Cancer immunotherapy: it's time to better predict patients' response

Impact of metabolic and nutritional disorders on the synergy between radiotherapy and immunotherapy in non-small-cell lung cancer

BACKGROUND

Patient conditions including metabolic and nutritional status were reported to be prognostic or predictive biomarkers of anti-cancer treatment, while little attention has been paid to its association with the synergistic effect of radiotherapy (RT) and immune checkpoint inhibitors (ICIs).

METHODS

Metastatic non-small-cell lung cancer (mNSCLC) patients who received concurrent RT and ICIs between 2018 and 2023 were included in this study. In addition, mNSCLC patients treated with ICIs alone were enrolled to confirm the synergetic effect of RT and ICIs. Clinicopathological, metabolic and nutritional factors were collected to analyze their influence on progression-free survival (PFS), overall survival and abscopal control time. Abdominal CT was used to obtain body composition data including abdominal obesity and muscle mass.

RESULTS

A total of 96 mNSCLC patients who received RT concurrent with ICIs were included, and a synergistic effect of significantly improved PFS was observed when compared with patients treated with ICIs alone. Among patients undergoing concurrent RT and ICIs, both total adipose area(HR = 2.81,P = 0.029) and prognosis nutritional index (HR = 0.24, P<0.001) were confirmed as independent positive prognostic markers for PFS. Later-line of immunotherapy (HR = 3.67, P = 0.006), low visceral-to-subcutaneous ratio (VSR, HR = 5.53, P = 0.002), high total adipose area (HR = 5.21, P = 0.0016) and high prognostic nutritional index (HR = 0.24, P = 0.002) were independent risk factors for abscopal progression. Then, we established a scoring system consisting of metabolic and nutritional factors to stratify patients into three groups. Patients with non-visceral obesity and good nutrition status have the longest PFS and abscopal control survival, while patients with poor nutritional status regardless of body composition represent the worst prognosis.

CONCLUSION

Metabolic and nutritional status, particularly the combined assessment of body composition and nutritional index, serves as a valuable predictor for the synergistic efficacy of concurrent RT and ICIs.

Emerging therapeutics targeting tumor-associated macrophages for the treatment of solid organ cancers

INTRODUCTION

Over the last decade, immune checkpoint inhibitors (ICIs) like PD-1/PD-L1 or CTLA-4, which reinvigorate T cells for tumor control have become standard-of-care treatment options. In response to the increasingly recognized mechanisms of resistance to T cell activation in immunologically cold tumors, immuno-oncology drug development has started to shift beyond T cell approaches. These include tumor-associated macrophages (TAMs), a major pro-tumor immune cell population in the tumor microenvironment known to silence immune responses.

AREAS COVERED

Here we outline anti-TAM therapies in current development, either as monotherapy or in combination with other treatment modalities. We describe emerging drugs targeting TAMs under investigation in phase II and III testing with a focus on their distinguishing mechanism of action which include (1) reprogramming of TAMs toward anti-tumor function and immune surveillance, (2) blockade of recruitment, and (3) reduction and ablation of TAMs.

EXPERT OPINION

Several new immuno-oncology agents are under investigation to harness anti-tumor functions of TAMs. While robust anti-tumor efficacy of anti-TAM therapies across advanced solid organ cancers remains elusive to-date, TAM reprogramming therapies have yielded benefits in select cancers. The inherent heterogeneity of the diverse TAM population will require enhanced investments into biomarker-driven approaches to fully leverage its therapeutic potential.

Development of a novel Cu-Mn hydroxide layered nanosheet-loaded drug modulating the tumour microenvironment and enhancing antitumor effects

The tumor microenvironment (TME) impedes the effectiveness of therapeutic strategies such as chemodynamic therapy (CDT). This study presents a novel nanoscale drug delivery system designed for the precise release of the chemotherapeutic agent doxorubicin (DOX), aiming to overcome treatment limitations, reduce systemic toxicity, and enhance antitumor efficacy. Mn(III) serves as an immunomodulatory agent, while Cu(II) regulates the levels of glutathione (GSH). Layered double hydroxides (LDHs) were synthesized and efficiently loaded with DOX, followed by surface modification with hyaluronic acid (HA). The HA-coated LDH/DOX nanocarriers showed effective internalization by tumor cells and provided a pH-responsive release of DOX. In vitro, the LDH/HA/DOX complex exhibited strong catalytic activity in the Fenton reaction. In vivo studies using an H22 hepatocarcinoma model confirmed its potent antitumor activity and excellent biocompatibility. Immunohistochemical analyses revealed that treatment with LDH/HA/DOX significantly increased infiltration of M1-polarized tumor-associated macrophages (TAMs), CD4 + T cells and CD8 + T cells, while decreasing M2-polarized TAMs. This change in immune cell profile was associated with notable tumor growth inhibition.

Biomaterial-driven 3D scaffolds for immune cell expansion toward personalized immunotherapy

Immunotherapy has emerged as a transformative medical approach in recent years, providing novel treatments for cancer eradication, autoimmune disorders, and infectious diseases. Fundamental to the success of therapy is the enrichment of the immune cell population, particularly T cells, natural killer cells, and dendritic cells. However, achieving a robust and long-term proliferation of immune cells is still challenging both in vivo and ex vivo. In vivo expansion leverages the patient's natural microenvironment and regulatory mechanisms through therapeutic interventions like immune checkpoint inhibitors, cytokine therapy, and targeted antibodies. This approach fosters long-term immune memory and sustained protection. In contrast, ex vivo expansion involves isolation, manipulation, and expansion of the immune cells under controlled conditions before reinfusion, allowing for precise control over the process and generating potent immune cell populations. Hydrogels, due to their tunable biomechanical properties, high biocompatibility, and ability to mimic the extracellular matrix, provide an ideal platform for both in vivo and ex vivo immune cell expansion. For instance, hydrogel-based scaffolds or beads can facilitate a controlled and efficient expansion of immune cells ex vivo, whereas injectable and implantable hydrogels can provide innovative solutions for enhancing immune cell activity within the patient supporting prolonged immune cell activity. This review aims to elucidate the importance of hydrogel-based strategies in immune cell expansion, advancing the development of effective, personalized immunotherapies to improve patient outcomes. STATEMENT OF SIGNIFICANCE: This review highlights the transformative potential of hydrogel-based 3D scaffolds in advancing personalized immunotherapy. By integrating in vivo and ex vivo strategies, hydrogels provide an innovative platform to enhance immune cell expansion, addressing critical challenges in immunotherapy. The discussion emphasizes the unique biomechanical and biochemical tunability of hydrogels, enabling precise mimicry of the extracellular matrix to support T cell proliferation, activation, and memory formation. These advances offer scalable, cost-effective solutions for producing high-quality immune cells, contributing to more effective cancer treatments, autoimmune disease management, and infectious disease control. By bridging materials science and immunology, this work underscores the pivotal role of hydrogels in shaping the future of immune-based therapies.

Deep Learning-Powered Whole Slide Image Analysis in Cancer Pathology

Pathology is the cornerstone of modern cancer care. With the advancement of precision oncology, the demand for histopathologic diagnosis and stratification of patients is increasing as personalized cancer therapy relies on accurate biomarker assessment. Recently, rapid development of whole slide imaging technology has enabled digitalization of traditional histologic slides at high resolution, holding promise to improve both the precision and efficiency of histopathologic evaluation. In particular, deep learning approaches, such as Convolutional Neural Network, Graph Convolutional Network, and Transformer, have shown great promise in enhancing the sensitivity and accuracy of whole slide image (WSI) analysis in cancer pathology because of their ability to handle high-dimensional and complex image data. The integration of deep learning models with WSIs enables us to explore and mine morphologic features beyond the visual perception of pathologists, which can help automate clinical diagnosis, assess histopathologic grade, predict clinical outcomes, and even discover novel morphologic biomarkers. In this review, we present a comprehensive framework for incorporating deep learning with WSIs, highlighting how deep learning-driven WSI analysis advances clinical tasks in cancer care. Furthermore, we critically discuss the opportunities and challenges of translating deep learning-based digital pathology into clinical practice, which should be considered to support personalized treatment of cancer patients.

CD8+ T cell-based immunotherapy: Promising frontier in human diseases

The abundant cell components of the adaptive immune system called T lymphocytes (T cells) play important roles in mediating immune responses to eliminate the invaders and create the memory of the germs to form a new immunity for the next encounter. Among them, cytotoxic T cells expressing cell-surface CD8 are the most critical effector cells that directly eradicate the target infected cells by recognizing antigens presented by major histocompatibility complex class I molecules to protect our body from pathological threats. In the continuous evolution of immunotherapy, various CD8+ T cell-based therapeutic strategies have been developed based on the role and molecular concept of CD8+ T cells. The emergence of such remarkable therapies provides promising hope for multiple human disease treatments such as autoimmunity, infectious disease, cancer, and other non-infectious diseases. In this review, we aim to discuss the current knowledge on the utilization of CD8+ T cell-based immunotherapy for the treatment of various diseases, the molecular basis involved, and its limitations. Additionally, we summarize the recent advances in the use of CD8+ T cell-based immunotherapy and provide a comprehensive overview of CD8+ T cells, including their structure, underlying mechanism of function, and markers associated with CD8+ T cell exhaustion. Building upon these foundations, we delineate the advancement of CD8+ T cell-based immunotherapies with fundamental operating principles followed by research studies, and challenges, as well as illustrate human diseases involved in this development.

Tumor-stroma proportion is associated with increased M2 macrophage abundance and predicts the resistance to immune checkpoint blockade in breast cancer

BACKGROUND

The tumor stroma has been reported to be associated with worse prognosis in several solid tumors, but its prognostic value in breast cancer (BRCA) is still undefined.

METHODS

In this research, multiple public and in-house patient cohorts were collected to demonstrate the clinical and immune correlations of tumor-stroma proportion (TSP) in BRCA. In addition, in vitro assays uncovered the oncogenic role of TSP-related collagen in BRCA.

RESULTS

High TSP status based on hematoxylin and eosin (HE) staining was associated with positive hormone receptor status, advanced clinical stages, and poor immune checkpoint blockade (ICB) response. In addition, we developed a RNA-sequencing (RNA-seq)-based stromal score based on four critical genes expression (AEBP1, COL6A3, CTSK, and PLAC9). Both TSP status and stromal score were positively associated with increased M2 macrophage abundance in BRCA. Moreover, tumor collagen has been found to be enriched in samples with the high TSP status, and collagen promoted BRCA cells aggressiveness and macrophage M2 polarization.

CONCLUSIONS

The tumor stroma was found to be notably related to poor ICB response in patients with BRCA as a result of tumor stroma-macrophage interactions. Thus, the TSP status could predict the clinical outcomes of BRCA patients receiving ICB therapy.

Immune checkpoint inhibitor-associated myocarditis: a historical and comprehensive review

The most fatal side effect associated with revolutionary immune checkpoint inhibitor (ICI) cancer therapies is myocarditis, a rare and devastating complication with a mortality rate approaching 40%. This review comprehensively examines the limited knowledge surrounding this recently recognized condition, emphasizing the absence of evidence-based therapeutic strategies, diagnostic modalities, and reliable biomarkers that hinder effective management. It explores advancements in preclinical models that are uncovering disease mechanisms and enabling the identification of therapeutic targets. These efforts have informed the design of early clinical trials aimed at reducing mortality. With the growing prevalence of ICI therapies in oncology, addressing critical gaps, such as long-term outcomes and risk stratification, has become increasingly urgent. By synthesizing current evidence, this work seeks to enhance understanding and guide the development of strategies to improve patient outcomes and ensure the continued safe use of ICIs in cancer care.

The association between blood sodium levels and survival in patients treated with immune checkpoint inhibitors

BACKGROUND

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape for solid tumors, offering substantial survival benefits. Despite this progress, many patients do not achieve durable responses, highlighting the need for novel prognostic biomarkers. This study investigates the association between serum sodium levels and survival outcomes in patients treated with ICIs.

RESEARCH DESIGN AND METHODS

We conducted a retrospective cohort study involving 509 patients with metastatic solid tumors treated with ICIs. We assessed overall survival (OS), progression-free survival (PFS), and response rates using Kaplan-Meier survival analysis and multivariate cox regression analysis.

RESULTS

The median age was 62 years (interquartile range (IQR): 54-69), and 76.6% of the patients were male. Multivariate analysis revealed that serum sodium levels between 135-140 mmol/L were an independent predictor of improved OS (HR: 0.58; 95% CI: 0.44-0.77) and PFS (HR: 0.76; 95%CI: 0.58-0.99) and those with levels > 140 mmol/L had an even lower HR of 0.43 (95% CI:0.31-0.62) for OS and HR of 0.62 (95% CI:0.45-0.86) for PFS.

CONCLUSION

This study highlights that ICI-treated patients with higher sodium levels had significantly better OS, PFS, and anti-tumor responses. Baseline serum sodium levels could be cost-effective and valuable predictive biomarker for ICIs across diverse tumor types and ICI agents.

In-silico analysis unveiling the role of cancer stem cells in immunotherapy resistance of immune checkpoint-high pancreatic adenocarcinoma

Although immune checkpoint (IC) inhibition is a major treatment modality in cancer-immunotherapy, multiple cancers show low response. Our in-silico exploration by mining cancer datasets using R2, available clinical trial data, and Kaplan-Meier analysis from GEPIA depicted that unlike low-responder (LR) cancers, high-responder (HR) cancers furnish higher IC expression, that upon lowering may provide better prognosis. Contrastingly, pancreatic adenocarcinoma (PAAD) demonstrated high IC expression but low immunotherapy-response. Infiltration scores from TIMER2.0 revealed higher pro-tumor immune subsets and cancer-associated fibroblasts (CAFs) while depicting lower anti-tumor immune subsets in PAAD as compared to HR lung adenocarcinoma (LUAD). Additionally, bioinformatic tool cBioportal showed lesser tumor mutational burden, mismatch repair deficiency and greater percent of driver mutations in TP53, KRAS and CDKN2A in PAAD, supporting its higher immunotherapy-resistance than LUAD. Our search for the 'key' immunotherapy response-deciding factor(s) revealed cancer stem cells (CSCs), the known contributors of therapy-resistance and immuno-evasion, to be positively correlated with above-mentioned driver mutations, pro-tumor immune and CAF subsets; and that PAAD furnished higher expression of CSC genes than LUAD. UMAP/tSNE analyses revealed that high CSC signature is positively correlated with immunotherapy-resistance genes and pro-cancer immune cells, while negatively with cytotoxic-T cells in PAAD. Our in-silico study explains the low immunotherapy-response in high IC-expressing PAAD, wherein CSC plays a pivotal role. Further exploration portrayed correlation of CSCs with immunotherapy-resistance in other LR and HR cancers too, substantiating the need for personalized CSC evaluation and targeting for successful immunotherapy outcomes.

Effects of gut microbiota on immune checkpoint inhibitors in multi-cancer and as microbial biomarkers for predicting therapeutic response

BACKGROUND

Gut bacteria are related to immune checkpoint inhibitors (ICIs). However, there is inconsistency in ICI-associated species, while the role of non-bacterial microbes in immunotherapy remains elusive. Here, we evaluated the association of trans-kingdom microbes with ICIs by multi-cohort multi-cancer analyses.

METHODS

We retrieved fecal metagenomes from 1,359 ICI recipients with four different cancers (metastatic melanoma [MM], non-small cell lung carcinoma [NSCLC], renal cell cancer [RCC], and hepatocellular carcinoma) from 12 published datasets. Microbiota composition was analyzed using the Wilcoxon rank test. The performance of microbial biomarkers in predicting ICI response was assessed by random forest. Key responder-associated microbes were functionally examined in vitro and in mice.

FINDINGS

Trans-kingdom gut microbiota (bacteria, eukaryotes, viruses, and archaea) was significantly different between ICI responders and non-responders in multi-cancer. Bacteria (Faecalibacterium prausnitzii, Coprococcus comes) and eukaryotes (Nemania serpens, Hyphopichia pseudoburtonii) were consistently enriched in responders of ≥2 cancer types or from ≥3 cohorts, contrasting with the depleted bacterium Hungatella hathewayi. Responder-associated species in each cancer were revealed, such as F. prausnitzii in MM and 6 species in NSCLC. These signature species influenced ICI efficacy by modulating CD8+ T cell activity in vitro and in mice. Moreover, bacterial and eukaryotic biomarkers showed great performance in predicting ICI response in patients from discovery and two validation cohorts (MM: area under the receiver operating characteristic curve [AUROC] = 72.27%-80.19%; NSCLC: AUROC = 72.70%-87.98%; RCC: AUROC = 83.33%-89.58%).

CONCLUSIONS

This study identified trans-kingdom microbial signatures associated with ICI in multi-cancer and specific cancer types. Trans-kingdom microbial biomarkers are potential predictors of ICI response in patients with cancer.

FUNDING

Funding information is shown in the acknowledgments.

Design, Synthesis, and Pharmacological Evaluation of Quinazoline and Quinoline Derivatives as Potent ENPP1 Inhibitors for Cancer Immunotherapy

ENPP1, a transmembrane glycoprotein overexpressed in various cancers, has become a promising target for tumor immunotherapy. Several ENPP1 inhibitors have been reported, but only a few have been validated in vivo. Herein, based on the reported inhibitors 3 and 6, we carried out a structural optimization by designing a variety of 8-methoxyquinazoline and its equivalent 8-methoxy-3-cyano-quinoline derivatives featuring bridged- or spirobicycles as the linker. Compound 30 was identified as a promising ENPP1 inhibitor. This compound exhibited IC50 values of 8.05 nM against ENPP1 and 1.53 nM in MDA-MB-231 cells with no significant inhibitory effects against both hERG and a panel of 97 kinases. It effectively activated the intracellular STING pathway by inhibiting cGAMP degradation. In the murine CT-26 tumor model, 30 inhibited tumor growth with increased immune cell infiltration in the tumor microenvironment and enhanced type I interferon responses. Meanwhile, compound 30 synergically enhanced the antitumor efficacy of anti-PD-L1 antibody.

Revolutionary Cancer Therapy for Personalization and Improved Efficacy: Strategies to Overcome Resistance to Immune Checkpoint Inhibitor Therapy

Cancer remains a significant public health issue worldwide, standing as a primary contributor to global mortality, accounting for approximately 10 million fatalities in 2020 [...].

Artificial intelligence-based biomarkers for treatment decisions in oncology

The development of new therapeutic strategies such as immune checkpoint inhibitors (ICIs) and targeted therapies has increased the complexity of the treatment landscape for solid tumors. At the current rate of annual FDA approvals, the potential treatment options could increase by tenfold over the next 5 years. The cost of personalized medicine technologies limits its accessibility, thus increasing socioeconomic disparities in the treated population. In this review we describe artificial intelligence (AI)-based solutions - including deep learning (DL) methods for routine medical imaging and large language models (LLMs) for electronic health records (EHRs) - to support cancer treatment decisions with cost-effective biomarkers. We address the current limitations of these technologies and propose the next steps towards their adoption in routine clinical practice.

MAP1LC3C repression reduces CIITA- and HLA class II expression in non-small cell lung cancer

In the last decade, advancements in understanding the genetic landscape of lung squamous cell carcinoma (LUSC) have significantly impacted therapy development. Immune checkpoint inhibitors (ICI) have shown great promise, improving overall and progression-free survival in approximately 25% of the patients. However, challenges remain, such as the lack of predictive biomarkers, difficulties in patient stratification, and identifying mechanisms that cancers use to become immune-resistant ("immune-cold"). Analysis of TCGA datasets reveals reduced MAP1LC3C expression in cancer. Further analysis indicates that low MAP1LC3C is associated with reduced CIITA and HLA expression and with decreased immune cell infiltration. In tumor cells, silencing MAP1LC3C inhibits CIITA expression and suppresses HLA class II production. These findings suggest that cancer cells are selected for low MAP1LC3C expression to evade efficient immune responses.

Soluble CD146 Cooperates with VEGFa to Generate an Immunosuppressive Microenvironment in CD146-Positive Tumors: Interest of a Combined Antibody-Based Therapy

Tumor development necessitates immune escape through different mechanisms. To counteract these effects, the development of therapies targeting immune checkpoints (ICP) has generated interest as they have produced lasting objective responses in patients with advanced metastatic tumors. However, many tumors do not respond to inhibitors of ICPs, necessitating to further study the underlying mechanisms of exhaustion. VEGFa, a proangiogenic molecule secreted by tumors, was described to participate to tumor immune exhaustion by increasing ICPs, justifying in part the use of an anti-VEGFa mAb, bevacizumab, in patients. However, recent studies from our group have demonstrated that tumors can escape anti-VEGFa therapy through the secretion of soluble CD146 (sCD146). In this study, we show that both VEGFa and sCD146 cooperate to create an immunosuppressive microenvironment by increasing the expression of ICPs. In addition, sCD146 favors protumoral M2-type macrophages and induces the secretion of proinflammatory cytokines. An anti-sCD146 mAb reverses these effects and displays additive effects with the anti-VEGFa antibody to eliminate tumors in a syngeneic murine model grafted with melanoma cells. Combining bevacizumab with mucizumab could thus be of major therapeutic interest to prevent immune escape in malignant melanoma and other CD146-positive tumors.

Immunogenomic determinants of exceptional response to immune checkpoint inhibition in renal cell carcinoma

Immune checkpoint inhibitors can lead to 'exceptional', durable responses in a subset of persons. However, the molecular basis of exceptional response (ER) to immunotherapy in metastatic clear cell renal cell carcinoma (mccRCC) has not been well characterized. Here we analyzed pretherapy genomic and transcriptomic data in treatment-naive persons with mccRCC treated with standard-of-care immunotherapies: (1) combination of programmed cell death protein and ligand 1 (PD1/PDL1) and cytotoxic T lymphocyte-associated protein 4 inhibitors (IO/IO) or (2) combination of PD1/PDL1 and vascular endothelial growth factor (VEGF) receptor inhibitors (IO/VEGF). In the IO/IO cohort, clonal neoantigen load was significantly higher in persons with ER. In the IO/VEGF cohort, ER participants displayed strong enrichment of B cell receptor signaling-related pathways, tertiary lymphoid structure (TLS) signatures and evidence of increased metabolic activity. Our results suggest that ER may be related to clonal neoantigen-driven cytotoxic T cell responses and TLS formation in tumor microenvironments. Therapeutic combinations that elicit both T cell-directed and B cell-directed antitumor immunity may be important to achieve exceptional benefit to IO-based treatment in ccRCC.

Advanced Polymeric Nanoparticles for Cancer Immunotherapy: Materials Engineering, Immunotherapeutic Mechanism and Clinical Translation

Cancer immunotherapy, which leverages immune system components to treat malignancies, has emerged as a cornerstone of contemporary therapeutic strategies. Yet, critical concerns about the efficacy and safety of cancer immunotherapies remain formidable. Nanotechnology, especially polymeric nanoparticles (PNPs), offers unparalleled flexibility in manipulation-from the chemical composition and physical properties to the precision control of nanoassemblies. PNPs provide an optimal platform to amplify the potency and minimize systematic toxicity in a broad spectrum of immunotherapeutic modalities. In this comprehensive review, the basics of polymer chemistry, and state-of-the-art designs of PNPs from a physicochemical standpoint for cancer immunotherapy, encompassing therapeutic cancer vaccines, in situ vaccination, adoptive T-cell therapies, tumor-infiltrating immune cell-targeted therapies, therapeutic antibodies, and cytokine therapies are delineated. Each immunotherapy necessitates distinctively tailored design strategies in polymeric nanoplatforms. The extensive applications of PNPs, and investigation of their mechanisms of action for enhanced efficacy are particularly focused on. The safety profiles of PNPs and clinical research progress are discussed. Additionally, forthcoming developments and emergent trends of polymeric nano-immunotherapeutics poised to transform cancer treatment paradigms into clinics are explored.

Immunotherapy in thymic epithelial tumors: an attractive dilemma

Thymomas and thymic carcinomas are the most prevalent tumors that develop in the thymus's epithelial tissue. Thymomas are malignant tumors that develop from the epithelial cells of the thymus and frequently include mixed populations of lymphocytes. In contrast, thymic carcinomas are also tumors of the thymic epithelium, but they are characterized by a lack of lymphocytes, exhibit more aggressive behavior, and are associated with a poorer prognosis. Surgical intervention is the primary approach for managing resectable cases, while advanced, unresectable tumors are treated with platinum-based chemotherapy. The recurrence of the disease can happen months to years after initial treatment. Some patients do benefit from biologic therapies, but there is still a significant need for new treatment options. Immune checkpoint inhibitors have proven safe and clinically effective, improving survival in various cancers. However, their use in thymic cancers is currently limited to treating recurrent thymic carcinoma due to potential immune toxicity risks. This manuscript reviews the current applications of immunotherapy for thymic epithelial tumors and discusses strategies to enhance safety and expand treatment options for patients with these cancers.

Integrating AI into Cancer Immunotherapy-A Narrative Review of Current Applications and Future Directions

BACKGROUND

Cancer remains a leading cause of morbidity and mortality worldwide. Traditional treatments like chemotherapy and radiation often result in significant side effects and varied patient outcomes. Immunotherapy has emerged as a promising alternative, harnessing the immune system to target cancer cells. However, the complexity of immune responses and tumor heterogeneity challenges its effectiveness.

OBJECTIVE

This mini-narrative review explores the role of artificial intelligence [AI] in enhancing the efficacy of cancer immunotherapy, predicting patient responses, and discovering novel therapeutic targets.

METHODS

A comprehensive review of the literature was conducted, focusing on studies published between 2010 and 2024 that examined the application of AI in cancer immunotherapy. Databases such as PubMed, Google Scholar, and Web of Science were utilized, and articles were selected based on relevance to the topic.

RESULTS

AI has significantly contributed to identifying biomarkers that predict immunotherapy efficacy by analyzing genomic, transcriptomic, and proteomic data. It also optimizes combination therapies by predicting the most effective treatment protocols. AI-driven predictive models help assess patient response to immunotherapy, guiding clinical decision-making and minimizing side effects. Additionally, AI facilitates the discovery of novel therapeutic targets, such as neoantigens, enabling the development of personalized immunotherapies.

CONCLUSIONS

AI holds immense potential in transforming cancer immunotherapy. However, challenges related to data privacy, algorithm transparency, and clinical integration must be addressed. Overcoming these hurdles will likely make AI a central component of future cancer immunotherapy, offering more personalized and effective treatments.

Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction

BACKGROUND

The efficacy of immune checkpoint inhibitors (ICIs) depends on the tumor immune microenvironment (TIME), with a preference for a T cell-inflamed TIME. However, challenges in tissue-based assessments via biopsies have triggered the exploration of non-invasive alternatives, such as radiomics, to comprehensively evaluate TIME across diverse cancers. To address these challenges, we develop an ICI response signature by integrating radiomics with T cell-inflamed gene-expression profiles.

METHODS

We conducted a pan-cancer investigation into the utility of radiomics for TIME assessment, including 1360 tumors from 428 patients. Leveraging contrast-enhanced CT images, we characterized TIME through RNA gene expression analysis, using the T cell-inflamed gene expression signature. Subsequently, a pan-cancer CT-radiomic signature predicting inflamed TIME (CT-TIME) was developed and externally validated. Machine learning was employed to select robust radiomic features and predict inflamed TIME. The study also integrated independent cohorts with longitudinal CT images, baseline biopsies, and comprehensive immunohistochemistry panel evaluation to assess the pan-cancer biological associations, spatiotemporal landscape and clinical utility of the CT-TIME.

RESULTS

The CT-TIME signature, comprising four radiomic features linked to a T-cell inflamed microenvironment, demonstrated robust performance with AUCs (95% CI) of 0.85 (0.73 to 0.96) (training) and 0.78 (0.65 to 0.92) (external validation). CT-TIME scores exhibited positive correlations with CD3, CD8, and CD163 expression. Intrapatient analysis revealed considerable heterogeneity in TIME between tumors, which could not be assessed using biopsies. Evaluation of aggregated per-patient CT-TIME scores highlighted its promising clinical utility for dynamically assessing the immune microenvironment and predicting immunotherapy response across diverse scenarios in advanced cancer. Despite demonstrating progression disease at the first follow-up, patients within the inflamed status group, identified by CT-TIME, exhibited significantly prolonged progression-free survival (PFS), with some surpassing 5 months, suggesting a potential phenomenon of pseudoprogression. Cox models using aggregated CT-TIME scores from baseline images revealed a statistically significant reduction in the risk of PFS in the pan-cancer cohort (HR 0.62, 95% CI 0.44 to 0.88, p=0.007), and Kaplan-Meier analysis further confirmed substantial differences in PFS between patients with inflamed and uninflamed status (log-rank test p=0.009).

CONCLUSIONS

The signature holds promise for impacting clinical decision-making, pan-cancer patient stratification, and treatment outcomes in immune checkpoint therapies.

Understanding the role of the gut microbiome in solid tumor responses to immune checkpoint inhibitors for personalized therapeutic strategies: a review

Immunotherapy, especially immune checkpoint inhibitor (ICI) therapy, has yielded remarkable outcomes for some patients with solid cancers, but others do not respond to these treatments. Recent research has identified the gut microbiota as a key modulator of immune responses, suggesting that its composition is closely linked to responses to ICI therapy in cancer treatment. As a result, the gut microbiome is gaining attention as a potential biomarker for predicting individual responses to ICI therapy and as a target for enhancing treatment efficacy. In this review, we discuss key findings from human observational studies assessing the effect of antibiotic use prior to ICI therapy on outcomes and identifying specific gut bacteria associated with favorable and unfavorable responses. Moreover, we review studies investigating the possibility of patient outcome prediction using machine learning models based on gut microbiome data before starting ICI therapy and clinical trials exploring whether gut microbiota modulation, for example via fecal microbiota transplantation or live biotherapeutic products, can improve results of ICI therapy in patients with cancer. We also briefly discuss the mechanisms through which the gut microbial-derived products influence immunotherapy effectiveness. Further research is necessary to fully understand the complex interactions between the host, gut microbiota, and immunotherapy and to develop personalized strategies that optimize responses to ICI therapy.

Introducing Neoadjuvant Immunotherapy for Colorectal Cancer: Advancing the Frontier

OBJECTIVE

To give surgeons a review of the current and future use of neoadjuvant immunotherapy in patients with localized colorectal cancer (CRC).

BACKGROUND

Immunotherapy has revolutionized the standard of care in oncology and improved survival outcomes in several cancers. However, the applicability of immunotherapy is still an ongoing challenge. Some cancer types are less responsive to immunotherapy, and the heterogeneity in responses within cancer types is poorly understood. Clinical characteristics of the patient, the timing of immunotherapy in relation to surgery, diversities in the immune responses, clonal heterogeneity, different features of the tumor microenvironment, and genetic alterations are some factors among many that may influence the efficacy of immunotherapy.

RESULTS

In this narrative review, we describe the major types of immunotherapy used to treat localized CRC. Furthermore, we discuss the prediction of response to immunotherapy in relation to biomarkers and radiologic assessment. Finally, we consider the future perspectives of clinical implications and response patterns, as well as the potential and challenges of neoadjuvant immunotherapy in localized CRC.

CONCLUSIONS

Establishing mismatch repair (MMR) status at the time of diagnosis is central to the potential use of neoadjuvant immunotherapy, in particular immune checkpoint inhibitors, in localized CRC. To date, efficacy is primarily seen in patients with deficient MMR status and polymerase epsilon mutations, although a small group of patients with proficient MMR does respond. In conclusion, neoadjuvant immunotherapy shows promising complete response rates, which may open a future avenue of an organ-sparing watch-and-wait approach for a group of patients.

Identification of a distinctive immunogenomic gene signature in stage-matched colorectal cancer

BACKGROUND

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Despite advances in diagnosis and treatment, including surgery, chemotherapy, and immunotherapy, accurate clinical markers are still lacking. The development of prognostic and predictive indicators, particularly in the context of personalized medicine, could significantly improve CRC patient management.

METHOD

In this retrospective study, we used FFPE blocks of tissue samples from CRC patients at Augusta University (AU) to quantify a custom 15-gene panel. To differentiate the tumor and adjacent normal regions (NAT), H&E staining was utilized. For the quantification of transcripts, we used the NanoString nCounter platform. Kaplan-Meier and Log-rank tests were used to perform survival analyses. Several independent datasets were explored to validate the gene signature. Orthogonal analyses included single-cell profiling, differential gene expression, immune cell deconvolution, neoantigen prediction, and biological pathway assessment.

RESULTS

A 3-gene signature (GTF3A, PKM, and VEGFA) was found to be associated with overall survival in the AU cohort (HR = 2.26, 95% CI 1.05-4.84, p = 0.02, 93 patients), TCGA cohort (HR = 1.57, 95% CI 1.05-2.35, p < 0.02, 435 patients) and four other GEO datasets. Independent single-cell analysis identified relatively higher expression of the 3-gene signature in the tumor region. Differential analysis revealed dysregulated tissue inflammation, immune dysfunction, and neoantigen load of cell cycle processes among high-risk patients compared to low-risk patients.

CONCLUSION

We developed a 3-gene signature with the potential for prognostic and predictive clinical assessment of CRC patients. This gene-based stratification offers a cost-effective approach to personalized cancer management. Further research using similar methods could identify therapy-specific gene signatures to strengthen the development of personalized medicine for CRC patients.

Unravelling the Reasons Behind Limited Response to Anti-PD Therapy in ATC: A Comprehensive Evaluation of Tumor-Infiltrating Immune Cells and Checkpoints

Inhibiting the immune checkpoint (ICP) PD-1 based on PD-L1 expression status has revolutionized the treatment of various cancers, yet its efficacy in anaplastic thyroid carcinoma (ATC) remains limited. The therapeutic response depends upon multiple factors, particularly the conduciveness of the tumor's immune milieu. This study comprehensively evaluated and classified ATC's immune microenvironment (IME) to elucidate the factors behind suboptimal response to anti-PD therapy. Utilizing multiplex-immunofluorescence and immunohistochemistry, we retrospectively analyzed 26 cases of ATC for expression of ICPs PD-L1, PD-1, CTLA4, TIM3, and Galectin-9 and tumor-infiltrating cytotoxic T lymphocytes (CTL)-the effector cells, the anti-tumor NK cells, the immune-inhibitory myeloid-derived suppressor (MDSC) and regulatory T (Treg) cells, and B lymphocytes. Most ATCs (65%) exhibited PD-L1 positivity, but only 31%, in addition, had abundant CTL (type I IME), a combination associated with a better response to ICP inhibition. Additionally, PD-1 expression levels on CTL were low/absent in most cases-a "target-missing" situation-unfavorable for an adequate therapeutic response. All but one ATC showed nuclear Galectin-9 expression. The documentation of nuclear expression of Galectin-9 akin to benign thyroid is a first, and its role in ATC pathobiology needs further elucidation. In addition to less abundant PD-1 expression on CTL, the presence of MDSC, Treg, and exhausted cytotoxic T lymphocytes in the immune milieu of ATC can contribute to anti-PD resistance. TIM3, the most frequently expressed ICP on CTL, followed by CTLA4, provides alternate therapeutic targets in ATC. The co-expression of multiple immune checkpoints is of great interest for ATC since these data also open the avenue for combination therapies.

The Potential of PD-1 and PD-L1 as Prognostic and Predictive Biomarkers in Colorectal Adenocarcinoma Based on TILs Grading

AIM

Colorectal cancer (CRC) is a prevalent malignancy with a high mortality rate. Tumor-infiltrating lymphocytes (TILs) play a crucial role in the immune response against tumors. Programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1) are key immune checkpoints regulating T cells in the tumor microenvironment. This study aimed to assess the relationships among PD-1 expression on TILs, PD-L1 expression in tumors, and TIL grading in colorectal adenocarcinoma.

METHODS

A cross-sectional design was employed to analyze 130 colorectal adenocarcinoma samples. The expression of PD-1 and PD-L1 was assessed through immunohistochemistry. A semi-quantitative scoring system was applied. Statistical analysis with the chi-square test was performed to explore correlations, with the data analyzed in SPSS version 27.

RESULTS

PD-1 expression on TILs significantly correlated with a higher TIL grading (p < 0.001), while PD-L1 expression in tumors showed an inverse correlation with TIL grading (p < 0.001).

CONCLUSIONS

The expression of PD-1 on TILs and PD-L1 on tumor cells correlated significantly with the grading of TILs in colorectal adenocarcinoma. This finding shows potential as a predictive biomarker for PD-1/PD-L1 blockade therapy. Further studies are needed to strengthen these results.

Harnessing Variability Signatures and Biological Noise May Enhance Immunotherapies' Efficacy and Act as Novel Biomarkers for Diagnosing and Monitoring Immune-Associated Disorders

Lack of response to immunotherapies poses a significant challenge in treating immune-mediated disorders and cancers. While the mechanisms associated with poor responsiveness are not well defined and change between and among subjects, the current methods for overcoming the loss of response are insufficient. The Constrained Disorder Principle (CDP) explains biological systems based on their inherent variability, bounded by dynamic boundaries that change in response to internal and external perturbations. Inter and intra-subject variability characterize the immune system, making it difficult to provide a single therapeutic regimen to all patients and even the same patients over time. The dynamicity of the immune variability is also a significant challenge for personalizing immunotherapies. The CDP-based second-generation artificial intelligence system is an outcome-based dynamic platform that incorporates personalized variability signatures into the therapeutic regimen and may provide methods for improving the response and overcoming the loss of response to treatments. The signatures of immune variability may also offer a method for identifying new biomarkers for early diagnosis, monitoring immune-related disorders, and evaluating the response to treatments.

Toripalimab in combination with HBM4003, an anti-CTLA-4 heavy chain-only antibody, in advanced melanoma and other solid tumors: an open-label phase I trial

BACKGROUND

HBM4003 is a novel anti-CTLA-4 heavy chain-only antibody, designed to enhance Treg ablation and antibody-dependent cell-mediated cytotoxicity while ensuring a manageable safety profile. This phase I trial investigated the safety, pharmacokinetics, immunogenicity and preliminary efficacy of HBM4003 plus with anti-PD-1 antibody toripalimab in patients with advanced solid tumors, especially focusing on melanoma.

METHODS

The multicenter, open-label phase I trial was divided into two parts: dose-escalation phase (part 1) and dose-expansion phase (part 2). In part 1, HBM4003 was administered at doses of 0.03, 0.1, 0.3 mg/kg in combination with toripalimab with fixed dosage of 240 mg every 3 weeks. The recommended phase II dose (RP2D) was used in the expansion phase. Primary endpoints were safety and RP2D in part 1 and objective response rate (ORR) in part 2. Biomarkers based on cytokines and multiplex immunofluorescence staining were explored.

RESULTS

A total of 40 patients received study treatment, including 36 patients treated with RP2D of HBM4003 0.3 mg/kg plus toripalimab 240 mg every 3 week. 36 participants (90.0%) experienced at least one treatment-related adverse event (TRAE), of which 10 (25.0%) patients experienced grade ≥3 TRAEs and 5 (12.5%) experienced immune-mediated adverse events (irAEs) with maximum severity of grade 3. No grade 4 or 5 irAEs occurred. Efficacy analysis set included 32 melanoma patients treated with RP2D and with available post-baseline imaging data. The ORRs of anti-PD-1/PD-L1 treatment-naïve subgroup and anti-PD-1/PD-L1 treatment-failed subgroup were 33.3% and 5.9%, respectively. In mucosal melanoma, the ORR of the two subgroups were 40.0% and 10.0%, respectively. Baseline high Treg/CD4+ratio in the tumor serves as an independent predictive factor for the efficacy of immunotherapy.

CONCLUSIONS

HBM4003 0.3 mg/kg plus toripalimab 240 mg every 3 week demonstrated manageable safety in solid tumors and no new safety signal. Limited data demonstrated promising antitumor activity, especially in PD-1 treatment-naïve mucosal melanoma.

TRIAL REGISTRATION NUMBER

NCT04727164.

Pooled Rate of Pseudoprogression, Patterns of Response, and Tumor Burden Analysis in Patients Undergoing Immunotherapy Oncologic Trials for Different Malignancies

AIMS

Assess rates of true pseudoprogression in unconfirmed progressive disease (iUPD) in a pool of immunotherapy clinical trials for different cancers, analyze tumor characteristics that drive iUPD classification, and investigate potentials predictors of pseudoprogression.

MATERIALS AND METHODS

Retrospective interpretation of prospectively acquired data. Patients from 18 immunotherapy clinical trials with two arms (RECIST 1.1, iRECIST), of 10 cancer types were selected. Pooled rate of true pseudoprogression among iUPD was estimated using a common effect meta-analysis. Target, Non-target, and new lesions as the trigger of confirmed-vs pseudo-progression were compared using Chi-Square and Fisher exact tests. Conditional logistic regression was used to investigate the association between age, sex, tumor burden at baseline, and number of follow ups and pseudoprogression.

RESULTS

60/287 (21%) patients (17 women) were classified as iUPD with at least one subsequent confirmatory timepoint. The overall pooled estimate of pseudoprogression was 15% (95%CI: 8%--26%). Nontarget lesions were significantly more frequent the cause of iUPD than change in Target lesions size (p< 0.001). Most observations of true pseudoprogression occurred in the first follow-up (77%), whereas confirmed progression occurred in later time points during the trial. Pseudoprogression was not significantly associated with age, sex, tumor burden at baseline, or number of timepoints.

CONCLUSION

In a pool of immunotherapy trials, the rate of true pseudoprogression was 15%, most often in the first timepoint after baseline than later in treatment. iUPD categorization was mostly driven by changes in NT lesions rather than objective changes in measurements of target lesions.

Microbiome and Immunotherapy for Melanoma: Are We Ready for Clinical Application?

The microbiome plays a substantial role in the efficacy of immune checkpoint blockade (ICB) in patients with metastatic melanoma. While the exact gut microbiome composition and the pathways involved in this interaction are not clearly delineated, novel studies and ongoing clinical trials are likely to reveal findings applicable to the clinical setting for the prediction and optimization of response to ICB. Nevertheless, lifestyle modifications, including high fiber diet, avoidance of unnecessary antibiotic prescriptions, and careful use of probiotics may be helpful to optimize the "health" of the gut microbiome and potentially enhance response to ICB in patients with melanoma.

Innovative optical imaging strategies for monitoring immunotherapy in the tumor microenvironments

BACKGROUND

The tumor microenvironment (TME) plays a critical role in cancer progression and response to immunotherapy. Immunotherapy targeting the immune system has emerged as a promising treatment modality, but challenges in understanding the TME limit its efficacy. Optical imaging strategies offer noninvasive, real-time insights into the interactions between immune cells and the TME.

OBJECTIVE

This review assesses the progress of optical imaging technologies in monitoring immunotherapy within the TME and explores their potential applications in clinical trials and personalized cancer treatment.

METHODS

This is a comprehensive literature review based on the advances in optical imaging modalities including fluorescence imaging (FLI), bioluminescence imaging (BLI), and photoacoustic imaging (PAI). These modalities were analyzed for their capacity to provide high-resolution, real-time imaging of immune cell dynamics, tumor vasculature, and other critical components of the TME.

RESULTS

Optical imaging techniques have shown significant potential in tracking immune cell infiltration, assessing immune checkpoint inhibitors, and visualizing drug delivery within the TME. Technologies like FLI and BLI are pivotal in tracking immune responses in preclinical models, while PAI provides functional imaging with deeper tissue penetration. The integration of these modalities with immunotherapy holds promise for improving treatment monitoring and outcomes.

CONCLUSION

Optical imaging is a powerful tool for understanding the complexities of the TME and optimizing immunotherapy. Further advancements in imaging technologies, combined with nanomaterial-based approaches, could pave the way for enhanced diagnostic accuracy and therapeutic efficacy in cancer treatment.

Cancer Patient-Derived Cell-Based Models: Applications and Challenges in Functional Precision Medicine

The field of oncology has witnessed remarkable progress in personalized cancer therapy. Functional precision medicine has emerged as a promising avenue for achieving superior treatment outcomes by integrating omics profiling and sensitivity testing of patient-derived cancer cells. This review paper provides an in-depth analysis of the evolution of cancer-directed drugs, resistance mechanisms, and the role of functional precision medicine platforms in revolutionizing individualized treatment strategies. Using two-dimensional (2D) and three-dimensional (3D) cell cultures, patient-derived xenograft (PDX) models, and advanced functional assays has significantly improved our understanding of tumor behavior and drug response. This progress will lead to identifying more effective treatments for more patients. Considering the limited eligibility of patients based on a genome-targeted approach for receiving targeted therapy, functional precision medicine provides unprecedented opportunities for customizing medical interventions according to individual patient traits and individual drug responses. This review delineates the current landscape, explores limitations, and presents future perspectives to inspire ongoing advancements in functional precision medicine for personalized cancer therapy.

Prediction of Survival in Patients With Esophageal Cancer After Immunotherapy Based on Small-Size Follow-Up Data

Esophageal cancer (EC) poses a significant health concern, particularly among the elderly, warranting effective treatment strategies. While immunotherapy holds promise in activating the immune response against tumors, its specific impact and associated reactions in EC patients remain uncertain. Precise prognosis prediction becomes crucial for guiding appropriate interventions. This study, based on data from the First Affiliated Hospital of Xiamen University (January 2017 to May 2021), focuses on 113 EC patients undergoing immunotherapy. The primary objectives are to elucidate the effectiveness of immunotherapy in EC treatment and to introduce a stacking ensemble learning method for predicting the survival of EC patients who have undergone immunotherapy, in the context of small sample sizes, addressing the imperative of supporting clinical decision-making for healthcare professionals. Our method incorporates five sub-learners and one meta-learner. Leveraging optimal features from the training dataset, this approach achieved compelling accuracy (89.13%) and AUC (88.83%) in predicting three-year survival status, surpassing conventional techniques. The model proves efficient in guiding clinical decisions, especially in scenarios with small-size follow-up data.

Causal role of the gut microbiome in certain human diseases: a narrative review

Composed of an elaborate ecosystem of bacteria, fungi, viruses and protozoa residing in the human digestive tract, the gut microbiome influences metabolism, immune modulation, bile acid homeostasis and host defence. Through observational and preclinical data, the gut microbiome has been implicated in the pathogenesis of a spectrum of chronic diseases ranging from psychiatric to gastrointestinal in nature. Until recently, the lack of unequivocal evidence supporting a causal link between gut microbiome and human health outcomes incited controversy regarding its significance. However, recent randomised controlled trial (RCT) evidence in conditions, such as Clostridioides difficile infection, cancer immunotherapy and ulcerative colitis, has supported a causal relationship and has underscored the potential of the microbiome as a therapeutic target. This review delineates the RCT evidence substantiating the potential for a causal relationship between the gut microbiome and human health outcomes, the seminal observational evidence that preceded these RCTs and the remaining knowledge gaps.

Hybridizing mechanistic modeling and deep learning for personalized survival prediction after immune checkpoint inhibitor immunotherapy

We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when trained on only mechanistic model-derived values or only clinical data. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in increasing prediction accuracy, further supporting the advantage of our hybrid approach.

Novel therapeutic agents in clinical trials: emerging approaches in cancer therapy

Novel therapeutic agents in clinical trials offer a paradigm shift in the approach to battling this prevalent and destructive disease, and the area of cancer therapy is on the precipice of a trans formative revolution. Despite the importance of tried-and-true cancer treatments like surgery, radiation, and chemotherapy, the disease continues to evolve and adapt, making new, more potent methods necessary. The field of cancer therapy is currently witnessing the emergence of a wide range of innovative approaches. Immunotherapy, including checkpoint inhibitors, CAR-T cell treatment, and cancer vaccines, utilizes the host's immune system to selectively target and eradicate malignant cells while minimizing harm to normal tissue. The development of targeted medicines like kinase inhibitors and monoclonal antibodies has allowed for more targeted and less harmful approaches to treating cancer. With the help of genomics and molecular profiling, "precision medicine" customizes therapies to each patient's unique genetic makeup to maximize therapeutic efficacy while minimizing unwanted side effects. Epigenetic therapies, metabolic interventions, radio-pharmaceuticals, and an increasing emphasis on combination therapy with synergistic effects further broaden the therapeutic landscape. Multiple-stage clinical trials are essential for determining the safety and efficacy of these novel drugs, allowing patients to gain access to novel treatments while also furthering scientific understanding. The future of cancer therapy is rife with promise, as the integration of artificial intelligence and big data has the potential to revolutionize early detection and prevention. Collaboration among researchers, and healthcare providers, and the active involvement of patients remain the bedrock of the ongoing battle against cancer. In conclusion, the dynamic and evolving landscape of cancer therapy provides hope for improved treatment outcomes, emphasizing a patient-centered, data-driven, and ethically grounded approach as we collectively strive towards a cancer-free world.

Modulating cancer mechanopathology to restore vascular function and enhance immunotherapy

Solid tumor pathology, characterized by abnormalities in the tumor microenvironment (TME), challenges therapeutic effectiveness. Mechanical factors, including increased tumor stiffness and accumulation of intratumoral forces, can determine the success of cancer treatments, defining the tumor's "mechanopathology" profile. These abnormalities cause extensive vascular compression, leading to hypoperfusion and hypoxia. Hypoperfusion hinders drug delivery, while hypoxia creates an unfavorable TME, promoting tumor progression through immunosuppression, heightened metastatic potential, drug resistance, and chaotic angiogenesis. Strategies targeting TME mechanopathology, such as vascular and stroma normalization, hold promise in enhancing cancer therapies with some already advancing to the clinic. Normalization can be achieved using anti-angiogenic agents, mechanotherapeutics, immune checkpoint inhibitors, engineered bacterial therapeutics, metronomic nanomedicine, and ultrasound sonopermeation. Here, we review the methods developed to rectify tumor mechanopathology, which have even led to cures in preclinical models, and discuss their bench-to-bedside translation, including the derivation of biomarkers from tumor mechanopathology for personalized therapy.

Expression of immune checkpoint molecules TIGIT and TIM-3 by tumor-infiltrating lymphocytes predicts poor outcome in sinonasal mucosal melanoma

BACKGROUND

Sinonasal mucosal melanoma (SNMM) is a rare but aggressive tumor with a poor prognosis. The co-inhibitory receptors T cell immunoglobulin and mucinodomain containing-3 (TIM-3), lymphocyte activation gene-3 (LAG-3) and T cell immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT) are promising new targets in anti-cancer immunotherapy. The expression profiles of these immune checkpoint molecules (ICMs) and potential prognostic implications have not been characterized in SNMM yet.

METHODS

Immunohistochemical staining for TIGIT, LAG-3 and TIM-3 was performed on tumor tissue samples from 27 patients with primary SNMM. Associations between ICM expression and demographic parameters, AJCC tumor stage, overall survival, and recurrence-free survival were retrospectively analyzed.

RESULTS

SNMM patients with low numbers of TIGIT+ and TIM-3+ tumor infiltrating lymphocytes (TILs) in the primary tumor survived significantly longer than patients with a high degree of TIGIT+ and TIM-3+ TILs. High infiltration with TIM-3+ or TIGIT+ lymphocytes was associated with the higher T4 stage and decreased 5-year survival.

CONCLUSION

We identified high densities of TIM-3+ and TIGIT+ TILs as strong negative prognostic biomarkers in SNMM. This suggests that TIM-3 and TIGIT contribute to immunosuppression in SNMM and provides a rationale for novel treatment strategies based on this next generation of immune checkpoint inhibitors. Prospective studies with larger case numbers are warranted to confirm our findings and their implications for immunotherapy.

Thetumor microenvironment'sinpancreatic cancer:Effects onimmunotherapy successandnovel strategiestoovercomethehostile environment

Cancer is a significant global health issue that poses a considerable burden on both patients and healthcare systems. Many different types of cancers exist that often require unique treatment approaches and therapies. A hallmark of tumor progression is the creation of an immunosuppressive environment, which poses complex challenges for current treatments. Amongst the most explored characteristics is a hypoxic environment, high interstitial pressure, and immunosuppressive cells and cytokines. Traditional cancer treatments involve radiotherapy, chemotherapy, and surgical procedures. The advent of immunotherapies was regarded as a promising approach with hopes of greatly increasing patients' survival and outcome. Although some success is seen with various immunotherapies, the vast majority of monotherapies are unsuccessful. This review examines how various aspects of the tumor microenvironment (TME) present challenges that impede the success of immunotherapies. Subsequently, we review strategies to manipulate the TME to facilitate the success of immunotherapies.

Enhanced Proteomic Coverage in Tissue Microenvironment by Immune Cell Subtype Library-Assisted DIA-MS

Immune cells that infiltrate the tumor microenvironment (TME) play crucial roles in shaping cancer development and influencing clinical outcomes and therapeutic responses. However, obtaining a comprehensive proteomic snapshot of tumor-infiltrating immunity in clinical specimens is often hindered by small sample amounts and a low proportion of immune infiltrating cells in the TME. To enable in-depth and highly sensitive profiling of microscale tissues, we established an immune cell-enriched library-assisted strategy for data-independent acquisition mass spectrometry (DIA-MS). Firstly, six immune cell subtype-specific spectral libraries were established from sorted cluster of differentiation markers, CD8+, CD4+ T lymphocytes, B lymphocytes, natural killer cells, dendritic cells, and macrophages in murine mesenteric lymph nodes (MLNs), covering 7815 protein groups with surface markers and immune cell-enriched proteins. The feasibility of microscale immune proteomic profiling was demonstrated on 1 μg tissue protein from the tumor of murine colorectal cancer (CRC) models using single-shot DIA; the immune cell-enriched library increased coverage to quantify 7419 proteins compared to directDIA analysis (6978 proteins). The enhancement enabled the mapping of 841 immune function-related proteins and exclusive identification of many low-abundance immune proteins, such as CD1D1, and CD244, demonstrating high sensitivity for immune landscape profiling. This approach was used to characterize the MLNs in CRC models, aiming to elucidate the mechanism underlying their involvement in cancer development within the TME. Even with a low percentage of immune cell infiltration (0.25-3%) in the tumor, our results illuminate downregulation in the adaptive immune signaling pathways (such as C-type lectin receptor signaling, and chemokine signaling), T cell receptor signaling, and Th1/Th2/Th17 cell differentiation, suggesting an immunosuppressive status in MLNs of CRC model. The DIA approach using the immune cell-enriched libraries showcased deep coverage and high sensitivity that can facilitate illumination of the immune proteomic landscape for microscale samples.

Current approaches in glioblastoma multiforme immunotherapy

Glioblastoma multiform (GBM) is the most prevalent CNS (central nervous system) tumor in adults, with an average survival length shorter than 2 years and rare metastasis to organs other than CNS. Despite extensive attempts at surgical resecting, the inherently permeable nature of this disease has rendered relapse nearly unavoidable. Thus, immunotherapy is a feasible alternative, as stimulated immune cells can enter into the remote and inaccessible tumor cells. Immunotherapy has revolutionized patient upshots in various malignancies and might introduce different effective ways for GBM patients. Currently, researchers are exploring various immunotherapeutic strategies in patients with GBM to target both the innate and acquired immune responses. These approaches include reprogrammed tumor-associated macrophages, the use of specific antibodies to inhibit tumor progression and metastasis, modifying tumor-associated macrophages with antibodies, vaccines that utilize tumor-specific dendritic cells to activate anti-tumor T cells, immune checkpoint inhibitors, and enhanced T cells that function against tumor cells. Despite these findings, there is still room for improving the response faults of the many currently tested immunotherapies. This study aims to review the currently used immunotherapy approaches with their molecular mechanisms and clinical application in GBM.

Orthotopic Models Using New, Murine Lung Adenocarcinoma Cell Lines Simulate Human Non-Small Cell Lung Cancer Treated with Immunotherapy

Understanding tumor-host immune interactions and the mechanisms of lung cancer response to immunotherapy is crucial. Current preclinical models used to study this often fall short of capturing the complexities of human lung cancer and lead to inconclusive results. To bridge the gap, we introduce two new murine monoclonal lung cancer cell lines for use in immunocompetent orthotopic models. We demonstrate how our cell lines exhibit immunohistochemical protein expression (TTF-1, NapA, PD-L1) and common driver mutations (KRAS, p53, and p110α) seen in human lung adenocarcinoma patients, and how our orthotopic models respond to combination immunotherapy in vivo in a way that closely mirrors current clinical outcomes. These new lung adenocarcinoma cell lines provide an invaluable, clinically relevant platform for investigating the intricate dynamics between tumor and the immune system, and thus potentially contributes to a deeper understanding of immunotherapeutic approaches to lung cancer treatment.

A Mesoporous Gold Sensor Unveils Phospho PD-L1 in Extracellular Vesicles as a Proxy for PD-L1 Expression in Lung Cancer Tissue

Immune checkpoint inhibitors (ICIs) targeting programmed cell death ligand 1 (PD-L1), or its receptor, PD-1 have improved survival in patients with non-small-cell lung cancer (NSCLC). Assessment of PD-L1 expression requires tissue biopsy or fine needle aspiration that are currently used to identify patients most likely to respond to single agent anti-PD-1/PD-L1 therapy. However, obtaining sufficient tissue to generate a PD-L1 tissue proportion score (TPS) ≥ 50% using immunohistochemistry remains a challenge that potentially may be overcome by liquid biopsies. This study utilized a mesoporous gold sensor (MGS) assay to examine the phosphorylation status of PD-L1 in plasma extracellular vesicles (EV pPD-L1) and PD-L1 levels in plasma from NSCLC patient samples and their association with tumor PD-L1 TPS. The 3-dimensional mesoporous network of the electrodes provides a large surface area, high signal-to-noise ratio, and a superior electro-conductive framework, thereby significantly improving the detection sensitivity of PD-L1 nanosensing. Test (n = 20) (Pearson's r = 0.99) and validation (n = 45) (Pearson's r = 0.99) cohorts show that EV pPD-L1 status correlates linearly with the tumor PD-L1 TPS assessed by immunohistochemistry irrespective of the tumor stage, with 64% of patients overall showing detectable EV pPD-L1 levels in plasma. In contrast to the EV pPD-L1 results, plasma PD-L1 levels did not correlate with the tumor PD-L1 TPS score or EV pPD-L1 levels. These data demonstrate that EV pPD-L1 levels may be used to select patients for appropriate PD-1 and PD-L1 ICI therapy regimens in early, locally advanced, and advanced NSCLC and should be tested further in randomized controlled trials. Most importantly, the assay used has a less than 24h turnaround time, facilitating adoption of the test into the routine diagnostic evaluation of patients prior to therapy.

Challenges and opportunities in cancer immunotherapy: a Society for Immunotherapy of Cancer (SITC) strategic vision

Cancer immunotherapy has flourished over the last 10-15 years, transforming the practice of oncology and providing long-term clinical benefit to some patients. During this time, three distinct classes of immune checkpoint inhibitors, chimeric antigen receptor-T cell therapies specific for two targets, and two distinct classes of bispecific T cell engagers, a vaccine, and an oncolytic virus have joined cytokines as a standard of cancer care. At the same time, scientific progress has delivered vast amounts of new knowledge. For example, advances in technologies such as single-cell sequencing and spatial transcriptomics have provided deep insights into the immunobiology of the tumor microenvironment. With this rapid clinical and scientific progress, the field of cancer immunotherapy is currently at a critical inflection point, with potential for exponential growth over the next decade. Recognizing this, the Society for Immunotherapy of Cancer convened a diverse group of experts in cancer immunotherapy representing academia, the pharmaceutical and biotechnology industries, patient advocacy, and the regulatory community to identify current opportunities and challenges with the goal of prioritizing areas with the highest potential for clinical impact. The consensus group identified seven high-priority areas of current opportunity for the field: mechanisms of antitumor activity and toxicity; mechanisms of drug resistance; biomarkers and biospecimens; unique aspects of novel therapeutics; host and environmental interactions; premalignant immunity, immune interception, and immunoprevention; and clinical trial design, endpoints, and conduct. Additionally, potential roadblocks to progress were discussed, and several topics were identified as cross-cutting tools for optimization, each with potential to impact multiple scientific priority areas. These cross-cutting tools include preclinical models, data curation and sharing, biopsies and biospecimens, diversification of funding sources, definitions and standards, and patient engagement. Finally, three key guiding principles were identified that will both optimize and maximize progress in the field. These include engaging the patient community; cultivating diversity, equity, inclusion, and accessibility; and leveraging the power of artificial intelligence to accelerate progress. Here, we present the outcomes of these discussions as a strategic vision to galvanize the field for the next decade of exponential progress in cancer immunotherapy.

Stepping forward: T-cell redirecting bispecific antibodies in cancer therapy

T cell-redirecting bispecific antibodies are specifically designed to bind to tumor-associated antigens, thereby engaging with CD3 on the T cell receptor. This linkage between tumor cells and T cells actively triggers T cell activation and initiates targeted killing of the identified tumor cells. These antibodies have emerged as one of the most promising avenues within tumor immunotherapy. However, despite success in treating hematological malignancies, significant advancements in solid tumors have yet to be explored. In this review, we aim to address the critical challenges associated with T cell-redirecting bispecific antibodies and explore novel strategies to overcome these obstacles, with the ultimate goal of expanding the application of this therapy to include solid tumors.

High serum sodium predicts immunotherapy response in metastatic renal cell and urothelial carcinoma

OBJECTIVES

The development of reliable biomarkers for the prediction of immune checkpoint inhibition (ICI) response in patients with metastatic renal cell carcinoma (mRCC) and urothelial carcinoma (mUC) remains an unresolved challenge. Conventional ICI biomarkers typically focus on tumor-related factors such as PD-L1 expression. However, a comprehensive evaluation of the predictive value of serum electrolyte levels, a so far widely unexplored area, is still pending.

METHODS

We conducted a post-hoc analysis of baseline sodium, potassium, chloride, magnesium and calcium levels in two independent phase 3 clinical trials: IMvigor211 for mUC comparing atezolizumab to chemotherapy, and IMmotion151 for mRCC comparing atezolizumab+bevacizumab to sunitinib. This analysis aimed to evaluate the prognostic and predictive value of these electrolyte levels in these clinical settings. A total of 1787 patients (IMvigor211 n = 901; IMmotion151 n = 886) were analyzed.

RESULTS

We found a linear correlation of baseline serum sodium and chloride with prognosis across both trials, which was not found for potassium, magnesium and calcium. In multivariate analysis, the prognostic capacity of sodium was limited to patients receiving ICI as compared to the control group. Interestingly, in both studies, the chance of achieving an objective response was highest in the patient subgroup with high baseline serum sodium levels of > 140 mmol/L (IMmotion151: Complete response in 17.9% versus 2.0% in patients with mRCC with baseline sodium < 135 mmol/L). Serum sodium outperformed tumor PD-L1 expression as a predictor for immunotherapy efficacy.

CONCLUSIONS

Patients exhibiting elevated serum sodium levels derive the greatest benefit from immunotherapy, suggesting that baseline serum concentration could serve as a valuable and cost-effective predictive biomarker for immunotherapy across entities.

Advancing cancer immunotherapy through siRNA-based gene silencing for immune checkpoint blockade

Cancer immunotherapy represents a revolutionary strategy, leveraging the patient's immune system to inhibit tumor growth and alleviate the immunosuppressive effects of the tumor microenvironment (TME). The recent emergence of immune checkpoint blockade (ICB) therapies, particularly following the first approval of cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitors like ipilimumab, has led to significant growth in cancer immunotherapy. The extensive explorations on diverse immune checkpoint antibodies have broadened the therapeutic scope for various malignancies. However, the clinical response to these antibody-based ICB therapies remains limited, with less than 15% responsiveness and notable adverse effects in some patients. This review introduces the emerging strategies to overcome current limitations of antibody-based ICB therapies, mainly focusing on the development of small interfering ribonucleic acid (siRNA)-based ICB therapies and innovative delivery systems. We firstly highlight the diverse target immune checkpoint genes for siRNA-based ICB therapies, incorporating silencing of multiple genes to boost anti-tumor immune responses. Subsequently, we discuss improvements in siRNA delivery systems, enhanced by various nanocarriers, aimed at overcoming siRNA's clinical challenges such as vulnerability to enzymatic degradation, inadequate pharmacokinetics, and possible unintended target interactions. Additionally, the review presents various combination therapies that integrate chemotherapy, phototherapy, stimulatory checkpoints, ICB antibodies, and cancer vaccines. The important point is that when used in combination with siRNA-based ICB therapy, the synergistic effect of traditional therapies is strengthened, improving host immune surveillance and therapeutic outcomes. Conclusively, we discuss the insights into innovative and effective cancer immunotherapeutic strategies based on RNA interference (RNAi) technology utilizing siRNA and nanocarriers as a novel approach in ICB cancer immunotherapy.

High FLT3 expression increases immune-cell infiltration in the tumor microenvironment and correlates with prolonged disease-free survival in patients with non-small cell lung cancer

Most of the currently used cancer immunotherapies inhibit the programmed cell death protein 1 (PD1)-programmed cell death 1 ligand 1 (PDL1) axis of T-cells. However, dendritic cells (DCs) controlled by natural killer (NK) cells via the FMS-related tyrosine kinase 3 (FLT3) axis are necessary for activation of T-cells. The aim of the study was to evaluate FLT3 as a prognostic factor and determine its role in immune infiltration (with emphasis on NK cells and DCs). Using The Cancer Genome Atlas (TCGA) database, we performed bioinformatic analysis of the gene expression datasets of 501 lung squamous cell carcinoma (LUSC) and 515 lung adenocarcinoma (LUAD) patient who had corresponding clinical data [analysis was performed in R (version 4.2.0)]. Disease-free survival (DFS) differed between the FLT3-low and FLT3-high expression groups, respectively, in LUSC (61.0 vs 71.3 months P = 0.075) and LUAD (32.7 vs 47.5 months P = 0.045). A tumor microenvironment (TME) with high immune infiltration and rich in T-cell exhaustion markers was observed in the FLT3-high group. We showed overexpression of NK cell and DC gene signatures in the FLT3-high expression group as well as overexpression of key effector genes of the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes protein (STING) pathway, which is crucial in response to radiotherapy. High expression of FLT3 in the TME was associated with immune cell infiltration (especially of NK cells and DCs), increased expression of T-cell exhaustion markers and expression of effector genes of the cGAS-STING pathway, which may consequently increase susceptibility to immunotherapy and radiotherapy. High FLT3 expression correlated with prolonged DFS in the LUSC and LUAD cohorts.

CDK9 inhibition activates innate immune response through viral mimicry

Cancer cells frequently exhibit hyperactivation of transcription, which can lead to increased sensitivity to compounds targeting the transcriptional kinases, in particular CDK9. However, mechanistic details of CDK9 inhibition-induced cancer cell-selective anti-proliferative effects remain largely unknown. Here, we discover that CDK9 inhibition activates the innate immune response through viral mimicry in cancer cells. In MYC over-expressing prostate cancer cells, CDK9 inhibition leads to the gross accumulation of mis-spliced RNA. Double-stranded RNA (dsRNA)-activated kinase can recognize these mis-spliced RNAs, and we show that the activity of this kinase is required for the CDK9 inhibitor-induced anti-proliferative effects. Using time-resolved transcriptional profiling (SLAM-seq), targeted proteomics, and ChIP-seq, we show that, similar to viral infection, CDK9 inhibition significantly suppresses transcription of most genes but allows selective transcription and translation of cytokines related to the innate immune response. In particular, CDK9 inhibition activates NFκB-driven cytokine signaling at the transcriptional and secretome levels. The transcriptional signature induced by CDK9 inhibition identifies prostate cancers with a high level of genome instability. We propose that it is possible to induce similar effects in patients using CDK9 inhibition, which, we show, causes DNA damage in vitro. In the future, it is important to establish whether CDK9 inhibitors can potentiate the effects of immunotherapy against late-stage prostate cancer, a currently lethal disease.