Engineered T Cells: CAR T Cell Therapy and Beyond

Purpose of review: This article reviews the current data and future directions of engineered T cell therapies in non-Hodgkin lymphomas.
Recent findings: Currently, four chimeric antigen receptor (CAR) T cell products are approved: axicabtagene ciloleucel, tisagenlecleucel, lisocabtagene maraleucel, and brexucabtagene autoleucel. These products differ in construct, indication, manufacturing, clinical trial design, and toxicity profile, but all are autologous products targeting CD19. Encouraging early data is also emerging with the use of these products in additional subtypes of B cell lymphoma. Alternative engineered T cell products are also in development, including dual CD19/22 targeting CAR T cells, CD30-directed CAR T cells, allogeneic CAR T cells, and engineered natural killer (NK) cells. Preclinical data using novel CAR constructs such as cytokine-secreting CARs targeted gene delivery into the T cell receptor α constant (TRAC) locus, combination strategies, and third-generation CARs holds promise for additional novel approaches. CAR T cells have transformed the therapeutic landscape for patients with relapsed/refractory B cell lymphomas. Early data with Gentaur Excursion-Trac novel engineered cellular products is encouraging and holds promise for future clinical use.

HLA-independent T cell receptors for targeting tumors with low antigen density

Chimeric antigen receptors (CARs) are receptors for antigen that direct potent immune responses. Tumor escape associated with low target antigen expression is emerging as one potential limitation of their efficacy. Here we edit the TRAC locus in human peripheral blood T cells to engage cell-surface targets through their T cell receptor-CD3 complex reconfigured to utilize the same immunoglobulin heavy and light chains as a matched CAR. We demonstrate that these HLA-independent T cell receptors (HIT receptors) consistently afford high antigen sensitivity and mediate tumor recognition beyond what CD28-based CARs, the most sensitive design to date, can provide.
We demonstrate that the functional persistence of HIT T cells can be augmented by constitutive coexpression of CD80 and 4-1BBL. Finally, we validate the increased antigen sensitivity afforded by HIT receptors in xenograft mouse models of B cell leukemia and acute myeloid leukemia, targeting CD19 and CD70, respectively. Overall, HIT receptors are well suited for targeting cell surface antigens of low abundance.

Pembrolizumab outperforms tyrosine kinase inhibitors as adjuvant treatment in patients with high-risk renal cell carcinoma after nephrectomy

We determined the oncologic outcomes and safety profiles of adjuvant immune checkpoint inhibitors (ICIs) compared to adjuvant tyrosine kinase inhibitors (TKIs) in patients at high risk after nephrectomy for clinically nonmetastatic renal cell carcinoma (RCC). Network meta-analyses were conducted for disease-free survival (DFS), overall survival (OS), and adverse events (AEs) with placebo as the common comparator arm. Six trials (KEYNOTE-564, S-TRAC, ASSURE, PROTECT, ATLAS, and SORCE) were included in our analysis.
Compared to placebo, both pembrolizumab (hazard ratio [HR] 0.68, 95% confidence interval [CI] 0.51-0.92) and pazopanib 800 mg (HR 0.69, 95% CI 0.49-0.97) were significantly associated with better DFS. Adjuvant pembrolizumab (HR 0.54, 95% CI 0.30-0.97) was significantly associated with better OS compared to TKIs (HR 0.93, 95% CI 0.83-1.04).
Analysis of treatment ranking revealed that pembrolizumab was the best treatment with regard to both DFS and OS and had the lowest likelihood of any-grade and high-grade AEs in comparison to TKIs. The superior oncologic benefit of pembrolizumab and its better toxicity profile support it as the new standard of care in the adjuvant setting for nephrectomy patients at high risk of RCC relapse. PATIENT SUMMARY: For patients with kidney cancer at high risk of relapse after surgical removal of their kidney, postoperative therapy with the immune checkpoint inhibitor pembrolizumab offers the best risk/benefit ratio.

Using the Theoretical Domains Framework to Identify Barriers and Enablers to Implementing a Virtual Tertiary-Regional Telemedicine Rounding and Consultation for Kids (TRaC-K) Model: Qualitative Study

Background: Inequities in access to health services are a global concern and a concern for Canadian populations living in rural areas. Rural children hospitalized at tertiary children’s hospitals have higher rates of medical complexity and experience more expensive hospitalizations and more frequent readmissions. The 2 tertiary pediatric hospitals in Alberta, Canada, have already been operating above capacity, but the pediatric beds at regional hospitals are underused.
Such imbalance could lead to poor patient safety and increased readmission risk at tertiary pediatric hospitals and diminish the clinical exposure of regional pediatric health care providers, erode their confidence, and compel health systems to further reduce the capacity at regional sites. A Telemedicine Rounding and Consultation for Kids (TRaC-K) model was proposed to enable health care providers at Alberta Children’s Hospital to partner with their counterparts at Medicine Hat Regional Hospital to provide inpatient clinical care for pediatric patients who would otherwise have to travel or be transferred to the tertiary site.
Objective: The aim of this study is to identify perceived barriers and enablers to implementing the TRaC-K model.
Methods: This study was guided by the Theoretical Domains Framework (TDF) and used qualitative methods. We collected qualitative data from 42 participants from tertiary and regional hospitals through 31 semistructured interviews and 2 focus groups. These data were thematically analyzed to identify major subthemes within each TDF domain. These subthemes were further aggregated and categorized into barriers or enablers to implementing the TRaC-K model and were tabulated separately.
Results: Our study identified 31 subthemes in 14 TDF domains, ranging from administrative issues to specific clinical conditions. We were able to merge these subthemes into larger themes and categorize them into 4 barriers and 4 enablers. Our findings showed that the barriers were lack of awareness of telemedicine, skills to provide virtual clinical care, unclear processes and resources to support TRaC-K, and concerns about clear roles and responsibilities. The enablers were health care providers’ motivation to provide care closer to home, supporting system resource stewardship, site and practice compatibility, and motivation to strengthen tertiary-regional relationships.
Conclusions: This systematic inquiry into the perceived barriers and enablers to the implementation of TRaC-K helped us to gain insights from various health care providers’ and family members’ perspectives. We will use these findings to design interventions to overcome the identified barriers and harness the enablers to encourage successful implementation of TRaC-K. These findings will inform the implementation of telemedicine-based interventions in pediatric settings in other parts of Canada and beyond.

cLoops2: a full-stack comprehensive analytical tool for chromatin interactions

Investigating chromatin interactions between regulatory regions such as enhancer and promoter elements is vital for understanding the regulation of gene expression. Compared to Hi-C and its variants, the emerging 3D mapping technologies focusing on enriched signals, such as TrAC-looping, reduce the sequencing cost and provide higher interaction resolution for cis-regulatory elements. A robust pipeline is needed for the comprehensive interpretation of these data, especially for loop-centric analysis.
Therefore, we have developed a new versatile tool named cLoops2 for the full-stack analysis of these 3D chromatin interaction data. cLoops2 consists of core modules for peak-calling, loop-calling, differentially enriched loops calling and loops annotation. It also contains multiple modules for interaction resolution estimation, data similarity estimation, features quantification, feature aggregation analysis, and visualization. cLoops2 with documentation and example data are open source and freely available at GitHub.

2021 Updated European Association of Urology Guidelines on the Use of Adjuvant Pembrolizumab for Renal Cell Carcinoma

Adjuvant treatment of nonmetastatic high-risk renal cell carcinoma is an unmet medical need. In the past, several tyrosine kinase inhibitor trials have failed to demonstrate an improvement of disease-free survival (DFS) in this setting. Only one trial (S-TRAC) provided evidence for improved DFS with sunitinib but without an overall survival (OS) signal. Keynote-564 is the first trial of an immune checkpoint inhibitor that significantly improved DFS with adjuvant pembrolizumab, a programmed death receptor-1 antibody, in clear cell renal cell carcinoma with a high risk of relapse. The intention-to-treat population, which included a group of patients after metastasectomy and no evidence of disease (M1 NED), had a significant DFS benefit.
The OS data are not mature as yet. The Renal Cell Carcinoma Guideline Panel issues a weak recommendation for the adjuvant use of pembrolizumab for high-risk clear cell renal carcinoma, as defined by the trial until final OS data are available. However, the trial reilluminates the discussion on when and in whom metastasectomy should be performed.
Here, caution is necessary not to perform metastasectomy in patients with poor prognostic features and rapid progressive disease, which must be excluded by a confirmatory scan of disease status prior to planned metastasectomy.
PATIENT SUMMARY: New data from the adjuvant immune checkpoint inhibitor trial with pembrolizumab (a programmed death receptor-1 antibody) for the treatment of high-risk clear cell renal cell carcinoma (ccRCC) after surgery showed that the drug prolonged the period of being cancer free significantly, although whether it prolonged survival remained uncertain. Consequently, pembrolizumab is cautiously recommended as an additional (ie, adjuvant) treatment in high-risk ccRCC after kidney cancer surgery.

TRAC Antibody

1-CSB-PA024144LA01HU Cusabio
  • 317.00 EUR
  • 335.00 EUR
  • 100ug
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TRAC Antibody

DF14496 Affbiotech 100ul 350 EUR

TRAC Polyclonal Antibody

A55102 EpiGentek 100 µg 570.55 EUR

TRAC Antibody, HRP conjugated

1-CSB-PA024144LB01HU Cusabio
  • 317.00 EUR
  • 335.00 EUR
  • 100ug
  • 50ug

TRAC Antibody, FITC conjugated

1-CSB-PA024144LC01HU Cusabio
  • 317.00 EUR
  • 335.00 EUR
  • 100ug
  • 50ug

TRAC Antibody, Biotin conjugated

1-CSB-PA024144LD01HU Cusabio
  • 317.00 EUR
  • 335.00 EUR
  • 100ug
  • 50ug

TRAC-1 Polyclonal Antibody

ES6685-100ul ELK Biotech 100ul 279 EUR

TRAC-1 Polyclonal Antibody

ES6685-50ul ELK Biotech 50ul 207 EUR

TRAC-1 Polyclonal Antibody

ABP55686-003ml Abbkine 0.03ml 158 EUR

TRAC-1 Polyclonal Antibody

ABP55686-01ml Abbkine 0.1ml 289 EUR

TRAC-1 Polyclonal Antibody

ABP55686-02ml Abbkine 0.2ml 414 EUR

Anti-TRAC-1 antibody

STJ96080 St John's Laboratory 200 µl 197 EUR

Anti-TCR alpha/TRAC Antibody

A05315 BosterBio 100ug/vial 334 EUR

TRAC Polyclonal Antibody, Biotin Conjugated

A55099 EpiGentek 100 µg 570.55 EUR

TRAC Polyclonal Antibody, FITC Conjugated

A55100 EpiGentek 100 µg 570.55 EUR

TRAC Polyclonal Antibody, HRP Conjugated

A55101 EpiGentek 100 µg 570.55 EUR

Anti-TRAC-1 / RNF125 antibody

STJ70554 St John's Laboratory 100 µg 359 EUR

TRAC ORF Vector (Human) (pORF)

ORF034213 ABM 1.0 ug DNA Ask for price

T Cell Receptor Alpha (TRAC) Antibody

abx415683-01mg Abbexa 0.1 mg 592 EUR

T Cell Receptor Alpha (TRAC) Antibody

abx415684-50ug Abbexa 50 ug 537 EUR

TRAC sgRNA CRISPR Lentivector set (Human)

K2432401 ABM 3 x 1.0 ug 339 EUR

Polyclonal TRAC-1 / RNF125 Antibody (C-Term)

APG00437G Leading Biology 0.1mg 484 EUR

TRAC sgRNA CRISPR Lentivector (Human) (Target 1)

K2432402 ABM 1.0 ug DNA 154 EUR

TRAC sgRNA CRISPR Lentivector (Human) (Target 2)

K2432403 ABM 1.0 ug DNA 154 EUR

TRAC sgRNA CRISPR Lentivector (Human) (Target 3)

K2432404 ABM 1.0 ug DNA 154 EUR

TRAC 3'UTR GFP Stable Cell Line

TU076204 ABM 1.0 ml 2333 EUR
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