Biology subjects

Foo, J.

Publications and source records attributed to Foo, J..

14 recordsLinked to original sources

bioRxiv2026

Explaining and predicting pseudoprogression in immunotherapythrough joint modeling of immune infiltration and ctDNA dynamics

Pseudoprogression in response to immune checkpoint inhibitor (ICI) therapy can be a complex challenge during treatment response evaluation. Erroneous classification of progression may result in stopping effective treatment for patients, while delayed confirmation of progression can prolong an ineffective treatment course. We develop a mechanistic mathematical model of tumor-immune dynamics under ICI treatment in which observed tumor volume includes contributions from both tumor burden and immune infiltration. We show analytically how pseudoprogression can arise from increased immune infiltration and proliferation as well as continued tumor growth during the ``ramp-up" phase. The model also incorporates circulating tumor DNA (ctDNA) dynamics, which we demonstrate are able to closely fit existing longitudinal tumor and ctDNA data and extract realistic parameter ranges to generate representative in silico data. Using model-generated cohorts, we train a random forest (RF) classifier using longitudinal tumor and ctDNA measurements to distinguish pseudoprogression and true progression, and demonstrate that it improves response classification relative to the Response Evaluation Criteria in Solid Tumors (RECIST 1.1) and provides classification earlier than the immunotherapy-specific iRECIST, even in the presence of tumor measurement noise. This work predicts that paired longitudinal tumor-ctDNA measurements may enable earlier discrimination of true progression from pseudoprogression, and motivates collection of imaging and ctDNA data synchronously during ICI therapy, especially in the setting of suspected disease progression.

cancer biology↗

bioRxiv2026

Systematic modeling of phenotypic drug response profiles inpatient-derived organoids

Patient-derived tumor organoids provide a physiologically relevant 3D disease model for preclinical drug discovery, surpassing the limitations of conventional 2D cell lines. To better capture the dynamic nature of organoid drug responses, we developed a new systematic evaluation method called SCOPE (Systematic Classification of Organoids for Phenotypic Evaluation), harnessing phenotypic assessments from multi-timepoint 3D imaging data. By integrating artificial intelligence (AI)-based image analysis of organoid viability with tracking and mathematical modeling of organoid growth over time, we captured temporal-and dose-dependent dynamics of phenotypic changes, culminating in two novel metrics: a combined growth and viability (GV) score as well as a cytostatic-cytotoxic transition range (CCTR) that separates drug effects on organoid growth and viability. Our approach supports classification of specific drug responses into four distinct phenotypic groups: (1) cytotoxic, (2) cytostatic plus cytotoxic, (3) late cytotoxic, and (4) cytostatic. This novel drug evaluation system can identify previously unknown drug effects or new therapeutic use cases for existing drugs, facilitating the design of alternative therapeutic options to overcome efficacy or drug resistance challenges and improving the clinical applicability of organoid-based drug discovery results.

bioengineering↗

bioRxiv2026

Modeling CISH-KO TIL therapy: T-cell persistence and endogenous competition determine response in gastrointestinal cancer

BackgroundTumor-infiltrating lymphocyte (TIL) therapies have shown promise in murine models; however, early-phase clinical trials reveal substantial heterogeneity in treatment response across patients. Elucidating the biological drivers of this variability is critical for improving patient selection and therapeutic design. MethodsWe developed a mechanistic mathematical model of Cish-inactivated TIL therapy capturing interactions among tumor cells, endogenous TILs, and infused CISH-KO (Cish gene knockout) T-cells. The model was calibrated to longitudinal tumor size data from two murine studies and also fitted to clinical data from a first-in-human phase 1 trial enrolling 22 and treating 12 patients with metastatic gastrointestinal cancers (27 independently tracked tumor lesions). We used the calibrated model to identify patient-level determinants of response and to simulate combination and fractionation treatment strategies in silico. FindingsFour mechanistically distinct parameters emerged as dominant determinants of therapeutic outcome: CISH-KO T-cell persistence (r = 0.827, p < 10-4) was the single strongest predictor of therapeutic failure; other major determinants included endogenous T-cell proliferation rate (r = 0.648, p < 10-4) , CISH-KO T-cell killing rate, and intrinsic tumor growth rate. In silico simulations predicted that the addition of anti-PD-1 checkpoint blockade to standard-dose CISH-KO TIL therapy achieved comparable or superior tumor control relative to dose doubling, while maintaining lower peak T-cell level. Additionally, fractionating the same total dose across multiple delayed infusions further improved predicted tumor control by prolonging CISH-KO T-cell persistence, again without raising peak effector burden. InterpretationThe efficacy of current CISH-KO TIL protocols is influenced not only by the cytotoxic potency of infused T-cells, but also by their persistence within the tumor microenvironment and competitive pressure from the reconstituting host immune compartment. These findings support strategies that enhance the functional efficiency and prolong the availability of infused T-cells--such as concurrent PD-1 blockade or fractionated dosing of the same total cell product--rather than dose escalation alone and identify CISH-KO T-cell persistence and endogenous immune competition as actionable targets for improving TIL-based immunotherapies.

Cancer Biology↗

bioRxiv2026

A novel pharmacogenetic testing panel for CYP2C19 genetic polymorphisms

Specific drug metabolism rates are defined by the constituency of the cytochrome P450 (CYP) genome, including polymorphic changes in any of 200+ CYP genes. An example is CYP2C19, where associations of gene polymorphisms with variability in certain drug metabolism rates have been linked to inter-individual and inter-ethnic differences in therapeutic outcomes. While pharmacogenomic screening for these variants prior to drug and dosage prescription has well-defined links to better treatment outcomes, current implementation is limited to complex and costly variant-probing and DNA sequencing protocols, which have limited availability in clinical laboratories, leading to slow turnaround times, impacting effective clinical intervention. Here we describe a novel, cost-effective, multiplex genotyping approach to screening CYP2C19 variants. Fluorescence nested allele-specific (FAS) PCR was used with primers to detect CYP2C19 variants of interest in specific hot spots, including the Tier 1 haplotypes identified by the Association for Molecular Pathology (AMP): CYP2C19*2, *3, and *17. The presence/absence of wild-type and mutant alleles were identified independently as haplotypes, and in a multiplex reaction as diplotypes representing the 10 possible genotype combinations/profiles. FAS-PCR achieved the same genotype calls as a pyrosequencing protocol optimized for validating genotypes, but with a simpler and more sensitive interface. The FAS-PCR method correctly identified the genotypes of both synthesized DNA and a human genomic DNA standard. Uniquely, the FAS-PCR protocol generates patterns using one fluorescently-labeled primer irrespective of the number of variants targeted, establishing it as considerably more cost-effective than other allele-specific PCR-based techniques that involve labeling both the forward and reverse primers.

pharmacology and toxicology↗

bioRxiv2026

Understanding heterogeneous responses to T cell engagers: Binding characteristics and dosing thresholds determine cytotoxic efficacy

Therapeutic efficacy of multivalent T cell engagers varies widely across individuals, but the basis for this heterogeneity remains poorly understood. Here, we integrate in vitro experiments of antitumor immune responses with a mechanistic modeling framework to investigate sources of response variability across T cell donors and TE constructs, focusing on a novel multivalent bispecific T cell engager currently in development. We identify parameter regimes that accurately recapitulate dose-response behaviors across T cell donors and doses, and perform cross-validation studies that demonstrate the models predictive accuracy. We find that variability in therapy efficacy is governed by the relationship between binding affinity and dose. When dose exceeds the binding affinity, responses are relatively robust across donors; when dose is below the binding affinity, responses are more donor dependent. At smaller doses, the TE-specific shape characteristics of the tumor-binding dose response, its steepness in particular, is a key marker of therapy efficacy. More generally, our integrated modeling and experimental framework offers insights and tools that are applicable to other bispecific T-cell engagers, and provides a quantitative foundation for the systematic, in silico optimization of TE design and dosing strategies.

immunology↗

bioRxiv2026

Ketone body beta-hydroxybutyrate restores neuronal Tau proteostasis via ketolysis-independent mechanism

Metabolic interventions that induce ketosis, including ketogenic diets, caloric restriction, intermittent fasting, and exercise, show promise in the treatment of Alzheimers disease (AD) and related tauopathies. {beta}-hydroxybutyrate ({beta}HB), the primary ketone body produced during ketosis, reproduces key features of these metabolic interventions, but the molecular mechanism underlying its neuroprotective properties is not fully understood. Here, we demonstrate that a {beta}HB precursor diet is sufficient to ameliorate Tau pathophysiology in a tauopathy mouse model. Furthermore, across in vitro, ex vivo, and in vivo models, we find that {beta}HB enhances neuronal Tau proteostasis and reduces Tau aggregation and secretion. Importantly, these effects are independent of {beta}HBs oxidation for ATP production, as its ketolysis-resistant enantiomer reproduces these benefits, indicating that ketolysis is dispensable for these effects. Overall, these data position {beta}HB as a novel therapeutic avenue for AD and tauopathy and elucidate a novel mechanism of action of metabolic interventions in neurodegenerative disease.

neuroscience↗

bioRxiv2025

Deep Modeling of Gain of Function Mutations on Androgen Receptor

The efficiency of Androgen Receptor (AR) pathway inhibitors for prostate cancer (PCa) is on decline due to resistance mechanisms including the occurrence of gain-of-function mutations on human androgen receptor (AR). Hence, understanding and predicting such mutations is crucial for developing effective PCa treatment strategies. Leveraging accumulated data on clinically relevant AR mutants with recent advances in deep modeling techniques, this study aims to unveil and quantify critical AR mutation-drug relationships. By incorporating molecular descriptors for drugs and mutated genes sequences, this work represented these features as single vectors and demonstrates their effectiveness in modeling AR mutant responses to conventional antiandrogens. The developed approach achieves up to 80% accuracy in predicting the gain-of-function behavior of AR mutants and therefore can potentially uncover unknown agonist/antagonist relationships among mutant-drug pairs.

bioinformatics↗

bioRxiv2025

AR-V7 condensates drive androgen-independent transcription in castration resistant prostate cancer

Biomolecular condensates organize cellular environments and regulate key processes such as transcription. We previously showed that full-length androgen receptor (AR-FL), a major oncogenic driver in prostate cancer (PCa), forms nuclear condensates upon androgen stimulation in androgen-sensitive PCa cells. Disrupting these condensates impairs AR-FL transcriptional activity, highlighting their functional importance. However, resistance to androgen deprivation therapy often leads to castration-resistant prostate cancer (CRPC), driven by constitutively active splice variants like AR variant 7 (AR-V7). The mechanisms underlying AR-V7s role in CRPC remain unclear. In this study, we characterized the condensate-forming ability of AR-V7 and compared its phase behavior with AR-FL across a spectrum of PCa models and in vitro conditions. Our findings indicate that cellular context can influence AR-V7s condensate-forming capacity. Unlike AR-FL, AR-V7 spontaneously forms condensates in the absence of androgen stimulation and functions independently of AR-FL in CRPC models. However, AR-V7 requires a higher concentration to form condensates, both in cellular contexts and in vitro. We further reveal that AR-V7 drives transcription via both condensate-dependent and condensate-independent mechanisms. Using an AR-V7 mutant incapable of forming condensates, while retaining nuclear localization and DNA-binding ability, we reveal that the condensate-dependent regime activates part of the oncogenic KRAS pathway in CRPC models. Genes under this condensate-dependent regime were found to harbor significantly higher numbers of AR-binding sites and exhibited boosted expression in response to AR-V7. These findings uncover a previously unrecognized role of AR-V7 condensate formation in driving oncogenic transcriptional programs and shed light on its unique contribution to CRPC progression. HighlightsO_LIAR-V7 condensates form independently of both androgens and AR-FL in CRPC models. C_LIO_LIAR-V7 mediates condensate-dependent and independent transcription C_LIO_LICondensate-dependent transcription enables boosted expression of oncogenic KRAS genes C_LIO_LICondensate-dependent genes exhibit an exponential increase in expression, with a higher number of AR binding sites potentially playing a key role in their reliance on condensate formation. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/631986v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@16b50d1org.highwire.dtl.DTLVardef@86e3b6org.highwire.dtl.DTLVardef@1cfe7f3org.highwire.dtl.DTLVardef@8532a2_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Early ctDNA kinetics as a dynamic biomarker of cancer treatment response

Circulating tumor DNA assays are promising tools for the prediction of cancer treatment response. Here, we build a framework for the design of ctDNA biomarkers of therapy response that incorporate variations in ctDNA dynamics driven by specific treatment mechanisms. We develop mathematical models of ctDNA kinetics driven by tumor response to several therapy classes, and utilize them to simulate randomized virtual patient cohorts to test candidate biomarkers. Using this approach, we propose specific biomarkers, based on ctDNA longitudinal features, for targeted therapy, chemotherapy and radiation therapy. We evaluate and demonstrate the efficacy of these biomarkers in predicting treatment response within a randomized virtual patient cohort dataset. These biomarkers are based on novel proposals for ctDNA sampling protocols, consisting of frequent sampling within a compact time window surrounding therapy initiation - which we hypothesize to hold valuable prognostic information on longer-term treatment response. This study highlights a need for tailoring ctDNA sampling protocols and interpretation methodology to specific biological mechanisms of therapy response, and it provides a novel modeling and simulation framework for doing so. In addition, it highlights the potential of ctDNA assays for making early, rapid predictions of treatment response within the first days or weeks of treatment, and generates hypotheses for further clinical testing.

cancer biology↗

bioRxiv2022

Statistical inference of the rates of cell proliferation and phenotypic switching in cancer

Recent evidence suggests that nongenetic (epigenetic) mechanisms play an important role at all stages of cancer evolution. In many cancers, these mechanisms have been observed to induce dynamic switching between two or more cell states, which commonly show differential responses to drug treatments. To understand how these cancers evolve over time, and how they respond to treatment, we need to understand the state-dependent rates of cell proliferation and phenotypic switching. In this work, we propose a rigorous statistical framework for estimating these parameters, using data from commonly performed cell line experiments, where phenotypes are sorted and expanded in culture. The framework explicitly models the stochastic dynamics of cell division, cell death and phenotypic switching, and it provides likelihood-based confidence intervals for the model parameters. The input data can be either the fraction of cells or the number of cells in each state at one or more time points. Through a combination of theoretical analysis and numerical simulations, we show that when cell fraction data is used, the rates of switching may be the only parameters that can be estimated accurately. On the other hand, using cell number data enables accurate estimation of the net division rate for each phenotype, and it can even enable estimation of the state-dependent rates of cell division and cell death. We conclude by applying our framework to a publicly available dataset.

bioinformatics↗

bioRxiv2022

Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data

Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses, and estimates their drug-sensitivities and frequencies within the bulk. We apply PhenoPop to synthetically-generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples, and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response. MotivationTumors are typically comprised of heterogeneous cell populations exhibiting diverse phenotypes. This heterogeneity, which is correlated with tumor aggressiveness and treatment failure, confounds current drug screening efforts aimed at informing therapy selection. In order to optimally select treatments, understanding the frequency and drug-response profile of individual subpopulations within a tumor is critical. Furthermore, quantitative profiles of tumor drug-response heterogeneity, in combination with predictive mathematical modeling of tumor dynamics, can be used to design effective temporal drug-sequencing strategies for tumor reduction. Here, we present a method that enables the deconvolution of tumor samples into individual sub-components exhibiting differential drug-response. This method relies on standard bulk drug-screen measurements and outputs the frequencies and drug-sensitivities of tumor subpopulations. This framework can also be applied more broadly to deconvolve cellular populations with heterogeneous responses to a variety of external stimuli and environmental factors.

cancer biology↗

bioRxiv2018

Memory in trait macroevolution

The history of a trait within a lineage may influence its future evolutionary trajectory, but macroevolutionary theory of this process is not well developed. For example, consider the simple binary trait of living in cave versus surface habitat. The longer a species has been cave-dwelling, the more may accumulated loss of vision, pigmentation, and defense restrict future adaptation if the species encounters the surface environment. However, the Markov model of discrete trait evolution that is widely adopted in phylogenetics does not allow the rate of cave-to-surface transition to decrease with longer duration as a cave-dweller. Here, we describe three models of evolution that remove this memory-less constraint, using a renewal process to generalize beyond the typical Poisson process of discrete trait macroevolution. We then show how the two-state renewal process can be used for inference, and we investigate the potential of phylogenetic comparative data to reveal different influences of trait duration, or memory in trait evolution. We hope that such approaches may open new avenues for modeling trait evolution and for broad comparative tests of hypotheses that some traits become entrenched.

evolutionary biology↗

bioRxiv2018

Glioblastoma recurrence and the role of MGMT promoter methylation

Tumor recurrence in glioblastoma multiforme (GBM) is often attributed to acquired resistance to the standard chemotherapeutic agent temozolomide (TMZ). Promoter methylation of the DNA repair gene MGMT has been associated with sensitivity to TMZ, while increased expression of MGMT has been associated with TMZ resistance. Clinical studies have observed a downward shift in MGMT methylation percentage from primary to recurrent stage tumors. However, the evolutionary processes driving this shift, and more generally the emergence and growth of TMZ-resistant tumor subpopulations, are still poorly understood. Here we develop a mathematical model, parameterized using clinical and experimental data, to investigate the role of MGMT methylation in TMZ resistance during the standard treatment regimen for GBM (surgery, chemotherapy and radiation). We first find that the observed downward shift in MGMT promoter methylation status between detection and recurrence cannot be explained solely by evolutionary selection. Next, our model suggests that TMZ has an inhibitory effect on maintenance methylation of MGMT after cell division. Finally, incorporating this inhibitory effect, we study the optimal number of TMZ doses per adjuvant cycle for GBM patients with high and low levels of MGMT methylation at diagnosis.

cancer biology↗

bioRxiv2018

Trans-ancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders

Liability to alcohol dependence (AD) is heritable, but little is known about its complex polygenic architecture or its genetic relationship with other disorders. To discover loci associated with AD and characterize the relationship between AD and other psychiatric and behavioral outcomes, we carried out the largest GWAS to date of DSM - IV diagnosed AD. Genome - wide data on 14,904 individuals with AD and 37,944 controls from 28 case / control and family - based studies were meta - analyzed, stratified by genetic ancestry (European, N = 46,568; African; N = 6,280). Independent, genome - wide significant effects of different ADH1B variants were identified in European (rs1229984; p = 9.8E - 13) and African ancestries (rs2066702; p = 2.2E - 9). Significant genetic correlations were observed with schizophrenia, ADHD, depression, and use of cigarettes and cannabis. There was only modest genetic correlation with alcohol consumption and inconsistent associations with problem drinking. The genetic underpinnings of AD only partially overlap with those for alcohol consumption, underscoring the genetic distinction between pathological and non - pathological drinking behaviors.

genetics↗