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Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to noninvasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that our method will contribute to the better understanding of cancer progression and therapeutic response.

Chimeric antigen receptor T cell (CAR-T) therapy is an emerging strategy to improve treatment outcomes for recurrent high-grade glioma, a cancer that responds poorly to current therapies. Here we report a completed phase I trial evaluating IL-13Rα2-targeted CAR-T cells in 65 patients with recurrent high-grade glioma, the majority being recurrent glioblastoma (rGBM). Primary objectives were safety and feasibility, maximum tolerated dose/maximum feasible dose and a recommended phase 2 dose plan. Secondary objectives included overall survival, disease response, cytokine dynamics and tumor immune contexture biomarkers. This trial evolved to evaluate three routes of locoregional T cell administration (intratumoral (ICT), intraventricular (ICV) and dual ICT/ICV) and two manufacturing platforms, culminating in arm 5, which utilized dual ICT/ICV delivery and an optimized manufacturing process. Locoregional CAR-T cell administration was feasible and well tolerated, and as there were no dose-limiting toxicities across all arms, a maximum tolerated dose was not determined. Probable treatment-related grade 3+ toxicities were one grade 3 encephalopathy and one grade 3 ataxia. A clinical maximum feasible dose of 200 × 106 CAR-T cells per infusion cycle was achieved for arm 5; however, other arms either did not test or achieve this dose due to manufacturing feasibility. A recommended phase 2 dose will be refined in future studies based on data from this trial. Stable disease or better was achieved in 50% (29/58) of patients, with two partial responses, one complete response and a second complete response after additional CAR-T cycles off protocol. For rGBM, median overall survival for all patients was 7.7 months and for arm 5 was 10.2 months. Central nervous system increases in inflammatory cytokines, including IFNγ, CXCL9 and CXCL10, were associated with CAR-T cell administration and bioactivity. Pretreatment intratumoral CD3 T cell levels were positively associated with survival. These findings demonstrate that locoregional IL-13Rα2-targeted CAR-T therapy is safe with promising clinical activity in a subset of patients. ClinicalTrials.gov Identifier: NCT02208362 .

Chronic myeloid leukemia (CML) is initiated and maintained by BCR::ABL which is clinically targeted using tyrosine kinase inhibitors (TKIs). TKIs can induce long-term remission but are also not curative. Thus, CML is an ideal system to test our hypothesis that transcriptome-based state-transition models accurately predict cancer evolution and treatment response. We collected time-sequential blood samples from tetracycline-off (Tet-Off) BCR::ABL-inducible transgenic mice and wild-type controls. From the transcriptome, we constructed a CML state-space and a three-well leukemogenic potential landscape. The potential's stable critical points defined observable disease states. Early states were characterized by anti-CML genes opposing leukemia; late states were characterized by pro-CML genes. Genes with expression patterns shaped similarly to the potential landscape were identified as drivers of disease transition. Re-introduction of tetracycline to silence the BCR::ABL gene returned diseased mice transcriptomes to a near healthy state, without reaching it, suggesting parts of the transition are irreversible. TKI only reverted the transcriptome to an intermediate disease state, without approaching a state of health; disease relapse occurred soon after treatment. Using only the earliest time-point as initial conditions, our state-transition models accurately predicted both disease progression and treatment response, supporting this as a potentially valuable approach to time clinical intervention, before phenotypic changes become detectable.

The plasma membrane is enriched for receptors and signaling proteins that are accessible from the extracellular space for pharmacological intervention. Here we conducted a series of CRISPR screens using human cell surface proteome and integrin family libraries in multiple cancer models. Our results identified ITGAV (integrin αV) and its heterodimer partner ITGB5 (integrin β5) as the essential integrin α/β pair for cancer cell expansion. High-density CRISPR gene tiling further pinpointed the integral pocket within the β-propeller domain of ITGAV for integrin αVβ5 dimerization. Combined with in silico compound docking, we developed a CRISPR-Tiling-Instructed Computer-Aided (CRISPR-TICA) pipeline for drug discovery and identified Cpd_AV2 as a lead inhibitor targeting the β-propeller central pocket of ITGAV. Cpd_AV2 treatment led to rapid uncoupling of integrin αVβ5 and cellular apoptosis, providing a unique class of therapeutic action that eliminates the integrin signaling via heterodimer dissociation. We also foresee the CRISPR-TICA approach to be an accessible method for future drug discovery studies.

Contrast transport models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.

An overview is given of the path of research that led from asking how hailstones originate to the discovery that ice nucleation can be initiated by bacteria and other microorganisms at temperatures as high as -2 [degrees]C. The major steps along that path were finding exceptionally effective ice nucleators in soils with a high content of decayed vegetative matter, then in decaying tree leaves, and then in plankton-laden ocean water. Eventually, it was shown that Pseudomonas syringae bacteria were responsible for most of the observed activity. That identification coincided with the demonstration that the same bacteria cause frost damage on plants. Ice nucleation by bacteria meant an unexpected turn in the understanding of ice nucleation and of ice formation in the atmosphere. Subsequent research confirmed the unique effectiveness of ice nucleating particles (INP) of biological origin, referred to as bio-INPs, so that bio-INPs are now considered to be important elements of lower-tropospheric cloud processes. Nonetheless, some of the questions which originally motivated the research are still unresolved, so that revisiting the early work may be helpful to current endeavors. Part I of this manuscript summarizes how the discovery progressed. Part II (Schnell and Vali) shows the relationship between bio-INPs in soils and in precipitation with climate and other findings. The online supplemental material contains a bibliography of recent work about bio-INPs. SIGNIFICANCE STATEMENT: The story here recounts how the authors found, in the 1970s, that bacteria from soils and vegetation when lifted into clouds as small aerosol particles are very efficient in initiating ice crystals for snow, hail, and rain. Curiously, the same bacteria are also involved in causing frost damage to plants. The importance of this finding for weather and climate is underscored by the knowledge accumulated over the years about this connection between the biosphere and the atmosphere. KEYWORDS: Atmosphere; Cloud microphysics; Biosphere atmosphere interaction; History, 1. Introduction Ice formation in the atmosphere is crucial for precipitation formation and is a large factor in Earth's radiation budget. At Earth's surface, it influences diverse processes and has [...]

The ERBB tyrosine kinase receptors and their ligands belong to a complex family that has diverse biological effects and expression profiles in the developing mammary glands, where its members play an essential role in translating hormone signals into local effects. While our understanding of these processes stems mostly from mouse models, there is the potential for differences in how this family functions in the mammary glands of other species, particularly in light of their unique histomorphological features. Herein we review the postnatal distribution and function of ERBB receptors and their ligands in the mammary glands of rodents and humans, as well as for livestock and companion animals. Our analysis highlights the diverse biology for this family and its members across species, the regulation of their expression, and how their roles and functions might be modulated by varying stromal composition and hormone interactions. Given that ERBB receptors and their ligands have the potential to influence processes ranging from normal mammary development to diseased states such as cancer and/or mastitis, both in human and veterinary medicine, a more complete understanding of their biological functions should help to direct future research and the identification of new therapeutic targets.

ObjectiveAmyotrophic lateral sclerosis (ALS) is a heterogeneous disease with a complex etiology that lacks biomarkers predicting disease progression. The objective of this study was to use longitudinal cerebrospinal fluid (CSF) samples to identify biomarkers that distinguish fast progression (FP) from slow progression (SP) and assess their temporal response.MethodsWe utilized mass spectrometry (MS)-based proteomics to identify candidate biomarkers using longitudinal CSF from a discovery cohort of SP and FP ALS patients. Immunoassays were used to quantify and validate levels of the top biomarkers. A state-transition mathematical model was created using the longitudinal MS data that also predicted FP versus SP.ResultsWe identified a total of 1148 proteins in the CSF of all ALS patients. Pathway analysis determined enrichment of pathways related to complement and coagulation cascades in FPs and synaptogenesis and glucose metabolism in SPs. Longitudinal analysis revealed a panel of 59 candidate markers that could segregate FP and SP ALS. Based on multivariate analysis, we identified three biomarkers (F12, RBP4, and SERPINA4) as top candidates that segregate ALS based on rate of disease progression. These proteins were validated in the discovery and a separate validation cohort. Our state-transition model determined that the overall variance of the proteome over time was predictive of the disease progression rate.InterpretationWe identified pathways and protein biomarkers that distinguish rate of ALS disease progression. A mathematical model of the CSF proteome determined that the change in entropy of the proteome over time was predictive of FP versus SP.

The goal of Working Group 1 in the Breastmilk Ecology: Genesis of Infant Nutrition (BEGIN) Project was to outline factors influencing biological processes governing human milk secretion and to evaluate our current knowledge of these processes. Many factors regulate mammary gland development in utero, during puberty, in pregnancy, through secretory activation, and at weaning. These factors include breast anatomy, breast vasculature, diet, and the lactating parent's hormonal milieu including estrogen, progesterone, placental lactogen, cortisol, prolactin, and growth hormone. We examine the effects of time of day and postpartum interval on milk secretion, along with the role and mechanisms of lactating parent-infant interactions on milk secretion and bonding, with particular attention to the actions of oxytocin on the mammary gland and the pleasure systems in the brain. We then consider the potential effects of clinical conditions including infection, pre-eclampsia, preterm birth, cardiovascular health, inflammatory states, mastitis, and particularly, gestational diabetes and obesity. Although we know a great deal about the transporter systems by which zinc and calcium pass from the blood stream into milk, the interactions and cellular localization of transporters that carry substrates such as glucose, amino acids, copper, and the many other trace metals present in human milk across plasma and intracellular membranes require more research. We pose the question of how cultured mammary alveolar cells and animal models can help answer lingering questions about the mechanisms and regulation of human milk secretion. We raise questions about the role of the lactating parent and the infant microbiome and the immune system during breast development, secretion of immune molecules into milk, and protection of the breast from pathogens. Finally, we consider the effect of medications, recreational and illicit drugs, pesticides, and endocrine-disrupting chemicals on milk secretion and composition, emphasizing that this area needs much more research attention.

Background In adults with large‐vessel occlusion stroke, the extent of the baseline infarct measured using the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) predicts outcome and is used during patient selection for mechanical thrombectomy. The relationship between ASPECTS and clinical outcome is unknown in pediatric large‐vessel occlusion stroke. Methods Secondary analysis of a retrospective multicenter case–control study assessing mechanical thrombectomy versus medical management alone for pediatric large‐vessel occlusion stroke across 5 centers in Australia and Canada from 2011 to 2022. ASPECTS was measured on baseline computed tomography or magnetic resonance imaging–diffusion‐weighted imaging while blinded to clinical outcome and treatment status. The relationship between ASPECTS and pediatric modified Rankin scale scores at 3 months following stroke was assessed using ordinal logistic regression. Results In total, n = 24 thrombectomy patients (F = 10, mean age, 11.3 years [SD, 4.36]) and n = 24 control patients (F = 10, mean age, 10.2 years [SD, 4.32) were included. Mean ASPECTS was 6.3 (SD, 2.37) in the thrombectomy group and 6.1 (SD, 2.64) in the control group. In patients undergoing thrombectomy, baseline ASPECTS significantly correlated with pediatric modified Rankin scale scores at 3 months (odds ratio, 1.58 [95% CI, 1.10–2.27]; P = 0.013). In control patients, there was no significant correlation (odds ratio, 1.17 [95% CI, 0.87–1.55]; P = 0.298). Six of 7 thrombectomy patients with a large baseline infarct (ASPECTS

Abstract Dose optimization of sirolimus may further improve outcomes in allogeneic hematopoietic cell transplant (HCT) patients receiving post‐transplantation cyclophosphamide (PTCy) to prevent graft‐versus‐host disease (GVHD). Sirolimus exposure–response association studies in HCT patients (i.e., the association of trough concentration with clinical outcomes) have been conflicting. Sirolimus has important effects on T‐cells, including conventional (Tcons) and regulatory T‐cells (Tregs), both of which have been implicated in the mechanisms by which PTCy prevents GVHD, but there is an absence of validated biomarkers of sirolimus effects on these cell subsets. Considering the paucity of existing biomarkers and the complexities of the immune system, we conducted a literature review to inform a quantitative systems pharmacology (QSP) model of GVHD. The published literature presented multiple challenges. The sirolimus pharmacokinetic models insufficiently describe sirolimus distribution to relevant physiological compartments. Despite multiple publications describing sirolimus effects on Tcons and Tregs in preclinical and human ex vivo models, consistent parameters relating sirolimus concentrations to circulating Tcons and Tregs could not be found. Each aspect presents a challenge in building a QSP model of sirolimus and its temporal effects on T‐cell subsets and GVHD prevention. To optimize GVHD prevention regimens, phase I studies and systematic studies of immunosuppressant concentration–effect association are needed for QSP modeling.

IntroductionGenetic predisposition to autoimmune encephalitis with antibodies against N-methyl-D-aspartate receptor (NMDAR) is poorly understood. Given the diversity of associated environmental factors (tumors, infections), we hypothesized that human leukocyte antigen (HLA) and killer-cell immunoglobulin-like receptors (KIR), two extremely polymorphic gene complexes key to the immune system, might be relevant for the genetic predisposition to anti-NMDAR encephalitis. Notably, KIR are chiefly expressed by Natural Killer (NK) cells, recognize distinct HLA class I allotypes and play a major role in anti-tumor and anti-infection responses.MethodsWe conducted a Genome Wide Association Study (GWAS) with subsequent control-matching using Principal Component Analysis (PCA) and HLA imputation, in a multi-ethnic cohort of anti-NMDAR encephalitis (n=479); KIR and HLA were further sequenced in a large subsample (n=323). PCA-controlled logistic regression was then conducted for carrier frequencies (HLA and KIR) and copy number variation (KIR). HLA-KIR interaction associations were also modeled. Additionally, single cell sequencing was conducted in peripheral blood mononuclear cells from 16 cases and 16 controls, NK cells were sorted and phenotyped.ResultsAnti-NMDAR encephalitis showed a weak HLA association with DRB1*01:01~DQA1*01:01~DQB1*05:01 (OR=1.57, 1.51, 1.45; respectively), and DRB1*11:01 (OR=1.60); these effects were stronger in European descendants and in patients without an underlying ovarian teratoma. More interestingly, we found increased copy number variation of KIR2DL5B (OR=1.72), principally due to an overrepresentation of KIR2DL5B*00201. Further, we identified two allele associations in framework genes, KIR2DL4*00103 (25.4% vs. 12.5% in controls, OR=1.98) and KIR3DL3*00302 (5.3% vs. 1.3%, OR=4.44). Notably, the ligands of these KIR2DL4 and KIR3DL3, respectively, HLA-G and HHLA2, are known to act as immune checkpoint with immunosuppressive functions. However, we did not find differences in specific KIR-HLA ligand interactions or HLA-G polymorphisms between cases and controls. Similarly, gene expression of CD56dim or CD56bright NK cells did not differ between cases and controls.DiscussionOur observations for the first time suggest that the HLA-KIR axis might be involved in anti-NMDAR encephalitis. While the genetic risk conferred by the identified polymorphisms appears small, a role of this axis in the pathophysiology of this disease appears highly plausible and should be analyzed in future studies.

Chimeric antigen receptor (CAR) T-cell based immunotherapy has shown its potential in treating blood cancers, and its application to solid tumors is currently being extensively investigated. For glioma brain tumors, various CAR T-cell targets include IL13Rα2, EGFRvIII, HER2, EphA2, GD2, B7-H3, and chlorotoxin. In this work, we are interested in developing a mathematical model of IL13Rα2 targeting CAR T-cells for treating glioma. We focus on extending the work of Kuznetsov et al. (1994) by considering binding of multiple CAR T-cells to a single glioma cell, and the dynamics of these multi-cellular conjugates. Our model more accurately describes experimentally observed CAR T-cell killing assay data than the models which do not consider multi-cellular conjugates. Moreover, we derive conditions in the CAR T-cell expansion rate that determines treatment success or failure. Finally, we show that our model captures distinct CAR T-cell killing dynamics from low to high antigen receptor densities in patient-derived brain tumor cells.

Efficacious stem cell-based therapies for traumatic brain injury (TBI) depend on successful delivery, migration, and engraftment of stem cells to induce neuroprotection. L-myc expressing human neural stem cells (LMNSC008) demonstrate an inherent tropism to injury sites after intranasal (IN) administration. We hypothesize that IN delivered LMNSC008 cells migrate to primary and secondary injury sites and modulate biomarkers associated with neuroprotection and tissue regeneration. To test this hypothesis, immunocompetent adult female rats received either controlled cortical impact injury or sham surgery. LMNSC008 cells or a vehicle were administered IN on postoperative days 7, 9, 11, 13, 15, and 17. The distribution and migration of eGFP-expressing LMNSC008 cells were quantified over 1 mm-thick optically cleared (CLARITY) coronal brain sections from TBI and SHAM controls. NSC migration was observed along white matter tracts projecting toward the hippocampus and regions of TBI. ELISA and Nanostring assays revealed a shift in tissue gene expression in LMNSC008 treated rats relative to controls. LMNSC008 treatment reduced expression of genes and pathways involved in inflammatory response, microglial function, and various cytokines and receptors. Our proof-of-concept studies, although preliminary, support the rationale of using intranasal delivery of LMNSC008 cells for functional studies in preclinical models of TBI and provide support for potential translatability in TBI patients.

Non-small cell lung cancer (NSCLC) has a high rate of brain metastasis. The purpose of this study was to assess the differential distribution of brain metastases from primary NSCLC based on mutation status. Brain MRI scans of patients with brain metastases from primary NSCLC were retrospectively analyzed. Brain metastatic tumors were grouped according to mutation status of their primary NSCLC and the neuroimaging features of these brain metastases were analyzed. A total of 110 patients with 1386 brain metastases from primary NSCLC were included in this study. Gray matter density at the tumor center peaked at ~0.6 for all mutations. The median depths of tumors were 7.9 mm, 8.7 mm and 9.1 mm for EGFR, ALK and KRAS mutation groups, respectively (p = 0.044). Brain metastases for the EGFR mutation-positive group were more frequently located in the left cerebellum, left cuneus, left precuneus and right precentral gyrus. In the ALK mutation-positive group, brain metastases were more frequently located in the right middle occipital gyrus, right posterior cingulate, right precuneus, right precentral gyrus and right parietal lobe. In the KRAS mutation-positive patient group, brain metastases were more frequently located in the posterior left cerebellum. Our study showed differential spatial distribution of brain metastases in patients with NSCLC according to their mutation status. Information regarding distribution of brain metastases is clinically relevant as it could be helpful to guide treatment planning for targeted therapy, and for predicting prognosis.

In the development of cell-based cancer therapies, quantitative mathematical models of cellular interactions are instrumental in understanding treatment efficacy. Efforts to validate and interpret mathematical models of cancer cell growth and death hinge first on proposing a precise mathematical model, then analyzing experimental data in the context of the chosen model. In this work, we present the first application of the sparse identification of non-linear dynamics (SINDy) algorithm to a real biological system in order discover cell-cell interaction dynamics in in vitro experimental data, using chimeric antigen receptor (CAR) T-cells and patient-derived glioblastoma cells. By combining the techniques of latent variable analysis and SINDy, we infer key aspects of the interaction dynamics of CAR T-cell populations and cancer. Importantly, we show how the model terms can be interpreted biologically in relation to different CAR T-cell functional responses, single or double CAR T-cell-cancer cell binding models, and density-dependent growth dynamics in either of the CAR T-cell or cancer cell populations. We show how this data-driven model-discovery based approach provides unique insight into CAR T-cell dynamics when compared to an established model-first approach. These results demonstrate the potential for SINDy to improve the implementation and efficacy of CAR T-cell therapy in the clinic through an improved understanding of CAR T-cell dynamics.

Small-ring cage hydrocarbons are popular bioisosteres (molecular replacements) for commonly found para-substituted benzene rings in drug design1. The utility of these cage structures derives from their superior pharmacokinetic properties compared with their parent aromatics, including improved solubility and reduced susceptibility to metabolism2,3. A prime example is the bicyclo[1.1.1]pentane motif, which is mainly synthesized by ring-opening of the interbridgehead bond of the strained hydrocarbon [1.1.1]propellane with radicals or anions4. By contrast, scaffolds mimicking meta-substituted arenes are lacking because of the challenge of synthesizing saturated isosteres that accurately reproduce substituent vectors5. Here we show that bicyclo[3.1.1]heptanes (BCHeps), which are hydrocarbons for which the bridgehead substituents map precisely onto the geometry of meta-substituted benzenes, can be conveniently accessed from [3.1.1]propellane. We found that [3.1.1]propellane can be synthesized on a multigram scale, and readily undergoes a range of radical-based transformations to generate medicinally relevant carbon- and heteroatom-substituted BCHeps, including pharmaceutical analogues. Comparison of the absorption, distribution, metabolism and excretion (ADME) properties of these analogues reveals enhanced metabolic stability relative to their parent arene-containing drugs, validating the potential of this meta-arene analogue as an sp3-rich motif in drug design. Collectively, our results show that BCHeps can be prepared on useful scales using a variety of methods, offering a new surrogate for meta-substituted benzene rings for implementation in drug discovery programmes.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to noninvasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that our method will contribute to the better understanding of cancer progression and therapeutic response.

Summary: Background: Kabuki syndrome (KS) is a genetic disorder caused by DNA mutations in KMT2D, a lysine methyltransferase that methylates histones and other proteins, and therefore modifies chromatin structure and subsequent gene expression. Ketones, derived from the ketogenic diet, are histone deacetylase inhibitors that can ‘open’ chromatin and encourage gene expression. Preclinical studies have shown that the ketogenic diet rescues hippocampal memory neurogenesis in mice with KS via the epigenetic effects of ketones. Methods: Single-cell RNA sequencing and mass spectrometry-based proteomics were used to explore molecular mechanisms of disease in individuals with KS (n = 4) versus controls (n = 4). Findings: Pathway enrichment analysis indicated that loss of function mutations in KMT2D are associated with ribosomal protein dysregulation at an RNA and protein level in individuals with KS (FDR

Contrast transport models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.

IntroductionCancer combination treatments involving immunotherapies with targeted radiation therapy are at the forefront of treating cancers. However, dosing and scheduling of these therapies pose a challenge. Mathematical models provide a unique way of optimizing these therapies. MethodsUsing a preclinical model of multiple myeloma as an example, we demonstrate the capability of a mathematical model to combine these therapies to achieve maximum response, defined as delay in tumor growth. Data from mice studies with targeted radionuclide therapy (TRT) and chimeric antigen receptor (CAR)-T cell monotherapies and combinations with different intervals between them was used to calibrate mathematical model parameters. The dependence of progression-free survival (PFS), overall survival (OS), and the time to minimum tumor burden on dosing and scheduling was evaluated. Different dosing and scheduling schemes were evaluated to maximize the PFS and optimize timings of TRT and CAR-T cell therapies. ResultsTherapy intervals that were too close or too far apart are shown to be detrimental to the therapeutic efficacy, as TRT too close to CAR-T cell therapy results in radiation related CAR-T cell killing while the therapies being too far apart result in tumor regrowth, negatively impacting tumor control and survival. We show that splitting a dose of TRT or CAR-T cells when administered in combination is advantageous only if the first therapy delivered can produce a significant benefit as a monotherapy. DiscussionMathematical models are crucial tools for optimizing the delivery of cancer combination therapy regimens with application along the lines of achieving cure, maximizing survival or minimizing toxicity.