Cell type-specific properties and environment shape tissue specificity of cancer genes

Emerging importance of m6A modification in liver cancer and its potential therapeutic role

Liver cancer refers to malignant tumors that form in the liver and is usually divided into several types, the most common of which is hepatocellular carcinoma (HCC), which originates in liver cells. Other rare types of liver cancer include intrahepatic cholangiocarcinoma (iCCA). m6A modification is a chemical modification of RNA that usually manifests as the addition of a methyl group to adenine in the RNA molecule to form N6-methyladenosine. This modification exerts a critical role in various biological processes by regulating the metabolism of RNA, affecting gene expression. Recent studies have shown that m6A modification is closely related to the occurrence and development of liver cancer, and m6A regulators can further participate in the pathogenesis of liver cancer by regulating the expression of key genes and the function of specific cells. In this review, we provided an overview of the latest advances in m6A modification in liver cancer research and explored in detail the specific functions of different m6A regulators. Meanwhile, we deeply analyzed the mechanisms and roles of m6A modification in liver cancer, aiming to provide novel insights and references for the search for potential therapeutic targets. Finally, we discussed the prospects and challenges of targeting m6A regulators in liver cancer therapy.

CytoSpatio: Learning cell type spatial relationships using multirange, multitype point process models

Recent advances in multiplexed fluorescence imaging have provided new opportunities for deciphering the complex spatial relationships among various cell types across diverse tissues. We introduce CytoSpatio, open-source software that constructs generative, multirange, and multitype point process models that capture interactions among multiple cell types at various distances simultaneously. On analyzing five cell types across five tissues, our software showed consistent spatial relationships within the same tissue type, with certain cell types like proliferating T cells consistently clustering across tissue types. It also revealed that the attraction-repulsion relationships between cell types like B cells and CD4-positive T cells vary with tissue type. CytoSpatio can also generate synthetic tissue structures that preserve the spatial relationships seen in training images, a capability not provided by previous descriptive, motif-based approaches. This potentially allows spatially realistic simulations of how cell relationships affect tissue biochemistry.

When Do Tumours Develop? Neoplastic Processes Across Different Timescales: Age, Season and Round the Circadian Clock

While it is recognised that most, if not all, multicellular organisms harbour neoplastic processes within their bodies, the timing of when these undesirable cell proliferations are most likely to occur and progress throughout the organism's lifetime remains only partially documented. Due to the different mechanisms implicated in tumourigenesis, it is highly unlikely that this probability remains constant at all times and stages of life. In this article, we summarise what is known about this variation, considering the roles of age, season and circadian rhythm. While most studies requiring that level of detail be done on humans, we also review available evidence in other animal species. For each of these timescales, we identify mechanisms or biological functions shaping the variation. When possible, we show that evolutionary processes likely played a role, either directly to regulate the cancer risk or indirectly through trade-offs. We find that neoplastic risk varies with age in a more complex way than predicted by early epidemiological models: rather than resulting from mutations alone, tumour development is dictated by tissue- and age-specific processes. Similarly, the seasonal cycle can be associated with risk variation in some species with life-history events such as sexual competition or mating being timed according to the season. Lastly, we show that the circadian cycle influences tumourigenesis in physiological, pathological and therapeutic contexts. We also highlight two biological functions at the core of these variations across our three timescales: immunity and metabolism. Finally, we show that our understanding of the entanglement between tumourigenic processes and biological cycles is constrained by the limited number of species for which we have extensive data. Improving our knowledge of the periods of vulnerability to the onset and/or progression of (malignant) tumours is a key issue that deserves further investigation, as it is key to successful cancer prevention strategies.

Clonal expansion of cancer driver gene mutants investigated using advanced sequencing technologies

Advanced sequencing technologies (ASTs) have revolutionized the quantitation of cancer driver mutations (CDMs) as rare events, which has utility in clinical oncology, cancer research, and cancer risk assessment. This review focuses on studies that have used ASTs to characterize clonal expansion (CE) of cells carrying CDMs and to explicate the selective pressures that shape CE. Importantly, high-sensitivity ASTs have made possible the characterization of mutant clones and CE in histologically normal tissue samples, providing the means to investigate nascent tumor development. Some ASTs can identify mutant clones in a spatially defined context; others enable integration of mutant data with analyses of gene expression, thereby elaborating immune, inflammatory, metabolic, and/or stromal microenvironmental impacts on CE. As a whole, these studies make it clear that a startlingly large fraction of cells in histologically normal tissues carry CDMs, CDMs may confer a context-specific selective advantage leading to CE, and only a small fraction of cells carrying CDMs eventually result in neoplasia. These observations were integrated with available literature regarding the mechanisms underlying clonal selection to interpret how measurements of CDMs and CE can be interpreted as biomarkers of cancer risk. Given the stochastic nature of carcinogenesis, the potential functional latency of driver mutations, the complexity of potential mutational and microenvironmental interactions, and involvement of other types of genetic and epigenetic changes, it is concluded that CDM-based measurements should be viewed as probabilistic rather than deterministic biomarkers. Increasing inter-sample variability in CDM levels (as a consequence of CE) may be interpretable as a shift away from normal tissue homeostasis and an indication of increased future cancer risk, a process that may reflect normal aging or carcinogen exposure. Consequently, analyses of variability in levels of CDMs have the potential to bolster existing approaches for carcinogenicity testing.

Prognostic significance of alpha-2-macrglobulin and low-density lipoprotein receptor-related protein-1 in various cancers

Cancer is the leading cause of death worldwide. The World Health Organization (WHO) estimates that 10 million fatalities occurred in 2023. Breast cancer (BC) ranked first among malignancies with 2.26 million cases, lung cancer (LC) second with 2.21 million cases, and colon and rectum cancers (CC, CRC) third with 1.93 million cases. These results highlight the importance of investigating novel cancer prognoses and anti-cancer markers. In this study, we investigated the potential effects of alpha-2 macroglobulin and its receptor, LRP1, on the outcomes of breast, lung, and colorectal malignancies. Immunohistochemical staining was used to analyze the expression patterns of A2M and LRP1 in 545 cases of invasive ductal breast carcinoma (IDC) and 51 cases of mastopathies/fibrocystic breast disease (FBD); 256 cases of non-small cell lung carcinomas (NSCLCs) and 45 cases of non-malignant lung tissue (NMLT); and 108 cases of CRC and 25 cases of non-malignant colorectal tissue (NMCT). A2M and LRP1 expression levels were also investigated in breast (MCF-7, BT-474, SK-BR-3, T47D, MDA-MB-231, and MDA-MB-231/BO2), lung (NCI-H1703, NCI-H522, and A549), and colon (LS 180, Caco-2, HT-29, and LoVo) cancer cell lines. Based on our findings, A2M and LRP1 exhibited various expression patterns in the examined malignancies, which were related to one another. Additionally, the stroma of lung and colorectal cancer has increased levels of A2M/LRP1 areas, which explains the significance of the stroma in the development and maintenance of tumor homeostasis. A2M expression was shown to be downregulated in all types of malignancies under study and was positively linked with an increase in cell line aggressiveness. Although more invasive cells had higher levels of A2M expression, an IHC analysis showed the opposite results. This might be because exogenous alpha-2-macroglobulin is present, which has an inhibitory effect on several cancerous enzymes and receptor-dependent signaling pathways. Additionally, siRNA-induced suppression of the transcripts for A2M and LRPP1 revealed their connection, which provides fresh information on the function of the LRP1 receptor in A2M recurrence in cancer. Further studies on different forms of cancer may corroborate the fact that both A2M and LRP1 have high potential as innovative therapeutic agents.

Transcriptomic analysis reveals a tissue-specific loss of identity during ageing and cancer

INTRODUCTION

Understanding changes in cell identity in cancer and ageing is of great importance. In this work, we analyzed how gene expression changes in human tissues are associated with tissue specificity during cancer and ageing using transcriptome data from TCGA and GTEx.

RESULTS

We found significant downregulation of tissue-specific genes during ageing in 40% of the tissues analyzed, which suggests loss of tissue identity with age. For most cancer types, we have noted a consistent pattern of downregulation in genes that are specific to the tissue from which the tumor originated. Moreover, we observed in cancer an activation of genes not usually expressed in the tissue of origin as well as an upregulation of genes specific to other tissues. These patterns in cancer were associated with patient survival. The age of the patient, however, did not influence these patterns.

CONCLUSION

We identified loss of cellular identity in 40% of the tissues analysed during human ageing, and a clear pattern in cancer, where during tumorigenesis cells express genes specific to other organs while suppressing the expression of genes from their original tissue. The loss of cellular identity observed in cancer is associated with prognosis and is not influenced by age, suggesting that it is a crucial stage in carcinogenesis.

BRCA1 the Versatile Defender: Molecular to Environmental Perspectives

The evolving history of BRCA1 research demonstrates the profound interconnectedness of a single protein within the web of crucial functions in human cells. Mutations in BRCA1, a tumor suppressor gene, have been linked to heightened breast and ovarian cancer risks. However, despite decades of extensive research, the mechanisms underlying BRCA1's contribution to tissue-specific tumor development remain elusive. Nevertheless, much of the BRCA1 protein's structure, function, and interactions has been elucidated. Individual regions of BRCA1 interact with numerous proteins to play roles in ubiquitination, transcription, cell checkpoints, and DNA damage repair. At a cellular scale, these BRCA1 functions coordinate tumor suppression, R-loop prevention, and cellular differentiation, all of which may contribute to BRCA1's role in cancer tissue specificity. As research on BRCA1 and breast cancer continues to evolve, it will become increasingly evident that modern materials such as Bisphenol A should be examined for their relationship with DNA stability, cancer incidence, and chemotherapy. Overall, this review offers a comprehensive understanding of BRCA1's many roles at a molecular, cellular, organismal, and environmental scale. We hope that the knowledge gathered here highlights both the necessity of BRCA1 research and the potential for novel strategies to prevent and treat cancer in individuals carrying BRCA1 mutations.

Analysis of context-specific KRAS-effector (sub)complexes in Caco-2 cells

Ras is a key switch controlling cell behavior. In the GTP-bound form, Ras interacts with numerous effectors in a mutually exclusive manner, where individual Ras-effectors are likely part of larger cellular (sub)complexes. The molecular details of these (sub)complexes and their alteration in specific contexts are not understood. Focusing on KRAS, we performed affinity purification (AP)-mass spectrometry (MS) experiments of exogenously expressed FLAG-KRAS WT and three oncogenic mutants ("genetic contexts") in the human Caco-2 cell line, each exposed to 11 different culture media ("culture contexts") that mimic conditions relevant in the colon and colorectal cancer. We identified four effectors present in complex with KRAS in all genetic and growth contexts ("context-general effectors"). Seven effectors are found in KRAS complexes in only some contexts ("context-specific effectors"). Analyzing all interactors in complex with KRAS per condition, we find that the culture contexts had a larger impact on interaction rewiring than genetic contexts. We investigated how changes in the interactome impact functional outcomes and created a Shiny app for interactive visualization. We validated some of the functional differences in metabolism and proliferation. Finally, we used networks to evaluate how KRAS-effectors are involved in the modulation of functions by random walk analyses of effector-mediated (sub)complexes. Altogether, our work shows the impact of environmental contexts on network rewiring, which provides insights into tissue-specific signaling mechanisms. This may also explain why KRAS oncogenic mutants may be causing cancer only in specific tissues despite KRAS being expressed in most cells and tissues.

BRCA Mutations-The Achilles Heel of Breast, Ovarian and Other Epithelial Cancers

Two related tumor suppressor genes, BRCA1 and BRCA2, attract a lot of attention from both fundamental and clinical points of view. Oncogenic hereditary mutations in these genes are firmly linked to the early onset of breast and ovarian cancers. However, the molecular mechanisms that drive extensive mutagenesis in these genes are not known. In this review, we hypothesize that one of the potential mechanisms behind this phenomenon can be mediated by Alu mobile genomic elements. Linking mutations in the BRCA1 and BRCA2 genes to the general mechanisms of genome stability and DNA repair is critical to ensure the rationalized choice of anti-cancer therapy. Accordingly, we review the literature available on the mechanisms of DNA damage repair where these proteins are involved, and how the inactivating mutations in these genes (BRCAness) can be exploited in anti-cancer therapy. We also discuss a hypothesis explaining why breast and ovarian epithelial tissues are preferentially susceptible to mutations in BRCA genes. Finally, we discuss prospective novel therapeutic approaches for treating BRCAness cancers.

An evolution-based machine learning to identify cancer type-specific driver mutations

Identifying cancer type-specific driver mutations is crucial for illuminating distinct pathologic mechanisms across various tumors and providing opportunities of patient-specific treatment. However, although many computational methods were developed to predict driver mutations in a type-specific manner, the methods still have room to improve. Here, we devise a novel feature based on sequence co-evolution analysis to identify cancer type-specific driver mutations and construct a machine learning (ML) model with state-of-the-art performance. Specifically, relying on 28 000 tumor samples across 66 cancer types, our ML framework outperformed current leading methods of detecting cancer driver mutations. Interestingly, the cancer mutations identified by sequence co-evolution feature are frequently observed in interfaces mediating tissue-specific protein-protein interactions that are known to associate with shaping tissue-specific oncogenesis. Moreover, we provide pre-calculated potential oncogenicity on available human proteins with prediction scores of all possible residue alterations through user-friendly website (http://sbi.postech.ac.kr/w/cancerCE). This work will facilitate the identification of cancer type-specific driver mutations in newly sequenced tumor samples.

The Mutational Landscape of Cancer's Vulnerability to Ionizing Radiation

PURPOSE

Large-scale sequencing efforts have established that cancer-associated genetic alterations are highly diverse, posing a challenge to the identification of variants that regulate complex phenotypes like radiation sensitivity. The impact of the vast majority of rare or common genetic variants on the sensitivity of cancers to radiotherapy remains largely unknown.

EXPERIMENTAL DESIGN

We developed a scalable gene editing and irradiation platform to assess the role of categories of variants in cells. Variants were prioritized on the basis of genotype-phenotype associations from a previously completed large-scale cancer cell line radiation profiling study. Altogether, 488 alleles (396 unique single-nucleotide variants) from 92 genes were generated and profiled in an immortalized lung cell line, BEAS-2B. We validated our results in other cell lines (TRT-HU1 and NCI-H520), in vivo via the use of both cell line and patient-derived murine xenografts, and in clinical cohorts.

RESULTS

We show that resistance to radiation is characterized by substantial inter- and intra-gene allelic variation. Some genes (e.g., KEAP1) demonstrated significant intragenic allelic variation in the magnitude of conferred resistance and other genes (e.g., CTNNB1) displayed both resistance and sensitivity in a protein domain-dependent manner. We combined results from our platform with gene expression and metabolite features and identified the upregulation of amino acid transporters that facilitate oxidative reductive capacity and cell-cycle deregulation as key regulators of radiation sensitivity.

CONCLUSIONS

Our results reveal new insights into the genetic determinants of tumor sensitivity to radiotherapy and nominate a multitude of cancer mutations that are predicted to impact treatment efficacy.

Cucurbitacin B regulates lung cancer cell proliferation and apoptosis via inhibiting the IL-6/STAT3 pathway through the lncRNA XIST/miR-let-7c axis

CONTEXT

Lung cancer, the most common type of cancer, has a high mortality rate. Cucurbitacin B (CuB), a natural compound extracted from Cucurbitaceae plants, has antitumor effects.

OBJECTIVE

We investigated the role of CuB on lung cancer and its potential mechanisms.

MATERIALS AND METHODS

A549 cells were treated with 0.1, 0.3, 0.6, and 0.9 μM CuB for 12, 24, and 48 h or untreated. Gene and protein levels were evaluated by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. Enzyme-linked immunosorbent assay (ELISA) detected inflammatory factors levels (TNF-α and IL-10). 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), flow cytometry, and colony formation assays measured cell viability, apoptosis, and proliferation. The interaction between miR-let-7c and long non-coding RNA X inactive-specific transcript (XIST) or interleukin-6 (IL-6) was verified by dual-luciferase reporter assays.

RESULTS

CuB treatment inhibited the proliferation of lung cancer cells and promoted cell apoptosis, and increased the expression of Bax and cleaved caspase3, decreased cyclin B1 and Bcl-2 expression. CuB suppressed XIST and IL-6 expression, and enhanced miR-let-7c expression. XIST silencing enhanced the inhibitory effect of CuB on cell proliferation and the promotion effect on apoptosis via upregulating miR-let-7c. Moreover, XIST targeted miR-let-7c to activate the IL-6/STAT axis. MiR-let-7c overexpression enhanced the regulatory effect of CuB on proliferation and apoptosis via suppressing the IL-6/STAT3 pathway.

DISCUSSION AND CONCLUSION

CuB regulated cell proliferation and apoptosis by inhibiting the XIST/miR-let-7c/IL-6/STAT3 axis in lung cancer. These findings indicate CuB may have the possibility of clinical application in lung cancer treatment.

New Drug Development and Clinical Trial Design by Applying Genomic Information Management

Depending on the patients' genotype, the same drug may have different efficacies or side effects. With the cost of genomic analysis decreasing and reliability of analysis methods improving, vast amount of genomic information has been made available. Several studies in pharmacology have been based on genomic information to select the optimal drug, determine the dose, predict efficacy, and prevent side effects. This paper reviews the tissue specificity and genomic information of cancer. If the tissue specificity of cancer is low, cancer is induced in various organs based on a single gene mutation. Basket trials can be performed for carcinomas with low tissue specificity, confirming the efficacy of one drug for a single gene mutation in various carcinomas. Conversely, if the tissue specificity of cancer is high, cancer is induced in only one organ based on a single gene mutation. An umbrella trial can be performed for carcinomas with a high tissue specificity. Some drugs are effective for patients with a specific genotype. A companion diagnostic strategy that prescribes a specific drug for patients selected with a specific genotype is also reviewed. Genomic information is used in pharmacometrics to identify the relationship among pharmacokinetics, pharmacodynamics, and biomarkers of disease treatment effects. Utilizing genomic information, sophisticated clinical trials can be designed that will be better suited to the patients of specific genotypes. Genomic information also provides prospects for innovative drug development. Through proper genomic information management, factors relating to drug response and effects can be determined by selecting the appropriate data for analysis and by understanding the structure of the data. Selecting pre-processing and appropriate machine-learning libraries for use as machine-learning input features is also necessary. Professional curation of the output result is also required. Personalized medicine can be realized using a genome-based customized clinical trial design.

Genome-wide DNA methylation profiling and identification of potential pan-cancer and tumor-specific biomarkers

DNA methylation alterations have already been linked to cancer, and their usefulness for therapy and diagnosis has encouraged research into the human epigenome. Several biomarker studies have focused on identifying cancer types individually, yet common cancer and multi-cancer markers are still underexplored. We used The Cancer Genome Atlas (TCGA) to investigate genome-wide methylation profiles of 14 different cancer types and developed a three-step computational approach to select candidate biomarker CpG sites. In total, 1,991 pan-cancer and between 75 and 1,803 cancer-specific differentially methylated CpG sites were discovered. Differentially methylated blocks and regions were also discovered for the first time on such a large-scale. Through a three-step computational approach, a combination of four pan-cancer CpG markers was identified from these sites and externally validated (AUC = 0.90), maintaining comparable performance across tumor stages. Additionally, 20 tumor-specific CpG markers were identified and made up the final type-specific prediction model, which could accurately differentiate tumor types (AUC = 0.87-0.99). Our study highlights the power of the methylome as a rich source of cancer biomarkers, and the signatures we identified provide a new resource for understanding cancer mechanisms on the wider genomic scale with strong applicability in the context of new minimally invasive cancer detection assays.

OUP accepted manuscript

Motivation

There is a plethora of measures to evaluate functional similarity (FS) of genes based on their co-expression, protein–protein interactions and sequence similarity. These measures are typically derived from hand-engineered and application-specific metrics to quantify the degree of shared information between two genes using their Gene Ontology (GO) annotations.

Results

We introduce deepSimDEF, a deep learning method to automatically learn FS estimation of gene pairs given a set of genes and their GO annotations. deepSimDEF’s key novelty is its ability to learn low-dimensional embedding vector representations of GO terms and gene products and then calculate FS using these learned vectors. We show that deepSimDEF can predict the FS of new genes using their annotations: it outperformed all other FS measures by >5–10% on yeast and human reference datasets on protein–protein interactions, gene co-expression and sequence homology tasks. Thus, deepSimDEF offers a powerful and adaptable deep neural architecture that can benefit a wide range of problems in genomics and proteomics, and its architecture is flexible enough to support its extension to any organism.

Availability and implementation

Source code and data are available at https://github.com/ahmadpgh/deepSimDEF

Supplementary information

Supplementary data are available at Bioinformatics online.

Cancer Type Classification in Liquid Biopsies Based on Sparse Mutational Profiles Enabled through Data Augmentation and Integration

Identifying the cell of origin of cancer is important to guide treatment decisions. Machine learning approaches have been proposed to classify the cell of origin based on somatic mutation profiles from solid biopsies. However, solid biopsies can cause complications and certain tumors are not accessible. Liquid biopsies are promising alternatives but their somatic mutation profile is sparse and current machine learning models fail to perform in this setting. We propose an improved method to deal with sparsity in liquid biopsy data. Firstly, data augmentation is performed on sparse data to enhance model robustness. Secondly, we employ data integration to merge information from: (i) SNV density; (ii) SNVs in driver genes and (iii) trinucleotide motifs. Our adapted method achieves an average accuracy of 0.88 and 0.65 on data where only 70% and 2% of SNVs are retained, compared to 0.83 and 0.41 with the original model, respectively. The method and results presented here open the way for application of machine learning in the detection of the cell of origin of cancer from liquid biopsy data.

Dysregulated expression of claudins in cancer

Claudins are adhesion molecules located at the tight junctions between epithelial cells. A series of studies have now reported aberrant expression of claudin proteins in the context of neoplastic transformation, suggesting its role in tumorigenesis. However, the precise mechanisms are still not well understood. Studies on expression alterations of claudins have revealed a range of outcomes that reflect the complexity of claudins in terms of spatial localization, tumor type and stage of disease. The diverse and dynamic expression patterns of claudins in cancer are tightly controlled by a wide range of regulatory mechanisms, which are commonly modulated by oncogenic signaling pathways. The present review summarizes the recent knowledge describing the dysregulation of claudin expression in cancer and discusses the intrinsic and extrinsic determinants of the context-specific expression patterns of claudins.

Context-Dependent Roles of Claudins in Tumorigenesis

The barrier and fence functions of the claudin protein family are fundamental to tissue integrity and human health. Increasing evidence has linked claudins to signal transduction and tumorigenesis. The expression of claudins is frequently dysregulated in the context of neoplastic transformation. Studies have uncovered that claudins engage in nearly all aspects of tumor biology and steps of tumor development, suggesting their promise as targets for treatment or biomarkers for diagnosis and prognosis. However, claudins can be either tumor promoters or tumor suppressors depending on the context, which emphasizes the importance of taking various factors, including organ type, environmental context and genetic confounders, into account when studying the biological functions and targeting of claudins in cancer. This review discusses the complicated roles and intrinsic and extrinsic determinants of the context-specific effects of claudins in cancer.

DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

Targeted mRNA expression panels, measuring up to 800 genes, are used in academic and clinical settings due to low cost and high sensitivity for archived samples. Most samples assayed on targeted panels originate from bulk tissue comprised of many cell types, and cell-type heterogeneity confounds biological signals. Reference-free methods are used when cell-type-specific expression references are unavailable, but limited feature spaces render implementation challenging in targeted panels. Here, we present DeCompress, a semi-reference-free deconvolution method for targeted panels. DeCompress leverages a reference RNA-seq or microarray dataset from similar tissue to expand the feature space of targeted panels using compressed sensing. Ensemble reference-free deconvolution is performed on this artificially expanded dataset to estimate cell-type proportions and gene signatures. In simulated mixtures, four public cell line mixtures, and a targeted panel (1199 samples; 406 genes) from the Carolina Breast Cancer Study, DeCompress recapitulates cell-type proportions with less error than reference-free methods and finds biologically relevant compartments. We integrate compartment estimates into cis-eQTL mapping in breast cancer, identifying a tumor-specific cis-eQTL for CCR3 (C-C Motif Chemokine Receptor 3) at a risk locus. DeCompress improves upon reference-free methods without requiring expression profiles from pure cell populations, with applications in genomic analyses and clinical settings.

Zooming in on the WNT/CTNNB1 Destruction Complex: Functional Mechanistic Details with Implications for Therapeutic Targeting

WNT/CTNNB1 signaling is crucial for balancing cell proliferation and differentiation in all multicellular animals. CTNNB1 accumulation is the hallmark of WNT/CTNNB1 pathway activation and the key downstream event in both a physiological and an oncogenic context. In the absence of WNT stimulation, the cytoplasmic and nuclear levels of CTNNB1 are kept low because of its sequestration and phosphorylation by the so-called destruction complex, which targets CTNNB1 for proteasomal degradation. In the presence of WNT proteins, or as a result of oncogenic mutations, this process is impaired and CTNNB1 levels become elevated.Here we discuss recent advances in our understanding of destruction complex activity and inactivation, focusing on the individual components and interactions that ultimately control CTNNB1 turnover (in the "WNT off" situation) and stabilization (in the "WNT on" situation). We especially highlight the insights gleaned from recent quantitative, image-based studies, which paint an unprecedentedly detailed picture of the dynamic events that control destruction protein complex composition and function. We argue that these mechanistic details may reveal new opportunities for therapeutic intervention and could result in the destruction complex re-emerging as a target for therapy in cancer.

Why Tumor Genetic Heterogeneity May Require Rethinking Cancer Genesis and Treatment

Tumor genetic heterogeneity, in which individual tumors contain both multiple variant cancer-associated and normal genes, has been widely reported, although its significance has yet to be fully understood. We propose a genetic heterogeneity-based selection-centric hypothesis in which genetic heterogeneity, caused by the temporary reduction of DNA repair efficiency, occurs very early in human development, resulting in a small minority of cells in normal tissues acquiring cancer-associated genes that remain dormant. Cancer develops when precancer cells are selected for by altered tissue microenvironments; similar scenarios occur with development of metastases and therapeutic resistance in established cancer. This suggests that a normal cell selection treatment approach based on preferentially selecting normal cells within tumors may be effective in treating cancer.

Why we do what we do. A brief analysis of cancer therapies

The goal of all medical activity is to preserve health in fit people, and to restore the sick into a state of complete physical, mental and social wellbeing. In an effort to determine whether we are achieving this last goal in oncology, herein we review the biological and clinical framework that has led to the foundations of the current anticancer treatment paradigm. Currently, cancer therapy is still based on the ancient axiom that states that the complete eradication of the tumor burden is the only way to achieve a cure. This strategy has led to a substantial improvement in survival rates as cancer mortality rates have dropped in an unprecedented way. Despite this progress, more than 9 million people still die from cancer every year, indicating that the current treatment strategy is not leading to a cancer cure, but to a cancer remission, that is "the temporary absence of manifestations of a particular disease"; after months or years of remission, in most patients, cancer will inevitably recur. Our critical analysis indicates that it is time to discuss about the new key challenges and future directions in clinical oncology. We need to generate novel treatment strategies more suited to the current clinical reality.

Untangling a complex web: Computational analyses of tumor molecular profiles to decode driver mechanisms

Genome-scale studies focusing on molecular profiling of cancers across tissue types have revealed a plethora of aberrations across the genomic, transcriptomic, and epigenomic scales. The significant molecular heterogeneity across individual tumors even within the same tissue context complicates decoding the key etiologic mechanisms of this disease. Furthermore, it is increasingly likely that biologic mechanisms underlying the pathobiology of cancer involve multiple molecular entities interacting across functional scales. This has motivated the development of computational approaches that integrate molecular measurements with prior biological knowledge in increasingly intricate ways to enable the discovery of driver genomic aberrations across cancers. Here, we review diverse methodological approaches that have powered significant advances in our understanding of the genomic underpinnings of cancer at the cohort and at the individual tumor scales. We outline the key advances and challenges in the computational discovery of cancer mechanisms while motivating the development of systems biology approaches to comprehensively decode the biologic drivers of this complex disease.

Human genes differ by their UV sensitivity estimated through analysis of UV-induced silent mutations in melanoma

We hypothesized that human genes differ by their sensitivity to ultraviolet (UV) exposure. We used somatic mutations detected by genome-wide screens in melanoma and reported in the Catalog Of Somatic Mutations In Cancer. As a measure of UV sensitivity, we used the number of silent mutations generated by C>T transitions in pyrimidine dimers of a given transcript divided by the number of potential sites for this type of mutations in the transcript. We found that human genes varied by UV sensitivity by two orders of magnitude. We noted that the melanoma-associated tumor suppressor gene CDKN2A was among the top five most UV-sensitive genes in the human genome. Melanoma driver genes have a higher UV-sensitivity compared with other genes in the human genome. The difference was more prominent for tumor suppressors compared with oncogene. The results of this study suggest that differential sensitivity of human transcripts to UV light may explain melanoma specificity of some driver genes. Practical significance of the study relates to the fact that differences in UV sensitivity among human genes need to be taken into consideration whereas predicting melanoma-associated genes by the number of somatic mutations detected in a given gene.

Effects of doxorubicin associated with amniotic membrane stem cells in the treatment of canine inflammatory breast carcinoma (IPC-366) cells

BACKGROUND

Tumours in mammary glands represent the most common neoplasia in bitches, as in humans. This high incidence results in part from the stimulation of sex hormones on these glands. Among mammary tumours, inflammatory carcinoma is the most aggressive, presenting a poor prognosis to surgical treatment and chemotherapy. One of the most widely used chemotherapy drugs for breast cancer treatment is doxorubicin (DOXO). Alternative therapies have been introduced in order to assist in these treatments; studies on treatments using stem cells have emerged, since they have anti-inflammatory and immunomodulatory properties. The aim of this study was to evaluate the effects of DOXO and canine amniotic membrane stem cells (AMCs) on the triple-negative canine inflammatory mammary carcinoma cell line IPC-366.

METHODS

Four experimental groups were analysed: a control group without treatment; Group I with DOXO, Group II with AMC and Group III with an association of DOXO and AMCs. We performed the MTT assay with DOXO in order to select the best concentration for the experiments. The growth curve was performed with all groups (I-III) in order to verify the potential of treatments to reduce the growth of IPC-366. For the cell cycle, all groups (I-III) were tested using propidium iodide. While in the flow cytometry, antibodies to progesterone receptor (PR), estrogen receptor (ER), PCNA, VEGF, IL-10 and TGF-β1 were used. For steroidogenic pathway hormones, an ELISA assay was performed.

RESULTS

The results showed that cells treated with 10 µg/mL DOXO showed a 71.64% reduction in cellular growth after 72 h of treatment. Reductions in the expression of VEGF and PCNA-3 were observed by flow cytometry in all treatments when compared to the control. The intracellular levels of ERs were also significantly increased in Group III (4.67% vs. 27.1%). Regarding to the levels of steroid hormones, significant increases in the levels of estradiol (E2) and estrone sulphate (S04E1) were observed in Groups I and III. On the other hand, Group II did not show differences in steroid hormone levels in relation to the control. We conclude that the association of DOXO with AMCs (Group III) promoted a reduction in cell growth and in the expression of proteins related to proliferation and angiogenesis in IPC-366 triple-negative cells.

CONCLUSIONS

This treatment promoted ER positive expression, suggesting that the accumulated oestrogen conducted these cells to a synergistic state, rendering these tumour cells responsive to ERs and susceptible to new hormonal cancer therapies.

Somatic selection distinguishes oncogenes and tumor suppressor genes

Motivation

Functions of cancer driver genes vary substantially across tissues and organs. Distinguishing passenger genes, oncogenes (OGs) and tumor-suppressor genes (TSGs) for each cancer type is critical for understanding tumor biology and identifying clinically actionable targets. Although many computational tools are available to predict putative cancer driver genes, resources for context-aware classifications of OGs and TSGs are limited.

Results

We show that the direction and magnitude of somatic selection of protein-coding mutations are significantly different for passenger genes, OGs and TSGs. Based on these patterns, we develop a new method (genes under selection in tumors) to discover OGs and TSGs in a cancer-type specific manner. Genes under selection in tumors shows a high accuracy (92%) when evaluated via strict cross-validations. Its application to 10 172 tumor exomes found known and novel cancer drivers with high tissue-specificities. In 11 out of 13 OGs shared among multiple cancer types, we found functional domains selectively engaged in different cancers, suggesting differences in disease mechanisms.

Availability and implementation

An R implementation of the GUST algorithm is available at https://github.com/liliulab/gust. A database with pre-computed results is available at https://liliulab.shinyapps.io/gust.

Supplementary information

Supplementary data are available at Bioinformatics online.

Tissue-Specific Carcinogens as Soil to Seed BRCA1/2-Mutant Hereditary Cancers

Despite their ubiquitous expression, the inheritance of monoallelic germline mutations in breast cancer susceptibility gene type 1 or 2 (BRCA1/2) poses tissue-specific variations in cancer risks and primarily associate with familial breast and ovarian cancers. The molecular basis of this tissue-specific tumor incidence remains unknown and intriguing to cancer researchers. A plethora of recent reports support the idea that several nongenetic factors present in the tissue microenvironment could induce tumors in the mutant BRCA1/2 background. This Opinion article summarizes the recent advances on tissue-specific carcinogens and their complex crosstalk with the compromised DNA repair machinery of BRCA1/2-mutant cells. Finally, we present our perspective on the therapeutic and chemopreventive interpretations of these developments.

Not all cancers are created equal: Tissue specificity in cancer genes and pathways

Tumors arise through waves of genetic alterations and clonal expansion that allow tumor cells to acquire cancer hallmarks, such as genome instability and immune evasion. Recent genomic analyses showed that the vast majority of cancer driver genes are mutated in a tissue-dependent manner, that is, are altered in some cancers but not others. Often the tumor type also affects the likelihood of therapy response. What is the origin of tissue specificity in cancer? Recent studies suggest that both cell-intrinsic and cell-extrinsic factors play a role. On one hand, cell type-specific wiring of the cell signaling network determines the outcome of cancer driver gene mutations. On the other hand, the tumor cells' exposure to tissue-specific microenvironments (e.g. immune cells) also contributes to shape the tissue specificity of driver genes and of therapy response. In the future, a more complete understanding of tissue specificity in cancer may inform methods to better predict and improve therapeutic outcomes.

The leading role of pathology in assessing the somatic molecular alterations of cancer: Position Paper of the European Society of Pathology

Molecular pathology is an essential part of pathology complementing conventional morphological tools to obtain a correct integrated diagnosis with appropriate assessment of prognosis and prediction of response to therapy, particularly in cancer. There is a concern about the situation of molecular pathology in some areas of Europe, namely, regarding the central role of pathologists in assessing somatic genomic alterations in cancer. In some countries, there are attempts that other laboratory medicine specialists perform the molecular analysis of somatic alterations in cancer, particularly now when next generation sequencing (NGS) is incorporated into clinical practice. In this scenario, pathologists may play just the role of “tissue providers,” and other specialists may take the lead in molecular analysis. Geneticists and laboratory medicine specialists have all background and skills to perform genetic analysis of germline alterations in hereditary disorders, including familial forms of cancers. However, interpretation of somatic alterations of cancer belongs to the specific scientific domain of pathology. Pathologists are necessary to guarantee the quality of the results, for several reasons: (1) The identified molecular alterations should be interpreted in the appropriate morphologic context, since most of them are context-specific; (2) pre-analytical issues must be taken into consideration; (3) it is crucial to check the proportion of tumor cells in the sample subjected to analysis and presence of inflammatory infiltrate and necrosis should be monitored; and 4) the role of pathologists is crucial to select the most appropriate methods and to control the turnaround time in which the molecular results are delivered in the context of an integrated diagnosis. Obviously, there is the possibility of having core facilities for NGS in a hospital to perform the sequence analysis that are open to other specialties (microbiologists, geneticists), but also in this scenario, pathologists should have the lead in assessing somatic alterations of cancer. In this article, we emphasize the importance of interpreting somatic molecular alterations of the tumors in the context of morphology. In this Position Paper of the European Society of Pathology, we strongly support a central role of pathology departments in the process of analysis and interpretation of somatic molecular alterations in cancer.

Framing cancer progression: influence of the organ- and tumour-specific matrisome

The extracellular matrix (ECM) plays a crucial role in regulating organ homeostasis. It provides mechanical and biochemical cues directing cellular behaviour and, therefore, has control over the progression of diseases such as cancer. Recent efforts have greatly enhanced our knowledge of the protein composition of the ECM and its regulators, the so-called matrisome, in healthy and cancerous tissues; yet, an overview of the common signatures and organ-specific ECM in cancer is missing. Here, we address this by taking a detailed approach to review why cancer grows in certain organs, and focus on the influence of the matrisome at primary and metastatic tumour sites. Our in-depth and comprehensive review of the current literature and general understanding identifies important commonalities and distinctions, providing insight into the biology of metastasis, which could pave the way to improve future diagnostics and therapies.

Diversity spectrum analysis identifies mutation-specific effects of cancer driver genes

Mutation-specific effects of cancer driver genes influence drug responses and the success of clinical trials. We reasoned that these effects could unbalance the distribution of each mutation across different cancer types, as a result, the cancer preference can be used to distinguish the effects of the causal mutation. Here, we developed a network-based framework to systematically measure cancer diversity for each driver mutation. We found that half of the driver genes harbor cancer type-specific and pancancer mutations simultaneously, suggesting that the pervasive functional heterogeneity of the mutations from even the same driver gene. We further demonstrated that the specificity of the mutations could influence patient drug responses. Moreover, we observed that diversity was generally increased in advanced tumors. Finally, we scanned potentially novel cancer driver genes based on the diversity spectrum. Diversity spectrum analysis provides a new approach to define driver mutations and optimize off-label clinical trials.

Xiaobao Dong et al. developed a network-based framework to measure cancer diversity for cancer driver mutations, finding that half of driver genes contain both cancer type-specific and pancancer mutations. They show that the specificity of mutations could influence therapeutic responses.

Secretome profiling identifies neuron-derived neurotrophic factor as a tumor-suppressive factor in lung cancer

Clinical and preclinical studies show tissue-specific differences in tumorigenesis. Tissue specificity is controlled by differential gene expression. We prioritized genes that encode secreted proteins according to their preferential expression in normal lungs to identify candidates associated with lung cancer. Indeed, most of the lung-enriched genes identified in our analysis have known or suspected roles in lung cancer. We focused on the gene encoding neuron-derived neurotrophic factor (NDNF), which had not yet been associated with lung cancer. We determined that NDNF was preferentially expressed in the normal adult lung and that its expression was decreased in human lung adenocarcinoma and a mouse model of this cancer. Higher expression of NDNF was associated with better clinical outcome of patients with lung adenocarcinoma. Purified NDNF inhibited proliferation of lung cancer cells, whereas silencing NDNF promoted tumor cell growth in culture and in xenograft models. We determined that NDNF is downregulated through DNA hypermethylation near CpG island shores in human lung adenocarcinoma. Furthermore, the lung cancer-related DNA hypermethylation sites corresponded to the methylation sites that occurred in tissues with low NDNF expression. Thus, by analyzing the tissue-specific secretome, we identified a tumor-suppressive factor, NDNF, which is associated with patient outcomes in lung adenocarcinoma.

An Emerging Regulatory Role for the Tumor Microenvironment in the DNA Damage Response to Double-Strand Breaks

Radiation, alkylating agents, and platinum-based chemotherapy treatments eliminate cancer cells through the induction of excessive DNA damage. The resultant DNA damage challenges the cancer cell's DNA repair capacity. Among the different types of DNA damage induced in cells, double-strand breaks (DSB) are the most lethal if left unrepaired. Unrepaired DSBs in tumor cells exacerbate existing gene deletions, chromosome losses and rearrangements, and aberrant features that characteristically enable tumor progression, metastasis, and drug resistance. Tumor microenvironmental factors like hypoxia, inflammation, cellular metabolism, and the immune system profoundly influence DSB repair mechanisms. Here, we put into context the role of the microenvironment in governing DSB repair mechanisms.

TissGDB: tissue-specific gene database in cancer

Tissue-specific gene expression is critical in understanding biological processes, physiological conditions, and disease. The identification and appropriate use of tissue-specific genes (TissGenes) will provide important insights into disease mechanisms and organ-specific therapeutic targets. To better understand the tissue-specific features for each cancer type and to advance the discovery of clinically relevant genes or mutations, we built TissGDB (Tissue specific Gene DataBase in cancer) available at http://zhaobioinfo.org/TissGDB. We collected and curated 2461 tissue specific genes (TissGenes) across 22 tissue types that matched the 28 cancer types of The Cancer Genome Atlas (TCGA) from three representative tissue-specific gene expression resources: The Human Protein Atlas (HPA), Tissue-specific Gene Expression and Regulation (TiGER), and Genotype-Tissue Expression (GTEx). For these 2461 TissGenes, we performed gene expression, somatic mutation, and prognostic marker-based analyses across 28 cancer types using TCGA data. Our analyses identified hundreds of TissGenes, including genes that universally kept or lost tissue-specific gene expression, with other features: cancer type-specific isoform expression, fusion with oncogenes or tumor suppressor genes, and markers for protective or risk prognosis. TissGDB provides seven categories of annotations: TissGeneSummary, TissGeneExp, TissGene-miRNA, TissGeneMut, TissGeneNet, TissGeneProg, TissGeneClin.

The X-linked tumor suppressor TSPX downregulates cancer-drivers/oncogenes in prostate cancer in a C-terminal acidic domain dependent manner

TSPX is a tumor suppressor gene located at Xp11.22, a prostate cancer susceptibility locus. It is ubiquitously expressed in most tissues but frequently downregulated in various cancers, including lung, brain, liver and prostate cancers. The C-terminal acidic domain (CAD) of TSPX is crucial for the tumor suppressor functions, such as inhibition of cyclin B/CDK1 phosphorylation and androgen receptor transactivation. Currently, the exact role of the TSPX CAD in transcriptional regulation of downstream genes is still uncertain. Using different variants of TSPX, we showed that overexpression of either TSPX, that harbors a CAD, or a CAD-truncated variant (TSPX[∆C]) drastically retarded cell proliferation in a prostate cancer cell line LNCaP, but cell death was induced only by overexpression of TSPX. Transcriptome analyses showed that TSPX or TSPX[∆C] overexpression downregulated multiple cancer-drivers/oncogenes, including MYC and MYB, in a CAD-dependent manner and upregulated various tumor suppressors in a CAD-independent manner. Datamining of transcriptomes of prostate cancer specimens in the Cancer Genome Atlas (TCGA) dataset confirmed the negative correlation between the expression level of TSPX and those of MYC and MYB in clinical prostate cancer, thereby supporting the hypothesis that the CAD of TSPX plays an important role in suppression of cancer-drivers/oncogenes in prostatic oncogenesis.

BRCA1 and Breast Cancer: a Review of the Underlying Mechanisms Resulting in the Tissue-Specific Tumorigenesis in Mutation Carriers

Since the first cloning of BRCA1 in 1994, many of its cellular interactions have been elucidated. However, its highly specific role in tumorigenesis in the breast tissue—carriers of BRCA1 mutations are predisposed to life-time risks of up to 80%—relative to many other tissues that remain unaffected, has not yet been fully enlightened. In this article, we have applied a universal model of tissue-specificity of cancer genes to BRCA1 and present a systematic review of proposed concepts classified into 4 categories. Firstly, tissue-specific differences in levels of BRCA1 expression and secondly differences in expression of proteins with redundant functions are outlined. Thirdly, cell-type specific interactions of BRCA1 are presented: its regulation of aromatase, its interaction with Progesterone- and receptor activator of nuclear factor-κB ligand-signaling that controls proliferation of luminal progenitor cells, and its influence on cell differentiation via modulation of the key regulators jagged 1-NOTCH and snail family transcriptional repressor 2. Fourthly, factors specific to the cell-type as well as the environment of the breast tissue are elucidated: distinct frequency of losses of heterozygosity, interaction with X inactivation specific transcript RNA, estrogen-dependent induction of genotoxic metabolites and nuclear factor (erythroid-derived 2)-like 2, and regulation of sirtuin 1. In conclusion, the impact of these concepts on the formation of hormone-sensitive and -insensitive breast tumors is outlined.

HCCDB: A Database of Hepatocellular Carcinoma Expression Atlas

Hepatocellular carcinoma (HCC) is highly heterogeneous in nature and has been one of the most common cancer types worldwide. To ensure repeatability of identified gene expression patterns and comprehensively annotate the transcriptomes of HCC, we carefully curated 15 public HCC expression datasets that cover around 4000 clinical samples and developed the database HCCDB to serve as a one-stop online resource for exploring HCC gene expression with user-friendly interfaces. The global differential gene expression landscape of HCC was established by analyzing the consistently differentially expressed genes across multiple datasets. Moreover, a 4D metric was proposed to fully characterize the expression pattern of each gene by integrating data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). To facilitate a comprehensive understanding of gene expression patterns in HCC, HCCDB also provides links to third-party databases on drug, proteomics, and literatures, and graphically displays the results from computational analyses, including differential expression analysis, tissue-specific and tumor-specific expression analysis, survival analysis, and co-expression analysis. HCCDB is freely accessible at http://lifeome.net/database/hccdb.

Explaining cancer type specific mutations with transcriptomic and epigenomic features in normal tissues

Most cancer driver genes are involved in generic cellular processes such as DNA repair, cell proliferation and cell adhesion, yet their mutations are often confined to specific cancer types. To resolve this paradox, we explained mutation frequencies of selected genes across tumor types with four features in the corresponding normal tissues from cancer-free subjects: mRNA expression and chromatin accessibility of mutated genes, mRNA expressions of their neighbors in curated pathways and the protein-protein interaction network. Encouragingly, these transcriptomic/epigenomic features in normal tissues were closely associated with mutational/functional characteristics in tumors. First, chromatin accessibility was a necessary but not sufficient condition for frequent mutations. Second, variations of mutation frequencies in selected genes across tissue types were significantly associated with all four features. Third, the genes possessing significant associations between mutation frequency variations and pathway gene expression were enriched with documented cancer genes. We further proposed a novel bivariate gene set enrichment analysis and confirmed that the pathway gene expression was the dominant factor in cancer gene enrichment. These findings shed lights on the functional roles of genes in normal tissues in shaping the mutational landscape during tumor genome evolution.

PO2-based biodosimetry evaluation using an EPR technique acts as a sensitive index for chemotherapy

The partial pressure of oxygen (PO₂) in the tumor microenvironment directly affects tumor sensitivity to chemotherapy. In the present study, a lithium phthalocyanine probe was implanted into MCF-7 human breast cancer cells, followed by transplant of the cells into nude mice. The present study used an electron paramagnetic resonance (EPR) oximetry measuring technique to dynamically monitor PO₂ in the tumor microenvironment prior to and following chemotherapy, and aimed to determine the precise time window in which the microenvironmental PO₂ peaked following chemotherapy. The results indicated that PO₂ was significantly higher in breast cancer compared with control (P<0.05). Following four cycles of chemotherapy, the activity of NADH dehydrogenase, succinate-cytochrome c reductase and cytochrome c oxidase in the mitochondria of cells was significantly reduced when compared with their activity prior to chemotherapy (P<0.05). Regional blood flow in tumor tissues undergoing chemotherapy was significantly lower than that prior to chemotherapy (P<0.05). The rate of cellular apoptosis in the PO₂ peak-based chemotherapy group was significantly greater than that in the conventional chemotherapy group after two and four cycles of chemotherapy (P<0.05). Tumor volume in the PO₂ peak-based chemotherapy group was significantly reduced compared with that in the 0.9% NaCl solution control and the conventional chemotherapy groups after four cycles of chemotherapy (P<0.05). The tumor inhibitory rate of the experimental group was significantly higher than that of the conventional chemotherapy group (P<0.01). In conclusion, the present study may provide guidance for the development of effective strategies depending on tumor-maximal response to chemotherapy in an oxygen-rich environment. Additionally, the present study aimed to establish a foundation for a clinical noninvasive assessment intended to guide treatment and formulate individual regimens, in order to improve cancer therapeutics, sensitivity monitoring and curative effect estimation.

Negative selection in tumor genome evolution acts on essential cellular functions and the immunopeptidome

Background

Natural selection shapes cancer genomes. Previous studies used signatures of positive selection to identify genes driving malignant transformation. However, the contribution of negative selection against somatic mutations that affect essential tumor functions or specific domains remains a controversial topic.

Results

Here, we analyze 7546 individual exomes from 26 tumor types from TCGA data to explore the portion of the cancer exome under negative selection. Although we find most of the genes neutrally evolving in a pan-cancer framework, we identify essential cancer genes and immune-exposed protein regions under significant negative selection. Moreover, our simulations suggest that the amount of negative selection is underestimated. We therefore choose an empirical approach to identify genes, functions, and protein regions under negative selection. We find that expression and mutation status of negatively selected genes is indicative of patient survival. Processes that are most strongly conserved are those that play fundamental cellular roles such as protein synthesis, glucose metabolism, and molecular transport. Intriguingly, we observe strong signals of selection in the immunopeptidome and proteins controlling peptide exposition, highlighting the importance of immune surveillance evasion. Additionally, tumor type-specific immune activity correlates with the strength of negative selection on human epitopes.

Conclusions

In summary, our results show that negative selection is a hallmark of cell essentiality and immune response in cancer. The functional domains identified could be exploited therapeutically, ultimately allowing for the development of novel cancer treatments.

Electronic supplementary material

The online version of this article (10.1186/s13059-018-1434-0) contains supplementary material, which is available to authorized users.

Expression of the three components of linear ubiquitin assembly complex in breast cancer

Proteins belonging to the linear ubiquitin assembly complex (LUBAC) are believed to be important in tumorigenesis. LUBAC has been demonstrated to be composed of RBCK1, RNF31 and SHARPIN. The aim of this study was to explore all members of the LUBAC complex as novel biomarkers in breast cancer. We have already reported that RNF31 mRNA levels are higher in breast cancer samples compared to adjacent non-tumor tissue. In this study we extend these findings by demonstrating that the mRNA levels of RBCK1 and SHARPIN are also higher in tumors compared to adjacent non-tumor tissue in the same cross sectional study of samples (p < 0.001). In addition, up-regulated mRNA expression of all three members of the LUBAC complex displayed high predictive value in distinguishing tumor tissues from adjacent non-tumor tissue as determined by ROC curve analysis. Furthermore, we investigated whether there is an association between the mRNA and protein expression levels of RBCK1, RNF31 and SHARPIN and clinicopathological parameters including estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor (HER2) status and found that RNF31 protein is significantly higher in ERalpha-negative tumors than ERalpha-positive tumors (p = 0.034). Collectively, our findings indicate that up-regulated mRNA expression of RNF31, RBCK1 and SHARPIN could potentially be diagnostic biomarkers of breast cancer and RNF31 might be a drug target for ERalpha-negative breast cancers.

Tissue-Dependent Tumor Microenvironments and Their Impact on Immunotherapy Responses

Recent advances in cancer immunology have led to a better understanding of the role of the tumor microenvironment (TME) in tumor initiation, progression, and metastasis. Tumors can occur at many locations within the body and coevolution between malignant tumor cells and non-malignant cells sculpts the TME at these sites. It has become increasingly clear that there are specific differences of the TMEs at different anatomical locations, and these tissue-specific TMEs regulate tumor growth, determine metastatic progression, and impact on the outcome of therapy responses. Herein, we review the scientific advances in understanding tissue-specific TMEs, discuss their impact on immunotherapeutic response, and assess the current clinical knowledge in this emerging field. A deeper understanding of the tissue-specific TME will help to develop effective immunotherapies against tumors and their metastases and assist in predicting clinical outcomes.

Cancer and intercellular cooperation

The major transitions approach in evolutionary biology has shown that the intercellular cooperation that characterizes multicellular organisms would never have emerged without some kind of multilevel selection. Relying on this view, the Evolutionary Somatic view of cancer considers cancer as a breakdown of intercellular cooperation and as a loss of the balance between selection processes that take place at different levels of organization (particularly single cell and individual organism). This seems an elegant unifying framework for healthy organism, carcinogenesis, tumour proliferation, metastasis and other phenomena such as ageing. However, the gene-centric version of Darwinian evolution, which is often adopted in cancer research, runs into empirical problems: proto-tumoural and tumoural features in precancerous cells that would undergo 'natural selection' have proved hard to demonstrate; cells are radically context-dependent, and some stages of cancer are poorly related to genetic change. Recent perspectives propose that breakdown of intercellular cooperation could depend on 'fields' and other higher-level phenomena, and could be even mutations independent. Indeed, the field would be the context, allowing (or preventing) genetic mutations to undergo an intra-organism process analogous to natural selection. The complexities surrounding somatic evolution call for integration between multiple incomplete frameworks for interpreting intercellular cooperation and its pathologies.

Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types

Isoforms of human miRNAs (isomiRs) are constitutively expressed with tissue- and disease-subtype-dependencies. We studied 10 271 tumor datasets from The Cancer Genome Atlas (TCGA) to evaluate whether isomiRs can distinguish amongst 32 TCGA cancers. Unlike previous approaches, we built a classifier that relied solely on ‘binarized’ isomiR profiles: each isomiR is simply labeled as ‘present’ or ‘absent’. The resulting classifier successfully labeled tumor datasets with an average sensitivity of 90% and a false discovery rate (FDR) of 3%, surpassing the performance of expression-based classification. The classifier maintained its power even after a 15× reduction in the number of isomiRs that were used for training. Notably, the classifier could correctly predict the cancer type in non-TCGA datasets from diverse platforms. Our analysis revealed that the most discriminatory isomiRs happen to also be differentially expressed between normal tissue and cancer. Even so, we find that these highly discriminating isomiRs have not been attracting the most research attention in the literature. Given their ability to successfully classify datasets from 32 cancers, isomiRs and our resulting ‘Pan-cancer Atlas’ of isomiR expression could serve as a suitable framework to explore novel cancer biomarkers.

Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks

Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 108 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.