Signaling network analysis of genomic alterations predicts breast cancer drug targets
Bibliographic record
Abstract
Introduction Thousands upon thousands of tumors have been sequenced so far, representing over 20 different cancer types. These efforts allowed scientists to take a closer look at the genomic alterations (i.e., mutations and copy number variations) within a tumor's genome, in order to be able to potentially explain the underlying mechanism that drives cancer cell survival and proliferation (Banerji et al ., 2012; Cancer Genome Atlas Network, 2012; Stephens et al ., 2012). However, extracting useful information from a vast source of various data types to establish a link between genomic alterations and the driving force behind cancer cells remains a challenge (Chin et al ., 2011). Over the past decades of cancer research, scientists have learned that during the evolution of normal cells to cancer cells, different genomic alterations are compiled. These alterations can impact gene expression and protein function to modulate certain fundamental characteristics (i.e., cancer hallmarks) of a cancer cell. Cell survival, proliferation, and apoptosis are among the most primitive cancer hallmarks (Hanahan and Weinberg, 2011). The accumulation of genomic alterations allows cancer cells to reach a neoplastic state that enables them to proliferate indefinitely and become nearly immortal. However, these changes appear to be random, with no patterns that can be used for classifications of patients or identifying drug targets for treatment. Recent studies (Schlabach et al ., 2008; Silva et al ., 2008) have gone on to identify genes that are required for cancer cell survival and proliferation (i.e., essential genes). They accomplished this by performing a genome-wide RNAi knockdown screening for different cancer cell lines from three different cancer types, whereupon a gene was considered to be an essential gene if the knockdown of that gene reduced the cell's survival and proliferation based on p -values. A key observation to note in these RNAi knockdown studies is that different cancer cell lines had different sets of essential genes, implying that the cancer hallmark traits, such as survival and proliferation, can be affected by different sets of genes. This was true for cell lines that belong to the same cancer type. Therefore, no two lung cancer cell lines, for example, had identical sets of essential genes, although there was some overlap. In addition, there was no one gene that appeared to be essential across all the different lung cancer cell lines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".