Abstract 1968: Early on-treatment genomic instability level in cell free DNA as a predictive and prognostic marker in metastatic breast cancer patients
Bibliographic record
Abstract
Abstract Metastatic breast cancer is mostly incurable and patients are treated with serial chemotherapy regimens to prolong life and decrease symptoms. Treatment efficacy is usually monitored by imaging every 3 months and by the measurement of blood tumor biomarkers despite their poor sensitivity and specificity. Novel and more timely approaches to monitoring treatment response and estimating prognosis are needed to enable more rapid treatment decisions. The Genomic Instability Number (GIN) is a recently published measure of DNA copy number changes across the genome that can be derived from low pass whole genome sequencing of cell free DNA in plasma. As it is genome wide, this method does not require previous tumor sequencing and offers a non-invasive option for liquid biopsy testing. We measured the GIN in plasma from a cohort of metastatic breast cancer patients undergoing therapy and correlated GIN levels with clinical response and outcome data. Methods: Blood samples (n=69) were collected from 27 patients with metastatic breast cancer undergoing standard of care therapy. Blood was collected prior to the beginning of a new line of therapy (T0), and then at T1 within 10 days and T2 within 22 days post-treatment initiation. The GIN was calculated for all 69 samples analyzed and we used a pre-determined detection threshold of GIN>170. Treatment response was assessed at 3 and at 6 months post-treatment. Results: 20 of the 27 metastatic patients analyzed had detectable GIN levels in at least one sample. Pre-treatment baseline GIN levels ranged from 80 to 6599 with undetectable GIN in 33% of patients. Baseline plasma GIN was not significantly associated with clinical characteristics, clinical response, progression free survival (PFS) or overall survival (OS). However, the average GIN level at early on-treatment time points, T1 or T2, was associated with clinical response with lower levels in patients who presented tumor response at 3 months (p<0.05) but not at 6 months. We found that detectable GIN at T1 was associated with significantly lower OS (p=0.014) but not with PFS, while detectable GIN values at T2 were associated with both poorer PFS (P=0.017) and OS (p=0.0085). The dynamic changes in GIN during treatment were also associated with treatment response and outcome. In fact, the degree of fall in GIN from baseline to T1 and from baseline to T2 was associated with early clinical response (at 3 months) (p<0.001 and p=0034 respectively). Interestingly, the fall of detectable GIN at T1 was highest in TNBC patients (average 73%) compared to non TNBCs (average 36%) (p=0.016). Finally, the percentage drop of GIN at T1 but not atT2 was significantly associated with PFS (p=0.016). In conclusion, the results from our study highlight the value of early on-treatment ctDNA measurements as a predictor of therapeutic response in the metastatic setting, and provide an approach that does not need to rely on tumor sequencing, thus making it much more clinically feasible. Citation Format: Adriana Aguilar-Mahecha, Josiane Lafleur, Susie Brousse, Cristiano Ferrario, Kimberly A. Holden, Graham McLennan, Taylor J. Jensen, Mark Basik. Early on-treatment genomic instability level in cell free DNA as a predictive and prognostic marker in metastatic breast cancer patients [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1968.
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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 | 0.000 | 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 | 0.000 |
| 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".