Immunohistochemical analysis based Ep-ICD subcellular localization index (ESLI) is a novel marker for metastatic papillary thyroid microcarcinoma
Bibliographic record
Abstract
BACKGROUND: Thyroid cancer is among the fastest growing malignancies; almost fifty-percent of these rapidly increasing incidence tumors are less than or equal to 1cm in size, termed papillary thyroid microcarcinoma (PTMC). The management of PTMC remains a controversy due to differing natural history of these patients. Epithelial cell adhesion molecule (EpCAM) is comprised of an extracellular domain (EpEx), a single transmembrane domain and an intracellular domain (Ep-ICD). Our group reported nuclear Ep-ICD correlated with poor prognosis in thyroid cancer (Ralhan et al., BMC Cancer 2010,10:331). Here in, we hypothesized nuclear and cytoplasmic accumulation of Ep-ICD and loss of membranous EpEx may aid in distinguishing metastatic from non-metastatic PTMC, which is an important current clinical challenge. To test our hypothesis, Ep-ICD and EpEx expression levels were analyzed in PTMC and the staining was correlated with metastatic potential of these carcinomas. METHODS: Thirty-six PTMC patients (tumor size 0.5 - 1cm; metastatic 8 cases and non-metastatic 28 cases) who underwent total thyroidectomy were selected. The metastatic group consisted of patients who developed lymph node or distant metastasis at diagnosis or during follow up. The patients' tissues were stained for Ep-ICD and EpEx using domain specific antibodies by immunohistochemistry and evaluated. RESULTS: PTMC patients with metastasis had higher scores for nuclear and cytoplasmic Ep-ICD immunostaining than the patients without metastasis (1.96 ± 0.86 vs. 1.22 ± 0.45; p = 0.007 and 5.37 ± 0.33 vs. 4.72 ± 1.07; p = 0.016, respectively). Concomitantly, the former had lower scores for membrane EpEx than the non-metastatic group (4.64 ± 1.08 vs. 5.64 ± 1.51; p = 0.026). An index of aggressiveness, Ep-ICD subcellular localization index (ESLI), was defined as sum of the IHC scores for accumulation of nuclear and cytoplasmic Ep-ICD and loss of membranous EpEx; ESLI = [Ep - ICDnuc + Ep - ICDcyt + loss of membranous EpEx]. Notably, ESLI correlated significantly with lymph node metastasis in PTMC (p = 0.008). CONCLUSION: Nuclear and cytoplasmic Ep-ICD expression and loss of membranous EpEx were found to correlate positively with metastasis in PTMC patients. In addition, ESLI had the potential to identify metastatic behavior in PTMC which could serve as a valuable tool for solving a current dilemma in clinical practice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 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".