Targeting cancer metabolism to overcome treatment resistance in HER2-positive breast cancer
Bibliographic record
Abstract
M adaption of tumour cells is necessary to meet biosynthesis and energy needs of a growing cancer. The energy sensing kinase AMPK is responsible for maintaining AMP/ATP ratio, serving as a metabolic checkpoint that is activated when phosphorylated by the LKB1, a tumour suppressor kinase. LKB1 is frequently found mutated in numerous cancers including 31% of HER2 breast cancer. In our LKB1-/-NIC model, loss of LKB1 expression resulted in reduced tumour latency where tumours were biochemically characterized as hyperactive mTOR, along with metabolic changes characteristic of Warburg effect, namely elevated ATP, LDH, PDH expression and enhanced lactate. Based on these finding, we conducted a pre-clinical studies to evaluate novel combinatorial therapies on tumourigenesis. We report that targeting PI3K-p70S6K pathways with competitive NVP-BEZ235 inhibitor was not as effective at reducing tumourigenesis as targeting mTOR and glycolysis with AZD8055 and 2-DG monotherapies, respectively. Interestingly, simultaneous inhibition of these pathways with AZD8055/2-DG combination was significantly more effective at reducing mitochondria function, tumour volume and burden, culminating in reduced tumourigenesis. At the molecular level, combination treatment inhibited both mTOR signalling and blocked MAPK survival signalling that is responsible for ERK-p90RSK pathway engagement. Finally, loss of LKB1 expression in cancers should be considered a marker for metabolic dysfunction given the role LKB1 plays in regulating both AMPK activity and mTOR function. The results of our pre-clinical studies suggest that combinatorial therapy that target mTORC1/ mTORC2 and glycolytic pathways in cancer, is critical for inhibiting tumour growth. Importantly, our discovery showed that the drug combination inhibited the activation of feedback loops that are drivers of resistance, namely ERK and p90RSK. We believe that simultaneous targeting of these pathways will provide the best clinical outcome for the treatment of metabolically active cancers, as well as reduce the likelihood of recurrence.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".