Abstract B34: Development of novel small molecule inhibitors targeting DNA repair proteins
Bibliographic record
Abstract
Abstract More than 1.6 million new cases of cancer will be diagnosed in the US in 2016 and a third of these will be from solid tumor of the lung, pancreas, breast, and ovary. These cancers represent a continuing clinical challenge in treatment and together account for over 250,000 deaths in the US alone, representing over 40% of all cancer deaths. There are limited therapeutic options for these patients, and targeted and combination therapies remain necessary for treating these aggressive cancers. The opportunity exists to exploit recent scientific advances in our knowledge of the underlying biology behind these cancers to create novel targeted therapeutics to dramatically enhance patient response to therapy and ultimately survival. To this end, we have developed a series of novel small chemical molecules that disrupt critical protein-DNA interactions in the nucleotide excision and non-homologous end joining DNA repair pathways. It is well understood that various cancer treatments like cisplatin, etoposide and ionizing radiation impart their chemotherapeutic effect by the formation of direct DNA damage which block DNA replication and transcription culminating in apoptosis. It is also well established that repair of this DNA damage by various DNA repair pathways reduces the effectiveness of chemo- or radio- therapy. It is our contention, borne out by analysis of clinical data to be presented on the development of our DNA repair inhibitors, that inhibition of these DNA repair pathways sensitizes these difficult to treat cancers to traditional DNA-damaging therapy. We anticipate both direct mechanisms of action on the repair pathways and synthetic lethal interactions can be exploited for therapeutic benefit. The series of novel small molecule inhibitors that we have developed targeting DNA repair proteins exhibit single-agent anti-cancer activity in cancer cell lines, and potentiate cellular sensitivity to chemotherapy and ionizing radiation treatment. Our data demonstrate that this class of inhibitors can be further developed as anti-cancer therapeutics with considerable potential to be used in conjunction with radiation therapy and other cancer therapies that induce DNA damage. Citation Format: Katherine S. Pawelczak, Navnath Gavande, Pamela VanderVere-Carozza, John Turchi. Development of novel small molecule inhibitors targeting DNA repair proteins [abstract]. In: Proceedings of the AACR Special Conference on DNA Repair: Tumor Development and Therapeutic Response; 2016 Nov 2-5; Montreal, QC, Canada. Philadelphia (PA): AACR; Mol Cancer Res 2017;15(4_Suppl):Abstract nr B34.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".