Administration of Hypoxia-Activated Prodrug Evofosfamide after Conventional Adjuvant Therapy Enhances Therapeutic Outcome and Targets Cancer-Initiating Cells in Preclinical Models of Colorectal Cancer
Bibliographic record
Abstract
Abstract Purpose: Cancer-initiating cells (C-IC) have been described in multiple cancer types, including colorectal cancer. C-ICs are defined by their capacity to self-renew, thereby driving tumor growth. C-ICs were initially thought to be static entities; however, recent studies have determined these cells to be dynamic and influenced by microenvironmental cues such as hypoxia. If hypoxia drives the formation of C-ICs, then therapeutic targeting of hypoxia could represent a novel means to target C-ICs. Experimental Design: Patient-derived colorectal cancer xenografts were treated with evofosfamide, a hypoxia-activated prodrug (HAP), in combination with 5-fluorouracil (5-FU) or chemoradiotherapy (5-FU and radiation; CRT). Treatment groups included both concurrent and sequential dosing regimens. Effects on the colorectal cancer-initiating cell (CC-IC) fraction were assessed by serial passage in vivo limiting dilution assays. FAZA-PET imaging was utilized as a noninvasive method to assess intratumoral hypoxia. Results: Hypoxia was sufficient to drive the formation of CC-ICs and colorectal cancer cells surviving conventional therapy were more hypoxic and C-IC-like. Using a novel approach to combination therapy, we show that sequential treatment with 5-FU or CRT followed by evofosfamide not only inhibits tumor growth of xenografts compared with 5-FU or CRT alone, but also significantly decreases the CC-IC fraction. Furthermore, noninvasive FAZA-PET hypoxia imaging was predictive of a tumor's response to evofosfamide. Conclusions: Our data demonstrate a novel means to target the CC-IC fraction by adding a HAP sequentially after conventional adjuvant therapy, as well as the use of FAZA-PET as a biomarker for hypoxia to identify tumors that will benefit most from this approach. Clin Cancer Res; 24(9); 2116–27. ©2018 AACR.
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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".