The whole-genome landscape of Burkitt lymphoma subtypes
Authors: Yuan Zhuang
Devang Thakkar
Ann M Moormann
John Michael Ong&#x27;echa
Christopher R Flowers
Cliff I Oduor
Rodney R Miles
Nestory Masalu
Razvan I Panea
Kristin M S Schroeder
Jennifer R Shingleton
Cassandra L Love
Nelson J Chao
Randy D Gascoyne
Bachir Alobeid
Guojie Li
Megan Agajanian
Yuri Fedoriw
Harshit Sahay
Tushar Dave
Yen-Yu Lin
David B Dunson
Micah A Luftig
Govind Bhagat
So Young Kim
Sandeep S Dave
Michael B Major
Wolfgang Hartmann
Eric D Hsi
Vidya Seshadri
Anupama Reddy
Shawn Levy
Alexander Waldrop
Grzegorz Rymkiewicz
Kristy L Richards
Rex K H Au-Yeung
Brooke C Palus
William W L Choi
Georg Lenz
Minerva Mukhopadyay
Howard Meyerson
Jeffrey A Bailey
Sarah L Ondrejka
Juliana A Otieno
Journal: Blood (2019)
Discipline: CLINICAL MEDICINE
Keywords: Burkitt lymphoma; genomic basis; driver genes; whole-genome sequencing; CRISPR screens
Affiliations: Center for Genomic and Computational Biology and Department of Medicine, Duke University, Durham, NC., Department of Pathology and Laboratory Medicine, Brown University, Providence, RI., Department of Medicine, University of Massachusetts, Worcester, MA., Jaramogi Oginga Odinga Teaching and Referral Hospital, Ministry of Health, Kisumu, Kenya., Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya;, Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya., Bugando Medical Center, Mwanza, Tanzania., Department of Cell Biology and Physiology, Washington University in St. Louis, St. Louis, MO., University of North Carolina, Chapel Hill, NC., Poland Flow Cytometry Laboratory, Department of Pathology and Laboratory Diagnostics, Maria Sklodowska-Curie Institute-Oncology Center, Warsaw, Poland., Department of Pathology, University of Utah, Salt Lake City, UT., Department of Pathology and Cell Biology, Columbia University, New York, NY., Department of Hematology and Medical Oncology, Emory University, Atlanta, GA., Department of Laboratory Medicine, Cleveland Clinic, Cleveland, OH., Department of Pathology, Hong Kong Sanatorium and Hospital, Hong Kong, China., The University of Hong Kong, Queen Mary Hospital, Hong Kong, China., Institute of Human Genetics, Christian-Albrechts-University, Kiel, Germany., Division of Translational Pathology, Gerhard-Domagk-Institute of Pathology, University Hospital Münster, Münster, Germany., Medical Department A, Hematology, Oncology and Pneumology, University of Münster, Münster, Germany., Department of Pathology, Case Western Reserve University, Cleveland, OH., Department of Immunology and., Department of Molecular Genetics and Microbiology, Duke University, Durham, NC., Department of Pathology and Experimental Therapeutics, BC Cancer Agency and BC Cancer Research Centre, Vancouver, BC, Canada., HudsonAlpha Institute for Biotechnology, Huntsville, AL; and., Department of Statistical Science, Duke University, Durham, NC.

Abstract:  Burkitt lymphoma (BL) is an aggressive, MYC-driven lymphoma comprising 3 distinct clinical subtypes: sporadic BLs that occur worldwide, endemic BLs that occur predominantly in sub-Saharan Africa, and immunodeficiency-associated BLs that occur primarily in the setting of HIV. In this study, we comprehensively delineated the genomic basis of BL through whole-genome sequencing (WGS) of 101 tumors representing all 3 subtypes of BL to identify 72 driver genes. These data were additionally informed by CRISPR screens in BL cell lines to functionally annotate the role of oncogenic drivers. Nearly every driver gene was found to have both coding and non-coding mutations, highlighting the importance of WGS for identifying driver events. Our data implicate coding and non-coding mutations in IGLL5, BACH2, SIN3A, and DNMT1. Epstein-Barr virus (EBV) infection was associated with higher mutation load, with type 1 EBV showing a higher mutational burden than type 2 EBV. Although sporadic and immunodeficiency-associated BLs had similar genetic profiles, endemic BLs manifested more frequent mutations in BCL7A and BCL6 and fewer genetic alterations in DNMT1, SNTB2, and CTCF. Silencing mutations in ID3 were a common feature of all 3 subtypes of BL. In vitro, mass spectrometry-based proteomics demonstrated that the ID3 protein binds primarily to TCF3 and TCF4. In vivo knockout of ID3 potentiated the effects of MYC, leading to rapid tumorigenesis and tumor phenotypes consistent with those observed in the human disease. 

Retracted: No

PubPeer comments:
 While attempting to determine the source of the data issue described in a separate comment, we noted another oddity in the data. This study describes the use of whole genome sequencing (WGS) to characterize these tumors. The WGS technique involves the (semi)random sampling of sheared or enzymatically fragmented genomic DNA. This results in reads that, on average, stack on the genome to a  depth that fluctuates around the average target depth. In DNA extracted from formalin-fixed tissue there are some sources of bias that cause higher variability in coverage and some consistent sites of extreme depth. We observed this pattern in the data but we also observed another pattern that was unexpected. The libraries each have significantly higher coverage across the exons of genes. This is the pattern that is only see in libraries that have undergone some sort of targeted enrichment (some form of  exome capture is the most common cause of this). When generating exome-enriched libraries, these are depleted for DNA from the rest of the genome and thus are by definition, not WGS. Using the read groups, which designate sets of sequencing data generated on different runs of the sequencer, we were able to separate two read groups for each bam file. The images below show the coverage of each sample with the upper section showing the read group that appears to be random (WGS-pattern) data and the lower section showing the exome-like coverage. There is no mention of exome capture in the methods of this paper and the data was, throughout, described as WGS. The non-exome portion of each bam is variable but is usually quite low coverage, nowhere near the depth at which one would consider these as having been sequenced. The coverage across the coding space is low in a substantial number of cases even when the exome reads are included. As per the paper, they report  WGS to a mean genome-coverage of 75x . The plot below shows the distribution of coverage (read depth) across variants reported in the study. Essentially this summarizes the coverage of regions where mutations were supposedly found. The majority of these sites have less than 50x coverage in all patients and in some cases, the average coverage appears closer to 10x or 20x. Some of the IGV displays at the bottom show examples of coverage at this level when we look at individual genes. It should be noted that this metric for coverage is generous because it focuses on places in the genome where mutations were reported, which are already highly enriched for coding regions (because of the exome reads). If we quantify genome-wide coverage (or view intergenic space), which is where the true utility of WGS comes into play, the numbers are much lower and clearly nowhere near the reported depth of 75x. ![file] It is very important to note that some of the results and many of the conclusions of this study would only be possible to properly achieve if WGS was performed and this seems to not be the case. For example, the mutation of introns and the detection of structural variants would be inadequate with these data. It s simply misleading for this to have been described as a WGS study. The authors should clarify how their data was generated and the limitations of the depth of sequencing achieved here should be acknowledged. ![file] Example 1. Coverage of MYC with non-exome (top) and exome-like (bottom) reads in sample 1296 ![file] Example 2. Coverage of MYC in three more samples showing exome-like coverage. ![file] Example 3. Coverage of the GNA13 gene showing a similar exome-like coverage. ![file] Example 4. Sparse coverage across GNA13 in sample 1296 ![file] Example 5. Sparse coverage of ID3 in sample 1296 , 
 We performed a more direct analysis of the duplicated mutations in the supplemental data. The positions of every mutation reported in the table was checked in the bam for non-reference reads that match the mutant allele in the table. All mutations in the supplement should have some corroborating reads. To account for sequencing error, we considered a variant allele fraction (VAF) of 5% to be equivalent to zero. We didn t consider insertion deletion mutations here due to the added complexity of inexact positions in the alignment. A startlingly high proportion of the variants reported in the supplement did not have read support at, or exceeding, this level of support (31% of the ~4500 variants). Put a more direct way, almost 1 3 of the data reported in that table is not supported by the raw data above the level of noise. In fact, many of them had zero reads supporting the mutant allele and in some cases had zero reads covering the region. This leads to the obvious question that if these variants are in the supplement but not supported by the raw data, where do the come from? It seems that many of them can be explained by the pattern shown in our first comment. The mutations that are shared between patients, usually two but sometimes many more. For this analysis, we ll call the mutations reported in exactly two patients  duplicates . Mutations reported in more than two patients will be called n-plicates here. The plot below shows a breakdown of how many unconfirmed duplicate mutations were present in each patient, only for patients with at least one such mutation meeting this criterion. Some samples have had many duplicates attributed to them and others have had few or none. ![file] Oddly, we see a different set of samples that had unvalidated n-plicates attributed to them. This implies that a different process has led to the spread of these variants to those samples. ![file] What could this process be and why did it happen? It didn t escape our notice that the most significantly affected sample of the first variety (duplicate-contaminated) had IDs mostly in the 2000 range. These were the African BL samples. Many of them also had lower sequencing depth across coding regions. Could it be that mutations were artificially attributed to these genomes from another patient to bolster the appearance of higher mutation yield? If so, is there any pattern or directionality to the migration of mutations between patients? It certainly looks that way. The heat map below is representing the relative overlap of shared mutations reported per pair (out of all mutations in the patient). This matrix should look like a red (hot) diagonal on a blue void. Instead, we see some red off the diagonal, mostly either 2 or 3 away. This looks like a pattern of a subset of mutations flowing (i.e. being duplicated) mostly from patient N to patient N+2 or N+3 among the African subset of samples. The duplication of n-plicates is more complicated and is probably what causes the warm area on the lower right of the heat map. Although it s conceivable that this massive data inconsistency was caused by a substantial error rather than intentional manipulation, we cannot think of any accident that would cause this specific pattern in the data. It would be extremely helpful if the authors could offer any insights into this. ![file] , 
 #2 It s a bit hard to comment as the data you are analyzing is protect by a DAC. If you read their supplemental methods they state in the  Whole Genome DNA and Transcriptome Library Preparation section that some of the samples were prepared via  SureSelectXT All Exon V6 + UTR which can be used as a WES sequencing kit. In that sense your statement of  There is no mention of exome capture in the methods of this paper is false. Although it is odd that it is not mentioned within the manuscript itself... it says 24 samples were analyzed this way. Are there multiple sequencing files per patient? That might explain your IGV screenshots above if they have both WGS and exome coverage... , 
 #4  100-1000 ng of total RNA was subjected to ribosomal depletion by hybridization, RNase H and DNase I digestion (NEB, Ipswich, MA) and bead purification. Library preparation was performed using the KAPA Stranded RNA-Seq Library Prep kit according to the manufactureru2019s specifications (Roche, Wilmington, MA). Multiplexed capture was performed on up to 24 RNA libraries using the SureSelectXT All Exon V6 + UTR bait set, according to the manufactureru2019s specifications (Agilent, Santa Clara, CA). Subsequent libraries were sequenced on the Illumina Hiseq 2500 V4 platform, 50 paired end. I did see that in the text. My take on this section is that it s only referring to the RNA-seq analysis. They appear to have used that capture to enrich for mRNA. This is sometimes used in lieu of enrichment for polyadenylated RNA with oligo dT. The exome-like reads I refer to above are in the same bam files as the reads that look like low-coverage WGS libraries (not separate files). I separated them out this way within IGV. I have never seen a single bam with two read groups from different library preparation methods pooled together this way. I will clarify my earlier statement: **There is no mention of exome capture in the context of the DNA sequencing methods.** , 
 The pattern of mutations that are not supported by the data is much more clear when we focus on the subset of mutations that are not supported by the corresponding bam of at least one sample. There are more than 700 examples of such  false positive variants reported in the supplement. Most of these appear to be mutations that are duplicates of existing mutations but are only supported in data from one of the two patients. The heat map below shows only those mutations and their status in each patient (red: confirmed or blue: false positive). There appear to be blocks of mutations misattributed between 28 individual samples. mostly within pairs, but sometimes more complex swaps among 3 or more cases. This pattern is entirely restricted to the cases indicated as the African (or endemic) subtype. There are two other patterns in this image. The region containing mostly red appears to be actual recurrent mutations that arise in multiple patients. I expect these are recurrent hot spot mutations, which are rare overall. In contrast to the pattern in the samples discussed above, this is the pattern that might be expected in such a data set. There is a third set of mutations that do not appear to validate in any sample. The origin of these is unclear but does not seem to be explainable by any swapping or mis-assignment of mutations, at least not among samples in these data. It would be very interesting to find out how this pattern of widespread mis-attribution of mutations arose. If this reflects the mutation calls that were used for subsequent analyses, it draws into question the validity of many of the conclusions of this paper. ![file] , 
 If the mutations that are not supported by the data were indeed used for the various downstream analyses discussed in the paper, these conclusions should all be drawn into question. One easy result where we can check this is the mutation load per genome. Figure 2B shows a box plot that shows the mutation load with patients separated by EBV type (or EBV-negative). ![file] Below is the reproduction of this figure in which we have overlaid the individual points for a bit more transparency. This is using all the mutations reported in the supplement. You should be able to see that the numbers match up. The second image is the subset of mutations that we could confirm using the associated bam file for that sample (i.e. the mutations that show up as red in the heat map above). You can hopefully see that all groups shift downward due to a loss of many reported mutations that were not corroborated. This dropout is not evenly affecting the three groups. It appears that the Type2 EBV cases shift down more and Type 1 EBV cases barely change. The paper reports a significant difference in the mutational burden between these groups using ANOVA. Since that test doesn t tell us which of the groups differ, we tried a Wilcoxon test, comparing Type 1 and Type 2 (the difference that is supposedly a novel finding of the paper). The P value from this comparison on the original data is **0.094**, so we can t reject the null hypothesis that the mutation loads in Type 1 and Type 2 do not differ. Owing to the shift of Type 1 downward from the loss of more mutations among those cases, the P value is **0.3795** when only the validated mutations are considered (i.e. the data that underlies second box plot). Although this change didn t shift a significant result to an insignificant result because the original difference wasn t significant when these two groups were directly compared, this comparison still suggests that other results would also be affected by the many unsupported mutations. ![file] ![file] , 
 Another main finding reported in the paper is the significant enrichment of mutations in a few genes among the sporadic BLs, namely DNMT1, SNTB2, and CTCF. This is in contrast to the abundance of mutations in BCL7A and BCL6 reported in the Endemic cases. Given the association of mutations in the latter with aberrant somatic hypermutation (caused by AID activity), we were interested in exploring the three genes with the opposite trend. The mutations from the supplement for these genes are shown in the heat map below. The pattern is quite striking. ![file] Using our very lenient criteria for validating a mutation, indeed many of the mutations in DNMT1, SNTB2 and CTCF do have support in the raw data (unlike those discussed above). Of note, when we look at the data using VAF we can see that virtually all of these had extremely low VAFs, which means the data supporting them is quite limited. This can be attributed to subclonal mutations but is more often explained by some sort of systematic artifact. ![file] Reported mutations with VAF showing low read support for mutations in DNMT1, SNTB2 and CTCF. One way to determine which phenomenon explains these variants is to determine whether this pattern extends to additional samples. This would be an indication that poor-quality alignments or an artifact in the libraries are causing the pattern. If we show the VAF of the same variant across the entire cohort, the pattern becomes more clear. Additional cases in which these variants weren t reported appear to have similar (low) VAF support of these. This is strongly suggestive that all of these variants are caused by an artifact. The enrichment of them in Sporadic cases is likely a batch effect that could be caused by a number of variables such as different handling of DNA or library preparation methods. ![file] Does this issue explain all the mutations that appear more common among Sporadic BLs in this study? That would take much deeper investigation to rigorously determine. This at least shows that those specific differences noted in the abstract are unlikely real. Extending this view to a larger set of variants (in this case, SNVs reported in 5 or more cases), the pattern holds true with the exception of those at the bottom of the image and a handful at the very top. The variants at the bottom are all in MYC or PAX5 and appear enriched in Endemic cases, which is no surprise as it was reported previously. Although they are sparse, the few variants at the top may be the only ones enriched among Sporadic BLs that don t represent artifacts in the data. ![file] , 
 A [correction] for this study was just published. In the correction, the authors assert that  This EGA entry also includes exome sequencing data that were not part of the analysis in this study. In other words, this analysis is more appropriately done after removing the read groups that are clearly exome reads. We did this and found discrepancies that are not consistent with the statement above. Specifically, there are a significant number of variants reported in Supplemental table 2 that have zero read support in the genome bam and yet did have read support in the corresponding exome data for the same patient. The only explanation for this is that the exome reads were, in fact, used in the analysis. Four such examples are included below. ![file] ![file] ![file] ![file] To explore whether there were any patterns for variants that appeared only in the exome data, the VAF for each variant was separately determined from the exome and the merged genome + exome (i.e. the bams as they exist in EGA). The heatmap below shows the VAF from each of these per patient (column). Each patient has three separate columns. The first is a white blue bar indicating whether or not that variant was reported in the patient (blue = reported in the supplement). The most common scenario seen in this are variants with little to no support in either the exome+genome data or the genome-restricted data (i.e. blue bar with no accompanying red in the adjacent columns). There were a limited number of patients with many examples of these exome-supported variants with no read support in the genome (e.g. 1288, 1296, 2979). These appear as a blue bar with a red bar to the right in the column that corresponds to the exome+ genome data. The examples shown above are all selected variants in these patients. Given the authors have stated that the exome data was not used in their analysis, how can it be that the genomes from these patients have no support for any of these variants and yet they were reported in the supplement? ![file] , 
 Using the genome reads (with the exome reads removed), the analysis described in earlier posts was repeated. The image below shows that the result is essentially the same albeit now there is an even larger number of unsubstantiated or False Positive (FP) variants. The heatmap has one column per patient and one row per variant. It shows only the subset of variants that were uncorroborated in at least one patient. The stacked bar plot on the left indicates the relative proportion of genomes in which the variant was either a TP (blue) or FP (red). The most common scenario among these is that the variant is confirmed in 50% (i.e. one of two) of the patients in which it was reported. The runs of red adjacent to blue appear due to clustering of patients. The patients clustered together here share a large number of variants in the supplemental table but seemingly the variant only ever exists in the data from one of the two patients. The bar chart at the top summarizes how many of the variants attributed to each patient were TP or FP using the genome reads. It seems as though the authors submitted a correction for this paper but did not address the fundamental problems with the data, i.e. a seemingly massive rate of duplication mis-attribution of mutations to the wrong patient. ![file] , 
 All analyses described above all used the most lenient criteria possible to allow a variant to be considered valid. Another important question we were interested in was the overall quality of the variant calls. Using the original bams from EGA (which contain both the exome and genome data combined), we performed indel and SNV calling using pipeline that involves four somatic variant callers. We then compared those variants to those reported in the supplement. The figure below contrasts the mutations that were reported to those that were corroborated by this variant calling pipeline. The genes shown here are the same genes used in Figure 2 of the study (with the same ordering). Unlike Figure 2 from the paper, which includes copy number information and non-coding variants, this figure only shows the coding variants. It is meant to emphasize the different representation of known suspected driver mutations between the analysis from Panea et al and our re-analysis. ![file] There are numerous examples of genes with striking differences between the reported and actual frequency of coding mutations between the reported data and our variant calls. Those affecting firmly established lymphoma genes are summarized below: |Gene|reported frequency|detected frequency| |---|---|--| |DDX3X|48%|28%| |HIST1H1E|42%|10%| |HIST1H2BK|38%|7%| |BTG2|14%|3%| |ID3|37%|23%| |HIST1H3H|26%|2%| |HIST1H1C|19%|5%| |SMARCA4|20%|10%| |GNAI2|20%|7%| |HIST1H3J|13%|4%| |KMT2D|13%|4%| The difference between these values can be explained by a combination of unsubstantiated variants (discussed earlier in this thread) and variants with low read support. This can be illustrated by visualizing the distribution of read support for all corroborated variants. In many of these samples, the median read support for all corroborated variants is less than 5. It s reasonable to expect that many of those variants would not have been detected by a somatic variant caller. It also seems reasonable to speculate that the variants with minimal support (i.e. those not shown in the right half of the image above) are likely highly over-represented by false positives. ![file] , 
 Another striking observation that does not even require access to the raw data is the large discrepancies even between two of the supplemental tables. Specifically, Table S2 (mutation status for all reported variants across all patients) and S3 (mutation clusters) cannot be consolidated. The first image compares the number of regions with consistent mutation status between all pairs of samples. The upper diagonal of this matrix is based on Table S3, so only has values for the 80 (of 101) patients that were represented in that table. The lower diagonal is instead calculated by counting the shared mutations between each pair of patients based on the information in Table S2. The samples in the top-left area that appear red (below the diagonal) are those that we had previously identified as having a pattern of shared mutations or  directional contamination. The samples we identified as being donors (i.e. real mutations that were duplicated in another patient) are indicated in the legend at the top. In theory, this matrix should show symmetry across the diagonal. The lack of red in the corresponding positions above the diagonal shows that these samples were not identified as having high levels of mutation sharing based on the analysis presented in Table S3. This could imply that the issue with the data relating to those samples arose *after* Table S3 had been prepared. The samples in the lower-right region of this image show a high degree of shared mutations according to Table S3 but not S2, which is more difficult to understand without more information. ![file] To clarify the potential cause of the samples sharing many mutations in Table S3, we peformed clustering based on the mutation status of each patient in each of the reported mutation clusters, according to Table S3. The set of 21 samples absent from S3 are included for completeness but appear entirely blue here because their mutation status is unknown, at least according to that table. The lower section of this heatmap tracks with a limited number of patients (the first seven from the left). These regions appear almost exclusively mutated in those patients. Although it is unclear why these genomes share a distinct set of mutations, this sharing explains the high degree of correlation for these patients in the image above. ![file] We find it concerning that the supplemental tables have such striking discrepancies. The authors should explain why 21 genomes were not used in the analysis presented in Table S3 and, moreover, why the high degree of shared mutations among the patients in Table S2 is not reflected in Table S3. , 
 This study generated RNA-seq data from 81 of the 101 patients. How the RNA-seq was used in the study (if at all) was never explicitly stated. The only other plausible source of the discrepancies we saw is that the variants with no support have come from RNA-seq. Indeed, if we check for support for each uncorroborated mutation in the RNA-seq bam from that same patient, many of the variants have reads that support the non-reference allele. There are 1287 such mutations with non-zero VAFs in the corresponding RNA-seq bam (not counting the mutations that were corroborated by the patient s WGS data). The naive view of this observation is that the uncorroborated variants are actually real (i.e. validated by the RNA-seq). Unfortunately, that isn t the case for most of these. The VAF distribution is shown below. Most of these have a VAF either near zero or near 1, whereas the VAF should be closer to 0.5 but with some variation around it due to different purity and allelic expression differences. Another interpretation of this finding is that the RNA-seq data was the primary source, or in the case of these specific variants, the ONLY source of data used to call these mutations to begin with. What this would mean is that the authors purposefully combined the WGS and corresponding RNA-seq data for the purpose of mutation calling but without any explicit mention of this. For a study that was framed as a genome landscape study, using RNA-seq as the primary source of variant detection is extremely troublesome. The fact that so many variants have zero read support in the genome is a clear indication of why this is problematic. The low VAF variants are quite likely to be caused by noise. The high VAF variants are more difficult to explain away. ![file] If we look at which patients have high-VAF support for their uncorroborated mutations, the picture becomes much more clear. The heatmap below has ordered the patients with those appearing to be  recipients of the directional data contamination detailed in earlier posts. In other words, the samples on the left are the ones that had many uncorroborated mutations that were _identical to mutations that were real in one other patient in the study._ What we can easily see in this image is groups of  mutations with impossibly high VAF support in each of these patients RNA-seq data. Why, then, would so many mutations appear in the RNA-seq but with no support in the patient s genomes? ![file] The explanation lies in the series of images below. Each of these shows a region containing some of these uncorroborated mutations with the WGS and matched RNA-seq from that patient. In fact, each image shows this for a pair of patients that were identified as having directional contamination of mutations from one to the other. It s clear from these and many other examples that each situation in which a patient appears to share many mutations with one other patient is caused by a series of mis-attributed RNA-seq libraries to the wrong patient. This mix-up would not have been detected if the variants weren t called from the RNA-seq. The high VAF of so many mutations becomes more easily explained in this case because many of the mutations are in MYC, which is expressed only from the translocated allele. The variants with high VAF exhibiting essentially mono-allelic expression are clear in many of the examples shown here. This appears to unequivocally demonstrate that the authors used RNA-seq data as a primary source of mutations in these patients (and quite possibly in all 81 patients with RNA-seq). Their use of the RNA-seq introduced numerous errors and artificially introduced many false mutations that do not even exist in the patient they were attributed to. I hope I m not alone in thinking this is entirely inappropriate and the authors need to be more explicit about their approach up-front as it would likely have affected how this study was handled during the editorial and peer review process. ![file] RNA-seq bam from patient 2970 matches mutations from 2969 ![file] RNA-seq bam from patient 2978 matches mutations from 2976 ![file] RNA-seq bam from patient 2987 matches mutations from 2984 ![file] RNA-seq bam from patient 2990 matches mutations from 2987 , 
 The authors of this study claim to have sequenced the tumor genomes from 101 different Burkitt lymphoma patients using whole genome sequencing. They provide a supplemental table that details which mutations were detected in each patient. The number of identical mutations observed in multiple patients (i.e. recurrent mutations) is strikingly high. Overall, the majority of mutations in Supplemental Table 2 are found in more than one patient. The pattern of recurrence is concerning because many of the mutations are reported to have occurred in exactly two patients. The plot below shows how cases vary by the relative fraction of mutations that are non-unique in the data. This number should be very low for tumors that are from different patients (close to zero). ![file] If we visualize the pattern of mutations each of these patient samples tends to have one other patient sample that it shares these mutations with. This image shows a random sampling of 300 exact duplicate mutations in the patient samples that have the highest relative proportion of non-unique mutations. ![file] Below is an annotated version of the same plot with yellow boxes showing the exact duplicates shared between each pair. Almost all cases share mutations with the same sample (rather than multiple other samples). This is very unlikely to happen by chance and is better explained by sample cross-contamination or manipulation of the data. ![file] We can check for cross-contamination (or accidental re-sequencing of the same samples twice) by determining if these mutations are also present in the raw data. The aligned sequencing reads for affected patients were manually checked in IGV. We noted a consistent trend that for these duplicate mutations, the raw data only supports the existence of the mutation in one of the two samples. The examples below are representative of the pattern we see for most of the shared mutations. In other words, these mutations really only exist in one of the two samples and have mis-attributed to the other. For many of these samples, the duplicate mis-attributed mutations comprise the majority of mutations attributed to that sample. This seems to rule out cross-contamination or accidental sequencing of duplicates and leaves the mis-handling (or manipulation) of the data as the only remaining explanation for this pattern. It is notable that the samples with shared mutations are usually separated by 2 or 3 in the original data matrix (based on their numeric identifiers). We have also noticed several other patterns in the data from this paper that are concerning and that draw the conclusions into question. To the best of our knowledge, the authors were made aware of these concerns and the journal has been contacted. ![file] **Example 1. Mutation is in sample 2973 but not 2975** ![file] **Example 2. Mutation is in sample 2977 but not 2979** ![file] **Example 3. Mutation is in sample 2983 but not 2986** ![file] **Example 4. Mutation is in 2989 but not 2992** , 
 The use of RNA-seq to increase the number of variants does not explain all the uncorroborated variants. This is easiest to confirm from the cases that had no RNA-seq data. For example, patient 1288 had only a genome and no RNA-seq in the EGA submission. This patient also had several mutations attributed to it in the supplemental table that were not supported by their genome reads but WERE supported by the exome reads. Based on the data available, the only explanation for this is that exome data was indeed used for calling at least some of the variants in this study. This directly contradicts the statement in their erratum. In other words, it looks as though the erratum that was published to explain the existence of exome data in EGA is not entirely factual unless the authors have some other genome data for this patient that was not submitted to EGA?  This EGA entry also includes exome sequencing data that were not part of the analysis in this study.u201d ![file] ![file] ![file] ,