Abstract 2495: The metabolic secretome of cachexia inducing pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Abstract Cachexia is an underexplored and yet devastating consequence of cancer that is the cause of 20% of all cancer related deaths1. Cachexia inducing tumors cause a ‘wasting away’ of the body. The condition is associated with poor treatment outcome2, fatigue, and extremely poor quality of life2,3. Because of the multi-factorial characteristics of this condition, it has been difficult to understand the mechanisms driving the impact of the tumor on body organs and the sequence of events that leads to this lethal condition. Here we have used 1H MRS to characterize the metabolic profile of tumor interstitial fluid (TIF) obtained from noncachexia (Panc1) and cachexia inducing (Pa04C) tumors to further understand the impact of the deranged metabolism of cachexia-inducing tumors on the tumor metabolic secretome. The human pancreatic cancer cell line, Panc1, was obtained from ATCC. The human pancreatic cancer cell line, Pa04C, was provided by Dr. Maitra4. Six to 8 week old male SCID mice were inoculated in both the right and left flank with cancer cells (5×106, Panc1 N=2, Pa04C N=2). We created a collection chamber to collect TIF. The chamber was implanted together with small tumor pieces harvested from the subcutaneous flank tumors, into the subcutaneous flank space of SCID mice (Panc1 N=8 and Pa04C N=6) until the tumor encompassed the chamber (4-5 weeks). The tumor was then removed and the tumor tissue and TIF were collected. To obtain control TIF, an empty chamber was implanted in the subcutaneous flank space of healthy mice. Dual phase solvent extraction was performed on tumor tissue. The water phase was separated, freeze dried, reconstituted in D2O PBS for spectral acquisition. All 1H MR spectra were acquired on an Avance III 750 MHz (17.6T) Bruker NMR spectrometer equipped with a 5 mm broad band inverse (BBI) probe. Spectral acquisition, processing and quantification were performed using TOPSPIN 2.1 software. Notable differences between Pa04C compared to Panc1 TIF or normal interstitial fluid were a significant decrease of polyunsaturated fatty acids (PUFA) and lipids, and formate, pyruvate, glutamine, and glucose. Lactate, glutamate, succinate, glycine and acetone significantly increased in Pa04C TIF compared to Panc1 TIF or normal interstitial fluid. These differences in TIF cannot be explained solely by the differences in the tumor metabolic profile. Our data provide new insights into changes in the metabolic secretome with induction of cachexia that may shed new light on the cachexia cascade, and identify metabolic strategies to reverse the syndrome. References: (1) Argiles, J. M., et al. Nature reviews. Cancer 2014, 14, 754-762. (2) Ozola Zalite, I., et al. Pancreatology 2015, 15, 19-24. (3) Fearon, K. C., et al. HPB (Oxford) 2010, 12, 323-324. (4) Penet, M. F., et al. Clinical Cancer Research 2015, 21, 386-395. Acknowledgment: This work was supported by NIH R01 CA193365, NIH P50CA013175 and NIH P30CA06973. Citation Format: Santosh K. Bharti, Paul T. Winnard, Louis Dore-Savard, Yelena Mironchik, Marie-France Penet, Zaver M. Bhujwalla. The metabolic secretome of cachexia inducing pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2495. doi:10.1158/1538-7445.AM2017-2495
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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 | 0.000 | 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 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".