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1. A Pilot Study of Blood-Based Methylation Markers Associated With Pancreatic Cancer

   https://doi.org/10.3389/fgene.2022.849839  

   Jansen, Rick J. ; Orr, Megan ; Bamlet, William R. ; Petersen, Gloria M. ( March 2022 , Frontiers in Genetics)

   Over the past several decades in the United States, incidence of pancreatic cancer (PCa) has increased, with the 5-year survival rate remaining extremely low at 10.8%. Typically, PCa is diagnosed at an advanced stage, with the consequence that there is more tumor heterogeneity and increased probability that more cells are resistant to treatments. Risk factors for PCa can serve as a way to select a high-risk population and develop biomarkers to improve early detection and treatment. We focus on blood-based methylation as an approach to identify a marker set that can be obtained in a minimally invasive way (through peripheral blood) and could be applied to a high-risk subpopulation [those with recent onset type 2 diabetes (DM)]. Blood samples were collected from 30 patients, 15 had been diagnosed with PCa and 15 had been diagnosed with recent onset DM. HumanMethylationEPIC Beadchip (Illumina, CA, United States) was used to quantify methylation of approximately 850,000 methylation sites across the genome and to analyze methylation markers associated with PCa or DM or both. Exploratory analysis conducted to propose importance of top CpG (5′—C—phosphate—G—3′) methylation site associated genes and visualized using boxplots. A methylation-based age predictor was also investigated for ability to distinguish disease groups from controls. No methylation markers were observed to be significantly associated with PCa or new onset diabetes compared with control the respective control groups. In our exploratory analysis, one methylation marker, CpG04969764, found in the Laminin Subunit Alpha 5 ( LAMA5 ) gene region was observed in both PCa and DM Top 100 methylation marker sets. Modification of LAMA5 methylation or LAMA5 gene function may be a way to distinguish those recent DM cases with and without PCa, however, additional studies with larger sample sizes and different study types (e.g., cohort) will be needed to test this hypothesis. 

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2. Identification of prognostic factors in childhood T‐cell acute lymphoblastic leukemia: Results from DFCI ALL Consortium Protocols 05‐001 and 11‐001

   https://doi.org/10.1002/pbc.28719  

   Burns, Melissa A. ; Place, Andrew E. ; Stevenson, Kristen E. ; Gutiérrez, Alejandro ; Forrest, Suzanne ; Pikman, Yana ; Vrooman, Lynda M. ; Harris, Marian H. ; Weinberg, Olga K. ; Hunt, Sarah K. ; et al ( October 2020 , Pediatric Blood & Cancer)

   While outcomes for pediatric T‐cell acute lymphoblastic leukemia (T‐ALL) are favorable, there are few widely accepted prognostic factors, limiting the ability to risk stratify therapy.

   Dana‐Farber Cancer Institute (DFCI) Protocols 05‐001 and 11‐001 enrolled pediatric patients with newly diagnosed B‐ or T‐ALL from 2005 to 2011 and from 2012 to 2015, respectively. Protocol therapy was nearly identical for patients with T‐ALL (N = 123), who were all initially assigned to the high‐risk arm. End‐induction minimal residual disease (MRD) was assessed by reverse transcription polymerase chain reaction (RT‐PCR) or next‐generation sequencing (NGS), but was not used to modify postinduction therapy. Early T‐cell precursor (ETP) status was determined by flow cytometry. Cases with sufficient diagnostic DNA were retrospectively evaluated by targeted NGS of known genetic drivers of T‐ALL, including Notch, PI3K, and Ras pathway genes.

   The 5‐year event‐free survival (EFS) and overall survival (OS) for patients with T‐ALL was 81% (95% CI, 73‐87%) and 90% (95% CI, 83‐94%), respectively. ETP phenotype was associated with failure to achieve complete remission, but not with inferior OS. Low end‐induction MRD (<10⁻⁴) was associated with superior disease‐free survival (DFS). Pathogenic mutations of the PI3K pathway were mutually exclusive of ETP phenotype and were associated with inferior 5‐year DFS and OS.

   Together, our findings demonstrate that ETP phenotype, end‐induction MRD, and PI3K pathway mutation status are prognostically relevant in pediatric T‐ALL and should be considered for risk classification in future trials. DFCI Protocols 05‐001 and 11‐001 are registered atwww.clinicaltrials.govas NCT00165087 and NCT01574274, respectively.

    

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3. Methylation of CpG Sites as Biomarkers Predictive of Drug-Specific Patient Survival in Cancer

   https://doi.org/10.1177/11769351221131124  

   Neary, Bridget ; Lin, Shuting ; Qiu, Peng ( January 2022 , Cancer Informatics)

   Background: Though the development of targeted cancer drugs continues to accelerate, doctors still lack reliable methods for predicting patient response to standard-of-care therapies for most cancers. DNA methylation has been implicated in tumor drug response and is a promising source of predictive biomarkers of drug efficacy, yet the relationship between drug efficacy and DNA methylation remains largely unexplored. Method: In this analysis, we performed log-rank survival analyses on patients grouped by cancer and drug exposure to find CpG sites where binary methylation status is associated with differential survival in patients treated with a specific drug but not in patients with the same cancer who were not exposed to that drug. We also clustered these drug-specific CpG sites based on co-methylation among patients to identify broader methylation patterns that may be related to drug efficacy, which we investigated for transcription factor binding site enrichment using gene set enrichment analysis. Results: We identified CpG sites that were drug-specific predictors of survival in 38 cancer-drug patient groups across 15 cancers and 20 drugs. These included 11 CpG sites with similar drug-specific survival effects in multiple cancers. We also identified 76 clusters of CpG sites with stronger associations with patient drug response, many of which contained CpG sites in gene promoters containing transcription factor binding sites. Conclusion: These findings are promising biomarkers of drug response for a variety of drugs and contribute to our understanding of drug-methylation interactions in cancer. Investigation and validation of these results could lead to the development of targeted co-therapies aimed at manipulating methylation in order to improve efficacy of commonly used therapies and could improve patient survival and quality of life by furthering the effort toward drug response prediction. 

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4. Robust explanation supervision for false positive reduction in pulmonary nodule detection

   https://doi.org/10.1002/mp.16937  

   Zhao, Qilong ; Chang, Chih‐Wei ; Yang, Xiaofeng ; Zhao, Liang ( January 2024 , Medical Physics)

   Lung cancer is the deadliest and second most common cancer in the United States due to the lack of symptoms for early diagnosis. Pulmonary nodules are small abnormal regions that can be potentially correlated to the occurrence of lung cancer. Early detection of these nodules is critical because it can significantly improve the patient's survival rates. Thoracic thin‐sliced computed tomography (CT) scanning has emerged as a widely used method for diagnosing and prognosis lung abnormalities.

   The standard clinical workflow of detecting pulmonary nodules relies on radiologists to analyze CT images to assess the risk factors of cancerous nodules. However, this approach can be error‐prone due to the various nodule formation causes, such as pollutants and infections. Deep learning (DL) algorithms have recently demonstrated remarkable success in medical image classification and segmentation. As an ever more important assistant to radiologists in nodule detection, it is imperative ensure the DL algorithm and radiologist to better understand the decisions from each other. This study aims to develop a framework integrating explainable AI methods to achieve accurate pulmonary nodule detection.

   A robust and explainable detection (RXD) framework is proposed, focusing on reducing false positives in pulmonary nodule detection. Its implementation is based on an explanation supervision method, which uses nodule contours of radiologists as supervision signals to force the model to learn nodule morphologies, enabling improved learning ability on small dataset, and enable small dataset learning ability. In addition, two imputation methods are applied to the nodule region annotations to reduce the noise within human annotations and allow the model to have robust attributions that meet human expectations. The 480, 265, and 265 CT image sets from the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC‐IDRI) dataset are used for training, validation, and testing.

   Using only 10, 30, 50, and 100 training samples sequentially, our method constantly improves the classification performance and explanation quality of baseline in terms of Area Under the Curve (AUC) and Intersection over Union (IoU). In particular, our framework with a learnable imputation kernel improves IoU from baseline by 24.0% to 80.0%. A pre‐defined Gaussian imputation kernel achieves an even greater improvement, from 38.4% to 118.8% from baseline. Compared to the baseline trained on 100 samples, our method shows less drop in AUC when trained on fewer samples. A comprehensive comparison of interpretability shows that our method aligns better with expert opinions.

   A pulmonary nodule detection framework was demonstrated using public thoracic CT image datasets. The framework integrates the robust explanation supervision (RES) technique to ensure the performance of nodule classification and morphology. The method can reduce the workload of radiologists and enable them to focus on the diagnosis and prognosis of the potential cancerous pulmonary nodules at the early stage to improve the outcomes for lung cancer patients.

    

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5. Identifying gene expression patterns associated with drug-specific survival in cancer patients

   https://doi.org/10.1038/s41598-021-84211-y  

   Neary, Bridget ; Zhou, Jie ; Qiu, Peng ( December 2021 , Scientific Reports)

   null (Ed.)

   Abstract The ability to predict the efficacy of cancer treatments is a longstanding goal of precision medicine that requires improved understanding of molecular interactions with drugs and the discovery of biomarkers of drug response. Identifying genes whose expression influences drug sensitivity can help address both of these needs, elucidating the molecular pathways involved in drug efficacy and providing potential ways to predict new patients’ response to available therapies. In this study, we integrated cancer type, drug treatment, and survival data with RNA-seq gene expression data from The Cancer Genome Atlas to identify genes and gene sets whose expression levels in patient tumor biopsies are associated with drug-specific patient survival using a log-rank test comparing survival of patients with low vs. high expression for each gene. This analysis was successful in identifying thousands of such gene–drug relationships across 20 drugs in 14 cancers, several of which have been previously implicated in the respective drug’s efficacy. We then clustered significant genes based on their expression patterns across patients and defined gene sets that are more robust predictors of patient outcome, many of which were significantly enriched for target genes of one or more transcription factors, indicating several upstream regulatory mechanisms that may be involved in drug efficacy. We identified a large number of genes and gene sets that were potentially useful as transcript-level biomarkers for predicting drug-specific patient survival outcome. Our gene sets were robust predictors of drug-specific survival and our results included both novel and previously reported findings, suggesting that the drug-specific survival marker genes reported herein warrant further investigation for insights into drug mechanisms and for validation as biomarkers to aid cancer therapy decisions. 

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