Impact of Chemical-Induced Mutational Load Increase on Immune Checkpoint Therapy in Poorly Responsive Murine Tumors
Bibliographic record
Abstract
Abstract A recurring historic finding in cancer drug development is encouraging antitumor effects observed in tumor-bearing mice that fail to translate into the clinic. An intriguing exception to this pattern is immune checkpoint therapy, as the sustained tumor regressions observed in subsets of cancer patients are rare in mice. Reasoning that this may be due in part to relatively low mutational loads of mouse tumors, we mutagenized transplantable mouse tumor cell lines EMT-6/P, B16F1, RENCA, CT26, and MC38 in vitro with methylnitro-nitrosoguanidine (MNNG) or ethylmethane sulfonate (EMS) and tested their responsiveness to PD-L1 blockade. Exome sequencing confirmed an increase in somatic mutations by mutagen treatment, an effect mimicked in EMT-6 variants chronically exposed in vivo to cisplatin or cyclophosphamide. Certain mutagenized variants of B16F1, EMT-6/P, CT26, and MC38 (but not RENCA) were more immunogenic than their parents, yet anti-PD-L1 sensitization developed only in some EMT-6/P and B16F1 variants. Treatment response patterns corresponded with changes in immune cell infiltration and especially increases in CD8+ T cells. Chronically cisplatin-exposed EMT-6 variants were also more responsive to anti-PD-L1 therapy. Although tumor PD-L1 expression was upregulated in in vivo chemotherapy-exposed variants, PD-L1 expression levels were not consistently associated with anti-PD-L1 treatment activity across mutagenized or chemotherapy-exposed variants. In summary, mutagenized and more immunogenic mouse tumors were not universally sensitized to PD-L1 blockade. Chemically mutagenized variants may be useful to evaluate the impact of immunologically “hot” or “cold” tumors with a high mutational load, to which certain chemotherapy agents may contribute, on immunotherapy outcomes. Mol Cancer Ther; 17(4); 869–82. ©2018 AACR.
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 | 0.001 |
| 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".