Adaptive Immunity in Genitourinary Cancers
Bibliographic record
Abstract
CONTEXT: While urothelial and renal cell cancers have exhibited modest responses to novel immune checkpoint inhibitors targeting the programmed death ligand 1 and its receptor, response rates in patients with prostate cancer have remained poor. The factors underlying suboptimal outcomes observed in patients treated with novel immunotherapies are still to be resolved. OBJECTIVE: To review the literature and describe the key adaptive immune physiological events associated with cancer progression and therapeutic response in genitourinary (GU) cancers. EVIDENCE ACQUISITION: We performed a nonsystematic, collaborative narrative review to highlight recent advancements leading to the current state of knowledge on the critical mediators of antitumor adaptive immunity to GU cancers. Further, we discuss the findings on the pre- and post-treatment immunological events that either are unique to each of the three cancer types or exhibit overlapping clinical associations. EVIDENCE SYNTHESIS: Aging-associated immune function decline is a major factor underlying poor outcomes observed in patients treated with both conventional and novel immunotherapies. Other cancer immunobiological aspects associated with suboptimal responses in GU cancers include the overall tumor mutational burden, mutations in specific tumor suppressor/DNA damage repair genes (KDM6A, PTEN, STAG2, TP53, ATM, and BRCA2), and abundance of multiple functional states of adaptive immune cells and their spatiotemporal localization within the tumor immune microenvironment. Understanding these mechanisms may potentially lead to the development of prognostic and predictive biomarkers such as immune cell infiltration profiles and tertiary lymphoid structures (TLSs) that associate with variable clinical outcomes depending on the nature of the novel immunotherapeutic approach. Implementation of newer immune-monitoring technologies and improved preclinical modeling systems will augment our understanding of the host and tumor intrinsic factors contributing to the variability of responses to immunotherapies. CONCLUSIONS: Despite the tremendous progress made in the understanding of dynamic and static adaptive immune elements within the tumor immune landscape, several knowledge gaps remain. A comprehensive knowledge thus gained will lead to precision immunotherapy, improved drug sequencing, and a therapeutic response. PATIENT SUMMARY: We performed a collaborative review by a diverse group of experts in the field to examine our understanding of the events and crosstalk between cancer cells and the patient's immune system that are associated with responses to novel immunotherapies. An evolving understanding of tumor-intrinsic and host-related immune alterations, both before and after therapy, will aid in the discovery of promising markers of responses to immunotherapy as well as the development of unique therapeutic approaches for the management of genitourinary cancers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".