Abstract AP12: CYSTEINE METABOLISM IN CLEAR CELL OVARIAN CANCER
Bibliographic record
Abstract
Abstract Our past research has used genomic screens to identify unique features of ovarian cancer subtypes. This has resulted in the description of frequent mutations in ARID1A in both clear cell (CCOC) and endometrioid (ENOC) ovarian cancers and patterns of genomic rearrangements indicative of each subtype. While genomic and RNA expression profiling have been extremely informative, they do not provide a view of the protein expression landscape of cancers. We therefore set out to perform proteomic profiling to better define the key features of the different subtypes and to understand the biology that underpin these diseases. We have developed a novel mass spectrometry proteomic profiling technique based on para–magnetic bead technology called SP3–Clinical Tumor Proteomics (SP3–CTP). This technique is able to perform sensitive proteomic profiling off of a single formalin–fixed paraffin–embedded (FFPE) section. Using this platform, we performed proteomic profiling of three subtypes of ovarian cancer: high grade serous (HGS), ENOC and CCOC. Many proteins known to be enriched in a particular subtype were validated by our screen. WT1 levels were higher in HGS and HNF1B levels were higher in CCOC, as expected. Perhaps more importantly, this screen also revealed that several proteins were enriched in one subtype, which had not previously been described. One such protein was cystathionine gamma lyase (CTH), which was enriched in CCOC compared to the other two subtypes. We validated these results on an ovarian tumor tissue microarray, which confirmed that the majority of CCOC (75%) express high levels of CTH. Conversely, only 10% ENOCa and less than 2% of HGS express high levels of CTH. CTH is a transulfuration enzyme which, along with cystathione beta lyase, form the biosynthetic pathway in which methionine is converted to cysteine. High levels of CTH activity can lead to the generation of hydrogen sulfide (H2S), which has been increasingly implicated as a gaseous intracellular signaling molecule. Overexpression of CTH in CCOC could explain the unique metabolism and other clinical features of this disease. We have found that CTH exhibits a unique staining pattern in the normal endometrium, staining some cells very dark while the rest exhibit little or no CTH staining. It is well documented that endometriosis is the presumed precursor lesion of both CCOC and ENOC. It is not known, however, how such phenotypically different cancers arise from the same precursor. It may be that the metabolic state of cells in the endometrium determines whether it becomes a CCOC or and ENOCa upon receiving a genetic hit. Our initial findings from our proteomic screen have highlighted CTH overexpression as being a feature common in CCOC and comparatively rare in ENOC and HGS. We believe that high CTH expression could provide a unique perspective into the initiation and pathology of CCOC. Citation Format: Dawn R. Cochrane, Christopher S. Hughes, Tayyebeh Nazeran, Anthony N. Karnezis, Melissa K. McConechy, Gregg B. Morin and David G. Huntsman. CYSTEINE METABOLISM IN CLEAR CELL OVARIAN CANCER [abstract]. In: Proceedings of the 11th Biennial Ovarian Cancer Research Symposium; Sep 12-13, 2016; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(11 Suppl):Abstract nr AP12.
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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 | 0.000 |
| 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".