Emerging landscape of oncogenic signatures across human cancers. Nat Genet. 2013;45:1127-1133. [PMID: 24071851 PMCID: PMC4320046 DOI: 10.1038/ng.2762]

Escape from Cellular Senescence Is Associated with Chromosomal Instability in Oral Pre-Malignancy

An escape from cellular senescence through the development of unlimited growth potential is one of the hallmarks of cancer, which is thought to be an early event in carcinogenesis. In this review, we propose that the molecular effectors of senescence, particularly the inactivation of TP53 and CDKN2A, together with telomere attrition and telomerase activation, all lead to aneuploidy in the keratinocytes from oral potentially malignant disorders (OPMD). Premalignant keratinocytes, therefore, not only become immortal but also develop genotypic and phenotypic cellular diversity. As a result of these changes, certain clonal cell populations likely gain the capacity to invade the underlying connective tissue. We review the clinical implications of these changes and highlight a new PCR-based assay to identify aneuploid cell in fluids such as saliva, a technique that is extremely sensitive and could facilitate the regular monitoring of OPMD without the need for surgical biopsies and may avoid potential biopsy sampling errors. We also draw attention to recent studies designed to eliminate aneuploid tumour cell populations that, potentially, is a new therapeutic approach to prevent malignant transformations in OPMD.

Targeting Transcription Factors in Cancer: From "Undruggable" to "Druggable"

Deregulation of transcription factors is critical to hallmarks of cancer. Genetic mutations, gene fusions, amplifications or deletions, epigenetic alternations, and aberrant post-transcriptional modification of transcription factors are involved in the regulation of various stages of carcinogenesis, including cancer initiation, progression, and metastasis. Thus, targeting the dysfunctional transcription factors may lead to new cancer therapeutic strategies. However, transcription factors are conventionally considered as "undruggable." Here, we summarize the recent progresses in understanding the regulation of transcription factors in cancers and strategies to target transcription factors and co-factors for preclinical and clinical drug development, particularly focusing on c-Myc, YAP/TAZ, and β-catenin due to their significance and interplays in cancer.

Multiparameter single-cell proteomic technologies give new insights into the biology of ovarian tumors

High-grade serous ovarian cancer (HGSOC) is the most lethal gynecological malignancy. Its diagnosis at advanced stage compounded with its excessive genomic and cellular heterogeneity make curative treatment challenging. Two critical therapeutic challenges to overcome are carboplatin resistance and lack of response to immunotherapy. Carboplatin resistance results from diverse cell autonomous mechanisms which operate in different combinations within and across tumors. The lack of response to immunotherapy is highly likely to be related to an immunosuppressive HGSOC tumor microenvironment which overrides any clinical benefit. Results from a number of studies, mainly using transcriptomics, indicate that the immune tumor microenvironment (iTME) plays a role in carboplatin response. However, in patients receiving treatment, the exact mechanistic details are unclear. During the past decade, multiplex single-cell proteomic technologies have come to the forefront of biomedical research. Mass cytometry or cytometry by time-of-flight, measures up to 60 parameters in single cells that are in suspension. Multiplex cellular imaging technologies allow simultaneous measurement of up to 60 proteins in single cells with spatial resolution and interrogation of cell-cell interactions. This review suggests that functional interplay between cell autonomous responses to carboplatin and the HGSOC immune tumor microenvironment could be clarified through the application of multiplex single-cell proteomic technologies. We conclude that for better clinical care, multiplex single-cell proteomic technologies could be an integral component of multimodal biomarker development that also includes genomics and radiomics. Collection of matched samples from patients before and on treatment will be critical to the success of these efforts.

Identification of anoikis-related molecular patterns to define tumor microenvironment and predict immunotherapy response and prognosis in soft-tissue sarcoma

Background: Soft-tissue sarcoma (STS) is a massive threat to human health due to its high morbidity and malignancy. STS also represents more than 100 histologic and molecular subtypes, with different prognosis. There is growing evidence that anoikis play a key role in the proliferation and invasion of tumors. However, the effects of anoikis in the immune landscape and the prognosis of STS remain unclear. Methods: We analyzed the genomic and transcriptomic profiling of 34 anoikis-related genes (ARGs) in patient cohort of pan-cancer and STS from The Cancer Genome Atlas (TCGA) database. Single-cell transcriptome was used to disclose the expression patterns of ARGs in specific cell types. Gene expression was further validated by real-time PCR and our own sequencing data. We established the Anoikis cluster and Anoikis subtypes by using unsupervised consensus clustering analysis. An anoikis scoring system was further built based on the differentially expressed genes (DEGs) between Anoikis clusters. The clinical and biological characteristics of different groups were evaluated. Results: The expressions of most ARGs were significantly different between STS and normal tissues. We found some common ARGs profiles across the pan-cancers. Network of 34 ARGs demonstrated the regulatory pattern and the association with immune cell infiltration. Patients from different Anoikis clusters or Anoikis subtypes displayed distinct clinical and biological characteristics. The scoring system was efficient in prediction of prognosis and immune cell infiltration. In addition, the scoring system could be used to predict immunotherapy response. Conclusion: Overall, our study thoroughly depicted the anoikis-related molecular and biological profiling and interactions of ARGs in STS. The Anoikis score model could guide the individualized management.

Transcriptomic pan-cancer analysis using rank-based Bayesian inference

The analysis of whole genomes of pan-cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this unsupervised problem via a novel rank-based Bayesian clustering method. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model, the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions, and the transparent biological interpretation of the identified clusters since each cluster is characterized by its top-ranked genomic features. We applied our method to RNA-seq data from cancer samples from 12 tumor types from the Cancer Genome Atlas. We identified a robust clustering that mostly reflects tissue of origin but also includes pan-cancer clusters. Importantly, we identified three pan-squamous clusters composed of a mix of lung squamous cell carcinoma, head and neck squamous carcinoma, and bladder cancer, with different biological functions over-represented in the top genes that characterize the three clusters. We also found two novel subtypes of kidney cancer that show different prognosis, and we reproduced known subtypes of breast cancer. Taken together, our method allows the identification of robust and biologically meaningful clusters of pan-cancer samples.

Apoptosis as a Barrier against CIN and Aneuploidy

Aneuploidy is the gain or loss of entire chromosomes, chromosome arms or fragments. Over 100 years ago, aneuploidy was described to be a feature of cancer and is now known to be present in 68-90% of malignancies. Aneuploidy promotes cancer growth, reduces therapy response and frequently worsens prognosis. Chromosomal instability (CIN) is recognized as the main cause of aneuploidy. CIN itself is a dynamic but stochastic process consisting of different DNA content-altering events. These can include impaired replication fidelity and insufficient clearance of DNA damage as well as chromosomal mis-segregation, micronuclei formation, chromothripsis or cytokinesis failure. All these events can disembogue in segmental, structural and numerical chromosome alterations. While low levels of CIN can foster malignant disease, high levels frequently trigger cell death, which supports the "aneuploidy paradox" that refers to the intrinsically negative impact of a highly aberrant karyotype on cellular fitness. Here, we review how the cellular response to CIN and aneuploidy can drive the clearance of karyotypically unstable cells through the induction of apoptosis. Furthermore, we discuss the different modes of p53 activation triggered in response to mitotic perturbations that can potentially trigger CIN and/or aneuploidy.

Utilization of Cancer Cell Line Screening to Elucidate the Anticancer Activity and Biological Pathways Related to the Ruthenium-Based Therapeutic BOLD-100

BOLD-100 (sodium trans-[tetrachlorobis(1H indazole)ruthenate(III)]) is a ruthenium-based anticancer compound currently in clinical development. The identification of cancer types that show increased sensitivity towards BOLD-100 can lead to improved developmental strategies. Sensitivity profiling can also identify mechanisms of action that are pertinent for the bioactivity of complex therapeutics. Sensitivity to BOLD-100 was measured in a 319-cancer-cell line panel spanning 24 tissues. BOLD-100's sensitivity profile showed variation across the tissue lineages, including increased response in esophageal, bladder, and hematologic cancers. Multiple cancers, including esophageal, bile duct and colon cancer, had higher relative response to BOLD-100 than to cisplatin. Response to BOLD-100 showed only moderate correlation to anticancer compounds in the Genomics of Drug Sensitivity in Cancer (GDSC) database, as well as no clear theme in bioactivity of correlated hits, suggesting that BOLD-100 may have a differentiated therapeutic profile. The genomic modalities of cancer cell lines were modeled against the BOLD-100 sensitivity profile, which revealed that genes related to ribosomal processes were associated with sensitivity to BOLD-100. Machine learning modeling of the sensitivity profile to BOLD-100 and gene expression data provided moderative predictive value. These findings provide further mechanistic understanding around BOLD-100 and support its development for additional cancer types.

Molecular profiling identifies distinct subtypes across TP53 mutant tumors

Tumor protein 53 mutation (TP53mut) is one of the most important driver events facilitating tumorigenesis, which could induce a series of chain reactions to promote tumor malignant transformation. However, the malignancy progression patterns under TP53 mutation remain less known. Clarifying the molecular landscapes of TP53mut tumors will help us understand the process of tumor development and aid precise treatment. Here, we distilled genetic and epigenetic features altered in TP53mut cancers for cluster-of-clusters analysis. Using integrated classification, we derived 5 different subtypes of TP53mut patients. These subtypes have distinct features in genomic alteration, clinical relevance, microenvironment dysregulation, and potential therapeutics. Among the 5 subtypes, COCA3 was identified as the subtype with worst prognosis, causing an immunosuppressive microenvironment and immunotherapeutic resistance. Further drug efficacy research highlighted olaparib as the most promising therapeutic agents for COCA3 tumors. Importantly, the therapeutic efficacy of olaparib in COCA3 and immunotherapy in non-COCA3 tumors was validated via in vivo experimentation. Our study explored the important molecular events and developed a subtype classification system with distinct targeted therapy strategies for different subtypes of TP53mut tumors. These multiomics classification systems provide a valuable resource that significantly expands the knowledge of TP53mut tumors and may eventually benefit in clinical practice.

The genomic and immune landscape of long-term survivors of high-grade serous ovarian cancer

Fewer than half of all patients with advanced-stage high-grade serous ovarian cancers (HGSCs) survive more than five years after diagnosis, but those who have an exceptionally long survival could provide insights into tumor biology and therapeutic approaches. We analyzed 60 patients with advanced-stage HGSC who survived more than 10 years after diagnosis using whole-genome sequencing, transcriptome and methylome profiling of their primary tumor samples, comparing this data to 66 short- or moderate-term survivors. Tumors of long-term survivors were more likely to have multiple alterations in genes associated with DNA repair and more frequent somatic variants resulting in an increased predicted neoantigen load. Patients clustered into survival groups based on genomic and immune cell signatures, including three subsets of patients with BRCA1 alterations with distinctly different outcomes. Specific combinations of germline and somatic gene alterations, tumor cell phenotypes and differential immune responses appear to contribute to long-term survival in HGSC.

Epistasis and evolutionary dependencies in human cancers

Cancer evolution is driven by the concerted action of multiple molecular alterations, which emerge and are selected during tumor progression. An alteration is selected when it provides an advantage to the tumor cell. However, the advantage provided by a specific alteration depends on the tumor lineage, cell epigenetic state, and presence of additional alterations. In this case, we say that an evolutionary dependency exists between an alteration and what influences its selection. Epistatic interactions between altered genes lead to evolutionary dependencies (EDs), by favoring or vetoing specific combinations of events. Large-scale cancer genomics studies have discovered examples of such dependencies, and showed that they influence tumor progression, disease phenotypes, and therapeutic response. In the past decade, several algorithmic approaches have been proposed to infer EDs from large-scale genomics datasets. These methods adopt diverse strategies to address common challenges and shed new light on cancer evolutionary trajectories. Here, we review these efforts starting from a simple conceptualization of the problem, presenting the tackled and still unmet needs in the field, and discussing the implications of EDs in cancer biology and precision oncology.

WNK3 inhibition elicits antitumor immunity by suppressing PD-L1 expression on tumor cells and activating T-cell function

Immune checkpoint therapies, such as programmed cell death ligand 1 (PD-L1) blockade, have shown remarkable clinical benefit in many cancers by restoring the function of exhausted T cells. Hence, the identification of novel PD-L1 regulators and the development of their inhibition strategies have significant therapeutic advantages. Here, we conducted pooled shRNA screening to identify regulators of membrane PD-L1 levels in lung cancer cells targeting druggable genes and cancer drivers. We identified WNK lysine deficient protein kinase 3 (WNK3) as a novel positive regulator of PD-L1 expression. The kinase-dead WNK3 mutant failed to elevate PD-L1 levels, indicating the involvement of its kinase domain in this function. WNK3 perturbation increased cancer cell death in cancer cell-immune cell coculture conditions and boosted the secretion of cytokines and cytolytic enzymes, promoting antitumor activities in CD4⁺ and CD8⁺ T cells. WNK463, a pan-WNK inhibitor, enhanced CD8⁺ T-cell-mediated antitumor activity and suppressed tumor growth as a monotherapy as well as in combination with a low-dose anti-PD-1 antibody in the MC38 syngeneic mouse model. Furthermore, we demonstrated that the c-JUN N-terminal kinase (JNK)/c-JUN pathway underlies WNK3-mediated transcriptional regulation of PD-L1. Our findings highlight that WNK3 inhibition might serve as a potential therapeutic strategy for cancer immunotherapy through its concurrent impact on cancer cells and immune cells.

Multiregional Sequencing Analysis Reveals Extensive Genetic Heterogeneity in Gastric Tumors from Latinos

Gastric cancer is a leading cause of cancer mortality and health disparities in Latinos. We evaluated gastric intratumoral heterogeneity using multiregional sequencing of >700 cancer genes in 115 tumor biopsies from 32 patients, 29 who were Latinos. Analyses focused on comparisons with The Cancer Genome Atlas (TCGA) and on mutation clonality, druggability, and signatures. We found that only approximately 30% of all mutations were clonal and that only 61% of the known TCGA gastric cancer drivers harbored clonal mutations. Multiple clonal mutations were found in new candidate gastric cancer drivers such as EYS, FAT4, PCDHA1, RAD50, EXO1, RECQL4, and FSIP2. The genomically stable (GS) molecular subtype, which has the worse prognosis, was identified in 48% of our Latino patients, a fraction that was >2.3-fold higher than in TCGA Asian and White patients. Only a third of all tumors harbored clonal pathogenic mutations in druggable genes, with most (93%) GS tumors lacking actionable clonal mutations. Mutation signature analyses revealed that, in microsatellite-stable (MSS) tumors, DNA repair mutations were common for both tumor initiation and progression, while tobacco, POLE, and inflammation signatures likely initiate carcinogenesis. MSS tumor progression was likely driven by aging- and aflatoxin-associated mutations, as these latter changes were usually nonclonal. In microsatellite-unstable tumors, nonclonal tobacco-associated mutations were common. Our study, therefore, contributed to advancing gastric cancer molecular diagnostics and suggests clonal status is important to understanding gastric tumorigenesis. Our findings of a higher frequency of a poor prognosis associated molecular subtype in Latinos and a possible new aflatoxin gastric cancer etiology also advance cancer disparities research.

Significance

Our study contributes to advancing our knowledge of gastric carcinogenesis, diagnostics, and cancer health disparities.

Clonal somatic copy number altered driver events inform drug sensitivity in high-grade serous ovarian cancer

Chromosomal instability is a major challenge to patient stratification and targeted drug development for high-grade serous ovarian carcinoma (HGSOC). Here we show that somatic copy number alterations (SCNAs) in frequently amplified HGSOC cancer genes significantly correlate with gene expression and methylation status. We identify five prevalent clonal driver SCNAs (chromosomal amplifications encompassing MYC, PIK3CA, CCNE1, KRAS and TERT) from multi-regional HGSOC data and reason that their strong selection should prioritise them as key biomarkers for targeted therapies. We use primary HGSOC spheroid models to test interactions between in vitro targeted therapy and SCNAs. MYC chromosomal copy number is associated with in-vitro and clinical response to paclitaxel and in-vitro response to mTORC1/2 inhibition. Activation of the mTOR survival pathway in the context of MYC-amplified HGSOC is statistically associated with increased prevalence of SCNAs in genes from the PI3K pathway. Co-occurrence of amplifications in MYC and genes from the PI3K pathway is independently observed in squamous lung cancer and triple negative breast cancer. In this work, we show that identifying co-occurrence of clonal driver SCNA genes could be used to tailor therapeutics for precision medicine.

AZIN1 RNA editing alters protein interactions, leading to nuclear translocation and worse outcomes in prostate cancer

The transcript encoding Antizyme Inhibitor 1 (AZIN1) is frequently edited in various cancers, and this editing is associated with enhanced tumor aggressiveness. After comparison of wild-type AZIN1 (wtAZIN1) and edited AZIN1 (edAZIN1, which contains a Ser367Gly substitution), we report differential binding of edAZIN1 to a small set of proteins; specifically, edAZIN1 binds to alpha-smooth muscle actin (ACTA2), gamma actin 1 (ACTG1), and myosin9, whereas wtAZIN1 does not. This binding enables nuclear translocation of edAZIN1. In contrast to overexpression of edAZIN1 and, to a lesser extent, (editable) wtAZIN1, overexpression of an uneditable AZIN1 allele does not promote a cellular phenotype associated with increased tumorigenicity. In patients, both editing and nuclear localization of AZIN1 are common and are associated with tumor aggressiveness, i.e., a higher Gleason score, higher genomic instability, and a shorter progression-free survival time. In conclusion, the data indicate that binding of edAZIN1 to the actin/myosin9 complex supports its nuclear translocation, leading to enhanced cellular aggressiveness, and is associated with worse prostate cancer outcomes.

A Novel Bayesian Framework Infers Driver Activation States and Reveals Pathway-Oriented Molecular Subtypes in Head and Neck Cancer

Head and neck squamous cell cancer (HNSCC) is an aggressive cancer resulting from heterogeneous causes. To reveal the underlying drivers and signaling mechanisms of different HNSCC tumors, we developed a novel Bayesian framework to identify drivers of individual tumors and infer the states of driver proteins in cellular signaling system in HNSCC tumors. First, we systematically identify causal relationships between somatic genome alterations (SGAs) and differentially expressed genes (DEGs) for each TCGA HNSCC tumor using the tumor-specific causal inference (TCI) model. Then, we generalize the most statistically significant driver SGAs and their regulated DEGs in TCGA HNSCC cohort. Finally, we develop machine learning models that combine genomic and transcriptomic data to infer the protein functional activation states of driver SGAs in tumors, which enable us to represent a tumor in the space of cellular signaling systems. We discovered four mechanism-oriented subtypes of HNSCC, which show distinguished patterns of activation state of HNSCC driver proteins, and importantly, this subtyping is orthogonal to previously reported transcriptomic-based molecular subtyping of HNSCC. Further, our analysis revealed driver proteins that are likely involved in oncogenic processes induced by HPV infection, even though they are not perturbed by genomic alterations in HPV+ tumors.

DEGBOE: Discrete time Evolution modeling of Gene mutation through Bayesian inference using qualitative Observation of mutation Events

An important aspect of cancer progression concerns the way in which gene mutations accumulate in cellular lineages. Comprehensive efforts into cataloging cancer genes have revealed that tumors demonstrate variability in genes that accumulate mutations which depend on the presence or absence of other mutations. However, understanding the stochastic process by which mutations arise across the genome is an important open problem of this nature in biology due to modeling discrete variate time-series is the most challenging, and, as yet, least well-developed of all areas of research in time-series. In this paper, a DEGBOE framework is proposed to model the mutation time-series given the sequence data of the gene mutations. The method relates the discrete-time, nonlinear and nonstationary series of gene mutations to the time-varying autoregressive moving average model. It presents the observation as a nonlinear function dependent on two variables: gene mutation, and gene-gene interactions characterizing the effects of the varying presence or absence of other gene mutations on a mutations' occurrence and evolution. DEGBOE is applied to model the dynamics of frequently mutated genes in lung cancer, includingEGFR,KRAS, and TP53. The results of the model are analyzed and compared to the original simulated data of theDNAwalk, and experimental lung cancer mutations data. It identifies the driver role of TP53 mutations in lung cancer progression.

Effective hybrid feature selection using different bootstrap enhances cancers classification performance

BACKGROUND

Machine learning can be used to predict the different onset of human cancers. Highly dimensional data have enormous, complicated problems. One of these is an excessive number of genes plus over-fitting, fitting time, and classification accuracy. Recursive Feature Elimination (RFE) is a wrapper method for selecting the best subset of features that cause the best accuracy. Despite the high performance of RFE, time computation and over-fitting are two disadvantages of this algorithm. Random forest for selection (RFS) proves its effectiveness in selecting the effective features and improving the over-fitting problem.

METHOD

This paper proposed a method, namely, positions first bootstrap step (PFBS) random forest selection recursive feature elimination (RFS-RFE) and its abbreviation is PFBS- RFS-RFE to enhance cancer classification performance. It used a bootstrap with many positions included in the outer first bootstrap step (OFBS), inner first bootstrap step (IFBS), and outer/ inner first bootstrap step (O/IFBS). In the first position, OFBS is applied as a resampling method (bootstrap) with replacement before selection step. The RFS is applied with bootstrap = false i.e., the whole datasets are used to build each tree. The importance features are hybrid with RFE to select the most relevant subset of features. In the second position, IFBS is applied as a resampling method (bootstrap) with replacement during applied RFS. The importance features are hybrid with RFE. In the third position, O/IFBS is applied as a hybrid of first and second positions. RFE used logistic regression (LR) as an estimator. The proposed methods are incorporated with four classifiers to solve the feature selection problems and modify the performance of RFE, in which five datasets with different size are used to assess the performance of the PFBS-RFS-RFE.

RESULTS

The results showed that the O/IFBS-RFS-RFE achieved the best performance compared with previous work and enhanced the accuracy, variance and ROC area for RNA gene and dermatology erythemato-squamous diseases datasets to become 99.994%, 0.0000004, 1.000 and 100.000%, 0.0 and 1.000, respectively.

CONCLUSION

High dimensional datasets and RFE algorithm face many troubles in cancers classification performance. PFBS-RFS-RFE is proposed to fix these troubles with different positions. The importance features which extracted from RFS are used with RFE to obtain the effective features.

Chromosome 11q loss and MYCN amplification demonstrate synthetic lethality with checkpoint kinase 1 inhibition in neuroblastoma

Neuroblastoma is the most common extracranial solid tumor found in children and despite intense multi-modal therapeutic approaches, low overall survival rates of high-risk patients persist. Tumors with heterozygous loss of chromosome 11q and MYCN amplification are two genetically distinct subsets of neuroblastoma that are associated with poor patient outcome. Using an isogenic 11q deleted model system and high-throughput drug screening, we identify checkpoint kinase 1 (CHK1) as a potential therapeutic target for 11q deleted neuroblastoma. Further investigation reveals MYCN amplification as a possible additional biomarker for CHK1 inhibition, independent of 11q loss. Overall, our study highlights the potential power of studying chromosomal aberrations to guide preclinical development of novel drug targets and combinations. Additionally, our study builds on the growing evidence that DNA damage repair and replication stress response pathways offer therapeutic vulnerabilities for the treatment of neuroblastoma.

EWS/FLI mediated reprogramming of 3D chromatin promotes an altered transcriptional state in Ewing sarcoma

Ewing sarcoma is a prototypical fusion transcription factor-associated pediatric cancer that expresses EWS/FLI or a highly related FET/ETS chimera. EWS/FLI dysregulates transcription to induce and maintain sarcomagenesis, but the mechanisms utilized are not fully understood. We therefore sought to define the global effects of EWS/FLI on chromatin conformation and transcription in Ewing sarcoma cells using a well-validated ‘knock-down/rescue’ model of EWS/FLI function in combination with next generation sequencing assays to evaluate how the chromatin landscape changes with loss, and recovery, of EWS/FLI expression. We found that EWS/FLI (and EWS/ERG) genomic localization is largely conserved across multiple patient-derived Ewing sarcoma cell lines. This EWS/FLI binding signature is associated with establishment of topologically-associated domain (TAD) boundaries, compartment activation, enhancer-promoter looping that involve both intra- and inter-TAD interactions, and gene activation. In addition, EWS/FLI co-localizes with the loop-extrusion factor cohesin to promote chromatin loops and TAD boundaries. Importantly, local chromatin features provide the basis for transcriptional heterogeneity in regulation of direct EWS/FLI target genes across different Ewing sarcoma cell lines. These data demonstrate a key role of EWS/FLI in mediating genome-wide changes in chromatin configuration and support the notion that fusion transcription factors serve as master regulators of three-dimensional reprogramming of chromatin.

The role of DNA damage repair (DDR) system in response to immune checkpoint inhibitor (ICI) therapy

As our understanding of the mechanisms of cancer treatment has increased, a growing number of studies demonstrate pathways through which DNA damage repair (DDR) affects the immune system. At the same time, the varied response of patients to immune checkpoint blockade (ICB) therapy has prompted the discovery of various predictive biomarkers and the study of combination therapy. Here, our investigation explores the interactions involved in combination therapy, accompanied by a review that summarizes currently identified and promising predictors of response to immune checkpoint inhibitors (ICIs) that are useful for classifying oncology patients. In addition, this work, which discusses immunogenicity and several components of the tumor immune microenvironment, serves to illustrate the mechanism by which higher response rates and improved efficacy of DDR inhibitors (DDRi) in combination with ICIs are achieved.

Establishment of tumor protein p53 mutation-based prognostic signatures for acute myeloid leukemia

PURPOSE

The tumor protein p53 gene (TP53) mutations are associated with poor prognosis of patients with acute myeloid leukemia (AML). This study aimed to establish TP53 mutation-based prognostic risk signatures.

PATIENTS AND METHODS

The transcriptomes and clinical characteristics of AML patients were acquired from The Cancer Genome Atlas database, including 11 TP53-mutant samples and 114 TP53-wildtype samples. Differentially expressed mRNAs and long non-coding RNAs (lncRNA) in TP53-mutant samples were identified. Weighted gene correlation network analysis was performed to generate survival-associated co-expression modules. LASSO regression analysis was conducted to build mRNA- and lncRNA-based prognostic risk signatures. Kaplan-Meier curve analysis and multivariate regression analysis were carried out to assess the prognostic values of the risk signatures. Receiver operating characteristic (ROC) analysis was performed to evaluate the accuracy of the signatures.

RESULTS

Based on the co-expression modules, a 5-mRNA risk signature and a 13-lncRNA risk signature were constructed to predict the overall survival for AML patients. Kaplan-Meier curves revealed that the high-risk patients had significantly shorter overall survival than the low-risk patients. ROC analysis yielded 1-, 3-, and 5-year AUCs of 0.681, 0.783, and 0.827 for mRNA signature and 0.85, 0.835, and 0.908 for lncRNA signature. Multivariate regression analysis revealed that both mRNA (HR = 1.45, P< 0.001) and lncRNA (HR = 1.19, P< 0.001) risk scores were independent prognostic factors for AML patients.

CONCLUSION

We provided a potential patients stratification tool for AML prognosis prediction and management, which established by effective TP53 mutation-related gene signatures.

Whole-Exome Sequencing Reveals the Genomic Features of the Micropapillary Component in Ground-Glass Opacities

Background: Micropapillary components are observed in a considerable proportion of ground-glass opacities (GGOs) and contribute to the poor prognosis of patients with invasive lung adenocarcinoma (LUAD). However, the underlying mutational processes related to the presence of micropapillary components remain obscure, limiting the development of clinical interventions. Methods: We collected 31 GGOs, which were separated into paired micropapillary and non-micropapillary components using microdissection. Whole-exome sequencing (WES) was performed on the GGO components, and bioinformatics analysis was conducted to reveal the genomic features of the micropapillary component in invasive LUAD. Results: The micropapillary component had more genomic variations, including tumor mutation burden, intratumoral heterogeneity, and copy number variation. We also observed the enrichment of AID/APOBEC mutation signatures and an increased activation of the RTK/Ras, Notch, and Wnt oncogenic pathways within the micropapillary component. A phylogenetic analysis further suggested that ERBB2/3/4, NCOR1/2, TP53, and ZNF469 contributed to the micropapillary component’s progression during the early invasion of LUAD, a finding that was validated in the TCGA cohort. Conclusions: Our results revealed specific mutational characteristics of the micropapillary component of invasive LUAD in an Asian population. These characteristics were associated with the formation of high-grade invasive patterns. These preliminary findings demonstrated the potential of targeting the micropapillary component in patients with early-stage LUAD.

Low-cost and clinically applicable copy number profiling using repeat DNA

BACKGROUND

Somatic copy number alterations (SCNAs) are an important class of genomic alteration in cancer. They are frequently observed in cancer samples, with studies showing that, on average, SCNAs affect 34% of a cancer cell's genome. Furthermore, SCNAs have been shown to be major drivers of tumour development and have been associated with response to therapy and prognosis. Large-scale cancer genome studies suggest that tumours are driven by somatic copy number alterations (SCNAs) or single-nucleotide variants (SNVs). Despite the frequency of SCNAs and their clinical relevance, the use of genomics assays in the clinic is biased towards targeted gene panels, which identify SNVs but provide limited scope to detect SCNAs throughout the genome. There is a need for a comparably low-cost and simple method for high-resolution SCNA profiling.

RESULTS

We present conliga, a fully probabilistic method that infers SCNA profiles from a low-cost, simple, and clinically-relevant assay (FAST-SeqS). When applied to 11 high-purity oesophageal adenocarcinoma samples, we obtain good agreement (Spearman's rank correlation coefficient, r_s=0.94) between conliga's inferred SCNA profiles using FAST-SeqS data (approximately £14 per sample) and those inferred by ASCAT using high-coverage WGS (gold-standard). We find that conliga outperforms CNVkit (r_s=0.89), also applied to FAST-SeqS data, and is comparable to QDNAseq (r_s=0.96) applied to low-coverage WGS, which is approximately four-fold more expensive, more laborious and less clinically-relevant. By performing an in silico dilution series experiment, we find that conliga is particularly suited to detecting SCNAs in low tumour purity samples. At two million reads per sample, conliga is able to detect SCNAs in all nine samples at 3% tumour purity and as low as 0.5% purity in one sample. Crucially, we show that conliga's hidden state information can be used to decide when a sample is abnormal or normal, whereas CNVkit and QDNAseq cannot provide this critical information.

CONCLUSIONS

We show that conliga provides high-resolution SCNA profiles using a convenient, low-cost assay. We believe conliga makes FAST-SeqS a more clinically valuable assay as well as a useful research tool, enabling inexpensive and fast copy number profiling of pre-malignant and cancer samples.

Oncogenic signaling pathway dysregulation landscape reveals the role of pathways at multiple omics levels in pan-cancer

Dysregulation of signaling pathways plays an essential role in cancer. However, there is not a comprehensive understanding on how oncogenic signaling pathways affect the occurrence and development with a common molecular mechanism of pan-cancer. Here, we investigated the oncogenic signaling pathway dysregulation by using multi-omics data on patients from TCGA from a pan-cancer perspective to identify commonalities across different cancer types. First, the pathway dysregulation profile was constructed by integrating typical oncogenic signaling pathways and the gene expression of TCGA samples, and four molecular subtypes with significant phenotypic and clinical differences induced by different oncogenic signaling pathways were identified: TGF-β+ subtype; cell cycle, MYC, and NF2− subtype; cell cycle and TP53+ subtype; and TGF-β and TP53− subtype. Patients in the TGF-β+ subtype have the best prognosis; meanwhile, the TGF-β+ subtype is associated with hypomethylation. Moreover, there is a higher level of immune cell infiltration but a slightly worse survival prognosis in the cell cycle, MYC, and NF2− subtype patients due to the effect of T-cell dysfunction. Then, the prognosis and subtype classifiers constructed by differential genes on a multi-omics level show great performance, indicating that these genes can be considered as biomarkers with potential therapeutic and prognostic significance for cancers. In summary, our study identified four oncogenic signaling pathway–driven patterns presented as molecular subtypes and their related potential prognostic biomarkers by integrating multiple omics data. Our discovery provides a perspective for understanding the role of oncogenic signaling pathways in pan-cancer.

Genomic and transcriptomic determinants of response to neoadjuvant therapy in rectal cancer

The incidence of rectal cancer is increasing in patients younger than 50 years. Locally advanced rectal cancer is still treated with neoadjuvant radiation, chemotherapy and surgery, but recent evidence suggests that patients with a complete response can avoid surgery permanently. To define correlates of response to neoadjuvant therapy, we analyzed genomic and transcriptomic profiles of 738 untreated rectal cancers. APC mutations were less frequent in the lower than in the middle and upper rectum, which could explain the more aggressive behavior of distal tumors. No somatic alterations had significant associations with response to neoadjuvant therapy in a treatment-agnostic manner, but KRAS mutations were associated with faster relapse in patients treated with neoadjuvant chemoradiation followed by consolidative chemotherapy. Overexpression of IGF2 and L1CAM was associated with decreased response to neoadjuvant therapy. RNA-sequencing estimates of immune infiltration identified a subset of microsatellite-stable immune hot tumors with increased response and prolonged disease-free survival.

Targeted Mutational Analysis of Circulating Tumor DNA to Decipher Temporal Heterogeneity of High-Grade Serous Ovarian Cancer

Simple Summary

The issue of spatial and temporal heterogeneity of high-grade serous ovarian cancer (HGS-EOC) has hampered the possibility to shape the molecular portrait of relapsed disease, which ultimately impacts our ability to develop a more rational second-line treatment. Liquid biopsy offers the unique opportunity to track tumor evolution over time and infer the dynamic changes of tumor clonal architecture. Differently from other tumors, no actionable driving lesions characterize HGS-EOC, thus genome-scale analysis like whole-exome sequencing is not compatible with the clinical turnaround time. In the present work, we provided a novel framework based on the analysis of both qualitative and quantitative features of circulating tumor DNA (ctDNA) in order to identify, at the time of molecular relapse, the early genetic vulnerabilities that will characterize the clinical recurrence and thus be amenable of a more rational second-line treatment.

Abstract

We have previously demonstrated that longitudinal untargeted analysis of plasma samples withdrawn from patients with high-grade serous ovarian cancer (HGS-EOC) can intercept the presence of molecular recurrence (TRm) earlier than the diagnosis of clinical recurrence (TRc). This finding opens a clinical important temporal window to acquire through plasma sample analysis a real-time picture of those emerging molecular lesions that will drive and sustain the growth of relapsed disease and ultimately will confer resistance. In this proof of principle study, the same genomic libraries obtained at the diagnosis (T0), TRm and TRc were further analyzed by targeted resequencing approach to sequence the coding region of a panel of 65 genes to provide longitudinal analysis of clonal evolution as a novel strategy to support clinical decisions for the second-line treatment. Experiments were performed on plasma and tumor tissues withdrawn on a selection of previously analyzed cohorts of cases (i.e., 33 matched primary and synchronous lesions and 43 plasma samples from 18 patients). At T0, the median concordance of mutations shared by each tumor tissue biopsy and its matched plasma sample was 2.27%. This finding confirms the limit of a single tumor biopsy to be representative of the entire disease, while plasma analysis can recapitulate most of the main molecular lesions of the disease. A comparable scenario was observed during longitudinal analysis, where, with the exception of the TP53 gene and germline mutations in BRCA1/2 genes, no other gene shared the same locus specific gene mutation across T0, TRm and TRc time points. This high level of temporal heterogeneity has important implications for planning second-line treatment. For example, in three out of 13 cases, plasma ctDNA analysis at TRm or TRc reported acquired novel variants in the TP53BP1 gene not present at T0. In particular, patient 21564, potentially eligible for PARP-inhibitor (PARPi) treatment at the time of diagnosis (BRCA1 c.5182delA mutation), would unlikely respond to these drugs in second-line therapy due to the presence of eight distinct TP53BP1 variants in plasma samples collected TRc. This study demonstrates that liquid biopsy provides a real-time molecular picture to intercept those actionable genetic vulnerabilities or drug resistance mechanisms that could be used to plan a more rational second-line treatment.

Chromosome instability and aneuploidy as context-dependent activators or inhibitors of antitumor immunity

Chromosome instability (CIN) and its major consequence, aneuploidy, are hallmarks of human cancers. In addition to imposing fitness costs on tumor cells through several cell-intrinsic mechanisms, CIN/aneuploidy also provokes an antitumor immune response. However, as the major contributor to genomic instability, intratumor heterogeneity generated by CIN/aneuploidy helps tumor cells to evolve methods to overcome the antitumor role of the immune system or even convert the immune system to be tumor-promoting. Although the interplay between CIN/aneuploidy and the immune system is complex and context-dependent, understanding this interplay is essential for the success of immunotherapy in tumors exhibiting CIN/aneuploidy, regardless of whether the efficacy of immunotherapy is increased by combination with strategies to promote CIN/aneuploidy or by designing immunotherapies to target CIN/aneuploidy directly.

Comparative molecular dynamics analyses on PIK3CA hotspot mutations with PI3Kα specific inhibitors and ATP

PI3K pathway is heavily emphasized in cancer where PIK3CA, which encodes for the p110α subunit of PI3Kα, presents itself as the second most common mutated gene. A lot of effort has been put in developing PI3K inhibitors, opening promising avenues for the treatment of cancer. Among these, PI3Kα specific inhibitor alpelisib was approved by FDA for breast cancer and other α-isoform specific inhibitors such as inavolisib and serabelisib reached clinical trials. However, the mode of action of these inhibitors on mutated PI3Kα and how they interact with mutant structures has not been fully elucidated yet. In this study, we are revealing the calculated interactions and binding affinities of these inhibitors within the context of PIK3CA hotspot mutations (E542K, E545K and H1047R) by employing molecular dynamics (MD) simulations. We performed principal component analysis to understand the motions of the protein complex during our simulations and also checked the correlated motions of all amino acids. Binding affinity calculations with MM-PBSA confirmed the consistent binding of alpelisib across mutations and revealed relatively higher affinities for inavolisib towards wild-type and H1047R mutant structures in comparison to other inhibitors. On the other hand, E542K mutation significantly impaired the interaction of inavolisib and serabelisib with PI3Kα. We also investigated the structural relationship of the natural ligand ATP with PI3Kα, and interestingly realized a significant reduction in binding affinity for the mutants, with potentially unexpected implications on the mechanisms that render these mutations oncogenic. Moreover, correlated motions of all residues were generally higher for ATP except the H1047R mutation which exhibited a distinguishable reduction. The results presented here could be guiding for pre-clinical and clinical studies of personalized medicine where individual mutations are a strong consideration point.

PaintOmics 4: new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases

PaintOmics is a web server for the integrative analysis and visualisation of multi-omics datasets using biological pathway maps. PaintOmics 4 has several notable updates that improve and extend analyses. Three pathway databases are now supported: KEGG, Reactome and MapMan, providing more comprehensive pathway knowledge for animals and plants. New metabolite analysis methods fill gaps in traditional pathway-based enrichment methods. The metabolite hub analysis selects compounds with a high number of significant genes in their neighbouring network, suggesting regulation by gene expression changes. The metabolite class activity analysis tests the hypothesis that a metabolic class has a higher-than-expected proportion of significant elements, indicating that these compounds are regulated in the experiment. Finally, PaintOmics 4 includes a regulatory omics module to analyse the contribution of trans-regulatory layers (microRNA and transcription factors, RNA-binding proteins) to regulate pathways. We show the performance of PaintOmics 4 on both mouse and plant data to highlight how these new analysis features provide novel insights into regulatory biology. PaintOmics 4 is available at https://paintomics.org/.

Candidate therapeutic agents in a newly established triple wild-type mucosal melanoma cell line

BACKGROUND

Mucosal melanoma has characteristically distinct genetic features and typically poor prognosis. The lack of representative mucosal melanoma models, especially cell lines, has hindered translational research on this melanoma subtype. In this study, we aimed to establish and provide the biological properties, genomic features and the pharmacological profiles of a mucosal melanoma cell line that would contribute to the understanding and treatment optimization of molecularly-defined mucosal melanoma subtype.

METHODS

The sample was collected from a 67-year-old mucosal melanoma patient and processed into pieces for the establishment of cell line and patient-derived xenograft (PDX) model. The proliferation and tumorigenic property of cancer cells from different passages were evaluated, and whole-genome sequencing (WGS) was performed on the original tumor, PDX, established cell line, and the matched blood to confirm the establishment and define the genomic features of this cell line. AmpliconArchitect was conducted to depict the architecture of amplified regions detected by WGS. High-throughput drug screening (HTDS) assay including a total of 103 therapeutic agents was implemented on the established cell line, and selected candidate agents were validated in the corresponding PDX model.

RESULTS

A mucosal melanoma cell line, MM9H-1, was established which exhibited robust proliferation and tumorigenicity after more than 100 serial passages. Genomic analysis of MM9H-1, corresponding PDX, and the original tumor showed genetic fidelity across genomes, and MM9H-1 was defined as a triple wild-type (TWT) melanoma subtype lacking well-characterized "driver mutations". Instead, the amplification of several oncogenes, telomerase reverse transcriptase (TERT), v-Raf murine sarcoma viral oncogene homolog B1 (BRAF), melanocyte Inducing transcription factor (MITF) and INO80 complex ATPase subunit (INO80), via large-scale genomic rearrangement potentially contributed to oncogenesis of MM9H-1. Moreover, HTDS identified proteasome inhibitors, especially bortezomib, as promising therapeutic candidates for MM9H-1, which was verified in the corresponding PDX model in vivo.

CONCLUSIONS

We established and characterized a new mucosal melanoma cell line, MM9H-1, and defined this cell line as a TWT melanoma subtype lacking well-characterized "driver mutations". The MM9H-1 cell line could be adopted as a unique model for the preclinical investigation of mucosal melanoma.

Discovery Proteomics Analysis Determines That Driver Oncogenes Suppress Antiviral Defense Pathways Through Reduction in Interferon-β Autocrine Stimulation

Since the discovery of oncogenes, there has been tremendous interest to understand their mechanistic basis and to develop broadly actionable therapeutics. Some of the most frequently activated oncogenes driving diverse cancers are c-MYC, EGFR, HER2, AKT, KRAS, BRAF, and MEK. Using a reductionist approach, we explored how cellular proteomes are remodeled in isogenic cell lines engineered with or without these driver oncogenes. The most striking discovery for all oncogenic models was the systematic downregulation of scores of antiviral proteins regulated by type 1 interferon. These findings extended to cancer cell lines and patient-derived xenograft models of highly refractory pancreatic cancer and osteosarcoma driven by KRAS and MYC oncogenes. The oncogenes reduced basal expression of and autocrine stimulation by type 1 interferon causing remarkable convergence on common phenotypic and functional profiles. In particular, there was dramatically lower expression of dsRNA sensors including DDX58 (RIG-I) and OAS proteins, which resulted in attenuated functional responses when the oncogenic cells were treated with the dsRNA mimetic, polyI:C, and increased susceptibility to infection with an RNA virus shown using SARS-CoV-2. Our reductionist approach provides molecular and functional insights connected to immune evasion hallmarks in cancers and suggests therapeutic opportunities.

The Genomic Landscape of Early-Stage Ovarian High-Grade Serous Carcinoma

PURPOSE

Ovarian high-grade serous carcinoma (HGSC) is usually diagnosed at late stage. We investigated whether late-stage HGSC has unique genomic characteristics consistent with acquisition of evolutionary advantage compared with early-stage tumors.

EXPERIMENTAL DESIGN

We performed targeted next-generation sequencing and shallow whole-genome sequencing (sWGS) on pretreatment samples from 43 patients with FIGO stage I-IIA HGSC to investigate somatic mutations and copy-number (CN) alterations (SCNA). We compared results to pretreatment samples from 52 patients with stage IIIC/IV HGSC from the BriTROC-1 study.

RESULTS

Age of diagnosis did not differ between early-stage and late-stage patients (median 61.3 years vs. 62.3 years, respectively). TP53 mutations were near-universal in both cohorts (89% early-stage, 100% late-stage), and there were no significant differences in the rates of other somatic mutations, including BRCA1 and BRCA2. We also did not observe cohort-specific focal SCNA that could explain biological behavior. However, ploidy was higher in late-stage (median, 3.0) than early-stage (median, 1.9) samples. CN signature exposures were significantly different between cohorts, with greater relative signature 3 exposure in early-stage and greater signature 4 in late-stage. Unsupervised clustering based on CN signatures identified three clusters that were prognostic.

CONCLUSIONS

Early-stage and late-stage HGSCs have highly similar patterns of mutation and focal SCNA. However, CN signature analysis showed that late-stage disease has distinct signature exposures consistent with whole-genome duplication. Further analyses will be required to ascertain whether these differences reflect genuine biological differences between early-stage and late-stage or simply time-related markers of evolutionary fitness. See related commentary by Yang et al., p. 2730.

Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma

BACKGROUND

At present, the diagnostic ability of hepatocellular carcinoma (HCC) based on serum alpha-fetoprotein level is limited. Finding markers that can effectively distinguish cancer and non-cancerous tissues is important for improving the diagnostic efficiency of HCC.

RESULTS

In this study, we developed a predictive model for HCC diagnosis using personalized biological pathways combined with a machine learning algorithm based on regularized regression and carry out relevant examinations. In two training sets, the overall cross-study-validated area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve and the Brier score of the diagnostic model were 0.987 [95%confidence interval (CI): 0.979-0.996], 0.981 and 0.091, respectively. Besides, the model showed good transferability in external validation set. In TCGA-LIHC cohort, the AUROC, AURPC and Brier score were 0.992 (95%CI: 0.985-0.998), 0.967 and 0.112, respectively. The diagnostic model has accomplished very impressive performance in distinguishing HCC from non-cancerous liver tissues. Moreover, we further analyzed the extracted biological pathways to explore molecular features and prognostic factors. The risk score generated from a 12-gene signature extracted from the characteristic pathways was correlated with some immune related pathways and served as an independent prognostic factor for HCC.

CONCLUSION

We used personalized biological pathways analysis and machine learning algorithm to construct a highly accurate HCC diagnostic model. The excellent interpretable performance and good transferability of this model enables it with great potential for personalized medicine, which can assist clinicians in diagnosis for HCC patients.

Treatment represents a key driver of metastatic cancer evolution

Metastasis is the main cause of cancer death, yet the evolutionary processes behind it remain largely unknown. Here, through analysis of large panel-based genomic datasets from the AACR GENIE project, including 40,979 primary and metastatic tumors across 25 distinct cancer types, we explore how the evolutionary pressure of cancer metastasis shapes the selection of genomic drivers of cancer. The most commonly affected genes were TP53, MYC, and CDKN2A, with no specific pattern associated with metastatic disease. This suggests that, on a driver mutation level, the selective pressure operating in primary and metastatic tumors is similar. The most highly enriched individual driver mutations in metastatic tumors were mutations known to drive resistance to hormone therapies in breast and prostate cancer (ESR1 and AR), anti-EGFR therapy in non-small cell lung cancer (EGFR T790M), and imatinib in gastrointestinal cancer (KIT V654A). Specific mutational signatures were also associated with treatment in three cancer types, supporting clonal selection following anti-cancer therapy. Overall, this implies that initial acquisition of driver mutations is predominantly shaped by the tissue of origin, where specific mutations define the developing primary tumor and drive growth, immune escape, and tolerance to chromosomal instability. However, acquisition of driver mutations that contribute to metastatic disease is less specific, with the main genomic drivers of metastatic cancer evolution associating with resistance to therapy.

Clinical significance of chromosomal integrity in gastric cancers

BACKGROUND

A whole-exome or targeted cancer genes panel by next-generation sequencing has been used widely in assisting individualized treatment decisions. Currently, multiple algorithms are developed to estimate DNA copy numbers based on sequencing data, which makes a comprehensive global glance at chromosomal integrity possible. We aim to classify gastric cancers based on chromosomal integrity to guide personalized therapy.

METHODS

We investigated copy number variations (CNV) across the entire genome of 124 gastric carcinomas via exome or targeted sequencing. Chromosomal integrity was classified as chromosomal stability (CS), chromosomal instability (CIN) and intermediate state (CIN/CS) based on CNV results. Chromosomal integrity was correlated to molecular features and clinical characteristics.

RESULTS

According the states of chromosomal integrity, gastric carcinomas can be stratified into two cohorts: CS and CIN. Our results showed a significant relationship between CIN status and TP53 mutation, but not RB1, phosphatase and tensin homolog (PTEN), or other reported DNA damage repair genes. The mutation frequency of the TP53 gene had great relevance. Our study initially revealed clinical significance of chromosomal integrity that CIN patients were prone to HER2-positive and mucinous adenocarcinoma, while CS patients were a diffuse subtype and poorly differentiated but had longer overall survival.

CONCLUSIONS

We classified gastric carcinomas into two states of chromosomal integrity with clinical implications. The dichotomy is applicable to clinical transformation. We proposed that classifying gastric cancers based on chromosomal integrity would enable us to achieve personalized therapy for patients and may be beneficial to patient stratification in future clinical trials.

Improved Uveal Melanoma Copy Number Subtypes Including an Ultra–High-Risk Group

Purpose

Design

Participants

Methods

Main Outcome Measures

Results

Conclusions

Robustness of the Autophagy Pathway to Somatic Copy Number Losses

Autophagy allows cells to temporarily tolerate energy stress by replenishing critical metabolites through self-digestion, thereby attenuating the cytotoxic effects of anticancer drugs that target tumor metabolism. Autophagy defects could therefore mark a metabolically vulnerable cancer state and open a therapeutic window. While mutations of autophagy genes (ATGs) are notably rare in cancer, haploinsufficiency network analyses across many cancers have shown that the autophagy pathway is frequently hit by somatic copy number losses of ATGs such as MAP1LC3B/ATG8F (LC3), BECN1/ATG6 (Beclin-1), and ATG10. Here, we used CRISPR/Cas9 technology to delete increasing numbers of copies of one or more of these ATGs in non-small cell lung cancer cells and examined the effects on sensitivity to compounds targeting aerobic glycolysis, a hallmark of cancer metabolism. Whereas the complete knockout of one ATG blocked autophagy and led to profound metabolic vulnerability, this was not the case for combinations of different nonhomozygous deletions. In cancer patients, the effect of ATG copy number loss was blunted at the protein level and did not lead to the accumulation of p62 as a sign of reduced autophagic flux. Thus, the autophagy pathway is shown to be markedly robust and resilient, even with the concomitant copy number loss of key autophagy genes.

Aberrantly Activated APOBEC3B Is Associated With Mutant p53-Driven Refractory/Relapsed Diffuse Large B-Cell Lymphoma

Tumor protein 53 (TP53) mutation predicts an unfavorable prognosis in diffuse large B-cell lymphoma (DLBCL), but the molecular basis for this association remains unclear. In several malignancies, the cytidine deaminase apolipoprotein B mRNA editing enzyme catalytic subunit 3B (APOBEC3B) has been reported to be associated with the TP53 G/C-to-A/T mutation. Here, we show that the frequency of this mutation was significantly higher in relapsed/refractory (R/R) than in non-R/R DLBCL, which was positively associated with the APOBEC3B expression level. APOBEC3B overexpression induced the TP53 G/C-to-A/T mutation in vitro, resulting in a phenotype similar to that of DLBCL specimens. Additionally, APOBEC3B-induced p53 mutants promoted the growth of DLBCL cells and enhanced drug resistance. These results suggest that APOBEC3B is a critical factor in mutant p53-driven R/R DLBCL and is therefore a potential therapeutic target.

Adoptive tumor infiltrating lymphocyte transfer as personalized immunotherapy

Cancer is a leading cause of death worldwide and, despite new targeted therapies and immunotherapies, a large group of patients fail to respond to therapy or progress after initial response, which brings the need for additional treatment options. Manipulating the immune system using a variety of approaches has been explored for the past years with successful results. Sustained progress has been made to understand the T cell-mediated anti-tumor responses counteracting the tumorigenesis process. The T-lymphocyte pool, especially its capacity for antigen-directed cytotoxicity, has become a central focus for engaging the immune system in defeating cancer. The adoptive cell transfer of autologous tumor-infiltrating lymphocytes has been used in humans for over 30 years to treat metastatic melanoma. In this review, we provide a brief history of ACT-TIL and discuss the current state of ACT-TIL clinical development in solid tumors. We also discuss how key advances in understanding genetic intratumor heterogeneity, to accurately identify neoantigens, and new strategies designed to overcome T-cell exhaustion and tumor immunosuppression have improved the efficacy of the TIL-therapy infusion. Characteristics of the TIL products will be discussed, as well as new strategies, including the selective expansion of specific fractions from the cell product or the genetic manipulation of T cells for improving the in-vivo survival and functionality. In summary, this review outlines the potential of ACT-TIL as a personalized approach for epithelial tumors and continued discoveries are making it increasingly more effective against other types of cancers.

APOBEC3 mutational signatures are associated with extensive and diverse genomic instability across multiple tumour types

BACKGROUND

The APOBEC3 (apolipoprotein B mRNA editing enzyme catalytic polypeptide 3) family of cytidine deaminases is responsible for two mutational signatures (SBS2 and SBS13) found in cancer genomes. APOBEC3 enzymes are activated in response to viral infection, and have been associated with increased mutation burden and TP53 mutation. In addition to this, it has been suggested that APOBEC3 activity may be responsible for mutations that do not fall into the classical APOBEC3 signatures (SBS2 and SBS13), through generation of double strand breaks.Previous work has mainly focused on the effects of APOBEC3 within individual tumour types using exome sequencing data. Here, we use whole genome sequencing data from 2451 primary tumours from 39 different tumour types in the Pan-Cancer Analysis of Whole Genomes (PCAWG) data set to investigate the relationship between APOBEC3 and genomic instability (GI).

RESULTS AND CONCLUSIONS

We found that the number of classical APOBEC3 signature mutations correlates with increased mutation burden across different tumour types. In addition, the number of APOBEC3 mutations is a significant predictor for six different measures of GI. Two GI measures (INDELs attributed to INDEL signatures ID6 and ID8) strongly suggest the occurrence and error prone repair of double strand breaks, and the relationship between APOBEC3 mutations and GI remains when SNVs attributed to kataegis are excluded.We provide evidence that supports a model of cancer genome evolution in which APOBEC3 acts as a causative factor in the development of diverse and widespread genomic instability through the generation of double strand breaks. This has important implications for treatment approaches for cancers that carry APOBEC3 mutations, and challenges the view that APOBECs only act opportunistically at sites of single stranded DNA.

[Influence of the number of high-risk cytogenetic abnormalities on the clinical characteristics and prognosis in 360 patients with newly diagnosed multiple myeloma]

Objective: To investigate the influence of the number of high-risk cytogenetic abnormalities (HRCA) on the clinical characteristics and prognosis of patients with newly diagnosed multiple myeloma (MM) . Methods: A total of 360 patients with newly diagnosed MM admitted to Jiangsu Province Hospital between November 2013 and September 2020 were included in this study. Cytoplasmic light chain immunofluorescence with fluorescence in situ hybridization (cIg-FISH) was used to detect HRCA. Cytogenetic abnormalities were combined with clinical characteristics and outcomes for further analysis. Results: Among the 360 patients, 120 patients (33.3%) presented with no HRCAs, and 175 (48.6%) , 61 (16.9%) , and four (1.1%) patients had one, two, and three HRCA (s) , respectively. Patients were divided into three groups, including the no-HRCA group, one-HRCA group, and ≥two-HRCA group, according to the number of HRCAs. There were significant differences in the R-ISS stage, hemoglobin level, albumin level, and the proportion of bone marrow plasma cells among the three groups (P<0.05) . The COX proportional-hazards model identified extramedullary disease (P=0.018) , HRCA ≥ 2 (P=0.001) , and absence of autologous hematopoietic stem cell transplantation (P<0.001) as independent risk factors for progression free survival (PFS) and identified lactate dehydrogenase (LDH) level ≥ 220 U/L (P<0.001) , HRCA ≥2 (P=0.001) , and absence of autologous hematopoietic stem cell transplantation (P=0.005) as independent risk factors for overall survival (OS) . The median PFS was 28 months, 22 months, and 14 months (P=0.005) for the three cohorts, and their OS was not reached,60 months, and 30 months (P=0.001) , respectively. Conclusions: HRCA ≥ 2 is an independent risk factor for decreased survival in patients with newly diagnosed MM. More HRCAs result in heavier tumor burden, as well as a higher risk of disease progression and death.

OMEN: Network-based Driver Gene Identification using Mutual Exclusivity

MOTIVATION

Network-based driver identification methods that can exploit mutual exclusivity typically fail to detect rare drivers because of their statistical rigor. Propagation-based methods in contrast allow recovering rare driver genes, but the interplay between network topology and high-scoring nodes often results in spurious predictions. The specificity of driver gene detection can be improved by taking into account both gene-specific and gene-set properties. Combining these requires a formalism that can adjust gene-set properties depending on the exact network context within which a gene is analyzed.

RESULTS

We developed OMEN: a logic programming framework based on random walk semantics. OMEN presents a number of novel concepts. In particular, its design is unique in that it presents an effective approach to combine both gene-specific driver properties and gene-set properties, and includes a novel method to avoid restrictive, a priori filtering of genes by exploiting the gene-set property of mutual exclusivity, expressed in terms of the functional impact scores of mutations, rather than in terms of simple binary mutation calls. Applying OMEN to a benchmark data set derived from TCGA illustrates how OMEN is able to robustly identify driver genes and modules of driver genes as proxies of driver pathways.

AVAILABILITY

The source code is freely available for download at www.github.com/DriesVanDaele/OMEN The data set is archived at https://doi.org/10.5281/zenodo.6419097 and the code at https://doi.org/10.5281/zenodo.6419764.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genomic landscape of chemical-induced lung tumors under Nrf2 different expression levels

The transcription factor Nrf2 plays a crucial role in the anti-oxidative stress response, protection of DNA from injury, and DNA repair mechanisms. Nrf2 activity reduces cancer initiation, but how Nrf2 affects whole-genome alterations upon carcinogenic stimulus remains unexplored. Although recent genome-wide analysis using next-generation sequencing revealed landscapes of nucleotide mutations and copy number alterations in various human cancers, genomic changes in murine cancer models have not been thoroughly examined. We elucidated the relationship between Nrf2 expression levels and whole exon mutation patterns using an ethyl-carbamate (urethane)-induced lung carcinogenesis model employing Nrf2-deficient and Keap1-kd mice, the latter of which express high levels of Nrf2. Exome analysis demonstrated that single nucleotide and trinucleotide mutation patterns and the Kras mutational signature differed significantly and were dependent on the expression level of Nrf2. The Nrf2-deficient tumors exhibited fewer copy number alterations relative to the Nrf2-wt and Keap1-kd tumors. The observed trend in genomic alterations likely prevented the Nrf2-deficient tumors from progressing into malignancy. For the first time, we present whole-exome sequencing results for chemically-induced lung tumors in the Nrf2 gain or loss of function mouse models. Our results demonstrate that different Nrf2 expression levels lead to distinct gene mutation patterns that underly different oncogenic mechanisms in each tumor genotype.

Aurora Kinase A Is an Independent Predictor of Invasive Recurrence in Breast Ductal Carcinoma in situ

INTRODUCTION

Aurora Kinase A (AURKA/STK15) has a role in centrosome duplication and is a regulator of mitotic cell proliferation. It is over-expressed in breast cancer and other cancers, however; its role in ductal carcinoma in situ (DCIS) remains to be defined. This study aims to characterize AURKA protein expression in DCIS and evaluate its prognostic significance.

METHODS

AURKA was assessed immunohistochemically in a large well-characterized cohort of DCIS (n = 776 pure DCIS and 239 DCIS associated with invasive breast cancer [DCIS-mixed]) with long-term follow-up data (median = 105 months) and basic molecular characterization.

RESULTS

High AURKA expression was observed in 15% of DCIS cases and was associated with features of aggressiveness including larger tumour size, high nuclear grade, hormone receptor negativity, HER2 positivity, and high Ki67 proliferation index. AURKA expression was higher in DCIS associated with invasive breast cancer than in pure DCIS (p < 0.0001). In the DCIS-mixed cohort, the invasive component showed higher AURKA expression than the DCIS component (p < 0.0001). Outcome analysis revealed that AURKA was a predictor of invasive recurrence (p = 0.002).

CONCLUSION

High AURKA expression is associated with poor prognosis in DCIS and might be a potential marker to predict DCIS progression to invasive disease.

Copy number variation of urine exfoliated cells by low-coverage whole genome sequencing for diagnosis of prostate adenocarcinoma: a prospective cohort study

Background

Non-invasive, especially the urine-based diagnosis of prostate cancer (PCa) remains challenging. Although prostate cancer antigen (PSA) is widely used in prostate cancer screening, the false positives may result in unnecessary invasive procedures. PSA elevated patients are triaged to further evaluation of free/total PSA ratio (f/t PSA), to find out potential clinically significant PCa before undergoing invasive procedures. Genomic instability, especially chromosomal copy number variations (CNVs) were proved much more tumor specific. Here we performed a prospective study to evaluate the diagnostic value of CNV via urine-exfoliated cell DNA analysis in PCa.

Methods

We enrolled 28 PSA elevated patients (≥ 4 ng/ml), including 16 PCa, 9 benign prostate hypertrophy (BPH) and 3 prostatic intraepithelial neoplasia (PIN). Fresh initial portion urine was collected after hospital admission. Urine exfoliated cell DNA was analyzed by low coverage Whole Genome Sequencing, followed by CNV genotyping by the prostate cancer chromosomal aneuploidy detector (ProCAD). CNVs were quantified in absolute z-score (|Z|). Serum free/total PSA ratio (f/t PSA) was reported altogether.

Results

In patients with PCa, the most frequent CNV events were chr3q gain (n = 2), chr8q gain (n = 2), chr2q loss (n = 4), and chr18q loss (n = 3). CNVs were found in 81.2% (95% Confidence Interval (CI) 53.7–95.0%) PCa. No CNV was identified in BPH patients. A diagnosis model was established by incorporating all CNVs. At the optimal cutoff of |Z|≥ 2.50, the model reached an AUC of 0.91 (95% CI 0.83–0.99), a sensitivity of 81.2% and a specificity of 100%. The CNV approach significantly outperformed f/t PSA (AUC = 0.62, P = 0.012). Further analyses showed that the CNV positive rate was significantly correlated with tumor grade. CNVs were found in 90.9% (95% CI 57.1–99.5%) high grade tumors and 60.0% (95% CI 17.0–92.7%) low grade tumors. No statistical significance was found for patient age, BMI, disease history and family history.

Conclusions

Urine exfoliated cells harbor enriched CNV features in PCa patients. Urine detection of CNV might be a biomarker for PCa diagnosis, especially in terms of the clinically significant high-grade tumors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01253-5.

Integrative proteogenomic characterization of hepatocellular carcinoma across etiologies and stages

Proteogenomic analyses of hepatocellular carcinomas (HCC) have focused on early-stage, HBV-associated HCCs. Here we present an integrated proteogenomic analysis of HCCs across clinical stages and etiologies. Pathways related to cell cycle, transcriptional and translational control, signaling transduction, and metabolism are dysregulated and differentially regulated on the genomic, transcriptomic, proteomic and phosphoproteomic levels. We describe candidate copy number-driven driver genes involved in epithelial-to-mesenchymal transition, the Wnt-β-catenin, AKT/mTOR and Notch pathways, cell cycle and DNA damage regulation. The targetable aurora kinase A and CDKs are upregulated. CTNNB1 and TP53 mutations are associated with altered protein phosphorylation related to actin filament organization and lipid metabolism, respectively. Integrative proteogenomic clusters show that HCC constitutes heterogeneous subgroups with distinct regulation of biological processes, metabolic reprogramming and kinase activation. Our study provides a comprehensive overview of the proteomic and phophoproteomic landscapes of HCCs, revealing the major pathways altered in the (phospho)proteome.

Proteogenomic analyses of hepatocellular carcinomas (HCC) have focused on early-stage, HBV-associated tumours and lacked information about the phosphoproteome. Here, the authors present a comprehensive HCC proteogenomics and phosphoproteomics study in patient samples from multiple etiologies and stages.

Serial Tumor Molecular Profiling of Newly Diagnosed HER2-Negative Breast Cancers During Chemotherapy in Combination with Angiogenesis Inhibitors

Background

Breast cancers are heterogeneous with variable clinical courses and treatment responses.

Objective

We sought to evaluate dynamic changes in the molecular landscape of HER2-negative tumors treated with chemotherapy and anti-angiogenic agents.

Patients and Methods

Newly diagnosed HER2-negative breast cancer patients received low-dose sunitinib or bevacizumab prior to four 2-weekly cycles of dose-dense doxorubicin and cyclophosphamide. Tumor biopsies were obtained at baseline, after 2 weeks and after 8 weeks of chemotherapy. Next-generation sequencing was performed to assess for single nucleotide variants (SNVs) and copy number alterations (CNAs) of 440 cancer-related genes (ACTOnco^®). Observed genomic changes were correlated with the Miller-Payne histological response to treatment.

Results

Thirty-four patients received sunitinib and 18 received bevacizumab. In total, 77% were hormone receptor positive (HER2−/HR+) and 23% were triple negative breast cancers (TNBC). New therapy-induced mutations were infrequent, occurring only in 13%, and appeared early after a single cycle of treatment. Seventy-two percent developed changes in the variant allele frequency (VAF) of pathogenic SNVs; the majority (51%) of these changes occurred early at 2 weeks and were sustained for 8 weeks. Changes in VAF of SNVs were most commonly seen in the PI3K/mTOR/AKT pathway; 13% developed changes in pathogenic mutations, which potentially confer sensitivity to PIK3CA inhibitors. Tumors with poor Miller-Payne response to treatment were less likely to experience changes in VAF of SNVs compared with those with good response (50% [7/14] vs 15% [4/24] had no changes observed at any timepoint, p = 0.029).

Conclusions

Serial molecular profiling identifies early therapy-induced genomic alterations, which may guide future selection of targeted therapies in breast cancer patients who progress after standard chemotherapy.

Clinical trial registration

ClinicalTrials.gov: NCT02790580 (first posted June 6, 2016).

Supplementary Information

The online version contains supplementary material available at 10.1007/s11523-022-00886-x.