IDO Downregulation Induces Sensitivity to Pemetrexed, Gemcitabine, FK866, and Methoxyamine in Human Cancer Cells
Bibliographic record
Abstract
Indoleamine 2,3-dioxygenase-1 (IDO) is an immune regulatory enzyme expressed by most human tumors. IDO levels in tumor cells correlate with increased metastasis and poor patient outcome and IDO is linked to tumor cell resistance to immunotherapy, radiation therapy, and chemotherapy. Knowledge of tumor cell-autonomous effects of IDO, independent of its well-known role in regulating and suppressing anti-tumor immune responses, is limited. Clonal populations of A549 human lung adenocarcinoma cells stably transfected with anti-IDO shRNA or scrambled control shRNA were used to study IDO effects on drug sensitivity and resistance. IFNγ was used to induce IDO in those cells. We show, for the first time, that IDO mediates human tumor cell resistance to the candidate anticancer drugs FK866 (an NAD+ inhibitor), methoxyamine (MX, a base excision repair [BER] inhibitor) and approved anticancer drugs pemetrexed (a folate anti-metabolite) and gemcitabine (a nucleoside analogue), and combined treatment with pemetrexed and MX, in the absence of immune cells. Concurrent knockdown of IDO and thymidylate synthase (TS, a key rate-limiting enzyme in DNA synthesis and repair) sensitizes human lung cancer cells to pemetrexed and 5FUdR to a greater degree than knockdown of either target alone. We conclude that BER in IDO-expressing A549 cells plays a major role in mediating resistance to a range of approved and candidate anticancer drugs. IDO inhibitors are undergoing clinical trials primarily to improve antitumor immune responses. We show that targeting IDO alone or in combination with TS is a potentially valuable therapeutic strategy for cancer treatment, independent of immune activity and in combination with conventional chemotherapy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.000 |
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 teacher head, 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".