Abstract PD4-02: PD4-02 Spatial and temporal heterogeneity of predictive and prognostic signatures in triple-negative breast cancer treated with neoadjuvant combination immune-chemotherapy
Authors: Denkert Carsten , Schneeweiss Andreas , Rey Julia , Hattesohl Akira , Karn Thomas , Braun Michael , Jank Paul , Huober Jens , Sinn Hans-Peter , Zahm Dirk-Michael , Hanusch Claus , Marmé Frederik , Furlanetto Jenny , Thomalla Jörg , Blohmer Jens-Uwe , van Mackelenbergh Marion , Stiewe Thorsten , Staib Peter , Jackisch Christian , Teply-Szymanski Julia , Fasching Peter A. , Sinn Bruno V. , Untch Michael , Weber Karsten , Loibl Sibylle 
Published: March 2023

Background: It is well known that immunological pathways are relevant for response to classical neoadjuvant chemotherapy as well as combined chemo-immunotherapy. In addition, it has been shown that combined chemo-immunotherapy significantly improves survival, even in the context of only moderate effects on pCR. Due to the window therapy with durvalumab-alone and the option to analyze multiple consecutive biopsies, the GeparNuevo trial offers the opportunity to 1) determine gene expression patterns for pCR and DDFS endpoints 2) identify pathways most relevant for pCR and DDFS 3) identify genes specifically regulated by immunotherapy (comparison of samples pre-and post-window) 4) identify genes specifically regulated by chemotherapy (comparison of samples pre-Tx and after 4 cycles of chemotherapy 5) identify longitudinal patterns of gene expression by comparison of up to four time points and 6) identify changes in the tumor microenvironment by spatial sequencing of tumor cell and stroma areas. Methods: 292 tumor samples were evaluated by gene expression analysis: 162 pretherapeutic core biopsies, 79 post-window biopsies, 32 biopsies during chemotherapy and 19 biopsies of the residual tumor after therapy. These samples were analyzed by HTG OBP panel targeting 2549 genes which are assigned to 25 different biological mechanisms or cellular pathways. In addition, spatial profiling was compared in a subset of pre-and post-window samples using Nanostring GeoMx spatial profiling system. Endpoints were pCR and DDFS. Results: A total of more than 600 genes were significantly associated with either the pCR or the DDFS endpoint in either the complete GeparNuevo cohort or one of the two therapy arms. Interestingly, there was a large number of predictive or prognostic genes (n=247 for pCR and n=179 for DDFS) in the durvalumab arm, while the number of genes in the placebo arm was considerably lower (n=113 for pCR and n=61 for DDFS). We used existing pathway information for HTG OBP panel to analyze the contribution of different cellular processes to pCR and DDFS signatures in different therapy arms. Immune pathways were particularly relevant for durvalumab signatures (pCR and DDFS), while cell cycle related gene expression patterns were particularly involved in signatures predictive of pCR in both therapy arms. To further assign genes to the cellular response to durvalumab-alone or chemotherapy-alone, we compared gene expression patterns in durvalumab arm before and after the window phase (gene expression patterns induced by one dose of durvalumab) with gene expression patterns in placebo arm before and after 4 cycles of chemotherapy. Further longitudinal alterations were analyzed by comparison of longitudinal samples for 4 different time-points (a: before NACT, n=162; b: after window phase, n=79; c: after 4 cycles, n=31 and d: at surgery, n=19). Using the Nanostring GeoMx spatial RNA profiling system guided by cytokeratine immunofluorescence, we compared areas with high tumor cell content with stromal areas with or without TILs. In combination with the HTG gene expression data, we were able allocate the changes induced by durvalumab vs chemotherapy to the stromal cell and tumor cell compartment, indicating a re-organization of the tumor-microenvironment. Conclusions: In our analysis, we show that immune gene signatures are particularly relevant for neoadjuvant response to durvalumab as well as prognosis after durvalumab treatment, while proliferation signatures are involved in pCR-signatures after durvalumab as well as chemotherapy. The spatial analysis showed that relevant changes occur in the stromal compartment, indicating a re-organization of the tumor microenvironment. The parallel targeting of immune- and proliferation pathways might explain why a combined immunotherapy-chemotherapy approach is more successful than each single therapy strategy alone.

Citation Format: Carsten Denkert, Andreas Schneeweiss, Julia Rey, Akira Hattesohl, Thomas Karn, Michael Braun, Paul Jank, Jens Huober, Hans-Peter Sinn, Dirk-Michael Zahm, Claus Hanusch, Frederik Marmé, Jenny Furlanetto, Jörg Thomalla, Jens-Uwe Blohmer, Marion van Mackelenbergh, Thorsten Stiewe, Peter Staib, Christian Jackisch, Julia Teply-Szymanski, Peter A. Fasching, Bruno V. Sinn, Michael Untch, Karsten Weber, Sibylle Loibl. PD4-02 Spatial and temporal heterogeneity of predictive and prognostic signatures in triple-negative breast cancer treated with neoadjuvant combination immune-chemotherapy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD4-02.