A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers

The LOXL1 antisense RNA 1 (LOXL1-AS1)/microRNA-423-5p (miR-423-5p)/ectodermal-neural cortex 1 (ENC1) axis promotes cervical cancer through the mitogen-activated protein kinase (MEK)/extracellular signal-regulated kinase (ERK) pathway

As the fourth commonest malignancy among females worldwide, cervical cancer (CC) poses a huge challenge to human health. The pivotal regulatory roles of lncRNAs in cancers have been highlighted. LOXL1 antisense RNA 1 (LOXL1-AS1) has been reported to play a key role in cervical squamous cell carcinoma and other various cancers. Thus, we investigated the roles and mechanisms of lncRNA LOXL1-AS1 in CC. The in vivo experiments demonstrated that LOXL1-AS1 downregulation inhibited tumor growth and metastasis and proliferation of CC cells. The results of RT-qPCR demonstrated that LOXL1-AS1 and ectodermal-neural cortex 1 (ENC1) expression levels were upregulated in CC cells and tissues, while microRNA-423-5p (miR-423-5p) level was downregulated. As subcellular fractionation assays, RNA pull down assays and luciferase reporter assays revealed, LOXL1-AS1 bound to miR-423-5p and miR-423-5p targeted ENC1. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays, wound healing and colony formation assays demonstrated that miR-423-5p upregulation and LOXL1-AS1 downregulation inhibited CC cell proliferation and migration, while ENC1 upregulation attenuated the inhibitory effects of miR-423-5p upregulation on the malignant phenotypes of CC cells. Western blotting was conducted to measure protein levels and the results showed that ENC1 knockdown inhibited the activation of ERK/MEK pathway. In summary, the LOXL1-AS1/miR-423-5p/ENC1 axis accelerates CC development through the MEK/ERK pathway.

Clinical Significance and Regulation of ERK5 Expression and Function in Cancer

Extracellular signal-regulated kinase 5 (ERK5) is a unique kinase among MAPKs family members, given its large structure characterized by the presence of a unique C-terminal domain. Despite increasing data demonstrating the relevance of the ERK5 pathway in the growth, survival, and differentiation of normal cells, ERK5 has recently attracted the attention of several research groups given its relevance in inflammatory disorders and cancer. Accumulating evidence reported its role in tumor initiation and progression. In this review, we explore the gene expression profile of ERK5 among cancers correlated with its clinical impact, as well as the prognostic value of ERK5 and pERK5 expression levels in tumors. We also summarize the importance of ERK5 in the maintenance of a cancer stem-like phenotype and explore the major known contributions of ERK5 in the tumor-associated microenvironment. Moreover, although several questions are still open concerning ERK5 molecular regulation, different ERK5 isoforms derived from the alternative splicing process are also described, highlighting the potential clinical relevance of targeting ERK5 pathways.

Real-World Data From a Molecular Tumor Board: Improved Outcomes in Breast and Gynecologic Cancers Patients With Precision Medicine

PURPOSE

Next-generation sequencing is increasingly used in gynecologic and breast cancers. Multidisciplinary Molecular Tumor Board (MTB) may guide matched therapy; however, outcome data are limited. We evaluate the effect of the degree of matching of tumors to treatment as well as compliance to MTB recommendations on outcomes.

METHODS

Overall, 164 patients with consecutive gynecologic and breast cancers presented at MTB were assessed for clinicopathologic data, next-generation sequencing results, MTB recommendations, therapy received, and outcomes. Matching score (MS), defined as percentage of alterations targeted by treatment over total pathogenic alterations, and compliance to MTB recommendations were analyzed in context of oncologic outcomes.

RESULTS

Altogether, 113 women were evaluable for treatment after MTB; 54% received matched therapy. Patients with MS ≥ 40% had higher overall response rate (30.8% v 7.1%; P = .001), progression-free survival (PFS; hazard ratio [HR] 0.51; 95% CI, 0.31 to 0.85; P = .002), and a trend toward improved overall survival (HR 0.64; 95% CI, 0.34 to 1.25; P = .082) in univariate analysis. The PFS advantage remained significant in multivariate analysis (HR 0.5; 95% CI, 0.3 to 0.8; P = .006). Higher MTB recommendation compliance was significantly associated with improved median PFS (9.0 months for complete; 6.0 months for partial; 4.0 months for no compliance; P = .004) and overall survival (17.1 months complete; 17.8 months partial; 10.8 months none; P = .046). Completely MTB-compliant patients had higher MS (P < .001). In multivariate analysis comparing all versus none MTB compliance, overall response (HR 9.5; 95% CI, 2.6 to 35.0; P = .001) and clinical benefit (HR 8.8; 95% CI, 2.4 to 33.2; P = .001) rates were significantly improved with higher compliance.

CONCLUSION

Compliance to MTB recommendations resulted in higher degrees of matched therapy and correlates with improved outcomes in patients with gynecologic and breast cancers.

Genomic alterations and evolution of cell clusters in metastatic invasive micropapillary carcinoma of the breast

Invasive micropapillary carcinoma (IMPC) has very high rates of lymphovascular invasion and lymph node metastasis and has been reported in several organs. However, the genomic mechanisms underlying its metastasis are unclear. Here, we perform whole-genome sequencing of tumor cell clusters from primary IMPC and paired axillary lymph node metastases. Cell clusters in multiple lymph node foci arise from a single subclone of the primary tumor. We find evidence that the monoclonal metastatic ancestor in primary IMPC shares high frequency copy-number loss of PRDM16 and IGSF9 and the copy number gain of ALDH2. Immunohistochemistry analysis further shows that low expression of IGSF9 and PRDM16 and high expression of ALDH2 are associated with lymph node metastasis and poor survival of patients with IMPC. We expect these genomic and evolutionary profiles to contribute to the accurate diagnosis of IMPC.

Discovering dominant tumor immune archetypes in a pan-cancer census

Cancers display significant heterogeneity with respect to tissue of origin, driver mutations, and other features of the surrounding tissue. It is likely that individual tumors engage common patterns of the immune system-here "archetypes"-creating prototypical non-destructive tumor immune microenvironments (TMEs) and modulating tumor-targeting. To discover the dominant immune system archetypes, the University of California, San Francisco (UCSF) Immunoprofiler Initiative (IPI) processed 364 individual tumors across 12 cancer types using standardized protocols. Computational clustering of flow cytometry and transcriptomic data obtained from cell sub-compartments uncovered dominant patterns of immune composition across cancers. These archetypes were profound insofar as they also differentiated tumors based upon unique immune and tumor gene-expression patterns. They also partitioned well-established classifications of tumor biology. The IPI resource provides a template for understanding cancer immunity as a collection of dominant patterns of immune organization and provides a rational path forward to learn how to modulate these to improve therapy.

Long non-coding RNA OIP5-AS1 (Cyrano): A context-specific regulator of normal and disease processes

Long non-coding (lnc) RNAs have been implicated in a plethora of normal biological functions, and have also emerged as key molecules in various disease processes. OIP5-AS1, also commonly known by the alias Cyrano, is a lncRNA that displays broad expression across multiple tissues, with significant enrichment in particular contexts including within the nervous system and skeletal muscle. Thus far, this multifaceted lncRNA has been found to have regulatory functions in normal cellular processes including cell proliferation and survival, as well as in the development and progression of a myriad disease states. These widespread effects on normal and disease states have been found to be mediated through context-specific intermolecular interactions with dozens of miRNAs and proteins identified to date. This review explores recent studies to highlight OIP5-AS1's contextual yet pleiotropic roles in normal homeostatic functions as well as disease oetiology and progression, which may influence its utility in the generation of future theranostics.

Generation and Culture of Organotypic Breast Carcinoma Spheroids for the Study of Drug Response in a 3D Microfluidic Device

Breast cancer (BC) is a leading cause of cancer death among women worldwide. To better understand and predict therapeutic response in BC patient developing a fast, low-cost, and reliable preclinical tumor from patient's tumor specimen is needed. Here, we describe the development of a preclinical model of BC through the generation and ex vivo culture of patient-derived organotypic tumor spheroids (PDOTS) in a 3D microfluidic device. Moreover, the real-time screening of conventional chemotherapy agents on cultured PDOTS is also described.

Clinical, Virological and Immunological Subphenotypes in a Cohort of Early Treated HIV-Infected Children

Background

Identifying subphenotypes within heterogeneous diseases may have an impact in terms of therapeutic options. In this study, we aim to assess different subphenotypes in children living with human immunodeficiency virus (HIV-1), according to the clinical, virological, and immunological characteristics.

Methods

We collected clinical and sociodemographic data, baseline viral load (VL), CD4 and CD8 count and percentage, age at initiation of ART, HIV DNA reservoir size in peripheral blood mononuclear cells (PBMCs), cell-associated RNA (CA-RNA), ultrasensitive VL, CD4 subsets (T effector CD25+, activated memory cells, Treg cells), humoral-specific HIV response (T-bet B cells), innate response (CD56dim natural killer (NK) cells, NKp46+, perforin), exhaustion markers (PD-1, PD-L1, DNAM), CD8 senescence, and biomarkers for T-lymphocyte thymic output (TREC) and endothelial activation (VCAM). The most informative variables were selected using an unsupervised lasso-type penalty selection for sparse clustering. Hierarchical clustering was performed using Pearson correlation as the distance metric and WARD.D2 as the clustering method. Internal validation was applied to select the best number of clusters. To compare the characteristics among clusters, boxplot and Kruskal Wallis test were assessed.

Results

Three subphenotypes were discovered (cluster1: n=18, 45%; cluster2: n=11, 27.5%; cluster3: n=11, 27.5%). Patients in cluster1 were treated earlier, had higher baseline %CD4, low HIV reservoir size, low western blot score, higher TREC values, and lower VCAM values than the patients in the other clusters. In contrast, cluster3 was the less favorable. Patients were treated later and presented poorer outcomes with lower %CD4, and higher reservoir size, along with a higher percentage of CD8 immunosenescent cells, lower TREC, higher VCAM cytokine, and a higher %CD4 PD-1. Cluster2 was intermediate. Patients were like those of cluster1, but had lower levels of t-bet expression and higher HIV DNA reservoir size.

Conclusions

Three HIV pediatric subphenotypes with different virological and immunological features were identified. The most favorable cluster was characterized by a higher rate of immune reconstitution and a slower disease progression, and the less favorable with more senescence and high reservoir size. In the near future therapeutic interventions for a path of a cure might be guided or supported by the different subphenotypes.

Androgen receptor in breast cancer: The "5W" questions

Androgen receptor (AR) is expressed in 60-70% of breast cancers (BCs) and the availability of anti-AR compounds, currently used for treating prostate cancer, paves the way to tackle specifically AR-positive BC patients. The prognostic and predictive role of AR in BC is a matter of debate, since the results from clinical trials are not striking, probably due to both technical and biological reasons. In this review, we aimed to highlight WHAT is AR, describing its structure and functions, WHAT to test and HOW to detect AR, WHERE AR should be tested (on primary tumor or metastasis) and WHY studying this fascinating hormone receptor, exploring and debating on its prognostic and predictive role. We considered AR and its ratio with other hormone receptors, analyzing also studies including patients with ductal carcinoma in situ and with early and advanced BC, as well. We also emphasized the effects that both other hormone receptors and the newly emerging androgen-inducible non coding RNAs may have on AR function in BC pathology and the putative implementation in the clinical setting. Moreover, we pointed out the latest results by clinical trials and we speculated about the use of anti-AR therapies in BC clinical practice.

Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies

A typical genome-wide association study (GWAS) analyzes millions of single-nucleotide polymorphisms (SNPs), several of which are in a region of the same gene. To conduct gene set analysis (GSA), information from SNPs needs to be unified at the gene level. A widely used practice is to use only the most relevant SNP per gene; however, there are other methods of integration that could be applied here. Also, the problem of nonrandom association of alleles at two or more loci is often neglected. Here, we tested the impact of incorporation of different integrations and linkage disequilibrium (LD) correction on the performance of several GSA methods. Matched normal and breast cancer samples from The Cancer Genome Atlas database were used to evaluate the performance of six GSA algorithms: Coincident Extreme Ranks in Numerical Observations (CERNO), Gene Set Enrichment Analysis (GSEA), GSEA-SNP, improved GSEA for GWAS (i-GSEA4GWAS), Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA), and Over-Representation Analysis (ORA). Association of SNPs to phenotype was calculated using modified McNemar's test. Results for SNPs mapped to the same gene were integrated using Fisher and Stouffer methods and compared with the minimum p-value method. Four common measures were used to quantify the performance of all combinations of methods. Results of GSA analysis on GWAS were compared to the one performed on gene expression data. Comparing all evaluation metrics across different GSA algorithms, integrations, and LD correction, we highlighted CERNO, and MAGENTA with Stouffer as the most efficient. Applying LD correction increased prioritization and specificity of enrichment outcomes for all tested algorithms. When Fisher or Stouffer were used with LD, sensitivity and reproducibility were also better. Using any integration method was beneficial in comparison with a minimum p-value method in specific combinations. The correlation between GSA results from genomic and transcriptomic level was the highest when Stouffer integration was combined with LD correction. We thoroughly evaluated different approaches to GSA in GWAS in terms of performance to guide others to select the most effective combinations. We showed that LD correction and Stouffer integration could increase the performance of enrichment analysis and encourage the usage of these techniques.

Co-existing TP53 and ARID1A mutations promote aggressive endometrial tumorigenesis

TP53 and ARID1A are frequently mutated across cancer but rarely in the same primary tumor. Endometrial cancer has the highest TP53-ARID1A mutual exclusivity rate. However, the functional relationship between TP53 and ARID1A mutations in the endometrium has not been elucidated. We used genetically engineered mice and in vivo genomic approaches to discern both unique and overlapping roles of TP53 and ARID1A in the endometrium. TP53 loss with oncogenic PIK3CA^H1047R in the endometrial epithelium results in features of endometrial hyperplasia, adenocarcinoma, and intraepithelial carcinoma. Mutant endometrial epithelial cells were transcriptome profiled and compared to control cells and ARID1A/PIK3CA mutant endometrium. In the context of either TP53 or ARID1A loss, PIK3CA mutant endometrium exhibited inflammatory pathway activation, but other gene expression programs differed based on TP53 or ARID1A status, such as epithelial-to-mesenchymal transition. Gene expression patterns observed in the genetic mouse models are reflective of human tumors with each respective genetic alteration. Consistent with TP53-ARID1A mutual exclusivity, the p53 pathway is activated following ARID1A loss in the endometrial epithelium, where ARID1A normally directly represses p53 pathway genes in vivo, including the stress-inducible transcription factor, ATF3. However, co-existing TP53-ARID1A mutations led to invasive adenocarcinoma associated with mutant ARID1A-driven ATF3 induction, reduced apoptosis, TP63+ squamous differentiation and invasion. These data suggest TP53 and ARID1A mutations drive shared and distinct tumorigenic programs in the endometrium and promote invasive endometrial cancer when existing simultaneously. Hence, TP53 and ARID1A mutations may co-occur in a subset of aggressive or metastatic endometrial cancers, with ARID1A loss promoting squamous differentiation and the acquisition of invasive properties.

Endometrial cancer is the most commonly diagnosed gynecologic malignancy in the United States, with annual incidence continuing to rise. Although the majority of endometrial cancer patients have an excellent overall prognosis if the disease is confined to the endometrium, myometrial invasion and metastasis to other sites correlate with poor survival. Here, we used genetically engineered mice, in vivo genomics, and public cancer patient data to understand the relationship between TP53 and ARID1A, two of the most commonly mutated genes in endometrial cancer, in the context of mutant PIK3CA. Mutations in TP53 and ARID1A change different aspects of endometrial cell health but also share some similarities. ARID1A mutations specifically promote cancer cells to invade nearby tissue, a hallmark of metastasis, associated with squamous differentiation. Mice with co-existing TP53 and ARID1A mutations developed more invasive disease. Our studies suggest that co-existing TP53 and ARID1A tumor mutations may promote invasion and metastasis.

Integrative multi-omics identifies high risk multiple myeloma subgroup associated with significant DNA loss and dysregulated DNA repair and cell cycle pathways

Background

Despite significant therapeutic advances in improving lives of multiple myeloma (MM) patients, it remains mostly incurable, with patients ultimately becoming refractory to therapies. MM is a genetically heterogeneous disease and therapeutic resistance is driven by a complex interplay of disease pathobiology and mechanisms of drug resistance. We applied a multi-omics strategy using tumor-derived gene expression, single nucleotide variant, copy number variant, and structural variant profiles to investigate molecular subgroups in 514 newly diagnosed MM (NDMM) samples and identified 12 molecularly defined MM subgroups (MDMS1-12) with distinct genomic and transcriptomic features.

Results

Our integrative approach let us identify NDMM subgroups with transversal profiles to previously described ones, based on single data types, which shows the impact of this approach for disease stratification. One key novel subgroup is our MDMS8, associated with poor clinical outcome [median overall survival, 38 months (global log-rank p-value < 1 × 10⁻⁶)], which uniquely presents a broad genomic loss (> 9% of entire genome, t-test p value < 1e−5) driving dysregulation of various transcriptional programs affecting DNA repair and cell cycle/mitotic processes. This subgroup was validated on multiple independent datasets, and a master regulator analyses identified transcription factors controlling MDMS8 transcriptomic profile, including CKS1B and PRKDC among others, which are regulators of the DNA repair and cell cycle pathways.

Conclusion

Using multi-omics unsupervised clustering we were able to discover a new high-risk multiple myeloma patient segment. This high-risk group presents diverse previously known genetic markers, but also a new characteristic defined by accumulation of genomic loss which seems to drive transcriptional dysregulation of cell cycle, DNA repair and DNA damage. Finally, our work identified various master regulators, including E2F2 and CKS1B as the genes controlling these key biological pathways.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-01140-5.

Pan-cancer investigation of CENPK gene: clinical significance and oncogenic immunology

Many studies have confirmed that the CENPK gene regulates the progression of cancers, but its specific molecular mechanism remains unidentified, as does its significance in the analysis of human cancers. We specify a comprehensive genomic architecture of the CENPK gene associated with the tumor immune microenvironment and its clinical relevance across a broad spectrum of solid tumors. Statistics from The Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia (CCLE) of over 30 solid tumors were examined. CENPK was expressed differentially in several cancers and is significantly associated in survival outcomes, with higher CENPK signifying a worse prognosis for ACC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, MESO, and SARC. We further examined its clinical relevance with tumor immunogenic features. The expression level of CENPK was not only strongly linked to the tumor infiltration, such as tumor-infiltrating immune cells and immune scores but also linked to microsatellite instability and tumor mutation burden in diverse cancers (P<0.05). I mmune markers such as TNFRSF14 and VSIR were highly expressed on over 20 kinds of human cancer and mismatch repair genes like MLH1, MSH2, MSH6, and PMS2 were positively related with CENPK expression. Moreover, the methyltransferases and functional pathways also seem to have a relationship with the CENPK. CENPK is expected to be a guiding marker gene for clinical prognosis and tumor personalized immunotherapy.

Identification of Immune-Related lncRNA Prognostic Signature and Molecular Subtypes for Glioblastoma

Background

Glioblastoma multiforme (GBM) is extensively genetically and transcriptionally heterogeneous, which poses challenges for classification and management. Long noncoding RNAs (lncRNAs) play a critical role in the development and progression of GBM, especially in tumor-associated immune processes. Therefore, it is necessary to develop an immune-related lncRNAs (irlncRNAs) signature.

Methods

Univariate and multivariate Cox regression analyses were utilized to construct a prognostic model. GBM-specific CeRNA and PPI network was constructed to predict lncRNAs targets and evaluate the interactions of immune mRNAs translated proteins. GO and KEGG pathway analyses were used to show the biological functions and pathways of CeRNA network-related immunity genes. Consensus Cluster Plus analysis was used for GBM gene clustering. Then, we evaluated GBM subtype-specific prognostic values, clinical characteristics, genes and pathways, immune infiltration access single cell RNA-seq data, and chemotherapeutics efficacy. The hub genes were finally validated.

Results

A total of 17 prognostically related irlncRNAs were screened to build a prognostic model signature based on six key irlncRNAs. Based on GBM-specific CeRNAs and enrichment analysis, PLAU was predicted as a target of lncRNA-H19 and mainly enriched in the malignant related pathways. GBM subtype-A displayed the most favorable prognosis, high proportion of genes (IDH1, ATRX, and EGFR) mutation, chemoradiotherapy, and low risk and was characterized by low expression of four high-risk lncRNAs (H19, HOTAIRM1, AGAP2-AS1, and AC002456.1) and one mRNA KRT8. GSs with poor survival were mainly infiltrated by mesenchymal stem cells (MSCs) and astrocyte, and were more sensitive to gefitinib and roscovitine. Among GSs, three hub genes KRT8, NGFR, and TCEA3, were screened and validated to potentially play feasible oncogenic roles in GBM.

Conclusion

Construction of lncRNAs risk model and identification of GBM subtypes based on 17 irlncRNAs, which suggesting that irlncRNAs had the promising potential for clinical immunotherapy of GBM.

TMB and TCR Are Correlated Indicators Predictive of the Efficacy of Neoadjuvant Chemotherapy in Breast Cancer

Immune characteristics were reported correlated to benefit neoadjuvant chemotherapy (NAC) in breast cancer, yet integration of comprehensive genomic alterations and T-cell receptors (TCR) to predict efficacy of NAC needs further investigation. This study simultaneously analyzed TMB (Tumor Mutation Burden), TCRs, and TILs (tumor infiltrating lymphocyte) in breast cancers receiving NAC was conducted in a prospective cohort (n = 22). The next-generation sequencing technology-based analysis of genomic alterations and TCR repertoire in paired breast cancer samples before and after NAC was conducted in a prospective cohort (n = 22). Fluorescent multiplex immunohistochemistry was used to stain CD4, CD8, PD1, TIM3, and cytokeratins simultaneously in those paired samples. TMB in pretreatment tumor tissues and TCR diversity index are higher in non-pCR patients than in pCR patients (10.6 vs. 2.3; p = 0.043) (2.066 vs. 0.467; p = 0.010). TMB and TCR diversity index had linear correlation (y = 5.587x − 0.881; r = 0.522, p = 0.012). Moreover, infiltrating T cells are significantly at higher presence in pCR versus non-pCR patients. Dynamically, the TMB reduced significantly after therapy in non-pCR patients (p = 0.010) but without TCR index change. The CDR3 peptide AWRSAGNYNEQF is the most highly expressed in pre-NAC samples of pCR patients and in post-NAC samples of non-pCR patients. In addition to pCR, high clonality of TCR and high level of CD8+ expression are associated with disease-free survival (DFS). TCR index and TMB have significant interaction and may guide neo-adjuvant treatment in operable breast cancers. Response to NAC in tumors with high TCR clonality may be attributable to high infiltration and expansion of tumor-specific CD8 positive effector cells.

A Prognostic Signature of Glycolysis-Related Long Noncoding RNAs for Molecular Subtypes in the Tumor Immune Microenvironment of Lung Adenocarcinoma

Purpose

Long noncoding RNAs (lncRNAs) and glycolysis regulate multiple types of cancer. However, the prognostic roles and biological functions of glycolysis-related lncRNAs in lung adenocarcinoma (LUAD) remain unclear. In this study, we investigated the role of glycolysis-related lncRNAs in LUAD.

Patients and Methods

We retrieved glycolysis-related genes from the Molecular Signatures Database and screened for prognostic glycolysis-related lncRNAs from The Cancer Genome Atlas.

Results

We identified three LUAD subtypes (clusters 1–3) by univariate Cox regression analysis and consensus clustering. Patients in cluster 1 had the best overall survival rates. Immune, stromal, and cytolytic-activity scores were the highest in cluster 1. The expression of immune checkpoint molecules (programmed cell death protein 1 and cytotoxic T-lymphocyte-associated protein 4) and other immune-related indicators was the highest in cluster 1, whereas that of epithelial cell biomarkers (Cadherin 1, Cadherin 2, and MET) was the lowest. Therefore, patients in cluster 1 may benefit from immunotherapy. Lasso–Cox regression and multivariate Cox regression analyses were used to select nine lncRNAs to build a robust prognostic model of LUAD. The area under the curve classifier values and a nomogram performed well in predicting survival times for patients with LUAD. The expression levels of nine lncRNAs were validated by quantitative reverse transcriptase-polymerase chain reaction analysis, and most of these lncRNAs were significantly related to immune-related mRNAs. Gene set enrichment analysis revealed that the high-risk group was enriched for cell cycle-related pathways and the low-risk group was enriched for pathways associated with immunity or immune-related diseases.

Conclusion

The LUAD subtypes and prognostic model developed here may help in clinical risk stratification, prognosis management, and treatment decisions for patients with LUAD.

Discovery of clinically relevant fusions in pediatric cancer

Background

Pediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions.

Results

Our Ensemble Fusion (EnFusion) approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and Amazon Web Services (AWS) serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a “known fusion list” prevents failure to report known pathogenic events. We have employed the EnFusion pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient’s medical record, both known and novel fusions provided medically meaningful information.

Conclusions

The EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08094-z.

Heat Shock Factor 1 (HSF1) cooperates with estrogen receptor α (ERα) in the regulation of estrogen action in breast cancer cells

Heat shock factor 1 (HSF1), a key regulator of transcriptional responses to proteotoxic stress, was linked to estrogen (E2) signaling through estrogen receptor α (ERα). We found that an HSF1 deficiency may decrease ERα level, attenuate the mitogenic action of E2, counteract E2-stimulated cell scattering, and reduce adhesion to collagens and cell motility in ER-positive breast cancer cells. The stimulatory effect of E2 on the transcriptome is largely weaker in HSF1-deficient cells, in part due to the higher basal expression of E2-dependent genes, which correlates with the enhanced binding of unliganded ERα to chromatin in such cells. HSF1 and ERα can cooperate directly in E2-stimulated regulation of transcription, and HSF1 potentiates the action of ERα through a mechanism involving chromatin reorganization. Furthermore, HSF1 deficiency may increase the sensitivity to hormonal therapy (4-hydroxytamoxifen) or CDK4/6 inhibitors (palbociclib). Analyses of data from The Cancer Genome Atlas database indicate that HSF1 increases the transcriptome disparity in ER-positive breast cancer and can enhance the genomic action of ERα. Moreover, only in ER-positive cancers an elevated HSF1 level is associated with metastatic disease.

About 70% of breast cancers rely on supplies of a hormone called estrogen – which is the main hormone responsible for female physical characteristics – to grow. Breast cancer cells that are sensitive to estrogen possess proteins known as estrogen receptors and are classified as estrogen-receptor positive. When estrogen interacts with its receptor in a cancer cell, it stimulates the cell to grow and migrate to other parts of the body. Therefore, therapies that decrease the amount of estrogen the body produces, or inhibit the receptor itself, are widely used to treat patients with estrogen receptor-positive breast cancers.

When estrogen interacts with an estrogen receptor known as ERα it can also activate a protein called HSF1, which helps cells to survive under stress. In turn, HSF1 regulates several other proteins that are necessary for ERα and other estrogen receptors to work properly. Previous studies have suggested that high levels of HSF1 may worsen the outcomes for patients with estrogen receptor-positive breast cancers, but it remains unclear how HSF1 acts in breast cancer cells.

Vydra, Janus, Kuś et al. used genetics and bioinformatics approaches to study HSF1 in human breast cancer cells. The experiments revealed that breast cancer cells with lower levels of HSF1 also had lower levels of ERα and responded less well to estrogen than cells with higher levels of HSF1. Further experiments suggested that in the absence of estrogen, HSF1 helps to keep ERα inactive. However, when estrogen is present, HSF1 cooperates with ERα and enhances its activity to help cells grow and migrate. Vydra, Janus, Kuś et al. also found that cells with higher levels of HSF1 were less sensitive to two drug therapies that are commonly used to treat estrogen receptor-positive breast cancers.

These findings reveal that the effect HSF1 has on ERα activity depends on the presence of estrogen. Therefore, cancer therapies that decrease the amount of estrogen a patient produces may have a different effect on estrogen receptor-positive tumors with high HSF1 levels than tumors with low HSF1 levels.

Downregulation of the Coiled-Coil Domain Containing 80 and Its Perspective Mechanisms in Ovarian Carcinoma: A Comprehensive Study

INTRODUCTION

We aimed to explore the downregulation of the coiled-coil domain containing 80 (CCDC80) and its underlying molecular mechanisms in ovarian carcinoma (OVCA). Materials/Methods. Immunohistochemical staining was performed to confirm the expression status of CCDC80 protein. Combining the data from in-house tissue microarrays and high-throughput datasets, we identified the expression level of CCDC80 in OVCA. We utilized cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm and single-sample gene set enrichment analysis (ssGSEA) to explore the relationship between CCDC80 and the tumor microenvironment (TME) landscape in OVCA. Pathway enrichment, function annotation, and transcription factor (TFs) exploration were conducted to study the latent molecular mechanisms. Moreover, the cell line data in the Genomics of Drug Sensitivity in Cancer (GDSC) database was used to discover the relationship between CCDC80 and drug sensitivity.

RESULTS

An integrated standard mean difference (SMD) of -0.919 (95% CI: -1.515-0.324, P = 0.002) identified the downregulation of CCDC80 in OVCA based on 1048 samples, and the sROC (AUC = 0.76) showed a moderate discriminatory ability of CCDC80 in OVCA. The fraction of infiltrating naive B cells showed significant differences between the high- and low-CCDC80 expression groups. Also, CCDC80-related genes are enriched in the Ras signaling pathway and metabolic of lipid. Nuclear receptor subfamily three group C member 1 (NR3C1) may be an upstream TF of CCDC80, and CCDC80 may be related to the sensitivity of mitocycin C and nilotinib.

CONCLUSION

CCDC80 was downregulated in OVCA and may play a role as a tumor suppressor in OVCA.

Multi-omics mapping of human papillomavirus integration sites illuminates novel cervical cancer target genes

BACKGROUND

Integration of human papillomavirus (HPV) into the host genome is a dominant feature of invasive cervical cancer (ICC), yet the tumorigenicity of cis genomic changes at integration sites remains largely understudied.

METHODS

Combining multi-omics data from The Cancer Genome Atlas with patient-matched long-read sequencing of HPV integration sites, we developed a strategy for using HPV integration events to identify and prioritise novel candidate ICC target genes (integration-detected genes (IDGs)). Four IDGs were then chosen for in vitro functional studies employing small interfering RNA-mediated knockdown in cell migration, proliferation and colony formation assays.

RESULTS

PacBio data revealed 267 unique human-HPV breakpoints comprising 87 total integration events in eight tumours. Candidate IDGs were filtered based on the following criteria: (1) proximity to integration site, (2) clonal representation of integration event, (3) tumour-specific expression (Z-score) and (4) association with ICC survival. Four candidates prioritised based on their unknown function in ICC (BNC1, RSBN1, USP36 and TAOK3) exhibited oncogenic properties in cervical cancer cell lines. Further, annotation of integration events provided clues regarding potential mechanisms underlying altered IDG expression in both integrated and non-integrated ICC tumours.

CONCLUSIONS

HPV integration events can guide the identification of novel IDGs for further study in cervical carcinogenesis and as putative therapeutic targets.

Neoadjuvant chemotherapy induces genomic and transcriptomic changes in ovarian cancer

The growing use of neoadjuvant chemotherapy to treat advanced-stage high-grade serous ovarian cancer (HGSOC) creates an opportunity to better understand chemotherapy-induced mutational and gene expression changes. Here we performed a cohort study including 34 patients with advanced stage IIIC or IV HGSOC to assess changes in the tumor genome and transcriptome in women receiving neoadjuvant chemotherapy. RNA-sequencing and panel DNA-sequencing of 596 cancer-related genes was performed on paired FFPE specimens collected before and after chemotherapy, and differentially expressed genes (DEGs) and CNVs in pre- and post-chemotherapy samples were identified. Following tissue and sequencing quality control, the final patient cohort consisted of 32 paired DNA and 20 paired RNA samples. Genomic analysis of paired samples did not reveal any recurrent chemotherapy-induced mutations. Gene expression analyses found that most DEGs were upregulated by chemotherapy, primarily in the chemotherapy resistant specimens. AP-1 transcription factor family genes (FOS, FOSB, FRA-1) were particularly upregulated in chemotherapy resistant samples. CNV analysis identified recurrent 11q23.1 amplification, which encompasses SIK2. In vitro, combined treatment with AP-1 or SIK2 inhibitors with carboplatin or paclitaxel demonstrated synergistic effects. These data suggest that AP-1 activity and SIK2 copy number amplification are induced by chemotherapy and may represent mechanisms by which chemotherapy resistance evolves in HGSOC. AP-1 and SIK2 are druggable targets with available small molecule inhibitors and represent potential targets to circumvent chemotherapy resistance.

Identifying Molecular Biomarkers for Diseases With Machine Learning Based on Integrative Omics

Molecular biomarkers are certain molecules or set of molecules that can be of help for diagnosis or prognosis of diseases or disorders. In the past decades, thanks to the advances in high-throughput technologies, a huge amount of molecular 'omics' data, e.g., transcriptomics and proteomics, have been accumulated. The availability of these omics data makes it possible to screen biomarkers for diseases or disorders. Accordingly, a number of computational approaches have been developed to identify biomarkers by exploring the omics data. In this review, we present a comprehensive survey on the recent progress of identification of molecular biomarkers with machine learning approaches. Specifically, we categorize the machine learning approaches into supervised, un-supervised and recommendation approaches, where the biomarkers including single genes, gene sets and small gene networks. In addition, we further discuss potential problems underlying bio-medical data that may pose challenges for machine learning, and provide possible directions for future biomarker identification.

A multi-omic single-cell landscape of human gynecologic malignancies

Deconvolution of regulatory mechanisms that drive transcriptional programs in cancer cells is key to understanding tumor biology. Herein, we present matched transcriptome (scRNA-seq) and chromatin accessibility (scATAC-seq) profiles at single-cell resolution from human ovarian and endometrial tumors processed immediately following surgical resection. This dataset reveals the complex cellular heterogeneity of these tumors and enabled us to quantitatively link variation in chromatin accessibility to gene expression. We show that malignant cells acquire previously unannotated regulatory elements to drive hallmark cancer pathways. Moreover, malignant cells from within the same patients show substantial variation in chromatin accessibility linked to transcriptional output, highlighting the importance of intratumoral heterogeneity. Finally, we infer the malignant cell type-specific activity of transcription factors. By defining the regulatory logic of cancer cells, this work reveals an important reliance on oncogenic regulatory elements and highlights the ability of matched scRNA-seq/scATAC-seq to uncover clinically relevant mechanisms of tumorigenesis in gynecologic cancers.

SMARCA4 Depletion Induces Cisplatin Resistance by Activating YAP1-Mediated Epithelial-to-Mesenchymal Transition in Triple-Negative Breast Cancer

Simple Summary

SMARCA4 mutations were over-representative in cisplatin resistance and metastatic triple-negative breast cancer (TNBC). Additionally, SMARCA4 inactivation induced the mesenchymal-like subtype TNBC. The epithelial-to-mesenchymal transition and Hippo-YAP/TAZ pathways were activated in SMARCA4 inactivation samples of both SMARCA4 knockout cell lines and TNBC patients. In SMARCA4 knockout cells, the YAP1 inhibitor verteporfin suppressed YAP1 target genes. This study depicts the clinical importance of SMARCA4 depletion in TNBC and suggests YAP/TAZ as a novel target for cisplatin-resistant patients.

Abstract

The role of SMARCA4, an ATPase subunit of the SWI/SNF chromatin remodeling complex, in genomic organization is well studied in various cancer types. However, its oncogenic role and therapeutic implications are relatively unknown in triple-negative breast cancer (TNBC). We investigated the clinical implication and downstream regulation induced by SMARCA4 inactivation using large-scale genome and transcriptome profiles. Additionally, SMARCA4 was knocked out in MDA-MB-468 and MDA-MB-231 using CRISPR/Cas9 to identify gene regulation and a targetable pathway. First, we observed an increase in SMARCA4 mutations in cisplatin resistance and metastasis in TNBC patients. Its inactivation was associated with the mesenchymal-like (MSL) subtype. Gene expression analysis showed that the epithelial-to-mesenchymal transition (EMT) pathway was activated in SMARCA4-deficient patients. Next, the Hippo pathway was activated in the SMARCA4 inactivation group, as evidenced by the higher CTNNB1, TGF-β, and YAP1 oncogene signature scores. In SMARCA4 knockout cells, EMT was upregulated, and the cell line transcriptome changed from the SL to the MSL subtype. SMARCA4 knockout cells showed cisplatin resistance and Hippo-YAP/TAZ target gene activation. The YAP1 inhibitor verteporfin suppressed the expression of YAP1 target genes, and decreased cell viability and invasiveness on SMARCA4 knockout cells. SMARCA4 inactivation in TNBC endowed the resistance to cisplatin via EMT activation. The YAP1 inhibitor could become a novel strategy for patients with SMARCA4-inactivated TNBC.

Allele-specific genomic data elucidate the role of somatic gain and copy-number neutral loss of heterozygosity in cancer

Pan-cancer studies sketched the genomic landscape of the tumor types spectrum. We delineated the purity- and ploidy-adjusted allele-specific profiles of 4,950 patients across 27 tumor types from the Cancer Genome Atlas (TCGA). Leveraging allele-specific data, we reclassified as loss of heterozygosity (LOH) 9% and 7% of apparent copy-number wild-type and gain calls, respectively, and overall observed more than 18 million allelic imbalance somatic events at the gene level. Reclassification of copy-number events revealed associations between driver mutations and LOH, pointing out the timings between the occurrence of point mutations and copy-number events. Integrating allele-specific genomics and matched transcriptomics, we observed that allele-specific gene status is relevant in the regulation of TP53 and its targets. Further, we disclosed the role of copy-neutral LOH in the impairment of tumor suppressor genes and in disease progression. Our results highlight the role of LOH in cancer and contribute to the understanding of tumor progression.

QNBC Is Associated with High Genomic Instability Characterized by Copy Number Alterations and miRNA Deregulation

Triple-negative breast cancer (TNBC) can be further classified into androgen receptor (AR)-positive TNBC and AR-negative TNBC or quadruple-negative breast cancer (QNBC). Here, we investigated genomic instability in 53 clinical cases by array-CGH and miRNA expression profiling. Immunohistochemical analysis revealed that 64% of TNBC samples lacked AR expression. This group of tumors exhibited a higher level of copy number alterations (CNAs) and a higher frequency of cases affected by CNAs than TNBCs. CNAs in genes of the chromosome instability 25 (CIN25) and centrosome amplification (CA) signatures were more frequent in the QNBCs and were similar between the groups, respectively. However, expression levels of CIN25 and CA20 genes were higher in QNBCs. miRNA profiling revealed 184 differentially expressed miRNAs between the groups. Fifteen of these miRNAs were mapped at cytobands with CNAs, of which eight (miR-1204, miR-1265, miR-1267, miR-23c, miR-548ai, miR-567, miR-613, and miR-943), and presented concordance of expression and copy number levels. Pathway enrichment analysis of these miRNAs/mRNAs pairings showed association with genomic instability, cell cycle, and DNA damage response. Furthermore, the combined expression of these eight miRNAs robustly discriminated TNBCs from QNBCs (AUC = 0.946). Altogether, our results suggest a significant loss of AR in TNBC and a profound impact in genomic instability characterized by CNAs and deregulation of miRNA expression.

Single-cell transcriptomics reveal the heterogeneity and dynamic of cancer stem-like cells during breast tumor progression

Breast cancer stem-like cells (BCSCs) play vital roles in tumorigenesis and progression. However, the origin and dynamic changes of BCSCs are still to be elucidated. Using the breast cancer mouse model MMTV-PyMT, we constructed a single-cell atlas of 31,778 cells from four distinct stages of tumor progression (hyperplasia, adenoma/MIN, early carcinoma and late carcinoma), during which malignant transition occurs. We identified that the precise cell type of ER^low epithelial cell lineage gave rise to the tumors, and the differentiation of ER^high epithelial cell lineage was blocked. Furthermore, we discovered a specific signature with a continuum of gene expression profiles along the tumor progression and significantly correlated with clinical outcomes, and we also found a stem-like cell cluster existed among ER^low epithelial cells. Further clustering on this stem-like cluster showed several sub-clusters indicating heterogeneity of stem-like epithelial cells. Moreover, we distinguished normal and cancer stem-like cells in this stem-like epithelial cell cluster and profiled the molecular portraits from normal stem-like cell to cancer stem-like cells during the malignant transition. Finally, we found the diverse immune cell infiltration displayed immunosuppressive characteristics along tumor progression. We also found the specific expression pattern of cytokines and their corresponding cytokine receptors in BCSCs and immune cells, suggesting the possible cross-talk between BCSCs and the immune cells. These data provide a useful resource for illuminating BCSC heterogeneity and the immune cell remodeling during breast tumor progression, and shed new light on transcriptomic dynamics during the progression at the single-cell level.

Interleukin-30 feeds breast cancer stem cells via CXCL10 and IL23 autocrine loops and shapes immune contexture and host outcome

Background

Breast cancer (BC) progression to metastatic disease is the leading cause of death in women worldwide. Metastasis is driven by cancer stem cells (CSCs) and signals from their microenvironment. Interleukin (IL) 30 promotes BC progression, and its expression correlates with disease recurrence and mortality. Whether it acts by regulating BCSCs is unknown and could have significant therapeutic implications.

Methods

Human (h) and murine (m) BCSCs were tested for their production of and response to IL30 by using flow cytometry, confocal microscopy, proliferation and sphere-formation assays, and PCR array. Immunocompetent mice were used to investigate the role of BCSC-derived IL30 on tumor development and host outcome. TCGA PanCancer and Oncomine databases provided gene expression data from 1084 and 75 hBC samples, respectively, and immunostaining unveiled the BCSC microenvironment.

Results

hBCSCs constitutively expressed IL30 as a membrane-anchored glycoprotein. Blocking IL30 hindered their proliferation and self-renewal efficiency, which were boosted by IL30 overexpression. IL30 regulation of immunity gene expression in human and murine BCSCs shared a significant induction of IL23 and CXCL10. Both immunoregulatory mediators stimulated BCSC proliferation and self-renewal, while their selective blockade dramatically hindered IL30-dependent BCSC proliferation and mammosphere formation. Orthotopic implantation of IL30-overexpressing mBCSCs, in syngeneic mice, gave rise to poorly differentiated and highly proliferating MYC⁺KLF4⁺LAG3⁺ tumors, which expressed CXCL10 and IL23, and were infiltrated by myeloid-derived cells, Foxp3⁺ T regulatory cells and NKp46⁺RORγt⁺ type 3 innate lymphoid cells, resulting in increased metastasis and reduced survival. In tumor tissues from patients with BC, expression of IL30 overlapped with that of CXCL10 and IL23, and ranked beyond the 95th percentile in a Triple-Negative enriched BC collection from the Oncomine Platform. CIBERSORTx highlighted a defective dendritic cell, CD4⁺ T and γδ T lymphocyte content and a prominent LAG3 expression in IL30^highversus IL30^low human BC samples from the TCGA PanCancer collection.

Conclusions

Constitutive expression of membrane-bound IL30 regulates BCSC viability by juxtacrine signals and via second-level mediators, mainly CXCL10 and IL23. Their autocrine loops mediate much of the CSC growth factor activity of IL30, while their paracrine effect contributes to IL30 shaping of immune contexture. IL30-related immune subversion, which also emerged from computational analyses, strongly suggests that targeting IL30 can restrain the BCSC compartment and counteract BC progression.

Different members of the APOBEC3 family of DNA mutators have opposing associations with the landscape of breast cancer

APOBEC enzymes are strong mutagenic factors. In breast cancer, expression of APOBEC3B is increased and associated with mutation load and poor outcome. Other APOBEC3s can also mutate DNA but their clinical significance in breast cancer and its underpinnings have not been comprehensively studied. In our examination of 1,091 breast carcinoma cases, high expression of APOBEC3A or APOBEC3B genes was associated with greater tumor burden of mutations and other genomic aberrations. Expression of none of the five APOBEC3C-H genes had any correlation with these features, including T[C-T/G]W mutations, but their high expression levels indicated a robust anti-cancer immune response within tumors, with elevated CD8⁺ T cell abundance, T cell receptor diversity, and immune cytolytic activity. Concordantly, survival analyses of this and two other cohorts with > 3,000 patients each showed favorable prognostic benefit of high APOBEC3C-H expression for both cancer progression and mortality. A detrimental prognostic value was observed for APOBEC3A and APOBEC3B. Single-cell data revealed cancer epithelial and stromal immune cells as major sources of APOBEC3B and APOBEC3C-H expression in tumors, respectively. These observations on opposing associations with breast cancer of different APOBEC3s highlight the contrasting roles of these enzymes, promoting cancer through mutagenesis while antagonizing it through immune response.

Comprehensive study of a novel immune-related lncRNA for prognosis and drug treatment of cervical squamous cell carcinoma

A comprehensive study focusing on immune-related long non-coding RNAs (lncRNAs) in cervical cancer (CC) was performed. Through the integration of TCGA data, a total of 266 immune-related lncRNAs were obtained. We defined all samples as an entire set, and randomly divided them into train set and test set at a ratio of 1:1. Univariate, LASSO and multivariate Cox regression analyses were carried out based on train set for key lncRNAs (UBL7-AS1, AC083809.1, LIPE-AS1, PCED1B-AS1, ELFN1-AS1 and NCK1-DT) to construct a prognostic model, while the others were used for validation. The overall survival (OS) suggested that we may have longer survival expectations for patients classified into the low-risk group. The P values of risk score in univariate analysis and multivariate analysis were all less than 0.05, indicating the ability of risk score to independently assess the prognosis of patients. For clinical application, a nomogram with a high degree of agreement between the predicted curve and the actual curve was constructed. Subsequently, immune status and chemotherapy response were investigated in two prognostic subtypes. The associations between risk score and immune cell were estimated, in which CD8+ T cells showed the highest positive correlation and activated mast cell showed the highest negative correlation. In addition, checkpoint proteins (CTLA4, LAG3, PD-1, and TIGIT) showing negative correlation with risk score were found to be upregulated in low-risk group. A total of 3 chemotherapy drugs including paclitaxel, vinorelbine and methotrexate were considered effective in patients of high-risk group. Using 6 key immune-related lncRNAs, we identified two prognostic subtypes and provided new insights for CC immunotherapy.

An Adaptive Transfer-Learning-Based Deep Cox Neural Network for Hepatocellular Carcinoma Prognosis Prediction

Background

Predicting hepatocellular carcinoma (HCC) prognosis is important for treatment selection, and it is increasingly interesting to predict prognosis through gene expression data. Currently, the prognosis remains of low accuracy due to the high dimension but small sample size of liver cancer omics data. In previous studies, a transfer learning strategy has been developed by pre-training models on similar cancer types and then fine-tuning the pre-trained models on the target dataset. However, transfer learning has limited performance since other cancer types are similar at different levels, and it is not trivial to balance the relations with different cancer types.

Methods

Here, we propose an adaptive transfer-learning-based deep Cox neural network (ATRCN), where cancers are represented by 12 phenotype and 10 genotype features, and suitable cancers were adaptively selected for model pre-training. In this way, the pre-trained model can learn valuable prior knowledge from other cancer types while reducing the biases.

Results

ATRCN chose pancreatic and stomach adenocarcinomas as the pre-training cancers, and the experiments indicated that our method improved the C-index of 3.8% by comparing with traditional transfer learning methods. The independent tests on three additional HCC datasets proved the robustness of our model. Based on the divided risk subgroups, we identified 10 HCC prognostic markers, including one new prognostic marker, TTC36. Further wet experiments indicated that TTC36 is associated with the progression of liver cancer cells.

Conclusion

These results proved that our proposed deep-learning-based method for HCC prognosis prediction is robust, accurate, and biologically meaningful.

From Cellular Infiltration Assessment to a Functional Gene Set-Based Prognostic Model for Breast Cancer

Background

Cancer heterogeneity is a major challenge in clinical practice, and to some extent, the varying combinations of different cell types and their cross-talk with tumor cells that modulate the tumor microenvironment (TME) are thought to be responsible. Despite recent methodological advances in cancer, a reliable and robust model that could effectively investigate heterogeneity with direct prognostic/diagnostic clinical application remained elusive.

Results

To investigate cancer heterogeneity, we took advantage of single-cell transcriptome data and constructed the first indication- and cell type-specific reference gene expression profile (RGEP) for breast cancer (BC) that can accurately predict the cellular infiltration. By utilizing the BC-specific RGEP combined with a proven deconvolution model (LinDeconSeq), we were able to determine the intrinsic gene expression of 15 cell types in BC tissues. Besides identifying significant differences in cellular proportions between molecular subtypes, we also evaluated the varying degree of immune cell infiltration (basal-like subtype: highest; Her2 subtype: lowest) across all available TCGA-BRCA cohorts. By converting the cellular proportions into functional gene sets, we further developed a 24 functional gene set-based prognostic model that can effectively discriminate the overall survival (P = 5.9 × 10-33, n = 1091, TCGA-BRCA cohort) and therapeutic response (chemotherapy and immunotherapy) (P = 6.5 × 10-3, n = 348, IMvigor210 cohort) in the tumor patients.

Conclusions

Herein, we have developed a highly reliable BC-RGEP that adequately annotates different cell types and estimates the cellular infiltration. Of importance, the functional gene set-based prognostic model that we have introduced here showed a great ability to screen patients based on their therapeutic response. On a broader perspective, we provide a perspective to generate similar models in other cancer types to identify shared factors that drives cancer heterogeneity.

Clinically Translatable Approaches of Inhibiting TGF-β to Target Cancer Stem Cells in TNBC

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that disproportionally accounts for the majority of breast cancer-related deaths due to the lack of specific targets for effective treatments. In this review, we highlight the complexity of the transforming growth factor-beta family (TGF-β) pathway and discuss how the dysregulation of the TGF-β pathway promotes oncogenic attributes in TNBC, which negatively affects patient prognosis. Moreover, we discuss recent findings highlighting TGF-β inhibition as a potent method to target mesenchymal (CD44⁺/CD24^-) and epithelial (ALDH^high) cancer stem cell (CSC) populations. CSCs are associated with tumorigenesis, metastasis, relapse, resistance, and diminished patient prognosis; however, due to differential signal pathway enrichment and plasticity, these populations remain difficult to target and persist as a major barrier barring successful therapy. This review highlights the importance of TGF-β as a driver of chemoresistance, radioresistance and reduced patient prognosis in breast cancer and highlights novel treatment strategies which modulate TGF-β, impede cancer progression and reduce the rate of resistance generation via targeting the CSC populations in TNBC and thus reducing tumorigenicity. Potential TGF-β inhibitors targeting based on clinical trials are summarized for further investigation, which may lead to the development of novel therapies to improve TNBC patient prognosis.

Characterization of Molecular Subtypes in Head and Neck Squamous Cell Carcinoma With Distinct Prognosis and Treatment Responsiveness

Head and neck squamous cell carcinoma (HNSCC) is one of the most aggressive malignancies with complex phenotypic, etiological, biological, and clinical heterogeneities. Previous studies have proposed different clinically relevant subtypes of HNSCC, but little is known about its corresponding prognosis or suitable treatment strategy. Here, we identified 101 core genes from three prognostic pathways, including mTORC1 signaling, unfold protein response, and UV response UP, in 124 pairs of tumor and matched normal tissues of HNSCC. Moreover, we identified three robust subtypes associated with distinct molecular characteristics and clinical outcomes using consensus clustering based on the gene expression profiles of 944 HNSCC patients from four independent datasets. We then integrated the genomic information of The Cancer Genome Atlas (TCGA) HNSCC cohort to comprehensively evaluate the molecular features of different subtypes and screen for potentially effective therapeutic agents. Cluster 1 had more arrested oncogenic signaling, the highest immune cell infiltration, the highest immunotherapy and chemotherapeutic responsiveness, and the best prognosis. By contrast, Cluster 3 showed more activated oncogenic signaling, the lowest immune cell infiltration, the lowest immunotherapy and chemotherapy responsiveness, and the worst prognosis. Our findings corroborate the molecular diversity of HNSCC tumors and provide a novel classification strategy that may guide for prognosis and treatment allocation.

Involvement of ATMIN-DYNLL1-MRN axis in the progression and aggressiveness of serous ovarian cancer

The loss of DYNLL1 contributes to chemoresistance in ovarian cancer. DYNLL1 binds to MRE11, a component of MRN complex (MRE11-RAD50-NBS1), and limits its function in homologous recombination (HR) repair in BRCA1-mutant cells. Decreased activity of MRE11 results in less HR-repair events and thus leads to higher sensitivity against DNA-damaging agents such as cisplatin. Therefore, a better understanding of the cellular changes in DYNLL1-MRN axis in ovarian cancer is needed. Here, we showed that DYNLL1 overexpression leads to decreased chemoresistance even in BRCA-proficient ovarian cancer cells. ATMIN, a transcriptional activator of DYNLL1, showed decreased expression; however, two components of MRN complex, MRE11 and NBS1 (NBN), showed increased expression in high grade compared to low grade serous ovarian cancer. We found that the components of MRN complex (MRE11-RAD50-NBS1) have higher protein levels in sites of omental metastasis and serous tubal intraepithelial carcinoma (STIC) compared to surrounding non-malignant stromal cells in patients with high grade serous ovarian cancer. We showed that the percentage of copy number variation (CNV) events in genes encoding ATMIN, DYNLL1, MRE11 and NBN are the highest in ovarian cancer among other cancer types. ATMIN and DYNLL1 genes are mostly characterized by copy number losses; however, CNV events in MRN complex components are mostly copy number gains. This study highlights the importance of ATMIN-DYNLL1-MRN axis in the development, progression and therapy response of ovarian cancer. MRN levels in ovarian cancer that differ from adjacent, non-malignant tissues may represent actionable therapeutic vulnerabilities.

Moving pan-cancer studies from basic research toward the clinic

Although all cancers share common hallmarks, we have long realized that there is no silver-bullet treatment for the disease. Many clinical oncologists specialize in a single cancer type, based predominantly on the tissue of origin. With advances brought by genetics and cancer genomic research, we now know that cancers are profoundly different, both in origins and in genetic alterations. At the same time, commonalities such as key driver mutations, altered pathways, mutational, immune and microbial signatures and other areas (many revealed by pan-cancer studies) point to the intriguing possibility of targeting common traits across diverse cancer types with the same therapeutic strategies. Studies designed to delineate differences and similarities across cancer types are thus critical in discerning the basic dynamics of oncogenesis, as well as informing diagnoses, prognoses and therapies. We anticipate growing emphases on the development and application of therapies targeting underlying commonalities of different cancer types, while tailoring to the unique tissue environment and intrinsic molecular fingerprints of each cancer type and subtype. Here we summarize the facets of pan-cancer research and how they are pushing progress toward personalized medicine.

Combined homologous recombination repair deficiency and immune activation analysis for predicting intensified responses of anthracycline, cyclophosphamide and taxane chemotherapy in triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is a clinically aggressive disease with abundant variants that cause homologous recombination repair deficiency (HRD). Whether TNBC patients with HRD are sensitive to anthracycline, cyclophosphamide and taxane (ACT), and whether the combination of HRD and tumour immunity can improve the recognition of ACT responders are still unknown.

METHODS

Data from 83 TNBC patients in The Cancer Genome Atlas (TCGA) was used as a discovery cohort to analyse the association between HRD and ACT chemotherapy benefits. The combined effects of HRD and immune activation on ACT chemotherapy were explored at both the genome and the transcriptome levels. Independent cohorts from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO) were adopted to validate our findings.

RESULTS

HRD was associated with a longer ACT chemotherapy failure-free interval (FFI) with a hazard ratio of 0.16 (P = 0.004) and improved patient prognosis (P = 0.0063). By analysing both HRD status and ACT response, we identified patients with a distinct TNBC subtype (ACT-S&HR-P) that showed higher tumour lymphocyte infiltration, IFN-γ activity and NK cell levels. Patients with ACT-S&HR-P had significantly elevated immune inhibitor levels and presented immune activation associated with the increased activities of both innate immune cells and adaptive immune cells, which suggested treatment with immune checkpoint blockade as an option for this subtype. Our analysis revealed that the combination of HRD and immune activation enhanced the efficiency of identifying responders to ACT chemotherapy (AUC = 0.91, P = 1.06e-04) and synergistically contributed to the clinical benefits of TNBC patients. A transcriptional HRD signature of ACT response-related prognostic factors was identified and independently validated to be significantly associated with improved survival in the GEO cohort (P = 0.0038) and the METABRIC dataset (P < 0.0001).

CONCLUSIONS

These findings highlight that HR deficiency prolongs FFI and predicts intensified responses in TNBC patients by combining HRD and immune activation, which provides a molecular basis for identifying ACT responders.

Discovery of synthetic lethal and tumor suppressor paralog pairs in the human genome

CRISPR screens have accelerated the discovery of important cancer vulnerabilities. However, single-gene knockout phenotypes can be masked by redundancy among related genes. Paralogs constitute two-thirds of the human protein-coding genome, so existing methods are likely inadequate for assaying a large portion of gene function. Here, we develop paired guide RNAs for paralog genetic interaction mapping (pgPEN), a pooled CRISPR-Cas9 single- and double-knockout approach targeting more than 2,000 human paralogs. We apply pgPEN to two cell types and discover that 12% of human paralogs exhibit synthetic lethality in at least one context. We recover known synthetic lethal paralogs MEK1/MEK2, important drug targets CDK4/CDK6, and other synthetic lethal pairs including CCNL1/CCNL2. Additionally, we identify ten tumor suppressor paralog pairs whose compound loss promotes cell proliferation. These findings nominate drug targets and suggest that paralog genetic interactions could shape the landscape of positive and negative selection in cancer.

Relevant and Non-Redundant Feature Selection for Cancer Classification and Subtype Detection

Simple Summary

Here we introduce a new feature selection algorithm DTA, which selects important, non-redundant, and relevant features from diverse omics data. DTA selects non-redundant features by maximizing the similarity between each patient pair by an approximate k-cover algorithm. We successfully applied this algorithm to three different biological problems: (a) disease to healthy sample classification, (b) multiclass classification of different disease samples, and (c) disease subtypes detection. DTA outperformed other feature selection techniques in the binary classification of healthy and disease samples and multiclass classification of various diseases. It also improved the performance of a subtype detection algorithm by selecting the important features for few cancer types.

Abstract

Biologists seek to identify a small number of significant features that are important, non-redundant, and relevant from diverse omics data. For example, statistical methods such as LIMMA and DEseq distinguish differentially expressed genes between a case and control group from the transcript profile. Researchers also apply various column subset selection algorithms on genomics datasets for a similar purpose. Unfortunately, genes selected by such statistical or machine learning methods are often highly co-regulated, making their performance inconsistent. Here, we introduce a novel feature selection algorithm that selects highly disease-related and non-redundant features from a diverse set of omics datasets. We successfully applied this algorithm to three different biological problems: (a) disease-to-normal sample classification; (b) multiclass classification of different disease samples; and (c) disease subtypes detection. Considering the classification of ROC-AUC, false-positive, and false-negative rates, our algorithm outperformed other gene selection and differential expression (DE) methods for all six types of cancer datasets from TCGA considered here for binary and multiclass classification problems. Moreover, genes picked by our algorithm improved the disease subtyping accuracy for four different cancer types over state-of-the-art methods. Hence, we posit that our proposed feature reduction method can support the community to solve various problems, including the selection of disease-specific biomarkers, precision medicine design, and disease sub-type detection.

A systematic comparison of data- and knowledge-driven approaches to disease subtype discovery

Typical clustering analysis for large-scale genomics data combines two unsupervised learning techniques: dimensionality reduction and clustering (DR-CL) methods. It has been demonstrated that transforming gene expression to pathway-level information can improve the robustness and interpretability of disease grouping results. This approach, referred to as biological knowledge-driven clustering (BK-CL) approach, is often neglected, due to a lack of tools enabling systematic comparisons with more established DR-based methods. Moreover, classic clustering metrics based on group separability tend to favor the DR-CL paradigm, which may increase the risk of identifying less actionable disease subtypes that have ambiguous biological and clinical explanations. Hence, there is a need for developing metrics that assess biological and clinical relevance. To facilitate the systematic analysis of BK-CL methods, we propose a computational protocol for quantitative analysis of clustering results derived from both DR-CL and BK-CL methods. Moreover, we propose a new BK-CL method that combines prior knowledge of disease relevant genes, network diffusion algorithms and gene set enrichment analysis to generate robust pathway-level information. Benchmarking studies were conducted to compare the grouping results from different DR-CL and BK-CL approaches with respect to standard clustering evaluation metrics, concordance with known subtypes, association with clinical outcomes and disease modules in co-expression networks of genes. No single approach dominated every metric, showing the importance multi-objective evaluation in clustering analysis. However, we demonstrated that, on gene expression data sets derived from TCGA samples, the BK-CL approach can find groupings that provide significant prognostic value in both breast and prostate cancers.

MEK inhibition overcomes resistance to EphA2-targeted therapy in uterine cancer

BACKGROUND

Our pilot clinical study of EphA2 inhibitor (dasatinib) plus paclitaxel and carboplatin showed interesting clinical activity in endometrial cancer with manageable toxicity. However, the underlying mechanisms of dasatinib resistance in uterine cancer are unknown. Here, we investigated potential mechanisms underlying resistance to EphA2 inhibitors in uterine cancer and examined the anti-tumor activity of EphA2 inhibitors alone and in combination with a MEK inhibitor.

METHODS

We evaluated the antitumor activity of EphA2 inhibitors plus a MEK inhibitor using in vitro and in vivo orthotopic models of uterine cancer.

RESULTS

EphA2 inhibitor induced MAPK in dasatinib-resistant uterine cancer cells (HEC-1A and Ishikawa) and BRAF/CRAF heterodimerization in HEC-1A cells. EphA2 inhibitor and trametinib significantly increased apoptosis in cancer cells resistant to EphA2 inhibitors compared with controls (p < 0.01). An in vivo study with the orthotopic HEC-1A model showed significantly greater antitumor response to combination treatment compared with dasatinib alone (p < 0.01). Combination treatment increased EphrinA1 and BIM along with decreased pMAPK, Jagged 1, and c-MYC expression in dasatinib-resistant cells. In addition, Spearman analysis using the TCGA data revealed that upregulation of EphA2 was significantly correlated with JAG1, MYC, NOTCH1, NOTCH3 and HES1 expression (p < 0.001, r = 0.25-0.43). Specifically, MAP3K15 and the NOTCH family genes were significantly related to poor clinical outcome in patients with uterine cancer.

CONCLUSIONS

These findings indicate that the MAPK pathway is activated in dasatinib-resistant uterine cancer cells and that EphrinA1-mediated MEK inhibition overcomes dasatinib resistance. Dual targeting of both EphA2 and MEK, combined with chemotherapy, should be considered for future clinical development.

Inhibition of HMGB1/RAGE axis suppressed the lipopolysaccharide (LPS)-induced vicious transformation of cervical epithelial cells

The chronic inflammation operates as one of the critical causes of cervical cancer. Activation of HMGB1/RAGE axis could induce the inflammation and lead to multiple types of cancer. However, whether the HMGB1/RAGE axis could affect the development of cervical cancer by regulating the inflammation is unclear. Here, we stimulated normal cervical epithelial cells with lipopolysaccharide (LPS). Next, the expression of RAGE in these cells was suppressed by the RAGE inhibitor. CCK-8 and wound healing assays were performed to detect the proliferation and invasion. To determine how inflammatory factors (IL-1β, IL-6 and TNF-α) expressed in supernatant of these cells, ELISA was conducted. Western blotting was used for the detection of the expression of pyroptosis-related proteins (NLRP3 and caspase4). It was found that stimulation of LPS enhanced the proliferation and invasion of normal cervical epithelial cells. The expression of inflammatory factors (IL-1β, IL-6 and TNF-α) in these cells was promoted as well. Application of RAGE inhibitor abolished the efficacy of LPS on these cells. Furthermore, LPS promoted the expression of NLRP3 and caspase4 in these cells while RAGE inhibitor exerted suppressive effects on the expression of these proteins. In summary, LPS-induced inflammation of normal cervical epithelial cells resulted in the malignant transformation of these cells by activating HMGB1/RAGE axis.

Talc, body powder, and ovarian cancer: A summary of the epidemiologic evidence

Many women apply powder to the genital area as a drying agent. Talc, an inert mineral with a high capacity to absorb water, has historically been a major component of body powders. Due to its similarity and co-occurrence with asbestos, the association of body powder/talc use and gynecological cancer risk, specifically ovarian cancer risk, has been a long-standing research question. Retrospective case-control studies have shown associations between genital powder use and ovarian cancer risk, with summary relative risk estimates from meta-analyses and pooled analyses ranging from 1.24 to 1.35 for ever versus never use. In contrast, prospective cohort studies have not shown a statistically significant association until recently, when a pooled analysis of four large cohorts demonstrated a weak, but statistically significant association among women with patent reproductive tracts (hazard ratio 1.13). Taken together, the epidemiological data from case-control studies and cohort studies suggest that there may be a small, positive association between genital powder use and ovarian cancer. The causal factors underlying this association are not clear. Proposed factors include talc, other minerals, such as asbestos or quartz, that are known carcinogens and may contaminate talc products, or other powder ingredients that could cause inflammation of the reproductive tracts. Given the rarity of ovarian cancer in the general population, the small increase in relative risk translates to a very low increase in absolute risk. Further research is needed to understand the underpinnings of the observed association between genital powder use and ovarian cancer risk.

Transcriptome Analysis Identifies GATA3-AS1 as a Long Noncoding RNA Associated with Resistance to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer Patients

Breast cancer is one of the leading causes of mortality in women worldwide, and neoadjuvant chemotherapy has emerged as an option for the management of locally advanced breast cancer. Extensive efforts have been made to identify new molecular markers to predict the response to neoadjuvant chemotherapy. Transcripts that do not encode proteins, termed long noncoding RNAs (lncRNAs), have been shown to display abnormal expression profiles in different types of cancer, but their role as biomarkers in response to neoadjuvant chemotherapy has not been extensively studied. Herein, lncRNA expression was profiled using RNA sequencing in biopsies from patients who subsequently showed either response or no response to treatment. The GATA3-AS1 transcript was overexpressed in the nonresponder group and was the most stable feature when performing selection in multiple random forest models. GATA3-AS1 was experimentally validated by RT-qPCR in an extended group of 68 patients. Expression analysis confirmed that GATA3-AS1 is overexpressed primarily in patients who were nonresponsive to neoadjuvant chemotherapy, with a sensitivity of 92.9%, a specificity of 75.0%, and an area under the curve of approximately 0.90, as measured by receiver operating characteristic curve analysis. The statistical model was based on luminal B-like patients and adjusted by menopausal status and phenotype (odds ratio, 37.49; 95% CI, 6.74-208.42; P = 0.001); GATA3-AS1 was established as an independent predictor of response. Thus, lncRNA GATA3-AS1 is proposed as a potential predictive biomarker of nonresponse to neoadjuvant chemotherapy.

Canine tumor mutational burden is correlated with TP53 mutation across tumor types and breeds

Spontaneous canine cancers are valuable but relatively understudied and underutilized models. To enhance their usage, we reanalyze whole exome and genome sequencing data published for 684 cases of >7 common tumor types and >35 breeds, with rigorous quality control and breed validation. Our results indicate that canine tumor alteration landscape is tumor type-dependent, but likely breed-independent. Each tumor type harbors major pathway alterations also found in its human counterpart (e.g., PI3K in mammary tumor and p53 in osteosarcoma). Mammary tumor and glioma have lower tumor mutational burden (TMB) (median < 0.5 mutations per Mb), whereas oral melanoma, osteosarcoma and hemangiosarcoma have higher TMB (median ≥ 1 mutations per Mb). Across tumor types and breeds, TMB is associated with mutation of TP53 but not PIK3CA, the most mutated genes. Golden Retrievers harbor a TMB-associated and osteosarcoma-enriched mutation signature. Here, we provide a snapshot of canine mutations across major tumor types and breeds.

SPINT1-AS1 Drives Cervical Cancer Progression via Repressing miR-214 Biogenesis

Accumulating evidences have revealed the dysregulated expressions and critical roles of non-coding RNAs in various malignancies, including cervical cancer. Nevertheless, our knowledge about the vast majority of non-coding RNAs is still lacking. Here we identified long non-coding RNA (lncRNA) SPINT1-AS1 as a novel cervical cancer-associated lncRNA. SPINT1-AS1 was increased in cervical cancer and correlated with advanced stage and poor prognosis. SPINT1-AS1 was a direct downstream target of miR-214, a well-known tumor suppressive microRNA (miRNA) in cervical cancer. Intriguingly, SPINT1-AS1 was also found to repress miR-214 biogenesis via binding DNM3OS, the primary transcript of miR-214. The interaction between SPINT1-AS1 and DNM3OS repressed the binding of DROSHA and DGCR8 to DNM3OS, blocked DNM3OS cleavage, and therefore repressed mature miR-214 biogenesis. The expression of SPINT1-AS1 was significantly negatively correlated with miR-214 in cervical cancer tissues, supporting the reciprocal repression between SPINT1-AS1 and miR-214 in vivo. Through downregulating mature miR-214 level, SPINT1-AS1 upregulated the expression of β-catenin, a target of miR-214. Thus, SPINT1-AS1 further activated Wnt/β-catenin signaling in cervical cancer. Functionally, SPINT1-AS1 drove cervical cancer cellular proliferation, migration, and invasion in vitro, and also tumorigenesis in vivo. Deletion of the region mediating the interaction between SPINT1-AS1 and DNM3OS, overexpression of miR-214, and inhibition of Wnt/β-catenin signaling all reversed the roles of SPINT1-AS1 in cervical cancer. Collectively, these findings identified SPINT1-AS1 as a novel cervical cancer-associated oncogenic lncRNA which represses miR-214 biogenesis and activates Wnt/β-catenin signaling, highlighting its potential as prognostic biomarker and therapeutic target for cervical cancer.

Breast cancer heterogeneity through the lens of single-cell analysis and spatial pathologies

Breast cancer ecosystems are composed of complex cell types, including tumor, stromal and immune cells, each of which can assume diverse phenotypes. Both the heterogeneous composition and spatially distinct tumor microenvironment impact breast cancer progression, treatment response and therapeutic resistance. Thus, a deeper understanding of breast cancer heterogeneity may help facilitate the development of novel therapies and improve outcomes for patients. The advent of paradigm shifting single-cell analysis and spatial pathologies allows for a comprehensive analysis of the tumor ecosystem as well as the interactions between its components at unprecedented resolution. In this review, we discuss the insights gained through single-cell analysis and spatial pathologies on breast cancer heterogeneity.

Evaluation of treatment effects in patients with endometrial cancer and POLE mutations: An individual patient data meta-analysis

BACKGROUND

Endometrial cancers (ECs) with somatic mutations in DNA polymerase epsilon (POLE) are characterized by unfavorable pathological features, which prompt adjuvant treatment. Paradoxically, women with POLE-mutated EC have outstanding clinical outcomes, and this raises concerns of overtreatment. The authors investigated whether favorable outcomes were independent of treatment.

METHODS

A PubMed search for POLE and endometrial was restricted to articles published between March 1, 2012, and March 1, 2018, that provided individual patient data (IPD), adjuvant treatment, and survival. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) reporting guidelines for IPD, the authors used univariate and multivariate one-stage meta-analyses with mixed effects Cox models (random effects for study cohorts) to infer the associations of treatment, traditional prognostic factors, and outcome, which was defined as the time from first diagnosis to any adverse event (progression/recurrence or death from EC).

RESULTS

Three hundred fifty-nine women with POLE-mutated EC were identified; 294 (82%) had pathogenic mutations. Worse outcomes were demonstrated in patients with nonpathogenic POLE mutations (hazard ratio, 3.42; 95% confidence interval, 1.47-7.58; log-rank P < .01). Except for stage (P < .01), traditional prognosticators were not associated with progression/recurrence or death from disease. Adverse events were rare (11 progressions/recurrences and 3 disease-specific deaths). Salvage rates in patients who experienced recurrence were high and sustained, with 8 of 11 alive without evidence of disease (range, 5.5-14.2 years). Adjuvant treatment was not associated with outcome.

CONCLUSIONS

Clinical outcomes for ECs with pathogenic POLE mutations are not associated with most traditional risk parameters, and patients do not appear to benefit from adjuvant therapy. The observed low rates of recurrence/progression and the high and sustained salvage rates raise the possibility of safely de-escalating treatment for these patients.

LAY SUMMARY

Ten percent of all endometrial cancers have mutations in the DNA repair gene DNA polymerase epsilon (POLE). Women who have endometrial cancers with true POLE mutations experience almost no recurrences or deaths from their cancer even when their tumors appear to have very unfavorable characteristics. Additional therapy (radiation and chemotherapy) does not appear to improve outcomes for women with POLE-mutated endometrial cancer, and this supports the move to less therapy and less associated toxicity. Diligent classification of endometrial cancers by molecular features provides valuable information to inform prognosis and to direct treatment/no treatment.