Biology subjects

Karam, S.

Publications and source records attributed to Karam, S..

3 recordsLinked to original sources

bioRxiv2024

IL15/IL15Rα complex induces an anti-tumor immune response following radiation therapy only in the absence of Tregs and fails to induce expansion of progenitor TCF1+ CD8 T cells

BackgroundThis work seeks to understand whether IL15-incorporating treatments improve response to radiotherapy and uncover mechanistic rationale for overcoming resistance to IL15 agonism using novel therapeutic combinations. Experimental DesignOrthotopic tumor models of PDAC were used to determine response to treatment. IL15-/- and Rag1-/- mouse models were employed to determine dependence on IL15 and CTLs, respectively. Flow cytometry was used to assess immune cell frequency and activation state. Phospho-proteomic analyses were used to characterize intracellular signaling pathways. ResultsWe show that the combination of radiation therapy (RT) and an IL15/IL15Ra fusion complex (denoted IL15c) fails to confer anti-tumor efficacy; however, a CD8-driven anti-tumor immune response is elicited with the concurrent administration of an aCD25 Treg-depleting antibody. Using IL15-/- and Rag1-/- mice, we demonstrate that response to RT + IL15c + aCD25 is dependent on both IL15 and CTLs. Furthermore, despite an equivalent survival benefit following treatment with RT + IL15c + aCD25 and combination RT + PD1-IL2v, a novel immunocytokine with PD-1 and IL2R{beta}{gamma} binding domains, CTL immunophenotyping and phospho-proteomic analysis of intracellular metabolites showed significant upregulation of activation and functionality in CD8 T cells treated with RT + PD1-IL2v. Finally, we show the immunostimulatory response to RT + PD1-IL2v is significantly diminished with a concurrent lack of TCF+ CD8 T cell generation in the absence of functional IL15 signaling. ConclusionsOur results are illustrative of a mechanism wherein unimpeded effector T cell activation through IL2R{beta} signaling and Treg inhibition are necessary in mediating an anti-tumor immune response.

cancer biology↗

bioRxiv2021

Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease

BackgroundInflammatory bowel disease (IBD) patients wait months and undergo numerous invasive procedures between the initial appearance of symptoms and receiving a diagnosis. In order to reduce time until diagnosis and improve patient wellbeing, machine learning algorithms capable of diagnosing IBD from the gut microbiomes composition are currently being explored. To date, these models have had limited clinical application due to decreased performance when applied to a new cohort of patient samples. Various methods have been developed to analyze microbiome data which may improve the generalizability of machine learning IBD diagnostic tests. With an abundance of methods, there is a need to benchmark the performance and generalizability of various machine learning pipelines (from data processing to training a machine learning model) for microbiome-based IBD diagnostic tools. ResultsWe collected fifteen 16S rRNA microbiome datasets (7707 samples) from North America to benchmark combinations of gut microbiome features, data normalization methods, batch effect reduction methods, and machine learning models. Pipeline generalizability to new cohorts of patients was evaluated with four binary classification metrics following leave-one dataset-out cross validation, where all samples from one study were left out of the training set and tested upon. We demonstrate that taxonomic features obtained from QIIME2 lead to better classification of samples from IBD patients than inferred functional features obtained from PICRUSt2. In addition, machine learning models that identify non-linear decision boundaries between labels are more generalizable than those that are linearly constrained. Prior to training a non-linear machine learning model on taxonomic features, it is important to apply a compositional normalization method and remove batch effects with the naive zero-centering method. Lastly, we illustrate the importance of generating a curated training dataset to ensure similar performance across patient demographics. ConclusionsThese findings will help improve the generalizability of machine learning models as we move towards non-invasive diagnostic and disease management tools for patients with IBD.

microbiology↗

bioRxiv2020

Transcriptional control of DNA repair networks by CDK7 regulates sensitivity to radiation in Myc-driven Medulloblastoma

Myc-driven Medulloblastoma remains a major therapeutic challenge due to frequent metastasis and a poor 5-year survival rate. Myc gene amplification results in transcriptional dysregulation, proliferation, and survival of malignant cells. To identify therapeutic targets in Myc-amplified medulloblastoma we performed a CRISPR-Cas9 essentiality screen targeting 1140 genes annotated as the druggable genome. CDK7 was identified as a mediator of medulloblastoma tumorigenesis. Using covalent inhibitors and genetic depletion of CDK7 we observe the cessation of tumor growth in xenograft mouse models and increase in apoptotic mechanisms. The results are attributed to repression of a core set of Myc-driven transcriptional programs mediating DNA repair. We further establish that blocking CDK7 activity sensitizes cells to ionizing radiation leading to accrual of DNA damage and extended survival and tumor latency in medulloblastoma xenograft mouse models. Our studies establish a mechanism for selective inhibition of Myc-driven MB by CDK7 inhibition combined with radiation as a viable therapeutic strategy for Myc-amplified medulloblastoma.

cancer biology↗