Persistent mutation burden drives sustained anti-tumor immune responses

Targeting cellular senescence as a therapeutic vulnerability in gastric cancer

AIMS

Cellular senescence (CS) represents an intracellular defense mechanism responding to stress signals and can be leveraged as a "vulnerability" in cancer treatment. This study aims to construct a CS atlas for gastric cancer (GC) and uncover potential therapeutics for GC patients.

MATERIALS AND METHODS

38 senescence-associated regulators with prognostic significance in GC were obtained from the CellAge database to construct Gastric cancer-specific Senescence Score (GSS). Using eXtreme Sum algorism, GSS-based drug repositioning was conducted to identify drugs that could antagonize GSS in CMap database. In vitro experiments were conducted to test the effect of combination of palbociclib and exisulind in eliminating GC cells.

KEY FINDINGS

Patients with high GSS exhibited CS-related features, such as CS markers upregulation, adverse clinical outcomes and hypomethylation status. scRNA-seq data showed malignant cells with high GSS exhibited enhanced senescence state and more immunosuppressive signals such as PVR-CD96 compared with malignant cells with low GSS. In addition, the GSS-High cancer associated fibroblasts might secrete cytokines and chemokines such as IL-6, CXCL1, CXCL12, and CCL2 to from an immunosuppressive microenvironment, and GSS could serve as an indicator for immunotherapy resistance. Exisulind exhibited the greatest potential to reverse GSS. In vitro experiments demonstrated that exisulind could induce apoptosis and suppress the proliferation of palbociclib-induced senescent GC cells.

SIGNIFICANCE

Overall, GSS offers a framework for better understanding of correlation between senescence and GC, which might provide new insights into the development of novel therapeutics in GC.

Integrated analysis reveals critical cisplatin-resistance regulators E2F7 contributed to tumor progression and metastasis in lung adenocarcinoma

BACKGROUND

Drug resistance poses a significant challenge in cancer treatment, particularly as a leading cause of therapy failure. Cisplatin, the primary drug for lung adenocarcinoma (LUAD) chemotherapy, shows effective treatment outcomes. However, the development of resistance against cisplatin is a major obstacle. Therefore, identifying genes resistant to cisplatin and adopting personalized treatment could significantly improve patient outcomes.

METHODS

By examining transcriptome data of cisplatin-resistant LUAD cells from the GEO database, 181 genes associated with cisplatin resistance were identified. Using univariate regression analysis, random forest and multivariate regression analyses, two prognostic genes, E2F7 and FAM83A, were identified. This study developed a prognostic model utilizing E2F7 and FAM83A as key indicators. The Cell Counting Kit 8 assay, Transwell assay, and flow cytometry were used to detect the effects of E2F7 on the proliferation, migration, invasiveness and apoptosis of A549/PC9 cells. Western blotting was used to determine the effect of E2F7 on AKT/mTOR signaling pathway.

RESULTS

This study has pinpointed two crucial genes associated with cisplatin resistance, E2F7 and FAM83A, and developed a comprehensive model to assist in the diagnosis, prognosis, and evaluation of relapse risk in LUAD. Analysis revealed that patients at higher risk, according to these genetic markers, had elevated levels of immune checkpoints (PD-L1 and PD-L2). The prognostic and diagnosis values of E2F7 and FAM83A were further confirmed in clinical data. Furthermore, inhibiting E2F7 in lung cancer cells markedly reduced their proliferation, migration, invasion, and increased apoptosis. In vivo experiments corroborated these findings, showing reduced tumor growth and lung metastasis upon E2F7 suppression in lung cancer models.

CONCLUSION

Our study affirms the prognostic value of a model based on two DEGs, offering a reliable method for predicting the success of tumor immunotherapy in patients with LUAD. The diagnostic and predictive model based on these genes demonstrates excellent performance. In vitro, reducing E2F7 levels shows antitumor effects by blocking LUAD growth and progression. Further investigation into the molecular mechanisms has highlighted E2F7's effect on the AKT/mTOR signaling pathway, underscoring its therapeutic potential. In the era of personalized medicine, this DEG-based model promises to guide clinical practice.

Adjuvant PD-1 Checkpoint Inhibition in Early Cutaneous Melanoma: Immunological Mode of Action and the Role of Ultraviolet Radiation

Melanoma ranks as the fifth most common solid cancer in adults worldwide and is responsible for a significant proportion of skin-tumor-related deaths. The advent of immune checkpoint inhibition with anti-programmed death protein-1 (PD-1) antibodies has revolutionized the adjuvant treatment of high-risk, completely resected stage III/IV melanoma. However, not all patients benefit equally. Current strategies for improving outcomes involve adjuvant treatment in earlier disease stages (IIB/C) as well as perioperative treatment approaches. Interfering with T-cell exhaustion to counteract cancer immune evasion and the immunogenic nature of melanoma is key for anti-PD-1 effectiveness. Yet, the biological rationale for the efficacy of adjuvant treatment in clinically tumor-free patients remains to be fully elucidated. High-dose intermittent sun exposure (sunburn) is a well-known primary risk factor for melanomagenesis. Also, ultraviolet radiation (UVR)-induced immunosuppression may impair anti-cancer immune surveillance. In this review, we summarize the current knowledge about adjuvant anti-PD-1 blockade, including a characterization of the main cell types most likely responsible for its efficacy. In conclusion, we propose that local and systemic immunosuppression, to some extent UVR-mediated, can be restored by adjuvant anti-PD-1 therapy, consequently boosting anti-melanoma immune surveillance and the elimination of residual melanoma cell clones.

Are we getting closer to a successful neoantigen cancer vaccine?

Although significant advances in immunotherapy have revolutionized the treatment of many cancer types over the past decade, the field of vaccine therapy, an important component of cancer immunotherapy, despite decades-long intense efforts, is still transmitting signals of promises and awaiting strong data on efficacy to proceed with regulatory approval. The field of cancer vaccines faces standard challenges, such as tumor-induced immunosuppression, immune response in inhibitory tumor microenvironment (TME), intratumor heterogeneity (ITH), permanently evolving cancer mutational landscape leading to neoantigens, and less known obstacles: neoantigen gain/loss upon immunotherapy, the timing and speed of appearance of neoantigens and responding T cell clonotypes and possible involvement of immune interference/heterologous immunity, in the complex interplay between evolving tumor epitopes and the immune system. In this review, we discuss some key issues related to challenges hampering the development of cancer vaccines, along with the current approaches focusing on neoantigens. We summarize currently well-known ideas/rationales, thus revealing the need for alternative vaccine approaches. Such a discussion should stimulate vaccine researchers to apply out-of-box, unconventional thinking in search of new avenues to deal with critical, often yet unaddressed challenges on the road to a new generation of therapeutics and vaccines.

Tissue-specific thresholds of mutation burden associated with anti-PD-1/L1 therapy benefit and prognosis in microsatellite-stable cancers

Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 or its ligand (PD-1/L1) have expanded the treatment landscape against cancers but are effective in only a subset of patients. Tumor mutation burden (TMB) is postulated to be a generic determinant of ICI-dependent tumor rejection. Here we describe the association between TMB and survival outcomes among microsatellite-stable cancers in a real-world clinicogenomic cohort consisting of 70,698 patients distributed across 27 histologies. TMB was associated with survival benefit or detriment depending on tissue and treatment context, with eight cancer types demonstrating a specific association between TMB and improved outcomes upon treatment with anti-PD-1/L1 therapies. Survival benefits were noted over a broad range of TMB cutoffs across cancer types, and a dose-dependent relationship between TMB and outcomes was observed in a subset of cancers. These results have implications for the use of cancer-agnostic and universal TMB cutoffs to guide the use of anti-PD-1/L1 therapies, and they underline the importance of tissue context in the development of ICI biomarkers.

Activated B-Cells enhance epitope spreading to support successful cancer immunotherapy

Immune checkpoint therapies (ICT) have transformed the treatment of cancer over the past decade. However, many patients do not respond or suffer relapses. Successful immunotherapy requires epitope spreading, but the slow or inefficient induction of functional antitumoral immunity delays the benefit to patients or causes resistances. Therefore, understanding the key mechanisms that support epitope spreading is essential to improve immunotherapy. In this review, we highlight the major role played by B-cells in breaking immune tolerance by epitope spreading. Activated B-cells are key Antigen-Presenting Cells (APC) that diversify the T-cell response against self-antigens, such as ribonucleoproteins, in autoimmunity but also during successful cancer immunotherapy. This has important implications for the design of future cancer vaccines.

Prognostic and Immune Landscape Analysis of Ubiquitination-related Genes in Hepatocellular Carcinoma: Based on Bulk and Single-cell RNA Sequencing Data

Background: Despite significant advances in tumor immunotherapy, hepatocellular carcinoma (HCC) remains a malignancy with a challenging prognosis. The increasing research emphasizes the crucial role of ubiquitination in tumor immunotherapy. However, the establishment of prognostic signatures based on ubiquitination-related genes (UbRGs) and their role in immunotherapy are still lacking in HCC. Methods: We employed datasets from TCGA and GEO for transcriptome differential expression analysis and single-cell RNA sequencing analysis. Applying weighted gene co-expression network analysis, cox regression, lasso, selection and visualization of the most relevant features, and gradient boosting machine, we identified hub UbRGs as a gene signature to develop a prognostic model. We evaluated the predictive utility concerning clinical characteristics as well as its role in the immune landscape and immunotherapy potential. Additionally, western blotting, reverse transcription-quantitative PCR, and immunofluorescence were employed to detect the expression and sub-localization of hub genes. Results: Three hub UbRGs (BOP1, CDC20, and UBE2S) were identified as a gene signature. In particular, the high-risk group exhibited notable characteristics, including higher tumor mutation burden, enrichment in immune-related pathways, up-regulation immune checkpoint, and higher immunity scores. Treatment response to immunotherapy varied based on the expression of PD-1 and CTLA-4. Furthermore, single-cell data analysis revealed heterogeneous expression of hub UbRGs across different cell subtypes, while cytological experiments provided additional confirmation of the high expression of hub UbRGs in HCC. Conclusion: Our study provides valuable insights into the identification of novel ubiquitination-related biomarkers with potential applications for prognosis, immunotherapy prediction, and drug sensitivity in HCC.

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.

Islands of genomic stability in the face of genetically unstable metastatic cancer

Introduction

Metastatic cancer affects millions of people worldwide annually and is the leading cause of cancer-related deaths. Most patients with metastatic disease are not eligible for surgical resection, and current therapeutic regimens have varying success rates, some with 5-year survival rates below 5%. Here we test the hypothesis that metastatic cancer can be genetically targeted by exploiting single base substitution mutations unique to individual cells that occur as part of normal aging prior to transformation. These mutations are targetable because ~10% of them form novel tumor-specific "NGG" protospacer adjacent motif (PAM) sites targetable by CRISPR-Cas9.

Methods

Whole genome sequencing was performed on five rapid autopsy cases of patient-matched primary tumor, normal and metastatic tissue from pancreatic ductal adenocarcinoma decedents. CRISPR-Cas9 PAM targets were determined by bioinformatic tumor-normal subtraction for each patient and verified in metastatic samples by high-depth capture-based sequencing.

Results

We found that 90% of PAM targets were maintained between primary carcinomas and metastases overall. We identified rules that predict PAM loss or retention, where PAMs located in heterozygous regions in the primary tumor can be lost in metastases (private LOH), but PAMs occurring in regions of loss of heterozygosity (LOH) in the primary tumor were universally conserved in metastases.

Conclusions

Regions of truncal LOH are strongly retained in the presence of genetic instability, and therefore represent genetic vulnerabilities in pancreatic adenocarcinomas. A CRISPR-based gene therapy approach targeting these regions may be a novel way to genetically target metastatic cancer.

Genetic insights into carbohydrate sulfotransferase 8 and its impact on the immunotherapy efficacy of cancer

Immune checkpoint blockade (ICB) is a promising therapy for solid tumors, but its effectiveness depends on biomarkers that are not precise. Here, we utilized genome-wide association study to investigate the association between genetic variants and tumor mutation burden to interpret ICB response. We identified 16 variants (p < 5 × 10-8) probed to 17 genes on 9 chromosomes. Subsequent analysis of one of the most significant loci in 19q13.11 suggested that the rs111308825 locus at the enhancer is causal, as its A allele impairs KLF2 binding, leading to lower carbohydrate sulfotransferase 8 (CHST8) expression. Breast cancer cells expressing CHST8 suppress T cell activation, and Chst8 loss attenuates tumor growth in a syngeneic mouse model. Further investigation revealed that programmed death-ligand 1 (PD-L1) and its homologs could be sulfated by CHST8, resulting in M2-like macrophage enrichment in the tumor microenvironment. Finally, we confirmed that low-CHST8 tumors have better ICB response, supporting the genetic effect and clinical value of rs111308825 for ICB efficacy prediction.

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.

Predicting patient outcomes after treatment with immune checkpoint blockade: A review of biomarkers derived from diverse data modalities

Immune checkpoint blockade (ICB) therapy targeting cytotoxic T-lymphocyte-associated protein 4, programmed death 1, and programmed death ligand 1 has shown durable remission and clinical success across different cancer types. However, patient outcomes vary among disease indications. Studies have identified prognostic biomarkers associated with immunotherapy response and patient outcomes derived from diverse data types, including next-generation bulk and single-cell DNA, RNA, T cell and B cell receptor sequencing data, liquid biopsies, and clinical imaging. Owing to inter- and intra-tumor heterogeneity and the immune system's complexity, these biomarkers have diverse efficacy in clinical trials of ICB. Here, we review the genetic and genomic signatures and image features of ICB studies for pan-cancer applications and specific indications. We discuss the advantages and disadvantages of computational approaches for predicting immunotherapy effectiveness and patient outcomes. We also elucidate the challenges of immunotherapy prognostication and the discovery of novel immunotherapy targets.

Neoadjuvant chemotherapy plus camrelizumab for locally advanced cervical cancer (NACI study): a multicentre, single-arm, phase 2 trial

BACKGROUND

Locally advanced cervical cancer constitutes around 37% of cervical cancer cases globally and has a poor prognosis due to limited therapeutic options. Immune checkpoint inhibitors in the neoadjuvant setting could address these challenges. We aimed to investigate the efficacy and safety of neoadjuvant chemo-immunotherapy for locally advanced cervical cancer.

METHODS

In this single-arm, phase 2 trial, which was done across eight tertiary hospitals in China, we enrolled patients aged 18-70 years with untreated cervical cancer (IB3, IIA2, or IIB/IIIC1r with a tumour diameter ≥4 cm [International Federation of Gynecology and Obstetrics, 2018]) and an Eastern Cooperative Oncology Group performance status of 0 or 1. Eligible patients underwent one cycle of priming doublet chemotherapy (75-80 mg/m2 cisplatin, intravenously, plus 260 mg/m2 nab-paclitaxel, intravenously), followed by two cycles of a combination of chemotherapy (cisplatin plus nab-paclitaxel) on day 1 with camrelizumab (200 mg, intravenously) on day 2, with a 3-week interval between treatment cycles. Patients with stable disease or progressive disease received concurrent chemoradiotherapy, and patients with a complete response or partial response proceeded to radical surgery. The primary endpoint was the objective response rate, by independent central reviewer according to Response Evaluation Criteria in Solid Tumours, version 1.1. Activity and safety were analysed in patients who received at least one dose of camrelizumab. This study is registered with ClinicalTrials.gov, NCT04516616, and is ongoing.

FINDINGS

Between Dec 1, 2020, and Feb 10, 2023, 85 patients were enrolled and all received at least one dose of camrelizumab. Median age was 51 years (IQR 46-57) and no data on race or ethnicity were collected. At data cutoff (April 30, 2023), median follow-up was 11·0 months (IQR 6·0-14·5). An objective response was noted in 83 (98% [95% CI 92-100]) patients, including 16 (19%) patients who had a complete response and 67 (79%) who had a partial response. The most common grade 3-4 treatment-related adverse events during neoadjuvant chemo-immunotherapy were lymphopenia (21 [25%] of 85), neutropenia (ten [12%]), and leukopenia (seven [8%]). No serious adverse events or treatment-related deaths occurred.

INTERPRETATION

Neoadjuvant chemo-immunotherapy showed promising antitumour activity and a manageable adverse event profile in patients with locally advanced cervical cancer. The combination of neoadjuvant chemo-immunotherapy with radical surgery holds potential as a novel therapeutic approach for locally advanced cervical cancer.

FUNDING

National Key Technology Research and Development Program of China and the National Clinical Research Center of Obstetrics and Gynecology.

Challenges and Future Directions in the Management of Tumor Mutational Burden-High (TMB-H) Advanced Solid Malignancies

A standardized assessment of Tumor Mutational Burden (TMB) poses challenges across diverse tumor histologies, treatment modalities, and testing platforms, requiring careful consideration to ensure consistency and reproducibility. Despite clinical trials demonstrating favorable responses to immune checkpoint inhibitors (ICIs), not all patients with elevated TMB exhibit benefits, and certain tumors with a normal TMB may respond to ICIs. Therefore, a comprehensive understanding of the intricate interplay between TMB and the tumor microenvironment, as well as genomic features, is crucial to refine its predictive value. Bioinformatics advancements hold potential to improve the precision and cost-effectiveness of TMB assessments, addressing existing challenges. Similarly, integrating TMB with other biomarkers and employing comprehensive, multiomics approaches could further enhance its predictive value. Ongoing collaborative endeavors in research, standardization, and clinical validation are pivotal in harnessing the full potential of TMB as a biomarker in the clinic settings.

Tumor mutation burden in the prognosis and response of lung cancer patients to immune-checkpoint inhibition therapies

Immune checkpoint inhibition (ICI) therapies have reshaped the therapeutic landscape in lung cancer management, providing first-time improvements in patient response, prognosis, and overall survival. Despite their clinical effectiveness, variability in treatment responsiveness, as well as drug resistance, have led to a compelling need for predictive biomarkers facilitating the individualized selection of the most efficient therapeutic approach. Significant progress has been made in the identification of such biomarkers, with tumor mutation burden (ΤΜΒ) appearing as the leading and most promising predictive biomarker for the efficacy of ICIs in non-small cell lung cancer (NSCLC) among other tumors. Anti-PD-1/PD-L1 and anti-CTLA-4 antibodies have been extensively studied and clinically utilized. However, the overall efficiency of these drugs remains unsatisfactory, urging for the investigation of novel inhibitors, such as those targeting LAG-3, TIM-3, TIGIT and VISTA, which could be used either as a monotherapy or synergistically with the PD-1/PD-L1 or CTLA-4 blockers. Here, we investigate the role of TMB and cancer neoantigens as predictive biomarkers in the response of lung cancer patients to different ICI therapies, specifically focusing on the most recent immune checkpoint inhibitors, against LAG-3, TIM-3, TIGIT and VISTA. We further discuss the new trends in immunotherapies, including CAR T-cell therapy and personalized tumor vaccines. We also review further potential biomarkers that could be used in lung cancer response to immunotherapy, such as PD-L1+ IHC, MSI/dMMR, tumor infiltrating lymphocytes (TILs), as well as the role of the microbiome and circulating tumor DNA (ctDNA). Finally, we discuss the limitations and challenges of each.

Detection, mechanisms, and therapeutic implications of oncometabolites

Metabolic abnormalities are a hallmark of cancer cells and are essential to tumor progression. Oncometabolites have pleiotropic effects on cancer biology and affect a plethora of processes, from oncogenesis and metabolism to therapeutic resistance. Targeting oncometabolites, therefore, could offer promising therapeutic avenues against tumor growth and resistance to treatments. Recent advances in characterizing the metabolic profiles of cancer cells are shedding light on the underlying mechanisms and associated metabolic networks. This review summarizes the diverse detection methods, molecular mechanisms, and therapeutic targets of oncometabolites, which may lead to targeting oncometabolism for cancer therapy.

Circulating tumor DNA as liquid biopsy in lung cancer: Biological characteristics and clinical integration

Lung cancer maintains high morbidity and mortality rate globally despite significant advancements in diagnosis and treatment in the era of precision medicine. Pathological analysis of tumor tissue, the current gold standard for lung cancer diagnosis, is intrusive and intrinsically confined to evaluating the limited amount of tissues that could be physically extracted. However, tissue biopsy has several limitations, including the invasiveness of the procedure and difficulty in obtaining samples for patients at advanced stages., there Additionally,has been no major breakthrough in tumor biomarkers with high specificity and sensitivity, particularly for early-stage lung cancer. Liquid biopsy has been considered a feasible auxiliary tool for tearly dianosis, evaluating treatment responses and monitoring prognosis of lung cancer. Circulating tumor DNA (ctDNA), an ideal biomarker of liquid biopsy, has emerged as one of the most reliable tools for monitoring tumor processes at molecular levels. Herein, this review focuses on tumor heterogeneity to elucidate the superiority of liquid biopsy and retrospectively discussdeciphersolution. We systematically elaborate ctDNA biological characteristics, introduce methods for ctDNA detection, and discuss the current role of plasma ctDNA in lung cancer management. Finally, we summarize the drawbacks of ctDNA analysis and highlight its potential clinical application in lung cancer.

Genetic Testing Enhances the Precision Diagnosis and Treatment of Breast Cancer

The contemporary comprehension of breast cancer has progressed to the molecular level. As a heterogeneous malignancy, conventional pathological diagnosis and histological classification could no longer meet the needs of precisely managing breast cancer. Genetic testing based on gene expression profiles and gene mutations has emerged and substantially contributed to the precise diagnosis and treatment of breast cancer. Multigene assays (MGAs) are explored for early-stage breast cancer patients, aiding the selection of adjuvant therapy and predicting prognosis. For metastatic breast cancer patients, testing specific genes indicates potentially effective antitumor agents. In this review, genetic testing in early-stage and metastatic breast cancer is summarized, as well as the advantages and challenges of genetic testing in breast cancer.

Clinical and Molecular Features of Long-term Response to Immune Checkpoint Inhibitors in Patients with Advanced Non-Small Cell Lung Cancer

PURPOSE

We sought to identify features of patients with advanced non-small cell lung cancer (NSCLC) who achieve long-term response (LTR) to immune checkpoint inhibitors (ICI), and how these might differ from features predictive of short-term response (STR).

EXPERIMENTAL DESIGN

We performed a multicenter retrospective analysis of patients with advanced NSCLC treated with ICIs between 2011 and 2022. LTR and STR were defined as response ≥ 24 months and response < 12 months, respectively. Tumor programmed death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), next-generation sequencing (NGS), and whole-exome sequencing (WES) data were analyzed to identify characteristics enriched in patients achieving LTR compared with STR and non-LTR.

RESULTS

Among 3,118 patients, 8% achieved LTR and 7% achieved STR, with 5-year overall survival (OS) of 81% and 18% among LTR and STR patients, respectively. High TMB (≥50th percentile) enriched for LTR compared with STR (P = 0.001) and non-LTR (P < 0.001). Whereas PD-L1 ≥ 50% enriched for LTR compared with non-LTR (P < 0.001), PD-L1 ≥ 50% did not enrich for LTR compared with STR (P = 0.181). Nonsquamous histology (P = 0.040) and increasing depth of response [median best overall response (BOR) -65% vs. -46%, P < 0.001] also associated with LTR compared with STR; no individual genomic alterations were uniquely enriched among LTR patients.

CONCLUSIONS

Among patients with advanced NSCLC treated with ICIs, distinct features including high TMB, nonsquamous histology, and depth of radiographic improvement distinguish patients poised to achieve LTR compared with initial response followed by progression, whereas high PD-L1 does not.

Alkylating Agent-Induced High Tumor Mutational Burden in Medullary Thyroid Cancer and Response to Immune Checkpoint Inhibitors: Two Case Reports

Background: Patients with metastatic medullary thyroid cancer (MTC) who progressed under tyrosine kinase inhibitors can benefit from an alkylating agent such as dacarbazine or temozolomide. Patient Findings: We describe two patients with metastatic MTC who developed a hypermutant phenotype after alkylating agent treatment. This phenotype was characterized by a high tumor mutational burden (TMB) and a mutational signature indicative of alkylating agent mutagenesis (single-base substitution 11). Both patients received immune checkpoint inhibitors, with partial morphological responses, clinical benefit, and progression-free survival of 6 and 9 months, respectively. Summary and Conclusions: Based on the described observations, we suggest that a hypermutant phenotype may be induced after alkylating agent treatment for MTC and the sequential use of immunotherapy should be further explored as a treatment option for MTC patients with increased TMB.

Senescence risk score: a multifaceted prognostic tool predicting outcomes, stemness, and immune responses in colorectal cancer

Colorectal cancer (CRC) remains a primary cause of cancer mortality globally, necessitating precise prognostic indicators for effective clinical management. Our study introduces the Senescence Risk Score (SRRS), based on several senescence-related genes (SRGs), a potent prognostic tool designed to measure cellular senescence in CRC. The higher SRRS predicts a poorer prognosis, providing a novel and efficient approach to patient stratification. Notably, we found that SRRS correlates with methylation and mutation variations, and increased immune infiltration in the tumor microenvironment, thus revealing potential therapeutic targets. We also discovered an inverse relationship between SRRS and cell stemness, which could have significant implications for cancer treatment strategies. Utilizing bioinformatics resources and machine learning, we identified LIMK1 and WRN as key genes associated with SRRS, further enhancing its prognostic value. Importantly, the modulation of these genes significantly impacts cellular senescence, proliferation, and stemness in CRC cells. In summary, our development of SRRS offers a powerful tool for CRC prognosis and paves the way for novel therapeutic strategies, underscoring its potential in transforming CRC patient management.

A workflow to study mechanistic indicators for driver gene prediction with Moonlight

Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2, SF3B4, KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4, KIF26B, ARL15, ARHGAP25, EMCN, GMFG, TPK1, NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R.

Neoantigen identification: Technological advances and challenges

Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.

Neoadjuvant immune checkpoint blockade: A window of opportunity to advance cancer immunotherapy

Among new treatment approaches for patients with cancer, few have accelerated as quickly as neoadjuvant immune checkpoint blockade (ICB). Neoadjuvant cancer therapy is administered before curative-intent surgery in treatment-naïve patients. Conventional neoadjuvant chemotherapy and radiotherapy are primarily intended to reduce tumor size, improving surgical resectability. However, recent scientific evidence outlined here suggests that neoadjuvant immunotherapy can expand and transcriptionally modify tumor-specific T cell clones to enhance both intratumoral and systemic anti-tumor immunity. It further offers a unique "window of opportunity" to explore mechanisms and identify novel biomarkers of ICB response and resistance, opening possibilities for refining long-term clinical outcome predictions and developing new, more highly effective ICB combination therapies. Here, we examine advances in clinical and scientific knowledge gleaned from studies in select cancers and describe emerging key principles relevant to neoadjuvant ICB across many cancer types.

TAGAP expression influences CD4+ T cell differentiation, immune infiltration, and cytotoxicity in LUAD through the STAT pathway: implications for immunotherapy

Background

T-cell Activation GTPase Activating Protein (TAGAP) plays a role in immune cell regulation. This study aimed to investigate TAGAP's expression and its potential impact on CD4+ T cell function and prognosis in lung adenocarcinoma (LUAD).

Methods

We analyzed TAGAP expression and its correlation with immune infiltration and clinical data in LUAD patients using multiple datasets, including The Cancer Genome Atlas (TCGA-LUAD), Gene Expression Omnibus (GEO), and scRNA-seq datasets. In vitro and in vivo experiments were conducted to explore the role of TAGAP in CD4+ T cell function, chemotaxis, and cytotoxicity.

Results

TAGAP expression was significantly lower in LUAD tissues compared to normal tissues, and high TAGAP expression correlated with better prognosis in LUAD patients. TAGAP was positively correlated with immune/stromal/ESTIMATE scores and immune cell infiltration in LUAD. Single-cell RNA sequencing revealed that TAGAP was primarily distributed in CD4+/CD8+ T cells. In vitro experiments showed that TAGAP overexpression enhanced CD4+ T cell cytotoxicity, proliferation, and chemotaxis. Gene Set Enrichment Analysis (GSEA) indicated that TAGAP was enriched in the JAK-STAT signaling pathway. In vivo experiments in a xenograft tumor model demonstrated that TAGAP overexpression suppressed tumor growth and promoted CD4+ T cell cytotoxicity.

Conclusions

TAGAP influences CD4+ T cell differentiation and function in LUAD through the STAT pathway, promoting immune infiltration and cytotoxicity. This study provides a scientific basis for developing novel LUAD immunotherapy strategies and exploring new therapeutic targets.

Immune checkpoint therapy for solid tumours: clinical dilemmas and future trends

Immune-checkpoint inhibitors (ICBs), in addition to targeting CTLA-4, PD-1, and PD-L1, novel targeting LAG-3 drugs have also been approved in clinical application. With the widespread use of the drug, we must deeply analyze the dilemma of the agents and seek a breakthrough in the treatment prospect. Over the past decades, these agents have demonstrated dramatic efficacy, especially in patients with melanoma and non-small cell lung cancer (NSCLC). Nonetheless, in the field of a broad concept of solid tumours, non-specific indications, inseparable immune response and side effects, unconfirmed progressive disease, and complex regulatory networks of immune resistance are four barriers that limit its widespread application. Fortunately, the successful clinical trials of novel ICB agents and combination therapies, the advent of the era of oncolytic virus gene editing, and the breakthrough of the technical barriers of mRNA vaccines and nano-delivery systems have made remarkable breakthroughs currently. In this review, we enumerate the mechanisms of each immune checkpoint targets, associations between ICB with tumour mutation burden, key immune regulatory or resistance signalling pathways, the specific clinical evidence of the efficacy of classical targets and new targets among different tumour types and put forward dialectical thoughts on drug safety. Finally, we discuss the importance of accurate triage of ICB based on recent advances in predictive biomarkers and diagnostic testing techniques.

The core role of macrophages in hepatocellular carcinoma: the definition of molecular subtypes and the prognostic risk system

Background: In patients with hepatocellular carcinoma (HCC), the tumor microenvironment (TME) is resistant to immunotherapy because of its specificity. It is meaningful to explore the role of macrophage, which is one of the most abundant immune cells in the TME, in cellular communication and its effect on the prognosis and immunotherapy of HCC. Methods: Dimensionality reduction and clustering of the single-cell RNA-seq data from the GSE149614 dataset were carried out to identify the cellular composition of HCC. CellChat was used to analyze the communication between different cells. The specifically highly expressed genes of macrophages were extracted for univariate Cox regression analysis to obtain prognostic genes for HCC cluster analysis, and the risk system of macrophage-specifically highly expressed genes was developed by random forest analysis and multivariate Cox regression analysis. Prognosis, TME infiltration, potential responses to immunotherapy, and antineoplastic drugs were compared among molecular subtypes and between risk groups. Results: We found that HCC included nine identifiable cell types, of which macrophages had the highest communication intensity with each of the other eight cell types. Of the 179 specifically highly expressed genes of macrophage, 56 were significantly correlated with the prognosis of HCC, which classified HCC into three subtypes, which were reproducible and produced different survival outcomes, TME infiltration, and immunotherapy responses among the subtypes. In the integration of four macrophage-specifically highly expressed genes for the development of a risk system, the risk score was significantly involved in higher immune cell infiltration, poor prognosis, immunotherapy response rate, and sensitivity of six drugs. Conclusion: In this study, through single-cell RNA-seq data, we identified nine cell types, among which macrophage had the highest communication intensity with the rest of the cell types. Based on specifically highly expressed genes of macrophage, we successfully divided HCC patients into three clusters with distinct prognosis, TME, and therapeutic response. Additionally, a risk system was constructed, which provided a potential reference index for the prognostic target and preclinical individualized treatment of HCC.

Tumour mutational burden as a biomarker in patients with mismatch repair deficient/microsatellite instability-high metastatic colorectal cancer treated with immune checkpoint inhibitors

BACKGROUND

Immune checkpoint inhibitors (ICIs) are the standard treatment in patients with mismatch repair deficient (dMMR)/microsatellite instability-high (MSI-H) metastatic colorectal cancer (mCRC). Tumour mutational burden (TMB) is a promising biomarker for the prediction of treatment outcomes.

PATIENTS AND METHODS

We screened 203 patients with dMMR/MSI-H mCRC treated with an anti-PD-(L)1 (anti-Programmed-Death-(Ligand)1) plus or minus an anti-Cytotoxic T-Lymphocyte Antigen 4 (anti-CTLA-4) agent at three Italian Academic Centers. TMB was tested by Foundation One Next Generation Sequencing assay and correlated with clinical outcomes, in the overall population and according to ICI regimen.

RESULTS

We included 110 patients with dMMR/MSI-H mCRC. Eighty patients received anti-PD-(L)1 monotherapy and 30 received anti-CTLA-4 combinations. Median TMB was 49 mut/Mb (range: 8-251 mut/Mb). The optimal prognostic cut-off for progression-free survival (PFS) stratification was 23 mut/Mb. Patients with TMB ≤23 mut/Mb had significantly worse PFS (adjusted Hazard Ratio [aHR] = 4.26, 95% confidence interval [CI]:1.85-9.82, p = 0.001) and overall survival (OS) (aHR = 5.14, 95% CI: 1.76-14.98, p = 0.003). Using a cut-off optimised for predicting treatment outcome, anti-CTLA-4 combination was associated with a significant PFS/OS benefit versus anti-PD-(L)1 monotherapy in patients with TMB>40 mut/Mb (2-year PFS: 100.0% versus 70.7%, p = 0.002; 2-year OS: 100.0% versus 76.0%, p = 0.025), but not in those with TMB ≤40 mut/Mb (2-year PFS: 59.7% versus 68.6%, p = 0.888; 2-year OS: 80.0% versus 81.0%, p = 0.949).

CONCLUSION

Patients with dMMR/MSI-H mCRC and relatively lower TMB value displayed early disease progression when receiving ICIs, whereas patients with the highest TMB values may obtain the maximal benefit from intensified anti-CTLA-4/PD-1 combination.

PANoptosis-based molecular subtyping and HPAN-index predicts therapeutic response and survival in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a highly prevalent and fatal cancer. The role of PANoptosis, a novel form of programmed cell death, in HCC is yet to be fully understood. This study focuses on identifying and analyzing PANoptosis-associated differentially expressed genes in HCC (HPAN_DEGs), aiming to enhance our understanding of HCC pathogenesis and potential treatment strategies.

Methods

We analyzed HCC differentially expressed genes from TCGA and IGCG databases and mapped them to the PANoptosis gene set, identifying 69 HPAN_DEGs. These genes underwent enrichment analyses, and consensus clustering analysis was used to determine three distinct HCC subgroups based on their expression profiles. The immune characteristics and mutation landscape of these subgroups were evaluated, and drug sensitivity was predicted using the HPAN-index and relevant databases.

Results

The HPAN_DEGs were mainly enriched in pathways associated with the cell cycle, DNA damage, Drug metabolism, Cytokines, and Immune receptors. We identified three HCC subtypes (Cluster_1, SFN+PDK4-; Cluster_2, SFN-PDK4+; Cluster_3, SFN/PDK4 intermediate expression) based on the expression profiles of the 69 HPAN_DEGs. These subtypes exhibited distinct clinical outcomes, immune characteristics, and mutation landscapes. The HPAN-index, generated by machine learning using the expression levels of 69 HPAN_DEGs, was identified as an independent prognostic factor for HCC. Moreover, the high HPAN-index group exhibited a high response to immunotherapy, while the low HPAN-index group showed sensitivity to small molecule targeted drugs. Notably, we observed that the YWHAB gene plays a significant role in Sorafenib resistance.

Conclusion

This study identified 69 HPAN_DEGs crucial to tumor growth, immune infiltration, and drug resistance in HCC. Additionally, we discovered three distinct HCC subtypes and constructed an HPAN-index to predict immunotherapeutic response and drug sensitivity. Our findings underscore the role of YWHAB in Sorafenib resistance, presenting valuable insights for personalized therapeutic strategy development in HCC.

TMBserval: a statistical explainable learning model reveals weighted tumor mutation burden better categorizing therapeutic benefits

A high tumor mutation burden (TMB) is known to drive the response to immune checkpoint inhibitors (ICI) and is associated with favorable prognoses. However, because it is a one-dimensional numerical representation of non-synonymous genetic alterations, TMB suffers from clinical challenges due to its equal quantification. Since not all mutations elicit the same antitumor rejection, the effect on immunity of neoantigens encoded by different types or locations of somatic mutations may vary. In addition, other typical genomic features, including complex structural variants, are not captured by the conventional TMB metric. Given the diversity of cancer subtypes and the complexity of treatment regimens, this paper proposes that tumor mutations capable of causing various degrees of immunogenicity should be calculated separately. TMB should therefore, be segmented into more exact, higher dimensional feature vectors to exhaustively measure the foreignness of tumors. We systematically reviewed patients' multifaceted efficacy based on a refined TMB metric, investigated the association between multidimensional mutations and integrative immunotherapy outcomes, and developed a convergent categorical decision-making framework, TMBserval (Statistical Explainable machine learning with Regression-based VALidation). TMBserval integrates a multiple-instance learning concept with statistics to create a statistically interpretable model that addresses the broad interdependencies between multidimensional mutation burdens and decision endpoints. TMBserval is a pan-cancer-oriented many-to-many nonlinear regression model with discrimination and calibration power. Simulations and experimental analyses using data from 137 actual patients both demonstrated that our method could discriminate between patient groups in a high-dimensional feature space, thereby rationally expanding the beneficiary population of immunotherapy.

The Efficacy of Tumor Mutation Burden as a Biomarker of Response to Immune Checkpoint Inhibitors

Cancer is one of the leading causes of death in the world; therefore, extensive research has been dedicated to exploring potential therapeutics, including immune checkpoint inhibitors (ICIs). Initially, programmed-death ligand-1 was the biomarker utilized to predict the efficacy of ICIs. However, its heterogeneous expression in the tumor microenvironment, which is critical to cancer progression, promoted the exploration of the tumor mutation burden (TMB). Research in various cancers, such as melanoma and lung cancer, has shown an association between high TMB and response to ICIs, increasing its predictive value. However, the TMB has failed to predict ICI response in numerous other cancers. Therefore, future research is needed to analyze the variations between cancer types and establish TMB cutoffs in order to create a more standardized methodology for using the TMB clinically. In this review, we aim to explore current research on the efficacy of the TMB as a biomarker, discuss current approaches to overcoming immunoresistance to ICIs, and highlight new trends in the field such as liquid biopsies, next generation sequencing, chimeric antigen receptor T-cell therapy, and personalized tumor vaccines.