Abstract PO-049: Exploiting tumor acidic microenvironment for improved therapeutics
Bibliographic record
Abstract
Abstract The tumor acidic microenvironment is a fundamental characteristic of solid tumors which distinguishes the tumor from adjacent normal tissues. This unique tumor microenvironment (TME) feature directly contributes to various aspect of tumor progression including gene expression, immune suppression, drug resistance, invasion and metastasis. Acidic TME poses great challenges for the efficacy of therapeutics; extracellular acidity of cancer cells provides less permissive conditions for optimal target engagement and drug efficacy. In addition targeting the effectors of acid-base balance enriched in hypoxia and acidic regions of the tumor provide valuable tools for normalizing the extracellular acidity and delivery of therapeutics agents to areas of the tumor less malleable to conventional therapies. Thus in the case of therapeutic antibodies, strategies that specifically integrates this TME specific feature in the design could improve binding to the target under low pH conditions and serve as delivery moiety that improve tumor specificity and functional efficacy. Here we describe two examples of these therapeutic strategies that leverage the acidic and hypoxic microenvironment to overcome the challenges posed by this hostile environment with the aim of generating improved therapeutic antibodies. A. Characterization of a set of function blocking therapeutic antibodies against key target expressed in response to hypoxia which contributes to tumor acidification, we demonstrate functional efficacy of this antibody to block the activity of the target in vitro and consequently reduce tumor spheroid growth. This and similar antibodies provide tools for specific targeting of the tumor areas that are often inaccessible to most therapeutics. B. Identification of targets enriched in acidic tumor microenvironment and design of pH selective therapeutic antibodies that preferentially bind to the target and exert their function under tumor acidic conditions. We demonstrate the functional selectivity of a variant of antibody targeting HER 2, with optimized binding under low pH conditions, on the growth of BT474 spheroids. The pH selective antibody variant blocked the tumor spheroid growth when spheroids were grown in TME-relevant- low pH conditions but it remained ineffective under normal physiological pH conditions. Suggesting increased selectivity of this antibody variant towards the acidic TME that in turn lower the on-target -off-tumor toxicity. In conclusion here we provide two examples of the strategies which exploit the targeting of hypoxia induced genes and pH selective design of the therapeutic antibodies in order to improve tumor targeting capacity and reduce systemic toxicity of conventional therapeutics. Citation Format: Nazanin Rohani Larijani, Traian Sulea, Mehdi Arbabi Ghahroudi, Beatrice Paul Roc, Mylene Gosselin, Joey Sheff, John C. Zwaagstra, Anne E.G. Lenferink. Exploiting tumor acidic microenvironment for improved therapeutics [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-049.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".