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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 inin vitroexperimental 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.

Temporal dynamics of gene expression inform cellular and molecular perturbations associated with disease development and evolution. Given the complexity of high-dimensional temporal genomic data, an analytic framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Here we model temporal dynamics of the transcriptome of peripheral blood mononuclear cells in a two-dimensional state-space representing states of health and leukemia using time-sequential bulk RNA-seq data from a murine model of acute myeloid leukemia (AML). The state-transition model identified critical points that accurately predict AML development and identifies stepwise transcriptomic perturbations that drive leukemia progression. The geometry of the transcriptome state-space provided a biological interpretation of gene dynamics, aligned gene signals that are not synchronized in time across mice, and allowed quantification of gene and pathway contributions to leukemia development. Our state-transition model synthesizes information from multiple cell types in the peripheral blood and identifies critical points in the transition from health to leukemia to guide interpretation of changes in the transcriptome as a whole to predict disease progression.Significance:These findings apply the theory of state transitions to model the initiation and development of acute myeloid leukemia, identifying transcriptomic perturbations that accurately predict time to disease development.See related commentary by Kuijjer, p. 3072

Table S1. Differentially expressed genes for c_1 vs c_1^* Table S2. Differentially expressed genes for c_2 vs c_1^* Table S3. Differentially expressed genes for c_3 vs c_1^* Table S4. Differentially expressed genes for c_2 vs c_1 Table S5. Differentially expressed genes for c_3 vs c_1 Table S6. Differentially expressed genes for c_3 vs c_2 Tables S7-S10. Differentially expressed genes for early, transition, persistent, and leukemia events. Tables S11-S14. GO terms enriched for early, transition, persistent, and leukemia events. Table S15. Top 1% of eigengenes.

Figure S1. Details of animal model and fusion gene CM, Figure S2. Ex vivo flow cytometry analysis of bone marrow, Figure S3. Details of principal component analysis, Figure S4. Comparison of state-space construction with different dimension reduction methods, Figure S5. Hierarchical clustering of time-series RNA-seq data and relation to state-space, Figure S6. Details of critical point estimation, construction of quasi-potential, and state-space dynamics, Figure S7. Correlation of state-space geometry with Kit and CM gene expression. Sensitivity of state-space geometry to inclusion of Kit and CM, Figure S8. Volcano plots of critical-point based differential gene expression analysis, Figure S9. Heatmaps of selected GO pathways in early, transition, persistent, and leukemic events, Figure S10. Computation of eigengene angle in state-space, Figure S11. Details of principal component analysis of validation cohorts and state-space dynamics, Figure S12. Mean-squared displacement analysis of particle trajectories in state-space and calibration of Fokker-Planck diffusion coefficient with training cohort, Figure S13. Sensitivity analysis of state-space construction to sample and normalization thresholds, Figure S14. Bootstrap cross validation of state-space construction.

In this supplement we provide additional data and methods to support our results. In particular, we show: details of our mouse model of AML; demonstrate the robustness of our state-space construction to normalization methods and gene selection criteria; compare dimension reduction methods; show bootstrap cross-validation of our predictions; compare hierarchical clustering with our state-space approach; and finally, provide full lists of differentially expressed genes and gene ontology (GO) term enrichment.

Gliomas are uniformly fatal forms of primary brain neoplasms that vary from low- to high-grade (glioblastoma). Whereas low-grade gliomas are weakly angiogenic, glioblastomas are among the most angiogenic tumors. Thus, interactions between glioma cells and their tissue microenvironment may play an important role in aggressive tumor formation and progression. To quantitatively explore how tumor cells interact with their tissue microenvironment, we incorporated the interactions of normoxic glioma cells, hypoxic glioma cells, vascular endothelial cells, diffusible angiogenic factors, and necrosis formation into a first-generation, biologically based mathematical model for glioma growth and invasion. Model simulations quantitatively described the spectrum of in vivo dynamics of gliomas visualized with medical imaging. Furthermore, we investigated how proliferation and dispersal of glioma cells combine to induce increasing degrees of cellularity, mitoses, hypoxia-induced neoangiogenesis and necrosis, features that characterize increasing degrees of “malignancy,” and we found that changes in the net rates of proliferation (ρ) and invasion (D) are not always necessary for malignant progression. Thus, although other factors, including the accumulation of genetic mutations, can change cellular phenotype (e.g., proliferation and invasion rates), this study suggests that these are not required for malignant progression. Simulated results are placed in the context of the current clinical World Health Organization grading scheme for studying specific patient examples. This study suggests that through the application of the proposed model for tumor–microenvironment interactions, predictable patterns of dynamic changes in glioma histology distinct from changes in cellular phenotype (e.g., proliferation and invasion rates) may be identified, thus providing a powerful clinical tool. Cancer Res; 71(24); 7366–75. ©2011 AACR.

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Glioblastoma multiforme is the most aggressive type of primary brain tumor. Glioblastoma growth dynamics vary widely across patients, making it difficult to accurately gauge their response to treatment. We developed a model-based metric of therapy response called Days Gained that accounts for this heterogeneity. Here, we show in 63 newly diagnosed patients with glioblastoma that Days Gained scores from a simple glioblastoma growth model computed at the time of the first postradiotherapy MRI scan are prognostic for time to tumor recurrence and overall patient survival. After radiation treatment, Days Gained also distinguished patients with pseudoprogression from those with true progression. Because Days Gained scores can be easily computed with routinely available clinical imaging devices, this model offers immediate potential to be used in ongoing prospective studies. Cancer Res; 73(10); 2976–86. ©2013 AACR.

Neural stem cells (NSCs) offer a potential solution to treating brain tumors. This is because NSCs can circumvent the blood-brain barrier and migrate to areas of damage in the central nervous system, including tumors, stroke, and wound injuries. However, for successful clinical application of NSC treatment, a sufficient number of viable cells must reach the diseased or damaged area(s) in the brain, and evidence suggests that it may be affected by the paths the NSCs take through the brain, as well as the locations of tumors. To study the NSC migration in brain, we develop a mathematical model of therapeutic NSC migration towards brain tumor, that provides a low cost platform to investigate NSC treatment efficacy. Our model is an extension of the model developed in Rockne et al. (PLoS ONE 13, e0199967, 2018) that considers NSC migration in non-tumor bearing naive mouse brain. Here we modify the model in Rockne et al. in three ways: (i) we consider three-dimensional mouse brain geometry, (ii) we add chemotaxis to model the tumor-tropic nature of NSCs into tumor sites, and (iii) we model stochasticity of migration speed and chemosensitivity. The proposed model is used to study migration patterns of NSCs to sites of tumors for different injection strategies, in particular, intranasal and intracerebral delivery. We observe that intracerebral injection results in more NSCs arriving at the tumor site(s), but the relative fraction of NSCs depends on the location of injection relative to the target site(s). On the other hand, intranasal injection results in fewer NSCs at the tumor site, but yields a more even distribution of NSCs within and around the target tumor site(s).

Acute myeloid leukemia (AML) harboring inv(16)(p13q22) expresses high levels of miR-126. Here we show that the CBFB-MYH11 (CM) fusion gene upregulates miR-126 expression through aberrant miR-126 transcription and perturbed miR-126 biogenesis via the HDAC8/RAN-XPO5-RCC1 axis. Aberrant miR-126 upregulation promotes survival of leukemia-initiating progenitors and is critical for initiating and maintaining CM-driven AML. We show that miR-126 enhances MYC activity through the SPRED1/PLK2-ERK-MYC axis. Notably, genetic deletion of miR-126 significantly reduces AML rate and extends survival in CM knock-in mice. Therapeutic depletion of miR-126 with an anti-miR-126 (miRisten) inhibits AML cell survival, reduces leukemia burden and leukemia stem cell (LSC) activity in inv(16) AML murine and xenograft models. The combination of miRisten with chemotherapy further enhances the anti-leukemia and anti-LSC activity. Overall, this study provides molecular insights for the mechanism and impact of miR-126 dysregulation in leukemogenesis and highlights the potential of miR-126 depletion as a therapeutic approach for inv(16) AML., miR-126 is highly expressed in inv(16) Acute myeloid leukemia (AML) but its role is unclear. Here, the authors show that the aberrant expression of miR-126 in inv(16) AML is directly due to the CBFB-MYH11 fusion gene and that it can promote AML development and leukemia stem cell maintenance, highlighting miR-126 as a therapeutic target for inv(16) AML patients

Chemotherapy may impair cognition and contribute to accelerated aging. The purpose of this study was to assess the effects of chemotherapy on the connectivity of the default mode network (DMN) in older women with breast cancer. This prospective longitudinal study enrolled women aged ≥ 60 years with stage I–III breast cancer (CTx group) and matched healthy controls (HC group). Study assessments, consisting of resting-state functional MRI (rs-fMRI) and the Picture Sequence Memory (psm) test for episodic memory from the NIH Toolbox for Cognition, were obtained at baseline and within one month after the completion of chemotherapy for the CTx group and at matched intervals for the HC group. Two-sample t-test and FDR multiple comparison were used for statistical inference. Our analysis of the CTx group (N = 19; 60–82 years of age, mean = 66.6, SD = 5.24) compared to the HC group (N = 14; 60–78 years of age, mean = 68.1, SD = 5.69) revealed weaker DMN subnetwork connectivity in the anterior brain but stronger connectivity in the posterior brain at baseline. After chemotherapy, this pattern was reversed, with stronger anterior connectivity and weaker posterior connectivity. In addition, the meta-level functional network connectivity (FNC) among the DMN subnetworks after chemotherapy was consistently weaker than the baseline FNC as seen in the couplings between anterior cingulate cortex (ACC) and retrosplenial (rSplenia) region, with ΔFNC(‘ACC’,’rSplenia’)=-0.14, t value=-2.44, 95 %CI=[-0.27,-0.10], pFDR

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 IL13Ra2, EGFRvIII, HER2, EphA2, GD2, B7-H3, and chlorotoxin. In this work, we are interested in developing a mathematical model of IL13Ra2 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 a model which does not consider cell binding. 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 at low, medium, and high antigen receptor densities in patient-derived brain tumor cells., Comment: 12 pages, 9 figures

BACKGROUND As a rare subtype of glioblastoma (GBM), the genetic features and outcomes in cystic GBM (cGBM) are largely unknown. This study aimed to evaluate the survival of cystic features, investigate genomic patterns and the immune microenvironment in patients with cGBM. METHODS Gd T1-weighted, T2-weighted, T2-Flair, DWI or ADC images were used to classify cGBM or noncystic GBM (non-cGBM). Clinical information from IvyGAP, EGA databases and Chinese cohort were used for survival analysis. Fresh cystic fluid and residual tumor tissue were collected to evaluate immunological components, pathological and genomic differences between cGBM and non-cGBM. Data from IvyGAP were included to determine difference of molecular feature among substructure. RESULTS 143 cases with cGBM and non-cGBM were screened to compare survival outcomes. Cystic features were to confer a survival benefit. It was an independent prognostic factor irrespective of IDH, MGMT and therapeutic regimen. cGBM patients have a hypomethylation state compared to non-cGBM. Active immune cells such as CD4+, CD8+ T cells were present in the cystic fluid and infiltrated into tumor. 10 cytokines/chemokines were only detected in cystic fluid, 8 cytokines/chemokines increased two-fold in cystic fluid than in peripheral blood serum (PBPs). Much differential methylated position (DMPs), differentially expressed genes (DEGs), Gene Set Enrichment Analysis (GSAE), Functional Epigenetic Modules (FEM) networks and molecular feature of substructural regions were different between cGBM and non-cGBM. CONCLUSION These results imply that cGBM could be a new subtype and has distinctive immune microenvironment which may have potential value correlating with prognosis.

Cell fate commitment is driven by dynamic changes in chromatin architecture and activity of lineage-specific transcription factors (TFs). The chromatin assembly factor-1 (CAF-1) is a histone chaperone that regulates chromatin architecture by facilitating nucleosome assembly during DNA replication. Accumulating evidence supports a substantial role of CAF-1 in cell fate maintenance, but the mechanisms by which CAF-1 restricts lineage choice remain poorly understood. Here, we investigate how CAF-1 influences chromatin dynamics and TF activity during lineage differentiation. We show that CAF-1 suppression triggers rapid differentiation of myeloid stem and progenitor cells into a mixed lineage state. We find that CAF-1 sustains lineage fidelity by controlling chromatin accessibility at specific loci, and limiting the binding of ELF1 TF at newly-accessible diverging regulatory elements. Together, our findings decipher key traits of chromatin accessibility that sustain lineage integrity and point to a powerful strategy for dissecting transcriptional circuits central to cell fate commitment.

MicroRNAs (miRNAs) have been shown to hold prognostic value in acute myeloid leukemia (AML); however, the temporal dynamics of miRNA expression in AML are poorly understood. Using serial samples from a mouse model of AML to generate time-series miRNA sequencing data, we are the first to show that the miRNA transcriptome undergoes state-transition during AML initiation and progression. We modeled AML state-transition as a particle undergoing Brownian motion in a quasi-potential and validated the AML state-space and state-transition model to accurately predict time to AML in an independent cohort of mice. The critical points of the model provided a framework to align samples from mice that developed AML at different rates. Our mathematical approach allowed discovery of dynamic processes involved during AML development and, if translated to humans, has the potential to predict an individual’s disease trajectory.

Lung cancer metastases comprise most of all brain metastases in adults and most brain metastases are diagnosed by magnetic resonance (MR) scans. The purpose of this study was to conduct an MR imaging-based radiomic analysis of brain metastatic lesions from patients with primary lung cancer to classify mutational status of the metastatic disease. We retrospectively identified lung cancer patients with brain metastases treated at our institution between 2009 and 2017 who underwent genotype testing of their primary lung cancer. Brain MR Images were used for segmentation of enhancing tumors and peritumoral edema, and for radiomic feature extraction. The most relevant radiomic features were identified and used with clinical data to train random forest classifiers to classify the mutation status. Of 110 patients in the study cohort (mean age 57.51 ± 12.32 years; M: F = 37:73), 75 had an EGFR mutation, 21 had an ALK translocation, and 15 had a KRAS mutation. One patient had both ALK translocation and EGFR mutation. Majority of radiomic features most relevant for mutation classification were textural. Model building using both radiomic features and clinical data yielded more accurate classifications than using either alone. For classification of EGFR, ALK, and KRAS mutation status, the model built with both radiomic features and clinical data resulted in area-under-the-curve (AUC) values based on cross-validation of 0.912, 0.915, and 0.985, respectively. Our study demonstrated that MR imaging-based radiomic analysis of brain metastases in patients with primary lung cancer may be used to classify mutation status. This approach may be useful for devising treatment strategies and informing prognosis.

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 (O6-methylguanine–DNA methyltransferase) has been associated with sensitivity to TMZ, whereas 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 that drive 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 found 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 patients with GBM with high and low levels of MGMT methylation at diagnosis.

High-grade (WHO grades III-IV) glioma remains one of the most lethal human cancers. Adoptive transfer of tumor-targeting chimeric antigen receptor (CAR)-redirected T cells for high-grade glioma has revealed promising indications of anti-tumor activity, but objective clinical responses remain elusive for most patients. A significant challenge to effective immunotherapy is the highly heterogeneous structure of these tumors, including large variations in the magnitudes and distributions of target antigen expression, observed both within individual tumors and between patients. To obtain a more detailed understanding of immunotherapy target antigens within patient tumors, we immunochemically mapped at single cell resolution three clinically-relevant targets, IL13Rα2, HER2 and EGFR, on tumor samples drawn from a 43-patient cohort. We observed that within individual tumor samples, expression of these antigens was neither random nor uniform, but rather that they mapped into local neighborhoods - phenotypically similar cells within regions of cellular tumor - reflecting not well understood properties of tumor cells and their milieu. Notably, tumor cell neighborhoods of high antigen expression were not arranged independently within regions. For example, in cellular tumor regions, neighborhoods of high IL13Rα2 and HER2 expression appeared to be reciprocal to those of EGFR, while in areas of pseudopalisading necrosis, expression of IL13Rα2 and HER2, but not EGFR, appeared to reflect the radial organization of tumor cells around hypoxic cores. Other structural features affecting expression of immunotherapy target antigens remain to be elucidated. This structured but heterogeneous organization of antigen expression in high grade glioma is highly permissive for antigen escape, and combinatorial antigen targeting is a commonly suggested potential mitigating strategy. Deeper understanding of antigen expression within and between patient tumors will enhance optimization of combination immunotherapies, the most immediate clinical application of the observations presented here being the importance of including (wild-type) EGFR as a target antigen.

As a rare subtype of glioblastomas (GBM), genetic features in cystic GBM (cGBM) are still largely unknown. We have previously identified a series of active T cell subsets and positive cytokines/chemocytokines in the cystic fluid of cGBMs, including IFN-gamma, IL-2, IL-6, IL-8, TNF-alpha, and MIP-1alpha/beta. This study aimed to evaluate the prognosis of cGBM through DNA methylation patterns and immune microenvironment transcriptional signatures. IvyGAP, EGA and fresh samples from a prospective Chinese cohort were collected to investigate genomic signatures, immune microenvironment and survival outcomes in patients with cGBMs. Gd T1-weighted, T2-weighted, T2-Flair, DWI or ADC images were used to classify cGBM. 143 cases with MGMT/IDH record were used to compare survival time and genomic analysis. Among the three cohorts, Kaplan-Meier analysis showed cystic features have benefit to overall survival (OS). Moreover, cyst feature is the forth factor (HR=0.65, [95% CI 0.42, 1.01]) of survival after IDH, MGMT and Stupp therapeutic regimen. Interestingly, in IDH-wild/MGMT-unmethyled subtype, cGBM patients have longer OS vs noncystic GBM (noncGBM) patients (HR = 0.57, [95% CI 0.33, 0.99]). Compared to noncGBM, cGBM patients have hypomethylation state both in whole gene region and cpG islands. Ivygap database showed between cystic and noncystic group, in sub-structures such as CT (cellular tumor region), CTpan (pseudopalisading cells around necrosis) and CTpnz (perinecrotic zone) there are differential gene expressions and different enrichment pathways. GSEA analysis showed within cGBM group, many gene sets associated with immune function activation such as TRL1/2/3/7 and IFN pathways. These results suggest that cysts of GBM may be associated with hypomethylation status an activated immune microenvironment which is associated with longer survival and may define a unique subgroup of GBM with intrinsically different biology and prognosis.

Cell-based therapies to the brain are promising for the treatment of multiple brain disorders including neurodegeneration and cancers. In order to access the brain parenchyma, there are multiple physiological barriers that must be overcome depending on the route of delivery. Specifically, the blood–brain barrier has been a major difficulty in drug delivery for decades, and it still presents a challenge for the delivery of therapeutic cells. Other barriers, including the blood-cerebrospinal fluid barrier and lymphatic-brain barrier, are less explored, but may offer specific challenges or opportunities for therapeutic delivery. Here we discuss the barriers to the brain and the strategies currently in place to deliver cell-based therapies, including engineered T cells, dendritic cells, and stem cells, to treat diseases. With a particular focus on cancers, we also highlight the current ongoing clinical trials that use cell-based therapies to treat disease, many of which show promise at treating some of the deadliest illnesses. Graphical abstract: [Figure not available: see fulltext.]. Accepted version

Chimeric antigen receptor (CAR) T-cell therapy is potentially an effective targeted immunotherapy for glioblastoma, yet there is presently little known about the efficacy of CAR T-cell treatment when combined with the widely used anti-inflammatory and immunosuppressant glucocorticoid, dexamethasone. Here we present a mathematical model-based analysis of three patient-derived glioblastoma cell lines treated in vitro with CAR T-cells and dexamethasone. Advanced in vitro experimental cell killing assay technologies allow for highly resolved temporal dynamics of tumor cells treated with CAR T-cells and dexamethasone, making this a valuable model system for studying the rich dynamics of nonlinear biological processes with translational applications. We model the system as a nonautonomous, two-species predator-prey interaction of tumor cells and CAR T-cells, with explicit time-dependence in the clearance rate of dexamethasone. Using time as a bifurcation parameter, we show that (1) dexamethasone destabilizes coexistence equilibria between CAR T-cells and tumor cells in a dose-dependent manner and (2) as dexamethasone is cleared from the system, a stable coexistence equilibrium returns in the form of a Hopf bifurcation. With the model fit to experimental data, we demonstrate that high concentrations of dexamethasone antagonizes CAR T-cell efficacy by exhausting, or reducing the activity of CAR T-cells, and by promoting tumor cell growth. Finally, we identify a critical threshold in the ratio of CAR T-cell death to CAR T-cell proliferation rates that predicts eventual treatment success or failure that may be used to guide the dose and timing of CAR T-cell therapy in the presence of dexamethasone in patients., Author summary Bioengineering and gene-editing technologies have paved the way for advance immunotherapies that can target patient-specific tumor cells. One of these therapies, chimeric antigen receptor (CAR) T-cell therapy has recently shown promise in treating glioblastoma, an aggressive brain cancer often with poor patient prognosis. Dexamethasone is a commonly prescribed anti-inflammatory medication due to the health complications of tumor associated swelling in the brain. However, the immunosuppressant effects of dexamethasone on the immunotherapeutic CAR T-cells are not well understood. To address this issue, we use mathematical modeling to study in vitro dynamics of dexamethasone and CAR T-cells in three patient-derived glioblastoma cell lines. We find that in each cell line studied there is a threshold of tolerable dexamethasone concentration. Below this threshold, CAR T-cells are successful at eliminating the cancer cells, while above this threshold, dexamethasone critically inhibits CAR T-cell efficacy. Our modeling suggests that in the presence of high dexamethasone reduced CAR T-cell efficacy, or increased exhaustion, can occur and result in CAR T-cell treatment failure.

Simple Summary Targeted radionuclide therapy (TRT) and immunotherapy, an example being chimeric antigen receptor T cells (CAR-Ts), represent two potent means of eradicating systemic cancers. Although each one as a monotherapy might have a limited effect, the potency can be increased with a combination of the two therapies. The complications involved in the dosing and scheduling of these therapies make the mathematical modeling of these therapies a suitable solution for designing combination treatment approaches. Here, we investigate a mathematical model for TRT and CAR-T cell combination therapies. Through an analysis of the mathematical model, we find that the tumor proliferation rate is the most important factor affecting the scheduling of TRT and CAR-T cell treatments with faster proliferating tumors requiring a shorter interval between the two therapies. Abstract Targeted radionuclide therapy (TRT) has recently seen a surge in popularity with the use of radionuclides conjugated to small molecules and antibodies. Similarly, immunotherapy also has shown promising results, an example being chimeric antigen receptor T cell (CAR-T) therapy in hematologic malignancies. Moreover, TRT and CAR-T therapies possess unique features that require special consideration when determining how to dose as well as the timing and sequence of combination treatments including the distribution of the TRT dose in the body, the decay rate of the radionuclide, and the proliferation and persistence of the CAR-T cells. These characteristics complicate the additive or synergistic effects of combination therapies and warrant a mathematical treatment that includes these dynamics in relation to the proliferation and clearance rates of the target tumor cells. Here, we combine two previously published mathematical models to explore the effects of dose, timing, and sequencing of TRT and CAR-T cell-based therapies in a multiple myeloma setting. We find that, for a fixed TRT and CAR-T cell dose, the tumor proliferation rate is the most important parameter in determining the best timing of TRT and CAR-T therapies.

SIGNIFICANCE Radiomics may improve precision medicine in CAR-T (Chimeric Antigen Receptor T Cell) therapy patient selection. BACKGROUND High-Grade Glioma (HGG) is a heterogenous primary CNS neoplasm with a high recurrence rate and poor outcomes. Many studies are exploring CAR T cells to combat HGG. Radiomic models have shown value in identifying biomarkers predictive of tumor genetics, response, and patient prognosis. In this study, we explore radiomic features derived from four clinical sequences and volumes of edema and enhancing tumor to predict treatment response to the first three doses of CAR-T therapy. METHODS In this IRB-approved phase 1 clinical trial (IRB 13384), patients underwent surgical resection of the tumor and received CAR-T cell therapy. Of the 82 patients accrued, 59 (20 females, median age = 49) completed three cycles of therapy and had 3T field strength MRI scans with minimal imaging artifacts. T1 weighted pre-contrast, T1 weighted post-contrast, T2 weighted, and T2 FLuid Attenuated Inversion Recovery sequences were used to generate 3D and 2D radiomics features. In total 28,541 radiomic features were generated per patient using the images prior to CAR-T administration. Each patient’s response to treatment after three cycles was determined to be either stable disease (29 patients) or progression. The radiomic feature set dimensionality was reduced using Maximum Relevance Minimum Redundancy. 10-fold cross-validation XGBoost was used to determine radiomic features predictive of treatment response with a randomized grid search for hyperparameter tuning. RESULTS Six radiomic features (four shape-based), had high SHapley Additive exPlanations (SHAP)-based importance feature predictive of RANO response with an AUC >0.73. CONCLUSION Despite the limited study size, such imaging-based radiomic models can serve as a potential basis for optimizing clinical trial design through more precise patient screening and providing potential predictive imaging biomarkers of whether a patient will respond to CAR-T cell therapy in HGGs.

The role of plasticity and epigenetics in shaping cancer evolution and response to therapy has taken center stage with recent technological advances including single cell sequencing. This roadmap article is focused on state-of-the-art mathematical and experimental approaches to interrogate plasticity in cancer, and addresses the following themes and questions: is there a formal overarching framework that encompasses both non-genetic plasticity and mutation-driven somatic evolution? How do we measure and model the role of the microenvironment in influencing/controlling non-genetic plasticity? How can we experimentally study non-genetic plasticity? Which mathematical techniques are required or best suited? What are the clinical and practical applications and implications of these concepts?

Background: We have demonstrated that 64Cu-DOTA trastuzumab is an effective PET imaging agent for HER2 positive breast cancer and are now seeking to evaluate the methodology for prediction of response and benefit to ado-trastuzumab emtansine (TDM1) in women with metastatic disease. Methods: Patients with metastatic breast cancer were eligible if they had biopsy confirmed HER2 + disease, at least 1 site of metastasis 2.0 cm or larger outside the biopsy site, and were to receive TDM1 as therapy. Pretreatment staging included 18F-FDG/PET. Prior to injection of 64Cu-DOTA-trastuzumab, patients received 45 mg of cold trastuzumab to reduce liver uptake. PET-CT scans were obtained at 21-25 h and 47-48 h over fields of view chosen in reference to 18F-FDG scans. TDM1 was administered at a dose of 3.6 mg/kg every 3 weeks. Restaging 18F-FDG/PET was performed every 2 cycles, and response to therapy was determined by PERCIST criteria. The radiolabel uptake was measured in terms of maximum voxel standardized uptake value (SUVmax). Results: Ten women enrolled on study and are evaluable for response; three continue on TDM1 for 22-40 months and four patients remained on treatment for at least 1 year. The median age was 54.5 years (48-83 years); seven received prior trastuzumab-containing chemotherapy 3 wks to 55 months prior to study entry. HER2 was positive by IHC in 5 and by FISH in 5 (3 were 2+ by IHC; 1 was 1+ and 1 indeterminate). Complete or partial metabolic response was observed in 5 patients. Patients had their average SUVmax on 64Cu-DOTA trastuzumab PET (aSUVmax) assessed in addition to individual assessments on up to 8 lesions on both Day1 and Day2. The mean aSUVmax was (6.3, 8.8) for responding patients and (4.4, 5.2) for non-responder (day1, day2). The difference between responders and non-responders on Day1 aSUVmax was marginally significant (p=0.06), but significant on Day2 (p=0.04). The three highest aSUVmax on both day1 and day2 were three of the four patients with PFS>1 year. Data on the relationship of 64Cu-DOTA trastuzumab PET to IHC and FISH, and individual lesion SUVmax including evidence suggesting a potential threshold effect will be presented. Conclusions: In women with biopsy confirmed HER2 positive metastatic disease, 64Cu-DOTA-trastuzumab PET imaging is predictive for response to TDM1. Citation Format: Mortimer JE, Bading JR, Frankel P, Tumyan L, Tran TT, Rockne RC, Shively JE, Gidwaney N, Park J, Colcher DM. Use of 64Cu-DOTA-trastuzumab positron emission tomography (PET) to predict response to ado-trastuzumab emtansine (TDM1) [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr PD4-12.

In this work we aim to understand how cellular aggregation and angiogenic pattern formation develop in bone marrow endothelial networks by integrating spatial-temporal mathematical models with theory regarding how cellular metabolism scales with cell number. Presently, efforts to model angiogenic pattern formation fail to accurately predict spatial characteristics often observed. Biological allometries, such as the scaling of metabolism to mass or cell number, have been hypothesized to result from natural selection to maximize how vascular networks fill space yet minimize internal transport distances and resistance to motion. Metabolic scaling theory argues that two guiding principles—optimization of motion and space-filling fractal distributions—describe a diversity of biological networks and predict how the geometry of these networks influences organismal metabolism. Recently, metabolic scaling theory has been applied to study tumor growth rates, offering a mechanistic link between tumor growth and vascular patterning. Remarkably, there exists two classes of spatial-temporal mathematical models where equivalent angiogenic pattern formation occurs. These two classes are determined primarily by either cellular interactions as proposed by Hillen and Painter, or biomechanical forces as proposed by Manoussaki and Murray. Examples of underlying mechanisms are chemotactic quorum-sensing and resource competition for the former, and advection-diffusion driven stresses and strains in the extra-cellular matrix for the latter. We compare these models by examining theoretical predictions for pattern forming criteria with numerical simulations. Furthermore, we identify experimentally testable predictions for angiogenic pattern formation related to the spatial configuration of the extra-cellular matrix. We test these predictions against in vitro confocal microscopy imaging data for bone marrow endothelial networks with experimental control over adhesion molecules and the resulting network structure. Specifically, we measure time-dependent geometric features in the highly reticulated endothelial networks related to steady-state equilibration, connectivity and space-filling. Example metrics are the scaling of network edge lengths and enclosed areas within the extra-cellular matrix that indicate changes to network morphology as adhesion molecules are present or absent. These metrics are informed by metabolic scaling theory and are predicted by the steady-state solutions of the underlying models. By conducting metric and model selection, this work identifies data-driven mechanisms for angiogenic pattern formation and quantifies normality in bone-marrow endothelial networks. This is an essential step for future work in quantifying and understanding abnormality in bone marrow endothelial networks. Citation Format: Alexander B. Brummer, Young-Woong Kim, Nadia Carlesso, Russell C. Rockne. Biophysical models of pattern formation in bone marrow endothelial networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 481.

Previous studies have shown that approximately 40% of estrogen receptor positive (ER+) breast cancer (BC) patients harbor immune signaling defects in their blood at diagnosis that predicts overall survival. The aim of this work is to apply communication theory and signal processing methods to model cytokine-mediated signaling errors in immune cells in the peripheral blood. Our approach uses communication theory concepts to develop a communication system model consisting of a transmitter, receiver, and channel, applied to the JAK/STAT signaling pathway and tested using multi-color flow cytometry analysis of peripheral blood mononuclear cells stimulated with IFNγ, IL-2, IL-6, IL-4, IL-10, IL-12, or TGFβ in samples from 2 BC patients and 4 healthy controls. We use our model to estimate the detection error rate when one of the 7 cytokines is being transduced through the JAK/STAT signaling pathway, but its signal is being received on multiple phosphorylated signal transducers and activators of transcription (pSTAT) molecules. In addition, we extend the measured error rate value to an error rate curve by varying the signal-to-noise ratio available to the receiver in our system model. Our results show a trend towards higher detection error rates in immune cells from BC patients as compared to healthy controls, which may indicate altered immune signaling and incorrect cell decisions in these patients. Also, considering different cell subtypes, our results show two orders of magnitude increase in error rate in CD4+ memory T cells compared to CD4+ naïve T cells in BC patients. With further development, we believe that our communication theory model and quantification of optimal signal detection in cells will provide a better understanding into how immune cells can accurately “decode” information to respond properly to various signals and how the immune signaling is altered in BC patients, causing better or worse outcomes. The benefit of our novel approach is that it integrates all the transmitted (cytokines) and received signals (pSTATs) into a single communications framework that can be efficiently analyzed together. Citation Format: Adina Matache, Aleksandra Karolak, Sergio Branciamore, Andrei Rodin, Peter P. Lee, Russell C. Rockne. A communication theory framework for modeling cytokine-mediated signaling in healthy and breast cancer derived peripheral blood immune cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2741.

Background: Immunotherapy with chimeric antigen receptor - T (CAR-T) cells and targeted radionuclide therapy (TRT) are two highly promising therapies in cancer treatment. Often, these therapies show limited efficacy in complete eradication of cancer cells making the combination of these two therapies an attractive cancer treatment option. The complications involved in dosing and scheduling of these therapies make mathematical modeling an appropriate method for analyzing and predicting disease response to these therapies. Here we propose a mathematical model evaluating disease response to the combination of these two therapies and explore the optimization of their dosing and scheduling. Methods: An ordinary differential equation-based formalism is proposed for simulation of tumor response to CAR-T cell therapy as well as TRT. CAR-T cell dose and injected radioactivity was input to the model. Among others, key model parameters included tumor proliferation rate, tumor cell and CAR-T cell radiosensitivity, CAR-T cell killing rate, CAR-T cell decay rate indicating persistence. Preclinical experiments involving CS1- CAR-T cell therapy and 225Ac-DOTA-Daratumumab TRT in a multiple myeloma mice model were used to parameterize the model. Sensitivity study of the model parameters using overall survival (OS) and progression-free survival (PFS) as evaluation metrics, was performed to elucidate the parameters with highest impact. Results: OS and PFS were 97 and 55 days when CAR-T cell therapy was given prior to TRT as compared to OS of 43 days for untreated control mice. Sensitivity study of model parameters showed that tumor proliferation has the highest impact on survival metrics. For a ±50% change in tumor proliferation rate, OS changed by -41%/+111% and PFS changed by -62%/+147%. Similar changes in TRT injected activity and CAR-T cell dose changed OS by ± 15% and ±21% respectively. Accordingly, PFS changed by roughly ±32% and ±45% respectively. A variation of the interval between the therapies showed that faster growing tumors required a shorter interval between the two therapies. The sequence of therapies was also changed and TRT prior to CAR-T cell therapy demonstrated shorter PFS (43 days) due to the adverse effects of radiation on CAR-T cells. Conclusion: For a fixed dose of TRT and CAR-T cells, tumor proliferation rate was found to be the prime factor impacting therapy interval. The presented work shows the key parameters required for planning and optimizing preclinical experiments and clinical trials. Using disease, CAR-T cell and radionuclide-specific parameters as shown in this work as well as incorporating immune stimulating effects of radiation would make it an extremely potent tool for optimizing combination therapies. Citation Format: Vikram Adhikarla, Dennis Awuah, Alexander B. Brummer, Enrico Caserta, Amrita Krishnan, Flavia Pichiorri, Megan M. Minnix, John E. Shively, Jeffrey Y.C. Wong, Xiuli Wang, Russell C. Rockne. A mathematical model for optimization of combination therapy involving targeted radionuclide and CAR-T cell therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2732.

Background During acute myeloid leukemia (AML) growth, the bone marrow (BM) niche acquires significant vascular changes that can be offset by therapeutic blast cytoreduction. The molecular mechanisms of this vascular plasticity remain to be fully elucidated. Herein, we report on the changes that occur in the vascular compartment of the FLT3-ITD+ AML BM niche pre and post treatment and their impact on leukemic stem cells (LSCs). Methods BM vasculature was evaluated in FLT3-ITD+ AML models (MllPTD/WT/Flt3ITD/ITD mouse and patient-derived xenograft) by 3D confocal imaging of long bones, calvarium vascular permeability assays, and flow cytometry analysis. Cytokine levels were measured by Luminex assay and miR-126 levels evaluated by Q-RT-PCR and miRNA staining. Wild-type (wt) and MllPTD/WT/Flt3ITD/ITD mice with endothelial cell (EC) miR-126 knockout or overexpression served as controls. The impact of treatment-induced BM vascular changes on LSC activity was evaluated by secondary transplantation of BM cells after administration of tyrosine kinase inhibitors (TKIs) to MllPTD/WT/Flt3ITD/ITD mice with/without either EC miR-126 KO or co-treatment with tumor necrosis factor alpha (TNFα) or anti-miR-126 miRisten. Results In the normal BM niche, CD31+Sca-1high ECs lining arterioles have miR-126 levels higher than CD31+Sca-1low ECs lining sinusoids. We noted that during FLT3-ITD+ AML growth, the BM niche lost arterioles and gained sinusoids. These changes were mediated by TNFα, a cytokine produced by AML blasts, which induced EC miR-126 downregulation and caused depletion of CD31+Sca-1high ECs and gain in CD31+Sca-1low ECs. Loss of miR-126high ECs led to a decreased EC miR-126 supply to LSCs, which then entered the cell cycle and promoted leukemia growth. Accordingly, antileukemic treatment with TKI decreased the BM blast-produced TNFα and increased miR-126high ECs and the EC miR-126 supply to LSCs. High miR-126 levels safeguarded LSCs, as shown by more severe disease in secondary transplanted mice. Conversely, EC miR-126 deprivation via genetic or pharmacological EC miR-126 knock-down prevented treatment-induced BM miR-126high EC expansion and in turn LSC protection. Conclusions Treatment-induced CD31+Sca-1high EC re-vascularization of the leukemic BM niche may represent a LSC extrinsic mechanism of treatment resistance that can be overcome with therapeutic EC miR-126 deprivation. Graphic abstract

Background: Brain metastases are associated with poor survival. Molecular genetic testing informs on targeted therapy and survival. The purpose of this study was to perform a MR imaging-based radiomic analysis of brain metastases from non-small cell lung cancer (NSCLC) to identify radiomic features that were important for predicting survival duration.Methods: We retrospectively identified our study cohort via an institutional database search for patients with brain metastases from EGFR, ALK, and/or KRAS mutation-positive NSCLC. We segmented the brain metastatic tumors on the brain MR images, extracted radiomic features, constructed radiomic scores from significant radiomic features based on multivariate Cox regression analysis (p < 0.05), and built predictive models for survival duration.Result: Of the 110 patients in the cohort (mean age 57.51 ± 12.32 years; range: 22–85 years, M:F = 37:73), 75, 26, and 15 had NSCLC with EGFR, ALK, and KRAS mutations, respectively. Predictive modeling of survival duration using both clinical and radiomic features yielded areas under the receiver operative characteristic curve of 0.977, 0.905, and 0.947 for the EGFR, ALK, and KRAS mutation-positive groups, respectively. Radiomic scores enabled the separation of each mutation-positive group into two subgroups with significantly different survival durations, i.e., shorter vs. longer duration when comparing to the median survival duration of the group.Conclusion: Our data supports the use of radiomic scores, based on MR imaging of brain metastases from NSCLC, as non-invasive biomarkers for survival duration. Future research with a larger sample size and external cohorts is needed to validate our results.

The homeostasis of vascular endothelium is crucial for cardiovascular health and endothelial cell (EC) aging and dysfunction could negatively impact vascular function. Leveraging transcriptome profiles from ECs subjected to various stimuli, including time-series data obtained from ECs under physiological pulsatile flow vs. pathophysiological oscillatory flow, we performed principal component analysis (PCA) to identify key genes contributing to divergent transcriptional states of ECs. Through bioinformatics analysis, we identified that a long non-coding RNA (lncRNA) RAMP2-AS1 encoded on the antisense of RAMP2, a determinant of endothelial homeostasis and vascular integrity, is a novel regulator essential for EC homeostasis and function. Knockdown of RAMP2-AS1 suppressed RAMP2 expression and caused EC functional changes promoting aging, including impaired angiogenesis and increased senescence. Our study demonstrates an integrative approach to quantifying EC aging based on transcriptome changes, which also identified a number of novel regulators, including protein-coding genes and many lncRNAs involved EC functional modulation, exemplified by RAMP2-AS1.

As the success of stem cell-based therapies is contingent on efficient cell delivery to damaged areas, neural stem cells (NSCs) have promising therapeutic potential because they inherently migrate to sites of central nervous system (CNS) damage. To explore the possibility of NSC-based therapy after traumatic brain injury (TBI), isoflurane-anesthetized adult male rats received a controlled cortical impact (CCI) of moderate severity (2.8 mm deformation at 4 m/s) or sham injury (i.e., no cortical impact). Beginning 1-week post-injury, the rats were immunosuppressed and 1 × 10 6 human NSCs (LM-NS008.GFP.fLuc) or vehicle (VEH) (2% human serum albumen) were administered intranasally (IN) on post-operative days 7, 9, 11, 13, 15, and 17. To evaluate the spatial distributions of the LM-NSC008 cells, half of the rats were euthanized on day 25, one day after completion of the cognitive task, and the other half were euthanized on day 46. 1 mm thick brain sections were optically cleared (CLARITY), and volumes were imaged by confocal microscopy. In addition, LM-NSC008 cell migration to the TBI site by immunohistochemistry for human-specific Nestin was observed at day 39. Acquisition of spatial learning was assessed in a well-established Morris water maze task on six successive days beginning on post-injury day 18. IN administration of LM-NSC008 cells after TBI ( TBI + NSC ) significantly facilitated spatial learning relative to TBI + VEH rats ( p < 0.05 ) and had no effect on sham + NSC rats. Overall, these data indicate that IN-administered LM-NSC008 cells migrate to sites of TBI damage and that their presence correlates with cognitive improvement. Future studies will expand on these preliminary findings by evaluating other LM-NSC008 cell dosing paradigms and evaluating mechanisms by which LM-NSC008 cells contribute to cognitive recovery.

Background Dynamic contrast-enhanced MRI (DCE-MRI) parameters have been shown to be biomarkers for treatment response in glioblastoma (GBM). However, variations in analysis and measurement methodology complicate determination of biological changes measured via DCE. The aim of this study is to quantify DCE-MRI variations attributable to analysis methodology and image quality in GBM patients. Methods The Extended Tofts model (eTM) and Leaky Tracer Kinetic Model (LTKM), with manually and automatically segmented vascular input functions (VIFs), were used to calculate perfusion kinetic parameters from 29 GBM patients with double-baseline DCE-MRI data. DCE-MRI images were acquired 2–5 days apart with no change in treatment. Repeatability of kinetic parameters was quantified with Bland–Altman and percent repeatability coefficient (%RC) analysis. Results The perfusion parameter with the least RC was the plasma volume fraction (vp), with a %RC of 53%. The extra-cellular extra-vascular volume fraction (ve) %RC was 82% and 81%, for extended Tofts-Kety Model (eTM) and LTKM respectively. The %RC of the volume transfer rate constant (Ktrans) was 72% for the eTM, and 82% for the LTKM, respectively. Using an automatic VIF resulted in smaller %RCs for all model parameters, as compared to manual VIF. Conclusions As much as 72% change in Ktrans (eTM, autoVIF) can be attributable to non-biological changes in the 2–5 days between double-baseline imaging. Poor Ktrans repeatability may result from inferior temporal resolution and short image acquisition time. This variation suggests DCE-MRI repeatability studies should be performed institutionally, using an automatic VIF method and following quantitative imaging biomarkers alliance guidelines.

Background: Glioblastoma is the deadliest, yet most common, brain tumor in adults, with poor survival and response to aggressive therapy. Therapeutic failure results from a number of causes inherent to these tumors. Imaging, computational, and drug delivery approaches can aid in the quest to access and kill each tumor cell in patients. One factor, interstitial fluid flow, is a driving force therapeutic delivery. However, convective and diffusive transport mechanisms are un-der-studied. In this study, we examine the application of a novel image analysis method to meas-ure fluid flow and diffusion in glioblastoma patients with MRI and compare to patient outcomes. Methods: Building on a prior imaging methodology tested and validated in vitro, in silico and in preclinical models of disease, here we apply our analysis method to archival patient data from the Ivy GAP dataset. Results: We characterize interstitial fluid flow and diffusion patterns in patients. We find strong correlations between flow rates measured within tumors and in the surrounding parenchymal space, where we hypothesized that velocities would be higher. Looking at overall magnitudes, there is significant correlation with both age and survival in this patient cohort. Additionally, we find that tumor size nor resection significantly alter the velocity magnitude. Last, we map the flow pathways in patient tumors and find variability in degree of directionality that we hypothesize in future studies may lead to information concerning treatment, invasive spread, and progression. Conclusions: Analysis of standard DCE-MRI in patients with glioblastoma offers more infor-mation regarding transport within and around tumor, can be measured post-resection and mag-nitudes correlate with patient prognosis.

Background: Glioblastoma (GBM) is the deadliest and most common brain tumor in adults, with poor survival and response to aggressive therapy. Limited access of drugs to tumor cells is one reason for such grim clinical outcomes. A driving force for therapeutic delivery is interstitial fluid flow (IFF), both within the tumor and in the surrounding brain parenchyma. However, convective and diffusive transport mechanisms are understudied. In this study, we examined the application of a novel image analysis method to measure fluid flow and diffusion in GBM patients. Methods: Here, we applied an imaging methodology that had been previously tested and validated in vitro, in silico, and in preclinical models of disease to archival patient data from the Ivy Glioblastoma Atlas Project (GAP) dataset. The analysis required the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is readily available in the database. The analysis results, which consisted of IFF flow velocity and diffusion coefficients, were then compared to patient outcomes such as survival. Results: We characterized IFF and diffusion patterns in patients. We found strong correlations between flow rates measured within tumors and in the surrounding parenchymal space, where we hypothesized that velocities would be higher. Analyzing overall magnitudes indicated a significant correlation with both age and survival in this patient cohort. Additionally, we found that neither tumor size nor resection significantly altered the velocity magnitude. Lastly, we mapped the flow pathways in patient tumors and found a variability in the degree of directionality that we hypothesize may lead to information concerning treatment, invasive spread, and progression in future studies. Conclusions: An analysis of standard DCE-MRI in patients with GBM offers more information regarding IFF and transport within and around the tumor, shows that IFF is still detected post-resection, and indicates that velocity magnitudes correlate with patient prognosis. Published version