Intra-tumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from the Cancer Genome Atlas

A machine learning-derived angiogenesis signature for clinical prognosis and immunotherapy guidance in colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most prevalent malignancies worldwide and its prognosis is extremely poor. Angiogenesis has been linked to clinical outcomes, tumor progression, and treatment sensitivity. However, the role of angiogenesis in the COAD microenvironment and its interaction with immunotherapy remains unclear. In this study, an integrative machine learning approach, including ten algorithms, was used to construct a prognostic consensus angiogenesis-related signature (CARS) for COAD. The optimal CARS constructed using the RSF + StepCox [forward] algorithm had superior performance for clinical prognostic prediction and served as an independent risk predictor for COAD. Patients in the low-CARS group, characterized by immune activation, elevated tumor mutation/neoantigen burden, and greater responsiveness to immunotherapy, had a superior prognosis. Patients in the high-CARS group exhibited a poor prognosis with higher angiogenesis activity and immunosuppressive status, indicating lower immunotherapy benefits. However, axitinib and olaparib may be promising treatment options for such patients. Taken together, we constructed a prognostic CARS that provides prognostic stratification and elucidates the characteristics of the tumor microenvironment, which might guide the selection of personalized treatments for patients with COAD.

Innovative implications of botulinum toxin in head and neck cancer: Exploring novel opportunities and future prospects

Head and neck cancer (HNC) represents a significant global health challenge, with over 660,000 annual diagnoses and a mortality rate exceeding 49 %, making it the seventh most common cancer worldwide. Current standard treatments, including surgery, radiation, and chemotherapy, often result in significant side effects, underscoring the need for innovative and personalized therapeutic approaches. In recent years, botulinum toxin (BoNT) has emerged as a transformative adjunctive therapy in HNC management. Initially recognized for its neuromuscular blocking properties, BoNT has expanded its applications to alleviate complications such as sialorrhea, gustatory sweating, and neuropathic pain associated with HNC treatment. Beyond symptomatic relief, emerging evidence suggests that BoNT may influence tumor biology by modulating the tumor microenvironment, disrupting nerve-cancer interactions, and altering key molecular pathways to inhibit tumor growth and metastasis. This article explores BoNT's achievements and therapeutic potential in treating HNC, examining its molecular mechanisms, clinical efficacy, and implications for future research. By elucidating BoNT's role in symptom management and its potential as an anti-tumor agent, this review highlights a promising avenue for advancing personalized medicine and improving outcomes for HNC patients.

Advances in research on receptor heterogeneity in breast cancer liver metastasis

Breast cancer liver metastasis (BCLM) presents a critical challenge in breast cancer treatment and has substantial epidemiological and clinical significance. Receptor status is pivotal in managing both primary breast cancer and its liver metastases. Moreover, shifts in these statuses can have a profound impact on patient treatment strategies and prognoses. Research has indicated that there is significant heterogeneity in receptor status between primary breast cancer and liver metastases. This variation may be influenced by a multitude of factors, such as therapeutic pressure, inherent tumor heterogeneity, clonal evolution, and the unique microenvironment of the liver. Changes in the receptor status of BCLM are crucial for adjusting treatment strategies, and liver biopsy plays an important role in the treatment process. Directions for future research targeting changes in receptor status include in-depth study of molecular mechanisms, combined treatment strategies for receptor status reversal, development of artificial intelligence deep learning models to predict receptor status in liver metastases, and clinical research on new drug development and combination therapies. That research will provide more precise treatment strategies for patients with BCLM and improve their prognosis.

Multi‑omics analysis identifies different molecular subtypes with unique outcomes in early-stage poorly differentiated lung adenocarcinoma

INTRODUCTION

Early-stage poorly differentiated lung adenocarcinoma (LUAD) is plagued by a high risk of postoperative recurrence, and its prognostic heterogeneity complicates treatment and surveillance planning. We conducted this integrative multi-omics study to identify those patients with a truly high risk of adverse outcomes.

METHODS

Whole-exome, RNA and whole methylome sequencing were carried out on 101 treatment-naïve early-stage poorly differentiated LUADs. Integrated analyses were conducted to disclose molecular characteristics and explore molecular subtyping. Functional validation of key molecules was carried out through in vitro and in vivo experiments.

RESULTS

Recurrent tumors exhibited significantly higher ploidy (p = 0.024), the fraction of the genome altered (FGA, p = 0.042), and aneuploidy (p < 0.05) compared to non-recurrent tumors, as well as a higher frequency of CNVs. Additionally, recurrent tumors showed hypomethylation at both the global level and in CpG island regions. Integrative transcriptomic and methylation analyses identified three molecular subtypes (C1, C2, and C3), with the C1 subtype presenting the worst prognosis (p = 0.024). Although frequently mutated genes showed similar mutation frequencies across the three subtypes, the C1 subtype exhibited the highest tumor mutation burden (TMB), mutant-allele tumor heterogeneity (MATH), aneuploidy, and HLA loss of heterozygosity (HLA-LOH), along with relatively lower immune cell infiltration. Furthermore, GINS1 and CPT1C were found to promote LUAD progression, and their high expression correlated with a poor prognosis.

CONCLUSIONS

This multi-omics study identified three integrative subtypes with distinct prognostic implications, paving the way for more precise management and postoperative monitoring of early-stage poorly differentiated LUAD.

Single-cell transcriptomic analysis reveals efferocytosis signature predicting immunotherapy response in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a substantial global health challenge owing to its high mortality rate and limited therapeutic options. We aimed to develop an efferocytosis-related gene signature (ER.Sig) and conduct a transcriptomic analysis to predict the prognosis and immunotherapeutic responses of patients with HCC.

METHODS

Single-cell RNA sequencing data and bulk RNA sequencing data were obtained from public databases. Based on single-sample gene set enrichment analysis and Weighted Gene Co-expression Network analyses, efferocytosis-related genes (ERGs) were selected at both the single-cell and bulk transcriptome levels. A machine-learning framework employing ten different algorithms was used to develop the ER.Sig. Subsequently, a multi-omics approach (encompassing genomic analysis, single-cell transcriptomics, and bulk transcriptomics) was employed to thoroughly elucidate the prognostic signatures.

RESULTS

Analysis of the HCC single-cell transcriptomes revealed significant efferocytotic activity in macrophages, endothelial cells, and fibroblasts within the HCC microenvironment. We then constructed a weighted co-expression network and identified six modules, among which the brown module (168 genes) was most highly correlated with the efferocytosis score (cor = 0.84). Using the univariate Cox regression analysis, 33 prognostic ERGs were identified. Subsequently, a predictive model was constructed using 10 machine-learning algorithms, with the random survival forest model showing the highest predictive performance. The final model, ER.Sig, comprised nine genes and demonstrated robust prognostic capabilities across multiple datasets. High-risk patients exhibited greater intratumoral heterogeneity and higher TP53 mutation frequencies than did low-risk patients. Immune landscape analysis revealed that compared with high-risk patients, low-risk patients exhibited a more favorable immune environment, characterized by higher proportions of CD8+ T and B cells, tumor microenvironment score, immunophenoscore, and lower Tumor Immune Dysfunction and Exclusion scores, indicating better responses to immunotherapy. Additionally, an examination of an independent immunotherapy cohort (IMvigor210) demonstrated that low-risk patients exhibited more favorable responses to immunotherapy and improved prognoses than did their high-risk counterparts.

CONCLUSIONS

The developed ER.Sig effectively predicted the prognosis of patients with HCC and revealed significant differences in tumor biology and treatment responses between the risk groups.

Immunogenic cell death genes in single-cell and transcriptome analyses perspectives from a prognostic model of cervical cancer

Background

The role of immunogenic cell death (ICD) in cervical cancer (CESC) is not well understood. This study sought to investigate the significance of ICD in CESC and to establish an ICDRs prognostic model to improve immunotherapy efficacy for patients with cervical cancer.

Methods

ICD-associated genes were screened at the single-cell and transcriptome levels based on AddModuleScore, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. Immunogenic cell death-related features (ICDRs) were constructed using multiple machine algorithms, and ICDRs were evaluated in training and validation sets to provide quantitative tools for predicting prognosis in clinical practice. Predictive models were used to risk subgroups for response to immunotherapy, as well as drug sensitivity. Finally, the expression of ICD-related genes was verified by RT-qPCR.

Results

Through an integrated analysis of single-cell data, transcriptomic profiling, and computational modeling, seven ICD-related genes were identified as highly prognostic for CESC patients. Multivariate analysis demonstrated that low-risk patients had significantly better overall survival compared to high-risk patients, confirming the model as an independent prognostic tool. Assessments of the tumor microenvironment (TME), mutation characteristics, and drug sensitivity within ICDRs risk subgroups indicated a stronger immunotherapy response in the low-risk group.

Integrating multi-omics and machine learning methods reveals the metabolism of amino acids and derivatives-related signature in colorectal cancer

Objective

The metabolism of amino acids and derivatives (MAAD) is closely related to the occurrence and development of colorectal cancer (CRC), but the specific regulatory mechanisms are not yet clear. This study aims to explore the role of MAAD in the progression of colorectal cancer and ultimately identify key molecules that may become potential therapeutic targets for CRC.

Methods

This study integrates bulk transcriptome and single-cell transcriptome to analyze and identify key MAAD-related genes from multiple levels. Subsequently, numerous machine learning methods were incorporated to construct MAAD-related prognostic models, and the infiltration of immune cells, tumor heterogeneity, tumor mutation burden, and potential pathway changes under different modes were analyzed. Finally, key molecules were identified for experimental validation.

Results

We successfully constructed prognostic models and Nomograms based on key MAAD-related molecules. There was a notable survival benefit observed for low-risk patients when contrasted with their high-risk counterparts. In addition, the high-risk group had a poorer response to immunotherapy and stronger tumor heterogeneity compared with the low-risk group. Further research found that by knocking down the MAAD-related gene. LSM8, the malignant characteristics of colorectal cancer cell lines were significantly alleviated, suggesting that LSM8 may become a potential therapeutic target.

Conclusion

The MAAD-related gene LSM8 is likely involved in the progression of CRC and could be a hopeful target for therapeutic intervention.

Spatial deconvolution from bulk DNA methylation profiles determines intratumoral epigenetic heterogeneity

BACKGROUND

Intratumoral heterogeneity emerges from accumulating genetic and epigenetic changes during tumorigenesis, which may contribute to therapeutic failure and drug resistance. However, the lack of a quick and convenient approach to determine the intratumoral epigenetic heterogeneity (eITH) limit the application of eITH in clinical settings. Here, we aimed to develop a tool that can evaluate the eITH using the DNA methylation profiles from bulk tumors.

METHODS

Genomic DNA of three laser micro-dissected tumor regions, including digestive tract surface, central bulk, and invasive front, was extracted from formalin-fixed paraffin-embedded sections of colorectal cancer patients. The genome-wide methylation profiles were generated with methylation array. The most variable methylated probes were selected to construct a DNA methylation-based heterogeneity (MeHEG) estimation tool that can deconvolve the proportion of each reference tumor region with the support vector machine model-based method. A PCR-based assay for quantitative analysis of DNA methylation (QASM) was developed to specifically determine the methylation status of each CpG in MeHEG assay at single-base resolution to realize fast evaluation of epigenetic heterogeneity.

RESULTS

In the discovery set with 79 patients, the differentially methylated CpGs among the three tumor regions were found. The 7 most representative CpGs were identified and subsequently selected to develop the MeHEG algorithm. We validated its performance of deconvolution of tumor regions in an independent cohort. In addition, we showed the significant association of MeHEG-based epigenetic heterogeneity with the genomic heterogeneity in mutation and copy number variation in our in-house and TCGA cohorts. Besides, we found that the patients with higher MeHEG score had worse disease-free and overall survival outcomes. Finally, we found dynamic change of epigenetic heterogeneity based on MeHEG score in cancer cells under the treatment of therapeutic drugs.

CONCLUSION

By developing a 7-loci panel using a machine learning approach combined with the QASM assay for PCR-based application, we present a valuable method for evaluating intratumoral heterogeneity. The MeHEG algorithm offers novel insights into tumor heterogeneity from an epigenetic perspective, potentially enriching current knowledge of tumor complexity and providing a new tool for clinical and research applications in cancer biology.

Construction of a prognostic model for gastric cancer based on immune infiltration and microenvironment, and exploration of MEF2C gene function

BACKGROUND

Advanced gastric cancer (GC) exhibits a high recurrence rate and a dismal prognosis. Myocyte enhancer factor 2c (MEF2C) was found to contribute to the development of various types of cancer. Therefore, our aim is to develop a prognostic model that predicts the prognosis of GC patients and initially explore the role of MEF2C in immunotherapy for GC.

METHODS

Transcriptome sequence data of GC was obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and PRJEB25780 cohort for subsequent immune infiltration analysis, immune microenvironment analysis, consensus clustering analysis and feature selection for definition and classification of gene M and N. Principal component analysis (PCA) modeling was performed based on gene M and N for the calculation of immune checkpoint inhibitor (ICI) Score. Then, a Nomogram was constructed and evaluated for predicting the prognosis of GC patients, based on univariate and multivariate Cox regression. Functional enrichment analysis was performed to initially investigate the potential biological mechanisms. Through Genomics of Drug Sensitivity in Cancer (GDSC) dataset, the estimated IC50 values of several chemotherapeutic drugs were calculated. Tumor-related transcription factors (TFs) were retrieved from the Cistrome Cancer database and utilized our model to screen these TFs, and weighted correlation network analysis (WGCNA) was performed to identify transcription factors strongly associated with immunotherapy in GC. Finally, 10 patients with advanced GC were enrolled from Sun Yat-sen University Cancer Center, including paired tumor tissues, paracancerous tissues and peritoneal metastases, for preparing sequencing library, in order to perform external validation.

RESULTS

Lower ICI Score was correlated with improved prognosis in both the training and validation cohorts. First, lower mutant-allele tumor heterogeneity (MATH) was associated with lower ICI Score, and those GC patients with lower MATH and lower ICI Score had the best prognosis. Second, regardless of the T or N staging, the low ICI Score group had significantly higher overall survival (OS) compared to the high ICI Score group. For its mechanisms, consistently, for Camptothecin, Doxorubicin, Mitomycin, Docetaxel, Cisplatin, Vinblastine, Sorafenib and Paclitaxel, all of the IC50 values were significantly lower in the low ICI Score group compared to the high ICI Score group. As a result, based on univariate and multivariate Cox regression, ICI Score was considered to be an independent prognostic factor for GC. And our Nomogram showed good agreement between predicted and actual probabilities. Based on CIBERSORT deconvolution analysis, there was difference of immune cell composition found between high and low ICI Score groups, probably affecting the efficacy of immunotherapy. Then, MEF2C, a tumor-related transcription factor, was screened out by WGCNA analysis. Higher MEF2C expression is significantly correlated with a worse OS. Moreover, its higher expression is also negatively correlated with tumor mutation burden (TMB) and microsatellite instability (MSI), but positively correlated with several immunosuppressive molecules, indicating MEF2C may exert its influence on tumor development by upregulating immunosuppressive molecules. Finally, based on transcriptome sequencing data on 10 paired tumor tissues from Sun Yat-sen University Cancer Center, MEF2C expression was significantly lower in paracancerous tissues compared to tumor tissues and peritoneal metastases, and it was also lower in tumor tissues compared to peritoneal metastases, indicating a potential positive association between MEF2C expression and tumor invasiveness.

CONCLUSIONS

Our prognostic model can effectively predict outcomes and facilitate stratification GC patients, offering valuable insights for clinical decision-making. The identified transcription factor MEF2C can serve as a biomarker for assessing the efficacy of immunotherapy for GC.

Deciphering molecular relapse and intra-tumor heterogeneity in non-metastatic resectable head and neck squamous cell carcinoma using circulating tumor DNA

OBJECTIVES

Head and neck squamous cell carcinoma (HNSCC) is characterized by significant genetic intra-tumor heterogeneity (ITH), which may hinder precision medicine strategies that depend on results from single tumor-biopsy specimens. Treatment response assessment relies on radiologic imaging, which cannot detect minimal residual disease (MRD). We assessed the relevance of circulating tumor DNA (ctDNA) as a biomarker for ITH and MRD in HNSCC.

MATERIALS AND METHODS

We recruited 41 non-metastatic resectable HNSCC patients treated with upfront curative-intent surgery in the prospective biobanking SCANDARE study (NCT03017573). Thirty-one patients (76 %) showed recurrent disease at a median follow-up of 41 months. Targeted next-generation sequencing was performed on resected tumor tissues, as well as on serial blood samples obtained at surgery, within 14 weeks after surgery, at six months and at recurrence.

RESULTS

ctDNA was detected in 21/41 patients at surgery (sensitivity: 51 %; 95 % CI, 35-67 %) and 15/22 patients at recurrence (sensitivity: 68 %; 95 % confidence interval [CI], 45-86 %). Among patients with mutations identified in longitudinal plasma samples, additional mutations missed in tumor tissues were reported in 3/21 patients (14 %), while emerging mutations were reported in 9/21 patients (43 %). In the postoperative surveillance setting, ctDNA-based MRD detection anticipated clinical recurrence with a median lead-time of 9.9 months (interquartile range, 8.0-14.5 months) in 17/27 patients (63 %). When detected within 14 weeks after surgery, MRD correlated with disease recurrence after adjusting for classical prognostic variables (HR = 3.0; 95 % CI, 1.1-7.9; p = 0.03).

CONCLUSIONS

ctDNA detection is a useful biomarker for ITH and MRD in resectable HNSCC patients.

Integrated machine learning reveals the role of tryptophan metabolism in clear cell renal cell carcinoma and its association with patient prognosis

BACKGROUND

Precision oncology's implementation in clinical practice faces significant constraints due to the inadequacies in tools for detailed patient stratification and personalized treatment methodologies. Dysregulated tryptophan metabolism has emerged as a crucial factor in tumor progression, encompassing immune suppression, proliferation, metastasis, and metabolic reprogramming. However, its precise role in clear cell renal cell carcinoma (ccRCC) remains unclear, and predictive models or signatures based on tryptophan metabolism are conspicuously lacking.

METHODS

The influence of tryptophan metabolism on tumor cells was explored using single-cell RNA sequencing data. Genes involved in tryptophan metabolism were identified across both single-cell and bulk-cell dimensions through weighted gene co-expression network analysis (WGCNA) and its single-cell data variant (hdWGCNA). Subsequently, a tryptophan metabolism-related signature was developed using an integrated machine-learning approach. This signature was then examined in multi-omics data to assess its associations with patient clinical features, prognosis, cancer malignancy-related pathways, immune microenvironment, genomic characteristics, and responses to immunotherapy and targeted therapy. Finally, the genes within the signature were validated through experiments including qRT-PCR, Western blot, CCK8 assay, and transwell assay.

RESULTS

Dysregulated tryptophan metabolism was identified as a potential driver of the malignant transformation of normal epithelial cells. The tryptophan metabolism-related signature (TMRS) demonstrated robust predictive capability for overall survival (OS) and progression-free survival (PFS) across multiple datasets. Moreover, a high TMRS risk score correlated with increased tumor malignancy, significant metabolic reprogramming, an inflamed yet dysfunctional immune microenvironment, heightened genomic instability, resistance to immunotherapy, and increased sensitivity to certain targeted therapeutics. Experimental validation revealed differential expression of genes within the signature between RCC and adjacent normal tissues, with reduced expression of DDAH1 linked to enhanced proliferation and metastasis of tumor cells.

CONCLUSION

This study investigated the potential impact of dysregulated tryptophan metabolism on clear cell renal cell carcinoma, leading to the development of a tryptophan metabolism-related signature that may provide insights into patient prognosis, tumor biological status, and personalized treatment strategies. This signature serves as a valuable reference for further exploring the role of tryptophan metabolism in renal cell carcinoma and for the development of clinical applications based on this metabolic pathway.

CT-based clinical-radiomics model to predict progression and drive clinical applicability in locally advanced head and neck cancer

BACKGROUND

Definitive chemoradiation is the primary treatment for locally advanced head and neck carcinoma (LAHNSCC). Optimising outcome predictions requires validated biomarkers, since TNM8 and HPV could have limitations. Radiomics may enhance risk stratification.

METHODS

This single-centre observational study collected clinical data and baseline CT scans from 171 LAHNSCC patients treated with chemoradiation. The dataset was divided into training (80%) and test (20%) sets, with a 5-fold cross-validation on the training set. Researchers extracted 108 radiomics features from each primary tumour and applied survival analysis and classification models to predict progression-free survival (PFS) and 5-year progression, respectively. Performance was evaluated using inverse probability of censoring weights and c-index for the PFS model and AUC, sensitivity, specificity, and accuracy for the 5-year progression model. Feature importance was measured by the SHapley Additive exPlanations (SHAP) method and patient stratification was assessed through Kaplan-Meier curves.

RESULTS

The final dataset included 171 LAHNSCC patients, with 53% experiencing disease progression at 5 years. The random survival forest model best predicted PFS, with an AUC of 0.64 and CI of 0.66 on the test set, highlighting 4 radiomics features and TNM8 as significant contributors. It successfully stratified patients into low and high-risk groups (log-rank p < 0.005). The extreme gradient boosting model most effectively predicted a 5-year progression, incorporating 12 radiomics features and four clinical variables, achieving an AUC of 0.74, sensitivity of 0.53, specificity of 0.81, and accuracy of 0.66 on the test set.

CONCLUSION

The combined clinical-radiomics model improved the standard TNM8 and clinical variables in predicting 5-year progression though further validation is necessary.

KEY POINTS

Question There is an unmet need for non-invasive biomarkers to guide treatment in locally advanced head and neck cancer. Findings Clinical data (TNM8 staging, primary tumour site, age, and smoking) plus radiomics improved 5-year progression prediction compared with the clinical comprehensive model or TNM staging alone. Clinical relevance SHAP simplifies complex machine learning radiomics models for clinicians by using easy-to-understand graphical representations, promoting explainability.

Exploring the impact of intra-tumoural heterogeneity on liquid biopsy cell-free DNA methylation and copy number in head and neck squamous cell carcinoma

Liquid biopsy profiling is gaining increasing promise towards biomarker-led identification and disease stratification of tumours, particularly for tumours displaying significant intra-tumoural heterogeneity (ITH). For head and neck squamous cell carcinoma (HNSCC), which display high levels of genetic ITH, identification of epigenetic modifications and methylation signatures has shown multiple uses in stratification of HNSCC for prognosis, treatment, and HPV status. In this study, we investigated the potential of liquid biopsy methylomics and genomic copy number to profile HNSCC. We conducted multi-region sampling of tumour core, tumour margin and normal adjacent mucosa, as well as plasma cell-free DNA (cfDNA) across 9 HNSCC patients. Collectively, our work highlights the prevalence of methylomic ITH in HNSCC, and demonstrates the potential of cfDNA methylation as a tool for ITH assessment and serial sampling.

Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort

Introduction

The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies are needed to investigate the prognostic significance of the interaction between immune-related genes and cell death in LUAD.

Methods

In this study, 10 clustering algorithms were applied to perform molecular typing based on cell death-related genes, immune-related genes, methylation data and somatic mutation data. And a powerful computational framework was used to investigate the relationship between immune genes and cell death patterns in LUAD patients. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations, and we constructed an immune-associated programmed cell death model (PIGRS) using the machine learning model that exhibited the best performance. Finally, based on a series of in vitro experiments used to explore the role of PSME3 in LUAD.

Results

We used 10 clustering algorithms and multi-omics data to categorize TCGA-LUAD patients into three subtypes. patients with the CS3 subtype had the best prognosis, whereas patients with the CS1 and CS2 subtypes had a poorer prognosis. PIGRS, a combination of 15 high-impact genes, showed strong prognostic performance for LUAD patients. PIGRS has a very strong prognostic efficacy compared to our collection. In conclusion, we found that PSME3 has been little studied in lung adenocarcinoma and may be a novel prognostic factor in lung adenocarcinoma.

Discussion

Three LUAD subtypes with different molecular features and clinical significance were successfully identified by bioinformatic analysis, and PIGRS was constructed using a powerful machine learning framework. and investigated PSME3, which may affect apoptosis in lung adenocarcinoma cells through the PI3K/AKT/Bcl-2 signaling pathway.

Genomic alterations in the stepwise progression from normal mucosa to metastasizing oral squamous cell carcinoma

Introduction

The aim of this study was to investigate the genomic changes that occur in the development from dysplasia, cancer and to regional metastases in patients with oral cavity squamous cell carcinoma (OSCC).

Material and methods

We included OSCC patients with lymph node metastases at diagnosis, treated with primary surgery at Rigshospitalet, University of Copenhagen in the period 2007-2014. The resected tumor specimens were evaluated by a pathologist, who marked areas of morphologically normal tissue and dysplasia surrounding the cancer, two areas from the cancer tissue, and one area within the lymph node metastases. From these areas a punch biopsy was taken, and DNA from each sample was extracted and sequenced using Illumina's TSO500 HT cancer panel.

Results

From 51 OSCC patients, 255 samples were included, comprising a wide variety of genomic alterations. Substantial intratumor heterogeneity was found. The most commonly mutated gene was TP53, mutated in 65% of all samples. Only two patients had no TP53 mutation in any samples. We found that morphologically normal appearing mucosa as well as surrounding dysplasia also contained malignant mutations, supporting the theory of field cancerization. There was a significant lower average tumor mutational burden (TMB) in the lymph node metastases compared to the primary tumors, supporting the theory of clonal selection.

Conclusion

Substantial inter- and intratumor genomic heterogeneity was found. Mutation of TP53 was the most common and was present in all but two patients. Our data strongly supports the theory of clonal selection and the theory of field cancerization.

TCF3 as a multidimensional biomarker: oncogenicity, genomic alterations, and immune landscape in pan-cancer analysis

Transcription factor 3 (TCF3), a pivotal member of the TCF/LEF family, plays a critical role in tumorigenesis. Nonetheless, its impact on the tumor microenvironment (TME) and cancer phenotypes remains elusive. We perform an exhaustive analysis of TCF3 expression, DNA variation profiles, prognostic implications, and associations with the TME and immunological aspects. This study is based on a large-scale pan-cancer cohort, encompassing over 17,000 cancer patients from multiple independent datasets, validated by in vitro assays. Our results show that TCF3/4/7 exhibits differential expression patterns between normal and tumor tissues across pan-cancer analyses. Mutational analysis of TCF3 across diverse cancer types reveals the highest alteration rates in biliary tract cancer. Additionally, mutations and single nucleotide variants in TCF3/4/7 are found to exert varied effects on patient prognosis. Importantly, TCF3 emerges as a robust predictor of survival across all cancer cohorts and among patients receiving immune checkpoint inhibitors. Elevated TCF3 expression is correlated with more aggressive cancer subtypes, as validated by immunohistochemistry and diverse cohort data. Furthermore, TCF3 expression is positively correlated with intratumoral heterogeneity and angiogenesis. In vitro investigations demonstrate that TCF3 is involved in epithelial-mesenchymal transition, migration, invasion, and angiogenesis. These effects are likely mediated through the interaction of TCF3 with the NF-κB/MMP2 pathway, which is modulated by IL-17A in human uveal melanoma MUM2B cells. This study elucidates, for the first time, the significant associations of TCF3 with DNA variation profiles, prognostic outcomes, and the TME in multiple cancer contexts. TCF3 holds promise as a molecular marker for diagnosis and as a potential target for novel therapeutic strategies, particularly in uveal melanoma.

Highlighting immune features of the tumor ecosystem and prognostic value of Tfh and Th17 cell infiltration in head and neck squamous cell carcinoma by single-cell RNA-seq

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) typically present with a complex anatomical distribution, often accompanied by insidious symptoms. This combination contributes to its high incidence and poor prognosis. It is now understood that the immune features of cellular components within the tumor ecosystem and their complex interactions are critical factors influencing both tumor progression and the effective immune response.

METHODS

We obtained single-cell RNA sequencing data of 26,496 cells from three tumor tissues and five normal tissues and performed subsequent analyses. Immunohistochemical staining on tumor sections was used to validate the presence of malignant cells. Additionally, we included bulk RNA sequencing data from 502 HNSCC patients. Kaplan-Meier analysis and the log-rank test were employed to assess predictors of patient outcomes.

RESULTS

We identified three epithelial subclusters exhibiting immune-related features. These subclusters promoted the infiltration of T cells, dendritic cells, and monocytes into the tumor microenvironment. Additionally, cancer-associated fibroblasts displayed tumor-promoting and angiogenesis characteristics, contrasting with the predominant antigen-presenting and inflammatory roles observed in fibroblasts from normal tissues. Furthermore, tumor endothelial subsets exhibited a double-sided effect, promoting tumor progression and enhancing the effectiveness of immune response. Finally, follicular helper T cells and T helper 17 cells were found to be significantly correlated with improved outcomes in HNSCC patients. These CD4+ T cell subpopulations could promote the anti-tumor immune response by recruiting and activating B and T cells.

CONCLUSION

Our findings provide deeper insights into the immune features of the tumor ecosystem and reveal the prognostic significance of follicular helper T cells and T helper 17 cells. These findings may pave the way for the development of therapeutic approaches.

A Novel Affordable and Reliable Framework for Accurate Detection and Comprehensive Analysis of Somatic Mutations in Cancer

Accurate detection and analysis of somatic variants in cancer involve multiple third-party tools with complex dependencies and configurations, leading to laborious, error-prone, and time-consuming data conversions. This approach lacks accuracy, reproducibility, and portability, limiting clinical application. Musta was developed to address these issues as an end-to-end pipeline for detecting, classifying, and interpreting cancer mutations. Musta is based on a Python command-line tool designed to manage tumor-normal samples for precise somatic mutation analysis. The core is a Snakemake-based workflow that covers all key cancer genomics steps, including variant calling, mutational signature deconvolution, variant annotation, driver gene detection, pathway analysis, and tumor heterogeneity estimation. Musta is easy to install on any system via Docker, with a Makefile handling installation, configuration, and execution, allowing for full or partial pipeline runs. Musta has been validated at the CRS4-NGS Core facility and tested on large datasets from The Cancer Genome Atlas and the Beijing Institute of Genomics. Musta has proven robust and flexible for somatic variant analysis in cancer. It is user-friendly, requiring no specialized programming skills, and enables data processing with a single command line. Its reproducibility ensures consistent results across users following the same protocol.

Comprehensive molecular characterization of long-term glioblastoma survivors

Fewer than 5 % glioblastoma (GBM) patients survive over five years and are termed long-term survivors (LTS), yet their molecular background is unclear. The present cohort included 72 isocitrate dehydrogenase (IDH)-wildtype GBM patients, consisting of 35 LTS and 37 short-term survivors (STS), and we employed whole exome sequencing, RNA-seq and DNA methylation array to delineate this largest LTS cohort to date. Although LTS and STS demonstrated analogous clinical characters and classical GBM biomarkers, CASC5 (P = 0.002) and SPEN (P = 0.013) mutations were enriched in LTS, whereas gene-to-gene fusions were concentrated in STS (P = 0.007). Importantly, LTS exhibited higher tumor mutation burden (P < 0.001) and copy number (CN) increase (P = 0.013), but lower mutant-allele tumor heterogeneity score (P < 0.001) and CN decrease (P = 0.026). Additionally, LTS demonstrated hypermethylated genome (P < 0.001) relative to STS. Differentially expressed and methylated genes both enriched in olfactory transduction. Further, analysis of the tumor microenvironment revealed higher infiltration of M1 macrophages (P = 0.043), B cells (P = 0.016), class-switched memory B cells (P = 0.002), central memory CD4+ T cells (P = 0.031) and CD4+ Th1 cells (P = 0.005) in LTS. We also separately analyzed a subset of patients who were methylation class-defined GBM, contributing 70.8 % of the entire cohort, and obtained similar results relative to prior analyses. Finally, we demonstrated that LTS and STS could be distinguished using a subset of molecular features. Taken together, the present study delineated unique molecular attributes of LTS GBM.

Characterization of a Diverse Set of Conditionally Reprogrammed Head and Neck Cancer Cell Cultures

OBJECTIVE

To establish and characterize a diverse library of head and neck squamous cell cancer (HNSCC) cultures using conditional reprogramming (CR).

METHODS

Patients enrolled on an IRB-approved protocol to generate tumor cell cultures using CR methods. Tumor and blood samples were collected and clinical information was recorded. Successful CR cultures were validated against banked reference tumors with short tandem repeat genotyping. Cell morphology was archived with photodocumentation. Clinical and demographic factors were evaluated for associations with successful establishment of CR culture. Human papilloma virus (HPV) genotyping, clonogenic survival, MTT assays, spheroid growth, and whole exome sequencing were carried out in selected cultures.

RESULTS

Forty four patients were enrolled, with 31 (70%) successful CR cultures, 32% derived from patients who identified as Black and 61% as Hispanic. All major head and neck disease sites were represented, including 15 (48%) oral cavity and 8 (26%) p16-positive oropharynx cancers. Hispanic ethnicity and first primary tumors (vs. second primary or recurrent tumors) were significantly associated with successful CR culture. HPV expression was conserved in CR cultures, including CR-024, which carried a novel HPV-69 serotype. CR cultures were used to test cisplatin responses using MTT assays. Previous work has also demonstrated these models can be used to assess response to radiation and can be engrafted in mouse models. Whole exome sequencing demonstrated that CR cultures preserved tumor mutation burden and driver mutations.

CONCLUSION

CR culture is highly successful in propagating HNSCC cells. This study included a high proportion of patients from underrepresented minority groups.

LEVEL OF EVIDENCE

Not Applicable Laryngoscope, 134:2748-2756, 2024.

Artificial neural network identified a 20-gene panel in predicting immunotherapy response and survival benefits after anti-PD1/PD-L1 treatment in glioblastoma patients

BACKGROUND

Immune checkpoint inhibitors (ICIs) are a promising immunotherapy approach, but glioblastoma clinical trials have not yielded satisfactory results.

OBJECTIVE

To screen glioblastoma patients who may benefit from immunotherapy.

METHODS

Eighty-one patients receiving anti-PD1/PD-L1 treatment from a large-scale clinical trial and 364 patients without immunotherapy from The Cancer Genome Atlas (TCGA) were included. Patients in the ICI-treated cohort were divided into responders and nonresponders according to overall survival (OS), and the most critical responder-relevant features were screened using random forest (RF). We constructed an artificial neural network (ANN) model and verified its predictive value with immunotherapy response and OS.

RESULTS

We defined two groups of ICI-treated glioblastoma patients with large differences in survival benefits as nonresponders (OS ≤6 months, n = 18) and responders (OS ≥17 months, n = 8). No differentially mutated genes were observed between responders and nonresponders. We performed RF analysis to select the most critical responder-relevant features and developed an ANN with 20 input variables, five hidden neurons and one output neuron. Receiver operating characteristic analysis and the DeLong test demonstrated that the ANN had the best performance in predicting responders, with an AUC of 0.97. Survival analysis indicated that ANN-predicted responders had significantly better OS rates than nonresponders.

CONCLUSION

The 20-gene panel developed by the ANN could be a promising biomarker for predicting immunotherapy response and prognostic benefits in ICI-treated GBM patients and may guide oncologists to accurately select potential responders for the preferential use of ICIs.

Circulating tumour DNA detects somatic variants contributing to spatial and temporal intra-tumoural heterogeneity in head and neck squamous cell carcinoma

Background

As circulating tumour DNA (ctDNA) liquid biopsy analysis is increasingly incorporated into modern oncological practice, establishing the impact of genomic intra-tumoural heterogeneity (ITH) upon data output is paramount. Despite advances in other cancer types the evidence base in head and neck squamous cell carcinoma (HNSCC) remains poor. We sought to investigate the utility of ctDNA to detect ITH in HNSCC.

Methods

In a pilot cohort of 9 treatment-naïve HNSCC patients, DNA from two intra-tumoural sites (core and margin) was whole-exome sequenced. A 9-gene panel was designed to perform targeted sequencing on pre-treatment plasma cell-free DNA and selected post-treatment samples.

Results

Rates of genomic ITH among the 9 patients was high. COSMIC variants from 19 TCGA HNSCC genes demonstrated an 86.9% heterogeneity rate (present in one tumour sub-site only). Across all patients, cell-free DNA (ctDNA) identified 12.9% (range 7.5-19.8%) of tumour-specific variants, of which 55.6% were specific to a single tumour sub-site only. CtDNA identified 79.0% (range: 55.6-90.9%) of high-frequency variants (tumour VAF>5%). Analysis of ctDNA in serial post-treatment blood samples in patients who suffered recurrence demonstrated dynamic changes in both tumour-specific and acquired variants that predicted recurrence ahead of clinical detection.

Conclusion

We demonstrate that a ctDNA liquid biopsy identified spatial genomic ITH in HNSCC and reliably detected high-frequency driver mutations. Serial sampling allowed post-treatment surveillance and early identification of treatment failure.

Spatial heterogeneity of tumor cells and the tissue microenvironment in oral squamous cell carcinoma

PURPOSE

This study describes the morphologic and phenotypic spatial heterogeneity of tumor cells and the tissue microenvironment (TME), focusing on immune infiltration in OSCCs.

STUDY DESIGN

Patients with OSCCs and planned surgical tumor resection were eligible for the study. Two biopsies each from the tumor center and the tumor rim were obtained. Immunohistochemical characterization of tumor and immune cells was performed using a panel of immunohistochemical markers.

RESULTS

Thirty-six biopsies were obtained from the 9 patients. All patients showed an individual marker expression profile with ITH. Within the same biopsy, the CPS and TPS scores showed relevant variations in PD-L1 expression. Comparisons between the tumor center and rim revealed significant differences in the up/downregulation of p53. Marker expression of patients with recurrences clustered similarly, with the higher expression of FoxP3, IDO, CD4, CD68, and CD163 at the tumor rim.

CONCLUSION

OSCCs were found to exhibit relevant ITH involving both tumor cells and TME, suggesting that biomarker analysis of multiple tumor regions may be helpful for clinical decision making and tumor characterization. The analysis of multiple spots within a biopsy is recommended for a reliable determination of PD-L1 expression and other biomarkers, impacting current clinical assessments.

Copper-binding protein modelling by single-cell transcriptome and Bulk transcriptome to predict overall survival in lung adenocarcinoma patients

Background: Copper and copper-binding proteins are key components of tumour progression as they play an important role in tumour invasion and migration, and abnormal accumulation of copper (Cu) may be intimately linked to with lung adenocarcinoma (LUAD). Methods: Data on lung adenocarcinoma were sourced from the Cancer Genome Atlas (TCGA) database and the National Centre for Biotechnology Information (GEO). 10x scRNA sequencing, which is from Bischoff P et al, was used for down-sequencing clustering and subgroup identification using TSNE. The genes for Copper-binding proteins (CBP) were acquired from the MSigDB database. LASSO-Cox analysis was subsequently used to construct a model for copper-binding proteins (CBPRS), which was then compared to lung adenocarcinoma models developed by others. External validation was carried out in the GSE31210 and GSE50081 cohorts. The effectiveness of immunotherapy was evaluated using the TIDE algorithm and the IMvigor210, GSE78220, and TCIA cohorts. Furthermore, differences in mutational profiles and the immune microenvironment between different risk groups were investigated. The CBPRS's key regulatory genes were screened using ROC diagnostic and KM survival curves. The differential expression of these genes was then verified by RT-qPCR. Results: The six CBP genes were identified as highly predictive of LUAD prognosis and significantly correlated with it. Multivariate analysis showed that patients in the low-risk group had a higher overall survival rate than those in the high-risk group, indicating that the model was an independent predictor of LUAD. The CBPRS demonstrated superior predictive ability compared to 11 previously published models. We constructed a column-line graph that includes CBPRS and clinical characteristics, which exhibits high predictive performance. Additionally, we observed significant differences in biological functions, mutational landscapes, and immune cell infiltration in the tumour microenvironment between the high-risk and low-risk groups. It is noteworthy that immunotherapy was also significant in both the high- and low-risk groups. These results suggest that the model has good predictive efficacy. Conclusions: The CBP model demonstrated good predictive performance, revealing characteristics of the tumour microenvironment. This provides a new method for assessing the efficacy of pre-immunisation and offers a potential strategy for future treatment of lung adenocarcinoma.

Prostate cancer: Molecular aspects, consequences, and opportunities of the multifocal nature

Prostate cancer is unique compared to other major cancers due to the presence of multiple primary malignant foci in the majority of patients at the time of diagnosis. Each malignant focus has distinct somatic mutations and gene expression patterns, which represents a challenge for the development of prognostic tests for localized prostate cancer. Additionally, the molecular heterogeneity of advanced prostate cancer has important implications for management, particularly for patients with metastatic and locally recurrent cancer. Studies have shown that prostate cancers with mutations in DNA damage response genes are more sensitive to drugs inhibiting the poly ADP-ribose polymerase (PARP) enzyme. However, testing for such mutations should consider both spatial and temporal heterogeneity. Here, we summarize studies where multiregional genomics and transcriptomics analyses have been performed for primary prostate cancer. We further discuss the vast interfocal heterogeneity and how prognostic biomarkers and a molecular definition of the index tumor should be developed. The concept of focal treatments in prostate cancer has been evolving as a demand from patients and clinicians and is one example where there is a need for defining an index tumor. Here, biomarkers must have proven value for individual malignant foci. The potential discovery and implementation of biomarkers that are agnostic to heterogeneity are also explored as an alternative to multisample testing. Thus, deciding upon whole-organ treatment, such as radical prostatectomy, should depend on information from biomarkers which are informative for the whole organ.

Identification of T-cell exhaustion-related genes and prediction of their immunotherapeutic role in lung adenocarcinoma

Background: Lung adenocarcinoma ranks as the second most widespread form of cancer globally, accompanied by a significant mortality rate. Several studies have shown that T cell exhaustion is associated with immunotherapy of tumours. Consequently, it is essential to comprehend the possible impact of T cell exhaustion on the tumor microenvironment. The purpose of this research was to create a TEX-based model that would use single-cell RNA-seq (scRNA-seq) and bulk-RNA sequencing to explore new possibilities for assessing the prognosis and immunotherapeutic response of LUAD patients. Methods: RNA-seq data from LUAD patients was downloaded from the Cancer Genome Atlas (TCGA) database and the National Center for Biotechnology Information (GEO). 10X scRNA sequencing data, as reported by Bischoff P et al., was utilized for down-sampling clustering and subgroup identification using TSNE. TEX-associated genes were identified through gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). We utilized LASSO-Cox analysis to establish predicted TEX features. External validation was conducted in GSE31210 and GSE30219 cohorts. Immunotherapeutic response was assessed in IMvigor210, GSE78220, GSE35640 and GSE100797 cohorts. Furthermore, we investigated differences in mutational profiles and immune microenvironment between various risk groups. We then screened TEXRS key regulatory genes using ROC diagnostic curves and KM survival curves. Finally, we verified the differential expression of key regulatory genes through RT-qPCR. Results: Nine TEX genes were identified as highly predictive of LUAD prognosis and strongly correlated with disease outcome. Univariate and multivariate analysis revealed that patients in the low-risk group had significantly better overall survival rates compared with those in the high-risk group, highlighting the model's ability to independently predict LUAD prognosis. Our analysis revealed significant variation in the biological function, mutational landscape, and immune cell infiltration within the tumor microenvironment of both high-risk and low-risk groups. Additionally, immunotherapy was found to have a significant impact on both groups, indicating strong predictive efficacy of the model. Conclusions: The TEX model showed good predictive performance and provided a new perspective for evaluating the efficacy of preimmunization, which provides a new strategy for the future treatment of lung adenocarcinoma.

Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer

BACKGROUND

Chemotherapy resistance is the main cause of ovarian cancer progression and even death. However, there are no clear indicators for predicting the risk of drug resistance in patients. Intra-tumor heterogeneity (ITH) is one of the characteristics of malignant tumors, which is associated with the treatment and prognosis of tumors. Accordingly, our study aims to investigate the correlation between the image features of intra-tumor heterogeneity and drug resistance of ovarian cancer based on artificial intelligence.

METHODS

We obtained hematoxylin and eosin staining frozen histopathological images of ovarian cancer and paracarcinoma tissues from the Cancer Genome Atlas. We extracted quantitative image features of whole-slide images based on the automatic image nuclear segmentation processing technology. After that, we used bioinformatics analysis to find the relationship between image features of intra-tumor heterogeneity and drug resistance.

RESULTS

Our results show that our automatic image processing process based on computer artificial intelligence can extract image features effectively, and the key image features extracted are closely related to ITH. Among them, the Perimeter.sd image feature with the most prominent ITH feature can accurately predict the risk of platinum-based chemotherapy drug resistance in ovarian cancer patients.

CONCLUSION

Automatic image processing and feature extraction based on artificial intelligence have excellent results. Perimeter.sd can be used as a useful image feature indicator for evaluating ITH. ITH is associated with drug resistance of ovarian cancer, so ITH characteristics can be used as an effective indicator to evaluate drug resistance in patients with ovarian cancer.

Genetic Intratumor Heterogeneity Remodels the Immune Microenvironment and Induces Immune Evasion in Brain Metastasis of Lung Cancer

INTRODUCTION

Brain metastasis, with the highest incidence in patients with lung cancer, significantly worsens prognosis and poses challenges to clinical management. To date, how brain metastasis evades immune elimination remains unknown.

METHODS

Whole-exome sequencing and RNA sequencing were performed on 30 matched brain metastasis, primary lung adenocarcinoma, and normal tissues. Data from The Cancer Genome Atlas primary lung adenocarcinoma cohort, including multiplex immunofluorescence, were used to support the findings of bioinformatics analysis.

RESULTS

Our study highlights the key role of intratumor heterogeneity of genomic alterations in the metastasis process, mainly caused by homologous recombination deficiency or other somatic copy number alteration-associated mutation mechanisms, leading to increased genomic instability and genomic complexity. We further proposed a selection model of brain metastatic evolution in which intratumor heterogeneity drives immune remodeling, leading to immune escape through different mechanisms under local immune pressure.

CONCLUSIONS

Our findings provide novel insights into the metastatic process and immune escape mechanisms of brain metastasis and pave the way for precise immunotherapeutic strategies for patients with lung cancer with brain metastasis.

Novel MFSD7-ATP5I fusion promotes migration and invasion of human sarcoma

Fusion genes have been implicated in the development and progression of several types of sarcomas, serving as valuable diagnostic and prognostic markers, as well as potential therapeutic targets. We discovered a novel major facilitator superfamily domain-containing 7 (MFSD7) and adenosine triphosphate 5I (ATP5I) gene fusion from sarcomas. In this study, the MFSD7-ATP5I fusion transcript was screened using RNA sequencing in 55 sarcoma samples and sixteen normal samples. The MFSD7-ATP5I fusion transcript was detected in 58% of sarcoma samples. The correlation between the expression of MFSD7-ATP5I fusion transcript and clinicopathological information was analyzed, and MFSD7-ATP5I expression is associated with marked pleomorphism and lower tumor necrosis. Cell migration and invasion was significantly reduced by knock-down of MFSD7-ATP5I. Cell migration and invasion was increased by overexpression of MFSD7-ATP5I. A phosphokinase assay demonstrated that MFSD7-ATP5I is involved in the GSK-3 pathway. The current study found that MFSD7-ATP5I is associated with increasing pleomorphism and decreasing necrosis of tumors. And our gain and loss of function experiments prove that MFSD7-ATP5I promotes the invasiveness of tumor cells.

Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and CT radiomics features: a multicentric study

BACKGROUND

This study aimed to investigate the value of clinical, radiomic features extracted from gross tumor volumes (GTVs) delineated on CT images, dose distributions (Dosiomics), and fusion of CT and dose distributions to predict outcomes in head and neck cancer (HNC) patients.

METHODS

A cohort of 240 HNC patients from five different centers was obtained from The Cancer Imaging Archive. Seven strategies, including four non-fusion (Clinical, CT, Dose, DualCT-Dose), and three fusion algorithms (latent low-rank representation referred (LLRR),Wavelet, weighted least square (WLS)) were applied. The fusion algorithms were used to fuse the pre-treatment CT images and 3-dimensional dose maps. Overall, 215 radiomics and Dosiomics features were extracted from the GTVs, alongside with seven clinical features incorporated. Five feature selection (FS) methods in combination with six machine learning (ML) models were implemented. The performance of the models was quantified using the concordance index (CI) in one-center-leave-out 5-fold cross-validation for overall survival (OS) prediction considering the time-to-event.

RESULTS

The mean CI and Kaplan-Meier curves were used for further comparisons. The CoxBoost ML model using the Minimal Depth (MD) FS method and the glmnet model using the Variable hunting (VH) FS method showed the best performance with CI = 0.73 ± 0.15 for features extracted from LLRR fused images. In addition, both glmnet-Cindex and Coxph-Cindex classifiers achieved a CI of 0.72 ± 0.14 by employing the dose images (+ incorporated clinical features) only.

CONCLUSION

Our results demonstrated that clinical features, Dosiomics and fusion of dose and CT images by specific ML-FS models could predict the overall survival of HNC patients with acceptable accuracy. Besides, the performance of ML methods among the three different strategies was almost comparable.

ZNF689 deficiency promotes intratumor heterogeneity and immunotherapy resistance in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive disease characterized by remarkable intratumor heterogeneity (ITH), which poses therapeutic challenges. However, the clinical relevance and key determinant of ITH in TNBC are poorly understood. Here, we comprehensively characterized ITH levels using multi-omics data across our center's cohort (n = 260), The Cancer Genome Atlas cohort (n = 134), and four immunotherapy-treated cohorts (n = 109). Our results revealed that high ITH was associated with poor patient survival and immunotherapy resistance. Importantly, we identified zinc finger protein 689 (ZNF689) deficiency as a crucial determinant of ITH formation. Mechanistically, the ZNF689-TRIM28 complex was found to directly bind to the promoter of long interspersed element-1 (LINE-1), inducing H3K9me3-mediated transcriptional silencing. ZNF689 deficiency reactivated LINE-1 retrotransposition to exacerbate genomic instability, which fostered ITH. Single-cell RNA sequencing, spatially resolved transcriptomics and flow cytometry analysis confirmed that ZNF689 deficiency-induced ITH inhibited antigen presentation and T-cell activation, conferring immunotherapy resistance. Pharmacological inhibition of LINE-1 significantly reduced ITH, enhanced antitumor immunity, and eventually sensitized ZNF689-deficient tumors to immunotherapy in vivo. Consistently, ZNF689 expression positively correlated with favorable prognosis and immunotherapy response in clinical samples. Altogether, our study uncovers a previously unrecognized mechanism underlying ZNF689 deficiency-induced ITH and suggests LINE-1 inhibition combined with immunotherapy as a novel treatment strategy for TNBC.

Molecular evolution in different subtypes of multifocal hepatocellular carcinoma

BACKGROUND

Multifocal hepatocellular carcinoma (MF-HCC) accounts for > 40% of HCCs, exhibiting a poor prognosis than single primary HCCs. Characterizing molecular features including dynamic changes of mutational signature along with clonal evolution, intrahepatic metastatic timing, and genetic footprint in the preneoplastic stage underlying different subtypes of MF-HCC are important for understanding their molecular evolution and developing a precision management strategy.

METHODS

We conducted whole-exome sequencing in 74 tumor samples from spatially distinct regions in 35 resected lesions and adjacent noncancerous tissues from 11 patients, 15 histologically confirmed preneoplastic lesions, and six samples from peripheral blood mononuclear cells. A previously published MF-HCC cohort (n = 9) was included as an independent validation dataset. We combined well-established approaches to investigate tumor heterogeneity, intrahepatic metastatic timing, and molecular footprints in different subtypes of MF-HCCs.

RESULTS

We classified MF-HCCs patients into three subtypes, including intrahepatic metastasis, multicentric occurrence, and mixed intrahepatic metastasis and multicentric occurrence. The dynamic changes in mutational signatures between tumor subclonal expansions demonstrated varied etiologies (e.g., aristolochic acid exposure) underlying the clonal progression in different MF-HCC subtypes. Furthermore, the clonal evolution in intrahepatic metastasis exhibited an early metastatic seeding at 10-4-0.01 cm3 in primary tumor volume (below the limits of clinical detection), further validated in an independent cohort. In addition, mutational footprints in the preneoplastic lesions for multicentric occurrence patients revealed common preneoplastic arising clones, evidently being ancestors of different tumor lesions.

CONCLUSION

Our study comprehensively characterized the varied tumor clonal evolutionary history underlying different subtypes of MF-HCC and provided important implications for optimizing personalized clinical management for MF-HCC.

Clinical implication of genetic intratumor heterogeneity for targeted therapy in head and neck cancer

BACKGROUND

Genomic profiling is increasingly used both in therapeutic decision-making and as inclusion criteria for trials testing targeted therapies. However, the mutational landscape may vary across different areas of a tumor and intratumor heterogeneity will challenge treatments or clinical decisions based on single tumor biopsies. The purpose of this study was to assess the clinical relevance of genetic intratumor heterogeneity in head and neck squamous cell carcinomas (HNSCC) using the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT).

MATERIALS AND METHODS

This prospective study included 33 whole tumor specimens from 28 patients with primary or recurrent HNSCC referred for surgery. Three tumor blocks were selected from central, semi-peripheral, and peripheral positions, mimicking biopsies in three different locations. Genetic analysis of somatic copy number alterations (SCNAs) was performed on the three biopsies using Oncoscan, focusing on 45 preselected HNSCC genes of interest. Clinical relevance was assessed using the ESCAT score to investigate whether and how treatment decisions would change based on the three biopsies from the same tumor.

RESULTS

The SCNAs identified among 45 preselected genes within the three tumor biopsies derived from the same tumor revealed distinct variations. The detected discrepancies could potentially influence treatment approaches or clinical decisions in 36% of the patients if only one tumor biopsy was used. Recurrent tumors exhibited significantly higher variation in SCNAs than primary tumors (p = .024). No significant correlation between tumor size and heterogeneity (p = .7) was observed.

CONCLUSION

In 36% of patients diagnosed with HNSCC, clinically significant intratumor heterogeneity was observed which may have implications for patient management. This finding substantiates the need for future studies that specifically investigate the clinical implications associated with intratumor heterogeneity.

Genomic profiling and immune landscape of olfactory neuroblastoma in China

Background

Olfactory neuroblastoma (ONB) is a rare malignant neoplasm of the olfactory mucosa. The paucity of genomic data has prevented the development of individualized ONB treatments. Here, we investigated the genomic and immune landscape of ONB in Chinese patients.

Methods

Whole exome sequencing (WES) and multiplex immunofluorescence (MIF) analysis were performed on tissue samples from 19 Chinese ONB patients. Patients were divided into low- and high-grade groups.

Results

Overall, 929 nonsynonymous alterations were identified in 18 (94.74%) ONB cases. The most prevalent altered cancer-related genes were CTNNB1 (16%) and ZNRF3 (16%). The most mutated oncogenic pathways were the WNT and RAS pathways. The median tumor mutation burden (TMB) was 0.45, ranging from 0 to 3.25. Only one case expressed PD-L1 (> 1%) in the tumor region. The percentage of CD8+ tumor-infiltrating lymphocytes (TILs) in the tumor region ranged from 0.03% to 84.9%, with a median of 1.08%. No significant differences were observed between the low- and high-grade groups for clinicopathological features, mutant genes, mutant pathways, TMB, tumor neoantigen burden (TNB), mutant-allele tumor heterogeneity (MATH), PD-L1 expression levels, or CD8+ TIL percentage. However, the low-grade group showed significantly more CD68+ macrophages in both the tumor and total region than the high-grade group. Notably, CD68+CD163- macrophages accounted for an average of 80.5% of CD68+ macrophages.

Conclusion

This study presents data on the genomic and immune landscape of ONB cases in China. CTNNB1 and ZNRF3 were the most prevalent altered cancer-related genes. The results of TMB, PD-L1, and CD8+ Tils suggest that ONB may be insensitive to immunotherapy. M1 macrophages may be positively associated with the prognosis of ONB.

Implications for Practice

In this study, the most prevalent altered cancer-related genes were CTNNB1 (16%) and ZNRF3 (16%). The most mutated oncogenic pathways were the WNT and RAS pathways. The median tumor mutation burden (TMB) was 0.45, ranging from 0 to 3.25. Only one (1/15) case expressed PD-L1 (> 1%) in the tumor region. However, the low-grade group showed significantly more CD68+ macrophages in both the tumor and total region than the high-grade group. The higher level of CD68-related macrophages indicates that M1 macrophages potentially play an important role in ONB development that is possibly associated with prognosis.

Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer

Digital pathology allows computerized analysis of tumor ecosystem using whole slide images (WSIs). Here, we present single-cell morphological and topological profiling (sc-MTOP) to characterize tumor ecosystem by extracting the features of nuclear morphology and intercellular spatial relationship for individual cells. We construct a single-cell atlas comprising 410 million cells from 637 breast cancer WSIs and dissect the phenotypic diversity within tumor, inflammatory and stroma cells respectively. Spatially-resolved analysis identifies recurrent micro-ecological modules representing locoregional multicellular structures and reveals four breast cancer ecotypes correlating with distinct molecular features and patient prognosis. Further analysis with multiomics data uncovers clinically relevant ecosystem features. High abundance of locally-aggregated inflammatory cells indicates immune-activated tumor microenvironment and favorable immunotherapy response in triple-negative breast cancers. Morphological intratumor heterogeneity of tumor nuclei correlates with cell cycle pathway activation and CDK inhibitors responsiveness in hormone receptor-positive cases. sc-MTOP enables using WSIs to characterize tumor ecosystems at the single-cell level.

PD-L1 expression-related PI3K pathway correlates with immunotherapy efficacy in gastric cancer

Background

The programed death ligand-1 combined positive score (PD-L1 CPS), the only FDA-approved biomarker for immune checkpoint inhibitor therapy in gastric cancer (GC) patients, is an important but imperfect predictive biomarker. The molecular characteristics of tumors that influence the PD-L1 CPS are largely unknown and would be helpful for screening patients who would benefit from immunotherapy.

Methods

PD-L1 immunohistochemistry (IHC) and targeted next-generation sequencing techniques were used to compare genomic alterations in 492 GC patients in two groups (PD-L1 CPS ⩾ 1, positive; CPS < 1, negative). Screened PD-L1 expression-related factors were analyzed for immunotherapy efficacy in three distinct GC cohorts from public databases.

Results

Positive PD-L1 expression occurred in 40% of GC patients and was associated with a higher proportion of phosphatidylinositol 3-kinase (PI3K), SWItch/Sucrose NonFermentable (SWI/SNF), lysine demethylase (KDM), and DNA (cytosine-5)-methyltransferase (DNMT) (all p < 0.01), pathway alterations. Compared to wild-type GC patients, those with PI3K pathway alterations had a higher response rate (p = 0.002) and durable clinical benefit rate with immunotherapy (p = 0.023, p = 0.038) as well as longer progression-free survival (p = 0.084, p = 0.0076) and overall survival (p = 0.2, p = 0.037) with immunotherapy.

Conclusion

This study revealed PD-L1 expression-related factors in the tumor genome in a GC cohort. Alterations in the PI3K pathway associated with PD-L1 positivity were shown to be associated with better immunotherapy efficacy in three distinct GC cohorts from public databases. Our results provide a potential avenue for patient selection and rational immune combination development for GC patients.

Loss of p53 and mutational heterogeneity drives immune resistance in an autochthonous mouse lung cancer model with high tumor mutational burden

The role of tumor mutational burden (TMB) in shaping tumor immunity is a key question that has not been addressable using genetically engineered mouse models (GEMMs) of lung cancer. To induce TMB in lung GEMMs, we expressed an ultra-mutator variant of DNA polymerase-E (POLE)P286R in lung epithelial cells. Introduction of PoleP286R allele into KrasG12D and KrasG12D; p53L/L (KP) models significantly increase their TMB. Immunogenicity and sensitivity to immune checkpoint blockade (ICB) induced by Pole is partially dependent on p53. Corroborating these observations, survival of NSCLC patients whose tumors have TP53truncating mutations is shorter than those with TP53WT with immunotherapy. Immune resistance is in part through reduced antigen presentation and in part due to mutational heterogeneity. Total STING protein levels are elevated in Pole mutated KP tumors creating a vulnerability. A stable polyvalent STING agonist or p53 induction increases sensitivity to immunotherapy offering therapeutic options in these polyclonal tumors.

The heterogeneity and clonal evolution analysis of the advanced prostate cancer with castration resistance

BACKGROUND

Nowadays, the incidence rate of advanced and metastatic prostate cancer at the first time of diagnosis grows higher in China yearly. At present, androgen deprivation therapy (ADT) is the primary treatment of advanced prostate cancer. However, after several years of ADT, most patients will ultimately progress to castration-resistant prostate cancer (CRPC). Previous studies mainly focus on Caucasian and very few on East Asian patients.

METHODS

In this study, the pre- and post-ADT tumor samples were collected from five Chinese patients with advanced prostate cancer. The whole-exome sequencing, tumor heterogeneity, and clonal evolution pattern were analyzed.

RESULTS

The results showed that the gene mutation pattern and heterogeneity changed significantly after androgen deprivation therapy. Tumor Mutational Burden (TMB) and Copy Number Alteration (CNA) were substantially reduced in the post-treatment group, but the Mutant-allele tumor heterogeneity (MATH), Socio-Demographic Index (SDI), Intratumor heterogeneity (ITH), and weighted Genome Instability Index (wGII) had no significant difference. According to the clone types and characteristics, the presence of main clones in five pre-and post-treatment samples, the clonal evolution pattern can be further classified into two sub-groups (the Homogeneous origin clonal model or the Heterogeneous origin clonal model). The Progression-free survival (PFS) of the patients with the "Homogeneous origin clonal model" was shorter than the "Heterogeneous origin clonal model". The longer PFS might relate to MUC7 and MUC5B mutations repaired. ZNF91 mutation might be responsible for resistance to ADT resistance.

CONCLUSION

Our findings revealed potential genetic regulators to predict the castration resistance and provide insights into the castration resistance processes in advanced prostate cancer. The crosstalk between clonal evolution patterns and tumor microenvironment may also play a role in castration resistance. A multicenter-research including larger populations with different background are needed to confirm our conclusion in the future.

Сell clusters isolation in glioblastomas and their functional and molecular characterization using new morphometric approaches

BACKGROUND

Digital pathology has come a long way in terms of creating tools to improve existing diagnostic approaches. However, several pathology fields, such as neuropathology, are still characterized by low coverage from machine learning tools and neural network analysis, which may be due to the complexity of the internal cellular and molecular structure of the corresponding neoplasms, including glioblastomas.

METHOD

In the framework of this study, using advanced proprietary tools for obtaining images of histological slides and their deep morphometric analysis, we studied samples of 198 patients with glioblastoma with the selection of morphometric cell clusters. Also, cells of each cluster were isolated, and their proliferative, migratory, invasive activity, survival ability, aerobic glycolysis activity, and chemo- and radioresistance were studied.

RESULTS

Four morphometric clusters were identified, including small-cell cluster, paracirculonuclear cluster, hypochromic cluster, and macronuclear cluster, which significantly differed in morphometric parameters and functional parameters. Hypochromic cluster cells demonstrated the highest proliferation activity; macronuclear cluster was the most active glucose consumer; paracirculonuclear cluster had the most prominent migratory and invasive activity and hypoxia resistance; small-cell cluster demonstrated predominantly average values of all parameters. Moreover, additional analysis revealed the presence of a separate subcluster of stem cell elements that correspond in their molecular properties to glioma stem cells and are present in all four clusters. It also turned out that several key molecular parameters of glioblastoma, such as mutational modifications in the EGFR, PDGFRA, and NF1 genes, along with the molecular GBM subtype, are significantly correlated with the identified cell clusters.

CONCLUSIONS

Thus, the results represent an up-and-coming innovation in the practical field of digital pathology and fundamental questions of glioma carcinogenesis.

Identification of the HNSC88 Molecular Signature for Predicting Subtypes of Head and Neck Cancer

Head and neck squamous cell carcinoma (HNSC) exhibits genetic heterogeneity in etiologies, tumor sites, and biological processes, which significantly impact therapeutic strategies and prognosis. While the influence of human papillomavirus on clinical outcomes is established, the molecular subtypes determining additional treatment options for HNSC remain unclear and inconsistent. This study aims to identify distinct HNSC molecular subtypes to enhance diagnosis and prognosis accuracy. In this study, we collected three HNSC microarrays (n = 306) from the Gene Expression Omnibus (GEO), and HNSC RNA-Seq data (n = 566) from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes (DEGs) and validate our results. Two scoring methods, representative score (RS) and perturbative score (PS), were developed for DEGs to summarize their possible activation functions and influence in tumorigenesis. Based on the RS and PS scoring, we selected candidate genes to cluster TCGA samples for the identification of molecular subtypes in HNSC. We have identified 289 up-regulated DEGs and selected 88 genes (called HNSC88) using the RS and PS scoring methods. Based on HNSC88 and TCGA samples, we determined three HNSC subtypes, including one HPV-associated subtype, and two HPV-negative subtypes. One of the HPV-negative subtypes showed a relationship to smoking behavior, while the other exhibited high expression in tumor immune response. The Kaplan-Meier method was used to compare overall survival among the three subtypes. The HPV-associated subtype showed a better prognosis compared to the other two HPV-negative subtypes (log rank, p = 0.0092 and 0.0001; hazard ratio, 1.36 and 1.39). Additionally, within the HPV-negative group, the smoking-related subgroup exhibited worse prognosis compared to the subgroup with high expression in immune response (log rank, p = 0.039; hazard ratio, 1.53). The HNSC88 not only enables the identification of HPV-associated subtypes, but also proposes two potential HPV-negative subtypes with distinct prognoses and molecular signatures. This study provides valuable strategies for summarizing the roles and influences of genes in tumorigenesis for identifying molecular signatures and subtypes of HNSC.

The joint role of methylation and immune-related lncRNAs in ovarian cancer: Defining molecular subtypes and developing prognostic signature

INTRODUCTION

Complex outcome of ovarian cancer (OC) stems from the tumor immune microenvironment (TIME) influenced by genetic and epigenetic factors. This study aimed to comprehensively explored the subclasses of OC through lncRNAs related to both N6-methyladenosine (m6A)/N1-methyladenosine (m1A)/N7-methylguanosine (m7G)/5-methylcytosine (m5C) in terms of epigenetic variability and immune molecules and develop a new set of risk predictive systems.

MATERIAL AND METHODS

The lncRNA data of OC were collected from TCGA. Spearman correlation analysis on lncRNA data of OC with immune-related gene expression and with m6A/m5C/m1A/m7G were respectively conducted. The m6A/m5C/m1A/m7G-related m6A/m5C/m1A/m7G related immune lncRNA subtypes were identified on the basis of the prognostic lncRNAs. Heterogeneity among subtypes was evaluated by tumor mutation analysis, tumor microenvironment (TME) component analysis, response to immune checkpoint blocked (ICB) and chemotherapeutic drugs. A risk predictive system was developed based on the results of Cox regression analysis and random survival forest analysis of the differences between each specific cluster and other clusters.

RESULTS

Three m6A/m5C/m1A/m7G-related immune lncRNA subtypes of OC showing distinct differences in prognosis, mutation pattern, TIME components, immunotherapy and chemotherapy response were identified. A set of risk predictive system consisting of 10 lncRNA for OC was developed, according to which the risk score of samples in each OC dataset was calculated and risk type was defined.

CONCLUSIONS

This study classified three m6A/m5C/m1A/m7G-related immune lncRNA subtypes with distinct heterogeneous mutation patterns, TME components, ICB therapy and immune response, and provided a set of risk predictive system consisted of 10 lncRNA for OC.

The applications and techniques of organoids in head and neck cancer therapy

Head and neck cancer (HNC) is one of the most common cancers on the planet, with approximately 600,000 new cases diagnosed and 300,000 deaths every year. Research into the biological basis of HNC has advanced slowly over the past decades, which has made it difficult to develop new, more effective treatments. The patient-derived organoids (PDOs) are made from patient tumor cells, resembling the features of their tumors, which are high-fidelity models for studying cancer biology and designing new precision medicine therapies. In recent years, considerable effort has been focused on improving "organoids" technologies and identifying tumor-specific medicine using head and neck samples and a variety of organoids. A review of improved techniques and conclusions reported in publications describing the application of these techniques to HNC organoids is presented here. Additionally, we discuss the potential application of organoids in head and neck cancer research as well as the limitations associated with these models. As a result of the integration of organoid models into future precision medicine research and therapeutic profiling programs, the use of organoids will be extremely significant in the future.

Genomic landscape and expression profile of consensus molecular subtype four of colorectal cancer

Background

Compared to other subtypes, the CMS4 subtype is associated with lacking of effective treatments and poorer survival rates.

Methods

A total of 24 patients with CRC were included in this study. DNA and RNA sequencing were performed to acquire somatic mutations and gene expression, respectively. MATH was used to quantify intratumoral heterogeneity. PPI and survival analyses were performed to identify hub DEGs. Reactome and KEGG analyses were performed to analyze the pathways of mutated or DEGs. Single-sample gene set enrichment analysis and Xcell were used to categorize the infiltration of immune cells.

Results

The CMS4 patients had a poorer PFS than CMS2/3. CTNNB1 and CCNE1 were common mutated genes in the CMS4 subtype, which were enriched in Wnt and cell cycle signaling pathways, respectively. The MATH score of CMS4 subtype was lower. SLC17A6 was a hub DEG. M2 macrophages were more infiltrated in the tumor microenvironment of CMS4 subtype. The CMS4 subtype tended to have an immunosuppressive microenvironment.

Conclusion

This study suggested new perspectives for exploring therapeutic strategies for the CMS4 subtype CRC.

Chronic obstructive pulmonary disease alters the genetic landscape and tumor immune microenvironment in lung cancer patients

Background

Chronic obstructive pulmonary disease (COPD) and lung cancer are leading causes of morbidity and mortality worldwide. Studies have reported molecular alterations in patients with lung cancer and in patients with COPD. However, few investigation has been conducted on the molecular characteristics of lung cancer patients with COPD.

Materials and methods

We performed a retrospective cohort study that included 435 patients with pathologically confirmed lung cancer at the Ruijin Hospital. For patients with documented spirometry, Global Initiative for Chronic Obstructive Lung Disease criteria were used to define COPD. For patients without documented spirometry, chest computed tomography and other clinical information were used to define COPD. Tumor tissue DNA was extracted from formalin-fixed paraffin-embedded samples. DNA mutation analysis, multiplex immunohistochemistry (mIHC), calculation of tumor mutational burden (TMB), mutant-allele tumor heterogeneity (MATH), and predication of neoantigens were performed.

Results

Although SNV mutations in lung cancer patients with COPD (G1 group) were generally higher than those in lung cancer patients without COPD (G2 group), the difference in the number of mutations was insignificant between the two groups. Of the 35 mutated genes, the number of them was higher in G1 than in G2, except that of EGFR. PI3K-Akt signaling pathway was enriched from significantly different genes. While TMB and MATH levels were not significantly different, the tumor neoantigen burdenwas markedly higher in G1 than that in G2. The level of CD68+ macrophages was significant higher in the stroma and total areas in the G1 group than in G2 group. The level of CD8+ lymphocytes was markedly higher in the stroma and showed a clear tendency forhigher expression in the G1 group than inthe G2 group. No significant differences were observed for the level of programmed death-ligand 1+ (PD-L1+), programmed death 1+ (PD-1+), and CD68PD-L1 in the stroma, tumor and total areas.

Conclusion

Our study revealed different genetic aberrations and pathways, higher neoantigen burden, and higher level of CD68+ macrophages and CD8+ T lymphocytes in lung cancer patients with COPD. Our investigation implies that the existence of COPD should be considered and immunotherapy is a potential choice when treating lung cancer patients with COPD.

Multi-omics identification of an immunogenic cell death-related signature for clear cell renal cell carcinoma in the context of 3P medicine and based on a 101-combination machine learning computational framework

Background

Clear cell renal cell carcinoma (ccRCC) is a prevalent urological malignancy associated with a high mortality rate. The lack of a reliable prognostic biomarker undermines the efficacy of its predictive, preventive, and personalized medicine (PPPM/3PM) approach. Immunogenic cell death (ICD) is a specific type of programmed cell death that is tightly associated with anti-cancer immunity. However, the role of ICD in ccRCC remains unclear.

Methods

Based on AddModuleScore, single-sample gene set enrichment analysis (ssGSEA), and weighted gene co-expression network (WGCNA) analyses, ICD-related genes were screened at both the single-cell and bulk transcriptome levels. We developed a novel machine learning framework that incorporated 10 machine learning algorithms and their 101 combinations to construct a consensus immunogenic cell death-related signature (ICDRS). ICDRS was evaluated in the training, internal validation, and external validation sets. An ICDRS-integrated nomogram was constructed to provide a quantitative tool for predicting prognosis in clinical practice. Multi-omics analysis was performed, including genome, single-cell transcriptome, and bulk transcriptome, to gain a more comprehensive understanding of the prognosis signature. We evaluated the response of risk subgroups to immunotherapy and screened drugs that target specific risk subgroups for personalized medicine. Finally, the expression of ICD-related genes was validated by qRT-PCR.

Results

We identified 131 ICD-related genes at both the single-cell and bulk transcriptome levels, of which 39 were associated with overall survival (OS). A consensus ICDRS was constructed based on a 101-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. ICDRS can also be used to predict the occurrence, development, and metastasis of ccRCC. Multivariate analysis verified it as an independent prognostic factor for OS, progression-free survival (PFS), and disease-specific survival (DSS) of ccRCC. The ICDRS-integrated nomogram provided a quantitative tool in clinical practice. Moreover, we observed distinct biological functions, mutation landscapes, and immune cell infiltration in the tumor microenvironment between the high- and low-risk groups. Notably, the immunophenoscore (IPS) score showed a significant difference between risk subgroups, suggesting a better response to immunotherapy in the high-risk group. Potential drugs targeting specific risk subgroups were also identified.

Conclusion

Our study constructed an immunogenic cell death-related signature that can serve as a promising tool for prognosis prediction, targeted prevention, and personalized medicine in ccRCC. Incorporating ICD into the PPPM framework will provide a unique opportunity for clinical intelligence and new management approaches.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00327-3.

Inferring early genetic progression in cancers with unobtainable premalignant disease

Analysis of premalignant tissue has identified the typical order of somatic events leading to invasive tumors in several cancer types. For other cancers, premalignant tissue is unobtainable, leaving genetic progression unknown. Here, we demonstrate how to infer progression from exome sequencing of primary tumors. Our computational method, PhylogicNDT, recapitulated the previous experimentally determined genetic progression of human papillomavirus-negative (HPV^-) head and neck squamous cell carcinoma (HNSCC). We then evaluated HPV⁺ HNSCC, which lacks premalignant tissue, and uncovered its previously unknown progression, identifying early drivers. We converted relative timing estimates of driver mutations and HPV integration to years before diagnosis based on a clock-like mutational signature. We associated the timing of transitions to aneuploidy with increased intratumor genetic heterogeneity and shorter overall survival. Our approach can establish previously unknown early genetic progression of cancers with unobtainable premalignant tissue, supporting development of experimental models and methods for early detection, interception and prognostication.

Circulating Tumor DNA in Head and Neck Squamous Cell Carcinoma

Tumors shed cell-free DNA (cfDNA) into the plasma. "Liquid biopsies" are a diagnostic test to analyze cfDNA in order to detect minimal residual cancer, profile the genomic tumor landscape, and monitor cancers non-invasively over time. This technique may be useful in patients with head and neck squamous cell carcinoma (HNSCC) due to genetic tumor heterogeneity and limitations in imaging sensitivity. However, there are technical challenges that need to be overcome for the widespread use of liquid biopsy in the clinical management of these patients. In this review, we discuss our current understanding of HNSCC genetics and the role of cfDNA genomic analyses as an emerging precision diagnostic tool.

Therapeutic Effects of Coumarins with Different Substitution Patterns

The use of derivatives of natural and synthetic origin has gained attention because of their therapeutic effects against human diseases. Coumarins are one of the most common organic molecules and are used in medicine for their pharmacological and biological effects, such as anti-inflammatory, anticoagulant, antihypertensive, anticonvulsant, antioxidant, antimicrobial, and neuroprotective, among others. In addition, coumarin derivates can modulate signaling pathways that impact several cell processes. The objective of this review is to provide a narrative overview of the use of coumarin-derived compounds as potential therapeutic agents, as it has been shown that substituents on the basic core of coumarin have therapeutic effects against several human diseases and types of cancer, including breast, lung, colorectal, liver, and kidney cancer. In published studies, molecular docking has represented a powerful tool to evaluate and explain how these compounds selectively bind to proteins involved in various cellular processes, leading to specific interactions with a beneficial impact on human health. We also included studies that evaluated molecular interactions to identify potential biological targets with beneficial effects against human diseases.

Patient-derived three-dimensional culture techniques model tumor heterogeneity in head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) outcomes remain stagnant, in part due to a poor understanding of HNSCC biology. The importance of tumor heterogeneity as an independent predictor of outcomes and treatment failure in HNSCC has recently come to light. With this understanding, 3D culture systems, including patient derived organoids (PDO) and organotypic culture (OTC), that capture this heterogeneity may allow for modeling and manipulation of critical subpopulations, such as p-EMT, as well as interactions between cancer cells and immune and stromal cells in the microenvironment. Here, we review work that has been done using PDO and OTC models of HNSCC, which demonstrates that these 3D culture models capture in vivo tumor heterogeneity and can be used to model tumor biology and treatment response in a way that faithfully recapitulates in vivo characteristics. As such, in vitro 3D culture models represent an important bridge between 2D monolayer culture and in vivo models such as patient derived xenografts.