Impact of adjuvant chemotherapy on T1N0M0 breast cancer patients: a propensity score matching study based on SEER database and external cohort

Shen, Kaiwen; Fu, Deyuan; Wu, Song; Gu, Xiaodong; Wang, Jie; Qian, Wei; Zheng, Zhenzhen

doi:10.1186/s12885-022-09952-z

Cited by 4 publications

(2 citation statements)

References 111 publications

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“…Clinical practice guidelines for testing with ROR/Prosigna and assessed prognostic classi cation schemes According to regional guidelines for GEP to guide clinical decision making during the study period, ROR/Prosigna was broadly recommended for postmenopausal women with node-negative, HR+/HER2breast cancer. In 2022, after the end of the study period, these guidelines were updated with new cut-off values for Ki-67 (low < 5%, intermediate 5-30%, high > 30%) and recommendation to omit testing in patients with pT1bN0 tumors due to the likely low absolute bene t from chemotherapy [13,25]. Both guidelines are presented in detail in Supplementary Tables 1 and 2.…”

Section: Study Populationmentioning

confidence: 99%

Adherence to and optimization of guidelines for Risk of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study

Kjällquist,

Tsiknakis,

Acs

et al. 2024

Preprint

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Purpose Gene expression profiles are used for decision making in the adjuvant setting of hormone receptor positive, HER2 negative (HR+/HER2-) breast cancer. Previous studies have reported algorithms to optimize the use of RS/Oncotype Dx but no such efforts have focused on ROR/Prosigna. We sought to improve pe-selection of patients before testing using machine learning. Methods Postmenopausal women with resected HR+/HER2- node negative breast cancer tested with ROR/Prosigna in four Swedish regions were included (n = 348). We used the ROR/Prosigna assessment results to compare the performance of four risk classifications in terms of over- and undertreatment. We developed and validated a machine learning model that comprised simple prognostic factors (size, progesterone receptor expression, grade and Ki67) for prediction of ROR/Prosigna outcome. Results Adherence to guidelines reached 66.3%, with non-tested patients being older and having more comorbidities (p < 0.001). Previous risk classifications led to excessive undertreatments (CTS5: 21.8%, MINDACT/TailorX risk definitions: 28.1%) or large intermediate groups that would need to be tested with gene expression profiling (Ki67 cut-offs according to Plan B: 86.5%). The model achieved AUC under ROC for predicting ROR/Prosigna result of 0.77 in the training and 0.83 in the validation cohort. By setting and validating upper and lower cut-offs in the model, we could improve correct risk stratification and decrease the proportion of patients needing testing with ROR/Prosigna compared to current management. Conclusion We show the feasibility of machine learning algorithms to improve patient selection for gene expression profiling. Further validation in external cohorts is needed.

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Section: Study Populationmentioning

confidence: 99%

Adherence to and optimization of guidelines for Risk of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study

Kjällquist,

Tsiknakis,

Acs

et al. 2024

Preprint

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“…In contrast, patients with pT1a tumors have excellent survival after surgery alone without substantial additional benefit from adjuvant chemotherapy . For pT1b tumors, the data on the magnitude of benefit from adjuvant chemotherapy are conflicting …”

Section: Introductionmentioning

confidence: 99%

Tumor-Infiltrating Lymphocytes in Patients With Stage I Triple-Negative Breast Cancer Untreated With Chemotherapy

Geurts,

Balduzzi,

Steenbruggen

et al. 2024

JAMA Oncol

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ImportanceThe absolute benefit of chemotherapy for all patients with stage I triple-negative breast cancer (TNBC) is unclear, and biomarkers are not currently available for selecting patients with an excellent outcome for whom neoadjuvant or adjuvant chemotherapy may have negligible benefit. High levels of stromal tumor-infiltrating lymphocytes (sTILs) are associated with favorable survival in TNBC, but data solely in stage I TNBC are lacking.ObjectiveTo examine the outcomes of patients of all ages with stage I TNBC solely and who received neither neoadjuvant nor adjuvant chemotherapy, according to centrally reviewed sTIL levels at prespecified cutoffs.Design, Setting, and ParticipantsThis cohort study used the Netherlands Cancer Registry to identify patients diagnosed with stage I TNBC between January 1, 2005, and December 31, 2015, who were not treated with chemotherapy. Only patients who did not receive neoadjuvant and/or adjuvant chemotherapy were selected. The clinical data were matched with their corresponding pathology data provided by the Dutch Pathology Registry. Data analysis was performed between February and October 2023.Main Outcomes and MeasuresThe primary end point was breast cancer–specific survival (BCSS) at 5, 10, and 15 years for the prespecified sTIL level cutoffs of 30%, 50%, and 75%. Hematoxylin and eosin–stained slides were used for central review of histologic subtype, grade, and lymphovascular invasion. The International Immuno-Oncology Biomarker Working Group guidelines were used to score the sTIL levels; these levels were determined for 1041 patients.ResultsA total of 4511 females with stage I TNBC (mean [SD] age at diagnosis, 64.4 [11.1] years; median follow-up, 11.4 [95% CI, 10.9-11.9] years) were included. Most tumors (952 [91.5%]) were invasive carcinomas of nonspecial histologic subtype. Most patients (548 [52.6%]) had pT1cN0 tumors. Median (range) sTIL level was 5% (1%-99%). A total of 775 patients (74.4%) had sTIL levels below 30%, 266 (25.6%) had 30% or greater, 203 (19.5%) had 50% or greater, and 141 (13.5%) had 75% or greater. Patients with pT1abN0 tumors had a more favorable outcome vs patients with pT1cN0 tumors, with a 10-year BCSS of 92% (95% CI, 89%-94%) vs 86% (95% CI, 82%-89%). In the overall cohort, sTIL levels of at least 30% were associated with better BCSS compared with sTIL levels less than 30% (96% and 87%, respectively; hazard ratio [HR], 0.45; 95% CI, 0.26-0.77). High sTIL levels of 50% or greater were associated with a better outcome than low sTIL levels of less than 50% (HR, 0.27; 95% CI, 0.10-0.74) in patients with pT1C tumors, with a 10-year BCSS of 95% increasing to 98% with sTIL levels of 75% or greater.Conclusions and RelevanceResults of this study showed that patients with stage I TNBC and high level of sTILs who did not receive neoadjuvant or adjuvant chemotherapy had excellent 10-year BCSS. The findings further support the role of sTILs as integral biomarkers in prospective clinical trials of therapy optimization for this patient population.

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The Impact of Neoadjuvant Chemotherapy on Patients With T1N0M0 Triple-Negative and HER-2 Positive Breast Cancer: A Retrospective Analysis Based on the SEER Database

Cheng,

Dai,

Liu

et al. 2024

Clinical Breast Cancer

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Impact of adjuvant chemotherapy on T1N0M0 breast cancer patients: a propensity score matching study based on SEER database and external cohort

Cited by 4 publications

References 111 publications

Adherence to and optimization of guidelines for Risk of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study

Adherence to and optimization of guidelines for Risk of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study

Tumor-Infiltrating Lymphocytes in Patients With Stage I Triple-Negative Breast Cancer Untreated With Chemotherapy

The Impact of Neoadjuvant Chemotherapy on Patients With T1N0M0 Triple-Negative and HER-2 Positive Breast Cancer: A Retrospective Analysis Based on the SEER Database

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