A functional genomic approach to actionable gene fusions for precision oncology

Oncogenic gene fusions in cancer: from biology to therapy

Oncogenic gene fusions occur across a broad range of cancers and are a defining feature of some cancer types. Cancers driven by gene fusion products tend to respond well to targeted therapies, where available; thus, detection of potentially targetable oncogenic fusions is necessary to select optimal treatment. Detection methods include non-sequencing methods, such as fluorescence in situ hybridization and immunohistochemistry, and sequencing methods, such as DNA- and RNA-based next-generation sequencing (NGS). While NGS is an efficient way to analyze multiple genes of interest at once, economic and technical factors may preclude its use in routine care globally, despite several guideline recommendations. The aim of this review is to present a summary of oncogenic gene fusions, with a focus on fusions that affect tyrosine kinase signaling, and to highlight the importance of testing for oncogenic fusions. We present an overview of the identification of oncogenic gene fusions and therapies approved for the treatment of cancers harboring gene fusions, and summarize data regarding treating fusion-positive cancers with no current targeted therapies and clinical studies of fusion-positive cancers. Although treatment options may be limited for patients with rare alterations, healthcare professionals should identify patients most likely to benefit from oncogenic gene fusion testing and initiate the appropriate targeted therapy to achieve optimal treatment outcomes.

Detection of fusion events by RNA sequencing in FFPE versus freshly frozen colorectal cancer tissue samples

Gene fusion events result in chimeric proteins that are frequently found in human cancers. Specific targeted therapies are available for several types of cancer fusions including receptor tyrosine kinase gene moieties. RNA sequencing (RNAseq) can directly be used for detection of gene rearrangements in a single test, along with multiple additional biomarkers. However, tumor biosamples are usually formalin-fixed paraffin-embedded (FFPE) tissue blocks where RNA is heavily degraded, which in theory may result in decreased efficiency of fusion detection. Here, for the first time, we compared the efficacy of gene fusion detection by RNAseq for matched pairs of freshly frozen in RNA stabilizing solution (FF) and FFPE tumor tissue samples obtained from 29 human colorectal cancer patients. We detected no statistically significant difference in the number of chimeric transcripts in FFPE and FF RNAseq profiles. The known fusion KANSL1-ARL17A/B occurred with a high frequency in 69% of the patients. We also detected 93 new fusion genes not mentioned in the literature or listed in the ChimerSeq database. Among them, 11 were found in two or more patients, suggesting their potential role in carcinogenesis. Most of the fusions detected most probably represented read-through, microdeletion or local duplication events. Finally, in one patient, we detected a potentially clinically actionable in-frame fusion of LRRFIP2 and ALK genes not previously described in colorectal cancer with an intact tyrosine kinase domain that can be potentially targeted by ALK inhibitors.

Oncofusions - shaping cancer care

Cancer manifests through a spectrum of mutations, including gene fusions termed oncofusions. These structural alterations influence tumorigenesis across various cancer types. Oncofusions arise primarily from genomic rearrangements and operate through deregulation or hybrid gene formation mechanisms. Notable examples such as BCR::ABL and EWS::FLI1 underscore their clinical significance. Several case studies exemplify the role of identifying and targeting oncofusions in guiding treatment decisions and improving patient outcomes. However, challenges persist in discerning drivers from passenger mutations and addressing acquired resistance. Despite advancements, the complexity of oncofusions warrants further exploration of their full potential as therapeutic targets, requiring a multidisciplinary approach integrating genomics, functional studies, and innovative drug discovery strategies to achieve precision in medicine.

Utilizing Patient-Derived Xenografts to Model Precision Oncology for Biliary Tract Cancer

PURPOSE

Biliary tract cancers, which are rare and aggressive malignancies, are rich in clinically actionable molecular alterations. A major challenge in the field is the paucity of clinically relevant biliary tract cancer models that recapitulate the diverse molecular profiles of these tumors. The purpose of this study was to curate a collection of patient-derived xenograft (PDX) models that reflect the spectrum of genomic alterations present in biliary tract cancers to create a resource for modeling precision oncology.

EXPERIMENTAL DESIGN

PDXs were derived from biliary tract cancer samples collected from surgical resections or metastatic biopsies. Alterations present in the PDXs were identified by whole-exome sequencing and RNA sequencing. PDXs were treated with approved and investigational agents. Efficacy was assessed by change in tumor volume from baseline. Event-free survival was defined as the time to tumor doubling from baseline. Responses were categorized at day 21: >30% decrease in tumor volume = partial response, >20% increase in tumor volume = progressive disease, and any non-partial response/progressive disease was considered stable disease.

RESULTS

Genomic sequencing demonstrated key actionable alterations across this cohort, including alterations in FGFR2, isocitrate dehydrogenase I, ERRB2, PIK3CA, PTEN, and KRAS. RNA sequencing demonstrated fusions and expression of antibody-drug conjugate targets, including TROP2, HER2, and Nectin4. Therapeutic matching revealed objective responses to approved and investigational agents that have been shown to have antitumor activity clinically.

CONCLUSIONS

In this study, we developed a catalog of biliary tract cancer PDXs that underwent comprehensive molecular profiling and therapeutic modeling. To date, this is one of the largest collections of biliary tract cancer PDX models and will facilitate the development of personalized treatments for patients with these aggressive malignancies.

Fusion Transcript Detection from Short-Read RNA-Seq

Fusion proteins have been shown to play an important role in many different cancers and other diseases. While the causal mutation can often be found in the genome as a structural variant (SV), differentiating between normal variation within individuals and somatic variants with functional consequences can be time-consuming as well as expensive since it requires a whole-genome sequencing (WGS) method. RNA Sequencing (RNA-Seq) provides a much cheaper and more straightforward approach to the detection of functional somatic events such as overexpression of proto-oncogenes as well as gene fusion. This chapter aims to discuss the utility of RNA-Seq for fusion detection as well as provide a detailed analysis pipeline for the detection of fusion transcripts.

Clinical bioinformatics desiderata for molecular tumor boards

Clinical Bioinformatics is a knowledge framework required to interpret data of medical interest via computational methods. This area became of dramatic importance in precision oncology, fueled by cancer genomic profiling: most definitions of Molecular Tumor Boards require the presence of bioinformaticians. However, all available literature remained rather vague on what are the specific needs in terms of digital tools and expertise to tackle and interpret genomics data to assign novel targeted or biomarker-driven targeted therapies to cancer patients. To fill this gap, in this article, we present a catalog of software families and human skills required for the tumor board bioinformatician, with specific examples of real-world applications associated with each element presented.

Fusion Challenges in Solid Tumors: Shaping the Landscape of Cancer Care in Precision Medicine

Targeting actionable fusions has emerged as a promising approach to cancer treatment. Next-generation sequencing (NGS)-based techniques have unveiled the landscape of actionable fusions in cancer. However, these approaches remain insufficient to provide optimal treatment options for patients with cancer. This article provides a comprehensive overview of the actionability and clinical development of targeted agents aimed at driver fusions. It also highlights the challenges associated with fusion testing, including the evaluation of patients with cancer who could potentially benefit from testing and devising an effective strategy. The implementation of DNA NGS for all tumor types, combined with RNA sequencing, has the potential to maximize detection while considering cost effectiveness. Herein, we also present a fusion testing strategy aimed at improving outcomes in patients with cancer.

Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities

Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of "wet-lab" experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets.

The Application of Long-Read Sequencing to Cancer

Cancer is a multifaceted disease arising from numerous genomic aberrations that have been identified as a result of advancements in sequencing technologies. While next-generation sequencing (NGS), which uses short reads, has transformed cancer research and diagnostics, it is limited by read length. Third-generation sequencing (TGS), led by the Pacific Biosciences and Oxford Nanopore Technologies platforms, employs long-read sequences, which have marked a paradigm shift in cancer research. Cancer genomes often harbour complex events, and TGS, with its ability to span large genomic regions, has facilitated their characterisation, providing a better understanding of how complex rearrangements affect cancer initiation and progression. TGS has also characterised the entire transcriptome of various cancers, revealing cancer-associated isoforms that could serve as biomarkers or therapeutic targets. Furthermore, TGS has advanced cancer research by improving genome assemblies, detecting complex variants, and providing a more complete picture of transcriptomes and epigenomes. This review focuses on TGS and its growing role in cancer research. We investigate its advantages and limitations, providing a rigorous scientific analysis of its use in detecting previously hidden aberrations missed by NGS. This promising technology holds immense potential for both research and clinical applications, with far-reaching implications for cancer diagnosis and treatment.

PTPRH promotes the progression of non-small cell lung cancer via glycolysis mediated by the PI3K/AKT/mTOR signaling pathway

BACKGROUND

The protein tyrosine phosphatase H receptor (PTPRH) is known to regulate the occurrence and development of pancreatic and colorectal cancer. However, its association with glycolysis in non-small cell lung cancer (NSCLC) is still unclear. In this study, we aimed to investigate the relationship between PTPRH expression and glucose metabolism and the underlying mechanism of action.

METHODS

The expression of PTPRH in NSCLC cells was evaluated by IHC staining, qRT‒PCR and Western blotting. The effect of PTPRH on cell biological behavior was evaluated by colony assays, EdU experiments, Transwell assays, wound healing assays and flow cytometry. Changes in F-18-fluorodeoxyglucose (18F-FDG) uptake and glucose metabolite levels after altering PTPRH expression were detected via a gamma counter and lactic acid tests. The expression of glycolysis-related proteins in NSCLC cells was detected by Western blotting after altering PTPRH expression.

RESULTS

The results showed that PTPRH was highly expressed in clinical patient tissue samples and closely related to tumor diameter and clinical stage. In addition, PTPRH expression was associated with glycometabolism indexes on 18F-FDG positron emission tomography/computed tomography (PET/CT) imaging, the expression level of Ki67 and the expression levels of glycolysis-related proteins. PTPRH altered cell behavior, inhibited apoptosis, and promoted 18F-FDG uptake, lactate production, and the expression of glycolysis-related proteins. In addition, PTPRH modulated the glycometabolism of NSCLC cells via the phosphatidylinositol-3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling pathway, as assessed using LY294002 and 740Y-P (an inhibitor and agonist of PI3K, respectively). The same results were validated in vivo using a xenograft tumor model in nude mice. Protein expression levels of PTPRH, glycolysis-related proteins, p-PI3K/PI3K and p-AKT/AKT were measured by IHC staining using a subcutaneous xenograft model in nude mice.

CONCLUSIONS

In summary, we report that PTPRH promotes glycolysis, proliferation, migration, and invasion via the PI3K/AKT/mTOR signaling pathway in NSCLC and ultimately promotes tumor progression, which can be regulated by LY294002 and 740Y-P. These results suggest that PTPRH is a potential therapeutic target for NSCLC.

Recurrent TRAK1::RAF1 Fusions in pediatric low-grade gliomas

Fusions involving CRAF (RAF1) are infrequent oncogenic drivers in pediatric low-grade gliomas, rarely identified in tumors bearing features of pilocytic astrocytoma, and involving a limited number of known fusion partners. We describe recurrent TRAK1::RAF1 fusions, previously unreported in brain tumors, in three pediatric patients with low-grade glial-glioneuronal tumors. We present the associated clinical, histopathologic and molecular features. Patients were all female, aged 8 years, 15 months, and 10 months at diagnosis. All tumors were located in the cerebral hemispheres and predominantly cortical, with leptomeningeal involvement in 2/3 patients. Similar to previously described activating RAF1 fusions, the breakpoints in RAF1 all occurred 5' of the kinase domain, while the breakpoints in the 3' partner preserved the N-terminal kinesin-interacting domain and coiled-coil motifs of TRAK1. Two of the three cases demonstrated methylation profiles (v12.5) compatible with desmoplastic infantile ganglioglioma (DIG)/desmoplastic infantile astrocytoma (DIA) and have remained clinically stable and without disease progression/recurrence after resection. The remaining tumor was non-classifiable; with focal recurrence 14 months after initial resection; the patient remains symptom free and without further recurrence/progression (5 months post re-resection and 19 months from initial diagnosis). Our report expands the landscape of oncogenic RAF1 fusions in pediatric gliomas, which will help to further refine tumor classification and guide management of patients with these alterations.

Genomic Landscape of Normal and Breast Cancer Tissues in a Hungarian Pilot Cohort

A limited number of studies have focused on the mutational landscape of breast cancer in different ethnic populations within Europe and compared the data with other ethnic groups and databases. We performed whole-genome sequencing of 63 samples from 29 Hungarian breast cancer patients. We validated a subset of the identified variants at the DNA level using the Illumina TruSight Oncology (TSO) 500 assay. Canonical breast-cancer-associated genes with pathogenic germline mutations were CHEK2 and ATM. Nearly all the observed germline mutations were as frequent in the Hungarian breast cancer cohort as in independent European populations. The majority of the detected somatic short variants were single-nucleotide polymorphisms (SNPs), and only 8% and 6% of them were deletions or insertions, respectively. The genes most frequently affected by somatic mutations were KMT2C (31%), MUC4 (34%), PIK3CA (18%), and TP53 (34%). Copy number alterations were most common in the NBN, RAD51C, BRIP1, and CDH1 genes. For many samples, the somatic mutational landscape was dominated by mutational processes associated with homologous recombination deficiency (HRD). Our study, as the first breast tumor/normal sequencing study in Hungary, revealed several aspects of the significantly mutated genes and mutational signatures, and some of the copy number variations and somatic fusion events. Multiple signs of HRD were detected, highlighting the value of the comprehensive genomic characterization of breast cancer patient populations.