Knowledge bases and software support for variant interpretation in precision oncology.

Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.

Bcl-xL as prognostic marker and potential therapeutic target in cholangiocarcinoma.

Intrahepatic, perihilar, and distal cholangiocarcinoma (iCCA, pCCA, dCCA) are highly malignant tumours with increasing mortality rates due to therapy resistances. Among the mechanisms mediating resistance, overexpression of anti-apoptotic Bcl-2 proteins (Bcl-2, Bcl-xL , Mcl-1) is particularly important. In this study, we investigated whether antiapoptotic protein patterns are prognostically relevant and potential therapeutic targets in CCA. Bcl-2 proteins were analysed in a pan-cancer cohort from the NCT/DKFZ/DKTK MASTER registry trial (n = 1140, CCA n = 72) via RNA-sequencing and transcriptome-based protein activity interference revealing high ranks of CCA for Bcl-xL and Mcl-1. Expression of Bcl-xL , Mcl-1, and Bcl-2 was assessed in human CCA tissue and cell lines compared with cholangiocytes by immunohistochemistry, immunoblotting, and quantitative-RT-PCR. Immunohistochemistry confirmed the upregulation of Bcl-xL and Mcl-1 in iCCA tissues. Cell death of CCA cell lines upon treatment with specific small molecule inhibitors of Bcl-xL (Wehi-539), of Mcl-1 (S63845), and Bcl-2 (ABT-199), either alone, in combination with each other or together with chemotherapeutics was assessed by flow cytometry. Targeting Bcl-xL induced cell death and augmented the effect of chemotherapy in CCA cells. Combined inhibition of Bcl-xL and Mcl-1 led to a synergistic increase in cell death in CCA cell lines. Correlation between Bcl-2 protein expression and survival was analysed within three independent patient cohorts from cancer centers in Germany comprising 656 CCA cases indicating a prognostic value of Bcl-xL in CCA depending on the CCA subtype. Collectively, these observations identify Bcl-xL as a key protein in cell death resistance of CCA and may pave the way for clinical application.

Prognostic Value of microRNA-221/2 and 17-92 Families in Primary Glioblastoma Patients Treated with Postoperative Radiotherapy.

MicroRNAs (miRs) are non-coding master regulators of transcriptome that could act as tumor suppressors (TSs) or oncogenes (oncomiRs). We aimed to systematically investigate the relevance of miRs as prognostic biomarkers in primary glioblastoma multiforme (GBM) treated with postoperative radio(chemo)therapy (PORT). For hypothesis generation, tumor miR expression by Agilent 8x15K human microRNA microarrays and survival data from 482 GBM patients of The Cancer Genome Atlas (TCGA cohort) were analyzed using Cox-PH models. Expression of candidate miRs with prognostic relevance (miR-221/222; miR-17-5p, miR-18a, miR-19b) was validated by qRT-PCR using Taqman technology on an independent validation cohort of GBM patients (n = 109) treated at Heidelberg University Hospital (HD cohort). In TCGA, 50 miRs showed significant association with survival. Among the top ranked prognostic miRs were members of the two miR families miR-221/222 and miR-17-92. Loss of miR-221/222 was correlated with improved prognosis in both cohorts (TCGA, HD) and was an independent prognostic marker in a multivariate analysis considering demographic characteristics (age, sex, Karnofsky performance index (KPI)), molecular markers (O-6-methylguanine-DNA methyltransferase (MGMT) methylation, IDH mutation status) and PORT as co-variables. The prognostic value of miR-17-92 family members was ambiguous and in part contradictory by direct comparison of the two cohorts, thus warranting further validation in larger prospective trials.

Germline SDHB-inactivating mutation in gastric spindle cell sarcoma.

Gastrointestinal stromal tumors (GISTs) are the most frequent mesenchymal tumors of the gastrointestinal tract. Inactivating mutations or epigenetic deregulation of succinate dehydrogenase complex (SDH) genes are considered defining features of a subset of GIST occurring in the stomach. Based on comprehensive molecular profiling and biochemical analysis within a precision oncology program, we identified hallmarks of SDH deficiency (germline SDHB-inactivating mutation accompanied by somatic loss of heterozygosity, lack of SDHB expression, global DNA hypermethylation, and elevated succinate/fumarate ratio) in a 40-year-old woman with undifferentiated gastric spindle cell sarcoma that did not meet the diagnostic criteria for other mesenchymal tumors of the stomach, including GIST. These data reveal that the loss of SDH function can be involved in the pathogenesis of non-GIST sarcoma of the gastrointestinal tract.

Patient-derived organoids and orthotopic xenografts of primary and recurrent gliomas represent relevant patient avatars for precision oncology.

Patient-based cancer models are essential tools for studying tumor biology and for the assessment of drug responses in a translational context. We report the establishment a large cohort of unique organoids and patient-derived orthotopic xenografts (PDOX) of various glioma subtypes, including gliomas with mutations in IDH1, and paired longitudinal PDOX from primary and recurrent tumors of the same patient. We show that glioma PDOXs enable long-term propagation of patient tumors and represent clinically relevant patient avatars that retain histopathological, genetic, epigenetic, and transcriptomic features of parental tumors. We find no evidence of mouse-specific clonal evolution in glioma PDOXs. Our cohort captures individual molecular genotypes for precision medicine including mutations in IDH1, ATRX, TP53, MDM2/4, amplification of EGFR, PDGFRA, MET, CDK4/6, MDM2/4, and deletion of CDKN2A/B, PTCH, and PTEN. Matched longitudinal PDOX recapitulate the limited genetic evolution of gliomas observed in patients following treatment. At the histological level, we observe increased vascularization in the rat host as compared to mice. PDOX-derived standardized glioma organoids are amenable to high-throughput drug screens that can be validated in mice. We show clinically relevant responses to temozolomide (TMZ) and to targeted treatments, such as EGFR and CDK4/6 inhibitors in (epi)genetically defined subgroups, according to MGMT promoter and EGFR/CDK status, respectively. Dianhydrogalactitol (VAL-083), a promising bifunctional alkylating agent in the current clinical trial, displayed high therapeutic efficacy, and was able to overcome TMZ resistance in glioblastoma. Our work underscores the clinical relevance of glioma organoids and PDOX models for translational research and personalized treatment studies and represents a unique publicly available resource for precision oncology.

Serum very long-chain fatty acid-containing lipids predict response to immune checkpoint inhibitors in urological cancers.

Checkpoint inhibitors (CPI) have significantly changed the therapeutic landscape of oncology. We adopted a non-invasive metabolomic approach to understand immunotherapy response and failure in 28 urological cancer patients. In total, 134 metabolites were quantified in patient sera before the first, second, and third CPI doses. Modeling the association between metabolites and CPI response and patient characteristics revealed that one predictive metabolite class  (n = 9/10) were very long-chain fatty acid-containing lipids (VLCFA-containing lipids). The best predictive performance was achieved through a multivariate model, including age and a centroid of VLCFA-containing lipids prior to first immunotherapy (sensitivity: 0.850, specificity: 0.825, ROC: 0.935). We hypothesize that the association of VLCFA-containing lipids with CPI response is based on enhanced peroxisome signaling in T cells, which results in a switch to fatty acid catabolism. Beyond use as a novel predictive non-invasive biomarker, we envision that nutritional supplementation with VLCFA-containing lipids might serve as an immuno sensitizer.

MetaboDiff: an R package for differential metabolomic analysis.

Summary: Comparative metabolomics comes of age through commercial vendors offering metabolomics for translational researchers outside the mass spectrometry field. The MetaboDiff packages aims to provide a low-level entry to differential metabolomic analysis with R by starting off with the table of metabolite measurements. As a key functionality, MetaboDiffs offers the exploration of sample traits in a data-derived metabolic correlation network. Availability and implementation: The MetaboDiff R package is platform-independent, available at http://github.com/andreasmock/MetaboDiff/ and released under the MIT licence. The package documentation comprises a step-by-step markdown tutorial. Supplementary information: Supplementary data are available at Bioinformatics online.

Impact of post-surgical freezing delay on brain tumor metabolomics.

INTRODUCTION: Translational cancer research has seen an increasing interest in metabolomic profiling to decipher tumor phenotypes. However, the impact of post-surgical freezing delays on mass spectrometric metabolomic measurements of the cancer tissue remains elusive. OBJECTIVES: To evaluate the impact of post-surgical freezing delays on cancer tissue metabolomics and to investigate changes per metabolite and per metabolic pathway. METHODS: We performed untargeted metabolomics on three cortically located and bulk-resected glioblastoma tissues that were sequentially frozen as duplicates at up to six different time delays (0-180 min, 34 samples). RESULTS: Statistical modelling revealed that 10% of the metabolome (59 of 597 metabolites) changed significantly after a 3 h delay. While carbohydrates and energy metabolites decreased, peptides and lipids increased. After a 2 h delay, these metabolites had changed by as much as 50-100%. We present the first list of metabolites in glioblastoma tissues that are sensitive to post-surgical freezing delays and offer the opportunity to define individualized fold change thresholds for future comparative metabolomic studies. CONCLUSION: More researchers should take these pre-analytical factors into consideration when analyzing metabolomic data. We present a strategy for how to work with metabolites that are sensitive to freezing delays.

The 2016 WHO classification of central nervous system tumors: what neurologists need to know.

PURPOSE OF REVIEW: The 2016 WHO classification of tumors of the central nervous system (2016 CNS WHO) features many changes that are relevant to neurologists treating patients with brain tumors as well as neurologists involved in basic, clinical, and epidemiological research. This review summarizes what neurologists need to know and will need to know in the next years. RECENT FINDINGS: The 2016 CNS WHO introduces diagnostic terms that 'integrate' histological and molecular information and suggests presenting diagnoses in a four-layered reporting format. In addition, it utilizes a 'not otherwise specified' designation to identify diagnostic categories that are not precisely defined. A better understanding of the biology of entities further led to changes in the tumor nosology, for example, diffuse gliomas based on IDH gene status. Meaningful molecular subgroups could also be identified in embryonal tumors and other entities. Given the pace of change in the field of brain tumor classification, there will likely be additional practical advances that emerge over the next few years. A new initiative entitled Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy intends to formulate recommendations between WHO updates. SUMMARY: The 2016 CNS WHO includes major changes in the way brain tumors are classified, with molecular parameters being incorporated into diagnostic criteria for a substantial number of such entities.

LOC283731 promoter hypermethylation prognosticates survival after radiochemotherapy in IDH1 wild-type glioblastoma patients.

MGMT promoter methylation status is currently the only established molecular prognosticator in IDH wild-type glioblastoma multiforme (GBM). Therefore, we aimed to discover novel therapy-associated epigenetic biomarkers. After enrichment for hypermethylated fractions using methyl-CpG-immunoprecipitation (MCIp), we performed global DNA methylation profiling for 14 long-term (LTS; >36 months) and 15 short-term (STS; 6-10 months) surviving GBM patients. Even after exclusion of the G-CIMP phenotype, we observed marked differences between the LTS and STS methylome. A total of 1,247 probes in 706 genes were hypermethylated in LTS and 463 probes in 305 genes were found to be hypermethylated in STS patients (p values < 0.05, log2 fold change ± 0.5). We identified 13 differentially methylated regions (DMRs) with a minimum of four differentially methylated probes per gene. Indeed, we were able to validate a subset of these DMRs through a second, independent method (MassARRAY) in our LTS/STS training set (ADCY1, GPC3, LOC283731/ISLR2). These DMRs were further assessed for their prognostic capability in an independent validation cohort (n = 62) of non-G-CIMP GBMs from the TCGA. Hypermethylation of multiple CpGs mapping to the promoter region of LOC283731 correlated with improved patient outcome (p = 0.03). The prognostic performance of LOC283731 promoter hypermethylation was confirmed in a third independent study cohort (n = 89), and was independent of gender, performance (KPS) and MGMT status (p = 0.0485, HR = 0.63). Intriguingly, the prediction was most pronounced in younger GBM patients (<60 years). In conclusion, we provide compelling evidence that promoter methylation status of this novel gene is a prognostic biomarker in IDH1 wild-type/non-G-CIMP GBMs.

Spatial transcriptome analysis reveals Notch pathway-associated prognostic markers in IDH1 wild-type glioblastoma involving the subventricular zone.

BACKGROUND: The spatial relationship of glioblastoma (GBM) to the subventricular zone (SVZ) is associated with inferior patient survival. However, the underlying molecular phenotype is largely unknown. We interrogated an SVZ-dependent transcriptome and potential location-specific prognostic markers. METHODS: mRNA microarray data of a discovery set (n = 36 GBMs) were analyzed for SVZ-dependent gene expression and process networks using the MetaCore™ workflow. Differential gene expression was confirmed by qPCR in a validation set of 142 IDH1 wild-type GBMs that was also used for survival analysis. RESULTS: Microarray analysis revealed a transcriptome distinctive of SVZ+ GBM that was enriched for genes associated with Notch signaling. No overlap was found to The Cancer Genome Atlas's molecular subtypes. Independent validation of SVZ-dependent expression confirmed four genes with simultaneous prognostic impact: overexpression of HES4 (p = 0.034; HR 1.55) and DLL3 (p = 0.017; HR 1.61) predicted inferior, and overexpression of NTRK2 (p = 0.049; HR 0.66) and PIR (p = 0.025; HR 0.62) superior overall survival (OS). Additionally, overexpression of DLL3 was predictive of shorter progression-free survival (PFS) (p = 0.043; HR 1.64). Multivariate analysis revealed overexpression of HES4 to be independently associated with inferior OS (p = 0.033; HR 2.03), and overexpression of DLL3 with inferior PFS (p = 0.046; HR 1.65). CONCLUSIONS: We identified four genes with SVZ-dependent expression and prognostic significance, among those HES4 and DLL3 as part of Notch signaling, suggesting further evaluation of location-tailored targeted therapies.

Prognostic value of the extent of resection in supratentorial WHO grade II astrocytomas stratified for IDH1 mutation status: a single-center volumetric analysis.

Current evidence supports a maximized extent of resection (EOR) in low-grade gliomas (LGG), regardless of different histological subtypes and molecular markers. We therefore evaluated the prognostic impact of extensive, mainly intraoperative (i)MRI-guided surgery in low-grade astrocytomas stratified for IDH1 mutation status. Retrospective assessment of 46 consecutive cases of newly diagnosed supratentorial WHO grade II astrocytomas treated during the last decade was performed. IDH1 mutation status was obtained for all patients. Volumetric analysis of tumor volumes was performed pre-, intra-, early postoperatively and at first follow-up. Survival analysis was conducted with uni-and multivariate regression models implementing clinical parameters and continuous volumetric variables. Median EOR was 90.4 % (range 17.5-100 %) and was increased to 94.9 % (range 34.8-100 %) in iMRI-guided resections (n = 33). A greater EOR was prognostic for increased progression-free survival (HR 0.23, p = 0.031) and time to re-intervention (TTR) (HR 0.23, p = 0.03). In IDH1 mutant patients, smaller residual tumor volumes were associated with increased TTR (HR 1.01, p = 0.03). IDH1 mutation (38/46 cases) was an independent positive prognosticator for overall survival (OS) in multivariate analysis (HR 0.09, p = 0.002), while extensive surgery had limited impact upon OS. In a subgroup of patients with ≥40 % EOR (n = 39), however, initial and residual tumor volumes were prognostic for OS (HR 1.03, p = 0.005 and HR 1.08, p = 0.007, respectively), persistent to adjustment for IDH1. No association between EOR and neurologic morbidity was found. In this analysis of low-grade astrocytomas stratified for IDH1, extensive tumor resections were prognostic for progression and TTR and, in patients with ≥40 % EOR, for OS.

Molecular profiling of long-term survivors identifies a subgroup of glioblastoma characterized by chromosome 19/20 co-gain.

Glioblastoma (GBM) is a devastating tumor and few patients survive beyond 3 years. Defining the molecular determinants underlying long-term survival is essential for insights into tumor biology and biomarker identification. We therefore investigated homogeneously treated, IDH (wt) long-term (LTS, n = 10) and short-term survivors (STS, n = 6) by microarray transcription profiling. While there was no association of clinical parameters and molecular subtypes with long-term survival, STS tumors were characterized by differential polarization of infiltrating microglia with predominance of the M2 phenotype detectable both on the mRNA and protein level. Furthermore, transcriptional signatures of LTS and STS predicted patient outcome in a large, IDH (wt) cohort (n = 468). Interrogation of overlapping genomic alterations identified concurrent gain of chromosomes 19 and 20 as a favorable prognostic marker. The strong association of this co-gain with survival was validated by aCGH in a second, independent cohort (n = 124). Finally, FISH and gene expression data revealed gains to constitute low-amplitude, clonal events with a strong impact on transcription. In conclusion, these findings provide important insights into the manipulation of the innate immune system by particularly aggressive GBM tumors. Furthermore, we genomically characterize a previously unknown, clinically relevant subgroup of glioblastoma, which can easily be identified through modern neuropathological workup.

Reduced promoter methylation and increased expression of CSPG4 negatively influences survival of HNSCC patients.

Proteoglycans are often overexpressed in tumors and can be found on several normal and neoplastic stem cells. In this study, we analyzed in-depth the role of CSPG4 in head and neck squamous cell carcinomas (HNSCC). Analysis of CSPG4 in a homogeneous study sample of HPV-negative stage IVa HNSCCs revealed overexpression of protein and mRNA levels in a subgroup of HNSCC tumors and a significant association of high CSPG4 protein levels with poor survival. This could be validated in three publicly available microarray datasets. As a potential cause for upregulated CSPG4 expression, we identified DNA hypomethylation in a CpG-island of the promoter region. Accordingly, we found an inverse correlation of methylation and patient outcome. Finally, CSPG4 re-expression was achieved by demethylating treatment of highly methylated HNSCC cell lines establishing a direct link between methylation and CSPG4 expression. In conclusion, we identified CSPG4 as a novel biomarker in HNSCC on several biological levels and established a causative link between DNA methylation and CSPG4 protein and mRNA expression.

Toward Explainable Artificial Intelligence for Precision Pathology.

The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect to its ability to analyze histological images and increasingly large molecular profiling data in a quantitative, integrative, and standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential to facilitate complex data analysis tasks, including clinical, histological, and molecular data for disease classification; tissue biomarker quantification; and clinical outcome prediction. This review provides a general introduction to AI and describes recent developments with a focus on applications in diagnostic pathology and beyond. We explain limitations including the black-box character of conventional AI and describe solutions to make machine learning decisions more transparent with so-called explainable AI. The purpose of the review is to foster a mutual understanding of both the biomedical and the AI side. To that end, in addition to providing an overview of the relevant foundations in pathology and machine learning, we present worked-through examples for a better practical understanding of what AI can achieve and how it should be done.

Reimbursement in the Context of Precision Oncology Approaches in Metastatic Breast Cancer: Challenges and Experiences.

Background: Precision oncology programs using next-generation sequencing to detect predictive biomarkers are extending therapeutic options for patients with metastatic breast cancer (mBC). Regularly, based on the recommendations of the interdisciplinary molecular tumor board (iMTB), an inclusion in a clinical trial is not possible. In this case, the German health insurance system allows for the application of reimbursement for an off-label drug use. Here, we describe the current challenges and our experience with reimbursement of molecular therapies in mBC. Methods: A total of 100 applications for reimbursement of off-label therapies recommended by an iMTB were filed for patients with mBC, of which 89 were evaluable for this analysis. The approval rate was correlated with the molecular level of evidence of the respective therapy according to the National Center for Tumor Diseases (NCT) and European Society for Medical Oncology Scale for Clinical Actionability of molecular Targets (ESCAT) classification as well as with pretreatment therapy lines. Findings: Overall, 53.9% (48/89) of reimbursement applications were approved. Applications for therapies based on level of evidence m1 (NCT classification), tier I and II (ESCAT classification) had a significantly and clinically relevant increased chance of reimbursement, while a greater number of previous treatment lines had no significantly increased chance of approval, though a trend of approval toward higher treatment lines was detectable. Interpretation: Currently, the German jurisdiction seems to aggravate the clinical implementation of clinically urgently needed molecular therapies.

[Explainable artificial intelligence in pathology]. / Erklärbare Künstliche Intelligenz in der Pathologie.

With the advancements in precision medicine, the demands on pathological diagnostics have increased, requiring standardized, quantitative, and integrated assessments of histomorphological and molecular pathological data. Great hopes are placed in artificial intelligence (AI) methods, which have demonstrated the ability to analyze complex clinical, histological, and molecular data for disease classification, biomarker quantification, and prognosis estimation. This paper provides an overview of the latest developments in pathology AI, discusses the limitations, particularly concerning the black box character of AI, and describes solutions to make decision processes more transparent using methods of so-called explainable AI (XAI).

Prognostic role of lymph node micrometastasis in oral and oropharyngeal cancer: A systematic review.

BACKGROUND: An estimated 20% of patients with oral and oropharyngeal squamous cell carcinoma (OOSCC) have micrometastases (Mi) or isolated tumor cells (ITC) in the cervical lymph nodes that evade detection by standard histological evaluation of lymph node sections. Lymph node Mi and ITC could be one reason for regional recurrence after neck dissection. The aim of this study was to review the existing data regarding the impact of Mi on the survival of patients with OOSCC. METHODS: PubMed and the Cochrane Library were searched for articles reporting the impact of Mi and ITC on patient survival. Two authors independently assessed the methodological quality of retrieved studies using the Downs and Black index. Data were also extracted on study type, number of included patients, mode of histological analysis, statistical analysis, and prognostic impact. RESULTS: Sixteen articles with a total of 2064 patients were included in the review. Among the 16 included studies, eight revealed a statistically significant impact of Mi on at least one endpoint in the Kaplan-Meier and/or multivariate analysis. Three studies regarded Mi as Ma, while five studies found no impact of Mi on survival. Only one study demonstrated an impact of ITC on patient's prognosis in the univariate but not in the multivariate analysis. CONCLUSION: The majority of cases included in the review were patients with oral cancer. The findings provide low-certainty evidence that Mi negatively impacts survival. Data on ITC were scarcer, so no conclusions can be drawn about their effect on survival. The lower threshold to discriminate between Mi and ITC should be defined for OOSCC since the existing thresholds are based on data from different tumors. The histological, immunohistological, and anatomical characteristics of Mi and ITC in OOSCC as well as the effect of radiotherapy on Mi should be further investigated separately for oral and oropharyngeal carcinomas.

Transcriptome profiling for precision cancer medicine using shallow nanopore cDNA sequencing.

Transcriptome profiling is a mainstay of translational cancer research and is increasingly finding its way into precision oncology. While bulk RNA sequencing (RNA-seq) is widely available, high investment costs and long data return time are limiting factors for clinical applications. We investigated a portable nanopore long-read sequencing device (MinION, Oxford Nanopore Technologies) for transcriptome profiling of tumors. In particular, we investigated the impact of lower coverage than that of larger sequencing devices by comparing shallow nanopore RNA-seq data with short-read RNA-seq data generated using reversible dye terminator technology (Illumina) for ten samples representing four cancer types. Coupled with ShaNTi (Shallow Nanopore sequencing for Transcriptomics), a newly developed data processing pipeline, a turnaround time of five days was achieved. The correlation of normalized gene-level counts between nanopore and Illumina RNA-seq was high for MinION but not for very low-throughput Flongle flow cells (r = 0.89 and r = 0.24, respectively). A cost-saving approach based on multiplexing of four samples per MinION flow cell maintained a high correlation with Illumina data (r = 0.56-0.86). In addition, we compared the utility of nanopore and Illumina RNA-seq data for analysis tools commonly applied in translational oncology: (1) Shallow nanopore and Illumina RNA-seq were equally useful for inferring signaling pathway activities with PROGENy. (2) Highly expressed genes encoding kinases targeted by clinically approved small-molecule inhibitors were reliably identified by shallow nanopore RNA-seq. (3) In tumor microenvironment composition analysis, quanTIseq performed better than CIBERSORT, likely due to higher average expression of the gene set used for deconvolution. (4) Shallow nanopore RNA-seq was successfully applied to detect fusion genes using the JAFFAL pipeline. These findings suggest that shallow nanopore RNA-seq enables rapid and biologically meaningful transcriptome profiling of tumors, and warrants further exploration in precision cancer medicine studies.