Algorithms to model single gene, single chromosome, and whole genome copy number changes jointly in tumor phylogenetics.

<p>We present methods to construct phylogenetic models of tumor progression at the cellular level that include copy number changes at the scale of single genes, entire chromosomes, and the whole genome. The methods are designed for data collected by fluorescence in situ hybridization (FISH), an experimental technique especially well suited to characterizing intratumor heterogeneity using counts of probes to genetic regions frequently gained or lost in tumor development. Here, we develop new provably optimal methods for computing an edit distance between the copy number states of two cells given evolution by copy number changes of single probes, all probes on a chromosome, or all probes in the genome. We then apply this theory to develop a practical heuristic algorithm, implemented in publicly available software, for inferring tumor phylogenies on data from potentially hundreds of single cells by this evolutionary model. We demonstrate and validate the methods on simulated data and published FISH data from cervical cancers and breast cancers. Our computational experiments show that the new model and algorithm lead to more parsimonious trees than prior methods for single-tumor phylogenetics and to improved performance on various classification tasks, such as distinguishing primary tumors from metastases obtained from the same patient population.</p

Network Signatures of Survival in Glioblastoma Multiforme

<div><p>To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included “protein kinase cascade,” “IκB kinase/NFκB cascade,” and “regulation of programmed cell death” – all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM.</p></div

Survival curves comparing various classifiers when tested on the dataset of Lee et al.

<p>(GEO ID: GSE13041). While the Verhaak subtypes – Proneural, Classical, Neural, and Mesenchymal – do not show statistically significant differences in survival, the top 5 CRANE subnetworks clearly distinguish short-term from long-term survivor groups.</p

Workflow of the CRANE algorithm for detecting combinatorially dysregulated subnetworks.

<p>We begin by mapping patient-specific, binarized mRNA expression data onto a protein interaction network. Then, we identify subnetworks whose pattern of expression – the subnetwork state function – can separate short-term and long-term survivors. Measures of separation are the support (the fraction of samples containing a particular subnetwork state), the fraction of long/short-term survivors, and the <i>J</i>-value (see text for description). In the table (bottom), the top ten states of the first TCGA subnetwork are shown. Each row represents a different state of the subnetwork. Each character in the state function (first column) represents the expression state of a particular gene in the subnetwork, where “L” and “H” stand for “low” and “high” expression, respectively.</p

The top five CRANE subnetworks representing a signature of survival in glioblastoma.

<p>Gene names are indicated within the nodes; edges represent either protein-protein interactions (turquoise), or proteins found together as partners within a complex (violet). Subnetworks are added into the classifier in clockwise fashion (from 1 to 5); after the addition of each subnetwork, an updated positive predictive value (PPV) is calculated, as shown along the periphery for prediction of both short-term (pink) and long-term (purple) survival.</p

Dysregulated proteins identified within the 50-gene subnetwork signature.

<p>Proteins with p-values<0.05 are in bold. Ratios (LTS-to-STS) were calculated from the raw data.</p

Proteomic detection and dysregulation of biomarkers discovered using various pipelines.

<p>(A) Comparison of the number of proteomic targets identified using a network-based algorithm for identifying combinatorial gene markers (“CRANE”) versus one using individual differentially expressed genes (“Individual Gene Markers”). (B) Comparison of the number of proteomic targets identified using the subtypes identified by Verhaak et al. We plot the total number of classifier targets detected in the proteomic experiment (“Identification”), as well as the subset of classifier genes showing evidence for differential expression (<i>p</i>-value≤0.05) at the protein level (“D.E.”).</p

Single-cell genetic analysis of ductal carcinoma in situ and invasive breast cancer reveals enormous tumor heterogeneity yet conserved genomic imbalances and gain of MYC during progression.

<p>Ductal carcinoma in situ (DCIS) is a precursor lesion of invasive ductal carcinoma (IDC) of the breast. To understand the dynamics of genomic alterations in this progression, we used four multicolor fluorescence in situ hybridization probe panels consisting of the oncogenes COX2, MYC, HER2, CCND1, and ZNF217 and the tumor suppressor genes DBC2, CDH1, and TP53 to visualize copy number changes in 13 cases of synchronous DCIS and IDC based on single-cell analyses. The DCIS had a lower degree of chromosomal instability than the IDC. Despite enormous intercellular heterogeneity in DCIS and IDC, we observed signal patterns consistent with a nonrandom distribution of genomic imbalances. CDH1 was most commonly lost, and gain of MYC emerged during progression from DCIS to IDC. Four of 13 DCISs showed identical clonal imbalances in the IDCs. Six cases revealed a switch, and in four of those, the IDC had acquired a gain of MYC. In one case, the major clone in the IDC was one of several clones in the DCIS, and in another case, the major clone in the DCIS became one of the two major clones in the IDC. Despite considerable chromosomal instability, in most cases the evolution from DCIS to IDC is determined by recurrent patterns of genomic imbalances, consistent with a biological continuum.</p

Single-Cell Genetic Analysis Reveals Insights into Clonal Development of Prostate Cancers and Indicates Loss of PTEN as a Marker of Poor Prognosis.

<p>Gauging the risk of developing progressive disease is a major challenge in prostate cancer patient management. We used genetic markers to understand genomic alteration dynamics during disease progression. By using a novel, advanced, multicolor fluorescence in situ hybridization approach, we enumerated copy numbers of six genes previously identified by array comparative genomic hybridization to be involved in aggressive prostate cancer [TBL1XR1, CTTNBP2, MYC (alias c-myc), PTEN, MEN1, and PDGFB] in six nonrecurrent and seven recurrent radical prostatectomy cases. An ERG break-apart probe to detect TMPRSS2-ERG fusions was included. Subsequent hybridization of probe panels and cell relocation resulted in signal counts for all probes in each individual cell analyzed. Differences in the degree of chromosomal and genomic instability (ie, tumor heterogeneity) or the percentage of cells with TMPRSS2-ERG fusion between samples with or without progression were not observed. Tumors from patients that progressed had more chromosomal gains and losses, and showed a higher degree of selection for a predominant clonal pattern. PTEN loss was the most frequent aberration in progressers (57%), followed by TBL1XR1 gain (29%). MYC gain was observed in one progresser, which was the only lesion with an ERG gain, but no TMPRSS2-ERG fusion. According to our results, a probe set consisting of PTEN, MYC, and TBL1XR1 would detect progressers with 86% sensitivity and 100% specificity. This will be evaluated further in larger studies.</p