Abstract B041: Mimicking tumor acidic and hypoxic microenvironment in vitro towards generation of more predictive screening platform for solid tumors
Bibliographic record
Abstract
Abstract The drug discovery process in oncology relies mainly on screening and preclinical validation platforms that use human cancer cells that are grown in standard monolayer (2D) cultures. However, 2D monolayer cell culture methods poorly reflect the tumor microenvironment (TME) and the transcriptome and epigenome attributes of the cancer cells in vivo, leading to inaccurate estimate of drug efficacy in clinic. The TME of solid tumors is complex yet it consist of generic hallmarks such as hypoxia and extracellular acidosis that are common across tumor types and directly modulate gene expression and epigenome. Here we hypothesized that in vitro culture systems that recapitulate the acidic and hypoxic features of the solid TME, more accurately reflect the gene expression profile of cells in in vivo tumors, and thus serve as improved drug screening platform that reliably determine drug efficacies. Here we surveyed systematically the impact on transcriptome and epigenome hallmarks of exposure to hypoxia and acidosis on 2D and 3D cultures of A549 lung adenocarcinoma cell line as a model for solid tumor. To recapitulate naturally occurring hypoxia in vitro, A549 cells were adapted over several passages to growth in 3D. Transcriptome data derived from cells adapted to 3D, when compared to 2D and patient tumor datasets suggested that time spent in 3D increased the concordance of transcriptome profile with tumors from patients. Furthermore adaptation to 3D revealed emergence of hypoxia hallmarks when compared to 2D. To model the combined effect of hypoxia and acidosis, the 3D adapted A549 spheroids were additionally exposed to low pH conditions (6.4) for a duration of 5 days. The additional exposure to low pH led to enhanced tumor progressive phenotypes as evidenced by enrichment in genes associated with hallmarks of inflammation, epithelial mesenchymal transition (EMT), cell invasion and drug resistance. To determine the impact of hypoxic and acidic TME on cytotoxic response to anti-cancer drugs, the 2D and 3D adapted spheroids with or without additional exposure to low pH were screened against a library consisting of 184 anti-cancer drugs. Additional exposure to low pH led to an overall increase in drug resistance. However the combined hypoxic and acidic conditions in 3D revealed novel susceptibilities for compounds that belong to the family of Aurora kinase inhibitors. These results provide indications that accurate modeling of hypoxic and acidic TME in vitro could reveal expected resistance mechanisms and specific drug susceptibilities. This study introduces a 3D screening platform that recapitulates the acidosis and hypoxia of solid TME and captures the transcriptomic profile in vitro that is highly concordant with tumors in patients. This model allows the study of resistance mechanisms and unravel novel susceptibilities that is otherwise undiscovered in standard 2D conditions and could result in the identification of anticancer drugs that are more clinically relevant. Citation Format: Nazanin Rohani Larijani, Marielle Huot, Anne Lenferink, Noël R. Raynal. Mimicking tumor acidic and hypoxic microenvironment in vitro towards generation of more predictive screening platform for solid tumors [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B041.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".