Publications by authors named "Kahn Rhrissorrakrai"

Motivation: The emergence of COVID-19 (C19) created incredible worldwide challenges but offers unique opportunities to understand the physiology of its risk factors and their interactions with complex disease conditions, such as metabolic syndrome. To address the challenges of discovering clinically relevant interactions, we employed a unique approach for epidemiological analysis powered by redescription-based topological data analysis (RTDA).

Results: Here, RTDA was applied to Explorys data to discover associations among severe C19 and metabolic syndrome.

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While the development of multiple primary tumors in smokers with lung cancer can be attributed to carcinogen-induced field cancerization, the occurrence of multiple primary tumors in individuals with -mutant lung cancer who lack known environmental exposures remains unexplained. We identified ten patients with early-stage, resectable non-small cell lung cancer who presented with multiple anatomically distinct -mutant tumors. We analyzed the phylogenetic relationships among multiple tumors from each patient using whole exome sequencing (WES) and hypermutable poly-guanine (poly-G) repeat genotyping, as orthogonal methods for lineage tracing.

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Although BCL2 mutations are reported as later occurring events leading to venetoclax resistance, many other mechanisms of progression have been reported though remain poorly understood. Here, we analyze longitudinal tumor samples from 11 patients with disease progression while receiving venetoclax to characterize the clonal evolution of resistance. All patients tested showed increased in vitro resistance to venetoclax at the posttreatment time point.

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Sampling circulating tumor DNA (ctDNA) using liquid biopsies offers clinically important benefits for monitoring cancer progression. A single ctDNA sample represents a mixture of shed tumor DNA from all known and unknown lesions within a patient. Although shedding levels have been suggested to hold the key to identifying targetable lesions and uncovering treatment resistance mechanisms, the amount of DNA shed by any one specific lesion is still not well characterized.

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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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Article Synopsis
  • Covalent inhibitors of BTK have revolutionized CLL treatment, but patients often develop resistance, primarily through mutations at the BTK 481 cysteine residue.
  • Pirtobrutinib is a noncovalent BTK inhibitor that remains effective in patients with CLL progression, regardless of BTK mutation status, by inhibiting B-cell receptor signaling and other key functions.
  • Studies show that while pirtobrutinib can initially reduce BCR signaling and improve patient outcomes, resistance can develop through alternative-site BTK mutations over time.
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Chimeric antigen receptor (CAR)-T cell therapy has revolutionized the treatment of hematologic malignancies. Approximately half of patients with refractory large B cell lymphomas achieve durable responses from CD19-targeting CAR-T treatment; however, failure mechanisms are identified in only a fraction of cases. To gain new insights into the basis of clinical response, we performed single-cell transcriptome sequencing of 105 pretreatment and post-treatment peripheral blood mononuclear cell samples, and infusion products collected from 32 individuals with large B cell lymphoma treated with either of two CD19 CAR-T products: axicabtagene ciloleucel (axi-cel) or tisagenlecleucel (tisa-cel).

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Acral melanoma, the most common melanoma subtype among non-White individuals, is associated with poor prognosis. However, its key molecular drivers remain obscure. Here, we perform integrative genomic and clinical profiling of acral melanomas from 104 patients treated in North America (n = 37) or China (n = 67).

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Background: All diseases containing genetic material undergo genetic evolution and give rise to heterogeneity including cancer and infection. Although these illnesses are biologically very different, the ability for phylogenetic retrodiction based on the genomic reads is common between them and thus tree-based principles and assumptions are shared. Just as the different frequencies of tumor genomic variants presupposes the existence of multiple tumor clones and provides a handle to computationally infer them, we postulate that the different variant frequencies in viral reads offers the means to infer multiple co-infecting sublineages.

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Sacituzumab govitecan (SG), the first antibody-drug conjugate (ADC) approved for triple-negative breast cancer, incorporates the anti-TROP2 antibody hRS7 conjugated to a topoisomerase-1 (TOP1) inhibitor payload. We sought to identify mechanisms of SG resistance through RNA and whole-exome sequencing of pretreatment and postprogression specimens. One patient exhibiting progression lacked TROP2 expression, in contrast to robust TROP2 expression and focal genomic amplification of observed in a patient with a deep, prolonged response to SG.

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During cancer therapy, tumor heterogeneity can drive the evolution of multiple tumor subclones harboring unique resistance mechanisms in an individual patient. Previous case reports and small case series have suggested that liquid biopsy (specifically, cell-free DNA (cfDNA)) may better capture the heterogeneity of acquired resistance. However, the effectiveness of cfDNA versus standard single-lesion tumor biopsies has not been directly compared in larger-scale prospective cohorts of patients following progression on targeted therapy.

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The confluence of deep sequencing and powerful machine learning is providing an unprecedented peek at the darkest of the dark genomic matter, the non-coding genomic regions lacking any functional annotation. While deep sequencing uncovers rare tumor variants, the heterogeneity of the disease confounds the best of machine learning (ML) algorithms. Here we set out to answer if the dark-matter of the genome encompass signals that can distinguish the fine subtypes of disease that are otherwise genomically indistinguishable.

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Article Synopsis
  • The publication contained an error regarding the name of the fourteenth author.
  • The incorrect name was initially printed in the article.
  • The correct name has now been provided to clarify the mistake.
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Article Synopsis
  • A clinical study was conducted in New York City with 30 glioblastoma patients to compare the effectiveness of whole genome sequencing (WGS) and RNA sequencing (RNA-seq) against targeted panel sequencing in identifying treatment options.
  • WGS/RNA-seq uncovered significantly more actionable clinical results—90% of the time—with an average of 16 times more unique variants identified, leading to 84 calls for actionable treatments that targeted panels missed.
  • The study found good agreement between manual and automated variant identification, showing that clinicians modified treatment plans based on this data in 10% of cases, marking a significant advancement in cancer treatment analysis.
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Background: Significant numbers of variants detected in cancer patients are often left labeled only as variants of unknown significance (VUS). In order to expand precision medicine to a wider population, we need to extend our knowledge of pathogenicity and drug response in the context of VUS's.

Methods: In this study, we analyzed variants from AACR Project GENIE Consortium APG (Cancer Discov 7:818-831, 2017) and compared them to the COSMIC database Forbes et al.

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Background: Using next-generation sequencing (NGS) to guide cancer therapy has created challenges in analyzing and reporting large volumes of genomic data to patients and caregivers. Specifically, providing current, accurate information on newly approved therapies and open clinical trials requires considerable manual curation performed mainly by human "molecular tumor boards" (MTBs). The purpose of this study was to determine the utility of cognitive computing as performed by Watson for Genomics (WfG) compared with a human MTB.

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The confluence of genomic technologies and cognitive computing has brought us to the doorstep of widespread usage of personalized medicine. Cognitive systems, such as Watson for Genomics (WG), integrate massive amounts of new omic data with the current body of knowledge to assist physicians in analyzing and acting on patient's genomic profiles.

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Article Synopsis
  • The study aimed to analyze a glioblastoma tumor specimen using three different methods to identify actionable variants.
  • Tumor DNA was assessed through targeted panel analysis, whole-genome sequencing (WGS), and RNA sequencing (RNA-seq), with data evaluated by both human experts and IBM Watson Genomic Analytics (WGA).
  • Results showed that WGS and RNA-seq identified more variants than targeted panels, and WGA performed the analysis much faster than human analysts, highlighting the potential for improved patient care with human-machine collaboration.
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Motivation: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs.

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Motivation: Inferring how humans respond to external cues such as drugs, chemicals, viruses or hormones is an essential question in biomedicine. Very often, however, this question cannot be addressed because it is not possible to perform experiments in humans. A reasonable alternative consists of generating responses in animal models and 'translating' those results to humans.

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