Dr. Manem’s research is centered on the application of translational bioinformatics and machine learning approaches to characterize the molecular mechanisms of cancer, with the aim to develop data-driven personalized diagnoses and treatments. With an emphasis on translational research, we closely collaborate with clinicians to develop novel biomarkers for improving cancer care. In particular, Dr. Manem and his team are analyzing high-throughput multimodal datasets to develop novel biomarkers for cancer diagnosis, prognosis, and therapy response. Our group is also studying the cross-scale associations of cancer biomarkers across multiple data modalities, i.e., from gene expression to immune phenotypes to radiological imaging and digital pathological slides.

Research synopsis :

  1. Immune checkpoint inhibitors (ICIs) have modified the therapeutic landscape of many solid cancers. Although clinical trials have indicated an increased likelihood of success to ICIs, the response rate varies widely. For example, only 20-30% of non-small cell lung cancer (NSCLC) patients respond to these regimens. The lack of biomarkers to accurately identify patients who will benefit from these expensive ICIs is a major drawback. Our lab has been developing approaches to predict a patient’s response to these expensive ICIs using radiological, pathological and clinical profiles.
  2. Most cancer-related deaths occur in patients who were initially diagnosed as early-stage and then later experienced a recurrence, which gets undetected until it has metastasized. For instance, early-stage NSCLC (stages I and II) patients develop cancer recurrence within the first 5 years of surgical resection. Reliable prediction tools of cancer recurrence can enable timely therapeutic interventions, thereby, improving the patient’s survival. To date, there is a paucity of models and clinical assays to identify patients with high risk for recurrence post-surgery. Our team has been developing data-driven approaches to identify which patients are likely to experience a recurrence by leveraging radiological images, pathological slides and clinical data.
  3. The paradigm that has guided radiation oncology is largely based on the widely adopted ‘one-size-fits-all’ philosophy that does not allow for patient-specific dose individualization. Radiotherapy is often used as a curative treatment strategy for early-stage curable cancers, and currently, there are no clinically implemented biomarkers predictive of radiotherapy response. There are several molecular diagnostic tools that are making inroads into clinical settings for other therapeutic interventions, such as immunotherapy, however, there has been a lack of analogous diagnostic indicators in the field of radiation medicine. Our lab has been building data-driven biomarkers predictive of radiation response using OMICS data.


L'Hôtel-Dieu de Québec
6 rue McMahon
Québec, QC
Canada G1R 3S3
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Active projects

  • Deciphering the role of the immune microenvironment in the response of lung cancer to immunotherapy, from 2021-03-31 to 2024-03-30
  • Fonds de démarrage - Nouveau chercheur, from 2023-09-05 to 2025-03-31

Recently finished projects

  • Guiding community-based lung cancer diagnostic follow-ups through Artificial Intelligence Tools, from 2022-03-01 to 2023-02-28
  • Linking clinical, research and imaging data in lung cancer: towards FAIR and AI-ready datasets, from 2022-03-15 to 2023-03-14
Data provided by the Université Laval research projects registery