Pierre Martinez

Pierre Martinez

Bioinformatics, Cancer Evolution

Research

With the aim of quantifying, predicting and controlling cancer evolution, here are the ongoing projects in the lab.

Quantifying Phenotypic Diversity and the impact of plasticity using Cellular Activities

Cancer cells stray from normal hierarchies and the expression markers to identify them during tissue homeostasis. They however display strong convergence on the phenotypic traits they must acquire to survive and invade distant organs (the Cancer “Hallmarks”). We thus aim to phenotypically describe cancer cells according to what they do, rather than what they are, using single-cell technologies. We are validating specific signatures adequate to single-cell data, via exogenous inductions of selected key cell functions in vitro. This will help us quantify phenotypic diversity and follow its dynamics in different cancer settings.

Relevant Publications:

  1. Refined cellular activity expression signatures provide a targeted framework to quantify phenotypic intra-tumor heterogeneity in single-cell data. bioRxiv, 2025.
  2. EMT-driven plasticity prospectively increases cell–cell variability to promote therapeutic adaptation in breast cancer. Cancer Cell International, 2025.
  3. Assessing Cell Activities rather than Identities to Interpret Intra-Tumor Phenotypic Diversity and Its Dynamics. iScience, 2020.

Evolutionary Dynamics of Metaplastic Breast Cancer

Metaplastic Breast Carcinomas (MpBC) are a rare breast cancer subtype, characterised by the presence of one or more transdifferenciated, phenotypically different tumour compartment(s) detected by histopatohological analyses. Their origin and evolutionary dynamics, along with the biological underpinnings of their development, are largely unknown. As a results, these patients are often treated with generic therapeutic options, to which they frequently resist. The team’s projects focus on using diverse multi-omics approaches (spatial and single cell transcriptomics, post-microdissection genetics and epigenetics) to better understand how these tumours emerge and develop. Using this knowledge, we aim identify molecular markers specific to different subtypes of transdifferenciation (spindle cell, osteoid, chondroid, squamous) and identify biological pathways that could be targeted by more specific therapeutic approaches. These projects involve a national consortium, and multiple interdisciplinary collaborations across France.

Relevant publications:

  1. Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes. Laboratory Investigation, 2023.
  2. Comprehensive characterization of claudin-low breast tumors reflects the impact of the cell-of-origin on cancer evolution. Nature Communications, 2020.

Dynamics of Progression in Oral Squamous Cell Cancers (OSCC)

We aim understand the genetic makeup of normal oral squamous mucosa as tissue ages, in order to model its genetic composition over time for both healthy and malignant evolutionary outcomes. This integrates a larger effort led by the Saintigny Team and European colleagues (EuroHNPACT) to understand the dynamics of progression, from normal to pre-malignancy to cancer in OSCC, and identify novel treatment and surveillance strategies. In particular, I am interested in providing a deeper understanding of oral potentially malignant disease (OPMD) development in the Fanconi Anaemia population, to improve devise better, minimally invasive, and early OSCC interception strategies in this vulnerable population. These project involve both and European collaborations on both sporadic and FA-related cases.

Relevant publications:

  1. Strategies for early detection and detailed characterization of oral lesions and head and neck squamous cell carcinoma in Fanconi anemia patients. Cancer Letters, 2025.
  2. Unmet Needs and Perspectives in Oral Cancer Prevention. Cancers, 2022.
  3. Identification of a Gene-Expression-Based Surrogate of Genomic Instability during Oral Carcinogenesis. Cancers, 2022.
  4. Separating the Local and Malignant Dimensions of Cancer Adaptation. Cancer Informatics, 2019.
  5. Quantifying local malignant adaptation in tissue‐specific evolutionary trajectories by harnessing cancer’s repeatability at the genetic level. Evolutionary Applications, 2019.