Clonal evolution and tumor-initiating cells: New dimensions in cancer patient treatment
Bibliographic record
Abstract
Human cancer is not a uniform disease but a plethora of disparate tumor types and subtypes. The differences that exist between individual tumors (intertumoral heterogeneity) present a significant roadblock to the eradication of cancer. It has also become increasingly clear that variations across individual tumors (intratumoral heterogeneity) have important implications to cancer progression and treatment efficacy. Therefore, in order to improve patient care and develop novel chemotherapeutics, the evolving tumor landscape needs to be further explored. Next-generation sequencing (NGS) technologies are revolutionizing the cancer research arena by providing state-of-the-art, high-speed methods of genome sequencing at single-nucleotide resolution, thus enabling an unprecedented detection of tumor-specific genetic abnormalities. These anomalies can be quantified to reveal specific frequencies of DNA alterations that correspond to distinct clonal populations within a given tumor. As such, NGS approaches have also been utilized to explore the heterogeneous landscape of patient tumors as well as to match metastatic and/or recurrent growths and patient-derived engrafts. By sequencing in this manner--through time so to speak--cancer researchers can track shifting clonal populations, make important inferences about tumor evolution and potentially identify tumor subclones that could be viably targeted. This exciting new territory has important implications for the competing clonal evolution and cancer stem cell models of tumor heterogeneity, and also offers a new dimension for cancer treatment and profound hope for patients in the coming years.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".