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Results for “Cancer Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 235 records · Page 13Linked to original sources

bioRxiv2018

Model-based analysis of positive selection significantly expands the list of cancer driver genes, including RNA methyltransferases

Identifying driver genes is a central problem in cancer biology, and many methods have been developed to identify driver genes from somatic mutation data. However, existing methods either lack explicit statistical models, or rely on very simple models that do not capture complex features in somatic mutations of driver genes. Here, we present driverMAPS (Model-based Analysis of Positive Selection), a more comprehensive model-based approach to driver gene identification. This new method explicitly models, at the single-base level, the effects of positive selection in cancer driver genes as well as highly heterogeneous background mutational process. Its selection model captures elevated mutation rates in functionally important sites using multiple external annotations, as well as spatial clustering of mutations. Its background mutation model accounts for both known covariates and unexplained local variation. Simulations under realistic evolutionary models demonstrate that driverMAPS greatly improves the power of driver gene detection over state-of-the-art approaches. Applying driverMAPS to TCGA data across 20 tumor types identified 159 new potential driver genes. Cross-referencing this list with data from external sources strongly supports these findings. The novel genes include the mRNA methytransferases METTL3-METTL14, and we experimentally validated METTL3 as a potential tumor suppressor gene in bladder cancer. Our results thus provide strong support to the emerging hypothesis that mRNA modification is an important biological process underlying tumorigenesis.

genomics↗

bioRxiv2020

A single N6-methyladenosine site in lncRNA HOTAIR regulates its function in breast cancer cells

N6-methyladenosine (m6A) modification of RNA plays important roles in normal and cancer biology, but knowledge of its function on long noncoding RNAs (lncRNAs) remains limited. Here, we investigate whether m6A regulates the function of the human HOTAIR lncRNA, which contributes to multiple pro-tumor phenotypes in triple-negative breast cancer (TNBC) cells. We identify at least 8 individual m6A sites within HOTAIR, with a single site (A783) consistently methylated. Mutation of A783 impairs cellular proliferation and invasion in HOTAIR-overexpressing TNBC cells. m6A at A783 regulates HOTAIRs ability to localize to chromatin and induce gene pathways that affect tumor progression. In contrast, A783U mutant HOTAIR demonstrates loss-of-function and antimorph behaviors by impairing gene expression changes induced by WT HOTAIR and, in some cases, inducing opposite changes in gene expression. HOTAIR interacts with nuclear m6A reader YTHDC1 and high HOTAIR is significantly associated with shorter overall patient survival, particularly in the context of high YTHDC1. At the molecular level, YTHDC1-HOTAIR interactions are required for chromatin localization and regulation of gene repression. Our work demonstrates how modification of one base in a lncRNA can elicit a distinct gene regulation mechanism and drive disease-associated phenotypic changes such as proliferation and invasion.

molecular biology↗

bioRxiv2021

Ab initio modelling of an essential mammalian protein: Transcription Termination Factor 1 (TTF1)

Transcription Termination Factor 1 (TTF1) is an essential mammalian protein that regulates cellular transcription, replication fork arrest, DNA damage repair, chromatin remodelling etc. TTF1 interacts with numerous cellular proteins to regulate various cellular phenomena, and plays a crucial role in maintaining normal cellular physiology, dysregulation of which has been reported towards cancerous transformation of the cells. However, despite its key role in cellular physiology, the complete structure of human TTF1 has not been elucidated to date, either experimentally or computationally. Hence, understanding the structure of human TTF1 becomes highly important for studying its functions and interactions with other cellular factors. Therefore, the aim of this study was to construct the complete structure of human TTF1 protein, using molecular modelling approaches. Owing to the lack of suitable homologues in the PDB, the complete structure of human TTF1 was constructed using ab initio modelling. The structural stability was determined using molecular dynamics (MD) simulations in explicit solvent, and trajectory analyses. The representative structure of human TTF1 was obtained by trajectory clustering, and the central residues were determined by centrality analyses of the residue interaction network of TTF1. Two residue clusters, in the oligomerisation domain and C-terminal domain, were determined to be central to the structural stability of human TTF1. To the best of our knowledge, this study is the first to report the complete structure of human TTF1, and the results obtained herein will provide structural insights for future research in cancer biology and related studies. Author SummaryThe transcription termination factor 1 (TTF1) is an essential multifunctional mammalian protein which plays important role in regulating important cellular process like transcription, replication, DNA damage repair, chromatin remodelling etc. and its dysregulation leads to various cancers. Despite its being such an important factor, the complete structure of human TTF1 has not been determined to date, either using experimental techniques or computationally. Therefore, the aim of this study was to construct the complete structure of human TTF1 using computational modelling. In this study the complete structure of human TTF1 was constructed by ab initio modelling using iTasser. The stability of this model was determined by 200 ns molecular dynamics (MD) simulations. The representative conformation of human TTF1 was further determined by clustering the simulation trajectory and the residues that are central to the stability of this structure were identified. The results demonstrate the presence of two residue clusters in human TTF1, one in the oligomerisation domain and other in the C-terminal domain, which were found to be crucial for the structural stability of this protein. Hence, the results of this study will aid future studies in this field towards engineering this important protein for further biochemistry and cell biology research.

bioinformatics↗

bioRxiv2021

A causality-inspired feature selection method for cancer imbalanced high-dimensional data

It is significant but challenging to explore a subset of robust biomarkers to distinguish cancer from normal samples on high-dimensional imbalanced cancer biological omics data. Although many feature selection methods addressing high dimensionality and class imbalance have been proposed, they rarely pay attention to the fact that most classes will dominate the final decision-making when the dataset is imbalanced, leading to instability when it expands downstream tasks. Because of causality invariance, causal relationship inference is considered an effective way to improve machine learning performance and stability. This paper proposes a Causality-inspired Least Angle Nonlinear Distributed (CLAND) feature selection method, consisting of two branches with a class-wised branch and a sample-wised branch representing two deconfounder strategies, respectively. We compared the performance of CLAND with other advanced feature selection methods in transcriptional data of six cancer types with different imbalance ratios. The genes selected by CLAND have superior accuracy, stability, and generalization in the downstream classification tasks, indicating potential causality for identifying cancer samples. Furthermore, these genes have also been demonstrated to play an essential role in cancer initiation and progression through reviewing the literature. Author SummarySelecting trustworthy biomarkers from high-dimensional data is an important step to help researchers and clinicians understand which genes play key roles in cancer development and progression. A large number of machine learning-based feature selection algorithms have been generated in recent years for biomarker discovery. However, these methods usually show unstable results in the face of class-imbalanced biological data, making it seem unreliable for researchers. Here we introduce the causal theory with the property of causal invariance to aid in the design of feature selection algorithms, analyze how imbalanced distributions affect feature selection methods, and propose a novel causality-based feature selection method. The method with bilateral structure adjusts the data distribution from both class-wise and sample-wise to eliminate the effect of imbalance on the results. Additionally, CLAND can simultaneously address the nonlinearity and high-dimensionality of cancer data, which broaden its application scope. We conducted extensive experiments on six real imbalance cancer datasets and obtained efficient and stable results, while the obtained biomarker has significant biological significance.

bioinformatics↗

bioRxiv2019

A Bayesian Nonparametric Approach to Discover Clinico-Genetic Associations across Cancer Types

MotivationPersonalized medicine aims at combining genetic, clinical, and environmental data to improve medical diagnosis and disease treatment, tailored to each patient. This paper presents a Bayesian nonparametric (BNP) approach to identify genetic associations with clinical/environmental features in cancer. We propose an unsupervised approach to generate data-driven hypotheses and bring potentially novel insights about cancer biology. Our model combines somatic mutation information at gene-level with features extracted from the Electronic Health Record. We propose a hierarchical approach, the hierarchical Poisson factor analysis (H-PFA) model, to share information across patients having different types of cancer. To discover statistically significant associations, we combine Bayesian modeling with bootstrapping techniques and correct for multiple hypothesis testing.\n\nResultsUsing our approach, we empirically demonstrate that we can recover well-known associations in cancer literature. We compare the results of H-PFA with two other classical methods in the field: case-control (CC) setups, and linear mixed models (LMMs).

bioinformatics↗

bioRxiv2020

Building a virtual summer research experience in cancer for high school and early undergraduate students: lessons from the COVID-19 pandemic

BackgroundThe COVID-19 pandemic posed a unique challenge for summer research programs in 2020, particularly for programs aimed at hands-on experience for younger trainees. The Indiana University Melvin and Bren Simon Comprehensive Cancer Center supports two pipeline programs, which traditionally immerse high school juniors, seniors, and early undergraduate students from underrepresented populations in science in hands-on projects in cancer biology labs. However, due to social distancing policies during the pandemic and reduction of research operations, these students were not physically allowed on campus. Thus, the authors set out to strategically pivot to a wholly virtual curriculum and evaluate the Virtual Summer Research Experience in Cancer outcomes. MethodsThe virtual program included four components: 1. a core science and professional development curriculum led by high school teachers and senior undergraduates; 2. faculty-delivered didactic sessions on cancer science; 3. mentored, virtual research projects with research faculty; and 4. online networking events to encourage vertical mentoring. Outcomes data were measured using an 11-item Research Preparation scale, daily electronic feedback, and structured evaluation and feedback via Zoom weekly. ResultsOutcome data suggested high self-reported satisfaction with the virtual program. Outcome data also revealed the importance of coordination between multiple entities for seamless program implementation. This includes the active recruitment and participation of high school teachers and further investment in information technology capabilities of institutions. ConclusionsFindings reveal a path to educate and train high school and early undergraduate students in cancer research when hands-on, in-person training is not feasible. Virtual research experiences are not only useful to engage students during public health crises but can provide an avenue for cancer centers to expand their cancer education footprints to remotely located schools and universities with limited resources to provide such experiences to their students.

scientific communication and education↗

bioRxiv2022

A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

Patient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H&E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of large human H&E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000 PDX and paired human progenitor H&E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions and to predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site. This repository enables PDX-specific, investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues.

bioinformatics↗

bioRxiv2025

Intratumor Heterogeneity Through the Lens of Gene Regulatory Networks

Intratumor heterogeneity (ITH), the presence of heterogeneous genomic and transcriptomic populations in tumor and its implications for the cancer therapies has been one of the focal points of computational cancer biology in the past years. While ITH is well characterized at the genomic level, the regulatory wiring connecting clonal genotypes to transcriptional states remains largely unmapped. To understand whether the evidence for ITH at the gene regulatory level can be inferred from scRNA-seq data, we analyzed scRNA-seq datasets spanning triple-negative breast cancer, colorectal cancer, glioblastoma, and a longitudinal stage IV breast cancer cohort, inferring clone-specific gene regulatory networks (GRNs) and developing quantitative tools to compare them: a Wasserstein distance-based measure of regulatory divergence, a kth order neighborhood similarity framework distinguishing structural from functional rewiring, bootstrap-based edge confidence, and an integrated regulon classification pipeline. Our analyses demonstrate that tumor clones exhibit reproducibly distinct network architectures, and dominant clones show significantly greater regulatory divergence from normal cells than smaller clones, linking regulatory rewiring to clonal fitness. Treatment-induced remodeling exceeds clonal variation, with rewiring exhibiting structural divergence but functional convergence. Regulon analysis further resolved clone-variable, sample-specific, and broadly conserved programs, some of which containing transcription factors whose target genes independently were associated with patient survival.

bioinformatics↗

bioRxiv2022

Direct estimation of genome mutation rates from pedigrees in free-ranging baleen whales

Current low germline mutation rate () estimates in baleen whales have greatly influenced research ranging from assessments of whaling impacts to evolutionary cancer biology. However, the reported rates were subject to methodological errors and uncertainty. We estimated directly from pedigrees in natural populations of four baleen whale species and the results were similar to primates. The implications of revised values include pre-exploitation population sizes at 14% of previous genetic diversity-based estimates and the conclusion that in itself is insufficient to explain low cancer rates in gigantic mammals (i.e., Petos Paradox). We demonstrate the feasibility of estimating from whole genome pedigree data in natural populations, which has wide-ranging implications for the many ecological and evolutionary inferences that rely on .

genetics↗

bioRxiv2026

A new genome-scale model enables prediction of cancer metabolic dependencies

Cancer cells rewire metabolism to support proliferation. Intriguingly, divergent metabolic choices are made to attain this common goal. Identifying the unique metabolic requirements for a specific cell has profound implications for cancer biology and precision medicine. Genome-scale metabolic models (GEMs) have emerged as powerful tools to systematically characterize, understand, and predict metabolism of cells and tissues. Despite being comprehensive, the current GEMs remain limited in their predictive power. Here, we present a new GEM of human cells, in silico Human Metabolic Essentiality (iHME), that significantly improves the prediction of metabolic dependencies at a reduced computational cost. Wse rationally downsized, curated, and corrected previous models to remove unsupported metabolic redundancies, which led to a slim model containing 4,377 reactions, 3,241 metabolites, and 1,825 genes. When used to reconstruct metabolic networks of 1,103 cancer cell lines, iHME recalled on average 84.6% of experimental essential genes, which is two-fold increase over previous models. Cholesterol biosynthesis was revealed to be the most reliably predicted pathway with alternative dependencies. Finally, we applied the model to reconstruct individualized networks and predict essential gene profiles for 8,384 patient tumor samples. Glucose transporter SLC2A1 (GLUT1) was identified as a context-specific dependency for head and neck cancers and ovarian cancer. Likewise, CDP-diacylglycerol synthase CDS2 was identified for skin cancer. Overall, iHME is a new genome-scale model for prediction of metabolic dependency at higher accuracy and computational efficiency.

systems biology↗

bioRxiv2022

Non-invasive Fluorescence Imaging of Gut Commensal Bacteria in Live Mice

In mammals, gut commensal microbiota interact extensively with the host and the same interactions can be dysregulated in diseased states. The development of methods to monitor gut microbiota in vivo can lead to improved foundational understanding of the biological events underpinning these interactions. The current standard for non-invasive monitoring of gut bacteria entails classification by 16S rRNA sequencing from fecal samples. This method has many advantages but also has serious limitations, especially for monitoring dynamic changes in the gut of live animals. In recent years, several imaging techniques have been widely adopted that afford non-invasive assessment of animal subjects - most notably in cancer biology; however, these technical gains have not translated to the imaging of gut bacterial communities. Herein, we describe a method to non-invasively image commensal bacteria based on the specific metabolic labeling of bacterial cell walls to illuminate the gut bacteria of live mice. This tagging strategy may additionally provide unprecedented insight into cell wall turnover of gut commensals, which has implications for bacterial cellular growth and division, in a live animal.

microbiology↗

bioRxiv2025

STmiR: A Novel XGBoost-Based Framework for Spatially Resolved miRNA Activity Prediction

MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology; however, their spatial dynamics within tumor microenvironments (TME) remain underexplored owing to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity predictions. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving a high predictive accuracy (Spearmans {rho} > 0.8) across four major cancer types (breast, lung, ovarian, and prostate). Applied to 10X Visium ST datasets from nine cancers, STmiR identified six pan-cancer conserved miRNAs (e.g., hsa-miR-21, hsa-let-7a) consistently ranked in the top 40 across malignancies, and uncovered cell-type-specific regulatory networks in fibroblasts, B cells, and malignant cells. A breast cancer case study validated the utility of STmiR by linking miR-205 to androgen receptor (AR) signaling and miR-200b to epithelial-mesenchymal transition (EMT). By enabling the spatial mapping of miRNA activity, STmiR provides a transformative tool to dissect miRNA-mediated regulatory mechanisms in cancer progression and TME remodeling, with implications for biomarker discovery and precision oncology. HighlightsO_LIFirst integration of XGBoost and spatial transcriptomics: STmiR pioneers a machine learning framework to predict miRNA activity in spatially heterogeneous tissues, overcoming limitations of linear correlation-based methods. C_LIO_LIHigh accuracy and generalizability: Demonstrates robust performance (Spearmans {rho} > 0.8) across four cancer types validated by independent datasets. C_LIO_LIPan-cancer conserved miRNAs: Six miRNAs (e.g., hsa-miR-21 and hsa-let-7a) were shared across nine cancers, implicating their roles in core oncogenic pathways. C_LI Key ContributionsO_LIMethodological innovation: Combines XGBoosts nonlinear modeling with spatial transcriptomics to resolve miRNA activity in multicellular contexts. C_LIO_LIBiological discovery: Uncovers the conserved and context-dependent roles of miRNAs in tumor progression and microenvironment crosstalk. C_LIO_LITranslational impact: Provides a computational platform for identifying spatially regulated therapeutic targets in cancer. C_LI

bioinformatics↗

bioRxiv2026

Bespoke sustainable 3D-printed labware for enhanced handling and standardization of tumor spheroid migration and invasion assays

Three-dimensional (3D) cell culture models, particularly multicellular tumor spheroids, have become essential tools for studying cancer biology, drug screening, and preclinical testing due to their ability to mimic physiological tumor microenvironments. However, traditional invasion assays, such as Boyden-chamber- or Transwell-based systems, often suffer from variability introduced by spheroid handling and transfer, compromising data reproducibility. Here, we present a novel, 3D-printed migration and invasion platform --the MQm-sert-- designed to standardize and streamline spheroid-based invasion assays while maintaining spheroid integrity. Fabricated via fused filament fabrication using biobased polylactic acid, the MQm-sert integrates a hanging-drop spheroid culture system (MQm-sert) with a membrane-based invasion chamber (M-sert), eliminating the need for disruptive spheroid transfer steps. Using synthetic tumor environment mimics (STEMs) composed of breast cancer cells (MDA-MB-231 and MCF7), mesenchymal stromal cells (MSCs), and human pulmonary microvascular endothelial cells (HPMECs), we quantified invasion dynamics and cellular interactions. This innovation significantly reduces experimental variability, as demonstrated by lower variance in invasive cell mass dimensions and cell density compared to conventional workflows. Beyond biological insights, the platform aligns with sustainability goals by leveraging cost-effective, open-source 3D printing, reducing reliance on commercial labware, and addressing key challenges in 3D cell culture standardization.

bioengineering↗

bioRxiv2018

Population assignment from cancer genome profiling data

For a variety of human malignancies, incidence, treatment efficacy and overall prognosis show considerable variation between different populations and ethnic groups. Disentangling the effects related to particular population backgrounds can help in both understanding cancer biology and in tailoring therapeutic interventions. Because self-reported or inferred patient data can be incomplete or misleading due to migration and genomic admixture, a data-driven ancestry estimation should be preferred. While algorithms to analyze ancestry structure from healthy individuals have been developed, an easy-to-use tool to assign population groups based on genotyping data from SNP profiles is still missing and benchmarking for the validity of population assignment strategy for aberrant cancer genomes was not tested.\n\nWe benchmarked the consistency and accuracy of cross-platform population assignment. We also demonstrated its high accuracy to process unaltered as well as cancer genomes. Despite widespread and extensive somatic mutations of cancer profiling data, population assignment consistency between germline and highly mutated samples from cancer patients reached of 97% and 92% for assignment into 5 and 26 populations re-spectively. Comparison of our benchmarked results with self-reported meta-data estimated a matching rate between 88% to 92%. Despite a relatively high matching rate, the ethnicity labels indicated in meta-data are vague compared to the standardized output from our tool.\n\nWe have developed a bioinformatics tool to assign the populations from genome profiling data and validated its performance in healthy as well as aberrant cancer genomes. It is ready-to-use for genotyping data from nine commercial SNP array platforms or sequencing data. This tool is effective to scrutinize the population structure in cancer genomes and provides better measure to integrate genotyping data from various platforms instead of self-reported information. It will facilitate research on interplay between ethnicity related genetic background and molecular patterns in cancer entities and disentangling possible hereditary contributions.\n\nThe docker image of the tool is provided in DockerHub as \"baudisgroup/snp2pop\".

bioinformatics↗

bioRxiv2024

Learning the Relationship Between Variants, Metabolic Fluxes and Phenotypes

In the dynamic field of cancer research, the fusion of genetics and machine learning presents a groundbreaking opportunity to understand the intricate links between genomic variations and cancer phenotypes. Despite the wealth of genetic data, translating it into actionable insights remains a challenge, particularly in the context of complex cellular metabolism. VaMP (Variants, Metabolic Fluxes & Phenotypes), our novel approach, addresses this by integrating neural networks and metabolic models, offering a comprehensive framework for deciphering cancer biology. The developed method leverages a novel end-to-end neural network architecture, integrating genome-scale metabolic models (GSMs) to capture the intricate relationship between genetic variations and cellular phenotypes. VaMPs encoder maps genetic variants to metabolic fluxes through a series of carefully designed steps utilizing neural network and GSM, while the decoder predicts phenotype probabilities using the differences between input and reference fluxes. The training data comprise mutation information and phenotypes, eliminating the need for explicit metabolic flux data preparation. Validation experiments on five cancer types demonstrate VaMPs ability to identify significant genes and metabolic signatures. Further utilizing SCREENER and co-occurrence analyses, the assessment reveals VaMPs capacity to anticipate established gene-disease relationships. The identified metabolic signatures are robustly substantiated by diverse literature grounded in experimental studies. Furthermore, an in-depth exploration of the outcomes from five VaMP models involved the correlation analysis for variants and fluxes to establish connections between significant genes and cancer-causing variants. Overall, VaMP can be used as a promising tool for unraveling the complex interplay between genetic alterations and cancer phenotypes, with implications for understanding disease mechanisms and identifying novel therapeutic targets.

bioinformatics↗

bioRxiv2026

tTEscanR: A user-friendly integrative R package for quantifying and visualizing translation efficiency from sequencing data in diverse biological systems

Translation elongation relies on accurate codon-anticodon pairing. Here, we present tTEscanR, an R package designed to investigate this translational interface. By quantifying mRNA codon demand alongside tRNA anticodon availability, tTEscanR provides scalable estimates of translation rates directly from standard transcriptomic and chromatin accessibility count matrices, where genomic features are represented as rows and experimental conditions as columns. tTEscanR seamlessly integrates into existing bulk and single-cell pipelines and provides modular functions for quality control, filtering, normalization, statistical analysis, and visualization. A structured data object centralizes workflow outputs and associated metadata. A built-in multilevel visualization module generates customizable, publication-ready graphical outputs to facilitate data interpretation and reproducibility of the complex translational landscape. We demonstrate the utility of tTEscanR across cancer biology, aging, and neurodegeneration datasets, uncovering critical translational regulatory programs overlooked by conventional analyses. tTEscanR is available in an open-source repository as a standalone tool or workflow plug-in.

genomics↗

bioRxiv2017

Cell proliferation depends on the direct binding between PKM2 and AKAP-Lbc

The M2 form of the glycolytic enzyme pyruvate kinase (PKM2) has generated much interest recently due to its important role in tumor metabolism. A yeast two-hybrid screen carried out by the Alliance for Cell Signaling suggests that PKM2 interacts with A-Kinase Anchoring Protein (AKAP)-Lbc.\n\nAKAP-Lbc (also known as AKAP13) is a scaffold protein that integrates signaling through multiple enzymes including protein kinases A and D and the small G protein Rho. AKAP-Lbc was originally identified in leukemic blast cells, and multiple reports implicate AKAP-Lbc in breast, prostate and thyroid cancers, however the role of AKAP-Lbc in cancer biology is not understood.\n\nCo-immunoprecipitation, pulldown and Bimolecular Fluorescence Complementation (BiFC) data indicate that PKM2 interacts with AKAP-Lbc. Mapping experiments indicate that PKM2 directly interacts with amino acid residues 1923-2817 of AKAP-Lbc. By disrupting the interaction between the two proteins with the expression of the AKAP-Lbc fragments, our data suggest that the binding between PKM2 and PKA plays a critical role in cell proliferation. The work indicates that the binding between AKAP-Lbc and PKM2 may be an important target to treat some cancers by reducing the cell proliferation.

biochemistry↗

bioRxiv2026

PACMOS: an R package for Projection And Classification of Multi-Omic Samples

MotivationIntegrated multi-omic analyses have transformed our understanding of cancer biology, giving rise to data-driven molecular classifications that capture disease heterogeneity beyond conventional histopathology. Among these approaches, multi-omic factor analysis (MOFA), a multimodal extension of principal component analysis, has been widely used to identify sources of molecular variation across omic layers and classify samples into molecular groups. However, classifying query samples according to an existing MOFA-based classification remains challenging, as there is no validated computational method for projecting samples into pretrained MOFA latent factor spaces. ResultsWe present PACMOS, an R package that provides a generalizable approach to project query samples into pretrained MOFA latent factor spaces. We validate PACMOS using two cancer datasets with published MOFA-based classifications--lung neuroendocrine neoplasms and pleural mesothelioma--showing that PACMOS preserves the existing MOFA latent factor space while allowing query samples to be classified. Availability and implementationPACMOS is an open-source R package available at https://github.com/IARCbioinfo/PACMOS and archived on Zenodo at https://doi.org/10.5281/zenodo.20933824, along with installation instructions and a vignette. Supplementary informationSupplementary data are available in separate files. Key messagesO_LIPACMOS enables the projection of query cancer samples into pretrained multi-omic latent factor spaces. C_LIO_LIThe package supports both continuous and discrete classifications. C_LIO_LIPACMOS provides a reproducible, per-sample workflow implemented in an R package. C_LIO_LIPACMOS demonstrates robust performance on pleural mesothelioma and lung neuroendocrine tumor datasets. C_LI

bioinformatics↗