Dual-target car-ts with on-and off-tumour activity may override immune suppression in solid cancers: A mathematical proof of concept

Chimeric antigen receptor (CAR)-T cell-based therapies have achieved substantial success against B-cell malignancies, which has led to a growing scientific and clinical interest in extending their use to solid cancers. However, results for solid tumours have been limited up to now, in part due to the immunosuppressive tumour microenvironment, which is able to inactivate CAR-T cell clones. In this paper we put forward a mathematical model describing the competition of CAR-T and tumour cells, taking into account their immunosuppressive capacity. Using the mathematical model, we show that the use of large numbers of CAR-T cells targetting the solid tumour antigens could overcome the immunosuppressive potential of cancer. To achieve such high levels of CAR-T cells we propose, and study computationally, the manufacture and injection of CAR-T cells targetting two antigens: CD19 and a tumour-associated antigen. We study in silico the resulting dynamics of the disease after the injection of this product and find that the expansion of the CAR-T cell population in the blood and lymphopoietic organs could lead to the massive production of an army of CAR-T cells targetting the solid tumour, and potentially overcoming its immune suppression capabilities. This strategy could benefit from the combination with PD-1 inhibitors and low tumour loads. Our computational results provide theoretical support for the treatment of different types of solid tumours using T cells engineered with combination treatments of dual CARs with on-and off-tumour activity and anti-PD-1 drugs after completion of classical cytoreductive treatmentsThis work has been funded by the James S. Mc. Donnell Foundation (USA) 21st Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer (Collaborative award 220020450), Ministerio de Ciencia e Innovación, Spain (grant number PID2019-110895RB-I00), and Junta de Comunidades de Castilla-La Mancha (grant number SBPLY/17/180501/000154). OLT is supported by a PhD Fellowship from the University of Castilla-La Mancha research pla

A Mathematical Description of the Bone Marrow Dynamics during CAR T-Cell Therapy in B-Cell Childhood Acute Lymphoblastic Leukemia

Chimeric Antigen Receptor (CAR) T-cell therapy has demonstrated high rates of response in recurrent B-cell Acute Lymphoblastic Leukemia in children and young adults. Despite this success, a fraction of patients' experience relapse after treatment. Relapse is often preceded by recovery of healthy B cells, which suggests loss or dysfunction of CAR T-cells in bone marrow. This site is harder to access, and thus is not monitored as frequently as peripheral blood. Understanding the interplay between B cells, leukemic cells, and CAR T-cells in bone marrow is paramount in ascertaining the causes of lack of response. In this paper, we put forward a mathematical model representing the interaction between constantly renewing B cells, CAR T-cells, and leukemic cells in the bone marrow. Our model accounts for the maturation dynamics of B cells and incorporates effector and memory CAR T-cells. The model provides a plausible description of the dynamics of the various cellular compartments in bone marrow after CAR T infusion. After exploration of the parameter space, we found that the dynamics of CAR T product and disease were independent of the dose injected, initial B-cell load, and leukemia burden. We also show theoretically the importance of CAR T product attributes in determining therapy outcome, and have studied a variety of possible response scenarios, including second dosage schemes. We conclude by setting out ideas for the refinement of the model.This work was partially supported by the Fundacion Espanola para la Ciencia y la Tecnologia (UCA PR214), the Asociacion Pablo Ugarte (APU, Spain), Junta de Comunidades de Castilla-La Mancha (SBPLY/17/180501/000154), Ministry of Science and Technology, Spain (PID2019110895RB-I00), and Inversion Territorial Integrada de la Provincia de Cadiz (ITI-0038-2019)

Increased hypothalamic anti‐inflammatory mediators in non‐diabetic insulin receptor substrate 2‐deficient mice

© 2021 by the authors.Insulin receptor substrate (IRS) 2 is a key mediator of insulin signaling and IRS-2 knockout (IRS2−/−) mice are a preclinical model to study the development of diabetes, as they develop peripheral insulin resistance and beta-cell failure. The differential inflammatory profile and insulin signaling in the hypothalamus of non-diabetic (ND) and diabetic (D) IRS2−/− mice might be implicated in the onset of diabetes. Because the lipid profile is related to changes in inflammation and insulin sensitivity, we analyzed whether ND IRS2−/− mice presented a different hypothalamic fatty acid metabolism and lipid pattern than D IRS2−/− mice and the relationship with inflammation and markers of insulin sensitivity. ND IRS2−/− mice showed elevated hypothalamic anti-inflammatory cytokines, while D IRS2−/− mice displayed a proinflammatory profile. The increased activity of enzymes related to the pentose-phosphate route and lipid anabolism and elevated polyunsaturated fatty acid levels were found in the hypothalamus of ND IRS2−/− mice. Conversely, D IRS2−/− mice have no changes in fatty acid composition, but hypothalamic energy balance and markers related to anti-inflammatory and insulin-sensitizing properties were reduced. The data suggest that the concurrence of an anti-inflammatory profile, increased insulin sensitivity and polyunsaturated fatty acids content in the hypothalamus may slow down or delay the onset of diabetes.This work was supported by the Spanish Ministry of Science and Innovation with the help of European FEDER funding (grant numbers FIS PI19/00166, BFU 2017-82565-C2-1-R, and RTI2018-094052-B-100), Comunidad de Madrid, Spain (S2017/BMD-3684) and the Network Center for Biomedical Research on Obesity and Nutrition (CIBEROBN) and Diabetes (CIBERDEM) Instituto Carlos III. S.C. was supported by CIBEROBN and A.G.M. by Fundación para la Investigación Biomédica Hospital Infantil Universitario Niño Jesús

High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia

B-cell Acute Lymphoblastic Leukaemia is one of the most common cancers in childhood, with 20% of patients eventually relapsing. Flow cytometry is routinely used for diagnosis and follow-up, but it currently does not provide prognostic value at diagnosis. The volume and the high-dimensional character of this data makes it ideal for its exploitation by means of Artificial Intelligence methods. We collected flow cytometry data from 56 patients from two hospitals. We analysed differences in intensity of marker expression in order to predict relapse at the moment of diagnosis. We finally correlated this data with biomolecular information, constructing a classifier based on CD38 expression. Artificial intelligence methods may help in unveiling information that is hidden in high-dimensional oncological data. Flow cytometry studies of haematological malignancies provide quantitative data with the potential to be used for the construction of response biomarkers. Many computational methods from the bioinformatics toolbox can be applied to these data, but they have not been exploited in their full potential in leukaemias, specifically for the case of childhood B-cell Acute Lymphoblastic Leukaemia. In this paper, we analysed flow cytometry data that were obtained at diagnosis from 56 paediatric B-cell Acute Lymphoblastic Leukaemia patients from two local institutions. Our aim was to assess the prognostic potential of immunophenotypical marker expression intensity. We constructed classifiers that are based on the Fisher's Ratio to quantify differences between patients with relapsing and non-relapsing disease. We also correlated this with genetic information. The main result that arises from the data was the association between subexpression of marker CD38 and the probability of relapse

Clonal heterogeneity and rates of specific chromosome gains are risk predictors in childhood high-hyperdiploid B-cell acute lymphoblastic leukemia

B-cell acute lymphoblastic leukemia (B-ALL) is the commonest childhood cancer. High hyperdiploidy (HHD) identifies the most frequent cytogenetic subgroup in childhood B-ALL. Although hyperdiploidy represents an important prognostic factor in childhood B-ALL, the specific chromosome gains with prognostic value in HHD-B-ALL remain controversial, and the current knowledge about the hierarchy of chromosome gains, clonal heterogeneity and chromosomal instability in HHD-B-ALL remains very limited. We applied automated sequential-iFISH coupled with single-cell computational modeling to identify the specific chromosomal gains of the eight typically gained chromosomes in a large cohort of 72 primary diagnostic (DX, n = 62) and matched relapse (REL, n = 10) samples from HHD-B-ALL patients with either favorable or unfavorable clinical outcome in order to characterize the clonal heterogeneity, specific chromosome gains and clonal evolution. Our data show a high degree of clonal heterogeneity and a hierarchical order of chromosome gains in DX samples of HHD-B-ALL. The rates of specific chromosome gains and clonal heterogeneity found in DX samples differ between HHD-B-ALL patients with favorable or unfavorable clinical outcome. In fact, our comprehensive analyses at DX using a computationally defined risk predictor revealed low levels of trisomies +18+10 and low levels of clonal heterogeneity as robust relapse risk factors in minimal residual disease (MRD)-negative childhood HHD-B-ALL patients: relapse-free survival beyond 5 years: 22.1% versus 87.9%, P < 0.0001 and 33.3% versus 80%, P < 0.0001, respectively. Moreover, longitudinal analysis of matched DX-REL HHD-B-ALL samples revealed distinct patterns of clonal evolution at relapse. Our study offers a reliable prognostic sub-stratification of pediatric MRD-negative HHD-B-ALL patients

Dynamical properties of feedback signalling in B lymphopoiesis: A mathematical modelling approach.

Haematopoiesis is the process of generation of blood cells. Lymphopoiesis generates lymphocytes, the cells in charge of the adaptive immune response. Disruptions of this process are associated with diseases like leukaemia, which is especially incident in children. The characteristics of self-regulation of this process make them suitable for a mathematical study. In this paper we develop mathematical models of lymphopoiesis using currently available data. We do this by drawing inspiration from existing structured models of cell lineage development and integrating them with paediatric bone marrow data, with special focus on regulatory mechanisms. A formal analysis of the models is carried out, giving steady states and their stability conditions. We use this analysis to obtain biologically relevant regions of the parameter space and to understand the dynamical behaviour of B-cell renovation. Finally, we use numerical simulations to obtain further insight into the influence of proliferation and maturation rates on the reconstitution of the cells in the B line. We conclude that a model including feedback regulation of cell proliferation represents a biologically plausible depiction for B-cell reconstitution in bone marrow. Research into haematological disorders could benefit from a precise dynamical description of B lymphopoiesis

Engraftment characterization of risk-stratified AML in NSGS mice

The authors thank Paola Romecin and Virginia Rodriguez-Cortez for technical assistance. This work was supported by the Spanish Ministry of Economy and Competitiveness (SAF2016-80481R, PID2019-108160RBI00), the Obra Social La Caixa (LCF/PR/HR19/52160011), Interreg V-A programme (POCTEFA) 2014-2020 (grant PROTEOblood EFA360/19), Health Canada (H4080-144541), and Deutsche Josep Carreras Leukämie Stiftung (P.M.). Additional funding was provided by Consejería de Salud y Familia (PI- 0119-2019) (R.D.d.l.G.), Health Institute Carlos III (ISCIII/FEDER, PI17/01028) and Asociación Española Contra el Cáncer (C.B.), Health Institute Carlos III/FEDER (CPII17/00032) (V.R.-M.), and Fundación Hay Esperanza (E.A.). CERCA/Generalitat de Catalunya and Fundación Josep Carreras-Obra Social la Caixa provided institutional support. B.L.-M. was supported by a Lady Tata Memorial Trust International Award and Asociación Española Contra el Cáncer (INVES20011LÓPE). O.M. and T.V.-H. were supported by Asociación Española Contra el Cáncer (INVES211226MOLI) and a Marie Sklodowska Curie Fellowship (792923), respectively. P.M. is an investigator in the Spanish Cell Therapy Network.Acute myeloid leukemia (AML) is the most common acute leukemia in adults. Disease heterogeneity is well documented, and patient stratification determines treatment decisions. Patient-derived xenografts (PDXs) from risk-stratified AML are crucial for studying AML biology and testing novel therapeutics. Despite recent advances in PDX modeling of AML, reproducible engraftment of human AML is primarily limited to high-risk (HR) cases, with inconsistent or very protracted engraftment observed for favorable-risk (FR) and intermediate-risk (IR) patients. We used NSGS mice to characterize the engraftment robustness/kinetics of 28 AML patient samples grouped according to molecular/ cytogenetic classification and assessed whether the orthotopic coadministration of patientmatched bone marrow mesenchymal stromal cells (BM MSCs) improves AML engraftment. PDX event-free survival correlated well with the predictable prognosis of risk-stratified AML patients. The majority (85-94%) of the mice were engrafted in bone marrow (BM) independently of the risk group, although HR AML patients showed engraftment levels that were significantly superior to those of FR or IR AML patients. Importantly, the engraftment levels observed in NSGS mice by week 6 remained stable over time. Serial transplantation and long-term culture-initiating cell (LTC-IC) assays revealed long-term engraftment limited to HR AML patients, fitter leukemia-initiating cells (LICs) in HR AML samples, and the presence of AML LICs in the CD342 leukemic fraction, regardless of the risk group. Finally, orthotopic coadministration of patient-matched BM MSCs and AML cells was dispensable for BM engraftment levels but favored peripheralization of engrafted AML cells. This comprehensive characterization of human AML engraftment in NSGS mice offers a valuable platform for in vivo testing of targeted therapies in risk-stratified AML patient samples.Spanish Ministry of Economy and Competitiveness (SAF2016-80481R, PID2019-108160RBI00)Obra Social La Caixa (LCF/PR/HR19/52160011)Interreg V-A programme (POCTEFA) 2014-2020 (grant PROTEOblood EFA360/19)Health Canada (H4080-144541)Deutsche Josep Carreras Leukämie StiftungConsejer ıa de Salud y Familia (PI- 0119-2019)Health Institute Carlos III (ISCIII/FEDER, PI17/01028)Asociación Española Contra el CáncerHealth Institute Carlos III/FEDER (CPII17/00032)Fundación Hay EsperanzaCERCA/Generalitat de CatalunyaFundació Josep Carreras-Obra Social la CaixaLady Tata Memorial Trust International AwardAsociación Española Contra el Cáncer (INVES20011LÓPE)Asociación Española Contra el Cáncer (INVES211226MOLI)Marie Sklodowska Curie Fellowship (792923

The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia.

Although children and adolescents with acute lymphoblastic leukaemia (ALL) have high survival rates, approximately 15-20% of patients relapse. Risk of relapse is routinely estimated at diagnosis by biological factors, including flow cytometry data. This high-dimensional data is typically manually assessed by projecting it onto a subset of biomarkers. Cell density and "empty spaces" in 2D projections of the data, i.e. regions devoid of cells, are then used for qualitative assessment. Here, we use topological data analysis (TDA), which quantifies shapes, including empty spaces, in data, to analyse pre-treatment ALL datasets with known patient outcomes. We combine these fully unsupervised analyses with Machine Learning (ML) to identify significant shape characteristics and demonstrate that they accurately predict risk of relapse, particularly for patients previously classified as 'low risk'. We independently confirm the predictive power of CD10, CD20, CD38, and CD45 as biomarkers for ALL diagnosis. Based on our analyses, we propose three increasingly detailed prognostic pipelines for analysing flow cytometry data from ALL patients depending on technical and technological availability: 1. Visual inspection of specific biological features in biparametric projections of the data; 2. Computation of quantitative topological descriptors of such projections; 3. A combined analysis, using TDA and ML, in the four-parameter space defined by CD10, CD20, CD38 and CD45. Our analyses readily extend to other haematological malignancies

Clonal heterogeneity and rates of specific chromosome gains are risk predictors in childhood high-hyperdiploid B-cell acute lymphoblastic leukemia

B-cell acute lymphoblastic leukemia (B-ALL) is the commonest childhood cancer. High hyperdiploidy (HHD) identifies the most frequent cytogenetic subgroup in childhood B-ALL. Although hyperdiploidy represents an important prognostic factor in childhood B-ALL, the specific chromosome gains with prognostic value in HHD-B-ALL remain controversial, and the current knowledge about the hierarchy of chromosome gains, clonal heterogeneity and chromosomal instability in HHD-B-ALL remains very limited. We applied automated sequential-iFISH coupled with single-cell computational modeling to identify the specific chromosomal gains of the eight typically gained chromosomes in a large cohort of 72 primary diagnostic (DX, n = 62) and matched relapse (REL, n = 10) samples from HHD-B-ALL patients with either favorable or unfavorable clinical outcome in order to characterize the clonal heterogeneity, specific chromosome gains and clonal evolution. Our data show a high degree of clonal heterogeneity and a hierarchical order of chromosome gains in DX samples of HHD-B-ALL. The rates of specific chromosome gains and clonal heterogeneity found in DX samples differ between HHD-B-ALL patients with favorable or unfavorable clinical outcome. In fact, our comprehensive analyses at DX using a computationally defined risk predictor revealed low levels of trisomies +18+10 and low levels of clonal heterogeneity as robust relapse risk factors in minimal residual disease (MRD)-negative childhood HHD-B-ALL patients: relapse-free survival beyond 5 years: 22.1% versus 87.9%, P < 0.0001 and 33.3% versus 80%, P < 0.0001, respectively. Moreover, longitudinal analysis of matched DX-REL HHD-B-ALL samples revealed distinct patterns of clonal evolution at relapse. Our study offers a reliable prognostic sub-stratification of pediatric MRD-negative HHD-B-ALL patients