An algorithm to quantify intratumor heterogeneity based on alterations of gene expression profiles

Analysis of germline-somatic mutational connections in colorectal cancer reveals differential tumorigenic patterns and a novel predictive marker for germline mutation carriers

Colorectal cancer (CRC) genetic testing of regions beyond clinical guidelines has revealed a substantial number of likely pathogenic germline mutations (GMs). It remains largely undetermined whether and how these GMs, typically located in non-mismatch repair (non-MMR) genes, are associated with the tumorigenesis of CRC. This study aimed to identify CRC-predisposing GMs among 93 cancer susceptibility genes and investigate their potential influences on CRC somatic mutational features. We secondarily aimed to investigate whether somatic ERBB2 amplification contributes to identifying GM carriers. This study incorporated a total of 3,240 Chinese CRC patients and 10,588 control individuals. CRC patients were subjected to paired tumor-normal sequencing with a 1,021-gene panel. A case-control analysis was conducted to profile the GM-associated CRC risk. A comprehensive germline-somatic association analysis was performed among 2,405 patients, with key findings subsequently validated in an independent 835-patient cohort and the TCGA CRC cohort. The case-control results supported CRC-predisposing effects of GMs in certain homologous recombination repair (HRR) and DNA damage checkpoint factor (CPF) genes, such as BRCA1/2, RecQ helicase genes, ATM, and CHEK2. HRR GMs were associated with an increased copy number alteration burden, more TP53 clonal mutations, and a higher probability of carrying somatic ERBB2 amplification. CPF GMs were inferred to have synergistic effects with ARID1A and KDM6A somatic mutations in CRC tumorigenesis. Among patients with onset age ≥55 years, stable microsatellites, and no cancer family history, ERBB2 amplification was significantly predictive of GM carriers. Our findings elucidate different germline tumorigenic patterns not driven by deficient MMR. Somatic ERBB2 amplification in CRC can serve as an indicator for germline genetic testing when traditional risk features are absent.

A comprehensive investigation of associations between cell death pathways and molecular and clinical features in pan-cancer

BACKGROUND

Regulated cell death (RCD) pathways play significant roles in tumorigenesis. However, systematic investigation into correlations between RCD and various molecular and clinical features, particularly anti-tumor immunity and immunotherapy response in pan-cancer remains lacking.

METHODS

Using the single-sample gene set enrichment analysis, we quantified the activities of six RCD pathways (apoptosis, autophagy, ferroptosis, cuproptosis, necroptosis, and pyroptosis) in each cancer specimen. Then, we explored associations of these six RCD pathways with tumor immunity, genomic instability, tumor phenotypes and clinical features, and responses to immunotherapy and targeted therapies in pan-cancer by statistical analyses.

RESULTS

Our results showed that the RCD (except autophagy) activities were oncogenic signatures, as evidenced by their hyperactivation in late stage or metastatic cancer patients, positive correlations with tumor proliferation, stemness, genomic instability and intratumor heterogeneity, and correlation with worse survival outcomes in cancer. In contrast, autophagy was a tumor suppressive signature as its associations with molecular and clinical features in cancer shows an opposite pattern compared to the other RCD pathways. Furthermore, heightened RCD (except cuproptosis) activities were correlated with increased sensitivity to immune checkpoint inhibitors. Additionally, elevated activities of pyroptosis, autophagy, cuproptosis and necroptosis were associated with increased drug sensitivity in a broad spectrum of anti-tumor targeted therapies, while the elevated activity of ferroptosis was correlated with decreased sensitivity to numerous targeted therapies.

CONCLUSION

RCD (except autophagy) activities correlate with unfavorable cancer prognosis, while the autophagy activity correlate with favorable clinical outcomes. RCD (except cuproptosis) activities are positive biomarkers for anti-tumor immunity and immunotherapy response.

Solid pancancer analysis reveals immune and hematopoietic stem cell and DNA damage repair signatures to distinguish different cancer subtypes

PURPOSE

Immunity, stemness, and DNA damage repair (DDR) are crucial for cancer development and therapy resistance. With advancements in multiomics technology, the exploration of cancers related to immunity, stemness, and the DDR has triggered interest, but the combination of these levels for analyzing multiple cancers remains insufficient.

METHODS

In this study, 9906 solid tumor samples from 31 TCGA cancer types were clustered on the basis of the enrichment levels of 13 gene sets associated with stemness, immunity, and DDR. Moreover, a soft ensemble model was constructed on the basis of the enrichment levels of these 13 gene sets to predict cancer subtypes via other omics data.

RESULTS

We identified four pancancer subtypes, termed C1, C2, C3, and C4, which presented distinct molecular and clinical features, including the immune microenvironment, stemness, genome instability, intratumor heterogeneity, methylation levels, tumor progression, sensitivity to chemotherapy and immunotherapy, and survival prognosis. The soft ensemble model validated this subtyping method in two breast cancer datasets (gene expression level), a pancancer proteomic dataset (protein expression level), and a pancancer cell line dataset (cell line gene expression level).

CONCLUSION

Our findings indicate that immune, stemness, and DDR signature-based subtyping offers new perspectives on cancer biology and holds promise for improving the clinical management of cancers.

Amino Acid Metabolism Subtypes in Neuroblastoma Identifying Distinct Prognosis and Therapeutic Vulnerabilities

Although amino acid (AA) metabolism is linked to tumor progression and could serve as an attractive intervention target, its association with neuroblastoma (NB) is unknown. Based on AA metabolism-related genes, we established three NB subtypes associated with distinct prognoses and specific functions, with C1 and C2 having better outcomes. The C1 displayed enhanced metabolic activity and recruited metabolism-associated cells. The C2 exhibited an activated immune microenvironment and was more vulnerable to immunotherapy. The C3, characterized by cell cycle peculiarity, possessed a dismal prognosis and high frequency of gene mutations and was susceptible to chemotherapy. Furthermore, single-cell RNA sequencing analysis revealed that the C3-associated Scissor+ cell subpopulation was characterized by notorious functional states and orchestrated an immunosuppressive microenvironment. Additionally, we identified that ALK and BIRC5 contributed to the shorter lifespan of C3 and their corresponding inhibitors were potential interventions. In conclusion, we identified three distinct subtypes of NB, which help us foster individualized therapeutic strategies to improve the prognosis of NB.

Intraneoplastic fungal dysbiosis is associated with colorectal cancer progression and host gene mutation

BACKGROUND

The relationship between intraneoplastic fungi and colorectal cancer (CRC) progression remains largely unclear. Here, we investigated fungal community changes in adenoma and CRC and their correlation with host genetic mutations.

METHODS

We obtained 261 tissue biopsies from two geographically distinct cohorts of CRC and adenoma patients, with each individual contributing 2-5 biopsies from lesions and 2 from adjacent normal tissues. 18S ribosomal RNA gene sequencing was used for microbial profiling. Host genetic alterations including KRAS mutations and microsatellite instability (MSI) were detected concurrently.

FINDINGS

Intra-neoplastic fungal composition significantly differed between CRC and adenoma in two independent cohorts, with enrichment of highly variable fungi (HVF) in CRC. Six HVFs exhibited higher abundances in adenoma and CRC compared to adjacent normal tissues with Malassezia showing a progressive increase from adenoma to CRC. Fungi intratumoral heterogeneity index also increased from adenoma through stages I to IV of CRC. Intra-tumoral fungi-fungi co-abundance analysis indicated stronger positive interactions in CRC than in adenoma, with increasingly robust links among intra-tumoral fungi along adenoma-CRC progression, primarily driven by Malassezia and Aspergillus. Furthermore, fungal heterogeneity was significantly correlated with host genetic mutations, with higher risk indices in CRC tissues harboring KRAS and MSI mutations. Thirteen fungi stratified CRC samples with KRAS mutations, achieving an area under the curve (AUC) of 0.86, while those associated with MSI status showed an AUC of 0.89.

INTERPRETATION

This study demonstrates that intraneoplastic fungal community alterations occur between adenoma and CRC, with increasing heterogeneity associated with host genetic mutations, emphasizing the role of fungal dysbiosis in CRC.

FUNDING

This work was supported by RGC Research Impact Fund Hong Kong (R4032-21F); RGC-CRF (C4008-23W); Strategic Seed Funding Collaboration Research Scheme CUHK (3133344); Strategic Impact Enhancement Fund CUHK (3135509); Impact case for RAE CUHK (3134277).

Comprehensive Analysis of Clinical and Molecular Features in Cancer Patients Associated With Major Human Oncoviruses

Viral infections contribute to a higher incidence of cancer than any other individual risk factor. This study aimed to compare the clinical and molecular features of four viral-associated cancers: stomach adenocarcinoma (STAD), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), and cervical squamous cell carcinoma (CESC). Patients were categorized based on viral infection status, as provided in the clinical data, into virus-associated and non-virus-associated groups, followed by a comprehensive comparison of clinical and molecular features. Our analysis disclosed that viral infections confer unique clinical and molecular signatures to their associated tumors. Specifically, human papillomavirus-associated (HPV+) HNSC and hepatitis B virus-associated (HBV+) LIHC patients were predominantly male, younger, and exhibited better clinical prognoses. Virus-associated tumors displayed enhanced immune microenvironments and high DNA damage response scores, while non-virus-associated tumors were enriched in stromal signatures. HPV+ HNSC and Epstein-Barr virus-associated (EBV+) STAD showed similarities across multi-omics features, including better responses to immunotherapy, lower TP53 mutation rates, tumor mutation burden (TMB), and copy number alteration (CNA). Conversely, HBV+, Hepatitis C virus-associated (HCV+) LIHCs and HPV+ CESC were more genomically unstable due to high TP53 mutation rates, TMB, and CNA. At the protein level, Caspase-7 and Syk were upregulated in HPV+ HNSC and EBV+ STAD, and positively correlated with the enrichment levels of CD8 + T cell, PD-L1, and cytolytic activity. Patient stratification based on infection status has significant clinical implications, particularly for patient prognosis and drug response.

Disulfidoptosis-related molecular subtypes and prognostic model for optimizing drug therapy in metastatic osteosarcoma patients

Disulfidotosis is a newly identified form of cell death associated with tumor response, patient outcomes, and cancer progression. This study aims to identify disulfidptosis-related genes (DiRGs) and their role in osteosarcoma (OS) to predict prognosis and optimize drug therapy for better patient survival. Gene expression matrices and clinical information on OS were obtained from the TARGET and GEO databases. Unsupervised clustering analysis identified two DiRG molecular subgroups with significantly different intratumoral heterogeneity and tumor microenvironment cell infiltrating characteristics in OS. A robust disulfidptosis-related prognostic model using five DiRGs was developed, demonstrating excellent predictive and prognostic power in OS with AUC values of 0.69, 0.78, and 0.85 for 1-, 3-, and 5-year periods, respectively. Investigations into the impact of disulfidoptosis on immune status in OS patients across different risk subgroups revealed that a low immune score and compromised immune status were associated with an unfavorable prognosis for OS patients. INF2 and MEGF10 genes are highly reliable predictors of metastasis among the hub DiRG genes. The validation results indicate a robust correlation between the expression of INF2 and MEGF10 and the severity of malignancy and metastasis in OS. Six drugs targeting osteosarcoma metastasis were identified, with INF2-BP-1-102 and MEGF10-AS703569 showing the best docking scores, indicating their potential to treat OS metastasis effectively. These findings provide valuable insight into improving treatment for OS patients.

Gene clusters-based pathway enrichment analysis identifies four pan-cancer subtypes with distinct molecular and clinical features

Pathways-based clustering methods have been proposed to explore tumor heterogeneity. However, such methods are currently disadvantageous in that specific pathways need to be explicitly claimed. We developed the PathClustNet algorithm, a pathway-based clustering method designed to identify cancer subtypes. This method first detects gene clusters and identifies overrepresented pathways associated with them. Based on the pathway enrichment scores, it reveals cancer subtypes by clustering analysis. We applied the method to TCGA pan-cancer data and identified four pan-cancer subtypes, termed C1, C2, C3 and C4. C1 exhibited high metabolic activity, favorable survival, and the lowest TP53 mutation rate. C2 had high immune, developmental, and stromal pathway activities, the lowest tumor purity, and intratumor heterogeneity. C3, which overexpressed cell cycle and DNA repair pathways, was the most genomically unstable and had the highest TP53 mutation rate. C4 overrepresented neuronal pathways, with the lowest response rate to chemotherapy, but the highest tumor purity and genomic stability. Furthermore, age showed positive correlations with most pathways but a negative correlation with neuronal pathways. Smoking, viral infections, and alcohol use were found to affect the activities of neuron, cell cycle, immune, stromal, developmental, and metabolic pathway in varying degrees. The PathClustNet algorithm unveils a novel classification of pan-cancer based on metabolic, immune, stromal, developmental, cell cycle, and neuronal pathways. These subtypes display different molecular and clinical features to warrant the investigation of precision oncology.

DEPTH2 score was associated with cell proliferation and immune cell infiltrations but not with systemic treatment response in breast cancer

Intratumoral genomic heterogeneity (ITGH), the existence of genotypic and phenotypic variation within an individual tumor, is known to be a key mechanism in treatment resistance. Deviating gene Expression Profiling Tumor Heterogeneity 2 (DEPTH2) algorithm was developed to estimate ITGH using solely RNA expression data unlike the others that require both DNA- and RNA-expression data. Total of 6,500 breast cancer patients from multiple independent cohorts were analyzed using DEPTH2. High DEPTH2 score patients were associated with worse overall survival consistently across all subtypes in METABRIC, but not in TCGA and SCAN-B cohort. Higher DEPTH2 score was linked to increased cell proliferation, as evidenced by elevated Nottingham histological grades and Ki67 gene expression, as well as enrichment of the cell proliferation-related gene sets, and immune cell infiltrations. DEPTH2 score was significantly higher in triple negative breast cancer among the subtypes but did not reflect with lymph node and distal metastasis. DEPTH2 scores decreased in two but showed no change in another two cohorts after neoadjuvant chemotherapy (NAC). DEPTH2 score was not associated with pathologic complete response after NAC in any subtypes across 3 cohorts. DEPTH2 score may not capture the entire biological aspects of ITGH in breast cancer patients.

Drug Repurposing for Cancer Treatment: A Comprehensive Review

Cancer ranks among the primary contributors to global mortality. In 2022, the global incidence of new cancer cases reached about 20 million, while the number of cancer-related fatalities reached 9.7 million. In Saudi Arabia, there were 13,399 deaths caused by cancer and 28,113 newly diagnosed cases of cancer. Drug repurposing is a drug discovery strategy that has gained special attention and implementation to enhance the process of drug development due to its time- and money-saving effect. It involves repositioning existing medications to new clinical applications. Cancer treatment is a therapeutic area where drug repurposing has shown the most prominent impact. This review presents a compilation of medications that have been repurposed for the treatment of various types of cancers. It describes the initial therapeutic and pharmacological classes of the repurposed drugs and their new applications and mechanisms of action in cancer treatment. The review reports on drugs from various pharmacological classes that have been successfully repurposed for cancer treatment, including approved ones and those in clinical trials and preclinical development. It stratifies drugs based on their anticancer repurpose as multi-type, type-specific, and mechanism-directed, and according to their pharmacological classes. The review also reflects on the future potential that drug repurposing has in the clinical development of novel anticancer therapies.

The baseline hemoglobin level is a positive biomarker for immunotherapy response and can improve the predictability of tumor mutation burden for immunotherapy response in cancer

Purpose

Because only a subset of cancer patients can benefit from immunotherapy, identifying predictive biomarkers of ICI therapy response is of utmost importance.

Methods

We analyzed the association between hemoglobin (HGB) levels and clinical outcomes in 1,479 ICIs-treated patients across 16 cancer types. We explored the dose-dependent associations between HGB levels and survival and immunotherapy response using the spline-based cox regression analysis. Furthermore, we investigated the associations across subgroups of patients with different clinicopathological characteristics, treatment programs and cancer types using the bootstrap resampling method.

Results

HGB levels correlated positively with clinical outcomes in cancer patients receiving immunotherapy but not in those without immunotherapy. Moreover, this association was independent of other clinicopathological characteristics (such as sex, age, tumor stage and tumor mutation burden (TMB)), treatment program and cancer type. Also, this association was independent of the established biomarkers of immunotherapy response, including TMB, PD-L1 expression and microsatellite instability. The combination of TMB and HGB level are more powerful in predicting immunotherapy response than TMB alone. Multi-omics analysis showed that HGB levels correlated positively with antitumor immune signatures and negatively with tumor properties directing antitumor immunosuppression, such as homologous recombination defect, stemness and intratumor heterogeneity.

Conclusion

The HGB measure has the potential clinical value as a novel biomarker of immunotherapy response that is easily accessible from clinically routine examination. The combination of TMB and HGB measures have better predictive performance for immunotherapy response than TMB.

Identifying Diffuse Glioma Subtypes Based on Pathway Enrichment Evaluation

Gliomas are highly heterogeneous in molecular, histology, and microenvironment. However, a classification of gliomas by integrating different tumor microenvironment (TME) components remains unexplored. Based on the enrichment scores of 17 pathways involved in immune, stromal, DNA repair, and nervous system signatures in diffuse gliomas, we performed consensus clustering to uncover novel subtypes of gliomas. Consistently in three glioma datasets (TCGA-glioma, CGGA325, and CGGA301), we identified three subtypes: Stromal-enriched (Str-G), Nerve-enriched (Ner-G), and mixed (Mix-G). Ner-G was charactered by low immune infiltration levels, stromal contents, tumor mutation burden, copy number alterations, DNA repair activity, cell proliferation, epithelial-mesenchymal transformation, stemness, intratumor heterogeneity, androgen receptor expression and EGFR, PTEN, NF1 and MUC16 mutation rates, while high enrichment of neurons and nervous system pathways, and high tumor purity, estrogen receptor expression, IDH1 and CIC mutation rates, temozolomide response rate and overall and disease-free survival rates. In contrast, Str-G displayed contrastive characteristics to Ner-G. Our analysis indicates that the heterogeneity between glioma cells and neurons is lower than that between glioma cells and immune and stromal cells. Furthermore, the abundance of neurons is positively associated with clinical outcomes in gliomas, while the enrichment of immune and stromal cells has a negative association with them. Our classification method provides new insights into the tumor biology of gliomas, as well as clinical implications for the precise management of this disease.

Spatial Transcriptome-Wide Profiling of Small Cell Lung Cancer Reveals Intra-Tumoral Molecular and Subtype Heterogeneity

Small cell lung cancer (SCLC) is a highly aggressive malignancy characterized by rapid growth and early metastasis and is susceptible to treatment resistance and recurrence. Understanding the intra-tumoral spatial heterogeneity in SCLC is crucial for improving patient outcomes and clinically relevant subtyping. In this study, a spatial whole transcriptome-wide analysis of 25 SCLC patients at sub-histological resolution using GeoMx Digital Spatial Profiling technology is performed. This analysis deciphered intra-tumoral multi-regional heterogeneity, characterized by distinct molecular profiles, biological functions, immune features, and molecular subtypes within spatially localized histological regions. Connections between different transcript-defined intra-tumoral phenotypes and their impact on patient survival and therapeutic response are also established. Finally, a gene signature, termed ITHtyper, based on the prevalence of intra-tumoral heterogeneity levels, which enables patient risk stratification from bulk RNA-seq profiles is identified. The prognostic value of ITHtyper is rigorously validated in independent multicenter patient cohorts. This study introduces a preliminary tumor-centric, regionally targeted spatial transcriptome resource that sheds light on previously unexplored intra-tumoral spatial heterogeneity in SCLC. These findings hold promise to improve tumor reclassification and facilitate the development of personalized treatments for SCLC patients.

Spatial intratumor heterogeneity of programmed death-ligand 1 expression predicts poor prognosis in resected non-small cell lung cancer

BACKGROUND

We quantified the pathological spatial intratumor heterogeneity of programmed death-ligand 1 (PD-L1) expression and investigated its relevance to patient outcomes in surgically resected non-small cell lung carcinoma (NSCLC).

METHODS

This study enrolled 239 consecutive surgically resected NSCLC specimens of pathological stage IIA-IIIB. To characterize the spatial intratumor heterogeneity of PD-L1 expression in NSCLC tissues, we developed a mathematical model based on texture image analysis and determined the spatial heterogeneity index of PD-L1 for each tumor. The correlation between the spatial heterogeneity index of PD-L1 values and clinicopathological characteristics, including prognosis, was analyzed. Furthermore, an independent cohort of 70 cases was analyzed for model validation.

RESULTS

Clinicopathological analysis showed correlations between high spatial heterogeneity index of PD-L1 values and histological subtype (squamous cell carcinoma; P < .001) and vascular invasion (P = .004). Survival analysis revealed that patients with high spatial heterogeneity index of PD-L1 values presented a significantly worse recurrence-free rate than those with low spatial heterogeneity index of PD-L1 values (5-year recurrence-free survival [RFS] = 26.3% vs 47.1%, P < .005). The impact of spatial heterogeneity index of PD-L1 on cancer survival rates was verified through validation in an independent cohort. Additionally, high spatial heterogeneity index of PD-L1 values were associated with tumor recurrence in squamous cell carcinoma (5-year RFS = 29.2% vs 52.8%, P < .05) and adenocarcinoma (5-year RFS = 19.6% vs 43.0%, P < .01). Moreover, we demonstrated that a high spatial heterogeneity index of PD-L1 value was an independent risk factor for tumor recurrence.

CONCLUSIONS

We presented an image analysis model to quantify the spatial intratumor heterogeneity of protein expression in tumor tissues. This model demonstrated that the spatial intratumor heterogeneity of PD-L1 expression in surgically resected NSCLC predicts poor patient outcomes.

TMPRSS2 is a tumor suppressor and its downregulation promotes antitumor immunity and immunotherapy response in lung adenocarcinoma

BACKGROUND

TMPRSS2, a key molecule for SARS-CoV-2 invading human host cells, has an association with cancer. However, its association with lung cancer remains insufficiently unexplored.

METHODS

In five bulk transcriptomics datasets, one single-cell RNA sequencing (scRNA-seq) dataset and one proteomics dataset for lung adenocarcinoma (LUAD), we explored associations between TMPRSS2 expression and immune signatures, tumor progression phenotypes, genomic features, and clinical prognosis in LUAD by the bioinformatics approach. Furthermore, we performed experimental validation of the bioinformatics findings.

RESULTS

TMPRSS2 expression levels correlated negatively with the enrichment levels of both immune-stimulatory and immune-inhibitory signatures, while they correlated positively with the ratios of immune-stimulatory/immune-inhibitory signatures. It indicated that TMPRSS2 levels had a stronger negative correlation with immune-inhibitory than with immune-stimulatory signatures. TMPRSS2 downregulation correlated with increased proliferation, stemness, genomic instability, tumor progression, and worse survival in LUAD. We further validated that TMPRSS2 was downregulated with tumor progression in the LUAD cohort we collected from Jiangsu Cancer Hospital, China. In vitro and in vivo experiments verified the association of TMPRSS2 deficiency with increased tumor cell proliferation and invasion and antitumor immunity in LUAD. Moreover, in vivo experiments demonstrated that TMPRSS2-knockdown tumors were more sensitive to BMS-1, an inhibitor of PD-1/PD-L1.

CONCLUSIONS

TMPRSS2 is a tumor suppressor, while its downregulation is a positive biomarker of immunotherapy in LUAD. Our data provide a potential link between lung cancer and pneumonia caused by SARS-CoV-2 infection.

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

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

Key Words

• predictive markers
• bladder cancer
• immunotherapy
• tumour immunology

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MESH Headings

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

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Grants

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

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Affiliation(s)

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

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Identification of Lung Adenocarcinoma Subtypes Based on MHC-II Gene Expression Profile and Immunological Analysis

INTRODUCTION

Major histocompatibility complex class II molecule (MHC-II) is pivotal in anti-tumor immunity, and targeting MHC-II in tumors may help improve patient survival. But function of MHC-II in the immunotherapy and prognosis of lung adenocarcinoma (LUAD) patients has not been thoroughly studied and reported.

METHODS

We selected LUAD-related MHC-II genes from public databases based on previous literature reports. We identified different subtypes according to expression differences of these genes in different LUAD samples through cluster analysis. We used R package to conduct a series of analyses on different subtypes, exploring their survival differences, gene expression differences, pathway enrichment differences, and differences in immune characteristics and immune therapy. Finally, we screened potential drugs from the cMAP database.

RESULTS

We identified two MHC-II-related LUAD subtypes. Our analyses presented that patients with cluster2 subtype showed better prognosis, higher immune scores, higher levels of immune cell infiltration and immune function activation. In addition, patients with this subtype had higher immunophenoscore, lower TIDE scores, and DEPTH scores. We also identified 10 small molecule drugs, such as lenalidomide, VX-745, and tyrphostin-AG-1295.

CONCLUSION

Overall, MHC-II is not only a potential biomarker for accurately distinguishing LUAD subtypes but also a predictive factor for their survival. Our study offers novel insights into understanding of impact of MHC-II in LUAD and offers a new perspective for improving the accurate classification of LUAD patients and enhancing drug treatment.

Identifying cancer subtypes based on embryonic and hematopoietic stem cell signatures in pan-cancer

PURPOSE

Cancer cells with stem cell-like properties may contribute to cancer development and therapy resistance. The advancement of multi-omics technology has sparked interest in exploring cancer stemness from a multi-omics perspective. However, there is a limited number of studies that have attempted to subtype cancer by combining different types of stem cell signatures.

METHODS

In this study, 10,323 cancer specimens from 33 TCGA cancer types were clustered based on the enrichment scores of six stemness gene sets, representing two types of stem cell backgrounds: embryonic stem cells (ESCs) and hematopoietic stem cells (HSCs).

RESULTS

We identified four subtypes of pan-cancer, termed StC1, StC2, StC3 and StC4, which displayed distinct molecular and clinical features, including stemness, genome integrity, intratumor heterogeneity, methylation levels, tumor microenvironment, tumor progression, responses to chemotherapy and immunotherapy, and survival prognosis. Importantly, this subtyping method for pan-cancer is reproducible at the protein level.

CONCLUSION

Our findings indicate that the ESC signature is an adverse prognostic factor in cancer, while the HSC signature and ratio of HSC/ESC signatures are positive prognostic factors. The subtyping of cancer based on ESC and HSC signatures may provide insights into cancer biology and clinical implications of cancer.

Exploring the molecular and immune-landscape of lung cancer associated with cystic airspaces

To explore the molecular biological characteristics of lung cancer associated with cystic airspaces (LCCA) and its potential roles on prognosis. A total of 165 LCCAs and 201 non-LCCAs were enrolled in this study. Bulk RNA sequencing was implemented in eight LCCAs and nine non-LCCAs to explore the differentially expressed genes. TCGA data were used to analyze LCCA-specific genes that associated with overall survival (OS). The median age was 60 (IQR 53 to 65) years in LCCA cohort. We found LCCA were predominant in men and had less visceral pleura invasion (VPI) or lympho-vascular invasion (LVI). Moreover, LCCA presented with higher histological heterogeneity. Kaplan-Meier analysis showed that patients of age more than 60 and positive VPI had significantly less PFS in LCCA. Cox regression suggested that LCCA, micropapillary subtype proportion and VPI were the independent risk factors for PFS. LCCA had up-regulated pathways associated with EMT, angiogenesis and cell migration. In addition, LCCA displayed higher levels of immunosuppressor infiltration (M2 macrophages, CAFs and MDSCs) and distinct cell death and metabolic patterns. BCR/TCR repertoire analysis revealed less BCR richness, clonality and high-abundance shared clonotypes in LCCA. Finally, Cox regression analysis identified that four cystic-specific genes, KCNK3, NRN1, PARVB and TRHDE-AS1, were associated with OS of lung adenocarcinoma (LUAD). And cystic-specific risk scores (CSRSs) were calculated to construct a nomogram, which performance well. Our study for the first time indicated significantly distinct molecular biological and immune characteristics between LCCA and non-LCCA, which provide complementary prognostic values in early-stage non-small cell lung cancer (NSCLC).

From complexity to clarity: unravelling tumor heterogeneity through the lens of tumor microenvironment for innovative cancer therapy

Despite the tremendous clinical successes recorded in the landscape of cancer therapy, tumor heterogeneity remains a formidable challenge to successful cancer treatment. In recent years, the emergence of high-throughput technologies has advanced our understanding of the variables influencing tumor heterogeneity beyond intrinsic tumor characteristics. Emerging knowledge shows that drivers of tumor heterogeneity are not only intrinsic to cancer cells but can also emanate from their microenvironment, which significantly favors tumor progression and impairs therapeutic response. Although much has been explored to understand the fundamentals of the influence of innate tumor factors on cancer diversity, the roles of the tumor microenvironment (TME) are often undervalued. It is therefore imperative that a clear understanding of the interactions between the TME and other tumor intrinsic factors underlying the plastic molecular behaviors of cancers be identified to develop patient-specific treatment strategies. This review highlights the roles of the TME as an emerging factor in tumor heterogeneity. More particularly, we discuss the role of the TME in the context of tumor heterogeneity and explore the cutting-edge diagnostic and therapeutic approaches that could be used to resolve this recurring clinical conundrum. We conclude by speculating on exciting research questions that can advance our understanding of tumor heterogeneity with the goal of developing customized therapeutic solutions.

Deciphering cellular plasticity in pancreatic cancer for effective treatments

Cellular plasticity and therapy resistance are critical features of pancreatic cancer, a highly aggressive and fatal disease. The pancreas, a vital organ that produces digestive enzymes and hormones, is often affected by two main types of cancer: the pre-dominant ductal adenocarcinoma and the less common neuroendocrine tumors. These cancers are difficult to treat due to their complex biology characterized by cellular plasticity leading to therapy resistance. Cellular plasticity refers to the capability of cancer cells to change and adapt to different microenvironments within the body which includes acinar-ductal metaplasia, epithelial to mesenchymal/epigenetic/metabolic plasticity, as well as stemness. This plasticity allows heterogeneity of cancer cells, metastasis, and evasion of host's immune system and develops resistance to radiation, chemotherapy, and targeted therapy. To overcome this resistance, extensive research is ongoing exploring the intrinsic and extrinsic factors through cellular reprogramming, chemosensitization, targeting metabolic, key survival pathways, etc. In this review, we discussed the mechanisms of cellular plasticity involving cellular adaptation and tumor microenvironment and provided a comprehensive understanding of its role in therapy resistance and ways to overcome it.

Prediction of prognostic and immune therapy response in lung adenocarcinoma based on MHC-I-related genes

OBJECTIVES

The study investigated the prognostic and immune predictive potential of major histocompatibility complex class I (MHC-I) in lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

With The Cancer Genome Atlas (TCGA)-LUAD and Gene Expression Omnibus datasets (GSE26939, GSE72094) as the training and validation sets, respectively, we used Cox regression analysis to construct a prognostic model, and verified independence of riskscore. The predictive capacity of the model was assessed in both sets using the receiver operating characteristic curve and Kaplan-Meier survival curves. Immune analysis was performed by using ssGSEA. Additionally, immune checkpoint blockade therapy was assessed by using immunophenoscore, Tumor Immune Dysfunction and Exclusion score. Based on the cMAP database, effective small molecule compounds were predicted.

RESULTS

A prognostic model was established based on 8 MHC-I-related genes, and the predictive capacity of the model was accurate. Immune analysis results revealed that patients classified as high-risk had lower levels of immune cell infiltration and impaired immune function. The low-risk group possessed a better response to immune checkpoint blockade therapy. Theobromine and pravastatin were identified as having great potential in improving the prognosis of LUAD.

CONCLUSION

Overall, the study revealed MHC-I-related molecular prognostic biomarkers as robust indicators for LUAD prognosis and immune therapy response.

Global analysis of iron metabolism-related genes identifies potential mechanisms of gliomagenesis and reveals novel targets

AIMS

This study aimed to investigate key regulators of aberrant iron metabolism in gliomas, and evaluate their effect on biological functions and clinical translational relevance.

METHODS

We used transcriptomic data from multiple cross-platform glioma cohorts to identify key iron metabolism-related genes (IMRGs) based on a series of bioinformatic and machine learning methods. The associations between IMRGs and prognosis, mesenchymal phenotype, and genomic alterations were analyzed in silico. The performance of the IMRGs-based signature in predicting temozolomide (TMZ) treatment sensitivity was evaluated. In vitro and in vivo experiments were used to explore the biological functions of these key IMRGs.

RESULTS

HMOX1, LTF, and STEAP3 were identified as the most essential IMRGs in gliomas. The expression levels of these genes were strongly related to clinicopathological and molecular features. The robust IMRG-based gene signature could be used for prognosis prediction. These genes facilitate mesenchymal transformation, driver gene mutations, and oncogenic alterations in gliomas. The gene signature was also associated with TMZ resistance. HMOX1, LTF, and STEAP3 knockdown in glioma cells significantly reduced cell proliferation, colony formation, migration, and malignant invasion.

CONCLUSION

The study presented a comprehensive view of key regulators underpinning iron metabolism in gliomas and provided new insights into novel therapeutic approaches.

Proteomics-based clustering of lung adenocarcinoma identifies three subtypes with significantly different clinical and molecular features

BACKGROUND

Lung adenocarcinoma (LUAD) is a predominant subtype of lung cancer. Although molecular classification of LUAD has been widely explored, proteomics-based subtyping of LUAD remains scarce.

METHODS

We proposed a subtyping method for LUAD based on the expression profiles of 500 proteins with the largest expression variability across LUAD. Furthermore, we comprehensively compared molecular and clinical features among the LUAD subtypes.

RESULTS

Consensus clustering identified three subtypes of LUAD, namely MtE, DrE, and StE. We demonstrated this subtyping method to be reproducible by analyzing two independent LUAD cohorts. MtE was characterized by high enrichment of metabolic pathways, high EGFR mutation rate, low stemness, proliferation, invasion, metastasis and inflammation signatures, favorable prognosis; DrE was characterized by high enrichment of DNA repair pathways, high TP53 mutation rate, and high levels of genomic instability, stemness, proliferation, and intratumor heterogeneity (ITH); and StE was characterized by high enrichment of stroma-related pathways, high KRAS mutation rate, and low levels of genomic instability.

CONCLUSIONS

The proteomics-based clustering analysis identified three LUAD subtypes with significantly different molecular and clinical properties. The novel subtyping method offers new perspectives on the cancer biology and holds promise in improving the clinical management of LUAD.

Identification of breast cancer subgroups and immune characterization based on glutamine metabolism-related genes

Immunotherapy is a promising treatment for breast cancer (BC). However, due to individual differences and tumor heterogeneity, immunotherapy is only applicable to some BC patients. Glutamine metabolism plays a role in inhibiting immunotherapy, but its role in BC is limitedly studied. Therefore, we aimed to identify different BC subgroups based on glutamine metabolism and characterize the features of different subgroups to provide guidance for personalized immunotherapy for BC patients. Using unsupervised clustering analysis, we classified BC patients in The Cancer Genome Atlas (TCGA) with glutamine metabolism-related genes and obtained low-risk (LR) and high-risk (HR) subgroups. Survival analysis revealed that prognosis of LR subgroup was notably better than HR subgroup. Through ssGSEA and CIBERSORT methods, we disclosed that infiltration levels of B cells, Mast cells, T helper cells, and Th2 cells, and Type II IFN Response immune function were notably higher in LR subgroup than in HR subgroup. The Wilcox algorithm comparison denoted that DEPTH of LR subgroup was significantly lower than HR subgroup. The TIDE of LR subgroup was significantly higher than HR subgroup. Functional annotation of differentially expressed genes revealed that channel activity and the Estrogen signaling pathway may be related to BC prognosis. Ten hub genes were selected between the subgroups through the STRING database and Cytoscape, and their correlation with drugs was predicted on the CellMiner website. This study analyzed the immune characteristics of BC subgroups based on glutamine metabolism and provided reference for prognosis prediction and personalized immunotherapy.

Innovative molecular subtypes of multiple signaling pathways in colon cancer and validation of FMOD as a prognostic-related marker

PURPOSE

Colon cancer is highly heterogeneous in terms of the immune and stromal microenvironment, genomic integrity, and oncogenic properties; therefore, molecular subtypes of the four characteristic dimensions are expected to provide novel clues for immunotherapy of colon cancer.

METHODS

According to the enrichment of four dimensions, we performed consensus cluster analysis and identified three robust molecular subtypes for colon cancer, namely immune enriched, immune deficiency, and stroma enriched. We characterized and validated the immune infiltration, gene mutations, copy number variants, methylation, protein expression, and clinical features in different datasets. Finally, we developed an 8-gene risk prognostic model and proposed the innovative RiskScore. In addition, a nomogram model was constructed combining clinical characteristics and RiskScore to validate its excellent clinical predictive power.

RESULTS

Combining clinical patient tissue samples and histochemical microarray data, we found that high FMOD expression in tumor epithelial cells was associated with poorer patient prognosis, but FMOD expression in the mesenchyme was not associated with prognosis. In pan-cancer, RiskScore, a prognostic model constructed based on characteristic pathway scores, was a poor prognostic factor for malignancy and was negatively associated with immunotherapy response.

CONCLUSION

The identification of molecular subtypes could provide innovative ideas for immunotherapy of colon cancer.

Mutant-allele dispersion correlates with prognosis risk in patients with advanced non-small cell lung cancer

BACKGROUND

Intra-tumor heterogeneity (ITH) contributes to lung cancer progression and resistance to therapy. To evaluate ITH and determine whether it may be employed as a predictive biomarker of prognosis in patients with advanced non-small cell lung cancer (NSCLC), we used a novel algorithm called mutant-allele dispersion (MAD).

METHODS

In the study, 103 patients with advanced NSCLC were enrolled. Using a panel of 425 cancer-related genes, next-generation sequencing (NGS) was performed on tumor specimens that had been collected. From NGS data, we derived MAD values, and we next looked into their relationships with clinical variables and different mutation subtypes.

RESULTS

The median MAD among 103 NSCLC patients was 0.73. EGFR mutation, tyrosine kinase inhibitor (TKI) therapy, radiotherapy, and chemotherapy cycles were all substantially correlated with the MAD score. In patients with lung adenocarcinoma (LUAD), correlation analysis revealed that the MAD score was substantially linked with Notch pathway mutation (P = 0.021). A significant relationship between high MAD and shorter progression-free survival (PFS) was found (HR = 2.004, 95%CI 1.269-3.163, P = 0.003). In patients with advanced NSCLC, histological type (P = 0.004), SMARCA4 mutation (P = 0.038), and LRP1B mutation (P = 0.006) were all independently associated with prognosis. The disease control rate was considerably greater in the low MAD group compared to the high MAD group in 19 LUAD patients receiving immunotherapy (92.9% vs. 40%, P = 0.037). TKI-PFS was longer in 37 patients with low MAD who received first-line TKI therapy (P = 0.014).

CONCLUSION

Our findings suggested that MAD is a practical and simple algorithm for assessing ITH, and populations with high MAD values are more likely to have EGFR mutations. MAD can be used as a potential biomarker to predict not only the prognosis of NSCLC but also the efficacy of immunotherapy and TKI therapy in patients with advanced NSCLC.

Advanced hepatocellular carcinoma with MET-amplified contained excellent response to crizotinib: a case report

Introduction

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide. Several novel therapeutic strategies have been developed to prolong the survival of patients with advanced HCC. However, therapeutic decision-making biomarkers owing to the extensive heterogeneity of HCC. Next-generation sequencing (NGS) is generally used in treatment decisions to help patients benefit from genome-directed targeting.

Case presentation

A 56 year-old male with type-B hepatitis for more than 20 years was admitted to our department and underwent laparoscopic left lateral hepatic lobectomy for hepatocellular carcinoma. Unfortunately, the tumor recurred 1 year later. Despite multiple treatments, the tumor continued to progress and invaded the patient's 5th thoracic vertebras, leading to hypoesthesia and hypokinesia below the nipple line plane 2 years later. NGS revealed MET amplification, and crizotinib, an inhibitor of MET, was recommended. After administration for a month, tumor marker levels decreased, and the tumor shrunk. The patient has remained in remission since that time.

Conclusions

We report that a patient with high MET amplification benefited from its inhibitor, which was recommended by NGS. This indicates the potential clinical decision support value of NGS and the satisfactory effect of MET inhibitors.

Defining multiple layers of intratumor heterogeneity based on variations of perturbations in multi-omics profiling

BACKGROUND

Intratumor heterogeneity (ITH) plays a crucial role in tumor progression, relapse, immune evasion, and drug resistance. Existing ITH quantification methods based on a single molecular level are inadequate to capture ITH evolving from genotype to phenotype.

METHODS

We designed a set of information entropy (IE)-based algorithms for quantifying ITH at the genome (somatic copy number alterations and mutations), mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), protein, and epigenome level, respectively. We evaluated the performance of these algorithms by analyzing the correlations between their ITH scores and ITH-associated molecular and clinical features in 33 TCGA cancer types. Moreover, we evaluated the correlations between the ITH measures at different molecular levels by Spearman correlation and clustering analysis.

RESULTS

The IE-based ITH measures had significant correlations with unfavorable prognosis, tumor progression, genomic instability, antitumor immunosuppression, and drug resistance. The mRNA ITH showed stronger correlations with the miRNA, lncRNA, and epigenome ITH than with the genome ITH, supporting the regulatory relationships of miRNA, lncRNA, and DNA methylation towards mRNA. The protein-level ITH displayed stronger correlations with the transcriptome-level ITH than with the genome-level ITH, supporting the central dogma of molecular biology. Clustering analysis based on the ITH scores identified four subtypes of pan-cancer showing significantly different prognosis. Finally, the ITH integrating the seven ITH measures displayed more prominent properties of ITH than that at a single level.

CONCLUSIONS

This analysis provides landscapes of ITH at various molecular levels. Combining the ITH observation from different molecule levels will improve personalized management for cancer patients.

Intratumor heterogeneity is associated with less CD8+ T cell infiltration and worse survival in patients with small cell lung cancer

PURPOSE

Small cell lung cancer (SCLC) is a heterogeneous malignancy with genetic and phenotypic disparity. However, the association between intratumor heterogeneity (ITH) and immunological features as well as the impact of ITH on prognosis has never been explored in SCLC. Hence, we investigated the relationship between ITH and their immunological features and explored the effect of ITH on overall survival (OS) in patients with SCLC.

METHODS

Programmed cell death-ligand 1 (PD-L1), CD8+ cell infiltration was calculated through immunohistochemical staining and tumor mutational burden (TMB), tumor neoantigen burden (TNB), and ITH levels via whole-exome sequencing (WES).

RESULTS

Significant correlation was not found in ITH versus TMB, ITH versus TNB (P = 0.1821, P = 0.0612). No significant variation in ITH was found between negative PD-L1 SCLC patients and positive PD-L1 ones (P = 0.0610 for TPS, P = 0.6347 for CPS). Interestingly, we demonstrated the negative correlation between CD8+ T cell infiltration and ITH (P = 0.0220). More importantly, significant OS benefit was detected in ITH-low SCLC patients in comparison with ITH-high ones (P = 0.0049). ITH was an independent prognostic factor on OS with clinicopathological variables adjusted (HR, 2.044; 95% CI 1.190-3.512; P = 0.010). We also demonstrated significantly different driver genes and CNV between ITH-low and ITH-high SCLC.

CONCLUSION

Our work pointed the negative association of ITH with CD8+ T cell infiltration and suggested ITH as a potential predictor of OS in SCLC, putting forward a direction for more precise and individualized therapeutic strategies for SCLC.

Dynamic single-cell RNA-seq analysis reveals distinct tumor program associated with microenvironmental remodeling and drug sensitivity in multiple myeloma

BACKGROUND

Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of malignant plasma cells. Despite extensive research, molecular mechanisms in MM that drive drug sensitivity and clinic outcome remain elusive.

RESULTS

Single-cell RNA sequencing was applied to study tumor heterogeneity and molecular dynamics in 10 MM individuals before and after 2 cycles of bortezomib-cyclophosphamide-dexamethasone (VCD) treatment, with 3 healthy volunteers as controls. We identified that unfolded protein response and metabolic-related program were decreased, whereas stress-associated and immune reactive programs were increased after 2 cycles of VCD treatment. Interestingly, low expression of the immune reactive program by tumor cells was associated with unfavorable drug response and poor survival in MM, which probably due to downregulation of MHC class I mediated antigen presentation and immune surveillance, and upregulation of markers related to immune escape. Furthermore, combined with immune cells profiling, we uncovered a link between tumor intrinsic immune reactive program and immunosuppressive phenotype in microenvironment, evidenced by exhausted states and expression of checkpoint molecules and suppressive genes in T cells, NK cells and monocytes. Notably, expression of YBX1 was associated with downregulation of immune activation signaling in myeloma and reduced immune cells infiltration, thereby contributed to poor prognosis.

CONCLUSIONS

We dissected the tumor and immune reprogramming in MM during targeted therapy at the single-cell resolution, and identified a tumor program that integrated tumoral signaling and changes in immune microenvironment, which provided insights into understanding drug sensitivity in MM.

The mutation in splicing factor genes correlates with unfavorable prognosis, genomic instability, anti-tumor immunosuppression and increased immunotherapy response in pan-cancer

Splicing abnormality resulting from somatic mutations in key splicing factor genes (SFG) has been detected in various cancers. Hence, an in-depth study of splicing factor genes mutations' impact on pan-cancer is meaningful. This study investigated associations of splicing factor genes mutations with clinical features, tumor progression phenotypes, genomic integrity, anti-tumor immune responses, and immunotherapy response in 12 common cancer types from the TCGA database. Compared to SFG-wildtype cancers, SFG-mutated cancers displayed worse survival prognosis, higher tumor mutation burden and aneuploidy levels, higher expression of immunosuppressive signatures, and higher levels of tumor stemness, proliferation potential, and intratumor heterogeneity (ITH). However, splicing factor genes-mutated cancers showed higher response rates to immune checkpoint inhibitors than splicing factor genes-wildtype cancers in six cancer cohorts. Single-cell data analysis confirmed that splicing factor genes mutations were associated with increased tumor stemness, proliferation capacity, PD-L1 expression, intratumor heterogeneity, and aneuploidy levels. Our data suggest that the mutation in key splicing factor genes correlates with unfavorable clinical outcomes and disease progression, genomic instability, anti-tumor immunosuppression, and increased immunotherapy response in pan-cancer. Thus, the splicing factor genes mutation is an adverse prognostic factor and a positive marker for immunotherapy response in cancer.

Immunological classification of hepatitis B virus-positive hepatocellular carcinoma by transcriptome analysis

BACKGROUND

Hepatitis B virus (HBV) infection is a major factor responsible for HBV+ hepatocellular carcinoma (HCC).

AIM

An immunological classification of HBV+ HCC may provide both biological insights and clinical implications for this disease.

METHODS

Based on the enrichment of 23 immune signatures, we identified two immune-specific subtypes (Imm-H and Imm-L) of HBV+ HCC by unsupervised clustering. We showed that this subtyping method was reproducible and predictable by analyzing three different datasets.

RESULTS

Compared to Imm-L, Imm-H displayed stronger immunity, more stromal components, lower tumor purity, lower stemness and intratumor heterogeneity, lower-level copy number alterations, higher global methylation level, and better overall and disease-free survival prognosis. Besides immune-related pathways, stromal pathways (ECM receptor interaction, focal adhesion, and regulation of actin cytoskeleton) and neuro-related pathways (neuroactive ligand-receptor interaction, and prion diseases) were more highly enriched in Imm-H than in Imm-L. We identified nine proteins differentially expressed between Imm-H and Imm-L, of which MYH11, PDCD4, Dvl3, and Syk were upregulated in Imm-H, while PCNA, Acetyl-a-Tubulin-Lys40, ER-α_pS118, Cyclin E2, and β-Catenin were upregulated in Imm-L.

CONCLUSION

Our data suggest that “hot” tumors have a better prognosis than “cold” tumors in HBV+ HCC and that “hot” tumors respond better to immunotherapy.

Multi-region sequencing with spatial information enables accurate heterogeneity estimation and risk stratification in liver cancer

BACKGROUND

Numerous studies have used multi-region sampling approaches to characterize intra-tumor heterogeneity (ITH) in hepatocellular carcinoma (HCC). However, conventional multi-region sampling strategies do not preserve the spatial details of samples, and thus, the potential influences of spatial distribution on patient-wise ITH (represents the overall heterogeneity level of the tumor in a given patient) have long been overlooked. Furthermore, gene-wise transcriptional ITH (represents the expression pattern of genes across different intra-tumor regions) in HCC is also under-explored, highlighting the need for a comprehensive investigation.

METHODS

To address the problem of spatial information loss, we propose a simple and easy-to-implement strategy called spatial localization sampling (SLS). We performed multi-region sampling and sequencing on 14 patients with HCC, collecting a total of 75 tumor samples with spatial information and molecular data. Normalized diversity score and integrated heterogeneity score (IHS) were then developed to measure patient-wise and gene-wise ITH, respectively.

RESULTS

A significant correlation between spatial and molecular heterogeneity was uncovered, implying that spatial distribution of sampling sites did influence ITH estimation in HCC. We demonstrated that the normalized diversity score had the ability to overcome sampling location bias and provide a more accurate estimation of patient-wise ITH. According to this metric, HCC tumors could be divided into two classes (low-ITH and high-ITH tumors) with significant differences in multiple biological properties. Through IHS analysis, we revealed a highly heterogenous immune microenvironment in HCC and identified some low-ITH checkpoint genes with immunotherapeutic potential. We also constructed a low-heterogeneity risk stratification (LHRS) signature based on the IHS results which could accurately predict the survival outcome of patients with HCC on a single tumor biopsy sample.

CONCLUSIONS

This study provides new insights into the complex phenotypes of HCC and may serve as a guide for future studies in this field.

TRPV1 Is a Potential Tumor Suppressor for Its Negative Association with Tumor Proliferation and Positive Association with Antitumor Immune Responses in Pan-Cancer

Background

Although numerous studies have shown that the expression and activation of TRPV1 have an important role in cancer development, a comprehensive exploration of associations between TRPV1 expression and tumor proliferation, microenvironment, and clinical outcomes in pan-cancer remains insufficient.

Methods

From The Cancer Genome Atlas (TCGA) program, we downloaded multiomics data of ten cancer cohorts and investigated the correlations between TRPV1 expression and immune signatures' enrichment, stromal content, genomic features, oncogenic signaling, and clinical features in these cancer cohorts and pan-cancer.

Results

Elevated expression of TRPV1 correlated with better clinical outcomes in pan-cancer and diverse cancer types. In multiple cancer types, TRPV1 expression correlated negatively with the expression of tumor proliferation marker genes (MKI67 and RACGAP1), proliferation scores, cell cycle scores, stemness scores, epithelial-mesenchymal transition scores, oncogenic pathways' enrichment, tumor immunosuppressive signals, intratumor heterogeneity, homologous recombination deficiency, tumor mutation burden, and stromal content. Moreover, TRPV1 expression was downregulated in late-stage versus early-stage tumors. In breast cancer, bladder cancer, and low-grade glioma, TRPV1 expression was more inferior in invasive than in noninvasive subtypes. Pathway analysis showed that the enrichment of cancer-associated pathways correlated inversely with TRPV1 expression levels.

Conclusion

TRPV1 upregulation correlates with decreased tumor proliferation, tumor driver gene expression, genomic instability, and tumor immunosuppressive signals in various cancers. Our results provide new understanding of the role of TRPV1 in both cancer biology and clinical practice.

The Variation of Transcriptomic Perturbations is Associated with the Development and Progression of Various Diseases

Background

Although transcriptomic data have been widely applied to explore various diseases, few studies have investigated the association between transcriptomic perturbations and disease development in a wide variety of diseases.

Methods

Based on a previously developed algorithm for quantifying intratumor heterogeneity at the transcriptomic level, we defined the variation of transcriptomic perturbations (VTP) of a disease relative to the health status. Based on publicly available transcriptome datasets, we compared VTP values between the disease and health status and analyzed correlations between VTP values and disease progression or severity in various diseases, including neurological disorders, infectious diseases, cardiovascular diseases, respiratory diseases, liver diseases, kidney diseases, digestive diseases, and endocrine diseases. We also identified the genes and pathways whose expression perturbations correlated positively with VTP across diverse diseases.

Results

VTP values were upregulated in various diseases relative to their normal controls. VTP values were significantly greater in define than in possible or probable Alzheimer's disease. VTP values were significantly larger in intensive care unit (ICU) COVID-19 patients than in non-ICU patients, and in COVID-19 patients requiring mechanical ventilatory support (MVS) than in those not requiring MVS. VTP correlated positively with viral loads in acquired immune deficiency syndrome (AIDS) patients. Moreover, the AIDS patients treated with abacavir or zidovudine had lower VTP values than those without such therapies. In pulmonary tuberculosis (TB) patients, VTP values followed the pattern: active TB > latent TB > normal controls. VTP values were greater in clinically apparent than in presymptomatic malaria. VTP correlated negatively with the cardiac index of left ventricular ejection fraction (LVEF). In chronic obstructive pulmonary disease (COPD), VTP showed a negative correlation with forced expiratory volume in the first second (FEV1). VTP values increased with H. pylori infection and were upregulated in atrophic gastritis caused by H. pylori infection. The genes and pathways whose expression perturbations correlated positively with VTP scores across diseases were mainly involved in the regulation of immune, metabolic, and cellular activities.

Conclusions

VTP is upregulated in the disease versus health status, and its upregulation is associated with disease progression and severity in various diseases. Thus, VTP has potential clinical implications for disease diagnosis and prognosis.

Molecular classification of human papillomavirus-positive cervical cancers based on immune signature enrichment

Background

Human papillomavirus-positive (HPV+) cervical cancers are highly heterogeneous in clinical and molecular characteristics. Thus, an investigation into their heterogeneous immunological profiles is meaningful in providing both biological and clinical insights into this disease.

Methods

Based on the enrichment of 29 immune signatures, we discovered immune subtypes of HPV+ cervical cancers by hierarchical clustering. To explore whether this subtyping method is reproducible, we analyzed three bulk and one single cell transcriptomic datasets. We also compared clinical and molecular characteristics between the immune subtypes.

Results

Clustering analysis identified two immune subtypes of HPV+ cervical cancers: Immunity-H and Immunity-L, consistent in the four datasets. In comparisons with Immunity-L, Immunity-H displayed stronger immunity, more stromal contents, lower tumor purity, proliferation potential, intratumor heterogeneity and stemness, higher tumor mutation burden, more neoantigens, lower levels of copy number alterations, lower DNA repair activity, as well as better overall survival prognosis. Certain genes, such as MUC17, PCLO, and GOLGB1, showed significantly higher mutation rates in Immunity-L than in Immunity-H. 16 proteins were significantly upregulated in Immunity-H vs. Immunity-L, including Caspase-7, PREX1, Lck, C-Raf, PI3K-p85, Syk, 14-3-3_epsilon, STAT5-α, GATA3, Src_pY416, NDRG1_pT346, Notch1, PDK1_pS241, Bim, NF-kB-p65_pS536, and p53. Pathway analysis identified numerous immune-related pathways more highly enriched in Immunity-H vs. Immunity-L, including cytokine-cytokine receptor interaction, natural killer cell-mediated cytotoxicity, antigen processing and presentation, T/B cell receptor signaling, chemokine signaling, supporting the stronger antitumor immunity in Immunity-H vs. Immunity-L.

Conclusion

HPV+ cervical cancers are divided into two subgroups based on their immune signatures' enrichment. Both subgroups have markedly different tumor immunity, progression phenotypes, genomic features, and clinical outcomes. Our data offer novel perception in the tumor biology as well as clinical implications for HPV+ cervical cancer.

ATAXIC: An Algorithm to Quantify Transcriptomic Perturbation Heterogeneity in Single Cancer Cells

The single-cell RNA sequencing (scRNA-seq) has recently been widely utilized to quantify transcriptomic profiles in single cells of bulk tumors. The transcriptomic profiles in single cells facilitate the investigation of intratumor heterogeneity that is unlikely confounded by the nontumor components. We proposed an algorithm (ATAXIC) to quantify the heterogeneity of transcriptomic perturbations (TPs) in single cancer cells. ATAXIC calculated the TP heterogeneity level of a single cell based on the standard deviations of the absolute z-scored gene expression values for tens of thousands of genes, reflecting the asynchronous degree of transcriptomic alterations relative to the central (mean) tendency. By analyzing scRNA-seq datasets for eight cancer types, we revealed that ATAXIC scores were likely to correlate positively with the enrichment scores of various proliferation and oncogenic signatures, DNA damage repair, treatment resistance, and unfavorable phenotypes and outcomes in cancer. The ATAXIC scores varied among different cancer types, with lung cancer and melanoma having the lowest average scores and clear cell renal cell carcinoma having the highest average scores. The low TP heterogeneity in lung cancer and melanoma could bestow relatively higher response rates to immune checkpoint inhibitors on both cancer types. In conclusion, ATAXIC is a useful algorithm to quantify the TP heterogeneity in single cancer cells, as well as providing new insights into tumor biology.

Identification of prostate cancer subtypes based on immune signature scores in bulk and single-cell transcriptomes

Prostate cancer (PC) is heterogeneous in the tumor immune microenvironment (TIME). Subtyping of PC based on the TIME could provide new insights into intratumor heterogeneity and its correlates of clinical features. Based on the enrichment scores of 28 immune cell types in the TIME, we performed unsupervised clustering to identify immune-specific subtypes of PC. The clustering analysis was performed in ten different bulk tumor transcriptomic datasets and in a single-cell RNA-Seq (scRNA-seq) dataset, respectively. We identified two PC subtypes: PC immunity high (PC-ImH) and PC immunity low (PC-ImL), consistently in these datasets. Compared to PC-ImL, PC-ImH displayed stronger immune signatures, worse clinical outcomes, higher epithelial-mesenchymal transition (EMT) signature, tumor stemness, intratumor heterogeneity (ITH) and genomic instability, and lower incidence of TMPRSS2-ERG fusion. Tumor mutation burden (TMB) showed no significant difference between PC-ImH and PC-ImL, while copy number alteration (CNA) was more significant in PC-ImL than in PC-ImH. PC-ImH could be further divided into two subgroups, which had significantly different immune infiltration levels and clinical features. In conclusion, "hot" PCs have stronger anti-tumor immune response, while worse clinical outcomes versus "cold" PCs. CNA instead of TMB plays a crucial role in the regulation of TIME in PC. TMPRSS2-ERG fusion correlates with decreased anti-tumor immune response while better disease-free survival in PC. The identification of immune-specific subtypes has potential clinical implications for PC immunotherapy.

High-Throughput Profiling of Colorectal Cancer Liver Metastases Reveals Intra- and Inter-Patient Heterogeneity in the EGFR and WNT Pathways Associated with Clinical Outcome

Simple Summary

Tumor heterogeneity can greatly influence therapy outcome and patient survival. In this study, we aimed at unraveling inter- and intra-patient heterogeneity of colorectal cancer liver metastases (CRLM). To this end, we comprehensively characterized CRLM using state-of-the-art high-throughput technologies combined with bioinformatics analyses. We found a high degree of inter- and intra-patient heterogeneity among the metastases, in particular in genes of the WNT and EGFR pathways. Through analyzing the master regulators and effectors associated with the regulation of these genes, we identified a specific gene signature that was highly expressed in a large cohort of colorectal cancer patients and associated with clinical outcome.

Abstract

Seventy percent of patients with colorectal cancer develop liver metastases (CRLM), which are a decisive factor in cancer progression. Therapy outcome is largely influenced by tumor heterogeneity, but the intra- and inter-patient heterogeneity of CRLM has been poorly studied. In particular, the contribution of the WNT and EGFR pathways, which are both frequently deregulated in colorectal cancer, has not yet been addressed in this context. To this end, we comprehensively characterized normal liver tissue and eight CRLM from two patients by standardized histopathological, molecular, and proteomic subtyping. Suitable fresh-frozen tissue samples were profiled by transcriptome sequencing (RNA-Seq) and proteomic profiling with reverse phase protein arrays (RPPA) combined with bioinformatic analyses to assess tumor heterogeneity and identify WNT- and EGFR-related master regulators and metastatic effectors. A standardized data analysis pipeline for integrating RNA-Seq with clinical, proteomic, and genetic data was established. Dimensionality reduction of the transcriptome data revealed a distinct signature for CRLM differing from normal liver tissue and indicated a high degree of tumor heterogeneity. WNT and EGFR signaling were highly active in CRLM and the genes of both pathways were heterogeneously expressed between the two patients as well as between the synchronous metastases of a single patient. An analysis of the master regulators and metastatic effectors implicated in the regulation of these genes revealed a set of four genes (SFN, IGF2BP1, STAT1, PIK3CG) that were differentially expressed in CRLM and were associated with clinical outcome in a large cohort of colorectal cancer patients as well as CRLM samples. In conclusion, high-throughput profiling enabled us to define a CRLM-specific signature and revealed the genes of the WNT and EGFR pathways associated with inter- and intra-patient heterogeneity, which were validated as prognostic biomarkers in CRC primary tumors as well as liver metastases.

DEPTH2: an mRNA-based algorithm to evaluate intratumor heterogeneity without reference to normal controls

Background

Intratumor heterogeneity (ITH) is associated with tumor progression, unfavorable prognosis, immunosuppression, genomic instability, and therapeutic resistance. Thus, evaluation of ITH levels is valuable in cancer diagnosis and treatment.

Methods

We proposed a new mRNA-based ITH evaluation algorithm (DEPTH2) without reference to normal controls. DEPTH2 evaluates ITH levels based on the standard deviations of absolute z-scored transcriptome levels in tumors, reflecting the asynchronous level of transcriptome alterations relative to the central tendency in a tumor.

Results

By analyzing 33 TCGA cancer types, we demonstrated that DEPTH2 ITH was effective in measuring ITH for its significant associations with tumor progression, unfavorable prognosis, genomic instability, reduced antitumor immunity and immunotherapy response, and altered drug response in diverse cancers. Compared to other five ITH evaluation algorithms (MATH, PhyloWGS, ABSOLUTE, DEPTH, and tITH), DEPTH2 ITH showed a stronger association with unfavorable clinical outcomes, and in characterizing other properties of ITH, such as its associations with genomic instability and antitumor immunosuppression, DEPTH2 also displayed competitive performance.

Conclusions

DEPTH2 is expected to have a wider spectrum of applications in evaluating ITH in comparison to other algorithms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03355-1.

Multifocal Organoid Capturing of Colon Cancer Reveals Pervasive Intratumoral Heterogenous Drug Responses

Intratumor heterogeneity (ITH) stands as one of the main difficulties in the treatment of colorectal cancer (CRC) as it causes the development of resistant clones and leads to heterogeneous drug responses. Here, 12 sets of patient-derived organoids (PDOs) and cell lines (PDCs) isolated from multiple regions of single tumors from 12 patients, capturing ITH by multiregion sampling of individual tumors, are presented. Whole-exome sequencing and RNA sequencing of the 12 sets are performed. The PDOs and PDCs of the 12 sets are also analyzed with a clinically relevant 24-compound library to assess their drug responses. The results reveal unexpectedly widespread subregional heterogeneity among PDOs and PDCs isolated from a single tumor, which is manifested by genetic and transcriptional heterogeneity and strong variance in drug responses, while each PDO still recapitulates the major histologic, genomic, and transcriptomic characteristics of the primary tumor. The data suggest an imminent drawback of single biopsy-originated PDO-based clinical diagnosis in evaluating CRC patient responses. Instead, the results indicate the importance of targeting common somatic driver mutations positioned in the trunk of all tumor subregional clones in parallel with a comprehensive understanding of the molecular ITH of each tumor.

Subtyping of sarcomas based on pathway enrichment scores in bulk and single cell transcriptomes

BACKGROUND

Sarcomas are highly heterogeneous in molecular, pathologic, and clinical features. However, a classification of sarcomas by integrating different types of pathways remains mostly unexplored.

METHODS

We performed hierarchical clustering analysis of sarcomas based on the enrichment scores of 14 pathways involved in immune, stromal, DNA damage repair (DDR), and oncogenic signatures in three bulk tumor transcriptome datasets.

RESULTS

Consistently in the three datasets, sarcomas were classified into three subtypes: Immune Class (Imm-C), Stromal Class (Str-C), and DDR Class (DDR-C). Imm-C had the strongest anti-tumor immune signatures and the lowest intratumor heterogeneity (ITH); Str-C showed the strongest stromal signatures, the highest genomic stability and global methylation levels, and the lowest proliferation potential; DDR-C had the highest DDR activity, expression of the cell cycle pathway, tumor purity, stemness scores, proliferation potential, and ITH, the most frequent TP53 mutations, and the worst survival. We further validated the stability and reliability of our classification method by analyzing a single cell RNA-Seq (scRNA-seq) dataset. Based on the expression levels of five genes in the pathways of T cell receptor signaling, cell cycle, mismatch repair, focal adhesion, and calcium signaling, we built a linear risk scoring model (ICMScore) for sarcomas. We demonstrated that ICMScore was an adverse prognostic factor for sarcomas and many other cancers.

CONCLUSIONS

Our classification method provides novel insights into tumor biology and clinical implications for sarcomas.

Subtyping of Human Papillomavirus-Positive Cervical Cancers Based on the Expression Profiles of 50 Genes

Background

Human papillomavirus-positive (HPV+) cervical cancers are highly heterogeneous in molecular and clinical features. However, the molecular classification of HPV+ cervical cancers remains insufficiently unexplored.

Methods

Based on the expression profiles of 50 genes having the largest expression variations across the HPV+ cervical cancers in the TCGA-CESC dataset, we hierarchically clustered HPV+ cervical cancers to identify new subtypes. We further characterized molecular, phenotypic, and clinical features of these subtypes.

Results

We identified two subtypes of HPV+ cervical cancers, namely HPV+G1 and HPV+G2. We demonstrated that this classification method was reproducible in two validation sets. Compared to HPV+G2, HPV+G1 displayed significantly higher immune infiltration level and stromal content, lower tumor purity, lower stemness scores and intratumor heterogeneity (ITH) scores, higher level of genomic instability, lower DNA methylation level, as well as better disease-free survival prognosis. The multivariate survival analysis suggests that the disease-free survival difference between both subtypes is independent of confounding variables, such as immune signature, stemness, and ITH. Pathway and gene ontology analysis confirmed the more active tumor immune microenvironment in HPV+G1 versus HPV+G2.

Conclusions

HPV+ cervical cancers can be classified into two subtypes based on the expression profiles of the 50 genes with the largest expression variations across the HPV+ cervical cancers. Both subtypes have significantly different molecular, phenotypic, and clinical features. This new subtyping method captures the comprehensive heterogeneity in molecular and clinical characteristics of HPV+ cervical cancers and provides potential clinical implications for the diagnosis and treatment of this disease.

Classification of lung adenocarcinoma based on stemness scores in bulk and single cell transcriptomes

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This study explores tumor stemness based on both bulk tumor and single cell transcriptome datasets.

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High-stemness tumors are less responsive to immunotherapy and targeted therapy compared with low-stemness tumors.

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Many high-stemness cells are at the beginning of the cell evolution trajectory, while most low-stemness cells are in the terminal or later phase.

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The correlations of tumor stemness with intratumor heterogeneity and tumor immunity were in the opposite direction between bulk tumors and single cells.

Tumor stemness is associated with tumor progression and therapy resistance. The recent advances in sequencing, genomics, and computational technologies have facilitated investigation into the tumor stemness cell-like characteristics. We identified subtypes of lung adenocarcinoma (LUAD) in bulk tumors or single cells based on the enrichment scores of 12 stemness signatures by clustering analysis of their transcriptomic profiles. Three stemness subtypes of LUAD were identified: St-H, St-M, and St-L, having high, medium, and low stemness signatures, respectively, consistently in six different datasets. Among the three subtypes, St-H was the most enriched in epithelial-mesenchymal transition, invasion, and metastasis signaling, genomically instable, irresponsive to immunotherapies and targeted therapies, and hence had the worst prognosis. We observed that intratumor heterogeneity was significantly higher in high-stemness than in low-stemness bulk tumors, but significantly lower in high-stemness than in low-stemness single cancer cells. Moreover, tumor immunity was stronger in high-stemness than in low-stemness cancer cells, but weaker in high-stemness than in low-stemness bulk tumors. These differences between bulk tumors and single cancer cells could be attributed to the non-tumor cells in bulk tumors that confounded the results of correlation analysis. Furthermore, pseudotime analysis showed that many St-H cells were at the beginning of the cell evolution trajectory, compared to most St-L cells in the terminal or later phase, suggesting that many low-stemness cells are originated from high-stemness cells. The stemness-based classification of LUAD may provide novel insights into the tumor biology as well as precise clinical management of this disease.

Analysis of the Single-Cell Heterogeneity of Adenocarcinoma Cell Lines and the Investigation of Intratumor Heterogeneity Reveals the Expression of Transmembrane Protein 45A (TMEM45A) in Lung Adenocarcinoma Cancer Patients

Simple Summary

Non-small cell lung cancer (NSCLC) is one of the main causes of cancer-related deaths worldwide. Intratumoral heterogeneity (ITH) is responsible for the majority of difficulties encountered in the treatment of lung-cancer patients. Therefore, the heterogeneity of NSCLC cell lines and primary lung adenocarcinoma was investigated by single-cell mass cytometry (CyTOF). Human NSCLC adenocarcinoma cells A549, H1975, and H1650 were studied at single-cell resolution for the expression pattern of 13 markers: GLUT1, MCT4, CA9, TMEM45A, CD66, CD274, CD24, CD326, pan-keratin, TRA-1-60, galectin-3, galectin-1, and EGFR. The intra- and inter-cell-line heterogeneity of A549, H1975, and H1650 cells were demonstrated through hypoxic modeling. Additionally, human primary lung adenocarcinoma, and non-involved healthy lung tissue were homogenized to prepare a single-cell suspension for CyTOF analysis. The single-cell heterogeneity was confirmed using unsupervised viSNE and FlowSOM analysis. Our results also show, for the first time, that TMEM45A is expressed in lung adenocarcinoma.

Abstract

Intratumoral heterogeneity (ITH) is responsible for the majority of difficulties encountered in the treatment of lung-cancer patients. Therefore, the heterogeneity of NSCLC cell lines and primary lung adenocarcinoma was investigated by single-cell mass cytometry (CyTOF). First, we studied the single-cell heterogeneity of frequent NSCLC adenocarcinoma models, such as A549, H1975, and H1650. The intra- and inter-cell-line single-cell heterogeneity is represented in the expression patterns of 13 markers—namely GLUT1, MCT4, CA9, TMEM45A, CD66, CD274 (PD-L1), CD24, CD326 (EpCAM), pan-keratin, TRA-1-60, galectin-3, galectin-1, and EGFR. The qRT-PCR and CyTOF analyses revealed that a hypoxic microenvironment and altered metabolism may influence cell-line heterogeneity. Additionally, human primary lung adenocarcinoma and non-involved healthy lung tissue biopsies were homogenized to prepare a single-cell suspension for CyTOF analysis. The CyTOF showed the ITH of human primary lung adenocarcinoma for 14 markers; particularly, the higher expressions of GLUT1, MCT4, CA9, TMEM45A, and CD66 were associated with the lung-tumor tissue. Our single-cell results are the first to demonstrate TMEM45A expression in human lung adenocarcinoma, which was verified by immunohistochemistry.

Quantification of tumor heterogeneity: from data acquisition to metric generation

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.