New therapeutic targets for cancer: the interplay between immune and metabolic checkpoints and gut microbiota
Bibliographic record
Abstract
Transformation and growth of tumor cells are associated with profound alterations in neighbouring cells and their environment, together forming the tumor microenvironment (TME). The TME provides a conducive but complex milieu for the tumors to thrive while incapacitating the immune cells that home there as part of our natural immunosurveillance mechanism. The orchestration of this successful survival strategy by tumor cells is associated with exploitation of numerous metabolic and immune checkpoints, as well as metabolic reprogramming in the tumor cells. Together these form an intricate network of feedback mechanisms that favor the growing tumor. In addition, an ecosystem of microbiota, proximal or distal to tumors, influences the successful survival or elimination of tumor cells mediated by immune cells. Discovery and clinical application of immune checkpoint inhibitors (ICIs) i.e., monoclonal antibodies (mAbs) blocking specific immune checkpoints CTLA-4 and PD-1/PD-L1, have revolutionized therapy of various cancers. However, they are still associated with limited response rates, severe immune-related adverse events, development of resistance, and more serious exacerbation of cancer progression termed hyper-progressive disease. Checkpoint inhibitors only represent a milestone and not the finish-line in the quest for treating and curing cancer. Efforts are underway to investigate and develop inhibitors of other immune as well as metabolic checkpoint molecules. Future therapy for various cancers is projected to target immune and metabolic checkpoints and the microbiota together.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".