Spontaneous mutations in the single TTN gene represent high tumor mutation burden

Immuno-genomic classification of colorectal cancer organoids reveals cancer cells with intrinsic immunogenic properties associated with patient survival

Background

The intrinsic immuno-ge7nomic characteristics of colorectal cancer cells that affect tumor biology and shape the tumor immune microenvironment (TIM) are unclear.

Methods

We developed a patient-derived colorectal cancer organoid (CCO) model and performed pairwise analysis of 87 CCOs and their matched primary tumors. The TIM type of the primary tumor was classified as immuno-active, immuno-exhausted, or immuno-desert.

Results

The gene expression profiles, signaling pathways, major oncogenic mutations, and histology of the CCOs recapitulated those of the primary tumors, but not the TIM of primary tumors. Two distinct intrinsic molecular subgroups of highly proliferative and mesenchymal phenotypes with clinical significance were identified in CCOs with various cancer signaling pathways. CCOs showed variable expression of cancer-specific immune-related genes such as those encoding HLA-I and HLA-II, and molecules involved in immune checkpoint activation/inhibition. Among these genes, the expression of HLA-II in CCOs was associated with favorable patient survival. K-means clustering analysis based on HLA-II expression in CCOs revealed a subgroup of patients, in whom cancer cells exhibited Intrinsically Immunogenic Properties (Ca-IIP), and were characterized by high expression of signatures associated with HLA-I, HLA-II, antigen presentation, and immune stimulation. Patients with the Ca-IIP phenotype had an excellent prognosis, irrespective of age, disease stage, intrinsic molecular type, or TIM status. Ca-IIP was negatively correlated with intrinsic E2F/MYC signaling. Analysis of the correlation between CCO immuno-genotype and TIM phenotype revealed that the TIM phenotype was associated with microsatellite instability, Wnt/β-catenin signaling, APC/KRAS mutations, and the unfolded protein response pathway linked to the FBXW7 mutation in cancer cells. However, Ca-IIP was not associated with the TIM phenotype.

Conclusions

We identified a Ca-IIP phenotype from a large set of CCOs. Our findings may provide an unprecedented opportunity to develop new strategies for optimal patient stratification in this era of immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-021-02034-1.

A BAYESIAN NONPARAMETRIC MODEL FOR INFERRING SUBCLONAL POPULATIONS FROM STRUCTURED DNA SEQUENCING DATA

There are distinguishing features or "hallmarks" of cancer that are found across tumors, individuals, and types of cancer, and these hallmarks can be driven by specific genetic mutations. Yet, within a single tumor there is often extensive genetic heterogeneity as evidenced by single-cell and bulk DNA sequencing data. The goal of this work is to jointly infer the underlying genotypes of tumor subpopulations and the distribution of those subpopulations in individual tumors by integrating single-cell and bulk sequencing data. Understanding the genetic composition of the tumor at the time of treatment is important in the personalized design of targeted therapeutic combinations and monitoring for possible recurrence after treatment. We propose a hierarchical Dirichlet process mixture model that incorporates the correlation structure induced by a structured sampling arrangement and we show that this model improves the quality of inference. We develop a representation of the hierarchical Dirichlet process prior as a Gamma-Poisson hierarchy and we use this representation to derive a fast Gibbs sampling inference algorithm using the augment-and-marginalize method. Experiments with simulation data show that our model outperforms standard numerical and statistical methods for decomposing admixed count data. Analyses of real acute lymphoblastic leukemia cancer sequencing dataset shows that our model improves upon state-of-the-art bioinformatic methods. An interpretation of the results of our model on this real dataset reveals co-mutated loci across samples.

LRP1B mutation: a novel independent prognostic factor and a predictive tumor mutation burden in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancies globally and the second leading cause of cancer-related death. Low-density lipoprotein (LDL) receptor-related protein 1B (LRP1B) is one of the commonly mutated genes in HCC, but its role in HCC remains unclear. In this study, we analyzed the role of LRP1B mutation in HCC. The bioinformatics results show that LRP1B had a frequency of mutation in HCC patients, and LRP1B mutation was associated with a higher tumor mutation burden (TMB), and survival analysis proved that the prognosis of HCC patients with LRP1B mutation was poor. Univariate and multivariate COX regression analysis indicated that LRP1B mutation was an independent risk factor in evaluating HCC patients' prognosis. Correlation analysis showed that LRP1B mutation status was associated with the infiltration of 2 types of immune cells and higher expression of immune checkpoint gene human endogenous retrovirus-H long terminal repeat-associating protein 2 (HHLA2) in HCC patients. In summary, the results show that LRP1B mutation is associated with the higher TMB and poor prognosis of patients with HCC, and it was an independent risk factor for clinical outcomes of HCC patients. LRP1B gene mutations can serve as predictors in HCC patients with higher TMB and higher expression of HHLA2. The results of this study will be beneficial to future studies on targeted therapy and immunotherapy for HCC.

Genomic Heterogeneity and Clonal Evolution in Gastroesophageal Junction Cancer Revealed by Single Cell DNA Sequencing

Gastroesophageal junction (GEJ) cancer is a tumor that occurs at the junction of stomach and esophagus anatomically. GEJ cancer frequently metastasizes to lymph nodes, however the heterogeneity and clonal evolution process are unclear. This study is the first of this kind to use single cell DNA sequencing to determine genomic variations and clonal evolution related to lymph node metastasis. Multiple Annealing and Looping Based Amplification Cycles (MALBAC) and bulk exome sequencing were performed to detect single cell copy number variations (CNVs) and single nucleotide variations (SNVs) respectively. Four GEJ cancer patients were enrolled with two (Pt.3, Pt.4) having metastatic lymph nodes. The most common mutation we found happened in the TTN gene, which was reported to be related with the tumor mutation burden in cancers. Significant intra-patient heterogeneity in SNVs and CNVs were found. We identified the SNV subclonal architecture in each tumor. To study the heterogeneity of CNVs, the single cells were sequenced. The number of subclones in the primary tumor was larger than that in lymph nodes, indicating the heterogeneity of primary site was higher. We observed two patterns of multi-station lymph node metastasis: one was skip metastasis and the other was to follow the lymphatic drainage. Taken together, our single cell genomic analysis has revealed the heterogeneity and clonal evolution in GEJ cancer.

Differential Mutation Detection Capability Through Capture-Based Targeted Sequencing in Plasma Samples in Hepatocellular Carcinoma

Circulating tumor DNA (ctDNA) is a promising biomarker for accurate monitoring and less invasive assessment of tumor burden and treatment response. Here, targeted next-generation sequencing (NGS) with a designed gene panel of 176 cancer-relevant genes was used to assess mutations in 90 ctDNA samples from 90 patients with multiple types of liver disease and 10 healthy donor samples for control. Using our ctDNA detection panel, we identified mutations in 98.89% (89/90) of patient plasma biopsy samples, and 19 coding variants located in 10 cancer-related genes [ACVR2A, PCLO, TBCK, adhesion G protein-coupled receptor (ADGRV1), COL1A1, GABBR1, MUC16, MAGEC1, FASLG, and JAK1] were identified in 96.7% of patients (87/90). The 10 top mutated genes were tumor protein p53 (TP53), ACVR2A, ADGRV1, MUC16, TBCK, PCLO, COL11A1, titin (TTN), DNAH9, and GABBR1. TTN and TP53 and TTN and DNAH9 mutations tended to occur together in hepatocellular carcinoma samples. Most importantly, we found that most of those variants were insertions (frameshift insertions) and deletions (frameshift deletions and in-frame deletions), such as insertion variants in ACVR2A, PCLO, and TBCK; such mutations were detected in almost 95% of patients. Our study demonstrated that the targeted NGS-based ctDNA mutation profiling was a useful tool for hepatocellular carcinoma (HCC) monitoring and could potentially be used to guide treatment decisions in HCC.

Identification of the Prognostic Significance of Somatic Mutation-Derived LncRNA Signatures of Genomic Instability in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is a highly heterogeneous tumor with substantial somatic mutations and genome instability, which are emerging hallmarks of cancer. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in genomic instability. However, the identification of genome instability-related lncRNAs (GInLncRNAs) and their clinical significance has not been investigated in LUAD.

Methods: We determined GInLncRNAs by combining somatic mutation and transcriptome data of 457 patients with LUAD and probed their potential function using co-expression network and Gene Ontology (GO) enrichment analyses. We then filtered GInLncRNAs by Cox regression and LASSO regression to construct a genome instability-related lncRNA signature (GInLncSig). We subsequently evaluated GInLncSig using correlation analyses with mutations, external validation, model comparisons, independent prognostic significance analyses, and clinical stratification analyses. Finally, we established a nomogram for prognosis prediction in patients with LUAD and validated it in the testing set and the entire TCGA dataset.

Results: We identified 161 GInLncRNAs, of which seven were screened to develop a prognostic GInLncSig model (LINC01133, LINC01116, LINC01671, FAM83A-AS1, PLAC4, MIR223HG, and AL590226.1). GInLncSig independently predicted the overall survival of patients with LUAD and displayed an improved performance compared to other similar signatures. Furthermore, GInLncSig was related to somatic mutation patterns, suggesting its ability to reflect genome instability in LUAD. Finally, a nomogram comprising the GInLncSig and tumor stage exhibited improved robustness and clinical practicability for predicting patient prognosis.

Conclusion: Our study identified a signature for prognostic prediction in LUAD comprising seven lncRNAs associated with genome instability, which may provide a useful indicator for clinical stratification management and treatment decisions for patients with LUAD.

The Landscape of Tumor-Specific Antigens in Colorectal Cancer

Over the last few decades, major efforts in cancer research and treatment have intensified. Apart from standard chemotherapy approaches, immunotherapy has gained substantial traction. Personalized immunotherapy has become an important tool for cancer therapy with the discovery of immune checkpoint inhibitors. Traditionally, tumor-associated antigens are used in immunotherapy-based treatments. Nevertheless, these antigens lack specificity and may have increased toxicity. With the advent of next-generation technologies, the identification of new tumor-specific antigens is becoming more important. In colorectal cancer, several tumor-specific antigens were identified and functionally validated. Multiple clinical trials from vaccine-based and adoptive cell therapy utilizing tumor-specific antigens have commenced. Herein, we will summarize the current landscape of tumor-specific antigens particularly in colorectal cancer.