Biology subjects

Spike, B. T.

Publications and source records attributed to Spike, B. T..

9 recordsLinked to original sources

bioRxiv2025

Determination of Drug Sensitivity in Patient Derived Models of Breast Cancer by Multiparametric QPI

Functional precision oncology seeks to match patients with effective therapies by empirically testing patient-derived samples for drug sensitivity in the laboratory. However, existing approaches require significant sample expansion time and expense prior to analysis, rendering them impractical for routine clinical testing. Quantitative phase imaging (QPI) provides a potential path forward by directly measuring responses at single cell resolution without the need for extensive sample expansion. In previous work, we demonstrated that multiple, independent parameters of cellular response to therapeutic agents can be derived from QPI data, an approach we call multiparametric QPI (mQPI). Here, we demonstrate application of mQPI using cells from patient derived xenograft organoid (PDxO) models, as well as cells viably frozen direct from patients. Using mQPI with breast cancer PDxO models, we uncover distinct drug responses for cells originating from different anatomic sites in the same patient and resolve cellular heterogeneity of response in a model of acquired therapeutic resistance. We also show that mQPI can detect drug responses in viably frozen primary patient samples, either direct from thaw or after a short term expansion of only 2 weeks. Overall, these data provide proof-of-principle for application of mQPI to a range of sample types, including cryopreserved material direct from patients. This underscores the clinical potential of mQPI as a time- and materials-efficient alternative to current methods in functional precision oncology.

bioengineering↗

bioRxiv2025

HNF4α controls growth, identity and response to KRAS inhibition of invasive mucinous adenocarcinoma of the lung

Cellular plasticity is a hallmark of cancer, enabling tumor cells to alter identity and evade therapeutic pressure. In invasive mucinous adenocarcinoma of the lung (IMA), NKX2-1 loss triggers a pulmonary to gastric switch marked by aberrant activation of HNF4, a master regulator of gastrointestinal/hepatic differentiation. We find that HNF4 promotes IMA growth and activates a gastric pit cell-like program. Hnf4a deletion induces IMA dedifferentiation, enabling FoxA1/2 to access de novo sites and activate alternative identities. HNF4 also induces a mucinous program associated with tolerance to KRAS blockade, and HNF4 loss enhances response to KRASG12D inhibition. Mechanistically, HNF4 blocks cell cycle exit in drug-tolerant persister cells and promotes activity of the antioxidant transcription factor NRF2. NRF2 activation partially rescues effects of Hnf4a deletion on KRASG12D inhibition, whereas NRF2 inhibition enhances sensitivity to KRASG12D blockade. Thus, HNF4 is a key regulator of identity and primary response to KRASG12D inhibition in IMA. SIGNIFICANCEIMA is a genetically and epigenetically distinct LUAD subtype for which targeted therapies are lacking due to the high proportion of KRAS mutations. This study points to blockade of the HNF4 -> NRF2 axis as a potential strategy to enhance primary response to KRAS inhibition in IMA.

cancer biology↗

bioRxiv2024

Tenascin-C in the early lung cancer tumor microenvironment promotes progression through integrin activation and FAK

Pre-cancerous lung lesions are commonly initiated by activating mutations in the RAS pathway, but do not transition to lung adenocarcinomas (LUAD) without additional oncogenic signals. Here, we show that expression of the extracellular matrix protein Tenascin-C (TNC) is increased in and promotes the earliest stages of LUAD development in oncogenic KRAS-driven lung cancer mouse models and in human LUAD. TNC is initially expressed by fibroblasts and its expression extends to tumor cells as the tumor becomes invasive. Genetic deletion of TNC in the mouse models reduces early tumor burden and high-grade pathology and diminishes tumor cell proliferation, invasion, and focal adhesion kinase (FAK) activity. TNC stimulates cultured LUAD tumor cell proliferation and migration through engagement of v-containing integrins and subsequent FAK activation. Intringuingly, lung injury causes sustained TNC accumulation in mouse lungs, suggesting injury can induce additional TNC signaling for early tumor cell transition to invasive LUAD. Biospecimens from patients with stage I/II LUAD show TNC in regions of FAK activation and an association of TNC with tumor recurrence after primary tumor resection. These results suggest that exogenous insults that elevate TNC in the lung parenchyma interact with tumor-initiating mutations to drive early LUAD progression and local recurrence.

cancer biology↗

bioRxiv2024

CRIPTO promotes extracellular vesicle uptake and activation of fibroblast exposed to triple negative breast cancer cells

Expression of CRIPTO, a factor involved in embryonic stem cells, fetal development, and wound healing, is tied to poor prognosis in multiple cancers. Prior studies in triple negative breast cancer (TNBC) models showed CRIPTO blockade inhibits tumor growth and dissemination. Here, we uncover a previously unidentified role for CRIPTO in orchestrating tumor-derived extracellular vesicle (TEV) uptake and fibroblast activation through discrete mechanisms. We found a novel mechanism by which CRIPTO drives aggressive TNBC phenotypes, involving CRIPTO-laden TEVs that program stromal fibroblasts, toward cancer associated fibroblast cell states, which in turn prompt tumor cell invasion. CRIPTO-bearing TEVs exhibited markedly elevated uptake in target fibroblasts and activated SMAD2/3 through NODAL-independent and - dependent mechanisms, respectively. Engineered expression of CRIPTO on EVs enhanced the delivery of bioactive molecules. In vivo, CRIPTO levels dictated TEV uptake in mouse lungs, a site of EV-regulated premetastatic niches important for breast cancer dissemination. These discoveries reveal a novel role for CRIPTO in coordinating heterotypic cellular crosstalk which offers novel insights into breast cancer progression, delivery of therapeutic molecules, and new, potentially targetable mechanisms of heterotypic cellular communication between tumor cells and the TME.

cancer biology↗

bioRxiv2023

OCA-B promotes autoimmune demyelination through control of stem-like CD4+ T cells

Stem-like T cells selectively contribute to autoimmunity, but the activities that promote their pathogenicity are incompletely understood. Here, we identify the transcription coregulator OCA-B as a driver of the pathogenic maturation of stem-like CD4+ T cell to promote autoimmune demyelination. Using two human multiple sclerosis (MS) datasets, we show that POU2AF1, the gene encoding OCA-B, is elevated in CD4+ T cells from MS patients. We show that T cell-intrinsic OCA-B loss protects mice from experimental autoimmune encephalomyelitis (EAE) while preserving responses to viral CNS infection. In EAE models driven by antigen reencounter, OCA-B deletion nearly eliminates CNS infiltration, proinflammatory cytokine production and clinical disease. OCA-B-expressing CD4+ T cells of mice primed with autoantigen express an encephalitogenic gene program and preferentially confer disease. In a relapsing-remitting EAE model, OCA-B loss protects mice specifically at relapse. During remission, OCA-B promotes the expression of Tcf7, Slamf6, and Sell in proliferating CNS T cell populations. At relapse timepoints, OCA-B loss results in both the accumulation of an immunomodulatory CD4+ T cell population expressing Ccr9 and Bach2, and loss of pro-inflammatory gene expression from Th17 cells. These results identify OCA-B as a driver of pathogenic CD4+ T cells.

immunology↗

bioRxiv2023

Mcam stabilizes a luminal progenitor-like breast cancer cell state via Ck2 control and Src/Akt/Stat3 attenuation

Breast cancers are categorized into subtypes with distinctive therapeutic vulnerabilities and prognoses based on their expression of clinically targetable receptors and gene expression patterns mimicking different cell types of the normal gland. Here, we tested the role of Mcam in breast cancer cell state control and tumorigenicity in a luminal progenitor-like murine tumor cell line (Py230) that exhibits lineage and tumor subtype plasticity. Mcam knockdown Py230 cells show augmented Stat3 and Pi3K/Akt activation associated with a lineage state switch away from a hormone-sensing/luminal progenitor state toward alveolar and basal cell related phenotypes that were refractory to growth inhibition by the anti-estrogen therapeutic, tamoxifen. Inhibition of Stat3, or the upstream activator Ck2, reversed these cell state changes. Mcam binds Ck2 and acts as a regulator of Ck2 substrate utilization across multiple mammary tumor cell lines. In Py230 cells this activity manifests as increased mesenchymal morphology, migration, and Src/Fak/Mapk/Paxillin adhesion complex signaling in vitro, in contrast to Mcams reported roles in promoting mesenchymal phenotypes. In vivo, Mcam knockdown reduced tumor growth and take rate and inhibited cell state transition to Sox10+/neural crest like cells previously been associated with tumor aggressiveness. This contrasts with human luminal breast cancers where MCAM copy number loss is highly coupled to Cyclin D amplification, increased proliferation, and the more aggressive Luminal B subtype. Together these data indicate a critical role for Mcam and its regulation of Ck2 in control of breast cancer cell state plasticity with implications for progression, evasion of targeted therapies and combination therapy design. HIGHLIGHTSO_LIMcam is expressed by diverse breast cancer subtypes. C_LIO_LIMcam regulates breast cancer cell state plasticity. C_LIO_LIMcam maintains the mammary luminal progenitor state. C_LIO_LIMcam attenuates Stat activity. C_LIO_LIMcam dictates Ck2 substrate utilization. C_LI

cancer biology↗

bioRxiv2021

Multiparametric quantitative phase imaging for real-time, single cell, drug screening in breast cancer

Quantitative phase imaging (QPI) measures the growth rate of individual cells by quantifying changes in mass versus time. Here, we use the breast cancer cell lines MCF-7, BT-474, and MDA-MB-231 to validate QPI as a multiparametric approach for determining response to single-agent therapies. Our method allows for rapid determination of drug sensitivity, cytotoxicity, heterogeneity, and time of response for up to 100,000 individual cells or small clusters in a single experiment. We find that QPI EC50 values are concordant with CellTiter-Glo (CTG), a gold standard metabolic endpoint assay. In addition, we apply multiparametric QPI to characterize cytostatic/cytotoxic and rapid/slow responses and track the emergence of resistant subpopulations. Thus, QPI reveals dynamic changes in response heterogeneity in addition to average population responses, a key advantage over endpoint viability or metabolic assays. Overall, multiparametric QPI reveals a rich picture of cell growth by capturing the dynamics of single-cell responses to candidate therapies.

bioengineering↗

bioRxiv2021

Factorization-based Imputation of Expression in Single-cell Transcriptomic Analysis (FIESTA) recovers Gene-Cell-State Relationships

Single cell RNA sequencing (scRNA-seq) is a gene expression profiling technique that is presently revolutionizing the study of complex cellular systems in the biological sciences. Existing scRNA-seq methods suffer from sub-optimal target recovery leading to inaccurate measurements including many false negatives. The resulting zero-inflated data may confound data interpretation and visualization. Since cells have coherent phenotypes defined by conserved molecular circuitries (i.e. multiple gene products working together) and since similar cells utilize similar circuits, information about each expression value or node in a multi-cell, multi-gene scRNA-seq data set is expected to also be predictable from other nodes in the data set. Based on this logic, several approaches have been proposed to impute missing values in a data set by extracting information from its non-zero measurements. In this study, we apply non-negative matrix factorization to a selection of published scRNA-seq data sets followed by multiplication of the factor matrices to generate idealized completed model versions of the data. From the model matrices, we recommend new values where original measurements are likely to be inaccurate and where zero measurements are predicted to be false negatives. The resulting imputed data model predicts novel type markers and expression patterns that match orthogonal measurements and field literature better than those obtained from pre-imputation data or alternative imputation strategies. Contactbenjamin.spike@hci.utah.edu Availability and implementationFIESTA is written in R and is available at https://github.com/elnazmirzaei/FIESTA and https://github.com/TheSpikeLab/FIESTA. Author summaryIn this work, we develop FIESTA, a novel, unsupervised, mathematical approach to impute missing values in scRNA-seq data. For each dataset, we use parts-based, non-negative matrix factorization to break the cells-by-genes expression matrix into optimized component matrices and then multiply these component matrices to generate an idealized, completed matrix. The completed matrix has many of the null values filled in because the optimized low rank factors from which it is generated, take multiple cells into account when estimating a particular component, including some cells with positive expression values for genes which are false negatives in other related cells. We also implement scaling and thresholding approaches based on intrinsic data topology for improved interpretability and graphical representation. Overall, FIESTA performs favorably relative to alternative imputation approaches and uncovers gene-gene and gene-cell relationships that are occluded in the raw data. The FIESTA computational pipeline is freely available for download and use by other researchers analyzing scRNA-seq data or other sparse data sets.

bioinformatics↗

bioRxiv2020

An NKX2-1/ERK/WNT feedback loop modulates gastric identity and response to targeted therapy in lung adenocarcinoma

Cancer cells undergo lineage switching during natural progression and in response to therapy. NKX2-1 loss in human and murine lung adenocarcinoma leads to invasive mucinous adenocarcinoma (IMA), a lung cancer subtype that exhibits gastric differentiation and harbors a distinct spectrum of driver oncogenes. NKX2-1 is required for optimal tumor initiation, but dispensable for growth of established tumors, in BRAFV600E driven disease. NKX2-1-deficient, BRAFV600E driven tumors resemble human IMA and exhibit a distinct response to BRAF/MEK inhibitors. Whereas BRAF/MEK inhibitors drive NKX2-1-positive tumor cells into quiescence, NKX2-1-negative cells fail to exit the cell cycle after the same therapy. BRAF/MEK inhibitors also induce cell identity switching in NKX2-1-negative lung tumors within the gastric lineage, which is driven in part by WNT signaling and FoxA1/2. These data elucidate a complex, reciprocal relationship between lineage specifiers and oncogenic signaling pathways in the regulation of lung adenocarcinoma identity that is likely to impact lineage-specific therapeutic strategies.

cancer biology↗