Identification and Validation of Protein Biomarkers of Response to Neoadjuvant Platinum Chemotherapy in Muscle Invasive Urothelial Carcinoma
Bibliographic record
Abstract
BACKGROUND: The 5-year cancer specific survival (CSS) for patients with muscle invasive urothelial carcinoma of the bladder (MIBC) treated with cystectomy alone is approximately 50%. Platinum based neoadjuvant chemotherapy (NAC) plus cystectomy results in a marginal 5-10% increase in 5-year CSS in MIBC. Interestingly, responders to NAC (<ypT2) have a 5-year CSS of 90% which is in stark contrast to the 30-40% CSS for those whose MIBC is resistance to NAC. While the implementation of NAC for MIBC is increasing, it is still not widely utilized due to concerns related to delay of cystectomy, potential side-effects, and inability to predict effectiveness. Recently suggested molecular signatures of chemoresponsiveness, which could prove useful in this setting, would be of considerable utility but are yet to be translated into clinical practice. METHODS: mRNA expression data from a prior report on a NAC-treated MIBC cohort were re-analyzed in conjunction with the antibody database of the Human Protein Atlas (HPA) to identify candidate protein based biomarkers detectable by immunohistochemistry (IHC). These candidate biomarkers were subsequently tested in tissue microarrays derived from an independent cohort of NAC naive MIBC biopsy specimens from whom the patients were treated with neoadjuvant gemcitabine cisplatin NAC and subsequent cystectomy. The clinical parameters that have been previously associated with NAC response were also examined in our cohort. RESULTS: Our analyses of the available mRNA gene expression data in a discovery cohort (n = 33) and the HPA resulted in 8 candidate protein biomarkers. The combination of GDPD3 and SPRED1 resulted in a multivariate classification tree that was significantly associated with NAC response status (Goodman-Kruskal γ = 0.85 p<0.0001) in our independent NAC treated MIBC cohort. This model was independent of the clinical factors of age and clinical tumor stage, which have been previously associated with NAC response by our group. The combination of both these protein biomarkers detected by IHC in biopsy specimens along with the relevant clinical parameters resulted in a prediction model able to significantly stratify the likelihood of NAC resistance in our cohort (n = 37) into two well separated halves: low-26% n = 19 and high-89% n = 18, Fisher's exact p = 0.0002). CONCLUSION: We illustrate the feasibility of translating a gene expression signature of NAC response from a discovery cohort into immunohistochemical markers readily applicable to MIBC biopsy specimens in our independent cohort. The results from this study are being characterized in additional validation cohorts. Additionally, we anticipate that emerging somatic mutations in MIBC will also be important for NAC response prediction. The relationship of the findings in this study to the current understanding of variant histologic subtypes of MIBC along with the evolving molecular subtypes of MIBC as it relates to NAC response remains to be fully characterized.
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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 | 0.001 |
| 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 | 0.000 | 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".