Biology subjects

Chen, G. M.

Publications and source records attributed to Chen, G. M..

5 recordsLinked to original sources

bioRxiv2024

TET2 regulates early and late transitions in exhausted CD8+ T-cell differentiation and limits CAR T-cell function

CD8+ T-cell exhaustion hampers disease control in cancer and chronic infections and limits efficacy of T-cell-based therapies, such as CAR T-cells. Epigenetic reprogramming of CAR T-cells by targeting TET2, a methylcytosine dioxygenase that mediates active DNA demethylation, has shown therapeutic potential; however, the role of TET2 in exhausted T-cell (TEX) development is unclear. In CAR T-cell exhaustion models and chronic LCMV infection, TET2 drove the conversion from stem cell-like, self-renewing TEX progenitors towards terminally differentiated and effector (TEFF)-like TEX. In mouse T-cells, TET2-deficient terminally differentiated TEX retained aspects of TEX progenitor biology, alongside decreased expression of the transcription factor TOX, suggesting that TET2 potentiates terminal exhaustion. TET2 also enforced a TEFF-like terminally differentiated CD8+ T-cell state in the early bifurcation between TEFF and TEX, indicating a broad role for TET2 in mediating the acquisition of an effector biology program that could be exploited therapeutically. Finally, we developed a clinically actionable strategy for TET2- targeted CAR T-cells, using CRISPR/Cas9 editing and site-specific adeno-associated virus transduction to simultaneously knock-in a CAR at the TRAC locus and a functional safety switch within TET2. Disruption of TET2 with this safety switch in CAR T-cells restrained terminal TEX differentiation in vitro and enhanced anti-tumor responses in vivo. Thus, TET2 regulates pivotal fate transitions in TEX differentiation and can be targeted with a safety mechanism in CAR T-cells for improved tumor control and risk mitigation. One Sentence SummaryModulation of exhausted CD8+ T-cell differentiation by targeting TET2 improves therapeutic potential of CAR T-cells in cancer.

immunology↗

bioRxiv2021

Decade-long remissions of leukemia sustained by the persistence of activated CD4+ CAR T-cells

The adoptive transfer of T lymphocytes reprogrammed to target tumor cells has demonstrated significant potential in various malignancies. However, little is known about the long-term potential and the clonal stability of the infused cells. Here, we studied the longest persisting CD19-redirected chimeric antigen receptor (CAR) T cells to date in two chronic lymphocytic leukemia (CLL) patients who achieved a complete remission in 2010. CAR T-cells were still detectable up to 10+ years post-infusion, with sustained remission in both patients. Surprisingly, a prominent, highly activated CD4+ population developed in both patients during the years post-infusion, dominating the CAR T-cell population at the late time points. This transition was reflected in the stabilization of the clonal make-up of CAR T-cells with a repertoire dominated by few clones. Single cell multi-omics profiling via Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) with TCR sequencing of CAR T-cells obtained 9.3 years post-infusion demonstrated that these long-persisting CD4+ CAR T-cells exhibited cytotoxic characteristics along with strong evidence of ongoing functional activation and proliferation. Our data provide novel insight into the CAR T-cell characteristics associated with long-term remission in leukemia.

immunology↗

bioRxiv2021

Network Analysis Reveals Synergistic Genetic Dependencies for Rational Combination Therapy in Philadelphia Chromosome-like Acute Lymphoblastic Leukemia

Systems biology approaches can identify critical targets in complex cancer signaling networks to inform therapy combinations and overcome conventional treatment resistance. Herein, we developed a data-driven, network controllability-based approach to identify synergistic key regulator targets in Philadelphia chromosome-like B-acute lymphoblastic leukemia (Ph-like B-ALL), a high-risk leukemia subtype associated with hyperactive signal transduction and chemoresistance. Integrated analysis of 1,046 childhood B-ALL cases identified 14 dysregulated network nodes in Ph-like ALL involved in aberrant JAK/STAT, Ras/MAPK, and apoptosis pathways and other critical processes. Consistent with network controllability theory, combination small molecule inhibitor therapy targeting a pair of key nodes shifted the transcriptomic state of Ph-like ALL cells to become less like kinase-activated BCR-ABL1-rearranged (Ph+) B-ALL and more similar to prognostically-favorable childhood B-ALL subtypes. Functional validation experiments further demonstrated enhanced anti-leukemia efficacy of combining the BCL-2 inhibitor venetoclax with tyrosine kinase inhibitors ruxolitinib or dasatinib in vitro in human Ph-like ALL cell lines and in vivo in multiple patient-derived xenograft models. Our study represents a broadly-applicable conceptual framework for combinatorial drug discovery, based on systematic interrogation of synergistic vulnerability pathways with pharmacologic targeted validation in sophisticated preclinical human leukemia models.

cancer biology↗

bioRxiv2017

Consensus on Molecular Subtypes of Ovarian Cancer

INTRODUCTIONVarious computational methods for gene expression-based subtyping of high-grade serous (HGS) ovarian cancer have been proposed. This resulted in the identification of molecular subtypes that are based on different datasets and were differentially validated, making it difficult to achieve consensus on which definitions to use in follow-up studies. We assess three major subtype classifiers for their robustness and association to outcome by a meta-analysis of publicly available expression data, and provide a classifier that represents their consensus.\n\nMETHODSWe use a compendium of 15 microarray datasets consisting of 1,774 HGS ovarian tumors to assess 1) concordance between published subtyping algorithms, 2) robustness of those algorithms to re-clustering across datasets, and 3) association of subtypes with overall survival. A consensus classifier is trained on concordantly classified samples, and validated by leave-one-dataset-out validation.\n\nRESULTSEach subtyping classifier identified subsets significantly differing in overall survival, but were not robust to re-fitting in independent datasets and grouped only approximately one third of patients concordantly into four subtypes. We propose a consensus classifier to identify the minority of unambiguously classifiable tumors across multiple gene expression platforms, using a 100-gene signature. The resulting consensus subtypes correlate with patient age, survival, tumor purity, and lymphocyte infiltration.\n\nCONCLUSIONSOur analysis demonstrates that most HGS ovarian cancers are not able to be subtyped. A minority of tumors can be classified and our proposed consensus classifier consolidates and improves on the robustness of three previously proposed subtype classifiers. It provides reliable stratification of patients with HGS ovarian tumors of clearly defined subtype, and will assist in studying the role of polyclonality in the majority of tumors that are not robustly classifiable.

cancer biology↗

bioRxiv2017

BPG: Seamless, Automated and Interactive Visualization of Scientific Data

We introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg

bioinformatics↗