Evaluation of an institutional series of low-grade oncocytic tumor (LOT) of the kidney and review of the mutational landscape of LOT
Bibliographic record
Abstract
The 2022 WHO classification of urinary and male genital tumors introduced several novel kidney entities exhibiting eosinophilic/oncocytic features with specific mutational backgrounds. Thus, molecular techniques, such as next-generation sequencing (NGS), became more commonly used for their evaluation. We studied 12 low-grade oncocytic tumors (LOT) of the kidney (from 11 patients), identified in a cohort of 210 eosinophilic/oncocytic renal tumors, diagnosed in our institution between October 2019 and May 2023, which represented 5.7% (12/210) of all eosinophilic/oncocytic renal tumors during this period. We reviewed their clinicopathologic, histologic, and immunohistochemical features, as well as their mutational profiles. We also reviewed the literature on NGS-derived data of LOT, by selecting papers in which LOT diagnosis was rendered according to the criteria proposed initially. Median age was 65 years (mean: 63.5; range 43-79) and median tumor size was 2.0 cm (mean: 2.2; range: 0.9-3.1). All tumors were positive for PAX8, CK7, and GATA3, and negative or focally positive for CD117/KIT. We found the following gene mutations: MTOR ((6/11), 54.5%)), TSC1 ((2/11), 18.2%)), and 1 had both NOTCH1 and NOTCH4 ((1/11), 9.1%)). Wild-type status was found in 2/11 (18.2%) patients and one tumor was not analyzable. A review of 8 previous studies that included 79 LOTs revealed frequent mutations in the genes that regulate the mammalian target of rapamycin (mTOR) pathway: MTOR (32/79 (40.5%)), TSC1 (21/79 (26.6%)), and TSC2 (9/79 (11.4%)). Other mutated genes included PIK3CA, NF2, and PTEN, not typically known to affect the mTOR pathway, but potentially acting as upstream and downstream effectors. Our study shows that LOT is increasingly diagnosed in routine practice when applying the appropriate diagnostic criteria. We also confirm that the mTOR pathway is strongly implicated in the pathogenesis of this tumor mainly through MTOR, TCS1, and TSC2 mutations, but other genes could also be involved in the pathway activation, especially in LOTs without "canonical" mutations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".