• Thematic field of study: Understanding immune dysfunction/suppression in glioma microenvironment
• Supervisor(s):Prof Abdallah Badou
• Contact details: abdallahbadou@yahoo.com
  

Summary of Hosting Lab

The Immuno-Genetics and Human Pathology Laboratory (LIGEP) is focused on conducting research that seeks to understand human diseases especially cancer, with a particular focus on immunological aspects. LIGEP comprises a team of 31 devoted professors spanning various
disciplines—clinical, fundamental, and engineering sciences—united in their mission to promote innovation and scientific discoveries, ultimately shaping the future of healthcare in the cancer field.

Project Description

Novel insights into the immunological aspects of the central nervous system (CNS) have played a crucial role in reshaping our understanding of its immune privilege, leading to a paradigm shift in the field of neuro-oncology. Among malignancies of CNS, glioma stands as the predominant primary brain tumor in adults, notably with glioblastoma emerging as the most aggressive and treatment-resistant subtype. A pivotal factor driving this resistance lies in the intricate interplay between glioma cells and the immune system within the tumor microenvironment. This interaction leads to a specific immunosuppressive pattern marked by diverse mechanisms impeding efficacious antitumoral immune response.
The aberrant cellular and molecular immunity within the glioma microenvironment is a critical barrier to achieving successful cancer immunotherapy, thereby improving patient survival. Notwithstanding recent breakthroughs, the precise mechanisms governing immune dysfunction and suppression in glioma patients remain incompletely elucidated, posing a challenge in optimizing the clinical management of this disease. Therefore, it is paramount to decipher in-depth and innovative dynamic pathways associated with immune dysregulation within the glioma microenvironment to improve patients’ outcomes.
In consideration of this context, our project seeks to comprehensively investigate novel mechanisms determining dysfunction and suppression of immunity in glioma patients through multidisciplinary approaches. The ultimate aim is to uncover novel therapeutic biomarkers and
immunobiology processes capable of enhancing immune-mediated anti-tumor responses, potentially offering clinical utility and advancing healthcare practices.

Publications

*Ait Ssi S, Chraa D, El Azhary K, Sahraoui S, Olive D, Badou A. Prognostic Gene Expression
Signature in Patients With Distinct Glioma Grades. Frontiers in Immunology (2021) 12:
https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2021.685213
[Accessed March 1, 2024]
Ghouzlani A, Lakhdar A, Rafii S, Karkouri M, Badou A. The immune checkpoint VISTA exhibits high expression levels in human gliomas and associates with a poor prognosis. Sci Rep (2021) 11:21504. doi: 10.1038/s41598-021-00835-0
Rafii S, Ghouzlani A, Naji O, Ait Ssi S, Kandoussi S, Lakhdar A, Badou A. A2AR as a Prognostic Marker and a Potential Immunotherapy Target in Human Glioma. Int J Mol Sci (2023) 24:6688. doi: 10.3390/ijms24076688 Boulhen C, AIT SSI S, Benthami H, Razzouki I, Lakhdar A, Karkouri M, Badou A. TMIGD2 as a potential therapeutic target in glioma patients. Front Immunol (2023) 14:1173518. doi: 10.3389/fimmu.2023.1173518

Miftah H, Naji O, Ssi SA, Ghouzlani A, Lakhdar A, Badou A. NR2F6, a new immune checkpoint that acts as a potential biomarker of immunosuppression and contributes to poor clinical outcome in human glioma. Front Immunol (2023) 14:1139268. doi: 10.3389/fimmu.2023.1139268

Major Lab techniques

RT-qPCR, Immunohistochemistry, Flow Cytometry, Western Blot, ELISA, Cell Culture, PBMC / TILs isolation, Multiomic and bioinformatic analysis.

• Thematic field of study: Emerging immune checkpoints in human glioma microenvironment
• Supervisor(s):Prof Abdallah Badou
• Contact details: abdallahbadou@yahoo.com
  

Summary of Hosting Lab

The Immuno-Genetics and Human Pathology Laboratory (LIGEP) is focused on conducting research that seeks to understand human diseases especially cancer, with a particular focus on immunological aspects. LIGEP comprises a team of 31 devoted professors spanning various
disciplines—clinical, fundamental, and engineering sciences—united in their mission to promote innovation and scientific discoveries, ultimately shaping the future of healthcare in the cancer field.

Project Description

Gliomas are the most severe and frequent primary brain tumors in adults. Characterized by their invasiveness and resistance to conventional therapies, gliomas pose a major problem in oncology. In the past few decades, a body of evidence has revealed the critical function of
immune checkpoints in controlling the immune response for several cancers.However, research on immunotherapy of glioma has extended in an exponential manner. Most of glioma patients weren't responsive to standard immunotherapies (CTLA-4 and PD1/PD-L1). This has piqued our curiosity in identifying novel immune checkpoints that might benefit glioma sufferers. The identification of novel and emerging immune checkpoints in the human glioma microenvironment could mark a significant step in cancer immunotherapy.These checkpoints, which control immune responses inside the glioma tumor microenvironment, provide fresh insights into the complicated interactions between tumour cells and the immune system.Understanding the function of these checkpoints is critical for creating novel immunotherapeutic approaches for glioma patients.The purpose of this project is to investigate the dynamics of immune checkpoint expression and function to better clarify their impact on tumor progression and patient outcome. A complete investigation of several immune checkpoint inhibitors is required to understand their effectiveness, boundaries, and possible synergies in the setting of glioma immunotherapy. This project offers enormous promise for improving our understanding of immune evasion mechanisms in gliomas, perhaps leading to more successful therapies in the future.

Publications

Ait Ssi S, Chraa D, El Azhary K, Sahraoui S, Olive D, Badou A. Prognostic Gene Expression Signature in Patients With Distinct Glioma Grades. Frontiers in Immunology (2021) 12: https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2021.685213
[Accessed March 1, 2024]

Ghouzlani A, Lakhdar A, Rafii S, Karkouri M, Badou A. The immune checkpoint VISTA exhibits high expression levels in human gliomas and associates with a poor prognosis. Sci Rep (2021) 11:21504. doi: 10.1038/s41598-021-00835-0
Rafii S, Ghouzlani A, Naji O, Ait Ssi S, Kandoussi S, Lakhdar A, Badou A. A2AR as a Prognostic Marker and a Potential Immunotherapy Target in Human Glioma. Int J Mol Sci(2023) 24:6688. doi: 10.3390/ijms24076688
Boulhen C, AIT SSI S, Benthami H, Razzouki I, Lakhdar A, Karkouri M, Badou A. TMIGD2 as a potential therapeutic target in glioma patients. Front Immunol (2023) 14:1173518. doi:10.3389/fimmu.2023.1173518
Miftah H, Naji O, Ssi SA, Ghouzlani A, Lakhdar A, Badou A. NR2F6, a new immune checkpoint that acts as a potential biomarker of immunosuppression and contributes to poor clinical outcome in human glioma. Front Immunol (2023) 14:1139268. doi: 10.3389/fimmu.2023.1139268

Major Lab techniques

RT-qPCR, Immunohistochemistry, Flow Cytometry, Western Blot, ELISA, Cell Culture, PBMC /
Tils isolation.

• Thematic field of study: Novel immune checkpoints in human breast cancer
• Supervisor(s):Prof Abdallah Badou
• Contact details: abdallahbadou@yahoo.com
  

Summary of Hosting Lab

The Immuno-Genetics and Human Pathology Laboratory (LIGEP) is focused on conducting research that seeks to understand human diseases especially cancer, with a particular focus on immunological aspects. LIGEP comprises a team of 31 devoted professors spanning various
disciplines—clinical, fundamental, and engineering sciences—united in their mission to promote innovation and scientific discoveries, ultimately shaping the future of healthcare in the cancer field.

Project Description

Breast cancer poses a significant global public health challenge, prompting intense research into therapeutic approaches like immunotherapy, which primarily centers on blocking the PD1/PD-L1 pathway. Despite this focus, a significant portion of breast cancer patients does not respond effectively to these treatments. This challenge underscores the complexity of the interplay between cancer cells and the immune system, a subject that has captivated scientific interest for decades. Cancer cells have demonstrated remarkable adaptability, employing a myriad of strategies to evade detection by the immune system and manipulate the tumor microenvironment to their advantage. One pivotal
breakthrough in this field has been the identification of inhibitory immune checkpoints, which serve as crucial mechanisms for cancer cells to escape immune surveillance. The aim of this project is to discover a novel immune checkpoint and study its mechanism of action in the breast tumor microenvironment. This exploration aims to pave the way for the development of innovative therapeutic pathways capable of effectively regulating immune responses and counteracting the evasion strategies used by cancer cells.

Publications

Rezouki I, Zohair B, Ssi SA, Karkouri M, Razzouki I, Elkarroumi M and Badou A (2023) High VISTA expression is linked to a potent epithelial-mesenchymal transition and is positively correlated with  PD1 in breast cancer. Front. Oncol. 13:1154631. doi: 10.3389/fonc.2023.1154631

Zohair B, Chraa D, Rezouki I, Benthami H, Razzouki I, Elkarroumi M, Olive D, Karkouri M and Badou A (2023) The immune checkpoint adenosine 2A receptor is associated with aggressive clinical outcomes and reflects an immunosuppressive tumor microenvironment in human breast cancer. Front. Immunol. 14:1201632. doi: 10.3389/fimmu.2023.1201632

Major Lab techniques

q-RT PCR | Cell Culture | Flow Cytometry | Immunohistochemistry | Animal Experimentation | Molecular
Screening and Docking | Molecular Modeling | Immunoinformatic tools

• Thematic field of study: The immune response in patients representing with breast cancer
• Supervisor(s):Prof Abdallah Badou
• Contact details: abdallahbadou@yahoo.com
  

Summary of Hosting Lab

The Immuno-Genetics and Human Pathology Laboratory (LIGEP) is focused on conducting research that seeks to understand human diseases especially cancer, with a particular focus on immunological aspects. LIGEP comprises a team of 31 devoted professors spanning various
disciplines—clinical, fundamental, and engineering sciences—united in their mission to promote innovation and scientific discoveries, ultimately shaping the future of healthcare in the cancer field.

Project Description

The immune response in breast cancer patients is a multifaceted process that significantly influences disease progression and treatment outcomes. Tumor-infiltrating lymphocytes (TILs) represent a key component of the anti-tumor immune response, with higher TIL levels
correlating with improved prognosis and response to therapy. Conversely, immunosuppressive mechanisms orchestrated by regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs) contribute to tumor immune evasion and disease advancement.
Recent research highlights the potential of harnessing the immune system for therapeutic interventions in breast cancer. Immunotherapeutic approaches, including adoptive cell therapy and cancer vaccines, aim to enhance anti-tumor immunity and mitigate immunosuppression
within the tumor microenvironment. Furthermore, immune-related biomarkers such as TILs are emerging as valuable prognostic indicators, aiding in risk stratification and treatment decision-making.
Understanding the dynamic interplay between the immune system and breast cancer is crucial for optimizing clinical management. Advances in immune profiling technologies offer insights into the composition and function of immune cells within the tumor, paving the way for
personalized treatment strategies. Additionally, ongoing efforts to identify predictive biomarkers of response to immunotherapy hold promise for tailoring treatment approaches to individual patient characteristics.
In conclusion, elucidating the immune response in breast cancer patients has significant clinical implications. By leveraging our understanding of immune-tumor interactions and developing innovative immunotherapeutic strategies, we can improve treatment outcomes and enhance
the quality of care for breast cancer patients.

Publications

Zohair B, Chraa D, Rezouki I, Benthami H, Razzouki I, Elkarroumi M, Olive D, Karkouri M, Badou A. The immune checkpoint adenosine 2A receptor is associated with aggressive clinical outcomes and reflects an immunosuppressive tumor microenvironment in human breast
cancer. Front Immunol. 2023 Sep 11;14:1201632. doi: 10.3389/fimmu.2023.1201632. PMID: 37753093; PMCID: PMC10518422.
Rezouki I, Zohair B, Ssi SA, Karkouri M, Razzouki I, Elkarroumi M, Badou A. High VISTA expression is linked to a potent epithelial-mesenchymal transition and is positively correlated with PD1 in breast cancer. Front Oncol. 2023 Apr 20;13:1154631. doi: 10.3389/fonc.2023.1154631. PMID: 37152039; PMCID: PMC10157209.
Chraa D, Naim A, Olive D, Badou A. T lymphocyte subsets in cancer immunity: Friends or foes. J Leukoc Biol. 2019 Feb;105(2):243-255. doi: 10.1002/JLB.MR0318-097R. Epub 2018 Nov 2.

Major Lab techniques

RT-qpcr , immunohistochemestry, flow cytometry , cell culture, western blot, ELISA, PBMC
isolation, TILs isolation

• Thematic field of study: A Comparative Analysis of Co-Clustering Algorithms for Gene Expression Data
• Supervisor(s): Professor Tayeb OUADERHMAN
• Contact details: abdallahbadou@yahoo.com
  

Summary of Hosting Lab

The Immuno-Genetics and Human Pathology Laboratory (LIGEP) is focused on conducting research that seeks to understand human diseases especially cancer, with a particular focus on immunological aspects. LIGEP comprises a team of 31 devoted professors spanning various
disciplines—clinical, fundamental, and engineering sciences—united in their mission to promote innovation and scientific discoveries, ultimately shaping the future of healthcare in the cancer field.

Project Description

The advent of high-throughput technologies has enabled the generation of vast amounts of gene expression data, offering valuable insights into the molecular mechanisms underlying various biological processes. Analyzing this data is crucial for understanding gene interactions, identifying biomarkers, and uncovering potential therapeutic targets. Co-clustering algorithms have emerged as powerful tools for extracting meaningful patterns from gene expression datasets by simultaneously clustering both genes and samples. This project aims to conduct a comprehensive comparative analysis of different co-clustering algorithms to discern their strengths, weaknesses, and applicability in the context of gene expression data. Co-clustering algorithms performance comparison is still an open research field.

Objectives:
 Evaluate the performance of various co-clustering algorithms on gene
expression datasets.
 Compare the ability of algorithms to identify biologically relevant gene
clusters and sample groups.
 Assess the robustness and scalability of different algorithms in handling
large-scale gene expression datasets.
 Investigate the impact of algorithm parameters on clustering results and
computational efficiency.
 Provide insights into the suitability of specific co-clustering algorithms for
different types of gene expression data.

Major Lab techniques

Algorithm Selection: Select a diverse set of co-clustering algorithms,
including but not limited to spectral co-clustering, non-negative matrix

factorization (NMF), bipartite spectral graph co-clustering, and other
state-of-the-art methods.
 Datasets: Gather publicly available gene expression datasets representing
diverse biological contexts, such as different tissues, diseases, and
experimental conditions.
 Evaluation Metrics: Define appropriate evaluation metrics to
quantitatively assess the performance of the selected algorithms.
 Statistical Analysis: Employ statistical methods to compare the
performance of different algorithms and draw meaningful conclusions.

Expected Outcomes:
 Identification of co-clustering algorithms that excel in specific biological
contexts.
 Insights into the impact of algorithm parameters on performance.
 Guidelines for selecting the most suitable co-clustering algorithm based
on the characteristics of gene expression data.

 Recommendations for future developments and improvements in co-
clustering algorithms for gene expression analysis.

Significance:

 This research is significant for advancing our understanding of co-
clustering algorithms’ effectiveness in uncovering hidden patterns within

gene expression data. The outcomes will contribute to the refinement and
development of future algorithms in the field of bioinformatics.
Conclusion:
By undertaking this comparative analysis, the project aims to enhance our
understanding of co-clustering algorithms’ performance on gene expression
data, paving the way for more informed and effective application of these
methods in biological research.

• Thematic field of study:  Enhancing tumor gene expression data Through Feature Selection in the Presence of Imbalanced Data
• Supervisor(s): Professor Hasna CHAMLAL
• Contact details: abdallahbadou@yahoo.com
  

Summary of Hosting Lab

Abstract: High-throughput technologies have revolutionized the field of genomics, providing vast amounts of gene expression data. However, the imbalance in sample sizes and the intricate nature of gene expression patterns pose challenges for accurate classification. This research project focuses on the development and evaluation of feature selection techniques to address imbalanced datasets in the context of gene expression analysis.

Objective: The primary goal of this research is to improve the interpretability and performance of gene expression models by identifying and prioritizing relevant features, particularly in scenarios with imbalanced class distribution.

Methodology: The project utilizes a combination of feature selection methods, including Recursive Feature Elimination (RFE), ReliefF, and hybrid approaches. The aim is to identify a subset of genes that contribute significantly to classification accuracy, considering the imbalance in expression levels across different biological conditions.

Results: Preliminary findings indicate that the proposed feature selection techniques enhance the performance of gene expression models, improving sensitivity and specificity in detecting relevant biological signals. The approach provides insights into potential biomarkers and pathways associated with specific conditions in the presence of imbalanced gene expression data.
Innovation: This project contributes to the genomics field by addressing the challenge of imbalanced gene expression data through advanced feature selection techniques. The hybrid approaches incorporate the strengths of multiple methods, ensuring a more comprehensive identification of informative genes relevant to specific biological conditions.

Conclusion: The application of sophisticated feature selection techniques in gene expression analysis demonstrates promise in unraveling complex biological patterns. Future work involves validation across diverse datasets, exploration of additional feature selection algorithms, and integration into existing genomic analysis pipelines.

Keywords: Gene Expression, Feature Selection, Imbalanced Data, Tumor gene
expression, discrimination criteria.

• Thematic field of study: Advancements in Deep Learning for Precision Segmentation of Medical Images: Methods and Applications
• Supervisor(s):Prof Habib Benlahmar
• Contact details: abdallahbadou@yahoo.com
  

Summary of Hosting Lab

The Immuno-Genetics and Human Pathology Laboratory (LIGEP) is focused on conducting research that seeks to understand human diseases especially cancer, with a particular focus on immunological aspects. LIGEP comprises a team of 31 devoted professors spanning various
disciplines—clinical, fundamental, and engineering sciences—united in their mission to promote innovation and scientific discoveries, ultimately shaping the future of healthcare in the cancer field.

Project Description

This research project focuses on leveraging deep learning techniques to enhance the precision of medical image segmentation. The primary goal is to develop and refine models that accurately delineate and classify various anatomical structures and pathologies in medical imaging, such as MRIs, CT scans, and histopathology images

Prerequisites

Major Lab techniques

RT-qpcr , immunohistochemestry, flow cytometry , cell culture, western blot, ELISA, PBMC
isolation, TILs isolation