My Cancer Genome: Coevolution of Precision Oncology and a Molecular Oncology Knowledgebase

DNA alterations in ovarian adult granulosa cell tumours: A scoping review protocol

BACKGROUND

Identifying and describing molecular alterations in tumors has become common with the development of high-throughput sequencing. However, DNA sequencing in rare tumors, such as ovarian adult granulosa cell tumor (aGCT), often lacks statistical power due to the limited number of cases in each study. Questions regarding personalized treatment or prognostic biomarkers for recurrence or other malignancies therefore still need to be elucidated. This scoping review protocol aims to systematically map the current evidence and identify knowledge gaps regarding DNA alterations, actionable variations and prognostic biomarkers in aGCT.

METHODS

This scoping review will be conducted based on Arksey and O'Malley's methodological framework and later modifications by JBI Evidence Synthesis. The protocol complies with Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. All original publications describing molecular alterations of aGCT will be included. The search will be performed in May 2024 in the following databases: MEDLINE (Ovid), Embase (Ovid), Web of Science Core Collection and Google Scholar (100-top ranked).

DISCUSSION

This scoping review will identify knowledge and gaps in the current understanding of the molecular landscape of aGCT, clinical trials on actionable variations and priorities for future research. As aGCT are rare, a possible limitation will be the small sample sizes and heterogenic study settings.

SCOPING REVIEW REGISTRATION

The review protocol is registered at Open Science Framework under https://doi.org/10.17605/OSF.IO/PX4MF.

Ku70 senses cytosolic DNA and assembles a tumor-suppressive signalosome

The innate immune response contributes to the development or attenuation of acute and chronic diseases, including cancer. Microbial DNA and mislocalized DNA from damaged host cells can activate different host responses that shape disease outcomes. Here, we show that mice and humans lacking a single allele of the DNA repair protein Ku70 had increased susceptibility to the development of intestinal cancer. Mechanistically, Ku70 translocates from the nucleus into the cytoplasm where it binds to cytosolic DNA and interacts with the GTPase Ras and the kinase Raf, forming a tripartite protein complex and docking at Rab5+Rab7+ early-late endosomes. This Ku70-Ras-Raf signalosome activates the MEK-ERK pathways, leading to impaired activation of cell cycle proteins Cdc25A and CDK1, reducing cell proliferation and tumorigenesis. We also identified the domains of Ku70, Ras, and Raf involved in activating the Ku70 signaling pathway. Therapeutics targeting components of the Ku70 signalosome could improve the treatment outcomes in cancer.

Tri©DB: an integrated platform of knowledgebase and reporting system for cancer precision medicine

BACKGROUND

With the development of cancer precision medicine, a huge amount of high-dimensional cancer information has rapidly accumulated regarding gene alterations, diseases, therapeutic interventions and various annotations. The information is highly fragmented across multiple different sources, making it highly challenging to effectively utilize and exchange the information. Therefore, it is essential to create a resource platform containing well-aggregated, carefully mined, and easily accessible data for effective knowledge sharing.

METHODS

In this study, we have developed "Consensus Cancer Core" (Tri©DB), a new integrative cancer precision medicine knowledgebase and reporting system by mining and harmonizing multifaceted cancer data sources, and presenting them in a centralized platform with enhanced functionalities for accessibility, annotation and analysis.

RESULTS

The knowledgebase provides the currently most comprehensive information on cancer precision medicine covering more than 40 annotation entities, many of which are novel and have never been explored previously. Tri©DB offers several unique features: (i) harmonizing the cancer-related information from more than 30 data sources into one integrative platform for easy access; (ii) utilizing a variety of data analysis and graphical tools for enhanced user interaction with the high-dimensional data; (iii) containing a newly developed reporting system for automated annotation and therapy matching for external patient genomic data. Benchmark test indicated that Tri©DB is able to annotate 46% more treatments than two officially recognized resources, oncoKB and MCG. Tri©DB was further shown to have achieved 94.9% concordance with administered treatments in a real clinical trial.

CONCLUSIONS

The novel features and rich functionalities of the new platform will facilitate full access to cancer precision medicine data in one single platform and accommodate the needs of a broad range of researchers not only in translational medicine, but also in basic biomedical research. We believe that it will help to promote knowledge sharing in cancer precision medicine. Tri©DB is freely available at www.biomeddb.org , and is hosted on a cutting-edge technology architecture supporting all major browsers and mobile handsets.

Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal

International cancer registries make real-world genomic and clinical data available, but their joint analysis remains a challenge. AACR Project GENIE, an international cancer registry collecting data from 19 cancer centers, makes data from >130,000 patients publicly available through the cBioPortal for Cancer Genomics (https://genie.cbioportal.org). For 25,000 patients, additional real-world longitudinal clinical data, including treatment and outcome data, are being collected by the AACR Project GENIE Biopharma Collaborative using the PRISSMM data curation model. Several thousand of these cases are now also available in cBioPortal. We have significantly enhanced the functionalities of cBioPortal to support the visualization and analysis of this rich clinico-genomic linked dataset, as well as datasets generated by other centers and consortia. Examples of these enhancements include (i) visualization of the longitudinal clinical and genomic data at the patient level, including timelines for diagnoses, treatments, and outcomes; (ii) the ability to select samples based on treatment status, facilitating a comparison of molecular and clinical attributes between samples before and after a specific treatment; and (iii) survival analysis estimates based on individual treatment regimens received. Together, these features provide cBioPortal users with a toolkit to interactively investigate complex clinico-genomic data to generate hypotheses and make discoveries about the impact of specific genomic variants on prognosis and therapeutic sensitivities in cancer.

SIGNIFICANCE

Enhanced cBioPortal features allow clinicians and researchers to effectively investigate longitudinal clinico-genomic data from patients with cancer, which will improve exploration of data from the AACR Project GENIE Biopharma Collaborative and similar datasets.

OncoCTMiner: streamlining precision oncology trial matching via molecular profile analysis

By establishing omics sequencing of patient tumors as a crucial element in cancer treatment, the extensive implementation of precision oncology necessitates effective and prompt execution of clinical studies for approving molecular-targeted therapies. However, the substantial volume of patient sequencing data, combined with strict clinical trial criteria, increasingly complicates the process of matching patients to precision oncology studies. To streamline enrollment in these studies, we developed OncoCTMiner, an automated pre-screening platform for molecular cancer clinical trials. Through manual tagging of eligibility criteria for 2227 oncology trials, we identified key bio-concepts such as cancer types, genes, alterations, drugs, biomarkers and therapies. Utilizing this manually annotated corpus along with open-source biomedical natural language processing tools, we trained multiple named entity recognition models specifically designed for precision oncology trials. These models analyzed 460 952 clinical trials, revealing 8.15 million precision medicine concepts, 9.32 million entity-criteria-trial triplets and a comprehensive precision oncology eligibility criteria database. Most significantly, we developed a patient-trial matching system based on cancer patients' clinical and genetic profiles, which can seamlessly integrate with the omics data analysis platform. This system expedites the pre-screening process for potentially suitable precision oncology trials, offering patients swifter access to promising treatment options. Database URL  https://oncoctminer.chosenmedinfo.com.

MRE11:p.K464R mutation mediates olaparib resistance by enhancing DNA damage repair in HGSOC

BACKGROUND

Although the clinical application of PARP inhibitors has brought hope to ovarian cancer, the problem of its resistance has become increasingly prominent. Therefore, clinical experts have been focused on finding specific indicators and therapeutic targets that can be used for resistance monitoring of PARP inhibitors.

RESULTS

By cfDNA detecting during Olaparib maintenance therapy in platinum-sensitive relapsed ovarian cancer, we found the presence of MRE11:p.K464R mutation was strongly associated with acquired Olaparib resistance. Structural analysis revealed that the MRE11:p.K464R mutation is situated at a critical site where the MRE11 protein interacts with other biomolecules, leading to potential structural and functional abnormalities of MRE11 protein. Functionally, MRE11:p.K464R mutation enhanced the tolerance of Olaparib by reducing the DNA damage. Mechanistically, MRE11:p.K464R mutation improved the efficiency of DNA damage repair and induce Olaparib resistance by enhancing its binding activity with the interacting proteins (including RAD50 and RPS3). Among them, the enhanced binding of MRE11:p.K464R mutation to RAD50/RPS3 facilitated non-homologous end joining (NHEJ) repair in tumor cells, thereby expanding the scope of research into acquired resistance to PARP inhibitors.

CONCLUSIONS

Our findings provide a theoretical basis for MRE11:p.K464R mutation as a specific indicator of resistance monitoring in Olaparib treatment, and the exploration of its resistance mechanism provides a novel insights for the formulation of combination ther therapies after Olaparib resistance.

In silico resources help combat cancer drug resistance mediated by target mutations

Drug resistance causes catastrophic cancer treatment failures. Mutations in target proteins with altered drug binding indicate a main mechanism of cancer drug resistance (CDR). Global research has generated considerable CDR-related data and well-established knowledge bases and predictive tools. Unfortunately, these resources are fragmented and underutilized. Here, we examine computational resources for exploring CDR caused by target mutations, analyzing these tools based on their functional characteristics, data capacity, data sources, methodologies and performance. We also discuss their disadvantages and provide examples of how potential inhibitors of CDR have been discovered using these resources. This toolkit is designed to help specialists explore resistance occurrence effectively and to explain resistance prediction to non-specialists easily.

Identification of neoepitope reactive T-cell receptors guided by HLA-A*03:01 and HLA-A*11:01 immunopeptidomics

BACKGROUND

Tumor-specific mutated proteins can create immunogenic non-self, mutation-containing 'neoepitopes' that are attractive targets for adoptive T-cell therapies. To avoid the complexity of defining patient-specific, private neoepitopes, there has been major interest in targeting common shared mutations in driver genes using off-the-shelf T-cell receptors (TCRs) engineered into autologous lymphocytes. However, identifying the precise naturally processed neoepitopes to pursue is a complex and challenging process. One method to definitively demonstrate whether an epitope is presented at the cell surface is to elute peptides bound to a specific major histocompatibility complex (MHC) allele and analyze them by mass spectrometry (MS). These MS data can then be prospectively applied to isolate TCRs specific to the neoepitope.

METHODS

We created mono-allelic cell lines expressing one class I HLA allele and one common mutated oncogene in order to eliminate HLA deconvolution requirements and increase the signal of recovered peptides. MHC-bound peptides on the surface of these cell lines were immunoprecipitated, purified, and analyzed using liquid chromatography-tandem mass spectrometry, producing a list of mutation-containing minimal epitopes. To validate the immunogenicity of these neoepitopes, HLA-transgenic mice were vaccinated using the minimal peptides identified by MS in order to generate neoepitope-reactive TCRs. Specificity of these candidate TCRs was confirmed by peptide titration and recognition of transduced targets.

RESULTS

We identified precise neoepitopes derived from mutated isoforms of KRAS, EGFR, BRAF, and PIK3CA presented by HLA-A*03:01 and/or HLA-A*11:01 across multiple biological replicates. From our MS data, we were able to successfully isolate murine TCRs that specifically recognize four HLA-A*11:01 restricted neoepitopes (KRAS G13D, PIK3CA E545K, EGFR L858R and BRAF V600E) and three HLA-A*03:01 restricted neoepitopes (KRAS G12V, EGFR L858R and BRAF V600E).

CONCLUSIONS

Our data show that an MS approach can be used to demonstrate which shared oncogene-derived neoepitopes are processed and presented by common HLA alleles, and those MS data can rapidly be used to develop TCRs against these common tumor-specific antigens. Although further characterization of these neoepitope-specific murine TCRs is required, ultimately, they have the potential to be used clinically for adoptive cell therapy.

Colon cancer transcriptome

Over the last four decades, methodological innovations have continuously changed transcriptome profiling. It is now feasible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples using RNA sequencing (RNA-seq). These transcriptomes serve as a connection between cellular behaviors and their underlying molecular mechanisms, such as mutations. This relationship, in the context of cancer, provides a chance to unravel tumor complexity and heterogeneity and uncover novel biomarkers or treatment options. Since colon cancer is one of the most frequent malignancies, its prognosis and diagnosis seem to be critical. The transcriptome technology is developing for an earlier and more accurate diagnosis of cancer which can provide better protectivity and prognostic utility to medical teams and patients. A transcriptome is a whole set of expressed coding and non-coding RNAs in an individual or cell population. The cancer transcriptome includes RNA-based changes. The combined genome and transcriptome of a patient may provide a comprehensive picture of their cancer, and this information is beginning to affect treatment decision-making in real-time. A full assessment of the transcriptome of colon (colorectal) cancer has been assessed in this review paper based on risk factors such as age, obesity, gender, alcohol use, race, and also different stages of cancer, as well as non-coding RNAs like circRNAs, miRNAs, lncRNAs, and siRNAs. Similarly, they have been examined independently in the transcriptome study of colon cancer.