Abstract CT011: Circulating tumor DNA (ctDNA) sequencing for <i>HER2</i> mutation (<i>HER2</i>mut) screening and response monitoring to neratinib in metastatic breast cancer (MBC)
Bibliographic record
Abstract
Abstract Introduction: MutHER is a phase II trial that demonstrated the anti-tumor activity of the pan-HER inhibitor neratinib in HER2mut, non-amplified MBC. The major challenges to accrue to this trial were the large number of pts to screen for the 2-3% HER2mut population and the high rate (24%) of poor quality tumor DNA for sequencing. The goals of this ctDNA study were: 1) the concordance of HER2mut detected by ctDNA versus tumor testing; 2) the incidence of ctDNA HER2mut in HER2 non-amplified MBC; 3) changes in HER2mut variant allele frequency (VAF) on neratinib therapy. Methods: A sample size of 30 negative (neg) controls was needed to ensure 90% confidence if ctDNA testing has >90% specificity in detecting HER2mut. Thus, plasma from MBC pts obtained at screening for MutHER trial (Neg control: 40 pts without HER2mut on tumor testing; Positive (pos) control: 14 pts with known HER2mut who received neratinib) were subjected to Guardant360 ctDNA 70-gene panel sequencing (all exons of HER2 included). ctDNA from the 14 neratinib treated pts were also analyzed at week (wk) 4 and upon progression. ctDNA data from MBC pts clinically tested at Guardant Health were interrogated for HER2mut incidence. Results: Among the 14 pts with tumor pos for HER2mut, ctDNA sequencing identified the same HER2mut in 11, discrepant HER2mut in 1, and neg in 2. The 2 pts with ctDNA neg for HER2mut had progressive disease (PD) and stable disease (SD > 6 months) on neratinib, respectively. Among the 40 neg controls, 8 were not evaluable (no detectable ctDNA or assay unsuccessful) and all 32 successfully sequenced cases were neg for HER2mut. The sensitivity and specificity of ctDNA for HER2mut detection was 11/14 (79%, 90% CI: 53-94%) and 32/32 (100%, 90% CI: 91-100%), respectively. Among the 11 paired baseline and wk 4 samples, 9 (82%) had lower HER2mut VAFs at wk 4 than at baseline, with 1 complete response (CR), 1 partial response (PR), 5 SD, and 4 PD at wk 8 as best tumor response. Two pts had higher wk 4 ctDNA HER2mut VAFs and both had radiographic PD at wk 8. The absolute HER2mut VAF levels at wk 4 were significantly associated with TTP (Spearman rho=-0.69, p=0.02) and tumor size change (rho=0.67, p=0.05). The HER2mut VAFs were significantly higher at progression than wk 4 in all pts (p<0.01). One pt acquired a new HER2mut T798I, which is analogous to the gate-keeper mutation EGFR T790M. The incidence of HER2mut without amplification in unselected consecutive MBC clinically tested by Guardant360 was 3% (48/1,584), with mutation pattern similar to published tumor testing data. Conclusions: ctDNA sequencing is sensitive and highly specific in detecting HER2mut, offering a non-invasive method to identify pts for trials of HER2mut-targeted therapy. Decreased HER2mut VAFs at wk 4 was observed in 82% of cases, consistent with the on-target effect of neratinib. Increased HER2mut VAFs at wk 4 is a potential early marker of progression. Citation Format: Cynthia Ma, Ron Bose, Feng Gao, Rachel Freedman, Melinda Telli, Gretchen Kimmick, Eric Winer, Michael Naughton, Matthew Goetz, Christy Russell, Debu Tripathy, Melody Cobleigh, Andres Forero, Timothy Pluard, Carey Anders, Shana Thomas, Jill Anderson, Caroline Bumb, Kimberly Banks, Richard Lanman, Richard Bryce, Alshad Lalani, John Pfeifer, Daniel Hays, Mark Pegram, Kimberly Blackwell, Philippe Bedard, Hussam Al-Kateb, Matthew Ellis. Circulating tumor DNA (ctDNA) sequencing for HER2 mutation (HER2mut) screening and response monitoring to neratinib in metastatic breast cancer (MBC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr CT011. doi:10.1158/1538-7445.AM2017-CT011
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".