Overcoming Multidrug Resistance in B Cell Malignancies By Antagonism of Stromal Mediated TGF-β Signalling
Bibliographic record
Abstract
Novel targeted therapies have substantially improved the prognosis of patients with B cell malignancies. However, a substantial fraction of patients still relapse, even after initially achieving deep remissions. Many studies have characterized the interactions between tumor cells and their microenvironment as integral to leukemia/ lymphoma homeostasis and for the provision of survival signals, also contributing to drug resistance (referred to as environment-mediated drug resistance (EMDR)). Therapeutic efforts to antagonize microenvironment-emanating survival cues have predominantly focused on perturbation of tumour cell adhesion enabling the physical displacement from protective niches (e.g. BCR-inhibitors). In an effort to address whether direct stromal targeting could more precisely mitigate EMDR, we recently characterised the molecular mechanisms underlying tumor-stroma interactions in B cell malignancies and identified a protein kinase C-β (PKC-β) as an essential kinase, required for activation of NF-κB in mesenchymal stromal cells (Lutzny et al Cancer Cell 2013). The dependency on stroma PKC-β was uniformly found for acute (ALL) and chronic (CLL, MCL) B cell malignancies. Importantly, our data further demonstrate that targeting stroma PKC-β is of key importance for multi-drug resistance of malignant B cells and can be used for therapeutic interventions (Park et al Science Trans Med 2020). Here we demonstrate novel mechanistic insights into stroma-mediated drug resistance in B cell malignancies. We identified that stroma PKC-β drives a transcriptional program in tumor cells, dependent on the activation of TGF-β and BMP-signaling, which ultimately leads to the stabilisation of BCL-XL. Our data show that BCL-XL expression in tumor cells is associated with SMAD1-induction by cytotoxic therapies, which simultaneously suppress SMAD4 expression. Importantly, SMAD1 expression was strictly dependent on stromal PKC-β activity. Antagonizing stroma signals with TGF-β inhibitors inhibits SMAD1 induction, abrogates the up-regulation of BCL-XL and overcomes stroma-dependent resistance to Venetoclax and conventional chemotherapy. The TGF-β pathway operates in parallel to the activation of the transcription factor EB (TFEB) as a down-stream target of PKC-β. Interference with these signaling pathways impairs plasma membrane integrity of stromal cells by down-regulation of numerous adhesion and signaling molecules, such as ADAM17, required for the reciprocal stabilization of BCL-XL in tumor cells. The significance of microenvironment PKC-β for drug resistance was demonstrated in vivo, using C57B/6 mice, diseased with EμTCL-1 driven B cell tumors and treated with Venetoclax in combination with or without PKC-β inhibitors. Combined treatment significantly prolonged survival, based on PKC-β mediated impairment of EMDR. Similarly, concurrent treatment of PKC-β inhibitors with chemotherapy also improved survival in an ALL-PDx model Our data demonstrate that mitigating EMDR with small molecule inhibitors of PKC-β or TGF-β signalling enhance the effectiveness of both targeted and non-targeted chemotherapies and moreover, has the ability to overcome Venetoclax resistance in B cell malignancies. Clinical trials with repurposed drugs inhibiting the here described pathways mediating EMDR are in planning. Disclosures No relevant conflicts of interest to declare. OffLabel Disclosure: Midostaurin as inhibitor of stroma PKC-β
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".