A5005626305- Cancer Genomics and Diagnostics
- Radiomics and Machine Learning in Medical Imaging
- AI in cancer detection
- Genomics and Rare Diseases
- Genetics, Bioinformatics, and Biomedical Research
- Genetic factors in colorectal cancer
- Lung Cancer Treatments and Mutations
- Bioinformatics and Genomic Networks
- Health Systems, Economic Evaluations, Quality of Life
- Oral microbiology and periodontitis research
- Genetic Associations and Epidemiology
- Lung Cancer Diagnosis and Treatment
- Gut microbiota and health
- BRCA gene mutations in cancer
- Cancer Diagnosis and Treatment
- Advanced biosensing and bioanalysis techniques
- Machine Learning in Healthcare
- Artificial Intelligence in Healthcare
- Oral Health Pathology and Treatment
- Esophageal Cancer Research and Treatment
- Pancreatic and Hepatic Oncology Research
- Head and Neck Cancer Studies
- Epigenetics and DNA Methylation
- Cytomegalovirus and herpesvirus research
- Telomeres, Telomerase, and Senescence
Kettering University
2023-2024
Memorial Sloan Kettering Cancer Center
2023-2024
New York Proton Center
2023
National Cancer Institute
2015-2021
Division of Cancer Epidemiology and Genetics
2015-2021
Frederick National Laboratory for Cancer Research
2018-2021
Leidos (United States)
2014-2021
National Institutes of Health
2020
ORCID
2020
Leidos Biomedical Research Inc. (United States)
2014
Germline mutations in telomere biology genes cause dyskeratosis congenita (DC), an inherited bone marrow failure and cancer predisposition syndrome. DC is a clinically heterogeneous disorder diagnosed by the triad of dysplastic nails, abnormal skin pigmentation, oral leukoplakia; Hoyeraal-Hreidarsson syndrome (HH), severe variant DC, also includes cerebellar hypoplasia, immunodeficiency, intrauterine growth retardation. Approximately 70% cases are associated with germline mutation one nine...
Genomics of radiation-induced damage The potential adverse effects exposures to radioactivity from nuclear accidents can include acute consequences such as radiation sickness, well long-term sequelae increased risk cancer. There have been a few studies examining transgenerational risks exposure but the results inconclusive. Morton et al. analyzed papillary thyroid tumors, normal tissue, and blood hundreds survivors Chernobyl accident compared them against those unexposed patients. findings...
Abstract Background Oral microbiota may be related to pancreatic cancer risk because periodontal disease, a condition linked multiple specific microbes, has been associated with increased of cancer. We evaluated the association between oral and in Iran. Methods A total 273 adenocarcinoma cases 285 controls recruited from tertiary hospitals specialty clinic Tehran, Iran provided saliva samples filled out questionnaire regarding demographics lifestyle characteristics. DNA was extracted V4...
Abstract Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, the most accurate methods not clinically feasible, relying derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use a data set 39,787 solid tumors sequenced using targeted gene panel to develop...
Familial acute myeloid leukemia is rare and linked to germline mutations in RUNX1, GATA2 or CCAAT/enhancer binding protein-α (CEBPA). We re-evaluated a large family with originally seen at NIH 1969. used whole exome sequencing study this family, conducted silico bioinformatics analysis, protein structural modeling laboratory experiments assess the impact of identified CEBPA Q311P mutation. Unlike most previously CEBPA, which were N-terminal frameshift mutations, we novel variant that was...
Epidemiologic studies use various biosample collection methods to study associations between human oral microbiota and health outcomes. However, the agreement different is unclear. We compared a commercially available OMNIgene ORAL kit three alternative methods: Saccomanno's fixative, Scope mouthwash, nonethanol mouthwash. Oral samples were collected from 40 individuals over 4 visits. Two each subject per visit: one with an method. DNA was extracted using DSP Virus Pathogen kit, V4 region of...
Abstract Background: Few studies have prospectively evaluated the association between oral microbiota and health outcomes. Precise estimates of intrasubject microbial metric stability will allow better study planning. Therefore, we conducted a to evaluate temporal variability microbiota. Methods: Forty individuals provided six samples using OMNIgene ORAL kit Scope mouthwash rinses approximately every two months over 10 months. DNA was extracted QIAsymphony V4 region 16S rRNA gene amplified...
Cigarette smoking and opium use are associated with periodontal disease caused by specific bacteria such as Porphyromonas gingivalis , which suggests a link between cigarette the oral microbiota. Alterations of microbiota in smokers compared to nonsmokers have been reported, but this has not studied across diverse populations.
4041 Background: ecDNA-enabled amplification of oncogenes is associated with increased expression and poor prognosis believed to contribute treatment resistance. However, the prognostic implications ecDNA on specific targeted therapy outcomes remains unknown. We investigated whether ERBB2ecDNA positivity (+) in EGC survival HER2-targeted agents. Methods: Demographics patients metastatic or recurrent treated first-line HER2-directed chemotherapy at Memorial Sloan Kettering between 2012 2021...
Abstract Background Focal high-level oncogene amplifications (FH-amp) (e.g., MYC, EGFR) frequently occur on extrachromosomal DNA (ecDNA), highly transcribed units of circular non-chromosomal DNA. FH-amp ecDNA promote intra-tumoral heterogeneity, resistance to therapies, and poor prognosis. Recently, a Phase 1 clinical trial exploring ecDNA-directed treatments has begun. However, date, bioinformatic detection relied whole-genome sequencing data, no standard method exists detect targeted NGS....
<p>Features leading to correct predictions of all 38 cancer types included, aggregated by broad feature category, using normalized Shapley value effect score, as previously described (Fig S6, Methods).</p>
<p>Top Shapley values per cancer type. Shapnorm = shapley effect score normalized across all predictions of the type</p>
<p>Assessment of GDD-ENS performance on the test set across reported purity values, binned in 10% increments. Count each bin (top), and corresponding overall accuracy (light pink), high-confidence (dark pink). Samples with NA values or >90% were removed, representing only 106 samples total.</p>
<p>For each broad category of features present within our training set, we trained individual models using the same regime as GDD-ENS. Results are shown across these categories, represented by a circle. We then iteratively combined and retrained models, adding feature groups in decreasing order accuracy their model, an X. The X corresponding to indicated on X-axis corresponds model all categories left it. has highest overall held-out data. CN; Copy Number.</p>
<p>Overall heatmap of top prediction across all confidence values. Heatmap is row-normalized and sorted by overall precision. Off-target values represent proportion the predicted type, row type that true along columns. NSCLC, Non-Small Cell Lung Cancer; GIST, Gastrointestinal Stromal Tumor; SQC, Squamous Carcinoma; SCLC, Small PNET, Pancreatic Neuroendocrine Lu-NET, GI-NET, Gastro-intestinal Carc., MPNST, Malignant Peripheral Nerve Sheath Tumor.</p>
<p>S11A: Expected Panel Performance (Masked Analysis), S11B: of GDD-ENS on UCSF test set. Acc: Recalibrated accuracy comparable to cohort after correction for difference in distribution cancer types within the cohort</p>
<p>S10: Accuracy across Purity Thresholds</p>
<p>Supplementary Methods and References</p>
<p>Flow of results for combination prior using either metastatic site (left) or histology (right). Metastatic Site is only applied to all samples (n = 2166), histological test set examples with annotations 4571). Arrow base represents pre-adjustment category, arrow head post-adjustment. Circle indicates the number that did not change categories after adjustment, i.e. 1406 were correct and high confidence before skewing biopsy annotations.</p>
<p>CUP Later confirmed Diagnoses</p>
<p>Cancer Type Organ System Mapping</p>
<p>Shapley values aggregated across 10 major organ systems, as described in Supp. Table S13. First column represents broad feature category importance all correct, in-distribution predictions. Second and third columns represent top features for correct incorrect predictions, respectively, regardless of or out distribution status. Number predictions within each shown bottom right figure.</p>
<p>(A) Overall proportion of all annotated ancestries across the training set (left) and testing (right). Numbers over each bar indicate total within category. EUR, European; ADM, Admixed; EAS, East Asian; AFR, African; SAS, South NAM, Native American (B) High-confidence accuracy (right) for European (EUR), Asian (EAS), African (AFR) (SAS) compared to overall high-confidence test set. P-values from a two-sided Fisher's exact comparing proportions these metrics distribution per ancestry...
<p>Shapley values significantly associated with cancer types. Shapnorm = shapley effect score normalized across all predictions of the type, stat_shap and pval_shap bonferonni corrected outputs Mann-Whitney U tests distribution Shapley value scores types vs non-cancer type predictions, shap_rank_ct rank feature association within it is (i.e. 1 top feature)</p>