Next generation proteomics with drug sensitivity screening identifies sub-clones informing therapeutic and drug development strategies for multiple myeloma patients

Treatment for a primary multidrug-resistant B-cell acute lymphoblastic leukemia patient carrying a SSBP2-CSF1R fusion gene: a case report

SSBP2-CSF1R is an important biomarker for clinical diagnosis and prognosis of Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL). This case report presents a pediatric Ph-like ALL patient carrying the SSBP2-CSF1R fusion gene. The patient was resistant to most conventional chemotherapy regimens and to dasatinib, an inhibitor that has been reported to have a therapeutic effect on SSBP2-CSF1R fusion Ph-like ALL, as she remained minimal residual disease (MRD) positive (detection by flow cytometry) and SSBP2-CSF1R fusion gene (detection by RT-PCR) positive after five rounds of such regimens. We thus conducted a large-scale in vitro screening to assess the sensitivity of the patient's leukemic cells to anti-cancer drugs. Based on the susceptibility results, we chose to combine cytarabine, homoharringtonine, dexamethasone, fludarabine, vindesine, and epirubicin for treatment. Clinical results showed that after a course of treatment, both MRD and SSBP2-CSF1R fusion gene turned negative, and there was no recurrence during an 18-month follow-up. In conclusion, our study suggests that the SSBP2-CSF1R fusion gene may be an important biomarker of primary drug resistance in Ph-like ALL, and indicate that the combination of cytarabine, homoharringtonine, dexamethasone, fludarabine, vindesine, and epirubicin can achieve optimal therapeutic results in this category of patients.

Using Proteomics Data to Identify Personalized Treatments in Multiple Myeloma: A Machine Learning Approach

This paper describes a machine learning (ML) decision support system to provide a list of chemotherapeutics that individual multiple myeloma (MM) patients are sensitive/resistant to, based on their proteomic profile. The methodology used in this study involved understanding the parameter space and selecting the dominant features (proteomics data), identifying patterns of proteomic profiles and their association to the recommended treatments, and defining the decision support system of personalized treatment as a classification problem. During the data analysis, we compared several ML algorithms, such as linear regression, Random Forest, and support vector machines, to classify patients as sensitive/resistant to therapeutics. A further analysis examined data-balancing techniques that emerged due to the small cohort size. The results suggest that utilizing proteomics data is a promising approach for identifying effective treatment options for patients with MM (reaching on average an accuracy of 81%). Although this pilot study was limited by the small patient cohort (39 patients), which restricted the training and validation of the explored ML solutions to identify complex associations between proteins, it holds great promise for developing personalized anti-MM treatments using ML approaches.

Lysin (K)-specific demethylase 1 inhibition enhances proteasome inhibitor response and overcomes drug resistance in multiple myeloma

BACKGROUND

Multiple myeloma (MM) is an incurable plasma cell malignancy, accounting for approximately 1% of all cancers. Despite recent advances in the treatment of MM, due to the introduction of proteasome inhibitors (PIs) such as bortezomib (BTZ) and carfilzomib (CFZ), relapses and disease progression remain common. Therefore, a major challenge is the development of novel therapeutic approaches to overcome drug resistance, improve patient outcomes, and broaden PIs applicability to other pathologies.

METHODS

We performed genetic and drug screens to identify new synthetic lethal partners to PIs, and validated candidates in PI-sensitive and -resistant MM cells. We also tested best synthetic lethal interactions in other B-cell malignancies, such as mantle cell, Burkitt's and diffuse large B-cell lymphomas. We evaluated the toxicity of combination treatments in normal peripheral blood mononuclear cells (PBMCs) and bone marrow stromal cells (BMSCs). We confirmed the combo treatment' synergistic effects ex vivo in primary CD138+ cells from MM patients, and in different MM xenograft models. We exploited RNA-sequencing and Reverse-Phase Protein Arrays (RPPA) to investigate the molecular mechanisms of the synergy.

RESULTS

We identified lysine (K)-specific demethylase 1 (LSD1) as a top candidate whose inhibition can synergize with CFZ treatment. LSD1 silencing enhanced CFZ sensitivity in both PI-resistant and -sensitive MM cells, resulting in increased tumor cell death. Several LSD1 inhibitors (SP2509, SP2577, and CC-90011) triggered synergistic cytotoxicity in combination with different PIs in MM and other B-cell neoplasms. CFZ/SP2509 treatment exhibited a favorable cytotoxicity profile toward PBMCs and BMSCs. We confirmed the clinical potential of LSD1-proteasome inhibition in primary CD138+ cells of MM patients, and in MM xenograft models, leading to the inhibition of tumor progression. DNA damage response (DDR) and proliferation machinery were the most affected pathways by CFZ/SP2509 combo treatment, responsible for the anti-tumoral effects.

CONCLUSIONS

The present study preclinically demonstrated that LSD1 inhibition could provide a valuable strategy to enhance PI sensitivity and overcome drug resistance in MM patients and that this combination might be exploited for the treatment of other B-cell malignancies, thus extending the therapeutic impact of the project.

The Future of Precision Oncology

Our understanding of the molecular mechanisms underlying cancer development and evolution have evolved rapidly over recent years, and the variation from one patient to another is now widely recognized. Consequently, one-size-fits-all approaches to the treatment of cancer have been superseded by precision medicines that target specific disease characteristics, promising maximum clinical efficacy, minimal safety concerns, and reduced economic burden. While precision oncology has been very successful in the treatment of some tumors with specific characteristics, a large number of patients do not yet have access to precision medicines for their disease. The success of next-generation precision oncology depends on the discovery of new actionable disease characteristics, rapid, accurate, and comprehensive diagnosis of complex phenotypes within each patient, novel clinical trial designs with improved response rates, and worldwide access to novel targeted anticancer therapies for all patients. This review outlines some of the current technological trends, and highlights some of the complex multidisciplinary efforts that are underway to ensure that many more patients with cancer will be able to benefit from precision oncology in the near future.

Proteomic and Metabolomic Analysis of Bone Marrow and Plasma from Patients with Extramedullary Multiple Myeloma Identifies Distinct Protein and Metabolite Signatures

Multiple myeloma (MM) is an incurable haematological malignancy of plasma cells in the bone marrow. In rare cases, an aggressive form of MM called extramedullary multiple myeloma (EMM) develops, where myeloma cells enter the bloodstream and colonise distal organs or soft tissues. This variant is associated with refractoriness to conventional therapies and a short overall survival. The molecular mechanisms associated with EMM are not yet fully understood. Here, we analysed the proteome of bone marrow mononuclear cells and blood plasma from eight patients (one serial sample) with EMM and eight patients without extramedullary spread. The patients with EMM had a significantly reduced overall survival with a median survival of 19 months. Label-free mass spectrometry revealed 225 proteins with a significant differential abundance between bone marrow mononuclear cells (BMNCs) isolated from patients with MM and EMM. This plasma proteomics analysis identified 22 proteins with a significant differential abundance. Three proteins, namely vascular cell adhesion molecule 1 (VCAM1), pigment epithelium derived factor (PEDF), and hepatocyte growth factor activator (HGFA), were verified as the promising markers of EMM, with the combined protein panel showing excellent accuracy in distinguishing EMM patients from MM patients. Metabolomic analysis revealed a distinct metabolite signature in EMM patient plasma compared to MM patient plasma. The results provide much needed insight into the phenotypic profile of EMM and in identifying promising plasma-derived markers of EMM that may inform novel drug development strategies.

Ex vivo drug response heterogeneity reveals personalized therapeutic strategies for patients with multiple myeloma

Multiple myeloma (MM) is a plasma cell malignancy defined by complex genetics and extensive patient heterogeneity. Despite a growing arsenal of approved therapies, MM remains incurable and in need of guidelines to identify effective personalized treatments. Here, we survey the ex vivo drug and immunotherapy sensitivities across 101 bone marrow samples from 70 patients with MM using multiplexed immunofluorescence, automated microscopy and deep-learning-based single-cell phenotyping. Combined with sample-matched genetics, proteotyping and cytokine profiling, we map the molecular regulatory network of drug sensitivity, implicating the DNA repair pathway and EYA3 expression in proteasome inhibitor sensitivity and major histocompatibility complex class II expression in the response to elotuzumab. Globally, ex vivo drug sensitivity associated with bone marrow microenvironmental signatures reflecting treatment stage, clonality and inflammation. Furthermore, ex vivo drug sensitivity significantly stratified clinical treatment responses, including to immunotherapy. Taken together, our study provides molecular and actionable insights into diverse treatment strategies for patients with MM.

S100 Calcium Binding Protein Family Members Associate With Poor Patient Outcome and Response to Proteasome Inhibition in Multiple Myeloma

Despite several new therapeutic options, multiple myeloma (MM) patients experience multiple relapses and inevitably become refractory to treatment. Insights into drug resistance mechanisms may lead to the development of novel treatment strategies. The S100 family is comprised of 21 calcium binding protein members with 17 S100 genes located in the 1q21 region, which is commonly amplified in MM. Dysregulated expression of S100 family members is associated with tumor initiation, progression and inflammation. However, the relationship between the S100 family and MM pathogenesis and drug response is unknown. In this study, the roles of S100 members were systematically studied at the copy number, transcriptional and protein level with patients’ survival and drug response. Copy number analysis revealed a predominant pattern of gains occurring in S100 genes clustering in the 1q21 locus. In general, gains of genes encoding S100 family members associated with worse patient survival. However, S100 gene copy number and S100 gene expression did not necessarily correlate, and high expression of S100A4 associated with poor patient survival. Furthermore, integrated analysis of S100 gene expression and ex vivo drug sensitivity data showed significant negative correlation between expression of S100 family members (S100A8, S100A9, and S100A12) and sensitivity to some drugs used in current MM treatment, including proteasome inhibitors (bortezomib, carfilzomib, and ixazomib) and histone deacetylase inhibitor panobinostat. Combined proteomic and pharmacological data exhibited significant negative association of S100 members (S100A4, S100A8, and S100A9) with proteasome inhibitors and panobinostat. Clinically, the higher expression of S100A4 and S100A10 were significantly linked to shorter progression free survival in patients receiving carfilzomib-based therapy. The results indicate an association and highlight the potential functional importance of S100 members on chromosome 1q21 in the development of MM and resistance to established myeloma drugs, including proteasome inhibitors.