Risk Factors Predicting Outcomes in Advanced Upper Gastrointestinal Cancers Treated With Immune Checkpoint Inhibitors
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors (ICIs) have moved to the frontline in recent years to manage upper gastrointestinal (UGI) tumors, such as esophageal and gastric cancers. This retrospective review sheds light on real-world data on ICI-treated UGI tumors to identify risk factors (clinical and pathological) impacting the outcome other than traditional biomarkers (programmed cell death ligand 1 (PD-L1) or microsatellite instability status). Methods: Patients with UGI tumors who received at least one dose of ICI for stage IV or recurrent disease between January 1, 2015, and July 31, 2021, at The Ohio State University were included in the study. The patients' baseline characteristics, labs, and blood counts (even at disease progression) were extracted with survival outcomes (progression-free survival (PFS) and overall survival (OS)). Descriptive statistics, log-rank test and Cox proportional hazard model for survival outcomes, Fisher exact test for categorical variables, were conducted using JMP Pro 16 (SAS Institute Inc., Cary, NC). Results: We had 64 patients (84% males) included in the study, with the racial distribution as follows: 88% Caucasian, 5% African American, 1% Asian, and 6% from other racial groups. Men and the use of ICI in third lines or more had a positive impact on PFS and OS. For OS, 1) history of surgery positively impacted the outcome, while bone metastases worsened it; 2) baseline red blood cell count (RBC), hemoglobin, and thyroid-stimulating hormone (TSH) negatively impacted the OS. For PFS, 1) PD-L1 positivity, baseline lymphocyte count, and aspartate transferase levels had a positive impact; 2) human epidermal growth factor receptor 2 (HER2) positivity, baseline RBC, TSH, alkaline phosphatase, and alanine transferase (AST) levels had a negative impact. A slight increase in white blood cell (WBC) count (by 1.54, P = 0.02) and a drop in lymphocyte count (by 0.1907, P = 0.003) was significantly associated with disease progression. Conclusions: Baseline risk factors and monitoring blood counts can help predict outcomes in ICI-treated UGI tumors. We need larger studies to confirm this.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".