The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies

Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study

INTRODUCTION

Molecular profiling of lung cancer is essential to identify genetic alterations that predict response to targeted therapy. While deep learning shows promise for predicting oncogenic mutations from whole tissue images, existing studies often face challenges such as limited sample sizes, a focus on earlier stage patients, and insufficient analysis of robustness and generalizability.

METHODS

This retrospective study evaluates factors influencing mutation prediction accuracy using the large Heidelberg Lung Adenocarcinoma Cohort (HLCC), a cohort of 2356 late-stage FFPE samples. Validation is performed in the publicly available TCGA-LUAD cohort.

RESULTS

Models trained on the larger HLCC cohort generalized well to the TCGA dataset for mutations in EGFR (AUC 0.76), STK11 (AUC 0.71) and TP53 (AUC 0.75), in line with the hypothesis that larger cohort sizes improve model robustness. Variation in performance due to pre-processing and modeling choices, such as mutation variant calling, affected EGFR prediction accuracy by up to 7 %.

DISCUSSION

Model explanations suggest that acinar and papillary growth patterns are critical for the detection of EGFR mutations, whereas solid growth patterns and large nuclei are indicative of TP53 mutations. These findings highlight the importance of specific morphological features in mutation detection and the potential of deep learning models to improve mutation prediction accuracy.

CONCLUSION

Although deep learning models trained on larger cohorts show improved robustness and generalizability in predicting oncogenic mutations, they cannot replace comprehensive molecular profiling. However, they may support patient pre-selection for clinical trials and deepen the insight in genotype-phenotype relationships.

A computational workflow for analysis of missense mutations in precision oncology

Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individual's tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutation's effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving a balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/ .Scientific contributionThis work presents a novel bioinformatics pipeline that integrates multiple computational tools to predict the effects of missense mutations on proteins of oncological interest. The pipeline uniquely combines fast protein modelling, stability prediction, and evolutionary analysis with virtual drug screening, while offering actionable insights for precision oncology. This comprehensive approach surpasses existing tools by automating the interpretation of mutations and suggesting potential treatments, thereby striving to bridge the gap between sequencing data and clinical application.

Evidence blocks for effective presentation of genomic findings at molecular tumor boards: Single institution experience

Genomic profiling, or molecular profiling of the tumor, is becoming a key component of therapeutic decision making in clinical oncology, and is typically carried out via next generation sequencing. However, the interpretation of the results and evaluation of rationale for targeting the uncovered alterations is challenging and requires a deep understanding of cancer biology, genetics, genomics and oncology. Multidisciplinary molecular tumor boards represent a promising strategy in the facilitation of molecularly-informed therapeutic decisions, and usually consist of specialists with various fields of expertise. To effectively communicate the biological and clinical significance of genomic findings, as well as to make molecular tumor board discussions more productive, we developed and implemented evidence blocks into case discussions in our center. We found that this approach facilitated clinicians' understanding of the results of genomic profiling, and resulted in shorter yet more efficient case discussions within the molecular tumor board. Here, we discuss our experience with evidence blocks and how their implementation influenced the molecular tumor board practice.

Clinical impact of panel gene sequencing on therapy of advanced cancers of the digestive system: a retrospective, single center study

BACKGROUND

Panel gene sequencing is an established diagnostic tool for precision oncology of solid tumors, but its utility for the treatment of cancers of the digestive system in clinical routine is less well documented.

METHODS

We retrospectively identified patients with advanced or metastatic gastrointestinal, pancreaticobiliary or hepatic cancers who received panel gene sequencing at a tertiary university hospital from 2015 to 2022. For these cases, we determined the spectrum of genetic alterations, clinicopathological parameters and treatment courses. Assessment of actionability of genetic alterations was based on the OncoKB database, cancer-specific ESMO treatment guidelines, and recommendations of the local molecular tumor board.

RESULTS

In total, 155 patients received panel gene sequencing using either the Oncomine Focus (62 cases), Comprehensive (91 cases) or Childhood Cancer Research Assay (2 cases). The mean age of patients was 61 years (range 24-90) and 37% were female. Most patients suffered from either colorectal cancer (53%) or cholangiocellular carcinoma (19%). 327 genetic alterations were discovered in 123 tumor samples, with an average number of 2.1 alterations per tumor. The most frequently altered genes were TP53, KRAS and PIK3CA. Actionable gene alterations were detected in 13.5-56.8% of tumors, according to ESMO guidelines or the OncoKB database, respectively. Thirteen patients were treated with targeted therapies based on identified molecular alterations, with a median progression-free survival of 8.8 months.

CONCLUSIONS

Actionable genetic alterations are frequently detected by panel gene sequencing in patients with advanced cancers of the digestive tract, providing clinical benefit in selected cases. However, for the majority of identified actionable alterations, sufficient clinical evidence for targeted treatments is still lacking.

NGS-Guided Precision Oncology in Breast Cancer and Gynecological Tumors-A Retrospective Molecular Tumor Board Analysis

Background: Precision oncology treatments are being applied more commonly in breast and gynecological oncology through the implementation of Molecular Tumor Boards (MTBs), but real-world clinical outcome data remain limited. Methods: A retrospective analysis was conducted in patients with breast cancer (BC) and gynecological malignancies referred to our center's MTB from 2018 to 2023. The analysis covered patient characteristics, next-generation sequencing (NGS) results, MTB recommendations, therapy received, and clinical outcomes. Results: Sixty-three patients (77.8%) had metastatic disease, and forty-four patients (54.3%) had previously undergone three or more lines of systemic treatment. Personalized treatment recommendations were provided to 50 patients (63.3%), while 29 (36.7%) had no actionable target. Ultimately, 23 patients (29.1%) underwent molecular-matched treatment (MMT). Commonly altered genes in patients with pan-gyn tumors (BC and gynecological malignancies) included TP53 (n = 42/81, 51.9%), PIK3CA (n = 18/81, 22.2%), BRCA1/2 (n = 10/81, 12.3%), and ARID1A (n = 9/81, 11.1%). Patients treated with MMT showed significantly prolonged progression-free survival (median PFS 5.5 vs. 3.5 months, p = 0.0014). Of all patients who underwent molecular profiling, 13.6% experienced a major clinical benefit (PFSr ≥ 1.3 and PR/SD ≥ 6 months) through precision oncology. Conclusions: NGS-guided precision oncology demonstrated improved clinical outcomes in a subgroup of patients with gynecological and breast cancers.

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.

Molecular tumour boards - current and future considerations for precision oncology

Over the past 15 years, rapid progress has been made in developmental therapeutics, especially regarding the use of matched targeted therapies against specific oncogenic molecular alterations across cancer types. Molecular tumour boards (MTBs) are panels of expert physicians, scientists, health-care providers and patient advocates who review and interpret molecular-profiling results for individual patients with cancer and match each patient to available therapies, which can include investigational drugs. Interpretation of the molecular alterations found in each patient is a complicated task that requires an understanding of their contextual functional effects and their correlations with sensitivity or resistance to specific treatments. The criteria for determining the actionability of molecular alterations and selecting matched treatments are constantly evolving. Therefore, MTBs have an increasingly necessary role in optimizing the allocation of biomarker-directed therapies and the implementation of precision oncology. Ultimately, increased MTB availability, accessibility and performance are likely to improve patient care. The challenges faced by MTBs are increasing, owing to the plethora of identifiable molecular alterations and immune markers in tumours of individual patients and their evolving clinical significance as more and more data on patient outcomes and results from clinical trials become available. Beyond next-generation sequencing, broader biomarker analyses can provide useful information. However, greater funding, resources and expertise are needed to ensure the sustainability of MTBs and expand their outreach to underserved populations. Harmonization between practice and policy will be required to optimally implement precision oncology. Herein, we discuss the evolving role of MTBs and current and future considerations for their use in precision oncology.

PredictONCO: a web tool supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning

PredictONCO 1.0 is a unique web server that analyzes effects of mutations on proteins frequently altered in various cancer types. The server can assess the impact of mutations on the protein sequential and structural properties and apply a virtual screening to identify potential inhibitors that could be used as a highly individualized therapeutic approach, possibly based on the drug repurposing. PredictONCO integrates predictive algorithms and state-of-the-art computational tools combined with information from established databases. The user interface was carefully designed for the target specialists in precision oncology, molecular pathology, clinical genetics and clinical sciences. The tool summarizes the effect of the mutation on protein stability and function and currently covers 44 common oncological targets. The binding affinities of Food and Drug Administration/ European Medicines Agency -approved drugs with the wild-type and mutant proteins are calculated to facilitate treatment decisions. The reliability of predictions was confirmed against 108 clinically validated mutations. The server provides a fast and compact output, ideal for the often time-sensitive decision-making process in oncology. Three use cases of missense mutations, (i) K22A in cyclin-dependent kinase 4 identified in melanoma, (ii) E1197K mutation in anaplastic lymphoma kinase 4 identified in lung carcinoma and (iii) V765A mutation in epidermal growth factor receptor in a patient with congenital mismatch repair deficiency highlight how the tool can increase levels of confidence regarding the pathogenicity of the variants and identify the most effective inhibitors. The server is available at https://loschmidt.chemi.muni.cz/predictonco.

NCT/DKFZ MASTER handbook of interpreting whole-genome, transcriptome, and methylome data for precision oncology

Analysis of selected cancer genes has become an important tool in precision oncology but cannot fully capture the molecular features and, most importantly, vulnerabilities of individual tumors. Observational and interventional studies have shown that decision-making based on comprehensive molecular characterization adds significant clinical value. However, the complexity and heterogeneity of the resulting data are major challenges for disciplines involved in interpretation and recommendations for individualized care, and limited information exists on how to approach multilayered tumor profiles in clinical routine. We report our experience with the practical use of data from whole-genome or exome and RNA sequencing and DNA methylation profiling within the MASTER (Molecularly Aided Stratification for Tumor Eradication Research) program of the National Center for Tumor Diseases (NCT) Heidelberg and Dresden and the German Cancer Research Center (DKFZ). We cover all relevant steps of an end-to-end precision oncology workflow, from sample collection, molecular analysis, and variant prioritization to assigning treatment recommendations and discussion in the molecular tumor board. To provide insight into our approach to multidimensional tumor profiles and guidance on interpreting their biological impact and diagnostic and therapeutic implications, we present case studies from the NCT/DKFZ molecular tumor board that illustrate our daily practice. This manual is intended to be useful for physicians, biologists, and bioinformaticians involved in the clinical interpretation of genome-wide molecular information.

Renal neuroendocrine tumors: clinical and molecular pathology with an emphasis on frequent association with ectopic Cushing syndrome

Renal neuroendocrine tumors (RenNETs) are rare malignancies with largely unknown biology, hormone expression, and genetic abnormalities. This study aims to improve our understanding of the RenNETs with emphasis of functional, hormonal, and genetic features. Surgically resected RenNETs (N = 13) were retrieved, and immunohistochemistry and next-generation sequencing (NGS) were performed in all cases. In addition, all published RenNETs were systematically reviewed. Our cohort (4 men and 9 women, mean age 42, mean tumor size 7.6 cm) included 2 patients with Cushing syndrome (CS). WHO grade (23% G1, 54% G2, and 23% G3) and tumor progression did not correlate. CS-associated RenNETs (CS-RenNETs) showed a solid and eosinophilic histology and stained for ACTH, while the remaining non-functioning tumors had a trabecular pattern and expressed variably hormones somatostatin (91%), pancreatic polypeptide (63%), glucagon (54%), and serotonin (18%). The transcription factors ISL1 and SATB2 were expressed in all non-functioning, but not in CS-RenNETs. NGS revealed no pathogenic alterations or gene fusions. In the literature review (N = 194), 15 (8%) of the patients had hormonal syndromes, in which CS being the most frequent (7/15). Large tumor size and presence of metastasis were associated with shorter patients' survival (p < 0.01). RenNETs present as large tumors with metastases. CS-RenNETs differ through ACTH production and solid-eosinophilic histology from the non-functioning trabecular RenNETs that produce pancreas-related hormones and express ISL1 and SATB2. MEN1 or DAXX/ARTX abnormalities and fusion genes are not detected in RenNETs, indicating a distinct yet unknown molecular pathogenesis.

AMBAR - Interactive Alteration annotations for molecular tumor boards

MOTIVATION

Personalized decision-making for cancer therapy relies on molecular profiling from sequencing data in combination with database evidence and expert knowledge. Molecular tumor boards (MTBs) bring together clinicians and scientists with diverse expertise and are increasingly established in the clinical routine for therapeutic interventions. However, the analysis and documentation of patients data are still time-consuming and difficult to manage for MTBs, especially as few tools are available for the amount of information required.

RESULTS

To overcome these limitations, we developed an interactive web application AMBAR (Alteration annotations for Molecular tumor BoARds), for therapeutic decision-making support in MTBs. AMBAR is an R shiny-based application that allows customization, interactive filtering, visualization, adding expert knowledge, and export to clinical systems of annotated mutations.

AVAILABILITY

AMBAR is dockerized, open source and available at https://sysbio.uni-ulm.de/?Software:Ambar Contact:hans.kestler@uni-ulm.de.

Investigation of PARP Inhibitor Resistance Based on Serially Collected Circulating Tumor DNA in Patients With BRCA-Mutated Ovarian Cancer

PURPOSE

Patient-specific molecular alterations leading to PARP inhibitor (PARPi) resistance are relatively unexplored. In this study, we analyzed serially collected circulating tumor DNA (ctDNA) from patients with BRCA1/2 mutations who received PARPis to investigate the resistance mechanisms and their significance in postprogression treatment response and survival.

EXPERIMENTAL DESIGN

Patients were prospectively enrolled between January 2018 and December 2021 (NCT05458973). Whole-blood samples were obtained before PARPi administration and serially every 3 months until progression. ctDNA was extracted from the samples and sequenced with a 531-gene panel; gene sets for each resistance mechanism were curated.

RESULTS

Fifty-four patients were included in this analysis. Mutation profiles of genes in pre-PARPi samples indicating a high tumor mutational burden and alterations in genes associated with replication fork stabilization and drug efflux were associated with poor progression-free survival on PARPis. BRCA hypomorphism and reversion were found in 1 and 3 patients, respectively. Among 29 patients with matched samples, mutational heterogeneity increased postprogression on PARPis, showing at least one postspecific mutation in 89.7% of the patients. These mutations indicate non-exclusive acquired resistance mechanisms-homologous recombination repair restoration (28%), replication fork stability (34%), upregulated survival pathway (41%), target loss (10%), and drug efflux (3%). We observed poor progression-free survival with subsequent chemotherapy in patients with homologous recombination repair restoration (P = 0.003) and those with the simultaneous involvement of two or more resistance mechanisms (P = 0.040).

CONCLUSIONS

Analysis of serial ctDNAs highlighted multiple acquired resistance mechanisms, providing valuable insights for improving postprogression treatment and survival.

Somatic and germline aberrations in homologous recombination repair genes in Chinese prostate cancer patients

Simple summary

Somatic and germline aberrations in homologous recombinant repair (HHR) genes are associated with increased incidence and poor prognosis for prostate cancer. Through next-generation sequencing of prostate cancer patients across all clinical states from north China, here the authors identified a somatic mutational rate of 3% and a germline mutational rate of 3.9% for HRR genes using 200 tumor tissues and 714 blood specimens. Thus, mutational rates in HRR genes were lower compared with previous studies.

Background

Homologous recombination repair deficiency is associated with higher risk and poorer prognosis for prostate cancer. However, the landscapes of somatic and germline mutations in these genes remain poorly defined in Chinese patients, especially for those with localized disease and those from north part of China. In this study, we explore the genomic profiles of these patients.

Methods

We performed next-generation sequencing with 200 tumor tissues and 714 blood samples from prostate cancer patients at Peking University First Hospital, using a 32 gene panel including 19 homologous recombination repair genes.

Results

TP53, PTEN, KRAS were the most common somatic aberrations; BRCA2, NBN, ATM were the most common germline aberrations. In terms of HRR genes, 3% (6/200) patients harbored somatic aberrations, and 3.8% (28/714) patients harbored germline aberrations. 98.0% (196/200) somatic-tested and 72.7% (519/714) germline tested patients underwent prostatectomy, of which 28.6% and 42.0% had Gleason scores ≥8 respectively. Gleason scores at either biopsy or prostatectomy were predictive for somatic aberrations in general and in TP53; while age of onset &lt;60 years old, PSA at diagnosis, and Gleason scores at biopsy were clinical factors associated with positive germline aberrations in BRCA2/ATM.

Conclusions

Our results showed a distinct genomic profile in homologous recombination repair genes for patients with prostate cancer across all clinical states from north China. Clinicians may consider to expand the prostate cancer patients receiving genetic tests to include more individuals due to the weak guiding role by the clinical factors currently available.

Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data

BACKGROUND

It remains an important challenge to predict the functional consequences or clinical impacts of genetic variants in human diseases, such as cancer. An increasing number of genetic variants in cancer have been discovered and documented in public databases such as COSMIC, but the vast majority of them have no functional or clinical annotations. Some databases, such as CiVIC are available with manual annotation of functional mutations, but the size of the database is small due to the use of human annotation. Since the unlabeled data (millions of variants) typically outnumber labeled data (thousands of variants), computational tools that take advantage of unlabeled data may improve prediction accuracy.

RESULT

To leverage unlabeled data to predict functional importance of genetic variants, we introduced a method using semi-supervised generative adversarial networks (SGAN), incorporating features from both labeled and unlabeled data. Our SGAN model incorporated features from clinical guidelines and predictive scores from other computational tools. We also performed comparative analysis to study factors that influence prediction accuracy, such as using different algorithms, types of features, and training sample size, to provide more insights into variant prioritization. We found that SGAN can achieve competitive performances with small labeled training samples by incorporating unlabeled samples, which is a unique advantage compared to traditional machine learning methods. We also found that manually curated samples can achieve a more stable predictive performance than publicly available datasets.

CONCLUSIONS

By incorporating much larger samples of unlabeled data, the SGAN method can improve the ability to detect novel oncogenic variants, compared to other machine-learning algorithms that use only labeled datasets. SGAN can be potentially used to predict the pathogenicity of more complex variants such as structural variants or non-coding variants, with the availability of more training samples and informative features.

A Genomically and Clinically Annotated Patient-Derived Xenograft Resource for Preclinical Research in Non-Small Cell Lung Cancer

Patient-derived xenograft (PDX) models are an effective preclinical in vivo platform for testing the efficacy of novel drugs and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathologic features, mutational profiles, gene expression, and copy-number aberrations. Most of the PDXs are models of non-small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors from The Cancer Genome Atlas and previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted PDX tumors. Treatment studies performed in a subset of the models recapitulated the responses expected on the basis of the observed genomic profiles. These models therefore serve as a valuable preclinical platform for translational cancer research.

SIGNIFICANCE

Patient-derived xenografts of lung cancer retain key features observed in the originating patient tumors and show expected responses to treatment with standard-of-care agents, providing experimentally tractable and reproducible models for preclinical investigations.

How to read a next-generation sequencing report—what oncologists need to know

Next-generation sequencing (NGS) of tumor cell-derived DNA/RNA to screen for targetable genomic alterations is now widely available and has become part of routine practice in oncology. NGS testing strategies depend on cancer type, disease stage and the impact of results on treatment selection. The European Society for Medical Oncology (ESMO) has recently published recommendations for the use of NGS in patients with advanced cancer. We complement the ESMO recommendations with a practical review of how oncologists should read and interpret NGS reports. A concise and straightforward NGS report contains details of the tumor sample, the technology used and highlights not only the most important and potentially actionable results, but also other pathogenic alterations detected. Variants of unknown significance should also be listed. Interpretation of NGS reports should be a joint effort between molecular pathologists, tumor biologists and clinicians. Rather than relying and acting on the information provided by the NGS report, oncologists need to obtain a basic level of understanding to read and interpret NGS results. Comprehensive annotated databases are available for clinicians to review the information detailed in the NGS report. Molecular tumor boards do not only stimulate debate and exchange, but may also help to interpret challenging reports and to ensure continuing medical education.

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NGS is routinely carried out in the diagnostic work-up of several cancer types.

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In many other malignancies NGS is carried out after exhaustion of standard therapy options.

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Minimal requirements for the NGS report are detailed in this review.

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Interpretation of NGS reports can be challenging and require interdisciplinary discussion.

Combined Focused Next-Generation Sequencing Assays to Guide Precision Oncology in Solid Tumors: A Retrospective Analysis from an Institutional Molecular Tumor Board

BACKGROUND

Increasing knowledge of cancer biology and an expanding spectrum of molecularly targeted therapies provide the basis for precision oncology. Despite extensive gene diagnostics, previous reports indicate that less than 10% of patients benefit from this concept.

METHODS

We retrospectively analyzed all patients referred to our center's Molecular Tumor Board (MTB) from 2018 to 2021. Molecular testing by next-generation sequencing (NGS) included a 67-gene panel for the detection of short-sequence variants and copy-number alterations, a 53- or 137-gene fusion panel and an ultra-low-coverage whole-genome sequencing for the detection of additional copy-number alterations outside the panel's target regions. Immunohistochemistry for microsatellite instability and PD-L1 expression complemented NGS.

RESULTS

A total of 109 patients were referred to the MTB. In all, 78 patients received therapeutic proposals (70 based on NGS) and 33 were treated accordingly. Evaluable patients treated with MTB-recommended therapy (n = 30) had significantly longer progression-free survival than patients treated with other therapies (n = 17) (4.3 vs. 1.9 months, p = 0.0094). Seven patients treated with off-label regimens experienced major clinical benefits.

CONCLUSION

The combined focused sequencing assays detected targetable alterations in the majority of patients. Patient benefits appeared to lie in the same range as with large-scale sequencing approaches.

OncoPubMiner: a platform for mining oncology publications

Updated and expert-quality knowledge bases are fundamental to biomedical research. A knowledge base established with human participation and subject to multiple inspections is needed to support clinical decision making, especially in the growing field of precision oncology. The number of original publications in this field has risen dramatically with the advances in technology and the evolution of in-depth research. Consequently, the issue of how to gather and mine these articles accurately and efficiently now requires close consideration. In this study, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customisation, publication search with online reading and project-centred and team-based data collection to form a one-stop 'keyword in-knowledge out' oncology publication mining platform. The platform was constructed by integrating all open-access abstracts from PubMed and full-text articles from PubMed Central, and it is updated daily. OncoPubMiner makes obtaining precision oncology knowledge from scientific articles straightforward and will assist researchers in efficiently developing structured knowledge base systems and bring us closer to achieving precision oncology goals.

Adolescent and parent perspectives on genomic sequencing to inform cancer care

Next-generation sequencing offers opportunities for targeted cancer therapies and may identify pathogenic germline variants. Adolescents' perception of testing is not well understood. We surveyed 16 adolescents and 59 parents regarding motivations, attitudes, and knowledge related to paired tumor/germline sequencing. Participants generally had a good objective understanding of germline genetics and cancer risk, with parents scoring higher than adolescents. Nearly all participants were motivated by a desire to help other patients and to treat their child/themselves. Most adolescents reported involvement in the decision to enroll in the study. Study findings suggest important similarities and differences between parent and adolescent views.

Cutting-Edge AI Technologies Meet Precision Medicine to Improve Cancer Care

To provide precision medicine for better cancer care, researchers must work on clinical patient data, such as electronic medical records, physiological measurements, biochemistry, computerized tomography scans, digital pathology, and the genetic landscape of cancer tissue. To interpret big biodata in cancer genomics, an operational flow based on artificial intelligence (AI) models and medical management platforms with high-performance computing must be set up for precision cancer genomics in clinical practice. To work in the fast-evolving fields of patient care, clinical diagnostics, and therapeutic services, clinicians must understand the fundamentals of the AI tool approach. Therefore, the present article covers the following four themes: (i) computational prediction of pathogenic variants of cancer susceptibility genes; (ii) AI model for mutational analysis; (iii) single-cell genomics and computational biology; (iv) text mining for identifying gene targets in cancer; and (v) the NVIDIA graphics processing units, DRAGEN field programmable gate arrays systems and AI medical cloud platforms in clinical next-generation sequencing laboratories. Based on AI medical platforms and visualization, large amounts of clinical biodata can be rapidly copied and understood using an AI pipeline. The use of innovative AI technologies can deliver more accurate and rapid cancer therapy targets.

[Molecular cytology: Opportunities and challenges]

Predictive marker (re-)analysis of tumor material can be a real obstacle in several tumor entities, like non-small cell lung cancer (NSCLC), due to difficult anatomic conditions and small biopsy samples. As reported in the literature, cytological samples comprise excellent starting material for predictive marker analysis like fluorescence in situ hybridization and next generation sequencing. As for formalin-fixed paraffin-embedded tissue samples, rigorous quality control and standardized laboratory operating procedures are mandatory. Further advantages of cytological specimens are the rapid and straightforward inspection of representativeness, for example by rapid on-site evaluation (ROSE). Another striking advantage is that the fresh cellular material from smears and serous cavity fluids can be used for the generation of two- and three-dimensional cell culture models. Hereby, in addition to the conventional biomarker testing, complex complementary functional genomic assays can also be applied, for example, to assess the effects of multiple variants in one sample and unknown variants of tumor driver genes and tumor suppressor genes. This information may provide additional vulnerabilities of the tumor to be considered for the therapy decision, for example in the molecular tumor board.

Hematopoietic differentiation persists in human iPSCs defective in de novo DNA methylation

BACKGROUND

DNA methylation is involved in the epigenetic regulation of gene expression during developmental processes and is primarily established by the DNA methyltransferase 3A (DNMT3A) and 3B (DNMT3B). DNMT3A is one of the most frequently mutated genes in clonal hematopoiesis and leukemia, indicating that it plays a crucial role for hematopoietic differentiation. However, the functional relevance of Dnmt3a for hematopoietic differentiation and hematological malignancies has mostly been analyzed in mice, with the specific role for human hematopoiesis remaining elusive. In this study, we therefore investigated if DNMT3A is essential for hematopoietic differentiation of human induced pluripotent stem cells (iPSCs).

RESULTS

We generated iPSC lines with knockout of either exon 2, 19, or 23 and analyzed the impact of different DNMT3A exon knockouts on directed differentiation toward mesenchymal and hematopoietic lineages. Exon 19-/- and 23-/- lines displayed an almost entire absence of de novo DNA methylation during mesenchymal and hematopoietic differentiation. Yet, differentiation efficiency was only slightly reduced in exon 19-/- and rather increased in exon 23-/- lines, while there was no significant impact on gene expression in hematopoietic progenitors (iHPCs). Notably, DNMT3A-/- iHPCs recapitulate some DNA methylation patterns of acute myeloid leukemia (AML) with DNMT3A mutations. Furthermore, multicolor genetic barcoding revealed growth advantage of exon 23-/- iHPCs in a syngeneic competitive differentiation assay.

CONCLUSIONS

Our results demonstrate that iPSCs with homozygous knockout of different exons of DNMT3A remain capable of mesenchymal and hematopoietic differentiation-and exon 23-/- iHPCs even gained growth advantage-despite loss of almost the entire de novo DNA methylation. Partial recapitulation of DNA methylation patterns of AML with DNMT3A mutations by our DNMT3A knockout iHPCs indicates that our model system can help to elucidate mechanisms of clonal hematopoiesis.

CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer

Several knowledgebases are manually curated to support clinical interpretations of thousands of hotspot somatic mutations in cancer. However, discrepancies or even conflicting interpretations are observed among these databases. Furthermore, many previously undocumented mutations may have clinical or functional impacts on cancer but are not systematically interpreted by existing knowledgebases. To address these challenges, we developed CancerVar to facilitate automated and standardized interpretations for 13 million somatic mutations based on the AMP/ASCO/CAP 2017 guidelines. We further introduced a deep learning framework to predict oncogenicity for these variants using both functional and clinical features. CancerVar achieved satisfactory performance when compared to several independent knowledgebases and, using clinically curated datasets, demonstrated practical utility in classifying somatic variants. In summary, by integrating clinical guidelines with a deep learning framework, CancerVar facilitates clinical interpretation of somatic variants, reduces manual work, improves consistency in variant classification, and promotes implementation of the guidelines.

Somatic Genomic Testing in Patients With Metastatic or Advanced Cancer: ASCO Provisional Clinical Opinion

PURPOSE

An ASCO provisional clinical opinion offers timely clinical direction to ASCO's membership following publication or presentation of potentially practice-changing data from major studies. This provisional clinical opinion addresses the appropriate use of tumor genomic testing in patients with metastatic or advanced solid tumors.

CLINICAL CONTEXT

An increasing number of therapies are approved to treat cancers harboring specific genomic biomarkers. However, there is a lack of clarity as to when tumor genomic sequencing should be ordered, what type of assays should be performed, and how to interpret the results for treatment selection.

PROVISIONAL CLINICAL OPINION

Patients with metastatic or advanced cancer should undergo genomic sequencing in a certified laboratory if the presence of one or more specific genomic alterations has regulatory approval as biomarkers to guide the use of or exclusion from certain treatments for their disease. Multigene panel-based assays should be used if more than one biomarker-linked therapy is approved for the patient's disease. Site-agnostic approvals for any cancer with a high tumor mutation burden, mismatch repair deficiency, or neurotrophic tyrosine receptor kinase (NTRK) fusions provide a rationale for genomic testing for all solid tumors. Multigene testing may also assist in treatment selection by identifying additional targets when there are few or no genotype-based therapy approvals for the patient's disease. For treatment planning, the clinician should consider the functional impact of the targeted alteration and expected efficacy of genomic biomarker-linked options relative to other approved or investigational treatments.Additional information is available at www.asco.org/assays-and-predictive-markers-guidelines.

Comprehensive characterization of PTEN mutational profile in a series of 34,129 colorectal cancers

Loss of expression or activity of the tumor suppressor PTEN acts similarly to an activating mutation in the oncogene PIK3CA in elevating intracellular levels of phosphatidylinositol (3,4,5)-trisphosphate (PIP3), inducing signaling by AKT and other pro-tumorigenic signaling proteins. Here, we analyze sequence data for 34,129 colorectal cancer (CRC) patients, capturing 3,434 PTEN mutations. We identify specific patterns of PTEN mutation associated with microsatellite stability/instability (MSS/MSI), tumor mutational burden (TMB), patient age, and tumor location. Within groups separated by MSS/MSI status, this identifies distinct profiles of nucleotide hotspots, and suggests differing profiles of protein-damaging effects of mutations. Moreover, discrete categories of PTEN mutations display non-identical patterns of co-occurrence with mutations in other genes important in CRC pathogenesis, including KRAS, APC, TP53, and PIK3CA. These data provide context for clinical targeting of proteins upstream and downstream of PTEN in distinct CRC cohorts.

Design of a targeted next-generation DNA sequencing panel for pediatric T-cell lymphoblastic lymphoma to unravel biology and optimize treatment

Low incidence and molecular heterogeneity of pediatric T-cell lymphoblastic lymphoma (T-LBL) require an international, large-scale effort to identify novel clinical biomarkers. The ongoing international clinical trial LBL2018 (NCT04043494) represents an ideal opportunity to implement a common analytic approach. Targeted next-generation sequencing is well-suited for this purpose; however, selection of relevant target genes for T-LBL remains subject of ongoing debates. Our group has recently designed and evaluated a first target panel of 80 candidate genes for T-LBL. The present study aimed at developing a novel optimized gene panel for large-scale application and to promote an international agreement on a common core panel. Small sequence variants obtained from our former study were systematically analyzed and classified with regards to pathogenic relevance, to prioritize candidate genes. Additional genes were curated from literature and online databases for a more comprehensive analysis of relevant functions and signaling pathways. The new target panel TGP-T-LBL entails 84 candidate genes which are key actors in NOTCH, PI3K-AKT, JAK-STAT, RAS signaling, epigenetic regulation, transcription, DNA repair, cell cycle regulation and ribosomal function. From our former gene panel, 35 out of 80 candidate genes were selected for the novel panel. Forty-six out of 84 genes are currently being analyzed in the ongoing international trial LBL2018. Exploratory analysis of prognostic relevance on mutation-level suggested a potential association of PIK3CA variants c.1624G > A(p.Glu542Lys) and c.1633G > A(p.Glu545Lys) to occurrence of relapse, emphasizing particular relevance of mutation analysis in PI3K-AKT signaling. Our approach promotes comprehensive and clinically relevant mutational profiling of pediatric T-LBL. This article is protected by copyright. All rights reserved.

Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification

Colorectal cancer (CRC) is the second most frequently diagnosed type of cancer and a major worldwide public health concern. Despite the global efforts in the development of modern therapeutic strategies, CRC prognosis is strongly correlated with the stage of the disease at diagnosis. Early detection of CRC has a huge impact in decreasing mortality while pre-lesion detection significantly reduces the incidence of the pathology. Even though the management of CRC patients is based on robust diagnostic methods such as serum tumor markers analysis, colonoscopy, histopathological analysis of tumor tissue, and imaging methods (computer tomography or magnetic resonance), these strategies still have many limitations and do not fully satisfy clinical needs due to their lack of sensitivity and/or specificity. Therefore, improvements of the current practice would substantially impact the management of CRC patients. In this view, liquid biopsy is a promising approach that could help clinicians screen for disease, stratify patients to the best treatment, and monitor treatment response and resistance mechanisms in the tumor in a regular and minimally invasive manner. Liquid biopsies allow the detection and analysis of different tumor-derived circulating markers such as cell-free nucleic acids (cfNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) in the bloodstream. The major advantage of this approach is its ability to trace and monitor the molecular profile of the patient's tumor and to predict personalized treatment in real-time. On the other hand, the prospective use of artificial intelligence (AI) in medicine holds great promise in oncology, for the diagnosis, treatment, and prognosis prediction of disease. AI has two main branches in the medical field: (i) a virtual branch that includes medical imaging, clinical assisted diagnosis, and treatment, as well as drug research, and (ii) a physical branch that includes surgical robots. This review summarizes findings relevant to liquid biopsy and AI in CRC for better management and stratification of CRC patients.

A platform for oncogenomic reporting and interpretation

Manual interpretation of variants remains rate limiting in precision oncology. The increasing scale and complexity of molecular data generated from comprehensive sequencing of cancer samples requires advanced interpretative platforms as precision oncology expands beyond individual patients to entire populations. To address this unmet need, we introduce a Platform for Oncogenomic Reporting and Interpretation (PORI), comprising an analytic framework that facilitates the interpretation and reporting of somatic variants in cancer. PORI integrates reporting and graph knowledge base tools combined with support for manual curation at the reporting stage. PORI represents an open-source platform alternative to commercial reporting solutions suitable for comprehensive genomic data sets in precision oncology. We demonstrate the utility of PORI by matching 9,961 pan-cancer genome atlas tumours to the graph knowledge base, calculating therapeutically informative alterations, and making available reports describing select individual samples.

The interpretation of somatic variants in cancer is challenging due to the scale and complexity of sequencing data. Here, the authors present PORI, an open-source framework for interpreting somatic variants in cancer using graph knowledge base tools, automated reporting, and manual curation.

ANSWER: Annotation Software for Electronic Reporting

PURPOSE

To better use genetic testing, which is used by clinicians to explain the molecular mechanism of disease and to suggest clinical actionability and new treatment options, clinical next-generation sequencing (NGS) laboratories must send the results into reports in PDF and discrete data element format (HL7). Although most clinical diagnostic tests have set molecular markers tested and have a set range of values or a binary result (positive or negative), the NGS genetic test could examine hundreds or thousands of genes with no predefined list of variants. Although there are some commercial and open-source tools for clinically reporting genomics results for oncology testing, they often lack necessary features.

METHODS

Using several available software tools for data storage including MySQL and MongoDB, database querying with Python, and a web-based user application using JAVA and JAVA script, we have developed a tool to store and query complex genomics and demographics data, which can be manually curated and reported by the user.

RESULTS

We have developed a tool, Annotation SoftWare for Electronic Reporting (ANSWER), that can allow molecular pathologists to (1) filter variants to find those meeting quality control metrics in the genes that are clinically actionable by diagnosis; (2) visualize variants using data generated in the bioinformatics analysis; (3) create annotations that can be reused in future reports with association specific to the gene, variant, or diagnosis; (4) select variants and annotations that should be reported to match the details of the case; and (5) generate a report that includes demographics, reported variants, clinical actionability annotation, and references that can be exported into PDF or HL7 format, which can be electronically sent to an electronic health record.

CONCLUSION

ANSWER is a tool that can be installed locally and is designed to meet the clinical reporting needs of a clinical oncology NGS laboratory for reporting.

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.

JCGA: the Japanese version of the Cancer Genome Atlas and its contribution to the interpretation of gene alterations detected in clinical cancer genome sequencing

With the emergence of next-generation sequencing (NGS)-based cancer gene panel tests in routine oncological practice in Japan, an easily interpretable cancer genome database of Japanese patients in which mutational profiles are unaffected by racial differences is needed to improve the interpretation of the detected gene alterations. Considering this, we constructed the first Japanese cancer genome database, called the Japanese version of the Cancer Genome Atlas (JCGA), which includes multiple tumor types. The database includes whole-exome sequencing data from 4907 surgically resected primary tumor samples obtained from 4753 Japanese patients with cancer and graphically provides genome information on 460 cancer-associated genes, including the 336 genes that are included in two NGS-based cancer gene panel tests approved by the Pharmaceuticals and Medical Devices Agency. Moreover, most of the contents of this database are written in Japanese; this not only helps physicians explain the results of NGS-based cancer gene panel tests but also enables patients and their families to obtain further information regarding the detected gene alterations.

Therapeutic Implications of Germline Testing in Patients With Advanced Cancers

PURPOSE

Tumor mutational profiling is increasingly performed in patients with advanced cancer. We determined the extent to which germline mutation profiling guides therapy selection in patients with advanced cancer.

METHODS

Patients with cancer undergoing tumor genomic profiling were prospectively consented for germline cancer predisposition gene analysis (2015-2019). In patients harboring germline likely pathogenic or pathogenic (LP/P) alterations, therapeutic actionability was classified using a precision oncology knowledge base. Patients with metastatic or recurrent cancer receiving germline genotype-directed therapy were determined.

RESULTS

Among 11,947 patients across > 50 malignancies, 17% (n = 2,037) harbored a germline LP/P variant. By oncology knowledge base classification, 9% (n = 1042) had an LP/P variant in a gene with therapeutic implications (4% level 1; 4% level 3B; < 1% level 4). BRCA1/2 variants accounted for 42% of therapeutically actionable findings, followed by CHEK2 (13%), ATM (12%), mismatch repair genes (11%), and PALB2 (5%). When limited to the 9,079 patients with metastatic or recurrent cancer, 8% (n = 710) harbored level 1 or 3B genetic findings and 3.2% (n = 289) received germline genotype-directed therapy. Germline genotype-directed therapy was received by 61% and 18% of metastatic cancer patients with level 1 and level 3B findings, respectively, and by 54% of BRCA1/2, 75% of mismatch repair, 43% of PALB2, 35% of RAD51C/D, 24% of BRIP1, and 19% of ATM carriers. Of BRCA1/2 patients receiving a poly(ADP-ribose) polymerase inhibitor, 45% (84 of 188) had tumors other than breast or ovarian cancer, wherein the drug, at time of delivery, was delivered in an investigational setting.

CONCLUSION

In a pan-cancer analysis, 8% of patients with advanced cancer harbored a germline variant with therapeutic actionability with 40% of these patients receiving germline genotype-directed treatment. Germline sequence analysis is additive to tumor sequence analysis for therapy selection and should be considered for all patients with advanced cancer.

Multicenter Comparison of Molecular Tumor Boards in The Netherlands: Definition, Composition, Methods, and Targeted Therapy Recommendations

BACKGROUND

Molecular tumor boards (MTBs) provide rational, genomics-driven, patient-tailored treatment recommendations. Worldwide, MTBs differ in terms of scope, composition, methods, and recommendations. This study aimed to assess differences in methods and agreement in treatment recommendations among MTBs from tertiary cancer referral centers in The Netherlands.

MATERIALS AND METHODS

MTBs from all tertiary cancer referral centers in The Netherlands were invited to participate. A survey assessing scope, value, logistics, composition, decision-making method, reporting, and registration of the MTBs was completed through on-site interviews with members from each MTB. Targeted therapy recommendations were compared using 10 anonymized cases. Participating MTBs were asked to provide a treatment recommendation in accordance with their own methods. Agreement was based on which molecular alteration(s) was considered actionable with the next line of targeted therapy.

RESULTS

Interviews with 24 members of eight MTBs revealed that all participating MTBs focused on rare or complex mutational cancer profiles, operated independently of cancer type-specific multidisciplinary teams, and consisted of at least (thoracic and/or medical) oncologists, pathologists, and clinical scientists in molecular pathology. Differences were the types of cancer discussed and the methods used to achieve a recommendation. Nevertheless, agreement among MTB recommendations, based on identified actionable molecular alteration(s), was high for the 10 evaluated cases (86%).

CONCLUSION

MTBs associated with tertiary cancer referral centers in The Netherlands are similar in setup and reach a high agreement in recommendations for rare or complex mutational cancer profiles. We propose a "Dutch MTB model" for an optimal, collaborative, and nationally aligned MTB workflow.

IMPLICATIONS FOR PRACTICE

Interpretation of genomic analyses for optimal choice of target therapy for patients with cancer is becoming increasingly complex. A molecular tumor board (MTB) supports oncologists in rationalizing therapy options. However, there is no consensus on the most optimal setup for an MTB, which can affect the quality of recommendations. This study reveals that the eight MTBs associated with tertiary cancer referral centers in The Netherlands are similar in setup and reach a high agreement in recommendations for rare or complex mutational profiles. The Dutch MTB model is based on a collaborative and nationally aligned workflow with interinstitutional collaboration and data sharing.

Clinical cancer genomic profiling

Technological innovation and rapid reduction in sequencing costs have enabled the genomic profiling of hundreds of cancer-associated genes as a component of routine cancer care. Tumour genomic profiling can refine cancer subtype classification, identify which patients are most likely to benefit from systemic therapies and screen for germline variants that influence heritable cancer risk. Here, we discuss ongoing efforts to enhance the clinical utility of tumour genomic profiling by integrating tumour and germline analyses, characterizing allelic context and identifying mutational signatures that influence therapy response. We also discuss the potential clinical utility of more comprehensive whole-genome and whole-transcriptome sequencing and ultra-sensitive cell-free DNA profiling platforms, which allow for minimally invasive, serial analyses of tumour-derived DNA in blood.

MUSTARD-a comprehensive resource of mutation-specific therapies in cancer

The steady increase in global cancer burden has fuelled the development of several modes of treatment for the disease. In the presence of an actionable mutation, targeted therapies offer a method to selectively attack cancer cells, increasing overall efficacy and reducing harmful side effects. However, different drug molecules are in different stages of development, with new molecules obtaining approvals from regulatory agencies each year. To augment clinical impact, it is important that this information reaches clinicians, patients and researchers swiftly and in a structured, well-annotated manner. To this end, we have developed Mutation-Specific Therapies Resource and Database in Cancer (MUSTARD), a database that is designed to be a centralized resource with diverse information such as cancer subtype, associated mutations, therapy offered and its effect observed, along with links to external resources for a more comprehensive annotation. In its current version, MUSTARD comprises over 2105 unique entries, including associations between 418 unique drug therapies, 189 cancer subtypes and 167 genes curated and annotated from over 862 different publications. To the best of our knowledge, it is the only resource that offers comprehensive information on mutation-specific, gene fusions and overexpressed gene-targeted therapies for cancer.

Database URL: http://clingen.igib.res.in/mustard/

A Curriculum for Genomic Education of Molecular Genetic Pathology Fellows: A Report of the Association for Molecular Pathology Training and Education Committee

Molecular genetic pathology (MGP) is a subspecialty of pathology and medical genetics and genomics. Genomic testing, which we define as that which generates large data sets and interrogates large segments of the genome in a single assay, is increasingly recognized as essential for optimal patient care through precision medicine. The most common genomic testing technologies in clinical laboratories are next-generation sequencing and microarray. It is essential to train in these methods and to consider the data generated in the context of the diagnosis, medical history, and other clinical findings of individual patients. Accordingly, updating the MGP fellowship curriculum to include genomics is timely, important, and challenging. At the completion of training, an MGP fellow should be capable of independently interpreting and signing out results of a wide range of genomic assays and, given the appropriate context and institutional support, of developing and validating new assays in compliance with applicable regulations. The Genomics Task Force of the MGP Program Directors, a working group of the Association for Molecular Pathology Training and Education Committee, has developed a genomics curriculum framework and recommendations specific to the MGP fellowship. These recommendations are presented for consideration and implementation by MGP fellowship programs with the understanding that MGP programs exist in a diversity of clinical practice environments with a spectrum of available resources.

Clinical Interpretation Challenges of Germline-Shared Somatic Variants in Cancer

OBJECTIVE

To investigate the interpretation differences of germline-shared somatic variants.

METHODS

A total of 123,302 COSMIC variants associated with hematologic malignant neoplasms were used. The pathogenicity and actionability of shared variants were analyzed based on the standardized guidelines.

RESULTS

The overall frequency of variants shared in ClinVar/HGMD and COSMIC was 10%. The pathogenicity of 54 shared variants was pathogenic/likely pathogenic (P/LP; n = 30), variants of unknown significance (n = 3), and benign/likely benign (n = 21). In total, 30 P/LP variants were reclassified to tier I/tier II (83%) and tier III (17%) variants.

CONCLUSIONS

This is the first study about different clinical interpretations of shared variants based on the current standard guidelines. This study takes a meaningful step in bridging the interpretation gap between the somatic and germline variants.

Criteria-based curation of a therapy-focused compendium to support treatment recommendations in precision oncology

While several resources exist that interpret therapeutic significance of genomic alterations in cancer, many regional real-world issues limit access to drugs. There is a need for a pragmatic, evidence-based, context-adapted tool to guide clinical management based on molecular biomarkers. To this end, we have structured a compendium of approved and experimental therapies with associated biomarkers following a survey of drug regulatory databases, existing knowledge bases, and published literature. Each biomarker-disease-therapy triplet was categorised using a tiering system reflective of key therapeutic considerations: approved and reimbursed therapies with respect to a jurisdiction (Tier 1), evidence of efficacy or approval in another jurisdiction (Tier 2), evidence of antitumour activity (Tier 3), and plausible biological rationale (Tier 4). Two resistance categories were defined: lack of efficacy (Tier R1) or antitumor activity (Tier R2). Based on this framework, we curated a digital resource focused on drugs relevant in the Australian healthcare system (TOPOGRAPH: Therapy Oriented Precision Oncology Guidelines for Recommending Anticancer Pharmaceuticals). As of November 2020, TOPOGRAPH comprised 2810 biomarker-disease-therapy triplets in 989 expert-appraised entries, including 373 therapies, 199 biomarkers, and 106 cancer types. In the 345 therapies catalogued, 84 (24%) and 65 (19%) were designated Tiers 1 and 2, respectively, while 271 (79%) therapies were supported by preclinical studies, early clinical trials, retrospective studies, or case series (Tiers 3 and 4). A companion algorithm was also developed to support rational, context-appropriate treatment selection informed by molecular biomarkers. This framework can be readily adapted to build similar resources in other jurisdictions to support therapeutic decision-making.

The KDR (VEGFR-2) Genetic Polymorphism Q472H and c-KIT Polymorphism M541L Are Associated With More Aggressive Behaviour in Astrocytic Gliomas

BACKGROUND/AIM

Better diagnostic and prognostic markers are required for a more accurate diagnosis and an earlier detection of glioma progression and for suggesting better treatment strategies. This retrospective study aimed to identify actionable gene variants to define potential markers of clinical significance.

MATERIALS AND METHODS

56 glioblastomas (GBM) and 44 grade 2-3 astrocytomas were profiled with next generation sequencing (NGS) as part of routine diagnostic workup and bioinformatics analysis was used for the identification of variants. CD34 immunohistochemistry (IHC) was used to measure microvessel density (MVD) and Log-rank test to compare survival and progression in the presence or absence of these variants.

RESULTS

Bioinformatic analysis highlighted frequently occurring variants in genes involved in angiogenesis regulation (KDR, KIT, TP53 and PIK3CA), with the most common ones being KDR (rs1870377) and KIT (rs3822214). The KDR variant was associated with increased MVD and shorter survival in GBM. We did not observe any correlation between the KIT variant and MVD; however, there was an association with tumour grade.

CONCLUSION

This study highlights the role of single-nucleotide variants (SNVs) that may be considered non-pathogenic and suggests the prognostic significance for survival of KIT rs3822214 and KDR rs1870377 and potential importance in planning new treatment strategies for gliomas.

Preclinical models and technologies to advance nanovaccine development

The remarkable success of targeted immunotherapies is revolutionizing cancer treatment. However, tumor heterogeneity and low immunogenicity, in addition to several tumor-associated immunosuppression mechanisms are among the major factors that have precluded the success of cancer vaccines as targeted cancer immunotherapies. The exciting outcomes obtained in patients upon the injection of tumor-specific antigens and adjuvants intratumorally, reinvigorated interest in the use of nanotechnology to foster the delivery of vaccines to address cancer unmet needs. Thus, bridging nano-based vaccine platform development and predicted clinical outcomes the selection of the proper preclinical model will be fundamental. Preclinical models have revealed promising outcomes for cancer vaccines. However, only few cases were associated with clinical responses. This review addresses the major challenges related to the translation of cancer nano-based vaccines to the clinic, discussing the requirements for ex vivo and in vivo models of cancer to ensure the translation of preclinical success to patients.

The My Cancer Genome clinical trial data model and trial curation workflow

OBJECTIVE

As clinical trials evolve in complexity, clinical trial data models that can capture relevant trial data in meaningful, structured annotations and computable forms are needed to support accrual.

MATERIAL AND METHODS

We have developed a clinical trial information model, curation information system, and a standard operating procedure for consistent and accurate annotation of cancer clinical trials. Clinical trial documents are pulled into the curation system from publicly available sources. Using a web-based interface, a curator creates structured assertions related to disease-biomarker eligibility criteria, therapeutic context, and treatment cohorts by leveraging our data model features. These structured assertions are published on the My Cancer Genome (MCG) website.

RESULTS

To date, over 5000 oncology trials have been manually curated. All trial assertion data are available for public view on the MCG website. Querying our structured knowledge base, we performed a landscape analysis to assess the top diseases, biomarker alterations, and drugs featured across all cancer trials.

DISCUSSION

Beyond curating commonly captured elements, such as disease and biomarker eligibility criteria, we have expanded our model to support the curation of trial interventions and therapeutic context (ie, neoadjuvant, metastatic, etc.), and the respective biomarker-disease treatment cohorts. To the best of our knowledge, this is the first effort to capture these fields in a structured format.

CONCLUSION

This paper makes a significant contribution to the field of biomedical informatics and knowledge dissemination for precision oncology via the MCG website.

KEY WORDS

knowledge representation, My Cancer Genome, precision oncology, knowledge curation, cancer informatics, clinical trial data model.

Swiss-PO: a new tool to analyze the impact of mutations on protein three-dimensional structures for precision oncology

Swiss-PO is a new web tool to map gene mutations on the 3D structure of corresponding proteins and to intuitively assess the structural implications of protein variants for precision oncology. Swiss-PO is constructed around a manually curated database of 3D structures, variant annotations, and sequence alignments, for a list of 50 genes taken from the Ion AmpliSeqTM Custom Cancer Hotspot Panel. The website was designed to guide users in the choice of the most appropriate structure to analyze regarding the mutated residue, the role of the protein domain it belongs to, or the drug that could be selected to treat the patient. The importance of the mutated residue for the structure and activity of the protein can be assessed based on the molecular interactions exchanged with neighbor residues in 3D within the same protein or between different biomacromolecules, its conservation in orthologs, or the known effect of reported mutations in its 3D or sequence-based vicinity. Swiss-PO is available free of charge or login at https://www.swiss-po.ch .

Comparison of Open-access Databases for Clinical Variant Interpretation in Cancer: A Case Study of MDS/AML

BACKGROUND

Recently, guidelines for variant interpretation in cancer have been established. However, these guidelines do not mention which databases are most suited to performing this task.

MATERIALS AND METHODS

We give an overview of existing databases and evaluate their benefit in practical application. We compared three meta-databases and 12 databases for a dataset of patients with myelodysplastic syndrome or acute myeloid leukemia.

RESULTS

Clinical implications were found for 13% of all variants. One-third of variants with therapeutic implications were uniquely contained in one database. The VICC meta-database was the most extensive source of information, featuring 92% of variants with a drug association. However, a comparison of meta-databases and original sources indicated that some variants are missing in those meta-databases.

CONCLUSION

Public databases provide decision support for interpreting variants but there is still need for manual curation. Meta-databases facilitate the use of multiple resources but should be interpreted with caution.

Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, and other web-based sources. Drug, gene, and interaction data are normalized and merged into conceptual groups. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major version release of this database. A primary focus of this update was integration with crowdsourced efforts, leveraging the Drug Target Commons for community-contributed interaction data, Wikidata to facilitate term normalization, and export to NDEx for drug-gene interaction network representations. Seven new sources have been added since the last major version release, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include the introduction of a more sophisticated Query Score for interaction search results, an updated Interaction Score, the inclusion of interaction directionality, and several additional improvements to search features, data releases, licensing documentation and the application framework.

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multiregion samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.

Methods and resources to access mutation-dependent effects on cancer drug treatment

In clinical cancer treatment, genomic alterations would often affect the response of patients to anticancer drugs. Studies have shown that molecular features of tumors could be biomarkers predictive of sensitivity or resistance to anticancer agents, but the identification of actionable mutations are often constrained by the incomplete understanding of cancer genomes. Recent progresses of next-generation sequencing technology greatly facilitate the extensive molecular characterization of tumors and promote precision medicine in cancers. More and more clinical studies, cancer cell lines studies, CRISPR screening studies as well as patient-derived model studies were performed to identify potential actionable mutations predictive of drug response, which provide rich resources of molecularly and pharmacologically profiled cancer samples at different levels. Such abundance of data also enables the development of various computational models and algorithms to solve the problem of drug sensitivity prediction, biomarker identification and in silico drug prioritization by the integration of multiomics data. Here, we review the recent development of methods and resources that identifies mutation-dependent effects for cancer treatment in clinical studies, functional genomics studies and computational studies and discuss the remaining gaps and future directions in this area.

Relevance and Effectiveness of Molecular Tumor Board Recommendations for Patients With Non–Small-Cell Lung Cancer With Rare or Complex Mutational Profiles

PURPOSE

Molecular tumor boards (MTBs) provide physicians with a treatment recommendation for complex tumor-specific genomic alterations. National and international consensus to reach a recommendation is lacking. In this article, we analyze the effectiveness of an MTB decision-making methodology for patients with non–small-cell lung cancer (NSCLC) with rare or complex mutational profiles as implemented in the University Medical Center Groningen (UMCG).

METHODS

The UMCG-MTB comprises (pulmonary) oncologists, pathologists, clinical scientists in molecular pathology, and structural biologists. Recommendations are based on reported actionability of variants and molecular interpretation of pathways affected by the variant and supported by molecular modeling. A retrospective analysis of 110 NSCLC cases (representing 106 patients) with suggested treatment of complex genomic alterations and corresponding treatment outcomes for targeted therapy was performed.

RESULTS

The MTB recommended targeted therapy for 59 of 110 NSCLC cases with complex molecular profiles: 24 within a clinical trial, 15 in accordance with guidelines (on label) and 20 off label. All but 16 recommendations involved patients with an EGFR or ALK mutation. Treatment outcome was analyzed for patients with available follow-up (10 on label and 16 off label). Adherence to the MTB recommendation (21 of 26; 81%) resulted in an objective response rate of 67% (14 of 21), with a median progression-free survival of 6.3 months (interquartile range, 3.2-10.6 months) and an overall survival of 10.4 months (interquartile range, 6.3-14.6 months).

CONCLUSION

Targeted therapy recommendations resulting from the UMCG-MTB workflow for complex molecular profiles were highly adhered to and resulted in a positive clinical response in the majority of patients with metastatic NSCLC.

Pan-cancer pharmacogenetics: targeted sequencing panels or exome sequencing?

Aim: This study provides clinicians and researchers with an informed choice between current commercially available targeted sequencing panels and exome sequencing panels in the context of pan-cancer pharmacogenetics. Materials & methods: Nine contemporary commercially available targeted pan-cancer panels and the xGen Exome Research Panel v2 were investigated to determine to what extent they cover the pharmacogenetic variant-drug interactions in five available cancer knowledgebases, and the driver mutations and fusion genes in the Cancer Genome Atlas. Results: xGen Exome Research Panel v2 and TrueSight Oncology 500 target 71.0 and 68.9% of the pharmacogenetic interactions in the available knowledgebases; and 93.7 and 86.0% of the driver mutations in the Cancer Genome Atlas, respectively. All other studied panels target lower percentages. Conclusion: Exome sequencing outperforms pan-cancer targeted sequencing panels in terms of covered cancer pharmacogenetic variant-drug interactions and pharmacogenetic cancer variants.