Prospective development and validation of a liquid immune profile-based signature (LIPS) to predict response of patients with recurrent/metastatic cancer to immune checkpoint inhibitors

The immune-reinforcements of Lenvatinib plus anti-PD-1 and their rationale to unite with TACE for unresectable hepatocellular carcinoma treatment

BACKGROUND

Despite encouraging clinical benefits have gained by anti-PD-1 and Lenvatinib combination, in-depth characterizations about the mechanisms of action remain poorly characterized. Furthermore, although the combination of systemic anti-PD-1 or Lenvatinib treatment and locoregional transcatheter arterial chemoembolization (TACE) is widely carried out to treat unresectable HCC in clinical, the efficacies of different combination regimens are uncertain due to limited researches.

METHODS

We firstly generated murine HCC models to validate the enhanced anti-tumor effects of anti-PD-1 and Lenvatinib combination therapy. Then single cell mass cytometry (CyTOF) was employed to phenotypically reveal their mechanisms of action. After that, we further compared the effectiveness of TACE plus Lenvatinib (i.e., TACE-Len) dual therapy with TACE, Lenvatinib plus anti-PD-1 (i.e., TACE-Len-PD-1) triple therapy as conversion therapy for unresectable HCC.

RESULTS

Lenvatinib and anti-PD-1 combination could generate activated immune profiles not only by increasing systemic CD4+, CD8+T cells and B cells proportions, but also by weakening the immune-tolerance functions derived from both immunosuppressive cells (i.e., MDSCs) and co-inhibitory mediators (i.e., PD-L1 and LAG-3). Meanwhile, our study also suggested that TACE-Len-PD-1 triple therapy could achieve better clinical responses with powerful immune profiles for unresectable HCC compared to TACE-Len dual therapy.

CONCLUSIONS

Our study provided a delicate immune landscape of anti-PD-1and Lenvatinib combination, and we also offered scientific evidences that TACE-Len-PD-1 triple therapy could fulfill better clinical benefits than TACE-Len dual therapy, which is anticipated to provide objective and effective evidences for clinical use.

Monocyte-related markers as predictors of immune checkpoint inhibitor efficacy and immune-related adverse events: a systematic review and meta-analysis

The efficacy and off-target effects of immune checkpoint inhibitors (ICI) in cancer treatment vary among patients. Monocytes likely contribute to this heterogeneous response due to their crucial role in immune homeostasis. We conducted a systematic review and meta-analysis to evaluate the impact of monocytes on ICI efficacy and immune-related adverse events (irAEs) in patients with cancer. We systematically searched PubMed, Web of Science, and Embase for clinical studies from January 2000 to December 2023. Articles were included if they mentioned cancer, ICI, monocytes, or any monocyte-related terminology. Animal studies and studies where ICIs were combined with other biologics were excluded, except for studies where two ICIs were used. This systematic review was registered with PROSPERO (CRD42023396297) prior to data extraction and analysis. Monocyte-related markers, such as absolute monocyte count (AMC), monocyte/lymphocyte ratio (MLR), specific monocyte subpopulations, and m-MDSCs were assessed in relation to ICI efficacy and safety. Bayesian meta-analysis was conducted for AMC and MLR. The risk of bias assessment was done using the Cochrane-ROBINS-I tool. Out of 5787 studies identified in our search, 155 eligible studies report peripheral blood monocyte-related markers as predictors of response to ICI, and 32 of these studies describe irAEs. Overall, based on 63 studies, a high MLR was a prognostic biomarker for short progression-free survival (PFS) and overall survival (OS) hazard ratio (HR): 1.5 (95% CI: 1.21-1.88) and 1.52 (95% CI:1.13-2.08), respectively. The increased percentage of classical monocytes was an unfavorable predictor of survival, while low baseline rates of monocytic myeloid-derived suppressor cells (m-MDSCs) were favorable. Elevated intermediate monocyte frequencies were associated but not significantly correlated with the development of irAEs. Baseline monocyte phenotyping may serve as a composite biomarker of response to ICI; however, more data is needed regarding irAEs. Monocyte-related variables may aid in risk assessment and treatment decision strategies for patients receiving ICI in terms of both efficacy and safety.

Peripheral blood PD-1+ T lymphocytes as biomarkers in liquid biopsies for solid tumors: Clinical significance and prognostic applications

A shift toward a T cell exhaustion phenotype is associated with the upregulation of expression of programmed cell death protein 1 (PD-1) on T lymphocytes in patients with malignant solid tumors. The interaction between PD-1 and programmed death-ligand 1 (PD-L1) inhibits PD-1+ T lymphocyte function, impacting their anti-tumor immune activity. Immune checkpoint inhibitors targeting PD-1/PD-L1 have revolutionized the treatment of various solid malignancies, improving therapeutic efficacy and survival outcomes. Peripheral blood analysis of liquid biopsies is being increasingly used to identify populations most likely to benefit from various treatment modalities. PD-1+ T lymphocytes represent the primary cell population responsive to immunotherapeutic interventions for patients with solid malignancies, as evidenced by the altered PD-1 expression levels and proportion of cells comprising the overall population of immunocytes. PD-1+ T cells in peripheral blood exert an associative and reciprocal predictive effect on homologous intratumoral cells. Distinct subpopulations of PD-1+ T cells exhibit differential ability to proliferate in the periphery and can be characterized by tumor antigen-specific and exhaustion phenotypes. These characteristics have prognostic implications, aiding in the prediction of the efficacy of antitumor therapy and predicting survival outcomes. We highlight distinct subpopulations of PD-1+ T cells, their exhaustion and antigen-specific phenotypes, and their dynamic changes over treatment, providing insights into their utility for tailoring personalized therapies. For the first time, this review discusses the role of peripheral PD-1+ T lymphocytes as prognostic biomarkers in liquid biopsies, focusing on their clinical significance, predictive value during therapy, and future research directions.

Flow cytometry-based monitoring of myeloid-derived suppressor cells in the peripheral blood of patients with solid tumors

Myeloid-derived suppressor cells (MDSCs) ameliorate inflammation by inhibiting T cell responses. In pathological conditions, such as autoimmunity, chronic infections or cancer they accumulate in the periphery. In cancer, MDSCs can also be part of the tumor microenvironment and are associated with a worse prognosis and limited response to immunotherapy. Nowadays attempts are made to specifically target MDSCs in cancer therapy. Still, the role of MDSCs in standard cancer treatment modalities, such as radiotherapy remains mostly elusive. Here, we describe a flow cytometry-based method to determine and monitor monocytic and granulocytic-derived MDSCs directly from whole blood in an easy, fast and reliable assay. As specific surface markers for MDSCs are lacking, the assay follows a gating strategy that excludes successively the main immune cells types and analyzes the remaining events for a set of molecules that are expressed on MDSCs. This assay is especially appropriate for longitudinal analyses and clinical trials and is suitable for being integrated into more complex immunophenotyping panels to generate a comprehensive immune status.

Combination of ataxia telangiectasia and Rad3-related inhibition with ablative radiotherapy remodels the tumor microenvironment and enhances immunotherapy response in lung cancer

We investigated the combined effects of ataxia telangiectasia and Rad3-related (ATR) inhibition, ablative radiotherapy, and immune checkpoint inhibitor (ICI) therapy against lung cancer. ATR inhibitor was administered combined with ablative radiotherapy to assess its radiosensitizing effect on lung cancer cells. Treatment response and survival were evaluated in vivo using A549 xenograft flank tumor and synchronous LLC lung and flank tumor mouse models. Mice received ablative radiotherapy (12 Gy/d for 2 d), ATR inhibitor, and ICI. The tumor microenvironment was assessed in irradiated flank and non-irradiated lung tumors. Programmed death-ligand 1 expression was upregulated after irradiation. ATR inhibition attenuated this upregulation. ATR inhibitor pretreatment decreased cell survival after irradiation by inhibiting DNA double-strand break repair, inducing mitotic cell death, and altering cell cycle progression. ATR inhibition enhanced radiation-induced damage-associated molecular patterns determined by high mobility group box 1 quantification and activated the cyclic GMP-AMP synthase-stimulator of interferon genes pathway. Combined ATR inhibition and ablative radiotherapy inhibited tumor growth and improved survival in mice. Adding ICI therapy further enhanced local antitumor effects, reducing the metastatic lung tumor burden and remodeling the tumor microenvironment through immunogenic cell death induction and enhanced immune cell infiltration. Triple therapy increased immune cell infiltration in distant non-irradiated lung tumors and stimulated the generation of protective T-cell immunity in splenocytes. Safety analysis showed minimal toxicity. ATR inhibition enhanced the efficacy of ablative radiotherapy and immunotherapy in lung cancer. These findings underscore the importance of combination therapies for enhancing systemic antitumor immune responses and outcomes.

Defining intra-tumoral and systemic immune biomarkers for locally advanced head-and-neck cancer - detailed protocol of a prospective, observatory multicenter trial (ImmunBioKHT) and first results of the immunophenotyping of the patients' peripheral blood

The approval and effectiveness of immune checkpoint inhibitors in head-and-neck squamous cell carcinoma (HNSCC) highlights the role of the immune system in this tumor entity. HNSCCs not only interacts with the immune system in the tumor tissue, but also induce systemic effects that may be additionally influenced by further factors such as the microbiome. Nonetheless, reliable immunological biomarkers that predict treatment response and outcome in HNSCC patients are lacking. The currently available biomarkers are mainly limited to analyses from tumor biopsies, while biomarkers from liquid biopsies, such as peripheral blood are not well-established. Thus, the here presented trial aims to identify interactions of intra-tumoral and systemic immune responses and to define prognostic immune signatures. Consequently, not only samples from the tumor tissue, but also from peripheral blood and the microbiome will be studied/are being evaluated and correlated with the clinical outcome. In this prospective, multi-center trial, 1000 HNSCC patients and 100 patients in the control cohort with non-tumor head-and-neck surgery will be enrolled. The local immune status from of the tumor and the microbiome will be sampled before treatment. In addition, the systemic immune status from peripheral blood will be analyzed before and after surgery and after the adjuvant and definitive radio-chemotherapy (RCT). Clinical baseline characteristics and outcome will additionally be collected. Data mining and modelling approaches will finally be applied to identify interactions of local and systemic immune parameters and to define prognostic immune signatures based on the evaluated immune markers. Approval from the institutional review board of the Friedrich-Alexander-Universität Erlangen-Nürnberg was granted in December 2021 (application number 21-440-B). By now, 150 patients have been enrolled in the intervention cohort. The results will be disseminated to the scientific audience and the general public via presentations at conferences and publication in peer-reviewed journals.

Peripheral immune biomarkers for immune checkpoint inhibition of solid tumours

BACKGROUND

With the rapid adoption of immunotherapy for the treatment of cancer comes the pressing need for readily accessible biomarkers to guide immunotherapeutic strategies and offer insights into outcomes with specific treatments. Regular sampling of solid tumour tissues outside of melanoma for immune monitoring is not often feasible; conversely, routine, frequent interrogation of circulating immune biomarkers is entirely possible. As immunotherapies and immune checkpoint inhibitors, in particular, are more widely used in first-line, neoadjuvant, and metastatic settings, the discovery and validation of peripheral immune biomarkers are urgently needed across solid tumour types for improved prediction and prognostication of clinical outcomes in response to immunotherapy, as well as elucidation of mechanistic underpinnings of the intervention. Careful experimental design, encompassing both retrospective and prospective studies, is required in such biomarker identification studies, and concerted efforts are essential for their advancement into clinical settings.

CONCLUSION

In this review, we summarize shared immune features between the tumour microenvironment and systemic circulation, evaluate exploratory peripheral immune biomarker studies, and discuss associations between candidate biomarkers with clinical outcomes. We also consider integration of multiple peripheral immune parameters for better prediction and prognostication and discuss considerations in study design to further evaluate the clinical utility of candidate peripheral immune biomarkers for immunotherapy of solid tumours.

HIGHLIGHTS

Peripheral immune biomarkers are critical for improved prediction and prognostication of clinical outcomes for patients with solid tumours treated with immune checkpoint inhibition. Candidate peripheral biomarkers, such as cytokines, soluble factors, and immune cells, have potential as biomarkers to guide immunotherapy of solid tumours. Multiple peripheral immune parameters may be integrated to improve prediction and prognostication. The potential of peripheral immune biomarkers to guide immunotherapy of solid tumours requires critical work in biomarker discovery, validation, and standardization.

Translational Frontiers and Clinical Opportunities of Immunologically Fitted Radiotherapy

Ionizing radiation can have a wide range of impacts on tumor-immune interactions, which are being studied with the greatest interest and at an accelerating pace by the medical community. Despite its undeniable immunostimulatory potential, it clearly appears that radiotherapy as it is prescribed and delivered nowadays often alters the host's immunity toward a suboptimal state. This may impair the full recovery of a sustained and efficient antitumor immunosurveillance posttreatment. An emerging concept is arising from this awareness and consists of reconsidering the way of designing radiation treatment planning, notably by taking into account the individualized risks of deleterious radio-induced immune alteration that can be deciphered from the planned beam trajectory through lymphocyte-rich organs. In this review, we critically appraise key aspects to consider while planning immunologically fitted radiotherapy, including the challenges linked to the identification of new dose constraints to immune-rich structures. We also discuss how pharmacologic immunomodulation could be advantageously used in combination with radiotherapy to compensate for the radio-induced loss, for example, with (i) agonists of interleukin (IL)2, IL4, IL7, IL9, IL15, or IL21, similarly to G-CSF being used for the prophylaxis of severe chemo-induced neutropenia, or with (ii) myeloid-derived suppressive cell blockers.

Deep Learning Based on Computed Tomography Predicts Response to Chemoimmunotherapy in Lung Squamous Cell Carcinoma

Non-small-cell lung carcinoma (NSCLC) often carries a dire prognosis. The advent of neoadjuvant chemoimmunotherapy (NCI) has become a promising approach in NSCLC treatment, making the identification of reliable biomarkers for major pathological response (MPR) crucial. This study aimed to devise a deep learning (DL) model to estimate the MPR to NCI in lung squamous cell carcinoma (LUSC) patients and uncover its biological mechanism. We enrolled a cohort of 309 LUSC patients from various medical institutions. A ResNet50 model, trained on contrast-enhanced computed tomography images, was developed, and validated to predict MPR. We examined somatic mutations, genomic data, tumor-infiltrating immune cells, and intra-tumor microorganisms. Post-treatment, 149 (48.22%) patients exhibited MPR. The DL model demonstrated excellent predictive accuracy, evidenced by an area under the receiver operating characteristic curve (AUC) of 0.95 (95% CI: 0.98-1.00) and 0.90 (95% CI: 0.81-0.98) in the first and second validation sets, respectively. Multivariate logistic regression analysis identified the DL model score (low vs. high) as an independent predictor of MPR. The prediction of MPR (P-MPR) correlated with mutations in four genes, as well as gene ontology and pathways tied to immune response and antigen processing and presentation. Analysis also highlighted diversity in immune cells within the tumor microenvironment and in peripheral blood. Moreover, the presence of four distinct bacteria varied among intra-tumor microorganisms. Our DL model proved highly effective in predicting MPR in LUSC patients undergoing NCI, significantly advancing our understanding of the biological mechanisms involved.

Machine Learning-assisted immunophenotyping of peripheral blood identifies innate immune cells as best predictor of response to induction chemo-immunotherapy in head and neck squamous cell carcinoma - knowledge obtained from the CheckRad-CD8 trial

PURPOSE

Individual prediction of treatment response is crucial for personalized treatment in multimodal approaches against head-and-neck squamous cell carcinoma (HNSCC). So far, no reliable predictive parameters for treatment schemes containing immunotherapy have been identified. This study aims to predict treatment response to induction chemo-immunotherapy based on the peripheral blood immune status in patients with locally advanced HNSCC.

METHODS

The peripheral blood immune phenotype was assessed in whole blood samples in patients treated in the phase II CheckRad-CD8 trial as part of the pre-planned translational research program. Blood samples were analyzed by multicolor flow cytometry before (T1) and after (T2) induction chemo-immunotherapy with cisplatin/docetaxel/durvalumab/tremelimumab. Machine Learning techniques were used to predict pathological complete response (pCR) after induction therapy.

RESULTS

The tested classifier methods (LDA, SVM, LR, RF, DT, and XGBoost) allowed a distinct prediction of pCR. Highest accuracy was achieved with a low number of features represented as principal components. Immune parameters obtained from the absolute difference (lT2-T1l) allowed the best prediction of pCR. In general, less than 30 parameters and at most 10 principal components were needed for highly accurate predictions. Across several datasets, cells of the innate immune system such as polymorphonuclear cells, monocytes, and plasmacytoid dendritic cells are most prominent.

CONCLUSIONS

Our analyses imply that alterations of the innate immune cell distribution in the peripheral blood following induction chemo-immuno-therapy is highly predictive for pCR in HNSCC.

Machine learning based on blood test biomarkers predicts fast progression in advanced NSCLC patients treated with immunotherapy

Objective

Fast progression (FP) represents a desperate situation for advanced non-small cell lung cancer (NSCLC) patients undergoing immune checkpoint inhibitor therapy. We aimed to develop a predictive framework based on machine learning (ML) methods to identify FP in advanced NSCLC patients using blood test biomarkers.

Methods and analysis

We extracted data of 1546 atezolizumab-treated patients from four multicentre clinical trials. In this study, patients from the OAK trial were taken for model training, whereas patients from the other trials were used for independent validations. The FP prediction model was developed using 21 pretreatment blood test variables in seven ML approaches. Prediction performance was evaluated by the receiver operating characteristic (ROC) curve.

Results

The prevalence of FP was 7.6% (118 of 1546) in all atezolizumab-treated patients. The most important variables for the prediction model were: C reactive protein, neutrophil count, lactate dehydrogenase and alanine transaminase. The Support Vector Machine (SVM) algorithm applied to these four blood test parameters demonstrated good performance: the area under the ROC curve obtained from the training cohort (OAK), validation cohort 1 (BIRCH) and cohort 2 (merged POPLAR and FIR) were 0.908, 0.666 and 0.776, respectively. In addition, the absolute difference in median survival between the SVM-predicted FP and non-FP groups was significant in both progression-free survival and overall survival (p<0.001).

Conclusion

SVM trained using a 4-biomarker panel has good performance in predicting the occurrence of FP regardless of programmed cell death ligand 1 expression, hence providing evidence for decision-making in single-agent atezolizumab immunotherapy for patients with advanced NSCLC.

A practical prognostic peripheral blood-based risk model for the evaluation of the likelihood of a response and survival of metastatic cancer patients treated with immune checkpoint inhibitors

BACKGROUND

Less than half of unselected metastatic cancer patients benefit from the immune checkpoint inhibitor (ICI) therapy. Systemic cancer-related inflammation may influence the efficacy of ICIs and thus, systemic inflammatory markers could have prognostic and/or predictive potential in ICI therapy. Here, we aimed to identify a combination of inflammation-related laboratory parameters to establish a practical prognostic risk model for the pretreatment evaluation of a response and survival of ICI-treated patients with different types of metastatic cancers.

METHODS

The study-cohort consisted of a real-world patient population receiving ICIs for metastatic cancers of different origins (n = 158). Laboratory parameters determined before the initiation of the ICI treatment were retrospectively collected. Six inflammation-related parameters i.e., elevated values of neutrophils, platelets, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and lactate dehydrogenase (LDH), and the presence of anemia, were each scored with one point, giving 0-6 risk points for each patient. The patients with information of all these six parameters (n = 109) were then stratified into low-risk (0-3 points) and high-risk (4-6 points) groups. The overall response rate (ORR), overall survival (OS), and progression-free survival (PFS) according to the risk scores were determined.

RESULTS

The risk model was strongly associated with the outcome of the patients. The ORR to ICI treatment in the high-risk group was 30.3% in comparison to 53.9% in the low-risk group (p = 0.023). The medians for OS were 10.0 months and 27.3 months, respectively (p < 0.001), and the corresponding medians for PFS were 3.9 months and 6.3 months (p = 0.002). The risk group remained as a significant prognostic factor for both OS (HR 3.04, 95% CI 1.64-5.64, p < 0.001) and PFS (HR 1.79, 95% CI 1.04-3.06, p = 0.035) in the Cox multivariate analyses.

CONCLUSIONS

We propose a readily feasible, practical risk model consisted of six inflammation-related laboratory parameters as a tool for outcome prediction in metastatic cancer patients treated with ICIs. The risk model was strongly associated with the outcome of the patients in terms of all the evaluated indicators i.e., ORR, OS and PFS. Yet, further studies are needed to validate the risk model.

Efficient plasma metabolic fingerprinting as a novel tool for diagnosis and prognosis of gastric cancer: a large-scale, multicentre study

OBJECTIVE

Metabolic biomarkers are expected to decode the phenotype of gastric cancer (GC) and lead to high-performance blood tests towards GC diagnosis and prognosis. We attempted to develop diagnostic and prognostic models for GC based on plasma metabolic information.

DESIGN

We conducted a large-scale, multicentre study comprising 1944 participants from 7 centres in retrospective cohort and 264 participants in prospective cohort. Discovery and verification phases of diagnostic and prognostic models were conducted in retrospective cohort through machine learning and Cox regression of plasma metabolic fingerprints (PMFs) obtained by nanoparticle-enhanced laser desorption/ionisation-mass spectrometry (NPELDI-MS). Furthermore, the developed diagnostic model was validated in prospective cohort by both NPELDI-MS and ultra-performance liquid chromatography-MS (UPLC-MS).

RESULTS

We demonstrated the high throughput, desirable reproducibility and limited centre-specific effects of PMFs obtained through NPELDI-MS. In retrospective cohort, we achieved diagnostic performance with areas under curves (AUCs) of 0.862-0.988 in the discovery (n=1157 from 5 centres) and independent external verification dataset (n=787 from another 2 centres), through 5 different machine learning of PMFs, including neural network, ridge regression, lasso regression, support vector machine and random forest. Further, a metabolic panel consisting of 21 metabolites was constructed and identified for GC diagnosis with AUCs of 0.921-0.971 and 0.907-0.940 in the discovery and verification dataset, respectively. In the prospective study (n=264 from lead centre), both NPELDI-MS and UPLC-MS were applied to detect and validate the metabolic panel, and the diagnostic AUCs were 0.855-0.918 and 0.856-0.916, respectively. Moreover, we constructed a prognosis scoring system for GC in retrospective cohort, which can effectively predict the survival of GC patients.

CONCLUSION

We developed and validated diagnostic and prognostic models for GC, which also contribute to advanced metabolic analysis towards diseases, including but not limited to GC.

Cisplatin and Albumin-Based Gold-Cisplatin Nanoparticles Enhance Ablative Radiation Therapy-Induced Antitumor Immunity in Local and Distant Tumor Microenvironment

PURPOSE

Ablative radiation therapy (RT) is an important strategy to eliminate primary tumor and can potentially induce the abscopal effect. Human serum albumin nanoparticle (NP) was used for controlled release of cisplatin to decrease cisplatin's systemic toxicity, and gold (Au) was added to increase RT-induced immunogenic cell death and potentiate the abscopal antitumor immunity.

METHODS AND MATERIALS

The designed albumin-based cisplatin-conjugated AuNPs were administered concurrently with ablative RT. C57BL/6 mice implanted with syngeneic murine Lewis lung carcinoma or murine MB49 tumor models were treated with ablative RT (12 Gy per fraction for 2 fractions, total 24 Gy), cisplatin, or Au-cisplatin NPs.

RESULTS

Combining ablative RT with cisplatin or Au-cisplatin NPs both destroyed the primary tumor effectively and elicited immunogenic cell death accompanied by release of danger-associated molecular patterns. This enhanced recruitment of effector tumor-infiltrating immune cells, including natural killer T cells and CD8+ T cells, and elicited an increased percentage of professional antigen-presenting CD11c+ dendritic cells. Transient weight loss, accompanying hepatotoxicity, nephrotoxicity, and hematopoietic suppression, was observed as a systemic adverse event in the cisplatin but not the Au-cisplatin NPs group. Cisplatin and Au-cisplatin NPs both showed equivalent ability to reduce metastatic potential when combined with ablative RT, confirmed by suppressed unirradiated flank tumor growth and decreased metastatic lung tumor burden, which translated to improved survival. Mobilization and abundance of effector tumor-infiltrating immune cells including CD8+ T cells and dendritic cells were observed in the distant lung tumor microenvironment after ablative RT with cisplatin or Au-cisplatin NPs, demonstrating increased antitumor immunotherapeutic activity as an abscopal effect.

CONCLUSIONS

Compared with cisplatin, the albumin-based Au-cisplatin NPs exhibited equivalent but no superior antitumor immunotherapeutic activity while reducing systemic adverse events and can be safely administered concurrently with ablative RT. Alternative NP formulations may be designed to further improve anticancer outcomes.

Changes in Peripheral Immune Cells after the Third Dose of SARS-CoV-2 mRNA-BNT162b2 Vaccine and Disease Outcomes in Cancer Patients Receiving Immune Checkpoint Inhibitors: A Prospective Analysis of the Vax-on-Third-Profile Study

BACKGROUND

Anti-SARS-CoV-2 mRNA vaccines can deeply affect cell-mediated immune responses in immunocompromised recipients, including cancer patients receiving active treatments. The clinical implications of changes in peripheral blood lymphocyte subsets following the third dose of mRNA-BNT162b2 vaccination (tozinameran) in patients on immune checkpoint blockade are not fully understood. We conducted a prospective analysis of the Vax-On-Third-Profile study to evaluate the impact of circulating lymphocyte dynamics on disease outcomes in this subgroup of patients.

METHODS

Recipients of booster dosing who had received before vaccination at least one course of an anti-PD-1/PD-L1 treatment for an advanced solid tumor were eligible. Immunophenotyping of peripheral blood was performed before the third dose of tozinameran (timepoint-1) and four weeks later (timepoint-2) to quantify the absolute counts of lymphocyte subpopulations, including CD3⁺CD4⁺ T cells, CD3⁺CD8⁺ T cells, B cells, and NK cells. Logistic regression was used to analyze the relationship between lymphocyte subsets and durable clinical benefit (DCB). The log-rank test and Cox regression model were applied to evaluate the relationship between lymphocyte subpopulations and both vaccine-related time-to-treatment failure (V-TTF) and overall survival (OS).

RESULTS

We included a total of 56 patients with metastatic disease who were given a third dose of tozinameran between 23 September and 7 October 2021 (median age: 66 years; male: 71%). Most recipients had a diagnosis of lung cancer and were being treated with pembrolizumab or nivolumab. Compared to baseline, the third immunization resulted in an incremental change in the median counts of all lymphocyte subpopulations, which was statistically significant only for NK cells (p < 0.001). A significant correlation was found between NK cell counts and DCB at timepoint-2 (p < 0.001). Multivariate logistic regression analysis of DCB confirmed the predictive significance of high-level NK cell counts (p = 0.020). In multivariate Cox regression analysis, high-level NK cell counts independently predicted longer V-TTF [HR 0.34 (95% CI 0.14-0.80), p = 0.014] and OS [HR 0.36 (95% CI 0.15-0.89), p = 0.027].

CONCLUSIONS

Our data suggest expansion of NK cell counts as the most noteworthy change in circulating lymphocytes after the third dose of tozinameran in cancer patients receiving PD-1/PD-L1-targeted agents. This change correlated with enhanced therapeutic efficacy, improving the rate of disease control, and prolonging survival outcomes. Similar findings have not been previously reported, implying that they have proof-of-concept value and warrant further confirmation.

Potential non-invasive biomarkers in tumor immune checkpoint inhibitor therapy: response and prognosis prediction

Immune checkpoint inhibitors (ICIs) have dramatically enhanced the treatment outcomes for diverse malignancies. Yet, only 15-60% of patients respond significantly. Therefore, accurate responder identification and timely ICI administration are critical issues in tumor ICI therapy. Recent rapid developments at the intersection of oncology, immunology, biology, and computer science have provided an abundance of predictive biomarkers for ICI efficacy. These biomarkers can be invasive or non-invasive, depending on the specific sample collection method. Compared with invasive markers, a host of non-invasive markers have been confirmed to have superior availability and accuracy in ICI efficacy prediction. Considering the outstanding advantages of dynamic monitoring of the immunotherapy response and the potential for widespread clinical application, we review the recent research in this field with the aim of contributing to the identification of patients who may derive the greatest benefit from ICI therapy.

Higher aorta dose increased neutrophil-to-lymphocyte ratio resulting in poorer outcomes in stage II-III non-small cell lung cancer

BACKGROUND

This study focused on the relationship between the neutrophil-to-lymphocyte ratio (NLR) and the dose of organs at risk in patients with stage II-III non-small cell lung cancer (NSCLC) receiving intensity-modulated radiotherapy.

METHODS

The clinical characteristics and dosimetric parameters of 372 patients were collected retrospectively. A high NLR was defined as that ≥1.525. Survival analysis was conducted using the Kaplan-Meier and Cox regression analysis. Least absolute shrinkage and selection operator (LASSO) analysis was conducted to select appropriate dosimetric parameters. The risk factors of NLR were evaluated using univariate and multivariate logistic regression analyses.

RESULTS

Patients with a high NLR had poorer progression-free survival (PFS) (p = 0.011) and overall survival (OS) (p = 0.061). A low NLR (<1.525) predicted better PFS (hazard ratio [HR] 0.676, 95% confidence interval [CI]: 0.508-0.900, p = 0.007) and OS (HR 0.664, 95% CI: 0.490-0.901, p = 0.009). The aorta dose differed between the low and high NLR groups (all <0.1) in the univariate analysis. An aorta V10 was confirmed as a significant risk factor for a high NLR (odds ratio [OR] 1.029, 95% CI: 1.011-1.048, p = 0.002). Receiving chemotherapy before (OR 0.428, 95% CI: 0.225-0.813, p = 0.010) and during (OR 0.491, 95% CI: 0.296-0.815, p = 0.006) radiotherapy were predictive factors of a low NLR.

CONCLUSION

The aorta dose was significantly associated with a high NLR. Patients with stage II-III NSCLC with a high NLR had poorer prognosis. Receiving chemotherapy before and/or during radiotherapy predicted a low NLR.

Dynamic peripheral blood immune cell markers for predicting the response of patients with metastatic cancer to immune checkpoint inhibitors

PURPOSE

Immune checkpoint inhibitors (ICIs) have shown durable responses in various malignancies. However, the response to ICI therapy is unpredictable, and investigation of predictive biomarkers needs to be improved.

EXPERIMENTAL DESIGN

In total, 120 patients receiving ICI therapy and 40 patients receiving non-ICI therapy were enrolled. Peripheral blood immune cell markers (PBIMs), as liquid biopsy biomarkers, were analyzed by flow cytometry before ICI therapy, and before the first evaluation. In the ICI cohort, patients were randomly divided into training (n = 91) and validation (n = 29) cohorts. Machine learning algorithms were applied to construct the prognostic and predictive immune-related models.

RESULTS

Using the training cohort, a peripheral blood immune cell-based signature (BICS) based on four hub PBIMs was developed. In both the training and the validation cohorts, and the whole cohort, the BICS achieved a high accuracy for predicting overall survival (OS) benefit. The high-BICS group had significantly shorter progression-free survival and OS than the low-BICS group. The BICS demonstrated the predictive ability of patients to achieve durable clinical outcomes. By integrating these PBIMs, we further constructed and validated the support vector machine-recursive and feature elimination classifier model, which robustly predicts patients who will achieve optimal clinical benefit.

CONCLUSIONS

Dynamic PBIM-based monitoring as a noninvasive, cost-effective, highly specific and sensitive biomarker has broad potential for prognostic and predictive utility in patients receiving ICI therapy.

Artificial intelligence-based immunoprofiling serves as a potentially predictive biomarker of nivolumab treatment for advanced hepatocellular carcinoma

Immune checkpoint inhibitors (ICI) have been applied in treating advanced hepatocellular carcinoma (aHCC) patients, but few patients exhibit stable and lasting responses. Moreover, identifying aHCC patients suitable for ICI treatment is still challenged. This study aimed to evaluate whether dissecting peripheral immune cell subsets by Mann-Whitney U test and artificial intelligence (AI) algorithms could serve as predictive biomarkers of nivolumab treatment for aHCC. Disease control group carried significantly increased percentages of PD-L1+ monocytes, PD-L1+ CD8 T cells, PD-L1+ CD8 NKT cells, and decreased percentages of PD-L1+ CD8 NKT cells via Mann-Whitney U test. By recursive feature elimination method, five featured subsets (CD4 NKTreg, PD-1+ CD8 T cells, PD-1+ CD8 NKT cells, PD-L1+ CD8 T cells and PD-L1+ monocytes) were selected for AI training. The featured subsets were highly overlapping with ones identified via Mann-Whitney U test. Trained AI algorithms committed valuable AUC from 0.8417 to 0.875 to significantly separate disease control group from disease progression group, and SHAP value ranking also revealed PD-L1+ monocytes and PD-L1+ CD8 T cells exclusively and significantly contributed to this discrimination. In summary, the current study demonstrated that integrally analyzing immune cell profiling with AI algorithms could serve as predictive biomarkers of ICI treatment.

Definition of a new blood cell count score for early survival prediction for non-small cell lung cancer patients treated with atezolizumab: Integrated analysis of four multicenter clinical trials

Importance

Blood cell count test (BCT) is a robust method that provides direct quantification of various types of immune cells to reveal the immune landscape to predict atezolizumab treatment outcomes for clinicians to decide the next phase of treatment.

Objective

This study aims to define a new BCTscore model to predict atezolizumab treatment benefits in non-small lung cell cancer (NSCLC) patients.

Design, Setting, and Participants

This study analyzed four international, multicenter clinical trials (OAK, BIRCH, POPLAR, and FIR trials) to conduct post-hoc analyses of NSCLC patients undergoing atezolizumab (anti–PD-L1) single-agent treatment (n = 1,479) or docetaxel single-agent treatment (n = 707). BCT was conducted at three time points: pre-treatment (T1), the first day of treatment cycle 3 (T2), and first day of treatment cycle 5 (T3). Univariate and multivariate Cox regression analyses were conducted to identify early BCT biomarkers to predict atezolizumab treatment outcomes in NSCLC patients.

Main Outcomes and Measures

Overall survival (OS) was used as the primary end point, whereas progression-free survival (PFS) according to Response Evaluation Criteria in Solid Tumors (RECIST), clinical benefit (CB), and objective response rate (ORR) were used as secondary end points.

Results

The BCT biomarkers of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) at time point T3 and neutrophil-to-monocyte ratio (NMR) at time point T2 with absolute cutoff values of NLR_T3 = 5, PLR_T3 = 180, and NMR_T2 = 6 were identified as strong predictive biomarkers for atezolizumab (Ate)–treated NSCLC patients in comparison with docetaxel (Dtx)–treated patients regarding OS (BCTscore low risk: HR _Ate vs_{. Dtx} = 1.54 (95% CI: 1.04–2.27), P = 0.031; high risk: HR _Ate vs_{. Dtx} = 0.84 (95% CI: 0.62–1.12), P = 0.235). The identified BCTscore model showed better OS AUC in the OAK (AUC_12month = 0.696), BIRCH (AUC_12month = 0.672) and POPLAR+FIR studies (AUC_12month = 0.727) than that of each of the three single BCT biomarkers.

Conclusion and Relevance

The BCTscore model is a valid predictive and prognostic biomarker for early survival prediction in atezolizumab-treated NSCLC patients.

The paradox of radiation and T cells in tumors

In this review we consider what appears to be a paradox in immunotherapies based around radiation therapy. The paradox is based on three main points. 1. That T cells are needed for radiation's efficacy; 2. That tumor-specific T cells are enriched in the field of treatment; and 3. That radiation kills T cells in the treatment field. We discuss evidence of the effect of radiation on T cells in the field given their ongoing movement in and out of tissues and the tumor, and how the movement of T cells impacts the treated primary tumor and untreated distant metastases. Given this evidence, we revisit the paradox to understand how the extraordinary efficacy of radiation and immunity in preclinical models is dependent on this radiation sensitive cell.

LOX and Its Methylation Impact Prognosis of Diseases and Correlate with TAM Infiltration in ESCA

Background

ESCA is one of the digestive tract tumors with a high fatality. It is implicated in an intricate gene regulation process, but the pathogenesis remains ambiguous.

Methods

The study used the packages of Limma from R software to analyze DEGs of ESCA in the GEO database and TCGA database. We employed the DAVID website for enrichment analysis, and the string database constructed the PPI network. Hub genes were identified from ESCA DEGs with Cytoscape MCODE. We evaluated the clinical relevance of LOX expression and its DNA methylation in the cBioPortal database and explored the roles of LOX in ESCA immunity, especially immune cell infiltration levels and immune checkpoint expression, by immunedeconv package of R software.

Conclusions

The overexpression of LOX in ESCA is regulated by DNA hypomethylation; LOX overexpression or LOX hypomethylation can predict a worse prognosis in patients with ESCA. Besides, LOX may be involved in TIME regulation, promoting the infiltration levels and function of TAM. Hence, high LOX expression affected by DNA hypomethylation has an essential role in patients with ESCA, which may become an effective prognostic marker and therapeutic target.

Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer

Lung cancer accounts for the main proportion of malignancy-related deaths and most patients are diagnosed at an advanced stage. Immunotherapy and targeted therapy have great advances in application in clinics to treat lung cancer patients, yet the efficacy is unstable. The response rate of these therapies varies among patients. Some biomarkers have been proposed to predict the outcomes of immunotherapy and targeted therapy, including programmed cell death-ligand 1 (PD-L1) expression and oncogene mutations. Nevertheless, the detection tests are invasive, time-consuming, and have high demands on tumor tissue. The predictive performance of conventional biomarkers is also unsatisfactory. Therefore, novel biomarkers are needed to effectively predict the outcomes of immunotherapy and targeted therapy. The application of artificial intelligence (AI) can be a possible solution, as it has several advantages. AI can help identify features that are unable to be used by humans and perform repetitive tasks. By combining AI methods with radiomics, pathology, genomics, transcriptomics, proteomics, and clinical data, the integrated model has shown predictive value in immunotherapy and targeted therapy, which significantly improves the precision treatment of lung cancer patients. Herein, we reviewed the application of AI in predicting the outcomes of immunotherapy and targeted therapy in lung cancer patients, and discussed the challenges and future directions in this field.

Ferroptosis-related lncRNA signature predicts prognosis and immunotherapy efficacy in cutaneous melanoma

Purpose

Ferroptosis-related lncRNAs are promising biomarkers for predicting the prognosis of many cancers. However, a ferroptosis-related signature to predict the prognosis of cutaneous melanoma (CM) has not been identified. The purpose of this study was to construct a ferroptosis-related lncRNA signature to predict prognosis and immunotherapy efficacy in CM.

Methods

Ferroptosis-related differentially expressed genes (FDEGs) and lncRNAs (FDELs) were identified using TCGA, GTEx, and FerrDb datasets. We performed Cox and LASSO regressions to identify key FDELs, and constructed a risk score to stratify patients into high- and low-risk groups. The lncRNA signature was evaluated using the areas under the receiver operating characteristic curves (AUCs) and Kaplan-Meier analyses in the training, testing, and entire cohorts. Multivariate Cox regression analyses including the lncRNA signature and common clinicopathological characteristics were performed to identify independent predictors of overall survival (OS). A nomogram was developed for clinical use. We performed gene set enrichment analyses (GSEA) to identify significantly enriched pathways. Differences in the tumor microenvironment (TME) between the 2 groups were assessed using 7 algorithms. To predict the efficacy of immune checkpoint inhibitors (ICI), we analyzed the association between PD1 and CTLA4 expression and the risk score. Finally, differences in Tumor Mutational Burden (TMB) and molecular drugs Sensitivity between the 2 groups were performed.

Results

We identified 5 lncRNAs (AATBC, AC145423.2, LINC01871, AC125807.2, and AC245041.1) to construct the risk score. The AUC of the lncRNA signature was 0.743 in the training cohort and was validated in the testing and entire cohorts. Kaplan-Meier analyses revealed that the high-risk group had poorer prognosis. Multivariate Cox regression showed that the lncRNA signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The 1-, 3-, and 5-year survival probabilities for CM patients were 92.7%, 57.2%, and 40.2% with an AUC of 0.804, indicating a good accuracy and reliability of the nomogram. GSEA showed that the high-risk group had lower ferroptosis and immune response. TME analyses confirmed that the high-risk group had lower immune cell infiltration (e.g., CD8+ T cells, CD4+ memory-activated T cells, and M1 macrophages) and lower immune functions (e.g., immune checkpoint activation). Low-risk patients whose disease expressed PD1 or CTLA4 were likely to respond better to ICIs. The analysis demonstrated that the TMB had significantly difference between low- and high- risk groups. Chemotherapy drugs, such as sorafenib, Imatinib, ABT.888 (Veliparib), Docetaxel, and Paclitaxel showed Significant differences in the estimated IC50 between the two risk groups.

Conclusion

Our novel ferroptosis-related lncRNA signature was able to accurately predict the prognosis and ICI outcomes of CM patients. These ferroptosis-related lncRNAs might be potential biomarkers and therapeutic targets for CM.

Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?

Strong rationale and a growing number of preclinical and clinical studies support combining radiotherapy and immunotherapy to improve patient outcomes. However, several critical questions remain, such as the identification of patients who will benefit from immunotherapy and the identification of the best modalities of treatment to optimize patient response. Imaging biomarkers and radiomics have recently emerged as promising tools for the non-invasive assessment of the whole disease of the patient, allowing comprehensive analysis of the tumor microenvironment, the spatial heterogeneity of the disease and its temporal changes. This review presents the potential applications of medical imaging and the challenges to address, in order to help clinicians choose the optimal modalities of both radiotherapy and immunotherapy, to predict patient's outcomes and to assess response to these promising combinations.

Detailed in vitro analyses of the impact of multimodal cancer therapy with hyperthermia and radiotherapy on the immune phenotype of human glioblastoma cells

PURPOSE

Improvements of heat-delivery systems have led to hyperthermia (HT) being increasingly recognized as an adjunct treatment modality also for brain tumors. But how HT affects the immune phenotype of glioblastoma cells is only scarcely known.

MATERIALS AND METHODS

We therefore investigated the effect of in vitro HT, radiotherapy (RT), and the combination of both (RHT) on cell death modalities, immune checkpoint molecule (ICM) expression and release of the danger signal HSP70 of two human glioblastoma cell lines (U87 and U251) by using multicolor flow cytometry and ELISA. Hyperthermia was performed once or twice for 60-minute sessions reaching temperatures of 39 °C, 41 °C, and 44 °C, respectively. RT was administered with 5 x 2 Gy.

RESULTS

A hyperthermia chamber for cell culture t-flasks regulating the temperature via a contact sensor was developed. While the glioblastoma cells were rather radioresistant, particularly in U251 cells, the combination of RT with HT significantly increased the percentage of apoptotic and necrotic cells for all temperatures examined and for both, single and double HT application. In line with that, an increased release of HSP 70 was seen only in U251 cells, mainly following treatment with HT at temperatures of 44 °C alone or in combination with RT. In contrast, immune suppressive (PD-L1, PD-L2, HVEM) and immune stimulatory (ICOS-L, CD137-L and Ox40-L) ICMs were significantly increased mostly on U87 cells, and particularly after RHT with 41 °C.

CONCLUSIONS

Individual assessment of the glioblastoma immune cell phenotype with regard to the planned treatment is mandatory to optimize multimodal radio-immunotherapy protocols including HT.

F18-FDG PET/CT imaging early predicts pathologic complete response to induction chemoimmunotherapy of locally advanced head and neck cancer: preliminary single-center analysis of the checkrad-cd8 trial

Aim

In the CheckRad-CD8 trial patients with locally advanced head and neck squamous cell cancer are treated with a single cycle of induction chemo-immunotherapy (ICIT). Patients with pathological complete response (pCR) in the re-biopsy enter radioimmunotherapy. Our goal was to study the value of F-18-FDG PET/CT in the prediction of pCR after induction therapy.

Methods

Patients treated within the CheckRad-CD8 trial that additionally received FDG- PET/CT imaging at the following two time points were included: 3–14 days before (pre-ICIT) and 21–28 days after (post-ICIT) receiving ICIT. Tracer uptake in primary tumors (PT) and suspicious cervical lymph nodes (LN +) was measured using different quantitative parameters on EANM Research Ltd (EARL) accredited PET reconstructions. In addition, mean FDG uptake levels in lymphatic and hematopoietic organs were examined. Percent decrease (Δ) in FDG uptake was calculated for all parameters. Biopsy of the PT post-ICIT acquired after FDG-PET/CT served as reference. The cohort was divided in patients with pCR and residual tumor (ReTu).

Results

Thirty-one patients were included. In ROC analysis, ΔSUVmax PT performed best (AUC = 0.89) in predicting pCR (n = 17), with a decline of at least 60% (sensitivity, 0.77; specificity, 0.93). Residual SUVmax PT post-ICIT performed best in predicting ReTu (n = 14), at a cutpoint of 6.0 (AUC = 0.91; sensitivity, 0.86; specificity, 0.88). Combining two quantitative parameters (ΔSUVmax ≥ 50% and SUVmax PT post-ICIT ≤ 6.0) conferred a sensitivity of 0.81 and a specificity of 0.93 for determining pCR. Background activity in lymphatic organs or uptake in suspected cervical lymph node metastases lacked significant predictive value.

Conclusion

FDG-PET/CT can identify patients with pCR after ICIT via residual FDG uptake levels in primary tumors and the related changes compared to baseline. FDG-uptake in LN + had no predictive value.

Trial registry

ClinicalTrials.gov identifier: NCT03426657.

The Effect of Hyperthermia and Radiotherapy Sequence on Cancer Cell Death and the Immune Phenotype of Breast Cancer Cells

Simple Summary

Hyperthermia (HT) is a cancer treatment which locally heats the tumor to supraphysiological temperature, and it is an effective sensitizer for radiotherapy (RT) and chemotherapy. HT is further capable of modulating the immune system. Thus, a better understanding of its effect on the immune phenotype of tumor cells, and particularly when combined with RT, would help to optimize combined anti-cancer treatments. Since in clinics, no standards about the sequence of RT and HT exist, we analyzed whether this differently affects the cell death and immunological phenotype of human breast cancer cells. We revealed that the sequence of HT and RT does not strongly matter from the immunological point of view, however, when HT is combined with RT, it changes the immunophenotype of breast cancer cells and also upregulates immune suppressive immune checkpoint molecules. Thus, the additional application of immune checkpoint inhibitors with RT and HT should be beneficial in clinics.

Abstract

Hyperthermia (HT) is an accepted treatment for recurrent breast cancer which locally heats the tumor to 39–44 °C, and it is a very potent sensitizer for radiotherapy (RT) and chemotherapy. However, currently little is known about how HT with a distinct temperature, and particularly, how the sequence of HT and RT changes the immune phenotype of breast cancer cells. Therefore, human MDA-MB-231 and MCF-7 breast cancer cells were treated with HT of different temperatures (39, 41 and 44 °C), alone and in combination with RT (2 × 5 Gy) in different sequences, with either RT or HT first, followed by the other. Tumor cell death forms and the expression of immune checkpoint molecules (ICMs) were analyzed by multicolor flow cytometry. Human monocyte-derived dendritic cells (moDCs) were differentiated and co-cultured with the treated cancer cells. In both cell lines, RT was the main stressor for cell death induction, with apoptosis being the prominent cell death form in MCF-7 cells and both apoptosis and necrosis in MDA-MB-231 cells. Here, the sequence of the combined treatments, either RT or HT, did not have a significant impact on the final outcome. The expression of all of the three examined immune suppressive ICMs, namely PD-L1, PD-L2 and HVEM, was significantly increased on MCF-7 cells 120 h after the treatment of RT with HT of any temperature. Of special interest for MDA-MB-231 cells is that only combinations of RT with HT of both 41 and 44 °C induced a significantly increased expression of PD-L2 at all examined time points (24, 48, 72, and 120 h). Generally, high dynamics of ICM expression can be observed after combined RT and HT treatments. There was no significant difference between the different sequences of treatments (either HT + RT or RT + HT) in case of the upregulation of ICMs. Furthermore, the co-culture of moDCs with tumor cells of any treatment had no impact on the expression of activation markers. We conclude that the sequence of HT and RT does not strongly affect the immune phenotype of breast cancer cells. However, when HT is combined with RT, it results in an increased expression of distinct immune suppressive ICMs that should be considered by including immune checkpoint inhibitors in multimodal tumor treatments with RT and HT. Further, combined RT and HT affects the immune system in the effector phase rather than in the priming phase.

Evaluation of radio-immunotherapy sequence on immunological responses and clinical outcomes in patients with melanoma brain metastases (ELEKTRA)

In patients with melanoma brain metastases (MBM), a combination of radiotherapy (RT) with immune checkpoint inhibitors (ICI) is routinely used. However, the best sequence of radio-immunotherapy (RIT) remains unclear. In an exploratory phase 2 trial, MBM patients received RT (stereotactic or whole-brain radiotherapy depending on the number of MBM) combined with ipilimumab (ipi) ± nivolumab (nivo) in different sequencing (Rad-ICI or ICI-Rad). Comparators arms included patients treated with ipi-free systemic treatment or without RT (in MBM-free patients). The primary endpoints were radiological and immunological responses in the peripheral blood. Secondary endpoints were progression-free survival (PFS) and overall survival (OS). Of 106 screened, 92 patients were included in the study. Multivariate analysis revealed an advantage for patients starting with RT (Rad-ICI) for overall response rate (RR: p = .007; HR: 7.88 (95%CI: 1.76–35.27)) and disease control rate (DCR: p = .036; HR: 6.26 (95%CI: 1.13–34.71)) with a trend for a better PFS (p = .162; HR: 1.64 (95%CI: 0.8–3.3)). After RT plus two cycles of ipi-based ICI in both RIT sequences, increased frequencies of activated CD4, CD8 T cells and an increase in melanoma-specific T cell responses were observed in the peripheral blood. Lasso regression analysis revealed a significant clinical benefit for patients treated with Rad-ICI sequence and immunological features, including high frequencies of memory T cells and activated CD8 T cells in the blood. This study supports increasing evidence that sequencing RT followed by ICI treatment may have better effects on the immunological responses and clinical outcomes in MBM patients.

Basal and one-month differed neutrophil, lymphocyte and platelet values and their ratios strongly predict the efficacy of checkpoint inhibitors immunotherapy in patients with advanced BRAF wild-type melanoma

Background

To evaluate the capability of basal and one-month differed white blood cells (WBC), neutrophil, lymphocyte and platelet values and their ratios (neutrophils-to-lymphocytes ratio, NLR, and platelets-to-lymphocytes ratio, PLR) in predicting the response to immune checkpoint inhibitors (ICI) in metastatic melanoma (MM).

Methods

We performed a retrospective study of 272 BRAF wild-type MM patients treated with first line ICI. Bivariable analysis was used to correlate patient/tumor characteristics with clinical outcomes. Variations between time 1 and time 0 (Δ) of blood parameters were also calculated and dichotomized using cut-off values assessed by ROC curve.

Results

At baseline, higher neutrophils and NLR negatively correlated with PFS, OS and disease control rate (DCR). Higher PLR was also associated with worse OS. In multivariable analysis, neutrophils (p = 0.003), WBC (p = 0.069) and LDH (p = 0.07) maintained their impact on PFS, while OS was affected by LDH (p < 0.001), neutrophils (p < 0.001) and PLR (p = 0.022), while DCR by LDH (p = 0.03) and neutrophils (p = 0.004). In the longitudinal analysis, PFS negatively correlated with higher Δplatelets (p = 0.039), ΔWBC (p < 0.001), and Δneutrophils (p = 0.020), and with lower Δlymphocytes (p < 0.001). Moreover, higher ΔNLR and ΔPLR identified patients with worse PFS, OS and DCR. In the multivariable model, only ΔNLR influenced PFS (p = 0.004), while OS resulted affected by higher ΔWBC (p < 0.001) and lower Δlymphocytes (p = 0.038). Higher ΔWBC also affected the DCR (p = 0.003). When clustering patients in 4 categories using basal LDH and ΔNLR, normal LDH/lower ΔNLR showed a higher PFS than high LDH/higher ΔNLR (20 vs 5 months). Moreover, normal LDH/higher Δlymphocytes had a higher OS than high LDH/lower Δlymphocytes (50 vs. 10 months).

Conclusions

Baseline and early variations of blood cells, together with basal LDH, strongly predict the efficacy of ICI in MM. Our findings propose simple, inexpensive biomarkers for a better selection of patient treatments. Prospective multicenter studies are warranted to confirm these data.

Induction chemoimmunotherapy followed by CD8+ immune cell-based patient selection for chemotherapy-free radioimmunotherapy in locally advanced head and neck cancer

PURPOSE

The first aim of the trial is to study feasibility of combined programmed death protein ligand 1/cytotoxic T-lymphocyte-associated protein 4 inhibition concomitant to radiotherapy. In addition, efficacy of the entire treatment scheme consisting of induction chemoimmunotherapy followed by chemotherapy-free radioimmunotherapy (RIT) after intratumoral CD8 +immune cell-based patient selection will be analyzed.

METHODS

Patients with stage III-IVB head and neck squamous cell carcinoma were eligible for this multicenter phase II trial. Treatment consisted of a single cycle of cisplatin 30 mg/m² days 1-3, docetaxel 75 mg/m² day 1, durvalumab 1500 mg fix dose day 5 and tremelimumab 75 mg fix dose day 5. Patients with increased intratumoral CD8 +immune cell density or pathological complete response (pCR) in the rebiopsy entered RIT up to a total dose of 70 Gy. Patients received further three cycles of durvalumab/tremelimumab followed by eight cycles of durvalumab mono (every 4 weeks). The intended treatment for patients not meeting these criteria was standard radiochemotherapy outside the trial. Primary endpoint was a feasibility rate of patients entering RIT to receive treatment until at least cycle 6 of immunotherapy of ≥80%.

RESULTS

Between September 2018 and May 2020, 80 patients were enrolled (one excluded). Out of these, 23 patients had human papilloma virus (HPV)-positive oropharyngeal cancer. Median follow-up was 17.2 months. After induction chemoimmunotherapy 41 patients had pCR and 31 had increased intratumoral CD8 +immune cells. Of 60 patients entering RIT (primary endpoint cohort), 10 experienced imiting toxic (mainly hepatitis) and four discontinued for other reasons, resulting in a feasibility rate of 82%. The RIT cohort (n=60) had a progression-free survival (PFS) rate at one and 2 years of 78% and 72%, respectively, and an overall survival rate at one and 2 years of 90% and 84%, respectively. Patients with HPV-positive oropharyngeal cancers had greater benefit from RIT with a 2-year PFS rate of 94% compared with 64% for HPV-negative oropharyngeal cancers and other locations. In the entire study cohort (n=79) the 2-year PFS rate was 68% (91% for HPV-positive oropharynx vs 59% for others). Toxicity grade 3-4 mainly consisted of dysphagia (53%), leukopenia (52%) and infections (32%).

CONCLUSIONS

The trial met the primary endpoint feasibility of RIT. Induction chemo-immunotherapy followed by chemotherapy-free RIT after intratumoral CD8 +immune cell-based patient selection has promising PFS.

TRIAL REGISTRATION NUMBER

The trial was registered with ClinicalTrials.gov (identifier: NCT03426657). The trial was conducted as investigator-sponsored trial (IST).

A Support Vector Machine Based on Liquid Immune Profiling Predicts Major Pathological Response to Chemotherapy Plus Anti-PD-1/PD-L1 as a Neoadjuvant Treatment for Patients With Resectable Non-Small Cell Lung Cancer

The biomarkers for the pathological response of neoadjuvant chemotherapy plus anti-programmed cell death protein-1/programmed cell death-ligand 1 (PD-1/PD-L1) (CAPD) are unclear in non-small cell lung cancer (NSCLC). Two hundred and eleven patients with stage Ib-IIIa NSCLC undergoing CAPD prior to surgical resection were enrolled, and 11 immune cell subsets in peripheral blood were prospectively analyzed using multicolor flow cytometry. Immune cell subtypes were selected by recursive feature elimination and least absolute shrinkage and selection operator methods. The support vector machine (SVM) was used to build a model. Multivariate analysis for major pathological response (MPR) was also performed. Finally, five immune cell subtypes were identified and an SVM based on liquid immune profiling (LIP-SVM) was developed. The LIP-SVM model achieved high accuracies in discovery and validation sets (AUC = 0.886, 95% CI: 0.823–0.949, P < 0.001; AUC = 0.874, 95% CI: 0.791–0.958, P < 0.001, respectively). Multivariate analysis revealed that age, radiological response, and LIP-SVM were independent factors for MPR in the two sets (each P < 0.05). The integration of LIP-SVM, clinical factors, and radiological response showed significantly high accuracies for predicting MPR in discovery and validation sets (AUC = 0.951, 95% CI: 0.916–0.986, P < 0.001; AUC = 0.943, 95% CI: 0.912–0.993, P < 0.001, respectively). Based on immune cell profiling of peripheral blood, our study developed a predictive model for the MPR of patients with NSCLC undergoing CAPD treatment that can potentially guide clinical therapy.

Predictive Value of Multiparametric MRI for Response to Single-Cycle Induction Chemo-Immunotherapy in Locally Advanced Head and Neck Squamous Cell Carcinoma

Objectives

To assess the predictive value of multiparametric MRI for treatment response evaluation of induction chemo-immunotherapy in locally advanced head and neck squamous cell carcinoma.

Methods

Twenty-two patients with locally advanced, histologically confirmed head and neck squamous cell carcinoma who were enrolled in the prospective multicenter phase II CheckRad-CD8 trial were included in the current analysis. In this unplanned secondary single-center analysis, all patients who received contrast-enhanced MRI at baseline and in week 4 after single-cycle induction therapy with cisplatin/docetaxel combined with the immune checkpoint inhibitors tremelimumab and durvalumab were included. In week 4, endoscopy with representative re-biopsy was performed to assess tumor response. All lesions were segmented in the baseline and restaging multiparametric MRI, including the primary tumor and lymph node metastases. The volume of interest of the respective lesions was volumetrically measured, and time-resolved mean intensities of the golden-angle radial sparse parallel-volume-interpolated gradient-echo perfusion (GRASP-VIBE) sequence were extracted. Additional quantitative parameters including the T1 ratio, short-TI inversion recovery ratio, apparent diffusion coefficient, and dynamic contrast-enhanced (DCE) values were measured. A model based on parallel random forests incorporating the MRI parameters from the baseline MRI was used to predict tumor response to therapy. Receiver operating characteristic (ROC) curves were used to evaluate the prognostic performance.

Results

Fifteen patients (68.2%) showed pathologic complete response in the re-biopsy, while seven patients had a residual tumor (31.8%). In all patients, the MRI-based primary tumor volume was significantly lower after treatment. The baseline DCE parameters of time to peak and wash-out were significantly different between the pathologic complete response group and the residual tumor group (p < 0.05). The developed model, based on parallel random forests and DCE parameters, was able to predict therapy response with a sensitivity of 78.7% (95% CI 71.24–84.93) and a specificity of 78.6% (95% CI 67.13–87.48). The model had an area under the ROC curve of 0.866 (95% CI 0.819–0.914).

Conclusions

DCE parameters indicated treatment response at follow-up, and a random forest machine learning algorithm based on DCE parameters was able to predict treatment response to induction chemo-immunotherapy.

Low Dose Radiation Therapy Induces Long-Lasting Reduction of Pain and Immune Modulations in the Peripheral Blood - Interim Analysis of the IMMO-LDRT01 Trial

The treatment of chronic inflammatory and degenerative diseases by low dose radiation therapy (LDRT) is promising especially for patients who were refractory for classical therapies. LDRT aims to reduce pain of patients and to increase their mobility. Although LDRT has been applied since the late 19th century, the immunological mechanisms remain elusive. Within the prospective IMMO-LDRT01 trial (NCT02653079) the effects of LDRT on the peripheral blood immune status, as well as on pain and life quality of patients have been analyzed. Blood is taken before and after every serial irradiation with a single dose per fraction of 0.5Gy, as well as during follow-up appointments in order to determine a detailed longitudinal immune status by multicolor flow cytometry. Here, we report the results of an interim analysis of 125 patients, representing half the number of patients to be recruited. LDRT significantly improved patients’ pain levels and induced distinct systemic immune modulations. While the total number of leukocytes remained unchanged in the peripheral blood, LDRT induced a slight reduction of eosinophils, basophils and plasmacytoid dendritic cells and an increase of B cells. Furthermore, activated immune cells were decreased following LDRT. Especially cells of the monocytic lineage correlated to LDRT-induced improvements of clinical symptoms, qualifying these immune cells as predictive biomarkers for the therapeutic success. We conclude that LDRT improves pain of the patients by inducing systemic immune modulations and that immune biomarkers could be defined for prediction by improved patient stratification in the future.

Hypofractionated Radiotherapy Upregulates Several Immune Checkpoint Molecules in Head and Neck Squamous Cell Carcinoma Cells Independently of the HPV Status While ICOS-L Is Upregulated Only on HPV-Positive Cells

While the treatment of squamous cell carcinoma of the head and neck (HNSCC) with radiotherapy (RT) is complemented more and more by immunotherapy in clinical trials, little is known about the impact of the human papillomavirus (HPV) status or the applied RT scheme on the immune phenotype of the tumor cells. Therefore, we aimed to examine the impact of the HPV status of four human HNSCC cell lines on cell death and the expression of immune checkpoint molecules (ICMs) after RT with either hypofractionation irradiation (5x3.0Gy) or a high single dose (1x19.3Gy) via multicolor flow cytometry and quantitative PCR at an early time point after therapy. In our study, 5x3.0Gy RT induced high numbers of early and late apoptotic cells independent of the HPV status, but necrosis was only increased in the HPV-positive UM-Scc-47 cells. Generally, the immune stimulatory ICMs (CD70, CD137-L, ICOS-L) were less affected by RT compared to the immune suppressive ones (PD-L1, PD-L2, and the herpesvirus entry mediator (HVEM)). A significant higher surface expression of the analyzed ICMs was found after hypofractionated RT compared to a single high dose; however, regardless of the HPV status, with the exception of ICOS-L. Here, HPV-positive HNSCC tumor cells showed a stronger response to 5x3.0Gy than HPV-negative ones. On the RNA level, only minor alterations of ICMs were observed following RT, with the exception of the HPV negative cell line CAL33 treated with 5x3.0Gy, where PD-L2, HVEM and CD70 were significantly increased. We conclude that the HPV status may not distinctly predict immunological responses following RT, and thus cannot be used as a single predictive marker for therapy responses in HNSCC. In contrast, the patient-specific individual expression of ICMs following RT is preferable for the targeted patient selection for immune therapy directed against distinct ICM.

Abscopal Effects of Local Radiotherapy Are Dependent on Tumor Immunogenicity

Although abscopal tumor regression remains a rare phenomenon, interest in exploiting how radiation stimulates the immune system to induce systemic abscopal response is increasing. Here, we tested the hypothesis that tumor immunogenicity determined the ability of radiotherapy to induce abscopal effects. We established highly (MC-38 and E.G7-OVA) or poorly (LL/2 and B16-F10) immunogenic tumor models in this study and treated them with sham radiation, a single dose of 15 Gy, or three fractions of 5 Gy on three consecutive days. Alterations in the tumor microenvironment after radiation were examined by flow cytometry and RNA sequencing. Our results demonstrated the positive correlation between tumor immunogenicity and the abscopal effect of radiotherapy. The single dose of 15 Gy radiation was an effective regimen for inducing abscopal effects in highly immunogenic tumors. Local radiation reshaped the tumor microenvironment of irradiated and non-irradiated distant tumors by increasing CD8 T-cell infiltration and reducing suppressive immune cell accumulation. However, radiation alone was insufficient to elicit abscopal effects in poorly immunogenic tumors. No significant alterations were detected in the non-irradiated distant tumor microenvironment after radiation of poorly immunogenic tumors. In addition, tumor immunogenic subtypes were associated with the radiological response and clinical outcome of patients receiving radiotherapy. These findings indicated that tumor immunogenicity was the dominant characteristic that could predict the abscopal effect of radiotherapy. Our study provides an in-depth understanding of the immunological mechanisms involved in abscopal effects and highlights the impact of tumor heterogeneity on the therapeutic efficacy of radiotherapy and their combination with immunotherapy in clinical trials.

Radiotherapy and the immune system: More than just immune suppression

Radiotherapy (RT) is still one of the standard cancer therapies, with up to two third of all cancer patients with solid tumors being irradiated in the course of their disease. The aim of using ionizing radiation in fractionated treatment schedules was always to achieve local tumor control by inducing DNA damage which can be repaired by surrounding normal tissue but leads to cell death in tumor cells. Meanwhile, it is known that RT also has immunological effects reshaping the tumor microenvironment. Nevertheless, RT alone often fails to elicit potent antitumor immune responses as these effects can be immunostimulatory as well as immunosuppressive. Here, we discuss how immunotherapies can be exploited in combined therapies to boost RT-induced antitumor immune responses or to counteract preexisting and RT-mediated immunosuppression to improve local and systemic tumor control. Furthermore, we highlight some parameters of radioimmunotherapies (RITs) which are under investigation for potential optimizations and how RIT approaches are tested in first phases II and III trials. Finally, we discuss how RT might affect normal and cancer stem cells.

Association of Dynamic Changes in Peripheral Blood Indexes With Response to PD-1 Inhibitor-Based Combination Therapy and Survival Among Patients With Advanced Non-Small Cell Lung Cancer

Background

PD-1 inhibitors have been routinely used in the treatment of advanced non-small cell lung cancer (NSCLC), and have demonstrated to significantly improve survivorship when combining with other conventional therapies, such as chemotherapy and anti-angiogenesis therapy. PD-L1 is the most commonly used biomarker to select benefiting groups, while not all patients with high PD-L1 expression benefit from immunotherapy. Therefore, identifying other prognostic and predictive biomarkers, including peripheral blood indexes, is essential.

Methods

We retrospectively collected medical records and hematological data of 151 patients with advanced NSCLC treated with PD-1 inhibitor-based combination therapy in our hospital. The peripheral blood indexes of interest were NLR, PLR, PAR, Hb, LDH, CEA, and NSE. The association between peripheral blood indexes and treatment responses or survival outcomes was examined by multivariable logistic regression and Cox regression, respectively.

Results

The decreased CEA at week 6 (OR = 4.209, 95%CI: 1.287-13.758) or 12 (OR = 7.267, 95%CI: 1.508-35.006) post-treatment was related to a higher disease control rate. The decrease or NLR at week 6 (OR = 3.081, 95%CI: 1.464-6.483) or 12 (OR = 3.304, 95%CI: 1.560-7.001) post-treatment, or CEA at week 12 post-treatment (OR = 2.469, 95%CI: 1.134-5.375), was associated with a higher objective response rate. Patients whose NLR (HR = 0.610, 95%CI: 0.411-0.907) or CEA (HR = 0.477, 95%CI: 0.320-0.710) decreased at week 6 post-treatment tended to have longer progression-free survival, and similar results were found in those with decreased NLR (HR = 0.587, 95%CI: 0.388-0.886) or CEA (HR = 0.406, 95%CI: 0.270-0.609) at week 12 post-treatment. Patients whose CEA (HR = 0.543, 95%CI: 0.339-0.871) or NSE (HR = 0.619, 95%CI: 0.386-0.994) decreased after 6 weeks post-treatment appeared to have longer overall survival, and the same was found for those whoseCEA (HR = 0.620, 95%CI: 0.390-0.986) or NSE (HR = 0.578, 95%CI: 0.353-0.947) was decreased at 12 weeks after treatment.

Conclusion

Post-treatment NLR, CEA and NSE changes are suggestive indicators for the prognosis of NSCLC patients after immunotherapy.

Development of Exhaustion and Acquisition of Regulatory Function by Infiltrating CD8+CD28- T Lymphocytes Dictate Clinical Outcome in Head and Neck Cancer

Head and neck squamous cell carcinoma (HNSCC) has a poor clinical outcome despite the presence of a rich CD8+ T cell tumor infiltrate in the majority of patients. This may be due to alterations of tumor infiltrating CD8+ T cells. Here, we performed a characterization of HNSCC infiltrating CD8+ T cells in a cohort of 30 patients. The results showed that differential intratumoral frequency of CD8+CD28+ T cells, CD8+CD28- T cells, and CD8+CD28-CD127-CD39+ Treg distinguished between HNSCC patients who did or did not respond to treatment. Moreover, high PD1 expression identified a CD8+CD28- T cell subpopulation, phenotypically/functionally corresponding to CD8+CD28-CD127-CD39+ Treg, which showed a high expression of markers of exhaustion. This observation suggests that development of exhaustion and acquisition of regulatory properties may configure the late differentiation stage for intratumoral effector T cells, a phenomenon we define as effector-to-regulatory T cell transition.