Testing <i>ERBB2</i> p.L755S kinase domain mutation as a druggable target in a patient with advanced colorectal cancer
Bibliographic record
Abstract
Recent advances in molecular profiling technologies allow genetic driver events in individual tumors to be identified. The hypothesis behind this ongoing molecular profiling effort is that improvement in patients' clinical outcomes will be achieved by inhibiting these discovered genetic driver events with matched targeted drugs. This hypothesis is currently being tested in oncology clinics with variable early results. Herein, we present our experience with a case of advanced colorectal cancer (CRC) with an ERBB2 p.L755S kinase domain mutation, a BRAF p.N581S mutation, and an APC p.Q1429fs mutation, together with a brief review of the literature describing the biological and clinical significance of ERRB2 kinase domain mutations in CRC. The patient was treated with trastuzumab combined with infusional 5-fluorouracil and leucovorin based on the presence of ERBB2 p.L755S kinase mutation in the tumor and based on the available evidence at the time when standard treatment options had been exhausted. However, there was no therapeutic response illustrating the challenges we face in managing patients with potentially targetable mutations where results from functional in vitro and in vivo studies lag behind those of genomic sequencing studies. Also lagging behind are clinical utility data from oncology clinics, hampering rapid therapeutic advances. Our case also highlights the logistical barriers associated with getting the most optimal therapeutic agents to the right patient in this era of personalized therapeutics based on cancer genomics.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".