Towards evidence-based response criteria for cancer immunotherapy

Artificial intelligence in immunotherapy PET/SPECT imaging

OBJECTIVE

Immunotherapy has dramatically altered the therapeutic landscape for oncology, but more research is needed to identify patients who are likely to achieve durable clinical benefit and those who may develop unacceptable side effects. We investigated the role of artificial intelligence in PET/SPECT-guided approaches for immunotherapy-treated patients.

METHODS

We performed a scoping review of MEDLINE, CENTRAL, and Embase databases using key terms related to immunotherapy, PET/SPECT imaging, and AI/radiomics through October 12, 2022.

RESULTS

Of the 217 studies identified in our literature search, 24 relevant articles were selected. The median (interquartile range) sample size of included patient cohorts was 63 (157). Primary tumors of interest were lung (n = 14/24, 58.3%), lymphoma (n = 4/24, 16.7%), or melanoma (n = 4/24, 16.7%). A total of 28 treatment regimens were employed, including anti-PD-(L)1 (n = 13/28, 46.4%) and anti-CTLA-4 (n = 4/28, 14.3%) monoclonal antibodies. Predictive models were built from imaging features using univariate radiomics (n = 7/24, 29.2%), radiomics (n = 12/24, 50.0%), or deep learning (n = 5/24, 20.8%) and were most often used to prognosticate (n = 6/24, 25.0%) or describe tumor phenotype (n = 5/24, 20.8%). Eighteen studies (75.0%) performed AI model validation.

CONCLUSION

Preliminary results suggest broad potential for the application of AI-guided immunotherapy management after further validation of models on large, prospective, multicenter cohorts.

CLINICAL RELEVANCE STATEMENT

This scoping review describes how artificial intelligence models are built to make predictions based on medical imaging and explores their application specifically in the PET and SPECT examination of immunotherapy-treated cancers.

KEY POINTS

• Immunotherapy has drastically altered the cancer treatment landscape but is known to precipitate response patterns that are not accurately accounted for by traditional imaging methods. • There is an unmet need for better tools to not only facilitate in-treatment evaluation but also to predict, a priori, which patients are likely to achieve a good response with a certain treatment as well as those who are likely to develop side effects. • Artificial intelligence applied to PET/SPECT imaging of immunotherapy-treated patients is mainly used to make predictions about prognosis or tumor phenotype and is built from baseline, pre-treatment images. Further testing is required before a true transition to clinical application can be realized.

The Immune Landscape of Pheochromocytoma and Paraganglioma: Current Advances and Perspectives

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors derived from neural crest cells from adrenal medullary chromaffin tissues and extra-adrenal paraganglia, respectively. Although the current treatment for PPGLs is surgery, optimal treatment options for advanced and metastatic cases have been limited. Hence, understanding the role of the immune system in PPGL tumorigenesis can provide essential knowledge for the development of better therapeutic and tumor management strategies, especially for those with advanced and metastatic PPGLs. The first part of this review outlines the fundamental principles of the immune system and tumor microenvironment, and their role in cancer immunoediting, particularly emphasizing PPGLs. We focus on how the unique pathophysiology of PPGLs, such as their high molecular, biochemical, and imaging heterogeneity and production of several oncometabolites, creates a tumor-specific microenvironment and immunologically "cold" tumors. Thereafter, we discuss recently published studies related to the reclustering of PPGLs based on their immune signature. The second part of this review discusses future perspectives in PPGL management, including immunodiagnostic and promising immunotherapeutic approaches for converting "cold" tumors into immunologically active or "hot" tumors known for their better immunotherapy response and patient outcomes. Special emphasis is placed on potent immune-related imaging strategies and immune signatures that could be used for the reclassification, prognostication, and management of these tumors to improve patient care and prognosis. Furthermore, we introduce currently available immunotherapies and their possible combinations with other available therapies as an emerging treatment for PPGLs that targets hostile tumor environments.

Tumor Size Is Not Everything: Advancing Radiomics as a Precision Medicine Biomarker in Oncology Drug Development and Clinical Care. A Report of a Multidisciplinary Workshop Coordinated by the RECIST Working Group

Radiomics, the science of extracting quantifiable data from routine medical images, is a powerful tool that has many potential applications in oncology. The Response Evaluation Criteria in Solid Tumors Working Group (RWG) held a workshop in May 2022, which brought together various stakeholders to discuss the potential role of radiomics in oncology drug development and clinical trials, particularly with respect to response assessment. This article summarizes the results of that workshop, reviewing radiomics for the practicing oncologist and highlighting the work that needs to be done to move forward the incorporation of radiomics into clinical trials.

PD-1/PD-L1 Inhibitor - Related Adverse Events and Their Management in Breast Cancer

As the positive results of multiple clinical trials were released, the Programmed cell death 1 (PD-1) and Programmed cell death ligand 1 (PD-L1) inhibitors emerge as the focus of integrative breast cancer treatment. PD-1/PD-L1 inhibitors are often used as a sequential agent to be combined with other agents such as chemotherapeutic agents, targeted agents, and radiation therapy. As multiple therapies are administered simultaneously or in sequence, they are prone to a variety of adverse effects on patients while achieving efficacy. It is a challenge for clinicians to maintaining the balance between immune-related adverse effects(irAEs) and treatment efficacy. Previous literatures have paid lots of attention on the adverse effects caused by immunosuppressive agents themselves, while there is a dearth of the research on the management of adverse immune effects during the combination of immunotherapy with other treatments. In this review, we discuss the overall incidence of irAEs caused by PD-1/PD-L1 inhibitors in combination with various types of treatments in breast cancer, including chemotherapy, CTLA-4 inhibitors, targeted therapy, and radiotherapy, and systematically summarizes the clinical management to each organ-related adverse immune reaction. It is important to emphasize that in the event of irAEs such as neurological, hematologic, and cardiac toxicity, there is no alternative treatment but to terminate immunotherapy. Thus, seeking more effective strategy of irAEs' management is imminent and clinicians are urged to raise the awareness of the management of adverse immune reactions.

A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?

The search for understanding immunotherapy response has sparked interest in diverse areas of oncology, with artificial intelligence (AI) and radiomics emerging as promising tools, capable of gathering large amounts of information to identify suitable patients for treatment. The application of AI in radiology has grown, driven by the hypothesis that radiology images capture tumor phenotypes and thus could provide valuable insights into immunotherapy response likelihood. However, despite the rapid growth of studies, no algorithms in the field have reached clinical implementation, mainly due to the lack of standardized methods, hampering study comparisons and reproducibility across different datasets. In this review, we performed a comprehensive assessment of published data to identify sources of variability in radiomics study design that hinder the comparison of the different model performance and, therefore, clinical implementation. Subsequently, we conducted a use-case meta-analysis using homogenous studies to assess the overall performance of radiomics in estimating programmed death-ligand 1 (PD-L1) expression. Our findings indicate that, despite numerous attempts to predict immunotherapy response, only a limited number of studies share comparable methodologies and report sufficient data about cohorts and methods to be suitable for meta-analysis. Nevertheless, although only a few studies meet these criteria, their promising results underscore the importance of ongoing standardization and benchmarking efforts. This review highlights the importance of uniformity in study design and reporting. Such standardization is crucial to enable meaningful comparisons and demonstrate the validity of biomarkers across diverse populations, facilitating their implementation into the immunotherapy patient selection process.

Precision USPIO-PEG-SLex Nanotheranostic Agent Targeted Photothermal Therapy for Enhanced Anti-PD-L1 Immunotherapy to Treat Immunotherapy Resistance

Background

The anti-Programmed Death-Ligand 1 (termed aPD-L1) immune checkpoint blockade therapy has emerged as a promising treatment approach for various advanced solid tumors. However, the effect of aPD-L1 inhibitors limited by the tumor microenvironment makes most patients exhibit immunotherapy resistance.

Methods

We conjugated the Sialyl Lewis X with a polyethylene glycol-coated ultrasmall superparamagnetic iron oxide (USPIO-PEG) to form UPS nanoparticles (USPIO-PEG-SLex, termed UPS). The physicochemical properties of UPS were tested and characterized. Transmission electron microscopy and ICP-OES were used to observe the cellular uptake and targeting ability of UPS. Flow cytometry, mitochondrial membrane potential staining, live-dead staining and scratch assay were used to verify the in vitro photothermal effect of UPS, and the stimulation of UPS on immune-related pathways at the gene level was analyzed by sequencing. Biological safety analysis and pharmacokinetic analysis of UPS were performed. Finally, the amplification effect of UPS-mediated photothermal therapy on aPD-L1-mediated immunotherapy and the corresponding mechanism were studied.

Results

In vitro experiments showed that UPS had strong photothermal therapy ability and was able to stimulate 5 immune-related pathways. In vivo, when the PTT assisted aPD-L1 treatment, it exhibited a significant increase in CD4+ T cell infiltration by 14.46-fold and CD8+ T cell infiltration by 14.79-fold, along with elevated secretion of tumor necrosis factor-alpha and interferon-gamma, comparing with alone aPD-L1. This PTT assisted aPD-L1 therapy achieved a significant inhibition of both primary tumors and distant tumors compared to the alone aPD-L1, demonstrating a significant difference.

Conclusion

The nanotheranostic agent UPS has been introduced into immunotherapy, which has effectively broadened its application in biomedicine. This photothermal therapeutic approach of the UPS nanotheranostic agent enhancing the efficacy of aPD-L1 immune checkpoint blockade therapy, can be instructive to address the challenges associated with immunotherapy resistance, thereby offering potential for clinical translation.

Advancements in dendritic cell vaccination: enhancing efficacy and optimizing combinatorial strategies for the treatment of glioblastoma

Glioblastomas (GBM) are highly invasive, malignant primary brain tumors. The overall prognosis is poor, and management of GBMs remains a formidable challenge, necessitating novel therapeutic strategies such as dendritic cell vaccinations (DCVs). While many early clinical trials demonstrate an induction of an antitumoral immune response, outcomes are mixed and dependent on numerous factors that vary between trials. Optimization of DCVs is essential; the selection of GBM-specific antigens and the utilization of 18F-fludeoxyglucose Positron Emission Tomography (FDG-PET) may add significant value and ultimately improve outcomes for patients undergoing treatment for glioblastoma. This review provides an overview of the mechanism of DCV, assesses previous clinical trials, and discusses future strategies for the integration of DCV into glioblastoma treatment protocols. To conclude, the review discusses challenges associated with the use of DCVs and highlights the potential of integrating DCV with standard therapies.

Non-coding RNAs in cancer immunotherapy: Predictive biomarkers and targets

BACKGROUND

To date, standardising clinical predictive biomarkers for assessing the response to immunotherapy remains challenging due to variations in personal genetic signatures, tumour microenvironment complexities and epigenetic onco-mechanisms.

MAIN BODY

Early monitoring of key non-coding RNA (ncRNA) biomarkers may help in predicting the clinical efficacy of cancer immunotherapy and come up with standard predictive ncRNA biomarkers. For instance, reduced miR-125b-5p level in the plasma of non-small cell lung cancer patients treated with anti-PD-1 predicts a positive outcome. The level of miR-153 in the plasma of colorectal cancer patients treated with chimeric antigen receptor T lymphocyte (CAR-T) cell therapy may indicate the activation of T-cell killing activity. miR-148a-3p and miR-375 levels may forecast favourable responses to CAR-T-cell therapy in B-cell acute lymphoblastic leukaemia. In cancer patients treated with the GPC3 peptide vaccine, serum levels of miR-1228-5p, miR-193a-5p and miR-375-3p were reported as predictive biomarkers of good response and improved overall survival. Therefore, there is a critical need for further studies to elaborate on the key ncRNA biomarkers that have the potential to predict early clinical responses to immunotherapy.

CONCLUSION

This review summarises important predictive ncRNA biomarkers that were reported in cancer patients treated with different immunotherapeutic modalities, including monoclonal antibodies, small molecule inhibitors, cancer vaccines and CAR-T cells. In addition, a concise discussion on forthcoming perspectives is provided, outlining technical approaches for the optimal utilisation of immunomodulatory ncRNA biomarkers as predictive tools and therapeutic targets.