With the advent of effective tools to study lipids, including mass spectrometry-based lipidomics, lipids are emerging as central players in cancer biology. Lipids function as essential building ...blocks for membranes, serve as fuel to drive energy-demanding processes and play a key role as signaling molecules and as regulators of numerous cellular functions. Not unexpectedly, cancer cells, as well as other cell types in the tumor microenvironment, exploit various ways to acquire lipids and extensively rewire their metabolism as part of a plastic and context-dependent metabolic reprogramming that is driven by both oncogenic and environmental cues. The resulting changes in the fate and composition of lipids help cancer cells to thrive in a changing microenvironment by supporting key oncogenic functions and cancer hallmarks, including cellular energetics, promoting feedforward oncogenic signaling, resisting oxidative and other stresses, regulating intercellular communication and immune responses. Supported by the close connection between altered lipid metabolism and the pathogenic process, specific lipid profiles are emerging as unique disease biomarkers, with diagnostic, prognostic and predictive potential. Multiple preclinical studies illustrate the translational promise of exploiting lipid metabolism in cancer, and critically, have shown context dependent actionable vulnerabilities that can be rationally targeted, particularly in combinatorial approaches. Moreover, lipids themselves can be used as membrane disrupting agents or as key components of nanocarriers of various therapeutics. With a number of preclinical compounds and strategies that are approaching clinical trials, we are at the doorstep of exploiting a hitherto underappreciated hallmark of cancer and promising target in the oncologist's strategy to combat cancer.
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The clinical successes in immunotherapy have been both astounding and at the same time unsatisfactory. Countless patients with varied tumor types have seen pronounced clinical response with ...immunotherapeutic intervention; however, many more patients have experienced minimal or no clinical benefit when provided the same treatment. As technology has advanced, so has the understanding of the complexity and diversity of the immune context of the tumor microenvironment and its influence on response to therapy. It has been possible to identify different subclasses of immune environment that have an influence on tumor initiation and response and therapy; by parsing the unique classes and subclasses of tumor immune microenvironment (TIME) that exist within a patient's tumor, the ability to predict and guide immunotherapeutic responsiveness will improve, and new therapeutic targets will be revealed.
The ability to non-invasively monitor tumor-infiltrating T cells in vivo could provide a powerful tool to visualize and quantify tumor immune infiltrates. For non-invasive evaluations in vivo, an ...anti-CD3 mAb was modified with desferrioxamine (DFO) and radiolabeled with zirconium-89 (Zr-89 or 89Zr). Radiolabeled 89Zr-DFO-anti-CD3 was tested for T cell detection using positron emission tomography (PET) in both healthy mice and mice bearing syngeneic bladder cancer BBN975. In vivo PET/CT and ex vivo biodistribution demonstrated preferential accumulation and visualization of tracer in the spleen, thymus, lymph nodes, and bone marrow. In tumor bearing mice, 89Zr-DFO-anti-CD3 demonstrated an 11.5-fold increase in tumor-to-blood signal compared to isotype control. Immunological profiling demonstrated no significant change to total T cell count, but observed CD4+ T cell depletion and CD8+ T cell expansion to the central and effector memory. This was very encouraging since a high CD8+ to CD4+ T cell ratio has already been associated with better patient prognosis. Ultimately, this anti-CD3 mAb allowed for in vivo imaging of homeostatic T cell distribution, and more specifically tumor-infiltrating T cells. Future applications of this radiolabeled mAb against CD3 could include prediction and monitoring of patient response to immunotherapy.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Genome‐scale sequencing creates vast amounts of genomic data, increasing the challenge of clinical sequence variant interpretation. The demand for high‐quality interpretation requires multiple ...specialties to join forces to accelerate the interpretation of sequence variant pathogenicity. With over 600 international members including clinicians, researchers, and laboratory diagnosticians, the Clinical Genome Resource (ClinGen), funded by the National Institutes of Health, is forming expert groups to systematically evaluate variants in clinically relevant genes. Here, we describe the first ClinGen variant curation expert panels (VCEPs), development of consistent and streamlined processes for establishing new VCEPs, and creation of standard operating procedures for VCEPs to define application of the ACMG/AMP guidelines for sequence variant interpretation in specific genes or diseases. Additionally, ClinGen has created user interfaces to enhance reliability of curation and a Sequence Variant Interpretation Working Group (SVI WG) to harmonize guideline specifications and ensure consistency between groups. The expansion of VCEPs represents the primary mechanism by which curation of a substantial fraction of genomic variants can be accelerated and ultimately undertaken systematically and comprehensively. We welcome groups to utilize our resources and become involved in our effort to create a publicly accessible, centralized resource for clinically relevant genes and variants.
ClinGen is organizing Variant Curation Expert Panels (VCEPs) to develop specifications to the ACMG/AMP guidelines for genes or diseases of interest, interpret variants according to these guidelines, and publish the expert interpretations through the publicly available ClinVar database. A stepwise process was iteratively developed for ClinGen VCEPs to apply to submit variant assertions to ClinVar at the Expert Panel level of review. Other groups that wish to assemble as VCEPs are encouraged, though not required, to follow these steps.
PurposeIntegrating genomic sequencing in clinical care requires standardization of variant interpretation practices. The Clinical Genome Resource has established expert panels to adapt the American ...College of Medical Genetics and Genomics/Association for Molecular Pathology classification framework for specific genes and diseases. The Cardiomyopathy Expert Panel selected MYH7, a key contributor to inherited cardiomyopathies, as a pilot gene to develop a broadly applicable approach.MethodsExpert revisions were tested with 60 variants using a structured double review by pairs of clinical and diagnostic laboratory experts. Final consensus rules were established via iterative discussions.ResultsAdjustments represented disease-/gene-informed specifications (12) or strength adjustments of existing rules (5). Nine rules were deemed not applicable. Key specifications included quantitative frameworks for minor allele frequency thresholds, the use of segregation data, and a semiquantitative approach to counting multiple independent variant occurrences where fully controlled case-control studies are lacking. Initial inter-expert classification concordance was 93%. Internal data from participating diagnostic laboratories changed the classification of 20% of the variants (n = 12), highlighting the critical importance of data sharing.ConclusionThese adapted rules provide increased specificity for use in MYH7-associated disorders in combination with expert review and clinical judgment and serve as a stepping stone for genes and disorders with similar genetic and clinical characteristics.
Standardized and accurate variant assessment is essential for effective medical care. To that end, Clinical Genome (ClinGen) Resource clinical domain working groups (CDWGs) are systematically ...reviewing disease-associated genes for sufficient evidence to support disease causality and creating disease-specific specifications of American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) guidelines for consistent and accurate variant classification.
The ClinGen RASopathy CDWG established an expert panel to curate gene information and generate gene- and disease-specific specifications to ACMG-AMP variant classification framework. These specifications were tested by classifying 37 exemplar pathogenic variants plus an additional 66 variants in ClinVar distributed across nine RASopathy genes.
RASopathy-related specifications were applied to 16 ACMG-AMP criteria, with 5 also having adjustable strength with availability of additional evidence. Another 5 criteria were deemed not applicable. Key adjustments to minor allele frequency thresholds, multiple de novo occurrence events and/or segregation, and strength adjustments impacted 60% of variant classifications. Unpublished case-level data from participating laboratories impacted 45% of classifications supporting the need for data sharing.
RAS-specific ACMG-AMP specifications optimized the utility of available clinical evidence and Ras/MAPK pathway-specific characteristics to consistently classify RASopathy-associated variants. These specifications highlight how grouping genes by shared features promotes rapid multigenic variant assessment without sacrificing specificity and accuracy.
High-grade urothelial cancer contains intrinsic molecular subtypes that exhibit differences in underlying tumor biology and can be divided into luminal-like and basal-like subtypes. We describe here ...the first subtype-specific murine models of bladder cancer and show that Upk3a-Cre
; Trp53
; Pten
; Rosa26
(UPPL, luminal-like) and BBN (basal-like) tumors are more faithful to human bladder cancer than the widely used MB49 cells. Following engraftment into immunocompetent C57BL/6 mice, BBN tumors were more responsive to PD-1 inhibition than UPPL tumors. Responding tumors within the BBN model showed differences in immune microenvironment composition, including increased ratios of CD8
:CD4
and memory:regulatory T cells. Finally, we predicted and confirmed immunogenicity of tumor neoantigens in each model. These UPPL and BBN models will be a valuable resource for future studies examining bladder cancer biology and immunotherapy.
This work establishes human-relevant mouse models of bladder cancer.
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In research settings, circulating tumor DNA (ctDNA) shows promise as a tumor-specific biomarker for pancreatic ductal adenocarcinoma (PDAC). This study aims to perform analytical and clinical ...validation of a
ctDNA assay in a Clinical Laboratory Improvement Amendments (CLIA) and College of American Pathology-certified clinical laboratory.
Digital-droplet PCR was used to detect the major PDAC-associated somatic
mutations (G12D, G12V, G12R, and Q61H) in liquid biopsies. For clinical validation, 290 preoperative and longitudinal postoperative plasma samples were collected from 59 patients with PDAC. The utility of ctDNA status to predict PDAC recurrence during follow-up was assessed.
ctDNA was detected preoperatively in 29 (49%) patients and was an independent predictor of decreased recurrence-free survival (RFS) and overall survival (OS). Patients who had neoadjuvant chemotherapy were less likely to have preoperative ctDNA than were chemo-naïve patients (21% vs. 69%;
< 0.001). ctDNA levels dropped significantly after tumor resection. Persistence of ctDNA in the immediate postoperative period was associated with a high rate of recurrence and poor median RFS (5 months). ctDNA detected during follow-up predicted clinical recurrence sensitivity 90% (95% confidence interval (CI), 74%-98%), specificity 88% (95% CI, 62%-98%) with a median lead time of 84 days (interquartile range, 25-146). Detection of ctDNA during postpancreatectomy follow-up was associated with a median OS of 17 months, while median OS was not yet reached at 30 months for patients without ctDNA (
= 0.011).
Measurement of
ctDNA in a CLIA laboratory setting can be used to predict recurrence and survival in patients with PDAC.
BackgroundTumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, demonstrates predictive biomarker potential for the identification ...of patients with cancer most likely to respond to immune checkpoint inhibitors. TMB is optimally calculated by whole exome sequencing (WES), but next-generation sequencing targeted panels provide TMB estimates in a time-effective and cost-effective manner. However, differences in panel size and gene coverage, in addition to the underlying bioinformatics pipelines, are known drivers of variability in TMB estimates across laboratories. By directly comparing panel-based TMB estimates from participating laboratories, this study aims to characterize the theoretical variability of panel-based TMB estimates, and provides guidelines on TMB reporting, analytic validation requirements and reference standard alignment in order to maintain consistency of TMB estimation across platforms.MethodsEleven laboratories used WES data from The Cancer Genome Atlas Multi-Center Mutation calling in Multiple Cancers (MC3) samples and calculated TMB from the subset of the exome restricted to the genes covered by their targeted panel using their own bioinformatics pipeline (panel TMB). A reference TMB value was calculated from the entire exome using a uniform bioinformatics pipeline all members agreed on (WES TMB). Linear regression analyses were performed to investigate the relationship between WES and panel TMB for all 32 cancer types combined and separately. Variability in panel TMB values at various WES TMB values was also quantified using 95% prediction limits.ResultsStudy results demonstrated that variability within and between panel TMB values increases as the WES TMB values increase. For each panel, prediction limits based on linear regression analyses that modeled panel TMB as a function of WES TMB were calculated and found to approximately capture the intended 95% of observed panel TMB values. Certain cancer types, such as uterine, bladder and colon cancers exhibited greater variability in panel TMB values, compared with lung and head and neck cancers.ConclusionsIncreasing uptake of TMB as a predictive biomarker in the clinic creates an urgent need to bring stakeholders together to agree on the harmonization of key aspects of panel-based TMB estimation, such as the standardization of TMB reporting, standardization of analytical validation studies and the alignment of panel-based TMB values with a reference standard. These harmonization efforts should improve consistency and reliability of panel TMB estimates and aid in clinical decision-making.
Human endogenous retroviruses (hERVs) are remnants of exogenous retroviruses that have integrated into the genome throughout evolution. We developed a computational workflow, hervQuant, which ...identified more than 3,000 transcriptionally active hERVs within The Cancer Genome Atlas (TCGA) pan-cancer RNA-Seq database. hERV expression was associated with clinical prognosis in several tumor types, most significantly clear cell renal cell carcinoma (ccRCC). We explored two mechanisms by which hERV expression may influence the tumor immune microenvironment in ccRCC: (i) RIG-I-like signaling and (ii) retroviral antigen activation of adaptive immunity. We demonstrated the ability of hERV signatures associated with these immune mechanisms to predict patient survival in ccRCC, independent of clinical staging and molecular subtyping. We identified potential tumor-specific hERV epitopes with evidence of translational activity through the use of a ccRCC ribosome profiling (Ribo-Seq) dataset, validated their ability to bind HLA in vitro, and identified the presence of MHC tetramer-positive T cells against predicted epitopes. hERV sequences identified through this screening approach were significantly more highly expressed in ccRCC tumors responsive to treatment with programmed death receptor 1 (PD-1) inhibition. hervQuant provides insights into the role of hERVs within the tumor immune microenvironment, as well as evidence that hERV expression could serve as a biomarker for patient prognosis and response to immunotherapy.
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