Journal articles on the topic '1D Anderson model'

Motivated by recent developments in the field of 1D topological superconductors (TSCs), we investigate the phase transition properties of p-wave superconductor with 50 sites. In this paper, we first map the p-wave fermion model into the transverse [Formula: see text] model with an incommensurate modulated transverse field. We study the phase transition from TSC phase to Anderson localization phase by using imaginary time-evolving block decimation (TEBD) algorithm. By calculating the correlation function [Formula: see text], we numerically calculate the average correlation function in different p-wave pairing amplitude [Formula: see text]. By plotting the average correlation function versus the strength of incommensurate [Formula: see text] for [Formula: see text] and [Formula: see text], we identify the phase transition from TSC phase to Anderson localization phase [X. M. Cai et al., Phys. Rev. Lett. 110, 176403 (2013)]. Last but not least, we give an average measure of the entanglement of a single site with the rest of the system and von Neumann entropy of two boundary sites for the same parameters showing that the system possesses very strong entanglement at the critical point. The finite size effect and Majorana fermions are discussed in this paper.

The methods to determine the Zak phase introduced by previous studies are usually limited to the periodic systems protected by the inversion symmetry. In this work, we build a one-dimensional chiral symmetric acoustic chain with controllable disorder to break its inversion symmetry. By the mean chiral displacement method, we detect the Zak phase in order to observe the topological phase transition induced purely by disorder. The finding exhibits the topological Anderson insulator phase, in which an otherwise trivial acoustic Su–Schrieffer–Heeger model is driven non-trivial by disorder accompanied by the change of the topological sign. This method could also be utilized in chiral symmetry broken and non-Hermitian systems. The result reveals that disorder introduced in the acoustic devices may induce the change of the topological phase, which is promising for the design of new acoustic devices.

ABSTRACTDirac materials are characterized by exceptional mobility, orders of magnitude higher than any semiconductor, due to the massless pseudorelativistic nature of the Dirac fermions. These systems being semimetallic, the lack of a genuine band-gap poses a serious limitation to their possible applications in electronics. We recently demonstrated that thin TiO2 nanowires can exhibit 1D Dirac states similar to metallic carbon nanotubes, with the crucial difference that these states lie inside the conduction band in proximity of a wide band gap. We analyze the robustness of the Dirac states respect to an Anderson disorder model and substitutional impurity and compare to different one dimensional systems. The results suggest that thin anatase TiO2 nanowires can be a promising candidate material for switching devices.

When nonlinearity is added to an infinite system with purely discrete linear spectrum, Anderson modes become coupled with one another by terms of higher order than linear, allowing energy exchange between them. It is generally believed, on the basis of numerical simulations in such systems, that any initial wave-packet with finite energy spreads down chaotically to zero amplitude with second moment diverging as a power law of time, slower than standard diffusion (subdiffusion). We present results which suggest that the interpretation of spreading cannot be described as initially believed and that new questions arise and still remain opened. We show that an initially localized wave-packet with finite norm may generate two kinds of trajectories both obtained with nonvanishing probability.The first kind consists of KAM trajectories which are recurrent and do not spread. Empirical investigations suggest that KAM theory may still hold in infinite systems under two conditions: (1) the linearized spectrum is purely discrete, (2) the considered solutions are square summable and not too large in amplitude. We check numerically that in appropriate regions of the parameter space, indeed many initial conditions can be found with finite probability that generate (nonspreading) infinite dimension tori (almost periodic solutions) in a fat Cantor set in (projected) phase space.The second kind consists of trajectories which look initially chaotic and often spread over long times. We first rigorously prove that initial chaos does not necessarily imply complete spreading e.g. for large norm initial wave-packet. Otherwise, in some modified models, no spreading at all is proven to be possible, despite the presence of initial chaos in contradiction with early beliefs. The nature of the limit state is still unknown.However, we attempt to present empirical arguments suggesting that if a trajectory starts chaotically spreading, there will necessarily exist (generally large) critical spreading distances that depend on the disorder realization where the trajectory will be sticking to a dense set of KAM tori. This effect should induce drastic slowing down of the spreading which could be viewed as "inverse Arnold diffusion" since the trajectory approaches KAM tori regions instead of leaving them. We suggest that this effect should self-organize the chaotic behavior and that at long time, the wave-packet might not be spread down to zero, but could have a limit profile with marginal chaos (with singular continuous spectrum), despite a long spatial tail. Further analytical and numerical investigations are required.

In the paper, we introduce a new model that addresses the generation of quantum droplets (QDs) in the binary Bose–Einstein condensate (BEC) mixture with mutually symmetric spinor components loaded in multi-color optical lattices (MOLs) of commensurate wavelengths and tunable intensities. The considered MOL confinement is the combination of the four-color optical lattice with an exponential periodic trap, which includes the complete set of the Fourier harmonics. Employing the one-dimensional (1D) extended Gross–Pitäevskii equation (eGPE), we calculate the exact analytical form of the wavefunction, MF/BMF nonlinearities, and MOL trap parameters. Utilizing the exact solutions, the formation of supersolid-like spatially periodic matter-wave droplet lattices and superlattices is illustrated under the space-periodic nonlinearity management. The precise positioning of the density maxima/minima of the droplet patterns at the center of the trap and tunable Anderson-like localization are observed by tuning the symmetry and amplitude of the considered MOL trap. The stability of the obtained solution is confirmed using the Vakhitov–Kolokolov (VK) criterion.

La caracterización petrofísica de yacimientos desempeña un rol importante en la industria petrolera, siendo primordial en el gerenciamiento integral y la optimización de procesos de recuperación. El siguiente trabajo planteó el modelado petrofísico y de facies para las unidades formacionales del Grupo Grant y el yacimiento Anderson dentro del Bloque-Bunda-3D-2009 de la cuenca Canning en Australia. Esta propuesta fue dividida en dos etapas. La etapa conceptual se basó en el estudio de la migración y acumulación de hidrocarburos en el área, y la creación de un inventario desde la información registrada en el Sistema de Gestión de Información Geotérmica y de Petróleo de Australia Occidental (WAPIMS). La segunda etapa se desarrolló considerando que la cantidad y distribución de lutitas presentes en las areniscas, tienen un gran impacto en la productividad de los yacimientos de hidrocarburos. Así, el primer paso fue calcular el volumen de lutitas a través del índice lineal de rayos gamma. Posteriormente, se modelaron las facies mediante el uso de redes neuronales y los resultados fueron comparados con las descripciones litológicas reportadas de los núcleos de diámetro completo de perforación. La porosidad efectiva fue modelada mediante el registro de densidad volumétrica de la roca y el tipo de distribución de arcilla; la saturación de agua mediante la correlación de Poupon-Leveaux y el modelo de permeabilidad horizontal fue generado con los datos de análisis convencionales de núcleos de diámetro completo de perforación. Se resalta que la presencia de pirita afectó la respuesta de los registros de densidad volumétrica, porosidad neutrón y de resistividad para algunos pozos del área. Igualmente, el hidrodinamismo actuante y la presencia de agua meteórica en los acuíferos incidió en la respuesta del registro eléctrico resistivo, resultando complejo la identificación de contactos agua - hidrocarburo

Background Adoptive therapy with regulatory T cells (Tregs) has already been established as a promising strategy for prevention of graft vs. host disease (GVHD) in clinical trials. Our group at MD Anderson Cancer Center has previously shown that a significantly lower dose of cord blood (CB) Tregs as compared to conventional T cells (Tcon) in the donor graft is able to prevent GVHD while preserving the graft vs. leukemia (GVL) effect. Therefore, we now examined the efficacy of using CB Tregs in the treatment of GVHD. Method: Xenogenic GVHD mouse model was established using NOD/SCID/IL2Rgnull (NSG) mice were sublethally irradiated at 300 cGy followed by injection of 1x107 peripheral blood (PB) mononuclear cells on day 0, as previously described. Ex vivo expanded CB Tregs were injected on day -1 (for prophylaxis) or at different days post PBMC injection for treatment. Mice were serially examined for appearance, weight, posture, GVHD score and survival. Serial peripheral blood sampling for flow cytometry and serum cytokine analysis. CB Tregs were also analyzed by flow cytometry. In order to understand the impact of the routine immunosuppressive agents on the function of CB Tregs, we incubated the CB Tregs in culture with cyclosporine (200ng/ml) or sirolimus (20 ng/ml) from day 8 to day 14. Cells were harvested on day 14 and analyzed by flow cytometry and CellTrace Violet suppression assay. Result: A single dose of 1x107 CB Tregs injected at day +7 did not result in a survival difference compared to the control arm (data not shown). Therefore, we froze multiple aliquots of expanded CB Tregs to be injected at different intervals post-transplant. Thawed CB Tregs showed stable phenotype of CD4+25+127lo: 94.7%; intracellular Helios+: 98.5% and intracellular FOXP3+: 99.4% and were able to suppress 87% of the proliferating conventional T-cells (Tcons). In order to compare the efficacy of the CB Tregs for GVHD treatment, we set up 3 arms: i) Control: PBMC alone; ii) Prophylaxis: 1x107 CB Tregs injected on day -1 and iii) Treatment: 1x107 CB Tregs injected on day +4, +7, +18 and +25. The mice in the prophylaxis and treatment arm retained their weight as compared to the control arm (p<0.003) (Fig 1A) and showed significantly better overall survival (P=0.01) (Fig 1B), which correlated with the decrease in circulating inflammatory cytokines including TNFa (Fig 1C). Since the standard of care for acute GVHD still remains high dose steroids, we evaluated the effect of continued exposure to steroids (prednisone-100ug/ml) for a period of 96 hours on the viability of CB Tregs. When compared to CB Tcons, 90.3% CB Tregs remained alive and viable compared to 64.7% of Tcons (Fig 1D). No differences were observed in the intracellular expression of FOXP3 or Helios in the control vs. cyclosporine or sirolimus exposed cells (Fig 1E). Similarly, no significant impact was observed on their suppressor function (Fig 1F). Conclusions: Multiple injections with CB Tregs can effectively treat GVHD. Combination therapy of CB Tregs with the commonly used GVHD treatments can be explored. Figure 1 Disclosures Iyer: Genentech/Roche: Research Funding; Incyte: Research Funding; Seattle Genetics, Inc.: Research Funding; Novartis: Research Funding; Bristol-Myers Squibb: Research Funding; Arog: Research Funding. Parmar:Cellenkos Inc.: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Background: Systemic Lupus Erythematosus (SLE) is a chronic inflammatory autoimmune disorder with multi-organ involvement, including skin rash, joint pain, neurological dysfunction, pulmonary fibrosis, vasculitis, and renal failure. Previously it has been reported that SLE patients have a lower percentage of regulatory T cells (Tregs) and when compared to healthy population, Tregs derived from SLE patients show defect in their suppressor function. Our group at MD Anderson Cancer Center has already shown that a significantly lower dose of cord blood (CB) Tregs as compared to conventional T cells (Tcon) in the donor graft is able to prevent graft vs host disease (GVHD). Furthermore, adoptive therapy with CB Tregs is being explored as a therapy for bone marrow failure including aplastic anemia as a single agent in the non-transplant setting. Therefore, we hypothesized that adoptive therapy with CB Tregs may be utilized for the treatment of SLE. Method: For examining the efficacy of CB Tregs in vivo, we developed a humanized SLE model, where female Rag2-/-γc-/- mice were transplanted with 3 ~ 4 x 106 human SLE-PBMCs by intravenous injection on day 0. The mice were allowed to develop disease (control) and at 1 weeks post-transplant a single dose of 1x107 CB Tregs was injected through the tail vein (treatment). Mice peripheral blood (PB) was assessed weekly for their cell compartment composition by using flow cytometry; double stranded DNA (dsDNA IgG) and plasma cytokines. Mice were monitored twice per week for weight loss, GVHD score and survival. Weekly urine collection was performed to analyze for albumin and creatinine. At the time of euthanasia, harvested organs were analyzed by flow cytometry, and immunohistochemistry. Result: Single injection of CB Tregs was sufficient to slow down the phenotype of SLE as shown in figure 1A, where the physical appearance of CB Treg treated mice was significantly better than the control and it correlated with a significantly lesser CD3+ infiltrates in the spleen of the treatment vs. control mice (Figure 1B) and a similar finding was observed in the CD20 infiltrate in the renal tissue (data not shown). While a widespread parakeratosis in the skin of the control mice, almost complete resolution was observed in the treatment arm (figure 1C). In addition, a lack of lymphoid infiltrate in the kidney and resolution of splenic lymphoid hyperplasia was observed in the CB Treg recipients (data not shown). In another experiment, we studied the role of weekly injection of 1x107 CB Tregs with the first injection administered at week 4 after the lupus inoculation. Significant improvement was observed in the urine albumin (p<0.02) (Fig 1D) as well as urine creatinine (p<0.0006) levels; which correlated with the significant improvement in the dsDNA IgG levels in the treatment arm compared to control (Fig 1E), respectively. We also measure the durability of single dose CB Treg injection, where a decrease in the 5-week plasma inflammatory cytokines including sCD40L (Fig 1F); TNFα (Fig 1G) and IFNγ (Fig 1H) was observed irrespective of CB Treg injection on day+7 or day+28 after the lupus PBMC injection. Conclusion: We conclude that adoptive therapy with CB Tregs is a viable option for SLE and additional studies are planned to optimize the dose and schedule details. Figure 1 Disclosures Khoury: Angle: Research Funding; Stemline Therapeutics: Research Funding; Kiromic: Research Funding. Iyer:Seattle Genetics, Inc.: Research Funding; Novartis: Research Funding; Bristol-Myers Squibb: Research Funding; Genentech/Roche: Research Funding; Incyte: Research Funding; Arog: Research Funding. Parmar:Cellenkos Inc.: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract Introduction:. Patients with locally advanced triple-negative breast cancer (TNBC) typically receive neoadjuvant therapy (NAT) to downstage the tumor and to improve the outcome of the subsequent breast conservation surgery. A critical unmet need is the lack of a method to accurately predict how a patient with TNBC will respond to NAT before surgery. In this work, we applied a clinical-computational framework to predict response of TNBC early in the course of NAT, by integrating quantitative MRI with mechanism-based mathematical modeling. Methods:. Patients and Data. Multiparametric quantitative MRI was acquired in patients (n = 46) before, and after 2 and 4 cycles of Adriamycin/Cyclophosphamide (A/C) regimen as part of the MD Anderson Cancer Center TNBC Moonshot Program. Within each imaging session, dynamic contrast-enhanced (DCE-), diffusion-weighted imaging (DWI), and a pre-contrast T1-map were acquired. Image processing. The processing pipeline consisted of three components. First, the images within each visit were registered to account for patient motion, and the parametric maps from the DCE and DWI images were computed. Second, inter-visit image registration was achieved by a non-rigid registration applied on breast, with a rigid penalty applied on the tumor region to preserve its size and shape. Third, post-processing was performed for preparation of modeling, including segmentation of the breast contour and tissues, and calculation of voxel-wise cellularity within tumors. Mathematical modeling. A predictive model was developed based on a reaction-diffusion equation (Eq. 1). The mobility of tumor cells is represented by diffusion coupled to mechanical properties of the tissue (Eq. 2), and the proliferation of the tumor is described with logistic growth. The injection and decay of administered therapies, inducing tumor cell death, is also represented in the model (Eq. 3). The variables and parameters used are listed in Table 1. Eq. 1: ∂N(x,t)/∂t = ∇⋅(D(x,t) ∇N(x,t)) + k(x) (1 - N(x,t)/θ)N(x,t) - (λ1(x,t) + λ2(x,t))N(x,t). Eq. 2: D(x,t) = D0 e-γσ(x,t). Eq. 3: λn(x,t) = αne-βn t C(x,t), n = 1, 2. For each patient, the domain and initial condition were generated from the pre-treatment images, and the images acquired during NAT were used for patient-specific calibration of parameters. The calibrated model was then used to predict the response to be observed at the end of NAT. We evaluated the model by comparing its predictions of tumor volume, longest axis, voxel-wise cellularity, and total tumor cellularity to the imaging measurements at the end of A/C. Results:. Our model predicted the tumor volume, total cellularity, and longest axis with a Pearson correlation coefficient (PCC) of 0.85, 0.80, and 0.60, respectively. The accuracy of voxel-wise cellularity achieved a PCC with the median (range) of 0.89 (0.77 - 0.93) between the prediction and the actual measurement. Moreover, we set criteria of 70% shrinkage of tumor volume to define response versus non-response cases, with which our model achieved a differentiation sensitivity/specificity of 0.90/0.73. Discussion:. Preliminary results of our study demonstrate the potential of the clinical-computational framework as a powerful tool for predicting response to NAT. Once validated, the method could also assist in optimizing treatment plans on a patient specific basis, or guiding patient selection in trials for novel NAT regimens. Table 1. Summary of the variables and parameters in the modelQuantitiesDefinition AssignmentDomainsΩbreast tissue domainGenerated from pre-treatment MRITEnd time point of NAT procedureDetermined from NAT schedulexCoordinate in breast tissueAssociated with spatial domain, ΩttimeAssociated with temporal domain, [0, T]VariablesN(x,t)Tumor cell numberInitialized from pre-treatment ADC, computed via Eq. 1D(x,t)Diffusive mobility of tumor cellsComputed via Eq. 2λn(x,t)Death rate induced by nth type of drugComputed via Eq. 3, n = 1 and 2 for A/Cσ(x,t)Von Mises stressComputed from gradient of N(x,t), based on Hormuth et al., 2018C(x,t)Spatiotemporal distribution of drugsAssigned based on NAT schedule and DCE imagesParametersk(x)Proliferation rate of tumor cellsLocally calibratedθTumor cells carry capacityGlobally calibratedαnEfficacy rate of nth type of drugGlobally calibratedβnDecay rate of of nth type of drugGlobally calibratedD0Diffusion coefficient of tumor cells in the absence of mechanical restrictionsGlobally calibratedγStress-tumor cell diffusion coupling constantAssigned based on Hormuth et al., 2018 Citation Format: Chengyue Wu, Angela M. Jarrett, Zijian Zhou, Nabil Elshafeey, Beatriz E. Adrada, Rosalind P. Candelaria, Rania M. Mohamed, Medine Boge, Lei Huo, Jason White, Debu Tripathy, Vicente Valero, Jennifer Litton, Stacy Moulder, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane M. Rauch, Thomas E. Yankeelov. Forecasting treatment response to neoadjuvant systemic therapy in triple negative breast cancer viamathematical modeling and quantitative MRI [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-08-08.

BackgroundAdoptive cell therapy with tumor-infiltrating lymphocytes (TILs) has demonstrated tremendous promise in clinical trials for patients with solid or metastatic tumors.1 However, current TIL therapy requires systemic administration of IL-2 to promote TIL survival, and IL-2-associated toxicities greatly limit patient eligibility and reduce the long-term clinical benefit of TIL therapy.2 3 Unlike IL-2, which promotes T cell exhaustion, IL-15 maintains antigen-independent TIL persistence through homeostatic proliferation and supports CD8+ T cell anti-tumor activity without stimulating regulatory T cells. We designed genetically engineered TILs to express a regulated form of membrane-bound IL-15 (mbIL15) for tunable long-term persistence, leading to enhanced efficacy and safety for the treatment of patients with solid tumors.MethodsObsidian’s cytoDRiVE™ platform includes small human protein sequences called drug responsive domains (DRD)s that enable regulated expression of a fused target protein under control of FDA-approved, bioavailable small molecule ligands. cytoTIL15 contains TILs engineered with mbIL15 under the control of a carbonic-anhydrase-2 DRD, controlled by the ligand acetazolamide (ACZ). After isolation from tumors, TILs were transduced and expanded in vitro through a proprietary TIL expansion process. cytoTIL15 were immunophenotyped and assessed for in vitro antigen-independent survival and co-cultured with tumor cells to assess polyfunctionality and cytotoxicity. In vivo TIL persistence and anti-tumor efficacy was evaluated through adoptive transfer of TILs into immunodeficient NSG mice, either naïve or implanted with subcutaneous patient-derived-xenograft (PDX) tumors.Results cytoTIL15 and conventional IL2-dependent TILs isolated from melanoma tumor samples expanded to clinically relevant numbers over 14 days. Throughout expansion, cytoTIL15 were enriched for CD8+ T cells and acquired enhanced memory-like characteristics, while maintaining diverse TCRVβ sub-family representation. cytoTIL15 demonstrated enhanced potency over conventional TILs, as measured by increased polyfunctionality and cytotoxicity against tumor and PDX lines in vitro (figure 1A). In a 10-day antigen-independent in vitro assay, cytoTIL15 persisted at greater frequencies than conventional TILs in the absence of IL-2 (figure 1B; *p<0.05). cytoTIL15 adoptively transferred into naïve NSG mice demonstrated ACZ-dependent long-term persistence without antigen or exogenous IL-2, whereas conventional TILs were undetectable >30 days following adoptive cell transfer (figure 1C). Importantly, cytoTIL15 achieved significant tumor control in a human PDX model (figure 1D), which correlated with increased TIL accumulation in secondary lymphoid organs.Abstract 166 Figure 1cytoTIL15 demonstrate superior persistence. cytoTIL15 is an engineered TIL product expressing regulatable mbIL15. (A) cytoTIL15 demonstrate enhanced in vitro cytotoxicity after co-culture with melanoma tumor lines (representative data from 3 TIL donors). (B) cytoTIL15 have improved persistence in antigen- and IL2- independent culture conditions in vitro compared to conventional TILs cultured in the absence of IL-2 as well as (C) in vivo compared to conventional TILs supplemented with IL-2, when engrafted into NSG mice (in vitro: representative data from 1 TIL donor, performed in >3 replicate donors, in vivo: n=5/group, representative of 1 TIL donor, performed in >3 replicate donors). (D) cytoTIL15 (with 200mg/kg ACZ PO QD) demonstrate enhanced anti-tumor efficacy in a xenograft melanoma model as compared to conventional TILs (with 50000 IU IL-2 q8h BID, IP for 5 days) (n=8/group, representative of 1 TIL donor, performed in >2 replicate donors; ACT = adoptive cell transfer).ConclusionsTaken together, the superior persistence and potency of cytoTIL15 in the complete absence of IL-2 highlights the clinical potential of cytoTIL15 as a novel TIL product with enhanced safety and efficacy for patients with melanomas, and other solid tumors.AcknowledgementsThe authors wish to acknowledge the Cooperative Human Tissue Network for the their supply of human tumor tissue, and the MD Anderson Cancer Center for technical support; schematic created with BioRender.com.ReferencesChandran SS, Somerville RPT, Yang JC, Sherry RM, Klebanoff CA, Goff SL, Wunderlich JR, Danforth DN, Zlott D, Paria BC, Sabesan AC, Srivastava AK, Xi L, Pham TH, Raffeld M, White DE, Toomey MA, Rosenberg SA, Kammula US. Treatment of metastatic uveal melanoma with adoptive transfer of tumour-infiltrating lymphocytes: a single-centre, two-stage, single-arm, phase 2 study. Lancet Oncol 2017 Jun;18(6):792–802. doi: 10.1016/S1470-2045(17)30251-6. Epub 2017 Apr 7. PMID: 28395880; PMCID: PMC5490083.Yang JC. Toxicities associated with adoptive T-cell transfer for Cancer. Cancer J 2015;21:506–9.Schwartz RN, Stover L, Dutcher JP. Managing toxicities of high-dose interleukin-2. Oncology (Williston Park) 2002 Nov;16(11 Suppl 13):11–20. PMID: 12469935.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 202222, “Fracture-Matrix Modeling of CO2 Enhanced Shale Gas Recovery in Compressible Shale,” by Dhruvit Satishchandra Berawala, SPE, Equinor, and Pål Østebø Andersen, University of Stavanger. The paper has not been peer reviewed. With technology available at the time of writing, only 3–10% of gas from tight shale is recovered economically through natural depletion, demonstrating a significant potential for enhanced shale gas recovery (ESGR). Experimental studies have demonstrated that shale kerogen/organic matter has a higher adsorption affinity for carbon dioxide (CO2) than methane (CH4). CO2 is preferentially adsorbed over CH4 with a ratio of as much as 5:1. The complete paper examines CO2 ESGR in compressible shale during huff ’n’ puff injection to understand better the parameters controlling its feasibility and effectiveness. The authors present a mathematical model in the complete paper in which the CO2/CH4 substitution mechanism is implemented in an injection/production setting representative of field implementation. Introduction Modeling of CO2 injection and the interplay between CO2 and CH4 sorption has been extremely challenging for scientists and engineers. The presence of CO2 with methane during the CO2 ESGR process makes gas-desorption behavior and measurement more difficult. Few researchers have evaluated the efficiency of CO2 ESGR in compressible shale. To improve the understanding of this technique, the authors present a numerical modeling approach using a 1D+1D fracture-matrix model in order to study the feasibility of CO2 injection in shale formations. The model consists of a high-permeability fracture extending from a well perforation, symmetrically surrounded by a shale matrix as shown in Fig. 1 of the complete paper. The fracture is assumed to have fixed width for simplicity. The system is assumed to consist of free gas in the pores as well as adsorbed gas in the matrix. When pressure in the well is reduced, free and adsorbed gas from the matrix flows toward the fracture and then to the well. The pressure-based advective forces, along with concentration-based diffusive forces, are considered the main transport mechanisms in this work. After the CH4 production cycle, CO2 is injected from the same well into the fracture and to the matrix. Injection of CO2 leads to an increase in total gas pressure in the system. CH4/CO2 adsorption kinetics are modeled using a multicomponent adsorption isotherm presented in earlier works by the authors. Apparent permeability is used to account for gas slippage effect, effective stress, adsorption, and other flow regimes relevant to the nanopore structure of the shale formation. The effect that compressible rock has on the porosity and apparent permeability changes with CH4 production. CO2 injection also is considered. The resulting model is composed of nonlinear partial differential equations that are solved numerically using an operator splitting approach. The geometry, mole conservation, pressure-dependent parameters, and initial and boundary conditions of the mathematical model are detailed in the complete paper, including mathematical definitions and solution procedures in its appendix.

INTRODUCTION: Atypical chronic myeloid leukemia (aCML) is a rare subtype of myelodysplastic/myeloproliferative neoplasms (MDS/MPN) associated with shorter survival and higher risk of transformation to acute myeloid leukemia (AML) than other MDS/MPNs. However, the clonal mechanisms underlying transformation to leukemia remain unclear. There is a need to develop predictive models and identify the optimal therapeutic management of these pts. METHODS: We evaluated all consecutive pts with aCML treated at the University of Texas MD Anderson Cancer Center from 2005 to 2020. Whole bone marrow (BM) DNA was subject to 28 or 81 gene targeted next-generation sequencing (NGS) analysis in a subset of pts. Variant allele frequency (VAF) estimates were used to evaluate clonal relationships within each sample using Pearson goodness-of-fit tests and VAF differences. Response to therapy was assessed following MDS/MPN IWG response criteria. Cox proportional hazards regression was used to study association of variables with survival. RESULTS: A total of 65 pts were identified. Median age was 67 years (range 46-89). Median WBC, Hgb and platelets were 44.5x109/L (5.9-474.9x109/L) 10.0g/dL (5.7-14.7g/dL) 93x109/L (12-560x109/L), respectively. Median neutrophil, promyelocyte, myelocyte and metamyelocyte percentages were 64%, 0%, 0% and 16%. Forty-one (63%) pts had normal karyotype, 5 (8%) trisomy 8, 2 (3%) i(17q) and 2 (3%) del(20q). Splenomegaly was observed in 26 (40%) pts and 7 (11%) had extramedullary disease. NGS data was available in 35 (54%) pts. The most frequently mutated genes included ASXL1 in 83%, SRSF2 in 68% and SETBP1 in 58%. Frequency and VAF of identified mutations is shown in Figure 1A. Mutations in SETBP1, SRSF2, TET2 and GATA2 tended to appear within dominant clones while other RAS pathway mutations were more likely to appear as minor clones. SRSF2 and SETBP1 tended to be co-dominant while ASXL1 appeared within minor clones in up to 50% of pts (Figure 1B). Therapy consisted of single agent hypomethylating agent (HMA) in 19 (29%), hydroxyurea in 8 (12%), HMA in combination with ruxolitinib in 7 (11%), other HMA combinations in 5 (8%), ruxolitinib single agent in 5 (8), induction chemotherapy in 3 (5%) and other investigational agents in 1 (2%) pts. Response outcomes by therapy are detailed in Figure 1C. With a median follow up of 35.6 months (95% CI 28.2-43.1) 18 (28%) of pts experienced transformation to AML within a median of 18 months (1-123 months). Median survival after transformation of 8.3 months (95% CI 5.5-11.0 months). NGS at the time of transformation was available in 12 (67%) pts with matched NGS at diagnosis of aCML and AML in 8 (44%) pts. Acquisition of new previously undetectable mutations was observed in 5 pts the most common involving signaling pathway mutations (Figure 1D). Acquisition of new cytogenetic abnormalities was observed in 9/14 pts (Figure 1D) the most frequent involving i(17q). The median OS was 25 months (95% CI 20.0-30.0) with pts who received intensive chemotherapy having significantly worse OS than those receiving HMA-based therapy or other agents such as ruxolitinib or hydroxyurea (p=0.012, Figure 1E). By multivariate analysis for survival, age, platelet count, BM blast percentage and serum LDH levels influenced prognosis. Based on these factors we developed a multivariable Cox model to generate a nomogram which assigned a score to each of the prognostic variables and allowed to predict 1-year and 3-year OS based on the total score among all prognostic variables (Figure 1F). CONCLUSIONS: aCML is characterized by high frequency of co-dominant SRSF2 and SETBP1 mutations. HMA therapy is associated with the best response outcomes. Clinicopathological features can help predict outcomes of these pts. Figure 1 Disclosures Sasaki: Daiichi Sankyo: Consultancy; Novartis: Consultancy, Research Funding; Pfizer Japan: Consultancy; Otsuka: Honoraria. Jabbour:Genentech: Other: Advisory role, Research Funding; Amgen: Other: Advisory role, Research Funding; Adaptive Biotechnologies: Other: Advisory role, Research Funding; AbbVie: Other: Advisory role, Research Funding; Pfizer: Other: Advisory role, Research Funding; Takeda: Other: Advisory role, Research Funding; BMS: Other: Advisory role, Research Funding. DiNardo:Agios: Consultancy, Honoraria, Research Funding; Takeda: Honoraria; Notable Labs: Membership on an entity's Board of Directors or advisory committees; ImmuneOnc: Honoraria; Novartis: Consultancy; MedImmune: Honoraria; AbbVie: Consultancy, Honoraria, Research Funding; Syros: Honoraria; Daiichi Sankyo: Consultancy, Honoraria, Research Funding; Calithera: Research Funding; Jazz: Honoraria; Celgene: Consultancy, Honoraria, Research Funding. Konopleva:F. Hoffmann La-Roche: Consultancy, Research Funding; Ablynx: Research Funding; Eli Lilly: Research Funding; Kisoji: Consultancy; Sanofi: Research Funding; Stemline Therapeutics: Consultancy, Research Funding; Agios: Research Funding; Genentech: Consultancy, Research Funding; Rafael Pharmaceutical: Research Funding; AbbVie: Consultancy, Research Funding; Forty-Seven: Consultancy, Research Funding; Calithera: Research Funding; Reata Pharmaceutical Inc.;: Patents & Royalties: patents and royalties with patent US 7,795,305 B2 on CDDO-compounds and combination therapies, licensed to Reata Pharmaceutical; Ascentage: Research Funding; Amgen: Consultancy; AstraZeneca: Research Funding; Cellectis: Research Funding. Pemmaraju:MustangBio: Honoraria; Incyte Corporation: Honoraria; Blueprint Medicines: Honoraria; Roche Diagnostics: Honoraria; LFB Biotechnologies: Honoraria; Stemline Therapeutics: Honoraria, Research Funding; Celgene: Honoraria; AbbVie: Honoraria, Research Funding; Pacylex Pharmaceuticals: Consultancy; Daiichi Sankyo: Research Funding; Affymetrix: Other: Grant Support, Research Funding; Plexxikon: Research Funding; Novartis: Honoraria, Research Funding; Samus Therapeutics: Research Funding; Cellectis: Research Funding; SagerStrong Foundation: Other: Grant Support; DAVA Oncology: Honoraria. Short:Takeda Oncology: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy; Amgen: Honoraria; Astellas: Research Funding. Issa:Novartis: Membership on an entity's Board of Directors or advisory committees; Syndax: Research Funding; Celegene: Research Funding. Kadia:Celgene: Research Funding; Abbvie: Honoraria, Research Funding; Novartis: Honoraria; Genentech: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Amgen: Research Funding; Incyte: Research Funding; Cellenkos: Research Funding; BMS: Honoraria, Research Funding; Ascentage: Research Funding; Astra Zeneca: Research Funding; Cyclacel: Research Funding; Pulmotec: Research Funding; Astellas: Research Funding; JAZZ: Honoraria, Research Funding. Ravandi:Amgen: Consultancy, Honoraria, Research Funding; Orsenix: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Xencor: Consultancy, Honoraria, Research Funding; Macrogenics: Research Funding; AstraZeneca: Consultancy, Honoraria; Jazz Pharmaceuticals: Consultancy, Honoraria, Research Funding; Astellas: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria. Daver:Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Research Funding; Servier: Research Funding; Genentech: Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novimmune: Research Funding; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Trovagene: Research Funding; Fate Therapeutics: Research Funding; ImmunoGen: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Jazz: Consultancy, Membership on an entity's Board of Directors or advisory committees; Trillium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Syndax: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Borthakur:AstraZeneca: Research Funding; PTC Therapeutics: Consultancy; Nkarta Therapeutics: Consultancy; Treadwell Therapeutics: Consultancy; Jannsen: Research Funding; Curio Science LLC: Consultancy; Novartis: Research Funding; Argenx: Consultancy; BioTherix: Consultancy; Polaris: Research Funding; BioLine Rx: Consultancy; Incyte: Research Funding; PTC Therapeutics: Research Funding; BioLine Rx: Research Funding; BMS: Research Funding; Cyclacel: Research Funding; Oncoceutics: Research Funding; Xbiotech USA: Research Funding; FTC Therapeutics: Consultancy; Abbvie: Research Funding; GSK: Research Funding. Verstovsek:Gilead: Research Funding; NS Pharma: Research Funding; Celgene: Consultancy, Research Funding; Genentech: Research Funding; Blueprint Medicines Corp: Research Funding; CTI Biopharma Corp: Research Funding; Protagonist Therapeutics: Research Funding; ItalPharma: Research Funding; Novartis: Consultancy, Research Funding; Incyte Corporation: Consultancy, Research Funding; Promedior: Research Funding; Roche: Research Funding; Sierra Oncology: Consultancy, Research Funding; PharmaEssentia: Research Funding; AstraZeneca: Research Funding. Kantarjian:Adaptive biotechnologies: Honoraria; Novartis: Honoraria, Research Funding; BioAscend: Honoraria; Daiichi-Sankyo: Honoraria, Research Funding; Immunogen: Research Funding; Jazz: Research Funding; Delta Fly: Honoraria; Janssen: Honoraria; Pfizer: Honoraria, Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Research Funding; Aptitute Health: Honoraria; BMS: Research Funding; Ascentage: Research Funding; Amgen: Honoraria, Research Funding; Abbvie: Honoraria, Research Funding; Oxford Biomedical: Honoraria. Bose:Blueprint Medicines Corporation: Honoraria, Research Funding; NS Pharma: Research Funding; Kartos Therapeutics: Honoraria, Research Funding; CTI BioPharma: Honoraria, Research Funding; Celgene Corporation: Honoraria, Research Funding; Astellas Pharmaceuticals: Research Funding; Incyte Corporation: Consultancy, Honoraria, Research Funding, Speakers Bureau; Pfizer, Inc.: Research Funding; Constellation Pharmaceuticals: Research Funding; Promedior, Inc.: Research Funding. Garcia-Manero:Celgene: Consultancy, Honoraria, Research Funding; H3 Biomedicine: Research Funding; Novartis: Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astex Pharmaceuticals: Consultancy, Honoraria, Research Funding; AbbVie: Honoraria, Research Funding; Helsinn Therapeutics: Consultancy, Honoraria, Research Funding; Onconova: Research Funding; Acceleron Pharmaceuticals: Consultancy, Honoraria; Merck: Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Jazz Pharmaceuticals: Consultancy; Amphivena Therapeutics: Research Funding.

INTRODUCTION: TP53 mutations are associated with adverse outcomes and shorter response to hypomethylating agents (HMA) in myelodysplastic syndromes (MDS). There is limited data evaluating the impact of the type, number, clonal size and patterns of TP53 mutations in response outcomes and prognosis. METHODS: We evaluated all patients with newly diagnosed myelodysplastic syndromes (MDS) treated at The University of Texas MD Anderson Cancer Center (MDACC) from 2013 to 2018. Genomic DNA was extracted from whole bone marrow aspirate samples and was subject to 28, 53 or 81-gene targeted PCR-based sequencing using a next generation sequencing (NGS) platform. Response assessment was performed following 2006 IWG criteria. The Kaplan-Meier product limit method was used to estimate survival outcomes for each clinical/demographic factor. Univariate Cox proportional hazards regression was used to identify any association with each of the variables and survival outcomes. RESULTS: 938 patients were evaluated including 261 (28%) with detectable TP53 mutations of which 189 (72%) received therapy: chemotherapy-based in 5 (3%) patients, single agent hypomethylating agents in 116 (61%) and hypomethylating agent in combination with novel agents in 65 (34%). Most (n=175, 67%) patients had one TP53 mutation with 75 (29%), 10 (4%) and 1 (0.4%) having 2, 3 and 4, respectively. TP53 dynamics on 18 patients with multiple TP53 mutations and longitudinal sequencing suggested both could present within the same clone in 8 (44%) patients. Median variant allele frequency (VAF) of all TP53 mutations was 39% (range 1-94%). TP53 deletion was more frequent in patients with mutated TP53 (31.8% vs 2.2%, p<0.001). Sixty-three patients (24%) suffered transformation to acute myeloid leukemia with a median transformation-free survival (TFS) of 10.6 months (95% CI 8.8-12.3). By univariate analysis the number of TP53 mutations (HR 2.03, 95% CI 1.3-3.05, p<0.001), TP53 mutation VAF (HR 1.02 increase per 1% VAF increase, 95% CI 1.01-1.02, p<0.001), TP53 deletion (HR 2.10, 95% CI 1.38-3.19, p<0.001) and complex karyotype (HR 2.58, 95% CI 1.70-3.91, p<0.001) were predictors of shorter TFS. By multivariate analysis only TP53 mutation VAF remained an independent predictor of shorter TFS (HR 1.02 increase per 1% VAF increase, 95% CI 1.00-1.03, p=0.005). With a median follow-up of 21.9 months (95% CI 20.3-25.6 months), there were no significant differences in ORR (58% vs 63%, p=0.303) or CR (27% vs 22%, p=0.288) based on the presence of TP53 mutation. Presence of TP53 deletion was associated with lower ORR (OR 0.53, p=0.021). Lower TP53 VAF correlated with higher ORR. Presence of TP53 abnormalities was associated with shorter response duration (HR 2.9, 95% CI 1.64-5.13, p<0.001). Longitudinal sequencing was available in 64 patients. TP53 VAF decreased more among responders (p=0.022) with subsequent increase of VAF at the time of relapse (Figure 1A). Presence of ³2 TP53 abnormalities was associated with shorter survival (HR 1.39, 95% CI 1.03-1.89, p=0.034, Figure 1B). TP53 VAF was associated with worse prognosis (HR 1.02 per 1% VAF increase, 95% CI 1.01-1.03, p<0.001). Patients could be classified into three prognostic groups based on TP53 VAF (Figure 1C). Integration of TP53 VAF and karyotypic complexity identified prognostic subgroups (Figure 1D). We developed a multivariable Cox model including TP53 VAF and IPSS-R categories with a corrected concordance index of 0.81 demonstrating a strong model fit. This model was used to generate a nomogram for overall survival (Figure 1E). CONCLUSION: This data suggests that the number and clonal size of TP53 mutations as well as other genomic events may help identify subgroups of patients with MDS with distinct prognosis and clinical outcomes. Figure 1 Disclosures Sasaki: Pfizer: Consultancy; Otsuka: Honoraria. Alvarado:Jazz Pharmaceuticals: Research Funding; Abbott: Honoraria. Kadia:Jazz: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Bioline RX: Research Funding; Celgene: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Research Funding; Genentech: Membership on an entity's Board of Directors or advisory committees. Ravandi:Selvita: Research Funding; Cyclacel LTD: Research Funding; Macrogenix: Consultancy, Research Funding; Xencor: Consultancy, Research Funding; Menarini Ricerche: Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Cortes:Astellas Pharma: Consultancy, Honoraria, Research Funding; Jazz Pharmaceuticals: Consultancy, Research Funding; Forma Therapeutics: Consultancy, Honoraria, Research Funding; Immunogen: Consultancy, Honoraria, Research Funding; Merus: Consultancy, Honoraria, Research Funding; Daiichi Sankyo: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Sun Pharma: Research Funding; Takeda: Consultancy, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Biopath Holdings: Consultancy, Honoraria; BiolineRx: Consultancy; Pfizer: Consultancy, Honoraria, Research Funding. Daver:Otsuka: Consultancy; Sunesis: Consultancy, Research Funding; Agios: Consultancy; Incyte: Consultancy, Research Funding; Karyopharm: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Hanmi Pharm Co., Ltd.: Research Funding; Glycomimetics: Research Funding; Servier: Research Funding; BMS: Consultancy, Research Funding; Jazz: Consultancy; Abbvie: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; NOHLA: Research Funding; Forty-Seven: Consultancy; Novartis: Consultancy, Research Funding; Astellas: Consultancy; Immunogen: Consultancy, Research Funding; Celgene: Consultancy; Pfizer: Consultancy, Research Funding. Takahashi:Symbio Pharmaceuticals: Consultancy. DiNardo:daiichi sankyo: Honoraria; agios: Consultancy, Honoraria; abbvie: Consultancy, Honoraria; celgene: Consultancy, Honoraria; notable labs: Membership on an entity's Board of Directors or advisory committees; syros: Honoraria; medimmune: Honoraria; jazz: Honoraria. Jabbour:Takeda: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Adaptive: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Cyclacel LTD: Research Funding; AbbVie: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding. Borthakur:Novartis: Research Funding; Merck: Research Funding; Incyte: Research Funding; Eli Lilly and Co.: Research Funding; Janssen: Research Funding; Cantargia AB: Research Funding; Eisai: Research Funding; Agensys: Research Funding; Oncoceutics: Research Funding; Bayer Healthcare AG: Research Funding; NKarta: Consultancy; BioLine Rx: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Xbiotech USA: Research Funding; Strategia Therapeutics: Research Funding; Tetralogic Pharmaceuticals: Research Funding; Argenx: Membership on an entity's Board of Directors or advisory committees; BioTheryX: Membership on an entity's Board of Directors or advisory committees; Oncoceutics, Inc.: Research Funding; Arvinas: Research Funding; Polaris: Research Funding; FTC Therapeutics: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; GSK: Research Funding; Cyclacel: Research Funding; PTC Therapeutics: Consultancy; AbbVie: Research Funding; AstraZeneca: Research Funding. Pemmaraju:cellectis: Research Funding; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; novartis: Consultancy, Research Funding; plexxikon: Research Funding; Daiichi-Sankyo: Research Funding; sagerstrong: Research Funding; affymetrix: Research Funding; incyte: Consultancy, Research Funding; mustangbio: Consultancy, Research Funding; abbvie: Consultancy, Honoraria, Research Funding; samus: Research Funding; celgene: Consultancy, Honoraria. Konopleva:Kisoji: Consultancy, Honoraria; Genentech: Honoraria, Research Funding; Reata Pharmaceuticals: Equity Ownership, Patents & Royalties; Ablynx: Research Funding; Astra Zeneca: Research Funding; Agios: Research Funding; Ascentage: Research Funding; Calithera: Research Funding; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; Forty-Seven: Consultancy, Honoraria; Eli Lilly: Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Cellectis: Research Funding; Amgen: Consultancy, Honoraria; F. Hoffman La-Roche: Consultancy, Honoraria, Research Funding. Bueso-Ramos:Incyte: Consultancy. Andreeff:BiolineRx: Membership on an entity's Board of Directors or advisory committees; CLL Foundation: Membership on an entity's Board of Directors or advisory committees; NCI-RDCRN (Rare Disease Cliln Network): Membership on an entity's Board of Directors or advisory committees; Leukemia Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; German Research Council: Membership on an entity's Board of Directors or advisory committees; NCI-CTEP: Membership on an entity's Board of Directors or advisory committees; Cancer UK: Membership on an entity's Board of Directors or advisory committees; Center for Drug Research & Development: Membership on an entity's Board of Directors or advisory committees; NIH/NCI: Research Funding; CPRIT: Research Funding; Breast Cancer Research Foundation: Research Funding; Oncolyze: Equity Ownership; Oncoceutics: Equity Ownership; Senti Bio: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Eutropics: Equity Ownership; Aptose: Equity Ownership; Reata: Equity Ownership; 6 Dimensions Capital: Consultancy; AstaZeneca: Consultancy; Daiichi Sankyo, Inc.: Consultancy, Patents & Royalties: Patents licensed, royalty bearing, Research Funding; Jazz Pharmaceuticals: Consultancy; Celgene: Consultancy; Amgen: Consultancy. Kantarjian:Ariad: Research Funding; Amgen: Honoraria, Research Funding; Takeda: Honoraria; Daiichi-Sankyo: Research Funding; Agios: Honoraria, Research Funding; Astex: Research Funding; Jazz Pharma: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Cyclacel: Research Funding; Novartis: Research Funding; Immunogen: Research Funding; AbbVie: Honoraria, Research Funding; BMS: Research Funding; Pfizer: Honoraria, Research Funding. Garcia-Manero:Amphivena: Consultancy, Research Funding; Helsinn: Research Funding; Novartis: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding; Merck: Research Funding.

BackgroundImmune-checkpoint inhibitors (ICIs) revolutionized the treatment of advanced non-small cell lung cancer (NSCLC).1–3 To date, tissue PD-L1 immunohistochemistry is one of the leading biomarkers for prediction of ICIs response but has several limitations.4 5Extracellular vesicles (EVs) are cell-derived structures involved in cell communication and represent a potential minimally invasive alternative to predicting ICI response.6–9 Based on this and our preliminary results presented at SITC 2020,10 we hypothesize that EV PD-L1 predicts response to ICIs in NSCLC.MethodsThis study evaluates an exploratory cohort of advanced/metastatic NSCLC patients receiving ICIs (cohort A) and a validation cohort receiving Pembrolizumab+docetaxel or docetaxel alone (PROLUNG Phase 2 randomized trial) (cohort B).11 Plasma samples were collected pre-treatment (T1) and at 3 treatment cycles (T2) (figure 1A). Response was assessed by computed-tomography scan at 3 (cohort A) and 6–8 treatment cycles (cohort B) according to mono- or chemotherapy combination therapy. Patients were classified as responders (partial, stable, or complete response) or non-responders (progressive disease) by RECISTv1.1.12 EVs were isolated by serial ultracentrifugation and characterized following ISEV recommendations.13,14 Tissue PD-L1 expression was measured by standardized immunohistochemistry (SP263, 22C3, or 28–8 clones)5 and EV PD-L1 expression by immunoblot and its ratio was calculated as EV PD-L1 T2/T1. Cut-offs from the exploratory cohort were applied to the validation cohort, being EV PD-L1 ratio <0.85 = Low.ResultsPaired samples from 30 ICIs, 23 pembrolizumab+docetaxel, and 15 docetaxel treated patients were analyzed. In cohort A, non-responders showed higher EV PD-L1 ratio than responders (p=0.012) (figure 1B) with an area-under-the-curve (AUC) of 77.3%, 83.3% sensitivity, and 61.1% specificity, while the tissue PD-L1 was not predictive (AUC=50%). As a validation, pembrolizumab+docetaxel treated non-responders showed higher EV PD-L1 ratio (p=0.036) than responders with an AUC=69.3%, sensitivity=75%, and specificity=63.6%, outperforming the tissue PD-L1 (figure 1C). No statistically significant differences were observed in the docetaxel group (p=0.885). Moreover, ICIs patients with higher EV PD-L1 ratio showed shorter progression-free survival (PFS) (HR=0.30, p=0.066) and overall survival (OS) (HR=0.17, p=0.016) (figure 1D) which was also observed in the pembrolizumab+docetaxel cohort with shorter PFS (HR=0.12, p=0.004) and OS (HR=0.23, p=0.010) (figure 1E). EV PD-L1 ratio did not predict survival in docetaxel-treated patients.Abstract 23 Figure 1(A) Study design and methodology. (B) EV PD-L1 ratio predicts response to ICIs in 30 NSCLC patients from the discovery cohort A and outperforms tissue PD-L1. (C) EV PD-L1 ratio is predictive for response to pembrolizumab+docetaxel in 23 NSCLC patients but not in 15 patients receiving docetaxel alone from cohort B. (D) Higher EV PD-L1 ratio predicts shorter PFS and OS in 30 patients from the discovery cohort A treated with ICIs. (E) Higher EV PD-L1 ratio is associated with shorter PFS and OS in 23 patients treated with pembrolizumab+docetaxel but not in patients treated with docetaxel alone. Abbreviations: CT: Computed tomography, EV: Extracellular vesicle; HR: Hazard Ratio; ICIs: Immune-checkpoint Inhibitors; IHC: Immunohistochemistry; NR: Non-Responders; OS: Overall Survival; p: p-value; PFS: Progression-free survival; R: Responders [Created with BioRender].ConclusionsWe demonstrated that treatment-associated changes in EV PD-L1 levels are predictive of response and survival in advanced NSCLC patients treated with ICIs. This model, if confirmed in a large prospective cohort, could have important clinical implications, guiding treatment decisions and improving the outcome of patients receiving ICIs.AcknowledgementsWe would like to extend our gratitude to the all the patients that participated in the study.ReferencesBorghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, et al. Nivolumab versus Docetaxel in Advanced Nonsquamous Non–Small-Cell Lung Cancer. N Engl J Med 2015;373:1627–39.Herbst RS, Baas P, Kim DW, Felip E, Pérez-Gracia JL, Han JY, et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): A randomised controlled trial. Lancet 2016;387:1540–50.Ruiz-Patiño A, Arrieta O, Cardona AF, Martín C, Raez LE, Zatarain-Barrón ZL, et al. Immunotherapy at any line of treatment improves survival in patients with advanced metastatic non-small cell lung cancer (NSCLC) compared with chemotherapy (Quijote-CLICaP). Thorac Cancer 2020;11:353–61.Doroshow DB, Bhalla S, Beasley MB, Sholl LM, Kerr KM, Gnjatic S, et al. PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nat Rev Clin Oncol 2021;18:345–362.Hirsch FR, McElhinny A, Stanforth D, Ranger-Moore J, Jansson M, Kulangara K, et al. PD-L1 immunohistochemistry assays for lung cancer: results from phase 1 of the blueprint PD-L1 IHC assay comparison project. J Thorac Oncol 2017;12:208–222.Poggio M, Hu T, Pai CC, Chu B, Belair CD, Chang A, et al. Suppression of exosomal PD-L1 induces systemic anti-tumor immunity and memory. Cell 2019;177:414–427.e13.Cordonnier M, Nardin C, Chanteloup G, Derangere V, Algros MP, Arnould L, et al. Tracking the evolution of circulating exosomal-PD-L1 to monitor melanoma patients. J Extracell Vesicles 2020;9:1710899.Del Re M, Cucchiara F, Rofi E, Fontanelli L, Petrini I, Gri N, et al. A multiparametric approach to improve the prediction of response to immunotherapy in patients with metastatic NSCLC. Cancer Immunol Immunother 2020;70:1667–1678.Chen G, Huang AC, Zhang W, Zhang G, Wu M, Xu W, et al. Exosomal PD-L1 contributes to immunosuppression and is associated with anti-PD-1 response. Nature. 2018;560:382–6.10 de Miguel Perez D, Russo A, Gunasekaran M, Cardona A, Lapidus R, Cooper B, et al. 31 Dynamic change of PD-L1 expression on extracellular vesicles predicts response to immune-checkpoint inhibitors in non-small cell lung cancer patients. 2020J Immunother Cancer;8(Suppl 3):A30–A30.Arrieta O, Barrón F, Ramírez-Tirado LA, Zatarain-Barrón ZL, Cardona AF, Díaz-García D, et al. Efficacy and safety of pembrolizumab plus docetaxel vs docetaxel alone in patients with previously treated advanced non–small cell lung cancer: the PROLUNG phase 2 randomized clinical trial. 2020JAMA Oncol;6:856–864.Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). 2009Eur J Cancer;45:228–47.Reclusa P, Verstraelen P, Taverna S, Gunasekaran M, Pucci M, Pintelon I, et al. Improving extracellular vesicles visualization: From static to motion. 2020Sci Rep;10:6494.Théry C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. 2018J Extracell Vesicles;7:1535750Ethics ApprovalPatients consented to Institutional Review Board–approved protocol, A.O. Pappardo, Messina, Italy for cohort A and Thoracic Oncology Unit, Instituto Nacional de Cancerología (INCan), México City, México in case of the cohort B. Biological material was transferred to the University of Maryland School of Medicine, Baltimore for EV analysis under signed MTA between institutions MTA/2020–13111 & MTA/2020–13113.

Background: Therapy-related acute myeloid leukemia (t-AML) is associated with higher risk cytogenetics and disease biology, which partly account for poorer outcomes compared with de novo AML. Normal karyotype (NK) among patients (pts) with t-AML is rare, and the relative contribution of prior chemotherapy or radiotherapy exposure to outcomes of pts with AML with NK is uncertain. Methods: We reviewed all pts with newly diagnosed AML treated at MD Anderson Cancer Center between 2007 and 2019. Patients were separated into two groups (t-AML and de novo AML) based on their prior administration of chemotherapy or radiotherapy for an antecedent neoplasm. We analyzed patients' characteristics and outcomes including remission rates, relapse rates and survival. Survival curves were estimated by Kaplan-Meier method. Gray's method was used for cumulative incidence of relapse (CIR) analysis. Multivariate analyses for relapse-free survival (RFS) and overall survival (OS) were conducted using Cox proportional hazards regression model including age, t-AML (vs. de novo AML), European LeukemiaNet (ELN) 2017 risk classification, and type of therapy (intensive chemotherapy vs. low intensity/hypomethylating agent-based therapy) as covariates. Results: A total of 1977 pts with AML who had complete cytogenetic information available were identified. Among 742 pts (38%) with NK, 61 pts (8%) had t-AML and 681 pts (92%) had de novo AML. NK was present in 18% of all t-AML (61/340 pts). Prior therapy in pts with NK t-AML was chemotherapy (24 pts, 39%), radiation (21 pts, 34%), or both (16 pts, 26%). Characteristics of pts with NK AML are summarized in Table 1. Median age was higher for t-AML vs. de novo AML (71 years [range, 48-89] vs. 64 years [range, 18-92], p < 0.01). No statistically significant difference was noted by mutational status or ELN 2017 risk category. Pts with t-AML were less likely to receive intensive induction chemotherapy (26% vs. 52%, p < 0.01). However, rates of allogeneic stem cell transplant were similar (15% and 22% in t-AML and de novo AML, respectively, p = 0.17). Response rates by type of treatment are shown in Table 2. In pts who received low-intensity therapy, no significant difference was seen in CR/CRi rates between t-AML and de novo AML (60% vs. 61%, p=0.92). However, in pts who received intensive chemotherapy, there was a trend for higher CR/CRi rates in pts with de novo AML compared with t-AML (86% vs. 69%, p=0.05). With a median follow-up of 54 months, median OS was significantly shorter for pts with t-AML compared with de novo AML: 10.2 months vs. 20.6 months (hazard ratio [HR] 2.07, 95% confidence interval [CI], 1.54-2.78, p < 0.01, Figure 1A). Similarly, RFS was significantly worse for t-AML with a median of 12.0 months compared to 14.8 months for de novo AML (HR 1.55, 95% CI 1.06-2.26, p = 0.02, Figure 1B). Although pts with t-AML had worse OS and RFS, interestingly, this was not driven by higher relapse rates. The 5-year CIR rate was similar for pts with t-AML and de novo AML (42% vs. 56%, p = 0.21, Figure 1C). In contrast, the 5-year cumulative incidence of death in CR/CRi was significantly higher in patients with t-AML compared to patients with de novo AML (51% vs 16%, p < 0.01, Figure 1D), suggesting that non-relapse death is the main driver of worse outcomes for pts with t-AML. In multivariate analysis, age ≥60 was independently associated with shorter OS (HR 1.79, 95% CI 1.06-3.01, p=0.03) whereas favorable ELN 2017 risk category was prognostic of longer OS (HR 0.62, 95% CI 0.40-0.96, p=0.03). Additionally, there was a trend toward poor OS for adverse ELN 2017 risk category (HR 1.35, 95% CI 0.99-1.86, p=0.06) and better OS with intensive chemotherapy (HR 0.64, 95% CI 0.40-1.01, p=0.06). However, t-AML was not independently associated with OS (HR 1.34, 95% CI 0.83-2.17, p = 0.24) or RFS (HR 1.02, 95% CI 0.53-1.98, p = 0.95). Conclusions: t-AML with NK is rare entity that comprises <5% of all AML cases. In this large cohort, although the mutation frequency of these pts was similar to those with de novo disease, t-AML with NK was associated with worse survival, which was mostly driven by older age, decreased likelihood of receiving intensive chemotherapy, and a higher rate of death in remission compared to pts with de novo AML with NK. Together, these findings suggest that a clinical history of prior chemotherapy or radiation in patients with NK AML should not impact prognostication or therapeutic decisions. Disclosures Ravandi: Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Cyclacel LTD: Research Funding; Macrogenix: Consultancy, Research Funding; Selvita: Research Funding; Menarini Ricerche: Research Funding; Xencor: Consultancy, Research Funding. Kadia:Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Bioline RX: Research Funding; BMS: Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Research Funding. Daver:Astellas: Consultancy; Sunesis: Consultancy, Research Funding; Servier: Research Funding; Karyopharm: Consultancy, Research Funding; Forty-Seven: Consultancy; Incyte: Consultancy, Research Funding; Agios: Consultancy; Hanmi Pharm Co., Ltd.: Research Funding; NOHLA: Research Funding; BMS: Consultancy, Research Funding; Glycomimetics: Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Otsuka: Consultancy; Daiichi Sankyo: Consultancy, Research Funding; Immunogen: Consultancy, Research Funding; Celgene: Consultancy; Abbvie: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Jazz: Consultancy. DiNardo:notable labs: Membership on an entity's Board of Directors or advisory committees; celgene: Consultancy, Honoraria; agios: Consultancy, Honoraria; syros: Honoraria; daiichi sankyo: Honoraria; abbvie: Consultancy, Honoraria; jazz: Honoraria; medimmune: Honoraria. Bose:Incyte Corporation: Consultancy, Research Funding, Speakers Bureau; Celgene Corporation: Consultancy, Research Funding; Blueprint Medicine Corporation: Consultancy, Research Funding; Kartos: Consultancy, Research Funding; Constellation: Research Funding; Pfizer: Research Funding; Astellas: Research Funding; NS Pharma: Research Funding; Promedior: Research Funding; CTI BioPharma: Research Funding. Borthakur:Bayer Healthcare AG: Research Funding; AstraZeneca: Research Funding; BMS: Research Funding; Eli Lilly and Co.: Research Funding; Argenx: Membership on an entity's Board of Directors or advisory committees; Oncoceutics: Research Funding; Xbiotech USA: Research Funding; Novartis: Research Funding; FTC Therapeutics: Membership on an entity's Board of Directors or advisory committees; Eisai: Research Funding; Cantargia AB: Research Funding; Merck: Research Funding; Arvinas: Research Funding; Agensys: Research Funding; Strategia Therapeutics: Research Funding; Polaris: Research Funding; BioLine Rx: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Research Funding; Incyte: Research Funding; GSK: Research Funding; Cyclacel: Research Funding; BioTheryX: Membership on an entity's Board of Directors or advisory committees; Tetralogic Pharmaceuticals: Research Funding; Janssen: Research Funding; PTC Therapeutics: Consultancy; NKarta: Consultancy; Oncoceutics, Inc.: Research Funding. Garcia-Manero:Amphivena: Consultancy, Research Funding; Helsinn: Research Funding; Novartis: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding; Merck: Research Funding. Cortes:Forma Therapeutics: Consultancy, Honoraria, Research Funding; Daiichi Sankyo: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Research Funding; Jazz Pharmaceuticals: Consultancy, Research Funding; Merus: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Immunogen: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Sun Pharma: Research Funding; Biopath Holdings: Consultancy, Honoraria; BiolineRx: Consultancy; Astellas Pharma: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding. Kantarjian:Cyclacel: Research Funding; Takeda: Honoraria; Immunogen: Research Funding; Pfizer: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Daiichi-Sankyo: Research Funding; Novartis: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Ariad: Research Funding; Jazz Pharma: Research Funding; Agios: Honoraria, Research Funding; BMS: Research Funding; AbbVie: Honoraria, Research Funding; Astex: Research Funding. Short:AstraZeneca: Consultancy; Takeda Oncology: Consultancy, Research Funding; Amgen: Honoraria.

AbstractParticle transport and localization phenomena in condensed-matter systems can be modeled using a tight-binding lattice Hamiltonian. The ideal experimental emulation of such a model utilizes simultaneous, high-fidelity control and readout of each lattice site in a highly coherent quantum system. Here, we experimentally study quantum transport in one-dimensional and two-dimensional tight-binding lattices, emulated by a fully controllable 3 × 3 array of superconducting qubits. We probe the propagation of entanglement throughout the lattice and extract the degree of localization in the Anderson and Wannier-Stark regimes in the presence of site-tunable disorder strengths and gradients. Our results are in quantitative agreement with numerical simulations and match theoretical predictions based on the tight-binding model. The demonstrated level of experimental control and accuracy in extracting the system observables of interest will enable the exploration of larger, interacting lattices where numerical simulations become intractable.

Proofs of localization for random Schrödinger operators with sufficiently regular distribution of the potential can take advantage of the fractional moment method introduced by Aizenman–Molchanov [Commun. Math. Phys. 157(2), 245–278 (1993)] or use the classical Wegner estimate as part of another method, e.g., the multi-scale analysis introduced by Fröhlich–Spencer [Commun. Math. Phys. 88, 151–184 (1983)] and significantly developed by Klein and his collaborators. When the potential distribution is singular, most proofs rely crucially on exponential estimates of events corresponding to finite truncations of the operator in question; these estimates in some sense substitute for the classical Wegner estimate. We introduce a method to “lift” such estimates, which have been obtained for many stationary models, to certain closely related non-stationary models. As an application, we use this method to derive Anderson localization on the 1D lattice for certain non-stationary potentials along the lines of the non-perturbative approach developed by Jitomirskaya–Zhu [Commun. Math. Physics 370, 311–324 (2019)] in 2019.