Abstract 4168: Pre-clinical demonstration of Raman spectroscopy-assisted photothermal therapy for prostate cancer
Bibliographic record
Abstract
Abstract Prostate cancer (PCa) ranks among the most prevalent malignancies in men. The combination of ultrasound and fine-needle aspiration biopsy has greatly facilitated early diagnosis and expanded the range of treatment options. Traditional treatments primarily involve anti-androgens and androgen deprivation therapy; however, a recurrence occurs in approximately 30% of cases following these interventions. Our current focus is on the exploration of photothermal therapies using nanoparticles for precise tumor ablation. Among these nanoparticles, multiwalled carbon nanotubes (MWCNTs) exhibit unique structural, optical, and thermal properties that make them exceptionally appealing for biomedical applications, especially as a foundation for photothermal therapy. Our platform employs the copper (I)-catalyzed azide alkyne cycloaddition of prostate-specific membrane antigen (PSMA)-targeting peptidomimetic (Glu-urea-Lys) ligand to the MWCNTs. We used confocal microscopy to observe a significantly enhanced binding of the targeted Glu-urea-Lys-MWCNTs to the surfaces of PSMA-expressing LNCaP cells compared to IgG-MWCNTs and to PSMA-null PC3 cells. Raman microscopy further confirmed the increased presence of Glu-urea-Lys-MWCNTs on LNCaP spheroids compared to non-targeting MWCNTs. We also confirmed enhanced photothermal cell ablation in vitro using the targeted MWCNTs. Based on in vitro evidence, we established LNCaP and PC3 xenografts in nu/nu mice and evaluated the therapeutic efficacy. Using fluorescence-based imaging, we observed the enhanced localization of Glu-urea-Lys-MWCNTs within the tumors. Furthermore, we collected the tumors and conducted ex vivo analysis using Raman microscopy for a label-free detection of the targeted MWCNTs, which complemented the fluorescence imaging and laying the foundation for real-time Raman imaging in vivo. Subsequently, we applied photothermal ablation of the tumor xenografts based on a pre-optimized treatment protocol established from COMSOL simulations and prior experiments. This treatment led to complete tumor ablation with minimal recurrence and minimal non-specific damage. In conclusion, we have assessed the effectiveness of our targeted nano-photothermal platform in a pre-clinical model of PCa. Further studies will aid in the transition of photothermal therapy to clinical trials. Moreover, we aim to investigate the role of photothermal therapy in the PCa tumor microenvironment and whether such an approach can trigger post-therapeutic activation of immune responses against the tumor. Ultimately, we anticipate that our nanotherapeutic platform will contribute to a focused therapeutic approach where targeted MWCNT-laser treatment exploits overexpressed tumor markers in conjunction with various irradiation strategies to enhance ablation while minimizing damage to surrounding tissue. Citation Format: Seung Soo Lee, Gabriel Beaudoin, Jun Hui Yeoh, Frederic Leblond, Mark Trifiro, Miltiadis Paliouras. Pre-clinical demonstration of Raman spectroscopy-assisted photothermal therapy for prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4168.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".