Biology subjects

Pienta, K. J.

Publications and source records attributed to Pienta, K. J..

17 recordsLinked to original sources

bioRxiv2026

Indirect genomic effects shape cancer risk across species

Tumour prevalence varies dramatically throughout the animal kingdom despite broadly conserved cellular and developmental processes, raising the question of how evolution has shaped susceptibility 1,2. Here, we link macroevolutionary variation in tumour prevalence to gene-level selection by integrating comparative genomics data from 109 species of birds and mammals using a Bayesian phylogenetic framework to estimate pangenome-wide rates of genetic evolution across >150 million years of evolutionary change. We identify 3,206 genes in which natural selection is associated with shifts in tumour prevalence, with more than 80% of which are linked to reduced prevalence, suggesting pervasive selection for cancer suppression. Using causal phylogenetic inference, we show that genes associated with reduced tumour prevalence act predominantly through indirect effects on body size, revealing growth as a key mediator of cancer risk across species. In contrast, genes associated with increased tumour prevalence exert direct effects independent of body size. Finally, at the species-level, we demonstrate that exceptionally low rates of benign tumours do not necessarily coincide with reduced malignancy, revealing that benign and malignant tumour processes are evolutionarily decoupled. Together, these results reveal how natural selection has fine-tuned the link between genotype, phenotype, and cancer risk across species.

evolutionary biology↗

bioRxiv2026

Drug-tolerant persister cells reallocate carbon sources to fuel antioxidant metabolism for survival

Therapy resistance is the leading cause of cancer-related deaths. Drug-tolerant persister cells (DTPs) represent a major barrier to cancer cure, mediating resistance through adaptive cell state transitions and driving tumor progression. Here, we investigate metabolic differences between DTPs and drug-sensitive cancer cells using integrated fluxomics. Proteomic profiling and extracellular flux analyses revealed that DTPs upregulate glycolysis and gluconeogenesis while reducing oxidative phosphorylation, indicating a shift in central carbon metabolism. Isotope tracing and metabolic modeling demonstrate that DTPs utilize glucose to fuel the pentose phosphate pathway (PPP) to generate NADPH and metabolize glutamine to provide carbons for the PPP via gluconeogenesis. Integrating our multi-omic datasets into a genome-scale model identified that DTPs sustain antioxidant metabolism by decreasing fluxes of other NADPH-consuming reactions upon in silico PPP knockout. These findings reveal a systems-level shift in DTP metabolism that maintains antioxidant activity for cell survival, highlighting potential new targets and treatment paradigms to overcome therapy resistance.

cancer biology↗

bioRxiv2025

Mitotic bypass and endocycling promote cancer cell survival after genotoxic chemotherapy

Genotoxic chemotherapies are central components of the treatment regimen for most cancers but are rarely curative. Drug-tolerant persister cells (DTPs) evade cell death during these treatments by accessing transient adaptive states and can contribute to cancer progression after treatment. Here, we demonstrate that cancer cells can survive genotoxic chemotherapy-induced stress by accessing a DTP state wherein stress-induced bypass of mitosis precipitates continued endocycling which promotes survival by allowing cells to evade mitotic catastrophe and cell death. Mechanistic studies indicate that persistent DNA damage signaling in endocycling persister cells triggers sustained p53-independent CDK1 inhibition by WEE1 and Myt1. Continued survival in endocycling persisters is dependent on activation of this G2 checkpoint, and disrupting WEE1 or Myt1 activity using clinical-stage small molecule inhibitors is sufficient to drive CDK1 reactivation, forcing mitotic entry, catastrophe, and cell death. Our results define endocycling DTPs as targetable mediators of cancer cell persistence after genotoxic therapy.

cell biology↗

bioRxiv2025

Cancer cells surviving cisplatin chemotherapy increase stress-induced OMA1 activity and mitochondrial fragmentation

Cancer is one of the leading causes of deaths worldwide. Once cancer cells acquire therapy resistance, they become the main driver of cancer lethality in patients. Thus, mechanisms of therapy resistance must be investigated to improve patient outcomes. Mitochondria are critical organelles in the cellular stress responses, undergoing dynamic morphological and functional changes in response to external stimuli. We and others have identified a chemotherapy-resistant cancer cell state where cells that survive treatment exhibit a dramatic increase in cell size and remain non-proliferative for weeks. In this study, we demonstrate that cancer cells that enter this resistant cell state in response to cisplatin increase OMA1 activity and decrease mitochondrial fusion and function to combat oxidative stress. These findings contribute to further understanding the role of the mitochondrial stress responses in therapy resistance in cancer and provide a potential therapeutic avenue to targeting cancer cells that enter this chemotherapy-resistant cell state.

cancer biology↗

bioRxiv2024

Polyploid cancer cells reveal signatures of chemotherapy resistance

Therapeutic resistance in cancer significantly contributes to mortality, with many patients eventually experiencing recurrence after initial treatment responses. Recent studies have identified therapy-resistant large polyploid cancer cells in patient tissues, particularly in late-stage prostate cancer, linking them to advanced disease and relapse. Here, we analyzed bone marrow aspirates from 44 advanced prostate cancer patients and found the presence of circulating tumor cells with increased genomic content (CTC-IGC) was significantly associated with poorer progression- free survival. Single cell copy number profiling of CTC-IGC displayed clonal origins with typical CTCs, suggesting complete polyploidization. Induced polyploid cancer cells from PC3 and MDA-MB-231 cell lines treated with docetaxel or cisplatin were examined through single cell DNA sequencing, RNA sequencing, and protein immunofluorescence. Novel RNA and protein markers, including HOMER1, TNFRSF9, and LRP1, were identified as linked to chemotherapy resistance. These markers were also present in a subset of patient CTCs and associated with recurrence in public gene expression data. This study highlights the prognostic significance of large polyploid tumor cells, their role in chemotherapy resistance, and their expression of markers tied to cancer relapse, offering new potential avenues for therapeutic development.

cancer biology↗

bioRxiv2024

Cells in the Polyaneuploid Cancer Cell State are Pro-Metastatic

There remains a large need for a greater understanding of the metastatic process within the prostate cancer field. Our research aims to understand the adaptive - ergo potentially metastatic - responses of cancer to changing microenvironments. Emerging evidence has implicated a role of the Polyaneuploid Cancer Cell (PACC) state in metastasis, positing the PACC state as capable of conferring metastatic competency. Mounting in vitro evidence supports increased metastatic potential of cells in the PACC state. Additionally, our recent retrospective study of prostate cancer patients revealed that PACC presence in the prostate at the time of radical prostatectomy was predictive of future metastatic progression. To test for a causative relationship between PACC state biology and metastasis, we leveraged a novel method designed for flow-cytometric detection of circulating tumor cells (CTCs) and disseminated tumor cells (DTCs) in subcutaneous, caudal artery, and intracardiac mouse models of metastasis. This approach provides both quantitative and qualitative information about the number and PACC-status of recovered CTCs and DTCs. Collating data from all models, we found that 74% of recovered CTCs and DTCs were in the PACC state. In vivo colonization assays proved PACC populations can regain proliferative capacity at metastatic sites following dormancy. Additional direct and indirect mechanistic in vitro analyses revealed a PACC-specific partial Epithelial-to-Mesenchymal-Transition phenotype and a pro-metastatic secretory profile, together providing preliminary evidence that PACCs are mechanistically linked to metastasis. Statement of SignificanceWe provide the first evidence that cells in the polyaneuploid cancer cell state contribute to increased metastatic competency in vivo.

cancer biology↗

bioRxiv2024

Punctuational evolution is pervasive in distal site metastatic colonization

The evolution of metastasis represents a lethal stage of cancer progression. Yet, the evolutionary kinetics of metastatic disease remain unresolved. Here, using single cell CRISPR-Cas9 lineage tracing data, we show that in metastatic disease, gradual molecular evolution is punctuated by episodes of rapid evolutionary change associated with lineage divergence. By measuring punctuational effects across the metastatic cascade, we show that punctuational effects contribute more to the molecular diversity at distal site metastases compared to the paired primary tumor, suggesting qualitatively different modes of evolution may drive primary and metastatic tumor progression. This is the first empirical evidence for distinct patterns of molecular evolution at early and late stages of metastasis and demonstrates the complex interplay of cell intrinsic and extrinsic factors that shape lethal cancer.

cancer biology↗

bioRxiv2023

Multiparameter flow cytometric detection and analysis of rare cells in in vivo models of cancer metastasis

Rapid and reliable circulating tumor cell (CTC) and disseminated tumor cell (DTC) detection forms a major underpinning of rigorous in vivo metastasis research. While many cancer cells initiate metastasis, very few can complete it. Clinical data evidences that each successive step of metastatic cascade presents increasing barriers to metastatic success, limiting the number of successful metastatic cells to fewer than 1 in 1,500,000,000. As such, it is critical to employ metastasis research approaches that allow scientists to discern which step(s) of the cascade present metastatic barriers to their model systems, and in which steps their model systems might display competency. Here, we present a novel flow-cytometry based method that allows for the simultaneous comparison of multiple steps of the cascade within one model system via the co-identification of CTC and DTCs from single animals. This approach is not only highly reliable and reproducible, but also broadly applicable and highly adaptable to a wide range of scientific inquiries.

cancer biology↗

bioRxiv2023

A Clear, Legible, Explainable, Transparent, and Elucidative (CLETE) Binary Classification Platform for Tabular Data

Therapeutic resistance continues to impede overall survival rates for those affected by cancer. Although driver genes are associated with diverse cancer types, a scarcity of instrumental methods for predicting therapy response or resistance persists. Therefore, the impetus for designing predictive tools for therapeutic response is crucial and tools based on machine learning open new opportunities. Here, we present an easily accessible platform dedicated to Clear, Legible, Explainable, Transparent, and Elucidative (CLETE) yet wholly modifiable binary classification models. Our platform encompasses both unsupervised and supervised feature selection options, hyperparameter search methodologies, under-sampling and over-sampling methods, and normalization methods, along with fifteen machine learning algorithms. The platform furnishes a k-fold receiver operating curve (ROC) - area under the curve (AUC) and accuracy plots, permutation feature importance, SHapley Additive exPlanations (SHAP) plots, and Local Interpretable Model-agnostic Explanations (LIME) plots to interpret the model and individual predictions. We have deployed a unique custom metric for hyperparameter search, which considers both training and validation scores, thus ensuring a check on under or over-fitting. Moreover, we introduce an innovative scoring method, NegLog2RMSL, which incorporates both training and test scores for model evaluation that facilitates the evaluation of models via multiple parameters. In a bid to simplify the user interface, we provide a graphical interface that sidesteps programming expertise and is compatible with both Windows and Mac OS. Platform robustness has been validated using pharmacogenomic data for 23 drugs across four diseases and holds the potential for utilization with any form of tabular data.

bioinformatics↗

bioRxiv2023

MYC-driven increases in mitochondrial DNA copy number occur early and persist throughout prostatic cancer progression

Increased mitochondrial function may render some cancers vulnerable to mitochondrial inhibitors. Since mitochondrial function is regulated partly by mitochondrial DNA copy number (mtDNAcn), accurate measurements of mtDNAcn could help reveal which cancers are driven by increased mitochondrial function and may be candidates for mitochondrial inhibition. However, prior studies have employed bulk macrodissections that fail to account for cell type-specific or tumor cell heterogeneity in mtDNAcn. These studies have often produced unclear results, particularly in prostate cancer. Herein, we developed a multiplex in situ method to spatially quantify cell type specific mtDNAcn. We show that mtDNAcn is increased in luminal cells of high-grade prostatic intraepithelial neoplasia (HGPIN), is increased in prostatic adenocarcinomas (PCa), and is further elevated in metastatic castration-resistant prostate cancer. Increased PCa mtDNAcn was validated by two orthogonal methods and is accompanied by increases in mtRNAs and enzymatic activity. Mechanistically, MYC inhibition in prostate cancer cells decreases mtDNA replication and expression of several mtDNA replication genes, and MYC activation in the mouse prostate leads to increased mtDNA levels in the neoplastic prostate cells. Our in situ approach also revealed elevated mtDNAcn in precancerous lesions of the pancreas and colon/rectum, demonstrating generalization across cancer types using clinical tissue samples.

cancer biology↗

bioRxiv2022

Cells in the Polyaneuploid Cancer Cell (PACC) state have increased metastatic potential

Although metastasis is the leading cause of cancer deaths, it is quite rare at the cellular level. Only a rare subset of cancer cells ([~]1 in 1.5 billion) can complete the entire metastatic cascade: invasion, intravasation, survival in the circulation, extravasation, and colonization (i.e. are metastasis competent). We propose that cells engaging a Polyaneuploid Cancer Cell (PACC) phenotype are metastasis competent. PACCs are enlarged, non-dividing cells with increased genomic content that form in response to stress. Single-cell tracking using time-lapse microscopy reveals that PACCs are more motile than nonPACCs. Additionally, PACCs exhibit increased capacity for environment-sensing and directional migration in chemotactic environments, predicting successful invasion. Magnetic Twisting Cytometry and Atomic Force Microscopy reveal that cells in the PACC state display hyper-elastic properties like increased peripheral deformability and maintained peri-nuclear cortical integrity that predict successful intravasation and extravasation. Furthermore, four orthogonal methods reveal that PACCs have increased expression of Vimentin, a known hyper-elastic biomolecule. Lastly, anoikis-resistance assays and detection of PACCs in the blood of a patient with metastatic castrate-resistant prostate cancer using a selection- free circulating tumor cell detection platform reveal that PACCs are capable of surviving in the circulation. Taken together with the knowledge that PACCs are capable of eventual depolyploidization and progeny formation (as a potential route to colonization), these data support PACCs as candidate metastasis-competent cells worthy of further analysis.

cancer biology↗

bioRxiv2022

Nuclear morphology and functional characteristics that enable survival of cells in the polyaneuploid cancer cell state following cytotoxic chemotherapy

In this study, we characterized nuclear morphology and function as cancer cells underwent recovery following chemotherapeutic treatment to identify the unique characteristics associated with treatment resistance and successful survival. Cells that survived following treatment and resisted therapy-induced cell death were predominantly mononucleated with increased nuclear/cellular size, enabled by continuous endocycling. We found that cells that survive after therapy release likely employ more efficient DNA damage repair and exhibit a distinct nucleolar phenotype - fewer but larger nucleoli - and increased rRNA levels. These data support a paradigm where soon after therapy release, the treated population mostly contains cells with a high level of widespread and catastrophic DNA damage that leads to apoptosis, while the minority of cells that have successful DDR are more likely to access a pro-survival state. These findings suggest that one way cancer cells can survive systemic therapy is to enter the polyaneuploid cancer cell (PACC) state, a recently-described mechanism of therapy resistance. Cancer cells in this state are physically enlarged, undergo whole-genome doubling resulting in polyaneuploid genomes, and are associated with worse prognosis in cancer patients. The PACC state is accessed when a cancer cell experiences external stress, such as genotoxic chemotherapy; after a period of recovery, cells exit the PACC state and resume proliferation to repopulate the tumor cell pool. Our findings demonstrate the fate of cancer cells following chemotherapy treatment and define key characteristics of the resistant PACC state. This work is essential for understanding and, ultimately, targeting, cancer resistance and recurrence.

cancer biology↗

bioRxiv2021

Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Herein, we report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

systems biology↗

bioRxiv2020

Characterization of extracellular vesicles and artificial nanoparticles with four orthogonal single-particle analysis platforms

We compared four orthogonal technologies for sizing, counting, and phenotyping of extracellular vesicles (EVs) and synthetic particles. The platforms were: single-particle interferometric reflectance imaging sensing (SP-IRIS) with fluorescence, nanoparticle tracking analysis (NTA) with fluorescence, microfluidic resistive pulse sensing (MRPS), and nanoflow cytometry measurement (NFCM). EVs from the human T lymphocyte line H9 (high CD81, low CD63) and the promonocytic line U937 (low CD81, high CD63) were separated from culture conditioned medium (CCM) by differential ultracentrifugation (dUC) or a combination of ultrafiltration (UF) and size exclusion chromatography (SEC) and characterized by transmission electron microscopy (TEM) and Western blot (WB). Mixtures of synthetic particles (silica and polystyrene spheres) with known sizes and/or concentrations were also tested. MRPS and NFCM returned similar particle counts, while NTA detected counts approximately one order of magnitude lower for EVs, but not for synthetic particles. SP-IRIS events could not be used to estimate particle concentrations. For sizing, SP-IRIS, MRPS, and NFCM returned similar size profiles, with smaller sizes predominating (per power law distribution), but with sensitivity typically dropping off below diameters of 60 nm. NTA detected a population of particles with a mode diameter greater than 100 nm. Additionally, SP-IRIS, MRPS, and NFCM were able to identify at least three of four distinct size populations in a mixture of silica or polystyrene nanoparticles. Finally, for tetraspanin phenotyping, the SP-IRIS platform in fluorescence mode was able to detect at least two markers on the same particle, while NFCM detected either CD81 or CD63. Based on the results of this study, we can draw conclusions about existing single-particle analysis capabilities that may be useful for EV biomarker development and mechanistic studies.

cell biology↗

bioRxiv2018

Computational modeling of the crosstalk between macrophage polarization and tumor cell plasticity in the tumor microenvironment

Tumor microenvironments contain multiple cell types interacting among one another via different signaling pathways. Furthermore, both cancer cells and different immune cells can display phenotypic plasticity in response to these communicating signals, thereby leading to complex spatiotemporal patterns that can impact therapeutic response. Here, we investigate the crosstalk between cancer cells and macrophages in a tumor microenvironment through in silico (computational) co-culture models. In particular, we investigate how macrophages of different polarization (M1 vs. M2) can interact with epithelial-mesenchymal plasticity of cancer cells, and conversely, how cancer cells exhibiting different phenotypes (epithelial vs. mesenchymal) can influence the polarization of macrophages. Based on interactions documented in the literature, an interaction network of cancer cells and macrophages is constructed. The steady states of the network are then analyzed. Various interactions were removed or added into the constructed-network to test the functions of those interactions. Also, parameters in the mathematical models were varied to explore their effects on the steady states of the network. In general, the interactions between cancer cells and macrophages can give rise to multiple stable steady-states for a given set of parameters and each steady state is stable against perturbations. Importantly, we show that the system can often reach one type of stable steady states where cancer cells go extinct. Our results may help inform efficient therapeutic strategies.

systems biology↗

bioRxiv2018

3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We used robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we computed geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compared over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieved accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.

bioinformatics↗

bioRxiv2017

3D cell nuclear morphology: microscopy imaging dataset and voxel-based morphometry classification results

Cell deformation is regulated by complex underlying biological mechanisms associated with spatial and temporal morphological changes in the nucleus that are related to cell differentiation, development, proliferation, and disease. Thus, quantitative analysis of changes in size and shape of nuclear structures in 3D microscopic images is important not only for investigating nuclear organization, but also for detecting and treating pathological conditions such as cancer. While many efforts have been made to develop cell and nuclear shape characteristics in 2D or pseudo-3D, several studies have suggested that 3D morphometric measures provide better results for nuclear shape description and discrimination. A few methods have been proposed to classify cell and nuclear morphological phenotypes in 3D, however, there is a lack of publicly available 3D data for the evaluation and comparison of such algorithms. This limitation becomes of great importance when the ability to evaluate different approaches on benchmark data is needed for better dissemination of the current state of the art methods for bioimage analysis. To address this problem, we present a dataset containing two different cell collections, including original 3D microscopic images of cell nuclei and nucleoli. In addition, we perform a baseline evaluation of a number of popular classification algorithms using 2D and 3D voxel-based morphometric measures. To account for batch effects, while enabling calculations of AUROC and AUPR performance metrics, we propose a specific cross-validation scheme that we compare with commonly used k-fold cross-validation. Original and derived imaging data are made publicly available on the project web-page: http://www.socr.umich.edu/projects/3d-cell-morphometry/data.html.

bioinformatics↗