Identification of breast cancer cell subtypes sensitive to ATG4B inhibition
Bibliographic record
Abstract
// Svetlana Bortnik 1, 2 , Courtney Choutka 1, 3 , Hugo M. Horlings 4, 5, 6 , Samuel Leung 4, 5 , Jennifer H. Baker 7 , Chandra Lebovitz 1, 3 , Wieslawa H. Dragowska 8 , Nancy E. Go 1 , Marcel B. Bally 8, 9, 10, 11 , Andrew I. Minchinton 7 , Karen A. Gelmon 12, 13 , Sharon M. Gorski 1, 2, 3, 14 1 The Genome Sciences Centre, BC Cancer Agency, Vancouver, BC, Canada 2 Interdisciplinary Oncology Program, University of British Columbia, Vancouver, BC, Canada 3 Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada 4 Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada 5 Vancouver General Hospital, BC Cancer Agency, Vancouver, BC, Canada 6 Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands 7 Radiation Biology Unit - Department of Integrative Oncology, BC Cancer Agency, Vancouver, BC, Canada 8 Department of Experimental Therapeutics, BC Cancer Agency, Vancouver, BC, Canada 9 Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada 10 Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada 11 Centre for Drug Research and Development, Vancouver, BC, Canada 12 Medical Oncology, BC Cancer Agency, Vancouver, BC, Canada 13 Department of Medicine, University of British Columbia, Vancouver, BC, Canada 14 Centre for Cell Biology, Development, and Disease, Simon Fraser University, Burnaby, BC, Canada Correspondence to: Svetlana Bortnik, email: sbortnik@bcgsc.ca Keywords: breast cancer, autophagy, ATG4B, HER2, trastuzumab Received: March 20, 2016 Accepted: August 09, 2016 Published: August 19, 2016 ABSTRACT Autophagy, a lysosome-mediated degradation and recycling process, functions in advanced malignancies to promote cancer cell survival and contribute to cancer progression and drug resistance. While various autophagy inhibition strategies are under investigation for cancer treatment, corresponding patient selection criteria for these autophagy inhibitors need to be developed. Due to its central roles in the autophagy process, the cysteine protease ATG4B is one of the autophagy proteins being pursued as a potential therapeutic target. In this study, we investigated the expression of ATG4B in breast cancer, a heterogeneous disease comprised of several molecular subtypes. We examined a panel of breast cancer cell lines, xenograft tumors, and breast cancer patient specimens for the protein expression of ATG4B, and found a positive association between HER2 and ATG4B protein expression. We showed that HER2-positive cells, but not HER2-negative breast cancer cells, require ATG4B to survive under stress. In HER2-positive cells, cytoprotective autophagy was dependent on ATG4B under both starvation and HER2 inhibition conditions. Combined knockdown of ATG4B and HER2 by siRNA resulted in a significant decrease in cell viability, and the combination of ATG4B knockdown with trastuzumab resulted in a greater reduction in cell viability compared to trastuzumab treatment alone, in both trastuzumab-sensitive and -resistant HER2 overexpressing breast cancer cells. Together these results demonstrate a novel association of ATG4B positive expression with HER2 positive breast cancers and indicate that this subtype is suitable for emerging ATG4B inhibition strategies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".