The ALLgorithMM: How to define the hemodilution of bone marrow samples in lymphoproliferative diseases

IntroductionMinimal residual disease (MRD) is commonly assessed in bone marrow (BM) aspirate. However, sample quality can impair the MRD measurement, leading to underestimated residual cells and to false negative results. To define a reliable and reproducible method for the assessment of BM hemodilution, several flow cytometry (FC) strategies for hemodilution evaluation have been compared. MethodsFor each BM sample, cells populations with a well-known distribution in BM and peripheral blood - e.g., mast cells (MC), immature (IG) and mature granulocytes (N) - have been studied by FC and quantified alongside the BM differential count. ResultsThe frequencies of cells' populations were correlated to the IG/N ratio, highlighting a mild correlation with MCs and erythroblasts (R=0.25 and R=0.38 respectively, with p-value=0.0006 and 0.0000052), whereas no significant correlation was found with B or T-cells. The mild correlation between IG/N, erythroblasts and MCs supported the combined use of these parameters to evaluate BM hemodilution, hence the optimization of the ALLgorithMM. Once validated, the ALLgorithMM was employed to evaluate the dilution status of BM samples in the context of MRD assessment. Overall, we found that 32% of FC and 52% of Next Generation Sequencing (NGS) analyses were MRD negative in samples resulted hemodiluted (HD) or at least mildly hemodiluted (mHD). ConclusionsThe high frequency of MRD-negative results in both HD and mHD samples implies the presence of possible false negative MRD measurements, impairing the correct assessment of patients' response to therapy and highlighs the importance to evaluate BM hemodilution

Association of kidney disease measures with risk of renal function worsening in patients with type 1 diabetes

Background: Albuminuria has been classically considered a marker of kidney damage progression in diabetic patients and it is routinely assessed to monitor kidney function. However, the role of a mild GFR reduction on the development of stage 653 CKD has been less explored in type 1 diabetes mellitus (T1DM) patients. Aim of the present study was to evaluate the prognostic role of kidney disease measures, namely albuminuria and reduced GFR, on the development of stage 653 CKD in a large cohort of patients affected by T1DM. Methods: A total of 4284 patients affected by T1DM followed-up at 76 diabetes centers participating to the Italian Association of Clinical Diabetologists (Associazione Medici Diabetologi, AMD) initiative constitutes the study population. Urinary albumin excretion (ACR) and estimated GFR (eGFR) were retrieved and analyzed. The incidence of stage 653 CKD (eGFR < 60 mL/min/1.73 m2) or eGFR reduction > 30% from baseline was evaluated. Results: The mean estimated GFR was 98 \ub1 17 mL/min/1.73m2 and the proportion of patients with albuminuria was 15.3% (n = 654) at baseline. About 8% (n = 337) of patients developed one of the two renal endpoints during the 4-year follow-up period. Age, albuminuria (micro or macro) and baseline eGFR < 90 ml/min/m2 were independent risk factors for stage 653 CKD and renal function worsening. When compared to patients with eGFR > 90 ml/min/1.73m2 and normoalbuminuria, those with albuminuria at baseline had a 1.69 greater risk of reaching stage 3 CKD, while patients with mild eGFR reduction (i.e. eGFR between 90 and 60 mL/min/1.73 m2) show a 3.81 greater risk that rose to 8.24 for those patients with albuminuria and mild eGFR reduction at baseline. Conclusions: Albuminuria and eGFR reduction represent independent risk factors for incident stage 653 CKD in T1DM patients. The simultaneous occurrence of reduced eGFR and albuminuria have a synergistic effect on renal function worsening

OAB-057: Temporal-weight estimation of the copy number alterations of of 1384 Multiple Myeloma patients defines an ancestrality index impacting patients survival

Background MM is a hematological malignancy always evolving from pre-malignant stages, with progressive increase of genomic complexity. MM is characterized by a large abundance of copy number alterations (CNA); many of them, regarded as “driver”, stack up progressively from early tumor stages, causing biological changes that give rise to tumor hallmarks and malignant phenotypes. The combined application of whole genome analysis and mathematical models allows to deeply describe these alterations and to infer their order of acquisition during oncogenesis from their clonality levels, assuming that clonal ones are more ancestral than subclonal. Aims: (1) To define the temporal order of acquisition of CNA, leading to the onset of symptomatic MM and (2) to define a scoring model able to stratify patients (pts) according to the ancestrality of the alterations observed in their genomic landscape. Methods Genomic data collected from a total of 1384 newly diagnosed MM pts were included in the study: SNPs array data were collected from 514 pts of our Institution (BO dataset); in 870 pts, WES data were downloaded from CoMMpass study. CN calls and clonality levels were harmonized by an analysis pipeline including ASCAT, GISTIC v2 and custom R scripts. Timing estimates were obtained with BradleyTerry2 package. Survival analysis were performed on R. Results A full call-set of CNAs was obtained by harmonizing BO and CoMMpass datasets. The clonality information was first extrapolated from the whole call-set, to define the temporal order of acquisition of non-primary CNAs. CNAs were then accurately ranked, by using the obtained timing estimates, characterized by a quite narrow confidence interval. Of interest, chr 1q gains and chr 13q losses were frequently clonal and ranked as ancestral events, whereas chr 17p losses were late occurring events. By weighting the CNAs carried by any given pts at diagnosis with their relative timing estimate in a combinatorial process, an Ancestrality Index (AI) was defined for each pts (median AI=3.4, IQR=1.7-6.0). The AI was found to be significantly associated with progression free (PFS) and overall survival (OS) (p3.4 (i.e. with a more “ancestral” profile) had a worse outcome as compared to the rest of pts (OS 40% vs 58%, PFS 42% vs 56%, at a median follow up of 92m and 34m, p<0.001).The risk attributed to this “ancestral” category was independent from other high-risk cytogenetic features (i.e. del17p, t(4;14), t(14;20), t(14;20)). Conclusions By means of whole genome analysis and dataset harmonizing, the temporal order of acquisition of MM CNAs has been confidently described. A score reflecting the disease ancestrality of MM pts at diagnosis was generated and associated to survival outcomes. Overall, these findings support the evidence that MM pts at diagnosis carrying an excess of ancestral alterations, expected to likely be drivers, are prone to have a dismal prognosis

A Methodologically Updated De-Novo Extraction of Copy Number Signatures in Multiple Myeloma: Clinical Significance and Putative Aetiologies

BACKGROUND: The tumor genomes of most Multiple Myeloma (MM) patients are heavily burdened with highly heterogenous copy number (CN) alterations, as detected by multiple molecular methods including whole genome sequencing (WGS), and have been shown to have a strong prognostic and predictive significance for patients' survival. Cutting-edge computational developments have made it possible to analyze complex genomic CN changes by identifying CN alterations' recurrent patterns among large cohorts of patients, named CN signatures (CNS), that are the result of cumulative chromosomic instability (CIN) processes throughout the course of cancer cells evolution. In fact, a robust methodological framework for CNS computation, along with a large compendium of CNS aetiologies have been recently published for many cancer types, notably not including MM (Drews 2022). To our knowledge, the only study that analyzes CNS in MM focused on the role of CNS in predicting chromothripsis events, but remarkably it did not include neither CNS aetiologies nor complete CNS characterization for all the discovered CNS (Maclachlan 2021). AIM: To define a novel methodological framework, based on previous studies, aimed at the identification of CNS in MM, in order to assess both the aetiologies and the biological significance of MM CNS and to evaluate their prognostic impact on MM clinical outcome. The newly extracted CNS defined at diagnosis will be also used as novel disease biomarkers, to develop an improved, aetiology-based MM patients' stratification in different molecular subtypes. METHODS: We calculated the MM genomic distributions of the six essential CN features (segment length, breakpoint per 10 Mb, breakpoint per chromosome arm, segment CN change, CN value, length of oscillating CN states) that have been previously shown to encode patterns of CN alterations, underlying the observed tumor CIN. Starting from 886 WGS generated CN profiles, included in the CoMMpass study, the above mentioned features were computed. Features were then categorized into components, by using mixture models' decomposition and CNS were finally extracted from the components, by using both a Hierarchical Dirichlet Process (HDP) and a Non-Negative Matrix Factorization (NNMF) approach. RESULTS: The main novel characteristics of the developed methodological framework aimed at CNS assessment were 1) the use of a "continuous CN value" feature, which enabled the evaluation of sub-clonal events and 2) the use of a logarithmic scale in "segment length" feature, which favored a higher resolution for categorizing focal and/or gene level CN events, that are very common in MM. Thanks to these implementations, 33 Gaussian mixture components were identified (as compared to 28 detected in Maclachlan 2021). After deriving a Sample x Component - Sum of Posteriors Matrix, the signatures were extracted by applying two parallel state-of-art approaches, namely HDP and NNMF. This allowed the extraction of 9 CNS that were characterized by their component's composition. The signature's exposure levels were correlated to well-known MM biomarkers (e.g. TP53 mut and/or del, 1q CN gain, 13q CN loss, t-IgH, hyperdiploidy), showing that all signatures correlated to at least one of the well known MM biomarkers; in particular, CN.SIG5 exposures was found to correlate to high-risk MM biomarkers (TP53 p<0.001, 1q CN gain p<0.001, t(4;14) p<0.001), thus suggesting its possible involvement in the aetiology of this peculiar genomic configuration. Finally, a survival analysis was performed in patients characterized by high exposure (4th quartile) to the CN.SIG5 (75 patients), as compared to the others (811 patients), showing a significant negative impact of this CNS on both overall (OS p<0.001)) and progression free survivals (PFS p<0.001). Cox-analysis revealed an OS HR= 1.37 p<0.001, PFS HR= 1.16, p<0.001, per 5% increase in exposure. CONCLUSION: By employing a novel bio-informatic approach, based on the use of continuous CN data for CNS extraction, 33 feature's components were identified. We observed that CN.SIG5 significantly affected patients carrying well-known high-risk genomic features, and patients highly exposed to this CNS had decreased PFS and OS. Additional characterizations are needed to unveil the biological meaning of CNS exposure; however, MM CNS, while informing on disease outcome, might be considered as new comprehensive biomarkers in this disease

Circulating Multiple Myeloma Cells (CMMCs) as Prognostic and Predictive Markers in Multiple Myeloma and Smouldering MM Patients

In recent years, liquid biopsy has emerged as a promising alternative to the bone marrow (BM) examination, since it is a minimally invasive technique allowing serial monitoring. Circulating multiple myeloma cells (CMMCs) enumerated using CELLSEARCH (R) were correlated with patients' prognosis and measured under treatment to assess their role in monitoring disease dynamics. Forty-four MM and seven smouldering MM (SMM) patients were studied. The CMMC medians at diagnosis were 349 (1 to 39,940) and 327 (range 22-2463) for MM and SMM, respectively. In the MM patients, the CMMC count was correlated with serum albumin, calcium, beta 2-microglobulin, and monoclonal components (p < 0.04). Under therapy, the CMMCs were consistently detectable in 15/40 patients (coMMstant = 1) and were undetectable or decreasing in 25/40 patients (coMMstant = 0). High-quality response rates were lower in the coMMstant = 1 group (p = 0.04), with a 7.8-fold higher risk of death (p = 0.039), suggesting that continuous CMMC release is correlated with poor responses. In four MM patients, a single-cell DNA sequencing analysis on residual CMMCs confirmed the genomic pattern of the aberrations observed in the BM samples, also highlighting the presence of emerging clones. The CMMC kinetics during treatment were used to separate the patients into two subgroups based on the coMMstant index, with different responses and survival probabilities, providing evidence that CMMC persistence is associated with a poor disease course

Multi-dimensional scaling techniques unveiled gain1q&loss13q co-occurrence in Multiple Myeloma patients with specific genomic, transcriptional and adverse clinical features

Abstract The complexity of Multiple Myeloma (MM) is driven by several genomic aberrations, interacting with disease-related and/or -unrelated factors and conditioning patients’ clinical outcome. Patient’s prognosis is hardly predictable, as commonly employed MM risk models do not precisely partition high- from low-risk patients, preventing the reliable recognition of early relapsing/refractory patients. By a dimensionality reduction approach, here we dissect the genomic landscape of a large cohort of newly diagnosed MM patients, modelling all the possible interactions between any MM chromosomal alterations. We highlight the presence of a distinguished cluster of patients in the low-dimensionality space, with unfavorable clinical behavior, whose biology was driven by the co-occurrence of chromosomes 1q CN gain and 13 CN loss. Presence or absence of these alterations define MM patients overexpressing either CCND2 or CCND1, fostering the implementation of biology-based patients’ classification models to describe the different MM clinical behaviors

Identification of a Maturation Plasma Cell Index through a Highly Sensitive Droplet Digital PCR Assay Gene Expression Signature Validation in Newly Diagnosed Multiple Myeloma Patients

DNA microarrays and RNA-based sequencing approaches are considered important discovery tools in clinical medicine. However, cross-platform reproducibility studies undertaken so far have highlighted that microarrays are not able to accurately measure gene expression, particularly when they are expressed at low levels. Here, we consider the employment of a digital PCR assay (ddPCR) to validate a gene signature previously identified by gene expression profile. This signature included ten Hedgehog (HH) pathways’ genes able to stratify multiple myeloma (MM) patients according to their self-renewal status. Results show that the designed assay is able to validate gene expression data, both in a retrospective as well as in a prospective cohort. In addition, the plasma cells’ differentiation status determined by ddPCR was further confirmed by other techniques, such as flow cytometry, allowing the identification of patients with immature plasma cells’ phenotype (i.e., expressing CD19+/CD81+ markers) upregulating HH genes, as compared to others, whose plasma cells lose the expression of these markers and were more differentiated. To our knowledge, this is the first technical report of gene expression data validation by ddPCR instead of classical qPCR. This approach permitted the identification of a Maturation Index through the integration of molecular and phenotypic data, able to possibly define upfront the differentiation status of MM patients that would be clinically relevant in the future

Single-Cell DNA Sequencing Reveals an Evolutionary Pattern of CHIP in Transplant Eligible Multiple Myeloma Patients

Clonal hematopoiesis of indeterminate potential (CHIP) refers to the phenomenon where a hematopoietic stem cell acquires fitness-increasing mutation(s), resulting in its clonal expansion. CHIP is frequently observed in multiple myeloma (MM) patients, and it is associated with a worse outcome. High-throughput amplicon-based single-cell DNA sequencing was performed on circulating CD34+ cells collected from twelve MM patients before autologous stem cell transplantation (ASCT). Moreover, in four MM patients, longitudinal samples either before or post-ASCT were collected. Single-cell sequencing and data analysis were assessed using the MissionBio Tapestri® platform, with a targeted panel of 20 leukemia-associated genes. We detected CHIP pathogenic mutations in 6/12 patients (50%) at the time of transplant. The most frequently mutated genes were TET2, EZH2, KIT, DNMT3A, and ASXL1. In two patients, we observed co-occurring mutations involving an epigenetic modifier (i.e., DNMT3A) and/or a gene involved in splicing machinery (i.e., SF3B1) and/or a tyrosine kinase receptor (i.e., KIT) in the same clone. Longitudinal analysis of paired samples revealed a positive selection of mutant high-fitness clones over time, regardless of their affinity with a major or minor sub-clone. Copy number analysis of the panel of all genes did not show any numerical alterations present in stem cell compartment. Moreover, we observed a tendency of CHIP-positive patients to achieve a suboptimal response to therapy compared to those without. A sub-clone dynamic of high-fitness mutations over time was confirmed

Kidney dysfunction and related cardiovascular risk factors among patients with type 2 diabetes

Background. Kidney dysfunction is a strong predictor of end-stage renal disease and cardiovascular (CV) events. The main goal was to study the clinical correlates of diabetic kidney disease in a large cohort of patients with type 2 diabetes mellitus (T2DM) attending 236 Diabetes Clinics in Italy.Methods. Clinical data of 120 903 patients were extracted from electronic medical records by means of an ad hoc-developed software. Estimated glomerular filtration rate (GFR) and increased urinary albumin excretion were considered. Factors associated with the presence of albuminuria only, GFR < 60 mL/min/1.73 m(2) only or both conditions were evaluated through multivariate analysis.Results. Mean age of the patients was 66.6 +/- 11.0 years, 58.1% were male and mean duration of diabetes was 11.1 +/- 9.4 years. The frequency of albuminuria, low GFR and both albuminuria and low GFR was 36.0, 23.5 and 12.2%, respectively. Glycaemic control was related to albuminuria more than to low GFR, while systolic and pulse pressure showed a trend towards higher values in patients with normal kidney function compared with those with both albuminuria and low GFR. Multivariate logistic analysis showed that age and duration of disease influenced both features of kidney dysfunction. Male gender was associated with an increased risk of albuminuria. Higher systolic blood pressure levels were associated with albuminuria, with a 4% increased risk of simultaneously having albuminuria and low GFR for each 5 mmHg increase.Conclusions. In this large cohort of patients with T2DM, reduced GFR and increased albuminuria showed, at least in part, different clinical correlates. A worse CV risk profile is associated with albuminuria more than with isolated low GFR