PhenoComb: a discovery tool to assess complex phenotypes in high-dimensional single-cell datasets.

Motivation: High-dimensional cytometry assays can simultaneously measure dozens of markers, enabling the investigation of complex phenotypes. However, as manual gating relies on previous biological knowledge, few marker combinations are often assessed. This results in complex phenotypes with the potential for biological relevance being overlooked. Here, we present PhenoComb, an R package that allows agnostic exploration of phenotypes by assessing all combinations of markers. PhenoComb uses signal intensity thresholds to assign markers to discrete states (e.g. negative, low, high) and then counts the number of cells per sample from all possible marker combinations in a memory-safe manner. Time and disk space are the only constraints on the number of markers evaluated. PhenoComb also provides several approaches to perform statistical comparisons, evaluate the relevance of phenotypes and assess the independence of identified phenotypes. PhenoComb allows users to guide analysis by adjusting several function arguments, such as identifying parent populations of interest, filtering of low-frequency populations and defining a maximum complexity of phenotypes to evaluate. We have designed PhenoComb to be compatible with a local computer or server-based use. Results: In testing of PhenoComb's performance on synthetic datasets, computation on 16 markers was completed in the scale of minutes and up to 26 markers in hours. We applied PhenoComb to two publicly available datasets: an HIV flow cytometry dataset (12 markers and 421 samples) and the COVIDome CyTOF dataset (40 markers and 99 samples). In the HIV dataset, PhenoComb identified immune phenotypes associated with HIV seroconversion, including those highlighted in the original publication. In the COVID dataset, we identified several immune phenotypes with altered frequencies in infected individuals relative to healthy individuals. Collectively, PhenoComb represents a powerful discovery tool for agnostically assessing high-dimensional single-cell data. Availability and implementation: The PhenoComb R package can be downloaded from https://github.com/SciOmicsLab/PhenoComb. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Phase II clinical and immune correlate study of adjuvant nivolumab plus ipilimumab for high-risk resected melanoma.

BACKGROUND: Adjuvant therapy for high-risk resected melanoma with programmed cell-death 1 blockade results in a median relapse-free survival (RFS) of 5 years. The addition of low dose ipilimumab (IPI) to a regimen of adjuvant nivolumab (NIVO) in CheckMate-915 did not result in increased RFS. A pilot phase II adjuvant study of either standard dose or low dose IPI with NIVO was conducted at two centers to evaluate RFS with correlative biomarker studies. METHODS: Patients with resected stages IIIB/IIIC/IV melanoma received either IPI 3 mg/kg and NIVO 1 mg/kg (cohort 4) or IPI 1 mg/kg and NIVO 3 mg/kg (cohorts 5 and 6) induction therapy every 3 weeks for 12 weeks, followed by maintenance NIVO. In an amalgamated subset of patients across cohorts, peripheral T cells at baseline and on-treatment were assessed by flow cytometry and RNA sequencing for exploratory biomarkers. RESULTS: High rates of grade 3-4 adverse events precluded completion of induction therapy in 50%, 35% and 7% of the patients in cohorts 4, 5 and 6, respectively. At a median of 63.9 months of follow-up, 16/56 patients (29%) relapsed. For all patients, at 5 years, RFS was 71% (95% CI: 60 to 84), and overall survival was 94% (95% CI: 88 to 100). Expansion of CD3+CD4+CD38+CD127-GARP- T cells, an on-treatment increase in CD39 expression in CD8+ T cells, and T-cell expression of phosphorylated signal-transducer-and-activator-of-transcription (STAT)2 and STAT5 were associated with relapse. CONCLUSIONS: Adjuvant IPI/NIVO at the induction doses used resulted in promising relapse-free and overall survival, although with a high rate of grade 3-4 adverse events. Biomarker analyses highlight an association of ectoenzyme-expressing T cells and STAT signaling pathways with relapse, warranting future validation. TRIAL REGISTRATION NUMBER: NCT01176474 and NCT02970981.

A biochemical network modeling of a whole-cell.

All cellular processes can be ultimately understood in terms of respective fundamental biochemical interactions between molecules, which can be modeled as networks. Very often, these molecules are shared by more than one process, therefore interconnecting them. Despite this effect, cellular processes are usually described by separate networks with heterogeneous levels of detail, such as metabolic, protein-protein interaction, and transcription regulation networks. Aiming at obtaining a unified representation of cellular processes, we describe in this work an integrative framework that draws concepts from rule-based modeling. In order to probe the capabilities of the framework, we used an organism-specific database and genomic information to model the whole-cell biochemical network of the Mycoplasma genitalium organism. This modeling accounted for 15 cellular processes and resulted in a single component network, indicating that all processes are somehow interconnected. The topological analysis of the network showed structural consistency with biological networks in the literature. In order to validate the network, we estimated gene essentiality by simulating gene deletions and compared the results with experimental data available in the literature. We could classify 212 genes as essential, being 95% of them consistent with experimental results. Although we adopted a relatively simple organism as a case study, we suggest that the presented framework has the potential for paving the way to more integrated studies of whole organisms leading to a systemic analysis of cells on a broader scale. The modeling of other organisms using this framework could provide useful large-scale models for different fields of research such as bioengineering, network biology, and synthetic biology, and also provide novel tools for medical and industrial applications.

Biological network border detection.

Complex networks have been widely used to model biological systems. The concept of accessibility has been proposed recently as a means to organize the nodes of complex networks as belonging to its border or center. Such an approach paves the way to investigating how the functional and structural properties of nodes vary with their respective position in the networks. In this work, we approach such a problem in a biological context applying border detection to Protein-Protein Interaction networks from four organisms of the Mycoplasma genus. We found evidence that the borderness of proteins bears a relation with their spatial organization and molecular function specificity.

Toward Community Standards and Software for Whole-Cell Modeling.

OBJECTIVE: Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate comprehensive models of complex cells. METHODS: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in the Systems Biology Markup Language. RESULTS: Our analysis revealed several challenges to representing WC models using the current standards. CONCLUSION: We, therefore, propose several new WC modeling standards, software, and databases. SIGNIFICANCE: We anticipate that these new standards and software will enable more comprehensive models.