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.

PLUS: Predicting cancer metastasis potential based on positive and unlabeled learning.

Metastatic cancer accounts for over 90% of all cancer deaths, and evaluations of metastasis potential are vital for minimizing the metastasis-associated mortality and achieving optimal clinical decision-making. Computational assessment of metastasis potential based on large-scale transcriptomic cancer data is challenging because metastasis events are not always clinically detectable. The under-diagnosis of metastasis events results in biased classification labels, and classification tools using biased labels may lead to inaccurate estimations of metastasis potential. This issue is further complicated by the unknown metastasis prevalence at the population level, the small number of confirmed metastasis cases, and the high dimensionality of the candidate molecular features. Our proposed algorithm, called Positive and unlabeled Learning from Unbalanced cases and Sparse structures (PLUS), is the first to use a positive and unlabeled learning framework to account for the under-detection of metastasis events in building a classifier. PLUS is specifically tailored for studying metastasis that deals with the unbalanced instance allocation as well as unknown metastasis prevalence, which are not considered by other methods. PLUS achieves superior performance on synthetic datasets compared with other state-of-the-art methods. Application of PLUS to The Cancer Genome Atlas Pan-Cancer gene expression data generated metastasis potential predictions that show good agreement with the clinical follow-up data, in addition to predictive genes that have been validated by independent single-cell RNA-sequencing datasets.

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.

Supervised clustering of high-dimensional data using regularized mixture modeling.

Identifying relationships between genetic variations and their clinical presentations has been challenged by the heterogeneous causes of a disease. It is imperative to unveil the relationship between the high-dimensional genetic manifestations and the clinical presentations, while taking into account the possible heterogeneity of the study subjects.We proposed a novel supervised clustering algorithm using penalized mixture regression model, called component-wise sparse mixture regression (CSMR), to deal with the challenges in studying the heterogeneous relationships between high-dimensional genetic features and a phenotype. The algorithm was adapted from the classification expectation maximization algorithm, which offers a novel supervised solution to the clustering problem, with substantial improvement on both the computational efficiency and biological interpretability. Experimental evaluation on simulated benchmark datasets demonstrated that the CSMR can accurately identify the subspaces on which subset of features are explanatory to the response variables, and it outperformed the baseline methods. Application of CSMR on a drug sensitivity dataset again demonstrated the superior performance of CSMR over the others, where CSMR is powerful in recapitulating the distinct subgroups hidden in the pool of cell lines with regards to their coping mechanisms to different drugs. CSMR represents a big data analysis tool with the potential to resolve the complexity of translating the clinical representations of the disease to the real causes underpinning it. We believe that it will bring new understanding to the molecular basis of a disease and could be of special relevance in the growing field of personalized medicine.

Atractylenolide I enhances responsiveness to immune checkpoint blockade therapy by activating tumor antigen presentation.

One of the primary mechanisms of tumor cell immune evasion is the loss of antigenicity, which arises due to lack of immunogenic tumor antigens as well as dysregulation of the antigen processing machinery. In a screen for small-molecule compounds from herbal medicine that potentiate T cell-mediated cytotoxicity, we identified atractylenolide I (ATT-I), which substantially promotes tumor antigen presentation of both human and mouse colorectal cancer (CRC) cells and thereby enhances the cytotoxic response of CD8+ T cells. Cellular thermal shift assay (CETSA) with multiplexed quantitative mass spectrometry identified the proteasome 26S subunit non-ATPase 4 (PSMD4), an essential component of the immunoproteasome complex, as a primary target protein of ATT-I. Binding of ATT-I with PSMD4 augments the antigen-processing activity of immunoproteasome, leading to enhanced MHC-I-mediated antigen presentation on cancer cells. In syngeneic mouse CRC models and human patient-derived CRC organoid models, ATT-I treatment promotes the cytotoxicity of CD8+ T cells and thus profoundly enhances the efficacy of immune checkpoint blockade therapy. Collectively, we show here that targeting the function of immunoproteasome with ATT-I promotes tumor antigen presentation and empowers T cell cytotoxicity, thus elevating the tumor response to immunotherapy.

IRIS-FGM: an integrative single-cell RNA-Seq interpretation system for functional gene module analysis.

SUMMARY: Single-cell RNA-Seq (scRNA-Seq) data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of condition-specific functional gene modules (FGM) can help to understand interactive gene networks and complex biological processes in different cell clusters. QUBIC2 is recognized as one of the most efficient and effective biclustering tools for condition-specific FGM identification from scRNA-Seq data. However, its limited availability to a C implementation restricted its application to only a few downstream analysis functionalities. We developed an R package named IRIS-FGM (Integrative scRNA-Seq Interpretation System for Functional Gene Module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can effectively identify condition-specific FGMs, predict cell types/clusters, uncover differentially expressed genes and perform pathway enrichment analysis. It is noteworthy that IRIS-FGM can also take Seurat objects as input, facilitating easy integration with the existing analysis pipeline. AVAILABILITY AND IMPLEMENTATION: IRIS-FGM is implemented in the R environment (as of version 3.6) with the source code freely available at https://github.com/BMEngineeR/IRISFGM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SSMD: a semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data.

Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models carry various genetic and physiological perturbations, making it questionable to assume fixed cell types and cell type marker genes for different data set scenarios. We developed a Semi-Supervised Mouse data Deconvolution (SSMD) method to study the mouse tissue microenvironment. SSMD is featured by (i) a novel nonparametric method to discover data set-specific cell type signature genes; (ii) a community detection approach for fixing cell types and their marker genes; (iii) a constrained matrix decomposition method to solve cell type relative proportions that is robust to diverse experimental platforms. In summary, SSMD addressed several key challenges in the deconvolution of mouse tissue data, including: (i) varied cell types and marker genes caused by highly divergent genotypic and phenotypic conditions of mouse experiment; (ii) diverse experimental platforms of mouse transcriptomics data; (iii) small sample size and limited training data source and (iv) capable to estimate the proportion of 35 cell types in blood, inflammatory, central nervous or hematopoietic systems. In silico and experimental validation of SSMD demonstrated its high sensitivity and accuracy in identifying (sub) cell types and predicting cell proportions comparing with state-of-the-arts methods. A user-friendly R package and a web server of SSMD are released via https://github.com/xiaoyulu95/SSMD.

MAL2 drives immune evasion in breast cancer by suppressing tumor antigen presentation.

Immune evasion is a pivotal event in tumor progression. To eliminate human cancer cells, current immune checkpoint therapy is set to boost CD8+ T cell-mediated cytotoxicity. However, this action is eventually dependent on the efficient recognition of tumor-specific antigens via T cell receptors. One primary mechanism by which tumor cells evade immune surveillance is to downregulate their antigen presentation. Little progress has been made toward harnessing potential therapeutic targets for enhancing antigen presentation on the tumor cell. Here, we identified MAL2 as a key player that determines the turnover of the antigen-loaded MHC-I complex and reduces the antigen presentation on tumor cells. MAL2 promotes the endocytosis of tumor antigens via direct interaction with the MHC-I complex and endosome-associated RAB proteins. In preclinical models, depletion of MAL2 in breast tumor cells profoundly enhanced the cytotoxicity of tumor-infiltrating CD8+ T cells and suppressed breast tumor growth, suggesting that MAL2 is a potential therapeutic target for breast cancer immunotherapy.

M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data.

BACKGROUND: Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. RESULTS: We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. CONCLUSION: A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S.

SHP2 inhibition reduces leukemogenesis in models of combined genetic and epigenetic mutations.

In patients with acute myeloid leukemia (AML), 10% to 30% with the normal karyotype express mutations in regulators of DNA methylation, such as TET2 or DNMT3A, in conjunction with activating mutation in the receptor tyrosine kinase FLT3. These patients have a poor prognosis because they do not respond well to established therapies. Here, utilizing mouse models of AML that recapitulate cardinal features of the human disease and bear a combination of loss-of-function mutations in either Tet2 or Dnmt3a along with expression of Flt3ITD, we show that inhibition of the protein tyrosine phosphatase SHP2, which is essential for cytokine receptor signaling (including FLT3), by the small molecule allosteric inhibitor SHP099 impairs growth and induces differentiation of leukemic cells without impacting normal hematopoietic cells. We also show that SHP099 normalizes the gene expression program associated with increased cell proliferation and self-renewal in leukemic cells by downregulating the Myc signature. Our results provide a new and more effective target for treating a subset of patients with AML who bear a combination of genetic and epigenetic mutations.

LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data.

A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene expression states regulated by different transcriptional regulatory inputs across individual cells, which is further complicated by largely observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model, from the kinetic relationships of the transcriptional regulatory inputs, mRNA metabolism and abundance in single cells. LTMG infers the expression multi-modalities across single cells, meanwhile, the dropouts and low expressions are treated as left truncated. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of scRNA-seq data, comparing to three other state-of-the-art models. Our biological assumption of the low non-zero expressions, rationality of the multimodality setting, and the capability of LTMG in extracting expression states specific to cell types or functions, are validated on independent experimental data sets. A differential gene expression test and a co-regulation module identification method are further developed. We experimentally validated that our differential expression test has higher sensitivity and specificity, compared with other five popular methods. The co-regulation analysis is capable of retrieving gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

Mammalian Near-Infrared Image Vision through Injectable and Self-Powered Retinal Nanoantennae.

Mammals cannot see light over 700 nm in wavelength. This limitation is due to the physical thermodynamic properties of the photon-detecting opsins. However, the detection of naturally invisible near-infrared (NIR) light is a desirable ability. To break this limitation, we developed ocular injectable photoreceptor-binding upconversion nanoparticles (pbUCNPs). These nanoparticles anchored on retinal photoreceptors as miniature NIR light transducers to create NIR light image vision with negligible side effects. Based on single-photoreceptor recordings, electroretinograms, cortical recordings, and visual behavioral tests, we demonstrated that mice with these nanoantennae could not only perceive NIR light, but also see NIR light patterns. Excitingly, the injected mice were also able to differentiate sophisticated NIR shape patterns. Moreover, the NIR light pattern vision was ambient-daylight compatible and existed in parallel with native daylight vision. This new method will provide unmatched opportunities for a wide variety of emerging bio-integrated nanodevice designs and applications. VIDEO ABSTRACT.

Anaconda: AN automated pipeline for somatic COpy Number variation Detection and Annotation from tumor exome sequencing data.

BACKGROUND: Copy number variations (CNVs) are the main genetic structural variations in cancer genome. Detecting CNVs in genetic exome region is efficient and cost-effective in identifying cancer associated genes. Many tools had been developed accordingly and yet these tools lack of reliability because of high false negative rate, which is intrinsically caused by genome exonic bias. RESULTS: To provide an alternative option, here, we report Anaconda, a comprehensive pipeline that allows flexible integration of multiple CNV-calling methods and systematic annotation of CNVs in analyzing WES data. Just by one command, Anaconda can generate CNV detection result by up to four CNV detecting tools. Associated with comprehensive annotation analysis of genes involved in shared CNV regions, Anaconda is able to deliver a more reliable and useful report in assistance with CNV-associate cancer researches. CONCLUSION: Anaconda package and manual can be freely accessed at http://mcg.ustc.edu.cn/bsc/ANACONDA/ .

CPSS 2.0: a computational platform update for the analysis of small RNA sequencing data.

SUMMARY: Next-generation sequencing has been widely applied to understand the complexity of non-coding RNAs (ncRNAs) in the last decades. Here, we present CPSS 2.0, an updated version of CPSS 1.0 for small RNA sequencing data analysis, with the following improvements: (i) a substantial increase of supported species from 10 to 48; (ii) improved strategies applied to detect ncRNAs; (iii) more ncRNAs can be detected and profiled, such as lncRNA and circRNA; (iv) identification of differentially expressed ncRNAs among multiple samples; (v) enhanced visualization interface containing graphs and charts in detailed analysis results. The new version of CPSS is an efficient bioinformatics tool for users in non-coding RNA research. AVAILABILITY AND IMPLEMENTATION: CPSS 2.0 is implemented in PHP + Perl + R and can be freely accessed at http://114.214.166.79/cpss2.0/. CONTACT: zyuanwei@ustc.edu.cn or qshi@ustc.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.