Analysis Of Lung Cancer Neoantigens And Infiltrated Lymphocytes. H/F INRIA

Analysis of lung cancer neoantigens and infiltrated lymphocytes.
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : Stage

Niveau de diplôme exigé : Bac +5 ou équivalent

Fonction : Stagiaire de la recherche

Niveau d'expérience souhaité : Jeune diplômé

A propos du centre ou de la direction fonctionnelle

Inria is the French National Institute for Research in Digital Science, of which the Inria Côte d'Azur University Center is a part. With strong expertise in computer science and applied mathematics, the research projects of the Inria Côte d'Azur University Center cover all aspects of digital science and technology and generate innovation. Based mainly in Sophia Antipolis, but also in Nice and Montpellier, it brings together 47 research teams and nine support services. It is active in the fields of artificial intelligence, data science, IT system security, robotics, network engineering, natural risk prevention, ecological transition, digital biology, computational neuroscience, health data, and more. The Inria Center at Université Côte d'Azur is a major player in terms of scientific excellence, thanks to the results it has achieved and its collaborations at both European and international level.

Contexte et atouts du poste

General Information
Duration: 5-6 month
Location: Marseille (on site)
Internship start: January to April
Internship stipend : ~670€ / month

COMPO team

The internship will take place within the COMPO team on the Timone Campus in Marseille. The ambition of the COMPO Inria-Inserm joint project-team is to develop novel mathematical models integrating data available in clinical oncology (from clinical trials and routine care), in order to provide decision-making tools to oncologists. To achieve this, the team uniquely
gathers mathematicians, pharmacologists and medical oncologists. It is integrated in the Center of Cancer Research of Marseille (Inserm U1068, CNRS UMR7258, Aix-Marseille Université UM105, Institut Paoli-Calmettes) and located in the La Timone Health Science campus of the University Hospitals of Marseille (AP-HM), close to the INCa-labeled center for early phase clinical trials (CLIP2). This joint project-team, built on strong expertise in mathematical modeling, pharmacometrics and experimental and clinical oncology, is committed to develop novel methodologies combining mechanistic and statistical modeling to be ultimately applied at bedside.

Mission confiée

Context and Objectives of the Internship
Motivation : The adaptive immune system, known for its role in differentiating self from non-
self, faces a unique challenge when confronted with cancer neoantigens. These neoantigens,
produced by the accumulation of non-synonymous somatic mutations in cancer cells, blur the
boundary between self and non-self. Detection requires neoantigen expression and
presentation on a tumor cell's major histocompatibility complex (MHC) I , the surface platform
that delivers peptide antigens to T-cells. Subsequent recognition of the neoantigen peptide-MHC
(pMHC) complex by a subset of specific T cell receptors (TCRs) leads to T cell activation and plays
a pivotal role in cytotoxic T cells' triggering of cancer cell death. Therefore, identifying
neoantigens capable of eliciting T cell recognition is crucial for predicting Immune Checkpoint
Inhibitors (ICIs) responses and enhancing them through targeted therapies like cancer vaccines
or adoptive T cell therapies.
Internship project : The PIONeeR (Precision Immuno-Oncology for advanced Non-Small CEll
Lung CancER) is a prospective multicenter trial that enrolled 443 advanced NSCLC patients
eligible for anti PD-(L)1 treatment strategies. The host lab is responsible for data analysis of this
trial, in which each patient underwent longitudinal collection of tissue and blood samples,
studied by extensive phenotyping and sequencing. The aim of this internship is the joint analysis
of neoantigens, from both solid tumor and circulating tumor nucleic acids, and tumor infiltrated
T lymphocytes, to derive aggregated statistics informative of therapeutic response to ICIs. This
internship will be in interaction with PhD students from the team carrying extensive work on the
statistical analysis of clinical and blood phenotyping data, and is part of a larger aim to decipher
immune-tumor interactions using statistical modeling approaches.

Keywords
Lung cancer; Adaptive immune system; T-cells; Neoantigens; Mutational Signatures;
Generative Models; Bioinformatics; Statistical data analysis

References

Addala, Venkateswar, Felicity Newell, John V. Pearson, et al. Computational Immunogenomic
Approaches to Predict Response to Cancer Immunotherapies. Nature Reviews Clinical Oncology 21,
no. 1 (2024): 28-46. https://doi.org/10.1038/s41571-023-00830-6.
Marcou, Quentin, Thierry Mora, and Aleksandra M. Walczak. High-Throughput Immune Repertoire
Analysis with IGoR. Nature Communications 9, no. 1(2018): https://doi.org/10.1038/s41467-018-02832-w.
Thorsson, Vésteinn, David L. Gibbs, Scott D. Brown, et al. The Immune Landscape of Cancer.
Immunity 48, no. 4 (2018): 812-830.e14. https://doi.org/10.1016/j.immuni.2018.03.023.
Mayer, Andreas, Christopher J. Russo, Quentin Marcou, William Bialek, and Benjamin D.
Greenbaum. How Different Are Self and Nonself? arXiv:2212.12049. Preprint, arXiv, December 22,
2022. https://doi.org/10.48550/arXiv.2212.12049.

Principales activités

Main activities (5 maximum) :

- Literature review & hypothesis framing
- Data analysis &Visualization
- Software development
- Communication of the results in team meetings
- Write reports

Compétences

Desired Skills
We are looking for a motivated candidate with skills in probability, statistics, and scientific
computing coming from a bio-informatics, applied mathematics or physics background.
Knowledge of biology is not required but valued, and an interest in applications to immunology
and cancer is expected.
Programming skills in Python (preferred) or R language is necessary. Knowledge of a lower
level programming language (e.g. C/C++), scientific computing (python ecosystem or Julia) or
machine learning libraries (scikit-learn, torch, ...) will be appreciated.

Avantages

- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage

Rémunération

Traineeship grant depending on attendance hours