Multiple myeloma is a plasma cell dyscrasia characterized by focal and non-focal bone lesions. Radiomic techniques extract morphological information from computerized tomography images and exploit them for stratification and risk prediction purposes. However, few papers so far have applied radiomics to multiple myeloma. A retrospective study approved by the institutional review board: n = 51 transplanted patients and n = 33 (64%) with focal lesion analyzed via an open-source toolbox that extracted 109 radiomics features. We also applied a dedicated tool for computing 24 features describing the whole skeleton asset. The redundancy reduction was realized via correlation and principal component analysis. Fuzzy clustering (FC) and Hough transform filtering (HTF) allowed for patient stratification, with effectiveness assessed by four skill scores. The highest sensitivity and critical success index (CSI) were obtained representing each patient, with 17 focal features selected via correlation with the 24 features describing the overall skeletal asset. These scores were higher than the ones associated with a standard cytogenetic classification. The Mann–Whitney U-test showed that three among the 17 imaging descriptors passed the null hypothesis. This AI-based interpretation of radiomics features stratified relapsed and non-relapsed MM patients, showing some potentiality for the determination of the prognostic image-based biomarkers in disease follow-up.

During the phase of proliferation needed for hematopoietic reconstitution following transplantation, hematopoietic stem/progenitor cells (HSPC) must express genes involved in stem cell self-renewal. We investigated the expression of genes relevant for self-renewal and expansion of HSPC (operationally defined as CD34+ cells) in steady state and after transplantation. Specifically, we evaluated the expression of ninety-one genes that were analyzed by real-time PCR in CD34+ cells isolated from (i) 12 samples from umbilical cord blood (UCB); (ii) 15 samples from bone marrow healthy donors; (iii) 13 samples from bone marrow after umbilical cord blood transplant (UCBT); and (iv) 29 samples from patients after transplantation with adult hematopoietic cells. The results show that transplanted CD34+ cells from adult cells acquire an asset very different from transplanted CD34+ cells from cord blood. Multivariate machine learning analysis (MMLA) showed that four specific gene signatures can be obtained by comparing the four types of CD34+ cells. In several, but not all cases, transplanted HSPC from UCB overexpress reprogramming genes. However, these remarkable changes do not alter the commitment to hematopoietic lineage. Overall, these results reveal undisclosed aspects of transplantation biology.

Chronic myeloid leukemia is a myeloproliferative neoplasm characterized by the presence of the Philadelphia chromosome that originates from the reciprocal translocation t(9;22)(q34;q11.2) and encodes for the constitutively active tyrosine kinase protein BCR-ABL1 from the Breakpoint Cluster Region (BCR) sequence and the Abelson (ABL1) gene. Despite BCR-ABL1 being one of the most studied oncogenic proteins, some molecular mechanisms remain enigmatic, and several of the proteins, acting either as positive or negative BCR-ABL1 regulators, are still unknown. The Drosophila melanogaster represents a powerful tool for genetic investigations and a promising model to study the BCR-ABL1 signaling pathway. To identify new components involved in BCR-ABL1 transforming activity, we conducted an extensive genetic screening using different Drosophila mutant strains carrying specific small deletions within the chromosomes 2 and 3 and the gmrGal4,UAS-BCR-ABL1 4M/TM3 transgenic Drosophila as the background. From the screening, we identified several putative candidate genes that may be involved either in sustaining chronic myeloid leukemia (CML) or in its progression. We also identified, for the first time, a tight connection between the BCR-ABL1 protein and Rab family members, and this correlation was also validated in CML patients. In conclusion, our data identified many genes that, by interacting with BCR-ABL1, regulate several important biological pathways and could promote disease onset and progression.