Neoadjuvant nivolumab or nivolumab plus ipilimumab in early-stage triple-negative breast cancer: a phase 2 adaptive trial.

Immune checkpoint inhibition (ICI) with chemotherapy is now the standard of care for stage II-III triple-negative breast cancer; however, it is largely unknown for which patients ICI without chemotherapy could be an option and what the benefit of combination ICI could be. The adaptive BELLINI trial explored whether short combination ICI induces immune activation (primary end point, twofold increase in CD8+ T cells or IFNG), providing a rationale for neoadjuvant ICI without chemotherapy. Here, in window-of-opportunity cohorts A (4 weeks of anti-PD-1) and B (4 weeks of anti-PD-1 + anti-CTLA4), we observed immune activation in 53% (8 of 15) and 60% (9 of 15) of patients, respectively. High levels of tumor-infiltrating lymphocytes correlated with response. Single-cell RNA sequencing revealed that higher pretreatment tumor-reactive CD8+ T cells, follicular helper T cells and shorter distances between tumor and CD8+ T cells correlated with response. Higher levels of regulatory T cells after treatment were associated with nonresponse. Based on these data, we opened cohort C for patients with high levels of tumor-infiltrating lymphocytes (≥50%) who received 6 weeks of neoadjuvant anti-PD-1 + anti-CTLA4 followed by surgery (primary end point, pathological complete response). Overall, 53% (8 of 15) of patients had a major pathological response (<10% viable tumor) at resection, with 33% (5 of 15) having a pathological complete response. All cohorts met Simon's two-stage threshold for expansion to stage II. We observed grade ≥3 adverse events for 17% of patients and a high rate (57%) of immune-mediated endocrinopathies. In conclusion, neoadjuvant immunotherapy without chemotherapy demonstrates potential efficacy and warrants further investigation in patients with early triple-negative breast cancer. ClinicalTrials.gov registration: NCT03815890 .

Automated classification of cellular expression in multiplexed imaging data with Nimbus.

Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference.

Facts and Hopes in Immunotherapy for Early-Stage Triple-Negative Breast Cancer.

A substantial fraction of early-stage triple-negative breast cancer (eTNBC) is characterized by high levels of stromal tumor-infiltrating lymphocytes (sTIL) and has a good prognosis even without systemic treatment, highlighting the importance of an endogenous anticancer immune response. Still, a considerable proportion of patients with eTNBC need some "therapeutical push" to kick-start this immune response. Exploiting this immune response with immune-checkpoint inhibition (ICI), in combination with chemotherapy, has made its way into standard of care in eTNBC. Major challenges in the near future include finding those patients with eTNBC who can be treated with ICI alone or with a reduced chemotherapy backbone. Exploring the optimal duration of ICI and finding biomarkers to predict response will be key to enable personalized implementation of ICI in patients with eTNBC. For patients who currently do not respond effectively to ICI plus chemotherapy, challenges lie in finding new immunomodulatory therapies and developing response-guided neoadjuvant approaches.

DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer.

We propose a Deep learning-based weak label learning method for analyzing whole slide images (WSIs) of Hematoxylin and Eosin (H&E) stained tumor tissue not requiring pixel-level or tile-level annotations using Self-supervised pre-training and heterogeneity-aware deep Multiple Instance LEarning (DeepSMILE). We apply DeepSMILE to the task of Homologous recombination deficiency (HRD) and microsatellite instability (MSI) prediction. We utilize contrastive self-supervised learning to pre-train a feature extractor on histopathology tiles of cancer tissue. Additionally, we use variability-aware deep multiple instance learning to learn the tile feature aggregation function while modeling tumor heterogeneity. For MSI prediction in a tumor-annotated and color normalized subset of TCGA-CRC (n=360 patients), contrastive self-supervised learning improves the tile supervision baseline from 0.77 to 0.87 AUROC, on par with our proposed DeepSMILE method. On TCGA-BC (n=1041 patients) without any manual annotations, DeepSMILE improves HRD classification performance from 0.77 to 0.81 AUROC compared to tile supervision with either a self-supervised or ImageNet pre-trained feature extractor. Our proposed methods reach the baseline performance using only 40% of the labeled data on both datasets. These improvements suggest we can use standard self-supervised learning techniques combined with multiple instance learning in the histopathology domain to improve genomic label classification performance with fewer labeled data.

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.

Interobserver Agreement of PD-L1/SP142 Immunohistochemistry and Tumor-Infiltrating Lymphocytes (TILs) in Distant Metastases of Triple-Negative Breast Cancer: A Proof-of-Concept Study. A Report on Behalf of the International Immuno-Oncology Biomarker Working Group.

Patients with advanced triple-negative breast cancer (TNBC) benefit from treatment with atezolizumab, provided that the tumor contains ≥1% of PD-L1/SP142-positive immune cells. Numbers of tumor-infiltrating lymphocytes (TILs) vary strongly according to the anatomic localization of TNBC metastases. We investigated inter-pathologist agreement in the assessment of PD-L1/SP142 immunohistochemistry and TILs. Ten pathologists evaluated PD-L1/SP142 expression in a proficiency test comprising 28 primary TNBCs, as well as PD-L1/SP142 expression and levels of TILs in 49 distant TNBC metastases with various localizations. Interobserver agreement for PD-L1 status (positive vs. negative) was high in the proficiency test: the corresponding scores as percentages showed good agreement with the consensus diagnosis. In TNBC metastases, there was substantial variability in PD-L1 status at the individual patient level. For one in five patients, the chance of treatment was essentially random, with half of the pathologists designating them as positive and half negative. Assessment of PD-L1/SP142 and TILs as percentages in TNBC metastases showed poor and moderate agreement, respectively. Additional training for metastatic TNBC is required to enhance interobserver agreement. Such training, focusing on metastatic specimens, seems worthwhile, since the same pathologists obtained high percentages of concordance (ranging from 93% to 100%) on the PD-L1 status of primary TNBCs.

Spatial immunophenotypes predict response to anti-PD1 treatment and capture distinct paths of T cell evasion in triple negative breast cancer.

Only a subgroup of triple-negative breast cancer (TNBC) responds to immune checkpoint inhibitors (ICI). To better understand lack of response to ICI, we analyze 681 TNBCs for spatial immune cell contextures in relation to clinical outcomes and pathways of T cell evasion. Excluded, ignored and inflamed phenotypes can be captured by a gene classifier that predicts prognosis of various cancers as well as anti-PD1 response of metastatic TNBC patients in a phase II trial. The excluded phenotype, which is associated with resistance to anti-PD1, demonstrates deposits of collagen-10, enhanced glycolysis, and activation of TGFß/VEGF pathways; the ignored phenotype, also associated with resistance to anti-PD1, shows either high density of CD163+ myeloid cells or activation of WNT/PPARγ pathways; whereas the inflamed phenotype, which is associated with response to anti-PD1, revealed necrosis, high density of CLEC9A+ dendritic cells, high TCR clonality independent of neo-antigens, and enhanced expression of T cell co-inhibitory receptors.

A High-Dimensional Window into the Micro-Environment of Triple Negative Breast Cancer.

Providing effective personalized immunotherapy for triple negative breast cancer (TNBC) patients requires a detailed understanding of the composition of the tumor microenvironment. Both the tumor cell and non-tumor components of TNBC can exhibit tremendous heterogeneity in individual patients and change over time. Delineating cellular phenotypes and spatial topographies associated with distinct immunological states and the impact of chemotherapy will be necessary to optimally time immunotherapy. The clinical successes in immunotherapy have intensified research on the tumor microenvironment, aided by a plethora of high-dimensional technologies to define cellular phenotypes. These high-dimensional technologies include, but are not limited to, single cell RNA sequencing, spatial transcriptomics, T cell repertoire analyses, advanced flow cytometry, imaging mass cytometry, and their integration. In this review, we discuss the cellular phenotypes and spatial patterns of the lymphoid-, myeloid-, and stromal cells in the TNBC microenvironment and the potential value of mapping these features onto tumor cell genotypes.

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials.

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting.

Comprehensive evaluation of methods to assess overall and cell-specific immune infiltrates in breast cancer.

BACKGROUND: Breast cancer (BC) immune infiltrates play a critical role in tumor progression and response to treatment. Besides stromal tumor infiltrating lymphocytes (sTILs) which have recently reached level 1B evidence as a prognostic marker in triple negative BC, a plethora of methods to assess immune infiltration exists, and it is unclear how these compare to each other and if they can be used interchangeably. METHODS: Two experienced pathologists scored sTIL, intra-tumoral TIL (itTIL), and 6 immune cell types (CD3+, CD4+, CD8+, CD20+, CD68+, FOXP3+) in the International Cancer Genomics Consortium breast cancer cohort using hematoxylin and eosin-stained (n = 243) and immunohistochemistry-stained tissue microarrays (n = 254) and whole slides (n = 82). The same traits were evaluated using transcriptomic- and methylomic-based deconvolution methods or signatures. RESULTS: The concordance correlation coefficient (CCC) between pathologists for sTIL was very good (0.84) and for cell-specific immune infiltrates slightly lower (0.63-0.66). Comparison between tissue microarray and whole slide pathology scores revealed systematically higher values in whole slides (ratio 2.60-5.98). The Spearman correlations between microscopic sTIL and transcriptomic- or methylomic-based assessment of immune infiltrates were highly variable (r = 0.01-0.56). Similar observations were made for cell type-specific quantifications (r = 0.001-0.54). We observed a strong inter-method variability between the omics-derived estimations, which is further cell type dependent. Finally, we demonstrated that most methods more accurately identify highly infiltrated (sTIL ≥ 60%; area under the curve, AUC, 0.64-0.99) as compared to lowly infiltrated tumors (sTIL ≤ 10%; AUC 0.52-0.82). CONCLUSIONS: There is a lower inter-pathologist concordance for cell-specific quantification as compared to overall infiltration quantification. Microscopic assessments are underestimated when considering small cores (tissue microarray) instead of whole slides. Results further highlight considerable differences between the microscopic-, transcriptomic-, and methylomic-based methods in the assessment of overall and cell-specific immune infiltration in BC. We therefore call for extreme caution when assessing immune infiltrates using current methods and emphasize the need for standardized immune characterization beyond TIL.

Publisher Correction: Immune induction strategies in metastatic triple-negative breast cancer to enhance the sensitivity to PD-1 blockade: the TONIC trial.

In the version of this article originally published, there was an error in Fig. 3j. A label on the heatmap read "TGF-α signaling via NF-κB". It should have read "TNF-α signaling via NF-κB". The error has been corrected in the HTML and PDF versions of this article.

Immune induction strategies in metastatic triple-negative breast cancer to enhance the sensitivity to PD-1 blockade: the TONIC trial.

The efficacy of programmed cell death protein 1 (PD-1) blockade in metastatic triple-negative breast cancer (TNBC) is low1-5, highlighting a need for strategies that render the tumor microenvironment more sensitive to PD-1 blockade. Preclinical research has suggested immunomodulatory properties for chemotherapy and irradiation6-13. In the first stage of this adaptive, non-comparative phase 2 trial, 67 patients with metastatic TNBC were randomized to nivolumab (1) without induction or with 2-week low-dose induction, or with (2) irradiation (3 × 8 Gy), (3) cyclophosphamide, (4) cisplatin or (5) doxorubicin, all followed by nivolumab. In the overall cohort, the objective response rate (ORR; iRECIST14) was 20%. The majority of responses were observed in the cisplatin (ORR 23%) and doxorubicin (ORR 35%) cohorts. After doxorubicin and cisplatin induction, we detected an upregulation of immune-related genes involved in PD-1-PD-L1 (programmed death ligand 1) and T cell cytotoxicity pathways. This was further supported by enrichment among upregulated genes related to inflammation, JAK-STAT and TNF-α signaling after doxorubicin. Together, the clinical and translational data of this study indicate that short-term doxorubicin and cisplatin may induce a more favorable tumor microenvironment and increase the likelihood of response to PD-1 blockade in TNBC. These data warrant confirmation in TNBC and exploration of induction treatments prior to PD-1 blockade in other cancer types.

Increased HLA-G Expression in Term Placenta of Women with a History of Recurrent Miscarriage Despite Their Genetic Predisposition to Decreased HLA-G Levels.

Human leukocyte antigen (HLA)-G is an immune modulating molecule that is present on fetal extravillous trophoblasts at the fetal-maternal interface. Single nucleotide polymorphisms (SNPs) in the 3 prime untranslated region (3'UTR) of the HLA-G gene can affect the level of HLA-G expression, which may be altered in women with recurrent miscarriages (RM). This case-control study included 23 women with a medical history of three or more consecutive miscarriages who delivered a child after uncomplicated pregnancy, and 46 controls with uncomplicated pregnancy. Genomic DNA was isolated to sequence the 3'UTR of HLA-G. Tissue from term placentas was processed to quantify the HLA-G protein and mRNA levels. The women with a history of RM had a lower frequency of the HLA-G 3'UTR 14-bp del/del genotype as compared to controls (Odds ratio (OR) 0.28; p = 0.039), which has previously been related to higher soluble HLA-G levels. Yet, HLA-G protein (OR 6.67; p = 0.006) and mRNA (OR 6.33; p = 0.010) expression was increased in term placentas of women with a history of RM as compared to controls. In conclusion, during a successful pregnancy, HLA-G expression is elevated in term placentas from women with a history of RM as compared to controls, despite a genetic predisposition that is associated with decreased HLA-G levels. These findings suggest that HLA-G upregulation could be a compensatory mechanism in the occurrence of RM to achieve an ongoing pregnancy.

Tipping the balance: inhibitory checkpoints in intestinal homeostasis.

The small intestinal and colonic lamina propria are populated with forkhead box P3 (FOXP3)+CD4+ regulatory T cells (Tregs) and interleukin-10-producing T cells that orchestrate intestinal tolerance to harmless microbial and food antigens. Expression of co-inhibitory receptors such as CTLA-4 and PD-1 serve as checkpoints to these cells controlling their T-cell receptor (TCR)-mediated and CD28-mediated activation and modulating the phenotype of neighboring antigen presenting cells. Recent discoveries on the diversity of co-inhibitory receptors and their selective cellular expression has shed new light on their tissue-dependent function. In this review, we provide an overview of the co-inhibitory pathways and checkpoints of Treg and effector T cells and their mechanisms of action in intestinal homeostasis. Better understanding of these inhibitory checkpoints is desired as their blockade harbors clinical potential for the treatment of cancer and their stimulation may offer new opportunities to treat chronic intestinal inflammation such as inflammatory bowel disease.

Risk Factors for Severe Acute Graft-versus-Host Disease in Donor Graft Composition.

Acute graft-versus-host disease (aGVHD) is 1 of the main major complications of post-hematopoietic stem cell transplantation (HSCT). Identifying patients at risk of severe aGVHD may lead to earlier intervention and treatment, resulting in increased survival and a better quality of life. We aimed to identify biomarkers in donor grafts and patient plasma around the time of transplantation that might be predictive of aGVHD development. We build on our previously published methods by using multiplex assays and multicolor flow cytometry. We identified 5 easily assessable cellular markers in donor grafts that combined could potentially be used to calculate risk for severe aGVHD development. Most noteworthy are the T cell subsets expressing IL-7 receptor-α (CD127) and PD-1. Additionally, we identified a potential role for elevated tumor necrosis factor-α levels in both graft and patient before HSCT in development of aGVHD.