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Our Research Work:

Our lab focuses on the study of immune-immune and immune-tumor interactions, under physiological and pathological conditions. By understanding the synergies and competitions between immune- and tumor- editing and immune- and tumor- cell communications we are able to better understand the basic biology of tumor-immune responses. We aim to capitalize on that basic understanding to identify and design better immunotherapeutic tools. 


The overaching themes of our lab include:

1- Understanding the molecular mechanisms of tumor- and immune- editing co-evolution

2- Identification of novel inter-cellular communications;  immune-immune and immune-tumor interactions.

3- Development of novel cancer diagnostic and immuno-therapeutic modalities.


Fig 1: Epigenomic modifications directing antibody-diversification processes somatic hypermutation (SHM) and CSR. Green core histones and associated modifications are involved in chromatin de-compaction and enable transcription through the immunoglobulin (Ig) locus. All factors above the locus are important for the generation of DSBs while everything below encourages mutagenic repair at the V region, and DSB repair at donor and acceptor S regions (Sμ and Sx, respectively). Blue histones and affiliated modifications help recruit or tether AID and other factors that facilitate production of DSBs. Purple DNA and RNA are linked with sequences and structures that facilitate AID recruitment or targeting. Red core histones and accessory modifications recruit DNA repair proteins to ensure excision of intervening CH region for successful class switching as well as error-prone polymerases to the V region.

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Fig 2: The rearranged immunoglobulin (Ig) locus where AID-induced mutagenesis and MMR-mediated error-prone DNA repair at Variable and Switch regions promote SHM & CSR, resp. Green arrowhead signify AID mutations. Off-target effect of this process on non-Ig genes (right) could lead to genomic instability and cancer. 

1- Understanding the molecular mechanisms of tumor- and immune- editing co-evolution

The AID/APOBEC cytosine deaminase enzymes are the only known mammalian genome editors that evolved important roles in immunity and cancer. AID, the ancestral gene in the family primarily mutates antibody genes to promote adaptive immunity. Other APOBEC genes are thought to either mutate viral genes during innate immunity and/or host genomes during tumorigenesis. Throughout immune editing, mutations are targeted by AID to antibody loci but when this process goes awry mutations can be mistargeted to other genes and cause cancer too. Through our prior work and others, we now have a basic understanding of the biological rules governing AID targeting of antibody genes but not of the underlying mechanisms governing tumor editing. Using a series of experimental approaches guided by machine learning algorithms based on kernels for structures developed by our collaborator, we aim to decipher the rules controlling tumor-editing and its co-evolution with immune-editing. We postulate that AID/APOBEC preferences coevolve with their respective antigenic targets be it pathogen or cancer. We aim to identify and ultimately predict negative and positive mutational “signatures” in tumor editing at the genetic and epigenetic levels. That is due to the unique ability of AID/APOBEC to mutate and “epi-mutate” cytosine and methyl-cytosine DNA bases, respectively. Furthermore, AID/APOBEC targeting offers a tractable mathematical tool to study this process because the mutational fingerprints are fairly well-characterized, and many genomes – of both healthy and cancerous tissues – are available through this URPP program or through other public databases. By evaluating the impact of these mutations on putative target loci using machine learning (ML) modelling, these methods will allow us to test and potentially explain how cancer genomes have subverted or hijacked the AID/APOBEC mutation machinery to a) enhance mutations, b) acquire immune resistance, and/or 3) promote the development of cancer neo-antigens.

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2- Identification of novel inter-cellular communications;  immune-immune and immune-tumor interactions.

Extracellular vesicles (EVs) are cell-derived vesicles that are present in many and perhaps all biological fluids, including blood, urine, and cultured cell medium. Exosomes contain various molecular constituents of their cell of origin, including proteins, DNA, and RNA. And its capacity to carry such wide array of signalling molecules provides it a distinctive capacity to re-wire host cells. It is becoming increasingly clear that EVs have specialized active functions and play a key role in intercellular communications through the cumulative effect of protein and non-coding RNA signalling networks (Fig 3). EV secretions from immune cells are known to be greatly exacerbated upon stress particularly through inflammation or tumor growth. That is why we postulate that EVs can offer a highly tractable amplifier of tumor and immune biomarkers. 

Fig 3: Cartoon depicting the novel  immune-tumor interactions mediated through immune and tumor extracellular vesicles

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3- Development of novel cancer diagnostic and immuno-therapeutic modalities.

Monoclonal antibodies have become a cornerstone in various diagnostic and drug modalities particularly cancer immune-therapeutics such as anti-PD1 and anti-CTLA4 drugs (Fig 4). And despite the promising advent of phage display and other antibody generating platforms such as the humanized antibody loci in mice (Regenron and KyMab), the process remains too artificial or dependent on animal models, respectively. The former – along all its technical variations – often lacks sufficient antibody specificity due to the absence of natural somatic hypermutation and the latter remains slow, inefficient, or inconsistent due to its dependency on animal responses. In fact, most monoclonal therapeutic antibodies in clinical use today are still being identified using the hybridoma technology developed in the 70s. To speed up the success rate and efficacy of monoclonal therapeutics, we are harnessing our basic science expertise in antibody maturation to create a novel proprietary tools that could generate human monoclonal antibodies more effectively. 

Fig 4: The distribution of therapeutic human  monoclonal antibody development in terms of methodologies (left) and usage (right). 

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