Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Conspectus This Account is the first comprehensive review article on the newly developed, photochemistry-based cancer therapy near-infrared (NIR) photoimmunotherapy (PIT). NIR-PIT is a molecularly targeted phototherapy for cancer that is based on injecting a conjugate of a near-infrared, water-soluble, silicon-phthalocyanine derivative, IRdye700DX (IR700), and a monoclonal antibody (mAb) that targets an expressed antigen on the cancer cell surface. Subsequent local exposure to NIR light turns on this photochemical “death” switch, resulting in the rapid and highly selective immunogenic cell death (ICD) of targeted cancer cells. ICD occurs as early as 1 min after exposure to NIR light and results in irreversible morphologic changes only in target-expressing cells based on the newly discovered photoinduced ligand release reaction that induces physical changes on conjugated antibody/antigen complex resulting in functional damage on cell membrane. Meanwhile, immediately adjacent receptor-negative cells are totally unharmed. Because of its highly targeted nature, NIR-PIT carries few side effects and healing is rapid. Evaluation of the tumor microenvironment reveals that ICD induced by NIR-PIT results in rapid maturation of immature dendritic cells adjacent to dying cancer cells initiating a host anticancer immune response, resulting in repriming of polyclonal CD8 + T cells against various released cancer antigens, which amplifies the therapeutic effect of NIR-PIT. NIR-PIT can target and treat virtually any cell surface antigens including cancer stem cell markers, that is, CD44 and CD133. A first-in-human phase 1/2 clinical trial of NIR-PIT using cetuximab-IR700 (RM1929) targeting EGFR in inoperable recurrent head and neck cancer patients successfully concluded in 2017 and led to “fast tracking” by the FDA and a phase 3 trial ( https://clinicaltrials.gov/ct2/show/NCT03769506 ) that is currently underway in 3 countries in Asia, US/Canada, and 4 countries in EU. The next step for NIR-PIT is to further exploit the immune response. Preclinical research in animals with intact immune systems has shown that NIT-PIT targeting of immunosuppressor cells within the tumor, such as regulatory T-cells, can further enhance tumor-cell-selective systemic host-immunity leading to significant responses in distant metastatic tumors, which are not treated with light. By combining cancer-targeting NIR-PIT and immune-activating NIR-PIT or other cancer immunotherapies, NIR-PIT of a local tumor, could lead to responses in distant metastases and may also inhibit recurrences due to activation of systemic anticancer immunity and long-term immune memory without the systemic autoimmune adverse effects often associated with immune checkpoint inhibitors. Furthermore, NIR-PIT also enhances nanodrug delivery into tumors up to 24-fold superior to untreated tumors with conventional EPR effects by intensively damaging cancer cells behind tumor vessels. We conclude by describing future advances in this novel photochemical cancer therapy that are likely to further enhance the efficacy of NIR-PIT.
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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 | 0.001 |
| 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) | 0.006 | 0.003 |
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".