Stabilized COre gene and Pathway Election uncovers pan-cancer shared pathways and a cancer specific driver

Approaches systematically characterizing interactions via transcriptomic data usually follow two systems: (1) co-expression network analyses focusing on correlations between genes; (2) linear regressions (usually regularized) to select multiple genes jointly. Both suffer from the problem of stability: a slight change of parameterization or dataset could lead to dramatic alternations of outcomes. Here, we propose Stabilized Core gene and Pathway Election, or SCOPE, a tool integrating bootstrapped LASSO and co-expression analysis, leading to robust outcomes insensitive to variations in data. By applying SCOPE to six cancer expression datasets (BRCA, COAD, KIRC, LUAD, PRAD and THCA) in The Cancer Genome Atlas, we identified core genes capturing interaction effects in crucial pan-cancer pathways related to genome instability and DNA damage response. Moreover, we highlighted the pivotal role of CD63 as an oncogenic driver and a potential therapeutic target in kidney cancer. SCOPE enables stabilized investigations towards complex interactions using transcriptome data.

In Silico Investigation of the Biological Implications of Complex DNA Damage with Emphasis in Cancer Radiotherapy through a Systems Biology Approach

Different types of DNA lesions forming in close vicinity, create clusters of damaged sites termed as “clustered/complex DNA damage” and they are considered to be a major challenge for DNA repair mechanisms resulting in significant repair delays and induction of genomic instability. Upon detection of DNA damage, the corresponding DNA damage response and repair (DDR/R) mechanisms are activated. The inability of cells to process clustered DNA lesions efficiently has a great impact on the normal function and survival of cells. If complex lesions are left unrepaired or misrepaired, they can lead to mutations and if persistent, they may lead to apoptotic cell death. In this in silico study, and through rigorous data mining, we have identified human genes that are activated upon complex DNA damage induction like in the case of ionizing radiation (IR) and beyond the standard DNA repair pathways, and are also involved in cancer pathways, by employing stringent bioinformatics and systems biology methodologies. Given that IR can cause repair resistant lesions within a short DNA segment (a few nm), thereby augmenting the hazardous and toxic effects of radiation, we also investigated the possible implication of the most biologically important of those genes in comorbid non-neoplastic diseases through network integration, as well as their potential for predicting survival in cancer patients.

The Clinical Value of miRNA-21 in Cervical Cancer: A Comprehensive Investigation Based on Microarray Datasets

Previous work has demonstrated that the expression of microRNA-21 (miR-21) is implicated in cervical cancer (CC). However, little is known regarding its associations with clinical parameters. We first conducted a meta-analysis using data from Gene Expression Omnibus (GEO) microarrays and The Cancer Genome Atlas (TCGA). Then, enrichment analysis and hub gene screening were performed by bioinformatics methods. Finally, the roles of the screened target genes in CC were explored. From the meta-analysis, more miR-21 was expressed in cancer tissues than in adjacent nontumor tissues (P < 0.05). In addition, 46 genes were predicted as potential targets of miR-21. After enrichment analyses, it was detected that these genes were enriched in various cancer pathways, including the phosphatidylinositol signaling system and mammalian target of rapamycin (mTOR) signaling pathway. In this study, bioinformatics tools and meta-analysis validated that miR-21 may function as a highly sensitive and specific marker for the diagnosis of CC, which may provide a novel approach to the diagnosis and treatment of CC.

P-L06 Establishing minimum datasets and disease specific forms for cancer patients in multidisciplinary team meetings

Background Multidisciplinary team (MDT) meetings aim to ensure standardised access to cancer pathways and treatment. It is thus important that minimum datasets are established and provided to the MDT clinicians to facilitate thorough discussion and encourage shared decision making. To determine the effectiveness of pre-established minimum dataset proformas for periampullary tumours, hepatocellular carcinoma (HCC), colorectal cancer with liver metastases (CRLM) and hilar cholangiocarcinoma in cancer MDT meetings. Methods Cancer specific minimum dataset forms were developed by consultants to be implemented in MDTs for the four types of hepatobiliary pancreatic cancers: HCC, periampullary cancer, hilar cholangiocarcinoma and CRLM. 189 MDT reports and 184 referral letters were analysed from three weekly MDT meetings to determine the outcomes reported against those outlined in the proformas. The proformas were then implemented by consultants leading the weekly MDT meetings for three consecutive weeks. 128 MDT reports and 126 referral letters were re-audited from these MDT meetings to determine if there was a change in the outcomes reported. Results In this quality improvement project, 21.25% (11/52) of outcomes in MDT and 19.23% (10/52) of outcomes in the referral letters had a statistically significant difference in the reporting of outcomes from Cycle 1 to Cycle 2 across all four cancers. Greater changes in reporting were observed for outcomes that may carry higher prognostic value, such as tumour size and vascular invasion. Despite the notable difference in reporting seen for datasets consisting of smaller cohorts, the change was not statistically significant due to the insufficient statistical power proving the need for trialling such proformas in a wider population. Conclusions The minimum dataset proformas were associated with a limited gain in compliance with data reporting. Further work, with particular focus on user acceptability and attitudes is required to improve use of minimum dataset proformas in MDT.

Identifying common transcriptome signatures of cancer by interpreting deep learning models

Cancer is a set of diseases characterized by unchecked cell proliferation and invasion of surrounding tissues. The many genes that have been genetically associated with cancer or shown to directly contribute to oncogenesis vary widely between tumor types, but common gene signatures that relate to core cancer pathways have also been identified, signifying that cancer cases display common hallmark molecular features. It is not clear however whether there exist additional sets of genes or transcriptomic features that are less well known in cancer biology but that are also commonly deregulated across several cancer types. Here, in order to agnostically identify transcriptomic features that are commonly shared between cancer types, we used RNA-Seq datasets encompassing thousands of samples from 19 healthy tissue types and 18 solid tumor types to train three feed-forward neural networks, based either on protein-coding gene expression, lncRNA expression or splice junction use, to distinguish between healthy and tumor samples. All three models achieve high precision, recall and accuracy on test sets derived from 13 datasets used during training and on an independent test dataset, indicating that our models recognize transcriptome signatures that are consistent across tumors. Analysis of attribution values extracted from our models reveals that genes that are commonly altered in cancer by expression or splicing variations are under strong evolutionary and selective constraints, suggesting that they have important cellular functions. Importantly, we found that genes composing our cancer transcriptome signatures are not frequently affected by mutations or genomic alterations and that their functions differ widely from the genes genetically associated with cancer. Finally, our results also highlighted that deregulation of RNA-processing genes and aberrant splicing are pervasive features across a large array of solid tumor types. The transcriptomic features that we highlight here define cancer signatures that may reflect causal variations or consequences of disease state, or a combination of both.

Intracellular delivery of protein drugs with an autonomously lysing bacterial system reduces tumor growth and metastases

Critical cancer pathways often cannot be targeted because of limited efficiency crossing cell membranes. Here we report the development of a Salmonella-based intracellular delivery system to address this challenge. We engineer genetic circuits that (1) activate the regulator flhDC to drive invasion and (2) induce lysis to release proteins into tumor cells. Released protein drugs diffuse from Salmonella containing vacuoles into the cellular cytoplasm where they interact with their therapeutic targets. Control of invasion with flhDC increases delivery over 500 times. The autonomous triggering of lysis after invasion makes the platform self-limiting and prevents drug release in healthy organs. Bacterial delivery of constitutively active caspase-3 blocks the growth of hepatocellular carcinoma and lung metastases, and increases survival in mice. This success in targeted killing of cancer cells provides critical evidence that this approach will be applicable to a wide range of protein drugs for the treatment of solid tumors.

RNA-Based Therapeutics: Current Developments in Targeted Molecular Therapy of Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a highly heterogeneous and aggressive cancer that has the highest mortality rate out of all breast cancer subtypes. Conventional clinical treatments targeting ER, PR, and HER2 receptors have been unsuccessful in the treatment of TNBC, which has led to various research efforts in developing new strategies to treat TNBC. Targeted molecular therapy of TNBC utilizes knowledge of key molecular signatures of TNBC that can be effectively modulated to produce a positive therapeutic response. Correspondingly, RNA-based therapeutics represent a novel tool in oncology with their ability to alter intrinsic cancer pathways that contribute to poor patient prognosis. Current RNA-based therapeutics exist as two major areas of investigation—RNA interference (RNAi) and RNA nanotherapy, where RNAi utilizes principles of gene silencing, and RNA nanotherapy utilizes RNA-derived nanoparticles to deliver chemotherapeutics to target cells. RNAi can be further classified as therapeutics utilizing either small interfering RNA (siRNA) or microRNA (miRNA). As the broader field of gene therapy has advanced significantly in recent years, so too have efforts in the development of effective RNA-based therapeutic strategies for treating aggressive cancers, including TNBC. This review will summarize key advances in targeted molecular therapy of TNBC, describing current trends in treatment using RNAi, combination therapies, and recent efforts in RNA immunotherapy, utilizing messenger RNA (mRNA) in the development of cancer vaccines.

Roles of circadian clocks in cancer pathogenesis and treatment

Circadian clocks are ubiquitous timing mechanisms that generate approximately 24-h rhythms in cellular and bodily functions across nearly all living species. These internal clock systems enable living organisms to anticipate and respond to daily changes in their environment in a timely manner, optimizing temporal physiology and behaviors. Dysregulation of circadian rhythms by genetic and environmental risk factors increases susceptibility to multiple diseases, particularly cancers. A growing number of studies have revealed dynamic crosstalk between circadian clocks and cancer pathways, providing mechanistic insights into the therapeutic utility of circadian rhythms in cancer treatment. This review will discuss the roles of circadian rhythms in cancer pathogenesis, highlighting the recent advances in chronotherapeutic approaches for improved cancer treatment.

SP4.1.7 Colorectal Cancer: Pathway Disruptions as a Result of COVID-19

Aims COVID-19 has impacted diagnosis and treatment of colorectal cancer. Efforts to minimise patient contact have caused delays in cancer pathways, generating a potential risk to patient care. We aim to identify the pandemic effect on colorectal referral pathways. Methods All cancer referrals during March-June 2020 were reviewed to obtain information on the timeline and planning of investigation and treatment. The data was compared to evidence from the same referral period in 2019. Results 681 referrals were received during March-June 2020, compared to 1032 in 2019, indicating a 34% decrease. The majority of patients were reviewed in telephone clinic (76.2%) rather than physical appointments (15.4%). Although the commonest mode of investigation was endoscopy(46.2%), there was increased use of CT scan(35.8%). 114(17.1%) patients were not investigated, of which 40(35.1%) declined investigation, primarily due to COVID-19 apprehension. 6 patients were re-referred and 67(58.8%) were removed from the pathway for unknown reasons. 1 patient was subsequently admitted as an emergency. There were 44 new diagnoses of colorectal cancer based on MDT discussion, of which 14(31.8%) breached the investigation and 20(45.5%) the treatment date. 18 underwent curative surgery compared to 47 in the same period in 2019, indicating 61.7% less operative activity. Conclusions COVID-19 has changed surgical practice, forcing alternative clinic, investigation and treatment methods. Disruption of colorectal pathways is causing reduced referrals, investigation delays and less surgical activity. Delayed presentation with advanced disease may deprive the opportunity of treatment with curative intent.