Molecular Classification

Abstract: Breast cancer is the most common and highly heterogeneous neoplastic disease comprised of several subtypes with distinct molecular etiology and clinical behaviours. The mortality observed over the past few decades and the failure in eradicating the disease is due to the lack of specific etiology, molecular mechanisms involved in initiation and progression of breast cancer. Understanding of the molecular classes of breast cancer may also lead to new biological insights and eventually to better therapies. The promising therapeutic targets and novel anti-cancer approaches emerging from these molecular targets that could be applied clinically in the near future are being highlighted. In addition, this review discusses some of the details of current molecular classification and available chemotherapeutics

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Bilateral Phyllodes Giant Tumor. A Case Report Analyzed by Array-CGH

Diagnostics ◽

10.3390/diagnostics10100825 ◽

2020 ◽

Vol 10 (10) ◽

pp. 825

Author(s):

Francesco Fortarezza ◽

Federica Pezzuto ◽

Gerardo Cazzato ◽

Clelia Punzo ◽

Antonio d’Amati ◽

...

Keyword(s):

Array Cgh ◽

Phyllodes Tumor ◽

Molecular Classification ◽

Left Breast ◽

Comparative Genomic ◽

Fish Analysis ◽

Molecular Tests ◽

Giant Tumor ◽

Metastatic Risk ◽

The Right

The breast phyllodes tumor is a biphasic tumor that accounts for less than of 1% of all breast neoplasms. It is classified as benign, borderline, or malignant, and can mimic benign masses. Some recurrent alterations have been identified. However, a precise molecular classification of these tumors has not yet been established. Herein, we describe a case of a 43-year-old woman that was admitted to the emergency room for a significant bleeding from the breast skin. A voluminous ulcerative mass of the left breast and multiple nodules with micro-calcifications on the right side were detected at a physical examination. A left total mastectomy and a nodulectomy of the right breast was performed. The histological diagnosis of the surgical specimens reported a bilateral giant phyllodes tumor, showing malignant features on the left and borderline characteristics associated with a fibroadenoma on the right. A further molecular analysis was carried out by an array-Comparative Genomic Hybridization (CGH) to characterize copy-number alterations. Many losses were detected in the malignant mass, involving several tumor suppressor genes. These findings could explain the malignant growth and the metastatic risk. In our study, genomic profiling by an array-CGH revealed a greater chromosomal instability in the borderline mass (40 total defects) than in the malignant (19 total defects) giant phyllodes tumor, reflecting the tumor heterogeneity. Should our results be confirmed with more sensitive and specific molecular tests (DNA sequencing and FISH analysis), they could allow a better selection of patients with adverse pathological features, thus optimizing and improving patient’s management.

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Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas

Brain Sciences ◽

10.3390/brainsci10070463 ◽

2020 ◽

Vol 10 (7) ◽

pp. 463 ◽

Cited By ~ 1

Author(s):

Muhaddisa Barat Ali ◽

Irene Yu-Hua Gu ◽

Mitchel S. Berger ◽

Johan Pallud ◽

Derek Southwell ◽

...

Keyword(s):

Molecular Subtypes ◽

Negative Impact ◽

Molecular Classification ◽

Tumor Segmentation ◽

Test Accuracy ◽

Low Grade ◽

Idh Mutation ◽

Molecular Type ◽

Low Grade Gliomas ◽

Domain Mapping

Brain tumors, such as low grade gliomas (LGG), are molecularly classified which require the surgical collection of tissue samples. The pre-surgical or non-operative identification of LGG molecular type could improve patient counseling and treatment decisions. However, radiographic approaches to LGG molecular classification are currently lacking, as clinicians are unable to reliably predict LGG molecular type using magnetic resonance imaging (MRI) studies. Machine learning approaches may improve the prediction of LGG molecular classification through MRI, however, the development of these techniques requires large annotated data sets. Merging clinical data from different hospitals to increase case numbers is needed, but the use of different scanners and settings can affect the results and simply combining them into a large dataset often have a significant negative impact on performance. This calls for efficient domain adaption methods. Despite some previous studies on domain adaptations, mapping MR images from different datasets to a common domain without affecting subtitle molecular-biomarker information has not been reported yet. In this paper, we propose an effective domain adaptation method based on Cycle Generative Adversarial Network (CycleGAN). The dataset is further enlarged by augmenting more MRIs using another GAN approach. Further, to tackle the issue of brain tumor segmentation that requires time and anatomical expertise to put exact boundary around the tumor, we have used a tight bounding box as a strategy. Finally, an efficient deep feature learning method, multi-stream convolutional autoencoder (CAE) and feature fusion, is proposed for the prediction of molecular subtypes (1p/19q-codeletion and IDH mutation). The experiments were conducted on a total of 161 patients consisting of FLAIR and T1 weighted with contrast enhanced (T1ce) MRIs from two different institutions in the USA and France. The proposed scheme is shown to achieve the test accuracy of 74 . 81 % on 1p/19q codeletion and 81 . 19 % on IDH mutation, with marked improvement over the results obtained without domain mapping. This approach is also shown to have comparable performance to several state-of-the-art methods.

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Normal tissue content impact on the GBM molecular classification

Briefings in Bioinformatics ◽

10.1093/bib/bbaa129 ◽

2020 ◽

Author(s):

Rodrigo Madurga ◽

Noemí García-Romero ◽

Beatriz Jiménez ◽

Ana Collazo ◽

Francisco Pérez-Rodríguez ◽

...

Keyword(s):

Normal Tissue ◽

In Silico Analysis ◽

Molecular Classification ◽

Gene Signature ◽

Normal Brain ◽

Normal Cells ◽

Final Model ◽

Tumor Subtypes ◽

Tissue Content

Abstract Molecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue. These samples can be misclassified preventing biological and clinical insights into the different tumor subtypes from coming to light. In this work, we present a model that tackles both the molecular classification of samples and discrimination of those with a high content of normal cells. We performed a transcriptomic in silico analysis on glioblastoma (GBM) samples (n = 810) and tested different criteria to optimize the number of genes needed for molecular classification. We used gene expression of normal brain samples (n = 555) to design an additional gene signature to detect samples with a high normal tissue content. Microdissection samples of different structures within GBM (n = 122) have been used to validate the final model. Finally, the model was tested in a cohort of 43 patients and confirmed by histology. Based on the expression of 20 genes, our model is able to discriminate samples with a high content of normal tissue and to classify the remaining ones. We have shown that taking into consideration normal cells can prevent errors in the classification and the subsequent misinterpretation of the results. Moreover, considering only samples with a low content of normal cells, we found an association between the complexity of the samples and survival for the three molecular subtypes.

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Multi-site tumor sampling highlights molecular intra-tumor heterogeneity in malignant pleural mesothelioma

Genome Medicine ◽

10.1186/s13073-021-00931-w ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Clément Meiller ◽

François Montagne ◽

Theo Z. Hirsch ◽

Stefano Caruso ◽

Julien de Wolf ◽

...

Keyword(s):

Tumor Microenvironment ◽

Malignant Pleural Mesothelioma ◽

Tumor Heterogeneity ◽

Immunological Synapse ◽

Side Wall ◽

Molecular Classification ◽

Suppressor Gene ◽

Pleural Mesothelioma ◽

Sequencing Analysis ◽

Methylome Analysis

Abstract Background Malignant pleural mesothelioma (MPM) is a heterogeneous cancer. Better knowledge of molecular and cellular intra-tumor heterogeneity throughout the thoracic cavity is required to develop efficient therapies. This study focuses on molecular intra-tumor heterogeneity using the largest series to date in MPM and is the first to report on the multi-omics profiling of a substantial series of multi-site tumor samples. Methods Intra-tumor heterogeneity was investigated in 16 patients from whom biopsies were taken at distinct anatomical sites. The paired biopsies collected from apex, side wall, costo-diaphragmatic, or highest metabolic sites as well as 5 derived cell lines were screened using targeted sequencing. Whole exome sequencing, RNA sequencing, and DNA methylation were performed on a subset of the cohort for deep characterization. Molecular classification, recently defined histo-molecular gradients, and cell populations of the tumor microenvironment were assessed. Results Sequencing analysis identified heterogeneous variants notably in NF2, a key tumor suppressor gene of mesothelial carcinogenesis. Subclonal tumor populations were shared among paired biopsies, suggesting a polyclonal dissemination of the tumor. Transcriptome analysis highlighted dysregulation of cell adhesion and extracellular matrix pathways, linked to changes in histo-molecular gradient proportions between anatomic sites. Methylome analysis revealed the contribution of epigenetic mechanisms in two patients. Finally, significant changes in the expression of immune mediators and genes related to immunological synapse, as well as differential infiltration of immune populations in the tumor environment, were observed and led to a switch from a hot to a cold immune profile in three patients. Conclusions This comprehensive analysis reveals patient-dependent spatial intra-tumor heterogeneity at the genetic, transcriptomic, and epigenetic levels and in the immune landscape of the tumor microenvironment. Results support the need for multi-sampling for the implementation of molecular-based precision medicine.

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