Abstract 1278: Determining the proteomic changes that define progressor and non-progressor clear cell renal cell carcinoma
Bibliographic record
Abstract
Abstract Renal cell carcinoma (RCC) is the most common type of kidney cancer, is increasing in incidence and is the most lethal genitourinary cancer. Due to the increased use of abdominal imaging, small renal masses (SRMs), mostly RCC, are now commonly detected in asymptomatic individuals and are the most common presentation of kidney cancer. Many of these SRMs grow slowly or not at all, while about 30% progress to advanced stages. However, in the absence of factors distinguishing masses that need treatment from those that can be managed by surveillance alone, most SRMs are treated by surgery. Clear cell RCC (ccRCC) is the most common form of RCC, has the worst prognosis and is characterized by inactivation of von Hippel-Lindau tumor suppressor gene (VHL). Systemic therapies developed to treat patients at advanced stages have targeted pVHL regulated genes particularly hypoxia inducible factor (HIF) downstream targets. However, these drugs rarely achieve cures suggesting that other pathways are implicated in ccRCC initiation and progression. Our national clinical trial, “Active Surveillance of Small Renal Masses” provides a unique opportunity to study the genetics and biology of the early stage ccRCC and characterize the genomic and proteomic signatures distinguishing non-progressor from progressor ccRCC-SRMs. Patients with ccRCC-SRMs (<4cm in size) are followed with time and if their mass progresses, they undergo surgical or other forms of intervention as appropriate. In this study, we determine the proteomic differences between a) non-progressor and progressor ccRCC-SRMs by characterizing biopsies taken at the time of diagnosis and recruitment into the surveillance protocol from both groups, and b) progressor ccRCC-SRM (at time of recruitment) and advanced ccRCC by comparing the initial, diagnostic biopsy of ccRCC-SRMs that subsequently progress to the surgical specimen at the time of treatment for progression (advanced stage). Using reverse phase protein array (RPPA), we identify proteomic signatures that distinguish progressor and non-progressor groups. We observed changes mostly in the PI3 kinase and ERK pathways as well as alterations in apoptotic pathways. Our results provide important insight for management of ccRCC-SRMs and define pathways involved in their initiation and progression. This could furthermore identify potential therapeutic targets that would lead to better management of ccRCC patients. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1278. doi:1538-7445.AM2012-1278
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".