For the first application, in addition to a similar phylogenetic tree to those shown in [Schwarz et al., 2015], our model is able to detect a resistant latent profile that was already present in the primary tumor. The exploration of these latent profiles could provide new genetic biomarker targets and help to develop original adapted drugs.
This chapter also summarizes a work done in collaboration with Institut Curie.
This collaboration enabled to apply our developed model on a completely new raw data set. However, it is difficult to draw relevant biological conclusions at this stage.
Although some patients were grouped after clustering, it appears that TNBC remains a heterogeneous disease for which it is difficult to highlight relevant biomarkers. Similar conclusions have been drawn from the analysis of the RNAseq data performed by Benjamin Sadacca.
Therefore, a key point of this Chapter is that we were able to apply the InCaSCN model on two different kinds of data sets. First, data from two technologies have been used (microarray and sequencing). Then, the model has been used to attempt to explain both intra and inter-tumoral heterogeneity. It could be interesting for the second one type of heterogeneity to apply the model to a study with a similar design than those of Institut Curie but on a less heterogeneous cancer type.
For both real data sets, we discovered that the inferred parental copy numbers present breakpoints simultaneously in minor and major copy number signals. However, such phenomenon is unlikely because it means that two major alterations occur at the same time. As a result, as mentioned in the Chapter 6, some modifications like the penalties that control the level of sparsity of the latent profiles could be added. We could also force jumps of latent profiles to occur only on the minor of major copy number. This phenomenon was not observed in the simulation study and may be that these results are due to the approximation on the estimations of the DoH in both of cases or to a poor segmentation.
We are currently writing an article that presents InCaSCN model and an application to a real data set.
Part III
Bioinformatic pipelines
Table of Contents
8 Estimation of DoH in absence of normal reference 139 8.1 Estimation of DoH . . . 140 8.2 Detection of a normal region after the segmentation . . . 142 8.3 Checking the assumptions . . . 144 8.4 Tentative to correct the bias. . . 148 8.5 Experiments on Illumina data set . . . 149 8.6 Conclusion . . . 152 9 Pipeline to estimate DNA copy number from sequencing data 153 9.1 Introduction . . . 153 9.2 Estimation of Copy number and B allele fraction . . . 153 9.3 ExCoBAF pipeline . . . 157 9.4 Parental copy number estimations . . . 160 9.5 Integration to a visualization tool . . . 161 9.6 Conclusion . . . 165
Chapter 8
Estimation of DoH in absence of normal reference
In absence of the knowledge of germline genotype of each locus, it is not possible to compute the DoH. Indeed, DoH is only computed on the heterozygous SNPs. Therefore, without DoH estimations, it is impossible to estimate the parental copy number signals and to use InCaSCN model described in Chapter6 to infer tumoral heterogeneity.
It is not uncommon, in cancer research, that the normal blood samples are not available because of non-consent of patients to sequence or genotype their genomes or because normal tissue samples were not taken at the time of the study. For instance in the case of retrospective or tumor cell lines studies.
It was the case of [Schwarz et al.,2015] data set used in Chapter7, where the data set was only composed of tumor samples. In order to compensate the absence of normal samples and therefore to not lost information from SNP arrays, we developed a method to estimate the DoH by regions and then the parental copy number signals. Indeed, the InCaSCN model only requires segment-level PSCN (parent specific copy number) estimates and not necessarily at each locus.
This chapter is divided into four sections. The first one describes how to estimate the DoH at a segment-level and the assumptions that are required. The second one deals with the detection of the normal region that is one of the main issues in the estimation of DoH in absence of a normal reference. Then, we attempt to check whether the assumptions of the model are realistic and whether the estimations are close to the reality. Some bias issues have been raised at the end of the section and we attempt to correct them in a next section. The last section is about experiments on another data
set from another technology.
The work done in of this Chapter is in progress and several issues have not been yet fixed.
8.1 Estimation of DoH
We would like to estimate the DoH at a segment-level. Then, in the following, a segment is denoted by s and we denote by ns = Card(s) the number of SNPs in segment s, nAAs ,nABs andnBBs are the number of SNPs AA, AB and BB in segmentsrespectively.
Note that, ns=nAAs +nBBs +nABs .
Then, we define the quantity δj for either heterozygous and homozygous SNPs j by:
δj = 2× |bj −1/2| (8.1)
where bj is the BAF for locij= 1, . . . , J.
δs=πsAAδAAs +πBBs δBBs +πABs δABs = 1 ns
X
j∈s
δj (8.2)
δABs = δs−πsAAδAAs −πBBs δBBs
πsAB (8.3)
where :
• πAAs = nnAAss ,
• πBBs = nnBBs
s
• and πsAB = nnABs
s
are respectively the proportion of SNPs AA, BB and AB in the segment s for s = 1, . . . S, and
• δAAs = nAA1 s
P
j∈sδjAA,
CHAPTER 8. ESTIMATION OF DOH IN ABSENCE OF NORMAL REFERENCE
• and δABs = nAB1 s
P
j∈sδABj
are respectively the mean of δ for SNPs AA,BB and AB in the segment s. Note thatδAB is in fact the DoH.
We assume that the proportion of the homozygous (πAA and πBB), and heterozy-gous SNPs (πsAB) are the same regardless of the type of region (gain, loss, normal,..).
Then, we can estimate the proportion of homozygous SNPs by detecting a normal re-gion and selecting SNPs with a BAF larger or lower than given threshold values. These two thresholds are dependent on the technology or even of the sample itself. The nor-mal regions have the particularity that BAF signals have only three modes that are centered at 1 (BB SNPs), 0 (AA SNPs) and 1/2 (AB SNPs). The Fig. 8.1 shows the raw BAF signal of the annotated data set fromacnrpackage (Affymetrix technology).
The first region is a normal one where it appears that the number of modes is three with the values quoted previously. The main difficulty is to detect these kinds of regions, we present a way to detect these regions in section 8.2.
Figure 8.1 – BAF signal
Then, once we know a normal region, we are able to estimate the mean ofδAAN and δBBN for a normal region. Assuming that the distribution of δ for homozygous SNPs is constant along the genome, we replace δAAs and δBBs in (8.3) respectively by their estimations in the normal region (δAAN andδBBN ).
δˆsAB = δs−πAAN δAAN −πsBBδBBN
1−(πNAA+πBBN ) (8.4)
Finally, the parental copy number estimations are equal to:
ys1= ¯cs×
1−δˆABs /2 ys2= ¯cs×
1 + ˆδABs /2
(8.5)
where ¯cs is the average of the TCN in the segment s.
To summarize, we have made three assumptions to estimate DoH that are:
A1 No breakpoints in the segments
A2 Proportion of the heterozygous and homozygous SNPs is the same along the genome (independent of the region).
A3 The distribution of the homozygous SNPs is the same regardless of the region.