Luciana Rodrigues Carvalho Barros’s research while affiliated with Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (46)


Figure 1. Schematic description of the CAR-T cell immunotherapy model in patients (left) and the corresponding interaction and target-antigen-dependent mechanisms (right). Interactions influenced by the target antigen are denoted by light magenta lines. The CAR-T cells, specifically designed to target the chosen antigen, undergo a rapid distribution phase upon injection into the patient. During this phase, they spread throughout the body and die naturally (C D ). A subset of these cells undergoes engraftment and successfully integrates into the bloodstream and tumour niche. Referred to as effector CAR-T cells (C T ), they expand upon target antigen contact, exhibit cytotoxicity against cells expressing the target antigen (both tumour and healthy B cells), differentiate into memory CAR-T cells, and are impaired by tumour-induced immunosuppressive
Figure 3. Theoretical relapse-free time (t T R ) recorded for profiles CR90, PR90, PD90, and patient K7. For each scenario, only the initial conditions were varied in the range of 10 4 to 10 9 , while maintaining the values of the model parameters. A null value indicates that the tumour cell count did not fall below the clinical detection threshold. On the other hand, (t T R = 900+) represents recurrence within 900 days or more.
Healthy B cells: allies or adversaries of CAR-T cell immunotherapy?
  • Preprint
  • File available

November 2024

·

23 Reads

·

Luciana Rodrigues Carvalho Barros

·

·

Chimeric antigen receptor (CAR)-T cell immunotherapy has achieved significant success against various haematological cancers, including B-cell malignancies. Its efficacy against B-cell cancers is influenced by the presence of healthy B-cells expressing the target antigen, and B-cell aplasia (BCA) serves as an indicator of successful therapy outcome. However, the precise influence of healthy B-cells on the in vivo dynamics of CAR-T cells and their ultimate impact on therapy outcomes remain unclear. Here, we propose a mathematical model to describe CAR-T cell immunotherapy in B-cell cancer patients. Our model successfully captured the interactions between different CAR-T cell phenotypes, tumour cells, and healthy B cells in patients who achieved a complete response. Using these cases, we constructed virtual scenarios to investigate how variations in baseline tumour and healthy B-cell populations, along with patient-specific factors related to CAR-T cell expansion and B-cell influx from the bone marrow, affect treatment outcomes. Our results suggest that the onset and duration of BCA is a patient-specific feature that depends primarily on the continuous influx of newly generated B cells, their proliferative capacity, and the expansion and cytotoxicity of CAR-T cells. Statement of significance This study presents a significant advancement in understanding the dynamics of CAR-T cell immunotherapy in B-cell malignancies by introducing a mathematical model that captures the complex interactions between CAR-T cells, tumour cells, and healthy B cells. The model provides crucial insights into how patient-specific factors, such as baseline tumour burden, B-cell populations, and CAR-T cell expansion, influence treatment outcomes, including the onset and duration of B-cell aplasia (BCA), a key marker of therapeutic success. Healthy B cells can act as allies, adversaries, or have a neutral effect on the therapy, depending on the tumour burden. These findings highlight the importance of personalised approaches in CAR-T cell immunotherapy, offering potential pathways to optimise treatment strategies for improved efficacy and patient outcomes.

Download

8696 Whole Exome Sequence Analysis in a Naïve Cohort of Congenital Hypopituitarism in a Single Center

October 2024

Journal of the Endocrine Society

Fernanda Queiroz Aratani

·

Debora Parreiras Di Matteo

·

Isabelle Pinheiro Amaro de Magalhães

·

[...]

·

Luciani Renata Silveira Carvalho

Disclosure: F.Q. Aratani: None. D.P. Di Matteo: None. I.P. de Magalhães: None. P. Frudit: None. L.S. Motomia: None. S.K. Beeby: None. L.R. Barros: None. D.D. Bissegatto: None. L.R. Carvalho: None. Background: Deficiency of one or more pituitary hormones is defined as hypopituitarism. In the literature, molecular diagnosis (MD) is defined in 12% by the Sanger sequencing and large scale sequencing increased this number to 15%. Aim: To perform whole exome sequencing (WES) in a cohort of patients with congenital hypopituitarism (CH) naïve to MD approach. Patients: Nine patients with CH followed in a single Brazilian tertiary center were included. Methods: Libraries were prepared with kits from Agilent and sequenced on Hi-Seq (Illumina). The variants were called following GATK best practices, using the Haplotypecaller and genome version hg38. They were classified in the Franklin by genoox (https://franklin.genoox.com/clinical-db/home). WES that did not pass the platform's initial quality control and variants that did not reach an adequate confidence level (low/medium confidence: VAF < 50%) were excluded. The remaining variants were classified according to ACMG criteria. Results: Three among 9 patients presented allelic variants and aredescribed here. Case 1: A 22-year-old female presented height of 143 cm (-3,13 SDS) at the first visit without signs of puberty, had delayed bone age (DBA), hypoplastic adenohypophysis, interrupted pituitary stalk (IPS) and ectopic neurohypophysis (ENH) at the magnetic resonance imaging (MRI). Hormonal profile showed low E2, GH, FSH and LH. Twoheterozygous missense variants in the ALMS1 gene (c.5450G>A : p.Arg1817GIn; and ALMS1:c.4225G>C: p.Ala1409Pro) were seen in the exome; both classified as VUS according to ACMG criteria. This allelic variant is related to Alstrom Syndrome. Case 2: A 7-year-old female presented with short stature and was diagnosed with GH followed by LH/FSH, TSH and ACTH deficiencies. At MRI presented IPS and ENH. A heterozygous variant in the PROK2 gene c.364C>T p.R122 with a premature stop codon and truncated protein was present and classified as probably pathogenic according to ACMG criteria. This variant was already described with hypogonadotropic hypogonadism (HH) with or without anosmia. Case3: A 15-year-old male was referred with growth hormone deficiency (GHD), DBA, and HH. Growth hormone replacement was started followed by testosterone. Despite delayed puberty, the testes were normal in size. WES showed a variant c.629G>A:p.Trp210*, in hemizygosity in IGSF1 gene and was classified as probably pathogenic according to ACMG criteria. Loss-of-function mutations in IGSF1 may cause an X-linked syndrome of central hypothyroidism, macroorchidism and delayed puberty (delayed rise of testosterone, but normal timing of testicular growth). Conclusion: WES in a cohort of patients with congenital hypopituitarism naïve to molecular diagnosis revealed 33% (3/9) with positive genetic findings, two of them with clinical significance and family segregation according to the genetic inheritance. Presentation: 6/3/2024


Solar explosion graph depicting spectrum of PGVs and VUSs in pancreatic cancer patients. Inner circle: Number of patients who are PGV carriers, VUS only carriers, neither PGV nor VUS carriers in the panel of genes (wild type); Medium circle: list of PGV affected genes in 37 participants; Outer circle: spectrum of VUS in the panel of genes in PGV carriers and non-carriers. *One patient who was a PGV carrier in CHEK2 and one patient who was a PGV carrier in ATM presented no VUS, but were considered together with the other patients who were PGV carriers in CHEK2 and ATM and were also VUS carriers.
Single Base Substitution (SBS) signatures across whole-exome-sequence tumor samples. Number of single nucleotide variants (SNVs) associated with each SBS in tumor samples of carriers of PGVs in MRE11 c.350A>G (PC32); ACD c.608_609delCT (plus RAD51C c.3G>A) (PC59), TERF2IP c.1152_1153del (PC67); SDHA c.666delG (PC109); POLE c.3721G>T (PC110); BRIP1 c.290_293del (PC115). PC: patient sample identification.
Individual genetic ancestry estimates of Brazilian pancreatic cancer patients in accordance with self-declared skin color. Patients self-declared skin color appears in the X axis. Genetic ancestry proportion estimates appear on the Y axis.
Overall survival estimates in PGV carriers and non-carriers. (A) All pancreatic cancer (PC) divided in PGV carriers in one of the 24 affected genes (P/LP variants present) and non-carriers (P/LP variants absent); (B) PC patients divided in homologous recombination repair (HRR) PGV carriers and non-carriers; (C) PC patients divided in PGV carriers in genes associated with pancreatic cancer (GAPC) and non-carriers; (D) Patients with non-European ancestry (admixed) PGV carriers and non-carriers. Abs: P/LP variants absent; Pres: P/LP variants present; CS: clinical stage.
Prevalence of germline variants in Brazilian pancreatic carcinoma patients

September 2024

·

26 Reads

We evaluated the prevalence of pathogenic/likely pathogenic germline variants (PGV) in Brazilian pancreatic adenocarcinoma (PC) patients, that represent a multiethnic population, in a cross-sectional study. We included 192 PC patients unselected for family history of cancer. We evaluated a panel of 113 cancer genes, through genomic DNA sequencing and 46 ancestry-informative markers, through multiplex PCR. The median age was 61 years; 63.5% of the patients presented disease clinical stages III or IV; 8.3% reported personal history of cancer; 4.7% and 16.1% reported first-degree relatives with PC or breast and/or prostate cancer, respectively. Although the main ancestry was European, there was considerable genetic composition admixture. Twelve patients (6.25%) were PGV carriers in PC predisposition genes (ATM, BRCA1, BRCA2, CDKN2A, MSH2, PALB2) and another 25 (13.0%) were PGV carriers in genes with a limited association or not previously associated with PC (ACD, BLM, BRIP1, CHEK2, ERCC4, FANCA, FANCE, FANCM, GALNT12, MITF, MRE11, MUTYH, POLE, RAD51B, RAD51C, RECQL4, SDHA, TERF2IP). The most frequently affected genes were CHEK2, ATM and FANC. In tumor samples from PGV carriers in ACD, BRIP1, MRE11, POLE, SDHA, TERF2IP, which were examined through exome sequencing, the main single base substitutions (SBS) mutational signature was SBS1+5+18, probably associated with age, tobacco smoking and reactive oxygen species. SBS3 associated with homologous repair deficiency was also represented, but on a lower scale. There was no difference in the frequency of PGV carriers between: (a) patients with or without first-degree relatives with cancer; and (b) patients with admixed ancestry versus those with predominantly European ancestry. Furthermore, there was no difference in overall survival between PGV carriers and non-carriers. Therefore, genetic testing should be offered to all Brazilian pancreatic cancer patients, regardless of their ancestry. Genes with limited or previously unrecognized associations with pancreatic cancer should be further investigated to clarify their role in cancer risk.


Protocol for the establishment of a serine integrase-based platform for functional validation of genetic switch controllers in eukaryotic cells

May 2024

·

50 Reads

·

1 Citation

Serine integrases (Ints) are a family of site-specific recombinases (SSRs) encoded by some bacteriophages to integrate their genetic material into the genome of a host. Their ability to rearrange DNA sequences in different ways including inversion, excision, or insertion with no help from endogenous molecular machinery, confers important biotechnological value as genetic editing tools with high host plasticity. Despite advances in their use in prokaryotic cells, only a few Ints are currently used as gene editors in eukaryotes, partly due to the functional loss and cytotoxicity presented by some candidates in more complex organisms. To help expand the number of Ints available for the assembly of more complex multifunctional circuits in eukaryotic cells, this protocol describes a platform for the assembly and functional screening of serine-integrase-based genetic switches designed to control gene expression by directional inversions of DNA sequence orientation. The system consists of two sets of plasmids, an effector module and a reporter module, both sets assembled with regulatory components (as promoter and terminator regions) appropriate for expression in mammals, including humans, and plants. The complete method involves plasmid design, DNA delivery, testing and both molecular and phenotypical assessment of results. This platform presents a suitable workflow for the identification and functional validation of new tools for the genetic regulation and reprogramming of organisms with importance in different fields, from medical applications to crop enhancement, as shown by the initial results obtained. This protocol can be completed in 4 weeks for mammalian cells or up to 8 weeks for plant cells, considering cell culture or plant growth time.


Figure 2. Clinical and tumor molecular characteristics of the patient harboring multiple somatic monosomy. (A) Bone scan and 18 F-FDG PET/CT showing multiple lytic lesions involving the skull, and axial and appendicular skeleton. (B) Result of NGS showing absence of RET germinative mutation and presence of a somatic M918T mutation. (C) Somatic copy number alteration analysis (SCNA) with losses of chromosomes 1p, 3, 4, 5, 9, and 13.
Patients characteristics of the study cohort
Somatic pathogenic variants identified in 19 young patients with sporadic medullary thyroid carcinoma
Not Only RET but NF1 and Chromosomal Instability Are Seen in Young Patients with Sporadic Medullary Thyroid Carcinoma

March 2024

·

39 Reads

·

1 Citation

Journal of the Endocrine Society

Genetic analysis of sporadic Medullary Thyroid Carcinoma (MTC) has revealed somatic variants in RET, RAS, and occasionally other genes. However, around 20% of sporadic MTC patients lack a known genetic driver. To uncover potential new somatic or germline drivers we analyze a distinct cohort of patients with sporadic, very early-onset and aggressive MTC. Germline and somatic DNA exome sequencing was performed in 19 patients, previously tested negative for germline RET variants. Exome sequencing of 19 germline samples confirmed the absence of RET and identified an NF1 pathogenic variant in one patient. Somatic sequencing was successful in 15 tumors revealing RET variants in 80%, predominantly p.Met918Thr which was associated with disease aggressiveness. In RET-negative tumors, pathogenic variants were found in HRAS and NF1. The NF1 germline and somatic variants were observed in a patient without a prior clinical diagnosis of Neurofibromatosis type 1, demonstrating that the loss of heterozygosity of NF1 functions as a potential MTC driver. Somatic copy number alterations analysis revealed chromosomal alterations in 53.3% of tumors, predominantly in RET-positive cases, with losses in chromosomes 9 and 22 being the most prevalent. This study reveals that within a cohort of early-onset non-hereditary MTC, RET remains the major driver gene. In RET-negative tumors, NF1 and RAS are drivers of sporadic MTC. In addition, in young patients without a RET germline mutation, a careful clinical evaluation with a consideration of germline NF1 gene analysis is ideal to exclude Neurofibromatosis type 1.




A systematic review of clinical trials for gene therapies for β-hemoglobinopathy around the world

June 2023

·

59 Reads

·

3 Citations

Cytotherapy

Background aims: Amidst the success of cell therapy for the treatment of onco-hematological diseases, the first recently Food and Drug Administration-approved gene therapy product for patients with transfusion-dependent β-thalassemia (TDT) indicates the feasibility of gene therapy as curative for genetic hematologic disorders. This work analyzed the current-world scenario of clinical trials involving gene therapy for β-hemoglobinopathies. Methods: Eighteen trials for patients with sickle cell disease (SCD) and 24 for patients with TDT were analyzed. Results: Most are phase 1 and 2 trials, funded by the industry and are currently recruiting volunteers. Treatment strategies for both diseases are fetal hemoglobin induction (52.4%); addition of wild-type or therapeutic β-globin gene (38.1%) and correction of mutations (9,5%). Gene editing (52.4%) and gene addition (40.5%) are the two most used techniques. The United States and France are the countries with the greatest number of clinical trials centers for SCD, with 83.1% and 4.2%, respectively. The United States (41.1%), China (26%) and Italy (6.8%) lead TDT trials centers. Conclusions: Geographic trial concentration indicates the high costs of this technology, logistical issues and social challenges that need to be overcome for gene therapy to reach low- and middle-income countries where SCD and TDT are prevalent and where they most impact the patient's health.


Figure 1: Modeling resistance mechanisms in CAR-T therapy. (a) Schematic description of the model structure. After infusion, effector CAR-T cells (CT ) expand in response to antigen contact, differentiate into a memory phenotype, are suppressed by tumor cells, and undergo natural cell death. Memory cells (CM ) have a longer persistence but may also die and differentiate back into effector CAR-T cells upon antigen re-exposure.Tumor cells are split into two constitutively different states: antigen-positive (Ag+, TP ) and antigen-negative (Ag-, TN ). All tumor cells proliferate but only Ag+ cells mutate into Ag-cells and experience transient changes in antigen expression. Since CAR-T cytotoxicity requires antigen-binding, tumor cells are killed with different rates, which depend on antigen-expression levels. (b) The initial distributions of tumor cells along the antigen space are given by Gaussian distributions with means xN and xP , representing the homeostatic values of each population antigen-density, and dN and dP are the intrinsic variabilities around this level (standard deviations). The threshold of antigen detection, x med , defines two regions of therapy responses: within the region 0 ≤ x < x med , tumor cells are considered resistant, while within the region x med ≤ x ≤ 1, they are considered sensible to CAR-T cell therapy. (c) Treatment pressure induces changes in antigen expression. This is modeled by the therapy-driven level of antigen-expression ¯ x(t) and its dependence on the CAR-T cell load. During CAR-T cells expansion (↑ CAR-T cells), antigen-positive cells lose antigen and escape immune response (¯ x(t) → xN ). As the number of CAR-T cells decreases (↓ CAR-T cells), these cells can restore their antigen expression to its homeostatic level (¯ x(t) → xP ). (d) The mutation kernel W (x) is defined as a Gaussian distribution with mean xN and standard deviation dN and denotes the probability that an antigen-positive tumor cell will mutate into a antigen-negative tumor cell with antigen expression x. (e) CAR-T cytotoxic function. The killing of tumor cells by CAR-T cells is regulated by antigen receptor signaling and, since tumor cells display heterogeneous antigen expression, the function g(x) represents the CAR-T cell cytotoxicity as an antigen-driven mechanism.
Figure 2: Model fits of tumor-CAR-T cell interactions for 18 patients divided into three cohorts: Relapse− (top panel), Relapse+ (middle panel), and CR (bottom panel). The total CAR-T cell population includes effector (CT ) and memory (CM ) phenotypes, while the total tumor population encompasses antigen-negative (TN ) and antigen-positive (TP ) cells. The qPCR detection threshold of 2.5 × 10 6 cells was represented by the gray area. The zoomed-in analysis of patient P02 was made from day 15 to day 45, while the remaining CR patients were analyzed from day 0 to day 30. B-ALL, B-cell acute lymphoblastic leukemia; ALL, acute lymphoblastic leukemia; CLL, chronic lymphoblastic leukemia; MCL, mantle cell lymphoma; DLBCL, diffuse large B cell lymphoma. These simulations consider SC1 (pre-existing Ag-cells, no mutations); corresponding fits for SC2 are presented in SM-4
Figure 3: Tumor dynamics in response to antigen-modulation for the representative patients P22 (Relapse− panels) and L2 (Relapse+ panels) in scenarios SC1 ( ) and SC2 ( ). (a) Tumor dynamics along the antigen space. During the treatment, Ag+ tumor cells are killed by CAR-T cells and change their antigen expression. Mechanisms like genetic mutations, the pre-existence of antigen-negative clones, or CAR-T impaired cytotoxicity may lead to tumor progression. (b) Time evolution of tumor sensible cells (TS) is defined in four stages: 1) initial decay due to CAR-T cell expansion; 2) slower decay (SC1) and re-growth (SC2) due to antigen loss; 3) secondary decay during antigen restoration; and 4) fast (SC1) and slow (SC2) re-growth due to CAR-T persistence. (c) Time evolution of the therapy-driven level of antigen expression ¯ x(t). Periods of the duration of the expansion, contraction, and persistence phases of CAR-T cells are indicated by the letters E, C, and P, respectively. During the expansion phase (E) of CAR-T cells, driven by the therapy pressure, antigen-positive tumor cells lose antigen and evade the immune attack. As the number of CAR-T cells decreases in the contraction phase (C), antigen-positive tumor cells restore their homeostatic level of antigen expression. (d) CAR-T cell cytotoxicity depends on the antigen density. Tumor cells with antigen downregulation (Ag−) are less likely to be killed by antigen-independent mechanisms, while tumor cells displaying high amounts of antigen (Ag+) are more susceptible to being killed by CAR-T cells.
Figure 4: Model simulations for different resistance mechanisms for patient P22 (first and second columns) and patient P12 (third and fourth columns). After fitting the model, parameters related to (a) the phenotypic transition (kP ), (b) the therapy-driven antigen loss (kI ), and (c) the intratumoral heterogeneity of antigen-positive tumor cells (dP ) were varied to assess changes in the dynamics of effector ( ) and memory ( ) CAR-T cells, as well as changes in antigen-positive ( ) and antigen-negative ( ) tumor cells. The corresponding scenarios are indicated in the CAR-T cell figures.
Figure 5: Different therapy scenarios assume different CAR-T cell cytotoxicity, resulting in different tumor dynamics. The upper panel shows the time evolution of the total tumor cells T (t) for scenario SC1 ( ) and scenario SC2 ( ). The rapid depletion of tumor cells that occurs at the beginning of CAR-T cell therapy (until C peak ) is hindered due to antigen loss. As the CAR-T cell population contracts and the therapy pressure decreases, antigen expression is restored, and the remaining CAR-T cells further reduce the tumor population (until Tmin. However, if cytotoxicity is insufficient (Relapse+) antigen-positive tumor cells escape; and if there is permanent antigen loss (Relapse−), the CAR-T cells cannot recognize its target, allowing the antigen-negative tumor cells to escape, although there is a high cytotoxic activity against antigen-positive cells. The middle and bottom panels display the cytotoxicity heatmaps, γg(x)f (CT , T ), for SC1 and SC2, respectively. For patients Relapse+, CAR-T cytotoxicity is severely impaired due to the minimal number of effector cells in the system and their weak antitumor activity, which cannot avoid antigen-positive cells escape. In patients, Relapse−, the mechanism of escape is related to the loss of the target-antigen and not due to CAR-T cytotoxicity. The points C peak and Tmin represent the time when the CAR-T cells reach its peak and when the tumor has reached its minimum population. All parameters used in this simulation are in Table SM-4.
Mechanisms of Resistance to CAR-T cell Immunotherapy: Insights from a Mathematical Model

May 2023

·

95 Reads

·

1 Citation

Chimeric Antigen Receptor (CAR)-T cell therapy long-term follow-up studies revealed non-durable remissions in a significant number of patients. Some of the mechanisms underlying these relapses include poor CAR T cell cytotoxicity or persistence, as well as antigen loss or lineage switching in tumor cells. In order to investigate how antigen-mediated resistance mechanisms affect therapy outcomes, we develop a mathematical model based on a set of integral-partial differential equations. Using a continuous variable to describe the level of antigen expression of tumor cells, we recapitulated important cellular mechanisms across patients with different therapeutic responses. Fitted with clinical data, the model successfully captured the dynamics of tumor and CAR-T cells for several hematological cancers. Furthermore, the role played by these mechanisms are explored with regard to different biological scenarios, such as pre-existing or acquired mutations, providing a deeper understanding of key factors underlying resistance to CAR-T cell immunotherapy. Statement of significance Our study introduces the first mathematical model to characterize the influence of a continuous level of antigen expression on the interplay between Chimeric Antigen Receptor (CAR)-T cells and cancer cells. We examine various cellular mechanisms across different hematologic cancers, taking into account both antigen-positive and antigen-negative relapses. Our findings shed light on the role of antigen density in CAR-T cell therapies and provide a valuable framework to investigate resistance with potential to improve patient’s outcomes.



Citations (27)


... This heralds a new era of innovation, where scientists will analyse large genetic data and design synthetic genomes with unprecedented speed and precision. This synergy accelerates the discovery process, enabling rapid advancements in genetic circuit design (Gomide et al., 2020;Oliveira et al., 2024), genetic manipulation of metabolic pathways (Murad et al., 2014;Ro et al., 2006), engineering whole and synthetic genomes Rothschild et al., 2024;Venter et al., 2022), among others. Computational approaches accelerate designing and constructing novel biological systems with tailored functionalities. ...

Reference:

Engineering biology and the positive regulatory pathway in Brazil
Protocol for the establishment of a serine integrase-based platform for functional validation of genetic switch controllers in eukaryotic cells

... Point mutations in RAS (HRAS and KRAS) represent another driver related to sporadic MTC, occurring in 20-50% of patients without RET mutations. Recently, inactivating variants of the NF1 gene have also been found to be a driver of non-RET/ non-RAS MTC (Ciampi et al. 2023;Castroneves et al. 2024). In approximately 20% of sporadic MTCs no variants in RET or RAS are identified, prompting ongoing studies to identify other drivers associated with MTC (WHO Classification of Tumours Editorial Board 2022; Agrawal et al. 2013). ...

Not Only RET but NF1 and Chromosomal Instability Are Seen in Young Patients with Sporadic Medullary Thyroid Carcinoma

Journal of the Endocrine Society

... RR comprises two subunits encoded by UL39 (large subunit) and UL40 (small subunit) [33], and its activity is required, especially in non-replicating cells such as neurons, which have a reduced pool of dNTPs [34]. The use of CRISPR/Cas9 in vitro assays using the VERO cell line can inhibit HSV-1 replication at a rate of more than 95% [35]. ...

CRISPR/Cas-9 vector system: targets UL-39 and inhibits Simplexvirus humanalpha1 (HSV-1) replication in vitro
  • Citing Article
  • July 2023

Cellular and molecular biology (Noisy-le-Grand, France)

... Previous studies on B-cell lymphoma have integrated the inactivation of immune cells upon exposure to tumor cells using mass-action terms (Kuznetsov et al. 1994;Owens and Bozic 2021;Roesch et al. 2013;Santurio et al. 2024). The addition of this saturating term for inactivation/exhaustion of CAR T-cells distinguishes this model from other CAR T-cell models (Viola and Lanzavecchia 1996;León-Triana et al. 2021a). ...

Mechanisms of Resistance to CAR-T cell Immunotherapy: Insights from a Mathematical Model
  • Citing Article
  • August 2023

Applied Mathematical Modelling

... Clinical trial centers for Food and Drug Administration-approved gene therapy products for patients with transfusion-dependent β-thalassemia are now available in high-income countries such as the United States (41.1%), China (26%), and Italy (6.8%) [54]. Though the timeline for the widespread availability of gene therapy remains uncertain, there is hope that in the future, the cost of gene therapy will be affordable and available for thalassemia treatment. ...

A systematic review of clinical trials for gene therapies for β-hemoglobinopathy around the world
  • Citing Article
  • June 2023

Cytotherapy

... Cellular immunotherapies might not be feasible where validated, cost-effective, antivirals exist. However, as the role of cell therapies becomes better understood, predictive modelling of these systems can help expedite the process by which new treatments are evaluated and optimized, reducing the cost associated with these therapies [90][91][92][93]. Another issue in the field is the lack of homogeneity and underpowering of clinical studies, which makes it difficult to draw robust conclusions from clinical trials. ...

Modeling Patient-Specific CAR-T Cell Dynamics: Multiphasic Kinetics via Phenotypic Differentiation

... These models also allow for the exploration of di↵erent treatment strategies [21,20,1]. In recent years, CAR T-cell therapies have drawn significant interest from mathematicians studying a range of tumors [60,17], including gliomas [65,6,66,47,7], melanomas [5], and B-cell malignancies [46,52,57]. These mathematical models have been used to investigate various cellular mechanisms, such as CAR T-cell activation, cancer cell killing in response to CAR T-cell dose, target antigen expression, and optimal dosing strategies. ...

A mathematical model for CAR-T cells on target off-tumor effect on gliomas

Frontiers in Systems Biology

... Despite intensive treatment with surgery, chemotherapy, and radiation, GBM remains incurable, with a median survival period of approximately 15-20 months [1,2]. Therefore, more effective treatments are required, and various therapies for GBM are being tested or are under development [3][4][5]. ...

Systematic Review of Available CAR-T Cell Trials around the World

... While both G and Q are binary adjacency matrices, experts agree that very little information is lost when sequence data are converted to the binary level [87]. For example, binary data have been used for the analysis of gene expression data and have produced reasonable results [87,33]. ...

A Stochastic Binary Model for the Regulation of Gene Expression to Investigate Responses to Gene Therapy

... If not regulated carefully, the release of immunogenic signals may hinder rather than assist in positive outcomes. Excessive or poorly regulated immune responses may damage healthy tissue, and an immune response against virotherapy may compromise its effectiveness by eliminating infected cells or neutralising the virus [5][6][7] . Therefore, the optimal design of immunostimulatory oncolytic viruses requires an in-depth analysis of various additional factors influencing the immune response generated by virotherapy, such as the presence of antigen-specific T-cells and the tumour microenvironment. ...

A systematic analysis on the clinical safety and efficacy of Onco-Virotherapy
  • Citing Article
  • October 2021

Molecular Therapy — Oncolytics