Bioinformatics Research Articles

Routine Whole Genome Sequencing Offers a More Comprehensive Assessment of IG/TCR Gene Rearrangements for MRD Targets in Acute Lymphoblastic Leukemia - Data from the First 62 Cases from the NHS England Genomic Medicine Service

Introduction Minimal residual disease (MRD) monitoring is fundamental for risk stratification and treatment in acute lymphoblastic leukemia (ALL). Sensitive molecular assay requires presentation samples to be screened for immunoglobulin and T-cell receptor (IG/TCR) gene rearrangements. Previously samples were screened by PCR, heteroduplex analysis and Sanger sequencing to identify clone defining IG/TCR gene rearrangements. Subsequently these sequences were used to develop qPCR assays for MRD follow-up monitoring. More recently, targeted IG/TCR high-throughput sequencing (HTS) has been used with similar panels to identify gene rearrangements. Both these methods have limitations, in that only rearrangements covered by the panels are detected, and the techniques are labour intensive and expensive. Whole genome sequencing (WGS) has potential to discover all IG/TCR rearrangements in a single assay giving greater potential to detect markers for follow up MRD monitoring. In England all patients with ALL are eligible for WGS at diagnosis via the National Health Service, Genomic Medicine Service (NHS GMS). Methods Sixty-two patients, presenting with ALL at either Great Ormond Street Hospital or Bristol Children's Hospital, had short-read WGS via the NHS GMS performed concurrently with standard of care (SOC) targeted HTS to identify IG/TCR rearrangements. A bespoke bioinformatic pipeline was developed to identify IG/TCR rearrangements in the WGS samples. SOC targeted HTS analysis was performed using the open source Vidjil bioinformatic software. A cut-off of 5% (clone proportion of total clonotypes) was used to define an acceptable disease-specific clone for monitoring. Results All 62 samples had a greater number of rearrangements detected by WGS than targeted HTS. The mean number of clones >5% detected was 2.7 by targeted HTS and 3.1 by WGS. For rearrangements identified by targeted HTS at a 5% cut-off, 97% (185 out of 190) were identified by WGS. All targets were identified by WGS using a 10% cut-off (Table 1).Despite the lower depth of coverage of the WGS assay, it retrieved 89% of sequences seen in SOC down to a level of 1%, as well as additional re-arrangements not seen by the SOC assay (Figure 1). As well as additional rearrangements being discovered by WGS, we observed novel rearrangements (dd3/ja rearrangements) not previously identified using a targeted approach. To establish if the additional sequences identified by WGS were clinically applicable as MRD targets, we designed allele specific oligonucleotides for a representative 32 rearrangements for testing by qPCR. Over half (n=16) were able to be used to track MRD in follow-up samples. New primer/probe sets were designed for the novel dd3/ja rearrangements identified by WGS, these were also tested by qPCR and performed well enough to be clinically applicable for MRD monitoring. Conclusions WGS approach outperformed targeted HTS, identifying more IG/TCR rearrangements suitable for molecular MRD monitoring in all patients. This is of particularly benefit in cases where targeted HTS only identifies 1 or 2 potential MRD targets. WGS also has the advantage of identifying other key gene rearrangements (BCR-ABL, KMT2A, SIL-TAL, IKZF1) that make excellent targets for MRD monitoring. Beyond this, WGS delivers all diagnostic information, germline and additional somatic findings that aid diagnosis. For ALL, in one test WGS can now replace all molecular diagnostics. In England all patients with leukemia are eligible for centralised WGS. With continued improvements in turnaround times, and reducing costs, we propose that WGS should be the new diagnostic standard for pediatric ALL

Specificity Testing for NGT PCR-Based Detection Methods in the Context of the EU GMO Regulations.

The term new genomic techniques (NGTs) is an umbrella term used to describe a variety of techniques that can alter the genetic material of an organism and that have emerged or have been developed since 2001, when the existing genetically modified organism (GMO) legislation was adopted. The analytical framework used to detect GMOs in Europe is an established single harmonized procedure that is mandatory for the authorization of GM food and feed, thus generating a reliable, transparent, and effective labeling scheme for GMO products. However, NGT products can challenge the implementation and enforcement of the current regulatory system in the EU, relating in particular to the detection of NGT products that contain no foreign genetic material. Consequently, the current detection methods might fail to meet the minimum performance requirements. Although existing detection methods may be able to detect and quantify even small alterations in the genome, this does not necessarily confirm the distinction between products resulting from NGTs subject to the GMO legislation and other products. Therefore, this study provides a stepwise approach for the in silico prediction of PCR systems' specificity by testing a bioinformatics pipeline for amplicon and primer set searches in current genomic databases. In addition, it also empirically tested the PCR system evaluated during the in silico analysis. Two mutant genotypes produced by CRISPR-Cas9 in Arabidopsis thaliana were used as a case study. Overall, our results demonstrate that the single PCR system developed for identifying a nucleotide insertion in the grf1-3 genotype has multiple matches in the databases, which do not enable the discrimination of this mutated event. Empirical assays further support this demonstration. In contrast, the second mutated genotype, grf8-61, which contains a -3 bp deletion, did not yield any matches in the sequence variant database. However, the primer sequences were not efficient during the empirical assay. Our approach represents a first step in decision making for analytical methods for NGT detection, identification, and quantification in light of the European labeling regulations.

Whole-Genome Sequencing Reveals Distinct Mutational Signatures in Multiple Myeloma Patients with African Ancestry Compared with Those of Non-African Ancestry

INTRODUCTION: Improving health outcomes for patients with African ancestry (AA) is a key healthcare aim, but there is uncertainty in whether disparities arise predominantly from socioeconomic differences or from genetic differences in tumor biology. It remains an open question as to whether multiple myeloma (MM) occurring in AA patients has a similar or different spectrum of genomic abberations when compared to patients having European ancestry. To date, studies have suggested that AA have an excess of t(11;14) and a deficiency of TP53 mutations. We have established a series of 302 cases of MM precursor and newly diagnosed MM with whole-genome sequencing (WGS) available that is enriched for self-declared AA or diverse ethnic background. METHODS: In collaboration with the NYGC and Polyethnic-1000 consortium, we sequenced tumor samples to a depth of 60-80X and normal tissue to 30-40X. We a employed bioinformatics pipeline with a consensus mechanism for somatic variant calling, including Mutect2, Strelka2, and VarScan2 for SNVs; Mutect2, Strelka2, VarScan2, and SvABA for InDels; Battenberg and FACETS for CNVs; Manta, SvABA, DELLY2, and IgCaller for SVs. Additionally, an admixture workflow was used to estimate each individual's ancestral lineage using continentally-distinct references, comprising 23 regional populations within 5 super-populations from the 1000 Genomes Project (https://github.com/pblaney/mgp1000). Mutational signature were calculated using the R package mmsig (Rustad et al. Comm. Bio. 2021). RESULTS: Using admixture estimations from 302 patients with high-coverage WGS together with 941 patients from the CoMMpass trial, we identified five clusters corresponding to single dominant genetic ancestries (median proportion >75% assignment to reference super-population), together with a cluster characterized by highly admixed individuals with no dominant genetic ancestry (median proportion <50%). Of the total, 53.0% are in the European dominant (EUR) cluster, 26.5% African dominant (AFR), 8.6% American dominant (AMR), 7.9% highly admixed, 2.0% East Asian dominant (EAS) and South-East Asian dominant (SAS) clusters, respectively. Stratifying patients by their cluster assignment and calculating the frequency of subtype translocations, we show that t(11;14) occurred in 25.8% of EUR patients while in only 14.6% of AFR (p=0.045) and 7.7% of AMR (p=0.05) patients. The frequency of the t(4;14) was more closely distributed, with 15.7% in EUR, 11.5% in AMR, and 11% in AFR clusters. For the acquired somatic mutations, the tumor mutational burden (TMB) was lowest in the AFR cluster 2.21 (median, somatic mutations per Mb), significantly lower by comparison to the EUR cluster at 2.94 (FDR adj. P=4.3x10 -6). The most striking genomic difference was observed when comparing the mutational signatures landscape between AA and the other racial groups. Using WGS, AA had lower SBS1 and SBS5 absolute contribution compared to EUR, and this was largely responsible for the difference in TMB. SBS1 and SBS5 are known to be clock-like signatures, accumulating at a constant rate over time. Because no differences in age, cancer cell fraction, and coverage were observed between AA and EUR, this finding suggest different mutational clock-like rate between AA and EUR. The higher TMB observed in EUR was also driven by the higher APOBEC-mutational activity (SBS2 and SBS13) compared to AA (p=0.05). Interestingly, 72% of all EUR had APOBEC-activity evident, in contrast to 45% of the AA (p=0.001). This difference was confirmed after excluding the MM precursor patients, previously demonstrated to have lower APOBEC-activity (Oben et al. Natur Comm. 2021), and was validated on CoMMpass whole-exomes. CONCLUSIONS: Leveraging one of the largest series of diverse patients with WGS, and integrating genomic data with comprehensive ancestry information, AA MM emerged as biologically different in term of genomic drivers and mutational signatures, suggesting potential differences in etiology and genomic evolution over time. Further analysis will include molecular timing of clonal copy number gains, and reconstruction of phylogenetic trees, with view to improving our understanding of the etiology of MM development across patient genetic backgrounds. FIGURE: Genomic characteristics of myeloma across ancestries. A) Translocation percentage (number per cluster) and B) Tumor mutational burden across clusters.

Nanopore Cas9-Targeted Long-Read Sequencing - a Fast and Flexible Diagnostic Tool for the Identification of B-Cell Acute Lymphoblastic Leukemia Associated Gene Rearrangements

B-cell acute lymphoblastic leukemia (B-ALL) is characterized by a multitude of recurrent genetic alterations, which drive malignant transformation. Besides their essential role in leukemia biology these aberrations also serve as predictors of treatment response and survival, making their reliable detection a prerequisite for risk stratification in state-of-the-art clinical trials. Currently, the genetic characterization of B-ALL in most diagnostic laboratories still relies on an integrative approach combining conventional cytogenetics, reverse transcription polymerase chain reaction (RT-PCR) and fluorescence in situ hybridization (FISH) assays as well as single nucleotide polymorphism (SNP) array analysis. However, these conventional diagnostic tools fail to identify a considerable number of genetic alterations, particularly cryptic rearrangements such as IGH::DUX4, IGH::EPOR or MEF2D involving fusions. Although next generation DNA- and RNA-based sequencing (NGS) assays have significantly improved the reliable detection of fusion genes, the need for large sample numbers to achieve acceptable turnaround times and cost effectiveness impairs their application in small- to medium-sized diagnostic laboratories. To address these issues, we developed a targeted sequencing assay, based on the commercially available combination of Cas9-mediated target enrichment and Nanopore long-read sequencing, which facilitates the identification of B-ALL associated gene rearrangements on the DNA level in a single sample format. The targeted panel design included an initial review of the literature to define the breakpoint-spanning regions involved in fusion gene formation followed by the design of specific guide RNAs (gRNA) to facilitate Cas9-mediated enrichment of these regions of interest (ROIs). We first conducted ROI enrichment and sequencing with a limited number of gRNAs targeting only ETV6, KMT2A and DUX4 fusions. Then we progressively extended the pool of gRNAs with each sequencing run until it comprised 91 gRNAs covering most B-ALL-associated rearrangements. This stepwise approach allowed us to identify negative effects of increasing panel size on overall enrichment efficacy and simplified the identification and replacement of poor performing gRNAs. Our initial cohort consisted of 12 well-characterized samples harboring 13 gene rearrangements, including ETV6::RUNX1, KMT2A::AFF1, MEF2D::BCL9, ZMIZ1::ABL1, SSBP2::CSF1R, PDGFRB::EBF1, IGH::EPOR, IGH::DUX4, IGH::CRLF2, IGH::CEBPE, IGH::MYC, and IGH::BCL2. To ensure easy implementation in diagnostic workflows, we used genomic DNA, isolated according to standard protocols. Cas9-based target enrichment and long-read sequencing on MinION flow cells (v9.4.1) were performed according to protocols provided by Oxford Nanopore and each sample was sequenced for 72 hours. On average, each run yielded 3.72±1.21 Gb, a mean read length of 7.40±1.00 kb with an N50 value of 12.87±1.71 kb and an average read quality (Q) score of 14.72±0.52. For data processing and analysis, we adapted the existing nextflow nf-core bioinformatics pipeline, nanoseq, to include publicly available structural variation callers and quality control tools. Our approach correctly identified 92.3% (12/13) of the rearrangements with a mean of 42 (range 2-97) breakpoint-supporting reads (BSR) per alteration. Only the IGH::MYC translocation was missed as the breakpoint was located outside of our predefined ROIs for MYC. To corroborate these results, we sequenced an additional cohort of 14 B-ALL samples, including ZNF384::DUX4, KMT2A::MLLT10, KMT2A::USP2, PAX5::SOX5, EP300::ZNF384, P2RY8::CRLF2, and IGH::DUX4 positive cases and were able to correctly identify 100% (14/14) of the fusion genes with an average of 83 (range 7-367) BSR. In conclusion, our newly developed targeted sequencing panel correctly identified 96.3% (26/27) analyzed gene rearrangements in B-ALL, including alterations evading standard diagnostic methodologies such as IGH::DUX4 or IGH::EPOR. Importantly, the assay allows individual analysis in a single sample format, thereby representing a valuable tool for B-ALL diagnostics in small to medium-sized laboratories.

Distinct T Cell Receptor Gene Repertoires and T Cell Subset Distribution in Peripheral Blood and Bone Marrow of Patients with Multiple Myeloma

Mounting evidence suggests that the T cell repertoire in multiple myeloma (MM) is actively shaped by antigen selection. However, the molecular underpinnings of this observation are not fully understood, while relevant studies have been mostly based on immunophenotypic characterization of T cell subsets, with considerably less information regarding T cell receptor (TR) gene repertoire profiles. Moreover, most available information derives from the analysis of a single tissue compartment [i.e. peripheral blood (PB) or bone marrow (BM)], thus hindering addressing the potential impact of local T cell responses. Here we sought to overcome these limitations through paired analysis of PB and BM aspirates of 20 newly diagnosed patients with MM before the administration of any treatment, from whom we isolated the PB and BM mononuclear cell (PBMC/BMMC) fraction, as well as CD4 + and CD8 + T cells. TR beta (TRB) chain gene rearrangements were RT-PCR amplified on RNA extracted from CD4 + and CD8 + T cells and then subjected to paired-end next generation sequencing (NGS). Raw reads (n=14,369,410 | median 179,618/sample) were processed through a purpose-built bioinformatics pipeline. Productive TRBV-TRBD-TRBJ gene rearrangements were taken into consideration (n=9,212,507 | median 115,156/sample) for the computation of clonotypes (i.e. TRB rearrangements with identical TRBV gene usage and amino acid complementarity-determining region 3 sequence). Overall, 1,005,150 TRΒ distinct clonotypes were assessed (median=12,565/sample). Both CD4 + and CD8 + T cell populations displayed skewed TRBV repertoires (7 TRBV genes and 3 TRBJ genes accounting for about ~40% and ~60% of the repertoire) in both BM and PB. All patients displayed oligoclonal T cell expansions in both CD4 + and CD8 + T cells, albeit these were significantly (p<0.001) more pronounced in CD8 + T cells for both BM and PB samples. That said, oligoclonality was more pronounced in BM vs. PB for both CD4 and CD8 T cells, reaching statistical significance in the former: in particular, the median cumulative frequency of the most expanded clonotypes/sample was ~40% higher in CD4 + BM T cells vs. CD4 + PB T cells (p<0.001). Additionally, a significant shift was noticed in the major clonotype repertoire of CD4 + and CD8 + T cells in both PB and BM. In more detail, the 10 most expanded clonotypes/sample in PB had significantly lower frequency in BM, and, vice versa, the 10 most expanded clonotypes in BM were represented at diminished frequency or were absent in the PB in 12/20 patients. Flow cytometry analysis revealed distinct T cell subset composition in CD4 + or CD8 + T cells, also in different tissue compartments (BM vs. PB). Significant (p<0.001) differences concerned the over-representation of: (i) effector T cells (CD45RA +/CCR7 -, Temra) in CD8 + vs. CD4 + T cells in both BM and PB (median frequencies in BM and PB: 22.5% and 7% for CD8 + T cells; 6% and 1.6% for CD4 + T cells); (ii) CD8 + Temra cells in BM vs. PB (median frequencies of 22.5% vs. 7% in BM and PB, respectively; p<0.001); (iii) CD4 + vs. CD8 + central memory T cells (CD45RO +/CCR7 +, Tcm) in both PB and BM (median frequencies in PB and BM: 42.7% and 49.1% for CD4 + Tcm and 14% and 18.8% for CD8 + Tcm); (iv) CD8 + effector memory T cells (CD45RO +/CCR7 -, Tem) in BM vs. PB (median frequencies of 21.8% vs. 2.7% in BM and PB, respectively); (v) CD8 + naïve T cells (CD45RA +/CCR7 +, Tn) in PB vs. BM (median frequencies of 67.7% vs. 24.2% in PB and BM, respectively). In conclusion, oligoclonal expansions of CD4 + and, particularly, CD8 + T cells in MM that are more pronounced in the BM (at least for CD4 + cells) argue for selection by BM-biased antigens, a claim also supported by the significant differences in the relative frequency of dominant clonotypes in BM vs. PB. The distinctive T cell subset distribution in the BM, characterized by a significant increase of effector at the expense of memory T cells, likely reflects an antigen-driven albeit ineffective immune response.

Dissecting the Single Cell Landscape of Leukemic Mantle Cell Lymphoma

Introduction: Resistance to therapy remains a major challenge for Mantle Cell Lymphoma (MCL) patients, despite a diverse and expanding treatment landscape. There is thus a major need to better understand the mechanistic underpinnings of therapy resistance in order to optimize treatment selection. Given the advent of immunotherapies in recent years, there has been increasing evidence that not only tumor-intrinsic factors affect therapy resistance, but that tumor-immune cell interactions are an additional major contributor. In the context of MCL, such interactions have been poorly characterized, but hold the promise of identifying novel treatment approaches and better understanding resistance mechanisms to commonly applied therapies such as BTK inhibition. Methods: This study aimed to investigate the single-cell landscape of leukemic MCL using single-cell profiling of 26 patients and 10 healthy donors. In addition, we performed in vitro drug testing on a subset of patient samples (n=12; 6 treatments) to understand the effects of drugs on both tumor and immune cells in MCL . To comprehensively characterize the phenotypic and functional features of immune and tumor cells within a single-cell landscape, we employed high-dimensional full-spectrum cytometry. Therefore, we developed 5 high-dimensional panels covering markers to deeply interrogate T and NK cells, immune evasion and immune checkpoints, cytokines and cytotoxicity, cellular trafficking and intracellular signaling/phosphoproteome (covering >120 proteins). We combined this with single-cell RNA sequencing and a scalable and reproducible bioinformatics pipeline. To assess the disease specificity of the immune landscape, we also obtained single-cell data from other B-NHL including MZL and CLL (n=60 patients). Results: Our data indicate profound changes in the immune landscape, in particular in the T cell compartment of MCL patients. In this regard, multi-omics factor analysis (MOFA) confirmed a highly distinct T cell landscape in MCL patients, characterized by higher effector marker expression in conventional CD4+ and CD8+ T cells in addition to higher fraction of effector and effector memory T cells compared to healthy donors (Fig. 1A and B). Moreover, our analysis revealed a pronounced expansion of highly suppressive regulatory T cells (~5% of T cells) (Fig.1B). The emergence of this effector Treg (eTreg) subset in MCL patients might limit efficient anti-tumor immunity of CD8+ and conventional CD4+ T cells. Consistent with this, we observed a high fraction of several dysfunctional CD8+ T cells subsets. Notably, we found a remodeling of this eTreg subset in patients undergoing ibrutinib treatment leading to a less pronounced suppressive phenotype and concomitantly observed an increase of proteins crucial for cytotoxic function such as Granzyme B and Interferon-γ in effector T cells. We further sought to link tumor cell signatures with the healthy immune cell signatures in order to assess potential interactions and mechanisms of immune escape. As such, the tumor cell compartment (MCL cells) displayed high-expression of inhibitory immune checkpoint ligands such as PVR, HLA-G, CD70 and PD-L1 and antiphagocytic proteins such as CD47 and CD24, which may contribute to the dysfunctional T cell compartment. In line with this, our analysis revealed that high expression of the immune checkpoint ligand CD70 on malignant B cells was inversely associated with an activation module in CD4+ T cells, providing a potential axis contributing to immune escape, which may be leveraged for future therapeutic use. Finally, we identified tumor and immune cell signatures associated with in vitro drug response that may not only be useful for improving treatment selection, but also offer novel biological insights into potential mechanisms of treatment resistance. Conclusion: This study provides valuable insights into the single-cell landscape of leukemic MCL, offering crucial understanding of how tumor-immune cell interactions may contribute to disease progression and treatment resistance. The findings hold potential for therapeutic exploitation to eventually improve patient outcomes in Mantle Cell Lymphoma.

Utility of Next-Generation Sequencing in the Detection of RNA Fusion Genes in Myeloid Malignancies in Singapore

Background Oncogenic fusion genes play an instrumental role in the pathogenesis of haematological malignancies and the identification of these genes is crucial for diagnosis, risk stratification and treatment decisions. Conventional diagnostic methods such as karyotyping and fluorescence in situ hybridization (FISH) are the current standard of care but are limited in the sensitivity and availability of different FISH probes for detection of fusion genes. In our institution, we have routinely integrated a targeted next-generation sequencing (NGS) panel in our diagnostic workflow for patients with suspected myeloid malignancies. Here we aim to review the cases with RNA fusion genes detected via NGS and compare these results with that of conventional diagnostic methods. Methods We analysed bone marrow samples from patients with suspected myeloid malignancies at the time of diagnosis in Singapore General Hospital (SGH). Extracted genomic DNA and RNA were amplified, and library preparation was performed with the Oncomine TM Myeloid Research assay (Thermo Fisher Scientific) to interrogate 40 DNA genes and 29 fusion driver genes. NGS libraries were sequenced on the Ion GeneStudio S5 System (Thermo Fisher Scientific) and analyzed using in-house bioinformatics pipelines and the Ion Reporter software. Limit of detection was determined as 3% allele frequency for DNA variants and 10 -2 for RNA fusions. RNA fusions detected by the NGS panel were consolidated and compared with cytogenetics (karyotyping and FISH) results. Results A total of 436 samples from adult patients with suspected myeloid malignancies were received for NGS testing from October 2021 to March 2023 in SGH, the largest tertiary hospital in Singapore. 306 had paired cytogenetics and NGS testing performed. 27 of the 306 cases (9%) had RNA fusions detected by NGS as summarized in Table 1 and Figure 1; their disease subgroups comprised of 16 acute myeloid leukaemia (AML), 5 chronic myeloid leukaemia (CML), 3 BCR-ABL1-negative myeloproliferative neoplasm (MPN), 2 acute promyelocytic leukaemia (APL) and 1 myelodysplastic syndrome (MDS). Eight AML cases had RUNX1-RUNX1T1.R3R3 fusions, with 6/8 (75%) harbouring additional DNA single nucleotide variants or indel mutations. Two AML cases had CBFB-MYH11 fusions, with 1 associated with KIT D419 deletion, and the other with U2AF1, NF1, and ASXL1 mutations. We also identified 2 adult AML cases (aged 49 and 65 years respectively) with DEK-NUP214.D9N18 fusions, both of which had associated FLT3-ITD mutations. There were 5 cases with KMT2A fusions, with a variety of partners including MLLT3.K9M6 (n=2), ELL.K9E2 (n=1), MLLT1.K8M4 (n=1), and MLLT6.K8M9 (n=1). Two patients with APL had PML-RARA detected for different variants: P3R3 and P6R3. Amongst the 8 MPN cases, 5 had BCR-ABL1 fusions consistent with diagnosis of CML. There was 1 case of MPN-Eo with FLP1L1-PDGFRA fusion, and the remaining 2 with ETV6-ABL1 and KMT2A-ELL.K9E2 rare fusions. The detected RNA fusions had clinical impact in establishing diagnosis (93%), prognosis (96%), and guidance of treatment decisions (89%). Out of the 27 cases with RNA fusions detected by NGS, 23 (85%) had concordant karyotype and FISH results. Four RNA fusions, namely KMT2A-ELL, CBFB-MYH11, FIP1L1-PDGFRA and ETV6-ABL1, were not detected by karyotyping or conventional FISH probes performed in our institution. It likely reflects the sensitivity of the NGS panel in detecting RNA fusions in cases of cryptic translocations. Conclusion We have demonstrated that NGS has a high sensitivity for identification of RNA fusion genes, is complementary to conventional cytogenetics testing, and has vast clinical impact in terms of diagnosis, prognostication and clinical management. We advocate for the integration of NGS with DNA and RNA sequencing into routine investigation of suspected myeloid malignancies for a more precise and comprehensive diagnostic approach.

Usefulness of Peripheral Blood Samples in Diagnosis and Prognosis Assessment of Myelodysplastic Syndromes and Chronic Myelomonocytic Leukemia Low Risk Patients Using Next Generation Sequencing

Introduction: myelodysplastic syndromes (MDS) are a heterogeneous group of hematological diseases. Morphologic bone marrow (BM) examination, cytogenetic analysis and molecular techniques are indispensable for accurate diagnosis and disease classification. BM constitutes the most suitable sample for molecular studies, however, peripheral blood (PB) can be obtained by minimally invasive venipuncture and would be ideal for comprehensive sequential monitoring of MDS over time. Aim: to assess whether targeted deep sequencing (TDS) allows the detection of somatic variants from diagnosis and follow-up PB samples and if the molecular profiles are comparable to those from BM samples. Methods: preliminary results include the study of 32 low risk patients (28 MDS and 4 chronic myelomonocytic leukemia, CMML). At least one sample, either BM or PB, was obtained at the moment of diagnosis and whenever possible, paired samples obtained the same day were collected. Over the course of the disease only PB samples were obtained. Libraries were prepared for all available samples using DNA from whole BM or whole PB using a custom hybridization-probe based panel (KAPA HyperCap, Roche), including selected exons of 50 myeloid-related genes. TDS was performed on Illumina MiSeq instrument at a mean coverage of 1000x. Data were analyzed using local bioinformatic pipeline. Only those variants with ≥100x locus coverage and ≥25 reads for the alternative allele (quality criteria) were considered for downstream analysis. Variants were filtered according to variant type, location, read depth (>100x) and population frequency (<1%). Results: patients´ characteristics are described in table 1. The combination of cytogenetics and TDS identified genetic alterations in 29/32 patients (91%). TDS allowed the identification of somatic variants in 27/32 patients (85%), being TET2 (n=23, 29%), SF3B1 (n=12, 15%) and ASXL1 (n=8, 10%) the most frequently mutated genes. Considering molecular information, restratification of IPSS-R to IPSS-M risk groups of MDS patients (n=28) was performed (Table 1). As previously described (Sauta et al, J Clin Oncol, 2023), we performed a five-to-five comparison of IPSS-R and IPSS-M patients' distribution (merging moderate low and moderate high to moderate in IPSS-M). This resulted in the restratification of 36% of MDS patients (10/28) of which 6% (n=2) were downstaged and 25% (n=8) were upstaged. Suitability of PB samples for NGS analysis was evaluated in 21 patients in which paired BM and PB samples were obtained at diagnosis. Overall, 65 variants were found, and 63 of them were detectable in both BM and PB samples. Despite the other two variants were also detected in PB samples at low variant allele frequency (VAF) values ( TET2 and NRAS, VAF=1,5% and 0,99%, respectively), they strictly did not meet our quality criteria (≥25 reads for the alternative allele). Nevertheless, a strong correlation was identified between VAF values for the individual variants detected in BM vs. PB (Figure 1). As part of this project, molecular profile during patients' follow-up is being studied using PB samples. To date, at least one follow-up sample of each patient was studied. The mean time between diagnosis and the first follow-up sample was 28 months (range 6-84 months). After this time, and despite molecular profiles are heterogeneous, variations could be detected in up to 72% of patients (23/32) using PB samples. The most commonly detected variation was an increasing in VAF values (9/23; defined by a difference >5% in relation to the VAF detected in the previous sample), followed by a new variant detection + increasing in VAF values (7/23), new variant detection only (6/23) and a single case where simultaneous decreasing VAF and disappearance of subclones was detected (single patient receiving treatment with hypomethylating agent). Conclusion: molecular testing has become increasingly important for accurate diagnosis and risk assessment in MDS and CMML patients, specially with the new classifications and prognostic scoring system. Here we demonstrate that the molecular analysis from PB cells constitutes an appropriate alternative for the detection and quantification of somatic variants in MDS patients at diagnosis and during disease course. Funding: Bristol Myers Squibb (PI-13279-IIT), FEHH and 2021 SGR 00560 (GRC).

The Novel Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) Platform Identifies Synergistic Activity of Lanraplenib and Ruxolitinib in Hematological Malignancies

Background: Investigation of drug combinations across different contexts can provide useful insights on the anti-cancer mechanism and can ultimately lead to new treatments. However, conventional drug combination screening methods are limited by throughput. Efforts to systematically identify the most effective active combinations and the optimal molecular contexts in a high throughput screening (HTS) format could greatly accelerate the development of combination treatments. Spleen Tyrosine Kinase, SYK, is a non-receptor tyrosine kinase known to regulate intracellular signaling, including FLT3, AKT/mTOR and STAT5 pathways, via its immunoreceptor tyrosine-based activation motif (ITAM). Deregulated SYK signaling has been reported to play a central role in pathogenesis of allergic and autoimmune diseases as well as hematological malignancies. Lanraplenib (LANRA) is a next-generation SYK inhibitor currently being evaluated in combination with gilteritinib, a FLT3 inhibitor, in patients with relapsed or refractory (R/R) FLT3-mutated acute myeloid leukemia (AML) (NCT05028751). Given its critical role in intracellular signaling and interaction with receptor tyrosine kinases(RTKs), we hypothesized that LANRA could synergize with ruxolitinib, a JAK inhibitor. To address this hypothesis, we performed a high throughput drug combination screen using the Broad Institute's Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) platform, which enables rapid screening of thousands of compounds in a 930-cell line panel across 45 different lineages. Methods: In this study, we performed a combination screen using the PRISM platform with ruxolitinib as the test compound, with a 10 μM top dose (7 dose concentrations with 3-fold dilutions), in combination with LANRA at two anchor doses, 2 μM and 10 μM. PRISM is a pooled, multiplexed cell viability assay that provides 7-point dose response curves, IC 50, AUC values, and relative abundance of unique cell line barcodes. To understand the drug synergy landscape across different lineages, we developed a bioinformatics pipeline which uses PRISM viability data to calculate synergy scores across all the cell lines and drug combinations. Secondary validation studies of the combinations used CTG viability measurements. Phospho-SYK expression was evaluated in archival formalin-fixed paraffin-embedded (FFPE) bone marrow biopsies from patients with myeloid proliferative neoplasm (MPN) by immunohistochemistry (IHC). RNA-seq was performed to evaluate differential changes in gene expression in response to LANRA. Gene set enrichment analysis (GSEA) was performed to evaluate perturbation in leukemogenic signaling pathways. Results: In the PRISM cell line panel, ruxolitinib in combination with LANRA demonstrated synergistic activity in hematological malignancy cell lines. Among AML cell lines in the panel, OCIAML5, OCIM1, HL60, HEL, EOL1, MONOMAC6, NB4, U937, PL21, and TF1 showed the highest synergy scores. The most sensitive cell lines to the combination showed up-regulation of JAK-STAT and inflammatory signaling pathways in a gene set enrichment analysis (GSEA) prior to treatment. Consistent with this, phosphorylated SYK was associated with inflammatory megakaryocytes and fibrosis in primary samples from patients with MPN. LANRA down-regulated JAK-STAT signaling in a reporter cell line and repressed gene expression associated with dysregulated inflammatory pathways in AML cells. Additionally, the combination of LANRA and ruxolitinib showed synergistic antiproliferative activity across a broad range of concentrations in FLT3-ITD primary AML cells and a panel of hematological malignancy cell lines, confirming the PRISM results. Conclusion: These studies demonstrate the utility of PRISM as a platform to rapidly identify rational combination agents. Importantly, LANRA is effective in combination with ruxolitinib in AML and other hematological malignancy preclinical models. This finding is consistent with the observation that SYK can regulate STAT signaling and cooperate with other RTKs like FLT3. Given the central role of SYK in regulation of oncogenic and inflammatory signaling, SYK inhibition with LANRA in combination with ruxolitinib may be a promising strategy for patients with myeloid malignancies.

Deciphering the Dynamics of the B-NHL Single-Cell Landscape during BTK Inhibition

Introduction: The inhibition of Bruton's tyrosine kinase (BTK) as a therapeutic strategy has dramatically improved the management of patients with chronic lymphocytic leukemia (CLL) and other B-cell-Non Hodgkin lymphomas (B-NHL). However, therapy resistance and treatment toxicity persist as significant clinical challenges. It has been previously reported that BTK inhibition not only targets the overactivated BCR pathway in CLL and other B-NHL, but also coincides with profound alterations in the tumor microenvironment including immune cells. Therefore, it is vital to understand these tumor-immune cell interactions during BTK inhibition to enhance response and reduce toxicity, advancing personalized care for CLL and B-NHL patients in the future. Methods: We developed a high-parametric analysis pipeline based on full spectrum cytometry, a novel technology allowing for the simultaneous assessment of 40 markers at single-cell level. In order to comprehensively assess the phosphoproteome relevant to BTK inhibition we established a signaling panel covering >30 proteins including pBTK, pSyk, pPLCg, pS6 and pAMPKa. Additionally, we designed 3 panels with up to 40 markers characterizing T cell activation and exhaustion, cellular trafficking, inhibitory immune checkpoints and cytokine production. Using our scalable and reproducible bioinformatics pipeline, we analyzed a cohort of 160 samples, longitudinally collected from 57 patients (CLL, MCL, MZL) before BTKi treatment, directly after treatment initiation, at leukocyte peak count and during therapy up to 6 months after therapy initiation. Results: Given that T cells are a crucial arm in governing anti-lymphoma immune response, we sought to elucidate the changes associated with BTK inhibition in the T cell compartment of the patients at high resolution (figure A). We observed certain changes in the fraction of T cell subtypes, notably a decrease in central memory stem T cells and an increase in γδ T cells at leukocyte peak and during later stages of therapy. Our analysis uncovered substantial changes in the T cell marker expression over the course of BTK inhibition. This includes a reversal of exhausted phenotype marked by downregulation of PD1, CD39 and CD38 and a skewed differentiation of CD4 T cells towards Th1 phenotype illustrated by a pronounced upregulation of the transcription factor T-bet. In addition, we detected restored proliferative and cytotoxic capacity in the CD8 and CD4 T cell compartments characterized by upregulation of GRZB, CD226, CD28, CD25 and Ki67. By leveraging our platform, we successfully captured the signalosome of malignant B cells at a high resolution. As anticipated, we detected significant heterogeneity in the malignant B cell signatures across individual patients. Interestingly, even at the initial sampling timepoint following treatment start, we observed substantially altered B cell signatures encompassing both the phosphoproteome and inhibitory immune checkpoints (figure B). Our preliminary data suggest that the dynamic changes induced by BTK inhibition extend beyond the signaling nodes associated with the BCR pathway and also involve the NFkB and p53 pathways. Conclusion: In this study, we established a platform for deep and longitudinal interrogation of the tumor and immune cell compartments to capture the dynamic and patient-specific changes occurring during BTK inhibition. Our preliminary data obtained from a large patient cohort of 57 B-NHL patients reveals a profound remodeling of not only the BCR signaling pathway in tumor cells, but also of the T cell compartment. Our data will be useful for a better understanding of treatment response and toxicity in the context of BTK inhibition.

Accelerated Progression in Lower-Risk Myelodysplastic Neoplasms: Attenuated Cell Cycle, DNA Damage Response, and Augmented Oncogenic Transcriptome Signatures

Patients with myelodysplastic neoplasms (MDS) face the risk of transformation to acute myeloid leukemia (AML). Although prognostic scoring systems exist for risk stratification and treatment decision making in MDS patients (Garcia-Manero G. Am J Hematol. 2023;98(8):1307-1325), disease management remains challenging due to the heterogeneity of clinical courses and long-term outcomes. The natural history of patients with lower-risk MDS (LR-MDS) is very heterogeneous and some LR-MDS patients experience rapid progression despite a generally favorable prognosis. The aim of our study was to uncover the molecular mechanisms underlying accelerated progression in hematopoietic stem/progenitor cells of patients with LR-MDS regardless of driver mutations. We focused on the transcriptome of bone marrow (BM) CD34 + cells from diagnostic samples using RNA-seq and used various bioinformatic pipelines to examine differentially expressed protein-coding genes, and long non-coding RNAs (lncRNAs) as well as differential alternative splicing events. RNA-seq dataset of CD34 + ribodepleted RNA samples from 53 LR-MDS patients without accelerated progression (stMDS) and 8 who progressed within 20 months (prMDS) showed 845 differentially expressed genes (ІlogFCІ > 1, FDR < 0.01) between these groups. CD34 + cells of prMDS exhibited a transcriptional pattern of quiescent-like cell state with overall decreased metabolism signatures and significantly reduced lineage differentiation compared to stMDS CD34 + cells. Gene set enrichment analysis (GSEA) showed that cell cycle- and cell cycle checkpoint- associated genes and activation of ATR in response to replication stress were all significantly under-represented in prMDS CD34 + BM (Fig. 1A). Cellular pathways from GO Biological Processes associated with cellular responses to stress and DNA damage responses (DDR) were downregulated in prMDS compared to stMDS. The only GO terms that were significantly upregulated in prMDS were processes related to cell-matrix adhesion or cell-cell adhesion. Indeed, key transcription factor controlling changes in cell-cell adhesion, ZEB1, was significantly overexpressed in prMDS BM CD34 + cells, whereas E-cadherin expression ( CDH1) was decreased; the expression levels of CDH1 and ZEB1 showed a significant moderate negative correlation (Fig. 1B). Additionally, prMDS samples exhibited high levels of aberrant splicing and global lncRNA expression. Analysis of aberrant transcripts, deregulated lncRNAs and their targets suggested their contribution to the attenuation of DDR pathways in prMDS. Given that the downregulation of DDR gene expression was the major feature distinguishing the prMDS CD34 + cell transcriptome from that of stMDS in our cohort, we selected - from the transcriptional profile of the top 50 downregulated genes in GSEA - a prognostic DDR-associated gene signature consisting of 19 genes. Using regularized Cox regression analysis of the expression profile of 19 DDR-associated genes, the probability of a progression-free disease was calculated in our cohort. High DDR gene expressors had a significantly higher probability of progression-free disease (p < 0.001) compared to low DDR gene expressors (Fig. 2A). Among the top 50 upregulated genes in the prMDS vs. stMDS samples, ranked by GSEA, overexpression of previously identified markers of leukemic progression and/or poor MDS/AML survival ( SPNS2, MN1, DOCK1 and others) was demonstrated. Interestingly, overexpression of NEK3, a gene not previously associated with MDS/AML progression, showed a predictive power for accelerated progression in LR-MDS with the best significance of curve separation among the genes tested (Fig. 2B). Furthermore, NEK3 expression was an independent prognostic factor (p<0.001) for progression-free disease in a multivariate analysis of important clinical and genetic factors. To conclude, our data suggest that DDR gene expression signature and NEK3 expression appear to be a significant predictor of LR-MDS progression and may be applicable at the time of diagnosis for the prognostic stratification of patients with LR-MDS, regardless of their mutational status. Supported by AZV (NU21-03-00565) and (NU23-03-00401); MH CZ-DRO (UHKT, 00023736); Program EXCELES, ID Project No. LX22NPO5102, and UP Young Researcher Grant Competition, ID Project JG_2023_016.

Gut Microbiome Links to Disease Stage and Immune Microenvironment in Patients with Chronic Lymphocytic Leukemia

Background Recent studies have shown extensive crosstalk between our immune system and the gut microbiome (GM). The host immune system plays a vital role in the maintenance of GM homeostasis by establishing a balance between eliminating invading pathogens and promoting the growth of beneficial microbes, whereas GM can modulate the immune system by cytokine production. As chronic lymphocytic leukemia (CLL) is characterized by a high rate of infectious complications and an altered immune microenvironment, it is warranted to uncover the link between microbiome composition and immune dysregulation in CLL patients. Thus, we investigated the microbiome pattern and potential connections to disease characteristics and phenotypes and distribution within the immune microenvironment in CLL patients requiring treatment. Methods Blood and feces samples were collected from 17 patients with CLL prior to targeted treatment with acalabrutinib or ibrutinib+venetoclax in clinical trials. T-cell maturation subsets, Treg/Th17 subsets, and activation- and exhaustion profiles were assessed in fresh blood samples by an 8-tube, 10 color flow cytometry panel (DuraClone), including one tube containing beads for calculating absolute concentrations. Fecal samples were subjected to shotgun sequencing using Illumina HiSeq and taxonomical profiling was done using an in-house bioinformatics pipeline. The bioinformatics analysis involved microbial species abundances comparison and co-occurrence identification across patient clusters. Electronic health records provided data on clinical baseline characteristics. Case report forms and data on use of antimicrobial drugs will be assessed at a later stage to investigate potential associations with infections or toxicity related adverse events. All patients provided written informed consent and the study was approved by the National Ethics Committee and the Data Protections Agency. Results Unsupervised clustering analyses based on GM composition data revealed two GM clusters, where we found an overlap constituting approximately 40% of the cohort between Binet stage C (7/10 patients) and GM cluster 2 (7/10 patients) (Figure 1A). GM cluster 2 was characterized by differentially abundant species such as Bacteroides fragilis and Eggerthela lenta, demonstrating the two highest fold change and elevated relative abundances (Figure 1B). Closer examination of the taxonomic composition characteristic for the 7 patients revealed a small set of indicator microbiome species, where Clostridium innocuum together with Clostridiales sp., Bacteroides fragilis and Escherichia coli, were the most significantly abundant co-occurring species for CLL patients with Binet C. Preliminary examination of T-cell subset data showed generally lower concentrations of T-cells in patients from GM cluster 2 compared to GM cluster 1, where the 7 patients overlapping with Binet C furthermore had decreased levels of Th17 CD4+ and Tc17 CD8+ cells compared to both the normal reference level and the rest of the cohort (Figure 1C). Conclusion Our preliminary results suggest a link between CLL stage at time of needing treatment, gut microbiome composition, represented by a subset of highly abundant bacterial species, and T-cell distribution with reduced Th17 and Tc17 subsets, to be further investigated. To our knowledge, we are thus among the first to present data connecting the gut microbiome to tumor microenvironmental changes in patients with CLL. Moving forward we will further explore microbiome-microenvironment interactions with focus on the production of short-chain fatty acids (bacterial metabolites which play crucial roles as immunomodulatory molecules) to investigate connections between microbiome, immune phenotypes, and clinical outcomes including infections, treatment-related toxicity, and disease progression, to light on novel avenues for therapeutic interventions in CLL.

Laboratories Can Reliably Detect Clinically Relevant Variants in the TP53 Gene below 10 % Allelic Frequency: A Multicenter Study of ERIC, the European Research Initiative on CLL

The presence of mutations in the TP53 gene is a powerful prognostic and predictive marker in chronic lymphocytic leukemia (CLL). Widespread use of NGS has enabled the detection of variants ≤10 % variant allelic frequency (low-VAF variants); however, the overall reliability and reproducibility of NGS techniques to identify such variants have been questioned repeatedly. Individual studies using sensitive, custom NGS-based assays have mostly demonstrated the shortened overall survival (OS) and event-free survival in patients with low-VAF TP53 variants treated with chemoimmunotherapy (CIT) regimens with median survival ranging between that of TP53 variants >10 % VAF (high-VAF) and wild-type TP53 (wt- TP53). Within an ERIC multicenter study, we tested the ability of NGS methods used in diagnostic and research laboratories to detect low-VAF TP53 variants and analyzed the impact of the identified low-VAF variants on patients' survival. In the first phase of the study (Fig. 1), seven sample mixes containing 23 pathogenic TP53 variants (range, 0.7-6.3% VAF) were analyzed in 41 ERIC centers using 44 NGS-based assays. All variants were validated with droplet digital PCR (ddPCR); obtained values were used as a reference for the assessment of each NGS method's performance. NGS results were categorized as true positive (TP), false positive (FP; not present in original samples and reported by one center each), and not reported/false negative (FN). In total, laboratories reported 77.8% of all variants (784 out of 1008), reaching a sensitivity [TP/ (TP + FN)] of 85.6%, 94.5%, and 94.8% at 1%, 2%, and 3% VAF cut-off, respectively. While the VAFs of individual variants reported by laboratories varied, median values strongly correlated with ddPCR (R 2=0.9841). Thirty-eight FP variants were reported by 10 laboratories, mainly <2% VAF (23 FP of VAF ≤1%, 14 FP of VAF >1 and ≤2%, 1 FP > 2%). Individual feedback was provided to improve the methods' performance and to help set an appropriate detection limit. In the second phase of the study, 12 centers provided results of TP53 NGS-based analysis of 1092 CLL clinical samples taken before first-line treatment (median time from sample to treatment 40 days). The impact of low-VAF variants (1-10% VAF; N=59) on time to second treatment (TTST; event: second treatment, death) and OS calculated from 1 st treatment initiation was compared to that of high-VAF variants (N=123) and wt- TP53 using logrank test with Benjamini-Hochberg correction of p-values. TTST (Fig. 2) of the low-VAF group was significantly shorter compared to wt- TP53 (P=0.013; median TTST wt- TP53 3.6 y, low-VAF 2.8 y, high-VAF 1 y) in patients not treated with targeted agents (N=999). If del(17p) status was considered, median TTST was the shortest in patients with a combination of del(17p) and either high (0.8 y) or low-VAF (1 y) TP53 mutations, followed by high-VAF (1.5 y) and low-VAF (2.8 y) mutations in the absence of del(17p) (P<0.001, P=0.032, P<0.001, P=0.026, respectively, compared to wt- TP53/no del(17p) (3.6 y)). In patients receiving frontline targeted agents (N=73; enriched for TP53 mutations), the results suggested shorter TTST for the high-VAF group only, but the difference was not significant (Fig. 2; P=0.06; median wt- TP53 n.r., low-VAF 4.8 y and high-VAF 3.6 y). OS of patients with low-VAF variants was significantly shorter compared to the wt- TP53 group in patients never treated with targeted treatment (P=0.033; median OS wt- TP53 6.6 y, low-VAF 3.2 y and high-VAF 2.1 y). Targeted therapy in 2 nd or later therapy lines diminished the difference and only OS of the high-VAF group differed significantly from wt- TP53 (P<0.001; median OS wt- TP53 10.6 y, low-VAF 8.6 y, and high-VAF 5.1 y). Altogether, we show that the cumulative reliability (no FN and FP) of methods tested increased continuously with VAF (Fig. 1), reaching 30% and 64% for variants ≥1.1% and 2% VAF, respectively. The reliability was affected by the type of NGS method and bioinformatic pipeline settings. We conclude that no strict threshold can be suggested from a technical standpoint. However, our results emphasize a strong need to validate/verify the NGS method, describe its limits, and report only reliable results. From a clinical standpoint, while low-VAF variants impact clinical outcomes for patients receiving CIT in the frontline setting, their clinical impact for patients treated with novel therapies remains to be evaluated in larger cohorts.

Whole-Genome Sequencing Identifies Structural Variation As a Key Driver of Disease Relapse and Aggressive Clinical Behavior in Multiple Myeloma

INTRODUCTION: Many key genomic events leading to relapse in multiple myeloma (MM) occur outside of coding regions and can only be identified using whole-genome sequencing (WGS). Studies of MM progression examining single nucleotide variants (SNVs) and insertions/deletions (InDels) have identified a distinct pattern of mutations associated with resistance, however these do not fully account for drivers of progression. Studies of relapsed patients provide hints about the molecular mechanisms driving progression based on identification of homozygous loss of tumor suppressor genes, aberrant copy-number variants (CNVs), and rare examples of complex structural variants (SV). We have analyzed a set of presentation and relapsed MM samples, including paired data, derived from WGS of plasma cells and focused on SVs as key drivers of progression. METHODS: WGS was performed on 338 MM patient samples using CD138-sorted plasma cells, among which 237 samples were paired including 101 patients (30%) at presentation and at relapse, 35 of whom had more than one sample at relapse. A bioinformatics pipeline employing a consensus mechanism for determining the final set of somatic events was used based on Mutect2, Strelka2, and VarScan2 for SNVs; Mutect2, Strelka2, VarScan2, and SvABA for InDels; Battenberg and FACETS for CNVs; Manta, SvABA, DELLY2, and IgCaller for SVs. Additionally, an admixture workflow was used to estimate each individual's ancestral lineage using continentally-distinct references, comprising 23 regional populations within 5 super-populations from the 1000 Genomes Project (https://github.com/pblaney/mgp1000). Complex rearranged genomes were reconstructed using the graph-based R package JaBbA (https://github.com/mskilab-org/JaBbA). Additionally, the python package Pairtree (https://github.com/morrislab/pairtree) was used to describe the evolutionary history of acquired mutations in these patients. RESULTS: Themedian age of the cases studied was 67, and 42% were female. Racial admixture correlated with self-identification, including 74% of European ancestry with the remainder being predominantly of African ancestry. High-risk cytogenetic features were found in 10% and 13% at diagnosis and relapse, respectively. As expected, the tumor mutational burden of the relapse cases was higher than that at presentation. The patterns of SNV mutational drivers were similar at relapse in comparison to presentation with again evidence for a role of biallelic tumor suppressor gene inactivation as being a key mechanism. We also found that the number of SVs increased at relapse. Complex SVs including templated insertions, chromoplexy, and chromothripsis were detected and occurred exclusively at relapse in some cases as well as earlier in the natural history in others, suggesting they can occur as both early and late driver events. Characterizing complex SVs, further we identified them at lower levels in cases at presentation compared to relapse, consistent with these being at a higher clonal fraction and providing further evidence for their role as driver events. We did not see the emergence of additional rearrangements at relapse in either the templated insertions or chromoplexy events, suggesting that these occur at a single timepoint and remain structurally stable overtime. CONCLUSIONS: Complex SVs provide a novel mechanism driving relapse in MM which can deregulate multiple genes simultaneously providing new potential markers of aggressive disease behavior and disease evolution.

High-throughput identification and quantification of bacterial cells in the microbiota based on 16S rRNA sequencing with single-base accuracy using BarBIQ.

Bacteria often function as a community, called the microbiota, consisting of many different bacterial species. The accurate identification of bacterial types and the simultaneous quantification of the cells of each bacterial type will advance our understanding of microbiota; however, this cannot be performed by conventional 16S rRNA sequencing methods as they only identify and quantify genes, which do not always represent cells. Here, we present a protocol for our developed method, barcoding bacteria for identification and quantification (BarBIQ). In BarBIQ, the 16S rRNA genes of single bacterial cells are amplified and attached to a unique cellular barcode in a droplet. Sequencing the tandemly linked cellular barcodes and 16S rRNA genes from many droplets (representing many cells with unique cellular barcodes) and clustering the sequences using the barcodes determines both the bacterial type for each cell based on 16S rRNA gene and the number of cells for each bacterial type based on the quantity of barcode types sequenced. Single-base accuracy for 16S rRNA sequencing is achieved via the barcodes and by avoiding chimera formation from 16S rRNA genes of different bacteria using droplets. For data processing, an easy-to-use bioinformatic pipeline is available ( https://github.com/Shiroguchi-Lab/BarBIQ_Pipeline_V1_2_0 ). This protocol allows researchers with experience in molecular biology but without bioinformatics experience to perform the process in ~2 weeks. We show the application of BarBIQ in mouse gut microbiota analysis as an example; however, this method is also applicable to other microbiota samples, including those from the mouth and skin, marine environments, soil and plants, as well as those from other terrestrial environments.

633. Nanopore Sequencing and In-house Bioinformatic Pipeline for Rapid and Accurate Detection of NRTI, NNRTI, PI, and INSTI Drug Resistance Mutations

Abstract Background Sanger-based sequencing (SBS) remains the gold standard for HIV-1 drug resistance testing (DRT). However, SBS output is still limited to an approximately 800-1000 bp read length. This is inadequate for amplifying the pol gene region that contains most of the clinically significant drug resistance mutation (DRM) positions, requiring separate steps for the protease (PR) and reverse transcriptase (RT) regions as is available in most commercial genotyping kits. Integrase (IN) gene is typically not sequenced, a major drawback since the WHO-recommended first-line treatment is now IN strand transfer inhibitor-based. We explore an alternative workflow for HIV-1 genotyping and DRT using the MinION sequencer to sequence a ∼2,800 bp amplicon covering PR and RT, and the first 224 codons of the IN region. This workflow also aims to reduce the turnaround time and cost of DRT. Methods Thirty samples (VL 3,160-3,520,000 copies/mL) previously tested for drug resistance underwent PCR amplification using customized primers targeting the pol gene. Agarose gel electrophoresis and library preparation (SQK-RBK110.96 kit, Oxford Nanopore Technologies) were then performed, followed by MinION sequencing. An in-house bioinformatic pipeline was used to analyze the sequencing data. Results MinION sequencing was able to detect all DRMs detected by SBS in all 27 samples and did not detect any DRMs in the ones without DRMs on SBS. A few additional DRMs undetected by SBS were detected by MinION sequencing which is expected since MinION is known to have higher minority variant sensitivity. Comparison of the resistance profiles showed that SBS and MinION sequencing are concordant with a kappa value of 95.8 interpreted as having perfect agreement (Landis & Koch, 1977). DRMs from IN could not be properly evaluated due to lack of positive SBS controls but will be assessed at a later time using deep-sequenced controls. The entire workflow from RNA extraction to data analysis for all samples was completed within 22 hours, significantly shorter than that of the WHO protocol for HIV-1 DRT. The projected cost per run is comparable with that of SBS with a more rapid turnaround time. Conclusion Our findings show that MinION sequencing and our in-house analysis pipeline can rapidly and accurately detect DRMs in HIV-1 positive patients. Disclosures Edsel Maurice Salvaña, MD, MSD: Advisor/Consultant|Pfizer: Advisor/Consultant

Machine learning-based peptide-spectrum match rescoring opens up the immunopeptidome.

Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and vaccine development. However, identifying immunopeptides remains challenging due to their non-tryptic nature, which results in distinct spectral characteristics. Moreover, the absence of strict digestion rules leads to extensive search spaces, further amplified by the incorporation of somatic mutations, pathogen genomes, unannotated open reading frames, and post-translational modifications. This inflation in search space leads to an increase in random high-scoring matches, resulting in fewer identifications at a given false discovery rate. Peptide-spectrum match rescoring has emerged as a machine learning-based solution to address challenges in mass spectrometry-based immunopeptidomics data analysis. It involves post-processing unfiltered spectrum annotations to better distinguish between correct and incorrect peptide-spectrum matches. Recently, features based on predicted peptidoform properties, including fragment ion intensities, retention time, and collisional cross section, have been used to improve the accuracy and sensitivity of immunopeptide identification. In this review, we describe the diverse bioinformatics pipelines that are currently available for peptide-spectrum match rescoring and discuss how they can be used for the analysis of immunopeptidomics data. Finally, we provide insights into current and future machine learning solutions to boost immunopeptide identification.

Comprehensive profiling of rRNA-derived small RNAs in Arabidopsis thaliana using rsRNAfinder pipeline

Ribosomal RNA (rRNA) gives rise to non-random small RNA fragments known as ribosomal-derived small RNAs (rsRNAs), which despite their biological importance, have been relatively understudied in comparison to other short non-coding RNAs. There exists a compelling necessity to develop a methodology for the identification, categorization, and quantification of rsRNAs from small RNA sequencing (sRNA-seq) data sets, considering the unique characteristics of ribosomal RNA (rRNA). To bridge this gap, we introduce ‘rsRNAfinder’ a specialized pipeline designed within the Snakemake framework. This analytical approach enables robust identification of rsRNAs using sRNA-seq datasets from Arabidopsis thaliana. Our methodology constitutes an integrated bioinformatic pipeline designed for different kinds of analysis.1.sRNA-seq data analysis: It performs in-depth analysis of reference-aligned sRNA-seq data, facilitating rsRNA annotation and quantification.2.Parametric reporting: Our pipeline provides comprehensive reports encompassing key parameters such as rsRNA size distributions, strandedness, genomic origin, and source rRNA origin.3.Illustrative validation: We have demonstrated the utility of our approach by conducting comprehensive rsRNA annotation in Arabidopsis thaliana. This validation reveals unique rsRNAs originating from all rRNA types, each of them distinguished by distinct identity, abundance, and length.

Bio Informatics Analysis of the Relationship Between Mammalian Target of Rapamycin and Colorectal Cancer.

The purpose of this paper is to promote the medical treatment of colorectal cancer in our country and to save the lives of patients with colorectal cancer by studying mammalian target of rapamycin (mTOR) and the biologic information analysis of colorectal cancer. We analyzed mTOR expression and survival differences using data from Coad & read from the TCGA public database and explored the coexpression regulatory network of mTOR. mTOR-regulated mirnas were screened using the Linked Omics database. In addition, we explored the association of mTOR with drug sensitivity, immune cell correlations, microsatellite deletions, tumor mutational burden, and mutational analysis. The expression and survival of mTOR were significant different in colorectal cancer, and were related to the sensitivity of Bleomycin, Cisplatin and Gemcitabine. mTOR is associated with dendritic cell activation, NK cell dormancy, dendritic cell dormancy, and eosinophil granulocyte. mTOR is associated with microsatellite deletions, and tumor mutational load. Based on these findings, we consumer mTOR as a biomarker for the diagnosis and prognosis of colorectal cancer.

Environmental DNA-based biomonitoring of Cuban Crocodylus and their accompanying vertebrate fauna from Zapata Swamp, Cuba

Crocodylians globally face considerable challenges, including population decline and extensive habitat modification. Close monitoring of crocodylian populations and their habitats is imperative for the timely detection of population trends, especially in response to management interventions. Here we use eDNA metabarcoding to identify the Critically Endangered Crocodylus rhombifer and the Vulnerable C. acutus, as well as vertebrate community diversity, in Cuba’s Zapata Swamp. We tested four different primer sets, including those used previously in Crocodylus population genetic and phylogenetic research, for their efficiency at detecting crocodylian eDNA. We detected C. rhombifer eDNA in 11 out of 15 sampled locations within its historical geographic distribution. We found that data analyses using the VertCOI primers and the mBRAVE bioinformatics pipeline were the most effective molecular marker and pipeline combination for identifying this species from environmental samples. We also identified 55 vertebrate species in environmental samples across the four bioinformatics pipelines— ~ 85% known to be present in the Zapata ecosystem. Among them were eight species previously undetected in the area and eight alien species, including known predators of hatchling crocodiles (e.g., Clarias sp.) and egg predators (e.g., Mus musculus). This study highlights eDNA metabarcoding as a powerful tool for crocodylian biomonitoring within fragile and diverse ecosystems, particularly where fast, non-invasive methods permit detection in economically important areas and will lead to a better understanding of complex human-crocodile interactions and evaluate habitat suitability for potential reintroductions or recovery programs for threatened crocodylian species.