Integrated analysis reveals prognostic value of HLA-I LOH in triple-negative breast cancer

Evolutionary and immune microenvironment dynamics during neoadjuvant treatment of esophageal adenocarcinoma

Locally advanced esophageal adenocarcinoma remains difficult to treat and the ecological and evolutionary dynamics responsible for resistance and recurrence are incompletely understood. Here, we performed longitudinal multiomic analysis of patients with esophageal adenocarcinoma in the MEMORI trial. Multi-region multi-timepoint whole-exome and paired transcriptome sequencing was performed on 27 patients before, during and after neoadjuvant treatment. We found major transcriptomic changes during treatment with upregulation of immune, stromal and oncogenic pathways. Genetic data revealed that clonal sweeps through treatment were rare. Imaging mass cytometry and T cell receptor sequencing revealed remodeling of the tumor microenvironment during treatment. The presence of genetic immune escape, a less-cytotoxic T cell phenotype and a lack of clonal T cell expansions were linked to poor treatment response. In summary, there were widespread transcriptional and environmental changes through treatment, with limited clonal replacement, suggestive of phenotypic plasticity.

GnRHR inhibits the malignant progression of triple-negative breast cancer by upregulating FOS and IFI44L

Gonadotropin-releasing hormone receptor (GnRHR) has been identified as a factor that hinders the malignant advancement of triple-negative breast cancer (TNBC). Nevertheless, the specific molecular mechanism responsible for this impact remains unclear. This study demonstrates that GnRHR exhibits low expression levels in TNBC, which correlates with an unfavorable prognosis for patients. GnRHR promotes the expression of FOS, subsequently enhancing IFI44L transcription and expression, thereby inhibiting TNBC cell proliferation, migration, and invasion. Goserelin reduced the growth rate of TNBC tumors in nude mice, resulting in elevated levels of GnRHR, FOS, and IFI44L in tumor tissues, while the expression of Ki67, vimentin, and N-cadherin decreased. Overall, our results reveal that GnRHR suppresses the malignant progression of TNBC by upregulating FOS and IFI44L. Goserelin slows down the proliferation of TNBC tumors in vivo through the GnRHR/FOS/IFI44L pathway, which may present a new therapeutic approach for the management of TNBC.

Loss of Heterozygosity (LOH) Affecting HLA Genes in Breast Cancer: Clinical Relevance and Therapeutic Opportunities

Major histocompatibility complex (MHC) class-I molecules (or Human Leucocyte Antigen class-I) play a key role in adaptive immunity against cancer. They present specific tumor neoantigens to cytotoxic T cells and provoke an antitumor cytotoxic response. The total or partial loss of HLA molecules can inhibit the immune system's ability to detect and destroy cancer cells. Loss of heterozygosity (LOH) is a common irreversible genetic alteration that occurs in the great majority of human tumors, including breast cancer. LOH at chromosome 6, which involves HLA genes (LOH-HLA), leads to the loss of an HLA haplotype and is linked to cancer progression and a weak response to cancer immunotherapy. Therefore, the loss of genes or an entire chromosomal region which are critical for antigen presentation is of particular importance in the search for novel prognostic and clinical biomarkers in breast cancer. Here, we review the role of LOH-HLA in breast cancer, its contribution to an understanding of cancer immune escape and tumor progression, and discuss how it can be targeted in cancer therapy.

HLA-class-I expression loss, tumor microenvironment and breast cancer prognosis

Loss of HLA-class-I molecule expression by cancer cells is a frequent event in human tumors that may lead to immune evasion from cytotoxic T-cells. We examined the expression patterns of HLA-class-I molecules in a series of 175 patients with operable breast cancer (BCa). Extensive loss of BCa cell HLA-class-I expression was noted 76.6 % of patients (27.5 % complete loss). A significant association of HLA-preservation with high TIL-density (p = 0.001) was documented. Preservation of HLA was evident only in BCa carcinomas with low HIF1α expression and high TIL-density. Cell line experiments (MCF7 and T47D) showed that induction of HLAs in cancer cells following incubation with lymphocytes or IFNγ, was abrogated under hypoxic conditions. HLA-preservation was linked with better distant metastasis-free survival (p = 0.01), which was confirmed also in multivariate analysis (p = 0.02, HR 3.17). Studying the expression of HLA-class-I molecules in parallel with TIL-density and HIF1α expression may identify subgroups of BCa patients who would benefit from immunotherapy.

The HLA-I landscape confers prognosis and antitumor immunity in breast cancer

Breast cancer is a highly heterogeneous disease with varied subtypes, prognoses and therapeutic responsiveness. Human leukocyte antigen class I (HLA-I) shapes the immunity and thereby influences the outcome of breast cancer. However, the implications of HLA-I variations in breast cancer remain poorly understood. In this study, we established a multiomics cohort of 1156 Chinese breast cancer patients for HLA-I investigation. We calculated four important HLA-I indicators in each individual, including HLA-I expression level, somatic HLA-I loss of heterozygosity (LOH), HLA-I evolutionary divergence (HED) and peptide-binding promiscuity (Pr). Then, we evaluated their distribution and prognostic significance in breast cancer subtypes. We found that the four breast cancer subtypes had distinct features of HLA-I indicators. Increased expression of HLA-I and LOH were enriched in triple-negative breast cancer (TNBC), while Pr was relatively higher in hot tumors within TNBCs. In particular, a higher Pr indicated a better prognosis in TNBCs by regulating the infiltration of immune cells and the expression of immune molecules. Using the matched genomic and transcriptomic data, we found that mismatch repair deficiency-related mutational signature and pathways were enriched in low-Pr TNBCs, suggesting that targeting mismatch repair deficiency for synthetic lethality might be promising therapy for these patients. In conclusion, we presented an overview of HLA-I indicators in breast cancer and provided hints for precision treatment for low-Pr TNBCs.

The Mutational, Prognostic, and Therapeutic Landscape of Neuroendocrine Neoplasms

BACKGROUND

Neuroendocrine neoplasms (NENs) represent clinically and genetically heterogeneous malignancies, thus a comprehensive understanding of underlying molecular characteristics, prognostic signatures, and potential therapeutic targets is urgently needed.

METHODS

Next-generation sequencing (NGS) and immunohistochemistry were applied to acquire genomic and immune profiles of NENs from 47 patients.

RESULTS

Difference was distinguished based on differentiation grade and primary localization. Poorly differentiated neuroendocrine carcinomas (NECs) and well-differentiated neuroendocrine tumors (NETs) harbored distinct molecular features; we observed that tumor mutational burden (TMB) and tumor neoantigen burden (TNB) were significantly higher in NECs versus NETs. Notably, we identified a 7-gene panel (MLH3, NACA, NOTCH1, NPAP1, RANBP17, TSC2, and ZFHX4) as a novel prognostic signature in NENs; patients who carried mutations in any of the 7 genes exhibited significantly poorer survival. Furthermore, loss of heterozygosity (LOH) and germline homogeneity in human leukocyte antigen (HLA) are common in NENs, accounting for 39% and 36%, respectively. Notably, HLA LOH was an important prognostic biomarker for a subgroup of NEN patients. Finally, we analyzed clinically actionable targets in NENs, revealing that TMB high (TMB-H) or gene mutations in TP53, KRAS, and HRAS were the most frequently observed therapeutic indicators, which granted eligibility to immune checkpoint blockade (ICB) and targeted therapy.

CONCLUSION

Our study revealed heterogeneity of NENs, and identified novel prognostic signatures and potential therapeutic targets, which directing improvements of clinical management for NEN patients in the foreseeable future.

Prognostic and Clinical Significance of Human Leukocyte Antigen Class I Expression in Breast Cancer: A Meta-Analysis

BACKGROUND

The value of human leukocyte antigen (HLA; also known as major histocompatibility complex) class I expression for the prediction of breast cancer survival outcomes remains unclear. We conducted a meta-analysis to explore the prognostic significance of this expression.

MATERIALS AND METHODS

We searched electronic databases to identify reports on associations of HLA class I protein or mRNA expression with survival outcomes and clinicopathological factors in the breast cancer context. Pooled hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CIs) were used to conduct a quantitative meta-analysis.

RESULTS

The sample comprised eight studies involving 3590 patients. Only the classical HLA class Ia (HLA-ABC) molecules studies were included in this meta-analysis. Elevated HLA class I protein expression was found to be significantly related to better disease-free survival (DFS) (HR 0.58, 95% CI 0.35-0.95, P = 0.03), particularly among patients with triple-negative breast cancer (TNBC) (HR 0.31, 95% CI 0.18-0.52, P < 0.001), but not to overall survival. It was also associated with estrogen receptor (ER) negativity (OR 1.71, 95% CI 1.24-2.35, P = 0.001), progesterone receptor (PR) negativity (OR 1.49, 95% CI 1.22-1.81, P < 0.001), human epidermal growth factor receptor 2 (HER2) positivity (OR 1.51, 95% CI 1.18-1.94, P = 0.001), TNBC (OR 1.68, 95% CI 1.15-2.45, P < 0.01), high Ki-67 indices (OR 2.06, 95% CI 1.62-2.61, P < 0.001), and high nuclear grades (OR 2.67, 95% CI 2.17-3.29, P < 0.001).

CONCLUSION

This meta-analysis demonstrated that enhanced HLA class I protein expression is significantly associated with the better DFS of patients with breast cancer, especially TNBC, as well as with ER and PR negativity, HER2 positivity, TNBC, and high Ki-67 indices and nuclear grades. The immune target HLA class I may serve as a prognostic indicator for breast cancer.

Oxidative stress genes define two subtypes of triple-negative breast cancer with prognostic and therapeutic implications

Introduction: Oxidative stress (OS)-related genes have been confirmed to be closely related to the prognosis of triple-negative breast cancer (TNBC) patients; despite this fact, there is still a lack of TNBC subtype strategies based on this gene guidance. Here, we aimed to explore OS-related subtypes and their prognostic value in TNBC. Methods: Data from The Cancer Genome Atlas (TCGA)-TNBC and Sequence Read Archive (SRA) (SRR8518252) databases were collected, removing batch effects using a combat method before analysis. Consensus clustering analysis identified two OS subtypes (clusters A and B), with cluster A showing a better prognosis. Immune infiltration characteristics were analyzed using ESTIMATE and single-sample gene set enrichment analysis (ssGSEA) algorithms, revealing higher ImmuneScore and ESTIMATEscore in cluster A. Tumor-suppressive immune cells, human leukocyte antigen (HLA) genes, and three immune inhibitors were more prevalent in cluster A. Results: An eight-gene signature, derived from differentially expressed genes, was developed and validated as an independent risk factor for TNBC. A nomogram combining the risk score and clinical variables accurately predicted patient outcomes. Finally, we also validated the classification effect of subtypes using hub markers of each subtype in the test dataset. Conclusion: Our study reveals distinct molecular clusters based on OS-related genes to better clarify the reactive oxygen species (ROS)-mediated progression and the crosstalk between the ROS and tumor microenvironment (TME) in this heterogenetic disease, and construct a risk prognostic model which could provide more support for clinical treatment decisions.

OBSCN restoration via OBSCN-AS1 long-noncoding RNA CRISPR-targeting suppresses metastasis in triple-negative breast cancer

Mounting evidence implicates the giant, cytoskeletal protein obscurin (720 to 870 kDa), encoded by the OBSCN gene, in the predisposition and development of breast cancer. Accordingly, prior work has shown that the sole loss of OBSCN from normal breast epithelial cells increases survival and chemoresistance, induces cytoskeletal alterations, enhances cell migration and invasion, and promotes metastasis in the presence of oncogenic KRAS. Consistent with these observations, analysis of Kaplan-Meier Plotter datasets reveals that low OBSCN levels correlate with significantly reduced overall and relapse-free survival in breast cancer patients. Despite the compelling evidence implicating OBSCN loss in breast tumorigenesis and progression, its regulation remains elusive, limiting any efforts to restore its expression, a major challenge given its molecular complexity and gigantic size (~170 kb). Herein, we show that OBSCN-Antisense RNA 1 (OBSCN-AS1), a novel nuclear long-noncoding RNA (lncRNA) gene originating from the minus strand of OBSCN, and OBSCN display positively correlated expression and are downregulated in breast cancer biopsies. OBSCN-AS1 regulates OBSCN expression through chromatin remodeling involving H3 lysine 4 trimethylation enrichment, associated with open chromatin conformation, and RNA polymerase II recruitment. CRISPR-activation of OBSCN-AS1 in triple-negative breast cancer cells effectively and specifically restores OBSCN expression and markedly suppresses cell migration, invasion, and dissemination from three-dimensional spheroids in vitro and metastasis in vivo. Collectively, these results reveal the previously unknown regulation of OBSCN by an antisense lncRNA and the metastasis suppressor function of the OBSCN-AS1/OBSCN gene pair, which may be used as prognostic biomarkers and/or therapeutic targets for metastatic breast cancer.

A comprehensive genomic and transcriptomic dataset of triple-negative breast cancers

Molecular subtyping of triple-negative breast cancer (TNBC) is essential for understanding the mechanisms and discovering actionable targets of this highly heterogeneous type of breast cancer. We previously performed a large single-center and multiomics study consisting of genomics, transcriptomics, and clinical information from 465 patients with primary TNBC. To facilitate reusing this unique dataset, we provided a detailed description of the dataset with special attention to data quality in this study. The multiomics data were generally of high quality, but a few sequencing data had quality issues and should be noted in subsequent data reuse. Furthermore, we reconduct data analyses with updated pipelines and the updated version of the human reference genome from hg19 to hg38. The updated profiles were in good concordance with those previously published in terms of gene quantification, variant calling, and copy number alteration. Additionally, we developed a user-friendly web-based database for convenient access and interactive exploration of the dataset. Our work will facilitate reusing the dataset, maximize the values of data and further accelerate cancer research.

Measurement(s)	RNA expression profiling • whole-exome sequencing (WES) • somatic mutations • copy number alterations (CNAs)
Technology Type(s)	RNA sequencing • DNA sequencing • OncoScan CNV assay
Factor Type(s)	Intervention or procedure
Sample Characteristic - Organism	Homo sapiens
Sample Characteristic - Location	China

Identifying common transcriptome signatures of cancer by interpreting deep learning models

Background

Cancer is a set of diseases characterized by unchecked cell proliferation and invasion of surrounding tissues. The many genes that have been genetically associated with cancer or shown to directly contribute to oncogenesis vary widely between tumor types, but common gene signatures that relate to core cancer pathways have also been identified. It is not clear, however, whether there exist additional sets of genes or transcriptomic features that are less well known in cancer biology but that are also commonly deregulated across several cancer types.

Results

Here, we agnostically identify transcriptomic features that are commonly shared between cancer types using 13,461 RNA-seq samples from 19 normal tissue types and 18 solid tumor types to train three feed-forward neural networks, based either on protein-coding gene expression, lncRNA expression, or splice junction use, to distinguish between normal and tumor samples. All three models recognize transcriptome signatures that are consistent across tumors. Analysis of attribution values extracted from our models reveals that genes that are commonly altered in cancer by expression or splicing variations are under strong evolutionary and selective constraints. Importantly, we find that genes composing our cancer transcriptome signatures are not frequently affected by mutations or genomic alterations and that their functions differ widely from the genes genetically associated with cancer.

Conclusions

Our results highlighted that deregulation of RNA-processing genes and aberrant splicing are pervasive features on which core cancer pathways might converge across a large array of solid tumor types.

Supplementary Information

The online version contains supplementary material available at (10.1186/s13059-022-02681-3).

The Genetic Evolution of Metastasis

Cancer is an evolutionary process that is characterized by the emergence of multiple genetically distinct populations or clones within the primary tumor. Intratumor heterogeneity provides a substrate for the selection of adaptive clones, such as those that lead to metastasis. Comparative molecular studies of primary tumors and metastases have identified distinct genomic features associated with the development of metastases. In this review, we discuss how these insights could inform clinical decision-making and uncover rational antimetastasis treatment strategies.