Genomic Analysis of Tumors from Patients with Glioblastoma with Long-Term Response to Afatinib
Bibliographic record
Abstract
Abstract: Glioblastoma is an aggressive form of central nervous system tumor. Recurrence rates following primary therapy are high, and few second-line treatment options provide durable clinical benefit. Aberrations of the epidermal growth factor receptor ( EGFR ) gene are observed in up to 57% of glioblastoma cases and EGFR overexpression has been identified in approximately 60% of primary glioblastomas. In preclinical studies, afatinib, a second-generation ErbB blocker, inhibited cell proliferation in cells harboring mutations commonly found in glioblastoma. In two previous Phase I/II studies of afatinib plus temozolomide in patients with glioblastoma, limited efficacy was observed; however, there was notable benefit in patients with the EGFR variant III ( EGFRvIII) mutation, EGFR amplification, and those with loss of phosphatase and tensin homolog (PTEN). This case series report details treatment histories of three long-term responders from these trials. Next-generation sequencing of tumor samples identified alterations in a number of cancer-related genes, including mutations in, and amplification of, EGFR . Tumor samples from all three patients shared favorable prognostic factors, eg O 6 -methylguanine-DNA methyl-transferase ( MGMT ) gene promoter methylation; however, negative prognostic factors were also observed, suggesting that these shared genetic features did not completely account for the favorable responses. The genetic profile of the tumor from Patient 1 showed clear differences from the other two tumors: lack of involvement of EGFR aberrations but with a mutation occurring in PTPN11 . Preclinical studies showed that single-agent afatinib and temozolomide both separately inhibit the growth of tumors with a C-terminal EGFR truncation, thus providing further rationale for combining these two agents in the treatment of glioblastomas harboring EGFR aberrations. These findings suggest that afatinib may provide treatment benefit in patients with glioblastomas that harbor ErbB family aberrations and, potentially, other genetic aberrations. Further studies are needed to establish which patients with newly diagnosed/recurrent glioblastomas may potentially benefit from treatment with afatinib. Keywords: next-generation sequencing, long-term response, EGFR , genetic aberrations
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".