Dual Relief of T-lymphocyte Proliferation and Effector Function Underlies Response to PD-1 Blockade in Epithelial Malignancies

Although understanding of T-cell exhaustion is widely based on mouse models, its analysis in patients with cancer could provide clues indicating tumor sensitivity to immune checkpoint blockade (ICB). Data suggest a role for costimulatory pathways, particularly CD28, in exhausted T-cell responsiveness to PD-1/PD-L1 blockade. Here, we used single-cell transcriptomic, phenotypic, and functional approaches to dissect the relation between CD8+ T-cell exhaustion, CD28 costimulation, and tumor specificity in head and neck, cervical, and ovarian cancers. We found that memory tumor–specific CD8+ T cells, but not bystander cells, sequentially express immune checkpoints once they infiltrate tumors, leading, in situ, to a functionally exhausted population. Exhausted T cells were nonetheless endowed with effector and tumor residency potential but exhibited loss of the costimulatory receptor CD28 in comparison with their circulating memory counterparts. Accordingly, PD-1 inhibition improved proliferation of circulating tumor–specific CD8+ T cells and reversed functional exhaustion of specific T cells at tumor sites. In agreement with their tumor specificity, high infiltration of tumors by exhausted cells was predictive of response to therapy and survival in ICB-treated patients with head and neck cancer. Our results showed that PD-1 blockade–mediated proliferation/reinvigoration of circulating memory T cells and local reversion of exhaustion occur concurrently to control tumors

Results of a worldwide survey on the currently used histopathological diagnostic criteria for invasive lobular breast cancer

Invasive lobular carcinoma (ILC) represents the second most common subtype of breast cancer (BC), accounting for up to 15% of all invasive BC. Loss of cell adhesion due to functional inactivation of E-cadherin is the hallmark of ILC. Although the current world health organization (WHO) classification for diagnosing ILC requires the recognition of the dispersed or linear non-cohesive growth pattern, it is not mandatory to demonstrate E-cadherin loss by immunohistochemistry (IHC). Recent results of central pathology review of two large randomized clinical trials have demonstrated relative overdiagnosis of ILC, as only similar to 60% of the locally diagnosed ILCs were confirmed by central pathology. To understand the possible underlying reasons of this discrepancy, we undertook a worldwide survey on the current practice of diagnosing BC as ILC. A survey was drafted by a panel of pathologists and researchers from the European lobular breast cancer consortium (ELBCC) using the online tool SurveyMonkey (R). Various parameters such as indications for IHC staining, IHC clones, and IHC staining procedures were questioned. Finally, systematic reporting of non-classical ILC variants were also interrogated. This survey was sent out to pathologists worldwide and circulated from December 14, 2020 until July, 1 2021. The results demonstrate that approximately half of the institutions use E-cadherin expression loss by IHC as an ancillary test to diagnose ILC and that there is a great variability in immunostaining protocols. This might cause different staining results and discordant interpretations. As ILC-specific therapeutic and diagnostic avenues are currently explored in the context of clinical trials, it is of importance to improve standardization of histopathologic diagnosis of ILC diagnosis

Deep Analysis of CNN Settings for New Cancer whole-slide Histological Images Segmentation: the Case of Small Training Sets

International audienceAccurate analysis and interpretation of stained biopsy images is a crucial step in the cancer diagnostic routine which is mainly done manually by expert pathologists. The recent progress of digital pathology gives us a challenging opportunity to automatically process these complex image data in order to retrieve essential information and to study tissue elements and structures. This paper addresses the task of tissue-level segmentation in intermediate resolution of histopathological breast cancer images. Firstly, we present a new medical dataset we developed which is composed of hematoxylin and eosin stained whole-slide images wherein all 7 tissues were labeled by hand and validated by expert pathologist. Then, with this unique dataset, we proposed an automatic end-to-end framework using deep neural network for tissue-level segmentation. Moreover, we provide a deep analysis of the framework settings that can be used in similar task by the scientific community

Several immune escape patterns in non-Hodgkin's lymphomas

International audienceFollicular Lymphomas (FL) and diffuse large B cell lymphomas (DLBCL) must evolve some immune escape strategy to develop from lymphoid organs, but their immune evasion pathways remain poorly characterized. We investigated this issue by transcriptome data mining and immunohistochemistry (IHC) of FL and DLBCL lymphoma biopsies. A set of genes involved in cancer immune-evasion pathways (Immune Escape Gene Set, IEGS) was defined and the distribution of the expression levels of these genes was compared in FL, DLBCL and normal B cell transcriptomes downloaded from the GEO database. The whole IEGS was significantly upregulated in all the lymphoma samples but not in B cells or other control tissues, as shown by the overexpression of the PD-1, PD-L1, PD-L2 and LAG3 genes. Tissue microarray immunostainings for PD-1, PD-L1, PD-L2 and LAG3 proteins on additional biopsies from 27 FL and 27 DLBCL patients confirmed the expression of these proteins. The immune infiltrates were more abundant in FL than DLBCL samples, and the microenvironment of FL comprised higher rates of PD-1+ lymphocytes. Further, DLBCL tumor cells comprised a higher proportion of PD-1+, PD-L1+, PD-L2+ and LAG3+ lymphoma cells than the FL tumor cells, confirming that DLBCL mount immune escape strategies distinct from FL. In addition, some cases of DLBCL had tumor cells co-expressing both PD-1, PD-L1 and PD-L2. Among the DLBCLs, the activated B cell (ABC) subtype comprised more PD-L1+ and PD-L2+ lymphoma cells than the GC subtype. Thus, we infer that FL and DLBCL evolved several pathways of immune escape

Training Set Class Distribution Analysis for Deep Learning Model - Application to Cancer Detection

International audienceDeep learning models specifically CNNs have been used successfully in many tasks including medical image classification. CNN effectiveness depends on the availability of large training data set to train which is generally costly to obtain for new applications or new cases. However, there is a little concrete recommendation about training set creation. In this research, we analyze the impact of different class distributions in the training data to a CNN model. We consider the case of cancer detection task from histopathological images for cancer diagnosis and derive some useful hypotheses about the distribution of classes in the training data. We found that using all the training data leads to the best recall-precision trade-off, while training with a reduced number of examples from some classes, it is possible to inflect the model toward a desired accuracy on a given class

Several immune escape patterns in non-Hodgkin's lymphomas

International audienceFollicular Lymphomas (FL) and diffuse large B cell lymphomas (DLBCL) must evolve some immune escape strategy to develop from lymphoid organs, but their immune evasion pathways remain poorly characterized. We investigated this issue by transcriptome data mining and immunohistochemistry (IHC) of FL and DLBCL lymphoma biopsies. A set of genes involved in cancer immune-evasion pathways (Immune Escape Gene Set, IEGS) was defined and the distribution of the expression levels of these genes was compared in FL, DLBCL and normal B cell transcriptomes downloaded from the GEO database. The whole IEGS was significantly upregulated in all the lymphoma samples but not in B cells or other control tissues, as shown by the overexpression of the PD-1, PD-L1, PD-L2 and LAG3 genes. Tissue microarray immunostainings for PD-1, PD-L1, PD-L2 and LAG3 proteins on additional biopsies from 27 FL and 27 DLBCL patients confirmed the expression of these proteins. The immune infiltrates were more abundant in FL than DLBCL samples, and the microenvironment of FL comprised higher rates of PD-1+ lymphocytes. Further, DLBCL tumor cells comprised a higher proportion of PD-1+, PD-L1+, PD-L2+ and LAG3+ lymphoma cells than the FL tumor cells, confirming that DLBCL mount immune escape strategies distinct from FL. In addition, some cases of DLBCL had tumor cells co-expressing both PD-1, PD-L1 and PD-L2. Among the DLBCLs, the activated B cell (ABC) subtype comprised more PD-L1+ and PD-L2+ lymphoma cells than the GC subtype. Thus, we infer that FL and DLBCL evolved several pathways of immune escape

A Study on the Impact of Class Distribution on Deep Learning - The Case of Histological Images and Cancer Detection

Extended AbstractInternational audienceStudies on deep learning tuning mostly focus on the neural network architectures and algorithms hyperparameters. Another core factor for accurate training is the class distribution of the training dataset. This paper contributes to understanding the optimal class distribution on the case for histological images used in cancer diagnosis. We formulate several hypotheses, which are then tested considering experiments with hundreds of trials. We considered both segmentation and classification tasks considering the U-net and group equivariant CNN (G-CNN). This paper is an extended abstract of another paper published by the authors 1

Finding a Suitable Class Distribution for Building Histological Images Data Sets Used in Deep Model Training - the Case of Cancer Detection

International audienceThe class distribution of a training data set is an important factor which influences the performance of a deep learning-based system. Understanding the optimal class distribution is therefore crucial when building a new training set which may be costly to annotate. This is the case for histological images used in cancer diagnosis where image annotation requires domain experts. In this paper we tackle the problem of finding the optimal class distribution of a training set to be able to train an optimal model that detects cancer in histological images. We formulate several hypotheses which are then tested in scores of experiments with hundreds of trials. The experiments have been designed to account for both segmentation and cla

Profiling Immune Escape in Hodgkin’s and Diffuse large B-Cell Lymphomas Using the Transcriptome and Immunostaining

IF 5.326International audienceTherapeutic blockade of PD-1/PD-L1 shows promising results in Hodgkin's lymphoma (HL) and in some diffuse large B-cell lymphoma (DLBCL) patients, but biomarkers predicting such responses are still lacking. To this end, we recently developed a transcriptional scoring of immune escape (IE) in cancer biopsies. Using this method in DLBCL, we identified four stages of IE correlated with overall survival, but whether Hodgkin's lymphomas (HL) also display this partition was unknown. Thus, we explored the transcriptomic profiles of ~1000 HL and DLBCL using a comparative meta-analysis of their bulk microarrays. Relative to DLBCL, the HL co-clustered at the advanced stage of immune escape, displaying significant enrichment of both IE and T-cell activation genes. Analyses via transcriptome deconvolution and immunohistochemistry showed more CD3⁺ and CD4⁺ tumor-infiltrating lymphocytes (TILs) in HL than DLBCL. Both HL and non-GCB DLBCL shared a high abundance of infiltrating CD8⁺ T-cells, but HL had less CD68⁺CD163⁺ macrophages. The same cellular distribution of PD-1 and TIM-3 was observed in HL and DLBCL, though HL had more PD-L1 tumor cells and LAG-3 ME cells. This study illuminates the advanced stage of immune activation and escape in HL, consistent with the response to checkpoint blockade therapies for this type of lymphoma

Finding a Suitable Class Distribution for Building Histological Images Data Sets Used in Deep Model Training - the Case of Cancer Detection

International audienceThe class distribution of a training data set is an important factor which influences the performance of a deep learning-based system. Understanding the optimal class distribution is therefore crucial when building a new training set which may be costly to annotate. This is the case for histological images used in cancer diagnosis where image annotation requires domain experts. In this paper we tackle the problem of finding the optimal class distribution of a training set to be able to train an optimal model that detects cancer in histological images. We formulate several hypotheses which are then tested in scores of experiments with hundreds of trials. The experiments have been designed to account for both segmentation and cla