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Book ChapterDOI

Genotyping techniques to address diversity in tumors.

01 Jan 2011-Advances in Cancer Research (Elsevier)-Vol. 112, pp 151-182
TL;DR: This chapter presents the basic principles for current array-based genotyping platforms and how they can be used to infer genotype and copy number for acquired genomic alterations, and describes how these techniques can beused to resolve tumor ploidy, normal cell admixture, and subclonality.
Abstract: Array-based genotyping platforms have during recent years been established as a valuable tool for the characterization of genomic alterations in cancer. The analysis of tumor samples, however, presents challenges for data analysis and interpretation. For example, tumor samples are often admixed with nonaberrant cells that define the tumor microenvironment, such as infiltrating lymphocytes and fibroblasts, or vasculature. Furthermore, tumors often comprise subclones harboring divergent aberrations that are acquired subsequent to the tumor-initiating event. The combined analysis of both genotype and copy number status obtained by array-based genotyping platforms provide opportunities to address these challenges. In this chapter, we present the basic principles for current array-based genotyping platforms and how they can be used to infer genotype and copy number for acquired genomic alterations. We describe how these techniques can be used to resolve tumor ploidy, normal cell admixture, and subclonality. We also exemplify how genotyping techniques can be applied in tumor studies to elucidate the hierarchy among tumor clones, and thus, provide means to study clonal expansion and tumor evolution.

Summary (5 min read)

I. INTRODUCTION

  • Cancer development and tumor formation involves acquired genomic aberrations, such as sequence mutations and copy number changes.
  • Thus, the amplitude of signal associated with a copy number alteration is dependent on the fraction of cells harboring the alteration.
  • Importantly, with aCGH data it is not straightforward to discriminate between contamination of normal genomes and varying magnitude of underlying net copy number changes, although there have been efforts aimed at resolving this issue (Tolliver et al., 2010) .
  • The authors finally discuss how these data can be used and interpreted with the aim of deducing intermixture of nonaberrant cells within tumor biopsies, as well as subclonal events and intra-tumor heterogeneity.

A. Platforms and Probe Design

  • There are two SNP array platforms predominantly in use, provided by Affymetrix and Illumina, respectively.
  • Here, the authors will confine to describe the basic principles of the platforms and highlight some of the differences between them.
  • Since the first SNP array platforms were presented (Wang et al., 1998) , array density has increased by several orders of magnitude and the current platforms comprise millions of probes in a single assay.
  • Illumina utilizes their BeadChip technology that permits probes to be immobilized on silica beads rather than directly onto the array surface.
  • After target hybridization, alleles are differentiated by a subsequent enzymatic single-base extension of the probe using the hybridized target as template.

B. Principles of Data Extraction and Normalization

  • Raw data acquisition and processing varies depending on array platform.
  • Arrays are hybridized and labeled according to chemistry-dependent experimental procedures followed by imaging and data extraction.
  • Preprocessing and normalization of probe data is performed to achieve pairs of allele-specific measurements for each SNP locus, and to this end there are various methods described (LaFramboise, 2009) .
  • For calling genotype and calculating allele ratio, observed normalized intensities are related to expected values derived from collections of reference data.
  • Transformation of intensities to relative copy number estimates is essentially also performed by relating values to a collection of normal reference samples or to a matched control.

C. The B Allele Frequency and Relative Copy Number

  • The B allele frequency (BAF), first presented using Illumina data (Peiffer et al., 2006) , is calculated for each SNP individually by transformation of allele intensities and represents the proportion of DNA content for allele B as compared to the total DNA content of A and B alleles together.
  • The proposed transformation involves linear interpolation of allele frequencies from reference data derived from normal samples.
  • These probes can be used for the analysis of CNVs but many are also added to provide increased power and resolution when analyzing acquired copy number aberrations in tumors.
  • Data from Affymetrix can be converted into BAF and LRR by appropriate normalization and transformation (Wang et al., 2007; Sun et al., 2009) .

D. Expected BAF and LRR for a Normal Genome

  • In a diploid genome, there are only three possible allele combinations for a given locus: homozygosity for the A allele (AA), heterozygosity (AB) or homozygosity for the B allele (BB).
  • Three seemingly horizontal bands representing AA, AB, and BB genotypes are apparent, closely clustered around the theoretical BAF values of 0, 0.5, and 1, respectively (Fig. 2B ).
  • Such chimeric patterns may be observed in clinical samples, for example, when 13 analyzing recurring leukemias after the patient has undergone bone marrow transplantation (Paulsson et al., 2011) .
  • In section II.C the authors described how SNP arrays estimate copy numbers for each SNP locus.
  • By definition, a normal diploid genome has two copies of each autosome.

III. WHOLE GENOME GENOTYPING OF TUMOR SAMPLES

  • Since the introduction of SNP arrays, a large number of studies have proved these platforms to be important means of analysis of acquired genomic changes.
  • Since SNP arrays can detect chromosomal imbalances at both the copy number level, measured as deviation of LRR, and at the genotype level, measured as deviations of BAF, the combined use of these two measurements can be used for interpretation of underlying genomic imbalances.
  • The authors will here discuss the basic concept of how copy number, and allelic ratios are affected by common genetic 14 alterations such as deletions, copy number gains, and copy number neutral events.

A. Changes in BAF and LLR upon Acquired Genomic Alterations

  • As described above, there are three possible genotypes for a given SNP locus in the normal diploid genome, either heterozygous (AB) or homozygous (AA or BB).
  • Thus, BAF values for all germline heterozygous SNPs are shifted from BAF=0.5 to either BAF=0, or BAF=1, depending on which chromosomal homologue that has been lost.
  • For more complex alterations involving higher allele copy numbers, multiple paired genotype combinations are possible within the gained region, again depending on which homologues are present and in what proportions.
  • It must be stressed that definition of copy number neutral alterations are intimately linked to the ploidy state of the tumor.
  • Due to its narrow definitionhomozygosity caused by two copies from the same parent -and close association with constitutional genetics, the authors will refrain from using the term UPD when discussing copy number neutral allelic imbalance events.

B. The Mirrored B Allele Frequency (mBAF)

  • In the examples above the authors demonstrated how different types of acquired chromosomal alterations influence the BAFs of constitutionally heterozygous SNP loci.
  • A consecutive series of SNP alleles (a haplotype series) on a chromosome homologue is in practice random with respect to its sequence of As and Bs.
  • If the authors consider a region affected by a specific genetic alteration they also note that BAF values for the SNPs within this region are symmetrically positioned around the 0.5 axis.
  • In Fig. 3B the authors demonstrate this inherent symmetry for the regions of copy number and/or allelic imbalance presented in Fig. 3A .

C. Delineating Regions of Genomic Imbalance

  • A number of computational methods have been described for the automated identification of altered regions in tumor genomes analyzed by SNP arrays.
  • Even in case of a matched normal genotype, individual SNPs are generally not sufficient for determining the genotype at a given loci due to possible technical noise.
  • The high resolution of SNP arrays permits inference of allelic imbalance from a continuous stretch of LOH without the need of a matched normal genotype.
  • Fig. 4 displays typical BAF and mBAF patterns obtained from a SNP array analysis of a tumor and illustrate how data can be segmented in order to reduce data dimensionality.
  • It then becomes intuitive that most acquired alterations will introduce a shift in BAF and/LRR, and that changing from one underlying state to another will involve breakpoints in the data delineating genomic alterations (Fig. 4 ).

D. BAF vs LRR Plots

  • The authors have shown that SNP array data provide both genotype and copy number estimates for each SNP that is queried, and that these can be visually represented using mBAF and LRR profile plots.
  • To interpret a specific genetic alteration it is needed to take both mBAF and LRR into account, and their respective relationship can be queried by plotting LRR versus mBAF (Fig. 5 ).
  • When plotting segmented LRR versus mBAF (or BAF) from a tumor with a diploid chromosomal number a characteristic pattern will emerge where genomic regions with identical allele combinations will appear close to each other within the mBAF/LRR space (Fig. 5 ).
  • Segments of one copy gain (BBA) will appear together as a cluster of values with elevated LRR and mBAF, approaching their theoretical values of mBAF=0.67 and LRR=0.58.
  • All unaltered segments (AB) will form a dense cluster at mBAF=0.5 and LRR=0.

IV. WGG ANALYSES OF COMPLEX AND HETEROGENEOUS CELL POPULATIONS

  • The authors have so far discussed relatively simple examples of alterations affecting one homogenous population of tumor cells.
  • In practice however, WGG analyses are often performed on heterogeneous tumor samples that contain more than one distinct population of cells.
  • Thus, the proportion of nonaberrant cells will vary from sample to sample.
  • Regardless of the cause and nature of included nonaberrant cells, the presence of normal diploid cells within a tumor sample can cause problems in downstream analyses and subsequent interpretations of the data.
  • Moreover, cancers may to varying degrees be composed of multiple clones harboring divergent aberrations that are acquired subsequent to the tumor-initiating event.

B. BAF and LRR in an Admixture of Tumor and Normal Cells

  • The above theoretical examples have focused on situations when there is only one clone present within the sample, i.e., all analyzed cells have identical genotypes.
  • Several studies have successfully demonstrated this using tumor biopsies by comparing BAF derived estimates with cellularity scores from histological examination (Nancarrow et al., 2007; Assie et al., 2008; Sun et al., 2009) .
  • The principles of estimating the fraction of normal cells can be illustrated using a simple example (Fig. 8D ).
  • The combination of normal contamination and increased clonal heterogeneity can rapidly increase the complexity of the data and thereby reduce the possibility to resolve underlying genotype status.

C. Tumor Subclonality

  • The presence of genetic variation between different subclones within a tumor mass is a well-known phenomenon.
  • Subclonal genetic alterations may readily be identified at the individual cell level by conventional cytogenetics or fluorescence in situ hybridization.
  • Current molecular analyses of bulk samples will however only give an average estimate of all imbalances.
  • If the authors further expand their example of a sample of 80% tumor cells and 20% normal diploid cells (Figs. 5 and 8D ) and hypothesize that 50% of the tumor cells carry 27 some additional alterations, they can simply calculate expected mBAF for these using Eq. ( 2).
  • Subsequent validation is necessary to definitively resolve the underlying states.

D. Tracing Clonal Relationships Using SNP Arrays

  • Depiction of copy number gain and loss frequencies across large tumor cohorts highlight recurrent alterations and can be used to classify tumors into 28 groups with related karyotypes (Russnes et al., 2010) .
  • To be able to discern and model the underlying chronology of events, repeated samples from the same individual has to be studied.
  • The authors will here present some hypothetical examples of how SNP array data can be used to analyze multiple tumors from the same patient in order to investigate clonal expansion, chronology of events, and divergence in clonal evolution.
  • The latter scenario will suggest that the two alterations were in fact confined to separate subclones in the primary.
  • The authors exemplify this for a deletion in which BAF is used to infer the complete haplotype sequences of the parental alleles (Fig.

V. CONCLUDING REMARKS

  • Throughout recent years, molecular techniques to study cancer have progressed in terms of resolution and sensitivity, but also with respect to accessibility due to decreased cost.
  • Undoubtedly, technologies will continue to evolve and much of what is considered at the forefront today will be superseded tomorrow.
  • The authors have aimed to present some basic concepts pertaining to the analysis of tumor-heterogeneity using genotyping techniques.
  • In the AAAB/ABBB two copy gain segment, the AAAB genotypes (BAF=0.2) will be transformed to the mirrored genotype (BBBA) with mBAF=0.8.
  • Non-informative homozygous SNPs are excluded from this plot.

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Genotyping techniques to address diversity in tumors.
Lindgren, David; Höglund, Mattias; Vallon-Christersson, Johan
Published in:
Advances in Cancer Research
DOI:
10.1016/B978-0-12-387688-1.00006-5
2011
Link to publication
Citation for published version (APA):
Lindgren, D., Höglund, M., & Vallon-Christersson, J. (2011). Genotyping techniques to address diversity in
tumors.
Advances in Cancer Research
,
112
, 151-182. https://doi.org/10.1016/B978-0-12-387688-1.00006-5
Total number of authors:
3
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1
Genotyping techniques to address diversity in tumors
David Lindgren,
a
Mattias Höglund,
b
and Johan Vallon-Christersson
b, c
a
Center for Molecular Pathology, Department of Laboratory Medicine, Lund
University, SUS Malmö, Malmö, Sweden
b
Department of Oncology, Clinical Sciences, Lund University, Lund, Sweden
c
CREATE Health Strategic Center for Translational Cancer Research, Lund
University, Lund, Sweden
Correspondence to: David Lindgren, Center for Molecular Pathology, Department
of Laboratory Medicine, Lund University, SUS Malmö, Entrance 78, SE-205 02
Malmö, Sweden.

2
Abstract
Array based genotyping platforms have during recent years been established
as a valuable tool for the characterization of genomic alterations in cancer. The
analysis of tumor samples, however, presents challenges for data analysis and
interpretation. For example, tumor samples are often admixed with nonaberrant
cells that define the tumor microenvironment, such as infiltrating lymphocytes
and fibroblasts, or vasculature. Furthermore, tumors often comprise subclones
harboring divergent aberrations that are acquired subsequent to the tumor-
initiating event. The combined analysis of both genotype and copy number status
obtained by array based genotyping platforms provide opportunities to address
these challenges. In this review, we present the basic principles for current array
based genotyping platforms and how they can be used to infer genotype and
copy number for acquired genomic alterations. We describe how these
techniques can be used to resolve tumor ploidy, normal cell admixture, and
subclonality. We also exemplify how genotyping techniques can be applied in
tumor studies to elucidate the hierarchy among tumor clones, and thus, provide
means to study clonal expansion and tumor evolution.

3
I. INTRODUCTION
Cancer development and tumor formation involves acquired genomic
aberrations, such as sequence mutations and copy number changes. Molecular
investigation of genomic alterations in tumors has traditionally been performed
using methods such as loss of heterozygosity (LOH) analyses and comparative
genomic hybridization (CGH). Conventional GCH, first described by Kallioniemi
and coworkers (Kallioniemi et al., 1992), use differentially fluorescently labeled
DNA from tumor sample and reference DNA to reveal regions of loss and gain by
competitive hybridization to immobilized normal metaphase chromosomes.
With the advent of array-technology (Schena et al., 1995), the analysis of cancer
genomes advanced rapidly with greatly increased resolution and sensitivity.
Array-based comparative genomic hybridization (aCGH) was first performed
using gene-centered arrays originally developed for gene expression analysis, or
using low-density arrays of large genomic segments cloned in bacterial artificial
chromosomes (BACs) (Pollack et al., 1999). Initial techniques were soon further
developed for genome-wide investigation of copy number aberrations at high-
resolution by tiling BAC arrays and subsequently by employing oligonucleotide
probe arrays. In short, aCGH utilizes the same strategy as conventional
metaphase CGH but DNA is hybridized to immobilized DNA probes mapped to
known genomic locations. Current array platforms, comprising from tens of
thousands up to millions of probes, allow for detection of breakpoints and copy
number alterations at sub-gene resolution and have been widely used to screen
for genomic alterations in cancer (Pinkel and Albertson, 2005). Such analyses
have provided a depiction of copy number gain and loss frequencies across large

4
tumor cohorts in a variety of cancers, highlighting recurrent alterations
important during oncogenesis and tumor development (Chin et al., 2006). LOH
analyses have, on the other hand, been widely used in cancer research to detect
regions of allelic imbalances indicating regions of genomic deletion or copy
number neutral LOH, and have been used to identify tumor suppressor genes
inactivated by mutation followed by loss of the wild-type allele. Traditionally,
LOH analysis use polymorphic markers, such as nucleotide repeat regions or
single nucleotide polymorphisms, to detect regions of allelic imbalance.
Whole genome genotyping (WGG) arrays based on Single Nucleotide
Polymorphisms (SNPs) (Wang et al., 1998) were developed to analyze blood
samples in association studies and have since its introduction successfully been
used in numerous studies for identification of genetic susceptibility loci in a
variety of diseases (Grant and Hakonarson, 2008). Progression of WGG arrays, or
SNP arrays, has followed the identification of SNPs in the human genome derived
from initiatives such as the international HapMap Project
(http://www.hapmap.org), and platforms currently in use allow for genotyping
of millions of SNPs simultaneously. Even though SNP arrays were not originally
designed for analysis of tumor samples, it was soon demonstrated that these
platforms are suitable for the analysis of cancer genomes (Lindblad-Toh et al.,
2000; Wang et al., 2004; LaFramboise et al., 2005; Zhao et al., 2005; Peiffer et al.,
2006). Allele specific interrogation of tumor DNA using SNP arrays provides
means to investigate the relative abundance of alleles and effectively combine
the advantages of LOH analysis and aCGH analysis. Thus, SNP arrays enable
researchers to detect copy neutral events in tumors along with copy number

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Abstract: Background Although the involvement of intra-tumor genetic heterogeneity in tumor progression, treatment resistance, and metastasis is established, genetic heterogeneity is seldom examined in clinical trials or practice. Many studies of heterogeneity have had prespecified markers for tumor subpopulations, limiting their generalizability, or have involved massive efforts such as separate analysis of hundreds of individual cells, limiting their clinical use. We recently developed a general measure of intra-tumor genetic heterogeneity based on wholeexome sequencing (WES) of bulk tumor DNA, called mutant-allele tumor heterogeneity (MATH). Here, we examine data collected as part of a large, multi-institutional study to validate this measure and determine whether intra-tumor heterogeneity is itself related to mortality.

219 citations

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Abstract: Screening for gene copy‐number alterations (CNAs) has improved by applying genome‐wide microarrays, where SNP arrays also allow analysis of loss of heterozygozity (LOH). We here analyzed 10 chronic lymphocytic leukemia (CLL) samples using four different high‐resolution platforms: BAC arrays (32K), oligonucleotide arrays (185K, Agilent), and two SNP arrays (250K, Affymetrix and 317K, Illumina). Cross‐platform comparison revealed 29 concordantly detected CNAs, including known recurrent alterations, which confirmed that all platforms are powerful tools when screening for large aberrations. However, detection of 32 additional regions present in 2–3 platforms illustrated a discrepancy in detection of small CNAs, which often involved reported copy‐number variations. LOH analysis using dChip revealed concordance of mainly large regions, but showed numerous, small nonoverlapping regions and LOH escaping detection. Evaluation of baseline variation and copy‐number ratio response showed the best performance for the Agilent platform and confirmed the robustness of BAC arrays. Accordingly, these platforms demonstrated a higher degree of platform‐specific CNAs. The SNP arrays displayed higher technical variation, although this was compensated by high density of elements. Affymetrix detected a higher degree of CNAs compared to Illumina, while the latter showed a lower noise level and higher detection rate in the LOH analysis. Large‐scale studies of genomic aberrations are now feasible, but new tools for LOH analysis are requested. © 2008 Wiley‐Liss, Inc.

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TL;DR: It is found that microdiversity predicts poor cancer-specific survival (60%; P=0.009), independent of other risk factors, in a cohort of 44 patients with chemotherapy-treated childhood kidney cancer, and survival was 100% for patients lacking microd diversity.
Abstract: Genetic differences among neoplastic cells within the same tumour have been proposed to drive cancer progression and treatment failure. Whether data on intratumoral diversity can be used to predict clinical outcome remains unclear. We here address this issue by quantifying genetic intratumoral diversity in a set of chemotherapy-treated childhood tumours. By analysis of multiple tumour samples from seven patients we demonstrate intratumoral diversity in all patients analysed after chemotherapy, typically presenting as multiple clones within a single millimetre-sized tumour sample (microdiversity). We show that microdiversity often acts as the foundation for further genome evolution in metastases. In addition, we find that microdiversity predicts poor cancer-specific survival (60%; P=0.009), independent of other risk factors, in a cohort of 44 patients with chemotherapy-treated childhood kidney cancer. Survival was 100% for patients lacking microdiversity. Thus, intratumoral genetic diversity is common in childhood cancers after chemotherapy and may be an important factor behind treatment failure.

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Abstract: Potential bias introduced during DNA isolation is inadequately explored, although it could have significant impact on downstream analysis. To investigate this in human brain, we isolated DNA from cerebellum and frontal cortex using spin columns under different conditions, and salting-out. We first analysed DNA using array CGH, which revealed a striking wave pattern suggesting primarily GC-rich cerebellar losses, even against matched frontal cortex DNA, with a similar pattern on a SNP array. The aCGH changes varied with the isolation protocol. Droplet digital PCR of two genes also showed protocol-dependent losses. Whole genome sequencing showed GC-dependent variation in coverage with spin column isolation from cerebellum. We also extracted and sequenced DNA from substantia nigra using salting-out and phenol / chloroform. The mtDNA copy number, assessed by reads mapping to the mitochondrial genome, was higher in substantia nigra when using phenol / chloroform. We thus provide evidence for significant method-dependent bias in DNA isolation from human brain, as reported in rat tissues. This may contribute to array "waves", and could affect copy number determination, particularly if mosaicism is being sought, and sequencing coverage. Variations in isolation protocol may also affect apparent mtDNA abundance.

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Frequently Asked Questions (14)
Q1. What are the contributions in "Genotyping techniques to address diversity in tumors" ?

Unless other specific re-use rights are stated the following general rights apply: Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. You may not further distribute the material or use it for any profit-making activity or commercial gain • 

Due to its narrow definition – homozygosity caused by two copies from the same parent – and close association with constitutional genetics, the authors will refrain from using the term UPD when discussing copy number neutral allelic imbalance events. 

LOH analyses have, on the other hand, been widely used in cancer research to detect regions of allelic imbalances indicating regions of genomic deletion or copy number neutral LOH, and have been used to identify tumor suppressor genes inactivated by mutation followed by loss of the wild-type allele. 

The combination of genotype and copy number measurements makes SNP arrays ideal for the identification of copy number neutral imbalances. 

The BAF profile of a homozygous genome, e.g., a haploid genome, will consequently present only 2 bands, restricted to theoretical BAF values 0 and 1, whereas a triploid genome will show four bands. 

Illumina utilizes their BeadChip technology that permits probes to be immobilized on silica beads rather than directly onto the array surface. 

Although values from individual SNPs can be plotted, various segmentation approaches can effectively reduce the complexity of data, i.e., defining regions of genomic balance or imbalance and treating these as individual events assigned representative mBAF and LRR values. 

Equation (1) can with some minor modifications be used to calculate BAFvalues for any given locus in case of heterogeneous samples. 

SNP array platforms have also successfully been applied to address problems regarding intermixture of nonaberrant cell populations. 

The interplay between cells within the tumor microenvironment has been highlighted as important hallmarks of cancer and its composition has been shown to represent an intrinsic property of tumors (Hanahan and Weinberg, 2011). 

Transformation of intensities to relative copy number estimates is essentially also performed by relating values to a collection of normal reference samples (HapMap) or to a matched control. 

The limited availability of multiple samples from individual patients can be circumvented by macro or micro dissection (Navin et al., 2010) or cell sorting procedures followed by expansion in animal models (Navin et al., 2011), effectively performing multiple samplings of the same tumor. 

Examples of expected BAF and LRR values for a normal genome and how these values are affected by acquired genetic aberrations is further discussed below.D. Expected BAF and LRR for a Normal GenomeIn a diploid genome, there are only three possible allele combinations for agiven locus: homozygosity for the A allele (AA), heterozygosity (AB) orhomozygosity for the B allele (BB). 

The authors previously described that, when considering a larger series of SNPs, a BAF plot will appear as banded and that three bands are seen when analyzing a normal diploid genome.