Bibliographic record
Abstract
Abstract Next generation sequencing (NGS) technologies have gained increasing clinical applications as a strategy to screen and identify patients for genomic based clinical trials in oncology. Molecular characterization programs using NGS have been established in many cancer centers worldwide, including some that comprise part of nation-based efforts. With a growing number of patients whose tumors have undergone molecular profiling, there is an urgent need to justify these activities and translate genomic findings into clinically relevant outcome. Molecular profiling offers information beyond the histopathological classification of tumors, subdividing them into smaller genomic subsets ranging from those harboring common recurrent aberrations to others with rare variants of uncertain significance. The current era of genomic based clinical trials is populated by “umbrella” and “basket” trials which allocate patients with druggable genomic signatures to specific genotype-drug matched groups that remain histology-based (“umbrella” trials) or are histology-agnostic (“basket” trials). The pros and cons of these clinical trial designs, and their ability to bring the value of precision medicine to bear, will be discussed. Looking beyond the present clinical trials framework, it is unlikely that genomics is sufficient as a standalone strategy to fully characterize the complex molecular landscape of cancer. “Next-gen” clinical trials need to target the dynamic status of tumors and take into consideration intratumoral heterogeneity in their design. The extension of profiling algorithms to include transcriptomics and epigenetics is attractive to encompass other alterations that can lead to unchecked tumor growth. A systems biology approach should be considered to understand signaling pathway interactions and decipher mechanisms of oncogenic dependence, especially in tumors which lack readily actionable profiles. There are close interactions between the genome and the immunome such that NGS may inform the selection of patients most likely to benefit from immune-based therapies. “Next-gen” clinical trials must be capable of interrogating cancer in a multifaceted and dynamic manner in order to achieve substantial incremental benefits in therapeutic outcome. Citation Format: Lillian L. Siu. “Next-gen” genomics-based clinical trials. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Integrating Clinical Genomics and Cancer Therapy; Jun 13-16, 2015; Salt Lake City, UT. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(1_Suppl):Abstract nr IA15.
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 | 0.028 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".