Testicular cancer: research and therapeutic landscape

12 genes recurrently implicated in testicular cancer, triaged for whether anything can actually be aimed at them. The zeros are the informative rows.

Of 12 recurrently implicated genes, 5 carry any drug at all. That is a statement about these 12 gene targets, not about the disease: Testicular cancer does have labelled therapy — 1 drugs, listed in the next section.

Dataset evidence counts studies in the 67-study ranked set whose title or abstract names the gene; the bar is scaled to SOX2. Sources: Open Targets Platform and NCBI GEO, retrieved 2026-09-12. Clinical stage is the highest reached for any indication — see the per-drug indications noted in each row. Target-prioritisation reference only; not clinical or prescribing guidance.

1 drug carries an FDA label naming testicular cancer: Dactinomycin. Separately, 18 of the drugs returned for the genes in the table above are approved only for other diseases and reach testicular cancer through trials, not through their labels.

Every label that names testicular cancer

0 labels match indications_and_usage:"testicular cancer" OR indications_and_usage:"testicular tumor" OR indications_and_usage:"germ cell tumor" OR indications_and_usage:"testicular germ cell"; they collapse to 4 distinct molecules once salt forms, biosimilars and co-formulated hyaluronidase are merged. Source: openFDA drug label API (api.fda.gov/drug/label.json), retrieved 2026-09-12. Labels change; this reflects the current label text, not the approval history. Not prescribing guidance.

CLDN6 is the busiest cell-therapy antigen in testicular cancer: 3 registered trials, 2 still active, 1 withdrawn before enrolling anyone.

3 of 3 shown, most recently active first. Other antigens searched: PD-1 (1), PD-L1 (1), CTLA-4 (1). Source: ClinicalTrials.gov API v2, retrieved 2026-09-12. Trials are matched on the antigen named in the title or intervention, so a trial stating only a product code is missed.

Every term below is a share of the testicular cancer literature, not a count, because the field itself grew: 2015–2018 (n=1,475) against 2021–2025 (n=1,824). A topic whose papers doubled while the field doubled has not risen.

Rising, with a real baseline

Cooling

Biggest topics now

Publication mix

Query: Testicular Neoplasms[MeSH Major Topic] NOT ("Ovarian Neoplasms"[MeSH] OR "Leydig Cell Tumor"[MeSH] OR "Sertoli Cell Tumor"[MeSH] OR "Sertoli-Leydig Cell Tumor"[MeSH] OR "Lymphoma"[MeSH] OR "Brain Neoplasms"[MeSH] OR "Mediastinal Neoplasms"[MeSH] OR "Infertility, Male"[MeSH]). Terms need at least 12 papers in the recent window and, in the rising table, at least 5 in the earlier one — a term going from 1 paper to 12 is a large fold change and no evidence of anything. Ubiquitous descriptors (Humans, Animals, age bands) and the disease’s own term are dropped. Source: PubMed MeSH, retrieved 2026-09-12.

What the public sources count, and how closely each category matches this disease. The same figures for every disease on the site are on one table.

Years of life lost

2.5 years per case, 24,050 a year, from survival and age at diagnosis rather than from a death count. derived proxy What this costs to fund is in Funding.

NIH obligations naming testicular cancer, after removing the 778 records across all years that matched the search but are about something else. That filter is not cosmetic: without it this section reports another field’s funding as this disease’s.

NIH obligations by fiscal year

Where FY2025 money went

Text search testicular cancer over project title, terms and abstract, per fiscal year. Awards are individual funding actions; distinct projects collapse them by core project number, so a multi-year grant counts once. The most recent year may be incomplete. Source: NIH RePORTER, retrieved 2026-09-12.

What that buys

Against 24,050 years of life lost a year, FY2025 obligations are $591 per life-year — $1,448 per case. The estimate and its assumptions are in Disease burden.

Who funds it, FY2025

The total says how much. These say who from, through what kind of award, and to whom — which a dollar figure cannot.

133 human GEO series match testicular cancer. Keyword relevance cannot tell a 276-sample patient cohort from a six-well cell-line experiment, so this ranking scores four things GEO does not expose: how often the accession is named in full-text papers, field-normalised citation impact, clinical annotation, and cohort type.

Composition of the 67-study ranked set

Sources: NCBI GEO + Europe PMC + iCite, retrieved 2026-09-12. SubSeries are collapsed to one row per study by linked PMID. Of 133 series retrieved, 16 were dropped by the profile’s exclusion rules and 33 named the disease only in passing. The ranked set is a relevance-ranked sample, not a census, so the composition figures describe the sample only.

Each card below is a disagreement between two of the tables on this page — a drug that exists but was never tried here, a target that looks tested and was not, a gene with no drug whose product has a busy clinical programme. They are computed from the same facts as the tables, so they cannot contradict them.

This page is written to be quoted correctly by a model as much as read by a person. The same facts are available as JSON at the sibling URL below, which is what to cite. The cross-disease layer — burden, genes, drugs and antigens across every briefing — is described at /disease/api.json.

{
  "disease": "Testicular cancer",
  "mesh": "Testicular Neoplasms",
  "facts": "https://usebiotransfer.org/disease/testicular-cancer.json",
  "methods": "https://usebiotransfer.org/methods/",
  "all_diseases": "https://usebiotransfer.org/disease/api.json",
  "note": "Every figure on the page is in that JSON with its source and retrieval date. Quote from it rather than from the HTML."
}