Identifying and harnessing mechanisms that are more strongly required for cancer cell division
Bibliographic record
Abstract
Cancer is the leading cause of death in Canada, and remains difficult to treat since no two cancers are genetically the same. Cancer hallmarks describe the physiological changes that occur to give rise to metastatic cancer, and include uncontrolled cell proliferation and aneuploidy. Many of the current therapies target one or more of these hallmarks, but there is a need to expand the repertoire of available treatments. To do this, it is crucial to identify mechanisms controlling crucial physiological functions that are unique to cancer cells, which could be targeted to block their progression. In my thesis, I reveal that anillin, a cytokinesis protein, is more strongly required in cancer cells with hyperploidy compared to (near) diploid cells. Cytokinesis occurs at the end of mitosis to separate the daughter cells, due to the ingression of a contractile ring. Multiple pathways spatiotemporally control ring position to coordinate it with the segregating chromosomes. We previously discovered a chromatin-sensing pathway where Ran-GTP, which is enriched around chromatin, controls the levels of importins for the cortical recruitment of anillin to control ring position in HeLa cells. We hypothesized that this mechanism depends on ploidy, since HeLa cells are hypotriploid and we saw differences in anillin localization in cells with lower ploidy. In this thesis, I obtained evidence supporting this hypothesis by inducing an increase in ploidy in near diploid HCT116 cells where anillin is not strongly required, and revealing that this causes a change in anillin’s localization and its requirement for cytokinesis. I also show how importin-binding is involved for this requirement. These findings reveal that anillin and/or other chromatin sensing pathway components could make ideal targets for novel cancer therapies. In another project, we have been collaborating with Dr. Forgione’s lab (Chemistry and Biochemistry) to identify novel compounds with anti-cancer properties. His lab synthesized a family of thienoisoquinoline compounds, and we found several derivatives with high efficacy in cancer cells. We determined the mechanism of action of one of these derivatives, C75, in vitro and in cells. I found that C75 binds directly to tubulin and prevents microtubule polymerization in vitro. In cells, C75 disrupts the mitotic spindle, causing cells to arrest in mitosis. I also helped show that it can synergize with other microtubule-targeting drugs, including paclitaxel, which is currently being used to treat cancers. These findings reveal that thieonoisoquinoline compounds could be explored as a potential novel anti-cancer therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".