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bioRxiv2021

Systems medicine dissection of chromosome 1q amplification reveals oncogenic regulatory circuits and informs targeted therapy in cancer

Understanding the biological and clinical impact of copy number aberrations (CNA) in cancer remains an unmet challenge. Genetic amplification of chromosome 1q (chr1q-amp) is a major CNA conferring adverse prognosis in several cancers, including the blood cancer, multiple myeloma (MM). Although several chr1q genes portend high-risk MM disease, the underpinning molecular aetiology remains elusive. Here we integrate patient multi-omics datasets with genetic variables to identify 103 adverse prognosis genes in chr1q-amp MM. Amongst these, the transcription factor PBX1 is ectopically expressed by genetic amplification and epigenetic activation of its own preserved 3D regulatory domain. By binding to reprogrammed super-enhancers, PBX1 directly regulates critical oncogenic pathways, whilst in co-operation with FOXM1, activates a proliferative gene signature which predicts adverse prognosis across multiple cancers. Notably, pharmacological disruption of the PBX1-FOXM1 axis, including with a novel PBX1 inhibitor is selectively toxic against chr1q-amp cancer cells. Overall, our systems medicine approach successfully identifies CNA-driven oncogenic circuitries, links them to clinical phenotypes and proposes novel CNA-targeted therapy strategies in cancer. SignificanceWe provide a comprehensive systems medicine strategy to unveil oncogenic circuitries and inform novel precision therapy decisions against CNA in cancer. This first clinical multi-omic analysis of chr1q-amp in MM identifies a central PBX1-FOXM1 regulatory axis driving high-risk prognosis, as a novel therapeutic target against chr1q-amp in cancer.

cancer biology↗

bioRxiv2023

Modelling cell adaptation using internal variables accounting for cell plasticity in continuum mathematical biology

AO_SCPLOWBSTRACTC_SCPLOWCellular adaptation is the ability of cells to change in response to different stimuli and environmental conditions. It occurs via phenotypic plasticity, that is, changes in gene expression derived from changes in the physiological environment. This phenomenon is important in many biological processes, in particular in cancer evolution and its treatment. Therefore, it is crucial to understand the mechanisms behind it. Specifically, the emergence of the cancer stem cell phenotype, showing enhanced proliferation and invasion rates, is an essential process in tumour progression. We present a mathematical framework to simulate phenotypic heterogeneity in different cell populations as a result of their interaction with chemical species in their microenvironment, through a continuum model using the well-known concept of internal variables to model cell phenotype. The resulting model, derived from conservation laws, incorporates the relationship between the phenotype and the history of the stimuli to which cells have been subjected, together with the inheritance of that phenotype. To illustrate the model capabilities, it is particularised for glioblastoma adaptation to hypoxia. A parametric analysis is carried out to investigate the impact of each model parameter regulating cellular adaptation, showing that it permits reproducing different trends reported in the scientific literature. The framework can be easily adapted to any particular problem of cell plasticity, with the main limitation of having enough cells to allow working with continuum variables. With appropriate calibration and validation, it could be useful for exploring the underlying processes of cellular adaptation, as well as for proposing favorable/unfavourable conditions or treatments.

cancer biology↗

bioRxiv2025

Serine auxotrophy is a targetable vulnerability driven by PSAT1 suppression in AML

Serine metabolism is of growing biologic and therapeutic interest in cancer. Upregulation of the serine synthesis pathway (SSP) can fuel tumor growth, and cancers with this phenotype are often sensitive to SSP inhibitors. In parallel, dietary restriction of serine and glycine (SG) can suppress some cancers, but the determinants of sensitivity to this approach are poorly understood. This is especially true in acute myeloid leukemia (AML), where serine metabolism has been less explored. We report that a subset of human AML cell lines and primary samples are completely dependent on external serine, known as serine auxotrophy. These leukemias consistently suppressed the SSP enzyme PSAT1, failed to synthesize serine, responded to SG restriction in vivo, and were rescued by restoring PSAT1. We also found that AML with an SF3B1 K700E mutation showed additional dependence on the SSP enzyme PHGDH, that SG restriction synergized with venetoclax in serine auxotrophic AML, and that MECOM rearrangement was strongly associated with PSAT1 suppression and serine auxotrophy. These findings define a metabolically distinct AML subtype and nominate it for targeting by SG restriction.

cancer biology↗

bioRxiv2018

Multi-omic tumor data reveal diversity of molecular mechanisms underlying survival

Outcomes for cancer patients vary greatly even within the same tumor type, and characterization of molecular subtypes of cancer holds important promise for improving prognosis and personalized treatment. This promise has motivated recent efforts to produce large amounts of multidimensional genomic ( multi-omic) data, but current algorithms still face challenges in the integrated analysis of such data. Here we present Cancer Integration via Multikernel Learning (CIMLR), a new cancer subtyping method that integrates multi-omic data to reveal molecular subtypes of cancer. We apply CIMLR to multi-omic data from 36 cancer types and show significant improvements in both computational efficiency and ability to extract biologically meaningful cancer subtypes. The discovered subtypes exhibit significant differences in patient survival for 27 of 36 cancer types. Our analysis reveals integrated patterns of gene expression, methylation, point mutations and copy number changes in multiple cancers and highlights patterns specifically associated with poor patient outcomes.

cancer biology↗

bioRxiv2018

KH-type splicing regulatory protein controls colorectal cancer cell growth and modulates the tumor microenvironment

KH-type splicing regulatory protein (KHSRP) is a multifunctional nucleic acid binding protein implicated in key aspects of cancer cell biology: inflammation and cell-fate determination. However, the role KHSRP plays in colorectal cancer (CRC) tumorigenesis remains largely unknown. Using a combination of in silico analysis of large datasets, ex vivo analysis of protein expression in patients, and mechanistic studies using in vitro models of CRC, we investigated the oncogenic role of KHSRP. We demonstrated KHSRP expression in the epithelial and stromal compartments of both primary and metastatic tumors. Elevated expression was found in tumor versus matched normal tissue, and we validated these findings in larger independent cohorts in silico. KHSRP expression was a prognostic indicator of worse overall survival (HR=3.74, 95% CI = 1.43-22.97, p=0.0138). Mechanistic data in CRC cell line models supported a role of KHSRP in driving epithelial cell proliferation in both a primary and metastatic setting, through control of the G1/S transition. Additionally, KHSRP promoted a pro-angiogenic extracellular environment by regulating the secretion of oncogenic proteins involved in diverse cellular processes such as migration and response to cellular stress. Our study provides novel mechanistic insight into the tumor-promoting effects of KHSRP in CRC.

cancer biology↗

bioRxiv2024

A deep profile of gene expression across 18 human cancers

Clinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprecedented efficacy but has thus far been hampered by a lack of biological interpretability and robustness. Here, we present DeepProfile, a comprehensive framework that addresses current challenges in applying unsupervised deep learning to gene expression profiles. We use DeepProfile to learn low-dimensional latent spaces for 18 human cancers from 50,211 transcriptomes. DeepProfile outperforms existing dimensionality reduction methods with respect to biological interpretability. Using DeepProfile interpretability methods, we show that genes that are universally important in defining the latent spaces across all cancer types control immune cell activation, while cancer type-specific genes and pathways define molecular disease subtypes. By linking DeepProfile latent variables to secondary tumor characteristics, we discover that tumor mutation burden is closely associated with the expression of cell cycle-related genes. DNA mismatch repair and MHC class II antigen presentation pathway expression, on the other hand, are consistently associated with patient survival. We validate these results through Kaplan-Meier analyses and nominate tumor-associated macrophages as an important source of survival-correlated MHC class II transcripts. Our results illustrate the power of unsupervised deep learning for discovery of cancer biology from existing gene expression data.

bioinformatics↗

bioRxiv2024

RBN-2397, a PARP7 Inhibitor, Synergizes with Paclitaxel to Inhibit Proliferation and Migration of Ovarian Cancer Cells

ObjectivesMono(ADP-ribosyl)ation (MARylation), a post translational modification of proteins, is emerging as an important regulator of the biology of cancer cells. PARP7 (TiPARP), a mono (ADP-ribosyl) transferase (MART), MARylates its substrate -tubulin in ovarian cancer cells, promoting destabilization of microtubules, cell growth, and migration. Recent development of RBN-2397, a potent inhibitor that selectively acts on PARP7, has provided a new tool for exploring the role of PARP7 catalytic activity in biological processes. In this study, we investigated the role of PARP7 catalytic activity in the regulation of ovarian cancer cell biology via MARylation of -tubulin. MethodsOvarian cancer cell lines (OVCAR4, OVCAR3) were treated with RBN-2397 and paclitaxel, both separately and in combination. Western blotting and immunoprecipitation confirmed the effects of RBN-2397 on -tubulin MARylation and stabilization. Cell proliferation and migration were assessed, and -tubulin stabilization was quantified using immunofluorescent imaging. RNA-sequencing was performed to assess the effects on gene expression changes. ResultsRBN-2397 inhibited PARP7 activity, decreasing -tubulin MARylation, leading to its stabilization, and reducing cancer cell proliferation and migration. The addition of paclitaxel further enhanced these effects, highlighting a synergistic interaction between the two drugs. Mutating the site of PARP7-mediated MARylation on -tubulin similarly resulted in microtubule stabilization and decreased cell migration in the presence of paclitaxel. ConclusionsThis study demonstrates that targeting PARP7 with RBN-2397, particularly in combination with paclitaxel, offers an effective strategy for inhibiting aggressive ovarian cancer cell phenotypes. Our findings underscore the potential of combining PARP7 inhibitors with established chemotherapeutics to enhance treatment efficacy in ovarian cancer.

cancer biology↗

bioRxiv2026

Systematic multivariate analysis of chromatin complex dependencies reveals Set1C/COMPASS as a melanoma-enriched epigenetic vulnerability

Epigenetic dysregulation is a common feature of cancer. It creates vulnerabilities arising from an increased reliance on chromatin-based mechanisms that sustain malignant transcriptional states. While many chromatin regulators are broadly required for cellular viability, others function in a context-dependent manner across distinct oncogenic settings, tissue lineages, and differentiation states. Moreover, chromatin regulators often operate within multi-subunit complexes; thus, epigenetic vulnerabilities emerge from coordinated complex activities. Here, we integrate large-scale genetic dependency maps from human cancer cell lines with curated epigenetic complex annotations to perform a systematic, multivariate analysis of complex-level epigenetic dependencies across cancer lineages. Our analysis reveals that dependencies frequently cluster among functionally related chromatin complexes and that biologically related cancer types (e.g., hematologic malignancies) share similar dependency patterns, consistent with shared underlying epigenetic requirements. Focusing on melanoma, we identify multiple enriched epigenetic complex dependencies, including complexes previously associated with recurrent genetic alterations or melanocyte lineage regulation, as well as a previously unrecognized vulnerability involving the H3K4 methyltransferase complex Set1C/COMPASS. This dependency is not restricted to a specific melanoma differentiation state, but genetic depletion of CXXC1 (a critical complex-specific subunit) suggests that CXXC1-dependent melanoma cells require Set1C/COMPASS activity to maintain global H3K4 trimethylation (H3K4me3) and proliferation. Integrative modeling links Set1C/COMPASS dependency to MYC- and E2F-driven transcriptional programs, which are suppressed upon complex inhibition. Together, this work combines integrative, multivariate analysis of lineage-enriched epigenetic dependencies with genetic perturbation, transcriptional profiling, and single-cell analysis to uncover an enriched epigenetic dependency on Set1C/COMPASS in melanoma cells. Author SummaryCancer cells often rely on abnormal regulation of gene activity to support uncontrolled growth and survival. This regulation is controlled not only by genetic mutations, but also by epigenetic mechanisms, chemical and structural modifications to DNA and its associated proteins that determine which genes are turned on or off. Several therapies that target epigenetic regulators have shown promise, particularly in blood cancers. However, identifying which epigenetic mechanisms are most important in specific cancers remains challenging, especially because epigenetic regulators frequently work together as multi-protein complexes. In this study, we combine large-scale public datasets across many cancer lineages with computational modeling to systematically identify lineage-enriched epigenetic vulnerabilities. We found that certain epigenetic complexes are selectively important in specific cancer lineages. In melanoma, an aggressive skin cancer, we identified a previously unrecognized dependence on a protein complex that modifies chromatin at gene promoters. We show that disrupting this complex can impair gene programs that drive cell division and block cancer cell growth. Moreover, our study demonstrate how integrative computational approaches combined with experiments can uncover new targets for potential cancer therapy studies.

cancer biology↗

bioRxiv2025

ALDH2 Deficiency Drives Proliferative Mammary Morphogenesis and Epithelial Cell Stemness via Oxidative Stress and Estrogen Receptor Activation

Alcohol consumption has been linked to breast cancer, partly due to the accumulation of toxic aldehydes like acetaldehyde, a carcinogenic byproduct of ethanol metabolism. ALDH2, a key mitochondrial enzyme, detoxifies acetaldehyde and other harmful aldehydes that drive oxidative stress, DNA damage, and hormonal dysregulation, key factors in carcinogenesis. Despite the possible link between alcohol consumption and breast cancer risk, little is known about how ALDH2 deficiency itself, independent of alcohol exposure, affects mammary gland biology and cancer susceptibility. Genetic variants leading to ALDH2 deficiency are highly prevalent in East Asian populations, where individuals carrying inactive ALDH2 alleles experience impaired aldehyde detoxification. While these individuals are at increased risk for alcohol-related cancers, the impact of ALDH2 deficiency on mammary gland development and homeostasis in the absence of alcohol exposure remains unexplored. To address this, we utilized a C57BL/6-based ALDH2 knockout (Aldh2-/-) mouse model to investigate its effects on mammary proliferation and development. Our findings revealed that Aldh2-/- mice exhibited hyperproliferative mammary glands, characterized by increased epithelial cell density, ductal expansion, and elevated Ki67+ cells. Flow cytometry analysis demonstrated a rise in luminal and basal epithelial subpopulations, alongside enhanced mammary epithelial stemness, as evidenced by increased mammosphere formation and colony-forming efficiency. At the molecular level, ALDH2 deficiency activated oxidative stress pathways, marked by elevated 8-OHdG, p38 MAPK, NF-{kappa}B, and Nrf2 signaling, alongside DNA damage responses involving p53 and H2A.X. Importantly, we also identified a previously unrecognized upregulation of RANKL in Aldh2-/- mammary glands, implicating the RANK/RANKL axis as a critical mediator linking aldehyde stress to NF-{kappa}B/p38 MAPK activation and enhanced mammary stemness. Furthermore, hormonal dysregulation was observed, with a significant increase in ER and PR expression and phosphorylation. Dysregulated ER signaling was further linked to enhanced erbB3 activation and downstream signaling, including the cyclin D1-pRb-E2F1 axis. Our results suggest that the accumulation of endogenous aldehydes, independent of alcohol exposure, profoundly alters mammary morphogenesis, epithelial repopulation, and stemness. Mechanistically, this occurs through oxidative stress activation and DNA damage pathways, leading to metabolic changes and upregulation of estrogen receptor and receptor tyrosine kinase signaling. This study highlights for the first time the potential role of ALDH2 deficiency in increasing mammary tissue susceptibility to oncogenic factors and breast cancer risk.

cancer biology↗

bioRxiv2024

A Bayesian framework to infer and cluster mutational signatures leveraging prior biological knowledge.

Mutational signatures provide key insights into cancer mutational processes, but the availability of signature catalogues generated by different groups using distinct methodologies underscores a need for standardisation. We introduce a Bayesian framework that offers a systematic approach to expanding existing signature catalogues for any type of mutational signature, while grouping patients based on shared signature patterns. We demonstrate that this approach can identify both known and novel molecular subtypes across nearly 8,000 samples spanning six cancer types, and show that stratifications derived from signature yield prognostic groups, further enhancing the translational potential of mutational signatures.

cancer biology↗

bioRxiv2026

Unraveling AMPK and BET regulation of immune checkpoint biology: implications for personalized medicine

Triple negative breast cancer (TNBC) patients with comorbid Type 2 diabetes (T2D) show worse survival compared to nondiabetic TNBC patients. Immune checkpoint blockade (ICB) has unclear benefit in TNBC. Immune suppression in T2D, and use of metformin, an activator of 5 Adenosine Monophosphate-activated Protein Kinase (AMPK), in such patients, prompted us to examine AMPK regulation of immune checkpoint expression. Improved ICB efficacy may optimize outcomes for certain TNBC patients. We have also been exploring the role of Bromodomain and ExtraTerminal domain (BET) proteins (BRD2, BRD3, BRD4) in regulation of checkpoint molecules in immune cell subsets, including CD4+, CD8+ T cells, and NK cells. BET proteins are important transcriptional co-regulators, critical for proliferation and metastasis in many cancer types, including TNBC. We observed differential BET regulation of immune checkpoint proteins, specifically TIM-3, TIGIT, PD-1 and CTLA-4, on CD3/CD28-stimulated peripheral blood mononuclear cells by flow cytometry. Chemical inhibition of AMPK with Compound C, and with the pan-BET inhibitor JQ1 or the BRD4-selective PROTAC inhibitor MZ-1, revealed that BET proteins regulate PD-1 and CTLA-4 through an AMPK-dependent pathway and TIM-3 and TIGIT through an AMPK-independent pathway. Personalized approaches to ICB treatment of TNBC patients with comorbid T2D should improve outcomes.

cancer biology↗

bioRxiv2021

Rational Drug Design of Targeted and Enzyme Cleavable Vitamin E Analogs as Neoadjuvant to Chemotherapy: In Vitro and In Vivo Evaluation on Reduction of Cardiotoxicity of Doxorubicin.

Traditional drug design focus on specific target (s) expressed by cancer cells. However, cancer cells outsmart the interventions by activating survival pathways and/or downregulating cell death pathways. As the research in molecular biology of cancer grows exponentially, new methods of drug designs are needed to target multiple pathways/targets which are involved in survival of cancer cells. Vitamin E analogues including a-tocopheryl succinate (-TOS) is a well-known anti-tumoregenic agent which is well studied both in vitro and in vivo tumor models. However, lack of targeting cancer cells and unexpected toxicity along with the poor water solubility of -TOS compelled a rational drug design using both targeting and cleavable technologies incorporated in the new drug design. A plethora of Vitamin E derivatives (AMP-001, 002 and 003) were synthesized, characterized and studied for the improved efficacy and lowered toxicity in various cancer cells in vitro. Preliminary studies revealed AAAPT leading candidates reduced the invasive potential of brain tumor stem cells, synergized with different drugs and different treatments. AAAPT leading drug AMP-001 enhanced the therapeutic index of front-line drug Doxorubicin in triple negative breast cancer (TNBC) tumor rat model preserving the ventricular function when used as a neoadjuvant to Doxorubicin. These results may pave the way for reducing the cardiotoxicity of chemotherapy in clinical settings.

cancer biology↗

bioRxiv2022

Extracellular heparan 6-O-endosulfatases SULF1 and SULF2 in HNSC and other malignancies

SULF1 and SULF2 are oncogenic in a number of human malignancies, including head and neck squamous cell carcinoma (HNSC). The function of these two heparan sulfate editing enzymes was previously considered largely redundant but the biology of cancer suggests differences that we explore in our RNAseq and RNAScope studies of HNSC and in a pan cancer analysis using the TCGA and CPTAC (proteomics) data. Our studies document a consistent upregulation of SULF1 and SULF2 in HNSC which is associated with poor survival outcomes. SULF2 expression increases in multiple malignancies but less consistently than SULF1, which uniformly increases in the tumor tissues and negatively impacts survival in several types of cancer. Meanwhile, SULF1 showed low expression in cancer cell lines and a scRNAseq study of HNSC shows that SULF1 is not supplied by epithelial tumor cells, like SULF2, but is secreted by cancer associated fibroblasts. Our RNAScope and PDX analysis of the HNSC tissues fully confirm the stromal source of SULF1 and explain the uniform impact of this enzyme on the biology of multiple malignancies. In summary, the SULF1 enzyme, supplied by a subset of cancer associated fibroblasts, is upregulated and negatively impacts HNSC survival at an early stage of the disease progression while the SULF2 enzyme, supplied by tumor cells, impacts survival at later stages of HNSC. This paradigm is common to multiple malignancies and suggests a potential for diagnostic and therapeutic targeting of the heparan sulfatases in cancer diseases.

cancer biology↗

bioRxiv2022

Therapeutic vulnerability to ATR inhibition in concurrent NF1 and ATRX-deficient high-grade solid tumors

Subsets of Neurofibromatosis Type 1 (NF1)-associated solid tumors have been shown to display high frequencies of ATRX mutations and presence of alternative lengthening of telomeres (ALT). In addition, a potential biologic vulnerability of ALT-positive cancer cells is ATR inhibition, a regulator of homologous recombination. We studied the phenotype of combined NF1 and ATRX deficiency in malignant solid tumors. For these experiments, cell lines derived from NF1-deficient sporadic glioblastomas (U251, SF188), a NF1-associated ATRX mutant glioblastoma cell line (JHH-NF1-GBM1), a NF1-derived sarcoma cell line (JHH-CRC65), and two NF1-deficient MPNST cell lines (ST88-14, NF90.8) were utilized. Cancer cells were treated with ATR inhibitors (AZD6738 and VE-822), in vitro, with or without a MEK inhibitor (AZD6244, selumetinib) or temozolomide. In contrast to the glioma cell line SF188, combined ATRX knockout (KO) and TERC KO led to ALT-like properties and sensitized U251 glioma cells to ATR inhibition (AZD6738 and VE-822) in vitro and in vivo. In addition, ATR inhibitors sensitized U251 cells to temozolomide, but not MEK inhibition (AZD6244), irrespective of ATRX level manipulation; whereas, the JHH-NF1-GBM1 cell line (ATRX loss/ALT-positive) demonstrated sensitivity to ATR inhibition (AZD6738), but not temozolomide. Similar effects were noted using the MPNST cell line NF90.8 after combined ATRX knockdown and TERC KO; however, not in the MPNST cell line ST88-14. Taken together, our study supports the feasibility of targeting the ATR pathway in subsets of NF1-deficient and associated tumors. Tumors with pre-existing ALT, or that subsequently develop ALT after ATRX downregulation, are particularly vulnerable to this therapeutic approach.

cancer biology↗

bioRxiv2023

Proteomic characterisation of Sarculator nomogram-defined risk groups in soft tissue sarcomas of the extremities and trunk wall

BackgroundHigh-risk soft tissue sarcomas of the extremities and trunk wall (eSTS), as defined by the Sarculator nomogram, are more likely to benefit from (neo)adjuvant anthracycline-based therapy compared to low/intermediate-risk patients. The biology underpinning these differential treatment outcomes remain unknown. MethodsWe analysed proteomic profiles and clinical outcomes of 123 eSTS patients. A Cox model for overall survival including the Sarculator was fitted to individual data to define 4 risk groups. A DNA replication protein signature - Sarcoma Proteomic Module 6 (SPM6) was evaluated for association with clinicopathological factors and risk groups. SPM6 was added as a covariate together with Sarculator in a multivariable Cox model to assess improvement in prognostic risk stratification. ResultsDNA replication and cell cycle proteins were upregulated in high risk versus very low risk patients. Evaluation of the functional effects of CRISPR-Cas9 gene knockdown of proteins enriched in high risk patients identified candidate drug targets. SPM6 was significantly associated with tumour malignancy grade (p = 1.6e-06), histology (p = 1.4e-05) and risk groups (p = 2.6e-06). Cox model analysis showed that SPM6 substantially contributed to a better calibration of the Sarculator nomogram (Index of Prediction Accuracy =0.109 for Sarculator alone versus 0.165 for Sarculator + SPM6). ConclusionsRisk stratification of patient with STS is defined by distinct biological pathways across a range of cancer hallmarks. Incorporation of SPM6 protein signature improves prognostic risk stratification of the Sarculator nomogram. This study highlights the utility of integrating protein signatures for the development of next-generation nomograms.

cancer biology↗

bioRxiv2025

Integrating Single-Cell Biophysical and Transcriptomic Features to Resolve Functional Heterogeneity in Mantle Cell Lymphoma

Intra-tumor heterogeneity impacts disease progression and therapeutic resistance but remains poorly characterized by conventional histologic, immunophenotypic, and molecular approaches. Single-cell biophysical properties distinguish functional phenotypes complementary to these approaches, providing additional insight into cellular diversity. Here we link both buoyant mass and stiffness to gene expression to identify clinically relevant phenotypes within primary mantle cell lymphoma (MCL) cells, employing MCL as a model of biological and clinical diversity in human cancer. Linked measurements reveal that buoyant mass and stiffness characterize B-cell development states from naive to plasma cell and correlate with expression of oncogenic B-cell receptor signaling genes such as BLK and CD79A. Additionally, changes in cell buoyant mass within primary patient specimens ex vivo correlate with sensitivity to Brutons Tyrosine Kinase inhibitors in vivo in MCL and chronic lymphocytic leukemia, another B-cell malignancy. These findings highlight the value of biophysical properties as biomarkers of response in pursuit of future precision therapeutic strategies.

cancer biology↗

bioRxiv2020

Modulation of the tumour promoting functions of cancer associated fibroblasts by phosphodiesterase type 5 inhibition increases the efficacy of chemotherapy in human preclinical models of esophageal adenocarcinoma.

Background and aimsEsophageal adenocarcinoma (EAC) is chemoresistant in the majority of cases. The tumor-promoting biology of cancer associated fibroblasts (CAF) make them a target for novel therapies. Phosphodiesterase type 5 inhibitors (PDE5i) have been shown to regulate the activated fibroblast phenotype in benign disease. We investigated the potential for CAF modulation in EAC using PDE5i to enhance the efficacy of chemotherapy. MethodsEAC fibroblasts were treated with PDE5i and phenotypic effects examined using immunoblotting, immunohistochemistry, gel contraction, transwell invasion, organotypics, single cell RNAseq and shotgun proteomics. The combination of PDE5i with standard-of-care chemotherapy (Epirubicin, 5-Fluorouracil and Cisplatin) was tested for safety and efficacy in validated near-patient model systems (3D tumor growth assays (3D-TGAs) and patient derived xenograft (PDX) mouse models). ResultsPDE5i treatment reduced -SMA expression in CAFs by 50% (p<0.05), associated with a significant reduction in the ability of CAFs to contract collagen-1 gels and induce cancer cell invasion, (p<0.05). RNAseq and proteomic analysis of CAF and EAC cell lines revealed PDE5i specific regulation of pathways related to fibroblast activation and tumor promotion. 3D-TGA assays confirmed the importance of stromal cells to chemoresistance in EAC, which could be attenuated by PDE5i. Chemotherapy+PDE5i in PDX-bearing mice was safe and significantly reduced PDX tumor volume (p<0.05). ConclusionPDE5 is a candidate for clinical trials to alter the fibroblast phenotype in esophageal cancer. We demonstrate the specificity of PDE5i for fibroblasts to prevent transdifferentiation and revert the CAF phenotype. Finally, we confirm the efficacy of PDE5i in combination with chemotherapy in close-to-patient in vitro and in vivo PDX-based model systems.

cancer biology↗

bioRxiv2019

An R Implementation of Tumor-Stroma-Immune Transcriptome Deconvolution Pipeline using DeMixT

The deconvolution of transcriptomic data from heterogeneous tissues in cancer studies remains challenging. Available software faces difficulties for accurately estimating both component-specific proportions and expression profiles for individual samples. To address these challenges, we present a new R-implementation pipeline for the more accurate and efficient transcriptome deconvolution of high dimensional data from mixtures of more than two components. The pipeline utilizes the computationally efficient DeMixT R-package with OpenMP and additional cancer-specific biological information to perform three-component deconvolution without requiring data from the immune profiles. It enables a wide application of DeMixT to gene expression datasets available from cancer consortium such as the Cancer Genome Atlas (TCGA) projects, where, other than the mixed tumor samples, a handful of normal samples are profiled in multiple cancer types. We have applied this pipeline to two TCGA datasets in colorectal adenocarcinoma (COAD) and prostate adenocarcinoma (PRAD). In COAD, we found varying distributions of immune proportions across the Consensus Molecular Subtypes, from the highest to the lowest being CMS1, CMS3, CMS4 and CMS2. In PRAD, we found the immune proportions are associated with progression-free survival (p<0.01) and negatively correlated with Gleason scores (p<0.001). Our DeMixT-centered analysis protocol opens up new opportunities to investigate the tumor-stroma-immune microenvironment, by providing both proportions and component-specific expressions, and thus better define the underlying biology of cancer progression.\n\nAvailability and implementation: An R package, scripts and data are available: https://github.com/wwylab/DeMixTallmaterials.

bioinformatics↗