Wrangle, John M

Background Tumor-infiltrating lymphocytes (TILs) are established determinants of clinical outcomes in breast cancer. 1–6 While stromal TILs (sTILs) are commonly evaluated due to easier scoring and higher interobserver reproducibility, intraepithelial TILs may provide additional insights into anti-tumor immunity. 7 We aimed to comprehensively characterize immunotype-associated cellular landscapes in triple-negative breast cancer (TNBC) and identify features associated with clinical outcomes. Methods 212 TNBC tumors were classified into three immunotypes based on immune infiltrate location and density: immune desert (ID), immune excluded (IE), and immune inflamed (IN). 8 Single-cell spatial transcriptomics was performed on 75 tumors to define the cellular composition and functional states of the tumor microenvironment across immunotypes. Findings were validated and integrated with bulk RNA sequencing data and clinical outcome datasets from FinXX, CALGB-40603, I-SPY 2, and Real-World Clinico-Genomic Data (RWCGD) cohorts. Results Patients with IN tumors had significantly improved outcomes compared with ID tumors. Despite high sTILs, IE tumors showed poor outcomes similar to ID tumors. Single-cell spatial analysis revealed that ID and IE tumors exhibited reduced major histocompatibility complex I/II expression and fewer tumor-resident plasmacytoid dendritic cells (pDCs) compared with IN tumors, which were enriched for interferon-alpha (IFNα) and interferon-gamma (IFNγ) responses. High IFN response scores were associated with favorable outcomes across multiple therapy types in independent datasets. Deconvolution of RWCGD bulk RNA-seq data confirmed that pDC abundance correlated with improved outcomes specifically in hormone receptor–negative subtypes. Conclusions Our study highlights pDCs and IFN signaling as hallmarks of effective anti-tumor immunity in TNBC. Immunotype-based profiling underscores the limited prognostic value of sTILs alone in immune-excluded tumors and supports pDCs and IFN pathways as potential biomarkers for prognosis and therapeutic development in TNBC.

PURPOSE BMI affects breast cancer risk and prognosis. In contrast to cytotoxic chemotherapy, CDK4/6 inhibitors are given at a fixed dose, irrespective of BMI or weight. This preplanned analysis of the global randomized PALLAS trial investigates the impact of BMI on the side-effect profile, treatment adherence, and efficacy of palbociclib. METHODS Patients were categorized at baseline according to WHO BMI categories. Neutropenia rates were assessed with univariable and multivariable logistic regression. Time to early discontinuation of palbociclib was analyzed with Fine and Gray competing risk models. Unstratified Cox models were used to investigate the association between BMI category and time to invasive disease-free survival (iDFS). 95% CIs were derived. RESULTS Of 5,698 patients included in this analysis, 68 (1.2%) were underweight, 2,082 (36.5%) normal weight, 1,818 (31.9%) overweight, and 1,730 (30.4%) obese at baseline. In the palbociclib arm, higher BMI was associated with a significant decrease in neutropenia (unadjusted odds ratio for 1-unit change, 0.93; 95% CI, 0.91 to 0.94; adjusted for age, race ethnicity, region, chemotherapy use, and Eastern Cooperative Oncology Group at baseline, 0.93; 95% CI, 0.92 to 0.95). This translated into a significant decrease in treatment discontinuation rate with higher BMI (adjusted hazard ratio [HR] for 10-unit change, 0.75; 95% CI, 0.67 to 0.83). There was no significant improvement in iDFS with the addition of palbociclib to ET in any weight category (normal weight HR, 0.84; 95% CI, 0.63 to 1.12; overweight HR, 1.10; 95% CI, 0.82 to 1.49; and obese HR, 0.95; 95% CI, 0.69 to 1.30) in this analysis early in follow-up (31 months). CONCLUSION This preplanned analysis of the PALLAS trial demonstrates a significant impact of BMI on side effects, dose reductions, early treatment discontinuation, and relative dose intensity. Additional long-term follow-up will further evaluate whether BMI ultimately affects outcome.

Abstract Background: It has become increasingly clear that effective treatment of metastatic breast cancer (MBC) requires an in-depth understanding of the molecular differences between primary tumors and metastases. The AURORA US Network was established to collect primary breast cancer-metastasis pairs for multi-platform genomic profiling in order to identify the molecular drivers of metastatic disease. AURORA US has both a retrospective and prospective phase. This is the first report of the retrospective phase. Methods: Archived tissue samples from the primary tumor and at least one distant metastasis were retrospectively collected from 83 MBC patients. Following internal quality assessment, samples from 55 pts, including 105 distinct metastatic lesions, were subject to DNA low pass whole genome and exome sequencing, DNA methylation arrays, and RNA sequencing. Early analyses of these multi-platform data include: DNA methylation, tumor gene expression and microenvironmental signatures, somatic and germline variants, DNA copy number changes, and structural variants between breast primaries and matched metastases. Results: Median age at diagnosis was 49 years (25-76); 32 (58%) were Stage I or II at presentation, 27 (49%) had a family history of breast cancer, and 20 (36%) had a second breast primary. Median disease-free interval before developing MBC was 2 years (range 0-36, 5 patients presented with Stage IV). Median overall survival from initial presentation was 4 years (range 0-37). Median survival after developing MBC was 1 year (range 0-13), with a median of three treatments. Primary tissue samples were banked from 1977-2017 and metastases were banked from 1999-2017. Clinical phenotypes of the primaries included 27 HR+ (49%), 15 triple negative (TNBC, 27%), and 11 HER2+ (20%, 12 missing HER2 status). Intrinsic subtype distribution of the primaries included 17 Basal-like (31%), 17 Luminal A (31%), 7 Normal-like (13%), 5 HER2-enriched (9%), and 1 Luminal B, with 8 pending. All metastases from the Basal-like cases remained Basal-like, while metastases from luminal primaries tended to gain HER2-Enriched subtype features (5/18, p = 0.01). Overall, we identified significant metastasis-enriched alterations in metabolism pathways, an increase in proliferation, and the loss of differentiation signatures and immune infiltrates with progression; the latter being the most pronounced in brain metastases. The most frequent somatic mutations in this cohort were in TP53, NCOR1, and RUNX1. Interestingly, ERBB2, EGFR, and ATM were also mutated in ≥10% of the tumors sequenced. In almost all cases, CpG island hypermethylation was clonally present in the primary tumor and persisted stably in the majority of metastatic lesions. Promoter CpG island hypermethylation was also identified in some metastatic lesions at JAM3, an important cellular adhesion molecule,and this was accompanied by reduced mRNA expression. CONCLUSIONS: Collection of banked primary and metastatic tissue pairs identified a young MBC cohort with a high frequency of breast cancer family history and second breast primaries. Molecular characterization of luminal tumor pairs highlighted acquisition of aggressive traits including increased proliferation and loss of differentiation in the metastases. In contrast, basal-like pairs remained relatively unchanged, except for the loss of immune activation. Ongoing analyses to be presented include clonal heterogeneity and phylogeny, novel metastasis signature discovery, gene fusion, and endogenous retrovirus detection. Citation Format: Tari A King, Minetta C Liu, Marni B McClure, Toshinori Hinoue, Benjamin J Kelly, Chad J Creighton, Jay Bowen, Kristen Leraas, Robyn T Burns, Sara Coppens, Salma Rezk, Amy L Garrett, Justin M Balko, Joel S Parker, Ben H Park, Ian Krop, Carey Anders, Katherine A Hoadley, Julie Gastier-Foster, Mothaffar F Rimawi, Rita Nanda, Nancy U Lin, Claudine Isaacs, P. Kelly Marcom, Anna Maria Storniolo, Fergus J Couch, Elaine R Mardis, Adrian V Lee, Uma Chandran, Peter W Laird, Susan G Hilsenbeck, Larry Norton, Andrea L Richardson, W. Fraser Symmans, Lisa A Carey, Antonio C Wolff, Nancy E Davidson, Charles M Perou, the AURORA US Network. Multiplatform analysis of matched primary and metastatic breast tumors from the AURORA US Network [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr GS3-08.

9517 Background: Although PD-1/L1 axis directed therapies induce durable responses in some mm patients (pts), biomarkers of response remain elusive. We hypothesized that quantifying key immune suppression mechanisms within the tumor microenvironment would provide superior predictors of response to anti-PD-1 compared with single marker assessment. Methods: Pre-treatment tumor biopsies from 124 mm pts treated with anti-PD-1 at 7 academic centers were fluorescently stained with multiple immune markers in discovery (n = 24) and validation (n = 100) cohorts. Selected biomarker signatures, PD-1/PD-L1 interaction score (proportion of PD-1+ cells co-localized with PD-L1) and IDO1/HLA-DR co-expression were evaluated for anti-PD-1 treatment response and survival. Slides were imaged using Vectra; biomarker positive cells and their co-localization were objectively quantified in pathologist-selected regions using novel Automated Quantitative Analysis (AQUA) algorithms. Results: In the discovery cohort, high levels of PD-1/PD-L1 interaction score and/or IDO1/HLA-DR coexpression was strongly positively associated with response to anti-PD-1 (p = 0.0005). In contrast, other individual biomarkers (PD-1, PD-L1, CD8) were not associated with response or survival (p > 0.10). This finding was replicated in the validation cohort: pts with high PD-1/PD-L1 and/or IDO1/HLA-DR were more likely to respond to treatment (p = 0.009). These pts also experienced a three-fold increase in progression free survival (hazards ratio (HR) = 0.33; p = 0.003) and overall survival (HR = 0.34; p = 0.004). Multivariate analyses revealed that these findings were independent of BRAF mutation, stage, LDH and prior therapy. In the combined cohort (n = 124), 80% of responding pts had higher levels of PD-1/PD-L1 interaction scores and/or IDO1/HLA-DR. In contrast, PD-L1 expression alone (≥1% or ≥50%) was not predictive of PFS or OS (p > 0.1). Conclusions: This novel multiplexed method profiling key tumor-immune suppression pathways identified mm pts likely to respond to anti-PD-1 therapy. This method could help stratify patients for PD-1 monotherapy and be useful in guiding future clinical trials.

Abstract Purpose: PD-1/L1 axis–directed therapies produce clinical responses in a subset of patients; therefore, biomarkers of response are needed. We hypothesized that quantifying key immunosuppression mechanisms within the tumor microenvironment by multiparameter algorithms would identify strong predictors of anti–PD-1 response. Experimental Design: Pretreatment tumor biopsies from 166 patients treated with anti–PD-1 across 10 academic cancer centers were fluorescently stained with multiple markers in discovery (n = 24) and validation (n = 142) cohorts. Biomarker-positive cells and their colocalization were spatially profiled in pathologist-selected tumor regions using novel Automated Quantitative Analysis algorithms. Selected biomarker signatures, PD-1/PD-L1 interaction score, and IDO-1/HLA-DR coexpression were evaluated for anti–PD-1 treatment outcomes. Results: In the discovery cohort, PD-1/PD-L1 interaction score and/or IDO-1/HLA-DR coexpression was strongly associated with anti–PD-1 response (P = 0.0005). In contrast, individual biomarkers (PD-1, PD-L1, IDO-1, HLA-DR) were not associated with response or survival. This finding was replicated in an independent validation cohort: patients with high PD-1/PD-L1 and/or IDO-1/HLA-DR were more likely to respond (P = 0.0096). These patients also experienced significantly improved progression-free survival (HR = 0.36; P = 0.0004) and overall survival (HR = 0.39; P = 0.0011). In the combined cohort, 80% of patients exhibiting higher levels of PD-1/PD-L1 interaction scores and IDO-1/HLA-DR responded to PD-1 blockers (P = 0.000004). In contrast, PD-L1 expression was not predictive of survival. Conclusions: Quantitative spatial profiling of key tumor-immune suppression pathways by novel digital pathology algorithms could help more reliably select melanoma patients for PD-1 monotherapy. Clin Cancer Res; 24(21); 5250–60. ©2018 AACR.