Recruiting cancer trials by gene
The Oncology Trial Activity Index tracks how many currently recruiting ClinicalTrials.gov studies mention each of 37 cancer-relevant genes. It is a research-attention snapshot, not a league table of biological importance or treatment benefit.
Genes monitored
37
With recruiting studies
37
Highest current count
HER2 · 1,020
Study-level monitoring pilot
Trace records and compare dated snapshots
The reproducible export includes NCT identifiers, source links, registry update dates and the fields in which each gene symbol was found. This is the first stored baseline, so no additions or removals are claimed yet.
Analysis from this snapshot
Three focused guides turn the table into answerable questions without stretching the data beyond the registry search.
The ten most-mentioned cancer genes
The dated ranking, what each row measures and why the counts cannot be added.
Why HER2 currently appears above EGFR
How synonyms, biomarker language and registry fields shape the result.
EGFR vs KRAS trial activity
Connected pathway biology, different molecular questions and overlapping studies.
Current activity index
Counts are exact at the snapshot date. One study can mention several genes, so rows overlap and must not be added together.
| Rank | Gene | Recruiting studies |
|---|---|---|
| 1 | HER2 | 1,020 |
| 2 | EGFR | 696 |
| 3 | KRAS | 311 |
| 4 | KIT | 218 |
| 5 | CDK4 | 180 |
| 6 | RB1 | 161 |
| 7 | BRAF | 156 |
| 8 | BCL2 | 125 |
| 9 | BRCA1 | 109 |
| 10 | TP53 | 102 |
| 11 | VEGFA | 95 |
| 12 | BRCA2 | 75 |
| 13 | APC | 65 |
| 14 | IDH1 | 59 |
| 15 | PIK3CA | 56 |
| 16 | MTOR | 50 |
| 17 | MGMT | 46 |
| 18 | ATM | 45 |
| 19 | PALB2 | 42 |
| 20 | POLE | 42 |
| 21 | MYC | 39 |
| 22 | PTEN | 37 |
| 23 | CDKN2A | 36 |
| 24 | MSH2 | 33 |
| 25 | MTAP | 33 |
| 26 | MLH1 | 31 |
| 27 | EZH2 | 27 |
| 28 | MDM2 | 14 |
| 29 | STAT3 | 14 |
| 30 | CHEK2 | 13 |
| 31 | AKT1 | 12 |
| 32 | ATRX | 8 |
| 33 | HIF1A | 5 |
| 34 | SMARCB1 | 2 |
| 35 | DAXX | 1 |
| 36 | KMT2D | 1 |
| 37 | MUTYH | 1 |
Method and limitations
GeneAnalyses queries the ClinicalTrials.gov API v2 separately for each curated gene, with condition set to cancer and overall status set to recruiting. An explicit editorial command stores the stripped study records as a dated, versioned snapshot; ordinary production builds consume that committed snapshot rather than silently changing the dataset.
The export records where an exact gene-symbol mention appears in the returned title, description, condition, intervention, eligibility or outcome fields. ClinicalTrials.gov can also return a match from its search index that is not present in those exported fields; those records are labelled accordingly rather than assigned a guessed field.
A symbol can appear as a biomarker, eligibility criterion, exploratory endpoint, background concept or treatment target. Counts therefore measure registry search matches, not positive results, approvals, patient numbers, prevalence or clinical importance. Gene-symbol ambiguity and synonyms can affect text search, and one study can appear under several genes.
Get the monthly Oncology Trial Activity Index
Receive the refreshed rankings, the largest changes and the new studies worth opening—delivered through the GeneAnalyses Substack.
Open the newsletterContinue through the strongest research clusters
Use the index as a starting point, then follow one connected mechanism rather than treating an isolated count as an answer.
EGFR–RAS–MAPK
Move from receptor biology to driver variants, resistance and targeted therapy.
TP53 tumour suppression
Follow p53 from gene function to mutation patterns and inherited risk.
BRCA and DNA repair
Connect trial activity with homologous-recombination biology and PARP inhibition.