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- Bai, Yalai, et al.
(författare)
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An Open Source, Automated Tumor Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
- 2021
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Ingår i: Clinical Cancer Research. - 1078-0432. ; 27:20, s. 5557-5565
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Although tumor infiltrating lymphocytes (TIL) assessment has been acknowledged to have both prognostic and predictive importance in triple negative breast cancer (TNBC), it is subject to inter and intra-observer variability that has prevented widespread adoption. Here we constructed a machine-learning based breast cancer TIL scoring approach and validated its prognostic potential in multiple TNBC cohorts. Experimental Design: Using the QuPath open source software, we built a neural-network classifier for tumor cells, lymphocytes, fibroblasts and “other” cells on hematoxylin-eosin (H&E) stained sections. We analyzed the classifier-derived TIL measurements with five unique constructed TIL variables. A retrospective collection of 171 TNBC cases was used as the discovery set to identify the optimal association of machine-read TIL variables with patient outcome. For validation we evaluated a retrospective collection of 749 TNBC patients comprised of four independent validation subsets. Results: We found that all five machine TIL variables had significant prognostic association with outcomes (p≤0.01 for all comparisons) but showed cell specific variation in validation sets. Cox regression analysis demonstrated that all five TIL variables were independently associated with improved overall survival after adjusting for clinicopathological factors including stage, age and histological grade (p≤0.003 for all analyses). Conclusions: Neural net driven cell classifier defined TIL variables were robust and independent prognostic factors in several independent validation cohorts of TNBC patients. These objective, open source TIL variables are freely available to download and can now be considered for testing in a prospective setting to assess clinical utility.
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- Zerdes, Ioannis, et al.
(författare)
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Interplay between copy number alterations and immune profiles in the early breast cancer Scandinavian Breast Group 2004-1 randomized phase II trial : results from a feasibility study
- 2021
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Ingår i: npj Breast Cancer. - : Springer Nature. - 2374-4677. ; 7:1
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Tidskriftsartikel (refereegranskat)abstract
- Emerging data indicate that genomic alterations can shape immune cell composition in early breast cancer. However, there is a need for complementary imaging and sequencing methods for the quantitative assessment of combined somatic copy number alteration (SCNA) and immune profiling in pathological samples. Here, we tested the feasibility of three approaches-CUTseq, for high-throughput low-input SCNA profiling, multiplexed fluorescent immunohistochemistry (mfIHC) and digital-image analysis (DIA) for quantitative immuno-profiling- in archival formalin-fixed paraffin-embedded (FFPE) tissue samples from patients enrolled in the randomized SBG-2004-1 phase II trial. CUTseq was able to reproducibly identify amplification and deletion events with a resolution of 100 kb using only 6 ng of DNA extracted from FFPE tissue and pooling together 77 samples into the same sequencing library. In the same samples, mfIHC revealed that CD4 + T-cells and CD68 + macrophages were the most abundant immune cells and they mostly expressed PD-L1 and PD-1. Combined analysis showed that the SCNA burden was inversely associated with lymphocytic infiltration. Our results set the basis for further applications of CUTseq, mfIHC and DIA to larger cohorts of early breast cancer patients.
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