COVID-19-neutralizing antibodies predict disease severity and survival.

Coronavirus disease 2019 (COVID-19) exhibits variable symptom severity ranging from asymptomatic to life-threatening, yet the relationship between severity and the humoral immune response is poorly understood. We examined antibody responses in 113 COVID-19 patients and found that severe cases resulting in intubation or death exhibited increased inflammatory markers, lymphopenia, pro-inflammatory cytokines, and high anti-receptor binding domain (RBD) antibody levels. Although anti-RBD immunoglobulin G (IgG) levels generally correlated with neutralization titer, quantitation of neutralization potency revealed that high potency was a predictor of survival. In addition to neutralization of wild-type SARS-CoV-2, patient sera were also able to neutralize the recently emerged SARS-CoV-2 mutant D614G, suggesting cross-protection from reinfection by either strain. However, SARS-CoV-2 sera generally lacked cross-neutralization to a highly homologous pre-emergent bat coronavirus, WIV1-CoV, which has not yet crossed the species barrier. These results highlight the importance of neutralizing humoral immunity on disease progression and the need to develop broadly protective interventions to prevent future coronavirus pandemics.

Rapid Tumor DNA Analysis of Cerebrospinal Fluid Accelerates Treatment of Central Nervous System Lymphoma.

Delays and risks associated with neurosurgical biopsies preclude timely diagnosis and treatment of central nervous system (CNS) lymphoma and other CNS neoplasms. We prospectively integrated targeted rapid genotyping of cerebrospinal fluid (CSF) into the evaluation of 70 patients with CNS lesions of unknown etiology. Participants underwent genotyping of CSF-derived DNA using a qPCR-based approach for parallel detection of single-nucleotide variants in the MYD88, TERT promoter, IDH1, IDH2, BRAF and H3F3A genes within 80 minutes of sample acquisition. Canonical mutations were detected in 42% of patients with neoplasms, including cases of primary and secondary CNS lymphoma, glioblastoma, IDH-mutant brainstem glioma and H3K27M-mutant diffuse midline glioma. Genotyping results eliminated the need for surgical biopsies in 7/33 (21.2%) cases of newly diagnosed neoplasms, resulting in significantly accelerated initiation of disease-directed treatment (median 3 vs 12 days; p = 0.027). This assay was then implemented in a Clinical Laboratory Improvement Amendments (CLIA) environment, with 2-day median turnaround for diagnosis of central nervous system lymphoma from 66 patients across 4 clinical sites. Our study prospectively demonstrates that targeted rapid CSF genotyping influences oncologic management for suspected CNS tumors.

Virus-Induced Acute Respiratory Distress Syndrome Causes Cardiomyopathy Through Eliciting Inflammatory Responses in the Heart.

BACKGROUND: Viral infections can cause acute respiratory distress syndrome (ARDS), systemic inflammation, and secondary cardiovascular complications. Lung macrophage subsets change during ARDS, but the role of heart macrophages in cardiac injury during viral ARDS remains unknown. Here we investigate how immune signals typical for viral ARDS affect cardiac macrophage subsets, cardiovascular health, and systemic inflammation. METHODS: We assessed cardiac macrophage subsets using immunofluorescence histology of autopsy specimens from 21 patients with COVID-19 with SARS-CoV-2-associated ARDS and 33 patients who died from other causes. In mice, we compared cardiac immune cell dynamics after SARS-CoV-2 infection with ARDS induced by intratracheal instillation of Toll-like receptor ligands and an ACE2 (angiotensin-converting enzyme 2) inhibitor. RESULTS: In humans, SARS-CoV-2 increased total cardiac macrophage counts and led to a higher proportion of CCR2+ (C-C chemokine receptor type 2 positive) macrophages. In mice, SARS-CoV-2 and virus-free lung injury triggered profound remodeling of cardiac resident macrophages, recapitulating the clinical expansion of CCR2+ macrophages. Treating mice exposed to virus-like ARDS with a tumor necrosis factor α-neutralizing antibody reduced cardiac monocytes and inflammatory MHCIIlo CCR2+ macrophages while also preserving cardiac function. Virus-like ARDS elevated mortality in mice with pre-existing heart failure. CONCLUSIONS: Our data suggest that viral ARDS promotes cardiac inflammation by expanding the CCR2+ macrophage subset, and the associated cardiac phenotypes in mice can be elicited by activating the host immune system even without viral presence in the heart.

Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models.

This work puts forth and demonstrates the utility of a reporting framework for collecting and evaluating annotations of medical images used for training and testing artificial intelligence (AI) models in assisting detection and diagnosis. AI has unique reporting requirements, as shown by the AI extensions to the Consolidated Standards of Reporting Trials (CONSORT) and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklists and the proposed AI extensions to the Standards for Reporting Diagnostic Accuracy (STARD) and Transparent Reporting of a Multivariable Prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklists. AI for detection and/or diagnostic image analysis requires complete, reproducible, and transparent reporting of the annotations and metadata used in training and testing data sets. In an earlier work by other researchers, an annotation workflow and quality checklist for computational pathology annotations were proposed. In this manuscript, we operationalize this workflow into an evaluable quality checklist that applies to any reader-interpreted medical images, and we demonstrate its use for an annotation effort in digital pathology. We refer to this quality framework as the Collection and Evaluation of Annotations for Reproducible Reporting of Artificial Intelligence (CLEARR-AI).

Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects.

Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging from the prospect of automation of routine diagnostic tasks to the discovery of new prognostic and predictive biomarkers from tissue morphology. Despite the promising potential of computational pathology, its integration in clinical settings has been limited by a range of obstacles including operational, technical, regulatory, ethical, financial, and cultural challenges. Here, we focus on the pathologists' perspective of computational pathology: we map its current translational research landscape, evaluate its clinical utility, and address the more common challenges slowing clinical adoption and implementation. We conclude by describing contemporary approaches to drive forward these techniques. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Dimensions of health data Integrity.

Health data integrity, as an emergent concept, stands to reshape the lifecycle of data-driven healthcare and research, ensuring a shared commitment to ethical practices and improved patient care.

Slide-to-Slide Tissue Transfer and Array Assembly From Limited Samples for Comprehensive Molecular Profiling.

Tissue microarrays (TMA) have become an important tool in high-throughput molecular profiling of tissue samples in the translational research setting. Unfortunately, high-throughput profiling in small biopsy specimens or rare tumor samples (eg, orphan diseases or unusual tumors) is often precluded owing to limited amounts of tissue. To overcome these challenges, we devised a method that allows tissue transfer and construction of TMAs from individual 2- to 5-µm sections for subsequent molecular profiling. We named the technique slide-to-slide (STS) transfer, and it requires a series of chemical exposures (so-called xylene-methacrylate exchange) in combination with rehydrated lifting, microdissection of donor tissues into multiple small tissue fragments (methacrylate-tissue tiles), and subsequent remounting on separate recipient slides (STS array slide). We developed the STS technique by assessing the efficacy and analytical performance using the following key metrics: (a) dropout rate, (b) transfer efficacy, (c) success rates using different antigen-retrieval methods, (d) success rates of immunohistochemical stains, (e) fluorescent in situ hybridization success rates, and (f) DNA and (g) RNA extraction yields from single slides, which all functioned appropriately. The dropout rate ranged from 0.7% to 6.2%; however, we applied the same STS technique successfully to fill these dropouts ("rescue" transfer). Hematoxylin and eosin assessment of donor slides confirmed a transfer efficacy of >93%, depending on the size of the tissue (range, 76%-100%). Fluorescent in situ hybridization success rates and nucleic acid yields were comparable with those of traditional workflows. In this study, we present a quick, reliable, and cost-effective method that offers the key advantages of TMAs and other molecular techniques-even when tissue is sparse. The perspectives of this technology in biomedical sciences and clinical practice are promising, given that it allows laboratories to create more data with less tissue.

Clinical Implications and Treatment Strategies for ESR1 Fusions in Hormone Receptor-Positive Metastatic Breast Cancer: A Case Series.

In hormone receptor-positive metastatic breast cancer (HR+ MBC), endocrine resistance is commonly due to genetic alterations of ESR1, the gene encoding estrogen receptor alpha (ERα). While ESR1 point mutations (ESR1-MUT) cause acquired resistance to aromatase inhibition (AI) through constitutive activation, far less is known about the molecular functions and clinical consequences of ESR1 fusions (ESR1-FUS). This case series discusses 4 patients with HR+ MBC with ESR1-FUS in the context of the existing ESR1-FUS literature. We consider therapeutic strategies and raise the hypothesis that CDK4/6 inhibition (CDK4/6i) may be effective against ESR1-FUS with functional ligand-binding domain swaps. These cases highlight the importance of screening for ESR1-FUS in patients with HR+ MBC while continuing investigation of precision treatments for these genomic rearrangements.

A rapid genotyping panel for detection of primary central nervous system lymphoma.

Diagnosing primary central nervous system lymphoma (PCNSL) frequently requires neurosurgical biopsy due to nonspecific radiologic features and the low yield of cerebrospinal fluid (CSF) studies. We characterized the clinical evaluation of suspected PCNSL (N = 1007 patients) and designed a rapid multiplexed genotyping assay for MYD88, TERT promoter, IDH1/2, H3F3A, and BRAF mutations to facilitate the diagnosis of PCNSL from CSF and detect other neoplasms in the differential diagnosis. Among 159 patients with confirmed PCNSL, the median time to secure a diagnosis of PCNSL was 10 days, with a range of 0 to 617 days. Permanent histopathology confirmed PCNSL in 142 of 152 biopsies (93.4%), whereas CSF analyses were diagnostic in only 15/113 samplings (13.3%). Among 86 archived clinical specimens, our targeted genotyping assay accurately detected hematologic malignancies with 57.6% sensitivity and 100% specificity (95% confidence interval [CI]: 44.1% to 70.4% and 87.2% to 100%, respectively). MYD88 and TERT promoter mutations were prospectively identified in DNA extracts of CSF obtained from patients with PCNSL and glioblastoma, respectively, within 80 minutes. Across 132 specimens, hallmark mutations indicating the presence of malignancy were detected with 65.8% sensitivity and 100% specificity (95% CI: 56.2%-74.5% and 83.9%-100%, respectively). This targeted genotyping approach offers a rapid, scalable adjunct to reduce diagnostic and treatment delays in PCNSL.

Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories.

OBJECTIVES: The field of artificial intelligence (AI) has grown in the past 10 years. Despite the crucial role of laboratory diagnostics in clinical decision-making, we found that the majority of AI studies focus on surgery, radiology, and oncology, and there is little attention given to AI integration into laboratory medicine. METHODS: We dedicated a session at the 3rd annual European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) strategic conference in 2022 to the topic of AI in the laboratory of the future. The speakers collaborated on generating a concise summary of the content that is presented in this paper. RESULTS: The five key messages are (1) Laboratory specialists and technicians will continue to improve the analytical portfolio, diagnostic quality and laboratory turnaround times; (2) The modularized nature of laboratory processes is amenable to AI solutions; (3) Laboratory sub-specialization continues and from test selection to interpretation, tasks increase in complexity; (4) Expertise in AI implementation and partnerships with industry will emerge as a professional competency and require novel educational strategies for broad implementation; and (5) regulatory frameworks and guidances have to be adopted to new computational paradigms. CONCLUSIONS: In summary, the speakers opine that the ability to convert the value-proposition of AI in the laboratory will rely heavily on hands-on expertise and well designed quality improvement initiative from within laboratory for improved patient care.

Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML.

BACKGROUND: Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality - for the specific purpose of assessing AI/ML improvements - is currently missing. METHODS: A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on "AI in the Laboratory of the Future" prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. RESULTS: The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. CONCLUSIONS: A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.

Exploring the Skin Brain Link: Biomarkers in the Skin with Implications for Aging Research and Alzheimer's Disease Diagnostics.

Neurodegenerative diseases, including Alzheimer's disease (AD), are challenging to diagnose. Currently the field must rely on imperfect diagnostic modalities. A recent study identified differences in several key bio-mechano-physiological parameters of the skin between AD patients and healthy controls. Here, we visually align these differences with the relevant histological, aging, and embryological paradigms to raise awareness for these potential biomarkers. In a study conducted by Wu et al., a series of n = 41 patients (n = 29 with AD and n = 12 healthy controls) were evaluated, demonstrating that AD patients exhibit a less acidic skin pH, increased skin hydration, and reduced skin elasticity compared to healthy controls. We constructed a visual overview and explored the relevant paradigms. We present a visual comparison of these factors, highlighting four paradigms: (1) the findings emphasize a shared ectodermal origin of the brain and the skin; (2) functional systems such as micro-vascularization, innervation, eccrine excretory functions, and the extracellular matrix undergo distinct changes in patients with AD; (3) the human skin mirrors the alterations in brain stiffness observed in aging studies; (4) assessment of physiological features of the skin is cost-effective, accessible, and easily amenable for monitoring and integration with cognitive assessment studies. Understanding the relationship between aging skin and aging brain is an exciting frontier, holding great promise for improved diagnostics. Further prospective and larger-scale investigations are needed to solidify the brain-skin link and determine the extent to which this relationship can be leveraged for diagnostic applications.

Implementation and Clinical Adoption of Precision Oncology Workflows Across a Healthcare Network.

BACKGROUND: Precision oncology relies on molecular diagnostics, and the value-proposition of modern healthcare networks promises a higher standard of care across partner sites. We present the results of a clinical pilot to standardize precision oncology workflows. METHODS: Workflows are defined as the development, roll-out, and updating of disease-specific molecular order sets. We tracked the timeline, composition, and effort of consensus meetings to define the combination of molecular tests. To assess clinical impact, we examined order set adoption over a two-year period (before and after roll-out) across all gastrointestinal and hepatopancreatobiliary (GI) malignancies, and by provider location within the network. RESULTS: Development of 12 disease center-specific order sets took ~9 months, and the average number of tests per indication changed from 2.9 to 2.8 (P = .74). After roll-out, we identified significant increases in requests for GI patients (17%; P < .001), compliance with testing recommendations (9%; P < .001), and the fraction of "abnormal" results (6%; P < .001). Of 1088 GI patients, only 3 received targeted agents based on findings derived from non-recommended orders (1 before and 2 after roll-out); indicating that our practice did not negatively affect patient treatments. Preliminary analysis showed 99% compliance by providers in network sites, confirming the adoption of the order sets across the network. CONCLUSION: Our study details the effort of establishing precision oncology workflows, the adoption pattern, and the absence of harm from the reduction of non-recommended orders. Establishing a modifiable communication tool for molecular testing is an essential component to optimize patient care via precision oncology.

Immune cell profiles in the tumor microenvironment of early-onset, intermediate-onset, and later-onset colorectal cancer.

BACKGROUND: Despite heightened interest in early-onset colorectal cancer (CRC) diagnosed before age 50, little is known on immune cell profiles of early-onset CRC. It also remains to be studied whether CRCs diagnosed at or shortly after age 50 are similar to early-onset CRC. We therefore hypothesized that immune cell infiltrates in CRC tissue might show differential heterogeneity patterns between three age groups (< 50 "early onset," 50-54 "intermediate onset," ≥ 55 "later onset"). METHODS: We examined 1,518 incident CRC cases with available tissue data, including 35 early-onset and 73 intermediate-onset cases. To identify immune cells in tumor intraepithelial and stromal areas, we developed three multiplexed immunofluorescence assays combined with digital image analyses and machine learning algorithms, with the following markers: (1) CD3, CD4, CD8, CD45RO (PTPRC), and FOXP3 for T cells; (2) CD68, CD86, IRF5, MAF, and MRC1 (CD206) for macrophages; and (3) ARG1, CD14, CD15, CD33, and HLA-DR for myeloid cells. RESULTS: Although no comparisons between age groups showed statistically significant differences at the stringent two-sided α level of 0.005, compared to later-onset CRC, early-onset CRC tended to show lower levels of tumor-infiltrating lymphocytes (P = 0.013), intratumoral periglandular reaction (P = 0.025), and peritumoral lymphocytic reaction (P = 0.044). Compared to later-onset CRC, intermediate-onset CRC tended to show lower densities of overall macrophages (P = 0.050), M1-like macrophages (P = 0.062), CD14+HLA-DR+ cells (P = 0.015), and CD3+CD4+FOXP3+ cells (P = 0.039). CONCLUSIONS: This hypothesis-generating study suggests possible differences in histopathologic lymphocytic reaction patterns, macrophages, and regulatory T cells in the tumor microenvironment by age at diagnosis.

Mucoepidermoid carcinoma (MEC) and adenosquamous carcinoma (ASC), the same or different entities?

Mucoepidermoid carcinoma (MEC) and adenosquamous carcinoma (ASC) have overlapping histopathological appearances and sites of occurrence, which may cause diagnostic difficulty impacting subsequent treatment. We conducted a systematic review of the scientific literature to determine whether molecular alterations were sufficiently different in MEC and ASC to aid in classifying the two entities. We searched Medline, Embase and Web of Science for studies reporting molecular determinations of ASC and/or MEC and screened retrieved records for eligibility. Two independent researchers reviewed included studies, assessed methodological quality and extracted data. Of 8623 identified records, 128 articles were included for analysis: 5 which compared the two tumors in the same investigation using the same methods and 123 which examined the tumors separately. All articles, except one were case series of moderate to poor methodological quality. The 5 publications examining both tumors showed that 52/88 (59%) MEC and 0% of 110 ASC had rearrangement of the MAML2 gene as detected by FISH and/or RT-PCR, but did not investigate other genes. In the entire series MEC had MAML2 gene rearrangement in 1337/2009 (66.6%) of tumors studied. The articles examining tumors separately found that MEC had mutations in EGFR (11/329 cases, 3.3%), KRAS (11/266, 4.1%) and ERBB2 (9/126, 7.1%) compared with ASC that had mutations in EGFR (660/1705, 38.7%), KRAS (143/625, 22.9%) and ERBB2 (6/196, 3.1%). The highest level of recurrent mutations was in pancreatic ASC where (108/126, 85.7%) reported mutations in KRAS. The EGFR mutations in ASC were similar in number and kind to those in lung adenocarcinoma. By standards of systematic review methodology and despite the large number of retrieved studies, we did not find adequate evidence for a distinctive molecular profile of either MEC or ASC that could definitively aid in its classification, especially in histologically difficult cases that are negative for MAML2 rearrangement. The case series included in this review indicate the relevance of MAML2 rearrangement to support the diagnosis of MEC, findings that should be confirmed by additional research with adequate study design.

Identification of fusions with potential clinical significance in melanoma.

Though uncommon in melanoma, gene fusions may have therapeutic implications. Next generation sequencing-based clinical assays, designed to detect relevant gene fusions, mutations, and copy number changes, were performed on 750 melanomas (375 primary and 375 metastases) at our institution from 2014-2021. These included 599 (80%) cutaneous, 38 (5%) acral, 11 (1.5%) anorectal, 23 (3%) sinonasal, 27 (3.6%) eye (uveal/ conjunctiva), 11 (1.5%) genital (vulva/penile), and 41 (5.5%) melanomas of unknown primary. Sixteen fusions (2%) were detected in samples from 16 patients: 12/599 (2%) cutaneous, 2/38 (5%) acral, 1/9 (11%) vulva, 1/23(4.3%) sinonasal; and 12/16 (75%) fusions were potentially targetable. We identified two novel rearrangements: NAGS::MAST2 and NOTCH1::GNB1; and two fusions that have been reported in other malignancies but not in melanoma: CANT1::ETV4 (prostate cancer) and CCDC6::RET (thyroid cancer). Additional fusions, previously reported in melanoma, included: EML4::ALK, MLPH::ALK, AGAP3::BRAF, AGK::BRAF, CDH3::BRAF, CCT8::BRAF, DIP2B::BRAF, EFNB1::RAF1, LRCH3::RAF1, MAP4::RAF1, RUFY1::RAF1, and ADCY2::TERT. Fusion positive melanomas harbored recurrent alterations in TERT and CDKN2A, among others. Gene fusions were exceedingly rare (0.2%) in BRAF/RAS/NF1-mutant tumors and were detected in 5.6% of triple wild-type melanomas. Interestingly, gene rearrangements were significantly enriched within the subset of triple wild-type melanomas that harbor TERT promoter mutations (18% versus 2%, p < 0.0001). Thirteen (81%) patients were treated with immunotherapy for metastatic disease or in the adjuvant setting. Six of 12 (50%) patients with potentially actionable fusions progressed on immunotherapy, and 3/6 (50%) were treated with targeted agents (ALK and MEK inhibitors), 2 off-label and 1 as part of a clinical trial. One patient with an AGAP3::BRAF fusion positive melanoma experienced a 30-month long response to trametinib. We show that, detecting fusions, especially in triple wild-type melanomas with TERT promoter mutations, may have a clinically significant impact in patients with advanced disease who have failed front-line immunotherapy.

Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology.

Machine learning (ML) is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but the lack of interoperability between ML systems and enterprise medical imaging systems has been a major barrier for clinical integration and evaluation. The DICOM® standard specifies information object definitions (IODs) and services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with datasets in DICOM format. Here we present the highdicom library, which provides a high-level application programming interface (API) for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library leverages NumPy arrays for efficient data representation and ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers and researchers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages. To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source at https://github.com/herrmannlab/highdicom .

Diagnostic Value of MAML2 Rearrangements in Mucoepidermoid Carcinoma.

Mucoepidermoid carcinoma (MEC) is often seen in salivary glands and can harbor MAML2 translocations (MAML2+). The translocation status has diagnostic utility as an objective confirmation of the MEC diagnosis, for example, when distinction from the more aggressive adenosquamous carcinoma (ASC) is not straightforward. To assess the diagnostic relevance of MAML2, we examined our 5-year experience in prospective testing of 8106 solid tumors using RNA-seq panel testing in combinations with a two-round Delphi-based scenario survey. The prevalence of MAML2+ across all tumors was 0.28% (n = 23/8106) and the majority of MAML2+ cases were found in head and neck tumors (78.3%), where the overall prevalence was 5.9% (n = 18/307). The sensitivity of MAML2 for MEC was 60% and most cases (80%) were submitted for diagnostic confirmation; in 24% of cases, the MAML2 results changed the working diagnosis. An independent survey of 15 experts showed relative importance indexes of 0.8 and 0.65 for "confirmatory MAML2 testing" in suspected MEC and ASC, respectively. Real-world evidence confirmed that the added value of MAML2 is a composite of an imperfect confirmation test for MEC and a highly specific exclusion tool for the diagnosis of ASC. Real-world evidence can help move a rare molecular-genetic biomarker from an emerging tool to the clinic.

MET D1228N and D1246N are the Same Resistance Mutation in MET Exon 14 Skipping.

Comprehensive genetic profiling using next-generation sequencing technologies has become an integral part of precision oncology. Variant annotation requires translating the DNA findings into protein level predictions. In this article we highlight inconsistencies in variant annotation for the MET D1228N exon 19 resistance mutations. MET D1228N and D1246N represent the same resistance mutation in MET exon 14 skipping alterations annotated on different transcripts. Additional examples of relevant variants annotated on different transcripts emphasize the importance of avoiding erroneous interpretation when realizing precision oncology.

Two In Cis Variants-Two Worlds Apart.

Precision oncology emphasizes genotyping as one of the mainstays of oncological decision-making. The core information element exchanged between the laboratory and the oncologist is the precise mutation. Specifically, it is the written representation typically in the form of a variant description at the DNA or protein level. These annotations can be confusing, and many commercial laboratories have abandoned DNA-level annotations. Here we present a complex double-point mutation to illustrate a situation where a formally "correct" reporting nomenclature can obscure clinically relevant and potentially clinically actionable information. KEY POINTS: The Human Genome Variation Society (HGVS) currently recommends that "two variants separated by one or more nucleotides should be described individually and not as a combined 'delins' (deletion-inserion)." There remains confusion about the appropriate nomenclature to report variants and the significance of these variants among clinicians. It is the clinically integrated molecular-genetic interpretation that will help clinicians make informed decisions to improve patient care.