Understanding Response to Immunotherapy Using Standard of Care and Experimental Imaging Approaches

Self-driven immune checkpoint blockade and spatiotemporal-sensitive immune response monitoring in acute myeloid leukemia using an all-in-one turn-on bionanoprobe

Immune checkpoint (ICP) blockade (ICB) is one of the most promising immunotherapies for acute myeloid leukemia (AML). However, owing to their heterogeneity, AML cells may cause uncoordinated metabolic fluxes and heterogeneous immune responses, inducing the release of a spatiotemporally sensitive immune response marker. Timely and in situ detection of immune responses in ICB therapy is important for therapeutic strategy adjustment. Herein, we constructed an all-in-one nanoprobe for self-driving ICB and simultaneously detecting an immune response in the same AML cell in vivo, thus enabling accurate evaluation of heterogenetic immune responses in living AML mice without additional drug treatment or probe processes. The nature-inspire polydopamine (PDA) nanoparticles loaded with an ICP blocker were targeted to the leukocyte immunoglobulin like receptor B4 (a new ICP) of AML cells to induce the release of immune response marker granzyme B (GrB). The PDA nanoparticles were additionally paired with carbon-derived graphene quantum dots (GQDs) to construct a full-organic 'turn-on' bionanoprobe that can transfer fluorescence resonance energy for GrB detection. This multifunctional nanoprobe was validated for triggering ICB therapy and monitoring the changes of GrB levels in real-time both in vitro and in vivo. The organic nanoprobe showed excellent permeability and retention in tumor cells and high biocompatibility in vivo. This bionanoprobe orderly interacted with the upstream ICP molecules and downstream signal molecule GrB, thereby achieving in situ immune response signals within the therapeutic efficacy evaluation window.

Interpretable artificial intelligence in radiology and radiation oncology

Artificial intelligence has been introduced to clinical practice, especially radiology and radiation oncology, from image segmentation, diagnosis, treatment planning and prognosis. It is not only crucial to have an accurate artificial intelligence model, but also to understand the internal logic and gain the trust of the experts. This review is intended to provide some insights into core concepts of the interpretability, the state-of-the-art methods for understanding the machine learning models, the evaluation of these methods, identifying some challenges and limits of them, and gives some examples of medical applications.

The radiological appearances of lung cancer treated with immunotherapy

Therapy and prognosis of several solid and hematologic malignancies, including non-small cell lung cancer (NSCLC), have been favourably impacted by the introduction of immune checkpoint inhibitors (ICIs). Their mechanism of action relies on the principle that some cancers can evade immune surveillance by expressing surface inhibitor molecules, known as "immune checkpoints". ICIs aim to conceal tumoural checkpoints on the cell surface and reinvigorate the ability of the host immune system to recognize tumour cells, triggering an antitumoural immune response.In this review, we will focus on the imaging patterns of different responses occurring in patients treated by ICIs. We will also discuss imaging findings of immune-related adverse events (irAEs), along with current and future perspectives of metabolic imaging. Finally, we will explore the role of radiomics in the setting of ICI-treated patients.

Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians

Immunotherapy using immune checkpoint inhibitors (ICIs) is a breakthrough in oncology development and has been applied to multiple solid tumors. However, unlike traditional cancer treatment approaches, immune checkpoint inhibitors (ICIs) initiate indirect cytotoxicity by generating inflammation, which causes enlargement of the lesion in some cases. Therefore, rather than declaring progressive disease (PD) immediately, confirmation upon follow-up radiological evaluation after four-eight weeks is suggested according to immune-related Response Evaluation Criteria in Solid Tumors (ir-RECIST). Given the difficulty for clinicians to immediately distinguish pseudoprogression from true disease progression, we need novel tools to assist in this field. Radiomics, an innovative data analysis technique that quantifies tumor characteristics through high-throughput extraction of quantitative features from images, can enable the detection of additional information from early imaging. This review will summarize the recent advances in radiomics concerning immunotherapy. Notably, we will discuss the potential of applying radiomics to differentiate pseudoprogression from PD to avoid condition exacerbation during confirmatory periods. We also review the applications of radiomics in hyperprogression, immune-related biomarkers, efficacy, and immune-related adverse events (irAEs). We found that radiomics has shown promising results in precision cancer immunotherapy with early detection in noninvasive ways.

Radiomic Models Predict Tumor Microenvironment Using Artificial Intelligence-the Novel Biomarkers in Breast Cancer Immune Microenvironment

Breast cancer is the most common malignancy in women, and some subtypes are associated with a poor prognosis with a lack of efficacious therapy. Moreover, immunotherapy and the use of other novel antibody‒drug conjugates have been rapidly incorporated into the standard management of advanced breast cancer. To extract more benefit from these therapies, clarifying and monitoring the tumor microenvironment (TME) status is critical, but this is difficult to accomplish based on conventional approaches. Radiomics is a method wherein radiological image features are comprehensively collected and assessed to build connections with disease diagnosis, prognosis, therapy efficacy, the TME, etc In recent years, studies focused on predicting the TME using radiomics have increasingly emerged, most of which demonstrate meaningful results and show better capability than conventional methods in some aspects. Beyond predicting tumor-infiltrating lymphocytes, immunophenotypes, cytokines, infiltrating inflammatory factors, and other stromal components, radiomic models have the potential to provide a completely new approach to deciphering the TME and facilitating tumor management by physicians.

Prostate cancer research in the 21st century; report from the 2021 Coffey-Holden prostate cancer academy meeting

INTRODUCTION

The 2021 Coffey-Holden Prostate Cancer Academy (CHPCA) Meeting, "Prostate Cancer Research in the 21st Century," was held virtually, from June 24-25, 2021.

METHODS

The CHPCA Meeting is organized by the Prostate Cancer Foundation as a unique discussion-oriented meeting focusing on critical topics in prostate cancer research envisioned to bridge the next major advances in prostate cancer biology and treatment. The 2021 CHPCA Meeting was virtually attended by 89 investigators and included 31 talks over nine sessions.

RESULTS

Major topic areas discussed at the meeting included: cancer genomics and sequencing, functional genomic approaches to studying mediators of plasticity, emerging signaling pathways in metastatic castration resistant prostate cancer, Wnt signaling biology and the challenges of targeted therapy, clonal hematopoiesis, neuroendocrine cell plasticity and antitumor immunity, cancer immunotherapy and its synergizers, and imaging the tumor microenvironment and metabolism.

DISCUSSION

This meeting report summarizes the research presented at the 2021 CHPCA Meeting. We hope that publication of this knowledge will accelerate new understandings and the development of new biomarkers and treatments for prostate cancer.

Exploring Response to Immunotherapy in Non-Small Cell Lung Cancer Using Delta-Radiomics

Delta-radiomics is a branch of radiomics in which features are confronted after time or after introducing an external factor (such as treatment with chemotherapy or radiotherapy) to extrapolate prognostic data or to monitor a certain condition. Immune checkpoint inhibitors (ICIs) are currently revolutionizing the treatment of non-small cell lung cancer (NSCLC); however, there are still many issues in defining the response to therapy. Contrast-enhanced CT scans of 33 NSCLC patients treated with ICIs were analyzed; altogether, 43 lung lesions were considered. The radiomic features of the lung lesions were extracted from CT scans at baseline and at first reassessment, and their variation (delta, Δ) was calculated by means of the absolute difference and relative reduction. This variation was related to the final response of each lesion to evaluate the predictive ability of the variation itself. Twenty-seven delta features have been identified that are able to discriminate radiologic response to ICIs with statistically significant accuracy. Furthermore, the variation of nine features significantly correlates with pseudo-progression.