Abstract P1-09-19: Tumor RNA disruption index as a tool to predict response to neoadjuvant chemotherapy in breast cancer: Optimizing timing of biopsy
Bibliographic record
Abstract
Abstract BACKGROUND. Early detection of tumor response to neoadjuvant therapy (NAT) could be used to tailor therapy and lower toxicity from ineffective treatments. The CCTG MA.22 trial has shown that RNA disruption is associated with breast cancer response to NAT when measured by image guided core biopsy mid-treatment. The objectives of this study were: 1) to determine the optimal time to measure the tumor RNA Disruption Index (RDI) after initiation of NAT when assessed by fine needle aspiration (FNA) in an office setting and 2) to determine if RDI could predict response to a second chemotherapy agent in patients that had a suboptimal response. METHODOLOGY. We performed a prospective pilot study including patients with palpable biopsy-proven breast cancer eligible for NAT. Chemotherapy and surgery were at the discretion of the treating physician. Two FNAs after cycles 1, 2, 3 and after initiation of a new chemotherapy agent were collected in RNA Protect Cell Reagent and sent to Rna Diagnostics Inc. to assess RDI. Prospectively recorded clinical tumor measurements and surgical pathology reports were obtained. Tumor pathological response (pR) after NAT was measured by pathological complete response (pCR: no invasive disease in breast) and residual cancer burden index (RCBI). RESULTS. 30 patients were accrued to the study. One patient withdrew consent, one patient was found to have metastatic disease and did not undergo surgery, and one patient had bilateral breast cancer (n= 29 evaluable tumors). ER+HER2-: 38% (11/29), ER-Her2-: 28% (8/29) and HER2+: 34% (10/29). 89% (25/28) of patients received taxane and anthracycline containing regimens. All HER2+ received trastuzumab. Our pCR and RCBI 0-1 rates were 24% (7/29) & 38% (11/29) respectively. At cycles 1, 2 and 3, RDI could be evaluated in 72% (21/29), 73% (16/22) and 44% (7/16) of palpable tumors. After the switch to a new agent, RDI could only be evaluated in 30% (3/10) of patients with palpable tumors. Using a tumor RDA cutoff at 5 (non-responder RDI < 5 (NR) and responder RDI ≥ 5 (R)), responder status between cycle 1 and 2 was concordant in 73% (11/15). After 1 cycle, NR vs. R was associated with numerically lower pCR (13% (2/15) vs. 33% (2/6), p=0.54) and RCBI 0-1 at surgery (20% (3/15) vs. 33% (2/6), p=0.60). These findings were similar after cycle 2. Non-analyzable samples (NAS) because of absence of RNA were associated with high pR (pCR: 38% (3/8) and RBCI 0-1: 75% (6/8)). The 3 NR at cycle 1 that achieved a significant pR had either non-palpable tumors or NAS after switching to a new chemotherapy agent. CONCLUSION. RDI can be measured by FNA in an office setting and could be helpful to identify early non-responders to NAT. The optimal time to perform RDI is after 1 or 2 cycles of treatment, which should be considered in ongoing and future trials. This study was underpowered to detect a statistically significant correlation between RDI and pR. NAS is associated with high pR and could represent responders to treatment. This early data suggests that RDI is unlikely to be helpful in assessing response to a second chemotherapy agent after receiving 4 cycles of standard chemotherapy. The use of RDI to tailor NAT needs to be evaluated in larger prospective trials. Citation Format: Samkari A, Chung W, Parissenti A, Pritzker L, Trabulsi N, Basik M, Boileau J-F. Tumor RNA disruption index as a tool to predict response to neoadjuvant chemotherapy in breast cancer: Optimizing timing of biopsy [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-09-19.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".