P200 | EXTRACELLULAR VESICLES MOLECULAR FINGERPRINTING WITH FOURIER TRANSFORM INFRARED SPECTROSCOPY: NOVEL APPROACH FOR MONITORING CHRONIC MYELOID LEUKEMIA TREATMENTS
Bibliographic record
Abstract
Tyrosine kinase inhibitors (TKIs) transformed Chronic Myeloid Leukemia (CML) treatment, enabling deep molecular responses and treatment-free remission (TFR). Yet, challenges such as relapse and adverse effects require improved monitoring tools. Extracellular vesicles (EV) are natural nanoparticles released by cells under physiological and pathological conditions, emerging as biomarkers in CML due to their ability to shuttle bioactive molecules that reflect the disease status. However, their heterogeneity and small size complicate standardization and clinical translation. Vibrational spectroscopies, like Fourier-transform infrared (FT-IR), offer label-free, high-resolution EV analysis. In this study, we used FT-IR to assess EV of CML patients on different treatment (Fig. 1), aiming to explore therapy-related molecular differences. EV were extracted from plasma samples of 51 CML patients who had attained at least a major molecular response under TKI treatment, using Norgen 58300 kit. For FT-IR analysis, 3µL of each sample was deposited onto a diamond window and dried under nitrogen. All spectra were recorded in triplicate. To distinguish the treatment groups, we applied Principal Component Analysis to reduce data dimensionality and Linear Discriminant Analysis to maximize class separation and enhance group discrimination based on treatment. Preliminary data show promising classification performance among the different TKIs, with sensitivity reaching 78.13%, specificity 94.54%, and an overall accuracy of 65.1% (Fig. 1). The result indicates a correlation between TKI treatment and the biochemical composition of EV that could be attributed to the drug being transported by the EV or to a distinct fingerprint present in EV released by CML and non-CML cells, revealing their off-target effects. Our findings support the potential of EV as non-invasive biomarkers in CML and highlight the effectiveness of FT-IR spectroscopy for their characterization, paving the way for new disease monitoring strategies. Moreover, since we analyzed patients undergoing TKI therapy and in molecular response, most EV originates from non-leukemic cells. Therefore, we are capturing a specific signal of the drug's effects on CML cells, but likely not exclusively, as it could also be related to side effects. This highlights the importance of monitoring patients to determine whether this fingerprint is specific to side effects and whether it could serve as a predictor for toxicity onset.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".