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Invasive lobular breast cancer (ILC) is the second most common histological breast cancer subtype, but ILC-specific trials are lacking. Translational research revealed an immune-related ILC subset, and in mouse ILC models, synergy between immune checkpoint blockade and platinum was observed. In the phase II GELATO trial (NCT03147040), patients with metastatic ILC were treated with weekly carboplatin (area under the curve 1.5 mg ml–1 min–1) as immune induction for 12 weeks and atezolizumab (PD-L1 blockade; triweekly) from the third week until progression. Four of 23 evaluable patients had a partial response (17%), and 2 had stable disease, resulting in a clinical benefit rate of 26%. From these six patients, four had triple-negative ILC (TN-ILC). We observed higher CD8+ T cell infiltration, immune checkpoint expression and exhausted T cells after treatment. With this GELATO trial, we show that ILC-specific clinical trials are feasible and demonstrate promising antitumor activity of atezolizumab with carboplatin, particularly for TN-ILC, and provide insights for the design of highly needed ILC-specific trials.

BACKGROUND: Cyclin E1 (CCNE1) is a potential predictive marker and therapeutic target in tubo-ovarian high-grade serous carcinoma (HGSC). Smaller studies have revealed unfavorable associations for CCNE1 amplification and CCNE1 overexpression with survival, but to date no large-scale, histotype-specific validation has been performed. The hypothesis was that high-level amplification of CCNE1 and CCNE1 overexpression, as well as a combination of the two, are linked to shorter overall survival in HGSC. METHODS: Within the Ovarian Tumor Tissue Analysis consortium, amplification status and protein level in 3029 HGSC cases and mRNA expression in 2419 samples were investigated. RESULTS: High-level amplification (>8 copies by chromogenic in situ hybridization) was found in 8.6% of HGSC and overexpression (>60% with at least 5% demonstrating strong intensity by immunohistochemistry) was found in 22.4%. CCNE1 high-level amplification and overexpression both were linked to shorter overall survival in multivariate survival analysis adjusted for age and stage, with hazard stratification by study (hazard ratio [HR], 1.26; 95% CI, 1.08-1.47, p = .034, and HR, 1.18; 95% CI, 1.05-1.32, p = .015, respectively). This was also true for cases with combined high-level amplification/overexpression (HR, 1.26; 95% CI, 1.09-1.47, p = .033). CCNE1 mRNA expression was not associated with overall survival (HR, 1.00 per 1-SD increase; 95% CI, 0.94-1.06; p = .58). CCNE1 high-level amplification is mutually exclusive with the presence of germline BRCA1/2 pathogenic variants and shows an inverse association to RB1 loss. CONCLUSION: This study provides large-scale validation that CCNE1 high-level amplification is associated with shorter survival, supporting its utility as a prognostic biomarker in HGSC. ispartof: CANCER vol:129 issue:5 pages:697-713 ispartof: location:United States status: published

Supplemental Figure 1. Tamoxifen treatment induces expression of NFκBB-related genes. Supplementary Figure 2. Immune cell composition and numbers in the tumor microenvironment do not significantly change upon tamoxifen treatment. Supplementary Figure 3. NFκB pathway and EMT/stemness factors in tamoxifen-tolerant persisters in T47D cells. Supplementary Figure 4. Expression of p65 and p50 proteins in MCF-7 CRISPR/Cas9 knock out clones. Supplementary Figure 5. NFκB pathway is not directly activated by tamoxifen. Supplementary Figure 6. Sorted NFκB+ vs NFκBâ^' or unsorted tamoxifen-tolerant cells based on GFP expression were reseeded for another round of clonogenic assay and the %GFP+ population was quantified in the second round.

Supplemental Table 3. GSEA enrichment plots

Supplemental Table 2. DE Genes

Supplemental Table 1. Patient characteristics.

Supplemental Materials and Methods and Figure Legends

Supplementary Table from Differential Survival and Therapy Benefit of Patients with Breast Cancer Are Characterized by Distinct Epithelial and Immune Cell Microenvironments

Supplementary Table S2 from Integration of DNA Copy Number Alterations and Prognostic Gene Expression Signatures in Breast Cancer Patients

Supplementary Data from Differential Survival and Therapy Benefit of Patients with Breast Cancer Are Characterized by Distinct Epithelial and Immune Cell Microenvironments

Purpose:Extensive work in preclinical models has shown that microenvironmental cells influence many aspects of cancer cell behavior, including metastatic potential and their sensitivity to therapeutics. In the human setting, this behavior is mainly correlated with the presence of immune cells. Here, in addition to T cells, B cells, macrophages, and mast cells, we identified the relevance of nonimmune cell types for breast cancer survival and therapy benefit, including fibroblasts, myoepithelial cells, muscle cells, endothelial cells, and seven distinct epithelial cell types.Experimental Design:Using single-cell sequencing data, we generated reference profiles for all these cell types. We used these reference profiles in deconvolution algorithms to optimally detangle the cellular composition of more than 3,500 primary breast tumors of patients that were enrolled in the SCAN-B and MATADOR clinical trials, and for which bulk mRNA sequencing data were available.Results:This large data set enables us to identify and subsequently validate the cellular composition of microenvironments that distinguish differential survival and treatment benefit for different treatment regimens in patients with primary breast cancer. In addition to immune cells, we have identified that survival and therapy benefit are characterized by various contributions of distinct epithelial cell types.Conclusions:From our study, we conclude that differential survival and therapy benefit of patients with breast cancer are characterized by distinct microenvironments that include specific populations of immune and epithelial cells.

Purpose: Several prognostic gene expression profiles have been identified in breast cancer. In spite of this progress in prognostic classification, the underlying mechanisms that drive these gene expression patterns remain unknown. Specific genomic alterations, such as copy number alterations, are an important factor in tumor development and progression and are also associated with changes in gene expression.Experimental Design: We carried out array comparative genomic hybridization in 68 human breast carcinomas for which gene expression and clinical data were available. We used a two-class supervised algorithm, Supervised Identification of Regions of Aberration in aCGH data sets, for the identification of regions of chromosomal alterations that are associated with specific sample labeling. Using gene expression data from the same tumors, we identified genes in the altered regions for which the expression level is significantly correlated with the copy number and validated our results in public available data sets.Results: Specific chromosomal aberrations are related to clinicopathologic characteristics and prognostic gene expression signatures. The previously identified poor prognosis, 70-gene expression signature is associated with the gain of 3q26.33-27.1, 8q22.1-24.21, and 17q24.3-25.1; the 70-gene good prognosis profile is associated with the loss at 16q12.1-13 and 16q22.1-24.1; basal-like tumors are associated with the gain of 6p12.3-23, 8q24.21-22, and 10p12.33-14 and losses at 4p15.31, 5q12.3-13.1, 5q33.1, 10q23.33, 12q13.13-3, 15q15.1, and 15q21.1; HER2+ breast show amplification at 17q11.1-12 and 17q21.31-23.2 (including HER2 gene).Conclusions: There is a strong correlation between the different gene expression signatures and underlying genomic changes. These findings help to establish a link between genomic changes and gene expression signatures, enabling a better understanding of the tumor biology that causes poor prognosis. Clin Cancer Res; 16(2); 651–63

Supplementary Figure from Differential Survival and Therapy Benefit of Patients with Breast Cancer Are Characterized by Distinct Epithelial and Immune Cell Microenvironments

Supplementary Figure 1 from MicroRNA Sequence and Expression Analysis in Breast Tumors by Deep Sequencing

Supplementary Figure 2A from MicroRNA Sequence and Expression Analysis in Breast Tumors by Deep Sequencing

Supplementary Figure 6 from MicroRNA Sequence and Expression Analysis in Breast Tumors by Deep Sequencing

Supplementary Methods, Figure and Table Legends from MicroRNA Sequence and Expression Analysis in Breast Tumors by Deep Sequencing

Supplementary Tables 1-8C from MicroRNA Sequence and Expression Analysis in Breast Tumors by Deep Sequencing

Supplementary Figure 4 from MicroRNA Sequence and Expression Analysis in Breast Tumors by Deep Sequencing

Supplementary Figure 5 from MicroRNA Sequence and Expression Analysis in Breast Tumors by Deep Sequencing

Supplementary Figure 3 from MicroRNA Sequence and Expression Analysis in Breast Tumors by Deep Sequencing

How the tumor’s genetic make up (i.e. molecular profiles) is associated with changes in the tumor microenvironment (TME) in high-grade serous ovarian cancer (HGSOC) is incompletely understood, while these factors influence survival. To investigate this we analyzed the TME and molecular profiles (homologous recombination deficiency (HRD) and Cyclin E1 (CCNE1) amplification) of HGSOC and assessed their associations with each other and overall survival (OS). Patients with advanced-stage HGSOC treated in three Dutch hospitals between 2008-2015 were included. Patient, treatment, and outcome data were collected from medical records. BRCA1/2 mutation, BRCA1 promotor methylation analyses, and copy number variations were used to define five molecular profiles. Immunecell densites were assessed with immunohistochemical staining for T- (CD8 and CD103), B- cells (CD20) and macrophages (CD68). This resulted in the assessability of 348 patients who were categorized as; BRCA mutation or promotor methylation (BRCAm) (30%), non-BRCA mutated HRD (19%), CCNE1-amplification (13%), non-BRCAmut HRD and CCNE1-amplification (double classifier) (20%), and no specific molecular profile (NSMP) (18%). BRCAm tumors showed the highest immune cell densities and CCNE1-amplification the lowest. BRCAm showed the most favorable OS (52.5months), compared to non-BRCAmut HRD (41.0months), CCNE1-amplification (28.0months), double classifier (27.8months), and NSMP (35.4months). CCNE1-amplification and double classifier remained to have a significantly worse OS in multivariable analysis. Higher immune cell densities showed a favorable OS compared to lower densities, also within the molecular profiles. CD8+, CD20+, and CD103+ cells remained associated with OS in multivariable analysis. These results depict that both molecular profiles and TME are associated with OS. Immune composition for lymphocytes and macrophages differ per molecular profile, with higher immune cell densities showing a favorable OS, even within the profiles. HGSOC does not reflect one entity but comprises different variants based on molecular profiles and TME, which ideally is translated into tailored treatment approaches.

PurposeInvasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy, however, only a fraction of the patients respond to it completely. To prevent over-treating patients with a toxic drug, there is an urgent need for biomarkers capable of predicting treatment response before administering the therapy. In this retrospective study, we developed interpretable, deep-learning based biomarkers to predict the pathological complete response (pCR, i.e. the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy from digital pathology H&E images of pre-treatment breast biopsies.Experimental DesignOur approach consists of two steps: In the first step, using deep learning, mitoses are detected and the tissue segmented into several morphology compartments including tumor, lymphocytes and stroma. In the second step, computational biomarkers are derived from the segmentation and detection output to encode slide-level relationships between the morphological structures with focus on tumor infiltrating lymphocytes (TILs). We developed and evaluated our method on slides from N=721 patients from three European medical centers with triple-negative and Luminal B breast cancers.ResultsThe investigated biomarkers yield statistically significant prediction performance for pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 depending on the cancer subtype and center.ConclusionThe proposed computational biomarkers predict pathological complete response, but will require more evaluation and finetuning for clinical application. The results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning.

Background: The addition of pertuzumab to neoadjuvant trastuzumab-containing chemotherapy significantly improves pathological complete response (pCR) rates in patients with HER2-positive breast cancer. However, effects on long-term survival outcomes have been modest. While prior studies suggest that only a subset of patients benefits most from the addition of pertuzumab, a reliable biomarker for response prediction has not been established. BluePrint is an 80-gene molecular subtyping test that classifies breast tumors as Basal-, Luminal- or HER2-subtype. Recent data shows that some tumors have more than one activated pathway and can be classified as dual subtype. Secondary analyses from the Aphinity trial (NCT01358877) suggest that pertuzumab benefit is largely restricted to patients with single-activated HER2-type tumors and less pronounced in patients with other single- or dual-activated subtypes. In this study, we evaluated BluePrint subtype as a biomarker for predicting response to trastuzumab-containing neoadjuvant chemotherapy with or without pertuzumab in a large nationwide cohort of patients. Methods: We collected data of all patients with stage II-III HER2-positive breast cancer who received neoadjuvant chemotherapy and trastuzumab between January 2013 and January 2016 from the Netherlands Cancer Registry. Pertuzumab was not reimbursed in the Netherlands during this period but was available to patients participating in the TRAIN2 clinical trial (NCT01996267). Pre-treatment biopsies were collected and tumor cell percentage was adequate in 404 patients who received pertuzumab and in 432 matched control patients who did not receive pertuzumab. Matching factors included treatment with anthracyclines (yes vs. no), age and hormone receptor status. BluePrint molecular subtypes were determined sucessfully through microarray in 680 patients. Pathological complete response (pCR) and overall survival (OS) were analyzed by treatment arm and BluePrint subtypes, using logistic and Cox regression analyses. Results: Microarray data was available for 334 patients who received pertuzumab and 346 patients who did not. Tumors were classified as single HER2 (77.6%), Luminal-HER2 (11.3%), single Luminal (8.4%), or other (2.6%). Overall, pCR rate was 65.0% in patients who received pertuzumab and 38.9% in patients who did not. In patients with single HER2-type tumors, pCR rate was significantly higher with pertuzumab (77.0% vs. 46.0%, multivariate adjusted odds ratio [aOR] 3.92, 95% confidence interval [CI] 2.60 - 5.98). In patients with other subtypes, pCR rate was lower overall and not significantly different with or without pertuzumab (23.5% vs. 13.5%, aOR 2.50, 95% CI 0.98 - 6.89). In addition, multivariate Cox regression analyses showed a significant OS benefit with pertuzumab in patients with single HER2-type tumors (adjusted hazard ratio [aHR] 0.33, 95% CI 0.16 - 0.67), but not in other subtypes (aHR 0.94, 95% CI 0.31 - 2.83). However, tests for interactions between pertuzumab and BluePrint subtypes were not significant. Conclusion: We confirmed the results of a prior secondary analysis of the Aphinity trial that the benefit of adding pertuzumab to (neo)adjuvant trastuzumab-based chemotherapy seems most pronounced in patients with a molecularly defined single-activated HER2-subtype. In other subtypes, pathological complete response rates and long-term outcomes are worse overall and no clear benefit of pertuzumab was seen, although tests for interaction between pertuzumab treatment and BluePrint subtype were not significant. Citation Format: Marte C Liefaard, Anna van der Voort, Joyce Sanders, Shiva Vonk, Hugo M Horlings, Sabine Siesling, Linda de Munck, Elise van Leeuwen-Stok, Miranda Kleijn, Lorenza Mittempergher, Midas M Kuilman, Esther H Lips, Gabe S Sonke. Effect of pertuzumab plus neoadjuvant trastuzumab-based chemotherapy in early-stage HER2-positive breast cancer according to BluePrint molecularly defined breast cancer subtypes [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD15-07.

The purpose of this study was to identify critical pathways promoting survival of tamoxifen-tolerant, estrogen receptor α positive (ER+) breast cancer cells, which contribute to therapy resistance and disease recurrence. Gene expression profiling and pathway analysis were performed in ER+ breast tumors of patients before and after neoadjuvant tamoxifen treatment and demonstrated activation of the NF-κB pathway and an enrichment of epithelial-to mesenchymal transition (EMT)/stemness features. Exposure of ER+ breast cancer cell lines to tamoxifen, in vitro and in vivo, gives rise to a tamoxifen-tolerant population with similar NF-κB activity and EMT/stemness characteristics. Small-molecule inhibitors and CRISPR/Cas9 knockout were used to assess the role of the NF-κB pathway and demonstrated that survival of tamoxifen-tolerant cells requires NF-κB activity. Moreover, this pathway was essential for tumor recurrence following tamoxifen withdrawal. These findings establish that elevated NF-κB activity is observed in breast cancer cell lines under selective pressure with tamoxifen in vitro and in vivo, as well as in patient tumors treated with neoadjuvant tamoxifen therapy. This pathway is essential for survival and regrowth of tamoxifen-tolerant cells, and, as such, NF-κB inhibition offers a promising approach to prevent recurrence of ER+ tumors following tamoxifen exposure.Implications:Understanding initial changes that enable survival of tamoxifen-tolerant cells, as mediated by NF-κB pathway, may translate into therapeutic interventions to prevent resistance and relapse, which remain major causes of breast cancer lethality.

AKT inhibitors have promising activity in AKT1E17K-mutant estrogen receptor (ER)–positive metastatic breast cancer, but the natural history of this rare genomic subtype remains unknown. Utilizing AACR Project GENIE, an international clinicogenomic data-sharing consortium, we conducted a comparative analysis of clinical outcomes of patients with matched AKT1E17K-mutant (n = 153) and AKT1–wild-type (n = 302) metastatic breast cancer. AKT1-mutant cases had similar adjusted overall survival (OS) compared with AKT1–wild-type controls (median OS, 24.1 vs. 29.9, respectively; P = 0.98). AKT1-mutant cases enjoyed longer durations on mTOR inhibitor therapy, an observation previously unrecognized in pivotal clinical trials due to the rarity of this alteration. Other baseline clinicopathologic features, as well as durations on other classes of therapy, were broadly similar. In summary, we demonstrate the feasibility of using a novel and publicly accessible clincogenomic registry to define outcomes in a rare genomically defined cancer subtype, an approach with broad applicability to precision oncology. Significance: We delineate the natural history of a rare genomically distinct cancer, AKT1E17K-mutant ER-positive breast cancer, using a publicly accessible registry of real-world patient data, thereby illustrating the potential to inform drug registration through synthetic control data. See related commentary by Castellanos and Baxi, p. 490.

Purpose: Extensive work in preclinical models has shown that microenvironmental cells influence many aspects of cancer cell behavior, including metastatic potential and their sensitivity to therapeutics. In the human setting, this behavior is mainly correlated with the presence of immune cells. Here, in addition to T cells, B cells, macrophages, and mast cells, we identified the relevance of nonimmune cell types for breast cancer survival and therapy benefit, including fibroblasts, myoepithelial cells, muscle cells, endothelial cells, and seven distinct epithelial cell types. Experimental Design: Using single-cell sequencing data, we generated reference profiles for all these cell types. We used these reference profiles in deconvolution algorithms to optimally detangle the cellular composition of more than 3,500 primary breast tumors of patients that were enrolled in the SCAN-B and MATADOR clinical trials, and for which bulk mRNA sequencing data were available. Results: This large data set enables us to identify and subsequently validate the cellular composition of microenvironments that distinguish differential survival and treatment benefit for different treatment regimens in patients with primary breast cancer. In addition to immune cells, we have identified that survival and therapy benefit are characterized by various contributions of distinct epithelial cell types. Conclusions: From our study, we conclude that differential survival and therapy benefit of patients with breast cancer are characterized by distinct microenvironments that include specific populations of immune and epithelial cells.