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Abstract PD13-02: Site of recurrence after neoadjuvant therapy: A multi-center pooled analysis

2021· article· en· W3129622461 on OpenAlexaff
Sonal Shad, Marieke van der Noordaa, Marie Osdoit, Diane De Croze, Anne‐Sophie Hamy, Marick Laé, Fabien Reyal, Miguel Martín, Marı́a del Monte-Millán, Sara López‐Tarruella, Judy C. Boughey, Matthew P. Goetz, Tanya L. Hoskin, Rebekah Gould, Vicente Valero, Gabe S. Sonke, Tessa G. Steenbruggen, Maartje van Seijen, Jelle Wesseling, John M.S. Bartlett, Stephen B. Edge, Mi‐Ok Kim, Jean Abraham, Carlos Caldas, Helena Earl, Elena Provenzano, Stephen‐John Sammut, David Cameron, A Graham, Peter S Hall, Lorna Mackintosh, Andrew K. Godwin, Kelsey Schwensen, Priyanka Sharma, Angela DeMichele, Janet Dunn, Louise Hiller, Larry Hayward, Jeremy Thomas, Kimberly Cole, Lajos Pusztai, Laura van’t Veer, Fraser Symmans, Laura Esserman, Christina Yau

Bibliographic record

VenueCancer Research · 2021
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
KeywordsMedicineCumulative incidenceInternal medicineOncologyIncidence (geometry)PopulationConfidence intervalBreast cancerDiseaseCancerCohort

Abstract

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Abstract Background: Achieving a pathologic complete response (pCR) has been shown on the patient level to predict excellent long-term event-free survival outcomes. Residual cancer burden (RCB) quantifies the extent of residual disease for patients who did not achieve pCR. We have previously observed in the I-SPY 2 TRIAL that while metastatic events outside the central nervous system (CNS) were dramatically reduced in the setting of pCR, the incidence of CNS metastasis remained similar across RCB classes, raising the possibility that these CNS events may be independent of response in the breast. In this study, we evaluate the type and sites of recurrences by RCB in a large pooled dataset, which allows for analysis within subtype, to validate these findings. Methods: 5161 patients pooled across 12 institutions/trials with available RCB and event-free survival (EFS) data were included in this analysis. EFS was calculated as the interval between treatment initiation, and locoregional recurrence, distant recurrence or death from any cause; patients without event are censored at time of last follow-up. The median follow-up is 4.6 years. We summarized the EFS event type, further sub-dividing the distant recurrence events (DR) by their site of relapse (CNS-only, CNS and other sites, Non-CNS). We used a competing risk (Fine-Gray) model to assess which of these site-specific relapses differ between RCB classes and estimated the cumulative incidence of CNS-only and non-CNS events at 5 years. Analyses were performed across the entire study population and within HR/HER2 defined subtypes. Results: Among the 5161 subjects, there were 1164 EFS events, including 92 (7.9%) local recurrences (without distant recurrence and/or death) and 1072 distant recurrence-free survival (DRFS) events. Among the DRFS events, 158 patients died without a distant recurrence. 914 experienced distant recurrences, including 90 (9.8%) with CNS-only, 145 (15.9%) with CNS and other sites, 664 (72.6%) with non-CNS distant recurrence; 15 (1.6%) patients had missing recurrence site information. Table 1 summarizes the cumulative incidence of CNS-only and non-CNS recurrence at 5 years and the proportion of CNS-only recurrences among DR events by RCB class overall and within each HR/HER2 subtypes. The incidence of CNS-only recurrences was low and similar across RCB classes. In contrast, the incidence of non-CNS recurrences increases with increasing RCB. As a result, CNS-only recurrences are proportionally higher within the RCB-0 and RCB-I than in the RCB-II and RCB-III groups, largely because of the low DR event rate and relative low frequency of non-CNS recurrence events within the RCB-0 and RCB-I classes. Overall, 27% of the recurrences in the setting of pCR (RCB-0) are due to CNS-only recurrences.Conclusions: Consistent with previous studies, our large pooled analysis confirmed that CNS-only recurrences are uncommon but appear similar across RCB groups, independent of response, suggesting that the CNS is a treatment sanctuary site. In contrast, non-CNS recurrence rates increase as RCB increases. These findings suggest that inclusion of CNS-only recurrences as an outcome event may impact the association between neoadjuvant therapy response and long-term outcomes in the context of current therapies. Novel therapies that cross the blood brain barrier will be needed to impact CNS recurrence rates. Table 1: Cumulative Incidence of CNS Only and non-CNS Distant Recurrences at 5 years and proportion of CNS-only events among DR eventsRCB Class0IIIIIIpOverall (5161)N16766622017806Cum. Inc. CNS Only2%2%2%1%0.627Cum. Inc. Non-CNS3%6%16%27%<0.001# CNS-Only / # DR events (%)26/96 (27%)14/74 (19%)39/443 (9%)11/301 (4%)HR-HER2- (1774)N770212590202Cum. Inc. CNS Only2%3%2%4%0.298Cum. Inc. Non-CNS4%11%19%42%<0.001# CNS-Only / # DR events (%)13/50 (26%)6/32 (19%)13/148 (9%)8/111 (7%)HR-HER2+ (572)N3766710029Cum. Inc. CNS Only1%5%5%0%0.022Cum. Inc. Non-CNS2%5%18%38%<0.001# CNS-Only / # DR events (%)4/17 (24%)3/10 (30%)6/31 (19%)0/13 (0%)HR+HER2+ (858)N31317229182Cum. Inc. CNS Only1%1%2%0%0.37Cum. Inc. Non-CNS2%3%15%26%<0.001# CNS-Only / # DR events (%)3/10 (30%)2/16 (12%)7/68 (10%)0/29 (0%)HR+HER2- (1957)N2172111036493Cum. Inc. CNS Only3%2%1%0.2%0.087Cum. Inc. Non-CNS5%4%13%20%<0.001# CNS-Only / # DR events (%)6/19 (32%)3/16 (19%)13/196 (7%)3/148 (2%) Citation Format: Sonal Shad, Marieke van der Noordaa, Marie Osdoit, Diane de Croze, Anne-Sophie Hamy, Marick Lae, Fabien Reyal, Miguel Martin, María Del Monte-Millán, Sara López-Tarruella, I-SPY 2 TRIAL Consortium, Judy C Boughey, Matthew P Goetz, Tanya Hoskin, Rebekah Gould, Vicente Valero, Gabe Sonke, Tessa G Steenbruggen, Maartje van Seijen, Jelle Wesseling, John Bartlett, Stephen Edge, Mi-Ok Kim, Jean Abraham, Carlos Caldas, Helena Earl, Elena Provenzano, Stephen-John Sammut, David Cameron, Ashley Graham, Peter Hall, Lorna Mackintosh, Fan Fang, Andrew K Godwin, Kelsey Schwensen, Priyanka Sharma, Angela DeMichele, Janet Dunn, Louise Hiller, Larry Hayward, Jeremy Thomas, Kimberly Cole, Lajos Pusztai, Laura Van't Veer, Fraser Symmans, Laura Esserman, Christina Yau. Site of recurrence after neoadjuvant therapy: A multi-center pooled analysis [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD13-02.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.016
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.062
GPT teacher head0.437
Teacher spread0.375 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes1
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