Tissue-specific identification of multi-omics features for pan-cancer drug response prediction

Publisher Copyright: © 2022 The Author(s)Current statistical models for drug response prediction and biomarker identification fall short in leveraging the shared and unique information from various cancer tissues and multi-omics profiles. We developed mix-lasso model that introduces an additional sample group penalty term to capture tissue-specific effects of features on pan-cancer response prediction. The mix-lasso model takes into account both the similarity between drug responses (i.e., multi-task learning), and the heterogeneity between multi-omics data (multi-modal learning). When applied to large-scale pharmacogenomics dataset from Cancer Therapeutics Response Portal, mix-lasso enabled accurate drug response predictions and identification of tissue-specific predictive features in the presence of various degrees of missing data, drug-drug correlations, and high-dimensional and correlated genomic and molecular features that often hinder the use of statistical approaches in drug response modeling. Compared to tree lasso model, mix-lasso identified a smaller number of tissue-specific features, hence making the model more interpretable and stable for drug discovery applications.Peer reviewe

Advanced machine-learning techniques in drug discovery

The popularity of machine learning (ML) across drug discovery continues to grow, yielding impressive results. As their use increases, so do their limitations become apparent. Such limitations include their need for big data, sparsity in data, and their lack of interpretability. It has also become apparent that the techniques are not truly autonomous, requiring retraining even post deployment. In this review, we detail the use of advanced techniques to circumvent these challenges, with examples drawn from drug discovery and allied disciplines. In addition, we present emerging techniques and their potential role in drug discovery. The techniques presented herein are anticipated to expand the applicability of ML in drug discovery

The Virtual Reality applied to the biology understanding: the in virtuo experimentation

International audienceThe advent of the computer and computer science, and in particular virtual reality, offers new experiment possibilities with numerical simulations and introduces a new type of investigation for the complex systems study: the in virtuo experiment. This work lies on the framework of multi-agent systems. We propose a generic model for systems biology based on reification of the interactions, on a concept of organization and on a multi-model approach. By 'reification' we understand that interactions are considered as autonomous agents. The aim has been to combine the systemic paradigm and the virtual reality to provide an application able to collect, simulate, experiment and understand the knowledge owned by different biologists working around an interdisciplinary subject. Here, we have been focused on the urticaria disease understanding. Autonomy is taken as a principle. The method permits to integrate different natures of model in the same application using chaotic asynchronous iterations and C++ library: AReVi. We have modeled biochemical reactions, molecular 3D diffusion, cell organizations and mechanical 3D interactions. It also permits to embed different expert system modeling methods like fuzzy cognitive maps. This work provides a toolbox easily adaptable to new biological studies

Discovery and effects of pharmacological inhibition of the E3 ligase Skp2 by small molecule protein-protein interaction disruptors

Skp2 (S-phase kinase-associated protein 2), one component of the SCF E3 ubiquitin ligase complex, directly interacts with Skp1 and indirectly associates with Cullin1 and Rbx1 to bridge the E2 conjugating enzyme with its protein substrate to execute its E3 ligase activity. Skp2 is an Fbox protein (due to it containing an Fbox domain) and it is the rate-limiting component of the SCF complex. Skp2 targets several cell-cycle regulatory proteins for ubiquitination and degradation; most notable and significant for cancer are the cyclin-dependent kinase inhibitor, p27. Skp2 is an oncogene and studies have shown that over-expression of Skp2 leads to increased degradation of p27 and increased proliferation in several tumor types. Additionally, Skp2 is over-expressed in multiple human cancers. Clearly, Skp2 represents an attractive target for attenuating p27 ubiquitination and subsequent cell cycle progression. However, Skp2 does not have an easily identifiable and druggable “pocket” on which small molecules can bind; it interacts with Skp1 through the Fbox domain and binds to an accessory protein called Cks1 to bind to p27. Despite this hurdle, in this study, two selective small molecule inhibitors of the Skp2 SCF complex were discovered via an in silico screen that disrupt two places: the Skp1/Skp2 interaction site and the p27 binding site via targeting hot-spot residues. The Skp1/Skp2 inhibitor disruption resulted in restoring p27 levels in the nucleus and blocks cancer progression and cancer stem cell traits. Additionally, the inhibitors phenocopy the effects of genetic Skp2 deficiency. Two specific residues on Skp2 were predicted to bind to this Skp1/Skp2 inhibitor: Trp97 and Asp98. When these residues were mutated to alanine, the inhibitor lost its ability to bind to Skp2. To investigate the flexibility and understand the conformational change upon inhibitor binding and dynamics of the SCF complex, molecular dynamics simulations, homology models, and structural analysis was carried out on the complex with and without the inhibitors. These simulations showed that the contributions of the N-terminal tail region of Skp2 does not contribute directly to the binding of these inhibitors; but its conformation is important in the context of the other members of the SCF complex. Further dynamics analysis validated the mutagenesis results, showing that the two Skp2 mutants (Trp97Ala, Asp98Ala) that retained Skp1 binding but blocked inhibitor binding were stable, whereas the mutant that was unable to retain Skp1 binding (Trp127Ala) showed destabilization in the Fbox domain. Finally, active recruitment events after post-translational modifications are shown to be possible by the interaction of phosphorylated Ser256 on Skp2 with Lys104 loop region on Cul1 The model shows that this is due to the significant flexibility in the F-box domain of Skp2, making this interaction very likely. These results show that Skp2 is a promising target on which protein-protein interaction disruptors can be designed, and consideration of the dynamics of protein complexes is required to understand ligand binding

The Role of a Monoclonal Gammopathy of Undetermined Significance Diagnosis in Healthcare Utilization

Background Monoclonal Gammopathy of Undetermined Significance (MGUS) is an understudied precursor of multiple myeloma (MM), the second most prevalent hematologic malignancy in the United States. This dissertation was designed to: (1) Describe the trajectories of serum biomarkers over time in patients with an MGUS diagnosis, (2) Determine if an MGUS diagnosis is associated with changes in healthcare service utilization, and (3) explore the patient- and provider-level drivers of healthcare utilization in patients with MGUS. Methods Data sources include health claims and electronic health records from a community-based population of patients seeking care in central Massachusetts and primary qualitative data collected from providers and patients’ interviews. The analyses included descriptive statistics, group-based trajectory modeling, conditional Poisson regression, and qualitative data analyses. Results (1) Three distinct multi-trajectory groups of creatinine and hemoglobin were identified. (2) The rates of emergency room, hospital, and outpatient visits were higher for patients with MGUS than patients without MGUS. (3) Patients have a basic understanding of MGUS; however, some patients feel anxiety, which may affect other aspects of their lives. Patients primarily see hematologists for follow-up care; other providers have less knowledge about MGUS. Conclusions Biomarker trajectories characterize specific subpopulations of patients with MGUS over time. We found that an MGUS diagnosis is associated with higher healthcare utilization, especially during the months surrounding the diagnosis date. Finally, our study suggests that some patients with MGUS may need psychosocial support services and identifies a gap in knowledge around caring for MGUS patients among primary care providers

Network controllability analysis of three multiple-myeloma patient genetic mutation datasets

Network controllability focuses on the concept of driving the dynamical system associated to a directed network of interactions from an arbitrary initial state to an arbitrary final state, through a well-chosen set of input functions applied in a minimal number of so-called input nodes. In earlier studies we and other groups demonstrated the potential of applying this concept in medicine. A directed network of interactions may be built around the main known drivers of the disease being studied, and then analysed to identify combinations of drug targets controlling survivability-essential genes in the network. This paper takes the next step and focuses on patient data. We demonstrate that comprehensive protein-protein interaction networks can be built around patient genetic data, and that network controllability can be used to identify possible personalised drug combinations. We discuss the algorithmic methods that can be used to construct and analyse these networks.</p

New approaches to the management of adult acute lymphoblastic leukemia

Traditional treatment regimens for adult acute lymphoblastic leukemia, including allogeneic hematopoietic cell transplantation, result in an overall survival of about 40%, a figure hardly comparable with the extraordinary 80-90% cure rate currently reported in children. When translated to the adult setting, modern pediatric-type regimens improve the survival to about 60% in young adults. The addition of tyrosine kinase inhibitors for patients with Philadelphia chromosome positive disease and the measurement of minimal residual disease to guide risk stratification and post-remission approaches has led to further improvements in outcomes. Relapsed disease and treatment toxicity - sparing no patient but representing a major concern especially in the elderly - are the most critical current issues awaiting further therapeutic advancement. Recently, there has been considerable progress in understanding the disease biology, specifically the Philadelphia-like signature as well as other high-risk subgroups. In addition, there are several new agents that will undoubtedly contribute to further improvement in the current outcomes. The most promising agents are new the monoclonal antibodies, immunomodulators, and chimeric antigen receptor T cells and, to a lesser extent, several new drugs targeting key molecular pathways involved in leukemic cell growth and proliferation. This review examines the evidence supporting the increasing role of the new therapeutic tools and treatment options in different disease subgroups, including frontline and relapsed/refractory disease. It is now possible to define the best individual approach based on to the emerging concepts of precision medicine

Preclinical Imaging of Multiple Myeloma Therapy Response

Multiple myeloma (MM) is a debilitating hematologic malignancy of terminally differentiated plasma cells in the bone marrow (BM). Advances in therapeutic regimens and the use of autologous stem cell transplantation have significantly improved survival rates and quality of life in patients. However, the disease remains incurable, with shorter, successive remission cycles following relapse. To reduce systemic, off-target toxicity and improve quality of life, there is a need for improved stratification of responding patients. Identification of specific, noninvasive, imaging biomarkers that correlate to therapeutic efficacy is an attractive strategy for stratifying responding patients, since the use of positron emission tomography (PET), computed tomography (CT), and magnetic resonance imaging (MRI) is clinically established. Here, we have developed a strategy for imaging MM disease pathogenesis and response to clinically relevant therapeutics by studying the bidirectional interactions between the BM microenvironment and myeloma cells at the cellular, environmental, and anatomical levels. Specifically, we have validated imaging markers that identify BM and myeloma-specific behaviors through three specific aims: The first aim validated the use of the phenylalanine analog 18F-FDOPA for monitoring the uptake and efficacy of the DNA alkylating agent melphalan, which is used extensively in elderly, non-transplant eligible patients and in relapsed, refractory disease. 18F-FDOPA uptake was significantly reduced in melphalan-treated mice with orthotopic myeloma tumors, and was concordant with the established 18F-FDG-PET imaging. Immunohistochemistry was used to validate 18F-FDOPA uptake results. Importantly, expression of LAT1, which is known to mediate 18F-FDOPA and melphalan uptake, was visibly increased, although this may be a result of increased tumor vascularity. Our results suggest that 18F-FDOPA-PET can provide complementary imaging to 18F-FDG-PET for monitoring response to melphalan therapy and overall LAT1 expression in MM. The second aim assessed the specificity and sensitivity of the peptidomimetic near-infrared fluorophore LLP2A-Cy5 for imaging the expression of the activated conformation of the VLA-4 integrin on the surface of myeloma cells. LLP2A-Cy5 imaging was also used to study response to treatment with the proteasome inhibitor bortezomib, which forms the backbone of several front-line MM therapy strategies. Uptake of LLP2A-Cy5 was significantly reduced in bortezomib-treated mice bearing intramedullary tumors, indicating a reduction in the expression of activated VLA-4. These observations are concordant with the known downregulation of adhesion-mediated drug resistance and VLA-4 by bortezomib. Our results indicate the viability of using LLP2A-Cy5 near-infrared imaging for sensitive, longitudinal assessment of VLA-4 expression for monitoring bortezomib treatment response. Finally, the third aim validated the use of preclinical, multi-parametric MRI for studying changes in the BM in a diffuse infiltrative intramedullary tumor model. Longitudinal imaging of the BM in the femur and tibia demonstrated significant regional differences in T1-weighted contrast uptake and parametric T2 that correlated to changes in viable tumor burden following treatment with bortezomib. Hematoxylin and eosin staining (H&E) was used to validate the MRI observations. H&E showed complete diffuse infiltration of the BM in untreated animals, while bortezomib therapy caused the concentration of tumor burden near the epiphyseal plate of the distal femur and proximal tibia. These observations, in combination with MRI results, establish the use of preclinical MRI for studying effects of disease progression and therapy response on the BM in a longitudinal, noninvasive manner. In summary, these studies established a combination of qualitative observations and quantitative results in PET, optical, and MRI based strategies. Thus, this project has integrated a structured, multi-modal approach for assessing changes in tumor burden and monitoring therapy response at varying granular levels within the myeloma/BM interaction spectrum. Future studies would adapt this approach into different cell lines and tumor models

Investigating the pathological mechanism of neuropathy in POEMS syndrome

POEMS syndrome (Polyneuropathy, Organomegaly, Endocrinopathy, Monoclonal gammopathy, Skin disorder) is a rare disease characterised by an inflammatory polyneuropathy an a monoclonal plasma cell dyscrasia. POEMS syndrome causes some of the most significant disability and mortality of any inflammatory neuropathy. The pathophysiology is unknown but recognised to be cytokine mediated, notably driven by vascular endothelial growth factor, however little is known about the other mediators at play. This thesis collates clinical data from the largest POEMS cohorti in Europe in order to study the characteristic disease features, optimise therapies and identify factors that influence outcome. Utilising our POEMS sample biobank, we carry out highly sensitive immunoassays to study the cytokines released in POEMS syndrome, and whether they correlate with disease activity. We go on to study the proteome of POEMS syndrome through mass spectrometry, to uncover the biological pathway involved and identify a number of novel, potentially pathogenic molecules. Fluid biomarkers of neuropathy in POEMS syndrome and related neuropathies are additionally explored. The development and optimisation of a homebrew immunoassay for peripherin, a peripheral nerve specific biomarker is detailed. The potential clinical utility of this biomarker is compared against that of serum neurofilament light. Finally, we attempt to model the neuropathogenesis of POEMS neuropathy in vitro using a novel human induced pleuripotent stem cell derived neuronal culture system