Cancer Genomic Alterations and Microenvironmental Features Encode Synergistic Interactions with Disease Outcomes
Bibliographic record
Abstract
Oncogenesis, tumor progression, and therapy response are shaped by somatic alterations in the cancer genome and features of the tumor-immune microenvironment (TME). How interactions between these two systems influence tumor evolution and clinical outcomes remains incompletely understood. To address this challenge, we developed the multi-omics analysis framework PACIFIC that systematically integrates genetic cancer drivers and infiltration profiles of immune cells to find pairwise combinations of drivers and TME characteristics that jointly associate with clinical outcomes. By analyzing 8,500 primary tumor samples of 26 cancer types, we report 34 immunogenomic interactions (IGX) in 13 cancer types in which context-specific combinations of genomic alterations and immune cell levels were significantly correlated with patient survival. Subsets of tumor samples defined by some IGXs were characterized by tumor-intrinsic and microenvironmental metrics of immunogenicity and differential expression of immunotherapy target genes. In luminal-A breast cancer, an IGX involving MEN1 deletion combined with reduced levels of neutrophils associated with lower progression-free survival and deregulation of immune signaling pathways, as observed in two independent cancer genomics datasets. These results showcase the ability of PACIFIC to integrate complex multi-omics datasets with clinical information, enabling the identification of clinically relevant IGXs. Such interactions provide a rich set of hypotheses for mechanistic studies and the development of biomarkers and therapeutic targets. IMPLICATIONS: Co-occurrence patterns of cancer drivers and TME characteristics highlight synergistic interactions with prognostic potential.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".