Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference

Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer

PURPOSE

A combination of fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is the standard for adjuvant therapy of resected early-stage colon cancer (CC). Oxaliplatin leads to lasting and disabling neurotoxicity. Reserving the regimen for patients who benefit from oxaliplatin would maximize efficacy and minimize unnecessary adverse side effects.

METHODS

We trained a new machine learning model, referred to as the colon oxaliplatin signature (COLOXIS) model, for predicting response to oxaliplatin-containing regimens. We examined whether COLOXIS was predictive of oxaliplatin benefits in the CC adjuvant setting among 1,065 patients treated with 5-fluorouracil plus leucovorin (FULV; n = 421) or FULV + oxaliplatin (FOLFOX; n = 644) from NSABP C-07 and C-08 phase III trials. The COLOXIS model dichotomizes patients into COLOXIS+ (oxaliplatin responder) and COLOXIS- (nonresponder) groups. Eight-year recurrence-free survival was used to evaluate oxaliplatin benefits within each of the groups, and the predictive value of the COLOXIS model was assessed using the P value associated with the interaction term (int P) between the model prediction and the treatment effect.

RESULTS

Among 1,065 patients, 526 were predicted as COLOXIS+ and 539 as COLOXIS-. The COLOXIS+ prediction was associated with prognosis for FULV-treated patients (hazard ratio [HR], 1.52 [95% CI, 1.07 to 2.15]; P = .017). The model was predictive of oxaliplatin benefits: COLOXIS+ patients benefited from oxaliplatin (HR, 0.65 [95% CI, 0.48 to 0.89]; P = .0065; int P = .03), but COLOXIS- patients did not (COLOXIS- HR, 1.08 [95% CI, 0.77 to 1.52]; P = .65).

CONCLUSION

The COLOXIS model is predictive of oxaliplatin benefits in the CC adjuvant setting. The results provide evidence supporting a change in CC adjuvant therapy: reserve oxaliplatin only for COLOXIS+ patients, but further investigation is warranted.

Redoxhigh phenotype mediated by KEAP1/STK11/SMARCA4/NRF2 mutations diminishes tissue-resident memory CD8+ T cells and attenuates the efficacy of immunotherapy in lung adenocarcinoma

Metabolism reprogramming within the tumor microenvironment (TME) can have a profound impact on immune cells. Identifying the association between metabolic phenotypes and immune cells in lung adenocarcinoma (LUAD) may reveal mechanisms of resistance to immune checkpoint inhibitors (ICIs). Metabolic phenotypes were classified by expression of metabolic genes. Somatic mutations and transcriptomic features were compared across the different metabolic phenotypes. The metabolic phenotype of LUAD is predominantly determined by reductase-oxidative activity and is divided into two categories: redoxhigh LUAD and redoxlow LUAD. Genetically, redoxhigh LUAD is mainly driven by mutations in KEAP1, STK11, NRF2, or SMARCA4. These mutations are more prevalent in redoxhigh LUAD (72.5%) compared to redoxlow LUAD (17.4%), whereas EGFR mutations are more common in redoxlow LUAD (19.0% vs. 0.7%). Single-cell RNA profiling of pre-treatment and post-treatment samples from patients receiving neoadjuvant chemoimmunotherapy revealed that tissue-resident memory CD8+ T cells are responders to ICIs. However, these cells are significantly reduced in redoxhigh LUAD. The redoxhigh phenotype is primarily attributed to tumor cells and is positively associated with mTORC1 signaling. LUAD with the redoxhigh phenotype demonstrates a lower response rate (39.1% vs. 70.8%, p = 0.001), shorter progression-free survival (3.3 vs. 14.6 months, p = 0.004), and overall survival (12.1 vs. 31.2 months, p = 0.022) when treated with ICIs. The redoxhigh phenotype in LUAD is predominantly driven by mutations in KEAP1, STK11, NRF2, and SMARCA4. This phenotype diminishes the number of tissue-resident memory CD8+ T cells and attenuates the efficacy of ICIs.

An individualized Bayesian method for estimating genomic variants of hypertension

BACKGROUND

Genomic variants of the disease are often discovered nowadays through population-based genome-wide association studies (GWAS). Identifying genomic variations potentially underlying a phenotype, such as hypertension, in an individual is important for designing personalized treatment; however, population-level models, such as GWAS, may not capture all the important, individualized factors well. In addition, GWAS typically requires a large sample size to detect the association of low-frequency genomic variants with sufficient power. Here, we report an individualized Bayesian inference (IBI) algorithm for estimating the genomic variants that influence complex traits, such as hypertension, at the level of an individual (e.g., a patient). By modeling at the level of the individual, IBI seeks to find genomic variants observed in the individual's genome that provide a strong explanation of the phenotype observed in this individual.

RESULTS

We applied the IBI algorithm to the data from the Framingham Heart Study to explore the genomic influences of hypertension. Among the top-ranking variants identified by IBI and GWAS, there is a significant number of shared variants (intersection); the unique variants identified only by IBI tend to have relatively lower minor allele frequency than those identified by GWAS. In addition, IBI discovered more individualized and diverse variants that explain hypertension patients better than GWAS. Furthermore, IBI found several well-known low-frequency variants as well as genes related to blood pressure that GWAS missed in the same cohort. Finally, IBI identified top-ranked variants that predicted hypertension better than GWAS, according to the area under the ROC curve.

CONCLUSIONS

The results support IBI as a promising approach for complementing GWAS, especially in detecting low-frequency genomic variants as well as learning personalized genomic variants of clinical traits and disease, such as the complex trait of hypertension, to help advance precision medicine.

Revealing the Impact of Genomic Alterations on Cancer Cell Signaling with an Interpretable Deep Learning Model

Cancer is a disease of aberrant cellular signaling resulting from somatic genomic alterations (SGAs). Heterogeneous SGA events in tumors lead to tumor-specific signaling system aberrations. We interpret the cancer signaling system as a causal graphical model, where SGAs affect signaling proteins, propagate their effects through signal transduction, and ultimately change gene expression. To represent such a system, we developed a deep learning model called redundant-input neural network (RINN) with a transparent redundant-input architecture. Our findings demonstrate that by utilizing SGAs as inputs, the RINN can encode their impact on the signaling system and predict gene expression accurately when measured as the area under ROC curves. Moreover, the RINN can discover the shared functional impact (similar embeddings) of SGAs that perturb a common signaling pathway (e.g., PI3K, Nrf2, and TGF). Furthermore, the RINN exhibits the ability to discover known relationships in cellular signaling systems.

An interpretable deep learning framework for genome-informed precision oncology

Cancers result from aberrations in cellular signaling systems, typically resulting from driver somatic genome alterations (SGAs) in individual tumors. Precision oncology requires understanding the cellular state and selecting medications that induce vulnerability in cancer cells under such conditions. To this end, we developed a computational framework consisting of two components: 1) A representation-learning component, which learns a representation of the cellular signaling systems when perturbed by SGAs, using a biologically-motivated and interpretable deep learning model. 2) A drug-response-prediction component, which predicts the response to drugs by leveraging the information of the cellular state of the cancer cells derived by the first component. Our cell-state-oriented framework significantly enhances the accuracy of genome-informed prediction of drug responses in comparison to models that directly use SGAs as inputs. Importantly, our framework enables the prediction of response to chemotherapy agents based on SGAs, thus expanding genome-informed precision oncology beyond molecularly targeted drugs.

Treatments and whole exon sequencing of a case with multiple primary lung cancer

INTRODUCTION

The number of patients with synchronous multiple primary lung cancer (sMPLC) has increased recently. However, diagnosing and selecting the appropriate therapeutic strategy for this type of disease is not simple.

CASE PRESENTATION

This report presented a case of sMPLC with lymph node metastasis. With no smoking and cancer history, this patient had seven nodules in the right lung and underwent single-portal video-assisted thoracoscopic surgery (VATS). In addition, she received four cycles of chemotherapy after the operation. Whole exon sequencing (WES) was performed in five resected tissue samples (four tumors and one lymph node). We conducted genomic profiling and clone evolution analysis of the five samples. Gene detection helped to confirm that the metastasis lymph node was transferred from one nodule. There was apparent heterogeneity of gene mutations among the five samples of the patient, with only one shared "neurofilament heavy polypeptide" (NEFH) mutation. A dominant substitution of C > T/G > A was found in all the samples. Pyclone model was used to calculate all tissues' cellular prevalence (CP) values, and NEFH mutations were thought to be the ancestral clones. During the follow-up period, residual lesions showed no apparent changes and limited response to chemotherapy.

CONCLUSIONS

This report showed an essential role in genomic detection and selecting the appropriate treatment of sMPLC. Surgery remains the primary treatment strategy for this type of disease, and the occurrence and development of sMPLC need more in-depth research.

An individualized causal framework for learning intercellular communication networks that define microenvironments of individual tumors

Cells within a tumor microenvironment (TME) dynamically communicate and influence each other's cellular states through an intercellular communication network (ICN). In cancers, intercellular communications underlie immune evasion mechanisms of individual tumors. We developed an individualized causal analysis framework for discovering tumor specific ICNs. Using head and neck squamous cell carcinoma (HNSCC) tumors as a testbed, we first mined single-cell RNA-sequencing data to discover gene expression modules (GEMs) that reflect the states of transcriptomic processes within tumor and stromal single cells. By deconvoluting bulk transcriptomes of HNSCC tumors profiled by The Cancer Genome Atlas (TCGA), we estimated the activation states of these transcriptomic processes in individual tumors. Finally, we applied individualized causal network learning to discover an ICN within each tumor. Our results show that cellular states of cells in TMEs are coordinated through ICNs that enable multi-way communications among epithelial, fibroblast, endothelial, and immune cells. Further analyses of individual ICNs revealed structural patterns that were shared across subsets of tumors, leading to the discovery of 4 different subtypes of networks that underlie disparate TMEs of HNSCC. Patients with distinct TMEs exhibited significantly different clinical outcomes. Our results show that the capability of estimating individual ICNs reveals heterogeneity of ICNs and sheds light on the importance of intercellular communication in impacting disease development and progression.

Genome wide DNA methylation analysis identifies novel molecular subgroups and predicts survival in neuroblastoma

BACKGROUND

Neuroblastoma is the most common malignancy in infancy, accounting for 15% of childhood cancer deaths. Outcome for the high-risk disease remains poor. DNA-methylation patterns are significantly altered in all cancer types and can be utilised for disease stratification.

METHODS

Genome-wide DNA methylation (n = 223), gene expression (n = 130), genetic/clinical data (n = 213), whole-exome sequencing (n = 130) was derived from the TARGET study. Methylation data were derived from HumanMethylation450 BeadChip arrays. t-SNE was used for the segregation of molecular subgroups. A separate validation cohort of 105 cases was studied.

RESULTS

Five distinct neuroblastoma molecular subgroups were identified, based on genome-wide DNA-methylation patterns, with unique features in each, including three subgroups associated with known prognostic features and two novel subgroups. As expected, Cluster-4 (infant diagnosis) had significantly better 5-year progression-free survival (PFS) than the four other clusters. However, in addition, the molecular subgrouping identified multiple patient subsets with highly increased risk, most notably infant patients that do not map to Cluster-4 (PFS 50% vs 80% for Cluster-4 infants, P = 0.005), and allowed identification of subgroup-specific methylation differences that may reflect important biological differences within neuroblastoma.

CONCLUSIONS

Methylation-based clustering of neuroblastoma reveals novel molecular subgroups, with distinct molecular/clinical characteristics and identifies a subgroup of higher-risk infant patients.

Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers

Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Interpreting somatic alterations within context-specific transcriptional programs will facilitate personalized therapeutic decisions but is a monumental task. Toward this goal, we develop a partially interpretable neural network model called Chromatin-informed Inference of Transcriptional Regulators Using Self-attention mechanism (CITRUS). CITRUS models the impact of somatic alterations on transcription factors and downstream transcriptional programs. Our approach employs a self-attention mechanism to model the contextual impact of somatic alterations. Furthermore, CITRUS uses a layer of hidden nodes to explicitly represent the state of transcription factors (TFs) to learn the relationships between TFs and their target genes based on TF binding motifs in the open chromatin regions of tumor samples. We apply CITRUS to genomic, transcriptomic, and epigenomic data from 17 cancer types profiled by The Cancer Genome Atlas. CITRUS predicts patient-specific TF activities and reveals transcriptional program variations between and within tumor types. We show that CITRUS yields biological insights into delineating TFs associated with somatic alterations in individual tumors. Thus, CITRUS is a promising tool for precision oncology.

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.

Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: an update of the Network of Cancer Genes (NCG) resource

Background

Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development.

Results

Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation.

Conclusions

Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02607-z.

Whole-exome sequencing in eccrine porocarcinoma indicates promising therapeutic strategies

Malignant sweat gland tumours are rare, with the most common form being Eccrine porocarcinoma (EP). To investigate the mutational landscape of EP, we performed whole-exome sequencing (WES) on 14 formalin-fixed paraffin-embedded samples of matched primary EP and healthy surrounding tissue. Mutational profiling revealed a high overall median mutation rate. This was attributed to signatures of mutational processes related to ultraviolet (UV) exposure, APOBEC enzyme dysregulation, and defective homologous double-strand break repair. All of these processes cause genomic instability and are implicated in carcinogenesis. Recurrent driving somatic alterations were detected in the EP candidate drivers TP53, FAT2, CACNA1S, and KMT2D. The analyses also identified copy number alterations and recurrent gains and losses in several chromosomal regions including that containing BRCA2, as well as deleterious alterations in multiple HRR components. In accordance with this reduced or even a complete loss of BRCA2 protein expression was detected in 50% of the investigated EP tumours. Our results implicate crucial oncogenic driver pathways and suggest that defective homologous double-strand break repair and the p53 pathway are involved in EP aetiology. Targeting of the p53 axis and PARP inhibition, and/or immunotherapy may represent promising treatment strategies.

Comprehensive analyses reveal TKI-induced remodeling of the tumor immune microenvironment in EGFR/ALK-positive non-small-cell lung cancer

Tyrosine kinase inhibitors (TKI) play a pivotal role in the treatment of non-small-cell lung cancer (NSCLC) with mutations in epidermal growth factor receptor (EGFR) and rearrangements in anaplastic lymphoma kinase (ALK). However, the influences of TKIs on the tumor immune microenvironment (TIM), especially dynamic changes of responders, have not yet been fully elucidated. Therefore, RNA sequencing and whole-exome sequencing were performed on EGFR/ALK-positive NSCLC samples before and after TKI treatment. In combination with neoantigen and mutational-load estimations, xCell and single-sample gene set enrichment analysis (ssGSEA) were used to assess tumor immune-cell infiltration and activity. Furthermore, weighted-gene correlation network analysis and the bottleneck method were used to identify the hub genes that affected treatment-related immune responses. We found that TKI treatment remodeled the TIM in treatment-responsive samples. Profound increases in the rate of anti-tumor cell infiltration and cytotoxicity was observed following TKI treatment, while antigen presentation was limited in ALK-rearranged samples. However, no significant change in anti-tumor cell infiltration or cytotoxicity was found between pre-treatment and post-progression samples. Subsequently, we found that neurofilament heavy (NEFH) mutations were enriched in samples after TKI treatment and were associated with reduced neutrophil infiltration. The cytotoxicity of EGFR-mutant NSCLCs with co-driver TP53 mutation and ALK-rearranged samples with wild-type TP53 seems to be more easily induced by TKI. Finally, the immune-associated score generated by hub genes was positively correlated with immune infiltration, immune activation, and a favorable prognosis. In conclusion, the dynamic changes in the TIM provide clues to drug selection and timing for TKI-immunotherapy combinations.

Efficient representations of tumor diversity with paired DNA-RNA aberrations

Cancer cells display massive dysregulation of key regulatory pathways due to now well-catalogued mutations and other DNA-related aberrations. Moreover, enormous heterogeneity has been commonly observed in the identity, frequency and location of these aberrations across individuals with the same cancer type or subtype, and this variation naturally propagates to the transcriptome, resulting in myriad types of dysregulated gene expression programs. Many have argued that a more integrative and quantitative analysis of heterogeneity of DNA and RNA molecular profiles may be necessary for designing more systematic explorations of alternative therapies and improving predictive accuracy. We introduce a representation of multi-omics profiles which is sufficiently rich to account for observed heterogeneity and support the construction of quantitative, integrated, metrics of variation. Starting from the network of interactions existing in Reactome, we build a library of “paired DNA-RNA aberrations” that represent prototypical and recurrent patterns of dysregulation in cancer; each two-gene “Source-Target Pair” (STP) consists of a “source” regulatory gene and a “target” gene whose expression is plausibly “controlled” by the source gene. The STP is then “aberrant” in a joint DNA-RNA profile if the source gene is DNA-aberrant (e.g., mutated, deleted, or duplicated), and the downstream target gene is “RNA-aberrant”, meaning its expression level is outside the normal, baseline range. With M STPs, each sample profile has exactly one of the 2^M possible configurations. We concentrate on subsets of STPs, and the corresponding reduced configurations, by selecting tissue-dependent minimal coverings, defined as the smallest family of STPs with the property that every sample in the considered population displays at least one aberrant STP within that family. These minimal coverings can be computed with integer programming. Given such a covering, a natural measure of cross-sample diversity is the extent to which the particular aberrant STPs composing a covering vary from sample to sample; this variability is captured by the entropy of the distribution over configurations. We apply this program to data from TCGA for six distinct tumor types (breast, prostate, lung, colon, liver, and kidney cancer). This enables an efficient simplification of the complex landscape observed in cancer populations, resulting in the identification of novel signatures of molecular alterations which are not detected with frequency-based criteria. Estimates of cancer heterogeneity across tumor phenotypes reveals a stable pattern: entropy increases with disease severity. This framework is then well-suited to accommodate the expanding complexity of cancer genomes and epigenomes emerging from large consortia projects.

A large variety of genomic and transcriptomic aberrations are observed in cancer cells, and their identity, location, and frequency can be highly indicative of the particular subtype or molecular phenotype, and thereby inform treatment options. However, elucidating this association between sets of aberrations and subtypes of cancer is severely impeded by considerable diversity in the set of aberrations across samples from the same population. Most attempts at analyzing tumor heterogeneity have dealt with either the genome or transcriptome in isolation. Here we present a novel, multi-omics approach for quantifying heterogeneity by determining a small set of paired DNA-RNA aberrations that incorporates potential downstream effects on gene expression. We apply integer programming to identify a small set of paired aberrations such that at least one among them is present in every sample of a given cancer population. The resulting “coverings” are analyzed for six cancer cohorts from the Cancer Genome Atlas, and facilitate introducing an information-theoretic measure of heterogeneity. Our results identify many known facets of tumorigenesis as well as suggest potential novel genes and interactions of interest.

The Role of Fibroblast Growth Factor 19 in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the fifth most common type of cancer and the third leading cause of cancer-related deaths worldwide. Liver resection or liver transplantation is the most effective therapy for HCC because drugs approved by the US Food and Drug Administration to treat patients with unresectable HCC have an unfavorable overall survival rate. Therefore, the development of biomarkers for early diagnosis and effective therapy strategies are still necessary to improve patient outcomes. Fibroblast growth factor (FGF) 19 was amplified in patients with HCC from various studies, including patients from The Cancer Genome Atlas. FGF19 plays a syngeneic function with other signaling pathways in primary liver cancer development, such as epidermal growth factor receptor, Wnt/β-catenin, the endoplasmic reticulum-related signaling pathway, STAT3/IL-6, RAS, and extracellular signal-regulated protein kinase, among others. The current review presents a comprehensive description of the FGF19 signaling pathway involved in liver cancer development. The use of big data and bioinformatic analysis can provide useful clues for further studies of the FGF19 pathway in HCC, including its application as a biomarker, targeted therapy, and combination therapy strategies.

Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

Background

Genes implicated in tumorigenesis often exhibit diverse sets of genomic variants in the tumor cohorts within which they are frequently mutated. For many genes, neither the transcriptomic effects of these variants nor their relationship to one another in cancer processes have been well-characterized. We sought to identify the downstream expression effects of these mutations and to determine whether this heterogeneity at the genomic level is reflected in a corresponding heterogeneity at the transcriptomic level.

Results

By applying a novel hierarchical framework for organizing the mutations present in a cohort along with machine learning pipelines trained on samples’ expression profiles we systematically interrogated the signatures associated with combinations of mutations recurrent in cancer. This allowed us to catalogue the mutations with discernible downstream expression effects across a number of tumor cohorts as well as to uncover and characterize over a hundred cases where subsets of a gene’s mutations are clearly divergent in their function from the remaining mutations of the gene. These findings successfully replicated across a number of disease contexts and were found to have clear implications for the delineation of cancer processes and for clinical decisions.

Conclusions

The results of cataloguing the downstream effects of mutation subgroupings across cancer cohorts underline the importance of incorporating the diversity present within oncogenes in models designed to capture the downstream effects of their mutations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04147-y.

Spatial Distribution of Private Gene Mutations in Clear Cell Renal Cell Carcinoma

Simple Summary

Tumours consist of multiple groups of similar cells resulting from differing evolutionary trajectories, i.e., subclones. These subclones are prevalent in clear cell renal cell carcinoma (ccRCC). The aim of this study is to determine how similar or dissimilar the subclones in 89 ccRCC tumours are from one another regarding their gene mutations and expression profiles, i.e., the extent of intra-tumour heterogeneity. The implications of these alterations with respect to signalling pathways is also assessed. Deep sequencing allows for the identification of mutations with low-allele frequencies, providing a more comprehensive view of the heterogeneity present in the tumours. With an average of 62% of mutations having been identified in only one of the two biopsies, some of which in turn are found to impact gene expression, the complex makeup of ccRCC tumours is evident, and this can drastically influence treatment outcome.

Abstract

Intra-tumour heterogeneity is the molecular hallmark of renal cancer, and the molecular tumour composition determines the treatment outcome of renal cancer patients. In renal cancer tumourigenesis, in general, different tumour clones evolve over time. We analysed intra-tumour heterogeneity and subclonal mutation patterns in 178 tumour samples obtained from 89 clear cell renal cell carcinoma patients. In an initial discovery phase, whole-exome and transcriptome sequencing data from paired tumour biopsies from 16 ccRCC patients were used to design a gene panel for follow-up analysis. In this second phase, 826 selected genes were targeted at deep coverage in an extended cohort of 89 patients for a detailed analysis of tumour heterogeneity. On average, we found 22 mutations per patient. Pairwise comparison of the two biopsies from the same tumour revealed that on average, 62% of the mutations in a patient were detected in one of the two samples. In addition to commonly mutated genes (VHL, PBRM1, SETD2 and BAP1), frequent subclonal mutations with low variant allele frequency (<10%) were observed in TP53 and in mucin coding genes MUC6, MUC16, and MUC3A. Of the 89 ccRCC tumours, 87 (~98%) harboured private mutations, occurring in only one of the paired tumour samples. Clonally exclusive pathway pairs were identified using the WES data set from 16 ccRCC patients. Our findings imply that shared and private mutations significantly contribute to the complexity of differential gene expression and pathway interaction and might explain the clonal evolution of different molecular renal cancer subgroups. Multi-regional sequencing is central for the identification of subclones within ccRCC.

The Role of Network Science in Glioblastoma

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.

From genome to phenome: Predicting multiple cancer phenotypes based on somatic genomic alterations via the genomic impact transformer

Cancers are mainly caused by somatic genomic alterations (SGAs) that perturb cellular signaling systems and eventually activate oncogenic processes. Therefore, understanding the functional impact of SGAs is a fundamental task in cancer biology and precision oncology. Here, we present a deep neural network model with encoder-decoder architecture, referred to as genomic impact transformer (GIT), to infer the functional impact of SGAs on cellular signaling systems through modeling the statistical relationships between SGA events and differentially expressed genes (DEGs) in tumors. The model utilizes a multi-head self-attention mechanism to identify SGAs that likely cause DEGs, or in other words, differentiating potential driver SGAs from passenger ones in a tumor. GIT model learns a vector (gene embedding) as an abstract representation of functional impact for each SGA-affected gene. Given SGAs of a tumor, the model can instantiate the states of the hidden layer, providing an abstract representation (tumor embedding) reflecting characteristics of perturbed molecular/cellular processes in the tumor, which in turn can be used to predict multiple phenotypes. We apply the GIT model to 4,468 tumors profiled by The Cancer Genome Atlas (TCGA) project. The attention mechanism enables the model to better capture the statistical relationship between SGAs and DEGs than conventional methods, and distinguishes cancer drivers from passengers. The learned gene embeddings capture the functional similarity of SGAs perturbing common pathways. The tumor embeddings are shown to be useful for tumor status representation, and phenotype prediction including patient survival time and drug response of cancer cell lines.

Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes

Cancer is a disease mainly caused by somatic genome alterations (SGAs) that perturb cellular signalling systems. Furthermore, the combination of pathway aberrations in a tumour defines its disease mechanism, and distinct disease mechanisms underlie the inter-tumour heterogeneity in terms of disease progression and responses to therapies. Discovering common disease mechanisms shared by tumours would provide guidance for precision oncology but remains a challenge. Here, we present a novel computational framework for revealing distinct combinations of aberrant signalling pathways in tumours. Specifically, we applied the tumour-specific causal inference algorithm (TCI) to identify causal relationships between SGAs and differentially expressed genes (DEGs) within tumours from the Cancer Genome Atlas (TCGA) study. Based on these causal inferences, we adopted a network-based method to identify modules of DEGs, such that the member DEGs within a module tend to be co-regulated by a common pathway. Using the expression status of genes in a module as a surrogate measure of the activation status of the corresponding pathways, we divided breast cancers (BRCAs) into five subgroups and glioblastoma multiformes (GBMs) into six subgroups with distinct combinations of pathway aberrations. The patient groups exhibited significantly different survival patterns, indicating that our approach can identify clinically relevant disease subtypes.