Pathology of Inflammatory Breast Cancer (IBC) A rare tumor

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1 Pathology of Inflammatory Breast Cancer (IBC) A rare tumor Jelle Wesseling 1, John Martens 2, Gabe Sonke 1, Carolien Schröder 3 1: Netherlands Cancer Institute Antoni van Leeuwenhoek Hospital, Amsterdam 2: Erasmus MC, Rotterdam 3: University Medical Center Groningen, Groningen 1

2 IBC is different from normal IBC Subtype Inflammatory Breast Cancer Normal breast cancer ER-negative 50% 20% HER2-postive 35-40% 10-15% Tumor emboli >90% ~10% Inflammatory response % <20% Peau d orange 100% <5% 2

3 Tumor emboli frequently seen in IBC 3

4 The worse prognosis of IBC not due to unfavorable subtypes Within subtypes, IBC-patients do worse Classical pathology does not capture the reason why 4

5 The tumor microenvironment is important Inflammatory Breast Cancer Gene expression profiling suggests T-cell signaling, PD-L1 overexpression in responders No comprehensive immune cell and associated molecular characterization What is the role of immunotherapy? Normal Breast Cancer TIL, CD8 T-cell infiltrate, PD-L1 expresion corelated with positive outcomes, especially in TNBC and HER2+ Tissue-associated macrophages play a critical role in breast cancer progression Checkpoint blockade effective in a subset of patients 5

6 Immune response most likely to be very relevant Immunologic aspects of IBC and microenvironment impact responses to neoadjuvant chemotherapy, and this immune microenvironment is affected by tumor genomic alterations Sangeeta Reddy et al., ASCO Annual Meeting

7 Study design focused on differences between pcr and non-pcr Sangeeta Reddy et al., ASCO Annual Meeting

8 Higher number of TILs associated lower stage and pcr Sangeeta Reddy et al., ASCO Annual Meeting

9 Other potential clues.. Mast cells inversely correlated with response HLA-DR expression inversely correlated with response 9

10 However, biomarkers, e.g. PD-L1, are frequently not really robust 10

11 Many potential clues are not clear at all PD-L1 Tumor mutational load Are differences in tumor microenvironment between responders and nonresponders cause or consequence? 11

12 No solid data yet how to treat IBC well Small series - Largest series: 137 IBCs compared with 252 non-ibcs 79-gene profile Activated signaling pathways: HER2, Myc, Ras, interferon (IFN)-α, IFN-γ, tumor necrosis factor (TNF)-α and vascular endothelial growth factor (VEGF) Relatively weakly activated pathways: ER, PR), P53 and Transforming Growth Factor (TGF)-β No validation! No (full) stratification per subtype! 12

13 Maybe, we look for an answer in the wrong way Static observations at a single point in time ptnm indirectly related to outcome Classification only partially biology-driven No integrated approach Small and/or biased series 13

14 Pathology is more than meets the eye Hanahan and Weinberg, Cell (2011) 144:646 14

15 Many factors are involved in prognosis and therapy response 15

16 Classical approach Potential factor #1 Potential factor #2 Study #1 Study #2 Study end-point (e.g. response or survival) Thanks to Bram Thijssen (PhD student in Lodewyk Wessels Group) 16

17 Proposed approach Potential factor #1 Potential factor #2 One study Study end-point (e.g. response or survival) Thanks to Bram Thijssen (PhD student in Lodewyk Wessels Group) 17

18 Proposed approach Potential factor #1 Potential factor #2 Intermediate phenotype Intermediate phenotype Study end-point (e.g. response or survival) Thanks to Bram Thijssen (PhD student in Lodewyk Wessels Group) 18

19 Possible outcome Thanks to Bram Thijssen (PhD student in Lodewyk Wessels Group) 19

20 The computational analysis is feasible Thanks to Bram Thijssen (PhD student in Lodewyk Wessels Group) 20

21 INFLAME to go Incorporate multiple explanatory factors in a single study On-treatment biopsies and blood samples to measure intermediate phenotypes Multi-level modeling to dissect the contributions 21

22 Intermediate endpoints are hallmarks of cancer Hanahan and Weinberg, Cell (2011) 144:646 22

23 Dashboard for read-out tumor biology (hallmarks of cancer) Proliferative signaling Evading growth supressors Avoiding immune destruction Enabling replicative immortality Tumor-promoting inflammation Activating invasion and metastasis Inducing angiongenesis Genome instability and mutation Resisting cell death Deregulation cellular energetics Tumor heterogeneity

24 Integrated approach to optimize treatment and outcome Standardized clinical data State-of-the-art technology Functional pathology Imaging & ` Radiomics Innovative surgery Sequential sampling ` Treatment Èxpression Analysis Genomic landscape ` Multiplex ` IHC (VECTRA) Proliferative signaling Activating invasion and metastasis Tumor heterogeneity Bioinformatics & Statistics Evading growth supressors Avoiding immune destruction Enabling replicative immortality Inducing Genome instability Resisting angiongenesis and mutation cell death. `... Hallmark Dashboard Tumor-promoting inflammation Deregulation cellular energetics.. Ongoing optimization of prognostic and predictive power CytOF Follow-up Data analysis control FEN1 inhibitor In vitro analyses In vivo ` analyses ` AACRgenie cbioportal 24

25 Questions? 25