Integrative multi-omics identifies high risk multiple myeloma subgroup associated with significant DNA loss and dysregulated DNA repair and cell cycle pathways.

BACKGROUND: Despite significant therapeutic advances in improving lives of multiple myeloma (MM) patients, it remains mostly incurable, with patients ultimately becoming refractory to therapies. MM is a genetically heterogeneous disease and therapeutic resistance is driven by a complex interplay of disease pathobiology and mechanisms of drug resistance. We applied a multi-omics strategy using tumor-derived gene expression, single nucleotide variant, copy number variant, and structural variant profiles to investigate molecular subgroups in 514 newly diagnosed MM (NDMM) samples and identified 12 molecularly defined MM subgroups (MDMS1-12) with distinct genomic and transcriptomic features. RESULTS: Our integrative approach let us identify NDMM subgroups with transversal profiles to previously described ones, based on single data types, which shows the impact of this approach for disease stratification. One key novel subgroup is our MDMS8, associated with poor clinical outcome [median overall survival, 38 months (global log-rank p-value < 1 × 10-6)], which uniquely presents a broad genomic loss (> 9% of entire genome, t-test p value < 1e-5) driving dysregulation of various transcriptional programs affecting DNA repair and cell cycle/mitotic processes. This subgroup was validated on multiple independent datasets, and a master regulator analyses identified transcription factors controlling MDMS8 transcriptomic profile, including CKS1B and PRKDC among others, which are regulators of the DNA repair and cell cycle pathways. CONCLUSION: Using multi-omics unsupervised clustering we were able to discover a new high-risk multiple myeloma patient segment. This high-risk group presents diverse previously known genetic markers, but also a new characteristic defined by accumulation of genomic loss which seems to drive transcriptional dysregulation of cell cycle, DNA repair and DNA damage. Finally, our work identified various master regulators, including E2F2 and CKS1B as the genes controlling these key biological pathways.

Quantitative Systems Pharmacology Modeling of Avadomide-Induced Neutropenia Enables Virtual Clinical Dose and Schedule Finding Studies.

Avadomide is a cereblon E3 ligase modulator and a potent antitumor and immunomodulatory agent. Avadomide trials are challenged by neutropenia as a major adverse event and a dose-limiting toxicity. Intermittent dosing schedules supported by preclinical data provide a strategy to reduce frequency and severity of neutropenia; however, the identification of optimal dosing schedules remains a clinical challenge. Quantitative systems pharmacology (QSP) modeling offers opportunities for virtual screening of efficacy and toxicity levels produced by alternative dose and schedule regimens, thereby supporting decision-making in translational drug development. We formulated a QSP model to capture the mechanism of avadomide-induced neutropenia, which involves cereblon-mediated degradation of transcription factor Ikaros, resulting in a maturation block of the neutrophil lineage. The neutropenia model was integrated with avadomide-specific pharmacokinetic and pharmacodynamic models to capture dose-dependent effects. Additionally, we generated a disease-specific virtual patient population to represent the variability in patient characteristics and response to treatment observed for a diffuse large B-cell lymphoma trial cohort. Model utility was demonstrated by simulating the avadomide effect in the virtual population for various dosing schedules and determining the incidence of high-grade neutropenia, its duration, and the probability of recovery to low-grade neutropenia.

Genetic program activity delineates risk, relapse, and therapy responsiveness in multiple myeloma.

Despite recent advancements in the treatment of multiple myeloma (MM), nearly all patients ultimately relapse and many become refractory to multiple lines of therapies. Therefore, we not only need the ability to predict which patients are at high risk for disease progression but also a means to understand the mechanisms underlying their risk. Here, we report a transcriptional regulatory network (TRN) for MM inferred from cross-sectional multi-omics data from 881 patients that predicts how 124 chromosomal abnormalities and somatic mutations causally perturb 392 transcription regulators of 8549 genes to manifest in distinct clinical phenotypes and outcomes. We identified 141 genetic programs whose activity profiles stratify patients into 25 distinct transcriptional states and proved to be more predictive of outcomes than did mutations. The coherence of these programs and accuracy of our network-based risk prediction was validated in two independent datasets. We observed subtype-specific vulnerabilities to interventions with existing drugs and revealed plausible mechanisms for relapse, including the establishment of an immunosuppressive microenvironment. Investigation of the t(4;14) clinical subtype using the TRN revealed that 16% of these patients exhibit an extreme-risk combination of genetic programs (median progression-free survival of 5 months) that create a distinct phenotype with targetable genes and pathways.

Bone marrow microenvironments that contribute to patient outcomes in newly diagnosed multiple myeloma: A cohort study of patients in the Total Therapy clinical trials.

BACKGROUND: The tumor microenvironment (TME) is increasingly appreciated as an important determinant of cancer outcome, including in multiple myeloma (MM). However, most myeloma microenvironment studies have been based on bone marrow (BM) aspirates, which often do not fully reflect the cellular content of BM tissue itself. To address this limitation in myeloma research, we systematically characterized the whole bone marrow (WBM) microenvironment during premalignant, baseline, on treatment, and post-treatment phases. METHODS AND FINDINGS: Between 2004 and 2019, 998 BM samples were taken from 436 patients with newly diagnosed MM (NDMM) at the University of Arkansas for Medical Sciences in Little Rock, Arkansas, United States of America. These patients were 61% male and 39% female, 89% White, 8% Black, and 3% other/refused, with a mean age of 58 years. Using WBM and matched cluster of differentiation (CD)138-selected tumor gene expression to control for tumor burden, we identified a subgroup of patients with an adverse TME associated with 17 fewer months of progression-free survival (PFS) (95% confidence interval [CI] 5-29, 49-69 versus 70-82 months, χ2 p = 0.001) and 15 fewer months of overall survival (OS; 95% CI -1 to 31, 92-120 versus 113-129 months, χ2 p = 0.036). Using immunohistochemistry-validated computational tools that identify distinct cell types from bulk gene expression, we showed that the adverse outcome was correlated with elevated CD8+ T cell and reduced granulocytic cell proportions. This microenvironment develops during the progression of premalignant to malignant disease and becomes less prevalent after therapy, in which it is associated with improved outcomes. In patients with quantified International Staging System (ISS) stage and 70-gene Prognostic Risk Score (GEP-70) scores, taking the microenvironment into consideration would have identified an additional 40 out of 290 patients (14%, premutation p = 0.001) with significantly worse outcomes (PFS, 95% CI 6-36, 49-73 versus 74-90 months) who were not identified by existing clinical (ISS stage III) and tumor (GEP-70) criteria as high risk. The main limitations of this study are that it relies on computationally identified cell types and that patients were treated with thalidomide rather than current therapies. CONCLUSIONS: In this study, we observe that granulocyte signatures in the MM TME contribute to a more accurate prognosis. This implies that future researchers and clinicians treating patients should quantify TME components, in particular monocytes and granulocytes, which are often ignored in microenvironment studies.

Avadomide monotherapy in relapsed/refractory DLBCL: safety, efficacy, and a predictive gene classifier.

Treatment options for relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) are limited, with no standard of care; prognosis is poor, with 4- to 6-month median survival. Avadomide (CC-122) is a cereblon-modulating agent with immunomodulatory and direct antitumor activities. This phase 1 dose-expansion study assessed safety and clinical activity of avadomide monotherapy in patients with de novo R/R DLBCL and transformed lymphoma. Additionally, a novel gene expression classifier, which identifies tumors with a high immune cell infiltration, was shown to enrich for response to avadomide in R/R DLBCL. Ninety-seven patients with R/R DLBCL, including 12 patients with transformed lymphoma, received 3 to 5 mg avadomide administered on continuous or intermittent schedules until unacceptable toxicity, disease progression, or withdrawal. Eighty-two patients (85%) experienced ≥1 grade 3/4 treatment-emergent adverse events (AEs), most commonly neutropenia (51%), infections (24%), anemia (12%), and febrile neutropenia (10%). Discontinuations because of AEs occurred in 10% of patients. Introduction of an intermittent 5/7-day schedule improved tolerability and reduced frequency and severity of neutropenia, febrile neutropenia, and infections. Among 84 patients with de novo R/R DLBCL, overall response rate (ORR) was 29%, including 11% complete response (CR). Responses were cell-of-origin independent. Classifier-positive DLBCL patients (de novo) had an ORR of 44%, median progression-free survival (mPFS) of 6 months, and 16% CR vs an ORR of 19%, mPFS of 1.5 months, and 5% CR in classifier-negative patients (P = .0096). Avadomide is being evaluated in combination with other antilymphoma agents. This trial was registered at www.clinicaltrials.gov as #NCT01421524.

Leveraging gene expression subgroups to classify DLBCL patients and select for clinical benefit from a novel agent.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, commonly described by cell-of-origin (COO) molecular subtypes. We sought to identify novel patient subgroups through an unsupervised analysis of a large public dataset of gene expression profiles from newly diagnosed de novo DLBCL patients, yielding 2 biologically distinct subgroups characterized by differences in the tumor microenvironment. Pathway analysis and immune deconvolution algorithms identified higher B-cell content and a strong proliferative signal in subgroup A and enriched T-cell, macrophage, and immune/inflammatory signals in subgroup B, reflecting similar biology to published DLBCL stratification research. A gene expression classifier, featuring 26 gene expression scores, was derived from the public dataset to discriminate subgroup A (classifier-negative, immune-low) and subgroup B (classifier-positive, immune-high) patients. Subsequent application to an independent series of diagnostic biopsies replicated the subgroups, with immune cell composition confirmed via immunohistochemistry. Avadomide, a CRL4CRBN E3 ubiquitin ligase modulator, demonstrated clinical activity in relapsed/refractory DLBCL patients, independent of COO subtypes. Given the immunomodulatory activity of avadomide and the need for a patient-selection strategy, we applied the gene expression classifier to pretreatment biopsies from relapsed/refractory DLBCL patients receiving avadomide (NCT01421524). Classifier-positive patients exhibited an enrichment in response rate and progression-free survival of 44% and 6.2 months vs 19% and 1.6 months for classifier-negative patients (hazard ratio, 0.49; 95% confidence interval, 0.280-0.86; P = .0096). The classifier was not prognostic for rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone or salvage immunochemotherapy. The classifier described here discriminates DLBCL tumors based on tumor and nontumor composition and has potential utility to enrich for clinical response to immunomodulatory agents, including avadomide.

Large-scale Analyses of Disease Biomarkers and Apremilast Pharmacodynamic Effects.

Finding biomarkers that provide shared link between disease severity, drug-induced pharmacodynamic effects and response status in human trials can provide number of values for patient benefits: elucidating current therapeutic mechanism-of-action, and, back-translating to fast-track development of next-generation therapeutics. Both opportunities are predicated on proactive generation of human molecular profiles that capture longitudinal trajectories before and after pharmacological intervention. Here, we present the largest plasma proteomic biomarker dataset available to-date and the corresponding analyses from placebo-controlled Phase III clinical trials of the phosphodiesterase type 4 inhibitor apremilast in psoriasis (PSOR), psoriatic arthritis (PsA), and ankylosing spondylitis (AS) from 526 subjects overall. Using approximately 150 plasma analytes tracked across three time points, we identified IL-17A and KLK-7 as biomarkers for disease severity and apremilast pharmacodynamic effect in psoriasis patients. Combined decline rate of KLK-7, PEDF, MDC and ANGPTL4 by Week 16 represented biomarkers for the responder subgroup, shedding insights into therapeutic mechanisms. In ankylosing spondylitis patients, IL-6 and LRG-1 were identified as biomarkers with concordance to disease severity. Apremilast-induced LRG-1 increase was consistent with the overall lack of efficacy in ankylosing spondylitis. Taken together, these findings expanded the mechanistic knowledge base of apremilast and provided translational foundations to accelerate future efforts including compound differentiation, combination, and repurposing.

ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells.

Immune cell infiltration of tumors and the tumor microenvironment can be an important component for determining patient outcomes. For example, immune and stromal cell presence inferred by deconvolving patient gene expression data may help identify high risk patients or suggest a course of treatment. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from single cell type purified gene expression data. Many methods from this family have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are difficult to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.

Comparison of RNA-seq and microarray platforms for splice event detection using a cross-platform algorithm.

BACKGROUND: RNA-seq is a reference technology for determining alternative splicing at genome-wide level. Exon arrays remain widely used for the analysis of gene expression, but show poor validation rate with regard to splicing events. Commercial arrays that include probes within exon junctions have been developed in order to overcome this problem. We compare the performance of RNA-seq (Illumina HiSeq) and junction arrays (Affymetrix Human Transcriptome array) for the analysis of transcript splicing events. Three different breast cancer cell lines were treated with CX-4945, a drug that severely affects splicing. To enable a direct comparison of the two platforms, we adapted EventPointer, an algorithm that detects and labels alternative splicing events using junction arrays, to work also on RNA-seq data. Common results and discrepancies between the technologies were validated and/or resolved by over 200 PCR experiments. RESULTS: As might be expected, RNA-seq appears superior in cases where the technologies disagree and is able to discover novel splicing events beyond the limitations of physical probe-sets. We observe a high degree of coherence between the two technologies, however, with correlation of EventPointer results over 0.90. Through decimation, the detection power of the junction arrays is equivalent to RNA-seq with up to 60 million reads. CONCLUSIONS: Our results suggest, therefore, that exon-junction arrays are a viable alternative to RNA-seq for detection of alternative splicing events when focusing on well-described transcriptional regions.

Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation.

Motivation: Protein-protein interactions (PPI) play a crucial role in our understanding of protein function and biological processes. The standardization and recording of experimental findings is increasingly stored in ontologies, with the Gene Ontology (GO) being one of the most successful projects. Several PPI evaluation algorithms have been based on the application of probabilistic frameworks or machine learning algorithms to GO properties. Here, we introduce a new training set design and machine learning based approach that combines dependent heterogeneous protein annotations from the entire ontology to evaluate putative co-complex protein interactions determined by empirical studies. Results: PPI annotations are built combinatorically using corresponding GO terms and InterPro annotation. We use a S.cerevisiae high-confidence complex dataset as a positive training set. A series of classifiers based on Maximum Entropy and support vector machines (SVMs), each with a composite counterpart algorithm, are trained on a series of training sets. These achieve a high performance area under the ROC curve of ≤0.97, outperforming go2ppi-a previously established prediction tool for protein-protein interactions (PPI) based on Gene Ontology (GO) annotations. Availability and implementation: https://github.com/ima23/maxent-ppi. Contact: sbh11@cl.cam.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Corrigendum: Large-scale production of megakaryocytes from human pluripotent stem cells by chemically defined forward programming.

This corrects the article DOI: 10.1038/ncomms11208.

Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics.

Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.

Large-scale production of megakaryocytes from human pluripotent stem cells by chemically defined forward programming.

The production of megakaryocytes (MKs)--the precursors of blood platelets--from human pluripotent stem cells (hPSCs) offers exciting clinical opportunities for transfusion medicine. Here we describe an original approach for the large-scale generation of MKs in chemically defined conditions using a forward programming strategy relying on the concurrent exogenous expression of three transcription factors: GATA1, FLI1 and TAL1. The forward programmed MKs proliferate and differentiate in culture for several months with MK purity over 90% reaching up to 2 × 10(5) mature MKs per input hPSC. Functional platelets are generated throughout the culture allowing the prospective collection of several transfusion units from as few as 1 million starting hPSCs. The high cell purity and yield achieved by MK forward programming, combined with efficient cryopreservation and good manufacturing practice (GMP)-compatible culture, make this approach eminently suitable to both in vitro production of platelets for transfusion and basic research in MK and platelet biology.

Brachyury and SMAD signalling collaboratively orchestrate distinct mesoderm and endoderm gene regulatory networks in differentiating human embryonic stem cells.

The transcription factor brachyury (T, BRA) is one of the first markers of gastrulation and lineage specification in vertebrates. Despite its wide use and importance in stem cell and developmental biology, its functional genomic targets in human cells are largely unknown. Here, we use differentiating human embryonic stem cells to study the role of BRA in activin A-induced endoderm and BMP4-induced mesoderm progenitors. We show that BRA has distinct genome-wide binding landscapes in these two cell populations, and that BRA interacts and collaborates with SMAD1 or SMAD2/3 signalling to regulate the expression of its target genes in a cell-specific manner. Importantly, by manipulating the levels of BRA in cells exposed to different signalling environments, we demonstrate that BRA is essential for mesoderm but not for endoderm formation. Together, our data illuminate the function of BRA in the context of human embryonic development and show that the regulatory role of BRA is context dependent. Our study reinforces the importance of analysing the functions of a transcription factor in different cellular and signalling environments.

Inhibition of phosphodiesterase-4 promotes oligodendrocyte precursor cell differentiation and enhances CNS remyelination.

The increasing effectiveness of new disease-modifying drugs that suppress disease activity in multiple sclerosis has opened up opportunities for regenerative medicines that enhance remyelination and potentially slow disease progression. Although several new targets for therapeutic enhancement of remyelination have emerged, few lend themselves readily to conventional drug development. Here, we used transcription profiling to identify mitogen-activated protein kinase (Mapk) signalling as an important regulator involved in the differentiation of oligodendrocyte progenitor cells (OPCs) into oligodendrocytes. We show in tissue culture that activation of Mapk signalling by elevation of intracellular levels of cyclic adenosine monophosphate (cAMP) using administration of either dibutyryl-cAMP or inhibitors of the cAMP-hydrolysing enzyme phosphodiesterase-4 (Pde4) enhances OPC differentiation. Finally, we demonstrate that systemic delivery of a Pde4 inhibitor leads to enhanced differentiation of OPCs within focal areas of toxin-induced demyelination and a consequent acceleration of remyelination. These data reveal a novel approach to therapeutic enhancement of remyelination amenable to pharmacological intervention and hence with significant potential for translation.

In vivo T-box transcription factor profiling reveals joint regulation of embryonic neuromesodermal bipotency.

The design of effective cell replacement therapies requires detailed knowledge of how embryonic stem cells form primary tissues, such as mesoderm or neurectoderm that later become skeletal muscle or nervous system. Members of the T-box transcription factor family are key in the formation of these primary tissues, but their underlying molecular activities are poorly understood. Here, we define in vivo genome-wide regulatory inputs of the T-box proteins Brachyury, Eomesodermin, and VegT, which together maintain neuromesodermal stem cells and determine their bipotential fates in frog embryos. These T-box proteins are all recruited to the same genomic recognition sites, from where they activate genes involved in stem cell maintenance and mesoderm formation while repressing neurogenic genes. Consequently, their loss causes embryos to form an oversized neural tube with no mesodermal derivatives. This collaboration between T-box family members thus ensures the continuous formation of correctly proportioned neural and mesodermal tissues in vertebrate embryos during axial elongation.

Single-cell gene expression profiling reveals functional heterogeneity of undifferentiated human epidermal cells.

Human epidermal stem cells express high levels of ß1 integrins, delta-like 1 (DLL1) and the EGFR antagonist LRIG1. However, there is cell-to-cell variation in the relative abundance of DLL1 and LRIG1 mRNA transcripts. Single-cell global gene expression profiling showed that undifferentiated cells fell into two clusters delineated by expression of DLL1 and its binding partner syntenin. The DLL1(+) cluster had elevated expression of genes associated with endocytosis, integrin-mediated adhesion and receptor tyrosine kinase signalling. Differentially expressed genes were not independently regulated, as overexpression of DLL1 alone or together with LRIG1 led to the upregulation of other genes in the DLL1(+) cluster. Overexpression of DLL1 and LRIG1 resulted in enhanced extracellular matrix adhesion and increased caveolin-dependent EGFR endocytosis. Further characterisation of CD46, one of the genes upregulated in the DLL1(+) cluster, revealed it to be a novel cell surface marker of human epidermal stem cells. Cells with high endogenous levels of CD46 expressed high levels of ß1 integrin and DLL1 and were highly adhesive and clonogenic. Knockdown of CD46 decreased proliferative potential and ß1 integrin-mediated adhesion. Thus, the previously unknown heterogeneity revealed by our studies results in differences in the interaction of undifferentiated basal keratinocytes with their environment.

The proteome of Toll-like receptor 3-stimulated human immortalized fibroblasts: implications for susceptibility to herpes simplex virus encephalitis.

BACKGROUND: Inborn errors in Toll-like receptor 3 (TLR3)-IFN type I and III pathways have been implicated in susceptibility to herpes simplex virus encephalitis (HSE) in children, but most patients studied do not carry mutations in any of the genes presently associated with HSE susceptibility. Moreover, many patients do not display any TLR3-IFN-related fibroblastic phenotype. OBJECTIVE: To study other signaling pathways downstream of TLR3 and/or other independent pathways that may contribute to HSE susceptibility. METHODS: We used the stable isotope labeling of amino acids in cell culture proteomics methodology to measure changes in the human immortalized fibroblast proteome after TLR3 activation. RESULTS: Cells from healthy controls were compared with cells from a patient with a known genetic etiology of HSE (UNC-93B-/-) and also to cells from an HSE patient with an unknown gene defect. Consistent with known variation in susceptibility of individuals to viral infections, substantial variation in the response level of different healthy controls was observed, but common functional networks could be identified, including upregulation of superoxide dismutase 2. The 2 patients with HSE studied show clear differences in functional response networks when compared with healthy controls and also when compared with each other. CONCLUSIONS: The present study delineates a number of novel proteins, TLR3-related pathways, and cellular phenotypes that may help elucidate the genetic basis of childhood HSE. Furthermore, our results reveal superoxide dismutase 2 as a potential therapeutic target for amelioration of the neurologic sequelae caused by HSE.

Popular computational methods to assess multiprotein complexes derived from label-free affinity purification and mass spectrometry (AP-MS) experiments.

Advances in sensitivity, resolution, mass accuracy, and throughput have considerably increased the number of protein identifications made via mass spectrometry. Despite these advances, state-of-the-art experimental methods for the study of protein-protein interactions yield more candidate interactions than may be expected biologically owing to biases and limitations in the experimental methodology. In silico methods, which distinguish between true and false interactions, have been developed and applied successfully to reduce the number of false positive results yielded by physical interaction assays. Such methods may be grouped according to: (1) the type of data used: methods based on experiment-specific measurements (e.g., spectral counts or identification scores) versus methods that extract knowledge encoded in external annotations (e.g., public interaction and functional categorisation databases); (2) the type of algorithm applied: the statistical description and estimation of physical protein properties versus predictive supervised machine learning or text-mining algorithms; (3) the type of protein relation evaluated: direct (binary) interaction of two proteins in a cocomplex versus probability of any functional relationship between two proteins (e.g., co-occurrence in a pathway, sub cellular compartment); and (4) initial motivation: elucidation of experimental data by evaluation versus prediction of novel protein-protein interaction, to be experimentally validated a posteriori. This work reviews several popular computational scoring methods and software platforms for protein-protein interactions evaluation according to their methodology, comparative strengths and weaknesses, data representation, accessibility, and availability. The scoring methods and platforms described include: CompPASS, SAINT, Decontaminator, MINT, IntAct, STRING, and FunCoup. References to related work are provided throughout in order to provide a concise but thorough introduction to a rapidly growing interdisciplinary field of investigation.