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bioRxiv2024

The three YTHDF paralogs and VIRMA are the major tumor drivers among the m6A core genes in a pan-cancer analysis

N6-methyladenosine (m6A) is the most abundant internal modification in mRNAs. Despite accumulating evidence for the profound impact of m6A on cancer biology, there are conflicting reports that alterations in genes encoding the m6A machinery proteins can either promote or suppress cancer, even in the same tumor type. Using data from The Cancer Genome Atlas, we performed a pan-cancer investigation of 15 m6A core factors in nearly 10,000 samples from 31 tumor types to reveal underlying cross-tumor patterns. Altered expression, largely driven by copy number variations at the chromosome arm level, results in the most common mode of dysregulation of these factors. YTHDF1, YTHDF2, YTHDF3, and VIRMA are the most frequently altered factors and the only ones to be uniquely altered when tumors are grouped according to the expression pattern of the m6A factors. These genes are also the only ones with coherent, pan-cancer predictive power for progression-free survival. On the contrary, METTL3, the most intensively studied m6A factor as a cancer target, shows much lower levels of alteration and no predictive power for patient survival. Therefore, we propose the non-enzymatic YTHDF and VIRMA genes as preferred subjects to dissect the role of m6A in cancer and as priority cancer targets. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/598899v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1e39eeborg.highwire.dtl.DTLVardef@d6628dorg.highwire.dtl.DTLVardef@e048a6org.highwire.dtl.DTLVardef@c19812_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

The Whole Proteome, Phosphoproteome, and Glycoproteome Landscape of Pan-Cancer Cell Lines Profiled by Mass Spectrometry and Reverse Phase Protein Array

Mammalian cancer cell lines are essential model systems in biomedical research. We conducted multi-level proteomics analyses on 54 widely used cancer cell lines derived from various tissue-of-origins using two prominent proteomics technologies: mass spectrometry (MS) and reverse-phase protein array (RPPA). Our analysis identified 10,088 proteins, 33,609 phosphorylation sites across 7,289 phosphoproteins, and 56,350 site-specific glycans on 16,296 glycosylation sites from 5,966 glycoproteins, along with 305 drug-relevant protein and phosphoprotein targets. Our results reveal both consistent and distinct patterns in protein expression and modification between MS and RPPA, underscoring their complementary strengths as discovery tools. Additionally, we identified protein features that distinguish tissue origins across different cell line lineages. This dataset supports model system selection for drug target-related studies in vitro and provides valuable insights into key signaling pathways. Overall, this comprehensive resource enables new opportunities for exploration in cancer biology and offers significant value to research communities focused on biomarker profiling, drug target discovery, and understanding mechanisms across diverse cancer types.

cancer biology↗

bioRxiv2024

Multi-omics analysis reveals CMTR1 upregulation in cancer and roles in ribosomal protein gene expression and tumor growth

The mRNA cap methyltransferase CMTR1 plays a crucial role in RNA metabolism and gene expression regulation, yet its significance in cancer remains largely unexplored. Here, we present a comprehensive multi-omics analysis of CMTR1 across various human cancers, revealing its widespread upregulation and potential as a therapeutic target. Integrating transcriptomic and proteomic data from a large set of cancer samples, we demonstrate that CMTR1 is upregulated at the mRNA, protein, and phosphoprotein levels across multiple cancer types. Functional studies using CRISPR-mediated knockout and siRNA knockdown in breast cancer models show that CMTR1 depletion significantly inhibits tumor growth both in vitro and in vivo. Transcriptomic analysis reveals that CMTR1 primarily regulates ribosomal protein genes and other transcripts containing 5 Terminal Oligopyrimidine (TOP) motifs. Additionally, CMTR1 affects the expression of snoRNA host genes and snoRNAs, suggesting a broader role in RNA metabolism. Mechanistically, we propose that CMTR1s target specificity is partly determined by mRNA structure, particularly the presence of 5TOP motifs. Furthermore, we identify a novel CMTR1 inhibitor, N97911, through in silico screening and biochemical assays, which demonstrates significant anti-tumor activity in vitro. Our findings establish CMTR1 as a key player in cancer biology, regulating critical aspects of RNA metabolism and ribosome biogenesis, and highlight its potential as a therapeutic target across multiple cancer types.

cancer biology↗

bioRxiv2025

Association of GADD45A and favorable outcome in hormone positive breast cancer

BackgroundHormone receptor-positive (HR(+)) breast cancer is the most prevalent subtype in breast cancer, and although treatment options are rapidly increasing, still poses as an unmet need in advanced cases. Growth arrest and DNA damage-inducible alpha (GADD45A) is a gene with known relationship with the cell cycle and DNA damage pathways. Conflicting roles in breast cancer biology have been reported for GADD45A. In our study, we investigate the clinical and molecular association of GADD45A in HR(+) breast cancer, evaluating its prognostic potential and relationship with estrogen signaling. MethodsGADD45A expression was assessed in 100 breast cancer tissue samples from a tissue microarray and correlated with clinicopathologic parameters. Public genomic datasets were analyzed to validate associations between GADD45A expression and clinical outcomes in HR(+)HER2(-) breast cancer. Gene set enrichment analysis (GSEA) was used to investigate pathway correlations. ResultsGADD45A protein levels were highest in HR(+)HER2(-) tumors and positively correlated with estrogen receptor (ER) levels, consistent with a luminal phenotype. No significant association was observed between GADD45A levels and progression-free survival in CDK4/6 inhibitor-treated patients, suggesting limited predictive utility in this context. However, higher GADD45A expression was significantly associated with improved overall and relapse-free survival in HR(+)HER2(-) breast cancer in the METABRIC cohort, but not in HER2(+) or triple- negative subtypes. GSEA revealed a positive association between GADD45A expression and the estrogen late response signature, suggesting a functional link with hormone signaling. ConclusionGADD45A expression is enriched in luminal-type HR(+) breast cancer and correlates with favorable clinical outcomes. While not predictive of CDK4/6 inhibitor response, GADD45A may serve as a surrogate marker of estrogen signaling and improved prognosis. These findings support further investigation into GADD45A as a biomarker for stratification and potentially as a therapeutic target in HR(+) breast cancer.

cancer biology↗

bioRxiv2025

When neighbours play a role: a systems-level analysis of protein interactions conditioning cancer driver mutation effects

BackgroundCancer is driven by the accumulation of somatic mutations, including driver mutations that confer a selective advantage to cancer cells. Driver proteins operate within complex interaction networks, and their activity is conditioned by neighbour proteins. Understanding the interplay between driver mutations and the expression of their neighbour proteins can provide insights into cancer biology and potential therapeutic targets. MethodsWe assessed associations between expression of neighbour proteins and driver mutation status, comparing both between and within cancer types. We further evaluated if neighbours were enriched in significant associations with multiple drivers and characterised the impact of neighbour expression on overall survival for all cancer types. ResultsWe found a significant correlation between the number of driver associations a neighbour gene has and the number of sign-coherent survival associations, particularly for neighbours enriched in positive associations, where high neighbour expression correlated with increased driver mutations and poorer survival. We identified 119 neighbours enriched in positive driver associations with at least two unfavourable survival associations and 25 neighbours enriched in negative driver associations with at least two favourable survival associations. ConclusionsOur study systematically identified neighbours associated with driver mutation status. Complementary evidence from survival analysis and the literature suggests that neighbours enriched in driver associations can be further explored as drug target candidates. Significance StatementCancers are caused by mutations in driver genes. The impact of those mutations in the cell can be influenced by other proteins in the cell that physically interact with the mutated protein. In this work, we analysed cancer patient data to uncover such neighbour proteins that may influence the outcome of driver mutations. We discovered a subset of neighbour proteins that are associated with mutations in multiple cancer drivers and, simultaneously, are associated with changes in survival times of patients for multiple cancer types. These neighbour proteins may help explain why some driver mutations are more common in certain cancer types. We also propose that these neighbour proteins should be explored as candidate drug targets for cancer therapy.

cancer biology↗

bioRxiv2025

Oxytocin receptor and HER2 interactions in breast cancer

Breast cancer affects women globally, with HER2 being one of the most aggressive subtypes. Despite advances in HER2-targeted therapies, many patients fail to respond to such treatments. In search of alternative therapeutic targets, we investigated the role of the oxytocin (OT) and oxytocin receptor (OTR) signalling system in HER2+ breast cancer. Survival analysis of HER2 subtype breast cancer patients revealed high OTR expression correlating with significantly improved relapse-free survival. OTR overexpression or knockdown in HER2+ SK-BR-3 cells decreased or increased cell viability, respectively. HER2 was downregulated in OTR-overexpressing cells, which could contribute to the decreased Pertuzumab efficacy. Importantly, HER2 was internalised upon OT treatment and interacted with OTR to form OTR-HER2 complexes. OT treatment furthermore induced a transient inhibition of HER2 phosphorylation without affecting the total HER2 protein levels. In addition, OT inhibited ERK1/2 phosphorylation but enhanced Akt phosphorylation, linked to increased cell viability. In summary, this work describes a novel HER2-OTR interaction and mechanism, expanding our understanding of HER2 cancer biology that might lead to new prognostic and therapeutic opportunities centred around the OT/OTR signalling system for patients suffering from HER2+ breast cancer.

cancer biology↗

bioRxiv2025

Explainable deep learning for identifying cancer driver genes based on the Cancer Dependency Map

Identifying cancer driver genes and their therapeutic impact remains a core challenge in computational cancer biology. We introduce xNNDriver and xAEDriver, two interpretable neural network frameworks that connect cancer mutations with genome-wide DepMap gene dependencies, pathway activity, and drug-response patterns. xNNDriver is a supervised pathway-guided model that evaluates whether a genes mutation status is encoded in the genome-wide dependency landscape; we interpret model fitness as a driver potential score, which quantifies the strength of this mutation-dependency signal and prioritizes genes with broad functional footprints. Across 3,008 candidate genes, xNNDriver recovers major established drivers and highlights literature-supported candidates, while pathway analyses reveal biologically coherent programs related to metabolism, growth factor signaling, and immune regulation. To capture combinatorial functional states, xAEDriver uses an unsupervised autoencoder to learn Driver Variant Representations (DVRs), latent binary features guided by the frequency distribution of known driver mutations. DVRs capture cell-line-specific dependency patterns and expression patterns and are associated with drug sensitivity and pathway activity. Together, these interpretable deep learning models demonstrate that gene dependency landscapes encode rich, interpretable signals of oncogenic function and provide a hypothesis-generating framework for prioritizing drivers, pathways, and therapeutic vulnerabilities for further experimental validation. Author summaryCancer is often driven by genetic changes that give tumor cells a growth advantage, but finding which changes matter and how they affect cell behavior remains difficult. In this study, we used large-scale gene-editing data from cancer cell lines to ask whether the pattern of genes a cell depends on can reveal information about its cancer-driving alterations. We developed interpretable neural-network models that connect mutation patterns, cell survival dependencies, and biological pathways. One model prioritizes genes whose mutation status leaves a clear functional signature across the cell. A second model summarizes broader, combined driver-like states in each cell line. These summaries were linked to known cancer biology, tissue type, and differences in drug response, suggesting that functional dependency data contain useful clues about cancer mechanisms. By connecting mutation patterns with functional dependencies and pathway activity, our approach helps identify candidate cancer drivers and interpretable drug-response patterns that can guide future experimental studies. Making the models interpretable allows researchers to move from large screening datasets toward biological explanations of cancer-driving processes.

bioinformatics↗

bioRxiv2024

Netrin G1 Ligand is a new stromal immunomodulator that promotes pancreatic cancer

Understanding pancreatic cancer biology is fundamental for identifying new targets and for developing more effective therapies. In particular, the contribution of the stromal microenvironment to pancreatic cancer tumorigenesis requires further exploration. Here, we report the stromal roles of the synaptic protein Netrin G1 Ligand (NGL-1) in pancreatic cancer, uncovering its pro-tumor functions in cancer-associated fibroblasts and in immune cells. We observed that the stromal expression of NGL-1 inversely correlated with patients overall survival. Moreover, germline knockout (KO) mice for NGL-1 presented decreased tumor burden, with a microenvironment that is less supportive of tumor growth. Of note, tumors from NGL-1 KO mice produced less immunosuppressive cytokines and displayed an increased percentage of CD8+ T cells than those from control mice, while preserving the physical structure of the tumor microenvironment. These effects were shown to be mediated by NGL-1 in both immune cells and in the local stroma, in a TGF-{beta}-dependent manner. While myeloid cells lacking NGL-1 decreased the production of immunosuppressive cytokines, NGL-1 KO T cells showed increased proliferation rates and overall polyfunctionality compared to control T cells. CAFs lacking NGL-1 were less immunosuppressive than controls, with overall decreased production of pro-tumor cytokines and compromised ability to inhibit CD8+ T cells activation. Mechanistically, these CAFs downregulated components of the TGF-{beta} pathway, AP-1 and NFAT transcription factor families, resulting in a less tumor-supportive phenotype. Finally, targeting NGL-1 genetically or using a functionally antagonistic small peptide phenocopied the effects of chemotherapy, while modulating the immunosuppressive tumor microenvironment (TME), rather than eliminating it. We propose NGL-1 as a new local stroma and immunomodulatory molecule, with pro-tumor roles in pancreatic cancer. Statement of SignificanceHere we uncovered the pro-tumor roles of the synaptic protein NGL-1 in the tumor microenvironment of pancreatic cancer, defining a new target that simultaneously modulates tumor cell, fibroblast, and immune cell functions. This study reports a new pathway where NGL-1 controls TGF-{beta}, AP-1 transcription factor members and NFAT1, modulating the immunosuppressive microenvironment in pancreatic cancer. Our findings highlight NGL-1 as a new stromal immunomodulator in pancreatic cancer.

cancer biology↗

bioRxiv2025

Sex Differences in the Cancer Proteome

Proteins play a central role in cancer biology: they are the most common drug targets and biomarkers. Sex influences the proteome in many diseases, ranging from neurological to cardiovascular. In cancer, sex is associated with incidence, progression and therapeutic response, as well as characteristics of the tumour genome and transcriptome. The extent to which sex differences impact the cancer proteome remains largely unknown. To fill this gap, we quantified sex differences across 1,590 proteomes from eight cancer types, identifying 901 genes with sex-differential proteins abundance in adenocarcinomas of the lung, and 20 genes across five other tumour types: squamous cell carcinoma of the lung, hepatocellular carcinomas, clear cell cancers of the kidney, adenocarcinomas of the pancreas and glioblastoma. A subset of these protein differences could be rationalized by sex-differential copy number aberrations. Pathway analysis showed that male-biased proteins in lung adenocarcinoma were enriched in MYC and E2F target pathways. These findings highlight the modest impact of sex on the cancer proteome, but the very limited power of existing proteomics cohorts for these analyses.

cancer biology↗

bioRxiv2020

High resolution discovery of regulatory DNA with synthetic wild-type and ablated genome constructs

Genomic enhancer elements play a role in modulating gene expression by interacting with effector proteins known as transcription factors. We present a new computational method, "STARR-scan", which identifies transcription factor motif appearances in synthetic DNA constructs that are correlated with enhancer activity. Using a tiled STARR-seq assay, we demonstrate that STARR-scan can suggest the biological function of transcription factors previously suspected to activate the APOBEC-3B gene. Using the same methodology, STARR-scan can suggest transcription factors with previously unknown biological activity which may regulate the APOBEC-3B gene. These novel factors may have biological significance in our understanding of cancer biology.

genomics↗

bioRxiv2024

Tracking of Lineage Mass via Quantitative Phase Imaging and Confinement in Low Refractive Index Microwells

Measurements of cell lineages is central to a variety of fundamental biological questions, ranging from developmental to cancer biology. However, accurate lineage tracing requires nearly perfect cell tracking, which can be challenging due to cell motion during imaging. Here we demonstrate the integration of microfabrication, imaging, and image processing approaches to demonstrate a platform for cell lineage tracing. We use quantitative phase imaging (QPI) a label-free imaging approach that quantifies cell mass. This gives an additional parameter, cell mass, that can be used to improve tracking accuracy. We confine lineages within microwells fabricated to reduce cell adhesion to sidewalls made of a low refractive index polymer. This also allows the microwells themselves to serve as references for QPI, enabling measurement of cell mass even in confluent microwells. We demonstrate application of this approach to immortalized adherent and nonadherent cell lines as well as stimulated primary B cells cultured ex vivo. Overall, our approach enables lineage tracking, or measurement of lineage mass, in a platform that can be customized to varied cell types.

bioengineering↗

bioRxiv2018

REVA: a rank-based multi-dimensional measure of correlation

The neighbors principle implicit in any machine learning algorithm says that samples with similar labels should be close to one another in feature space as well. For example, while tumors are heterogeneous, tumors that have similar genomics profiles can also be expected to have similar responses to a specific therapy. Simple correlation coefficients provide an effective way to determine whether this principle holds when features and labels are both scalar, but not when either is multivariate. A new class of generalized correlation coefficients based on inter-point distances addresses this need and is called \"distance correlation\". There is only one rank-based distance correlation test available to date, and it is asymmetric in the samples, requiring that one sample be distinguished as a fixed point of reference. Therefore, we introduce a novel, nonparametric statistic, REVA, inspired by the Kendall rank correlation coefficient. We use U-statistic theory to derive the asymptotic distribution of the new correlation coefficient, developing additional large and finite sample properties along the way. To establish the admissibility of the REVA statistic, and explore the utility and limitations of our model, we compared it to the most widely used distance based correlation coefficient in a range of simulated conditions, demonstrating that REVA does not depend on an assumption of linearity, and is robust to high levels of noise, high dimensions, and the presence of outliers. We also present an application to real data, applying REVA to determine whether cancer cells with similar genetic profiles also respond similarly to a targeted therapeutic.\n\nAuthor summarySometimes a simple question arises: how does the distance between two samples in multivariate space compare to another scalar value associated with each sample. Here, we propose theory for a nonparametric test to statistically test this association. This test is independent of the scale of the scalar data, and thus generalizable to any comparison of samples with both high-dimensional data and a scalar. We apply the resulting statistic, REVA, to problems in cancer biology motivated by the model that cancer cells with more similar gene expression profiles to one another can be expected to have a more similar response to therapy.

bioinformatics↗

bioRxiv2026

The metastasis susceptibility gene RRP1B is a stress-responsive regulator of nuclear RNA processing and splicing in breast cancer

The tumor microenvironment exposes cancer cells to mechanical, thermal, hypoxic, and acidic stresses, yet how cells integrate these signals to remodel RNA processing remains poorly understood. Here, we show that ribosomal RNA Processing 1B (RRP1B), previously characterized as a nucleolar ribosome biogenesis factor and metastasis modifier, functions as a broad-spectrum stress sensor that dynamically repositions among the nuclear envelope, nucleolus, and nuclear speckles (NS). Relocalization is governed by multi-site phosphorylation within intrinsically disordered regions (IDRs): phosphomimetic substitutions promote NS-proximal condensate formation in an RNA-dependent manner, while unphosphorylatable substitutions confine RRP1B to the nucleolus. Under stress, the RRP1B interactome shifts globally, with ribosomal processing partners replaced by pre-mRNA splicing components enriched for NS-resident proteins. RNA immunoprecipitation sequencing (RIP-seq) demonstrates that under basal conditions RRP1B associates with long, intron-rich, nuclear periphery-proximal transcripts, whereas heat shock redirects binding toward shorter, exon-dense transcripts enriched for motifs of serine/arginine-rich (SR) proteins near the NS. RRP1B overexpression nearly abolishes cytoplasmic retained intron accumulation and drives preferential export of specific transcript isoforms in a compartment- and temperature-dependent manner, establishing RRP1B as a regulator of RNA localization fidelity rather than transcriptional output. An RRP1B overexpression signature is most highly activated in basal-like and claudin-low breast tumors, and the RRP1B-associated retained intron splicing program correlates with reduced survival in a tumor-grade-dependent manner. These findings reframe RRP1B as a microenvironmentally sensitive regulator of nuclear RNA processing with direct implications for aggressive breast cancer biology.

Cancer Biology↗

bioRxiv2026

Comprehensive Pan-Cancer Analysis Reveals the Prognostic, Molecular, and Immunological Significance of EPS8

Epidermal Growth Factor Receptor Pathway Substrate 8 (EPS8) is believed to function as a tumor driver; however, to understand its molecular characteristics across tumors, a comprehensive pan-cancer analysis of EPS8 is lacking. Objective: This study aimed to investigate the prognostic, molecular, and immunological significance of EPS8 across multiple human cancers. Methods: Comprehensive analyses were conducted using GEPIA2, UALCAN, TIMER2.0, cBioPortal, SMART, GSCA, Enrichr, and the TCGAplot R package to evaluate survival prognosis, gene expression, DNA methylation, immune infiltration, proteomic expression, tumor mutational burden (TMB), microsatellite instability (MSI), genetic alterations, drug sensitivity, and enriched pathways. Results: EPS8 overexpression in LGG (p=7.2x10-5) and PAAD (p=9.4x10-6) was significantly associated with poor overall survival and disease-free survival. Amplification was the most common genetic alteration, with alteration frequencies approaching 6% in TGCT and UCEC. In KIRC and PAAD, EPS8 expression correlated with tumor stage. Immune infiltration analysis revealed significant associations between EPS8 expression and immune cells. EPS8 expression also showed significant correlations with both TMB and MSI in STAD and ESCA. Enrichment analysis indicated an association between EPS8 and regulation of the actin cytoskeleton and similar pathways. Conclusion: These findings suggest that EPS8 has prognostic and immunological significance across multiple cancer types and may serve as a potential biomarker. The observed associations with immune-related features and drug response provide a basis for future experimental studies to evaluate its role in cancer biology and its potential relevance to immunotherapy.

cancer biology↗

bioRxiv2026

Targeting tRNA-Arg-TCT-4-1 suppresses cancer cell growth and tumorigenesis

tRNAs play a critical role in protein synthesis, influencing mRNA translation dynamics to shape proteomes. Emerging evidence links dysregulated tRNA activity to cancer progression, with tRNA-Arg-TCT identified as an oncogenic driver when ectopically overexpressed in non-malignant cells. The requirement of endogenous tRNA-Arg-TCT in cancer biology, however, remains untested. Moreover, considering that the tRNA-Arg-TCT family comprises six genes in humans, the importance of an individual tRNA isodecoder in cancer remains unknown. Here, we find elevated levels of tRNA-Arg-TCT-4-1 isodecoder are associated with poor patient prognosis across multiple cancer types. We demonstrate that, using different antisense RNA strategies, specific inhibition of tRNA-Arg-TCT-4-1 suppresses the growth of glioblastoma (GBM) and liposarcoma (LPS) cancer cells. Mechanistically, we find that tRNA-Arg-TCT-4-1 inhibition leads to a codon-biased remodeling of mRNA translation and the proteome, preferentially suppressing expression of growth-promoting genes and pathways encoded by mRNAs enriched in arginine AGA codons. Strikingly, intratumoral delivery of an antisense oligonucleotide (ASO) targeting tRNA-Arg-TCT-4-1 suppresses tumor growth and extends survival in mouse xenograft experiments performed using either a human LPS cell line or a patient-derived soft tissue sarcoma model. This study provides a foundation for targeting tRNA dysregulation as a novel therapeutic approach for cancer. One Sentence SummaryThis study identifies tRNA-Arg-TCT-4-1 as a new anti-cancer therapeutic target and demonstrates that an antisense oligonucleotide (ASO) targeting this tRNA effectively suppresses tumorigenesis and extends survival in mouse xenograft models.

cancer biology↗

bioRxiv2022

A human mesenchymal spheroid prototype to replace moderate severity animal procedures in leukaemia drug testing

Patient derived xenograft (PDX) models are regarded as gold standard preclinical models in leukaemia research, especially in testing new drug combinations where typically 45-50 animals are used per assay. 9000 animal experiments are performed annually in leukaemia research with these expensive procedures being described as moderate severity, meaning they cause significant pain, suffering and visible distress to animals state. Furthermore, not all clinical leukaemia samples engraft and when they do data turnaround time can be between 6-12 months. Heavy dependence on animal models is because clinical leukaemia samples do not proliferate in vitro. Alternative cell line models though popular for drug testing are not biomimetic - they are not dependent on the microenvironment for survival, growth and treatment response and being derived from relapse samples they do not capture the molecular complexity observed at disease presentation. Here we have developed an in vitro platform to rapidly establish co-cultures of patient-derived leukaemia cells with 3D bone marrow mesenchyme spheroids, BM-MSC-spheroids. We optimise protocols for developing MSC-spheroid leukaemia co-culture using clinical samples and deliver drug response data within a week. Using three patient samples representing distinct cytogenetics we show that patient-derived-leukaemia cells show enhanced proliferation when co-cultured with MSC-spheroids. In addition, MSC-spheroids provided improved protection against treatment. This makes our spheroids suitable to model treatment resistance - a major hurdle in current day cancer management Given this 3Rs approach is 12 months faster (in delivering clinical data), is a human cell-based biomimetic model and 45-50 animals/drug-response assay cheaper the anticipated target end-users would include academia and pharmaceutical industry. This animal replacement prototype would facilitate clinically translatable research to be performed with greater ethical, social and financial sustainability. Research HighlightsO_ST_ABSScientific BenefitC_ST_ABSA 3D spheroid-based approach for ex vivo co-culture of clinical leukaemia samples for further investigation into cancer biology such as blast-niche interactions, blast proliferation and treatment resistance 3Rs BenefitTo replace moderate severity animal procedures in leukaemia research and drug testing Practical Benefit3Rs approach that yields drug response data quickly and is more ethically, socially and financially sustainable than its in vivo counterparts Current applicationsExploration of leukaemia biology such as blasts proliferation, blast-niche interactions, niche-impacted treatment resistance and obtain drug response data Potential applicationsExtend the approach to include other haematological cancers as well as bone cancers.

cancer biology↗

bioRxiv2023

Deconstructing the contributions of heterogeneity to combination treatment of hormone sensitive breast cancer

Combination therapies are fundamental to cancer treatments, including in breast cancer the most common invasive malignancy in women. Breast cancer treatment is determined based on molecular subtypes, and since 2016, combination palbociclib and fulvestrant has been used to treat hormone receptor-positive breast cancer. However, the impact of heterogeneity of the tumour landscape and tumour composition dynamics on scheduling decisions remains poorly understood. To elucidate the contributions of variability at multiple scales to treatment outcomes in hormone receptor-positive breast cancer, we developed a simple mathematical model of two unique estrogen receptor positive (ER+) breast cancer cell types and their response to combination treatment with palbociclib and fulvestrant. We used this model to understand how the initial fraction of either cell type may impact the fraction remaining after treatment and examined how heterogeneity in pharmacokinetics and pharmacodynamics result in a large distribution of outcomes. Our results suggest that the pharmacokinetics and pharmacodynamics of fulvestrant were the major drivers of final tumour size and composition. We then leveraged our model to guide therapeutic scheduling of combination palbociclib and fulvestrant, demonstrating the use of mathematical modelling to improve our understanding of cancer biology and treatments.

cancer biology↗

bioRxiv2021

PTHrP Induces STAT5 Activation, Secretory Differentiation and Mammary Tumor Progression

BackgroundParathyroid hormone-related protein (PTHrP) is required for embryonic breast development and has important functions during lactation, when it is produced by alveolar epithelial cells and secreted into the maternal circulation to mobilize skeletal calcium used for milk production. PTHrP is also produced by breast cancers and GWAS studies suggest that it influences breast cancer risk. However, the exact functions of PTHrP in breast cancer biology remain unsettled. MethodsWe developed a tetracyline-regulated, MMTV (mouse mammary tumor virus)-driven model of PTHrP overexpression in mammary epithelial cells (Tet-PTHrP mice) and bred these mice with the MMTV-PyMT (polyoma middle tumor-antigen) breast cancer model to analyze the impact of PTHrP overexpression on normal mammary gland biology and in breast cancer progression. ResultsOverexpression of PTHrP in luminal epithelial cells caused alveolar hyperplasia and secretory differentiation of the mammary epithelium with milk production. This was accompanied by activation of Stat5 and increased expression of E74-like factor-5 (Elf5). In MMTV-PyMT mice, overexpression of PTHrP (Tet-PTHrP;PyMT mice) shortened tumor latency and accelerated tumor growth, ultimately reducing overall survival. Tumors overproducing PTHrP also displayed increased expression of nuclear pSTAT5 and Elf5, increased expression of markers of secretory differentiation and milk constituents, and histologically resembled secretory carcinomas of the breast. Overexpression of PTHrP within cells isolated from tumors, but not PTHrP exogenously added to cell culture media, led to activation of STAT5 and milk protein gene expression. In addition, neither ablating the Type 1 PTH/PTHrP receptor (PTH1R) in epithelial cells or treating Tet-PTHrP;PyMT mice with an anti-PTH1R antibody prevented secretory differentiation or altered tumor latency. These data suggest that PTHrP acts in a cell-autonomous, intracrine manner. Finally, expression of PTHrP in human breast cancers is associated with expression of genes involved in milk production and STAT5 signaling. ConclusionsOur study suggests that PTHrP promotes pathways leading to secretory differentiation and proliferation in both normal mammary epithelial cells and in breast tumor cells.

cancer biology↗