Dataset Information

A comprehensive analysis of RHOA mutation positive and negative angioimmunoblastic T-cell lymphomas by targeted deep sequencing, expression profiling and single cell digital image analysis.

ABSTRACT: Angioimmunoblastic T?cell lymphoma (AITL) is a uniquely aggressive mature T?cell neoplasm. In recent years, recurrent genetic mutations in ras homolog family member A (RHOA), tet methylcytosine dioxygenase 2 (TET2), DNA methyltransferase 3 alpha (DNMT3A) and isocitrate dehydrogenase [NADP(+)] 2 (IDH2) have been identified as associated with AITL. However, a deep molecular study assessing both DNA mutations and RNA expression profile combined with digital image analysis is lacking. The present study aimed to evaluate the significance of molecular and morphologic features by high resolution digital image analysis in several cases of AITL. To do so, a total of 18 separate tissues from 10 patients with AITL were collected and analyzed. The results identified recurrent mutations in RHOA, TET2, DNMT3A, and IDH2, and demonstrated increased DNA mutations in coding, promoter and CCCTC binding factor (CTCF) binding sites in RHOA mutated AITLs vs. RHOA non?mutated cases, as well as increased overall survival in RHOA mutated patients. In addition, single cell computational digital image analysis morphologically characterized RHOA mutated AITL cells as distinct from cells from RHOA mutation negative patients. Computational analysis of single cell morphological parameters revealed that RHOA mutated cells have decreased eccentricity (more circular) compared with RHOA non?mutated AITL cells. In conclusion, the results from the present study expand our understanding of AITL and demonstrate that there are specific cell biological and morphological manifestations of RHOA mutations in cases of AITL.

SUBMITTER: Butzmann A

PROVIDER: S-EPMC7447311 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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A comprehensive analysis of RHOA mutation positive and negative angioimmunoblastic T-cell lymphomas by targeted deep sequencing, expression profiling and single cell digital image analysis.

Butzmann Alexandra A Sridhar Kaushik K Jangam Diwash D Kumar Jyoti J Sahoo Malaya Kumar MK Shahmarvand Nahid N Warnke Roger R Rangasamy Elumalai E Pinsky Benjamin Alan BA Ohgami Robert Shigeo RS

International journal of molecular medicine 20200728 4

Angioimmunoblastic T‑cell lymphoma (AITL) is a uniquely aggressive mature T‑cell neoplasm. In recent years, recurrent genetic mutations in ras homolog family member A (RHOA), tet methylcytosine dioxygenase 2 (TET2), DNA methyltransferase 3 alpha (DNMT3A) and isocitrate dehydrogenase [NADP(+)] 2 (IDH2) have been identified as associated with AITL. However, a deep molecular study assessing both DNA mutations and RNA expression profile combined with digital image analysis is lacking. The present st ...[more]

PMID: 32945366

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Project description:For a method to be widely adopted in medical research or clinical practice, it needs to be reproducible so that clinicians and regulators can have confidence in its use. Machine learning and deep learning have a particular set of challenges around reproducibility. Small differences in the settings or the data used for training a model can lead to large differences in the outcomes of experiments. In this work, three top-performing algorithms from the Camelyon grand challenges are reproduced using only information presented in the associated papers and the results are then compared to those reported. Seemingly minor details were found to be critical to performance and yet their importance is difficult to appreciate until the actual reproduction is attempted. We observed that authors generally describe the key technical aspects of their models well but fail to maintain the same reporting standards when it comes to data preprocessing which is essential to reproducibility. As an important contribution of the present study and its findings, we introduce a reproducibility checklist that tabulates information that needs to be reported in histopathology ML-based work in order to make it reproducible. Author summary For a method to be used a lot in medical research or clinical practice, it needs to be able to be reproduced so that people can trust it. Machine learning and deep learning have some challenges around this. For example, small changes in how a model is trained can lead to significant changes in the results of experiments. This makes it essential that researchers report how they do things in enough detail for the results of their experiments to be reproducible. In this work, we looked at three different algorithms used for digital pathology image analysis. We tried to reproduce them using only the information reported in their papers. We confirmed that even minor details could be essential. Authors often do not report all the details needed to reproduce their work. We also created a checklist of things that need to be reported to help other researchers make their work reproducible.

Dataset Information

A comprehensive analysis of RHOA mutation positive and negative angioimmunoblastic T-cell lymphomas by targeted deep sequencing, expression profiling and single cell digital image analysis.

Publications

A comprehensive analysis of RHOA mutation positive and negative angioimmunoblastic T-cell lymphomas by targeted deep sequencing, expression profiling and single cell digital image analysis.

Similar Datasets

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