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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 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.

Introduction Multiple Myeloma (MM) is a genetically complex disease, characterized by the recurrence of several chromosomal aberrations, which impair the disease prognosis. The use of genome-wide technologies has recently highlighted the existence of Complex Chromosomal Events (CCEs), caused by distinct phenomenons: Chromothripsis (CT), caused by single-step genomic events, and Stepwise Events (SE), consequence of multiple, small and sequential genomic events, occurring throughout subsequent cell cycles. The prognostic impact of CT in MM has not yet been fully elucidated. Aims of our study were: (1) to detect CCEs in MM, with a focus on CT, by using an original and reliable bio-informatic algorithm, (2) to characterize the genetic and genomic context of CT and (3) to correlate the presence of CTwith pts prognosis. Methods A total of 488 newly diagnosed MM patients (pts) have been included in the study. Genomic data have been obtained by SNPs arrays on bone marrow (BM) aspirates CD138+ enriched cell fractions; data were analysed by Affymetrix’s programs and R-scripts. Results An original algorithm able to discriminate among the 2 different CCEs (CT and SE),was set up and tested, by implementing the most commonly reported guidelines for CCEs identification with knowledges on MM-specific highly heterogeneous genomic context. CCEs were detected in 174 pts (36% with at least one CCE): in particular, 46/174 pts (26%) carried CT. CT can affect any chromosome, yet showing significant associations with the following genomic regions: chr1p (p=8.37E-15), chr2q (p=4.27E-8), chr11q (p=6.99E-5) and chr22q (p=5.15E-7). Pts carrying CT were more likely to carry also IgH translocations associated to bad prognosis (p=0.002, HR 3.4) and TP53 deletions (p=1.16E-5, HR=6.26). The presence of any CT event conferred hazard ratio of 1.52 (p=0.019) and 1.68 (p=0.019) to pts’ progression-free (PFS) and overall survival (OS), respectively, independently from the presence of both TP53 deletion and translocation t(4;14), as evaluated in a multivariated model. An association between CT events and XBP1 gene deletion was observed; since XBP1 expression has been correlated to an effective proteasome inhibitor (PI) therapy response, CT events particularly impacted survival expectancies of PI-treated pts, whose PFS and OS were significanlty worse than those of other pts (p=0.0098 and =0.023, respecitvely). Finally, the same CT events acquired at diagnosis were observed in those 4/55 pts, whose BM aspirates were analysed also at relapse, thus suggesting that, once occurred, CT might have a driver role for disease progression. Conclusion Our results showed that CT impact MM pts prognosis, independently from the genomic region affected and the pts’ genomic backgroung. Acknowledgment AIRC_IG2014, RF-2016.

Background The introduction of novel agents into the therapeutic algorithm of Multiple Myeloma (MM) has led to remarkable improvements in the rate of Minimal Residual Disease (MRD) negative status. Sustained MRD negativity is a robust prognosticator and a driver of treatment strategies. Therefore, it is expected that MRD molecular tracking would become critical in the management of MM, and could be included in daily clinical practice. Aim To evaluate the performances of next generation sequencing (NGS), implemented as best practice in MM MRD daily evaluation. Methods A cohort of 166 newly diagnosed transplant-eligible and -ineligible MM pts were screened to define the ID clonotypes. MRD was firstly assessed at achievement of at least a very good partial response during maintenance, and thereafter once a year. In high-risk pts, MRD was also detected between 1° and 2° autologous stem-cell transplantation (ASCT). The ID clonotypes screening has been performed by NGS, using an assay covering IgH and Igk genes (Invivoscribe®). The MRD tracking has been done both by conventional ASO-qPCR and by NGS. Data were analyzed by Lymphotrack Dx and MRD software (Invivoscribe®). Results The ID clonotypes screening was successful in 158/166 pts (95%), even though a small proportion of pts resulted polyclonal (14/158). Overall, 63, 27 and 54 pts had IgH, Igk and IgH/k ID clonotypes, respectively, mainly restricted to the VH3 (47%) family genes. MRD assessment was performed by NGS in 124/144 pts (overall, 96 MRD samples for 64 pts monitored to date), with 10-5 sensitivity in most cases (75/96). 44 pts were assessed during maintenance and 23 achieved MRD-negativity. On the contrary, 20 pts were evaluated between 1° and 2° ASCT, resulting MRD-positive in all cases. Overall, few FU samples (13/96) were reported as “positive-not-quantifiable” (PNQ), since had residual disease levels of 10-7, much lower than the internal control. In a subset of 20 pts with trackable IgH/Igk sequences, pts-specific assays were designed, according to the EuroMRD guidelines. In these pts, MRD was tested both by ASO-qPCR (including a standard curve and healthy donor cells, to define the sensitivity and the specificity, respectively) and by NGS. Overall, ASO-qPCR allowed the tracking of a single MRD clone (with up to 10-4 sensitivity) in 12/20 pts (60%), whereas NGS was feasible in all of them, confirming the ASO-qPCR results, yet with higher sensitivity and >95% confidence. Moreover, in 2 cases, NGS allowed to disclose ASO-qPCR PNQ results due to nonspecific amplification, thus unveiling MRD negativity with 10-5 sensitivity. Conclusion NGS has been successfully implemented as best practice in the management of MM pts, showing the superiority of this method over ASO-qPCR conventional approach. The ID clonotype was defined in almost all pts included in the workflow, thus allowing their MRD molecular monitoring, according to the clinical requirements. Acknowledgment AIRC IG2018-22059

Background Multiple Myeloma (MM) is a plasma cell (PC) disorder characterized by the presence of multiple lytic lesions at the time of diagnosis. Recently, cell-free DNA (cfDNA) has proven to resume the heterogeneity of spatially distributed clones. However, the potential of cfDNA to track the evolutionary dynamics and the heterogeneity of MM, possibly anticipating the emergence of therapy resistant residual cells, remains to be confirmed. Aim of this study is to evaluate cfDNA at diagnosis and during follow-up in order to compare this approach with both bone marrow (BM) molecular and whole-body imaging residual disease assessment. Methods A total of 97 patients (pts) were screened at baseline and during follow-up with 18F-FDG PET/CT and NMR, and molecularly assessed by Ultra Low Pass-Whole Genome Sequencing (ULP-WGS). ULP-WGS was used to characterize both the neoplastic PC clone from BM (gDNA) and the cfDNA from peripheral blood. Data were analysed by ichorCNA and Clonality R packages. Results Overall, cfDNA tumour fraction (TF) at diagnosis was significantly lower as compared to gDNA TF [median (M) TF: 3.0%vs.74.4%, respectively]. Nevertheless, high cfDNA TF levels (49/97 pts = 50%, M cfDNA TF: 10%, range : 3.0-40.6%) correlated with high gDNA TF levels (M gDNA TF: 84%, range: 5.9-95.2%). Similarly, a significant correlation was shown between cfDNA TF and the BM plasma cells clone throughout disease progression, highlighting a parallel dynamics of tumour burden from SMM to MM to +3m post-induction (cfDNA TF vs. % CD138+/CD38+ BM cells: 1.5 vs 1.0; 3.0 vs. 2.3; 1.2 vs. 0.01, respectively, p: 0.008). Pts with high cfDNA TF were likely to have more sites of extramedullary disease (EMD) at PET-CT, a higher number of PET lesions and higher tumour metabolic activity, as compared to pts with low TF (EMD 8/49, 17% vs. 2/49, 3.4%; M n. PET lesions: 8 vs. 2; SUVmax: 12.9 vs. 3.9). Similarly, bone involvement as detected by MRI, was more evident in pts with high cfDNA TF (M n. focal lesions: 6 vs. 1). More interestingly, after 3 cycles of induction therapy, imaging and cfDNA TF dynamics were concordant: indeed, in those cases where FDG activity and focal lesions still persisted (4/10=40%: SUVmax 5.8; >2 PET lesions), cfDNA TF as well was still detectable (>1 TF).Finally, the comparison between cfDNA and BM genomic profiles showed an overall concordance of the copy number alterations (CNAs) landscape in most patients (83/97; 86.4%), whereas three patients (10/97; 10,3%) displayed a different genomic profile in cfDNA, as compared to BM. Conclusions although only few cases showed genomic evidence of spatial heterogeneity, in pts with high cfDNA TF, imaging data overall suggested a propensity to a metastatic spread of the disease. Future studies will be addressed to exploit the use of cfDNA in disease monitoring. Acknowledgements AIRC IG2018-22059.