Bibliographic record
Abstract
Our group is studying changes in the proteome in the context of cancer and immunology. We are developing and applying differential ion mobility to enhance proteome coverage and the sensitivity quantitative proteomic measurements. We are also developing new protein chemistry approaches and mass spectrometry tools to profile protein modifications (e.g. phosphorylation, ubiquitylation, sumoylation, etc …) and further understand how these modifications affect cell signaling and protein functions. An important component of our research program is dedicated to the use of new mass spectrometry and bioinformatics approaches to identify peptides presented by the major histocompatibility complex class I (e.g. immunopeptidome) of normal and cancer cells and discover antigens that can be used in cancer immunotherapy. My very first exposure to mass spectrometry was when I worked as an occupational hygienist after completing my BSc in chemistry. We visited companies to evaluate health hazards often bringing back sorbent tubes of air samples to determine if they contained high concentrations of harmful chemicals. While I was not directly involved in the analysis of these chemicals, I was always fascinated by the sensitivity and wealth of information obtained from GC/MS, and this prompted me to purse a research career in bioanalytical mass spectrometry. My very first mass spectrometry project was to distinguish isomeric polyaromatic hydrocarbons such as benzo [a]/[e] pyrene by Mass-analyzed Ion Kinetic Energy Spectrometry (MIKES) on a Kratos MS-50 EBE instrument. After painstaking experiments, I found out that under typical electron impact ionization conditions these isomers gave superimposable MIKES spectra, and that their differentiation would be an uphill battle. It was at that time that my PhD director Michel Bertrand suggested that I move on to a totally different topic and use fast atom bombardment (FAB) to perform de novo peptide sequencing. FAB was just recently introduced and was a promising technique for peptide analysis. I went on to complete my PhD on a totally different topic then what I started on. One of the most gratifying projects that I worked on was the development of a mass spectrometry platform to profile the immunopeptidome of normal and cancer cells. This was a significant analytical challenge as we needed to isolate low-abundance MHC I peptides and determine by LC/MS/MS how their abundances change across patient samples or environmental conditions. This adventure led us to uncover that the immunopeptidome is not only dynamic but can also display unusual antigens that harbor polymorphic variants and peptides derived from non-canonical regions of the genome. By profiling the repertoire of MHC I peptides of lymphocytes from different patients, we identified hundreds of antigens that share optimal features for immunotherapy, and can be used in immunotherapy for the treatment of hematological cancers (HCs). Several of these antigens are currently being evaluated as part of a clinical trial in patients with HCs presenting a molecular or clinical relapse after hematopoietic stem cell transplantation. The possibility of contributing to a discovery that has broader medical application is most gratifying. Bioanalytical mass spectrometry research has never been more intellectually exciting. Pursuing a career in this field is demanding, but it is never boring and always filled with surprises. Boundless curiosity and resilience are always good ingredients to succeed in this field. After completing my PhD, I had the pleasure of conducting my postdoctoral studies with Bob Boyd at the Institute for Marine Biosciences in Halifax, Nova Scotia. Bob was a great mentor who instilled humor and passion into research. His clever mind and insights were an unlimited source of inspiration, and for a young apprentice like me, Bob had a great influence on my career development. Scientific research offers a constant challenge, and an intense thrill of learning something new about nature. Being passionate about what you do, and never be afraid to delve into unchartered waters are key to discovery, but most importantly science must be fun to be enjoyable! Mass spectrometry continues to make significant contributions to health research, and fundamental research has been key in developing new technologies that have opened up entire fields of research. Large-scale proteomic analyses have shed light on the vast complexity of cell extracts, and our limited abilities to capture the full protein repertoire including modification thereof. In the next decade, we are likely to see more development in ionization methods and ion sampling that will expand the sensitivity and comprehensives of mass spectrometry-based proteomics. These will in turn lead to exciting technological developments that will translate into applications in the medical field and new therapies for human health. The pace of discovery in mass spectrometry is exhilarating. We are far from reaching the senescence of this technology. We are regularly seeing new applications of mass spectrometry into medical research. For example, we have recently witnessed the expansion of ICP-MS into the field of mass cytometry, a technology that has significantly expanded the multiplex capability of flow cytometry and revolutionized the field of stem cell research. We are likely to see the adoption of different mass spectrometry tools that will contribute to the development of new personalized therapies in health research. Keeping a life balance is important to maintain sanity! Starting the day with a morning jog or playing squash with colleagues once a week has been quite therapeutic! Having a one-year paid leave is a great opportunity to explore new and complementary areas of research. For me, the possibility of leveraging large-scale data analysis in the field of immunology would be a unique opportunity to expand the perspective and impact of mass spectrometry in health sciences. Terrance Stanley (Terry) Fox is probably the most striking Canadian figure of courage, stamina and determination. Terry Fox was an inspiration to many for his courageous fight against cancer. Diagnosed with osteosarcoma at age 19, he took on an extensive period of chemotherapy that made him realize the benefits of medical advances, and decided to spend much of his life in a way that help others find courage. His fight against cancer brought him to run across Canada to raise awareness and money for cancer research. He was the youngest Canadian to be named Companion of the Order of Canada; his fight against cancer continues to live today through the annual Terry Fox Run for cancer research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.056 | 0.060 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".