Drug resistance in multiple myeloma: Soldiers and weapons in the bone marrow niche

Multiple myeloma (MM) is still an incurable disease, despite considerable improvements in treatment strategies, as resistance to most currently available agents is not uncommon. In this study, data on drug resistance in MM were analyzed and led to the following conclusions: resistance occurs via intrinsic and extrinsic mechanisms, including intraclonal heterogeneity, drug efflux pumps, alterations of drug targets, the inhibition of apoptosis, increased DNA repair and interactions with the bone marrow (BM) microenvironment, cell adhesion, and the release of soluble factors. Since MM involves the BM, interactions in the MM-BM microenvironment were examined as well, with a focus on the cross-talk between BM stromal cells (BMSCs), adipocytes, osteoclasts, osteoblasts, endothelial cells, and immune cells. Given the complex mechanisms that drive MM, next-generation treatment strategies that avoid drug resistance must target both the neoplastic clone and its non-malignant environment. Possible approaches based on recent evidence include: (i) proteasome and histone deacetylases inhibitors that not only target MM but also act on BMSCs and osteoclasts; (ii) novel peptide drug conjugates that target both the MM malignant clone and angiogenesis to unleash an effective anti-MM immune response. Finally, the role of cancer stem cells in MM is unknown but given their roles in the development of solid and hematological malignancies, cancer relapse, and drug resistance, their identification and description are of paramount importance for MM management

BoBafit: A copy number clustering tool designed to refit and recalibrate the baseline region of tumors’ profiles

Human cancer arises from a population of cells that have acquired a wide range of genetic alterations, most of which are targets of therapeutic treatments or are used as prognostic factors for patient's risk stratification. Among these, copy number alterations (CNAs) are quite frequent. Currently, several molecular biology technologies, such as microarrays, NGS and single-cell approaches are used to define the genomic profile of tumor samples. Output data need to be analyzed with bioinformatic approaches and particularly by employing computational algorithms. Molecular biology tools estimate the baseline region by comparing either the mean probe signals, or the number of reads to the reference genome. However, when tumors display complex karyotypes, this type of approach could fail the baseline region estimation and consequently cause errors in the CNAs call. To overcome this issue, we designed an R-package, BoBafit, able to check and, eventually, to adjust the baseline region, according to both the tumor-specific alterations’ context and the sample-specific clustered genomic lesions. Several databases have been chosen to set up and validate the designed package, thus demonstrating the potential of BoBafit to adjust copy number (CN) data from different tumors and analysis techniques. Relevantly, the analysis highlighted that up to 25% of samples need a baseline region adjustment and a redefinition of CNAs calls, thus causing a change in the prognostic risk classification of the patients. We support the implementation of BoBafit within CN analysis bioinformatics pipelines to ensure a correct patient's stratification in risk categories, regardless of the tumor type

Role of serum-free light chain assay for defining response and progression in immunoglobulin secretory multiple myeloma

The International Myeloma Working Group (IMWG) guidelines recommend using electrophoresis and immunofixation to define response and progressive disease (PD) in immunoglobulin (Ig) secretory multiple myeloma (Ig-MM), whereas the role of serum-free light chain (sFLC) is controversial. We retrospectively analyzed the value of adding sFLC assays in the definition of response and PD according to IMWG criteria in 339 Ig-MM patients treated with a first-line novel agent-based therapy (median follow-up 54 months). sFLC PD was defined according to conventional criteria plus increased sFLC levels, or sFLC escape (sFLCe); progression/sFLCe-free survival (ePFS) was the time from the start of treatment to the date of first PD or sFLCe, or death; overall survival after PD/sFLCe (OS after Pe) was the time from first PD or sFLCe to the date of death. 148 (44%) patients achieved a complete response and 198 (60%) a normal sFLC ratio (sFLCR). sFLCR normalization was an independent prognostic factor for extended PFS (HR = 0.46, p = 0.001) and OS (HR = 0.47, p = 0.006) by multivariable analysis. 175 (52%) patients experienced PD according to the IMWG criteria, whereas 180 (53%) experienced PD or sFLCe. Overall, a sFLCe was observed in 31 (9%) patients. Median PFS and ePFS were both equal to 36 (95% CI = 32–42, and 32–40, respectively) months. sFLC PD adversely affected the OS after Pe compared to PD with increasing monoclonal Ig only (HR = 0.52, p = 0.012). Our results support the inclusion of the sFLC assay for defining response and PD in Ig-MM

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

Synergism through WEE1 and CHK1 inhibition in acute lymphoblastic leukemia

Introduction: Screening for synthetic lethality markers has demonstrated that the inhibition of the cell cycle checkpoint kinases WEE1 together with CHK1 drastically affects stability of the cell cycle and induces cell death in rapidly proliferating cells. Exploiting this finding for a possible therapeutic approach has showed efficacy in various solid and hematologic tumors, though not specifically tested in acute lymphoblastic leukemia. Methods: The efficacy of the combination between WEE1 and CHK1 inhibitors in B and T cell precursor acute lymphoblastic leukemia (B/T-ALL) was evaluated in vitro and ex vivo studies. The efficacy of the therapeutic strategy was tested in terms of cytotoxicity, induction of apoptosis, and changes in cell cycle profile and protein expression using B/T-ALL cell lines. In addition, the efficacy of the drug combination was studied in primary B-ALL blasts using clonogenic assays. Results: This study reports, for the first time, the efficacy of the concomitant inhibition of CHK1/CHK2 and WEE1 in ALL cell lines and primary leukemic B-ALL cells using two selective inhibitors: PF-0047736 (CHK1/CHK2 inhibitor) and AZD-1775 (WEE1 inhibitor). We showed strong synergism in the reduction of cell viability, proliferation and induction of apoptosis. The efficacy of the combination was related to the induction of early S-phase arrest and to the induction of DNA damage, ultimately triggering cell death. We reported evidence that the efficacy of the combination treatment is independent from the activation of the p53-p21 pathway. Moreover, gene expression analysis on B-ALL primary samples showed that Chek1 and Wee1 are significantly co-expressed in samples at diagnosis (Pearson r = 0.5770, p = 0.0001) and relapse (Pearson r= 0.8919; p = 0.0001). Finally, the efficacy of the combination was confirmed by the reduction in clonogenic survival of primary leukemic B-ALL cells. Conclusion: Our findings suggest that the combination of CHK1 and WEE1 inhibitors may be a promising therapeutic strategy to be tested in clinical trials for adult ALL

Integrative analysis of the genomic and transcriptomic landscape of double-refractory multiple myeloma

In multiple myeloma, novel treatments with proteasome inhibitors (PIs) and immunomodulatory agents (IMiDs) have prolonged survival but the disease remains incurable. At relapse, next-generation sequencing has shown occasional mutations of drug targets but has failed to identify unifying features that underlie chemotherapy resistance. We studied 42 patients refractory to both PIs and IMiDs. Whole-exome sequencing was performed in 40 patients, and RNA sequencing (RNA-seq) was performed in 27. We found more mutations than were reported at diagnosis and more subclonal mutations, which implies ongoing evolution of the genome of myeloma cells during treatment. The mutational landscape was different from that described in published studies on samples taken at diagnosis. The TP53 pathway was the most frequently inactivated (in 45% of patients). Conversely, point mutations of genes associated with resistance to IMiDs were rare and were always subclonal. Refractory patients were uniquely characterized by having a mutational signature linked to exposure to alkylating agents, whose role in chemotherapy resistance and disease progression remains to be elucidated. RNA-seq analysis showed that treatment or mutations had no influence on clustering, which was instead influenced by karyotypic events. We describe a cluster with both amp(1q) and del(13) characterized by CCND2 upregulation and also overexpression of MCL1, which represents a novel target for experimental treatments. Overall, high-risk features were found in 65% of patients. However, only amp(1q) predicted survival. Gene mutations of IMiD and PI targets are not a preferred mode of drug resistance in myeloma. Chemotherapy resistance of the bulk tumor population is likely attained through differential, yet converging evolution of subclones that are overall variable from patient to patient and within the same patient