Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma.

PURPOSE: Plexiform neurofibromas (PNF) are peripheral nerve sheath tumors that cause significant morbidity in persons with neurofibromatosis type 1 (NF1), yet treatment options remain limited. To identify novel therapeutic targets for PNF, we applied an integrated multi-omic approach to quantitatively profile kinome enrichment in a mouse model that has predicted therapeutic responses in clinical trials for NF1-associated PNF with high fidelity. EXPERIMENTAL DESIGN: Utilizing RNA sequencing combined with chemical proteomic profiling of the functionally enriched kinome using multiplexed inhibitor beads coupled with mass spectrometry, we identified molecular signatures predictive of response to CDK4/6 and RAS/MAPK pathway inhibition in PNF. Informed by these results, we evaluated the efficacy of the CDK4/6 inhibitor, abemaciclib, and the ERK1/2 inhibitor, LY3214996, alone and in combination in reducing PNF tumor burden in Nf1flox/flox;PostnCre mice. RESULTS: Converging signatures of CDK4/6 and RAS/MAPK pathway activation were identified within the transcriptome and kinome that were conserved in both murine and human PNF. We observed robust additivity of the CDK4/6 inhibitor, abemaciclib, in combination with the ERK1/2 inhibitor, LY3214996, in murine and human NF1(Nf1) mutant Schwann cells. Consistent with these findings, the combination of abemaciclib (CDK4/6i) and LY3214996 (ERK1/2i) synergized to suppress molecular signatures of MAPK activation and exhibited enhanced antitumor activity in Nf1flox/flox;PostnCre mice in vivo. CONCLUSIONS: These findings provide rationale for the clinical translation of CDK4/6 inhibitors alone and in combination with therapies targeting the RAS/MAPK pathway for the treatment of PNF and other peripheral nerve sheath tumors in persons with NF1.

Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer.

Introduction: Glucose and glutamine are major carbon and energy sources that promote the rapid proliferation of cancer cells. Metabolic shifts observed on cell lines or mouse models may not reflect the general metabolic shifts in real human cancer tissue. Method: In this study, we conducted a computational characterization of the flux distribution and variations of the central energy metabolism and key branches in a pan-cancer analysis, including the glycolytic pathway, production of lactate, tricarboxylic acid (TCA) cycle, nucleic acid synthesis, glutaminolysis, glutamate, glutamine, and glutathione metabolism, and amino acid synthesis, in 11 cancer subtypes and nine matched adjacent normal tissue types using TCGA transcriptomics data. Result: Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of the TCA cycle, i.e., the Warburg effect, in almost all the analyzed cancer. However, increased lactate production and the second half of the TCA cycle were only seen in certain cancer types. More interestingly, we failed to detect significantly altered glutaminolysis in cancer tissues compared to their adjacent normal tissues. A systems biology model of metabolic shifts through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes; (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls; and (3) the different shifts in tissue-specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression. Discussion: This study strongly suggests the possibility of having a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors.

FLUXestimator: a webserver for predicting metabolic flux and variations using transcriptomics data.

Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies.

Pipeline for characterizing alternative mechanisms (PCAM) based on bi-clustering to study colorectal cancer heterogeneity.

The cells of colorectal cancer (CRC) in their microenvironment experience constant stress, leading to dysregulated activity in the tumor niche. As a result, cancer cells acquire alternative pathways in response to the changing microenvironment, posing significant challenges for the design of effective cancer treatment strategies. While computational studies on high-throughput omics data have advanced our understanding of CRC subtypes, characterizing the heterogeneity of this disease remains remarkably complex. Here, we present a novel computational Pipeline for Characterizing Alternative Mechanisms (PCAM) based on biclustering to gain a more detailed understanding of cancer heterogeneity. Our application of PCAM to large-scale CRC transcriptomics datasets suggests that PCAM can generate a wealth of information leading to new biological understanding and predictive markers of alternative mechanisms. Our key findings include: 1) A comprehensive collection of alternative pathways in CRC, associated with biological and clinical factors. 2) Full annotation of detected alternative mechanisms, including their enrichment in known pathways and associations with various clinical outcomes. 3) A mechanistic relationship between known clinical subtypes and outcomes on a consensus map, visualized by the presence of alternative mechanisms. 4) Several potential novel alternative drug resistance mechanisms for Oxaliplatin, 5-Fluorouracil, and FOLFOX, some of which were validated on independent datasets. We believe that gaining a deeper understanding of alternative mechanisms is a critical step towards characterizing the heterogeneity of CRC. The hypotheses generated by PCAM, along with the comprehensive collection of biologically and clinically associated alternative pathways in CRC, could provide valuable insights into the underlying mechanisms driving cancer progression and drug resistance, which could aid in the development of more effective cancer therapies and guide experimental design towards more targeted and personalized treatment strategies. The computational pipeline of PCAM is available in GitHub (https://github.com/changwn/BC-CRC).

Generalized Matrix Local Low Rank Representation by Random Projection and Submatrix Propagation.

Matrix low rank approximation is an effective method to reduce or eliminate the statistical redundancy of its components. Compared with the traditional global low rank methods such as singular value decomposition (SVD), local low rank approximation methods are more advantageous to uncover interpretable data structures when clear duality exists between the rows and columns of the matrix. Local low rank approximation is equivalent to low rank submatrix detection. Unfortunately, existing local low rank approximation methods can detect only submatrices of specific mean structure, which may miss a substantial amount of true and interesting patterns. In this work, we develop a novel matrix computational framework called RPSP (Random Probing based submatrix Propagation) that provides an effective solution for the general matrix local low rank representation problem. RPSP detects local low rank patterns that grow from small submatrices of low rank property, which are determined by a random projection approach. RPSP is supported by theories of random projection. Experiments on synthetic data demonstrate that RPSP outperforms all state-of-the-art methods, with the capacity to robustly and correctly identify the low rank matrices when the pattern has a similar mean as the background, background noise is heteroscedastic and multiple patterns present in the data. On real-world datasets, RPSP also demonstrates its effectiveness in identifying interpretable local low rank matrices.

Bias Aware Probabilistic Boolean Matrix Factorization.

Boolean matrix factorization (BMF) is a combinatorial problem arising from a wide range of applications including recommendation system, collaborative filtering, and dimensionality reduction. Currently, the noise model of existing BMF methods is often assumed to be homoscedastic; however, in real world data scenarios, the deviations of observed data from their true values are almost surely diverse due to stochastic noises, making each data point not equally suitable for fitting a model. In this case, it is not ideal to treat all data points as equally distributed. Motivated by such observations, we introduce a probabilistic BMF model that recognizes the object- and feature-wise bias distribution respectively, called bias aware BMF (BABF). To the best of our knowledge, BABF is the first approach for Boolean decomposition with consideration of the feature-wise and object-wise bias in binary data. We conducted experiments on datasets with different levels of background noise, bias level, and sizes of the signal patterns, to test the effectiveness of our method in various scenarios. We demonstrated that our model outperforms the state-of-the-art factorization methods in both accuracy and efficiency in recovering the original datasets, and the inferred bias level is highly significantly correlated with true existing bias in both simulated and real world datasets.

A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data.

The metabolic heterogeneity and metabolic interplay between cells are known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single-cell metabolomics technology, we are yet to establish systematic understanding of the intra-tissue metabolic heterogeneity and cooperative mechanisms. To mitigate this knowledge gap, we developed a novel computational method, namely, single-cell flux estimation analysis (scFEA), to infer the cell-wise fluxome from single-cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a systematically reconstructed human metabolic map as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network-based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multilayer neural networks to capitulate the nonlinear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq data set with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this data set showed the consistency between predicted flux and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics data sets and identified context- and cell group-specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analyses including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.

Retinal Ganglion Cells With a Glaucoma OPTN(E50K) Mutation Exhibit Neurodegenerative Phenotypes when Derived from Three-Dimensional Retinal Organoids.

Retinal ganglion cells (RGCs) serve as the connection between the eye and the brain, with this connection disrupted in glaucoma. Numerous cellular mechanisms have been associated with glaucomatous neurodegeneration, and useful cellular models of glaucoma allow for the precise analysis of degenerative phenotypes. Human pluripotent stem cells (hPSCs) serve as powerful tools for studying human disease, particularly cellular mechanisms underlying neurodegeneration. Thus, efforts focused upon hPSCs with an E50K mutation in the Optineurin (OPTN) gene, a leading cause of inherited forms of glaucoma. CRISPR/Cas9 gene editing introduced the OPTN(E50K) mutation into existing lines of hPSCs, as well as generating isogenic controls from patient-derived lines. RGCs differentiated from OPTN(E50K) hPSCs exhibited numerous neurodegenerative deficits, including neurite retraction, autophagy dysfunction, apoptosis, and increased excitability. These results demonstrate the utility of OPTN(E50K) RGCs as an in vitro model of neurodegeneration, with the opportunity to develop novel therapeutic approaches for glaucoma.