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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2026

Multi-level Analysis of Codon Usage Patterns Reveals Systematic Optimization of Oncogenic Gene Expression in Pancreatic Cancer

BackgroundCodon usage bias, the non-random usage of synonymous codons in coding sequences, represents a fundamental feature of genomic organization that has been largely understudied in cancer biology. Pancreatic ductal adenocarcinoma (PDAC), the predominant subtype of pancreatic cancer, is characterized by aggressive disease progression and limited therapeutic options, necessitating novel approaches to understand its molecular pathogenesis. Leveraging publicly available single-cell RNA sequencing data, we performed comprehensive codon usage analyses across different cellular populations in PDAC. ResultsEmploying a variety of computational codon usage indices uncovered the connections between cancer-specific cellular state features and codon usage signatures. Our findings reveal that malignant pancreatic cells express genes with significantly higher GC content, demonstrate preferential usage of optimal codons through increased frequency of preferred synonymous codons, and exhibit a marked preference for more cost-effective amino acids. Analysis of transcript-level bulk RNA-seq data from PDAC tumors revealed that these codon optimization patterns extend to alternative isoform usage, with highly expressed isoforms displaying increased codon optimality and enhanced mRNA stability. ConclusionThese codon usage-dependent adaptations operating at both gene expression and transcript isoform levels may enable malignant cells to enhance gene expression rates, potentially leading to increased translational efficiency and protein production. These insights into the codon usage landscape of PDAC may provide potential biomarkers for disease monitoring and treatment response prediction.

cancer biology↗

bioRxiv2026

Pan-cancer analysis of single-cell RNA sequencing data from 304 human tumors sheds light on the aneuploidy paradox

Aneuploidy poses a central paradox in cancer biology: it impairs cellular fitness in normal cells but drives cancer progression. To resolve this, we analyzed single cell transcriptomes from >665,000 cells - including [~]288,000 malignant cells - across 304 tumors and 15 cancer types. Integrating transcriptomics with inferred aneuploidy profiles, we characterized cell-intrinsic programs and interactions with the tumor microenvironment. Unexpectedly, highly aneuploid single cells exhibited reduced proliferation and metabolism, contrasting sharply with tumor-bulk profiles. We show this divergence is driven by karyotypic heterogeneity: in highly heterogeneous tumors, aneuploid cells display signatures of acute stress and negative selection. Conversely, in clonally aneuploid tumors, these detrimental signatures are lost and replaced by signatures of increased proliferation and enhanced metabolism, reflecting adaptation. Additionally, we identified consistent transcriptional programs driven by recurrent chromosome-arm alterations across both single cells and bulk tumors. These findings illuminate the selective forces shaping tumor evolution and the aneuploidy paradox. Statement Of SignificanceBy jointly evaluating gene expression and aneuploidy at single-cell resolution, we demonstrate that karyotypic heterogeneity underlies the transcriptional impact of aneuploidy, reveal distinct cellular responses to emerging versus stable states, and identify recurrent chromosomal alterations that drive conserved transcriptional programs. Our findings capture the dynamics of aneuploidy evolution in human tumors, providing novel insights into the aneuploidy paradox.

cancer biology↗

bioRxiv2021

The Progenetix oncogenomic resource in 2021

In cancer, copy number aberrations (CNA) represent a type of nearly ubiquitous and frequently extensive structural genome variations. To disentangle the molecular mechanisms underlying tumorigenesis as well as identify and characterize molecular subtypes, the comparative and meta-analysis of large genomic variant collections can be of immense importance. Over the last decades, cancer genomic profiling projects have resulted in a large amount of somatic genome variation profiles, however segregated in a multitude of individual studies and datasets. The Progenetix project, initiated in 2001, curates individual cancer CNA profiles and associated metadata from published oncogenomic studies and data repositories with the aim to empower integrative analyses spanning all different cancer biologies. During the last few years, the fields of genomics and cancer research have seen significant advancement in terms of molecular genetics technology, disease concepts, data standard harmonization as well as data availability, in an increasingly structured and systematic manner. For the Progenetix resource, continuous data integration, curation and maintenance have resulted in the most comprehensive representation of cancer genome CNA profiling data with 138663 (including 115357 tumor) CNV profiles. In this article, we report a 4.5-fold increase in sample number since 2013, improvements in data quality, ontology representation with a CNV landscape summary over 51 distinctive NCIt cancer terms as well as updates in database schemas, and data access including new web front-end and programmatic data access. Database URL: progenetix.org

cancer biology↗

bioRxiv2022

Development of a scoring function for comparing simulated and experimental tumor spheroids

Enormous progress continues in the field of cancer biology, yet much remains to be unveiled regarding the mechanisms of cancer invasion. In particular, complex biophysical mechanisms enable a tumor to remodel the surrounding extracellular matrix (ECM), thus allowing cells to escape and invade alone or as multicellular collectives. Tumor spheroids cultured in collagen represent a simplified, reproducible 3D model system, which is sufficiently complex to recapitulate the evolving internal organization of cells and external interaction with the ECM that occur during invasion. Recent experimental approaches enable high resolution imaging and quantification of the internal structure of invading tumor spheroids. Concurrently, computational modeling enables simulations of complex multicellular aggregates based on first principles. The comparison between real and simulated spheroids represents a way to fully exploit both data sources, but remains a challenging task. We hypothesize that comparing any two spheroids requires first the extraction of basic features from the raw data, and second the definition of key metrics to match such features. Here, we present a novel data-agnostic method to compare spatial features of spheroids in 3D. To do so, we define and extract features from spheroid point cloud data, which we simulated using Cells in Silico (CiS), a high-performance framework for large-scale tissue modeling previously developed by our group. We then define metrics to compare features between individual spheroids, and combine all metrics into an overall deviation score. Finally, we use our features to compare experimental data on invading spheroids in increasing collagen densities. We propose that our approach represents the basis for defining improved metrics to compare large 3D data sets. Moving forward, this approach will enable informing in silico spheroids based on their in vitro counterparts, and vice versa, thus enabling both basic and applied researchers to close the loop between modeling and experiments in cancer research. Author summaryCells within a tumor use various methods to escape and thereby invade into healthy parts of the body. These methods are studied experimentally by examining tumor spheroids, spherical aggregates of hundreds to thousands of individual cells. Such spheroids can also be simulated, and the comparison of simulation and experiment is desirable. Here, we present an analysis strategy for the comparison of tumor spheroids, a widely used workhorse of cancer research. Using this strategy, we aim to improve the collaborative potential between experimentalists and theorists.

cancer biology↗

bioRxiv2024

Super-enhancer profiling reveals ThPOK/ZBTB7B, a CD4+ cell lineage commitment factor, as a master regulator that restricts breast cancer cells to a luminal non-migratory phenotype

Despite efforts to understand breast cancer biology, metastatic disease remains a clinical challenge. Identifying suppressors of breast cancer progression and mechanisms of transition to more invasive phenotypes could provide game changing therapeutic opportunities. Transcriptional deregulation is central to all malignancies, highlighted by the extensive reprogramming of regulatory elements that underlie oncogenic programs. Among these, super-enhancers (SEs) stand out due to their enrichment in genes controlling cancer hallmarks. To reveal novel breast cancer dependencies, we integrated the analysis of the SE landscape with master regulator activity inference for a series of breast cancer cell lines. As a result, we identified T-helper-inducing Poxviruses and Zinc-finger (POZ)/Kruppel-like factor (ThPOK, ZBTB7B), a CD4+ cell lineage commitment factor, as a breast cancer master regulator that is recurrently associated with a SE. ThPOK expression is highest in luminal breast cancer but is significantly reduced in the basal subtype. Manipulation of ThPOK levels in cell lines shows that its repressive function restricts breast cancer cells to an epithelial phenotype by suppressing the expression of genes involved in the epithelial-mesenchymal transition (EMT), WNT/{beta}-catenin target genes, and the pro-metastatic TGF{beta} pathway. Our study reveals ThPOK as a master transcription factor that restricts the acquisition of metastatic features in breast cancer cells.

cancer biology↗

bioRxiv2018

Interrogating Mutant Allele Expression via Customized Reference Genomes to Define Influential Cancer Mutations

Genetic alterations are essential for cancer initiation and progression. However, differentiating mutations that drive the tumor phenotype from mutations that do not affect tumor fitness remains a fundamental challenge in cancer biology. To better understand the impact of a given mutation within cancer, RNA-sequencing data was used to categorize mutations based on their allelic expression. For this purpose, we developed the MAXX (Mutation Allelic Expression Extractor) software, which is highly effective at delineating the allelic expression of both single nucleotide variants and small insertions and deletions. Results from MAXX demonstrated that mutations can be separated into three groups based on their expression of the mutant allele, lack of expression from both alleles, or expression of only the wild-type allele. By taking into consideration the allelic expression patterns of genes that are mutated in PDAC, it was possible to increase the sensitivity of widely used driver mutation detection methods, as well as identify subtypes that have prognostic significance and are associated with sensitivity to select classes of therapeutic agents in cell culture. Thus, differentiating mutations based on their mutant allele expression via MAXX represents a means to parse somatic variants in tumor genomes, helping to elucidate of a genes respective role in cancer.

cancer biology↗

bioRxiv2017

Precise, pan-cancer discovery of gene fusions reveals a signature of selection in primary tumors

Short AbstractThe extent to which gene fusions function as drivers of cancer remains a critical open question in cancer biology. In principle, transcriptome sequencing provided by The Cancer Genome Atlas (TCGA) enables unbiased discovery of gene fusions and post-analysis that informs the answer to this question. To date, such an analysis has been impossible because of performance limitations in fusion detection algorithms. By engineering a new, more precise, algorithm and statistical approaches to post-analysis of fusions called in TCGA data, we report new recurrent gene fusions, including those that could be druggable; new candidate pan-cancer oncogenes based on their profiles in fusions; and prevalent, previously overlooked, candidate oncogenic gene fusions in ovarian cancer, a disease with minimal treatment advances in recent decades. The novel and reproducible statistical algorithms and, more importantly, the biological conclusions open the door for increased attention to gene fusions as drivers of cancer and for future research into using fusions for targeted therapy.

cancer biology↗

bioRxiv2019

Glioblastomas derived from genetically modified pluripotent stem cells recapitulate pathobiology

Many current cellular models aimed at elucidating cancer biology do not recapitulate pathobiology including tumor heterogeneity, an inherent feature of cancer that underlies treatment resistance. Here we introduce a new cancer modeling paradigm using genetically engineered human pluripotent stem cells (hiPSCs) that capture authentic cancer pathobiology. Orthotopic engraftment of neural progenitor cells derived from hiPSCs that have been genome-edited to contain tumor-associated genetic driver mutations revealed by The Cancer Genome Atlas project for glioblastoma (GBM) result in formation of high-grade gliomas. As observed in GBM patient samples, these models harbor inter-tumor heterogeneity resembling different GBM molecular subtypes, and intra-tumor heterogeneity. Further, re-engraftment of primary tumor neurospheres generates secondary tumors with features characteristic of patient samples and present mutation-dependent patterns of tumor evolution. Thus, these cancer avatar models provide a platform for a comprehensive longitudinal assessment of human tumor development as governed by molecular subtype mutations and lineage-restricted differentiation.

cancer biology↗

bioRxiv2023

Pan-cancer analysis reveals embryonic and hematopoietic stem cell signatures to distinguish different cancer subtypes

PurposeStem cells-like properties in cancer cells may confer cancer development and therapy resistance. With the advancement of multi-omics technology, the multi-omics-based exploration of cancer stemness has attracted certain interests. However, subtyping of cancer based on the combination of different types of stem cell signatures remains scarce. MethodsIn this study, 10,323 cancer specimens from 33 TCGA cancer types were clustered based on the enrichment scores of six stemness gene sets, representing two types of stem cell backgrounds: embryonic stem cells (ESCs) and hematopoietic stem cells (HSCs). ResultsWe identified four subtypes of pan-cancer, termed StC1, StC2, StC3 and StC4, which displayed distinct molecular and clinical features, including stemness, genome integrity, intratumor heterogeneity, methylation levels, tumor microenvironment, tumor progression, chemotherapy and immunotherapy responses, and survival prognosis. This subtyping method for pan-cancer is reproducible at the protein level. ConclusionOur findings indicate that the ESC signature is an adverse prognostic factor, while the HSC signature and ratio of HSC/ESC signatures are positive prognostic factors in cancer. The ESC and HSC signatures-based subtyping of cancer may provide insights into cancer biology and clinical implications of cancer.

bioinformatics↗

bioRxiv2019

Evidence for a role for BK channels in the regulation of ADAM17 activity.

Large-conductance voltage and calcium activated channels, KCa1.1, have a large single conductance (~p250) and are highly selective for potassium ions. As a result they have been termed big potassium channels (BK channels). Because of the channels ability to integrate multiple physical and chemical signals they have received much attention in excitable cells. In comparison they have received relatively little attention in non-excitable cells in those of the immune system. Here we report evidence that the BK channel regulates ADAM17 activity. Upon macrophage activation, BK channels translocate to the cell membrane. Genetic or pharmacological inhibition of the cell membrane BK channels resulted in elevated TNF- release and increased metalloproteinase a disintegrin and metalloproteinase domain 17 (ADAM17) activity. Inhibitors of BK channels also increased IL-6R release, a second ADAM17 substrate. In comparison, a BK channel opener decreases TNF- release. Taken together, our results demonstrate a novel mechanism by which ion channel regulates ADAM17 activity. Given the broad range of ADAM17 substrates, this finding has implications in many fields of cell biology including immunology, neurology and cancer biology.

pharmacology and toxicology↗

bioRxiv2026

The lncRNA SOX2OT Drives Non-Small Cell Lung Cancer Progression and Metastasis by Suppressing miR-143

In terms of cancer-related death, non-small cell lung cancer (NSCLC), the worlds leading cause, highlights the need for continued research into the genetic factors that influence tumor growth. Long non-coding RNAs (lncRNAs) are now well recognized as essential regulators of oncogenic signaling cascades; nevertheless, the specific role and molecular basis of the SOX2 overlapping transcript (SOX2OT) in NSCLC are not entirely understood. This study examined the functional importance of SOX2OT and its regulatory interactions with tumor-suppressive microRNAs in NSCLC cells. In A549 and Calu-3 cells, RNA interference-mediated SOX2OT silencing dramatically reduced cellular proliferation, migration, and invasiveness. Moreover, SOX2OT knockdown was associated with inhibition of epithelial-mesenchymal transition (EMT), alongside induction of cell cycle arrest and activation of apoptotic pathways. Integrated transcriptomic profiling and bioinformatic prediction analyses identified miR-143 as a putative downstream effector of SOX2OT activity. Consistently, depletion of SOX2OT resulted in marked elevation of miR-143 expression, which corresponded with downregulation of oncogenic mediators, including STAT3, EZH2, and CXCL13. As a result of SOX2OT suppression, both the transcript and the protein levels of PTEN were restored. Further functional characterization demonstrated that SOX2OT knockdown inhibits EMT progression by decreasing mesenchymal markers and EMT-related transcription factors (TFs) while concomitantly enhancing epithelial marker expression. Collectively, these findings suggest that SOX2OT contributes to NSCLC pathogenesis through regulation of a miR-143-centered signaling network that influences oncogenic signaling, cellular survival, and metastatic potential. Targeting the SOX2OT/miR-143 regulatory axis may therefore represent a promising therapeutic approach for NSCLC, while also underscoring the broader importance of lncRNA-mediated post-transcriptional regulation in lung cancer biology.

cancer biology↗

bioRxiv2021

Integrating Subclonal Response Heterogeneity to Define Cancer Organoid Therapeutic Sensitivity

Tumor heterogeneity is predicted to confer inferior clinical outcomes, however modeling heterogeneity in a manner that still represents the tumor of origin remains a formidable challenge. Sequencing technologies are limited in their ability to identify rare subclonal populations and predict response to the multitude of available treatments for patients. Patient-derived organotypic cultures have significantly improved the modeling of cancer biology by faithfully representing the molecular features of primary malignant tissues. Patient-derived cancer organoid (PCO) cultures contain numerous individual organoids with the potential to recapitulate heterogeneity, though PCOs are most commonly studied in bulk ignoring any diversity in the molecular profile or treatment response. Here we demonstrate the advantage of evaluating individual PCOs in conjunction with cellular level optical metabolic imaging to characterize the largely ignored heterogeneity within these cultures to predict clinical therapeutic response, identify subclonal populations, and determine patient specific mechanisms of resistance.

cancer biology↗

bioRxiv2024

The Curated Cancer Cell Atlas: comprehensive characterisation of tumours at single-cell resolution

Single-cell RNA-seq (scRNA-seq) has transformed the study of cancer biology. Recent years have seen a rapid expansion in the number of single-cell cancer studies, yet most of these studies profiled few tumours, such that individual datasets have limited statistical power. Combining the data and results across studies holds great promise but also involves various challenges. We recently began to address these challenges by curating a large collection of cancer scRNA-seq datasets, and leveraging it for systematic analyses of tumor heterogeneity. Here we significantly extend this repository to 124 datasets for over 40 cancer types, together comprising 2,822 samples, with improved data annotations, visualisations and exploration. Utilising this vast cohort, we systematically quantified context-dependent gene expression and proliferation patterns across cell types and cancer types. These data, annotations and analysis results are all freely available for exploration and download via the Curated Cancer Cell Atlas (3CA) website (https://www.weizmann.ac.il/sites/3CA/), a central source of data and analyses for the cancer research community that opens new avenues in cancer research.

cancer biology↗

bioRxiv2017

Effect sizes of somatic mutations in cancer

A major goal of cancer biology is determination of the relative importance of the genomic alterations that confer selective advantage to cancer cells. Tumor sequence surveys have frequently ranked the importance of substitutions to cancer growth by P value or a false-discovery conversion thereof. However, P values are thresholds for belief, not metrics of effect. Their frequent misuse as metrics of effect has often been vociferously decried. Here, we estimate the effect sizes of all recurrent single nucleotide variants in 23 cancer types, quantifying relative importance within and between driver genes. Some of the variants with the highest effect size, such as EGFR L858R in lung adenocarcinoma and BRAF V600E in primary skin cutaneous melanoma, have yielded remarkable therapeutic responses. Quantification of cancer effect sizes has immediate importance to the prioritization of clinical decision-making by tumor boards, selection and design of clinical trials, pharmacological targeting, and basic research prioritization.

cancer biology↗

bioRxiv2017

The Activation of MEK1 by Enhanced Homodimerization Drives Tumorigenesis

Hyperactive RAS/RAF/MEK/ERK signaling has a well-defined role in cancer biology. Aberrant pathway activation occurs mostly upstream of MEK; however, MEK mutations are prevalent in some cancer subsets. Here we show that cancer-related MEK mutants can be classified as those activated by relieving the inhibitory role of helix A, and those with in-frame deletions of {beta}3-C loop, which exhibit differential resistance to MEK inhibitors in vitro and in vivo. The {beta}3-C loop deletions activate MEK1 through enhancing homodimerization that can drive intradimer cross-phosphorylation of activation loop. Further, we demonstrate that MEK1 dimerization is required both for its activation by RAF and for its catalytic activity towards ERK. Our study identifies a novel group of MEK mutants, illustrates some key steps in RAF/MEK/ERK activation, and has important implications for the design of therapies targeting hyperactive RAS/RAF/MEK/ERK signaling in cancers.

cancer biology↗

bioRxiv2022

Genomic heterogeneity in pancreatic cancer organoids and its stability with culture

The establishment of patient-derived pancreatic cancer organoid culture in recent years creates an exciting opportunity for researchers to perform a wide range of in vitro studies on a model that closely recapitulates the tumor. Among the outstanding questions in pancreatic cancer biology are the causes and consequences of genomic heterogeneity observed in the disease. However, to use pancreatic cancer organoids as a model to study genomic variations, we need to first understand the degree of genomic heterogeneity and its stability within organoids. Here, we used single-cell whole-genome sequencing to investigate the genomic heterogeneity of two independent pancreatic cancer organoids, as well as their genomic stability with extended culture. Clonal populations with similar copy number profiles were observed within the organoids, and the proportion of these clones was shifted with extended culture, suggesting the growth advantage of some clones. However, sub-clonal genomic heterogeneity was also observed within each clonal population, indicating the genomic instability of the pancreatic cancer cells themselves. Furthermore, our transcriptomic analysis also revealed a positive correlation between copy number alterations and gene expression regulation, suggesting the functionality of these copy number alterations.

cancer biology↗

bioRxiv2024

CanLncG4: A database curated for the assessment of G4s in the lncRNAs dysregulated in various human cancers

Long non-coding RNAs (lncRNAs) comprise a substantive part of the human genome and have emerged as crucial participants of cellular processes and disease pathogenesis. Dysregulated expression of lncRNAs in cancer contributes to various hallmarks of the disease, presenting novel opportunities for diagnosis and therapy. G-quadruplexes (G4s) within lncRNAs have gained attention, though their systematic evaluation in cancer biology is yet to be performed. In this work, we have formulated CanLncG4, a comprehensive database integrating experimentally validated associations between lncRNAs and cancer, and detailed predictions of their G4-forming potential. CanLncG4 categorizes predicted G4 motifs into anticipated G4 types and offers insights into the subcellular localization of the corresponding lncRNAs. It provides information on lncRNA-RNA and lncRNA-protein interactions, together with the RNA G4-binding capabilities of these proteins. To ensure the accuracy and validity of the data sourced from various databases, a meticulous examination of the output data was conducted to identify any discrepancies, including incorrect, missing, or duplicate entries. Additionally, scientific literature mining was performed to cross-validate the gathered information. Data from G4-prediction tools was generated using multiple parameter combinations to determine the parameters that yield more relevant and accurate predictions of the G4-forming potential. We validate our in silico G4-prediction pipeline through in vitro experiments, affirming the presence of G4s within specific cancer-dysregulated lncRNAs, thereby illustrating the predictive capability of CanLncG4. CanLncG4 represents a valuable resource for investigating G4-mediated lncRNA functions in diverse human cancers. It is expected to provide distinctive leads about G4-mediated lncRNA-protein interactions. CanLncG4 comprehensively documents 17,666 entries, establishing correlations between 6,408 human lncRNAs encompassing their transcript variants, and 15 distinct types of human cancers. The database is freely available at https://canlncg4.com/, offering researchers a valuable tool for exploring lncRNA and G4 biology towards cancer diagnosis and therapeutics.

bioinformatics↗

bioRxiv2020

A comprehensive map of genetic interactions in childhood cancer reveals multiple underlying biological mechanisms

Childhood cancer is a major cause of child death in developed countries. Genetic interactions between mutated genes play an important role in cancer development. They can be detected by searching for pairs of mutated genes that co-occur more (or less) often than expected. Co-occurrence suggests a cooperative role in cancer development, while mutual exclusivity points to synthetic lethality, a phenomenon of interest in cancer treatment research. Little is known about genetic interactions in childhood cancer. We apply a statistical pipeline to detect genetic interactions in a combined dataset comprising over 2,500 tumors from 23 cancer types. The resulting genetic interaction map of childhood cancers comprises 15 co-occurring and 27 mutually exclusive candidates. The biological mechanisms underlying most candidates are either tumor subtype, pathway epistasis or cooperation while synthetic lethality plays a much smaller role. Thus, other explanations beyond synthetic lethality should be considered when interpreting results of genetic interaction tests.

cancer biology↗