Improved prediction of immune checkpoint blockade efficacy across multiple cancer types

Image-based deep learning model using DNA methylation data predicts the origin of cancer of unknown primary

Cancer of unknown primary (CUP) is a rare type of metastatic cancer in which the origin of the tumor is unknown. Since the treatment strategy for patients with metastatic tumors depends on knowing the primary site, accurate identification of the origin site is important. Here, we developed an image-based deep-learning model that utilizes a vision transformer algorithm for predicting the origin of CUP. Using DNA methylation dataset of 8,233 primary tumors from The Cancer Genome Atlas (TCGA), we categorized 29 cancer types into 18 organ classes and extracted 2,312 differentially methylated CpG sites (DMCs) from non-squamous cancer group and 420 DMCs from squamous cell cancer group. Using these DMCs, we created organ-specific DNA methylation images and used them for model training and testing. Model performance was evaluated using 394 metastatic cancer samples from TCGA (TCGA-meta) and 995 samples (693 primary and 302 metastatic cancers) obtained from 20 independent external studies. We identified that the DNA methylation image reveals a distinct pattern based on the origin of cancer. Our model achieved an overall accuracy of 96.95 % in the TCGA-meta dataset. In the external validation datasets, our classifier achieved overall accuracies of 96.39 % and 94.37 % in primary and metastatic tumors, respectively. Especially, the overall accuracies for both primary and metastatic samples of non-squamous cell cancer were exceptionally high, with 96.79 % and 96.85 %, respectively.

Metal-ion-chelating phenylalanine nanostructures reverse immune dysfunction and sensitize breast tumour to immune checkpoint blockade

An immunosuppressive tumour microenvironment strongly influences response rates in patients receiving immune checkpoint blockade-based cancer immunotherapies, such as programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1). Here we demonstrate that metal-ion-chelating L-phenylalanine nanostructures synergize with short-term starvation (STS) to remodel the immunosuppressive microenvironment of breast and colorectal tumours. These nanostructures modulate the electrophysiological behaviour of dendritic cells and activate them through the NLRP3 inflammasome and calcium-mediated nuclear factor-κB pathway. STS promotes the cellular uptake of nanostructures through amino acid transporters and plays a key role in dendritic cell maturation and tumour-specific cytotoxic T lymphocyte responses. This study demonstrates the potential role of metal-ion-chelating L-phenylalanine nanostructures in activating immune responses and the effect of STS treatment in improving nanomaterial-mediated cancer immunotherapy.

Clinical and molecular correlates of tumor aneuploidy in metastatic non-small cell lung cancer

Recent studies have linked elevated tumor aneuploidy to anti-tumor immune suppression and adverse survival following immunotherapy. Herein, we provide supportive evidence for tumor aneuploidy as a biomarker of response to immunotherapy in patients with non-small cell lung cancer (NSCLC). We identify a dose-response relationship between aneuploidy score and patient outcomes. In two independent NSCLC cohorts (n = 659 patients), we demonstrate a novel association between elevated aneuploidy and non-smoking-associated oncogenic driver mutations. Lastly, we report enrichment of TERT amplification and immune-suppressive phenotypes of highly aneuploid NSCLC. Taken together, our findings emphasize a potentially critical role for tumor aneuploidy in guiding immunotherapy treatment strategies.

Decision model for durable clinical benefit from front- or late-line immunotherapy alone or with chemotherapy in non-small cell lung cancer

BACKGROUND

Predictive biomarkers and models of immune checkpoint inhibitors (ICIs) have been extensively studied in non-small cell lung cancer (NSCLC). However, evidence for many biomarkers remains inconclusive, and the opaqueness of machine learning models hinders practicality. We aimed to provide compelling evidence for biomarkers and develop a transparent decision tree model.

METHODS

We consolidated data from 3,288 ICI-treated patients with NSCLC across real-world multicenter, public cohorts and the Choice-01 trial (ClinicalTrials.gov: NCT03856411). Over 50 features were examined for predicting durable clinical benefits (DCBs) from ICIs. Noteworthy biomarkers were identified to establish a decision tree model. Additionally, we explored the tumor microenvironment and peripheral CD8+ programmed death-1 (PD-1)+ T cell receptor (TCR) profiles.

FINDINGS

Multivariate logistic regression analysis identified tumor histology, PD-ligand 1 (PD-L1) expression, tumor mutational burden, line, and regimen of ICI treatment as significant factors. Mutation subtypes of EGFR, KRAS, KEAP1, STK11, and disruptive TP53 mutations were associated with DCB. The decision tree (DT10) model, using the ten clinicopathological and genomic markers, showed superior performance in predicting DCB in the training set (area under the curve [AUC] = 0.82) and consistently outperformed other models in test sets. DT10-predicted-DCB patients manifested longer survival, an enriched inflamed tumor immune phenotype (67%), and higher peripheral TCR diversity, whereas the DT10-predicted-NDB (non-durable benefit) group showed an enriched desert immune phenotype (86%) and higher peripheral TCR clonality.

CONCLUSIONS

The model effectively predicted DCB after front-/subsequent-line ICI treatment, with or without chemotherapy, for squamous and non-squamous lung cancer, offering clinicians valuable insights into efficacy prediction using cost-effective variables.

FUNDING

This study was supported by the National Key R&D Program of China.

Engineering an Organic Nanoplatform for Augmented Pyroeletroimmunotherapy

Photothermal immunotherapy has shown great promise in the treatment of tumor metastasis. However, the thermal resistance of tumor cells substantially compromises the treatment effect of photothermal immunotherapy. Herein, a high-performance organic pyroelectric nanoplatform, tBu-TPAD-BF2 nanoparticles (NPs), is rationally engineered for the effective pyroelectroimmunotherapy of tumor metastasis. Biocompatible tBu-TPAD-BF2 NPs with excellent pyroelectric and photothermal conversion properties are constructed by assembling organic, low-bandgap pyroelectric molecules with amphiphilic polymers. After internalization by tumor cells, treatment with tBu-TPAD-BF2 NPs causes an apparent temperature elevation upon near-infrared (NIR) laser irradiation, inducing potent immunogenic cell death (ICD). Additionally, the temperature variations under alternating NIR laser irradiation facilitate reactive oxygen species production for pyroelectric therapy, thus promoting ICD activation and lowering thermal resistance. Importantly, in vivo assessments illustrate that tBu-TPAD-BF2 NPs in combination with NIR laser exposure notably inhibit primary and distant tumor proliferation and prominently retarded lung metastasis. RNA profiling reveals that treatment with tBu-TPAD-BF2 NPs markedly suppresses metastasis under NIR laser illumination by downregulating metastasis-related genes and upregulating immune response-associated pathways. Therefore, this study provides a strategy for designing high-performance pyroelectric nanoplatforms to effectively cure tumor metastasis, thereby overcoming the inherent shortcomings of photothermal immunotherapy.

LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features

Despite the revolutionary impact of immune checkpoint blockade (ICB) in cancer treatment, accurately predicting patient responses remains challenging. Here, we analyzed a large dataset of 2,881 ICB-treated and 841 non-ICB-treated patients across 18 solid tumor types, encompassing a wide range of clinical, pathologic and genomic features. We developed a clinical score called LORIS (logistic regression-based immunotherapy-response score) using a six-feature logistic regression model. LORIS outperforms previous signatures in predicting ICB response and identifying responsive patients even with low tumor mutational burden or programmed cell death 1 ligand 1 expression. LORIS consistently predicts patient objective response and short-term and long-term survival across most cancer types. Moreover, LORIS showcases a near-monotonic relationship with ICB response probability and patient survival, enabling precise patient stratification. As an accurate, interpretable method using a few readily measurable features, LORIS may help improve clinical decision-making in precision medicine to maximize patient benefit. LORIS is available as an online tool at https://loris.ccr.cancer.gov/ .

Prognostic and immunotherapeutic significances of M2 macrophage-related genes signature in lung cancer

Objective: We aimed to investigate the immunological significance of M2 macrophage-related genes in lung cancer (LC) patients, specifically focusing on constructing a risk score to predict patient prognosis and response to immunotherapy. Methods: We developed a novel risk score by identifying and incorporating 12 M2 macrophage-related genes. The risk score was calculated by multiplying the expression levels of risk genes by their respective coefficients. Through comprehensive enrichment analysis, we explored the potential functions distinguishing high- and low-risk groups. Moreover, we examined the relationship between patients in different risk groups and immune infiltration as well as their response to immunotherapy. The single-cell RNA sequencing data were acquired to ascertain the spatial pattern of RNF130 expression. The expression of RNF130 was examined using TCGA datasets and verified by HPA. The qRT-PCR was employed to examine RNF130 expression in LC cells. Finally, in vitro experiments were carried out to validate the expression and function of RNF130. Results: Our results indicated that the risk score constructed from 12 M2 macrophage-related genes was an independent prognostic factor. Patients in the high-risk group had a significantly worse prognosis compared to those in the low-risk group. Functional enrichment analysis showed a significant relationship between the risk score and immunity. Furthermore, we explored immune infiltration in different risk groups using seven immune algorithms. The results demonstrated a negative correlation between high-risk group patients and immune infiltration of B cells, CD4+ cells, and CD8+ cells. We further validated these findings using an immunotherapy response database, which revealed that high-risk patients were more likely to exhibit immune evasion and might have poorer immunotherapy outcomes. Additionally, drug sensitivity analysis indicated that patients in the high-risk group were more sensitive to certain chemotherapeutic and targeted drugs than those in the low-risk group. Single-cell analysis indicated that macrophages were the primary site of RNF130 distribution. The results from the TCGA and HPA database demonstrated a trend toward a low expression of RNF130 in LC. Finally, in vitro experiments further validated the expression and function of RNF130 in LC cells. Conclusions: The high-risk group constructed with M2 macrophage-related genes in LC was closely associated with poor prognosis, low immune cell infiltration, and poorer response to immunotherapy. This risk score can help differentiate and predict the prognosis and immune status of LC patients, thereby aiding in the development of precise and personalized immunotherapy strategies.

Second-generation checkpoint inhibitors and Treg depletion synergize with a mouse cancer vaccine in accordance with tumor microenvironment characterization

BACKGROUND

Despite advances in checkpoint inhibitor (CPI) therapy for cancer treatment, many cancers remain resistant. Tumors deemed "cold" based on lack of T cell infiltration show reduced potential for CPI therapy. Cancer vaccines may overcome the inadequacy of existing T cells by inducing the needed antitumor T cell response to synergize with CPIs and overcome resistance.

METHODS

CT26 and TC1 tumor cells were injected subcutaneously into mice. Mice were treated with combinations of CPIs alone or a cancer vaccine specific to the tumor antigen E7 present in TC1 cells. CPIs for the TC1 model were selected because of immunophenotyping TC1 tumors. Antitumor and protumor immunity, tumor size and survival, sequence and timing of vaccine and CPI administration, and efficacy of treatment in young and aged mice were probed.

RESULTS

While "hot" CT26 tumors are treatable with combinations of second-generation CPIs alone or with anti-TGFβ, "cold" TC1 tumor reduction requires the synergy of a tumor-antigen-specific vaccine in combination with two CPIs, anti-TIGIT and anti-PD-L1, predicted by tumor microenvironment (TME) characterization. The synergistic triple combination delays tumor growth better than any pairwise combination and improves survival in a CD8+T cell-dependent manner. Depletion of CD4+T cells improved the treatment response, and depleting regulatory T cells (Treg) revealed Tregs to be inhibiting the response as also predicted from TME analysis. We found the sequence of CPI and vaccine administration dictates the success of the treatment, and the triple combination administered concurrently induces the highest E7-specific T cell response. Contrary to young mice, in aged mice, the cancer vaccine alone is ineffective, requiring the CPIs to delay tumor growth.

CONCLUSIONS

These findings show how pre-existing or vaccine-mediated de novo T cell responses can both be amplified by and facilitate synergistic CPIs and Treg depletion that together lead to greater survival, and how analysis of the TME can help rationally design combination therapies and precision medicine to enhance clinical response to CPI and cancer vaccine therapy.

Immune-related adverse events of antibody-based biological medicines in cancer therapy

Recombinant antibodies (Abs) are an integral modality for the treatment of multiple tumour malignancies. Since the Food and Drug Administration (FDA) approval of rituximab as the first monoclonal antibody (mAb) for cancer treatment, several mAbs and antibody (Ab)-based therapies have been approved for the treatment of solid tumour malignancies and other cancers. These Abs function by either blocking oncogenic pathways or angiogenesis, modulating immune response, or by delivering a conjugated drug. The use of Ab-based therapy in cancer patients who could benefit from the treatment, however, is still limited by associated toxicity profiles which may stem from biological features and processes related to target binding, alongside biochemical and/or biophysical characteristics of the therapeutic Ab. A significant immune-related adverse event (irAE) associated with Ab-based therapies is cytokine release syndrome (CRS), characterized by the development of fever, rash and even marked, life-threatening hypotension, and acute inflammation with secondary to systemic uncontrolled increase in a range of pro-inflammatory cytokines. Here, we review irAEs associated with specific classes of approved, Ab-based novel cancer immunotherapeutics, namely immune checkpoint (IC)-targeting Abs, bispecific Abs (BsAbs) and Ab-drug-conjugates (ADCs), highlighting the significance of harmonization in preclinical assay development for safety assessment of Ab-based biotherapeutics as an approach to support and refine clinical translation.

Characterization of Non-Monotonic Relationships between Tumor Mutational Burden and Clinical Outcomes

Potential clinical biomarkers are often assessed with Cox regressions or their ability to differentiate two groups of patients based on a single cutoff. However, both of these approaches assume a monotonic relationship between the potential biomarker and survival. Tumor mutational burden (TMB) is currently being studied as a predictive biomarker for immunotherapy, and a single cutoff is often used to divide patients. In this study, we introduce a two-cutoff approach that allows splitting of patients when a non-monotonic relationship is present and explore the use of neural networks to model more complex relationships of TMB to outcome data. Using real-world data, we find that while in most cases the true relationship between TMB and survival appears monotonic, that is not always the case and researchers should be made aware of this possibility.

SIGNIFICANCE

When a non-monotonic relationship to survival is present it is not possible to divide patients by a single value of a predictor. Neural networks allow for complex transformations and can be used to correctly split patients when a non-monotonic relationship is present.

Disentangling the splicing factor programs underlying complex molecular phenotypes

The regulation of exon inclusion through alternative splicing tunes the cell's behavior by increasing the functional diversity of the transcriptome and the proteome. Splicing factors work in concert to generate gene isoform pools that contribute to cell phenotypes yet their activity is controlled by multiple regulatory and signaling layers. This hinders identification of functional, phenotype-specific splicing factors using traditional single-omic measurements, such as their mutational state or expression. To address this challenge, we propose repurposing the virtual inference of protein activity by enriched regulon analysis (VIPER) to measure splicing factor activity solely from their downstream exon transcriptomic inclusion signatures. This approach is effective in assessing the effect of co-occurring splicing factor perturbations, as well as their post-translational regulation. As proof of concept, we dissect recurrent splicing factor programs underlying tumorigenesis including aberrantly activated factors acting as oncogenes and inactivated ones acting as tumor suppressors, which are undetectable by more conventional methodologies. Activation and inactivation of these cancer splicing programs effectively stratifies overall survival, as well as cancer hallmarks such as proliferation and immune evasion. Altogether, repurposing network-based inference of protein activity for splicing factor networks distills common, functionally relevant splicing programs in otherwise heterogeneous molecular contexts.

Serum albumin and derived neutrophil-to-lymphocyte ratio are potential predictive biomarkers for immune checkpoint inhibitors in small cell lung cancer

Background

Immune checkpoint inhibitors (ICIs) have reshaped the treatment landscape of small cell lung cancer (SCLC), but only a minority of patients benefit from this therapy. Therefore, it is critical to identify potential risk factors that could predict the efficacy of ICI treatment in SCLC patients and identify patient subgroups who may benefit the most from ICI therapy.

Methods

Our study included a total of 183 SCLC patients who had received at least one dose of ICI treatment. We utilized both logistic regression and Cox proportional hazard regression to evaluate whether various patient clinical factors and serum biomarkers could serve as predictors of patient response to treatment and overall survival (OS) during ICI therapy.

Results

Logistic regression showed that patients with a history of surgery (p=0.003, OR 9.06, 95% CI: (2.17, 37.9)) and no metastasis (p=0.008, OR 7.82, 95% CI: (1.73, 35.4)) exhibited a higher odds of response to ICI treatment. Cox regression analyses demonstrated that pretreatment blood albumin (p=0.003, HR 1.72, 95% CI: (1.21, 2.45)) and derived neutrophil to lymphocyte ratio (dNLR) (p=0.003, HR 1.71, 95% CI: (1.20-2.44)) were independent predictors for OS in SCLC patients. By establishing a pre-treatment prognostic scoring system based on baseline albumin and dNLR, we found that patients with high albumin and low dNLR exhibited a significantly better prognosis than those with low albumin and high dNLR in both the full (P<.0001, HR 0.33, 95% CI: 0.20-0.55) and the metastatic cohort (P<.0001, HR 0.28, 95% CI: 0.15-0.51). The better prognostic group also had younger age, higher BMI and lower systemic inflammatory biomarker values than the unfavorable group (P<.0001).

Conclusion

Our data reveals the significant role of metastasis status and treatment history in predicting the initial response of SCLC patients to ICI treatment. However, baseline serum albumin and dNLR provide a more precise prognostic prediction for patient OS. The scoring system based on albumin and dNLR enhances the ability to stratify patient prognosis and holds the potential to guide clinical decision-making for SCLC patients undergoing ICI therapy.

Efficacy and safety of anti-PD-1/PD-L1-based dual immunotherapies versus PD-1/PD-L1 inhibitor alone in patients with advanced solid tumor: a systematic review and meta-analysis

INTRODUCTION

Numerous randomized controlled trials (RCTs) have investigated PD-1/PD-L1 inhibitor-based combination therapies. The debate surrounding the potential additive clinical benefits of combination of two immune-oncology (IO) therapies for cancer patients persists.

METHODS

Both published and grey sources of randomized clinical trials that compared anti-PD-1/PD-L1-based immunotherapy combinations with monotherapy in patients with advanced or metastatic solid tumors were encompassed. The primary outcome was progression-free survival (PFS), and secondary outcomes included objective response rate (ORR), overall survival (OS) and treatment-related adverse events (TRAEs).

RESULTS

Our analysis encompassed 31 studies comprising 10,341 patients, which covered 12 distinct immune-oncology combination regimens. Across all patients, the immunotherapy combinations exhibited the capability to enhance the ORR (OR = 1.23 [95% CI 1.13-1.34]) and extend PFS (HR = 0.91 [95% CI 0.87-0.95]). However, the observed enhancement in OS (HR = 0.96 [95% CI 0.91-1.01]) was of no significance. Greater benefits in terms of PFS (HR = 0.82 [95% CI 0.72 to 0.93]) and OS (HR = 0.85 [95% CI 0.73 to 0.99]) may be particularly pronounced in cases where PD-L1 expression is negative. Notably, despite a heightened risk of any-grade TRAEs (OR = 1.72 [95% CI 1.40-2.11]) and grade greater than or equal to 3 TRAEs (OR = 2.01 [95% CI 1.67-2.43]), toxicity was generally manageable.

CONCLUSIONS

This study suggests that incorporating an additional immunotherapy agent with PD-1/PD-L1 inhibitors can elevate the response rate and reduce the risk of disease progression, all while maintaining manageable toxicity. However, there remains a challenge in translating these primary clinical benefits into extended overall survival.

Delivery of STING agonists for cancer immunotherapy

Agonists of the cyclic guanosine monophosphate-adenosine monophosphate synthase (cGAS)-Stimulator of Interferon Genes (STING) pathway, a critical mediator of innate immune response to foreign invaders with DNA, have gained significant interest in cancer immunotherapy. STING agonists are envisioned as a way of complementing the antitumor activity of the patient's immune system and immune checkpoint blockade therapy. However, their clinical development has been challenging due to the poor pharmacokinetic and physicochemical properties. This review discusses drug delivery efforts to circumvent the challenges, their accomplishment, and unmet needs based on the last five years of literature.

Liquid biopsy for human cancer: cancer screening, monitoring, and treatment

Currently, tumor treatment modalities such as immunotherapy and targeted therapy have more stringent requirements for obtaining tumor growth information and require more accurate and easy-to-operate tumor information detection methods. Compared with traditional tissue biopsy, liquid biopsy is a novel, minimally invasive, real-time detection tool for detecting information directly or indirectly released by tumors in human body fluids, which is more suitable for the requirements of new tumor treatment modalities. Liquid biopsy has not been widely used in clinical practice, and there are fewer reviews of related clinical applications. This review summarizes the clinical applications of liquid biopsy components (e.g., circulating tumor cells, circulating tumor DNA, extracellular vesicles, etc.) in tumorigenesis and progression. This includes the development process and detection techniques of liquid biopsies, early screening of tumors, tumor growth detection, and guiding therapeutic strategies (liquid biopsy-based personalized medicine and prediction of treatment response). Finally, the current challenges and future directions for clinical applications of liquid biopsy are proposed. In sum, this review will inspire more researchers to use liquid biopsy technology to promote the realization of individualized therapy, improve the efficacy of tumor therapy, and provide better therapeutic options for tumor patients.

Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole-genome sequencing (WGS). Here we now introduce MRD-EDGE, a machine-learning-guided WGS ctDNA single-nucleotide variant (SNV) and copy-number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by ~300× compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, vastly expanding its applicability within solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in TF in response to neoadjuvant immunotherapy in lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables plasma-only (non-tumor-informed) disease monitoring in advanced melanoma and lung cancer, yielding clinically informative TF monitoring for patients on immune-checkpoint inhibition.

Immune features are associated with response to neoadjuvant chemo-immunotherapy for muscle-invasive bladder cancer

Neoadjuvant cisplatin-based chemotherapy is standard of care for muscle-invasive bladder cancer (MIBC). Immune checkpoint inhibition (ICI) alone, and ICI in combination with chemotherapy, have demonstrated promising pathologic response ( Collapse

Key Words

• predictive markers
• bladder cancer
• immunotherapy
• tumour immunology

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MESH Headings

• Humans
• Urinary Bladder Neoplasms/drug therapy
• Urinary Bladder Neoplasms/immunology
• Urinary Bladder Neoplasms/therapy
• Urinary Bladder Neoplasms/pathology
• Neoadjuvant Therapy/methods
• Deoxycytidine/analogs & derivatives
• Deoxycytidine/therapeutic use
• Deoxycytidine/administration & dosage
• Gemcitabine
• Immunotherapy/methods
• Male
• Cisplatin/therapeutic use
• Cisplatin/administration & dosage
• Female
• Antibodies, Monoclonal, Humanized/therapeutic use
• Antineoplastic Combined Chemotherapy Protocols/therapeutic use
• Immune Checkpoint Inhibitors/therapeutic use
• Aged
• Middle Aged
• Neoplasm Invasiveness
• Interleukin-9/metabolism
• B7-H1 Antigen/metabolism
• Biomarkers, Tumor/blood
• Treatment Outcome

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Grants

• F30 CA278317 NCI NIH HHS
• 1F30CA278317-01A1 U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
• Merck & Co., Inc. | Merck Sharp and Dohme (Merck Sharp & Dohme)
• U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)

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Affiliation(s)

Wolfgang Beckabir Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Mi Zhou Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jin Seok Lee Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, USA
Steven P Vensko Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Mark G Woodcock Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Hsing-Hui Wang Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Sara E Wobker Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Gatphan Atassi Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Alec D Wilkinson Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Kenneth Fowler Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Leah M Flick Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jeffrey S Damrauer Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Michael R Harrison Division of Medical Oncology, Department of Medicine, Duke Cancer Institute, Duke University, Durham, NC, USA
Karen P McKinnon Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Tracy L Rose Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Matthew I Milowsky Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jonathan S Serody Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA. Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
William Y Kim Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Division of Hematology, Department of Medicine, UNC School of Medicine, Chapel Hill, NC, USA.
Benjamin G Vincent Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, USA. Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Computational Medicine Program, UNC School of Medicine, Chapel Hill, NC, USA.

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Multi-omic analysis identifies hypoalbuminemia as independent biomarker of poor outcome upon PD-1 blockade in metastatic melanoma

We evaluated the prognostic value of hypoalbuminemia in context of various biomarkers at baseline, including clinical, genomic, transcriptomic, and blood-based markers, in patients with metastatic melanoma treated with anti-PD-1 monotherapy or anti-PD-1/anti-CTLA-4 combination therapy (n = 178). An independent validation cohort (n = 79) was used to validate the performance of hypoalbuminemia compared to serum LDH (lactate dehydrogenase) levels. Pre-treatment hypoalbuminemia emerged as the strongest predictor of poor outcome for both OS (HR = 4.01, 95% CI 2.10-7.67, Cox P = 2.63e-05) and PFS (HR = 3.72, 95% CI 2.06-6.73, Cox P = 1.38e-05) in univariate analysis. In multivariate analysis, the association of hypoalbuminemia with PFS was independent of serum LDH, IFN-γ signature expression, TMB, age, ECOG PS, treatment line, treatment type (combination or monotherapy), brain and liver metastasis (HR = 2.76, 95% CI 1.24-6.13, Cox P = 0.0131). Our validation cohort confirmed the prognostic power of hypoalbuminemia for OS (HR = 1.98, 95% CI 1.16-3.38; Cox P = 0.0127) and was complementary to serum LDH in analyses for both OS (LDH-adjusted HR = 2.12, 95% CI 1.2-3.72, Cox P = 0.00925) and PFS (LDH-adjusted HR = 1.91, 95% CI 1.08-3.38, Cox P = 0.0261). In conclusion, pretreatment hypoalbuminemia was a powerful predictor of outcome in ICI in melanoma and showed remarkable complementarity to previously established biomarkers, including high LDH.

Commonly used software tools produce conflicting and overly-optimistic AUPRC values

The precision-recall curve (PRC) and the area under the precision-recall curve (AUPRC) are useful for quantifying classification performance. They are commonly used in situations with imbalanced classes, such as cancer diagnosis and cell type annotation. We evaluate 10 popular tools for plotting PRC and computing AUPRC, which were collectively used in more than 3000 published studies. We find the AUPRC values computed by the tools rank classifiers differently and some tools produce overly-optimistic results.

Current Landscape of Cancer Immunotherapy: Harnessing the Immune Arsenal to Overcome Immune Evasion

Cancer immune evasion represents a leading hallmark of cancer, posing a significant obstacle to the development of successful anticancer therapies. However, the landscape of cancer treatment has significantly evolved, transitioning into the era of immunotherapy from conventional methods such as surgical resection, radiotherapy, chemotherapy, and targeted drug therapy. Immunotherapy has emerged as a pivotal component in cancer treatment, harnessing the body's immune system to combat cancer and offering improved prognostic outcomes for numerous patients. The remarkable success of immunotherapy has spurred significant efforts to enhance the clinical efficacy of existing agents and strategies. Several immunotherapeutic approaches have received approval for targeted cancer treatments, while others are currently in preclinical and clinical trials. This review explores recent progress in unraveling the mechanisms of cancer immune evasion and evaluates the clinical effectiveness of diverse immunotherapy strategies, including cancer vaccines, adoptive cell therapy, and antibody-based treatments. It encompasses both established treatments and those currently under investigation, providing a comprehensive overview of efforts to combat cancer through immunological approaches. Additionally, the article emphasizes the current developments, limitations, and challenges in cancer immunotherapy. Furthermore, by integrating analyses of cancer immunotherapy resistance mechanisms and exploring combination strategies and personalized approaches, it offers valuable insights crucial for the development of novel anticancer immunotherapeutic strategies.

A specific enterotype derived from gut microbiome of older individuals enables favorable responses to immune checkpoint blockade therapy

Immunotherapy has revolutionized cancer treatment, but inconsistent responses persist. Our study delves into the intriguing phenomenon of enhanced immunotherapy sensitivity in older individuals with cancers. Through a meta-analysis encompassing 25 small-to-mid-sized trials of immune checkpoint blockade (ICB), we demonstrate that older individuals exhibit heightened responsiveness to ICB therapy. To understand the underlying mechanism, we reanalyze single-cell RNA sequencing (scRNA-seq) data from multiple studies and unveil distinct upregulation of exhausted and cytotoxic T cell markers within the tumor microenvironment (TME) of older patients. Recognizing the potential role of gut microbiota in modulating the efficacy of immunotherapy, we identify an aging-enriched enterotype linked to improved immunotherapy outcomes in older patients. Fecal microbiota transplantation experiments in mice confirm the therapeutic potential of the aging-enriched enterotype, enhancing treatment sensitivity and reshaping the TME. Our discoveries confront the prevailing paradox and provide encouraging paths for tailoring cancer immunotherapy strategies according to an individual's gut microbiome profile.

IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition

Background

Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition.

Method

To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy.

Results

We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity.

Conclusion

Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.

Host-related indexes in head and neck cancer

PURPOSE OF REVIEW

Peripheral blood host-related indexes have been widely studied in cancer patients. Several authors have shown the prognostic capacity of these indexes in head and neck cancer. Therefore, there has been an increasing interest in this topic recently.

RECENT FINDINGS

The main variables analyzed and used to create these host-related indexes are peripheral blood leukocytes - including neutrophils, monocytes and lymphocytes - albumin and hemoglobin levels. Other factors with proven prognostic capacity in some studies are: platelets, C-reactive protein, and BMI. Among all the combined indexes, the neutrophil-to-lymphocyte ratio has been the most accepted and used worldwide. Nonetheless, there are other indexes which group multiple of these factors that have shown better prognostic capacity, and are promising in the near future.

SUMMARY

Host-related indexes are ideal biomarkers to be used on our daily-basis. There is enough evidence to start considering them when assessing patients with head and neck cancer.

Immunotherapy in melanoma: Can we predict response to treatment with circulating biomarkers?

Melanoma is the most aggressive form of skin cancer, representing approximately 4% of all cutaneous neoplasms and accounting for up to 80% of deaths. Advanced stages of melanoma involve metastatic processes and are associated with high mortality and morbidity, mainly due to the rapid dissemination and heterogeneous responses to current therapies, including immunotherapy. Immune checkpoint inhibitors (ICIs) are currently used in the treatment of metastatic melanoma (MM) and despite being linked to an increase in patient survival, a high percentage of them still do not benefit from it. Accordingly, the number of therapeutic regimens for MM patients using ICIs either alone or in combination with other therapies has increased, together with the need for reliable biomarkers that can both predict and monitor response to ICIs. In this context, circulating biomarkers, such as DNA, RNA, proteins, and cells, have emerged due to their ability to reflect disease status. Moreover, blood tests are minimally invasive and provide an attractive option to detect biomarkers, avoiding stressful medical procedures. This systematic review aims to evaluate the possibility of a non-invasive biomarker signature that can guide therapeutic decisions. The studies reported here offer valuable insight into how circulating biomarkers can have a role in personalized treatments for melanoma patients receiving ICIs therapy, emphasizing the need for rigorous clinical trials to confirm findings and establish standardized procedures.

PIEZO1 mechanically regulates the antitumour cytotoxicity of T lymphocytes

The killing function of cytotoxic T cells can be enhanced biochemically. Here we show that blocking the mechanical sensor PIEZO1 in T cells strengthens their traction forces and augments their cytotoxicity against tumour cells. By leveraging cytotoxic T cells collected from tumour models in mice and from patients with cancers, we show that PIEZO1 upregulates the transcriptional factor GRHL3, which in turn induces the expression of the E3 ubiquitin ligase RNF114. RNF114 binds to filamentous actin, causing its downregulation and rearrangement, which depresses traction forces in the T cells. In mice with tumours, the injection of cytotoxic T cells collected from the animals and treated with a PIEZO1 antagonist promoted their infiltration into the tumour and attenuated tumour growth. As an immunomechanical regulator, PIEZO1 could be targeted to enhance the outcomes of cancer immunotherapies.

Predicting the prognosis of glioma patients with TERT promoter mutations and guiding the specific immune profile of immune checkpoint blockade therapy

The telomerase reverse transcriptase promoter (TERTp) is frequently mutated in gliomas. This study sought to identify immune biomarkers of gliomas with TERTp mutations. Data from TCGA were used to identify and validate survival-associated gene signatures, and immune and stromal scores were calculated using the ESTIMATE algorithm. High stromal or immune scores in patients with TERTp-mutant gliomas correlated with shorter overall survival compared to cases with low stromal or immune scores. Among TERTp-mutant gliomas with both high immune and high stromal scores, 213 commonly shared DEGs were identified. Among 71 interacting DEGs representing candidate hub genes in a PPI network, HOXC6, WT1, CD70, and OTP showed significant ability in establishing subgroups of high- and low-risk patients. A risk model based on these 4 genes showed strong prognostic potential for gliomas with mutated TERTp, but was inapplicable for TERTp-wild-type gliomas. TERTp-mutant gliomas with high-risk scores displayed a greater percentage of naïve B cells, plasma cells, naïve CD4 T cells, and activated mast cells than low-risk score gliomas. TIDE analysis indicated that immune checkpoint blockade (ICB) therapy may benefit glioma patients with TERTp mutations. The present risk model can help predict prognosis of glioma patients with TERTp mutations and aid ICB treatment options.

Proteomic characterization of esophageal squamous cell carcinoma response to immunotherapy reveals potential therapeutic strategy and predictive biomarkers

Immunotherapy is the first-line therapy for esophageal squamous cell carcinoma (ESCC), yet many patients do not respond due to drug resistance and the lack of reliable predictive markers. We collected 73 ESCC patients (including discovery cohort and validation cohort) without immune thrombocytopenia and undergoing anti-PD1 immunotherapy. Proteomic and phosphoproteomic analysis of 73 ESCC treatment-naive samples by mass spectrometry-based label-free quantification were applied to explore the potential resistant and sensitive mechanisms, and identify predictive markers of ESCC immunotherapy. Comparative analysis found the pathways related to immune and mitochondrial functions were associated with ESCC immunotherapy sensitivity; while platelet activation bioprocess showed negative correlation with CD8+ T cells and related to ESCC immunotherapy non-sensitivity. Finally, we identified 10 ESCC immunotherapy predictive biomarkers with high accuracy (≥ 0.90) to predict the immunotherapeutic response, which was validated in the independent cohort.

Identifying microRNAs associated with tumor immunotherapy response using an interpretable machine learning model

Predicting clinical responses to tumor immunotherapy is essential to reduce side effects and the potential for sustained clinical responses. Nevertheless, preselecting patients who are likely to respond to such treatments remains highly challenging. Here, we explored the potential of microRNAs (miRNAs) as predictors of immune checkpoint blockade responses using a machine learning approach. First, we constructed random forest models to predict the response to tumor ICB therapy using miRNA expression profiles across 19 cancer types. The contribution of individual miRNAs to each prediction process was determined by employing SHapley Additive exPlanations (SHAP) for model interpretation. Remarkably, the predictive performance achieved by using a small number of miRNAs with high feature importance was similar to that achieved by using the entire miRNA set. Additionally, the genes targeted by these miRNAs were closely associated with tumor- and immune-related pathways. In conclusion, this study demonstrates the potential of miRNA expression data for assessing tumor immunotherapy responses. Furthermore, we confirmed the potential of informative miRNAs as biomarkers for the prediction of immunotherapy response, which will advance our understanding of tumor immunotherapy mechanisms.

Informing immunotherapy with multi-omics driven machine learning

Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.

An immunogenetic basis for lung cancer risk

Cancer risk is influenced by inherited mutations, DNA replication errors, and environmental factors. However, the influence of genetic variation in immunosurveillance on cancer risk is not well understood. Leveraging population-level data from the UK Biobank and FinnGen, we show that heterozygosity at the human leukocyte antigen (HLA)-II loci is associated with reduced lung cancer risk in smokers. Fine-mapping implicated amino acid heterozygosity in the HLA-II peptide binding groove in reduced lung cancer risk, and single-cell analyses showed that smoking drives enrichment of proinflammatory lung macrophages and HLA-II+ epithelial cells. In lung cancer, widespread loss of HLA-II heterozygosity (LOH) favored loss of alleles with larger neopeptide repertoires. Thus, our findings nominate genetic variation in immunosurveillance as a critical risk factor for lung cancer.

Predictive indicators of immune therapy efficacy in hepatocellular carcinoma based on neutrophil-to-lymphocyte ratio

Hepatocellular carcinoma (HCC) exhibits high incidence and mortality rates in China. Most cases are often diagnosed at late stages and require multi-strategy therapies. In recent years, immune checkpoint inhibitors (ICIs), particularly programmed cell death protein 1 (PD-1) antibodies, have demonstrated effectiveness in comprehensive HCC treatment. However, the efficacy and prognosis vary greatly among patients. Screening suitable patients and predicting outcomes are crucial for improving the efficacy of ICIs. Although PD-L1 expression levels in tumor cells have been used as predictors of PD-1/PD-L1 antibody therapy, they may not consistently correlate with clinical response in some studies; thus, exploring new biomarkers is necessary. The neutrophil-to-lymphocyte ratio (NLR) emerged as a new predictor of ICI immunotherapy efficacy, and its application in HCC is worth exploring. This study utilizes the Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC) project in the Genomic Data Commons (GDC) database for methylation and transcriptome data analysis. The correlation between NLR and ICI immunotherapy efficacy for HCC was evaluated, identifying differentially expressed genes. Analysis revealed 74 up-regulated and 445 down-regulated genes in the high-NLR group compared to the low-NLR group. NLR-related differential methylation analysis identified 68 hypermethylated and 65 hypomethylated probes in the NLR high group. Furthermore, a machine learning model using 27 intersecting genes predicted PD-1 antibody therapy efficacy, achieving an AUC value of 0.813. In summary, we established a predictive model for HCC immunotherapy based on 27 genes related to differential expressions and NLR-associated methylation, showing significant potential for clinical research potential in this field.

Epigenome and early selection determine the tumour-immune evolutionary trajectory of colorectal cancer

Immune system control is a major hurdle that cancer evolution must circumvent. The relative timing and evolutionary dynamics of subclones that have escaped immune control remain incompletely characterized, and how immune-mediated selection shapes the epigenome has received little attention. Here, we infer the genome- and epigenome-driven evolutionary dynamics of tumour-immune coevolution within primary colorectal cancers (CRCs). We utilise our existing CRC multi-region multi-omic dataset that we supplement with high-resolution spatially-resolved neoantigen sequencing data and highly multiplexed imaging of the tumour microenvironment (TME). Analysis of somatic chromatin accessibility alterations (SCAAs) reveals frequent somatic loss of accessibility at antigen presenting genes, and that SCAAs contribute to silencing of neoantigens. We observe that strong immune escape and exclusion occur at the outset of CRC formation, and that within tumours, including at the microscopic level of individual tumour glands, additional immune escape alterations have negligible consequences for the immunophenotype of cancer cells. Further minor immuno-editing occurs during local invasion and is associated with TME reorganisation, but that evolutionary bottleneck is relatively weak. Collectively, we show that immune evasion in CRC follows a "Big Bang" evolutionary pattern, whereby genetic, epigenetic and TME-driven immune evasion acquired by the time of transformation defines subsequent cancer-immune evolution.

Commonly used software tools produce conflicting and overly-optimistic AUPRC values

The precision-recall curve (PRC) and the area under it (AUPRC) are useful for quantifying classification performance. They are commonly used in situations with imbalanced classes, such as cancer diagnosis and cell type annotation. We evaluated 10 popular tools for plotting PRC and computing AUPRC, which were collectively used in >3,000 published studies. We found the AUPRC values computed by the tools rank classifiers differently and some tools produce overly-optimistic results.

Treatment of nonmelanoma skin cancer with pro-differentiation agents and photodynamic therapy: Preclinical and clinical studies (Review)

Photodynamic therapy (PDT) is a nonscarring cancer treatment in which a pro-drug (5-aminolevulinic acid, ALA) is applied, converted into a photosensitizer (protoporphyrin IX, PpIX) which is then activated by visible light. ALA-PDT is now popular for treating nonmelanoma skin cancer (NMSC), but can be ineffective for larger skin tumors, mainly due to inadequate production of PpIX. Work over the past two decades has shown that differentiation-promoting agents, including methotrexate (MTX), 5-fluorouracil (5FU) and vitamin D (Vit D) can be combined with ALA-PDT as neoadjuvants to promote tumor-specific accumulation of PpIX, enhance tumor-selective cell death, and improve therapeutic outcome. In this review, we provide a historical perspective of how the combinations of differentiation-promoting agents with PDT (cPDT) evolved, including Initial discoveries, biochemical and molecular mechanisms, and clinical translation for the treatment of NMSCs. For added context, we also compare the differentiation-promoting neoadjuvants with some other clinical PDT combinations such as surgery, laser ablation, iron-chelating agents (CP94), and immunomodulators that do not induce differentiation. Although this review focuses mainly on the application of cPDT for NMSCs, the concepts and findings described here may be more broadly applicable towards improving the therapeutic outcomes of PDT treatment for other types of cancers.

Bioengineering strategies to enhance the interleukin-18 bioactivity in the modern toolbox of cancer immunotherapy

Cytokines are the first modern immunotherapeutic agents used for activation immunotherapy. Interleukin-18 (IL-18) has emerged as a potent anticancer immunostimulatory cytokine over the past three decades. IL-18, structurally is a stable protein with very low toxicity at biological doses. IL-18 promotes the process of antigen presentation and also enhances innate and acquired immune responses. It can induce the production of proinflammatory cytokines and increase tumor infiltration of effector immune cells to revert the immunosuppressive milieu of tumors. Furthermore, IL-18 can reduce tumorigenesis, suppress tumor angiogenesis, and induce tumor cell apoptosis. These characteristics present IL-18 as a promising option for cancer immunotherapy. Although several preclinical studies have reported the immunotherapeutic potential of IL-18, clinical trials using it as a monotherapy agent have reported disappointing results. These results may be due to some biological characteristics of IL-18. Several bioengineering approaches have been successfully used to correct its defects as a bioadjuvant. Currently, the challenge with this anticancer immunotherapeutic agent is mainly how to use its capabilities in a rational combinatorial therapy for clinical applications. The present study discussed the strengths and weaknesses of IL-18 as an immunotherapeutic agent, followed by comprehensive review of various promising bioengineering approaches that have been used to overcome its disadvantages. Finally, this study highlights the promising application of IL-18 in modern combinatorial therapies, such as chemotherapy, immune checkpoint blockade therapy, cell-based immunotherapy and cancer vaccines to guide future studies, circumventing the barriers to administration of IL-18 for clinical applications, and bring it to fruition as a potent immunotherapy agent in cancer treatment.

Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data

PURPOSE

Although immune checkpoint inhibitors (ICIs) have improved outcomes in certain patients with cancer, they can also cause life-threatening immunotoxicities. Predicting immunotoxicity risks alongside response could provide a personalized risk-benefit profile, inform therapeutic decision making, and improve clinical trial cohort selection. We aimed to build a machine learning (ML) framework using routine electronic health record (EHR) data to predict hepatitis, colitis, pneumonitis, and 1-year overall survival.

METHODS

Real-world EHR data of more than 2,200 patients treated with ICI through December 31, 2018, were used to develop predictive models. Using a prediction time point of ICI initiation, a 1-year prediction time window was applied to create binary labels for the four outcomes for each patient. Feature engineering involved aggregating laboratory measurements over appropriate time windows (60-365 days). Patients were randomly partitioned into training (80%) and test (20%) sets. Random forest classifiers were developed using a rigorous model development framework.

RESULTS

The patient cohort had a median age of 63 years and was 61.8% male. Patients predominantly had melanoma (37.8%), lung cancer (27.3%), or genitourinary cancer (16.4%). They were treated with PD-1 (60.4%), PD-L1 (9.0%), and CTLA-4 (19.7%) ICIs. Our models demonstrate reasonably strong performance, with AUCs of 0.739, 0.729, 0.755, and 0.752 for the pneumonitis, hepatitis, colitis, and 1-year overall survival models, respectively. Each model relies on an outcome-specific feature set, though some features are shared among models.

CONCLUSION

To our knowledge, this is the first ML solution that assesses individual ICI risk-benefit profiles based predominantly on routine structured EHR data. As such, use of our ML solution will not require additional data collection or documentation in the clinic.

Integrated germline and somatic features reveal divergent immune pathways driving ICB response

Immune Checkpoint Blockade (ICB) has revolutionized cancer treatment, however mechanisms determining patient response remain poorly understood. Here we used machine learning to predict ICB response from germline and somatic biomarkers and interpreted the learned model to uncover putative mechanisms driving superior outcomes. Patients with higher T follicular helper infiltrates were robust to defects in the class-I Major Histocompatibility Complex (MHC-I). Further investigation uncovered different ICB responses in MHC-I versus MHC-II neoantigen reliant tumors across patients. Despite similar response rates, MHC-II reliant responses were associated with significantly longer durable clinical benefit (Discovery: Median OS=63.6 vs. 34.5 months P=0.0074; Validation: Median OS=37.5 vs. 33.1 months, P=0.040). Characteristics of the tumor immune microenvironment reflected MHC neoantigen reliance, and analysis of immune checkpoints revealed LAG3 as a potential target in MHC-II but not MHC-I reliant responses. This study highlights the value of interpretable machine learning models in elucidating the biological basis of therapy responses.

Ultrasound-activated prodrug-loaded liposome for efficient cancer targeting therapy without chemotherapy-induced side effects

BACKGROUND

Off-targeted distribution of chemotherapeutic drugs causes severe side effects, further leading to poor prognosis and patient compliance. Ligand/receptor-mediated targeted drug delivery can improve drug accumulation in the tumor but it always attenuated by protein corona barriers.

RESULTS

To address these problems, a radically different strategy is proposed that can leave the off-targeted drugs inactive but activate the tumor-distributed drugs for cancer-targeting therapy in a tumor microenvironment-independent manner. The feasibility and effectiveness of this strategy is demonstrated by developing an ultrasound (US)-activated prodrug-loaded liposome (CPBSN38L) comprising the sonosensitizer chlorin e6 (Ce6)-modified lipids and the prodrug of pinacol boronic ester-conjugated SN38 (PBSN38). Once CPBSN38L is accumulated in the tumor and internalized into the cancer cells, under US irradiation, the sonosensitizer Ce6 rapidly induces extensive production of intracellular reactive oxygen species (ROS), thereby initiating a cascade amplified ROS-responsive activation of PBSN38 to release the active SN38 for inducing cell apoptosis. If some of the injected CPBSN38L is distributed into normal tissues, the inactive PBSN38 exerts no pharmacological activity on normal cells. CPBSN38L exhibited strong anticancer activity in multiple murine tumor models of colon adenocarcinoma and hepatocellular carcinoma with no chemotherapy-induced side effects, compared with the standard first-line anticancer drugs irinotecan and topotecan.

CONCLUSIONS

This study established a side-effect-evitable, universal, and feasible strategy for cancer-targeting therapy.

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

BACKGROUND

The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology.

MATERIALS AND METHODS

We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data.

RESULTS

A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data.

CONCLUSION

AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.

Loss of Human Leukocyte Antigen and Immune Escape in Head and Neck Cancer

OBJECTIVES/HYPOTHESIS

Cancer cells evade recognition by the immune system to survive. Head and neck squamous cell carcinoma (HNSCC) is characterized by high levels of immune infiltration and mutation-associated neoantigens; therefore, immune evasion is likely to be an important mechanism in HNSCC tumorigenesis and progression. A commonly employed mechanism of immune evasion is downregulation of human leukocyte antigen (HLA) or loss of heterozygosity (LOH) in tumor cells. The objective of this study was to integrate multi-dimensional genomic and transcriptomic data from HNSCC tumors to better understand the clinical and immunologic implications of HLA LOH.

STUDY TYPE/DESIGN

Cross-sectional integrated clinical and genomic analysis.

METHODS

Whole-exome sequencing and RNA-sequencing data from 522 tumors profiled in The Cancer Genome Atlas HNSCC cohort were analyzed and integrated with secondary analyses including immune cell deconvolution data. Associations were analyzed with categorical hypothesis testing and multivariable logistic and Cox regression.

RESULTS

HLA LOH was a prevalent event that was identified in 53% of HNSCC tumors; in many cases, more than one class I HLA gene was targeted for LOH. HLA LOH was more common in advanced-stage tumors. Tumors with somatic HLA LOH had tumor microenvironments defined by decreased lymphocyte and T cell infiltration.

CONCLUSIONS

HLA LOH is one of the most prevalent genetic alterations in HNSCC, and is associated with a cold immune microenvironment, suggesting that HLA LOH is a means of immune evasion. It may have value as a predictive biomarker or potential as a cancer cell-specific therapeutic target.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:160-165, 2024.

HLA Genes: A Hallmark of Functional Genetic Variation and Complex Evolution

The major histocompatibility complex (MHC) with its highly polymorphic HLA genes represents one of the most intensely studied genomic regions in the genome. MHC proteins play a key role in antigen-specific immunity and are associated with a wide range of complex diseases. Despite decades of research and many advances in the field, the characterization and interpretation of its genetic and genomic variability remain challenging. Here an overview is provided of the MHC, the nature of its exceptional variability, and the complex evolutionary processes assumed to drive this variability. Highlighted are also recent advances in the field that promise to improve our understanding of the variability in the MHC and in antigen-specific immunity more generally.

Nutritional status as prognostic factor of advanced oesophageal cancer patients treated with immune checkpoint inhibitors

BACKGROUND & AIMS

Malnutrition is reported in 60%-85% of oesophageal cancer (EC) patients. Indicators commonly used in the clinic to evaluate the nutritional status of patients include haemoglobin (Hb), body mass index (BMI), albumin (ALB), prognostic nutritional index (PNI), prealbumin (PAB), transferrin (TRF), and NRS2002 scores. In this study, we explored the associations between pretreatment nutrition-related indicators and clinical outcomes in patients with advanced EC who were treated with immune checkpoint inhibitors (ICIs).

METHODS

The general clinical data of patients, NRS2002 scores, PNI, and levels of BMI, ALB, Hb, PAB, and TRF at baseline were collected. Categorical variables were compared using the chi-squared test. The chi-squared test was used to compare the differences in the objective response rate (ORR) and the disease control rate (DCR) between groups. The Kaplan-Meier method was used to compute the survival curves. Cox proportional hazard regression was performed to evaluate factors independently associated with OS and PFS.

RESULT

Of the 1340 patients diagnosed with EC at Zhejiang Cancer Hospital between June 2018 and September 2022, 354 patients with advanced EC who underwent ICI therapy were enrolled. In total, the ORR and DCR were 38.1% and 82.2%, respectively. A significantly worse response to ICI therapy was observed in the Hb-L group and the BMI-L group. The median PFS and OS among all enrolled patients were 6.0 months and 13.3 months, respectively. PFS was significantly associated with pretreatment levels of Hb, ALB, and PAB. OS was significantly associated with pretreatment levels of Hb, ALB, BMI, PNI, TRF, and PAB. The multivariate analysis further demonstrated that Hb was an independent prognostic factor of both OS (P = 0.004) and PFS (P = 0.047), and ALB was an independent prognostic factor of OS (P = 0.045). No independent relevance for OS or PFS was found in the BMI, PNI, TRF, or PAB groups. In this study, the NRS2002 scores was not significantly correlated with the therapeutic response or prognosis of ICI therapy.

CONCLUSION

Our study indicated the relevance of nutritional status to the efficacy and prognosis of advanced EC patients treated with ICIs. The pretreatment levels of Hb and BMI were significantly related to therapeutic response. The pretreatment levels of Hb, BMI, ALB, PAB, TRF, and PNI were partially associated with survival. Notably, Hb is not only related to therapeutic response but is also an independent prognostic indicator of survival outcomes.

Pathways to hypermutation in high-grade gliomas: Mechanisms, syndromes, and opportunities for immunotherapy

Despite rapid advances in the field of immunotherapy, including the success of immune checkpoint inhibition in treating multiple cancer types, clinical response in high-grade gliomas (HGGs) has been disappointing. This has been in part attributed to the low tumor mutational burden (TMB) of the majority of HGGs. Hypermutation is a recently characterized glioma signature that occurs in a small subset of cases, which may open an avenue to immunotherapy. The substantially elevated TMB of these tumors most commonly results from alterations in the DNA mismatch repair pathway in the setting of extensive exposure to temozolomide or, less frequently, from inherited cancer predisposition syndromes. In this review, we discuss the genetics and etiology of hypermutation in HGGs, with an emphasis on the resulting genomic signatures, and the state and future directions of immuno-oncology research in these patient populations.

Efficacy and safety of regorafenib in combination with immune checkpoint inhibitor therapy as second-line and third-line regimen for patients with advanced hepatocellular carcinoma: a retrospective study

Background

Despite the emergence of immune checkpoint inhibitors (ICIs) as first-line treatment for advanced hepatocellular carcinoma (HCC), there is an unmet need regarding subsequent treatments in patients that fail ICI. Regorafenib is a vascular endothelial growth factor receptor (VEGFR) inhibitor, which could increase programmed death-ligand 1 (PD-L1) expression in tumors and increase intra-tumoral CD8+ T-cell infiltration by normalizing the cancer vasculature and improving the efficacy of the programmed cell death protein 1 (PD-1) antibody. Thus, we evaluated the combination of regorafenib and a PD-1 inhibitor for advanced HCC patients that had failed combined tyrosine kinase inhibitors (TKIs) plus ICI.

Methods

Data of patients with advanced HCC who had failed combined TKIs plus ICI treatment and were afterwards treated with combined regorafenib plus a PD-1 inhibitor were reviewed. All patients had received PD-1 inhibitors as part of the first-line treatment and regorafenib every 4 weeks until disease progression, intolerable toxicities, or physician/patient withdrawal. The clinical data, previous treatment strategies, follow-up imaging results, and adverse events (AEs) during follow-ups were recorded. Common Terminology Criteria for Adverse Events (CTCAE) v. 5.0 was used to evaluate AEs and Response Evaluation Criteria in Solid Tumors (RECIST) v. 1.1 was used to evaluate response. The primary endpoint was safety, and the secondary endpoints were the objective response rate (ORR), progression-free survival (PFS), disease control rate (DCR), overall survival (OS), and duration of response (DOR).

Results

From November 15, 2020, to January 31, 2022, data of 17 patients with advanced HCC that met the criteria were reviewed. The cohort included 16 men and 1 woman with a median age of 54 years (interquartile range, 46 to 63 years). Sixteen patients had Child-Pugh class A (n=16, 94.12%) and one with class B (n=1, 15.9%) liver disease. Thirteen patients received second-line treatment, and the remaining patients received third-line treatment. All patients received at least 1 dose of PD-1 inhibitors. The median follow-up duration was 7.62 months. Twelve recipients experienced treatment-related AEs. The most frequent AE (≥5%) included fatigue (17.64%), diarrhea (17.65%), proteinuria (5.88%), bleeding gums (11.76%), and hypertension (11.76%). No grade-4 AE or new safety signals were identified. The ORR and DCR were 41.2% and 64.7%, respectively, and the median PFS was 5.09 months.

Conclusions

Regorafenib combined with PD-1 inhibitor is a promising regimen in treating patients with advanced HCC owing to its safety and effectiveness as well as low incidence of serious AEs with its use.

Validation of a Machine Learning Model to Predict Immunotherapy Response in Head and Neck Squamous Cell Carcinoma

Head and neck squamous-cell carcinoma (HNSCC) is a disease with a generally poor prognosis; half of treated patients eventually develop recurrent and/or metastatic (R/M) disease. Patients with R/M HNSCC generally have incurable disease with a median survival of 10 to 15 months. Although immune-checkpoint blockade (ICB) has improved outcomes in patients with R/M HNSCC, identifying patients who are likely to benefit from ICB remains a challenge. Biomarkers in current clinical use include tumor mutational burden and immunohistochemistry for programmed death-ligand 1, both of which have only modest predictive power. Machine learning (ML) has the potential to aid in clinical decision-making as an approach to estimate a tumor's likelihood of response or a patient's likelihood of experiencing clinical benefit from therapies such as ICB. Previously, we described a random forest ML model that had value in predicting ICB response using 11 or 16 clinical, laboratory, and genomic features in a pan-cancer development cohort. However, its applicability to certain cancer types, such as HNSCC, has been unknown, due to a lack of cancer-type-specific validation. Here, we present the first validation of a random forest ML tool to predict the likelihood of ICB response in patients with R/M HNSCC. The tool had adequate predictive power for tumor response (area under the receiver operating characteristic curve = 0.65) and was able to stratify patients by overall (HR = 0.53 [95% CI 0.29-0.99], p = 0.045) and progression-free (HR = 0.49 [95% CI 0.27-0.87], p = 0.016) survival. The overall accuracy was 0.72. Our study validates an ML predictor in HNSCC, demonstrating promising performance in a novel cohort of patients. Further studies are needed to validate the generalizability of this algorithm in larger patient samples from additional multi-institutional contexts.

Synergetic regulation of cancer cells and exhausted T cells to fight cold tumors with a fluorinated EGCG-based nanocomplex

Immune therapy that targets PD-L1 (programmed cell death-ligand 1) is attractive to augment immune response by breaking the programmed cell death-1 (PD-1)/PD-L1 axis. However, T cell exhaustion associated with insufficient T cells infiltration may diminish the efficacy of cancer therapy. Here, we report a novel delivery system of FEGCG/FPEI@siTOX composed of fluorinated EGCG (FEGCG) and fluorinated polyethyleneimine (FPEI) for delivery of small interfering RNA anti-TOX (thymus high mobility group box protein, TOX) to treat tumor and metastasis. In this way, the reduction in PD-L1 expression by FEGCG can promote T-cell function, while inhibition of TOX expression with siTOX can alleviate T-cell exhaustion. FPEI are designed to deliver siRNA with high efficiency and low toxicity compared to classical PEI. Integrating FEGCG, FPEI and siTOX into such a novel system resulted in excellent anti-tumor and antimetastatic effects. It is a promising delivery system and potential strategy for the treatment of "cold" tumors.

Pretreatment albumin is a prognostic and predictive biomarker for response to atezolizumab across solid tumors

Objectives

Reliable predictive biomarkers for response to immune checkpoint inhibition (ICI) are lacking. Pretreatment serum albumin, a known prognostic and predictive factor in ICI-treated patients, has been proposed as a potential pharmacokinetic surrogate marker for anti-PD1/PD-L1 antibodies, as it shares a homeostatic pathway with IgG. However, this hypothesis is currently based on theoretical considerations and limited evidence from retrospective data. Therefore, we comprehensively investigated the prognostic and predictive value of pretreatment albumin and its relationship with anti-PD-L1 IgG levels.

Methods

We analysed pretreatment albumin and atezolizumab serum levels and clinical response in four trials (IMvigor210, IMvigor211, IMmotion151 and OAK) of patients with metastatic lung-, renal- or urothelial cancer who received atezolizumab alone or in combination.

Results

A total of 3391 patients were analysed. Correlation between serum albumin and atezolizumab levels was weak (Pearson's coefficient 0.23). We found a strong prognostic value for pretreatment serum albumin across all trials. Both atezolizumab serum levels and serum albumin were independently correlated with overall survival. Importantly, in the three randomised phase III clinical trials, the survival benefit for immunotherapy compared with the active comparator arm was limited to patients with pretreatment serum albumin > 35 g L-1.

Conclusion

Our data do not support the hypothesis that albumin serves as a surrogate for atezolizumab pharmacokinetics. However, we show that albumin on its own exerts strong prognostic value for patients treated with immunotherapy. As benefit from immunotherapy was limited to patients with normal/elevated serum albumin levels, baseline albumin could potentially be used as a predictive marker for immune checkpoint inhibition.

Quantifying the impact of immunotherapy on RNA dynamics in cancer

BACKGROUND

Checkpoint inhibitor (CPI) immunotherapies have provided durable clinical responses across a range of solid tumor types for some patients with cancer. Nonetheless, response rates to CPI vary greatly between cancer types. Resolving intratumor transcriptomic changes induced by CPI may improve our understanding of the mechanisms of sensitivity and resistance.

METHODS

We assembled a cohort of longitudinal pre-therapy and on-therapy samples from 174 patients treated with CPI across six cancer types by leveraging transcriptomic sequencing data from five studies.

RESULTS

Meta-analyses of published RNA markers revealed an on-therapy pattern of immune reinvigoration in patients with breast cancer, which was not discernible pre-therapy, providing biological insight into the impact of CPI on the breast cancer immune microenvironment. We identified 98 breast cancer-specific correlates of CPI response, including 13 genes which are known IO targets, such as toll-like receptors TLR1, TLR4, and TLR8, that could hold potential as combination targets for patients with breast cancer receiving CPI treatment. Furthermore, we demonstrate that a subset of response genes identified in breast cancer are already highly expressed pre-therapy in melanoma, and additionally we establish divergent RNA dynamics between breast cancer and melanoma following CPI treatment, which may suggest distinct immune microenvironments between the two cancer types.

CONCLUSIONS

Overall, delineating longitudinal RNA dynamics following CPI therapy sheds light on the mechanisms underlying diverging response trajectories, and identifies putative targets for combination therapy.

Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer

Tumor molecular datasets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning to analyze a single-cell, spatial, and highly multiplexed proteomic dataset from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcome. A novel multiplex immunohistochemistry antibody panel was used to audit T cell functionality and spatial localization in resected tumors from treatment-naive patients with localized pancreatic ductal adenocarcinoma (PDAC) compared to a second cohort of patients treated with neoadjuvant agonistic CD40 (αCD40) monoclonal antibody therapy. In total, nearly 2.5 million cells from 306 tissue regions collected from 29 patients across both treatment cohorts were assayed, and more than 1,000 tumor microenvironment (TME) features were quantified. We then trained machine learning models to accurately predict αCD40 treatment status and disease-free survival (DFS) following αCD40 therapy based upon TME features. Through downstream interpretation of the machine learning models' predictions, we found αCD40 therapy to reduce canonical aspects of T cell exhaustion within the TME, as compared to treatment-naive TMEs. Using automated clustering approaches, we found improved DFS following αCD40 therapy to correlate with the increased presence of CD44+ CD4+ Th1 cells located specifically within cellular spatial neighborhoods characterized by increased T cell proliferation, antigen-experience, and cytotoxicity in immune aggregates. Overall, our results demonstrate the utility of machine learning in molecular cancer immunology applications, highlight the impact of αCD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for αCD40-treated patients with PDAC.