Model Of Cancer Progression Research Articles

Fractional and stochastic modeling of breast cancer progression with real data validation.

This study presents a novel approach to modeling breast cancer dynamics, one of the most significant health threats to women worldwide. Utilizing a piecewise mathematical framework, we incorporate both deterministic and stochastic elements of cancer progression. The model is divided into three distinct phases: (1) initial growth, characterized by a constant-order Caputo proportional operator (CPC), (2) intermediate growth, modeled by a variable-order CPC, and (3) advanced stages, capturing stochastic fluctuations in cancer cell populations using a stochastic operator. Theoretical analysis, employing fixed-point theory for the fractional-order phases and Ito calculus for the stochastic phase, establishes the existence and uniqueness of solutions. A robust numerical scheme, combining the nonstandard finite difference method for fractional models and the Euler-Maruyama method for the stochastic system, enables simulations of breast cancer progression under various scenarios. Critically, the model is validated against real breast cancer data from Saudi Arabia spanning 2004-2016. Numerical simulations accurately capture observed trends, demonstrating the model's predictive capabilities. Further, we investigate the impact of chemotherapy and its associated cardiotoxicity, illustrating different treatment response scenarios through graphical representations. This piecewise fractional-stochastic model offers a powerful tool for understanding and predicting breast cancer dynamics, potentially informing more effective treatment strategies.

P53 and ΔNp63 Transcriptional Programs in Coordination With TGF‐β1 and RAS Activation Regulate HAS2 and HAS3 in Epithelial Cells

ABSTRACTHyaluronan, p53, and transforming growth factor‐β1 (TGF‐β1) play important roles in skin function. They are involved in keratinocyte growth, migration, and differentiation, although the way they interact is not well understood. Analysis of p63 genome‐wide chromatin immunoprecipitation and sequencing (ChIP‐seq) revealed that ΔNp63, a paralogue of p53, binds to the genomic loci of HAS2 and HAS3, encoding hyaluronan synthases, in human‐immortalized keratinocyte HaCaT cells which express mutant p53. Additionally, ΔNp63 binding to these loci, particularly onto the HAS2 gene, was further increased by constitutively active (ca)RAS‐ and TGF‐β1‐mediated suppression of p53. We found that p53 suppressed HAS2 and HAS3 mRNA expression, while ΔNp63 increased their expression. When mutant p53 was silenced, there was a significant increase in HAS2 and HAS3 expression in unstimulated HaCaT cells. After TGF‐β1 stimulation, HAS2 expression was further increased while HAS3 expression was suppressed. Additionally, p53 mutation and abrogation affected the levels of secreted hyaluronan. Moreover, activation of RAS played a key role in regulating TGF‐β1‐mediated expression of HAS2 and the hyaluronan receptor CD44 in a cell‐specific manner. The level of HAS3 was associated with the malignant phenotype in the MCF10A breast cancer progression model. In conclusion, we found that endogenous p53 and ΔNp63 activities are pivotal in regulating HAS2 and HAS3 mRNA expression in connection to TGF‐β1 stimulation and RAS activation.

Cancer diagnosis and prognosis using multi-Omics data: A data science and machine learning approach

Cancer remains one of the leading causes of morbidity and mortality worldwide, demanding innovative approaches for early diagnosis and accurate prognosis. Recent advances in multi-omics technologies, which integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics, provide comprehensive insights into the complex biological mechanisms underlying cancer. By capturing molecular signatures at multiple levels, multi-omics data offers unparalleled potential for identifying cancer biomarkers, stratifying patients, and predicting therapeutic responses. However, the volume, complexity, and heterogeneity of multi-omics data present significant analytical challenges, necessitating robust data science and machine learning techniques. Machine learning algorithms, including supervised, unsupervised, and deep learning approaches, are increasingly being utilized to unravel the patterns embedded in multi-omics datasets. These methods enable feature selection, dimensionality reduction, and the integration of multi-modal data, facilitating the identification of precise biomarkers and the development of predictive models for cancer progression. Furthermore, advanced frameworks such as explainable AI (XAI) provide interpretability to these models, ensuring their clinical applicability and enhancing trust among healthcare professionals. This review highlights recent breakthroughs in cancer diagnosis and prognosis using multi-omics data, emphasizing the synergy between data science and machine learning in transforming oncology research. It also explores the challenges in data integration, algorithmic bias, and model validation, proposing solutions to enhance predictive accuracy and generalizability. By bridging molecular biology and computational sciences, this interdisciplinary approach has the potential to revolutionize precision oncology, paving the way for personalized treatment strategies and improved patient outcomes.

Accelerated growth and local progression of radiorecurrent prostate cancer in an orthotopic bioluminescent mouse model

Globally, prostate cancer is the second most common malignancy in males, with over 400 thousand men dying from the disease each year. A common treatment modality for localized prostate cancer is radiotherapy. However, up to half of high-risk patients can relapse with radiorecurrent prostate cancer, the aggressive clinical progression of which remains severely understudied. To address this, we have established an orthotopic mouse model for study that recapitulates the aggressive clinical progression of radiorecurrent prostate cancer. Radiorecurrent DU145 cells which survived conventional fraction (CF) irradiation were orthotopically injected into the prostates of athymic nude mice and monitored with bioluminescent imaging. CF tumours exhibited higher take rates and grew more rapidly than treatment-naïve parental tumours (PAR). Pathohistological analysis revealed extensive seminal vesicle invasion and necrosis in CF tumours, recapitulating the aggressive progression towards locally advanced disease exhibited by radiorecurrent tumours clinically. RNA sequencing of CF and PAR tumours identified ROBO1, CAV1, and CDH1 as candidate targets of radiorecurrent progression associated with biochemical relapse clinically. Together, this study presents a clinically relevant orthotopic model of radiorecurrent prostate cancer progression that will enable discovery of targets for therapeutic intervention to improve outcomes in prostate cancer patients.

Application of Original Prostate Cancer Progression Model Interacting with Fibroblasts in Preclinical Research.

Prostate cancer (PCa) is a heterogeneous disease that exhibits androgen sensitivity and responsiveness to androgen deprivation therapy (ADT). However, ADT induces only temporary remission, and the majority of PCa cases eventually progress to castration-resistant PCa (CRPC). During the development and progression of CRPC, androgen sensitivity and androgen receptor (AR) dependency in PCa cells are often deceased or lost due to ADT or spontaneously arising AR variants even before starting ADT. To prevent CRPC, a clinical PCa model derived from an AR-positive cancer cell line with weak or no androgen sensitivity is required. The human prostate LNCaP cell line is a good model for PCa because of its androgen sensitivity and AR dependency in terms of cell growth and gene expression. Notably, LNCaP cells are heterogeneous cells comprising different clones with natural variations in androgen sensitivity and AR dependency resulting from spontaneously occurring changes. In our group, to obtain androgen-insensitive or weakly sensitive clones spontaneously derived from parental LNCaP cells, we performed a limiting dilution of parental LNCaP cells and obtained several sublines with varying levels of androgen sensitivity and AR dependency. In addition, we established an androgen-insensitive subline from parental LNCaP cells by continuous passage under hormone-depleted conditions. This article provides a unique perspective on our original PCa progression model interacting with fibroblasts and its application in preclinical research.

Estimation of Selected Metals in the Tissues of Prostate Carcinoma Patients

It Indicated that the Prostate cancer is common it affects nearly one-third of male over the age of 45. For two fundamental reasons, prostate cancer (PCa) is a serious health issue and a good prospect for tissue-scale, individualized modeling of cancer progression. In vivo validation might be allowed due to its compact size and the ability to measure serum PSA, a for prostate cancer blood biomarker can be diagnosed with support if certain markers identified by immunohistochemistry on tissue sections can aid in the diagnosis of adenocarcinoma that is primary in the prostate gland or metastatic. The biology of prostate cancer can be deeply understood by the gene identification and gene patterns expression. Using the Affymetrix Uranium95a, U95b, and Uranium95c chip sets (37,777 genes and expression sequence tags), Comprehensive gene expression is performed study on 152 hompsapien samples, which were from men of all ages and included cancer tissues, prostate tissues next to tumors, and prostate tissue of donor. In the prostate tissues, the majority of metals show a random distribution; nevertheless, patients show comparatively more unpredictability than controls. However, our findings indicate that the pattern of gene expression in cancer-related tissues is altered to such an extent that it resembles the effects of a cancer field. In contrast to controls, PCA and CA demonstrated how hazardous metals interfered with the critical metals' normal physiological and metabolic roles in the prostate tissues of both patient groups. In comparison to hyperthyroid patients and controls, hypothyroid patients' prostate tissues had greater mean levels of Fe, Ni, Cr, and Hg. Additionally; we discovered that by employing a unique model, Prostate cancer aggressiveness can be predicted by gene expression patterns.

TGFβ1-TNFα regulated secretion of neutrophil chemokines is independent of epithelial-mesenchymal transitions in breast tumor cells.

Neutrophils have tumor-promoting roles in breast cancer and are detected in higher numbers in aggressive breast tumors. How aggressive breast tumors recruit neutrophils remains undefined. Here, we investigated the roles of TGF-β1 and TNF-α in the regulation of neutrophil recruitment by breast cancer cells. TGF-β1 and TNF-α are pro-inflammatory factors upregulated in breast tumors and induce epithelial to mesenchymal transitions (EMT), a process linked to cancer cell aggressiveness. We report that, as expected, dual treatment with TGF-β1 and TNF-α induces EMT signatures in premalignant M2 cells, which are part of the MCF10A breast cancer progression model. Conditioned media (CM) harvested from M2 cells treated with TGF-β1/TNF-α gives rise to amplified neutrophil chemotaxis compared to CM from control M2 cells. This response correlates with higher levels of the neutrophil chemokines CXCL1, CXCL2, and CXCL8 and is significantly attenuated in the presence of a CXCL8-neutralizing antibody. Furthermore, we found that secretion of CXCL1 and CXCL8 from treated M2 cells depends on p38MAPK activity. By combining gene editing, immunological and biochemical approaches, we show that the regulation of neutrophil recruitment and EMT signatures are not mechanistically linked in treated M2 cells. Finally, analysis of publicly available cancer cell line transcriptomic databases revealed a significant correlation between CXCL8 and TGF-β1/TNF-α-regulated or effector genes in breast cancer. Together, our findings establish a novel role for the TGF-β1/TNF-α/p38 MAPK signaling axis in regulating neutrophil recruitment in breast cancer, independent of TGF-β1/TNF-α regulated EMT.

Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States.

We developed and validated a progression model of prostate cancer using >1,000 tumor and normal tissue samples. The model provided a comprehensive view of prostate tumor evolution and progression.

Loss of multi-level 3D genome organization during breast cancer progression.

Breast cancer entails intricate alterations in genome organization and expression. However, how three-dimensional (3D) chromatin structure changes in the progression from a normal to a breast cancer malignant state remains unknown. To address this, we conducted an analysis combining Hi-C data with lamina-associated domains (LADs), epigenomic marks, and gene expression in an in vitro model of breast cancer progression. Our results reveal that while the fundamental properties of topologically associating domains (TADs) are overall maintained, significant changes occur in the organization of compartments and subcompartments. These changes are closely correlated with alterations in the expression of oncogenic genes. We also observe a restructuring of TAD-TAD interactions, coinciding with a loss of spatial compartmentalization and radial positioning of the 3D genome. Notably, we identify a previously unrecognized interchromosomal insertion event, wherein a locus on chromosome 8 housing the MYC oncogene is inserted into a highly active subcompartment on chromosome 10. This insertion is accompanied by the formation of de novo enhancer contacts and activation of MYC , illustrating how structural genomic variants can alter the 3D genome to drive oncogenic states. In summary, our findings provide evidence for the loss of genome organization at multiple scales during breast cancer progression revealing novel relationships between genome 3D structure and oncogenic processes.

Modeling metastatic progression from cross-sectional cancer genomics data.

Metastasis formation is a hallmark of cancer lethality. Yet, metastases are generally unobservable during their early stages of dissemination and spread to distant organs. Genomic datasets of matched primary tumors and metastases may offer insights into the underpinnings and the dynamics of metastasis formation. We present metMHN, a cancer progression model designed to deduce the joint progression of primary tumors and metastases using cross-sectional cancer genomics data. The model elucidates the statistical dependencies among genomic events, the formation of metastasis, and the clinical emergence of both primary tumors and their metastatic counterparts. metMHN enables the chronological reconstruction of mutational sequences and facilitates estimation of the timing of metastatic seeding. In a study of nearly 5000 lung adenocarcinomas, metMHN pinpointed TP53 and EGFR as mediators of metastasis formation. Furthermore, the study revealed that post-seeding adaptation is predominantly influenced by frequent copy number alterations. All datasets and code are available on GitHub at https://github.com/cbg-ethz/metMHN.

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice.

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review,we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

FKBP10-inhibited β-catenin ubiquitination in the malignant transformation of breast epithelial cells.

e12580 Background: Current model of breast cancer (BCa) progression suggests a linear and three-stage process from normal epithelial tissue to ductal carcinoma in situ (DCIS), and then invasive ductal carcinoma (IDC). Since the detailed mechanisms for BCa oncogenesis remain largely unclear and there is still a lack of root-cause level interventions, exploring the underlying molecular alterations during three-stage process of BCa carcinogenesis is urgently needed. Methods: Three-stage specimens from the very same patients were collected. High-throughput sequencing was applied to obtain the differentially expressed genes among normal, DCIS, and IDC samples. Expression level and prognostic value of the selected gene were evaluated by PCR, western blot, tissue chip, and bioinformatics analysis. MCF10DCIS and MCF10Ca cell lines were respectively used to establish DCIS and IDC tumor models in breast epithelial cell-specific gene knockout mice. Patient-matched DCIS and IDC organoids were also constructed. Signaling pathways associated with malignant transformation of breast epithelial cells were detected by using high-throughput sequencing in mouse models and organoids. Protein binding was evaluated by protein mass spectrometry and co-immunoprecipitation. Results: By using high-throughput sequencing, we found that FKBP10 levels were gradually increased in three-stage specimens from the very same patients and were associated with poor prognosis. By establishing DCIS and IDC models in FKBP10flox/floxCre+/- and FKBP10flox/floxCre-/- control mice, we confirmed that FKBP10 deficiency could suppress BCa formation. Besides, FKBP10 knockdown could reduce oncogenic potentials in patient-matched DCIS and IDC organoids. Wnt/β-catenin signaling pathway was related to malignant transformation of breast epithelial cells, when MCF10DCIS+FKBP10flox/floxCre+/- model, MCF10Ca+FKBP10flox/floxCre+/- model, FKBP10-knockdown DCIS and IDC organoids, and their corresponding controls were comprehensively compared. Moreover, FKBP10 was found to inhibit β-catenin ubiquitination with the help of deubiquitinase Trim13, thus activating Wnt/β-catenin signaling pathway and accelerating malignant transformation of breast epithelial cells. Increased level of β-catenin was able to promote FKBP10 expression in a feedback loop. We also confirmed that exosome-loaded FKBP10-targeting peptide could suppress tumor growth both in vitro and in vivo. Conclusions: FKBP10-inhibited β-catenin ubiquitination was responsible in the malignant transformation from normal tissue to DCIS, and then IDC. Our study would uncover novel regulatory mechanisms of FKBP10 in three-stage process of BCa carcinogenesis and provide a new therapeutic approach for clinical treatment.

Spatial interactions modulate tumor growth and immune infiltration.

Lenia, a cellular automata framework used in artificial life, provides a natural setting to implement mathematical models of cancer incorporating features such as morphogenesis, homeostasis, motility, reproduction, growth, stimuli response, evolvability, and adaptation. Historically, agent-based models of cancer progression have been constructed with rules that govern birth, death and migration, with attempts to map local rules to emergent global growth dynamics. In contrast, Lenia provides a flexible framework for considering a spectrum of local (cell-scale) to global (tumor-scale) dynamics by defining an interaction kernel governing density-dependent growth dynamics. Lenia can recapitulate a range of cancer model classifications including local or global, deterministic or stochastic, non-spatial or spatial, single or multi-population, and off or on-lattice. Lenia is subsequently used to develop data-informed models of 1) single-population growth dynamics, 2) multi-population cell-cell competition models, and 3) cell migration or chemotaxis. Mathematical modeling provides important mechanistic insights. First, short-range interaction kernels provide a mechanism for tumor cell survival under conditions with strong Allee effects. Next, we find that asymmetric interaction tumor-immune kernels lead to poor immune response. Finally, modeling recapitulates immune-ECM interactions where patterns of collagen formation provide immune protection, indicated by an emergent inverse relationship between disease stage and immune coverage.

Abstract 7378: Using game theory to investigate the therapeutic potential of disrupting tumor-microenvironment interactions

Abstract Cancer is a complex disease involving the interplay of several types of malignant and nonmalignant cells. The growth of tumors and surrounding stromal cells can be modeled as an ecosystem, where populations of cancerous and healthy cells interact and compete for resources. In this work, we have explored the application of game theoretic models of cancer to the design of novel anticancer therapies. Notably, a game theoretic-model of tumor-stromal interactions can capture the impact of interactions between cell types and can be used to investigate frequency-dependent parameters describing the impact of drug action during the initiation and growth of metastatic cancers. Previous studies using game theoretic models to describe cancer have focused on explaining progression patterns of specific tumors with known tumor-stromal interactions. This study takes a more general approach to cancer growth dynamics, modeling cancer as a simple game between healthy and cancer cells. This simplified model is used to make general predictions about cancer progression and the efficacy of treatment strategies. Our work suggests that disruption of beneficial interactions between healthy and cancer cells may be a viable treatment strategy for small tumors and preventing metastasis by ensuring that the healthy cell strategy is evolutionarily stable. The efficacy of treatment strategies that disrupt cancer cell cooperation or tumor-stromal dynamics changes as the cancer cell frequency changes. Our work suggests that drugs that target tumor-stromal cell interactions would need to be combined with other types of treatments to be effective against large tumors. Although based on a simplistic model of cancer initiation and progression, the framework derived here can be extended to the real-world design of experimental anticancer therapeutics, and fundamental analyses of this nature can be of practical utility in setting strategy for Target Product Profile design. Citation Format: Debra Van Egeren, Andrew Chen, Madison Stoddard, Lin Yuan, Arijit Chakravarty. Using game theory to investigate the therapeutic potential of disrupting tumor-microenvironment interactions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7378.

Rare subclonal sequencing of breast cancers indicates putative metastatic driver mutations are predominately acquired after dissemination

BackgroundEvolutionary models of breast cancer progression differ on the extent to which metastatic potential is pre-encoded within primary tumors. Although metastatic recurrences often harbor putative driver mutations that are not detected in their antecedent primary tumor using standard sequencing technologies, whether these mutations were acquired before or after dissemination remains unclear.MethodsTo ascertain whether putative metastatic driver mutations initially deemed specific to the metastasis by whole exome sequencing were, in actuality, present within rare ancestral subclones of the primary tumors from which they arose, we employed error-controlled ultra-deep sequencing (UDS-UMI) coupled with FFPE artifact mitigation by uracil-DNA glycosylase (UDG) to assess the presence of 132 “metastasis-specific” mutations within antecedent primary tumors from 21 patients. Maximum mutation detection sensitivity was ~1% of primary tumor cells. A conceptual framework was developed to estimate relative likelihoods of alternative models of mutation acquisition.ResultsThe ancestral primary tumor subclone responsible for seeding the metastasis was identified in 29% of patients, implicating several putative drivers in metastatic seeding including LRP5 A65V and PEAK1 K140Q. Despite this, 93% of metastasis-specific mutations in putative metastatic driver genes remained undetected within primary tumors, as did 96% of metastasis-specific mutations in known breast cancer drivers, including ERRB2 V777L, ESR1 D538G, and AKT1 D323H. Strikingly, even in those cases in which the rare ancestral subclone was identified, 87% of metastasis-specific putative driver mutations remained undetected. Modeling indicated that the sequential acquisition of multiple metastasis-specific driver or passenger mutations within the same rare subclonal lineage of the primary tumor was highly improbable.ConclusionsOur results strongly suggest that metastatic driver mutations are sequentially acquired and selected within the same clonal lineage both before, but more commonly after, dissemination from the primary tumor, and that these mutations are biologically consequential. Despite inherent limitations in sampling archival primary tumors, our findings indicate that tumor cells in most patients continue to undergo clinically relevant genomic evolution after their dissemination from the primary tumor. This provides further evidence that metastatic recurrence is a multi-step, mutation-driven process that extends beyond primary tumor dissemination and underscores the importance of longitudinal tumor assessment to help guide clinical decisions.

Health and economic effects of introducing single-dose or two-dose human papillomavirus vaccination in India

BackgroundCervical cancer is a major public health problem in India, where access to prevention programmes is low. The WHO-Strategic Advisory Group of Experts recently updated their recommendation for human papillomavirus...

Stronger Together: Cancer Clones Cooperate to Alleviate Growth Barriers in Critical Cancer Progression Transitions.

Hershey and colleagues recently showed how clones in a triple-negative breast cancer cell line cooperate for their mutual fitness benefit. In this system, clones exchange soluble metabolites to increase their in vitro growth rate at low population densities, therefore mitigating the documented growth barrier that reduces individual fitness in small tumor cell populations (Allee effect). Such cooperation could aid important transitions in cancer progression in which cancer cell populations are small, like invasion or metastasis. Using orthotopic transplantation, the authors demonstrate that this cooperation is functional in one such transition in vivo, increasing the metastatic load and number of metastases, which are usually polyclonal. Together, these findings highlight the need to consider ecologic interactions to properly understand tumor growth dynamics, and how they complement the standing evolutionary model of cancer progression in our quest to understand and treat cancer.

Design of a mucin-selective protease for targeted degradation of cancer-associated mucins.

Targeted protein degradation is an emerging strategy for the elimination of classically undruggable proteins. Here, to expand the landscape of targetable substrates, we designed degraders that achieve substrate selectivity via recognition of a discrete peptide and glycan motif and achieve cell-type selectivity via antigen-driven cell-surface binding. We applied this approach to mucins, O-glycosylated proteins that drive cancer progression through biophysical and immunological mechanisms. Engineering of a bacterial mucin-selective protease yielded a variant for fusion to a cancer antigen-binding nanobody. The resulting conjugate selectively degraded mucins on cancer cells, promoted cell death in culture models of mucin-driven growth and survival, and reduced tumor growth in mouse models of breast cancer progression. This work establishes a blueprint for the development of biologics that degrade specific protein glycoforms on target cells.

Core Fucosylation Mediated by the FucT-8 Enzyme Affects TRAIL-Induced Apoptosis and Sensitivity to Chemotherapy in Human SW480 and SW620 Colorectal Cancer Cells.

Epithelial cells can undergo apoptosis by manipulating the balance between pro-survival and apoptotic signals. In this work, we show that TRAIL-induced apoptosis can be differentially regulated by the expression of α(1,6)fucosyltransferase (FucT-8), the only enzyme in mammals that transfers the α(1,6)fucose residue to the pentasaccharide core of complex N-glycans. Specifically, in the cellular model of colorectal cancer (CRC) progression formed using the human syngeneic lines SW480 and SW620, knockdown of the FucT-8-encoding FUT8 gene significantly enhanced TRAIL-induced apoptosis in SW480 cells. However, FUT8 repression did not affect SW620 cells, which suggests that core fucosylation differentiates TRAIL-sensitive premetastatic SW480 cells from TRAIL-resistant metastatic SW620 cells. In this regard, we provide evidence that phosphorylation of ERK1/2 kinases can dynamically regulate TRAIL-dependent apoptosis and that core fucosylation can control the ERK/MAPK pro-survival pathway in which SW480 and SW620 cells participate. Moreover, the depletion of core fucosylation sensitises primary tumour SW480 cells to the combination of TRAIL and low doses of 5-FU, oxaliplatin, irinotecan, or mitomycin C. In contrast, a combination of TRAIL and oxaliplatin, irinotecan, or bevacizumab reinforces resistance of FUT8-knockdown metastatic SW620 cells to apoptosis. Consequently, FucT-8 could be a plausible target for increasing apoptosis and drug response in early CRC.

PTHrP Regulates Fatty Acid Metabolism via Novel lncRNA in Breast Cancer Initiation and Progression Models.

Parathyroid hormone-related peptide (PTHrP) is the primary cause of malignancy-associated hypercalcemia (MAH). We previously showed that PTHrP ablation, in the MMTV-PyMT murine model of breast cancer (BC) progression, can dramatically prolong tumor latency, slow tumor growth, and prevent metastatic spread. However, the signaling mechanisms using lineage tracing have not yet been carefully analyzed. Here, we generated Pthrpflox/flox; Cre+ mT/mG mice (KO) and Pthrpwt/wt; Cre+ mT/mG tumor mice (WT) to examine the signaling pathways under the control of PTHrP from the early to late stages of tumorigenesis. GFP+ mammary epithelial cells were further enriched for subsequent RNA sequencing (RNAseq) analyses. We observed significant upregulation of cell cycle signaling and fatty acid metabolism in PTHrP WT tumors, which are linked to tumor initiation and progression. Next, we observed that the expression levels of a novel lncRNA, GM50337, along with stearoyl-Coenzyme A desaturase 1 (Scd1) are significantly upregulated in PTHrP WT but not in KO tumors. We further validated a potential human orthologue lncRNA, OLMALINC, together with SCD1 that can be regulated via PTHrP in human BC cell lines. In conclusion, these novel findings could be used to develop targeted strategies for the treatment of BC and its metastatic complications.