Publications by authors named "Taylor-Weiner A"
Article Synopsis
- - Clinical trials for metabolic dysfunction-associated steatohepatitis (MASH) need accurate histologic scoring to assess participants and outcomes, but varying interpretations have affected results.
- - The AI-based tool AIM-MASH showed strong consistency and agreement with expert pathologists in scoring MASH histology, achieving accuracy comparable to that of average pathologists.
- - AIM-MASH demonstrated a strong ability to predict patient outcomes, correlating well with pathologist scores and noninvasive biomarkers, indicating it could enhance the efficiency and reliability of clinical trials for MASH.
Article Synopsis
- The study focuses on creating a deep learning digital pathology tool for accurately detecting, segmenting, and classifying nuclei in cancer tissues, addressing challenges in quantifying nuclear morphology in histologic images.
- This tool was trained on nucleus annotations to analyze H&E-stained slides from various cancer cohorts (BRCA, LUAD, PRAD), revealing significant differences in nuclear features like shape and size linked to genomic instability and cancer prognosis.
- Results highlighted that certain nuclear characteristics, particularly in fibroblasts, were associated with patient survival outcomes and gene expression related to tumor biology, paving the way for better understanding of cancer biomarkers.
Introduction: Pathologic response (PathR) by histopathologic assessment of resected specimens may be an early clinical end point associated with long-term outcomes with neoadjuvant therapy. Digital pathology may improve the efficiency and precision of PathR assessment. LCMC3 (NCT02927301) evaluated neoadjuvant atezolizumab in patients with resectable NSCLC and reported a 20% major PathR rate.
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- Clinical trials for nonalcoholic steatohepatitis (NASH) rely on consistent histologic scoring, but variability in these interpretations has affected trial results.* -
- An AI tool called AIM-NASH was developed to provide standardized scoring for NASH histology, showing strong correlation with expert consensus scores and improving predictive accuracy for patient outcomes.* -
- In a retrospective analysis, AIM-NASH helped meet previously unmet pathological endpoints in the ATLAS trial, suggesting it could reduce variability in scoring and enhance the assessment of treatment responses in clinical trials.*
Article Synopsis
- * Researchers used machine learning and advanced analysis of tissue samples from NASH clinical trials to identify a 5-gene expression signature that could predict disease progression in patients with severe liver fibrosis (F3 and F4 stages).
- * This study found that the Notch signaling pathway, linked to liver diseases, was significantly present in the gene signature, and in a validation cohort, drugs that improved liver conditions also reduced levels of various Notch signaling components.
Richter syndrome (RS) arising from chronic lymphocytic leukemia (CLL) exemplifies an aggressive malignancy that develops from an indolent neoplasm. To decipher the genetics underlying this transformation, we computationally deconvoluted admixtures of CLL and RS cells from 52 patients with RS, evaluating paired CLL-RS whole-exome sequencing data. We discovered RS-specific somatic driver mutations (including IRF2BP2, SRSF1, B2M, DNMT3A and CCND3), recurrent copy-number alterations beyond del(9p21)(CDKN2A/B), whole-genome duplication and chromothripsis, which were confirmed in 45 independent RS cases and in an external set of RS whole genomes.
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- * A comprehensive analysis of data from 1,148 patients led to the identification of 202 genetic drivers of CLL, including 109 that were previously unrecognized, and refined the understanding of IGHV subtypes.
- * This research not only clarifies the genomic landscape of CLL but also uncovers new gene expression subtypes that serve as independent prognostic factors, enhancing the prediction of clinical outcomes.
Clonal hematopoiesis results from somatic mutations in cancer driver genes in hematopoietic stem cells. We sought to identify novel drivers of clonal expansion using an unbiased analysis of sequencing data from 84,683 persons and identified common mutations in the 5-methylcytosine reader, , as well as in , , and . We also identified these mutations at low frequency in myelodysplastic syndrome patients.
View Article and Find Full Text PDFKnowledge of the genomic landscape of chronic lymphocytic leukemia (CLL) grows increasingly detailed, providing challenges in contextualizing the accumulated information. To define the underlying networks, we here perform a multi-platform molecular characterization. We identify major subgroups characterized by genomic instability (GI) or activation of epithelial-mesenchymal-transition (EMT)-like programs, which subdivide into non-inflammatory and inflammatory subtypes.
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- Computational methods have enhanced pathology workflows for diagnostics and genomics but struggle with interpretability for clinical use.
- We developed a method using human-interpretable image features (HIFs) from histopathology images, trained on over 1.6 million annotations from certified pathologists.
- Our approach identifies specific cancer-related characteristics and predicts molecular signatures with similar accuracy to complex 'black-box' models, offering clear insights into tumor microenvironments.
Article Synopsis
- Manual histological assessment for diagnosing NASH (Nonalcoholic Steatohepatitis) is inconsistent and not very responsive to changes in disease state, highlighting the need for better tools.
- A machine learning approach was developed to analyze liver histology more accurately by utilizing deep learning to evaluate features like steatosis and fibrosis, correlating well with expert opinions and predicting disease progression.
- The study introduces a new metric, the Deep Learning Treatment Assessment Liver Fibrosis score, which detects treatment effects missed by traditional methods, showcasing the potential of machine learning in improving NASH diagnosis and treatment monitoring.
Purpose: Cohort-based germline variant characterization is the standard approach for pathogenic variant discovery in clinical and research samples. However, the impact of cohort size on the molecular diagnostic yield of joint genotyping is largely unknown.
Methods: Head-to-head comparison of the molecular diagnostic yield of joint genotyping in two cohorts of 239 cancer patients in the absence and then in the presence of 100 additional germline exomes.
Article Synopsis
- - We analyzed 1,048 melanoma samples and found significant differences in their genomic characteristics based on subtypes (BRAF, (N)RAS, NF1, triple wild-type (TWT)), identifying unique secondary driver genes and mutational processes for each subtype.
- - Each melanoma subtype showed distinct patterns of dysregulated pathways and co-mutation patterns that influence their response to immune checkpoint therapies, like increased TGF-β signaling in BRAF melanomas and disrupted SWI/SNF complex in (N)RAS melanomas.
- - The study also highlighted the TWT subtype's DNA-repair defects, offering new insights into potential therapeutic approaches based on genetic profiling for treating different melanoma patient groups.
Article Synopsis
- Less than 10% of cancer patients have detectable harmful genetic changes, which may be due to incomplete detection methods.
- The study aimed to assess whether deep learning can improve the identification of these genetic variants in cancer patients compared to standard methods.
- Results showed that deep learning techniques detected more patients with harmful variants in genes linked to cancer risk than traditional methods in cohorts of prostate and melanoma patients.
Article Synopsis
- Smoldering multiple myeloma (SMM) is a precursor to multiple myeloma (MM) with a significant risk of progression, indicating a need for better risk assessment tools beyond just clinical metrics.
- Researchers utilized next-generation sequencing on 214 SMM patients and discovered that key genetic alterations linked to progression to MM are usually already present at SMM diagnosis.
- The study identified specific genetic changes, particularly in certain pathways, as independent risk factors for progression, which were validated in an external cohort, suggesting potential for improved precision in predicting disease evolution from SMM to MM.
Cancer genomes contain large numbers of somatic mutations but few of these mutations drive tumor development. Current approaches either identify driver genes on the basis of mutational recurrence or approximate the functional consequences of nonsynonymous mutations by using bioinformatic scores. Passenger mutations are enriched in characteristic nucleotide contexts, whereas driver mutations occur in functional positions, which are not necessarily surrounded by a particular nucleotide context.
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- Current genomics methods need to scale from handling thousands of samples to millions to keep up with rapid data generation in biomedical science.
- By using general-purpose libraries like PyTorch and TensorFlow on GPUs, researchers can significantly reduce runtime and costs, achieving over 200 times faster processing and 5-10 times lower costs compared to traditional CPUs.
- The increased accessibility of these GPU libraries is expected to encourage more widespread use of GPU technology in the field of computational genomics.
Current statistical models for assessing hotspot significance do not properly account for variation in site-specific mutability, thereby yielding many false-positives. We thus (i) detail a Log-normal-Poisson (LNP) background model that accounts for this variability in a manner consistent with models of mutagenesis; (ii) use it to show that passenger hotspots arise from all common mutational processes; and (iii) apply it to a ∼10,000-patient cohort to nominate driver hotspots with far fewer false-positives compared with conventional methods. Overall, we show that many cancer hotspot mutations recurring at the same genomic site across multiple tumors are actually passenger events, recurring at inherently mutable genomic sites under no positive selection.
View Article and Find Full Text PDFPurpose: Leiomyosarcoma and liposarcoma are common subtypes of soft tissue sarcoma (STS). Patients with metastatic leiomyosarcoma or dedifferentiated liposarcoma (DDLPS) typically have worse outcomes compared with localized leiomyosarcoma or well-differentiated liposarcoma (WDLPS). A better understanding of genetic changes between primary/metastatic leiomyosarcoma and between WDLPS/DDLPS may provide insight into their genetic evolution.
View Article and Find Full Text PDFHow the genomic features of a patient's cancer relate to individual disease kinetics remains poorly understood. Here we used the indolent growth dynamics of chronic lymphocytic leukaemia (CLL) to analyse the growth rates and corresponding genomic patterns of leukaemia cells from 107 patients with CLL, spanning decades-long disease courses. We found that CLL commonly demonstrates not only exponential expansion but also logistic growth, which is sigmoidal and reaches a certain steady-state level.
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- Scientists studied how the length of telomeres (protective caps at the ends of chromosomes) relates to chronic lymphocytic leukemia (CLL), a type of cancer, using data from two large clinical trials.
- They found that shorter telomeres are linked to worse outcomes and survival rates in CLL patients.
- The research also showed that patients with certain genetic changes (like 17p- and 11q-) had the shortest telomeres, suggesting that telomere shortening happens before these high-risk changes and could help monitor the disease's progress.
SF3B1 is recurrently mutated in chronic lymphocytic leukemia (CLL), but its role in the pathogenesis of CLL remains elusive. Here, we show that conditional expression of Sf3b1-K700E mutation in mouse B cells disrupts pre-mRNA splicing, alters cell development, and induces a state of cellular senescence. Combination with Atm deletion leads to the overcoming of cellular senescence and the development of CLL-like disease in elderly mice.
View Article and Find Full Text PDFImportance: Approximately 50% of the risk for the development of testicular germ cell tumors (TGCTs) is estimated to be heritable, but no mendelian TGCT predisposition genes have yet been identified. It is hypothesized that inherited pathogenic DNA repair gene (DRG) alterations may drive susceptibility to TGCTs.
Objective: To systematically evaluate the enrichment of germline pathogenic variants in the mendelian cancer predisposition DRGs in patients with TGCTs vs healthy controls.
Article Synopsis
- Tumor mutational burden is linked to how well tumors respond to immune checkpoint therapy, but this connection is unclear in microsatellite-stable tumors.
- An analysis of 249 tumors and their normal tissue identified additional genomic factors influencing therapy response, including specific driver gene mutations and neoantigens, beyond just mutational burden.
- The findings emphasize the complexity of tumor genetics in creating an immunoresponsive environment and suggest a need for comprehensive analysis of large clinical data to find reliable predictive indicators for treatment response.
In the version of this article originally published, an asterisk was omitted from Fig. 1a. The asterisk has been added to the figure.
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