Pathway Mutations in Breast Cancer Using Whole-Exome Sequencing

Identification and validation of Golgi apparatus-related signature for predicting prognosis and immunotherapy response in breast cancer

BACKGROUND

The Golgi apparatus plays a pivotal role in various aspects of cancer. This study aims to investigate the predictive value of Golgi apparatus-related genes (GARGs) in breast cancer prognosis and immunotherapy response evaluation.

METHODS

Transcriptional and clinical data from the TCGA-BRCA cohort and GSE96058 cohort were utilized to construct and validate a prognostic model for breast cancer using Cox regression analysis. Differences in immune landscape, somatic mutations, gene expression, drug sensitivity, and immunotherapy response between different risk groups were assessed. A prognostic nomogram for breast cancer was further developed and evaluated. qPCR and single-cell sequencing analyses were performed to validate the expression of GARGs.

RESULTS

A total of 394 GARGs significantly associated with breast cancer prognosis were identified, leading to the construction of a prognostic risk feature comprising 10 GARGs. This feature effectively stratified breast cancer patients into high-risk and low-risk groups, with the high-risk group exhibiting significantly worse prognosis. Meanwhile, significant differences in clinicopathological features, immune infiltration, drug sensitivity, and immunotherapy response were observed between the high- and low-risk groups. The constructed nomogram incorporating these factors showed superior performance in prognostic assessment for breast cancer patients. Ultimately, the utilization of qPCR and single-cell sequencing techniques substantiated the disparate expression patterns of these prognostic genes in breast cancer.

CONCLUSION

Our findings demonstrate that a prognostic risk feature derived from GARGs holds promising application potential for predicting prognosis and evaluating immunotherapy response in breast cancer patients.

Haplotyping Using Long-Range PCR and Nanopore Sequencing to Phase Variants: Lessons Learned From the ABCA4 Locus

Short-read next-generation sequencing has revolutionized our ability to identify variants underlying inherited diseases; however, it does not allow the phasing of variants to clarify their diagnostic interpretation. The advent of widespread, increasingly accurate long-read sequencing has opened up new applications not currently available through short-read next-generation sequencing. One such use is the ability to phase variants to clarify their diagnostic interpretation and to investigate the increasingly prevalent role of cis-acting variants in the pathogenesis of the inherited disease, so-called complex alleles. Complex alleles are becoming an increasingly prevalent part of the study of genes associated with inherited diseases, for example, in ABCA4-related diseases. We sought to establish a cost-effective method to phase contiguous segments of the 130-kb ABCA4 locus by long-read sequencing of overlapping amplification products. Using the comprehensively characterized CEPH sample, NA12878, we verified the accuracy and robustness of our assay. However, in-field assessment of its utility using clinical test cases was hampered by the paucity and distribution of identified variants and by PCR chimerism, particularly where the number of PCR cycles was high. Despite this, we were able to construct robust phase blocks of up to 94.9 kb, representing 73% of the ABCA4 locus. We conclude that, although haplotype analysis of variants located within discrete amplification products was robust and informative, the stitching together of larger phase blocks using overlapping single-molecule reads remained practically challenging.

The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers

BACKGROUND

Female breast cancer remains the second leading cause of cancer-related death in the USA. The heterogeneity in the tumor morphology across the cohort and within patients can lead to unpredictable therapy resistance, metastasis, and clinical outcome. Hence, supplementing classic pathological markers with intrinsic tumor molecular markers can help identify novel molecular subtypes and the discovery of actionable biomarkers.

METHODS

We conducted a large multi-institutional genomic analysis of paired normal and tumor samples from breast cancer patients to profile the complex genomic architecture of breast tumors. Long-term patient follow-up, therapeutic regimens, and treatment response for this cohort are documented using the Breast Cancer Collaborative Registry. The majority of the patients in this study were at tumor stage 1 (51.4%) and stage 2 (36.3%) at the time of diagnosis. Whole-exome sequencing data from 554 patients were used for mutational profiling and identifying cancer drivers.

RESULTS

We identified 54 tumors having at least 1000 mutations and 185 tumors with less than 100 mutations. Tumor mutational burden varied across the classified subtypes, and the top ten mutated genes include MUC4, MUC16, PIK3CA, TTN, TP53, NBPF10, NBPF1, CDC27, AHNAK2, and MUC2. Patients were classified based on seven biological and tumor-specific parameters, including grade, stage, hormone receptor status, histological subtype, Ki67 expression, lymph node status, race, and mutational profiles compared across different subtypes. Mutual exclusion of mutations in PIK3CA and TP53 was pronounced across different tumor grades. Cancer drivers specific to each subtype include TP53, PIK3CA, CDC27, CDH1, STK39, CBFB, MAP3K1, and GATA3, and mutations associated with patient survival were identified in our cohort.

CONCLUSIONS

This extensive study has revealed tumor burden, driver genes, co-occurrence, mutual exclusivity, and survival effects of mutations on a US Midwestern breast cancer cohort, paving the way for developing personalized therapeutic strategies.

Omics-Based Investigations of Breast Cancer

Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.

Targeted nanopore sequencing enables complete characterisation of structural deletions initially identified using exon-based short-read sequencing strategies

BACKGROUND

The widespread adoption of exome sequencing has greatly increased the rate of genetic diagnosis for inherited conditions. However, the detection and validation of large deletions remains challenging. While numerous bioinformatics approaches have been developed to detect deletions from whole - exome sequencing and targeted panels, further work is typically required to define the physical breakpoints or integration sites. Accurate characterisation requires either expensive follow - up whole - genome sequencing or the time - consuming, laborious process of PCR walking, both of which are challenging when dealing with the repeat sequences which frequently intersect deletion breakpoints. The aim of this study was to develop a cost-effective, long-range sequencing method to characterise deletions.

METHODS

Genomic DNA was amplified with primers spanning the deletion using long-range PCR and the products purified. Sequencing was performed on MinION flongle flowcells. The resulting fast5 files were basecalled using Guppy, trimmed using Porechop and aligned using Minimap2. Filtering was performed using NanoFilt. Nanopore sequencing results were verified by Sanger sequencing.

RESULTS

Four cases with deletions detected following comparative read-depth analysis of targeted short-read sequencing were analysed. Nanopore sequencing defined breakpoints at the molecular level in all cases including homozygous breakpoints in EYS, CNGA1 and CNGB1 and a heterozygous deletion in PRPF31. All breakpoints were verified by Sanger sequencing.

CONCLUSIONS

In this study, a quick, accurate and cost - effective method is described to characterise deletions identified from exome, and similar data, using nanopore sequencing.

Potential predictive value of circulating tumor DNA (ctDNA) mutations for the efficacy of immune checkpoint inhibitors in advanced triple-negative breast cancer

Background: In recent years, tumor immunotherapy has become a viable treatment option for triple negative breast cancer (TNBC). Among these, immune checkpoint inhibitors (ICIs) have demonstrated good efficacy in advanced TNBC patients with programmed death-ligand 1 (PD-L1) positive expression. However, only 63% of PD-L1-positive individuals showed any benefit from ICIs. Therefore, finding new predictive biomarkers will aid in identifying patients who are likely to benefit from ICIs. In this study, we used liquid biopsies and next-generation sequencing (NGS) to dynamically detect changes in circulating tumor DNA (ctDNA) in the blood of patients with advanced TNBC treated with ICIs and focused on its potential predictive value.

Methods: From May 2018 to October 2020, patients with advanced TNBC treated with ICIs at Shandong Cancer Hospital were included prospectively. Patient blood samples were obtained at the pretreatment baseline, first response evaluation, and disease progression timepoints. Furthermore, 457 cancer-related genes were evaluated by NGS, and patients’ ctDNA mutations, gene mutation rates, and other indicators were determined and coupled with clinical data for statistical analysis.

Results: A total of 11 TNBC patients were included in this study. The overall objective response rate (ORR) was 27.3%, with a 6.1-month median progression-free survival (PFS) (95% confidence interval: 3.877–8.323 months). Of the 11 baseline blood samples, 48 mutations were found, with the most common mutation types being frame shift indels, synonymous single-nucleotide variations (SNVs), frame indel missenses, splicing, and stop gains. Additionally, univariate Cox regression analysis revealed that advanced TNBC patients with one of 12 mutant genes (CYP2D6 deletion and GNAS, BCL2L1, H3F3C, LAG3, FGF23, CCND2, SESN1, SNHG16, MYC, HLA-E, and MCL1 gain) had a shorter PFS with ICI treatment (p < 0.05). To some extent, dynamic changes of ctDNA might indicate the efficacy of ICIs.

Conclusion: Our data indicate that ICI efficacy in patients with advanced TNBC may be predicted by 12 mutant ctDNA genes. Additionally, dynamic alterations in peripheral blood ctDNA might be used to track the effectiveness of ICI therapy in those with advanced TNBC.

Potential Impact of PI3K-AKT Signaling Pathway Genes, KLF-14, MDM4, miRNAs 27a, miRNA-196a Genetic Alterations in the Predisposition and Progression of Breast Cancer Patients

Genome-wide association studies have reported link between SNPs and risk of breast cancer. This study investigated the association of the selected gene variants by predicting them as possible target genes. Molecular technique advances with the availability of whole-exome sequencing (WES), now offer opportunities for simultaneous investigations of many genes. The experimental protocol for PI3K, AKT-1, KLF-14, MDM4, miRNAs 27a, and miR-196a genotyping was done by ARMS-PCR and sanger sequencing. The novel and known gene variants were studied by Whole-exome sequencing using Illumina NovaSeq 6000 platform. This case control study reports significant association between BC patients, healthy controls with the polymorphic variants of PI3K C > T, AKT-1 G > A KLF 14 C > T, MDM4 A > G, miR-27a A > G, miR-196a-2 C > T genes (p < 0.05). MDM4 A > G genotypes were strongly associated with BC predisposition with OR 2.08 & 2.15, p < 0.05) in codominant and dominant models respectively. MDM4 A allele show the same effective (OR1.76, p < 0.05) whereas it remains protective in recessive model for BC risk. AKT1G > A genotypes were strongly associated with the BC susceptibility in all genetic models whereas PI3K C > T genotypes were associated with breast cancer predisposition in recessive model OR 6.96. Polymorphic variants of KLF-14 A > G, MDM4G > A, MiR-27aA >G, miR-196a-C > T were strongly associated with stage, tamoxifen treatment. Risk variants have been reported by whole exome sequencing in our BC patients. It was concluded that a strong association between the PI3K-AKT signaling pathway gene variants with the breast cancer susceptibility and progression. Similarly, KLF 14-AA, MDM4-GA, miR27a-GG and miR-196a-CT gene variants were associated with the higher risk probability of BC and were strongly correlated with staging of the BC patients. This study also reported Low, novel, and intermediate-genetic-risk variants of PI3K, AKT-1, MDM4G & KLF-14 by utilizing whole-exome sequencing. These variants should be further investigated in larger cohorts' studies.

ELP6 and PLIN5 Mutations Were Probably Prognostic Biomarkers for Patients With Gastric Cancer

Purpose

Gastric cancer (GC) is the fifth leading cancer around world. And prognosis of patients with GC is still undesirable. Our study aimed to explore potential prognostic biomarkers for patients with GC.

Methods

The clinical samples were collected from the Qinghai University Affiliated Hospital, which were subjected to the whole exome sequencing (WES). The other GC-related data were obtained from The Cancer Genome Atlas (TCGA) database. Cross analyses were done to determine the candidate genes. And the final mutated genes were determined by survival analyses, univariate and multivariate Cox regression analyses. CIBERSORT and GSEA were used for immune cell infiltration analysis and functional enrichment, respectively.

Results

After cross analyses, 160 candidate-mutated genes were identified. And mutated ELP6 and PLIN5 were significantly independently correlated with the overall survival (OS) of patients with GC. Patients with GC with ELP6 and PLIN5 mutations had worse and better prognosis, respectively. Totally 5 types of immune cells were significantly differentially infiltrated in wild-type and mutated ELP6 and PLIN5 GC samples. In mutated ELP6 and PLIN5 GC samples, totally 7 and 11 pathways were significantly enriched, respectively.

Conclusions

The ELP6 and PLIN5 mutations were probably prognostic biomarkers for patients with GC.

Construction and validation of a risk prediction model for clinical axillary lymph node metastasis in T1-2 breast cancer

The current diagnostic technologies for assessing the axillary lymph node metastasis (ALNM) status accurately in breast cancer (BC) remain unsatisfactory. Here, we developed a diagnostic model for evaluating the ALNM status using a combination of mRNAs and the T stage of the primary tumor as a novel biomarker. We collected relevant information on T1-2 BC from public databases. An ALNM prediction model was developed by logistic regression based on the screened signatures and then internally and externally validated. Calibration curves and the area under the curve (AUC) were employed as performance metrics. The prognostic value and tumor immune infiltration of the model were also determined. An optimal diagnostic model was created using a combination of 11 mRNAs and T stage of the primary tumor and showed high discrimination, with AUCs of 0.828 and 0.746 in the training sets. AUCs of 0.671 and 0.783 were achieved in the internal validation cohorts. The mean external AUC value was 0.686 and ranged between 0.644 and 0.742. Moreover, the new model has good specificity in T1 and hormone receptor-negative/human epidermal growth factor receptor 2- negative (HR-/HER2-) BC and good sensitivity in T2 BC. In addition, the risk of ALNM and 11 mRNAs were correlated with the infiltration of M2 macrophages, as well as the prognosis of BC. This novel prediction model is a useful tool to identify the risk of ALNM in T1-2 BC patients, particularly given that it can be used to adjust surgical options in the future.

Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines

There are many types of cancers. Although they share some hallmarks, such as proliferation and metastasis, they are still very different from many perspectives. They grow on different organ or tissues. Does each cancer have a unique gene expression pattern that makes it different from other cancer types? After the Cancer Genome Atlas (TCGA) project, there are more and more pan-cancer studies. Researchers want to get robust gene expression signature from pan-cancer patients. But there is large variance in cancer patients due to heterogeneity. To get robust results, the sample size will be too large to recruit. In this study, we tried another approach to get robust pan-cancer biomarkers by using the cell line data to reduce the variance. We applied several advanced computational methods to analyze the Cancer Cell Line Encyclopedia (CCLE) gene expression profiles which included 988 cell lines from 20 cancer types. Two feature selection methods, including Boruta, and max-relevance and min-redundancy methods, were applied to the cell line gene expression data one by one, generating a feature list. Such list was fed into incremental feature selection method, incorporating one classification algorithm, to extract biomarkers, construct optimal classifiers and decision rules. The optimal classifiers provided good performance, which can be useful tools to identify cell lines from different cancer types, whereas the biomarkers (e.g. NCKAP1, TNFRSF12A, LAMB2, FKBP9, PFN2, TOM1L1) and rules identified in this work may provide a meaningful and precise reference for differentiating multiple types of cancer and contribute to the personalized treatment of tumors.