Cancer Cell Populations Research Articles

Evaluating the role of wound-healing genes in conjunction with stool routine and serum tumor markers for colorectal cancer diagnosis and prognostic implications.

Colorectal cancer is a common malignant digestive tract tumour with high morbidity and mortality. Early detection, treatment and diagnosis are crucial for preventing and treating colorectal cancer, which develops through multi-stage accumulation and gene participation, affecting tumour marker levels. Chronic wounds can lead to the development of certain cancers, such as colorectal cancer. The prolonged inflammation and tissue repair caused by chronic wounds can trigger cellular changes, potentially promoting cancerous cell growth in the colon. The formation and progression of colorectal cancer involve changes in tumour markers, such as carcinoembryonic antigen (CEA), sugar chain antigen 19-9 (CA199) and CA125. This study explores the clinical application value of a stool routine combined with serum tumour marker detection in diagnosing colorectal cancer. The experiment team examined the clinical information of 56 colorectal cancer patients alongside a control group of 56 healthy patients. Distinct stool characteristics and heightened occult blood rates were evident in colorectal cancer cases. The combined approach integrating stool routine and serum tumour markers improved diagnostic accuracy, displaying enhanced sensitivity and specificity compared with individual markers or stool routines alone. Bioinformatics analysis indicated increased CEA and CA125 levels in colorectal cancer tissues versus normal tissues, hinting at potential prognostic implications. Exploring wound-healing genes like Vascular Endothelial Growth Factor A (VEGFA), Tumour Protein 53 (TP53) and Transforming Growth Factor Alpha (TGFA) revealed heightened expression in colorectal cancer, suggesting their potential role in disease progression. These markers showed associations with various immune cell types, suggesting their impact within the tumour microenvironment (p < 0.05). Single-cell RNA sequencing data highlighted varying CEA expressions across different cell populations in colorectal cancer. The findings indicated that integrating clinical assessments with accurate biomarkers may provide valuable insights into prognostic implications.

Simulation of the cancer cell growth and their invasion into healthy tissues using local radial basis function method

Applying mathematical models to simulate dynamic biological processes has been a common practice for a long time. In recent decades, cancer research has also adopted this approach to understand how cancer cell populations grow and spread. This study focuses on a mathematical model that uses a system of PDEs to explain the time-dependent reaction–diffusion interaction among cancer cells, extracellular matrix, and matrix degradation enzymes. We use a computational method that involves the discrete Galerkin technique by employing local radial basis functions (LRBFs) as its basis to approximate the behavior of cancer cells as they grow and invade neighboring healthy tissues. This novel approach employs a smaller set of nodes to approximate the solution, instead of considering all data in the given domain of the cancer growth model. Utilizing locally supported radial basis functions, this method significantly reduces the computational volume required, in contrast to globally supported radial basis functions. Finally, we provide experimental examples to validate and illustrate the effectiveness of this new scheme in modeling the growth and behavior of cancer cells at different stages.

Cell cycle heterogeneity and plasticity of colorectal cancer stem cells.

Cancer stem cells (CSCs) are a long-lived and self-renewing cancer cell population that drives tumor propagation and maintains cancer heterogeneity. They are also implicated in the therapeutic resistance of various types of cancer. Recent studies of CSCs in colorectal cancer (CRC) have uncovered fundamental paradigms that have increased understanding of CSC systems in solid tumors. Colorectal CSCs share multiple biological properties with normal intestinal stem cells (ISCs), including expression of the stem cell marker Lgr5. New evidence suggests that colorectal CSCs manifest substantial heterogeneity, as exemplified by the existence of both actively cycling Lgr5+ CSCs as well as quiescent Lgr5+ CSCs that are resistant to conventional anticancer therapies. The classical view of a rigid cell hierarchy and irreversible cell differentiation trajectory in normal and neoplastic tissues is now challenged by the finding that differentiated cells have the capacity to revert to stem cells through dynamic physiological reprogramming events. Such plasticity of CSC systems likely underlies both carcinogenesis and therapeutic resistance in CRC. Further characterization of the mechanisms underpinning the heterogeneity and plasticity of CSCs should inform future development of eradicative therapeutic strategies for CRC.

Dosage optimization for reducing tumor burden using a phenotype-structured population model with a drug-resistance continuum.

Drug resistance is a significant obstacle to effective cancer treatment. To gain insights into how drug resistance develops, we adopted a concept called fitness landscape and employed a phenotype-structured population model by fitting to a set of experimental data on a drug used for ovarian cancer, olaparib. Our modeling approach allowed us to understand how a drug affects the fitness landscape and track the evolution of a population of cancer cells structured with a spectrum of drug resistance. We also incorporated pharmacokinetic (PK) modeling to identify the optimal dosages of the drug that could lead to long-term tumor reduction. We derived a formula that indicates that maximizing variation in plasma drug concentration over a dosing interval could be important in reducing drug resistance. Our findings suggest that it may be possible to achieve better treatment outcomes with a drug dose lower than the levels recommended by the drug label. Acknowledging the current limitations of our work, we believe that our approach, which combines modeling of both PK and drug resistance evolution, could contribute to a new direction for better designing drug treatment regimens to improve cancer treatment.

Single-cell analysis identified POSTN+ cells associated with the aggressive phenotype and risk of esophageal squamous cell carcinoma

Tumors are intricate and heterogeneous systems characterized by mosaic cancer cell populations with diverse expression profiles. Leveraging single-cell technologies, we employed the Scissor algorithm to delineate an epithelial subpopulation associated with the aggressive phenotype in esophageal squamous cell carcinoma (ESCC). This identified subpopulation exhibited elevated expression of genes involved in critical pathways, such as epithelial-mesenchymal transition and PI3K-Akt. Key signature genes within this subpopulation, namely CAV1, COL3A1, COL6A1, POSTN, and TAGLN, demonstrated significant upregulation concomitant with both tumorigenesis and tumor progression across independent single-cell datasets. Furthermore, we selected 1,450 expression quantitative trait loci of the top 62 signature genes of this cell subpopulation to investigate their potential in predicting ESCC risk. The results showed that the POSTN loci were predominantly associated with ESCC susceptibility. Through functional annotation and replication analyses, we identified that the rs1028728 in the POSTN promoter was significantly associated with increased ESCC risk in 7,049 ESCC cases and 8,063 controls (odds ratio= 1.29, 95% confidence interval: 1.18-1.42, p= 4.03×10-8). Subsequent biochemical experiments showed that the rs1028728[T] allele enhanced POSTN expression by affecting the binding of PRRX1 in the POSTN promoter. In summary, our meticulous single-cell analysis delineates an invasive epithelial subpopulation in ESCC, with POSTN emerging as an important marker for the aggressive phenotype. These findings offer more insights into potential strategies for the prevention and intervention of ESCC, enriching our understanding of this complex cancer landscape.

Abstract B032: Comprehensive analysis of clonality, segregation errors, chromosomal instability and heterogeneity in cancer cell lines and cancer samples reveals that cancer evolution is often promoted by highly aberrant cellular divisions

Abstract Chromosomal instability (CIN) is the most common form of genomic instability in cancer. CIN leads to structural and numerical chromosomal aberrations and to heterogeneity of such aberrations inside the same tumor. Together with other forms of genomic and epigenomic instability, CIN underlies the most dangerous feature of cancer cells, namely, ability to evolve, allowing cancer cells to evade immunologic surveillance, to metastasize, to become resistant to drug treatments. Studies of chromosomal instability are significantly impeded by the lack of comprehensive approaches for simultaneous detection of different forms of ongoing chromosomal instability and their outcomes. We developed an approach that allows to analyze the clonal composition and chromosomal heterogeneity in cell lines and cancer samples, as well as rates of different forms of ongoing chromosomal instability in cancer cell lines. At the heart our analysis is a multiplex interphase FISH (miFISH) with seven sequential hybridizations of color probe panels resulting in the assessment of a total of 35 probes per single cell. Each probe panel is designed to include the centromeric probe and probes on the p- and q-arm for a given chromosome that allows for the simultaneous assessment of centromeric probes and gene-specific arm probes. This approach was applied to interphase nuclei, metaphase spreads and bi-nucleated/dividing cancer cells in colorectal and cervical cell lines and cancer samples. miFISH allows for automated acquisition of nuclei and metaphase spreads on a BioView spotcounting system. A gallery provides detailed views for semi-automatic as well as manual analysis of the obtained images. A total of 10000-25000 cell images were collected from each slide. The simultaneous evaluation of our strategically chosen centromeric and gene-specific probes within the same cells allows unprecedented insights in the patterns of distribution of genetic material among cancer cells in cancer cell population, and, especially, in dividing cells. We evaluated clonality, clonal and non-clonal chromosomal heterogeneity, ploidy distributions, and occurrence of endoreplication. Assessment of dividing cells based on equal or unequal distribution of miFISH centromeric and gene-specific signals allowed to estimate and compare the rates and outcomes of different forms of ongoing chromosomal instability such as structural rearrangements and amplifications, loss of chromosomes or chromosomal fragments in micronuclei, non-disjunctions, grossly asymmetrical cell divisions (including multipolar mitoses). Conclusions: Gradual changes in chromosomal/genomic content of cancer cells occur due to structural rearrangements, losses of chromosomes and chromosomal fragments in micronuclei, and by relatively rare single chromosome non-disjunctions. Grossly abnormal cell divisions (massive non-disjunctions and structural rearrangements) is the main source for the high level of chromosomal abnormalities and aneuploidy in evolved cancer cell populations. Citation Format: Anna Roschke. Comprehensive analysis of clonality, segregation errors, chromosomal instability and heterogeneity in cancer cell lines and cancer samples reveals that cancer evolution is often promoted by highly aberrant cellular divisions [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr B032.

Abstract IA006: Leveraging the extreme selection induced by therapeutic bottlenecks to subvert resistance via identifying evolutionarily-informed targets for orthogonal immunotherapy

Abstract Evolutionary dynamics underlie carcinogenesis, with tumor cells constantly adapting, resulting in cell populations that outgrow their non-tumor cell neighbors, evade treatment, and ultimately lead to death. Well-designed therapies to treating cancer induce immense and specific selection. The heterogenous cancer cell population either goes extinct or continues to evolve during and in response to the therapeutically induced bottleneck, leading to a subsequent selective sweep and newfound prominence of cells exhibiting the resistance phenotype. However, this bottleneck period provides a unique opportunity wherein the diversity of cells constituting the tumor is predictably reduced. Here, we introduce a framework to quantify the selection upon variants before, during, and after the therapeutic bottleneck, facilitating richer understanding of the mechanisms by which therapeutic resistance is acquired. We additionally couple measures of selection intensity with the prediction of neoantigens in a cohort of patients to identify potential orthogonal targets for immunotherapy. The result is a schema for leveraging predictability induced by the primary therapeutic bottleneck to drive the remnant tumor cell population to extinction. Finally, we demonstrate the utility of this schema in mutant EGFR lung adenocarcinoma. Citation Format: J. Nic Fisk, Katerina Politi, Scott Gettinger, Jeffrey P Townsend. Leveraging the extreme selection induced by therapeutic bottlenecks to subvert resistance via identifying evolutionarily-informed targets for orthogonal immunotherapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA006.

Abstract A023: Integrating mathematical modeling and in vitro experiments to measure ecological interactions in cancer

Abstract Integrating mathematical modeling and in vitro experiments to measure ecological interactions in cancer Maximilian Strobl, Dagim Tadele, Jeffrey Maltas, Rowan Barker-Clarke, Mina Dinh, Jacob Scott Over the past two decades it has become clear that tumours are complex and evolving ecosystems, in which different cancer cell populations interact with each other and with their non-tumour microenvironment. This understanding has given rise to the intriguing idea that we might be able to leverage these interactions to slow, or even revert, the expansion of resistant cells. In so-called “Adaptive Therapy” (AT) treatment is dynamically reduced to maintain the tumour burden tolerable whilst also preserving a pool of drug-sensitive cells which competitively suppress resistant cells. However, how strong and how common is this suppression, and how do we stratify patients who might benefit from AT from those who should receive standard-of-care continuous therapy? In this contribution, we present development of methodology and initial experimental results aimed at measuring the distribution of ecological interactions between drug-sensitive and resistant cells. Previously, we pioneered an assay with which to quantify ecological interactions in cancer in vitro. In the “Game Assay”, the two populations are co-cultured at a range of initial ratios in 2-D, and we record how the growth rate of each population is modulated by the relative abundance of the other. In the current study, we begin by using an agent-based mathematical model to characterize the accuracy and precision of this assay and how we may improve these by modifying different aspects of the experimental design (number of replicates, number of proportions) and analysis (regression technique, regression window). This shows that the choice of seeding ratios is important in being able to reliably identify interactions and we discuss guidelines for assay design. Upon identifying an interaction, a key next question is the underlying mechanism. Next, we thus use the model to investigate the degree to which we can use the assay to distinguish between local (e.g. contact inhibition) and non-local interactions (e.g. paracrine signaling). To conclude, we present preliminary experimental data in which we have used our optimized protocol to quantify the distribution of interactions between Osimertinib sensitive and resistant NSCLC cells (PC9). Our data show that while suppressive interactions do occur, they vary in their strength between different resistant lineages and can be attenuated in a frequency-dependent manner. Overall, we demonstrate how mathematical models can help in the design and interpretation of experimental assays and we contribute towards a more systematic and quantitative understanding of tumor ecology as a foundation for adaptive therapy. Citation Format: Maximilian A. R. Strobl, Dagim S. Tadele, Jeffrey Maltas, Rowan Barker-Clarke, Mina Dinh, Jacob Scott. Integrating mathematical modeling and in vitro experiments to measure ecological interactions in cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A023.

Abstract PR011: Comprehensive longitudinal tracking of lung cancer evolutionary clonal dynamics during therapy using circulating tumour DNA

Abstract Background: Outgrowth of resistance cancer cell populations is a common mechanism of therapy failure in oncology. Effective personalised medicine relies on targeting of ubiquitous aberrations; however, tumours are highly heterogenous. Liquid biopsies have the potential to provide representative tumour sampling at regular intervals through disease course, but extensive interrogation of longitudinal clonal evolution through therapy has been lacking, particularly for patients on a curative disease pathway. Methods: We analysed 1102 plasma samples from 202 early-stage non-small cell lung cancer patients who underwent multiregional whole exome sequencing of primary tumour, relapse and, where available, comprehensive post-mortem samples. We designed personalised panels targeting a median of 200 mutations for each patient and sequenced cell-free DNA (cfDNA) around these variant sites to saturation. For four patients we designed personalised panels including all clonal and subclonal mutations discovered (up to 600). One of these four patients received a personalised neoantigen vaccine. We used ECLIPSE to determine the clonal composition of low tumour fraction ctDNA. Results: We found a directly proportional correlation between the cancer cell fraction (CCF) of subclones estimated in plasma and multiregional tissue samples at surgery. Lower plasma CCFs were observed for clonal illusion mutations from randomly selected single samples, one for each TRACERx patient, when compared to truly clonal mutations (P &amp;lt; 0.001, OR = 0.44), distinguishing appropriate therapeutic targets without multiregional sampling. In 24 patients with both cfDNA and tissue available at relapse we detected 28/29 relapse tissue subclones in the cfDNA with an additional 8 cfDNA unique subclones. We found a strong bias for cfDNA unique subclones to be present in only subset of cells (P = 0.008, OR = 5.5), consistent with insufficient tissue sampling. We observed longitudinal clonal dynamics which were often, but not always, associated with treatment. Metastatic competent subclones had higher CCFs in the primary tumour, as measured in pre-operative plasma (P &amp;lt; 0.001, OR = 4.5). Upon receiving a personalised neoantigen vaccine a patient had regression of clones harbouring a targeted EGFR mutation. However, copy number loss of the targeted EGFR mutation occurred in a liver lesion which grew over the treatment period on surveillance radiology alongside a novel ctDNA subclone. Upon cessation vaccine treatment the resistance-associated subclone and liver lesion underwent regression. EGFR mutant clones become dominant in the tumour thereafter suggesting a fitness cost associated with vaccine resistant EGFR mutation loss in this patient, measured using ctDNA. Conclusions: We find evidence that comprehensive tracking of subclonal mutations in plasma can accurately profile the clonal structure of tumours over time, revealing clonal dynamics in response to treatment and biological determinants of metastasis with the potential to guide personalised medicine. Citation Format: Alexander M. Frankell, Christopher Abbosh, Kristiana Grigoriadis, Emilia Lim, Selvaraju Veeriah, Sophia Ward, Tak Karasaki, James R. M. Black, Mariam Jamal-Hanjani, Nicholas McGranahan, Charles Swanton. Comprehensive longitudinal tracking of lung cancer evolutionary clonal dynamics during therapy using circulating tumour DNA [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR011.

Abstract A022: Dissecting patterns of small cell lung cancer evolution using deep whole genome sequencing of circulating tumor DNA

Abstract Background: Small cell lung cancer (SCLC) is the most lethal form of lung cancer. A key driver of this near universal lethality is SCLC’s recalcitrance to therapy. The standard of care for SCLC is a chemotherapy doublet of etoposide and cis/carboplatin in combination with immunotherapy (EP+IO). While most tumors respond to these therapies, &amp;gt;80% of SCLCs progress within one year of treatment. Additionally, the aggressive clinical course of SCLC has historically precluded collection of patient-matched treatment naïve and recurrent tumor tissue traditionally required to study evolution. In lieu of tissue, several groups have used circulating tumor DNA (ctDNA) to study SCLC evolution. However, these studies used targeted sequencing panels that analyze &amp;lt;0.04% of the SCLC genome. Using new methods to comprehensively trace how SCLCs rapidly evolve to therapy resistance is a significant unmet need. Methods: Medical records were reviewed to identify treatment naïve SCLC patients treated at MD Anderson that had both pre- and post-EP+IO liquid biopsies collected under IRB approved protocols. ctDNA was isolated from plasma using a MiniMax High Efficiency Cell-Free DNA Isolation Kit (Apostle Bio). Germline DNA was extracted from buffy coat samples using a QIAamp DNA Blood Midi Kit (Qiagen). Deep whole genome sequencing (WGS) (&amp;gt;100X) of ctDNA and WGS of germline DNA (&amp;gt;60X) was performed using an Illumina NovaSeq X sequencer. Reads were aligned to the hg38 reference genome using BWA-mem. Somatic mutations were called using MuTect2, and clonal and subclonal copy number alterations were identified using Battenberg. DPClust was used to reconstruct cancer cell populations, leveraging mutation and copy number calls. Results: We identified several deleterious genomic alterations impacting TP53 and RB1 tumor suppressor genes, known genetic drivers of SCLC development. We also detected recurrent, whole chromosome arm loss of heterozygosity events impacting many chromosomes in treatment naïve samples, some of which are known to be present in &amp;gt;80% of SCLCs. Additionally, we found that both treatment naïve and recurrent SCLCs were composed of multiple subclones, each defined by dozens to thousands of unique somatic mutations. COSMIC signature analysis identified high activity of tobacco carcinogens, APOBEC, and replicative-based processes across ctDNA samples. We also identified emergence of novel subclones carrying unique copy number alterations following EP+IO recurrence. Importantly, the affected regions encode key effectors that regulate anti-tumor immune responses and cell survival following DNA damage, both of which are intimately related to the mechanism of action of EP+IO. Conclusions: Our data shows that deep WGS of ctDNA recapitulates known genetics of SCLC and allows for sensitive characterization of therapy resistant clones. Using this method, we have captured snapshots of both intrinsic and acquired resistance mechanisms to frontline EP+IO. Deep WGS of ctDNA is a promising approach to comprehensively dissect routes of SCLC evolution and therapy resistance. Citation Format: Benjamin B. Morris, Zhihui Zhang, Simon Heeke, Kyle Concannon, Bingan Zhang, Waree Rinsurongkawong, Vadeerat Rinsurongkawong, J. Jack Lee, Jianjun Zhang, Don L. Gibbons, Ara A. Vaporciyan, Carl M. Gay, Hai T. Tran, John V. Heymach, Lauren A. Byers, Peter Van Loo. Dissecting patterns of small cell lung cancer evolution using deep whole genome sequencing of circulating tumor DNA [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A022.

Integrative analysis of spatial and single-cell transcriptome data from human pancreatic cancer reveals an intermediate cancer cell population associated with poor prognosis

BackgroundRecent studies using single-cell transcriptomic analysis have reported several distinct clusters of neoplastic epithelial cells and cancer-associated fibroblasts in the pancreatic cancer tumor microenvironment. However, their molecular characteristics and biological significance have not been clearly elucidated due to intra- and inter-tumoral heterogeneity.MethodsWe performed single-cell RNA sequencing using enriched non-immune cell populations from 17 pancreatic tumor tissues (16 pancreatic cancer and one high-grade dysplasia) and generated paired spatial transcriptomic data from seven patient samples.ResultsWe identified five distinct functional subclusters of pancreatic cancer cells and six distinct cancer-associated fibroblast subclusters. We deeply profiled their characteristics, and we found that these subclusters successfully deconvoluted most of the features suggested in bulk transcriptome analysis of pancreatic cancer. Among those subclusters, we identified a novel cancer cell subcluster, Ep_VGLL1, showing intermediate characteristics between the extremities of basal-like and classical dichotomy, despite its prognostic value. Molecular features of Ep_VGLL1 suggest its transitional properties between basal-like and classical subtypes, which is supported by spatial transcriptomic data.ConclusionsThis integrative analysis not only provides a comprehensive landscape of pancreatic cancer and fibroblast population, but also suggests a novel insight to the dynamic states of pancreatic cancer cells and unveils potential therapeutic targets.Graphical

A Countable-Type Branching Process Model for the Tug-of-War Cancer Cell Dynamics.

We consider a time-continuous Markov branching process of proliferating cells with a countable collection of types. Among-type transitions are inspired by the Tug-of-War process introduced by McFarland et al. (Proc Natl Acad Sci 111(42):15138-15143, 2014) as a mathematical model for competition of advantageous driver mutations and deleterious passenger mutations in cancer cells. We introduce a version of the model in which a driver mutation pushes the type of the cell L-units up, while a passenger mutation pulls it 1-unit down. The distribution of time to divisions depends on the type (fitness) of cell, which is an integer. The extinction probability given any initial cell type is strictly less than 1, which allows us to investigate the transition between types (type transition) in an infinitely long cell lineage of cells. The analysis leads to the result that under driver dominance, the type transition process escapes to infinity, while under passenger dominance, it leads to a limit distribution. Implications in cancer cell dynamics and population genetics are discussed.

Abstract C024: Single-cell transcriptomics reveals unique stromal and metabolic heterogeneity in liver metastatic pancreatic cancer

Abstract Pancreatic ductal adenocarcinoma (PDA) is the 3rd leading cause of cancer related deaths in the US. This mortality is strongly linked to the complex PDA tumor microenvironment that drives resistance to chemotherapy and immune suppression. Most patients succumb to metastatic disease, with liver being the most frequently colonized organ. Despite this, most pre-clinical studies have focused on primary PDA models to study the disease. Accordingly, the extent that the liver metastatic niche promotes immune evasion and resistance to treatment remains unclear. To directly compare primary vs. metastatic tumors in PDA tumors, we have optimized a syngeneic experimental model to contrast primary vs. metastatic lesions. To define the similarities and differences between cell types present in these tumors, we employed a single-cell transcriptomics approach. Our analysis has identified shared populations of malignant epithelial, stromal, and immune cells between lesions, allowing for a direct comparison of programming. Here, we have observed that the dominant populations of PDA cells in pancreas vs. liver tumors engage in distinct metabolic programs that suggest different actionable vulnerabilities. Beyond these, we also observed fibroblasts and myeloid sub-populations that are exclusive to liver tumors. These data further support our hypothesis that alternative mechanisms of metabolic, immune, and stromal interactions take place in the metastatic tumor microenvironment. To define these crosstalk networks, we employed the interactome tool CellChat, which has uncovered unique signaling interactions between the CAF and cancer cell populations in pancreatic and liver tumors. Overall, these data have the potential to lead to new avenues to directly target cancer cells or engage an effective immune response that can be designed to treat metastatic pancreatic cancer- an urgent clinical need. Citation Format: Rima Singh, Natalie Yousefian, Nina Steele, Christopher Halbrook. Single-cell transcriptomics reveals unique stromal and metabolic heterogeneity in liver metastatic pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C024.

Modelling count data with partial differential equation models in biology

Partial differential equation (PDE) models are often used to study biological phenomena involving movement-birth–death processes, including ecological population dynamics and the invasion of populations of biological cells. Count data, by definition, is non-negative, and count data relating to biological populations is often bounded above by some carrying capacity that arises through biological competition for space or nutrients. Parameter estimation, parameter identifiability, and making model predictions usually involves working with a measurement error model that explicitly relating experimental measurements with the solution of a mathematical model. In many biological applications, a typical approach is to assume the data are normally distributed about the solution of the mathematical model. Despite the widespread use of the standard additive Gaussian measurement error model, the assumptions inherent in this approach are rarely explicitly considered or compared with other options. Here, we interpret scratch assay data, involving migration, proliferation and delays in a population of cancer cells using a reaction–diffusion PDE model. We consider relating experimental measurements to the PDE solution using a standard additive Gaussian measurement error model alongside a comparison to a more biologically realistic binomial measurement error model. While estimates of model parameters are relatively insensitive to the choice of measurement error model, model predictions for data realisations are very sensitive. The standard additive Gaussian measurement error model leads to biologically inconsistent predictions, such as negative counts and counts that exceed the carrying capacity across a relatively large spatial region within the experiment. Furthermore, the standard additive Gaussian measurement error model requires estimating an additional parameter compared to the binomial measurement error model. In contrast, the binomial measurement error model leads to biologically plausible predictions and is simpler to implement. We provide open source Julia software on GitHub to replicate all calculations in this work, and we explain how to generalise our approach to deal with coupled PDE models with several dependent variables through a multinomial measurement error model, as well as pointing out other potential generalisations by linking our work with established practices in the field of generalised linear models.

Navigating challenges: optimising methods for primary cell culture isolation

Primary cell lines are invaluable for exploring cancer biology and investigating novel treatments. Despite their numerous advantages, primary cultures are laborious to obtain and maintain in culture. Hence, established cell lines are still more common. This study aimed to evaluate a range of techniques for isolating primary breast cancer cultures, employing distinct enzymatic compositions, incubation durations, and mechanical approaches, including filtration. Out of several protocols, we opted for a highly effective method (Method 5) that gave rise to a primary cell culture (BC160). This method combines mechanical disaggregation and enzymatic digestion with hyaluronidase and collagenase. Moreover, the paper addresses common issues in isolating primary cultures, shedding light on the struggle against fibroblasts overgrowing cancer cell populations. To make primary cell lines a preferred model, it is essential to elaborate and categorise isolation methods, develop approaches to separate heterogeneous cultures and investigate factors influencing the establishment of primary cell lines.

Theoretical understanding of evolutionary dosing following tumor dynamics

Recent preclinical and clinical studies have suggested that evolution-based cancer therapies exploiting intratumor competition can significantly delay resistance. Various mathematical and computational models have been used to explain the effectiveness of particular treatment strategies. However, this evolutionary approach often assumes only preexisting resistance, although acquired resistance and cancer phenotype plasticity can dramatically affect treatment outcomes. We theoretically investigate whether an evolutionary therapy that capitalizes on plasticity could delay resistance. In particular, we propose a time-dependent evolutionary dose that can modulate the evolving competition among drug-sensitive, drug-resistant, and plastic cells. We assume a Lotka–Volterra type competition model of cancer cell populations with constant carrying capacity to analyze the dynamics of tumor growth to show that some tumors can be controlled with proper dosing, which balances the competition between different cell species so that they coexist in a stable tumor below a tolerable burden. The emergence of a new resistant species may further increase the tumor volume, which can be nullified by lowering the dose. In contrast, increasing the tumor volume due to new metastatic formation requires a higher dose. Additionally, we propose tumor-dynamics-driven adaptive scheduling consisting of subsequent treatment holidays and low- and high-dose periods. This approach can contain the tumor at variable volumes below the tolerable burden, which may facilitates reiteration of the treatment strategy in the case of any evolutionary changes. Overall, this study provides a theoretical understanding of the evolutionary dosing that can delay resistance.

The importance of cancer-associated fibroblasts in targeted therapies and drug resistance in breast cancer.

Cancer-associated fibroblasts (CAFs) play a substantial role in the tumor microenvironment, exhibiting a strong association with the advancement of various types of cancer, including breast, pancreatic, and prostate cancer. CAFs represent the most abundant mesenchymal cell population in breast cancer. Through diverse mechanisms, including the release of cytokines and exosomes, CAFs contribute to the progression of breast cancer by influencing tumor energy metabolism, promoting angiogenesis, impairing immune cell function, and remodeling the extracellular matrix. Moreover, CAFs considerably impact the response to treatment in breast cancer. Consequently, the development of interventions targeting CAFs has emerged as a promising therapeutic approach in the management of breast cancer. This article provides an analysis of the role of CAFs in breast cancer, specifically in relation to diagnosis, treatment, drug resistance, and prognosis. The paper succinctly outlines the diverse mechanisms through which CAFs contribute to the malignant behavior of breast cancer cells, including proliferation, invasion, metastasis, and drug resistance. Furthermore, the article emphasizes the potential of CAFs as valuable tools for early diagnosis, targeted therapy, treatment resistance, and prognosis assessment in breast cancer, thereby offering novel approaches for targeted therapy and overcoming treatment resistance in this disease.

Bioengineered hydrogels enhance ex vivo preservation of patient-derived tumor explants for drug evaluation

Ex vivo patient-derived tumor slices (PDTS) are currently limited by short-term viability in culture. Here, we show how bioengineered hydrogels enable the identification of key matrix parameters that significantly enhance PDTS viability compared to conventional culture systems. As demonstrated using single-cell RNA sequencing and high-dimensional flow cytometry, hydrogel-embedded PDTS tightly preserved cancer, cancer-associated fibroblast, and various immune cell populations and subpopulations in the corresponding original tumor. Cell-cell communication networks within the tumor microenvironment, including immune checkpoint ligand-receptor interactions, were also maintained. Remarkably, our results from a co-clinical trial suggest hydrogel-embedded PDTS may predict sensitivity to immune checkpoint inhibitors (ICIs) in head and neck cancer patients. Further, we show how these longer term-cultured tumor explants uniquely enable the sampling and detection of temporal evolution in molecular readouts when treated with ICIs. By preserving the compositional heterogeneity and complexity of patient tumors, hydrogel-embedded PDTS provide a valuable tool to facilitate experiments targeting the tumor microenvironment.

Cleanroom-Free Microfluidic Device for Natural Induction of Hypoxia in 2D and 3D Tumor Models.

Hypoxia is a common and critical feature of solid tumors that contributes to the plasticity and heterogeneity of the cancer cells. Cancer cell populations take on a region-specific adaptation induced by hypoxia, and each cancer cell population will show different levels of sensitivity and resistance to cancer therapeutics. Therefore, a faithful recapitulation of tumor hypoxia that allows for accurate assessments of hypoxia-induced adaptations, heterogeneity, and response to therapy is needed to develop new therapeutic approaches. The existing hypoxic tumor models rely on complex fabrication methods and external gas sources that make them unfavorable for the early-stage screening of new therapeutics. Here, we demonstrate how to establish a cleanroom-free microfluidic device that supports both 2D and 3D hypoxic tumor modeling through natural cancer cell metabolism and confirm the induction of the hypoxic gradient.

Optimal strategies of oncolytic virus-bortezomib therapy via the apoptotic, necroptotic, and oncolysis signaling network.

Bortezomib and oncolytic virotherapy are two emerging targeted cancer therapies. Bortezomib, a proteasome inhibitor, disrupts protein degradation in cells, leading to the accumulation of unfolded proteins that induce apoptosis. On the other hand, virotherapy uses genetically modified oncolytic viruses (OVs) to infect cancer cells, trigger cell lysis, and activate anti-tumor response. Despite progress in cancer treatment, identifying administration protocols for therapeutic agents remains a significant concern, aiming to strike a balance between efficacy, minimizing toxicity, and administrative costs. In this work, optimal control theory was employed to design a cost-effective and efficient co-administration protocols for bortezomib and OVs that could significantly diminish the population of cancer cells via the cell death program with the NF$ \kappa $B-BAX-RIP1 signaling network. Both linear and quadratic control strategies were explored to obtain practical treatment approaches by adapting necroptosis protocols to efficient cell death programs. Our findings demonstrated that a combination therapy commencing with the administration of OVs followed by bortezomib infusions yields an effective tumor-killing outcome. These results could provide valuable guidance for the development of clinical administration protocols in cancer treatment.