Abstract P6-02-03: Leptin receptor (OB-R) in breast carcinoma tissue: Ubiquitous expression and correlation with leptin-mediated signaling, but not with systemic markers of obesity
Bibliographic record
Abstract
Abstract Background/Aims: Obesity is associated with a 30-50% increased risk of breast-cancer (BC) mortality, most consistently in estrogen receptor (ER) positive disease, through unclear mechanisms. Leptin is a multi-functional protein with key actions on adipose tissue. In pre-clinical studies, leptin stimulates the growth, survival, and progression of BC cells through both estrogen dependent and other (e.g. JAK/STAT, PI3K/Akt, MAPK) pathways. Leptin has also been associated with increased BC risk and poor prognosis. Our aim was to correlate tumor leptin-receptor (OB-R) expression with tissue markers of cell signaling and systemic markers of obesity, inflammation, and metabolism in a cohort of ER+/HER2- BC patients. Methods: From our biorepository, we identified ER+/HER2- BC patients having both blood and tissue samples available. Data included BMI, menopausal status, and family/cancer/medical history, tumor histology, grade, stage, and ER/PgR/HER2 status. We performed blood assays for factors related to inflammation, tumor growth, hormonal regulation, and metabolism (see below). Immunohistochemistry for OB-R, pAkt (S473), pERK (T202/Y204), and insulin-receptor (IR) was performed on archived tissue, and scored for % positive cells and intensity of staining. Allred and H-scores were calculated. Associations with OB-R scores were calculated using Pearson, Spearman, and χ2 methods. Results: 129 patients were eligible; 69.8% were post-menopausal and mean BMI was 27.8 ± 6.5 kg/m2. Most tumors were no-special-type (79%), PgR+ (90%), and node-neg (78%). The tissue expression of OB-R and other markers was scorable in 118 (91%) cases. OB-R was expressed in all 118/118 cancers (Allred score range: 3 to 8; median 7, mean 6.61). High blood leptin did not downregulate OB-R (Spearman R=0), even though leptin was strongly correlated with BMI (Pearson r=0.78, p<0.00001). Increasing OB-R correlated with phosphorylation of Akt (R=0.19) but not ERK (R=0.08). By contrast, high BMI was associated with lower Akt (R=-0.18) and ERK (R=-0.11) phosphorylation. OB-R correlated with ER (Spearman R = 0.27), PgR (R=0.29), and insulin receptor (R = 0.24), weakly correlated with estradiol (Spearman, R=0.11) and fasting glucose (R=0.18), and negatively correlated with systemic IL-2 (R=-0.11) and IL-6 (R=-0.21). OB-R was not correlated with other blood markers (insulin, HOMA, PAI-1, IL-1ẞ, IL-8, VEGF, EGF, TNF-α,hsCRP, SHBG, or estrogens) or tumor grade. Conclusions: OB-R is highly expressed in breast tumor tissue even in non-obese patients. Although leptin and BMI did not modulate OB-R expression, downstream signaling (e.g. Akt, ERK) did show a BMI-dependent effect, albeit of limited magnitude. This suggests that leptin acts on breast cancer cells through OB-R activation and downstream Akt/ERK signaling, without a coupled change in total OB-R expression. Further work is needed to elucidate the roles of inflammation, estrogens, and regulatory mechanisms within the PI3K-PTEN and Ras-MAPK cell-signaling networks. The authors wish to acknowledge the generous support of the Breast Cancer Research Foundation and Hold'Em For Life Charity Challenge. Citation Format: Chang MC, Ennis M, Dowling RJO, Stambolic V, Goodwin PJ. Leptin receptor (OB-R) in breast carcinoma tissue: Ubiquitous expression and correlation with leptin-mediated signaling, but not with systemic markers of obesity [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-02-03.
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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) | 0.005 | 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".