The Information Technology (IT) Infrastructure of the Multicenter Archipelago of Ovarian Cancer Research Biobank: A Potential Blueprint for Other Biobanks.

Objective: Biobanks play a crucial role in fundamental and translational research by storing valuable biomaterials and data for future analyses. However, the design of their information technology (IT) infrastructures is often customized to specific requirements, thereby lacking the ability to be used for biobanks comprising other (types of) diseases. This results in substantial costs, time, and efforts for each new biobank project. The Dutch multicenter Archipelago of Ovarian Cancer Research (AOCR) biobank has developed an innovative, reusable IT infrastructure capable of adaptation to various biobanks, thereby enabling cost-effective and efficient implementation and management of biobank IT systems. Methods and Results: The AOCR IT infrastructure incorporates preexisting biobank software, mainly managed by Health-RI. The web-based registration tool Ldot is used for secure storage and pseudonymization of patient data. Clinicopathological data are retrieved from the Netherlands Cancer Registry and the Dutch nationwide pathology databank (Palga), both established repositories, reducing administrative workload and ensuring high data quality. Metadata of collected biomaterials are stored in the OpenSpecimen system. For digital pathology research, a hematoxylin and eosin-stained slide from each patient's tumor is digitized and uploaded to Slide Score. Furthermore, adhering to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles, genomic data derived from the AOCR samples are stored in cBioPortal. Conclusion: The IT infrastructure of the AOCR biobank represents a new standard for biobanks, offering flexibility to handle diverse diseases and types of biomaterials. This infrastructure bypasses the need for disease-specific, custom-built software, thereby being cost- and time-effective while ensuring data quality and legislative compliance. The adaptability of this infrastructure highlights its potential to serve as a blueprint for the development of IT infrastructures in both new and existing biobanks.

PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning.

BACKGROUND: Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy. METHODS: In this retrospective study, we developed hypothesis-driven interpretable biomarkers based on deep learning, to predict the pathological complete response (pCR, i.e., the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy solely using digital pathology H&E images of pre-treatment breast biopsies. Our approach consists of two steps: First, we use deep learning to characterize aspects of the tumor micro-environment by detecting mitoses and segmenting tissue into several morphology compartments including tumor, lymphocytes and stroma. Second, we derive computational biomarkers from the segmentation and detection output to encode slide-level relationships of components of the tumor microenvironment, such as tumor and mitoses, stroma, and tumor infiltrating lymphocytes (TILs). RESULTS: 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 and performed external independent validation on n = 126 patients from a public dataset. We report the predictive value of the investigated biomarkers for predicting pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 across the tested cohorts. CONCLUSION: The proposed computational biomarkers predict pCR, but will require more evaluation and finetuning for clinical application. Our results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning, along with automated mitoses quantification. We made our method publicly available to extract segmentation-based biomarkers for research purposes.

B-cells and regulatory T-cells in the microenvironment of HER2+ breast cancer are associated with decreased survival: a real-world analysis of women with HER2+ metastatic breast cancer.

BACKGROUND: Despite major improvements in treatment of HER2-positive metastatic breast cancer (MBC), only few patients achieve complete remission and remain progression free for a prolonged time. The tumor immune microenvironment plays an important role in the response to treatment in HER2-positive breast cancer and could contain valuable prognostic information. Detailed information on the cancer-immune cell interactions in HER2-positive MBC is however still lacking. By characterizing the tumor immune microenvironment in patients with HER2-positive MBC, we aimed to get a better understanding why overall survival (OS) differs so widely and which alternative treatment approaches may improve outcome. METHODS: We included all patients with HER2-positive MBC who were treated with trastuzumab-based palliative therapy in the Netherlands Cancer Institute between 2000 and 2014 and for whom pre-treatment tissue from the primary tumor or from metastases was available. Infiltrating immune cells and their spatial relationships to one another and to tumor cells were characterized by immunohistochemistry and multiplex immunofluorescence. We also evaluated immune signatures and other key pathways using next-generation RNA-sequencing data. With nine years median follow-up from initial diagnosis of MBC, we investigated the association between tumor and immune characteristics and outcome. RESULTS: A total of 124 patients with 147 samples were included and evaluated. The different technologies showed high correlations between each other. T-cells were less prevalent in metastases compared to primary tumors, whereas B-cells and regulatory T-cells (Tregs) were comparable between primary tumors and metastases. Stromal tumor-infiltrating lymphocytes in general were not associated with OS. The infiltration of B-cells and Tregs in the primary tumor was associated with unfavorable OS. Four signatures classifying the extracellular matrix of primary tumors showed differential survival in the population as a whole. CONCLUSIONS: In a real-world cohort of 124 patients with HER2-positive MBC, B-cells, and Tregs in primary tumors are associated with unfavorable survival. With this paper, we provide a comprehensive insight in the tumor immune microenvironment that could guide further research into development of novel immunomodulatory strategies.

Characterization of the Tumor Microenvironment of De Novo Oligometastatic Breast Cancer in a Nationwide Cohort.

PURPOSE: Oligometastatic breast cancer (OMBC) has a more favorable outcome than widespread metastatic breast cancer. Some patients with OMBC achieve long-term remission if treated with multimodality therapy, including systemic and locally ablative therapies. However, not all patients with OMBC benefit from such treatment, while all experience toxicity. To explore biomarkers identifying patients with OMBC and potential long-term survival, we compared tumor-immune characteristics of patients with OMBC and long-term versus shorter-term survival. MATERIALS AND METHODS: We collected tumor tissue of 97 patients with de novo OMBC (≤5 metastases) via the Dutch nationwide cancer and pathology registries using a case-control design. Long-term survivors (LTS) were defined as patients alive ≥10 years since OMBC diagnosis. Fifty-five LTS and 42 shorter-term survivors (STS) were included. Median follow-up was 15 years (IQR, 14-16). Tumor characteristics and infiltrating immune cells were assessed by immunohistochemistry and next-generation RNA-sequencing. Association of the resulting 52 biomarkers with long-term survival was assessed using logistic regression. Associations with survival within LTS were assessed using Cox-proportional hazards modeling. P values were adjusted for multiple hypothesis testing. RESULTS: Most patients had estrogen receptor (ER)-positive OMBC (n = 86; 89%) and 23 (24%) had human epidermal growth factor receptor 2-positive disease. ER positivity in primary tumors distinguished LTS from STS. In addition, extracellular matrix (ECM)2-low and ECM4-high distinguished between long-term and shorter-term survival. Immune levels in the primary tumor did not associate with LTS. However, within the LTS subset, higher immune levels associated with improved progression-free survival. CONCLUSION: We identified tumor and ECM features in the primary tumor of patients with de novo OMBC that were associated with long-term survival. Our data should be validated in other patients with OMBC before they can be used in clinical practice.

CMTM6 shapes antitumor T cell response through modulating protein expression of CD58 and PD-L1.

The dysregulated expression of immune checkpoint molecules enables cancer cells to evade immune destruction. While blockade of inhibitory immune checkpoints like PD-L1 forms the basis of current cancer immunotherapies, a deficiency in costimulatory signals can render these therapies futile. CD58, a costimulatory ligand, plays a crucial role in antitumor immune responses, but the mechanisms controlling its expression remain unclear. Using two systematic approaches, we reveal that CMTM6 positively regulates CD58 expression. Notably, CMTM6 interacts with both CD58 and PD-L1, maintaining the expression of these two immune checkpoint ligands with opposing functions. Functionally, the presence of CMTM6 and CD58 on tumor cells significantly affects T cell-tumor interactions and response to PD-L1-PD-1 blockade. Collectively, these findings provide fundamental insights into CD58 regulation, uncover a shared regulator of stimulatory and inhibitory immune checkpoints, and highlight the importance of tumor-intrinsic CMTM6 and CD58 expression in antitumor immune responses.

RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease.

Digital health data used in diagnostics, patient care, and oncology research continue to accumulate exponentially. Most medical information, and particularly radiology results, are stored in free-text format, and the potential of these data remains untapped. In this study, a radiological repomics-driven model incorporating medical token cognition (RadioLOGIC) is proposed to extract repomics (report omics) features from unstructured electronic health records and to assess human health and predict pathological outcome via transfer learning. The average accuracy and F1-weighted score for the extraction of repomics features using RadioLOGIC are 0.934 and 0.934, respectively, and 0.906 and 0.903 for the prediction of breast imaging-reporting and data system scores. The areas under the receiver operating characteristic curve for the prediction of pathological outcome without and with transfer learning are 0.912 and 0.945, respectively. RadioLOGIC outperforms cohort models in the capability to extract features and also reveals promise for checking clinical diagnoses directly from electronic health records.

Immune landscape of breast tumors with low and intermediate estrogen receptor expression.

Immune checkpoint blockade (ICB) is currently approved for patients with triple-negative breast cancer (TNBC), whereas responses to ICB are also observed in a small subgroup of Estrogen Receptor (ER)-positive breast cancer. The cut-off for ER-positivity (≥1%) is based on likelihood of endocrine treatment response, but ER-positive breast cancer represents a very heterogeneous group. This raises the question whether selection based on ER-negativity should be revisited to select patients for ICB treatment in the context of clinical trials. Stromal tumor-infiltrating lymphocytes (sTILs) and other immune parameters are higher in TNBC compared to ER-positive breast cancer, but it is unknown whether lower ER levels are associated with more inflamed tumor microenvironments (TME). We collected a consecutive series of primary tumors from 173 HER2-negative breast cancer patients, enriched for tumors with ER expression between 1 and 99% and found levels of stromal TILs, CD8 + T cells, and PD-L1 positivity in breast tumors with ER 1-9% and ER 10-50% to be comparable to tumors with ER 0%. Expression of immune-related gene signatures in tumors with ER 1-9% and ER 10-50% was comparable to ER 0%, and higher than in tumors with ER 51-99% and ER 100%. Our results suggest that the immune landscape of ER low tumors (1-9%) and ER intermediate tumors (10-50%) mimic that of primary TNBC.

PD-L1 blockade in combination with carboplatin as immune induction in metastatic lobular breast cancer: the GELATO trial.

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.

Predicting breast cancer types on and beyond molecular level in a multi-modal fashion.

Accurately determining the molecular subtypes of breast cancer is important for the prognosis of breast cancer patients and can guide treatment selection. In this study, we develop a deep learning-based model for predicting the molecular subtypes of breast cancer directly from the diagnostic mammography and ultrasound images. Multi-modal deep learning with intra- and inter-modality attention modules (MDL-IIA) is proposed to extract important relations between mammography and ultrasound for this task. MDL-IIA leads to the best diagnostic performance compared to other cohort models in predicting 4-category molecular subtypes with Matthews correlation coefficient (MCC) of 0.837 (95% confidence interval [CI]: 0.803, 0.870). The MDL-IIA model can also discriminate between Luminal and Non-Luminal disease with an area under the receiver operating characteristic curve of 0.929 (95% CI: 0.903, 0.951). These results significantly outperform clinicians' predictions based on radiographic imaging. Beyond molecular-level test, based on gene-level ground truth, our method can bypass the inherent uncertainty from immunohistochemistry test. This work thus provides a noninvasive method to predict the molecular subtypes of breast cancer, potentially guiding treatment selection for breast cancer patients and providing decision support for clinicians.

Homologous Recombination Deficiency and Cyclin E1 Amplification Are Correlated with Immune Cell Infiltration and Survival in High-Grade Serous Ovarian Cancer.

BACKGROUND: How molecular profiles are associated with tumor microenvironment (TME) in high-grade serous ovarian cancer (HGSOC) is incompletely understood. Therefore, we analyzed the TME and molecular profiles of HGSOC and assessed their associations with overall survival (OS). METHODS: Patients with advanced-stage HGSOC treated in three Dutch hospitals between 2008-2015 were included. Patient data were collected from medical records. BRCA1/2 mutation, BRCA1 promotor methylation analyses, and copy number variations were used to define molecular profiles. Immune cells were assessed with immunohistochemical staining. RESULTS: 348 patients were categorized as BRCA mutation (BRCAm) (BRCAm or promotor methylation) (30%), non-BRCA mutated HRD (19%), Cyclin E1 (CCNE1)-amplification (13%), non-BRCAmut HRD and CCNE1-amplification (double classifier) (20%), and no specific molecular profile (NSMP) (18%). BRCAm showed highest immune cell densities and CCNE1-amplification lowest. BRCAm showed the most favorable OS (52.5 months), compared to non-BRCAmut HRD (41.0 months), CCNE1-amplification (28.0 months), double classifier (27.8 months), and NSMP (35.4 months). Higher immune cell densities showed a favorable OS compared to lower, also within the profiles. CD8+, CD20+, and CD103+ cells remained associated with OS in multivariable analysis. CONCLUSIONS: Molecular profiles and TME are associated with OS. TME differs per profile, with higher immune cell densities showing a favorable OS, even within the profiles. HGSOC does not reflect one entity but comprises different entities based on molecular profiles and TME.

Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer.

To guide the choice of treatment, every new breast cancer is assessed for aggressiveness (i.e., graded) by an experienced histopathologist. Typically, this tumor grade consists of three components, one of which is the nuclear pleomorphism score (the extent of abnormalities in the overall appearance of tumor nuclei). The degree of nuclear pleomorphism is subjectively classified from 1 to 3, where a score of 1 most closely resembles epithelial cells of normal breast epithelium and 3 shows the greatest abnormalities. Establishing numerical criteria for grading nuclear pleomorphism is challenging, and inter-observer agreement is poor. Therefore, we studied the use of deep learning to develop fully automated nuclear pleomorphism scoring in breast cancer. The reference standard used for training the algorithm consisted of the collective knowledge of an international panel of 10 pathologists on a curated set of regions of interest covering the entire spectrum of tumor morphology in breast cancer. To fully exploit the information provided by the pathologists, a first-of-its-kind deep regression model was trained to yield a continuous scoring rather than limiting the pleomorphism scoring to the standard three-tiered system. Our approach preserves the continuum of nuclear pleomorphism without necessitating a large data set with explicit annotations of tumor nuclei. Once translated to the traditional system, our approach achieves top pathologist-level performance in multiple experiments on regions of interest and whole-slide images, compared to a panel of 10 and 4 pathologists, respectively.

Establishment of the Dutch Nationwide, Interdisciplinary Infrastructure and Biobank for Fundamental and Translational Ovarian Cancer Research: Archipelago of Ovarian Cancer Research.

OBJECTIVES: Ovarian cancer has the worst overall survival rate of all gynecologic malignancies. For the majority of patients, the 5-year overall survival rate of less than 50% has hardly improved over the last decades. To improve the outcome of patients with all subtypes of ovarian cancer, large-scale fundamental and translational research is needed. To accommodate these types of ovarian cancer research, we have established a Dutch nationwide, interdisciplinary infrastructure and biobank: the Archipelago of Ovarian Cancer Research (AOCR). The AOCR will facilitate fundamental and translational ovarian cancer research and enhance interdisciplinary, national, and international collaboration. DESIGN: The AOCR biobank is a prospective ovarian cancer biobank in which biomaterials are collected, processed, and stored in a uniform matter for future (genetic) scientific research. All 19 Dutch hospitals in which ovarian cancer surgery is performed participate and collaborate in the AOCR biobank. PARTICIPANTS/MATERIALS, SETTING, METHODS: Patients of 16 years and older with suspected or diagnosed ovarian, fallopian tube, or primary peritoneal cancer are recruited for participation. Patients who agree to participate give written informed consent for collection, storage, and issue of their biomaterials for future studies. After inclusion, different blood samples are taken at various predefined time points both before and during treatment. In case of a diagnostic paracentesis or biopsy, the residual biomaterials of these procedures are stored in the biobank. During surgery, primary tumor tissue and, if applicable, tissue from metastatic sites are collected and stored. From each patient, a representative histological hematoxylin and eosin stained slide is digitalized for research purposes, including reassessment by a panel of gynecologic pathologists. Clinical and pathological data are obtained on a per-study basis from Dutch registries. Research proposals for the issue of biomaterials and data are evaluated by both the Archipelago Scientific Committee and the Steering Committee. Researchers using the biomaterials from the AOCR biobank are encouraged to enrich the biobank with data and materials resulting from their analyses and experiments. LIMITATIONS: The implementation and first 4 years of collection are financed by an infrastructural grant from the Dutch Cancer Society. Therefore, the main limitation is that the costs for sustaining the biobank after the funding period will have to be covered. This coverage will come from incorporation of budget for biobanking in future grant applications and from fees from external researchers and commercial parties using the biomaterials stored in the AOCR biobank. Moreover, we will apply for grants aimed at sustaining and improving research infrastructures and biobanks. CONCLUSIONS: With the establishment of the Dutch nationwide, interdisciplinary Archipelago of Ovarian Cancer Research infrastructure and biobank, fundamental and translational research on ovarian cancer can be greatly improved. The ultimate aim of this infrastructure is that it will lead to improved diagnostics, treatment, and survival of patients with ovarian cancer.

Spatial interplay of tissue hypoxia and T-cell regulation in ductal carcinoma in situ.

Hypoxia promotes aggressive tumor phenotypes and mediates the recruitment of suppressive T cells in invasive breast carcinomas. We investigated the role of hypoxia in relation to T-cell regulation in ductal carcinoma in situ (DCIS). We designed a deep learning system tailored for the tissue architecture complexity of DCIS, and compared pure DCIS cases with the synchronous DCIS and invasive components within invasive ductal carcinoma cases. Single-cell classification was applied in tandem with a new method for DCIS ductal segmentation in dual-stained CA9 and FOXP3, whole-tumor section digital pathology images. Pure DCIS typically has an intermediate level of colocalization of FOXP3+ and CA9+ cells, but in invasive carcinoma cases, the FOXP3+ (T-regulatory) cells may have relocated from the DCIS and into the invasive parts of the tumor, leading to high levels of colocalization in the invasive parts but low levels in the synchronous DCIS component. This may be due to invasive, hypoxic tumors evolving to recruit T-regulatory cells in order to evade immune predation. Our data support the notion that hypoxia promotes immune tolerance through recruitment of T-regulatory cells, and furthermore indicate a spatial pattern of relocalization of T-regulatory cells from DCIS to hypoxic tumor cells. Spatial colocalization of hypoxic and T-regulatory cells may be a key event and useful marker of DCIS progression.

Serine metabolism remodeling after platinum-based chemotherapy identifies vulnerabilities in a subgroup of resistant ovarian cancers.

Resistance to platinum-based chemotherapy represents a major clinical challenge for many tumors, including epithelial ovarian cancer. Patients often experience several response-relapse events, until tumors become resistant and life expectancy drops to 12-15 months. Despite improved knowledge of the molecular determinants of platinum resistance, the lack of clinical applicability limits exploitation of many potential targets, leaving patients with limited options. Serine biosynthesis has been linked to cancer growth and poor prognosis in various cancer types, however its role in platinum-resistant ovarian cancer is not known. Here, we show that a subgroup of resistant tumors decreases phosphoglycerate dehydrogenase (PHGDH) expression at relapse after platinum-based chemotherapy. Mechanistically, we observe that this phenomenon is accompanied by a specific oxidized nicotinamide adenine dinucleotide (NAD+) regenerating phenotype, which helps tumor cells in sustaining Poly (ADP-ribose) polymerase (PARP) activity under platinum treatment. Our findings reveal metabolic vulnerabilities with clinical implications for a subset of platinum resistant ovarian cancers.

Effect of HIPEC according to HRD/BRCAwt genomic profile in stage III ovarian cancer: Results from the phase III OVHIPEC trial.

The addition of hyperthermic intraperitoneal chemotherapy (HIPEC) with cisplatin to interval cytoreductive surgery improves recurrence-free (RFS) and overall survival (OS) in patients with stage III ovarian cancer. Homologous recombination deficient (HRD) ovarian tumors are usually more platinum sensitive. Since hyperthermia impairs BRCA1/2 protein function, we hypothesized that HRD tumors respond best to treatment with HIPEC. We analyzed the effect of HIPEC in patients in the OVHIPEC trial, stratified by HRD status and BRCAm status. Clinical data and tissue samples were collected from patients included in the randomized, phase III OVHIPEC-1 trial. DNA copy number variation (CNV) profiles, HRD-related pathogenic mutations and BRCA1 promotor hypermethylation were determined. CNV-profiles were categorized as HRD or non-HRD, based on a previously validated algorithm-based BRCA1-like classifier. Hazard ratios (HR) and corresponding 99% confidence intervals (CI) for the effect of RFS and OS of HIPEC in the BRCAm, the HRD/BRCAwt and the non-HRD group were estimated using Cox proportional hazard models. Tumor DNA was available from 200/245 (82%) patients. Seventeen (9%) tumors carried a pathogenic mutation in BRCA1 and 14 (7%) in BRCA2. Ninety-one (46%) tumors classified as BRCA1-like. The effect of HIPEC on RFS and OS was absent in BRCAm tumors (HR 1.25; 99%CI 0.48-3.29), and most present in HRD/BRCAwt (HR 0.44; 99%CI 0.21-0.91), and non-HRD/BRCAwt tumors (HR 0.82; 99%CI 0.48-1.42), interaction P value: 0.024. Patients with HRD tumors without pathogenic BRCA1/2 mutation appear to benefit most from treatment with HIPEC, while benefit in patients with BRCA1/2 pathogenic mutations and patients without HRD seems less evident.

Low probability of disease cure in advanced ovarian carcinomas before the PARP inhibitor era.

BACKGROUND: In ovarian carcinomas, the likelihood of disease cure following first-line medical-surgical treatment has been poorly addressed. The objective was to: (a) assess the likelihood of long-term disease-free (LDF) > 5 years; and (b) evaluate the impact of the tumour primary chemosensitivity (assessed with the modelled CA-125 KELIM) with respect to disease stage, and completeness of debulking surgery. METHODS: Three Phase III trial datasets (AGO-OVAR 9; AGO-OVAR 7; ICON-7) were retrospectively investigated in an "adjuvant dataset", whilst the Netherlands Cancer Registry was used in a "neoadjuvant dataset". The prognostic values of KELIM, disease stage and surgery outcomes regarding the likelihood of LDF were assessed using univariate/multivariate analyses. RESULTS: Of 2029 patients in the "adjuvant dataset", 82 (4.0%) experienced LDF (Stage I-II: 25.9%; III: 2.1%; IV: 0.5%). Multivariate analyses identified disease stage and KELIM (OR = 4.24) as independent prognostic factors. Among the 1452 patients from the "neoadjuvant dataset", 36 (2.4%) had LDF (Stage II-III: 3.3%; IV: 1.3%). Using multivariate tests, high-risk diseases (OR = 0.18) and KELIM (OR = 2.96) were significant. CONCLUSION: The probability of LDF > 5 years after first-line treatment in 3486 patients (<4%) was lower than thought. These data could represent a reference for future studies meant to assess progress related to PARP inhibitors.

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.

IHC-based Ki67 as response biomarker to tamoxifen in breast cancer window trials enrolling premenopausal women.

Window studies are gaining traction to assess (molecular) changes in short timeframes. Decreased tumor cell positivity for the proliferation marker Ki67 is often used as a proxy for treatment response. Immunohistochemistry (IHC)-based Ki67 on tissue from neo-adjuvant trials was previously reported to be predictive for long-term response to endocrine therapy for breast cancer in postmenopausal women, but none of these trials enrolled premenopausal women. Nonetheless, the marker is being used on this subpopulation. We compared pathologist assessed IHC-based Ki67 in samples from pre- and postmenopausal women in a neo-adjuvant, endocrine therapy focused trial (NCT00738777), randomized between tamoxifen, anastrozole, or fulvestrant. These results were compared with (1) IHC-based Ki67 scoring by AI, (2) mitotic figures, (3) mRNA-based Ki67, (4) five independent gene expression signatures capturing proliferation, and (5) blood levels for tamoxifen and its metabolites as well as estradiol. Upon tamoxifen, IHC-based Ki67 levels were decreased in both pre- and postmenopausal breast cancer patients, which was confirmed using mRNA-based cell proliferation markers. The magnitude of decrease of Ki67 IHC was smaller in pre- versus postmenopausal women. We found a direct relationship between post-treatment estradiol levels and the magnitude of the Ki67 decrease in tumors. These data suggest IHC-based Ki67 may be an appropriate biomarker for tamoxifen response in premenopausal breast cancer patients, but anti-proliferative effect size depends on estradiol levels.

Characterization of Oligometastatic Disease in a Real-World Nationwide Cohort of 3447 Patients With de Novo Metastatic Breast Cancer.

Background: Observational studies in metastatic breast cancer (MBC) show that long-term overall survival (OS) is associated with limited tumor burden, or oligo-MBC (OMBC). However, a uniform definition of OMBC is lacking. In this real-world nationwide cohort, we aimed to define the optimal OMBC threshold and factors associated with survival in patients with OMBC. Methods: 3535 patients aged younger than 80 years at diagnosis of de novo MBC in the Netherlands between January 2000 and December 2007 were included. Detailed clinical, therapy, and outcome data were collected from medical records of a sample of the patients. Using inverse-sampling-probability weighting, the analysis cohort (n = 3447) was constructed. We assessed OS according to number of metastases at diagnosis to determine the optimal OMBC threshold. Next, we applied Cox regression models with inverse-sampling-probability weighting to study associations with OS and progression-free survival in OMBC. All statistical tests were 2-sided. Results: Compared with more than 5 distant metastases, adjusted hazard ratios for OS (with 95% confidence interval [CI] based on robust standard errors) for 1, 2-3, and 4-5 metastases were 0.70 (95% CI = 0.52 to 0.96), 0.63 (95% CI = 0.45 to 0.89), and 0.91 (95% CI = 0.61 to 1.37), respectively. Ten-year OS estimates for patients with no more than 3 vs more than 3 metastases were 14.9% and 3.4% (P < .001). In multivariable analyses, premenopausal andperimenopausal status, absence of lung metastases, and local therapy of metastases (surgery and/or radiotherapy) added to systemic therapy were statistically significantly associated with better OS and progression-free survival in OMBC, independent of local therapy of the primary tumor. Conclusion: OMBC defined as MBC limited to 1-3 metastases was associated with favorable OS. In OMBC, local therapy of metastases was associated with better OS, particularly if patients were premenopausal or perimenopausal without lung metastases.