From cancer genomes to oncogenic drivers, tumour dependencies and therapeutic targets

Role of TRP channels in carcinogenesis and metastasis: Pathophysiology and regulation by non-coding RNAs

In 2021, David Julius and Ardem Patapoutian received Nobel Prize in Physiology or Medicine for their ground-breaking discoveries in the functional characterization of receptors for temperature and touch. Transient receptor potential (TRP) channels have captivated tremendous appreciation as promising drug targets over the past few years because of central involvement in different cancers. Based on the insights gleaned from decades of high-quality research, basic and clinical scientists have unveiled how Transient receptor potential channels regulated cancer onset and progression. Pioneering studies have sparked renewed interest and researchers have started to scratch the surface of mechanistic role of these channels in wide variety of cancers. In this review we have attempted to provide a summary of most recent updates and advancements made in the biology of these channels in context of cancers. We have partitioned this review into different subsections on the basis of emerging evidence about characteristically distinct role of TRPV (TRPV1, TRPV5), TRPM (TRPM3, TRPM7) and TRPC in cancers. Regulation of TRP channels by non-coding RNAs is also a very exciting area of research which will be helpful in developing a sharper understanding of the multi-step aspects of cancers.

Basket Trials: Past, Present, and Future

Large-scale tumor molecular profiling has revealed that diverse cancer histologies are driven by common pathways with unifying biomarkers that can be exploited therapeutically. Disease-agnostic basket trials have been increasingly utilized to test biomarker-driven therapies across cancer types. These trials have led to drug approvals and improved the lives of patients while simultaneously advancing our understanding of cancer biology. This review focuses on the practicalities of implementing basket trials, with an emphasis on molecularly targeted trials. We examine the biologic subtleties of genomic biomarker and patient selection, discuss previous successes in drug development facilitated by basket trials, describe certain novel targets and drugs, and emphasize practical considerations for participant recruitment and study design. This review also highlights strategies for aiding patient access to basket trials. As basket trials become more common, steps to ensure equitable implementation of these studies will be critical for molecularly targeted drug development.

SP4 Facilitates Esophageal Squamous Cell Carcinoma Progression by Activating PHF14 Transcription and Wnt/Β-Catenin Signaling

Specificity protein 4 transcription factor (SP4), a member of the Sp/Krüppel-like family (KLF), could bind to GT and GC box promoters, and plays an essential role in transcriptional activating. Despite SP4 having been detected to be highly expressed in a variety of human tumors, its biological effect and underlying molecular mechanism in esophageal squamous cell carcinoma (ESCC) remains unclear. Our research discovered that high SP4 expression is detected in primary ESCC specimens and cell lines and is strongly associated with the ESCC tumor grade and poor prognosis. In vitro, knockdown of SP4 suppressed cell proliferation and cell-cycle progression and promoted apoptosis, whereas overexpression of SP4 did the opposite. In vivo, inhibiting SP4 expression in ESCC cells suppresses tumor growth. Subsequently, we demonstrated that SP4 acts as the transcriptional upstream of PHF14, which binds to PHF14 promoter region, thus promoting PHF14 transcription. PHF14 was also significantly expressed in patient tissues and various ESCC cell lines and its expression promoted cell proliferation and inhibited apoptosis. Moreover, knockdown of SP4 inhibited the Wnt/β-catenin signaling pathway, whereas overexpression of PHF14 eliminated the effects of SP4 knockdown in ESCC cells. These results demonstrate that SP4 activates the Wnt/β-catenin signaling pathway by driving PHF14 transcription, thereby promoting ESCC progression, which indicates that SP4 might act as a prospective prognostic indicator or therapeutic target for patients with ESCC.

IMPLICATIONS

This study identified SP4/PH14 axis as a new mechanism to promote the progression of ESCC, which may serve as a novel therapeutic target for patients with ESCC.

HOPE (SOLTI-1903) breast cancer study: real-world, patient-centric, clinical practice study to assess the impact of genomic data on next treatment decision-choice in patients with locally advanced or metastatic breast cancer

Background

Metastatic breast cancer (mBC) causes nearly all BC-related deaths. Next-generation sequencing (NGS) technologies allow for the application of personalized medicine using targeted therapies that could improve patients' outcomes. However, NGS is not routinely used in the clinical practice and its cost induces access-inequity among patients. We hypothesized that promoting active patient participation in the management of their disease offering access to NGS testing and to the subsequent medical interpretation and recommendations provided by a multidisciplinary molecular advisory board (MAB) could contribute to progressively overcome this challenge. We designed HOPE (SOLTI-1903) breast cancer trial, a study where patients voluntarily lead their inclusion through a digital tool (DT). The main objectives of HOPE study are to empower mBC patients, gather real-world data on the use of molecular information in the management of mBC and to generate evidence to assess the clinical utility for healthcare systems.

Trial design

After self-registration through the DT, the study team validates eligibility criteria and assists patients with mBC in the subsequent steps. Patients get access to the information sheet and sign the informed consent form through an advanced digital signature. Afterwards, they provide the most recent (preferably) metastatic archival tumor sample for DNA-sequencing and a blood sample obtained at the time of disease progression for ctDNA analysis. Paired results are reviewed by the MAB, considering patient's medical history. The MAB provides a further interpretation of molecular results and potential treatment recommendations, including ongoing clinical trials and further (germline) genetic testing. Participants self-document their treatment and disease evolution for the next 2 years. Patients are encouraged to involve their physicians in the study. HOPE also includes a patient empowerment program with educational workshops and videos about mBC and precision medicine in oncology. The primary endpoint of the study was to describe the feasibility of a patient-centric precision oncology program in mBC patients when a comprehensive genomic profile is available to decide on a subsequent line of treatment.

Clinical trial registration

www.soltihope.com, identifier NCT04497285.

Targeting oncogene and non-oncogene addiction to inflame the tumour microenvironment

Immune checkpoint inhibitors (ICIs) have revolutionized the clinical management of multiple tumours. However, only a few patients respond to ICIs, which has generated considerable interest in the identification of resistance mechanisms. One such mechanism reflects the ability of various oncogenic pathways, as well as stress response pathways required for the survival of transformed cells (a situation commonly referred to as 'non-oncogene addiction'), to support tumour progression not only by providing malignant cells with survival and/or proliferation advantages, but also by establishing immunologically 'cold' tumour microenvironments (TMEs). Thus, both oncogene and non-oncogene addiction stand out as promising targets to robustly inflame the TME and potentially enable superior responses to ICIs.

Evaluation of an EZH2 inhibitor in patient-derived orthotopic xenograft models of pediatric brain tumors alone and in combination with chemo- and radiation therapies

Brain tumors are the leading cause of cancer-related death in children. Tazemetostat is an FDA-approved enhancer of zeste homolog (EZH2) inhibitor. To determine its role in difficult-to-treat pediatric brain tumors, we examined EZH2 levels in a panel of 22 PDOX models and confirmed EZH2 mRNA over-expression in 9 GBM (34.6 ± 12.7-fold) and 11 medulloblastoma models (6.2 ± 1.7 in group 3, 6.0 ± 2.4 in group 4) accompanied by elevated H3K27me3 expression. Therapeutic efficacy was evaluated in 4 models (1 GBM, 2 medulloblastomas and 1 ATRT) via systematically administered tazemetostat (250 and 400 mg/kg, gavaged, twice daily) alone and in combination with cisplatin (5 mg/kg, i.p., twice) and/or radiation (2 Gy/day × 5 days). Compared with the untreated controls, tazemetostat significantly (Pcorrected < 0.05) prolonged survival times in IC-L1115ATRT (101% at 400 mg/kg) and IC-2305GBM (32% at 250 mg/kg, 45% at 400 mg/kg) in a dose-dependent manner. The addition of tazemetostat with radiation was evaluated in 3 models, with only one [IC-1078MB (group 4)] showing a substantial, though not statistically significant, prolongation in survival compared to radiation treatment alone. Combining tazemetostat (250 mg/kg) with cisplatin was not superior to cisplatin alone in any model. Analysis of in vivo drug resistance detected predominance of EZH2-negative cells in the remnant PDOX tumors accompanied by decreased H3K27me2 and H3K27me3 expressions. These data supported the use of tazemetostat in a subset of pediatric brain tumors and suggests that EZH2-negative tumor cells may have caused therapy resistance and should be prioritized for the search of new therapeutic targets.

SuperDendrix algorithm integrates genetic dependencies and genomic alterations across pathways and cancer types

Recent genome-wide CRISPR-Cas9 loss-of-function screens have identified genetic dependencies across many cancer cell lines. Associations between these dependencies and genomic alterations in the same cell lines reveal phenomena such as oncogene addiction and synthetic lethality. However, comprehensive identification of such associations is complicated by complex interactions between genes across genetically heterogeneous cancer types. We introduce and apply the algorithm SuperDendrix to CRISPR-Cas9 loss-of-function screens from 769 cancer cell lines, to identify differential dependencies across cell lines and to find associations between differential dependencies and combinations of genomic alterations and cell-type-specific markers. These associations respect the position and type of interactions within pathways: for example, we observe increased dependencies on downstream activators of pathways, such as NFE2L2, and decreased dependencies on upstream activators of pathways, such as CDK6. SuperDendrix also reveals dozens of dependencies on lineage-specific transcription factors, identifies cancer-type-specific correlations between dependencies, and enables annotation of individual mutated residues.

Using SuperDendrix, Park et al. examine associations between genetic dependencies in 769 cancer cell lines. They report 127 genetic dependencies explained by combinations of mutually exclusive somatic mutations congregating into a few oncogenic pathways across cancer subtypes. These present a small number of prominent and highly specific genetic vulnerabilities in cancer.

Graphical abstract

Modelling approaches for histology-independent cancer drugs to inform NICE appraisals: a systematic review and decision-framework

BACKGROUND

The first histology-independent marketing authorisation in Europe was granted in 2019. This was the first time that a cancer treatment was approved based on a common biomarker rather than the location in the body at which the tumour originated. This research aims to explore the implications for National Institute for Health and Care Excellence appraisals.

METHODS

Targeted reviews were undertaken to determine the type of evidence that is likely to be available at the point of marketing authorisation and the analyses required to support National Institute for Health and Care Excellence appraisals. Several challenges were identified concerning the design and conduct of trials for histology-independent products, the greater levels of heterogeneity within the licensed population and the use of surrogate end points. We identified approaches to address these challenges by reviewing key statistical literature that focuses on the design and analysis of histology-independent trials and by undertaking a systematic review to evaluate the use of response end points as surrogate outcomes for survival end points. We developed a decision framework to help to inform approval and research policies for histology-independent products. The framework explored the uncertainties and risks associated with different approval policies, including the role of further data collection, pricing schemes and stratified decision-making.

RESULTS

We found that the potential for heterogeneity in treatment effects, across tumour types or other characteristics, is likely to be a central issue for National Institute for Health and Care Excellence appraisals. Bayesian hierarchical methods may serve as a useful vehicle to assess the level of heterogeneity across tumours and to estimate the pooled treatment effects for each tumour, which can inform whether or not the assumption of homogeneity is reasonable. Our review suggests that response end points may not be reliable surrogates for survival end points. However, a surrogate-based modelling approach, which captures all relevant uncertainty, may be preferable to the use of immature survival data. Several additional sources of heterogeneity were identified as presenting potential challenges to National Institute for Health and Care Excellence appraisal, including the cost of testing, baseline risk, quality of life and routine management costs. We concluded that a range of alternative approaches will be required to address different sources of heterogeneity to support National Institute for Health and Care Excellence appraisals. An exemplar case study was developed to illustrate the nature of the assessments that may be required.

CONCLUSIONS

Adequately designed and analysed basket studies that assess the homogeneity of outcomes and allow borrowing of information across baskets, where appropriate, are recommended. Where there is evidence of heterogeneity in treatment effects and estimates of cost-effectiveness, consideration should be given to optimised recommendations. Routine presentation of the scale of the consequences of heterogeneity and decision uncertainty may provide an important additional approach to the assessments specified in the current National Institute for Health and Care Excellence methods guide.

FURTHER RESEARCH

Further exploration of Bayesian hierarchical methods could help to inform decision-makers on whether or not there is sufficient evidence of homogeneity to support pooled analyses. Further research is also required to determine the appropriate basis for apportioning genomic testing costs where there are multiple targets and to address the challenges of uncontrolled Phase II studies, including the role and use of surrogate end points.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Evidence Synthesis programme and will be published in full in Health Technology Assessment; Vol. 25, No. 76. See the NIHR Journals Library website for further project information.

Identifying Driver Genes for Individual Patients through Inductive Matrix Completion

MOTIVATION

The driver genes play a key role in the evolutionary process of cancer. Effectively identifying these driver genes is crucial to cancer diagnosis and treatment. However, due to the high heterogeneity of cancers, it remains challenging to identify the driver genes for individual patients. Although some computational methods have been proposed to tackle this problem, they seldom consider the fact that the genes functionally similar to the well-established driver genes may likely play similar roles in cancer process, which potentially promotes the driver gene identification. Thus, here we developed a novel approach of IMCDriver to promote the driver gene identification both for cohorts and individual patients.

RESULTS

IMCDriver first considers the well-established driver genes as prior information, and adopts the using multi-omics data (e.g., somatic mutation, gene expression and protein-protein interaction) to compute the similarity between patients/genes. Then, IMCDriver prioritizes the personalized mutated genes according to their functional similarity to the well-established driver genes via Inductive Matrix Completion. Finally, IMCDriver identifies the highly rank-ordered genes as the personalized driver genes. The results on five cancer datasets from TCGA show that our IMCDriver outperforms other existing state-of-the-art methods both in the cohort and patient-specific driver gene identification. IMCDriver also reveals some novel driver genes that potentially drive cancer development. In addition, even for the driver genes rarely mutated among a population, IMCDriver can still identify them and prioritize them with high priorities.

AVAILABILITY

Code available at https://github.com/NWPU-903PR/IMCDriver.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Exploring epigenetic biomarkers of universal specificities and commonalities among pan-cancer cohorts in The Cancer Genome Atlas

Aim: To explore the mechanism of cancer by employing a comprehensive analysis of DNA methylation patterns and variations among pan-cancer cohorts. Materials & methods: This research focused on the discovery of universally specific or common biomarkers by mathematical statistics and machine learning methods in The Cancer Genome Atlas. Results: We found 138 differently methylated CpGs (DMCs) with a common methylation trend and eight common differently methylated regions in different cancer cohorts. Additionally, we found 99 DMCs to distinguish 32 different cancer cohorts in random forest analysis because of the specificity mechanism, but each DMC still had high instability. Conclusion: Our results could facilitate the development of biomarkers that are universally specific and common features across pan-cancer cohorts.

A chemogenomic approach to identify personalized therapy for patients with relapse or refractory acute myeloid leukemia: results of a prospective feasibility study

Targeted next-generation sequencing (tNGS) and ex vivo drug sensitivity/resistance profiling (DSRP) have laid foundations defining the functional genomic landscape of acute myeloid leukemia (AML) and premises of personalized medicine to guide treatment options for patients with aggressive and/or chemorefractory hematological malignancies. Here, we have assessed the feasibility of a tailored treatment strategy (TTS) guided by systematic parallel ex vivo DSRP and tNGS for patients with relapsed/refractory AML (number NCT02619071). A TTS issued by an institutional personalized committee could be achieved for 47/55 included patients (85%), 5 based on tNGS only, 6 on DSRP only, while 36 could be proposed on the basis of both, yielding more options and a better rationale. The TSS was available in <21 days for 28 patients (58.3%). On average, 3 to 4 potentially active drugs were selected per patient with only five patient samples being resistant to the entire drug panel. Seventeen patients received a TTS-guided treatment, resulting in four complete remissions, one partial remission, and five decreased peripheral blast counts. Our results show that chemogenomic combining tNGS with DSRP to determine a TTS is a promising approach to propose patient-specific treatment options within 21 days.

A framework for identifying dysregulated chromatin regulators as master regulators in human cancer

MOTIVATION

Chromatin regulators (CRs) are frequently dysregulated to reprogram the epigenetic landscape of the cancer genome. However, the underpinnings of the dysregulation of CRs and their downstream effectors remain to be elucidated.

RESULTS

Here, we designed an integrated framework based on multi-omics data to identify candidate master regulatory CRs affected by genomic alterations across eight cancer types in The Cancer Genome Atlas. Most of them showed consistent activated or repressed (i.e. oncogenic or tumor-suppressive) roles in cancer initiation and progression. In order to further explore the insight mechanism of the dysregulated CRs, we developed an R package ModReg based on differential connectivity to identify CRs as modulators of transcription factors (TFs) involved in tumorigenesis. Our analysis revealed that the connectivity between TFs and their target genes (TGs) tended to be disrupted in the patients who had a high expression of oncogenic CRs or low-expression of tumor-suppressive CRs. As a proof-of-principle study, 14 (82.4%) of the top-ranked 17 driver CRs in liver cancer were able to be validated by literature mining or experiments including shRNA knockdown and dCas9-based epigenetic editing. Moreover, we confirmed that CR SIRT7 physically interacted with TF NFE2L2, and positively modulated the transcriptional program of NFE2L2 by affecting ∼64% of its TGs.

AVAILABILITY AND IMPLEMENTATION

ModReg is freely accessible at http://cis.hku.hk/software/ModReg.tar.gz.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

KRASG12C inhibition produces a driver-limited state revealing collateral dependencies

Inhibitors targeting KRAS^G12C, a mutant form of the guanosine triphosphatase (GTPase) KRAS, are a promising new class of oncogene-specific therapeutics for the treatment of tumors driven by the mutant protein. These inhibitors react with the mutant cysteine residue by binding covalently to the switch-II pocket (S-IIP) that is present only in the inactive guanosine diphosphate (GDP)-bound form of KRAS^G12C, sparing the wild-type protein. We used a genome-scale CRISPR interference (CRISPRi) functional genomics platform to systematically identify genetic interactions with a KRAS^G12C inhibitor in cellular models of KRAS^G12C mutant lung and pancreatic cancer. Our data revealed genes that were selectively essential in this oncogenic driver-limited cell state, meaning that their loss enhanced cellular susceptibility to direct KRAS^G12C inhibition. We termed such genes "collateral dependencies" (CDs) and identified two classes of combination therapies targeting these CDs that increased KRAS^G12C target engagement or blocked residual survival pathways in cells and in vivo. From our findings, we propose a framework for assessing genetic dependencies induced by oncogene inhibition.

MR4Cancer: a web server prioritizing master regulators for cancer

MOTIVATION

During cancer stage transition, a master regulator (MR) refers to the key gene controlling cancer initiation and progression by orchestrating the associated target genes (termed as its regulon). Due to their inherent importance, MRs can serve as critical biomarkers for cancer diagnosis and prognosis, and therapeutic targets. However, it is challenging to infer key MRs that might explain gene expression profile changes between two groups due to lack of context-specific regulons, whose expression level can collectively reflect the activity of likely MRs. There is also a need to design an easy-to-use tool of MR identification for research community.

RESULTS

First, we generated cancer-specific regulons for 26 cancer types by analyzing high-throughput omics data from TCGA, and extracted noncancer-specific regulons from public databases. We subsequently developed a web server MR4Cancer, integrating the regulons with statistical inference to identify and prioritize MRs driving a phenotypic divergence of interest. Based on the input gene list (e.g. differentially expressed genes) or expression profile with two groups, MR4Cancer outputs ranked MRs by enrichment testing against the predefined regulons. Gene Ontology and canonical pathway analyses are also conducted to elucidate the function of likely MRs. Moreover, MR4Cancer provides dynamic network visualization for MR-target relations, and users can interactively interrogate the network to produce new hypotheses and high-quality figures for publication. Finally, the presented case studies highlighted the performance of MR4Cancer. We expect this user-friendly and powerful web tool will provide researchers novel insights into tumorigenesis and therapeutic intervention.

AVAILABILITY AND IMPLEMENTATION

http://cis.hku.hk/MR4Cancer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Collaborating genomic, transcriptomic and microbiomic alterations lead to canine extreme intestinal polyposis

Extreme intestinal polyposis in pet dogs has not yet been reported in literature. We identified a dog patient who developed numerous intestinal polyps, with the severity resembling human classic familial adenomatous polyposis (FAP), except the jejunum-ileum junction being the most polyp-dense. We investigated this dog, in comparison with 22 other dogs with spontaneous intestinal tumors but no severe polyposis, and with numerous published human cancers. We found, not APC mutation, but three other alteration pathways as likely reasons of this canine extreme polyposis. First, somatic truncation mutation W411X of FBXW7, a component of an E3 ubiquitin ligase, over-activates MYC and cell cycle-promoting network, accelerating crypt cell proliferation. Second, genes of protein trafficking and localization are downregulated, likely associated with germline mutation G406D of STAMBPL1, a K63-deubiquitinase, and MYC network activation. This inhibits epithelial apical-basolateral polarity establishment, preventing crypt cell differentiation. Third, Bacteroides uniformis, a commensal gut anaerobe, thrives and expresses abundantly thioredoxin and nitroreductase. These bacterial products could reduce oxidative stress linked to host germline mutation R51X of CYB5RL, a cytochrome b5 reductase homologue, decreasing cell death. Our work emphasizes the close collaboration of alterations across the genome, transcriptome and microbiome in promoting tumorigenesis.

Module Analysis Captures Pancancer Genetically and Epigenetically Deregulated Cancer Driver Genes for Smoking and Antiviral Response

The availability of increasing volumes of multi-omics profiles across many cancers promises to improve our understanding of the regulatory mechanisms underlying cancer. The main challenge is to integrate these multiple levels of omics profiles and especially to analyze them across many cancers. Here we present AMARETTO, an algorithm that addresses both challenges in three steps. First, AMARETTO identifies potential cancer driver genes through integration of copy number, DNA methylation and gene expression data. Then AMARETTO connects these driver genes with co-expressed target genes that they control, defined as regulatory modules. Thirdly, we connect AMARETTO modules identified from different cancer sites into a pancancer network to identify cancer driver genes. Here we applied AMARETTO in a pancancer study comprising eleven cancer sites and confirmed that AMARETTO captures hallmarks of cancer. We also demonstrated that AMARETTO enables the identification of novel pancancer driver genes. In particular, our analysis led to the identification of pancancer driver genes of smoking-induced cancers and 'antiviral' interferon-modulated innate immune response.

SOFTWARE AVAILABILITY

AMARETTO is available as an R package at https://bitbucket.org/gevaertlab/pancanceramaretto.

Breast Cancer Risk Associated with Genotype Polymorphisms of the Aurora Kinase a Gene (AURKA): a Case-Control Study in a High Altitude Ecuadorian Mestizo Population

Breast cancer (BC) is the leading cause of cancer related death among women in 2014. The AURKA gene that encodes the protein called Aurora kinase A plays an important role in the progression of the cell cycle, by controlling and promoting the entry into the phase of mitosis. The single nucleotide polymorphism AURKA T91A (rs2273535) (Phe21Ile) has been identified as functional alternator of this kinase, the Ile allele is associated with the occurrence of chromosome segregation errors and tumor progression. Therefore, it is essential to know how BC risk is associated with histopathological characteristics, immunohistochemical characteristics, and genotype polymorphism in a high altitude Ecuadorian mestizo population. In this retrospective case-control study 200 individuals were analyzed. DNA was extracted from 100 healthy and 100 affected women. Genotypes were determined by genomic sequencing. We found significant association between the AURKA T91A (rs2273535) (Phe21Ile) genotype and an increased risk of BC development: Phe/Ile (odds ratio [OR] = 2.6; 95% confidence interval [CI] = 1.4-4.9; P = 0.004), Ile/Ile (OR = 3.8; 95% CI = 1.6-9.0; P = 0.002), and Phe/Ile + Ile/Ile (OR = 2.9; 95% CI = 1.6-5.2; P = 0.001). Additionally, the rs2273535 variant was associated with the tumor grade SBR III (OR = 9.6; 95% CI = 1.0-91.9; P = 0.048) and the Ki-67 ≥ 20 (OR = 16.5; 95% CI = 2.7-101.3; P = 0.002). In brief, this study provides the first evidence where the Ile allele of the AURKA gene could act as potentially predictive biomarker of BC in the high altitude Ecuadorian mestizo population that lives at 2800 m above sea level (masl).

Image-guided genomics of phenotypically heterogeneous populations reveals vascular signalling during symbiotic collective cancer invasion

Phenotypic heterogeneity is widely observed in cancer cell populations. Here, to probe this heterogeneity, we developed an image-guided genomics technique termed spatiotemporal genomic and cellular analysis (SaGA) that allows for precise selection and amplification of living and rare cells. SaGA was used on collectively invading 3D cancer cell packs to create purified leader and follower cell lines. The leader cell cultures are phenotypically stable and highly invasive in contrast to follower cultures, which show phenotypic plasticity over time and minimally invade in a sheet-like pattern. Genomic and molecular interrogation reveals an atypical VEGF-based vasculogenesis signalling that facilitates recruitment of follower cells but not for leader cell motility itself, which instead utilizes focal adhesion kinase-fibronectin signalling. While leader cells provide an escape mechanism for followers, follower cells in turn provide leaders with increased growth and survival. These data support a symbiotic model of collective invasion where phenotypically distinct cell types cooperate to promote their escape.

The mechanisms linking phenotypic heterogeneity to collective cancer invasion are unclear. Here the authors develop an image-guided genomic technique to select and amplify leader and follower cells from in vitro invading cell packs and find a cooperative symbiotic relationship between these two cell populations.

Tissue-specific tumorigenesis: context matters

How can we treat cancer more effectively? Traditionally, tumours from the same anatomical site are treated as one tumour entity. This concept has been challenged by recent breakthroughs in cancer genomics and translational research that have enabled molecular tumour profiling. The identification and validation of cancer drivers that are shared between different tumour types, spurred the new paradigm to target driver pathways across anatomical sites by off-label drug use, or within so-called basket or umbrella trials which are designed to test whether molecular alterations in one tumour entity can be extrapolated to all others. However, recent clinical and preclinical studies suggest that there are tissue- and cell type-specific differences in tumorigenesis and the organization of oncogenic signalling pathways. In this Opinion article, we focus on the molecular, cellular, systemic and environmental determinants of organ-specific tumorigenesis and the mechanisms of context-specific oncogenic signalling outputs. Investigation, recognition and in-depth biological understanding of these differences will be vital for the design of next-generation clinical trials and the implementation of molecularly guided cancer therapies in the future.

Systems Pharmacology-Based Discovery of Natural Products for Precision Oncology Through Targeting Cancer Mutated Genes

Massive cancer genomics data have facilitated the rapid revolution of a novel oncology drug discovery paradigm through targeting clinically relevant driver genes or mutations for the development of precision oncology. Natural products with polypharmacological profiles have been demonstrated as promising agents for the development of novel cancer therapies. In this study, we developed an integrated systems pharmacology framework that facilitated identifying potential natural products that target mutated genes across 15 cancer types or subtypes in the realm of precision medicine. High performance was achieved for our systems pharmacology framework. In case studies, we computationally identified novel anticancer indications for several US Food and Drug Administration-approved or clinically investigational natural products (e.g., resveratrol, quercetin, genistein, and fisetin) through targeting significantly mutated genes in multiple cancer types. In summary, this study provides a powerful tool for the development of molecularly targeted cancer therapies through targeting the clinically actionable alterations by exploiting the systems pharmacology of natural products.

Copy-number and gene dependency analysis reveals partial copy loss of wild-type SF3B1 as a novel cancer vulnerability

Genomic instability is a hallmark of human cancer, and results in widespread somatic copy number alterations. We used a genome-scale shRNA viability screen in human cancer cell lines to systematically identify genes that are essential in the context of particular copy-number alterations (copy-number associated gene dependencies). The most enriched class of copy-number associated gene dependencies was CYCLOPS (Copy-number alterations Yielding Cancer Liabilities Owing to Partial losS) genes, and spliceosome components were the most prevalent. One of these, the pre-mRNA splicing factor SF3B1, is also frequently mutated in cancer. We validated SF3B1 as a CYCLOPS gene and found that human cancer cells harboring partial SF3B1 copy-loss lack a reservoir of SF3b complex that protects cells with normal SF3B1 copy number from cell death upon partial SF3B1 suppression. These data provide a catalog of copy-number associated gene dependencies and identify partial copy-loss of wild-type SF3B1 as a novel, non-driver cancer gene dependency.

Mutant DNA methylation regulators endow hematopoietic stem cells with the preleukemic stem cell property, a requisite of leukemia initiation and relapse

Genetic mutations are considered to drive the development of acute myeloid leukemia (AML). With therapid progress in sequencing technologies, many newly reported genes that are recurrently mutated in AML have been found to govern the initiation and relapse of AML. These findings suggest the need to distinguish the driver mutations, especially the most primitive single mutation, from the subsequent passenger mutations. Recent research on DNA methyltransferase 3A (DNMT3A) mutations provides the first proof-of-principle investigation on the identification of preleukemic stem cells (pre-LSCs) in AML patients. Although DNMT3A mutations alone may only transform hematopoietic stem cells into pre-LSCs without causing the full-blown leukemia, the function of this driver mutation appear to persist from AML initiation up to relapse. Therefore, identifying and targeting preleukemic mutations, such as DNMT3A mutations, in AML is a promising strategy for treatment and reduction of relapse risk.

Jak-STAT3 pathway triggers DICER1 for proteasomal degradation by ubiquitin ligase complex of CUL4A(DCAF1) to promote colon cancer development

Chronic intestinal inflammation is closely associated with colon cancer development and STAT3 seems to take center stage in bridging chronic inflammation to colon cancer progress. Here, we discovered that DICER1 was significantly downregulated in response to IL-6 or LPS stimulation and identified a novel mechanism for DICER1 downregulation via proteasomal degradation by ubiquitin ligase complex of CUL4A(DCAF1) in colon cancer cells. Meanwhile, PI3K-AKT signaling pathway phosphorylated DICER1 and contributed to its proteasomal degradation. The regulation of DICER1 by CUL4A(DCAF1) affected cell growth and apoptosis which is controlled by IL-6 activated Jak-STAT3 pathway. Intervention of CUL4A(DCAF1) ubiquitin ligase complex led to fluctuation in expression levels of DICER1 and microRNAs, and thus affected tumor growth in a mouse xenograft model. A panel of microRNAs that were downregulated by IL-6 stimulation was rescued by siRNA-CUL4A, and their predicated functions are involved in regulation of cell proliferation, apoptosis and motility. Furthermore, clinical specimen analysis revealed that decreased DICER1 expression was negatively correlated with STAT3 activation and cancer progression in human colon cancers. DICER1 and p-STAT3 expression levels correlated with 5-year overall survival of colon cancer patients. Consequently, this study proposes that inflammation-induced Jak-STAT3 signaling leads to colon cancer development through proteasomal degradation of DICER1 by ubiquitin ligase complex of CUL4A(DCAF1), which suggests a novel therapeutic opportunity for colon cancer.

Understanding pancreatic cancer genomes

Pancreatic cancer is the fourth leading cause of cancer death in our society, with a mortality that virtually parallels its incidence, a median survival of <12 months even with maximal therapy, and a 5-year survival rate of <5 %. The diversity of clinical outcomes and the molecular heterogeneity of histopathologically similar cancer types, incomplete knowledge of the genomic aberrations that drive carcinogenesis and the lack of therapeutics that specifically target most known genomic aberrations necessitates large-scale detailed analysis of cancer genomes to identify novel potential therapeutic strategies. As part of the International Cancer Genome Consortium (ICGC), the Australian Pancreatic Cancer Genome Initiative (APGI) used exomic sequencing and copy number analysis to define genomic aberrations that characterize a large, clinically focused, prospectively accrued cohort of patients with pancreatic cancer. The cohort consisted of early (clinical stages I and II) non-pre-treated patients with pancreatic ductal adenocarcinoma who underwent operative resection with curative intent. We devised approaches to adjust for low epithelial content in primary tumours and to define the genomic landscape of pancreatic cancer to identify novel candidate driver genes and mechanisms. We aim to develop stratified, molecular phenotype-guided therapeutic strategies using existing therapeutics that are either rescued, repurposed, in development, or are known to be effective in an undefined subgroup of PC patients. These are then tested in primary patient-derived xenografts and cell lines from the above deeply characterized cohort. In addition, we return information to treating clinicians that influences patient care and are launching a clinical trial called IMPaCT (Individualized Molecular Pancreatic Cancer Therapy). This umbrella design trial randomizes patients with metastatic disease to either standard first-line therapy with gemcitabine, or a molecular phenotype-guided approach using next-generation sequencing strategies to screen for actionable mutations defined through the ICGC effort.

Cell-Line Selectivity Improves the Predictive Power of Pharmacogenomic Analyses and Helps Identify NADPH as Biomarker for Ferroptosis Sensitivity

Precision medicine in oncology requires not only identification of cancer-associated mutations but also effective drugs for each cancer genotype, which is still a largely unsolved problem. One approach for the latter challenge has been large-scale testing of small molecules in genetically characterized cell lines. We hypothesized that compounds with high cell-line-selective lethality exhibited consistent results across such pharmacogenomic studies. We analyzed the compound sensitivity data of 6,259 lethal compounds from the NCI-60 project. A total of 2,565 cell-line-selective lethal compounds were identified and grouped into 18 clusters based on their median growth inhibitory GI50 profiles across the 60 cell lines, which were shown to represent distinct mechanisms of action. Further transcriptome analysis revealed a biomarker, NADPH abundance, for predicting sensitivity to ferroptosis-inducing compounds, which we experimentally validated. In summary, incorporating cell-line-selectivity filters improves the predictive power of pharmacogenomic analyses and enables discovery of biomarkers that predict the sensitivity of cells to specific cell death inducers.

Personalization of the medical treatment of solid tumours using patient-derived tumour explants (Review)

Improving the pre-clinical characterization of therapeutic approaches and developing new biological assays that will enable treatment personalization for individual patients are promising developments in oncology. Here we describe a new approach consisting of culturing human tumour explants. This approach involves the preparation of slices from freshly-obtained, surgically-resected material that can be maintained ex vivo for several days. Recent studies have provided proof of principle that this approach can be easily implemented in order to explore the mode of action of various anticancer drugs and the responses of 'real' tumours at the individual patient level. We present the practical aspects and highlight the versatility of this approach, which allows for the analysis of the susceptibility of any individual tumour to multiple anticancer drugs in parallel. We discuss its potential as a companion assay in the design of optimal clinical trials and as a guide for the prescription of medical treatment. We discuss which future clinical and biological studies are needed to validate the information gathered from cultured tumour explants, and to integrate this information with that gathered from other assays in order to optimize the medical treatment of cancer.

SS18-SSX, the Oncogenic Fusion Protein in Synovial Sarcoma, Is a Cellular Context-Dependent Epigenetic Modifier

The prevalence and specificity of unique fusion oncogenes are high in a number of soft tissue sarcomas (STSs). The close relationship between fusion genes and clinicopathological features suggests that a correlation may exist between the function of fusion proteins and cellular context of the cell-of-origin of each tumor. However, most STSs are origin-unknown tumors and this issue has not yet been investigated in detail. In the present study, we examined the effects of the cellular context on the function of the synovial sarcoma (SS)-specific fusion protein, SS18-SSX, using human pluripotent stem cells (hPSCs) containing the drug-inducible SS18-SSX gene. We selected the neural crest cell (NCC) lineage for the first trial of this system, induced SS18-SSX at various differentiation stages from PSCs to NCC-derived mesenchymal stromal cells (MSCs), and compared its biological effects on each cell type. We found that the expression of FZD10, identified as an SS-specific gene, was induced by SS18-SSX at the PSC and NCC stages, but not at the MSC stage. This stage-specific induction of FZD10 correlated with stage-specific changes in histone marks associated with the FZD10 locus and also with the loss of the BAF47 protein, a member of the SWI/SNF chromatin-remodeling complex. Furthermore, the global gene expression profile of hPSC-derived NCCs was the closest to that of SS cell lines after the induction of SS18-SSX. These results clearly demonstrated that the cellular context is an important factor in the function of SS18-SSX as an epigenetic modifier.

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.

Cancer modelling in the NGS era - Part I: Emerging technology and initial modelling

It is today indisputable that great progresses have been made in our molecular understanding of cancer cells, but an effective implementation of such knowledge into dramatic cancer-cures is still belated and yet desperately needed. This review gives a snapshot at where we stand today in this search for cancer understanding and definitive treatments, how far we have progressed and what are the major obstacles we will have to overcome both technologically and for disease modelling. In the first part, promising 3rd/4th Generation Sequencing Technologies will be summarized (particularly IonTorrent and OxfordNanopore technologies). Cancer modelling will be then reviewed from its origin in XIX Century Germany to today's NGS applications for cancer understanding and therapeutic interventions. Developments after Molecular Biology revolution (1953) are discussed as successions of three phases. The first, PH1, labelled "Clonal Outgrowth" (from 1960s to mid 1980s) was characterized by discoveries in cytogenetics (Nowell, Rowley) and viral oncology (Dulbecco, Bishop, Varmus), which demonstrated clonality. Treatments were consequently dominated by a "cytotoxic eradication" strategy with chemotherapeutic agents. In PH2, (from the mid 1980s to our days) the description of cancer as "Gene Networks" led to targeted-gene-therapies (TGTs). TGTs are the focus of Section 3: in view of their apparent failing (Ephemeral Therapies), alternative strategies will be discussed in review part II (particularly cancer immunotherapy, CIT). Additional Pitfalls impinge on the concepts of tumour heterogeneity (inter/intra; ITH). The described pitfalls set the basis for a new phase, PH3, which is called "NGS Era" and will be also discussed with ten emerging cancer models in the Review 2nd part.

IndividualizedPath: identifying genetic alterations contributing to the dysfunctional pathways in glioblastoma individuals

Due to the extensive complexity and high genetic heterogeneity of genetic alterations in cancer, comprehensively depicting the molecular mechanisms of cancer remains difficult. Characterizing personalized pathogenesis in cancer individuals can help to reveal new details of the complex mechanisms. In this study, we proposed an integrative method called IndividualizedPath to identify genetic alterations and their downstream risk pathways from the perspective of individuals through combining the DNA copy number, gene expression data and topological structures of biological pathways. By applying the method to TCGA glioblastoma multiforme (GBM) samples, we identified 394 gene-pathway pairs in 252 GBM individuals. We found that genes with copy number alterations showed high heterogeneity across GBM individuals, whereas they affected relatively consistent biological pathways. A global landscape of gene-pathway pairs showed that EGFR linked with multiple cancer-related biological pathways confers the highest risk of GBM. GBM individuals with MET-pathway pairs showed significantly shorter survival times than those with only MET amplification. Importantly, we found that the same risk pathways were affected by different genes in distinct groups of GBM individuals with a significant pattern of mutual exclusivity. Similarly, GBM subtype analysis revealed some subtype-specific gene-pathway pairs. In addition, we found that some rare copy number alterations had a large effect on contribution to numerous cancer-related pathways. In summary, our method offers the possibility to identify personalized cancer mechanisms, which can be applied to other types of cancer through the web server (http://bioinfo.hrbmu.edu.cn/IndividualizedPath/).

From the Biology of PP2A to the PADs for Therapy of Hematologic Malignancies

Over the past decades, an emerging role of phosphatases in the pathogenesis of hematologic malignancies and solid tumors has been established. The tumor-suppressor protein phosphatase 2A (PP2A) belongs to the serine-threonine phosphatases family and accounts for the majority of serine-threonine phosphatase activity in eukaryotic cells. Numerous studies have shown that inhibition of PP2A expression and/or function may contribute to leukemogenesis in several hematological malignancies. Likewise, overexpression or aberrant expression of physiologic PP2A inhibitory molecules (e.g., SET and its associated SETBP1 and CIP2A) may turn off PP2A function and participate to leukemic progression. The discovery of PP2A as tumor suppressor has prompted the evaluation of the safety and the efficacy of new compounds, which can restore PP2A activity in leukemic cells. Although further studies are needed to better understand how PP2A acts in the intricate phosphatases/kinases cancer network, the results reviewed herein strongly support the development on new PP2A-activating drugs and the immediate introduction of those available into clinical protocols for leukemia patients refractory or resistant to current available therapies.

Identification of constrained cancer driver genes based on mutation timing

Cancer drivers are genomic alterations that provide cells containing them with a selective advantage over their local competitors, whereas neutral passengers do not change the somatic fitness of cells. Cancer-driving mutations are usually discriminated from passenger mutations by their higher degree of recurrence in tumor samples. However, there is increasing evidence that many additional driver mutations may exist that occur at very low frequencies among tumors. This observation has prompted alternative methods for driver detection, including finding groups of mutually exclusive mutations and incorporating prior biological knowledge about gene function or network structure. Dependencies among drivers due to epistatic interactions can also result in low mutation frequencies, but this effect has been ignored in driver detection so far. Here, we present a new computational approach for identifying genomic alterations that occur at low frequencies because they depend on other events. Unlike passengers, these constrained mutations display punctuated patterns of occurrence in time. We test this driver-passenger discrimination approach based on mutation timing in extensive simulation studies, and we apply it to cross-sectional copy number alteration (CNA) data from ovarian cancer, CNA and single-nucleotide variant (SNV) data from breast tumors and SNV data from colorectal cancer. Among the top ranked predicted drivers, we find low-frequency genes that have already been shown to be involved in carcinogenesis, as well as many new candidate drivers. The mutation timing approach is orthogonal and complementary to existing driver prediction methods. It will help identifying from cancer genome data the alterations that drive tumor progression.

A global assessment of cancer genomic alterations in epigenetic mechanisms

Background

The notion that epigenetic mechanisms may be central to cancer initiation and progression is supported by recent next-generation sequencing efforts revealing that genes involved in chromatin-mediated signaling are recurrently mutated in cancer patients.

Results

Here, we analyze mutational and transcriptional profiles from TCGA and the ICGC across a collection 441 chromatin factors and histones. Chromatin factors essential for rapid replication are frequently overexpressed, and those that maintain genome stability frequently mutated. We identify novel mutation hotspots such as K36M in histone H3.1, and uncover a general trend in which transcriptional profiles and somatic mutations in tumor samples favor increased transcriptionally repressive histone methylation, and defective chromatin remodeling.

Conclusions

This unbiased approach confirms previously published data, uncovers novel cancer-associated aberrations targeting epigenetic mechanisms, and justifies continued monitoring of chromatin-related alterations as a class, as more cancer types and distinct cancer stages are represented in cancer genomics data repositories.

Electronic supplementary material

The online version of this article (doi:10.1186/1756-8935-7-29) contains supplementary material, which is available to authorized users.

Evolving toward a human-cell based and multiscale approach to drug discovery for CNS disorders

A disruptive approach to therapeutic discovery and development is required in order to significantly improve the success rate of drug discovery for central nervous system (CNS) disorders. In this review, we first assess the key factors contributing to the frequent clinical failures for novel drugs. Second, we discuss cancer translational research paradigms that addressed key issues in drug discovery and development and have resulted in delivering drugs with significantly improved outcomes for patients. Finally, we discuss two emerging technologies that could improve the success rate of CNS therapies: human induced pluripotent stem cell (hiPSC)-based studies and multiscale biology models. Coincident with advances in cellular technologies that enable the generation of hiPSCs directly from patient blood or skin cells, together with methods to differentiate these hiPSC lines into specific neural cell types relevant to neurological disease, it is also now possible to combine data from large-scale forward genetics and post-mortem global epigenetic and expression studies in order to generate novel predictive models. The application of systems biology approaches to account for the multiscale nature of different data types, from genetic to molecular and cellular to clinical, can lead to new insights into human diseases that are emergent properties of biological networks, not the result of changes to single genes. Such studies have demonstrated the heterogeneity in etiological pathways and the need for studies on model systems that are patient-derived and thereby recapitulate neurological disease pathways with higher fidelity. In the context of two common and presumably representative neurological diseases, the neurodegenerative disease Alzheimer's Disease, and the psychiatric disorder schizophrenia, we propose the need for, and exemplify the impact of, a multiscale biology approach that can integrate panomic, clinical, imaging, and literature data in order to construct predictive disease network models that can (i) elucidate subtypes of syndromic diseases, (ii) provide insights into disease networks and targets and (iii) facilitate a novel drug screening strategy using patient-derived hiPSCs to discover novel therapeutics for CNS disorders.

Prioritizing targets for precision cancer medicine

The implementation of cancer genomic testing into the clinical setting has brought major opportunities. However, as our understanding of cancer initiation, maintenance and progression improves through detailed cancer genomic studies, the challenges associated with driver identification and target classification in the clinical setting become clearer. Here, we review recent insights into cancer genomic testing in the clinical setting, and suggest a target classification approach that considers the levels of evidence supporting the prioritization of tumour drivers for therapeutic targeting in light of complex cancer clonal and sub-clonal structures and clinical successes and failures in the field. We argue that such classification approaches, together with transparent reporting of both positive and negative clinical data and continued research to identify the sub-clonal dynamics of driver events during the disease course, will facilitate inter-trial comparisons, optimize patient informed consent and provide a critically balanced evaluation of genomic testing in clinical practice.

Computer-aided targeting of the PI3K/Akt/mTOR pathway: toxicity reduction and therapeutic opportunities

The PI3K/Akt/mTOR pathway plays an essential role in a wide range of biological functions, including metabolism, macromolecular synthesis, cell growth, proliferation and survival. Its versatility, however, makes it a conspicuous target of many pathogens; and the consequential deregulations of this pathway often lead to complications, such as tumorigenesis, type 2 diabetes and cardiovascular diseases. Molecular targeted therapy, aimed at modulating the deregulated pathway, holds great promise for controlling these diseases, though side effects may be inevitable, given the ubiquity of the pathway in cell functions. Here, we review a variety of factors found to modulate the PI3K/Akt/mTOR pathway, including gene mutations, certain metabolites, inflammatory factors, chemical toxicants, drugs found to rectify the pathway, as well as viruses that hijack the pathway for their own synthetic purposes. Furthermore, this evidence of PI3K/Akt/mTOR pathway alteration and related pathogenesis has inspired the exploration of computer-aided targeting of this pathway to optimize therapeutic strategies. Herein, we discuss several possible options, using computer-aided targeting, to reduce the toxicity of molecularly-targeted therapy, including mathematical modeling, to reveal system-level control mechanisms and to confer a low-dosage combination therapy, the potential of PP2A as a therapeutic target, the formulation of parameters to identify patients who would most benefit from specific targeted therapies and molecular dynamics simulations and docking studies to discover drugs that are isoform specific or mutation selective so as to avoid undesired broad inhibitions. We hope this review will stimulate novel ideas for pharmaceutical discovery and deepen our understanding of curability and toxicity by targeting the PI3K/Akt/mTOR pathway.

Clinical application of high-throughput genomic technologies for treatment selection in breast cancer

Large-scale collaborative initiatives using next-generation DNA sequencing and other high-throughput technologies have begun to characterize the genomic landscape of breast cancer. These landmark studies have identified infrequent driver mutations that are potential targets for therapeutic intervention with approved or investigational drug treatments, among other important discoveries. Recently, many institutions have launched molecular screening programs that apply high-throughput genomic technologies to patients with advanced solid malignancies, including breast cancer, to inform clinical decision-making. This article provides an overview of the recent molecular insights in breast cancer, including potentially actionable somatic alterations, the technological platforms currently available in a clinical diagnostics setting to detect these alterations, and ongoing institutional or regional molecular screening programs in advanced breast cancer.

From cancer genomics to precision oncology--tissue's still an issue

Rapidly evolving genome technology has enabled extensive molecular analysis of limited tumor biopsy material, thereby facilitating the broader implementation of personalized cancer medicine. However, genomics-based patient stratification across diverse tumor types is unlikely to supplant tissue-of-origin considerations in addressing clinical needs, including the development and application of novel "rationally targeted" cancer therapies.

Co-development of diagnostic vectors to support targeted therapies and theranostics: essential tools in personalized cancer therapy

Novel technologies are being developed to improve patient therapy through the identification of targets and surrogate molecular signatures that can help direct appropriate treatment regimens for efficacy and drug safety. This is particularly the case in oncology whereby patient tumor and biofluids are routinely isolated and analyzed for genetic, immunohistochemical, and/or soluble markers to determine if a predictive biomarker signature (i.e., mutated gene product, differentially expressed protein, altered cell surface antigen, etc.) exists as a means for selecting optimal treatment. These biomarkers may be drug-specific targets and/or differentially expressed nucleic acids, proteins, or cell lineage profiles that can directly affect the patient’s disease tissue or immune response to a therapeutic regimen. Improvements in diagnostics that can prescreen predictive response biomarker profiles will continue to optimize the ability to enhance patient therapy via molecularly defined disease-specific treatment. Conversely, patients lacking predictive response biomarkers will no longer needlessly be exposed to drugs that are unlikely to provide clinical benefit, thereby enabling patients to pursue other therapeutic options and lowering overall healthcare costs by avoiding futile treatment. While patient molecular profiling offers a powerful tool to direct treatment options, the difficulty in identifying disease-specific targets or predictive biomarker signatures that stratify a significant fraction within a disease indication remains challenging. A goal for drug developers is to identify and implement new strategies that can rapidly enable the development of beneficial disease-specific therapies for broad patient-specific targeting without the need of tedious predictive biomarker discovery and validation efforts, currently a bottleneck for development timelines. Successful strategies may gain an advantage by employing repurposed, less-expensive existing agents while potentially improving the therapeutic activity of novel, target-specific therapies that may otherwise have off-target toxicities or less efficacy in cells exhibiting certain pathways. Here, we discuss the use of co-developing diagnostic-targeting vectors to identify patients whose malignant tissue can specifically uptake a targeted anti-cancer drug vector prior to treatment. Using this system, a patient can be predetermined in real-time as to whether or not their tumor(s) can specifically uptake a drug-linked diagnostic vector, thus inferring the uptake of a similar vector linked to an anti-cancer agent. If tumor-specific uptake is observed, then the patient may be suitable for drug-linked vector therapy and have a higher likelihood of clinical benefit while patients with no tumor uptake should consider other therapeutic options. This approach offers complementary opportunities to rapidly develop broad tumor-specific agents for use in personalized medicine.

S-score: a scoring system for the identification and prioritization of predicted cancer genes

A new method, which allows for the identification and prioritization of predicted cancer genes for future analysis, is presented. This method generates a gene-specific score called the “S-Score” by incorporating data from different types of analysis including mutation screening, methylation status, copy-number variation and expression profiling. The method was applied to the data from The Cancer Genome Atlas and allowed the identification of known and potentially new oncogenes and tumor suppressors associated with different clinical features including shortest term of survival in ovarian cancer patients and hormonal subtypes in breast cancer patients. Furthermore, for the first time a genome-wide search for genes that behave as oncogenes and tumor suppressors in different tumor types was performed. We envisage that the S-score can be used as a standard method for the identification and prioritization of cancer genes for follow-up studies.

Multifocal epithelial tumors and field cancerization: stroma as a primary determinant

It is increasingly evident that cancer results from altered organ homeostasis rather than from deregulated control of single cells or groups of cells. This applies especially to epithelial cancer, the most common form of human solid tumors and a major cause of cancer lethality. In the vast majority of cases, in situ epithelial cancer lesions do not progress into malignancy, even if they harbor many of the genetic changes found in invasive and metastatic tumors. While changes in tumor stroma are frequently viewed as secondary to changes in the epithelium, recent evidence indicates that they can play a primary role in both cancer progression and initiation. These processes may explain the phenomenon of field cancerization, i.e., the occurrence of multifocal and recurrent epithelial tumors that are preceded by and associated with widespread changes of surrounding tissue or organ "fields."

Pancreatic cancer genomics

Pancreatic cancer is one of the most lethal malignancies. The overall median survival even with treatment is only 6-9 months, with almost 90% succumbing to the disease within a year of diagnosis. It is characterised by an intense desmoplastic stroma that may contribute to therapeutic resistance, and poses significant challenges for genomic sequencing studies. It is recalcitrant to almost all therapies and consequently remains the fourth leading cause of cancer death in Western societies. Genomic studies are unveiling a vast heterogeneity of mutated genes, and this diversity may explain why conventional clinical trial designs have mostly failed to demonstrate efficacy in unselected patients. Those that are available offer only marginal benefits overall, but are associated with clinically significant responses in as yet undefined subgroups. This chapter describes our current understanding of the genomics of pancreatic cancer and the potential impact of these findings on our approaches to treatment.

Learning subgroup-specific regulatory interactions and regulator independence with PARADIGM

High-dimensional '-omics' profiling provides a detailed molecular view of individual cancers; however, understanding the mechanisms by which tumors evade cellular defenses requires deep knowledge of the underlying cellular pathways within each cancer sample. We extended the PARADIGM algorithm (Vaske et al., 2010, Bioinformatics, 26, i237-i245), a pathway analysis method for combining multiple '-omics' data types, to learn the strength and direction of 9139 gene and protein interactions curated from the literature. Using genomic and mRNA expression data from 1936 samples in The Cancer Genome Atlas (TCGA) cohort, we learned interactions that provided support for and relative strength of 7138 (78%) of the curated links. Gene set enrichment found that genes involved in the strongest interactions were significantly enriched for transcriptional regulation, apoptosis, cell cycle regulation and response to tumor cells. Within the TCGA breast cancer cohort, we assessed different interaction strengths between breast cancer subtypes, and found interactions associated with the MYC pathway and the ER alpha network to be among the most differential between basal and luminal A subtypes. PARADIGM with the Naive Bayesian assumption produced gene activity predictions that, when clustered, found groups of patients with better separation in survival than both the original version of PARADIGM and a version without the assumption. We found that this Naive Bayes assumption was valid for the vast majority of co-regulators, indicating that most co-regulators act independently on their shared target.

AVAILABILITY

http://paradigm.five3genomics.com.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ChromoHub V2: cancer genomics

SUMMARY

Cancer genomics data produced by next-generation sequencing support the notion that epigenetic mechanisms play a central role in cancer. We have previously developed Chromohub, an open access online interface where users can map chemical, structural and biological data from public repositories on phylogenetic trees of protein families involved in chromatin mediated-signaling. Here, we describe a cancer genomics interface that was recently added to Chromohub; the frequency of mutation, amplification and change in expression of chromatin factors across large cohorts of cancer patients is regularly extracted from The Cancer Genome Atlas and the International Cancer Genome Consortium and can now be mapped on phylogenetic trees of epigenetic protein families. Explorators of chromatin signaling can now easily navigate the cancer genomics landscape of writers, readers and erasers of histone marks, chromatin remodeling complexes, histones and their chaperones.

AVAILABILITY AND IMPLEMENTATION

http://www.thesgc.org/chromohub/.

Digitization of medicine: how radiology can take advantage of the digital revolution

In the era of medical cost containment, radiologists must continually maintain their actual and perceived value to patients, payers, and referring providers. Exploitation of current and future digital technologies may be the key to defining and promoting radiology's "brand" and assure our continued relevance in providing predictive, preventive, personalized, and participatory medicine. The Association of University of Radiologists Radiology Research Alliance Digitization of Medicine Task Force was formed to explore the opportunities and challenges of the digitization of medicine that are relevant to radiologists, which include the reporting paradigm, computational biology, and imaging informatics. In addition to discussing these opportunities and challenges, we consider how change occurs in medicine, and how change may be effected in medical imaging community. This review article is a summary of the research of the task force and hopefully can be used as a stimulus for further discussions and development of action plans by radiology leaders.