Baughn, Linda B

ABSTRACT The prognostic relevance of multiple myeloma (MM)‐defined high‐risk (HR) cytogenetic abnormalities in AL amyloidosis is incompletely defined. We retrospectively evaluated 517 newly diagnosed AL amyloidosis patients seen at Mayo Clinic between 2012 and 2021 who had baseline plasma cell FISH. HR cytogenetics were defined as t (4;14), t (14;16), t (14;20), del(17p), or 1q gain/amplification (+1q). HR abnormalities were identified in 151 patients (29%); +1q accounted for most HR cases (122/151, 81%). Compared with standard‐risk (SR), patients harboring +1q demonstrated a greater clonal disease burden, including higher dFLC and bone marrow plasma cells, greater intact immunoglobulin secretion, higher plasma cell proliferative index, and enrichment for modified SLiM‐CRAB features. Hematologic and organ response patterns of +1q patients were generally comparable to SR patients, whereas non‐1q HR patients had a higher proportion of early hematologic responses. Patients harboring +1q demonstrated significantly inferior overall survival (OS) compared with both non‐1q HR patients and SR patients (median OS 57 months vs. not reached vs. 120 months, p = 0.02). In multivariable piecewise Cox analysis, the adverse prognostic impact of +1q was most evident beyond 24 months from diagnosis (HR 2.89, 95% CI 1.64–5.09, p < 0.001), whereas non‐1q HR lesions were not independently prognostic. These findings suggest that +1q identifies a biologically distinct AL amyloidosis subset characterized by plasma cell–dominant features and inferior long‐term disease control. In contrast, other MM‐defined HR cytogenetic abnormalities appear to have limited prognostic relevance in AL amyloidosis.

Abstract Studies have reported conflicting findings regarding the contribution of germline variants or somatic genomic drivers to racial disparities in multiple myeloma. To comprehensively investigate somatic drivers in relation to inherited genetics in multiple myeloma, we combined newly sequenced whole-genome sequencing data with publicly available datasets (total n = 1,286). Overall, we did not identify germline or somatic genomic differences that explain the different risk of developing multiple myeloma between patients with genetic similarity to African (AFR) or European (EUR) reference populations. A difference in the detectability and timing of APOBEC-associated and germinal center mutational activity was observed. Integrating epidemiologic data and mutational signature-based temporal estimates, we challenge the assumption that individuals in the AFR group develop multiple myeloma at a younger age. Finally, we demonstrate that, with equal access to efficacious therapies, patients in the AFR and EUR groups have equivalent clinical outcomes. Significance: Multiple myeloma is reported to occur at higher rates in individuals who self-identify as non-Hispanic Black. In this large dataset, genomic drivers occur at the same rate among ancestry groups, except for APOBEC mutagenesis. With equivalent therapy, clinical outcomes did not differ for patients grouped by genetic ancestry similarity.

Abstract Multiple myeloma is a malignancy of clonally expanded plasma cells shaped by complex interactions with the immune microenvironment (IME). To investigate immune correlates of treatment response and disease progression, 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. In longitudinal analyses, interferon-γ signaling associated with markers of impaired T-cell memory after autologous stem cell transplant, whereas naïve B-cell abundance and immunoglobulin diversity correlated with improved progression-free survival (HR = 0.48; P = 2.3e−4). At disease progression, multiple myeloma cells upregulated cancer–testis antigens (CTAg) and immune effector genes, with concurrent B-cell depletion, enrichment of myeloid-derived suppressor cell expression, and phenotypic T-cell exhaustion. These findings highlight dynamic immune–tumor interactions, identifying naïve B-cell reconstitution as a biomarker of durable response and CTAgs as potential targets for high-risk disease at progression. Significance: Longitudinal profiling of multiple myeloma and the IME revealed dynamic immune–tumor interactions across the disease course. Altered expression in CD8+ T cells limited memory phenotype after transplant, whereas naïve B-cell recovery associated with sustained treatment response. At progression, CTAg expression associated with immunosuppression, revealing novel mechanisms of immune dysregulation.

Abstract Motivation Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual’s genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry-informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. Results Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including whole exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using nonstandard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual’s genetic similarity to a continental group. Availability and implementation AncestryGeni pipeline is available at https://github.com/eelhaik/AncestryGeni/tree/main.

Abstract Clonal hematopoiesis (CH) of indeterminate potential (CHIP), driven by somatic mutations in leukemia-associated genes, confers increased risk of hematologic malignancies, cardiovascular disease, and all-cause mortality. In blood of healthy individuals, small CH clones can expand over time to reach 2% variant allele frequency (VAF), the current threshold for CHIP. Nevertheless, reliable detection of low-VAF CHIP mutations is challenging, often relying on deep targeted sequencing. Here, we present UNISOM, a streamlined workflow for enhancing CHIP detection from whole-genome and whole-exome sequencing data that are underpowered, especially for low VAFs. UNISOM utilizes a meta-caller for variant detection, in couple with machine learning models which classify variants into CHIP, germline, and artifact. In whole-exome sequencing data, UNISOM recovered nearly 80% of the CHIP mutations identified via deep targeted sequencing in the same cohort. Applied to whole-genome sequencing data from Mayo Clinic Biobank, it recapitulated the patterns previously established in much larger cohorts, including the most frequently mutated CHIP genes and predominant mutation types and signatures, as well as strong associations of CHIP with age and smoking status. Notably, 30% of the identified CHIP mutations had &lt; 5% VAFs, demonstrating its high sensitivity toward small mutant clones. This workflow is applicable to CHIP screening in population genomic studies. The UNISOM pipeline is freely available at https://github.com/shulanmayo/UNISOM and https://ngdc.cncb.ac.cn/biocode/tool/7816.

Abstract Acute myeloid leukemia (AML) with TP53 mutations (TP53Mut) has poor clinical outcomes. We investigated whether this AML subtype harbors a distinct gene expression profile (GEP) and whether this GEP is prognostic in TP53 wild type (TP53WT) AML. We applied a supervised machine-learning approach to detect the TP53Mut GEP. We divided the samples in the Beat-AML dataset into training and testing datasets. The TCGA LAML dataset was used as a validation dataset. We trained a ridge regression machine learning model to classify TP53Mut and TP53WT cases. This model was highly accurate in distinguishing TP53Mut versus TP53WT cases in both the test and validation datasets. Additionally, we identified TP53WT samples with high ridge regression scores. These high-scoring TP53WT samples also have poor overall survival, suggesting they share clinical and GEP features with TP53Mut AML. We defined these TP53WT samples as TP53Mut-like. Using drug sensitivity data in the Beat AML dataset, we found TP53Mut-like AMLs have distinct drug sensitivity patterns that phenocopy TP53Mut cases. Finally, we identified a 25-gene signature that can identify TP53Mut-like AML. This signature could be used clinically to identify this novel subset of poor-prognosis AML.

Abstract Overall Survival estimates from diagnosis are valuable for guiding treatment, but do not consider the years already survived. Conditional survival (CS) provides dynamic survival predictions over time. This study was conducted to estimate CS at 1–8 years from diagnosis and the impact of baseline prognostic factors on CS in multiple myeloma (MM) patients. This is a retrospective study including 2556 MM patients diagnosed between 2004–2019. CS (t|s) was defined as the probability of surviving t years given survival of s years. Median age was 64 years. Median follow-up was 6.2 years and median overall survival from diagnosis was 7.5 years. The 5-year CS estimates at s = 0, 1, 2, 3, and 5 years were 0.64, 0.61, 0.61, 0.61, and 0.58, respectively. On multivariate analysis, age ≥ 65 and proteasome inhibitor + immunomodulatory-based induction were associated with decreased survival and increased survival, respectively, retained at 5 years. The adverse impact of 1q gain/amplification, high-risk IgH translocation, and ISS-3 was significant at 1 and 3 years but not 5 years. Chromosome 17 abnormality was associated with decreased survival only at 1 year. Among MM patients, 5-year CS was stable at 1–5 years from diagnosis. The prognostic impact of high-risk cytogenetic factors decreased with additional years survived.

AbstractAcute lymphoblastic leukemia (B‐ALL) with intrachromosomal amplification of chromosome 21 (iAMP21‐ALL) represents a recurrent high‐risk cytogenetic abnormality and accurate identification is critical for appropriate clinical management. Identification of iAMP21‐ALL has historically relied on fluorescence in situ hybridization (FISH) using a RUNX1 probe. Current classification requires ≥ five copies of RUNX1 per cell and ≥ three additional copies of RUNX1 on a single abnormal iAMP21‐chromosome. We sought to evaluate the performance of the RUNX1 probe in the identification of iAMP21‐ALL. This study was a retrospective evaluation of iAMP21‐ALL in the Mayo Clinic and Children's Oncology Group cohorts. Of 207 cases of iAMP21‐ALL, 188 (91%) were classified as “typical” iAMP21‐ALL, while 19 (9%) cases were classified as “unusual” iAMP21‐ALL. The “unusual” iAMP21 cases did not meet the current definition of iAMP21 by FISH but were confirmed to have iAMP21 by chromosomal microarray. Half of the “unusual” iAMP21‐ALL cases had less than five RUNX1 signals, while the remainder had ≥ five RUNX1 signals with some located apart from the abnormal iAMP21‐chromosome. Nine percent of iAMP21‐ALL cases fail to meet the FISH definition of iAMP21‐ALL demonstrating that laboratories are at risk of misidentification of iAMP21‐ALL when relying only on the RUNX1 FISH probe. Incorporation of chromosomal microarray testing circumvents these risks.