenberg/Current_Research.html.

ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles

BackgroundTumour samples containing distinct sub-populations of cancer and normal cells present challenges in the development of reproducible biomarkers, as these biomarkers are based on bulk signals from mixed tumour profiles. ISOpure is the only mRNA computational purification method to date that does not require a paired tumour-normal sample, provides a personalized cancer profile for each patient, and has been tested on clinical data. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer.ResultsTo simplify the integration of ISOpure into standard R-based bioinformatics analysis pipelines, the algorithm has been implemented as an R package. The ISOpureR package performs analogously to the original code in estimating the fraction of cancer cells and the patient cancer mRNA abundance profile from tumour samples in four cancer datasets.ConclusionsThe ISOpureR package estimates the fraction of cancer cells and personalized patient cancer mRNA abundance profile from a mixed tumour profile. This open-source R implementation enables integration into existing computational pipelines, as well as easy testing, modification and extension of the model.

The evolutionary history of 2,658 cancers

Cancer develops through a process of somatic evolution. Here, we use whole-genome sequencing of 2,778 tumour samples from 2,658 donors to reconstruct the life history, evolution of mutational processes, and driver mutation sequences of 39 cancer types. The early phases of oncogenesis are driven by point mutations in a small set of driver genes, often including biallelic inactivation of tumour suppressors. Early oncogenesis is also characterised by specific copy number gains, such as trisomy 7 in glioblastoma or isochromosome 17q in medulloblastoma. By contrast, increased genomic instability, a nearly four-fold diversification of driver genes, and an acceleration of point mutation processes are features of later stages. Copy-number alterations often occur in mitotic crises leading to simultaneous gains of multiple chromosomal segments. Timing analysis suggests that driver mutations often precede diagnosis by many years, and in some cases decades, providing a window of opportunity for early cancer detection.

Computational purification of tumor gene expression data

Background Cancer gene expression profiling is an indispensable tool for identifying drivers of tumor progression, identifying subtypes, and predicting clinical outcome. An outstanding challenge faced by cancer gene expression studies is the limited concordance between studies [1], driven in part by lack of statistical power [2]. Part of this lack of statistical power is due to the fact that tumor samples from some solid cancers contain between 30%-70% healthy tissue [3]. This healthy tissue contaminates tumor expression profiles and variable amounts of healthy tissue leads to increased variability between tumor expression profiles. Physical purification of these tumor samples before profiling is often not feasible.