A Next-Generation Sequencing Based Analysis of Clonality across 39 Subjects Treated for Lymphoproliferative
Disorders Reveals Matching Clones in the Diverse IGH Locus

I033

Alyssa M. Zlotnicki1, Austin Jacobsen1, Edgar Vigil1, Kasey Hutt1, Andrew Carson1, Ying Huang1, Khedoudja Nafa2, Maria Arcila2,3, and Jeffrey E. Miller1
1Invivoscribe, Inc., San Diego, United States, 2Dept of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 3Dept of Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, UnitedStatesMethods

Inter-run overlap comparisonsa)

Discussion and Conclusions
Figure 1: Percent of unique clones shared between the repertoires of any two given subjects within a sequencing run or between two different sequencing runs. The first row contains comparisons
between subjects between different sequencing runs, and the second row contains comparisons between subjects within the same sequencing run. The latter set of comparisons is presented in order todemonstrate the level of expected overlap within a run due to barcode-hopping/crossover, and to provide as a basis for comparison to the former. The orange box indicates an inter-run comparison between
runs sequenced on the same MiSeq®. The turquoise boxes indicate an inter-run comparison between runs sequenced on the same day.

Results: Proportions of Overlapping Clones
Based on Run Average and Disease Types
Figure 2a: Average percent of
unique clones shared between
the repertoires of any twogiven subjects within asequencing run or between
two different sequencing runs.
This plot represents theaverage number of overlaps
between any two subjects
within the two given run
identifiers, e.g. the average
number of overlaps between
subjects in run1 and run2. Theorange box and turquoise boxes
indicate inter-run comparisons
between runs sequenced on
the same MiSeq® and sameday, respectively.

MiSeq® is a registered trademark of Illumina, Inc.

AMP, November 05-09, 2019, Baltimore, Maryland, USAb)

Clustering of inter-run overlap counts
Clustering of intra-run overlap counts
between run 3 and run 4
within run 4
Figure 3: Hierarchical clustering of subjects based on the number of clones in eachsubject that overlap with at least one other run.
a) clustering between subjects in two different MiSeq® runs based on unique clone
count between subjects. Other inter-run clustering graphs showed similar patterns.
b) clustering between subjects within the same MiSeq® run based on unique clone
count between subjects. Other intra-run clustering graphs showed similar patterns.
Hierarchical clustering was performed using R’s hclust function, using the single
agglomeration method. Counts were scaled using z-score scaling before clustering.

Intra-run overlap comparisons

The detection of clonality of the rearranged immunoglobulin heavy chain (IGH)
locus is the basis for early detection of B-cell malignancies. The genomic
diversity of this locus has been analyzed in many studies, showing that VDJrearrangement and the random additions of nucleotides that occur afterwards
produce a high diversity across healthy and affected individuals.1, 2 This isespecially true in the complementarity determining region 3 (CDR3) region,
which is the most variable region of the IGH locus.4 The resulting possiblecombinations generated from these rearrangements and additions generatesan immune repertoire of > 1 × 1012 for Ig receptors1. Based on these studies, it
is extremely rare to detect the presence of the exact same clone sequence inseparate human subject repertoires. Here we present an analysis of thediversity of the IGH locus in subjects treated for various lymphoproliferative
disorders using a Next-Generation Sequencing based clonality assay.

Results: Hierarchical Clustering

Results: Proportion of Overlapping Clones to Total Clones Between Subjects

Introduction

Figure 2b: Percent of uniqueclones shared between therepertoires of subjects withthe same disease between anytwo different sequencing runs.
This plot represents theaverage number of overlaps
between any two subjects indifferent sequencing runs thatwere treated for a particulardisease type or types: e.g. theaverage number of clones
shared between subjects with
B-ALL and subjects with PCN
between any two given
subjects between any givenrun.

Results: Subset Motifs Found in the CDR3 Regions of Clones
Shared Between All Runs
Table 1: Top 6 subsets found in 267 clusters of clones shared by all runs based on the major IGH CDR3subsets found in the repertoires of CLL subjects by Agathangelidis et. al. (2012)
SubsetNumberNumber of clone clusters

Ranked 1stby p-value

Ranked 2ndby p-value

CDR3 pattern

Ranked 3rdby p-valueCountPercent

CountPercent

CountPercent

#31

44

16.5%

00%

72.62%

ARxxxxxxxxxxxxYYYxMDx

#64B

00%

4215.7%00%

A[KRH][DE]xx[AVLI]VVxxx[AVLI]xYYYYGMDx#28A

72.62%

20.749%3312.4%

ARxxxGxxYYYYYGMDx#500%

72.62%

114.12%

ARxxxxxx[AVLI]xxxYYYYxMDx#2124.49%

00%

00%

[AVLI]x[DE]xxxM[DE]x0%
3.37%#120900%
ARDxxYYDSSGYY[ST]xxxDx# clusters
where no72.7%
73.8%
197
20476.4%
N/A
subset p < 0.05 194
Background: There were 1279 unique clone sequences shared between all four runs. Given the high variability of the IGH CDR3region, it would be rare to observe patterns in this region due to random chance, and so the CDR3 regions of the shared clones
were extracted for comparison. To determine if shared clone motifs were similar to motif patterns observed in clinical samples,the comparison was performed against a set of 19 IGH CDR3 subset motifs derived from 7424 clinical chronic lymphocytic
leukemia (CLL) samples; the largest study of its kind at its time of publication.3 Motif matches between the clones and thesubsets would indicate: 1) a strong consensus group can be formed from the clones, showing that some sort of genetic event(e.g. clonal evolution) has caused sequence convergence to develop post-treatment. 2) these motifs are possibly clinicallyrelevant, given they share motifs that are associated with certain clinical attributes (e.g. age of patient, aggressiveness ofdisease), and gives support to the idea that a similar clustering analysis performed with treatment information for the diseases
analyzed here (B-ALL, MCL, PCN), may reveal motifs similarly associated with clinical outcomes, given that clones in IGH locus
cause all aforementioned diseases, although this would not be the case if convergence is related to the treatment itself.
Methods: Clusters of CDR3 clone regions were generated using three of the criteria followed in other CDR3 clustering practices:
50% amino acid identity, 70% amino acid similarity based on physio-chemical properties, and same CDR3 amino acid sequencelength.3 Position-specific scoring matrices (PSSMs) were generated for each cluster, each CLL subset, and 31 random sequenceswith mean length 19 using MEME’s sites2meme with pseudocounts=0.0001 and background amino acid frequencies derived
from analyses of vertebrate polypetides.5,6,7 Each cluster PSSM was compared to the set of CLL subset and random sequencesPSSMs using MEME’s tomtom to determine motif matches. Any motif match that had a p-value higher than that of a random
sequence or 0.05, whichever was lower, was considered an insignificant match and was not used for cluster motif classification.

The proportions of overlapping clones (where an overlapping clone was defined as aexact match between two clones across the entire length of their sequences)
between subject samples across runs are lower than what is seen within runs, but isdistinctly present across runs. This demonstrates that the overlaps between runs donot mimic the pattern of overlap seen from what you would expect with sample
contamination, which is seen in the intra-run samples due to barcode-hopping. The
number of overlaps seen between runs sequenced on the same MiSeq® was notsubstantially higher or lower than other inter-run comparisons, which would indicatethat these overlaps are not due to contamination. Clustering revealed overlap counts
were similar based on run or subject obtained from, further indicating the substantive
presence of these overlaps. These overlaps were not expected to be present at thelevel observed in each subject’s repertoire given the high diversity of the IGH locus
and the number of possible unique Ig receptor sequences. This could be explained byconvergence of these IGH FR1 sequences in response to treatment. Clustering of theCDR3 regions of IGH clones shared by all sequencing runs revealed that a strong
consensus subset could be generated via clustering based on amino acid identity,amino acid similarity, and sequence length. When these motifs were compared to 19
major CLL subsets3, the proportion of sequences that were classified and unclassifiedwere similar to those found by Agathangelidis et. al. (2012) (27.3%/72.7% ascompared to 30%/70%), which indicates that the diversity of clones is not higher orlower than expected by this metric. The most significant CLL subset found in the mostshared clones in our dataset (#31) has the characteristics of being found in younger
subjects and subjects with aggressive disease. An analysis using subject samples withknown treatment status may inform future interpretations of results from posttreatment subject samples, particularly considering clonal evolution and tracking
future clones. If shared clones were found to be strongly associated with the subject’s
treatment type, it would provide evidence to suggest that their convergence istreatment driven. This would inform the analysis of the efficacy of newly developed
drugs, as this would indicate that certain clones may be generated from the drug inaddition to the disease. It is also possible that these clones are a sign of earlyprogression of the disease that remain artifacts post-treatment. Analysis of clones
present throughout the course of treatment would reveal if this is the case.

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