TiMMing: developing an innovative suite of bioinformatic tools to harmonize and track the origin of copy number alterations in the evolutive history of multiple myeloma

Multiple Myeloma (MM) is a hematologic cancer with heterogeneous and complex genomic landscape, where Copy Number Alterations (CNAs) play a key role in the disease's pathogenesis and prognosis. It is of biological and clinical interest to study the temporal occurrence of early alterations, as they play a disease "driver" function by deregulating key tumor pathways. This study presents an innovative bioinformatic tools suite created for harmonizing and tracing the origin of CNAs throughout the evolutionary history of MM. To this aim, large cohorts of newly-diagnosed MM (NDMM, N=1582) and Smoldering-MM (SMM, N=282) were aggregated. The tools developed in this study enable the harmonization of CNAs as obtained from different genomic platforms in such a way that a high statistical power can be obtained. By doing so, the high numerosity of those cohorts was harnessed for the identification of novel genes characterized as "driver" (NFKB2, NOTCH2, MAX, EVI5 and MYC-ME2-enhancer), and the generation of an innovative timing model, implemented with a statistical method to introduce confidence intervals in the CNAs-calls. By applying this model on both NDMM and SMM cohorts, it was possible to identify specific CNAs (1q(CKS1B)amp, 13q(RB1)del, 11q(CCND1)amp and 14q(MAX)del) and categorize them as "early"/ "driver" events. A high level of precision was guaranteed by the narrow confidence intervals in the timing estimates. These CNAs were proposed as critical MM alterations, which play a foundational role in the evolutionary history of both SMM and NDMM. Finally, a multivariate survival model was able to identify the independent genomic alterations with the greatest effect on patients’ survival, including RB1-del, CKS1B-amp, MYC-amp, NOTCH2-amp and TRAF3-del/mut. In conclusion, the alterations that were identified as both "early-drivers” and correlated with patients’ survival were proposed as biomarkers that, if included in wider survival models, could provide a better disease stratification and an improved prognosis definition.