ALK5/VEGFR2 dual inhibitor TU2218 alone or in combination with immune checkpoint inhibitors enhances immune-mediated antitumor effects

Transforming growth factor β (TGFβ) is present in blood of patients who do not respond to anti-programmed cell death (ligand) 1 [PD-(L)1] treatment, and through synergy with vascular endothelial growth factor (VEGF), it helps to create an environment that promotes tumor immune evasion and immune tolerance. Therefore, simultaneous inhibition of TGFβ/VEGF is more effective than targeting TGFβ alone. In this study, the dual inhibitory mechanism of TU2218 was identified through in vitro analysis mimicking the tumor microenvironment, and its antitumor effects were analyzed using mouse syngeneic tumor models. TU2218 directly restored the activity of damaged cytotoxic T lymphocytes (CTLs) and natural killer cells inhibited by TGFβ and suppressed the activity and viability of regulatory T cells. The inactivation of endothelial cells induced by VEGF stimulation was completely ameliorated by TU2218, an effect not observed with vactosertib, which inhibits only TGFβ signaling. The combination of TU2218 and anti-PD1 therapy had a significantly greater antitumor effect than either drug alone in the poorly immunogenic B16F10 syngeneic tumor model. The mechanism of tumor reduction was confirmed by flow cytometry, which showed upregulated VCAM-1 expression in vascular cells and increased influx of CD8 + CTLs into the tumor. As another strategy, combination of anti-CTLA4 therapy and TU2218 resulted in high complete regression (CR) rates in CT26 and WEHI-164 tumor models. In particular, immunological memory generated by the combination of anti-CTLA4 and TU2218 in the CT26 model prevented the development of tumors after additional tumor cell transplantation, suggesting that the TU2218-based combination has therapeutic potential in immunotherapy.

Repurposing discarded leukodepletion filters as a source of mononuclear cells for advanced in vitro research

In light of advancements in the field of immuno-oncology, the demand for obtaining mononuclear cells for in vitro assays has surged. However, obtaining these cells from healthy donors remains a challenging task due to difficulties in donor recruitment and the requirement for substantial blood volumes. Here, we present a protocol for isolating peripheral blood mononuclear cells (PBMCs) from leukodepletion filters used in whole blood and erythrocytes by apheresis donations at the Hemonucleus of the Barretos Cancer Hospital, Brazil. The method involves rinsing the leukodepletion filters and subsequent centrifugation using a Ficoll-Paque concentration gradient. The isolated PBMCs were analyzed by flow cytometry, which allowed the identification of various subpopulations, including CD4+ T lymphocytes (CD45+CD4+), CD8+ T lymphocytes (CD45+CD8+), B lymphocytes (CD45+CD20+CD19+), non-classical monocytes (CD45+CD64+CD14-), classical monocytes (CD45+CD64+CD14+), and granulocytes (CD45+CD15+CD14-). In our comparative analysis of filters, we observed a higher yield of PBMCs from whole blood filters than those obtained from erythrocytes through apheresis. Additionally, fresh samples exhibited superior viability when compared to cryopreserved ones. Given this, leukodepletion filters provide a practical and cost-effective means to isolate large quantities of pure PBMCs, making it a feasible source for obtaining mononuclear cells for in vitro experiments. SUMMARY: Here, we provide a detailed protocol for the isolation of mononuclear cells from leukodepletion filters, which are routinely discarded at the Barretos Cancer Hospital's Hemonucleus.

Real-world Treatment Sequencing and Outcomes With Cabozantinib After First-line Immune Checkpoint Inhibitor-based Combination Therapy For Patients With Advanced Renal Cell Carcinoma: CARINA Study Results

INTRODUCTION

Real-world data are limited on treatment sequencing and outcomes after first-line (1L) immune checkpoint inhibitor (CPI)-based combination treatment of advanced renal cell carcinoma (aRCC).

PATIENTS AND METHODS

In this real-world, UK-based, retrospective study (CARINA; NCT04957160), data were obtained from hospital and electronic prescribing records. Patients were aged ≥ 18 years at aRCC diagnosis and had received 1L CPI-CPI or tyrosine kinase inhibitor (TKI)-CPI combination therapy before second-line (2L) therapy including cabozantinib. We describe treatment outcomes including 1L and 2L durations of treatment (DoT) and overall survival (OS).

RESULTS

Data from April 2015 to June 2022 were collected on 281 patients from nine UK centres. Median 1L DoT was 2.3 months for CPI-CPI therapy (n = 171) and 5.0 months for TKI-CPI therapy (n = 58). After 1L CPI-CPI or TKI-CPI therapy, median 2L DoT was 5.8 versus 4.2 months, respectively, for cabozantinib (n = 163), and 3.8 versus 2.4 months for other therapies (n = 118); median 2L OS was 15.2 and 15.3 months, respectively, for cabozantinib, and 14.6 and 24.2 months for other therapies.

CONCLUSION

DoT for 2L treatment was numerically better for cabozantinib than for other therapies, and after 1L CPI-CPI therapy than after 1L TKI-CPI therapy. Median OS was similar for 2L cabozantinib and other 2L therapies, and median OS for 2L cabozantinib was similar after both 1L therapy types. These results demonstrate the antitumour effect of 2L therapies, including cabozantinib, after 1L CPI-based combination treatment, regardless of whether 1L CPI-CPI or TKI-CPI therapy is used.

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

A spatial proteomic study of platinum refractory HGSOC implicates dual AKT and WNT activity linked to an immunosuppressive tumor microenvironment

OBJECTIVE

Advanced-stage high-grade serous ovarian cancer (HGSOC) remains a deadly gynecologic malignancy with high rates of disease recurrence and limited, effective therapeutic options for patients. There is a significant need to better stratify HGSOC patients into platinum refractory (PRF) vs. sensitive (PS) cohorts at baseline to improve therapeutic responses and survival outcomes for PRF HGSOC.

METHODS

We performed NanoString for GeoMx Digital Spatial Profile (G-DSP) multiplex protein analysis on PRF and PS tissue microarrays (TMAs) to study the bidirectional communication of cancer cells with immune cells in the tumor microenvironment (TME) of HGSOC. We demonstrate robust stratification of PRF and PS tumors at baseline using multiplex spatial proteomic biomarkers with implications for tailoring subsequent therapy.

RESULTS

PS patients had elevated apoptotic and anti-tumor immune profiles, while PRF patients had dual AKT1 and WNT signaling with immunosuppressive profiles. We found that dual activity of AKT1 and WNT signaling supported the exclusion of immune cells, specifically tumor infiltrating lymphocytes (TILs), from the TME in PRF tumors, and this was not observed in PS tumors. The exclusion of immune cells from the TME of PRF tumors corresponded to abnormal endothelial cell structure in tumors with dual AKT1 and WNT signaling activity.

CONCLUSIONS

We believe our findings provide improved understanding of tumor-immune crosstalk in HGSOC TME highlighting the importance of the relationship between AKT and WNT pathways, immune cell function, and platinum response in HGSOC.

Development of a robust cell-based potency assay for a coxsackievirus A21 oncolytic virotherapy

Oncolytic viruses (OV) are part of a burgeoning field of investigational oncolytic therapy (OT), in which lytic viruses dissolve advanced tumors productively and specifically. One such OT is a Coxsackievirus A21 (CVA21) based OV that is currently under clinical evaluation. A tissue culture infectious dose (TCID50) assay was used for CVA21 potency release and stability testing in early clinical development. The titer measured in this method was an extrapolated value from cytopathic effect (CPE) observed during the serial dilution but doesn't represent direct viral killing of cells. Moreover, the assay was not deemed to be optimal to carry into late phase clinical development due to limitations in assay precision, turn-around time, and sample throughput. To address these points, we developed a plaque assay to measure viral plaque forming units to measure the potency value for drug substance (DS), drug product (DP) and virus seed (master and working) stocks. In this manuscript, we describe the steps taken to develop this plaque assay for the late-stage clinical development, which include the assay qualification, validation, and robustness protocols, and describe statistical methods for data analysis. Moreover, the method was validated for linearity, accuracy, precision, and specificity. Furthermore, the plaque assay quantifies OV infectivity with better precision (32% vs 58%), with higher sample throughput (22 samples/week vs 3 samples/week) and shorter assay turnaround time (4 days vs 7 days) than the TCID50 method. This assay development strategy can provide guidance for the development of robust cell-based potency methods for OVs and other infectious viral products.

Utility and impact of quantitative pharmacology on dose selection and clinical development of immuno-oncology therapy

Immuno-oncology (IO) therapies have changed the cancer treatment landscape. Immune checkpoint inhibitors (ICIs) have improved overall survival in 20-40% of patients with malignancies that were previously refractory. Due to the uniqueness in biology, modalities and patient responses, drug development strategies for IO differed from that traditionally used for cytotoxic and target therapies in oncology, and quantitative pharmacology utilizing modeling approach can be applied in all phases of the development process. In this review, we used case studies to showcase how various modeling methodologies were applied from translational science and dose selection through to label change, using examples that included anti-programmed-death-1 (anti-PD-1), anti-programmed-death ligand-1 (anti-PD-L1), anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4), and anti-glucocorticoid-induced tumor necrosis factor receptor-related protein (anti-GITR) antibodies. How these approaches were utilized to support phase I-III dose selection, the design of phase III trials, and regulatory decisions on label change are discussed to illustrate development strategies. Model-based quantitative approaches have positively impacted IO drug development, and a better understanding of the biology and exposure-response relationship may benefit the development and optimization of new IO therapies.

Evaluating the impact of PD-1 inhibitor treatment on key health outcomes for cancer patients in China

BACKGROUND

There is a lack of studies examining the influence of programmed cell death protein 1 (PD-1) inhibitors on the health outcomes of cancer patients in China.

AIM

This study aimed to evaluate prospective health outcomes associated with introducing PD-1 inhibitor treatment in China over five years.

METHOD

We constructed a partitioned survival model to assess disparities in health outcomes over a 5-year time frame between two scenarios: one involving the availability of PD-1 inhibitor class with standard of care and the other involving standard of care alone. The impact on various health outcomes were assessed, including life years (LYs) gained, quality-adjusted life years (QALYs) gained, progression-free survival (PFS) years gained, the reduction in the number of grade 3-5 adverse events (AEs), and the improvement in objective remission rates (ORR). A sensitivity analysis was conducted to assess the robustness and reliability of the model.

RESULTS

From 2023 to 2027, the incorporation of PD-1 inhibitor class treatments was anticipated to yield substantial improvements in health outcomes, with an estimated increase of 1,336,332 LYs (+ 24.7%), 1,065,359 QALYs (+ 30.3%), and 1,177,564 PFS years (+ 57.4%) compared to standard of care alone. Simultaneously, the number of grade 3-5 AEs decreased by 334,976 (- 13.0%), and the ORR saw a 19.1% increase (+ 105.6%) relative to standard of care treatment alone.

CONCLUSION

This study provides a analysis of the potential beneficial effects on health outcomes in the Chinese population after introducing PD-1 inhibitor class treatment. The findings suggest the PD-1 inhibitor class will significantly improve patient survival.

Immuno-oncological effects of standard anticancer agents and commonly used concomitant drugs: an in vitro assessment

BACKGROUND

It has become evident in the field of oncology that the outcome of medical treatment is influenced by the combined effect exerted on both cancer- and immune cells. Therefore, we evaluated potential immunological effects of 46 standard anticancer agents and 22 commonly administered concomitant non-cancer drugs.

METHODS

We utilized a miniaturized in vitro model system comprised of fluorescently labeled human colon and lung cancer cell lines grown as monocultures and co-cultured with activated peripheral blood mononuclear cells (PBMCs). The Bliss Independence Model was then applied to detect antagonism and synergy between the drugs and activated immune cells.

RESULTS

Among the standard anticancer agents, tyrosine kinase inhibitors (TKIs) stood out as the top inducers of both antagonism and synergy. Ruxolitinib and dasatinib emerged as the most notably antagonistic substances, exhibiting the lowest Bliss scores, whereas sorafenib was shown to synergize with activated PBMCs. Most concomitant drugs did not induce neither antagonism nor synergy. However, the statins mevastatin and simvastatin were uniquely shown to synergize with activated PBMC at all tested drug concentrations in the colon cancer model.

CONCLUSION

We utilized a miniaturized tumor-immune model to enable time and cost-effective evaluation of a broad panel of drugs in an immuno-oncology setting in vitro. Using this approach, immunomodulatory effects exerted by TKIs and statins were identified.

Emulation of Quantitative Systems Pharmacology models to accelerate virtual population inference in immuno-oncology

Quantitative Systems Pharmacology (QSP) models are increasingly being applied for target discovery and dose selection in immuno-oncology (IO). Typical application involves virtual trial, a simulation of a virtual population of hundreds of model instances with model inputs reflecting individual variability. While the structure of the model and initial parameterisation are based on literature describing the underlying biology, calibration of the virtual population by existing clinical data is frequently required to create tumour and patient population specific model instances. Since comparison of a virtual trial with clinical output requires hundreds of large-scale, non-linear model evaluations, the inference of a virtual population is computationally expensive, frequently becoming a bottleneck. Here, we present novel approach to virtual population inference in IO using emulation of the QSP model and an objective function based on Kolmogorov-Smirnov statistics to maximise congruence of simulated and observed clinical tumour size distributions. We sample the parameter space of a QSP IO model to collect a set of tumour growth time profiles. We evaluate performance of several machine learning approaches in interpolating these time profiles and create a surrogate model, which computes tumor growth profiles faster than the original model and allows examination of tens of millions of virtual patients. We use the surrogate model to infer a virtual population maximising congruence with the waterfall plot of a pembrolizumab clinical trial. We believe that our approach is applicable not only in QSP IO, but also in other applications where virtual populations need to be inferred for computationally expensive mechanistic models.

Treatment patterns and outcomes of high-grade immune checkpoint inhibitor-related pneumonitis in an oncology hospitalist service

PURPOSE

Immune checkpoint inhibitors (ICI) have become standard of care for some types of lung cancer. Along with expanding usage comes the emergence of immune-related adverse events (irAEs), including ICI-related pneumonitis (ICI-P). Treatment guidelines for managing irAEs have been developed; however, how clinicians manage irAEs in the real-world setting is less well known. We aimed to describe the outcomes and care patterns of grade ≥ 3 ICI-P in an onco-hospitalist service.

PATIENTS AND METHODS

We included patients with lung cancer treated with ICI who were admitted to an oncology hospitalist service with a suspicion of ICI-P. We described the hospitalization characteristics, treatment patterns, discharge practices, and clinical outcomes of patients with confirmed ICI-P. The primary outcome was time to start treatment for ICI-P.

RESULTS

Among 49 patients admitted with a suspicion of ICI-P, 31 patients were confirmed to have ICI-P and subsequently received ICI-P directed treatment. Pulmonology was consulted in 97% of patients. Median time to start treatment for ICI-P was 1 day (IQR 0-3.5 days). All 31 patients received corticosteroids. Inpatient mortality was 32%. Majority of patients discharged with steroids were prescribed prophylaxis for gastritis and opportunistic infections. Thirty-eight percent of patients were seen by pulmonology and 86% were seen by the oncology team post-discharge.

CONCLUSION

Our study confirms prior findings of high mortality among patients with high-grade ICI-P. Early diagnosis and treatment are key to improving clinical outcomes. Understanding the care patterns and adherence to treatment guidelines of clinicians caring for this patient population may help identify ways to further standardize management practices and improve patient outcomes.

A Microdissection Protocol for Proteogenomic Analysis of Histological Sections to Advance Drug Development

Combining proteogenomics with laser capture microdissection (LCM) in cancer research offers a targeted way to explore the intricate interactions between tumor cells and the different microenvironment components. This is especially important for immuno-oncology (IO) research where improvements in the predictability of IO-based drugs are sorely needed, and depends on a better understanding of the spatial relationships involving the tumor, blood supply, and immune cell interactions, in the context of their associated microenvironments. LCM is used to isolate and obtain distinct histological cell types, which may be routinely performed on complex and heterogeneous solid tumor specimens. Once cells have been captured, nucleic acids and proteins may be extracted for in-depth multimodality molecular profiling assays. Optimizing the minute tissue quantities from LCM captured cells is challenging. Following the isolation of nucleic acids, RNA-seq may be performed for gene expression and DNA sequencing performed for the discovery and analysis of actionable mutations, copy number variation, methylation profiles, etc. However, there remains a need for highly sensitive proteomic methods targeting small-sized samples. A significant part of this protocol is an enhanced liquid chromatography mass spectrometry (LC-MS) analysis of micro-scale and/or nano-scale tissue sections. This is achieved with a silver-stained one-dimensional sodium dodecyl sulfate polyacrylamide gel electrophoresis (1D-SDS-PAGE) approach developed for LC-MS analysis of fresh-frozen tissue specimens obtained via LCM. Included is a detailed in-gel digestion method adjusted and specifically designed to maximize the proteome coverage from amount-limited LCM samples to better facilitate in-depth molecular profiling. Described is a proteogenomic approach leveraged from microdissected fresh frozen tissue. The protocols may also be applicable to other types of specimens having limited nucleic acids, protein quantity, and/or sample volume.

Spatial and Compositional Biomarkers in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options, which warrants identification of novel therapeutic targets. Deciphering nuances in the tumor microenvironment (TME) may unveil insightful links between anti-tumor immunity and clinical outcomes, yet such connections remain underexplored. Here we employed a dataset derived from imaging mass cytometry of 58 TNBC patient specimens at single-cell resolution and performed in-depth quantifications with a suite of multi-scale computational algorithms. We detected distinct cell distribution patterns among clinical subgroups, potentially stemming from different infiltration related to tumor vasculature and fibroblast heterogeneity. Spatial analysis also identified ten recurrent cellular neighborhoods (CNs) - a collection of local TME characteristics with unique cell components. Coupling of the prevalence of pan-immune and perivasculature immune hotspot CNs, enrichment of inter-CN interactions was associated with improved survival. Using a deep learning model trained on engineered spatial data, we can with high accuracy (mean AUC of 5-fold cross-validation = 0.71) how a separate cohort of patients in the NeoTRIP clinical trial will respond to treatment based on baseline TME features. These data reinforce that the TME architecture is structured in cellular compositions, spatial organizations, vasculature biology, and molecular profiles, and suggest novel imaging-based biomarkers for treatment development in the context of TNBC.

Immunotherapy-related biomarkers: Confirmations and uncertainties

Immunotherapy profoundly changed oncology treatment, becoming one of the main therapeutical strategies. Remarkable improvement has been achieved in survival outcomes, but the percentage of patients who benefit from immunotherapy is still limited. Only one-third of patients receiving immune checkpoint inhibitors (ICIs) achieve long-term response. Several patients are not responsive to treatment or relapse after an initial response. To date, programmed death-ligand 1, microsatellite instability, and tumor mutational burden are the three biomarkers validated to predict the ICIs response, but a single variable seems still insufficient in the patient's selection. Considering the substantial and increasing use of these drugs, the identification of new predictive biomarkers of ICI response is of paramount importance. We summarize the state of the art and the clinical use of immune biomarkers in oncology, highlighting the strength and weaknesses of currently approved biomarkers, describing the emerging tissues and circulating biomarkers, and outlining future perspectives.

Neurotoxicity of Cancer Immunotherapies Including CAR T Cell Therapy

PURPOSE OF REVIEW

To outline the spectrum of neurotoxicity seen with approved immunotherapies and in pivotal clinical trials including immune checkpoint inhibitors, chimeric antigen receptor T-cell therapy, vaccine therapy, and oncolytic viruses.

RECENT FINDINGS

There has been an exponential growth in new immunotherapies, which has transformed the landscape of oncology treatment. With more widespread use of cancer immunotherapies, there have also been advances in characterization of its associated neurotoxicity, research into potential underlying mechanisms, and development of management guidelines. Increasingly, there is also mounting interest in long-term neurologic sequelae. Neurologic complications of immunotherapy can impact every aspect of the central and peripheral nervous system. Early recognition and treatment are critical. Expanding indications for immunotherapy to solid and CNS tumors has led to new challenges, such as how to reliably distinguish neurotoxicity from disease progression. Our evolving understanding of immunotherapy neurotoxicity highlights important areas for future research and the need for novel immunomodulatory therapeutics.

Predictors, surrogate, and patient-reported outcomes in immunotherapy and salvage surgery for unresectable lung cancer: a single-center retrospective study

Medical treatment has changed drastically in recent years, especially for advanced stages of non-small-cell lung cancer (NSCLC), for which the development of immunotherapy and molecular targeted therapy significantly increased survival and quality of life. This single-center retrospective study aimed to analyze the outcome predictors, the surrogate outcomes, and the patient-reported outcomes after neoadjuvant immunotherapy for initially unresectable NSCLC. Patients affected by an initially unresectable NSCLC and identified between March 2014 and December 2021 who received immunotherapy alone or in combination with platinum-based chemotherapy and/or radiotherapy were collected. Overall survival (OS) and disease-free survival (DFS) were estimated according to the Kaplan-Meier method. Patient-reported outcomes were recorded using the European Organization for Research and Treatment of Cancer (EORTC) Quality-of-Life (QoL) Group questionnaire-Lung Cancer 29 Module to compare differences in symptoms and QoL at two different times, 30 days and 1 year after surgery. Surgical, pathological records, and patient-reported outcomes (at 30 days and 1 year after surgery) were reviewed. Complete pathological remission was achieved in 7 patients (36.8%) and major pathological remission in 3 patients (15.7%). The median overall survival in the study group is 19 months (range: 2-57.4). Of 19 patients, 16 (84.2%) are alive to date, of which 2 (10.5%) have a local recurrence. At 30 days from surgery, the main symptoms reported by EORTC Module were coughing, shortness of breath, the side effect of treatment, fear of progression, and surgery-related problems. Induction immunotherapy with or without chemotherapy can be considered for unresectable locally advanced NSCLC, and after the downstaging, the possibility of surgery could be re-evaluated in a multidisciplinary setting with high rates of R0 resection. In this selected and highly motivated group of patients, the QoL and symptoms after salvage surgeries are acceptable and even better than those reported in the literature.

Comparison of Large Language Models in Answering Immuno-Oncology Questions: A Cross-Sectional Study

Background

The capability of large language models (LLMs) to understand and generate human-readable text has prompted the investigation of their potential as educational and management tools for cancer patients and healthcare providers.

Materials and Methods

We conducted a cross-sectional study aimed at evaluating the ability of ChatGPT-4, ChatGPT-3.5, and Google Bard to answer questions related to four domains of immuno-oncology (Mechanisms, Indications, Toxicities, and Prognosis). We generated 60 open-ended questions (15 for each section). Questions were manually submitted to LLMs, and responses were collected on June 30th, 2023. Two reviewers evaluated the answers independently.

Results

ChatGPT-4 and ChatGPT-3.5 answered all questions, whereas Google Bard answered only 53.3% (p <0.0001). The number of questions with reproducible answers was higher for ChatGPT-4 (95%) and ChatGPT3.5 (88.3%) than for Google Bard (50%) (p <0.0001). In terms of accuracy, the number of answers deemed fully correct were 75.4%, 58.5%, and 43.8% for ChatGPT-4, ChatGPT-3.5, and Google Bard, respectively (p = 0.03). Furthermore, the number of responses deemed highly relevant was 71.9%, 77.4%, and 43.8% for ChatGPT-4, ChatGPT-3.5, and Google Bard, respectively (p = 0.04). Regarding readability, the number of highly readable was higher for ChatGPT-4 and ChatGPT-3.5 (98.1%) and (100%) compared to Google Bard (87.5%) (p = 0.02).

Conclusion

ChatGPT-4 and ChatGPT-3.5 are potentially powerful tools in immuno-oncology, whereas Google Bard demonstrated relatively poorer performance. However, the risk of inaccuracy or incompleteness in the responses was evident in all three LLMs, highlighting the importance of expert-driven verification of the outputs returned by these technologies.

Inflammatory response-based prognostication and personalized therapy decisions in clear cell renal cell cancer to aid precision oncology

OBJECTIVE

The impact of inflammatory response on tumor development and therapeutic response is of significant importance in clear cell renal cell carcinoma (ccRCC). The customization of specialized prognostication approaches and the exploration of supplementary treatment options hold critical clinical implications in relation to the inflammatory response.

METHODS

In the present study, unsupervised clustering was implemented on TCGA-KIRC tumors using transcriptome profiles of inflammatory response genes, which was then validated in two ccRCC datasets (E-MATB-1980 and ICGC) and two immunotherapy datasets (IMvigor210 and Liu et al.) via SubMap and NTP algorithms. Combining co-expression and LASSO analyses, inflammatory response-based scoring system was defined, which was evaluated in pan-cancer.

RESULTS

Three reproducible inflammatory response subtypes (named IR1, IR2 and IR3) were determined and independently verified, each exhibiting distinct molecular, clinical, and immunological characteristics. Among these subtypes, IR2 had the best OS outcomes, followed by IR3 and IR1. In terms of anti-angiogenic agents, sunitinib may be appropriate for IR1 patients, while axitinib and pazopanib may be suitable for IR2 patients, and sorafenib for IR3 patients. Additionally, IR1 patients might benefit from anti-CTLA4 therapy. A scoring system called IRscore was defined for individual ccRCC patients. Patients with high IRscore presented a lower response rate to anti-PD-L1 therapy and worse prognostic outcomes. Pan-cancer analysis demonstrated the immunological features and prognostic relevance of the IRscore.

CONCLUSION

Altogether, characterization of inflammatory response subtypes and IRscore provides a roadmap for patient risk stratification and personalized treatment decisions, not only in ccRCC, but also in pan-cancer.

Stereotactic radiosurgery and anti-PD-1 + CTLA-4 therapy, anti-PD-1 therapy, anti-CTLA-4 therapy, BRAF/MEK inhibitors, BRAF inhibitors, or conventional chemotherapy for the management of melanoma brain metastases

BACKGROUND

Immunotherapy and targeted BRAF/MEK inhibitors (i) have revolutionised the systemic management of advanced melanoma. Given the role of stereotactic radiosurgery (SRS) in the local management of brain metastases, we sought to evaluate clinical outcomes in patients with melanoma brain metastases (MBM) treated with SRS and various systemic therapies.

METHODS

Patients were included if MBM were diagnosed and treated with SRS within 3 months of receiving anti-PD-1+CTLA-4 therapy, anti-PD-1 therapy, anti-CTLA-4 therapy, BRAF/MEK-i, BRAF-i, or conventional chemotherapy. Comparisons between groups were made for overall survival (OS), distant MBM control, local MBM, systemic progression-free survival (sPFS), and neurotoxicity.

RESULTS

In total, 257 patients with 1048 MBM treated over 368 SRS sessions between 2011 and 2020 were identified. On MVA, treatment with anti-PD1+anti-CTLA-4, anti-PD-1, and BRAF/MEK-i improved distant intracranial control over conventional chemotherapy. No significant differences were noted in local control (LC) between groups (p = 0.78). Kaplan-Meier OS at 12 months for anti-PD-1 + CTLA-4 therapy, anti-PD-1 therapy, anti-CTLA-4 therapy, BRAF/MEK-i, BRAF-i, and conventional chemotherapy was 68%, 59%, 45%, 62%, 21%, and 15%, respectively (p = <0.0001). The sPFS rates at 12 months were 57%, 53%, 42%, 45%, 14%, and 6% (p = <0.0001). No significant differences were noted in rates of radiation necrosis (p = 0.93).

CONCLUSIONS

This is among the largest series evaluating MBM treated with SRS and various systemic therapy regimens. Our analysis noted significant differences in OS, distant MBM control, and sPFS by systemic therapy. No differences in LC or radiation necrosis risk were noted.

CYFIP2 serves as a prognostic biomarker and correlates with tumor immune microenvironment in human cancers

BACKGROUND

The mechanisms whereby CYFIP2 acts in tumor development and drives immune infiltration have been poorly explored. Thus, this study aimed to identifying the role of CYFIP2 in tumors and immune response.

METHODS

In this study, we first explored expression patterns, diagnostic role and prognostic value of CYFIP2 in cancers, particularly in lung adenocarcinoma (LUAD). Then, we performed functional enrichment, genetic alterations, DNA methylation analysis, and immune cell infiltration analysis of CYFIP2 to uncover its potential mechanisms involved in immune microenvironment.

RESULTS

We found that CYFIP2 significantly differentially expressed in different tumors including LUAD compared with normal tissues. Furthermore, CYFIP2 was found to be significantly correlated with clinical parameters in LUAD. According to the diagnostic and survival analysis, CYFIP2 may be employed as a potential diagnostic and prognostic biomarker. Moreover, genetic alterations revealed that mutation of CYFIP2 was the main types of alterations in different cancers. DNA methylation analysis indicated that CYFIP2 mRNA expression correlated with hypomethylation. Afterwards, functional enrichment analysis uncovered that CYFIP2 was involved in tumor-associated and immune-related pathways. Immune infiltration analysis indicated that CYFIP2 was significantly correlated with immune cells infiltration. In particular, CYFIP2 was strongly linked with immune microenvironment scores. Additionally, CYFIP2 exhibited a significant relationship with immune regulators and immune-related genes including chemokines, chemokines receptors, and MHC genes.

CONCLUSION

Our results suggested that CYFIP2 may serve as a prognostic cancer biomarker for determining prognosis and might be a promising therapeutic strategy for tumor immunotherapy.

Fully closed and automated enrichment of primary blood dendritic cells for cancer immunotherapy

Dendritic cell (DC) vaccination is a promising approach to induce tumor-specific immune responses in cancer patients. Until recently, most DC vaccines were based on in vitro-differentiated monocyte-derived DCs. However, through development of efficient isolation techniques, the use of primary blood dendritic cell subsets has come within reach. Manufacturing of blood-derived DCs has multiple advances over monocytes-derived DCs, including more standardized isolation and culture protocols and shorter production processes. In peripheral blood, multiple DC subsets can be distinguished based on their phenotype and function. Plasmacytoid DC (pDC) and myeloid/conventional DCs (cDC) are the two main DC populations, moreover cDC can be further subdivided into CD141/BDCA3+ DC (cDC1) and CD1c/BDCA1+ DC (cDC2). In three separate clinical DC vaccination studies in melanoma and prostate cancer patients, we manufactured DC vaccines consisting of pDCs only, cDC2s only, or a combination of pDC and cDC2s, which we called natural DCs (nDC). Here, we describe a fully closed and automated GMP-compliant method to enrich naturally circulating DCs and present the results of enrichment of primary blood DCs from aphaeresis products of 8 healthy donors, 21 castrate-resistant prostate cancer patients, and 112 stage III melanoma patients. Although primary blood DCs are relatively scarce in aphaeresis material, our results show that it is feasible to isolate highly pure pDC, cDC2, or nDC with sufficient yield to manufacture DC vaccines for natural DC-based immunotherapy.

Polymeric nanoparticle gel for intracellular mRNA delivery and immunological reprogramming of tumors

Immuno-oncology therapies have been of great interest with the goal of inducing sustained tumor regression, but clinical results have demonstrated the need for improved and widely applicable methods. An antigen-free method of cancer immunotherapy can stimulate the immune system to recruit lymphocytes and produce immunostimulatory factors without prior knowledge of neoantigens, while local delivery reduces the risk of systemic toxicity. To improve the interactions between tumor cells and cytotoxic lymphocytes, a gene delivery nanoparticle platform was engineered to reprogram the tumor microenvironment (TME) in situ to be more immunostimulatory by inducing tumor-associated antigen-presenting cells (tAPCs) to activate cytotoxic lymphocytes against the tumor. Biodegradable, lipophilic poly (beta-amino ester) (PBAE) nanoparticles were synthesized and used to co-deliver mRNA constructs encoding a signal 2 co-stimulatory molecule (4-1BBL) and a signal 3 immuno-stimulatory cytokine (IL-12), along with a nucleic acid-based immunomodulatory adjuvant. Nanoparticles are combined with a thermoresponsive block copolymer for gelation at the injection site for local NP retention at the tumor. The reprogramming nanoparticle gel synergizes with immune checkpoint blockade (ICB) to induce tumor regression and clearance in addition to resistance to tumor rechallenge at a distant site. In vitro and in vivo studies reveal increases in immunostimulatory cytokine production and recruitment of immune cells as a result of the nanoparticles. Intratumoral injection of nanoparticles encapsulating mRNA encoding immunostimulatory agents and adjuvants via an injectable thermoresponsive gel has great translational potential as an immuno-oncology therapy that can be accessible to a wide range of patients.

The efficacy of albumin-globulin ratio to predict prognosis in cancer patients

The goal of this systematic review was to identify all of the research within the last 10 years that investigated both the Albumin-Globulin Ratio (AGR) and outcomes of solid tumor cancer patients via quantitative prognostic variables. Multiple scientific databases were researched for journal articles that included keywords relating AGR to prognosis. Once isolated from the databases, the articles were de-duplicated and manually screened based on standardized inclusion/exclusion criteria in a blind format via Rayyan. The collective data were sorted by cancer type, corrected for population size, and used to calculate the average cut-off values for the most popular prognostic variables. In total, 18 independent types of cancer have been evaluated to see if AGR is a prognostic indicator based on multivariate analyses. The average cut-off value for AGR in overall survival was 1.356, while the average cut-off value for AGR in progression free survival was 1.292. AGR was found to be significantly associated with at least one prognostic variable in every type of cancer evaluated based on multivariate analyses. The ease of access and affordability of AGR makes it an invaluable tool applicable to nearly all patients. Overall, AGR is a proven prognostic variable that should always be considered in the evaluation of a solid tumor cancer patient's prognosis. Further research needs to be conducted studying the potential prognostic effect in more types of solid tumors.

Advances in Radiotherapy Immune Modulation: From Bench-to-Bedside and Back Again

Pre-clinical and clinical data clearly demonstrate the immune modulatory effects of radiotherapy (RT) but clinical trials testing RT + immunotherapy have been equivocal. An improved understanding of the immune modulatory effects of RT and how practical parameters of RT delivery (site and number of lesions, dose, fractionation, timing) influence these effects are needed to optimally combine RT with immunotherapy. Additionally, increased exploration of immunotherapy combinations with RT, beyond immune checkpoint inhibitors, are needed. A "bench-to-bedside and back again" approach will improve our understanding of RT immune modulation and allow for the implementation of more effective RT + immunotherapy strategies.

Patient-derived preclinical models to develop immunotherapies

Cancer immunotherapy has revolutionized the treatment of some malignancies. Yet, many tumors do not unfortunately respond to immune-based therapies. Deeper insights into the biology of the immune response to cancer are required to identify novel therapeutic targets and advance immuno-oncology. To do so, we need to study cancer in patient-derived models that can faithfully recapitulate and capture the complexity and heterogeneity of the tumor-immune ecosystem. Platforms facilitating the analysis of the human tumor immune microenvironment of individual patients are crucial. Patient-derived models are fundamental not only to better understand the biology of the cancer immune system but to discern the mechanism of action of therapeutic compounds and perform preclinical studies toward improving the success of subsequent clinical testing. In this viewpoint, I present a brief review of patient-derived models for cancer immunotherapy.