Does the addition of molecular targeted therapy to standard treatments lead to better or worse outcomes overall? A systematic review of EGFR-targeted therapies used in combination with standard treatments.
Bibliographic record
Abstract
2572 Background: Combining novel targeted therapies with standard treatment is a common strategy in drug development. The primary aim of this systematic review was to provide a summary of the outcomes from studies combining EGFR targeted therapy (ETT) with standard treatments. Methods: A PubMed (1975-Jan, 2012) and ASCO (2005-2011) abstract database search was performed. Eligible studies were RCTs that compared standard therapies (chemotherapy, radiation therapy, or hormonal therapy) to standard therapy plus an ETT. Efficacy outcomes: overall survival (OS), progression free survival (PFS), objective response rate (ORR) time to progression (TTP), clinical benefit rate (CBR), and toxicity (total and grade 3+) were recorded. If any toxicity was significantly more frequent in one arm it was coded as such. Results: 128 studies (60 manuscripts, 68 abstracts) met criteria. Median study enrolment was 230 patients, with breast, lung, and colorectal cancer as the main tumor sites. ETT, cetuximab (41%), trastuzumab (19%), gefitinib (13%), erlotinib (13%), and lapatinib (16%), was combined with platinums, 5FU, taxanes, nucleoside analogs; hormonal therapy and/or radiation therapy. Refer to the table for efficacy results. Most frequent toxicities (common and grade 3+) in the combined arm were rash, diarrhea, hematological toxicity and fatigue. Conclusions: Despite enthusiasm for combining standard treatments with targeted therapies, we have demonstrated mixed efficacy results, with marginal benefit and worse toxicity. Quality of life (Q of L) was rarely reported. A similar analysis is underway for VEGFR, and mTor inhibitors, but raises the question of how best to approach the question of combination therapy. [Table: see text]
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".