Next Generation Imaging Techniques to Define Immune Topographies in Solid Tumors

Extracellular matrix stiffness and tumor-associated macrophage polarization: new fields affecting immune exclusion

In the malignant progression of tumors, there is deposition and cross-linking of collagen, as well as an increase in hyaluronic acid content, which can lead to an increase in extracellular matrix stiffness. Recent research evidence have shown that the extracellular matrix plays an important role in angiogenesis, cell proliferation, migration, immunosuppression, apoptosis, metabolism, and resistance to chemotherapeutic by the alterations toward both secretion and degradation. The clinical importance of tumor-associated macrophage is increasingly recognized, and macrophage polarization plays a central role in a series of tumor immune processes through internal signal cascade, thus regulating tumor progression. Immunotherapy has gradually become a reliable potential treatment strategy for conventional chemotherapy resistance and advanced cancer patients, but the presence of immune exclusion has become a major obstacle to treatment effectiveness, and the reasons for their resistance to these approaches remain uncertain. Currently, there is a lack of exact mechanism on the regulation of extracellular matrix stiffness and tumor-associated macrophage polarization on immune exclusion. An in-depth understanding of the relationship between extracellular matrix stiffness, tumor-associated macrophage polarization, and immune exclusion will help reveal new therapeutic targets and guide the development of clinical treatment methods for advanced cancer patients. This review summarized the different pathways and potential molecular mechanisms of extracellular matrix stiffness and tumor-associated macrophage polarization involved in immune exclusion and provided available strategies to address immune exclusion.

Phototruncation cell tracking with near-infrared photoimmunotherapy using heptamethine cyanine dye to visualise migratory dynamics of immune cells

BACKGROUND

Noninvasive in vivo cell tracking is valuable in understanding the mechanisms that enhance anti-cancer immunity. We have recently developed a new method called phototruncation-assisted cell tracking (PACT), that uses photoconvertible cell tracking technology to detect in vivo cell migration. This method has the advantages of not requiring genetic engineering of cells and employing tissue-penetrant near-infrared light.

METHODS

We applied PACT to monitor the migration of immune cells between a tumour and its tumour-draining lymph node (TDLN) after near-infrared photoimmunotherapy (NIR-PIT).

FINDINGS

PACT showed a significant increase in the migration of dendritic cells (DCs) and macrophages from the tumour to the TDLN immediately after NIR-PIT. This migration by NIR-PIT was abrogated by inhibiting the sphingosine-1-phosphate pathway or Gαi signaling. These results were corroborated by intranodal immune cell profiles at two days post-treatment; NIR-PIT significantly induced DC maturation and increased and activated the CD8+ T cell population in the TDLN. Furthermore, PACT revealed that NIR-PIT significantly enhanced the migration of CD8+ T cells from the TDLN to the tumour four days post-treatment, which was consistent with the immunohistochemical assessment of tumour-infiltrating lymphocytes and tumour regression.

INTERPRETATION

Immune cells dramatically migrated between the tumour and TDLN following NIR-PIT, indicating its potential as an immune-stimulating therapy. Also, PACT is potentially applicable to a wide range of immunological research.

FUNDING

This work was supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Centre for Cancer Research (grant number: ZIA BC011513 and ZIA BC011506).

Shifting the paradigm: engaging multicellular networks for cancer therapy

Most anti-cancer modalities are designed to directly kill cancer cells deploying mechanisms of action (MOAs) centered on the presence of a precise target on cancer cells. The efficacy of these approaches is limited because the rapidly evolving genetics of neoplasia swiftly circumvents the MOA generating therapy-resistant cancer cell clones. Other modalities engage endogenous anti-cancer mechanisms by activating the multi-cellular network (MCN) surrounding neoplastic cells in the tumor microenvironment (TME). These modalities hold a better chance of success because they activate numerous types of immune effector cells that deploy distinct cytotoxic MOAs. This in turn decreases the chance of developing treatment-resistance. Engagement of the MCN can be attained through activation of immune effector cells that in turn kill cancer cells or when direct cancer killing is complemented by the production of proinflammatory factors that secondarily recruit and activate immune effector cells. For instance, adoptive cell therapy (ACT) supplements cancer cell killing with the release of homeostatic and pro-inflammatory cytokines by the immune cells and damage associated molecular patterns (DAMPs) by dying cancer cells. The latter phenomenon, referred to as immunogenic cell death (ICD), results in an exponential escalation of anti-cancer MOAs at the tumor site. Other approaches can also induce exponential cancer killing by engaging the MCN of the TME through the release of DAMPs and additional pro-inflammatory factors by dying cancer cells. In this commentary, we will review the basic principles that support emerging paradigms likely to significantly improve the efficacy of anti-cancer therapy.

Whole Slide Imaging-Based Prediction of TP53 Mutations Identifies an Aggressive Disease Phenotype in Prostate Cancer

In prostate cancer, there is an urgent need for objective prognostic biomarkers that identify the metastatic potential of a tumor at an early stage. While recent analyses indicated TP53 mutations as candidate biomarkers, molecular profiling in a clinical setting is complicated by tumor heterogeneity. Deep learning models that predict the spatial presence of TP53 mutations in whole slide images (WSI) offer the potential to mitigate this issue. To assess the potential of WSIs as proxies for spatially resolved profiling and as biomarkers for aggressive disease, we developed TiDo, a deep learning model that achieves state-of-the-art performance in predicting TP53 mutations from WSIs of primary prostate tumors. In an independent multifocal cohort, the model showed successful generalization at both the patient and lesion level. Analysis of model predictions revealed that false positive (FP) predictions could at least partially be explained by TP53 deletions, suggesting that some FP carry an alteration that leads to the same histological phenotype as TP53 mutations. Comparative expression and histologic cell type analyses identified a TP53-like cellular phenotype triggered by expression of pathways affecting stromal composition. Together, these findings indicate that WSI-based models might not be able to perfectly predict the spatial presence of individual TP53 mutations but they have the potential to elucidate the prognosis of a tumor by depicting a downstream phenotype associated with aggressive disease biomarkers.

SIGNIFICANCE

Deep learning models predicting TP53 mutations from whole slide images of prostate cancer capture histologic phenotypes associated with stromal composition, lymph node metastasis, and biochemical recurrence, indicating their potential as in silico prognostic biomarkers. See related commentary by Bordeleau, p. 2809.

Cancer immune exclusion: breaking the barricade for a successful immunotherapy

Immunotherapy has changed the course of cancer treatment. The initial steps were made through tumor-specific antibodies that guided the setup of an antitumor immune response. A new and successful generation of antibodies are designed to target immune checkpoint molecules aimed to reinvigorate the antitumor immune response. The cellular counterpart is the adoptive cell therapy, where specific immune cells are expanded or engineered to target cancer cells. In all cases, the key for achieving positive clinical resolutions rests upon the access of immune cells to the tumor. In this review, we focus on how the tumor microenvironment architecture, including stromal cells, immunosuppressive cells and extracellular matrix, protects tumor cells from an immune attack leading to immunotherapy resistance, and on the available strategies to tackle immune evasion.

Histopathological Features of Parathyroid Adenoma and 18F-Choline Uptake in PET/MR of Primary Hyperparathyroidism

BACKGROUND

The aim of this study was to assess the relationship between the histopathological properties of hyperfunctioning parathyroids and parathyroid 18F-choline uptake.

PATIENTS AND METHODS

A total of 31 parathyroid adenomas were retrospectively analyzed in patients with primary hyperparathyroidism and preoperative 18F-choline PET/MR. PET/MR parameters of parathyroid glands (SUVmax and target-to-background ratio in early-phase [EP] and late-phase [LP]), MRI volume, preoperative parathyroid hormone (PTH) serum concentration, and postoperative histopathology (predominant cell type and growth pattern of adenoma cells, location and size of adenoma) were assessed. The relationship of PET/MR parameters, PTH, and histological parameters was determined using linear regression, Spearman correlation and Kruskal-Wallis test.

RESULTS

The median volume of parathyroid adenoma was 421.78 ± 142.46 mm3 (46.39-4412.69). Adenomas were predominantly composed of chief, water-clear, and oncocytic/oxyphilic cells in 27/31, 2/31, and 2/31 cases, respectively. The growth pattern was predominantly solid, follicular, and trabecular in 18/31, 8/31, and 5/31, respectively. The SUVmax was 6.71 ± 3.39 in EP and 6.91 ± 3.97 in LP. Follicular growth pattern had slightly higher EP SUVmax (trabecular: 4.12 ± 0.56; solid: 6.62 ± 3.19; follicular: 8.56 ± 3.96; P = 0.046). Spearman correlation showed strong positive correlation between volume and both EP and LP SUVmax (0.626; P = 0.0001 and 0.576; P = 0.0001, respectively). Linear regression analysis revealed significant correlation between PTH level and EP and LP SUVmax (both P = 0.001); in contrast, no correlation was found between PTH level and both cell type and growth pattern.

CONCLUSIONS

Our findings suggest that 18F-choline uptake of parathyroid adenomas might be associated both with the histological growth pattern and adenoma volume, but not with a specific cell type.

Non-invasive cell-tracking methods for adoptive T cell therapies

Adoptive T cell therapies (ACT) have demonstrated groundbreaking results in blood cancers and melanoma. Nevertheless, their significant cost, the occurrence of severe adverse events, and their poor performance in solid tumors are important hurdles hampering more widespread applicability. In vivo cell tracking allows instantaneous and non-invasive monitoring of the distribution, tumor homing, persistence, and redistribution to other organs of infused T cells in patients. Furthermore, cell tracking could aid in the clinical management of patients, allowing the detection of non-responders or severe adverse events at an early stage. This review provides a concise overview of the main principles and potential of cell tracking, followed by a discussion of the clinically relevant labeling strategies and their application in ACT.

NSCLC Biomarkers to Predict Response to Immunotherapy with Checkpoint Inhibitors (ICI): From the Cells to In Vivo Images

Lung cancer remains the leading cause of cancer-related death, and it is usually diagnosed in advanced stages (stage III or IV). Recently, the availability of targeted strategies and of immunotherapy with checkpoint inhibitors (ICI) has favorably changed patient prognosis. Treatment outcome is closely related to tumor biology and interaction with the tumor immune microenvironment (TME). While the response in molecular targeted therapies relies on the presence of specific genetic alterations in tumor cells, accurate ICI biomarkers of response are lacking, and clinical outcome likely depends on multiple factors that are both host and tumor-related. This paper is an overview of the ongoing research on predictive factors both from in vitro/ex vivo analysis (ranging from conventional pathology to molecular biology) and in vivo analysis, where molecular imaging is showing an exponential growth and use due to technological advancements and to the new bioinformatics approaches applied to image analyses that allow the recovery of specific features in specific tumor subclones.

The Future of Cancer Diagnosis, Treatment and Surveillance: A Systemic Review on Immunotherapy and Immuno-PET Radiotracers

Immunotherapy is an effective therapeutic option for several cancers. In the last years, the introduction of checkpoint inhibitors (ICIs) has shifted the therapeutic landscape in oncology and improved patient prognosis in a variety of neoplastic diseases. However, to date, the selection of the best patients eligible for these therapies, as well as the response assessment is still challenging. Patients are mainly stratified using an immunohistochemical analysis of the expression of antigens on biopsy specimens, such as PD-L1 and PD-1, on tumor cells, on peritumoral immune cells and/or in the tumor microenvironment (TME). Recently, the use and development of imaging biomarkers able to assess in-vivo cancer-related processes are becoming more important. Today, positron emission tomography (PET) with 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) is used routinely to evaluate tumor metabolism, and also to predict and monitor response to immunotherapy. Although highly sensitive, FDG-PET in general is rather unspecific. Novel radiopharmaceuticals (immuno-PET radiotracers), able to identify specific immune system targets, are under investigation in pre-clinical and clinical settings to better highlight all the mechanisms involved in immunotherapy. In this review, we will provide an overview of the main new immuno-PET radiotracers in development. We will also review the main players (immune cells, tumor cells and molecular targets) involved in immunotherapy. Furthermore, we report current applications and the evidence of using [18F]FDG PET in immunotherapy, including the use of artificial intelligence (AI).

Dive into Single, Seek Out Multiple: Probing Cancer Metastases via Single-Cell Sequencing and Imaging Techniques

Metastasis is the cause of most cancer deaths and continues to be the biggest challenge in clinical practice and laboratory investigation. The challenge is largely due to the intrinsic heterogeneity of primary and metastatic tumor populations and the complex interactions among cancer cells and cells in the tumor microenvironment. Therefore, it is important to determine the genotype and phenotype of individual cells so that the metastasis-driving events can be precisely identified, understood, and targeted in future therapies. Single-cell sequencing techniques have allowed the direct comparison of the genomic and transcriptomic changes among different stages of metastatic samples. Single-cell imaging approaches have enabled the live visualization of the heterogeneous behaviors of malignant and non-malignant cells in the tumor microenvironment. By applying these technologies, we are achieving a spatiotemporal precision understanding of cancer metastases and clinical therapeutic translations.