Ohia HLHS mutant mice show splenic defects, a phenotype associated with heterotaxy, and HLHS in Ohia mice arises in part from mutation in Sap130, a component of the Sin3A chromatin complex known to regulate Lefty1 and Snai1, genes essential for left–right patterning. Together, these findings point to laterality disturbance mediating the left-sided heart defects associated with HLHS. As laterality disturbance is also observed for other CHD, this suggests that heart development integration with left–right patterning may help to establish the left–right asymmetry of the cardiovascular system essential for efficient blood oxygenation.Hypoplastic left heart syndrome (HLHS) is a complex congenital heart disease characterized by hypoplasia of left-sided heart structures. The developmental basis for restriction of defects to the left side of the heart in HLHS remains unexplained. The observed clinical co-occurrence of rare organ situs defects such as biliary atresia, gut malrotation, or heterotaxy with HLHS would suggest possible laterality disturbance. Consistent with this, pathogenic variants in genes regulating left–right patterning have been observed in HLHS patients. Additionally,  Hypoplastic left heart syndrome (HLHS) is a congenital heart disease (CHD) with severe hypoplasia of left-sided heart structures, including hypoplasia of the aorta (Ao), aortic and mitral valves (MV), and the left ventricle (LV). While HLHS infants now survive their critical CHD ,2 with aHLHS has been reported to co-occur with various laterality defects, indicating a possible role for left–right patterning disturbances in the restriction of defects to the left side of the heart . Thus, HZIC3 [FGFR4 [FOXF1 associated with bowel malrotation [HAND1 associated with HLHS [HAND1 encodes a transcription factor exhibiting restricted expression in the embryonic left ventricle, indicating an essential role in formation of the LV. However, mutant mice with a deficiency in these laterality genes have not replicated the HLHS phenotype, nor have knockin of a A126FS HAND1 somatic mutation recovered from HLHS heart tissue. In the latter mutant mice, heart outflow tract defects and intraventricular abnormalities were observed, but the LV was of normal size [HLHS in conjunction with other closely related left ventricular outflow tract obstructive (LVOTO) CHD lesions have shown high heritability, providing strong evidence of a genetic etiology for HLHS ,33,34,35ZIC3 , FGFR4 [3 [FGFR4 , and FOXrotation , are assith HLHS ,39. HANDmal size .Ohia in which the HLHS causing mutations were identified. This was shown to be mediated by two genes—a point mutation in Protocadherin A9 (Pcdha9) and a splicing defect mutation in Sin3A associated protein 130 (Sap130). Mice with CRISPR gene edited alleles of Pcdha9/Sap130 replicated the HLHS phenotype, confirming their role in the pathogenesis of HLHS seen in the Ohia mutant line. While the Pcdha9 mutation was shown to cause the aorta/aortic valve defects in HLHS, the Sap130 mutation was shown to play a pivotal role in the LV hypoplasia [Sap130 also may contribute to the defects being left-sided is supported by observations related to the function of the Sin3A complex (see below).From a large-scale mouse mutagenesis screen for mutations causing CHD, we recovered eight independent mouse lines with HLHS, including one mutant line named poplasia . The facOhia mutant line spanned a spectrum, with 26% exhibiting HLHS among mice that are double homozygous for the Sap130/Pcdha9 mutations [Ohia mutant line, the incomplete penetrance and variable expressivity must have an epigenetic etiology that likely reflects gene–environment interactions.The cardiac phenotype in the utations . Of the Ohia mutant mice can have a hypoplastic spleen or asplenia (Ohia mutant mice would constitute a left-sided defect, in line with hypoplasia of left-sided heart structures in HLHS. This would suggest possible right isomerism. In contrast, we note clinically that HLHS has been suggested to involve left isomerism, as azygos or hemiazygos venous connection can be seen in HLHS patients. However, as azygous venous connection can occur in the context of normal atrial situs, identifying atrial situs via venous connections alone can also lead to erroneous diagnosis of left isomerism. In addition, as right atrial isomerism is one of the most severe forms of CHD, and is associated with high morbidity and mortality, right isomerism may be underrepresented in the viable HLHS patient population, resulting in ascertainment bias of left-isomerism [Ohia HLHS mouse model.It is also interesting to note that the asplenia g,h, defesomerism . We noteWhile severe hypoplasia of the LV is a major hallmark of HLHS, the hypoplasia is observed not only in the LV, but in all the structures on the left side of the heart, including the left atrium, aorta, and aortic and mitral valves. This suggests disturbance of heart organ size regulation, albeit restricted to the left side. Relevant to this, Hippo signaling has been shown to regulate heart organ size with the modulation of cell proliferation, apoptosis, and stem cell self-renewal . This inGenetic manipulation of Hippo-Yap signaling in mice has shown an essential role for the Hippo pathway in heart organ size regulation. This involves the inhibition of cardiomyocyte proliferation via repression of Wnt target genes . ReductiWe note a recent study which suggested that Hippo signaling does not play a role in normal organ growth. However, this study investigated only the mouse liver and used conditional deletion of Yap/Taz or hyperactivation with YAP overexpression or Lats1/2 conditional deletion . In contSap130 is a component of the Sin3A-HDAC repressor complex, a chromatin modifying complex with critical roles in a wide spectrum of developmental processes such as the maintenance of stem cell pools , muscle Nkx2.5-Cre mediated cardiac specific deletion of Yap1 resulted in embryos with smaller hearts, evident by E9.5 [Nkx2.5-Cre Yap1 deleted embryos died by E10.5 from heart failure with a thin myocardium. In a separate study, Nkx2.5-Cre deletion of a floxed Sav1 allele, a Hippo kinase in the Mst1/Mst2 complex, resulted in cardiomegaly due to failure to repress cardiomyocyte proliferation normally regulated by Hippo signaling. As the heart enlargement affected the RV and LV equally, this indicated that Hippo regulation of ventricular growth has no intrinsic “sidedness” [Sap130 likely plays a role in specifying the sidedness of the ventricular hypoplasia in HLHS. This may involve Sap130/Sin3A-mediated integration of pathways regulating left–right patterning with Hippo signaling and the regulation of cardiomyocyte proliferation.Consistent with a role in heart organ size regulation, Yap1 has been shown to regulate cardiomyocyte proliferation from the very earliest stages of mouse heart development, with Yap1 and Tead observed to be strongly expressed and nuclear localized in the E8.5 myocardium . The Nkx by E9.5 . This wa by E9.5 . These Ndedness” . This woLefty1 and Snai1, are both found in the Sin3A complex. Sin3A has been shown to recruit the DNA demethylase Tet1 to demethylate the promoter of Lefty1 and thus enhance its transcription [Lefty1 is required for the breaking of symmetry and the specification of left-sidedness. In addition, Snai1 is also found in the Sin3A complex mediating the transcriptional repression of cadherins [Snai1 knockout mice exhibit heterotaxy with disruption of left–right patterning [Snai1 and Pitx2c are likely Sap130 targets (Pitx2c being a transcription factor specifying left-sided visceral organ identity [This is supported by the observation that two genes with essential functions in left–right patterning, cription . Expressadherins  to promoadherins . Thus, Stterning . ChIPseq targets ; Pitx2c identity ,59. BothSmad2/Nodal double heterozygous KO mice exhibiting laterality defects in conjunction with CHD, comprising transposition of the great artery [The overall evidence in studies of patients, mice, and other animal models would suggest the involvement of laterality disturbance in the developmental etiology of HLHS. As none of the genes or variants recovered from HLHS patients known to regulate left–right patterning yielded HLHS or a small LV phenotype in KO/KI mouse models, this would indicate that these genes are likely functioning as genetic modifiers to bias the phenotype towards left-sided heart structures. This fits well with several studies indicating an oligogenic etiology for HLHS ,41. Sucht artery .Of further note, studies conducted using birth registries have also shown a wide spectrum of other CHD associated with laterality defects . Thus, sEstablishing the critical left–right asymmetry in the cardiovascular system is an evolutionary adaption essential for the separation of oxygenated from deoxygenated blood to allow efficient blood oxygenation in air-breathing animals. This integration of heart development with left–right patterning may account for the cardiovascular system being the most left–right asymmetric organ in the mammalian body plan. Thus, understanding how left–right patterning is integrated with cardiac morphogenesis may provide important insights not only into CHD such as HLHS, but likely a broad spectrum of other CHD beyond HLHS. Ultimately, insights into how left–right patterning is integrated with heart development may help to identify new druggable targets for the development of therapeutics for recovering LV growth in fetuses with evolving HLHS. This may provide a two-ventricle surgical path that holds promise for better long-term outcomes for patients otherwise diagnosed with HLHS.


Genomic profiling of hematologic malignancies has augmented our understanding of variants that contribute to disease pathogenesis and supported development of prognostic models that inform disease management in the clinic. Tumor only sequencing assays are limited in their ability to identify definitive somatic variants, which can lead to ambiguity in clinical reporting and patient management. Here, we describe the MSK-IMPACT Heme cohort, a comprehensive data set of somatic alterations from paired tumor and normal DNA using a hybridization capture-based next generation sequencing platform. We highlight patterns of mutations, copy number alterations, and mutation signatures in a broad set of myeloid and lymphoid neoplasms. We also demonstrate the power of appropriate matching to make definitive somatic calls, including in patients who have undergone allogeneic stem cell transplant. We expect that this resource will further spur research into the pathobiology and clinical utility of clinical sequencing for patients with hematologic neoplasms. Targeted sequencing panels such as MSK-IMPACT have been successfully used to profile solid tumours in clinical settings. Here, the authors develop and implement the MSK-IMPACT Heme sequencing panel and platform to profile haematologic malignancies using paired tumor and normal tissues. BRAF, CALR, JAK2 and MPL, have diagnostic utility in myeloid neoplasms, for example, others such as CEBPA, DNMT3A, FLT3, IDH1, IDH2, KIT, NPM1, and TP53 have prognostic and/or therapeutic implications, particularly when determining whether a patient should undergo an allogeneic stem cell transplant2. Clinically, as the evidence and the repertoire of molecularly targeted therapies for hematologic malignancies continue to expand5, so do the challenges and opportunities for molecular profiling to inform tumor classification, prognosis, disease monitoring, and treatment decisions.Hematologic malignancies are characterized by the presence of complex and dynamic genomic changes that are now increasingly utilized to classify and define independent disease subsets. With the rapid adoption of next-generation sequencing technology, a multitude of recurrent somatic alterations in genes regulating cell growth, DNA repair, and differentiation have been identified; these contribute not only to the onset and progression of disease, but also to the development of relapse and resistance to therapy. Genetic profiling has hence emerged as a key element in the workup of patients with hematologic malignancies, guiding patient management at various levels. While mutations in certain genes, such as 8, there are unique challenges to the evaluation of somatic alterations in hematologic malignancies. One distinct challenge is the lack of easily implementable sources of patient matched normal controls as comparators to confidently identify variants as distinctly somatic. The presence of leukemic contamination in buccal swabs and saliva, poor yield of DNA from hair follicles or nails and the extensive work required to sort normal cells or grow fibroblast cultures are all well-known challenges in clinical practice. Alternatively, unmatched interpretation brings its own challenges related to the discrimination of somatic and germline variants, especially given the large proportion of altered genes that do not have mutational hotspots or are not yet well-described. This precludes the reliance on publicly available databases for accurate curation of variants. Secondly, co-existing alterations that influence variant allele frequencies (VAF)  commonly occur, such that this metric cannot confidently guide the determination of somatic vs germline origin. These challenges are especially compounded in patients with a history of allogeneic hematopoietic stem cell (HSC) transplant, where determination of somatic vs germline and the source (host or donor) is often not possible.Given the growing number of clinically relevant genetic alterations, it has become necessary to develop high throughput approaches for the genomic characterization of neoplasms in clinical practice. Unlike the workflows that have successfully provided prospective tumor molecular profiling of solid cancers at large scaleHere, we show our experience addressing these unique challenges through the development and clinical experience of MSK-IMPACT Heme , a comprehensive molecular profiling platform, utilizing hybridization capture and high coverage next-generation sequencing of paired tumor and normal tissues.11. To confidently identify somatic mutations in hematologic tumor cells, we used either saliva or nail clippings12 as a source of germline DNA, since genomic material from whole blood may contain high levels of contaminating tumor cells and would not be suitable as a comparator  or history to suggest an evolving myeloid neoplasia, including prior chemotherapy/radiation exposure (n = 3), and/or persistent cytopenia (n = 2). Despite the presence of contamination, the variants detected in nail samples were found with high tumor:nail VAF ratios in virtually all cases , supporting the utility of nail control samples towards deciphering the germline versus somatic nature of variants detected in neoplastic patient samples. Somatic variants were rarely identified with VAFs >=10% in nail samples and were primarily confined to disease-defining alterations associated with loss of heterozygosity (LOH) in the tumor sample, such as JAK2 and TET2 in myeloproliferative neoplasms. These alterations were still easily identified as somatic variants owing to the retention of high tumor:nail VAF ratios  were sequenced with a saliva normal comparator, these saliva controls contained high levels of contaminating tumor DNA, up to 38% VAF  were present in COSMIC v94 database, and identified in 2,271 tumor samples, or 95% of our sequenced cohort with an average of 4 additional variants per sample. These represent germline variants that would have been incorrectly reported as somatic in an unmatched analysis, with potential adverse clinical implications. For instance, while specific mutations are not required in the FDA approval for hypomethylating agents (HMAs) in myeloid neoplasms, their presence has been associated with response to HMA treatment, and inaccurate reporting could alter choice of therapy15. In this analysis we identified a total of 54 germline variants from myeloid neoplasms in genes associated with response to HMA treatment that could have been misattributed to being of somatic origin without a matched normal sample: TET2 (n = 47), DNMT3A (n = 4), and TP53 (n = 3) , we developed an algorithm (FACETS2n), which leverages coverage data from patient unmatched normal samples and combines with patient matched allele frequencies to estimate integer level copy number values as well as allelic imbalances such as copy-neutral loss of heterozygosity (CN-LOH) (see Methods). A comparison of results from high-density single nucleotide polymorphism (SNP) array and MSK-IMPACT Heme FACETS2n analysis from 64 clinical samples showed highly concordant results with 92.9% sensitivity and 100% specificity. Discordant calls between SNP array and FACETS2n were attributed to sub-clonal calls made by SNP array and/or tumor fractions below the sensitivity of FACETS2n (less than 20%) as determined by serial dilution of a well characterized tumor sample. , and single nucleotide polymorphism (SNP) arrays have been used to detect clinically relevant SCNAs. The application of the FACETS2n algorithm to these sequencing data allowed the identification of focal amplifications and deletions as well as broad chromosomal arm level gains and losses. We detected focal copy number alterations in 854 patients (44.1%) whereas 1146 patients (59.2%) had a chromosome arm level copy number alteration detected. The identification of SNVs, indels and SCNAs in a single assay afforded efficiencies in tissue management and the ability to provide clinically actionable results from a single assay in a clinical setting.Having established saliva and nail tissues as suitable controls for identifying true somatic SNVs and indels, we sought to further leverage paired tumor-normal matched sequencing data to assess the allele specific copy number alterations of this cohort. The identification of somatic copy number alterations (SCNAs), including gains and losses in chromosomal arms, has both diagnostic and prognostic implications for hematologic malignanciesTP53 variant identified post-transplant was also detected in the donor blood sample, but it was not possible to distinguish the germline vs somatic nature based on VAF alone in the tumor sample. In a second patient we identified a putative donor-derived somatic variant, DNMT3A p.R882C, likely of clonal hematopoiesis origin. To further demonstrate the utility of a unified analysis using both host and donor normal tissues, we present the case of a 37-year-old female who underwent allogeneic stem cell transplantation from an HLA-matched unrelated donor for the treatment of acute myeloid leukemia. A bone marrow biopsy was performed on day 98 post-transplant for assessment of suspected relapse, which was confirmed with 56% myeloblasts. Engraftment assessment by short tandem repeat analysis (STR) showed a chimeric status with 56% host component  samples to calculate variant allele log odds ratios with the post-transplant sample and determine regions of allelic imbalance genome wide  mutation who underwent allogeneic stem cell transplant. FLT3 ITD mutations, such as the 60 bp FLT3-ITD detected in this bone marrow (See Methods), are recurrent somatic alterations in AML and typically detected using PCR and capillary electrophoresis assays. . Morphologic and immunophenotypic assessment of a bone marrow sample demonstrated low-level involvement by AITL (<5% by CD3/PD-1 immunohistochemistry) and the concurrent presence of a clonal plasma cell population, which accounted for 15% of cells on the aspirate smear and 1.9% of WBC by flow cytometry. Although clonal plasmacytosis has been reported in AITLons Fig. . In addions Fig. . Of noteKMT2D , TP53 , TET2  and CREBBP .  compared to myeloid neoplasms . The most prevalent arm-level SCNAs in lymphoid neoplasms were +7p , +18q , +12q , del 17p , del 6q , and del 13q . For myeloid neoplasms, trisomy 8 , +21q  and +1q  were the most prevalent broad gains, while del 7q , del 17p , del 5q , and del9p  were the most common broad chromosomal losses  =1.15 × 10–15, two-proportion Z-test), immune modulation , NOTCH signaling , chromatin modifiers , DNA damage response , and NF-kB signaling . The most prevalent focal copy number alterations in myeloid neoplasms were deletions of TP53 , JAK2 , FLT3 , TET2 , and EZH2 .We identified 12,893 somatic mutations, 4231 gene level and 7566 broad chromosome arm level somatic copy number alterations from 2290 samples. Implementation of the MSK-IMPACT Heme workflow enabled the characterization of complex tumor specimens, including flow-sorted samples and tumor samples from chimeric post-transplant patients. Somatic genomic alterations including nonsynonymous SNVs, indels, focal and chromosome arm level copy number alterations were identified in 1885 of 1937 patients (97.3%). A total of 1804 patients (93.1%) had at least one SNV or Indel identified . The most commonly identified SNVs were in %). Fig.  We obserses Fig. . These fn = 294/1357, 22%) compared to myeloid  including FL , DLBCL , and HGBCL . In myeloid malignancies, CN-LOH was observed in acute leukemias including AML (n = 36) and BLL (n = 6), or chronic myeloid neoplasms including PMF , CMML , and PV . The most frequent chromosome arm level CN-LOH events were identified in 6p (n = 81), 9p (n = 65), 16p (n = 50), 9q (n = 48), 16q (n = 46), 17q (n = 46), 15q (n = 44), 19p (n = 44), 13q (n = 43), and 17p (n = 41). Interestingly, CN-LOH has been shown to be a mechanism of HLA Class 1 loss in cancer and may underlie the 6p aberrations noted here29.In addition to gene level copy number alterations, FACETS2n enables accurate assessment of allele-specific copy number state, including copy neutral loss of heterozygosity (CN-LOH). CN-LOH was identified in 433 samples (19%) and, similar to global copy number changes, was more frequently noted in lymphoid neoplasms  as well as several other genes. In particular, within FL and DLBCL, the following genes were frequently affected by two hits via mutation and LOH: TNFRSF14 (n = 76), CREBBP (n = 71), TNFAIP3 (n = 30), and B2M (n = 29). We also identified genes which harbored multiple somatic variants in a single neoplastic sample, which may reflect bi-allelic inactivation, multiple subclones, or aberrant somatic hypermutation. In myeloid malignancies, multiple alterations were noted in TET2  and DNMT3A (n = 35), while in mantle cell lymphoma, ATM  frequently harbored multiple mutations. FL and DLBCL showed multiple mutations in the same patient of KMTD (n = 101), CREBBP (n = 39), and HIST1H1E (n = 20), in addition to aberrant somatic hypermutation of BCL2 (n = 58), PIM1 (n = 41), and SOCS1 (n = 22). TP53 harbored multiple mutations across lymphoid and myeloid malignancies (n = 82)  Fig. .40. In clinical tumor profiling, the deciphering of mutational signatures can aid diagnosis, disease prognosis, and treatment decisions44. However, identification of mutational signatures has occurred mostly in solid tumor cohorts, mainly due to the lower levels of somatic mutation in blood cancers relative to solid tumors45. In the MSK-IMPACT Heme cohort, we calculated the tumor mutation burden  for all samples  . For the 261 tumors (11%) with elevated tumor mutation burden , all synonymous and nonsynonymous single nucleotide variants were decomposed into COSMIC v3.1 SBS signatures with the inclusion of recently described MMRd signatures46 (See Methods).The application of DNA sequencing in conjunction with advances in mathematical models have aided the discovery and understanding of the mutational processes that underlie the acquired somatic variants of cancer genomesMb) Fig. . Relativn = 231) displayed dominant mutational signatures associated with genome instability as mediated by AID and the error-prone DNA polymerase eta in conjunction with clock-like mutational processes49. We observed ultraviolet light exposure as a dominant signature in cutaneous T-cell lymphomas (n = 11) in addition to DLBCL tumors from two patients, for which clinical histories indicated that these two tumors likely originated near the skin.We identified tumors with mutational processes attributable to activation-induced cytidine deaminase (AID) activity, DNA polymerase eta, mismatch repair (MMR) deficiency, exposure to ultraviolet light (UV), chemotherapy treatment, apolipoprotein B editing complex (APOBEC), and clock-like mutational processes Fig. . Mature MLH1 alterations in the two BLL samples with highest mutation burden concurrent with MSH6 frameshift variants and heterozygous loss of MSH2/6. In the first sample, we detected CN-LOH of MLH1, and in the other, a splice variant (c.790+1G>A) previously reported to result in exon 9–10 skipping and reported as a pathogenic germline variant in many individuals with a family history of Lynch-syndrome associated tumors exhibiting microsatellite instability51. No somatic PMS2 alterations were detected in tumors with dominant MMR signature. Both AML samples with elevated mutation burden had dominant mutational signatures for chemotherapy that corresponded with their treatment histories. The two samples with elevated mutation burden and a dominant APOBEC signature were plasma cell myelomas, which has been previously shown to be a poor prognostic indicator52. Taken together, we show the ability of mutation signature analysis from targeted sequencing of hematologic cancers with elevated TMB to identify underlying mutational processes, with potential to impact patient management using these data.Nine tumors from seven patients exhibited a dominant MMR signature, including all four relapse BLL tumor samples with elevated tumor mutation burden. Of the nine tumors with MMR signatures, only one DLBCL sample did not have a clear alteration in the MMR pathway. This case had a lower TMB (20 Mut/Mb) and estimated tumor purity (28%) relative to other MMR tumors and only 43% of mutations attributed to MMR signature. Other signatures attributed to the mutational profile of the tumor were associated with AID, polymerase eta, and clock-like mutational processes. We also observed somatic http://oncokb.org), an expert curated precision oncology knowledge base. OncoKB annotates the oncogenic effect and clinical implications of somatic molecular alterations and has recently expanded to include alterations in hematologic malignancies54. Key to OncoKB is its level of evidence system that annotates molecular variants based on the level of evidence that the alteration is either a predictive biomarker of drug sensitivity or important in informing diagnosis or prognostication. By classifying patient samples by the highest level of evidence assigned to detected variants in that sample, we found that 10.6% of patients profiled had at least one potential clinically actionable alteration, defined as carrying ≥1 alterations assigned an OncoKB level of evidence 1-3B55. and 71.5% had an oncogenic alteration . Of note, this analysis reflects an underestimate of actionability at a disease level in this cohort, as the MSK-IMPACT Heme assay does not include targets for the detection of actionable gene fusions or rearrangements which may be addressed in future panel design iterations. Instead, transcript fusion detection is accomplished by a companion RNA-based NGS assay56.We also sought to assess the clinical utility of prospective molecular profiling to guide patient management using OncoKB  model includes these features in combination with genomic profiling to improve risk stratification (https://mds-risk-model.com)57. Here, we applied the IPSS-M model to the 101 patients with MDS for whom we also had the required clinical and cytogenetic data to stratify each patient into IPSS-M risk categories. After application of this algorithm, 32.3% (n = 33) of cases were classified with a risk category of Very High, 15.8% (n = 16) as High, 10.9% (n = 11) as Low, and 6.9% (n = 7) as Very Low. ; ‘Moderate High’ -> ‘High’ (n = 2); ‘Moderate Low’ -> ‘Moderate High’ (n = 2); ‘Low’ -> ‘Moderate Low’ (n = 2); ‘Very Low’ -> ‘Low’ (n = 1). Combining somatic copy number alterations detected by MSK-IMPACT Heme with conventional karyotyping (G-banding) and FISH allowed for a more sensitive detection of copy number alterations, mainly due to the detection of CN-LOH via IMPACT-Heme. Amongst those patients categorized as very high risk, we identified 14 patients with multiple hits to TP53, two patients with multiple TP53 variants and 12 patients with a single variant and loss of heterozygosity. For patients with a single TP53 hit, 36% (n = 4) were classified as very high risk. We also demonstrate the ability of IMPACT-Heme to distinguish between subgroups of SF3B1 altered patients, with a single patient identified with mutated SF3B1 and isolated del5q versus 14 patients identified with SF3B1 alpha  and associated with favorable outcomes57  is the current standard for patient risk stratification, which relies on clinical parameters of cytopenias, bone marrow blast percentage, and cytogenetic features, but does not consider gene mutationsow. Fig. . Evaluats57 Fig. .6, we now show that this type of enterprise-scale sequencing of neoplastic and matched normal samples is feasible in hematologic cancers, including highly complex cases of patients following hematopoietic stem cell transplantation and those with multiple concurrent malignancies. For a comprehensive matched assessment, turnaround times may be variable and, in this cohort, generally ranged from 2-3 weeks. Shorter turnaround times are possible depending on the timeframe to obtain the tumor-normal pairs, further highlighting the importance of a multidisciplinary approach for workflow optimization. While this is a clinically actionable timeframe in a large proportion of cases, the implementation of alternate assays for rapid assessment of key genetic alterations may be needed as a bridging step in select cases. Through this comprehensive effort, we demonstrate the utility of our approach, going beyond the narrow assessment of selected genes in a tumor only sequencing model, which may be incomplete and ineffective for patient management. As the costs of sequencing and data analysis continue to fall, we believe that a similar matched approach will be applied to whole genome sequencing (WGS) for all cancer patients. In this future model, inclusion of germline controls will be even more critical than in a targeted panel where the normal variation of the targets is better understood. We have generated an extensive collection of manually reviewed mutations and SCNAs in 2,384 samples from 1,937 patients in 85 detailed tumor types. This cross-malignancy dataset will support explorations of driver alterations across all blood cancers to support discovery of rare and unanticipated clinically actionable alterations. With continuing growth in the realm of precision therapeutics, this data set will prove a transformative resource for identifying novel biomarkers to inform prognosis and predict response and resistance to therapy. This includes the further definition of putative passengers which may, in fact, have important functional and cooperative roles in driving cancer. In an unmatched model, a very high proportion of private SNP’s are classified as passengers or VUS’s, necessarily so because they are not yet part of well characterized variants in any of the current publicly available databases. In contrast to solid tumors where the primary focus of genomic profiling has been the selection of targeted therapy for key single genetic drivers, the aims of genomic profiling in hematologic cancers are heavily invested in refining a diagnosis and providing prognostic information, with therapy selection often supported by the former. Broad genomic profiling provides a more accurate diagnosis and risk stratification of individual patients at the time of diagnosis and may also predict response and/or outcomes after selected treatments. For instance, TP53 mutations are consistently associated with shorter survival after allogeneic stem cell transplantation and somatic mutations in epigenetic pathways  may confer increased sensitivity to hypomethylating agents59. Somatic mutations may require reassessment to update individual risk after treatment, at the time of significant clinical changes or before disease-modifying treatments. Our approach to testing, incorporating routine sequencing of appropriate control samples, enables the unequivocal identification of somatic genetic variants in a way that is scalable even in the context of an allogeneic transplant. It also allows the determination of donor-derived variants which may necessitate monitoring in both the recipient and donor for subsequent development of disease. Additionally, a separate analysis of the normal controls would also facilitate the assessment of key germline events that are relevant to hematologic malignancies. Although this was not included here due to a lack of patient consent for dedicated germline analysis and reporting, this is the topic of a manuscript in preparation with a more recent data set.We, herein, report the experience of a large institution-wide, prospective clinical sequencing effort to guide the diagnosis, prognosis, therapy selection and future monitoring of patients across the spectrum of hematologic malignancies. As we previously demonstrated in solid tumorsEZH2 in follicular lymphoma and FLT3, IDH1, and IDH2 in AML61. One emerging area of study in hematologic cancer is the study of mutation signatures. In solid tumors, mutation signatures such as MMR deficiency and TMB correlate with response to immune checkpoint inhibitors63. In contrast to solid tumors, hematologic malignancies tend to have lower levels of somatic mutation38, which may account for their relatively disappointing response to immune checkpoint inhibition68. The unambiguous identification of somatic alterations via the use of a matched normal affords a more accurate assessment of TMB in these neoplasms. Our identification of a small subset of patients with high TMB and/or MMR signatures suggests that these patients could be biological outliers and should be considered for trials of checkpoint inhibition based on these signatures.At the same time, as our understanding of the biology of hematologic malignancies has continued to expand, compounds targeting proteins or signaling pathways disrupted by recurrently mutated genes have become available, notably inhibitors to 70. In spite of this interest; however, it is unclear if suitable markers are available for all patients and some guidelines only specify molecular targets for select patients71. To address this shortcoming, some groups propose approaches which include the use of any somatic alteration as a potential target for monitoring73. These approaches highlight the power of a fully matched sample at initial tumor genotyping and the pitfalls of inaccurate somatic/germline assignment. By removing germline variants from reporting through genotyping of candidate variants in matched normal tissue(s), we are able to better identify appropriate markers for MRD assessment and prevent false positive calls. We have demonstrated through our tumor only analysis that these confounders occur in up to 95% of samples and could thus significantly limit the power of an “any variant” MRD approach.In addition to the biological insights and potential for therapeutic targeting afforded by our approach, there is also an opportunity for improved patient monitoring. There has been increasing interest in designing assays for monitoring minimal/measurable residual disease (MRD) following treatment across hematologic malignancies74, and it is our hope that work such as that presented here and by other groups will serve as a precedent for increased genomic profiling in blood cancer. The best approach to rapidly achieve these goals is through sharing of these datasets across institutions and establishing broad collaborations. To this end, we have deposited our full data set into the cBioPortal for Cancer Genomics . With continued testing and data sharing, it is our belief that broad genomic assessment will support understanding the pathobiology of, identifying novel drug targets for, and improving noninvasive monitoring for response in all patients with blood cancer.While this study represents a foray into the power of broad-scale genomic analysis in hematologic malignancy, additional work remains for the field of clinical genomic analysis to reach its full potential to improve patient care. Other groups have begun to explore even broader testing for a subset of hematologic malignancieshttp://www.cbioportal.org/oncotree/), an institutional tumor classification system was used to ensure consistent specimen annotation. Matched saliva was prioritized for lymphoid neoplasms owing to the ease of specimen collection and processing and the known paucity of lymphoid components in the samples. Patient-matched nail tissue is requested for all myeloid neoplasms due to the high level of neoplastic myeloid cells in the patient whole blood and saliva. Patients that had previously undergone hematopoietic stem cell transplantation were sequenced with pre-transplant host and/or donor normal specimens, dependent on engraftment status and tissue availability.This study complies with all relevant ethical regulations. Informed consent for the molecular profiling of patient tumors was obtained under protocol NCT01775072 “Tumor Genomic Profiling in Patients Evaluated for Targeted Cancer Therapy.” The protocol was approved by the Institutional Review Board at Memorial Sloan Kettering Cancer Center and written consent was obtained from all patients. Following consent, either archival or new tumor samples were obtained. The selection of appropriate matched normal was determined after a review of the patient clinical history and tumor diagnosis. OncoTree , small insertions and deletions (Indels), and somatic copy number alterations. Genomic DNA extraction was performed on the Chemagic STAR instrument (Hamilton) from peripheral blood, bone marrow, saliva, or formalin-fixed, paraffin-embedded (FFPE) tumors and patient-matched normal samples using the Chemagen magnetic bead technology (PerkinElmer). FFPE tissues were deparaffinized using mineral oil followed by digestion with the proteinase K enzyme. Extraction of genomic DNA from nail samples was performed by utilizing both physical and chemical digestion techniques. 10–25 mg of nail clippings were pulverized using high-speed agitation and centrifugation with zirconium beads in a BeadBlaster instrument  followed by chemical digestion with an adapted protocol using QIAamp® DNA investigator kit (Qiagen).DNA samples were normalized to yield 50–250 ng input and diluted with Tris-EDTA  to a total volume of 55 μl on the Biomek FX Laboratory Automation Workstation (Beckman Coulter), before undergoing shearing on the Covaris instrument. Sequence libraries were prepared through a series of enzymatic steps, including shearing, end-repair, A-base addition, ligation of barcoded sequence adaptors, and low-cycle PCR amplification (Kapa Biosystems). To enable multiplexed captures, tumors and matched normal samples were combined into pools of approximately 28 libraries, utilizing custom-designed biotinylated probes (Nimblegen). The captured DNA fragments were subsequently sequenced on an Illumina HiSeq2500 as paired-end 100-base pair reads.An automated data management system monitored the sequencers, initiating the analysis pipeline upon completion of the sequencing run. Sequence reads were aligned to the human genome (hg19) using BWA MEM (version 0.7.5a). ABRA (version 0.92) was employed to realign reads around indels to reduce alignment artifacts, and the Genome Analysis Toolkit (version 3.3-0) was used to recalibrate base quality scores. Duplicate reads were identified and marked for removal, resulting in BAM files that were utilized for variant candidate discovery.21 to allow the incorporation of multiple normal samples for normalization and determination of allelic imbalance in tumor samples, even those from chimeric patients. All genomic variants called by the analysis pipeline were loaded into MPath, an in-house genomic variant database and user interface that facilitates the manual review of variants and their assessment for therapeutic, diagnostic, and prognostic implications with OncoKB53. Through the incorporation of variant allele fraction (VAF) in tumor and normal tissues, patient clinical history, and annotated population frequencies9, we were able to eliminate variants with low sequencing quality and those of patient and/or donor germline origin.We implemented a custom analysis pipeline (see below) to integrate the analysis of any number of normal samples with a given tumor and provide a reliable assessment of somatic alterations, even in post-transplant chimeric patients. Copy number alterations were assessed using FACETS2n, an allele-specific copy number analysis pipeline for next-generation sequencing data, adapted from the FACETS algorithm75 prior to variant calling to resolve soft-flipped bases to insertions and deletions commonly missed by standard analysis workflows, such as FLT3-ITDs. To the union of calls made by MuTect (version 1.1.4)76, VarDict (version 1.4.6)77, and Somatic Indel Detector (version 2.3)78, the genotypes from the patient-matched normal sample(s) were incorporated and subjected to automated filtering to generate a complete list of somatic mutation calls, including SNVs and short and long indels. By incorporating the genotype information for patient and donor DNA of non-neoplastic origin, we were able to eliminate variant calls attributed to the germline present in tumor specimens. In detail, all variant calls require a VAF in the tumor ≥ 5 times that of an unmatched normal, a minimum of 20 total reads, 5 alt reads, at least 1% VAF, and presence in less than 20% of our standard normal samples. Furthermore, hotspot sites required an alt allele depth of 8 reads and a VAF ≥ 2%. Non-hotspot sites require more stringent secondary filtering of at least 10 alt reads, VAF ≥ 5%, and VAF in the matched normal and tumor ≤ 35%. Each alteration identified by the pipeline was annotated with The Ensembl Variant Effect Predictor (VEP)79 to be compliant with Human Genome Variation Society  standards and then manually reviewed to ensure that no false positives were reported.Variant calling was performed in paired sample mode using BAM files generated for the tumor sample and the pooled normal control sample processed with each sequencing run. Indel realignment of sequencing reads was performed with ABRA2 (version 2.13)11. The logOR of the variant-allele count in tumor versus patient-matched normal, an unbiased estimate of allelic copy ratio, was calculated for all heterozygous SNPs  in the patient-matched normal. For patients sequenced following allogeneic stem cell transplantation, logOR was limited to the subset of heterozygous SNPs common to the patient baseline normal sample and all donor samples. For the calculation of integer copy numbers, we utilized a two-pass implementation whereby a low-sensitivity run  first determines copy number log-ratio corresponding to diploidy. The copy number state of individual genes was determined by a run with higher sensitivity for focal events . The following gene level SCNAs were retained for analysis: amplifications (integer copy number ≥ 5 without whole genome doubling or > 6 with whole genome doubling), homozygous deletions, and heterozygous losses that co-occurred with an SNV or Indel. Broad chromosome arm level copy number gains (integer copy number ≥ 3 without whole genome doubling or > 5 with whole genome doubling) and losses were retained for analysis if they comprised at least 50% of the chromosome arm.Genome-wide total and allele-specific copy number states were calculated for all tumor samples using the open-source R package FACETS2n (v0.3.0). Library-specific coverage biases that stem from differences between tumor  and normal  tissues may result in log ratios with high levels of noise when calculated with matched normal samples. With FACETS2n, a single unmatched normal is selected from a pool of high-quality normal samples previously processed and sequenced with the MSK IMPACT-Heme assay. These normal samples were selected to have a representation of males and females from a variety of tissue types and with different insert size distributions. A single tumor sample was compared to multiple control normal samples to derive distinct log-ratio sets. The normal sample with the lowest sum-squared log-ratio served as the best reference diploid genome comparator for the tumor sampleWe evaluated the accuracy, sensitivity and reproducibility of MSK-IMPACT Heme in detecting somatic copy number alterations in a validation study of 11 select and clinically relevant regions: chromosomes 3q, 5q, 7q, 8, 11q, 12, 13q, 17p,19, 20q, and the single gene locus TP53. A total of 64 clinical samples were evaluated by both IMPACT-Heme and snp-array, using the results of snp-array as the set of true positive copy number alterations. In these clinical samples, somatic copy number alterations were detected with 92.9% sensitivity and 100% specificity. Three samples with copy number alterations from the validation set were studied over three different sequencing runs for inter-assay reproducibility in addition to being studied in triplicate in the same run for the intra-assay(precision). Concordant results were obtained for all cases in both intra- and inter-assay reproducibility studies. The sensitivity of FACETS2n was evaluated using one FFPE sample from a DLBCL patient with known 12p amplification and 13q loss. Five serial dilutions using DNA from patient-matched FFPE normal tissue were prepared .Tumor mutation burden was calculated as the number of nonsynonymous and synonymous SNVs and Indels per megabase of genome targeted by the MSK-IMPACT Heme panel (1.0837 Mb). We used the distribution of TMB across all tumors to identify highly mutated cases with the formula: median cohort TMB + 2*IQR. Those tumors with a TMB > 12.9 Mut/Mb were classified as TMB High and analyzed for mutational signatures.40. In order to limit inter-signature bleeding that stems from difficult to decipher flat signatures80 and elucidate the mutational processes that contribute to individual cancer genomes, we first performed de novo extraction of single base substitution (SBS) signatures on both individual tumor types and groups of tumors originating from either lymphoid or myeloid lineages. Discovered signatures were decomposed into COSMIC v3.1 SBS signatures with the inclusion of recently described MMRd signatures46. We then estimated the contribution of each signature to individual cancer genomes using a nonlinear convex optimization programming solver, SigProfilerAssignment. The set of known signatures applied to each genome was determined from those identified from de novo extraction in the same tumor type or cellular lineage40.Mutational signatures were assessed for the 261 tumor samples with elevated mutation rates using all synonymous and nonsynonymous SNVs and SigProfiler software57.Clinical parameters of percentage bone marrow blasts, hemoglobin levels, and platelet counts were curated for 101 patients with an MDS diagnosis. We then compiled the somatic genomic alterations  and pathologist-reviewed assessment of cytogenetic results to derive IPSS-R cytogenetic risk categories, identify complex karyotype, and encode the gene and chromosome level binary variables as input to the IPSS-M algorithm to derive the IPSS-M risk score75 which incorporates high-quality soft-clipped reads into the generation of contigs that represent variation from the reference genome. ITDs that were resolved via indel realignment were then identified as part of the somatic variant calling pipeline with either the SomaticIndelDetctor and/or VarDict algorithms78.De novo detection of FLT3 internal tandem duplications (ITDs) using NGS data was performed by adding an indel realignment step to aligned BAM files using ABRA2Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data


MYBPC3 was founded using sperm from a single heterozygous male cat. Cardiac function in four generations was assessed by periodic echocardiography and measurement of blood biomarkers. Results showed that HCM penetrance was age-dependent, and that penetrance occurred earlier and was more severe in successive generations, especially in homozygotes. Homozygosity was also associated with progression from preclinical to clinical disease. A31P homozygous cats represent a heritable model of HCM with early disease penetrance and a severe phenotype necessary for interventional studies aimed at altering disease progression. The occurrence of a more severe phenotype in later generations of cats, and the occasional occurrence of HCM in wildtype cats suggests the presence of at least one gene modifier or a second causal variant in this research colony that exacerbates the HCM phenotype when inherited in combination with the A31P mutation.We sought to establish a large animal model of inherited hypertrophic cardiomyopathy (HCM) with sufficient disease severity and early penetrance for identification of novel therapeutic strategies. HCM is the most common inherited cardiac disorder affecting 1 in 250–500 people, yet few therapies for its treatment or prevention are available. A research colony of purpose-bred cats carrying the A31P mutation in  HCM is traditionally characterized by asymmetric or symmetric left ventricular (LV) hypertrophy with impaired diastolic relaxation. However, clinical symptoms are heterogeneous, with most patients showing no clinical signs while others develop a range of consequences including congestive heart failure (CHF), malignant arrhythmias, or sudden cardiac death2.Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiomyopathy in people affecting 1:250–500 individuals1. However, up to ~ 70% of mutations occur in two genes, MYH7 and MYBPC3, encoding beta-myosin heavy chain (myosin) and cardiac myosin-binding protein-C (cMyBP-C), respectively. Myosin is the force-generating protein in muscle responsible for the power that drives cardiac contraction during each heartbeat, whereas, cMyBP-C is a regulatory protein that modifies actomyosin interactions to regulate the rate and extent of contraction and relaxation4. Because myosin, cMyBP-C, and most other proteins carrying causative mutations for HCM localize to sarcomeres (the basic contractile units of muscle), HCM has become known as a “disease of the sarcomere”. This basic understanding has led to intense efforts to target the sarcomere for drug therapies which have led to exciting new first-in-class drugs that directly affect muscle function by inhibiting or activating myosin6. While these drugs hold promise for effective treatments and potentially prevention of HCM, obstacles still remain; such as variable mutation penetrance and differences in expressivity that make it difficult to identify at-risk individuals that will benefit from treatment.Part of the complexity in understanding preclinical and clinical HCM phenotypes is due to heterogeneity in underlying genetic causes of the disease with more than 1,000 mutations in at least 15 genes associated with HCM8. In this regard, HCM is the most common naturally occurring inherited cardiomyopathy in domestic cats with a reported incidence in one study as high as 15%, and is the most common cause of CHF9. Unlike engineered rodent models of HCM, feline HCM shows similar clinical and pathological presentations as human HCM including left ventricular outflow tract obstruction (LVOTO)10. Because LVOTO is a significant factor affecting patient quality of life in people12, animal models that recapitulate hemodynamic complications of HCM are especially valuable. For instance, LVOTO is not typical of genetically engineered mouse models of HCM and therefore such models are limited in the pursuit of novel therapeutics that can mitigate LVOTO gradients. HCM in cats also appears to have similar underlying genetic causes as in people; to date, two causal mutations in MYBPC3 have been identified in Maine Coon and Ragdoll cats14. However, thus far other genetic causes have proven difficult to identify in cats and few longitudinal studies of feline HCM with a known causative mutation have been performed, thus, exacerbating gaps in our understanding of feline disease progression15.Animal models of HCM can be useful in understanding some of this variability, especially large animal (non-rodent) models that have disease etiologies and phenotypes similar to human HCM13. The mutation results in a single amino acid substitution causing a proline to be substituted for an alanine at the protein’s 31st amino acid position near the N’-terminus of the molecule . Previous studies showed that the mutation disrupts the folded domain structure of the first immunoglobulin (Ig)-like domain of cMyBP-C and increases susceptibility to proteolytic degradation, however, the mutation does not lead to significant loss of cMyBP-C (haploinsufficiency)16. Normal levels of cMyBP-C were also reported in another cat homozygous  for A31P where the mutation was reported to decrease binding to actin17.Here we aimed to characterize the long-term impact on cardiac function of the A31P missense mutation in cMyBP-C first identified in Maine Coon cats19, we hypothesized that disease phenotype would be more penetrant in cats homozygous for the mutation. Cats were purpose bred to obtain homozygous A31P genotypes to determine whether homozygous status would constitute a useful animal model of HCM with requisite high penetrance of disease in young animals (< 1 year) ideal for preventative therapeutic studies. This study was designed to minimize bias and confounders by prospectively and blindly assessing cats housed in a controlled environment over time from kitten to adult ages. Cats homozygous for the A31P mutation develop preclinical asymmetric ventricular growth and diastolic dysfunction suggesting that continued selective breeding in this unique research colony will be valuable for identifying interventional therapies for both human and veterinary medicine.Because the incidence of clinical HCM in cats heterozygous  for the A31P mutation appears low in the general cat populationMYBPC3 was first identified in a research colony of cats with HCM and was also identified in Maine Coon cats in general cat populations13. However, the original research colony consisted of a mixed genetic population of cats with HCM and not all cats in that colony with HCM carried the A31P mutation. Therefore, multiple genetic etiologies likely contributed to HCM in the original colony20. To focus exclusively on disease progression from a known genetic cause, we selected a single male cat from the original HCM colony that was heterozygous for the A31P mutation. Sperm from this heterozygous A31P male was used to develop a second-generation research colony using artificial insemination of three healthy, unrelated, wildtype DSH (domestic shorthair) female cats. Resulting heterozygous A31P cats were crossed to obtain cats homozygous for the A31P mutation. Homozygous A31P cats were born in Mendelian ratios and survived into adulthood. Homozygous X homozygous crosses produced healthy litters, albeit, litter sizes were frequently small for all colony breeding pairs regardless of genotype .The breeding strategy used to obtain cats carrying the A31P mutation in cMyBP-C is presented in Fig. Cardiac structure and function was assessed by echocardiography in kittens three-to-six months of age and thereafter at regular intervals of six-to-twelve months throughout the term of the study. Standard cardiac morphology and functional indices were measured via M-mode, 2-D, and color Doppler imaging for a complete echocardiographic exam at each time point. Blood samples were also collected at each exam for analysis of NT-proBNP and cardiac troponin-I (cTnI) serum biomarkers. Affected status was determined after each exam according to criteria described in “Analyses of longitudinal echocardiographic assessments revealed gradual progression of preclinical disease penetrance in most colony cats. The progression was evident as an increase in cats designated affected with increasing number of evaluations, increasing age at time of exam, and with A31P homozygous genotype Fig. . Individ55.LVOTOs were also common (present in ~ 18% of examinations) and dynamic within the colony. Although dynamic LVOTO was occasionally observed in wildtype cats, the incidence of mid-ventricular obstruction was common in cats with the A31P variant and HCM affected status. Juvenile cats with early onset and severely affected disease status also commonly demonstrated LVOTO. As seen in Table NT-proBNP values were significantly associated with HCM affected status, A31P genotype, and the presence of LVOTO in colony cats Tables  and 3. TInheritance of the A31P mutation in homozygous cats leads to a statistically significant steady progression of preclinical indices of HCM ; homozygotes are more likely to develop HCM compared to heterozygotes  Table . HoweverDisease penetrance, progression, and severity in cats homozygous for the A31P mutation have not been well described due in part to the small number of homozygous cats available for study in general cat populations. Here we studied 22 cats homozygous for the A31P mutation. Of these, three cats progressed from preclinical status to CHF during the course of the study. The first cat that progressed from preclinical to clinical disease was a cat from the first generation of cats obtained from heterozygous X heterozygous crosses  or before one year-of-age. Individuals that resulted from a cross involving a heterozygous male #2011-44) and homozygous female #2011-45) cat presented with a more severe phenotype at earlier ages than in previous generations. For instance, the second cat that progressed to CHF occurred in later generations . Only the previously identified MYBPC3 A31P variant was identified in the majority of HCM affected colony cats and not in any of the wildtype control cats or the general cat population  and was first identified in an inbred research HCM cat colony13. This mutation was also found in general populations of client-owned cats of the Maine Coon breed, making it the first identified, naturally occurring, causal mutation for HCM in any companion animal species25. Because heterozygous inheritance of the A31P allele in general cat populations was associated with low disease penetrance26, we bred cats to achieve homozygosity for the A31P allele. Our goals were to determine 1) whether homozygous inheritance of the A31P mutation was associated with higher disease penetrance and a more severe phenotype as a prerequisite for use of A31P cats as a feline model of HCM, and 2) provide a controlled, longitudinal assessment of preclinical disease progression in a population of purpose-bred cats with and without the A31P mutation. Result from this study confirm that the A31P mutation is causative for HCM, especially when inherited in the homozygous state  and ejection fraction (EF), were not significantly different in affected cats.Systolic abnormalities included dynamic LVOTO and systolic anterior motion of the mitral valve (SAM) frequently observed in affected cats. Consistent with previous reports, LVOTO was not associated with higher incidence of adverse outcomes33, mouse models cannot recapitulate significant aspects of disease comorbidities including hemodynamic complications such as LVOTO or susceptibility to arrhythmia. Because LVOTO is a significant factor impacting human patient quality of life12, non-rodent models are critically needed to identify new therapeutics that reduce LVOTO gradients.Taken together, these echocardiography results establish that cats homozygous for the A31P mutation reliably develop preclinical HCM with characteristics common to both human and feline diseases. Purpose-bred cats homozygous for the A31P mutation therefore represent a unique, naturally occurring, large animal model of HCM that can offer significant advantages over existing engineered rodent models of disease. For instance, while genetically engineered rodent models of HCM are essential for confirming deleterious effects of mutations identified in people37. This facilitates wider implementation in laboratory or clinical settings where access to secondary interventions may be limited. Because mutations in MYBPC3 are the most common cause of HCM in people, understanding the natural history of HCM due to a cMyBP-C mutation in cats may offer insights into critical points of decompensation as well as provide a highly relevant model for testing new drug or gene therapies39.Another advantage of homozygous A31P cats as an animal model of disease is that HCM occurs naturally as a result of an inherited mutation so that additional surgical, drug, or dietary interventions are not required to induce cardiac dysfunction as in other models of cardiac dysfunctionMYBPC3 mutations identified in people lead to premature protein truncation40. However, missense mutations coding for single amino acid substitutions, like A31P, also occur in people. In this respect, the A31P mutation occurs in the cardiac-specific C0 domain of cMyBP-C and other missense mutations have been identified in the C0 domain of HCM patients41. The precise function of C0 is not yet known, but C0 binds to both F-actin and the regulatory light chain (RLC) of myosin43. Disruption of these interactions could therefore affect the ability of cMyBP-C to activate the thin filament or to stabilize the super-relaxed (SRX) state of myosin46, either of which would be expected to directly cause contraction or relaxation abnormalities resulting in a cardiomyopathy.Most missense MYBPC3 (encoding R820W) was identified in Ragdolls14, cats expressing the R820W mutation are not currently available for research studies. HCM and dilated cardiomyopathy (DCM) have been identified as naturally occurring diseases in a number of large animals including dogs, pigs, and non-human primates, but a genetic basis has yet to be established in these species. Purpose-bred cats expressing the A31P mutation in cMyBP-C represent a unique genetic model of HCM with relevance to human disease and are currently the only available naturally occurring HCM model with a defined genetic mutation.While a second missense mutation in Despite significant advantages offered by use of purpose-bred A31P cats, challenges still remain in their development as an effective model of HCM. First, HCM penetrance in cats and people is variable with different expressivity observed even in closely related individuals that carry the same mutation. Here we attempted to overcome this limitation by breeding cats to homozygosity as gene dosage effects can lead to more severe disease phenotypes. This strategy led to higher penetrance of affected status in cats with a homozygous genotype relative to heterozygous cats carrying a single copy of the mutant allele or to wildtype cats that did not express the mutation  of the University of California Davis . In addition, this study was carried out in compliance with the ARRIVE guidelines (http://www.nc3rs.org.uk/page.asp?id=1357), the Animal Welfare Act, and the Institute for Laboratory Animal Research Guide for the Care and Use of Laboratory Animals. Priority at all times was given to cat comfort, health, and well-being. Briefly, cats were housed together in social groups in large enclosures with access to natural light, food and water (ad libitum), environmental enrichment including toys, scratching posts, perches, and beds, and were provided daily socialization with caretakers. Importantly, cats in the colony are maintained in a pre-clinical state, meaning they are without signs or symptoms of HCM and so experience minimal, if any, discomfort. Upon first development of CHF symptoms, cats are humanely euthanized.A purpose-bred colony of cats was created for research studies through selective breeding of cats with the A31P mutation in cMyBP-C20. Sperm from the founder cat was used to artificially inseminate three unrelated domestic short hair (DSH) females. Heterozygous A31P cats were then bred to obtain cats homozygous for the A31P mutation. A full colony pedigree chart is shown in Fig. The colony was founded using sperm from a single male Maine Coon/mix breed cat that was heterozygous for the A31P mutation obtained from a previously established research colony of cats with multiple causes of HCM at UC DavisAll cats were genotyped and underwent periodic complete echocardiography and cardiac biomarker assessment (details provided in “54 in the absence of any other systemic or metabolic disease. LVOTO was defined as color flow turbulence in association with a region of obstruction (LVOTO secondary to SAM or mid-ventricular) and a measured flow velocity of ≥ 2 m/sec, as previously reported55. All cats diagnosed with HCM were determined to have normal renal function via biochemistry, absence of any other known metabolic disease, a systolic blood pressure < 160 mmHg via Doppler sphymomanometry, and a total thyroxine level within the reference range if over the age of six years. CHF was defined as the presence of pleural and/or pericardial effusion via thoracic ultrasound and/or pulmonary edema on thoracic radiographs in conjunction with clinical signs of tachypnea or dyspnea. Cats diagnosed with CHF were euthanized at the first signs of clinical progression and no cats were maintained beyond diagnosis of CHF to ensure that there were no opportunities for cats to go untreated with progressive respiratory distress.HCM affected status was strictly defined by one of two possible scenarios: 1) a diastolic LV wall segment ≥ 6 mm identified in at least two separate echocardiographic examinations in the absence of any other systemic or metabolic disease, or 2) a diastolic LV wall segment ≥ 6 mm identified on a single echocardiographic examination in conjunction with a serum NT-proBNP concentration of > 100 pmol/L56.DNA for PCR genotyping was extracted from either buccal swabs or EDTA blood samples for all cats. Genotyping for the A31P mutation was carried out either by the UC Davis Veterinary Genetics Laboratory or by standard PCR and Sanger sequencing as previously reported55 of two-dimensional, M-mode, color, and spectral Doppler modalities. Cats were monitored via a continuous lead II ECG throughout the echocardiogram when tolerated at each evaluation. Sedated cats were first manually restrained in right then left lateral recumbencies to obtain echocardiographic images. All images were obtained and stored offline for measurement and analysis by a single, board-certified, veterinary cardiologist (J.A.S) using commercially available software and workstation . Cat images were identified by six-digit numbers such that the investigator was blinded to cat age, gender, and A31P genotype during measurement. When possible, the average value of three consecutive cardiac cycles was obtained for each measurement for all two-dimensional or M-mode measures. Spectral Doppler measures were recorded as the peak modal velocity. Measurements were taken to avoid ectopic cardiac cycles or cycles immediately following ectopy. LV wall measurements were obtained from both 2D and M-mode imaging as previously described with care to avoid the insertion sites of observed moderator bands9. LA diameter and LA/Ao measured by 2D echocardiogram in right parasternal short axis was obtained as previously described57. The left atrium was considered enlarged if the maximal left atrial diameter was ≥ 16 mm or the left atrium to aortic root ratio (LA:Ao) was ≥ 1.659. Peak LV outflow tract velocity was obtained from the left apical imaging plane with parallel alignment to blood flow and noted turbulence by color-flow Doppler imaging.Echocardiographic examinations were performed by a single, board-certified, veterinary cardiologist (J.A.S) using a 12-4 mHz sector-array transducer  and a standard approachCardiac biomarkers were assessed in venous blood samples obtained from each cat at each cardiovascular examination when possible. NT-proBNP (Cardiopet® proBNP) and high-sensitivity cardiac troponin-I  were measured in serum by a diagnostic laboratory according to sample handling and preparation guidelines . All cats had an indirect ophthalmic examination performed during one of their final two examinations to rule out retinal lesions consistent with systemic hypertension.Statistical analyses were performed using commercially available software packages . Normality testing for continuous echocardiographic and patient characteristic variables was done using D’Agostino-Pearson test. Descriptive statistics were performed and reported as mean ± SD for parametric values and median and interquartile range (IQR) for nonparametric values.max, Septal bulge, AoV, and PV) or lowest values  of echocardiographic parameters were also compared among cats with different A31P genotypes using Kruskal–Wallis test with Dunn’s post-hoc comparison.Echocardiographic parameters obtained during the last examination were compared among cats with different A31P genotypes using Kruskal–Wallis test with Dunn’s post-hoc comparison. The highest  were compared using Mann–Whitney U test or Kruskal–Wallis test with Dunn’s post-hoc comparisons. Correlations between the highest NT-proBNP values in the repeated measurement with continuous variables including age and body weight were assessed using the Spearman correlation method. Simple and multiple regression analysis after standard assumptions for linear regression were performed. Backward and forward selection techniques were performed by sequentially deleting or adding the variables with Logistic regression analysis was performed to determine the odds ratio of characteristic variables  of developing disease. The odd ratios of developing HCM was also obtained by multiple regression analysis by including all variables used for the simple regression analysis.Whole-blood or tissue samples were collected for 15 research colony cats following protocols approved by the University of California Davis IACUC. Of the 15 cats, three cats had HCM but were wildtype for the A31P mutation. An additional five cats had severe or early HCM and were either heterozygous (three cats) or homozygous (two cats) for the A31P mutation. Seven cats were healthy and were wildtype for the A31P mutation. DNA was extracted from EDTA whole blood or frozen tissue samples using a commercially available kit following the manufacturers protocol .56. All cats were sequenced to ~ 30X coverage. The generated sequences were processed as previously described56.Ten DNA samples were submitted for WGS . To ensure DNA integrity and sufficient concentration, DNA samples were run on a 2% electrophoresis agarose gel and Qubit Fluorometer  assessments were performed. DNA samples that passed quality control were selected, a 450 bp KAPA PCR-free library was constructed, and 150 bp pair-ended sequencing was performed on an Illumina NovaSeq S4 instrument . The additional five cat samples in this study previously underwent WGS using 350 bp and 550 bp PCR-free Illumina TruSeq libraries and sequenced on the Illumina HiSeq2500 sequencing platform as describedP-value less than 0.05 were further analyzed for the following effects: 5’—UTR premature start codon variants, disruptive inframe indels, frameshift variants, initiator codon variants, missense variants, splice acceptor/donor variants, splice region variants, and stop gain/loss variants. Variants were further filtered based on Ensembl IMPACT rating60. Variants that were modifiers or had a high, moderate, or low predicted effect on protein structure based on IMPACT rating and, when applicable, a SIFT (Sorting Intolerant From Tolerant) score less than or equal to 0.05 were further analyzed in a large cohort (n = 180) of unphenotyped whole-genome sequenced cats (http://felinegenetics.missouri.edu/99lives). The allele frequency of statistically significant variants was calculated for the initial 15 cats and the additional 180 cat population consisting of 43 different breeds.Variant call files were uploaded and analyzed via SNP & Variation Suite (SVS) commercial software . Variants that had a call rate less than 80% were removed from the analysis. The remaining variants were filtered with the Ensembl FelCat Genes 94 track using SVS software; intergenic variants were removed from further analysis. A total of 142 genes were analyzed which included genes that were previously associated with HCM, suspected to be associated with HCM, as well as electrophysiology genes . To identify statistically significant variants, a Chi-square test was completed. Variants located in exons or in the 3’ and 5’ untranslated regions (UTR) that had a Supplementary Information.


Drosophila embryos, epithelial tissues undergoing groove formation preferentially enhanced extracellular signal-regulated kinase (ERK) signaling. In EGFR mutant embryos at stage 11, sporadic apical cell extrusion in the head initiates a cascade of apical extrusions of apoptotic and non-apoptotic cells that sweeps the entire ventral body wall. Here, we show that this process is apoptosis dependent, and clustered apoptosis, groove formation, and wounding sensitize EGFR mutant epithelia to initiate massive tissue disintegration. We further show that tissue detachment from the vitelline membrane, which frequently occurs during morphogenetic processes, is a key trigger for the EGFR mutant phenotype. These findings indicate that, in addition to cell survival, EGFR plays a role in maintaining epithelial integrity, which is essential for protecting tissues from transient instability caused by morphogenetic movement and damage.Dying cells in the epithelia communicate with neighboring cells to initiate coordinated cell removal to maintain epithelial integrity. Naturally occurring apoptotic cells are mostly extruded basally and engulfed by macrophages. Here, we have investigated the role of Epidermal growth factor (EGF) receptor (EGFR) signaling in the maintenance of epithelial homeostasis. In  Highlighted Article: EGF receptor signaling in Drosophila protects epithelial tissues under mechanical stress from collapsing by preventing apical cell extrusion. Drosophila (22 h) compared with zebrafish (72 h) or mouse (3 weeks).The maintenance of epithelial integrity is essential for animal development and homeostasis. Cell proliferation often produces excess cells that are removed at a later point from the epithelium to maintain optimal cell numbers. The morphogenetic movement of epithelial folding or cell overcrowding applies mechanical stress to the cells to trigger programmed cell death . These cDrosophila embryogenesis starts from the syncytium, where diploid nuclei fill the surface area and the yolk occupies the interior. Drosophila embryos are internally pressurized, and their surfaces are supported by the vitelline membrane. Poking a hole in the vitelline membrane causes rapid cytoplasmic leakage; embryos do not survive after vitelline membrane removal. Embryonic morphogenesis proceeds via epithelial folding and invagination in the basal  direction against internal pressure, by increasing the apical surface tension of the epithelium, which drives inward tissue movement  probe of ERK activity (rhomboid (rho), which encodes the key protease required for the activation of the major EGFR ligand Spitz . In addition, perivitelline gaps were detected in the place above mitotic cells  and head involution (t=4 h) A. EGFR mMovie 2) showed that the amnioserosa was the first to lose continuity in EGFR mutants (Movie 2)  . After tdeformed (dfd), which stimulates the expression of the proapoptotic gene reaper (Df(3L)H99 mutant, in which four major proapoptotic genes are removed (Egfr and Df(3L)H99 double-mutant embryos showed very few apoptotic cells and no tissue disintegration . Before apical cell extrusion, the apical cell surface was tightly juxtaposed to the vitelline membrane . No apical cell extrusion was observed outside the salivary gland placode in the imaged region of mutant embryos at this stage.To further clarify the relationship between epithelial fold formation and apical cell extrusion, we further investigated posterior segments A. A ventIn order to detect any change in cellular adhesion, we monitored the expression of the adherens junction marker E-cadherin in the salivary gland placode . In contConsidering the results thus far, we reasoned that attachment to the vitelline membrane suppresses epithelial instability under the loss of EGFR function. To test this hypothesis, we observed epithelia mechanically detached from the vitelline membrane.+ tissue fragments expressing the caspase activity reporter apoliner . Strikingly, when EGFR mutant epithelial tissues were cultured, they rapidly lost integrity and almost completely collapsed within 5 h . Cross-sectional views showed that EGFR mutant epidermal fragments were folded and extruded several cells apically before completely collapsing . Cables of myosin appeared at the wound edge 10 min after wounding  laser that reaches deep into the target and avoids the risk of damaging the vitelline membrane . Laser iosophila . Under tosophila D, 5 min.wounding D′.n=15 embryos, Movie 7). When the EGFR mutant was wounded, the damage expanded to the neighboring region, separate from the epithelial disintegration initiated from the head region. The initial epidermal disintegration phenotype was limited to the side of laser wounding and took more than 30 min to initiate . Cross-sectional views showed that wounding caused ectopic detachment of the epithelia from the vitelline membrane around the vicinity of the wounding region  would buffer the negative impact of mitosis and invagination on epithelial stability and allow enough time for tissues to repair any damage that might occur during morphogenesis, making embryonic membranes dispensable. In addition, two cell adhesion systems, the septate junction mediating adhesion of lateral cell membranes and the integrin-basement membrane adhesion systems, develop only in the late embryonic stages  sorbitan monolaurate in PBS] for 30 min. The embryos were stained with primary antibodies at 4°C overnight. After five washes in blocking buffer, the embryos were stained with secondary antibodies at 4°C overnight. The source and working concentrations of primary and secondary antibodies and reagents are listed in Drosophila embryos were mounted on a glass-bottom med dish (Iwaki) coated with glue and extracted with heptane using double-sided sticky tape (Scotch). Each confocal image was acquired using a laser-scanning confocal microscope  with the following lenses: PlanApo N 60×NA1.42 oil (most of the live imaging), UPlanSApo 60×NA1.30 silicon  and covered with silicone oil (Shinetsu silicone). A solution of tetramethyl rhodamine-conjugated dextran, 3000 MW (1 µg/µl), was diluted in PBS and injected into the perivitelline space of the embryos. The glass needles were prepared by pulling a glass capillary with a needle puller (Sutter).Drosophila medium supplemented with 10% fetal bovine serum and antibiotics (penicillin and streptomycin). The vitelline membrane was punctured by pricking at the anterior end of the embryo with a stainless steel needle (Shiga Konchu), and half of the disrupted embryos were pushed out of the vitelline membrane. Tissue fragments were collected without vitelline membrane from at least 10 embryos in each experiment and placed in poly-L-lysine-coated glass-bottomed dishes  for confocal imaging. Epidermal tissue fragments facing the apical side towards the glass were imaged within 1 h of dissection. A list of reagents used is provided in Eggs were collected overnight at 18°C, and stage 11-12 embryos were selected from the dechorionated embryos. They were placed on a polystyrene tissue culture dish (Iwaki) and covered with Schneider's The IR-LEGO system  was combined with a confocal microscope  equipped with GaAsP detectors. The IR laser was introduced through the lateral camera port of an inverted microscope . The laser intensity was adjusted to 100 mW or 150 mW to produce local wounds . Time-laClick here for additional data file.10.1242/develop.201231_sup1Supplementary informationClick here for additional data file.


Millions of single nucleotide variants (SNVs) exist in the human genome; however, it remains challenging to identify functional SNVs associated with diseases. We propose a non-encoding SNVs analysis tool bpb3, BayesPI-BAR version 3, aiming to identify the functional mutation blocks (FMBs) by integrating genome sequencing and transcriptome data. The identified FMBs display high frequency SNVs, significant changes in transcription factors (TFs) binding affinity and are nearby the regulatory regions of differentially expressed genes. A two-level Bayesian approach with a biophysical model for protein-DNA interactions is implemented, to compute TF-DNA binding affinity changes based on clustered position weight matrices (PWMs) from over 1700 TF-motifs. The epigenetic data, such as the DNA methylome can also be integrated to scan FMBs. By testing the datasets from follicular lymphoma and melanoma, bpb3 automatically and robustly identifies FMBs, demonstrating that bpb3 can provide insight into patho-mechanisms, and therapeutic targets from transcriptomic and genomic data.  •Integration of genome sequencing and transcriptome data for regulatory SNP analysis•A Bayesian approach with a biophysical model to estimate TF-DNA binding affinity•Use of DNA methylome data for identification of functional regulatory mutations•The tool robustly identifies functional non-coding mutation regions in diseases Biological sciences; Bioinformatics; Biocomputational method; Omics; Biological sciences tools These large amounts of sequencing data need to be analyzed and explored. Especially, identifying the functional mutation regions from millions of genetic variants in the individual patient genome is an emerging task for understanding patho-mechanisms leading to disease, clustering patients, providing potential diagnostic biomarkers and screening of therapy targets.,,,Genetic variants, such as somatic single nucleotide variants (SNVs) including SNPs, structural variants, insertions, and deletions, can affect the binding affinity of DNA with TFs, and are considered as a significant mechanism in the development of cancer or other diseases.,,,,Recently, whole genome sequencing technologies have enabled new discoveries in the medical field, including genome-wide profiling of mutations by scanning SNVs in both coding and non-coding regions. Several attempts are made to identify mutations affecting gene regulation, such as the mutations in cis regulatory elements (CREs), in a large number of cancer or disease datasets.,https://bpb3.github.io/bpb3/).However, one challenge in the aforementioned tools is that the information of gene expression and epigenetic profiles from patients are not considered, which makes them less useful when correlating SNVs with patient-specific omics data. Currently, only few studies integrated patient-specific whole genome sequencing data with gene expression data,A Bayesian method similar to BayesPI-BAR2,,,in silico prediction of TF binding affinity changes due to DNA sequence variation. All calculations were done using 10 parallel processes on the same high-performance computer. The clustering of 1772 human TFs was obtained by applying the abc4pwm packageHere, ∼67 SNPsHere, whole-genome-sequencing (WGS) data and RNA-seq experiments for tumor-normal paired cancer patient cohorts  were obtained from ICGCIn this analysis, 4, 12, and 12 significant mutation blocks are predicted in FL14, second FL cohort and skin cancer data, respectively. Elapsed CPU times in five different calculations are recorded in BCL2 (B cell lymphoma 2).BCL2 gene.After comparing the accuracy and performance between the original method and the clustered PWMs method in 67 known regulatory SNPs and cancer cohort data, a more detailed evaluation of results between the methods will be illustrated here. For FL14 data, it is known that TFs bindings from nuclear receptors are affected by a mutation block in the promoter of TERT gene) are displayed in TERT (telomerase reverse transcriptase) gene in melanoma cancer patients.TERT gene in the melanoma cohort. This result is in agreement with a previous publicationTERT gene generate de novo binding motifs for ETS-related factors in skin cancer.To further evaluate the results in skin cancer cohort data, the predicted significant TF binding affinity changes at a mutation block  for the evaluation, where mutation scores for the verified mutations and random ones were obtained from the four tools; bpb3, BayesPI-BAR, CADD,Secondly, we performed the same analysis on a large number of verified regulatory mutations (∼3075 SNPs) based on massively parallel reporter assays (MPRAs) with three sets of randomly selected nonfunctional ones (∼3075 SNPs in each set) from a previous study,BCL2 gene  and overlapping with both DMR and enhancers. In particular, there is a significant FOX family TF binding affinity changes between the FL patients and the normal samples at block_66304, which was verified in lymphoma cancer cell lines previously.A previous publication,To study the potential applications with diverse functional genomic data  to predict putative functional regulatory mutation block-gene associations, we first identified genome-wide mutation blocks  and differentially expressed genes  from the second FL cohort (∼181135 of SNVs from 22 patients) by using the bpb3 package. Then, the 786 mutation blocks were annotated to four genomic regions , from which an initial list of mutation block-gene associations was built. Subsequently, the common topologically associating domains (TAD) information in five human cell lines, as well as the differential methylation regions (DMR) between tumor and control samples, were acquired from earlier publications.,BCL2 and BCL6) of twelve genes were known previously,Finally, a random permutation test was used to evaluate the enrichment of mutation blocks associated with genes across seven chromatin states. These seven chromatin states represent functional regions in the human genome , which were predicted previously by applying ML methods on multiple functional genomic datasets  across six human cell-lines.cis-regulation regions such as promoters or enhancers; (4) being nearby or overlapping with DEGs or/and other interesting function-associated regions such as differentially methylated regions (DMR), and thus functionally associating with dysregulation in gene expression. In summary, bpb3 can automatically predict functional mutation blocks by integrating genome, transcriptome and epigenomic data from patients.Bpb3 can identify functional mutation blocks and predict the effect of these mutations on TF-DNA binding based on whole genome sequencing data from cancer or other disease patients. Compared to other functional SNVs prediction and annotation tools, bpb3 is unique in terms of its advantage in searching for functional regulatory mutation regions by integrating multi-omics data from patients. The integration of transcriptomic and other omics data is one of its specific features. These features will not only improve the predication accuracy for functional mutation blocks, but also help finding the mutation target genes which may be of importance in precision medicine. The functional mutation blocks predicted by bpb3 are based on the following features: (1) carrying high mutation frequency with a cluster of SNVs cross patients; (2) resulting in significant change in protein-DNA binding affinity in a list of top-ranked TFs; (3) targeting in no-coding The bpb3 package is tested and evaluated on both small and large datasets from cancer patients. Its predicted results for 263 skin cancer of melanoma (MN) and 14 FL patients are accurate and reproducible, by using the three evaluation methods for detecting significant TF binding affinity changes caused by SNPs. The first method (PWMs_full) uses all available PWMs, while the second method only considers clustered PWMs (C1_full). The third one (C1fC2_N) uses a two-level approach to predict significant TF binding affinity changes: the clustered PWMs are used in foreground calculations (first level); the top N clustered PWMs from the first level predictions are extracted and PWMs within the selected clusters are used for further calculations (second level). The results of the three types of calculations match to each other to a large extent. In general, C1_full provides accurate prediction as the other two methods but with far fewer CPU hours. Thus, C1_full is the optimized method in general for big data computation and if the CPU time is one of the main concerns. However, C1_full identified less TFs compared to PWMs_full method, because the number of the reference TFs have decreased after PWMs classification. PWMs_full method is recommended for detailed studies in small datasets, while the C1fC2_N method is suited for narrow bottom-up TFs candidates, hence speeding up computation time.BCL2 and BCL6 genes,Remarkably, when the accuracy of the top ranked TFs from bpb3 was compared against the previous BayesPI-BARRecently, a sequence classes-based tool Sei, an improved version of DeepSEA Beluga, has been developed to classify and quantify (score) the regulatory impact of SNVs on DNA sequences.https://bpb3.github.io/bpb3/). This information may help users to estimate the cost of running bpb3 on a cloud machine based on commercial providers.Bpb3 is a command line tool, which not only includes all R language functions of previous BayesPI-BARTERT and BCL2 genes in MN and FL, respectively. It is applicable to genome sequence variants and gene expression data of any other diseases, to identify functional non-coding SNVs in whole genome data and provide reliable predictions of functional mutation blocks affecting TFs binding.In the current cancer cohort studies, bpb3 correctly predicted significant TF binding affinity changes at mutation blocks near the promoter of A limitation of current study is that only two crucial features  are considered in identifying functional regulatory mutation blocks. While these features are important in gene regulation, other relevant features, such as histone modification marker, DNA methylation patterns, nucleosome density, and Topologically Associating Domains (TADs), were not included in the analysis. Therefore, some functional regulatory mutation blocks may be missed in the prediction. In the future, a comprehensive analysis method shall be developed by incorporating diverse information , which will improve the understanding of the complex regulatory landscape affected by mutations.junbai.wang@medisin.uio.no).Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Junbai Wang  is the protein binding free energy in sequence S which corresponds to a position-specific affinity matrix (PSAM), or position-weight matrix (PWM)mu (μ) is the chemical potential (or protein concentration), k is gas constant, and T is the absolute temperature. The TF chemical potential mu (μ) at various conditions can be estimated by BayesPI2+,in vivo ChIP-seq experiment. Nevertheless, the mu (μ) can also be manually adjusted for a given PWMdifferential binding affinity (dbA) is used to distinguish between the direct and the indirect protein–DNA interactions,shifted differential binding affinity (δdbA) is designed to calculate the difference in TF binding affinities between the reference sequence  and the mutated DNA sequence : δdbA(Si) = dbA – dbA. A similar δdbA(Si) calculation is applied to a set of randomly mutated DNA sequences to estimate the significance of such TF binding affinity changes. Subsequently, it can rank TFs based on their impact on the TF-DNA binding affinity changes due to a DNA mutation. In other words, a TF with the strongest impact on TF-DNA binding affinity change will have the largest absolute value of δdbA due to a regulatory sequence variation. More information of BayesPI-BAR is given at online documentation (https://bpb3.github.io/bpb3/) and published papers.,Where (1)Foreground calculation: all predicted patient-specific mutation blocks are considered foreground mutation blocks, which are tested against all available PWMs of human TFs .(2)Background calculation: background mutation blocks are extracted randomly from regions of interest  with the same sequence length as patient-specific mutation blocks. Mutations in each background mutation block are generated based on the reference genome by altering the nucleotides in random positions with either tumor-derived mutations or a given k-mer mutation probability distribution (the mutation signature).(3)A comparison between the foreground and the background calculation: for each patient-specific mutation block, a TF with a distribution of δdbA values are significantly different  between the foreground and the background calculations is considered as a putative target for the mutation block.In order to predict functional regulatory mutations in cancer or other disease patients as BayesPI-BAR2,https://bpb3.github.io/bpb3/) and published work.More information about the computation please refer to bpb3 online documentation  of predicted true TFs for all mutations by using a scatterplot, where circles represent mutations, X axis and Y axis are the rankings of true TFs predicted by bpb3 and sTRAP/is-rSNP, respectively. Given that a lower rank shows a better prediction, mutations above the red diagonal line indicates a better ranking by bpb3. However, mutations below the diagonal line suggests the ranking by sTRAP/is-rSNP is more accurate. A significance of the difference of ranking between the two methods in a scatterplot is tested by a Wilcoxon signed-rank test.https://osf.io/pjxm4/wiki/home/) with three sets of randomly selected nonfunctional mutations (∼3075 SNPs in each set) from massively parallel reporter assays (MPRAs),https://github.com/CshlSiepelLab/LINSIGHT). The significant difference of mutation scores between the verified/functional mutations and the random ones is tested by Wilcoxon rank-sum test.Two datasets are used in this evaluation. First, 67 verified In general, the functional regulatory mutations exhibit higher mutation scores than those of the random occurring mutations. This characteristic allows the assessment of the prediction accuracy of various tools by applying a binary classifier on their provided mutation scores. For example, a better score will have a better binary-classification  accuracy. By applying a cutoff value on the scores that splits the mutations to two classes  then considering all possible cutoff values for the scores, the receiver operating characteristic (ROC) curves  can be plotted for the four methods, and the plot reveals performance comparison of the binary classification . An area under this curve (AUC) represents the degree of separability for the classification i.e., the higher the AUC, the better the method is at classifying two groups. For a random-guessing classifier, the AUC of the theoretical ROC curve is 0.5.,Here, bpb3 package was first applied on whole-genome somatic mutations (∼181135 of SNVs) of the second FL cohort (22 patients) obtained from ICGC public data release,(1)M mutation blocks associated to a gene, a fraction of blocks  located in the seven chromatin states is computed.For (2)M blocks in the first step, the same number of mutation blocks were selected randomly from the initial ∼786 blocks to obtain an expected fraction of blocks located in the seven chromatin states.By excluding the (3)For each chromatin state, if the expected fraction is greater than the actual one then the chromatin state is incremented by one.(4)After 10000 times random sampling in step 3, an expected P-value for the mutation blocks enriched in a chromatin state is calculated .By considering other functional genomic information such as the seven types of chromatin segmentation (or chromatin states) in human genome, a permutation enrichment test of mutation blocks (∼175 blocks) overlapping to the chromatin states was used to find the putative functional regulatory mutation block-gene associations . The seven chromatin states  were obtained from earlier publications,If we assume that the functional regulatory mutation blocks of a gene are often enriched in either TSS or enhancer regions,https://github.com/abc4pwm/abc4pwm).Affinity Based Clustering for Positions Weight Matrices - abc4pwmhttps://bpb3.github.io/bpb3/). The cost of CPU hours, memory usages, and data storage are supported by NRIS – Norwegian research infrastructure services through project number NN4605K and NS4605K.In this study, all parallel calculations were done in SAGA supercomputer with 10 parallel processes. SAGA is provided by Hewlett Packard Enterprise - Apollo 2000/6500 Gen10 , which is placed at NTNU in Trondheim but we are physically located in Oslo Norway. Thus, the work can be seen as an application of cloud computing in the evaluation of bpb3 performance. More information of high-performance computing please refer to bpb3 online documentation (https://bpb3.github.io/bpb3/).Statistical analyses were performed using Python or the designed bpb3 Python package. More information please refer to bpb3 online documentation (