We present chromosome-level, phased diploid genome assemblies of two widely used human fibroblast cell lines: BJ (46,XY) and IMR-90 (46,XX). Using Oxford Nanopore, PacBio HiFi, and Hi-C sequencing data, we generated assemblies spanning 5.9 and 6.0 Gbp with diploid quality values exceeding QV 60. To validate structural integrity, we developed KaryoScope, an alignment-free tool for generating computational karyograms from k-mer feature databases. We identify >50 000 structural variants relative to T2T-CHM13v2.0, the majority of which are heterozygous and cell-line-specific. Combining reference-based and de novo gene annotation, we uncover a previously unreported 1 Mbp homozygous duplication at the 16p11.2 locus in BJ, demonstrating that even karyotypically normal cell lines can harbor clinically relevant submicroscopic rearrangements. We show that mapping publicly available short-read, RNA-seq, and ChIP-seq data to sample-matched diploid assemblies substantially improves read alignment and enables haplotype phasing of 23%-28% of short reads. The BJ and IMR-90 assemblies and associated variant calls are publicly available as a resource for the research community.

INTRODUCTION:Growing data support interactions between host-gut microbes and treatment responses in multiple myeloma (MM), where a higher abundance of Eubacterium hallii in stool samples has been found among MM patients with negative minimal residual disease after induction therapy. Here, we evaluated changes in the gut microbiome associated with daratumumab (dara) based therapy in 40 MM patients, before and after therapy. PATIENTS AND METHODS:Patients with relapsed MM and prior autologous transplantation who had received 1 to 4 prior lines of therapy were eligible. Two stool samples were collected, one within 1 week prior to dara (predara) and one immediately after 4 doses of dara (postdara). Metagenomics sequencing was conducted. Microbiome taxonomic analyses were performed using MetaPhlAn4, and microbial functional pathway analyses were conducted using HUMAnN3.6. QIIME2 was used for compositional and statistical analyses. RESULTS:Of 40 participants enrolled, there were 5 nonresponders; 35 patients achieved partial response (PR) or better (responders). Among responders, 10 patients achieved complete remission (CR), and 25 patients achieved either very good partial response (VGPR) or PR. There were no statistically significant differences between overall pre and postdara gut microbiomes. Differential abundance analysis (ANCOM-BC) showed statistically significant (q ≤ 0.05) overgrowth of Alistipes finegoldii and Acidaminococcus intestini species in responders and Ruminococcus torques, Sellimonas intestinalis and Clostridium symbiosum in nonresponders. Compared to non-CR, CR samples showed enrichment of Faecalibacterium prausnitzii; non-CR samples were enriched in Segatella copri and Faecalimonas umbilicata. DISCUSSION/CONCLUSION:Our results suggest differences in species between clinical responders and nonresponders, but larger prospective studies are needed to confirm these results.

Abstract Here we report a newly developed method utilizing long-range PCR and long-read Pacific Biosciences HiFi sequencing that successfully obtained two full-length and annotated mitochondrial genomes from Culex quinquefasciatus Say, 1823 and Culex tarsalis Coquillett, 1896, both from Maricopa County, Arizona, USA. Given the substantial burden of West Nile virus in Maricopa County over the past decade, and that these vectors are primarily responsible for spillover to human populations in the county, it is critical to better understand their distribution over time and space. This study begins to approach this need by contributing a novel approach that has resulted in the first West Nile virus vector mitochondrial genomes from Arizona. Our circular Cx. quinquefasciatus mitogenome is 15,587 bp in length, making it the first USA-based mitogenome sequenced through the AT-rich control region. The Cx. tarsalis mitochondrial genome is 16,416 bp long, longer than recently published California-based CTarK1 and Texas-based PQ585801 mitogenomes. The increased length of the Cx. tarsalis mitogenome is a result of a 905 bp insertion in the AT-rich control region, not present in the species’ publicly available mitogenomes. A maximum likelihood-based phylogenetic reconstruction supports the species designation of these newly-sequenced mitogenomes. The newly developed methodology offers a unique approach to study medically-important vector species around the globe, providing a solution to study populations through pooled vector pathogen surveillance programs.

Abstract Purpose: Given that most patients with multiple myeloma (MM) become refractory to daratumumab (Dara) but retain CD38 expression, we developed a CD38-targeting radioimmunotherapy (RIT) by conjugating the α emitter actinium-225 to Dara using a DOTA chelator (225Ac-Dara). Methods: We conducted a first-in-human trial of 225Ac-Dara co-infused with the imaging agent 111Indium-DOTA-Dara (111In-Dara). Eligible participants had received available therapies with proven clinical benefit and were Dara-refractory (12-week Dara washout). Patients had adequate hematologic and organ function, performance status ECOG ≤2, no prior RIT or radiation ≥25% to marrow, liver, or kidneys. Three radioactivity dose levels (DLs) were 20, 40 and 60 kBq/kg. We used a BOIN design for dose escalation decisions. Patients received an IV dose of unlabeled Dara at 45mg 2-4h prior to RIT. They then received an IV infusion of 111In-Dara for imaging, immediately followed by a single infusion of 225Ac-Dara conjugated to 5mg of Dara. Serial planar (2h, 24h and 144h-168h) and SPECT/CT (24h) imaging were performed. Primary endpoint was toxicity to define MTD/RP2D. Secondary endpoints included ORR and PFS. Exploratory correlatives included tumor uptake, organ dosimetry, and assessment of immune microenvironment. Results: Nine patients were treated. One patient was not evaluable for DLTs and was replaced. One of 6 had a DLT at DL1. Both patients treated at DL2 had DLT. MTD was defined as DL1 (20 kBq/kg). Non-hematologic AEs were grade ≤ 2. All DLTs were hematologic, seen in patients with high disease burden or early progression. One patient with prolonged cytopenias recovered with autologous hematopoietic progenitor cell infusion. All nine patients were evaluable for secondary efficacy endpoints. Eight had best response of stable disease after the single 225Ac-Dara dose; the other had PD. Median PFS was 96d (95% CI: 29-154d). Mass cytometry of marrow plasma cells (CD138⁺CD38⁺) showed high baseline CD38 expression (median MFI ≈ 110), indicating continued target expression despite Dara-refractoriness. PBMC analysis revealed early reductions in CD38⁺ populations, checkpoint modulation, and innate activation with depletion of immunosuppressive subsets. Paired medians at baseline and ∼24 h showed marked declines in CD38⁺ NK cells ( −73%), total NK cells (−57.5%), and CD38⁺ Tregs (−28%). Post-hoc exploratory evaluation revealed an 80% ORR among those receiving bispecific antibody or CAR-T as next line of therapy, with median DOR of 620d (n=5). 111In-Dara imaging showed target specificity, with uptake in marrow and extramedullary disease. Conclusion: 225Ac-Dara in Dara-refractory MM is a novel strategy to circumvent immune exhaustion and repurpose CD38-targeting. Repeated administration of lower doses may improve the therapeutic window and duration. Translational and clinical observations from this trial support possible synergy between 225Ac-Dara and subsequent immunotherapies. We are modeling these hypotheses preclinically to support future study. Citation Format: Scott Ryan Goldsmith, Vikram Adhikarla, Murali Janakiram, Michael Rosenzweig, Savita Dandapani, Sarah Lee, Nitya Nathwani, Azra Borogovac, Myo Htut, Theophilus Tandoh, Alex Pozhitkov, Enrico Caserta, Mariam Murtadha, Ni-Chun Tsai, Arnab Chowdhury, Joycelynne Palmer, Jonathan Keats, James Sanchez, Erasmus Poku, Russell Rockne, Paul Yazaki, Jeffrey Wong, John E. Shively, Flavia Pichiorri, Amrita Krishnan. First-in-human trial of 225Actinium-DOTA-daratumumab, an alpha-emitting radioimmunotherapy, in patients with daratumumab-refractory multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr CT133.

Background. CTCs are independent prognostic biomarkers in NDMM patients (pts) stratified by International Staging System (ISS) and Revised ISS. However, the relevance of CTCs in the context of R2-ISS and the new IMS-IMWG definition of high-risk myeloma (Consensus Genomic Staging, CGS) has not been investigated. The aim of this analysis was to evaluate the clinical significance of CTCs in combination with R2-ISS and the IMS-IMWG CGS in a large cohort of NDMM pts. Methods. The European CTC Consortium collected patient-level data of NDMM pts from 5 collaborative groups (Czech Republic, Greece, Italy, HOVON/Netherlands-Belgium, PETHEMA/Spain). All included pts had CTC enumeration by flow cytometry prior to treatment. To investigate the role of CTCs in combination with R2-ISS we analyzed 1657 NDMM pts (R2-ISS cohort) with complete data on the R2-ISS defining variables [ISS, del(17p), t(4;14), LDH levels, 1q+]. A value was assigned to each risk feature and pts were stratified into 4 risk groups according to the total additive score (D'Agostino et al JCO 2022). To investigate the role of CTCs in combination with IMS-IMWG CGS we analyzed 190 NDMM pts (CGS cohort) with molecular data available. TP53 mutations, biallelic del(1p32) (CDKN2C log2 < -1.3219) and t(14;20) were analyzed by whole genome/exome sequencing of CD138+ tumor cells . CGS high risk was defined as per IMS/IMWG recommendations (Avet-Loiseau et al JCO 2025), In both cohorts CTCs were analyzed as log10 increase (as a continuous variable and as discrete intervals from ≤0.001% to >1%) and using a binary cutoff (£ 0.02% vs >0.02%, Bertamini et al EHA 2025) Results. In the R2-ISS cohort, 17% of pts were R2-ISS I, 29% R2-ISS II, 46% R2-ISS III and 9% R2-ISS IV. The distribution of pts with ≤0.001%, ≤0.01%, ≤0.1%, ≤1% and >1% CTCs was 20%, 20%, 31%, 18% and 10%. After a median follow-up of 50 months, both CTCs log10 increments (HR 1.15 p<0.001 for PFS; HR 1.13 p<0.001 for OS) and R2-ISS (R2-ISS II vs I HR 1.20 for PFS 1.16 for OS; R2-ISS III vs I HR 1.67 for PFS 1.90 for OS; R2-ISS IV vs I HR 2.66 for PFS HR 3.77 for OS; p<0.001) were independent predictors of outcome in a multivariable model including site, transplant eligibility and age. A prognostic model including CTCs as log10 intervals + R2-ISS (c-index 0.703 for PFS and 0.743 for OS) or CTCs with a binary cut-off + R2-ISS (c-index 0.698 for PFS and 0.736 for OS) performed better than R2-ISS alone (likelihood ratio test of both models vs R2-ISS p<0.001). Using a 0.02% cut-off, in R2-ISS I 77% vs 33% of pts have low vs high CTCs (median PFS 99 vs 72 months); in R2-ISS II 60% vs 40% of pts have low vs high CTCs (median PFS 83 vs 48 months); in R2-ISS III 41% vs 59% of pts have low vs high CTCs (median PFS 40 vs 31 months); in R2-ISS IV 22% vs 78% have low vs high CTCs (median PFS 29 vs 15 months) (p<0.001). In the IMS-IMWG CGS cohort, 45% of pts were high-risk possibly due to greater availability of molecular data in pts with high tumor burden. The distribution of pts with ≤0.001%, ≤0.01%, ≤0.1%, ≤1% and >1% CTCs was 17%, 9%, 31%, 29% and 13%. After a median follow-up of 91 months, both CTC log10 increments (HR 1.31 p<0.001 for PFS; HR 1.24 p=0.036 for OS) and IMS-IMWG CGS high risk (HR 1.55 p=0.047 for PFS; HR 2.51 p=0.003 for OS) were independent predictors of outcome in a multivariable model including site, transplant eligibility and age. A prognostic model including CTCs as log10 intervals + CGS (c-index 0.669 for PFS and 0.730 for OS) or CTCs with a binary cut-off + CGS (c-index 0.650 for PFS and 0.719 for OS) performed better than CGS alone (likelihood ratio test of both models vs CGS p<0.05) Using a 0.02% cut-off, 29% of pts were classified as CGS standard risk + low CTCs, 25% as CGS standard risk + high CTCs, 7% as CGS high risk + low CTCs and 38% as CGS high risk + high CTCs. Median PFS was not reached in CGS standard risk + low CTCs, 72 months in CGS standard risk + high CTCs, 64 months in CGS high risk + low CTCs and 33 months in CGS high risk + high CTCs (p<0.001). Overall CGS standard risk + low CTCs delineates a favorable prognosis group, CGS high risk + high CTCs delineates a poor prognosis group, while all other combinations identify pts with an intermediate prognosis. Conclusions. CTC levels have independent prognostic value in NDMM pts stratified with R2-ISS and IMS-IMWG CGS. These data support the investigation of CTCs in combination with current prognostic models and high-risk definitions.

Multiple myeloma, a hematopoietic malignancy of terminally differentiated B cells, is the second most common hematological malignancy after leukemia. While patients have benefited from numerous advances in treatment in recent years resulting in significant increases to average survival time following diagnosis, myeloma remains incurable and relapse is common. To help identify novel therapeutic agents with efficacy against the disease and to search for biomarkers associated with differential response to treatment, a large-scale pharmacological screen was performed with 1,912 small molecule compounds tested at 11 doses for 47 human myeloma cell lines (HMCL). Raw and processed versions of the drug screen dataset are provided, as well as supportive information including drug and cell line metadata and high-level characterization of the most salient features of each. The dataset is publicly available at Zenodo and the workflow code used for data processing and generation of supporting figures and tables are available on GitHub.

Introduction: The t(4;14) translocation subtype of multiple myeloma (MM) is characterized by overexpression of NSD2, a histone methyltransferase which catalyzes di-methylation of histone H3 lysine 36 (H3K36me2). About 12% of MM patients have t(4;14) and this is associated with poor outcome. If and how NSD2 drives unique epigenetic vulnerabilities in this subtype of MM is not well understood. Methods: Primary MM samples from the MMRF CoMMpass trial (NCT01454296) were analyzed by Whole Genome Bisulfite Sequencing (WGBS). CRISPR/Cas9 was used to genetically ablate the NSD2 translocated allele in KMS11 and KMS18 (hereafter referred to as NSD2-high and NSD2-low). Multiple single cell clones were isolated and confirmed by immunoblot analysis and sequencing. KMS11 NTKO and TKO models (Kuo et al. Molecular Cell 2011) were also used for CUT&Tag and DNAm analysis. Cell responses to decitabine (DAC) and GSK-3685032 were evaluated by flow cytometry for Annexin V and live/dead staining on day 4. Dynamic BH3 profiling (DBP) was performed similar to previously described (Matulis, ASH 2024). Gene expression was assessed using RNAseq with ribosomal RNA depletion (Kapa). Results: Analysis of CoMMpass samples showed higher levels of DNAm in the t(4;14) subtype compared to other subtypes (p=1.17x10-12). Consistent with DNAm being connected to NSD2 overexpression, KMS11 NSD2-high cells had higher DNAm than isogenic NSD2-low cells (p<1x10-9). Elevated DNAm in NSD2-high cells co-occurred in genomic regions with higher levels of H3K36me2 (OR=1.85, p<1x10-15). Treating KMS11 and KMS18 NSD2 isogenic cell lines with the DNA hypomethylating agent DAC showed NSD2-high cells are significantly more sensitive to DNAm inhibition (p<0.01 at ≥100 nM for KMS11 and p<0.001 at ≥500 nM for KMS18). This sensitivity to DNAm inhibition was seen again using the DNMT1 enzymatic inhibitor GSK-3685032 (p<0.001 at ≥100 nM). DBP of KMS18 showed NSD2-high cells increased mitochondrial priming when treated with DAC (p<0.001) whereas NSD2-low cells did not significantly change, consistent with the increased apoptosis observed in NSD2-high cells. RNAseq of KMS11 NSD2-high and NSD2-low clones treated with 0, 100, or 500 nM DAC showed 3,599 differentially expressed genes (FDR<0.01, fold-change>2). Genes downregulated with DAC treatment were common between NSD2-high and NSD2-low cells and included genes involved in cell cycle and MYC targets as indicated by Gene Set Enrichment Analysis (GSEA). Genes upregulated with DAC-treatment were more distinct between NSD2-high and NSD2-low cells, with GSEA indicating interferon response and TNFa signaling via NFKB more significantly induced in NSD2-high cells (FDR<0.0023) than in the NSD2-low cells (FDR>0.13). Further investigation of DAC-induced RNAseq shows an increase in transcription of endogenous retroviruses (ERVs), specifically in the NSD2-high KMS11 cells. This suggests that hypomethylating agents induce transcription of these ERVs, which then induce an intracellular interferon response, and this is specific to NSD2-high MM. Consistent with this, these ERVs had higher levels of DNAm in isogenic KMS11 NSD2-high cells as compared to NSD2-low cells (FDR<0.01). This was also observed in CoMMpass samples where higher DNA methylation was found at these ERVs in t(4;14) MM. Conclusions: NSD2-mediated increases in H3K36me2 in t(4;14) MM correspond with higher DNAm as compared to other MM subtypes. This is consistent with data from other cell types suggesting H3K36me2 is recognized by the PWWP domains of the DNMT3 DNA methyltransferasaes. Multiple NSD2 isogenic models suggest this creates a preferential sensitivity to DNA hypomethylating agents in NSD2-high MM. This phenomenon was shown both with DAC, a cytosine analog that incorporates into DNA, as well as with GSK-3685032, a DNMT1 enzymatic inhibitor. Both Annexin V staining and DBP indicated increased apoptosis, specific to NSD2-high cells. In addition, RNAseq data suggests this increased apoptosis is due to re-expression of ERVs that are triggering a viral defense response and interferon signaling. Consistent with this, t(4;14) cell line models and patient samples had higher levels of DNAm at ERVs, suggesting these are silenced through DNAm in t(4;14), but by other mechanisms in non-t(4;14) MM. Together, these data indicate NSD2 remodels the epigenome of t(4;14) MM creating unique epigenetic dependencies that can be exploited with readily available DNA hypomethylating agents.

Despite significant improvements in survival of patients with multiple myeloma (MM), outcomes remain heterogeneous, and a significant proportion of patients experience suboptimal outcomes. Importantly, traditional prognostic factors based on data from patients treated with older therapies no longer capture prognosis accurately in the contemporary era of novel triplet or quadruplet therapies. Therefore, risk stratification requires refinement in the context of available and investigational treatment options in routine practice and clinical trials, respectively. The current identification of high-risk MM (HRMM) in routine practice is based on the Revised International Staging System, which stratifies patients using a combination of widely available serum biomarkers and chromosomal abnormalities assessed via fluorescence in situ hybridization. In recent years, a substantial body of evidence concerning additional clinical, biological, and molecular/genomic prognostic factors has accumulated, along with new MM risk stratification tools and consensus reports. The International Myeloma Society, along with the International Myeloma Working Group, convened an Expert Panel with the primary aim of revisiting the definition of HRMM and formulating a practical and data-driven consensus definition, based on new evidence from molecular/genomic assays, updated clinical data, and contemporary risk stratification concepts. The Panel proposes the following Consensus Genomic Staging (CGS) of HRMM which relies upon the presence of at least one of these abnormalities: (1) del(17p), with a cutoff of >20% clonal fraction, and/or TP53 mutation; (2) an IgH translocation including t(4;14), t(14;16), or t(14;20) along with 1q+ and/or del(1p32); (3) monoallelic del(1p32) along with 1q+ or biallelic del(1p32); or (4) β2 microglobulin ≥5.5 mg/L with normal creatinine (<1.2 mg/dL).

Introduction: Multiple Myeloma (MM) disproportionately affects Black individuals, where it is 2-3 times more prevalent than in White populations. Prior studies have identified distinct germline risk alleles between Black and White individuals; however, somatic mutational differences remain limited with research suggesting Black patients exhibit lower mutational burdens of TP53 and IRF4. It remains unclear if the repertoire of genomic structural variants is similar between Black and White individuals or if they carry distinct prognostic implications. Methods: Institutionaldata from 977 patients uniformly treated with lenalidomide, bortezomib, and dexamethasone (hereafter RVD1000) were obtained from Joseph et al., 2020 and analyzed using self-identified race and cytogenetic FISH results. Whole Genome Sequencing (WGS) data from the MMRF CoMMpass were utilized. Germline variants were determined using HaplotypeCaller (v4.3.0.0) and phased using SHAPEIT (v5) before assessing global and local ancestry using RFMix (v2) with the 1000 Genomes project as a reference. Somatic mutations were determined with Mutect2 based on exome data and translocations were determined with Manta based on WGS. Copy number alterations were provided by TGen. Differences in somatic genetic events were determined using Fisher's exact test. Outcome analysis used Kaplan-Meier survival analysis and differences in prognostic impact used a Cox proportional hazards model considering the interaction of ancestry. Results: Of 977 RVD1000 patients with self-identified race, 36.7% (N=359) were Black, with no difference in PFS or OS observed between White and Black patients. The only significant differences were in age, 1q gain and del17p loss and all favored Black patients. Similarly, no differences in PFS or OS was observed between newly-diagnosed self-identified White (N=620) and Black (N=135) patients in CoMMpass. Genetic ancestry analysis of CoMMpass was concordant with self-identified race with all self-identified Black patients categorized as having primarily African (AFR) ancestry and only 7/620 self-identified White patients being categorized as having African ancestry. Analysis of somatic mutations in CoMMpass between AFR and European (EUR) patients indicated AFR patients had a lower overall mutational burden (P = 0.035) and lower number of non-synonymous mutations (P = 0.016). Analysis of gene mutations indicated a similar frequency in the most commonly mutated genes such as KRAS, NRAS, DIS3, and BRAF. However, consistent with previous reports AFR patients had fewer mutations in TP53 as well as DST and PABPC1 (P < 0.05), but were not significant after multiple hypothesis correction. Common copy number alterations (CNAs) were similar between AFR and EUR MM patients, although AFR trended to have a lower frequency of CNAs and this was significant for del(6q25) (FDR < 0.01). Consistently, analysis of structural variants (SVs) found AFR MM patients were less likely to have deletions (P = 0.0014). The total number of other SVs including duplications, deletions, and translocations between AFR and EUR were similar as were the most common translocations including both primary translocations t(11;14), t(4;14), t(14;16) and secondary events such as t(MYC) and t(IgL). Finally, we looked to see if common SVs conferred a similar prognosis in AFR and EUR patients. Surprisingly, we found several secondary translocations conferred a worse prognosis in AFR patients than those of EUR ancestry. These included t(8;22) and t(MAP3K14) where AFR patients with these translocations had a worse OS and PFS (P < 0.05; t(8;22) HR 3.07 PFS, 5.83 OS, t(MAPK3K14) HR 8.05 PFS, 4.52 OS) as compared to EUR patients with the same events. Conclusion: Our analyses indicate that White and Black MM patients have similar overall outcomes, despite Black patients have a lower mutational burden and fewer copy number deletions. This is somewhat surprising given Black patients tend to be diagnosed at a younger age and less commonly have TP53 loss as observed in RVD1000 which typically confer a worse prognosis. Specific secondary translocations were associated with significantly worse outcomes in patients of African ancestry, suggesting the influence of ancestry-specific genetic modifiers on disease progression. These findings indicate that risk markers may have different implications in White and Black MM patients.

AML accounts for one-third of adult leukemias. With an incidence of approximately 22, 000 new cases/year, AML is projected to cause over 11,000 deaths in the United States in 2025. Hence, new and more effective diagnostic, prognostic, and therapeutic approaches are highly needed. Genomic profiling is crucial to provide a proper prognosis and predict treatment response to emerging new therapeutics for both younger adult (<60 y/o) or older (>60 y/o) AML patients. This information is key to determine the best course of treatment and to make earlier decisions of whether to proceed to allogeneic hematopoietic cell transplants. However, the turnaround time for traditional “gold-standard” diagnostics — including FISH, cytogenetics, and targeted gene panels — can often exceed two weeks, depending on laboratory capacity and healthcare system logistics. Because many patients with AML require immediate initiation of therapy, clinicians are frequently forced to make treatment decisions without the benefit of genomic insights that could optimize therapy selection or trial eligibility — particularly in patients with high-risk, refractory, or relapsed disease. To address this unmet need, we developed ALTseqTM, a rapid whole genome sequencing (rWGS) assay for AML, at the TGen Clinical Laboratory (CAP: 8713783, CLIA: 03D2215886). ALTseqTM was specifically designed to deliver clinically actionable genomic results in under 48 hours, significantly faster than conventional testing. This accelerated turnaround time was enabled by streamlining laboratory workflows, enhancing bioinformatic pipelines, and expediting variant approval for reporting. The assay captures single nucleotide variants (SNVs), insertions and deletions (indels) — including FLT3-ITDs and KMT2A-PTDs —, 155 distinct structural variants (SVs including translocations or inversions) along with KMT2A rearrangements, and genome-wide copy number alterations, all in a single test. Specimens received must be whole blood or bone marrow in an EDTA tube and contain ≥20% blasts. DNA is extracted using the Qiagen DNA Blood Mini Kit (Qiagen, Hilden, Germany), prepared using the Watchmaker Genomics DNA Library Prep Kit (Watchmaker Genomics, Denver, CO, USA), and sequenced on the NovaSeq X Plus (Illumina, San Diego, CA, USA). The analytical pipeline utilizes both commercially available and proprietary algorithms with resulting VCFs compared to a knowledge base built in-house to create a json file used by the LIMS to produce the final clinical report. The curated report covers the genomic status of 41 genes with validated detection of key AML-related aberrations in both peripheral blood and bone marrow specimens. These include mutations in NPM1, TP53, RUNX1, IDH1/2, FLT3, MEN1, rearrangements involving KMT2A, MECOM, NUP98, and canonical translocations such as PML::RARA, RUNX1::RUNX1T1, and BCR::ABL1, among others. ALTseqTM has a limit of detection of 9%, 8%, 10%, and 7% for SNVs, indels, CNAs, and SVs, respectively, with sensitivities of 96%, 96.4%, 95.7%, and 100%, respectively. The positive predictive value for all variant types is ≥99.5%. Since implementation, ALTseqTM has been used in 35 AML cases, achieving an average turnaround time (TAT) of 35.2 hours, with a range of 29.4 to 46.3 hours from sample receipt to report delivery. Physician satisfaction with the assay's speed and clinical utility has been consistently high at City of Hope where the initial launch has occurred. In summary, we describe the successful deployment of a high-throughput, fast-turnaround rWGS platform for AML, capable of delivering comprehensive genomic profiling of 41 genes in <48 hours from sample receipt to clinical reporting. This approach has enabled earlier, more informed treatment decisions, and we are actively evaluating its impact on hospital length of stay and clinical trial enrollment, particularly where molecular eligibility criteria are required. Building on this success, we are now validating a similar rWGS approach for patients with multiple myeloma and plan to expand ALTseqTM to include acute lymphoblastic leukemia (ALL).

5569 Background: Hyperthermic intraperitoneal chemotherapy (HIPEC) is associated with improved overall survival in Stage III epithelial ovarian cancer (EOC) patients. We set out to evaluate the gene signatures associated with HIPEC response in EOC patients. Methods: Ninety-one EOC patients who underwent HIPEC with pre-operative tumor samples at City of Hope (51) and CHU Lyon (40) were identified between 2014 and 2022. RNA isolation was performed from formalin-fixed paraffin-embedded samples, followed by Whole-transcriptome library construction. Following exclusion of non-high grade serous (HGS) samples, and quality control steps, twenty-four samples were excluded. Progression-free survival (PFS) was used to define HIPEC response. Cut-off PFS values were used to distinguish good vs poor responders in primary EOC patients (18 months, based on KGOG, CARCINO-HIPEC trials), and recurrent EOC patients (12 months, based on MSK, CHIPOR HIPEC trials). Differential Gene Expression Analysis comparing good and poor HIPEC responders identified significantly changed genes. Pathway analysis was conducted using gene set enrichment analysis (GSEA) against Hallmark. Results: A total of sixty HGS tumor samples with available survival data were analyzed. 63.3% were primary EOC, 36.7% recurrent EOC. Germline BRCA mutations affected 21.7% of patients. With a median follow up of 31.9 months, median PFS was 29.3 (95%CI: 15.3, 63.5) months in primary EOC patients and 26.0 (95%CI: 14.7, 37.1) months in recurrent patients. Median OS was not reached in either group. 60.0% had a recurrence. Thirty-eight patients were identified as good responders, with a median PFS of 37.1 mos. (95%CI: 26.4, NR); 18 patients were identified as poor responders, with median PFS of 11.4 months (95%CI: 7.5, 14.2). Differential gene expression analysis between good and poor responders revealed 29 significantly upregulated 35 downregulated genes in HIPEC responders. Top upregulated genes in HIPEC responders include MAPK signaling pathway genes (RIB2, ETV5, CAPN8, IGFR1), in addition to CCND1 and CEACAM1. In HIPEC responders, the top-ranking gene sets in the transcriptional signature included Notch, KRAS, and Wnt/beta-catenin signaling pathways. In poor HIPEC responders, the DNA damage repair associated pathways E2F targets and G2M checkpoint, were activated. Similar transcriptomic pathway signatures were observed in Non-recurrent versus Recurrent HIPEC patients: Non-recurrent tumors were enriched with Notch signaling, while Recurrent tumors were enriched with E2F target and G2M checkpoint pathways. Conclusions: Good HIPEC response is characterized by transcriptional signatures consistent with Type I EOC characteristics of PI3K/RAS/Notch signaling. Recurrence after HIPEC in HGS ovarian cancer is higher in patients with E2F/G2M transcriptional signatures.

Circulating tumor cells (CTCs) have emerged as a key prognostic factor in newly diagnosed multiple myeloma (NDMM). However, it remains unclear if high CTC counts represent a mere surrogate of tumor burden or might reflect a distinct genomic or transcriptomic entity. In this study, we characterized the genomic and transcriptomic features associated with CTC burden and assessed their combined prognostic value in NDMM patients. We analyzed 540 NDMM patients from the CoMMpass dataset with available baseline CTC information and matched bone marrow transcriptomic (n = 374) and genomic (n = 460) sequencing data. We then validated the results on an external cohort of 135 NDMM patients with CTCs enumerated by next-generation flow cytometry. Higher CTC levels were significantly associated with high-risk clinical features (e.g., ISS or IMS/IMWG 2024). Furthermore, genomic analyses revealed that high CTC counts were associated with complex genomic features such as chromothripsis, APOBEC mutagenesis, and loss of key tumor suppressors, typically linked to high-risk disease. Transcriptomic analyses revealed that elevated CTCs were enriched in cell cycle and proliferation (PR) genes while presenting a reduced association with immune response. Importantly, CTCs also emerged as a surrogate for PR transcriptomic signatures and demonstrated prognostic superiority, potentially simplifying application in the clinical setting. Elevated CTC levels reflect aggressive biological features of multiple myeloma and outperform prognostic markers such as PR signatures. Integrating CTC data into genomic and transcriptomic classifiers could enhance risk stratification and provide a streamlined and powerful tool for clinical decision-making in NDMM.

Multiple myeloma (MM) is an incurable malignancy of clonally expanded plasma cells shaped by complex interactions with the immune microenvironment. To investigate immune factors driving treatment response and resistance, we conducted multi-omics profiling including CD138neg single-cell RNA sequencing of 243 bone marrow samples from 102 patients (631,226 cells) and CD138pos bulk RNA and whole-genome sequencing from 209 samples. Longitudinal analyses revealed that interferon gamma signaling impairs T cell memory after autologous stem cell transplant, while naïve B cell abundance and immunoglobulin diversity correlated with improved progression-free survival (HR = 0.48, p = 2.3e-4). At disease progression, MM cells upregulated cancer-testis antigens and immune effector genes, with concurrent B cell depletion, enrichment of myeloid-derived suppressor cell genes in monocytes, and T cell exhaustion. These findings highlight dynamic immune-tumor interactions, identifying naïve B cell reconstitution as a biomarker of durable response, and cancer-testis antigens as potential targets for high-risk disease at progression. Statement of Significance Longitudinal profiling of multiple myeloma and the immune microenvironment revealed dynamic immune-tumor interactions across the disease course. Dysfunctional CD8⁺ T cells limited memory formation post-transplant, while naïve B recovery associated with sustained treatment response. At progression, cancer-testis antigen expression associated with immunosuppression, revealing novel mechanisms of immune escape. ### Competing Interest Statement SG reports other research funding from Boehringer-Ingelheim, Bristol-Myers Squibb, Celgene, Genentech, Regeneron, and Takeda, and consulting from Taiho Pharmaceuticals, not related to this study. SK declares Research funding for clinical trials to the institution: Abbvie, Amgen, Allogene, BMS, Carsgen, GSK, Janssen, Roche-Genentech, Takeda, Regeneron Consulting/Advisory Board participation: (with no personal payments) Abbvie, BMS, Janssen, Roche-Genentech, Takeda, Pfizer, Loxo Oncology, K36, Sanofi, ArcellX, Beigene; TK declares research funding from Novartis, Pfizer. Advisory Board: BMS. DA declares grants from MMRF, CTN (NIHLBI), Celgene, Pharmacyclics and Kite Pharma. Other support from Juno, Partners TX, Karyopharm, BMS, Aviv MedTech Ltd., Takeda, Legend Bio Tech, Chugai, Caribou Biosciences, Janssen, Parexel, Sanofi, and Kowa.; DA has a patent for PCT/US2021/059199 pending.; ISV reports grants from NCI, NHLBI, NIDDK, Harvard Stem Cell Institute, and consulting for Mosaic LLC, AlphaSights, NextRNA, and Guidepoint Global outside of the submitted work; Other authors declare no competing financial or non-financial interests.

Despite recent advances in multiple myeloma (MM) treatment, a subset of patients continues to experience early disease progression and poor outcomes and are classified as high-risk (HR). The recently proposed new classification of HR includes patients with 17p deletion, TP53 mutations, chromosome 1q/1p abnormalities, IgH translocation including t(4;14), t(14;16), or t(14;20) along with 1q/1p alterations and β2 microglobulin ≥5.5 mg/L with normal creatinine. However, these criteria do not fully capture the molecular heterogeneity of MM stemming from genetic, clonal diversity and subclonal disease. Furthermore, the RNA-based molecular classifications of MM into unique subtypes, including HP, MS, MF, CD1, CD2 and PR have been clinically useful for risk stratification, but have lacked the ability to identify prognostic and predictive markers of HR disease. In this study, we have developed a new method to identify HR MM based on the presence of highly proliferative (PR) subclones detected at single-cell level in bone marrow (BM) aspirates of newly diagnosed (ND) and relapsed/refractory (RR) MM patients. Single-cell RNA analyses were performed in BM sorted MM cells obtained from 118 patients (56 ND and 62 RR) collected in 3 independent cohorts (Calgary, Heidelberg, and Toulouse). Serial samples were available for 33 patients. Unbiased mRNA profiling and sequencing were conducted using the 10x Genomics and Illumina platforms. Cell Ranger and Seurat were used for data processing and downstream analysis. A single-cell Gene Set Enrichment Analysis (scGSEA) score obtained by using the Zhan dataset (Zhan et al, Blood 2006) was developed to classify cells into each MM subgroup. FISH data were used to ensure accurate calls. Kaplan-Meier survival analysis was performed to evaluate the effect of PR cells on progression-free survival (PFS). P values were calculated using the log-rank test. The Calgary cohort which included 37 RRMM patients contained 184,032 cells and was used as a training set to test our scGSEA score. A threshold of ≥22% positive cells was required to confidently (accuracy= 0.69 and error rate= 0.30) assign each patient into the different MM subgroups. Of note our score outperformed the FISH classification and was further validated by confirming the overexpression of genes of interest for each subgroup (NSD2 and FGFR3 for t(4;14), CCND1 for t(11;14), CCND3 for t(6;14), MAF for t(14;16) and MAFB for t(14;20)). We next used the R package cutpointr to estimate the optimal proportion of PR cells associated with poor outcomes. As such, we found that the presence of ≥13% PR cells in the tumor was predictive of poor prognosis in RRMM with a median PFS of 7.5 months in PR patients and 13 months in non-PR patients (p=0.013). This cutoff of 13% PR cells was validated in the 2 independent cohorts of 28 RRMM. Of interest, this cutoff was predictive of poor survival also in early-relapse patients (1-3 lines of therapy). The median PFS was 8.5 months in PR patients and 15.5 months in non-PR patients (p=0.013). In addition, regardless of the original MM subtype, all patients with PR cells retained their PR signature overtime and an enrichment of PR cells was observed in 48% of patients at progression, consistent with Darwinian clonal evolution under therapeutic pressure.In NDMM patients, we detected PR cells in 33% of patients and the presence of ≥5% PR cells in the tumor was associated with poor prognosis with a median PFS of 24.1 months in PR patients and 43 months in non-PR patients (p=0.0064). Importantly, in both RR and ND patients the presence of PR cells conferred poor outcomes even in patients with favorable cytogenetic (HP, CD1, and CD2 groups). Lastly, to better understand the biology of PR cells and identify potential therapeutic targets for HR patients, we characterized the transcriptomic signature of PR cells. Pathway enrichment analysis revealed consistent activation of the MTORC1 signature, MYC targets, E2F targets, and DNA repair pathways in PR cells across all three analyzed cohorts, suggesting their potential role in HR disease. Further evaluation of these pathways is currently ongoing. In conclusion, we have here defined an RNA-based method to identify HR disease in ND and RR MM based on the subclonal presence of PR cells at single-cell level. Future HR classifications should account for the subclonal presence of PR cells to improve disease prognostication and develop new targeted therapeutics for HR patients.

Human cell lines are fundamental tools in biomedical research and are widely used in disease modeling, drug development, and many other domains. Here, we present chromosome-level, phased diploid genome assemblies of two popular human cell lines: the BJ foreskin fibroblast line and the IMR-90 fetal lung fibroblast line. Our high-quality assemblies, generated using long-read and Hi-C sequencing data, reveal substantial structural variation, including more than 50,000 insertions, deletions, duplications, and inversions compared to the recent T2T-CHM13v2.0 reference. Our assemblies provide detailed maps of genetic variation, enabling more accurate variant calling and the ability to phase reads when using newly generated or historical sequencing data on these cell lines or their derivatives. All assemblies and associated data have been made available as a resource for the research community. We envision that diploid genome assembly will become a cornerstone approach for personalized medicine in the near future.

The International Myeloma Society and International Myeloma Working Group recently updated the definition of high-risk multiple myeloma and for the first time this definition includes genomic features that can only be detected by DNA sequencing. As a consequence, the working group recommended clinical testing should transition from FISH to DNA sequencing-based technologies that can detect the translocations, copy number alterations, and coding mutations used to calculate risk along with beta-2-microglobulin levels. To support this transition, we characterized the translocation breakpoints in 68 commercially or publicly available myeloma cell lines to create a gold-standard reference set for the community. This was then used to evaluate a series of different structural callers or dedicated immunoglobulin translocation calling tools. Additionally, as part of the clinical validation of a rapid whole genome sequencing platform, we established the accuracy, limit of detection and precision of our platform and analytical workflow for the detection of immunoglobulin translocations. All cell lines and patient samples used for clinical validation were sequenced on a NovaSeq X Plus using PCR-free libraries sequenced to 30-40x and >118x, respectively. To confirm complex events in the cell lines, we performed long-read sequencing using a PacBio Revio with libraries selected to have inserts exceeding 15kb. We tested the ability of Manta v1.6, IgCaller v1.3, DRAGEN v4.4.6, and SCITAV v0.6.5 to call the individual derivative chromosomes or at least any one derivative from a given translocation. Within the panel of myeloma cell lines, we detected 160 junctions from 92 distinct balanced or unbalanced translocation events in 66 cell lines between a common target gene in myeloma (NSD2, CCND3, MYC, MAFA, CCND1, CCND2, MAF, MAFB) and one of the immunoglobulin loci (IgH, IgK, IgL). Of the individual junctions, we found 9 with breakpoints proximal but outside of the immunoglobulin loci, 15 with insertions of a tertiary part of the genome ranging from 42bp-338kb and one with an immunoglobulin breakpoint containing 565bp of novel sequence. Approximately, 5.6% of derivative chromosome junctions will be undetectable by standard short-read sequencing approaches given the 8/15 events with insertions exceeding a typical WGS library or the one with unmappable sequence. Although 7/15 events with insertions under 260bp could likely be detected with some whole genome sequencing tests, we limited our comparison of bioinformatics approaches to the 135 junctions from 82 translocations that are within the immunoglobulin loci and do not contain defined insertions at the junctions. Using each calling tool in a tumor-only mode, the individual derivative call rate ranged from 66.7% (Manta) to 97.8% (SCITAV), with IgCaller detecting 76.3% while DRAGEN detected 88.2%. Since the majority of translocations are balanced events with two detectable derivatives, we assessed which percentage of translocations are detectable when at least one derivative is detected. This raised the detection rate range from 81.7% (Manta) to 100% (SCITAV), with IgCaller detecting 87.8% and DRAGEN 91.5%. A common theme in undetected breakpoints was immunoglobulin windows with low mapping quality resulting in read pairs being distributed between homologous regions, which resulted in the same event being called multiple times or these reads being ignored, leading to a missed call. For clinical validation we compared the WGS results with gold standard FISH assays from each patient and SCITAV had a sensitivity and specificity of 100%, while DRAGEN had a sensitivity of 94.4% and specificity of 100%. The limit of detection for SCITAV was established to be 6% VAF by diluting tumor DNA into matched normal DNA and additional precision replicates at this VAF resulted in a 96% recovery of calls across all replicates. We have shown that the majority of immunoglobulin translocations are detectable with WGS, but comprehensive detection requires optimized approaches to overcome mapping quality issues existing within immunoglobulin loci. These gold standards and particularly the difficult to detect events can be used for the establishment of clinical sequencing assays for multiple myeloma, which will provide more uniform and complete risk assessments and hold the promise of impacting patient care by identifying novel therapeutic options or predicting if specific immunotherapies will be effective.

Evaluating genomic signatures may improve prognostic information and outcomes for patients with lung cancer. To determine if homologous recombination deficiency (HRD) is associated with clinical features or overall survival in patients with lung cancer. This is a retrospective cohort study derived from City of Hope’s Implementing Next-Generation Sequencing for Precision Intervention and Risk Evaluation (INSPIRE) study. Study participants with histologically confirmed lung cancer and adequate tissue for comprehensive paired tumor-normal whole exome sequencing were eligible for analysis. 197 patients were sequenced, excluding 14 with rare histologies. Clinical and treatment variables were obtained from cancer registries and the electronic medical record and were recorded by experienced data curation and abstraction specialists. HRD scores were calculated using the scarHRD R package. Mutational signatures were analyzed using SigProfiler and SigEstimation. Associations between clinical variables and HRD status were measured using univariate and multivariate methods. Unsupervised analysis was performed to identify genes in HRD-high (sum score greater than or equal to 42) and HRD-low samples (sum score less than 42). Overall survival was quantified using non-parametric Kaplan-Meier statistics and multivariate Cox-proportional hazards models. Of the 183 lung cancer samples analyzed, 63 (34.4%) samples were HRD-high. Of these, 43 had a TP53 alteration and 20 did not; 120 (65.6%) samples were HRD-low, 50 with a TP53 alteration and 70 without (OR 3.01 (95%CI 1.58, 5.72), p=0.0008). TP53 alterations were strongly associated with high HRD scores. Overall survival was improved in patients with HRD-high tumors treated with platinum therapy, although this difference did not reach statistical significance. Katherine G. Roth, Kevin J. McDonnell, Ernest Nadal, Joseph D. Bonner, Arman Seuylemezian, Suravi Nahar, Lawrence Shaktah, Jonathan Salazar, Sidney Lindsey, Xiaoyu Xia, Sara A. Byron, Jonathan J. Keats, Allen Mao, Adrien Larsen, Bryce Turner, Ravi Salgia, Stacy W. Gray, Stephen B. Gruber. Homologous recombination deficiency in lung cancer in the INSPIRE study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2047.

Genes whose expression are associated with ETV4 in NDMM and in RRMM patients enrolled in the CoMMpass study.