Bolli, Niccolò

Abstract Background: amyotrophic lateral sclerosis is the most common adult-onset motor neuron disease, characterised by the progressive degeneration of upper and lower motor neurons in the brain and spinal cord. Despite substantial advances in the understanding of underlying disease mechanisms, effective therapies remain lacking. Methods: we generated spinal cord organoids from patient-derived induced pluripotent stem cells and matched isogenic controls to model in vitro the molecular pathology associated with the hexanucleotide repeat expansion within the C9orf72 gene. Results: C9orf72 organoids displayed delayed neuronal maturation, axonal degeneration, mitochondrial abnormalities, enhanced apoptotic signaling, and accumulation of dipeptide repeat proteins. Integrated multi-omics analyses revealed a consistent down-regulation of Netrin-1 at both the transcript and protein levels across patient-derived organoids, suggesting that its loss may contribute to impaired neuronal connectivity, compromised axonal integrity, and increased motor neuron vulnerability in C9orf72 amyotrophic lateral sclerosis. Conclusions: together, these findings establish patient-derived spinal cord organoids as a biologically relevant and in vitro model that recapitulates key cellular and molecular hallmarks of C9orf72-associated amyotrophic lateral sclerosis. This platform provides a valuable framework to deepen disease mechanisms, toward the development of new effective therapeutic strategies.

Abstract Background: Multiple myeloma (MM) is always preceded by monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). While these precursor conditions are asymptomatic, they are not entirely benign and carry a lifelong risk of progression to MM. Unlike other cancers defined by pathology, malignant transformation from MGUS or SMM to MM has so far relied on demonstration of clinical end-organ damage or high tumor cell burden (i.e. SLiM criteria, SV Rajkumar Lancet Onc 2014), as morphology and cytogenetics cannot reliably distinguish them. Methods: To identify genomic features underlying clinical behavior in MGUS and SMM, a total of 374 patients with SMM (n= 290) or MGUS (n=84) with available whole exome (whole exome sequencing, WES, n=190) or whole genome sequencing (whole genome sequencing WGS, n=184) were included in the study. Overall, none of the patients had evidence of SLiM or CRAB criteria at the time of the biopsy. Based on the center of origin and availability of WES and WGS, 227 and 97 patients were used to form our study training and validation set. The study was designed in two parts: 1) defining a new classification for MM precursor conditions; 2) integrating genomics to existing prognostic score to improve accuracy in identifying patients at imminent risk of progression. Overall, 68 patients in the training set were either lost to follow-up or enrolled in a clinical trial and they were only included in the classification part of the study. Results: By leveraging the distribution of established MM-defining genomic events (Maura et al. JCO 2024) in the training set, we developed a workflow that differentiates MGUS and SMM into two genomically distinct entities: one with evidence of neoplastic transformation (i.e., genomic MM) and one without (i.e., genomic MGUS). Overall, 39% of MGUS and 91% of SMM cases had genomic evidence of neoplastic transformation in the training set (i.e., genomic MM). When we applied the same workflow and classification to the validation set, 46% and 83% of MGUS and SMM had evidence of neoplastic transformation, respectively. Importantly, none of the patients classified as genomic-MGUS progressed to MM in either the training or validation cohorts, with a median follow-up of 40 and 61 months, respectively. Moreover, all patients with IMWG 2/20/20 high risk SMM were classified as genomic MM in both the training and validation set. Interestingly, 5 out of 62 (8%) patients enrolled in early intervention trials for high risk SMM were genomically classified as genomic-MGUS. Overall, these data demonstrated that the MM genomic background and neoplastic transformation can be acquired very early in time, even before the SMM phase, in line with what observed in solid tumors. To avoid overestimating the prognostic impact of myeloma-defining genomic events—which are, by definition, enriched in the genomic-MM group compared to genomic-MGUS—we restricted our prognostic analysis to patients in whom transformation has already occurred (genomic-MM). In fact, distinct genomic drivers may predict malignant transformation but not necessarily the risk of imminent progression to MM once transformation has occurred. After running 5-fold cross validations corrected for IMWG 2/20/20, only presence of RAS mutations, genomic events involving MYC, presence of neoplastic copy number variant (CNV) signatures and APOBEC mutagenesis were predictive of early progression in the training set. Adding the presence of high-risk myeloma-defining genomic events to the IMWG 2/20/20 model (i.e., genomic-IMWG) significantly improves the accuracy of predicting MM progression in both the training (c-index 0.69 vs. 0.74) and validation sets (c-index 0.74 vs. 0.79), compared to the IMWG 2/20/20 alone. Conclusion: By leveraging comprehensive genomic profiling integrated with the clinical IMWG 2/20/20 risk score, we propose a novel classification and prognostication system for MM precursor conditions, which will better define patients who may benefit from early therapeutic intervention versus watch-and-wait monitoring.

Multiple myeloma (MM) is consistently preceded by monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). While these precursor conditions are asymptomatic, they are not entirely benign and carry a lifelong risk of progression to MM. Unlike other cancers defined by pathology, malignant transformation from MGUS or SMM to MM has so far relied on demonstration of clinical end-organ damage as morphology and cytogenetics cannot reliably distinguish them. In this study, using genomic data from 374 patients with MGUS or SMM (277 training, 97 validation), to our knowledge, we demonstrate for the first time the ability to identify malignant transformation in MGUS and SMM. We introduce the concept of genomic MM and genomic MGUS to differentiate the subsets of MGUS and SMM that are biologically malignant with genomic features indistinguishable from MM from the subset that is premalignant and unlikely to progress to malignancy. Importantly, we find that most SMM has biological features of malignant transformation indistinguishable from MM. As expected, this subset that we consider having genomic MM is associated with a high risk of progression to MM although some patients remained progression-free beyond 5 years. Conversely, 60% of MGUS and 10% of SMM have no evidence of malignant transformation (genomic MGUS), with no progression during follow-up. Integration of genomic features with the 2/20/20 International Myeloma Working Group model significantly improved the prediction of progression among genomic MM. These findings support the use of genomic criteria to refine the classification and the risk stratification in myeloma precursor conditions.

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).

Abstract Accurate prediction of risk of progression from smoldering (SMM) to active multiple myeloma (MM) is paramount to individualized early therapeutic strategies with minimum risk of overtreatment. Current risk stratification models do not account for evolving biomarker trajectories. We assembled the largest cohort to date of 2,270 SMM patients from six international centers with longitudinal clinical and biological data to train and validate the PANGEA 2.0 risk models. Four evolving biomarkers were significantly associated with shorter time-to-progression: M-protein increase ≥0.2g/dL, involved:uninvolved serum free light chain ratio increase ≥20, creatinine increase >25%, and hemoglobin decrease ≥1.5g/dL. PANGEA 2.0 outperforms established models including the 20/2/20 and IMWG models by more accurately predicting progression (C-statistics=0.69–0.84), even without biomarker history (C-statistics=0.69–0.83) or recent bone marrow biopsy. PANGEA 2.0 is an easy-to-use, open-access tool (https://ghobrial.shinyapps.io/pangea_2_calculator) to improve and individualize SMM risk stratification. Validation tools are available to compare PANGEA 2.0 to established models (https://ghobrial.shinyapps.io/pangea_validation).

PURPOSE Outcomes for patients with newly diagnosed multiple myeloma (NDMM) are heterogenous, with overall survival (OS) ranging from months to over 10 years. METHODS To decipher and predict the molecular and clinical heterogeneity of NDMM, we assembled a series of 1,933 patients with available clinical, genomic, and therapeutic data. RESULTS Leveraging a comprehensive catalog of genomic drivers, we identified 12 groups, expanding on previous gene expression–based molecular classifications. To build a model predicting individualized risk in NDMM (IRMMa), we integrated clinical, genomic, and treatment variables. To correct for time-dependent variables, including high-dose melphalan followed by autologous stem-cell transplantation (HDM-ASCT), and maintenance therapy, a multi-state model was designed. The IRMMa model accuracy was significantly higher than all comparator prognostic models, with a c-index for OS of 0.726, compared with International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625). Integral to model accuracy was 20 genomic features, including 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, APOBEC mutational signatures, and copy-number signatures (reflecting the complex structural variant chromothripsis). IRMMa accuracy and superiority compared with other prognostic models were validated on 256 patients enrolled in the GMMG-HD6 (ClinicalTrials.gov identifier: NCT02495922 ) clinical trial. Individualized patient risks were significantly affected across the 12 genomic groups by different treatment strategies (ie, treatment variance), which was used to identify patients for whom HDM-ASCT is particularly effective versus patients for whom the impact is limited. CONCLUSION Integrating clinical, demographic, genomic, and therapeutic data, to our knowledge, we have developed the first individualized risk-prediction model enabling personally tailored therapeutic decisions for patients with NDMM.

Abstract Introduction: The bone marrow (BM) niche promotes multiple myeloma (MM) growth, survival and drug resistance. Therapies targeting both cancer cells and the microenvironment are highly effective. We were interested in identifying novel signaling pathways supporting MM pathogenesis through MM-BM crosstalk. The transmembrane receptor Roundabout 1 (ROBO1) plays a role in growth and dissemination of solid tumors, however its function in MM is unknown. Material and Methods: We analyzed ROBO1 expression in cell lines and primary samples via western blot, immunohistochemistry (IHC) and gene expression profiling. We used short hairpin RNA and CRISPR-Cas9 for ROBO1 knock down (KD) and knock out (KO), respectively. For protein structure-function and rescue studies, we stably expressed full-length (FL) or mutant ROBO1 devoid of extracellular (Cyt) or intracellular domain (DeltaCyt), including patient-derived G674* truncation, with a C-terminus FLAG tag. We used a 3D hydrogel encapsulation system to study proliferation; FLAG immunoprecipitation (IP) followed by mass spectrometry to identify ROBO1 interacting partners; and immunofluorescence to detect ROBO1 localization. To study tumor growth in vivo, we performed PET-CT of mice inoculated subcutaneously or intramedullary with WT or ROBO1 KO MM cells and retrieved tumors for RNA sequencing. To study dissemination and homing, KO and FL addback MM cells were injected intravenously in SCID mice. Mice were monitored for development of tumors or hindlimb paralysis and femora/tumors harvested once mice reached endpoint. Results: ROBO1 is highly expressed in MM cell lines and primary cells but low/absent in normal plasma cells and other hematologic cancer cell lines. ROBO1 KD is specifically cytotoxic for MM cells and ROBO1 KO decreases proliferation, a phenotype fully rescued by FL ROBO1. Compared to WT, ROBO1 KO significantly decreases intramedullary (mean tumor volume (MTV): 1323 vs 457 mm3, p value= 0.02) and extramedullary (MTV: 2684 vs 823 mm3, p value= 0.001) tumor growth in vivo. We further discovered that ROBO1 KO decreases adhesion of MM to BM endothelial and BMSC, which is fully rescued by FL ROBO1. In a disseminated mouse model, ROBO1 KO cells generate bone plasmacytoma with reduced BM invasion, as compared to the extensive BM infiltration observed with ROBO1 FL cells. Consistently, in primary samples from patients, we detected ROBO1 expression only in 1 out of 10 solitary plasmacytoma (dim staining) as compared to 14 out of 14 MM bone marrow biopsy samples tested (11 strong, 3 dim, p value= 0.0001). Mechanistically, we show for the first time that ROBO1 C-terminus is cleaved in a ligand-independent fashion; translocates to the nucleus; and is necessary and sufficient to rescue ROBO1 KO proliferative defect. Viceversa, mutants lacking the cytoplasmic domain, including the G674* truncation, act dominantly negative. Interactomic and RNA sequencing studies point to a previously unknown function of ROBO1 in RNA processing, setting the bases for future studies. Conclusions: We show for the first time that ROBO1 is necessary for MM growth and homing to the BM. Cleaved ROBO1 cytosolic domain translocates to the nucleus and is necessary and sufficient to rescue ROBO1 KO proliferative defect, possibly by participating in RNA processing. These data suggest that ROBO1 C-terminus may be a novel molecular target in MM. Citation Format: Giada Bianchi, Peter G. Czarnecki, Matthew Ho, Aldo M. Roccaro, Antonio Sacco, Yawara Kawano, Annamaria Gulla, Anil Aktas Samur, Tianzeng Cheng, Kenneth Wen, Yu-Tzu Tai, Maria Moscvin, Xinchen Wu, Gulden Camci-Unal, Matteo Claudio Da VIa, Niccolo Bolli, Ruben D. Carrasco, Irene M. Ghobrial, Kenneth C. Anderson. ROBO1 promotes homing, dissemination, and survival of multiple myeloma within the bone marrow microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB217.

Abstract The bone marrow (BM) microenvironment actively promotes multiple myeloma pathogenesis, and therapies targeting both cancer cells and the niche are highly effective. We were interested in identifying novel signaling pathways supporting multiple myeloma–BM cross-talk. Mutations in the transmembrane receptor Roundabout 1 (ROBO1) were recently identified in patients with multiple myeloma; however, their functional consequences are uncertain. Through protein structure–function studies, we discovered that ROBO1 is necessary for multiple myeloma adhesion to BM stromal and endothelial cells and that ROBO1 knockout (KO) compromises BM homing and engraftment in a disseminated mouse model. ROBO1 KO significantly decreases multiple myeloma proliferation in vitro and intra- and extramedullary tumor growth in vivo. Mechanistically, the ROBO1 C-terminus is cleaved in a ligand-independent fashion and is sufficient to promote multiple myeloma proliferation. Vice versa, mutants lacking the cytoplasmic domain, including the human-derived G674* truncation, act dominantly negative. Interactomic and RNA-sequencing studies suggest that ROBO1 may be involved in RNA processing, supporting further studies. Significance: ROBO1 is highly expressed in t(4;14) multiple myeloma and supports homing and dissemination to the BM niche. ROBO1 knockout causes reduced tumor growth in intramedullary and extramedullary myeloma animal models, while the ROBO1 C-terminus is cleaved in multiple fragments and it is necessary and sufficient to sustain myeloma proliferation.

Key Points Using the largest set of patients with newly diagnosed myeloma, we identified 63 mutated driver genes. We identified oncogenic dependencies, particularly relating to primary translocations, indicating a nonrandom accumulation of genetic hits.