One-Step Robust Low-Rank Subspace Segmentation for Tumor Sample Clustering

Clustering of tumor samples can help identify cancer types and discover new cancer subtypes, which is essential for effective cancer treatment. Although many traditional clustering methods have been proposed for tumor sample clustering, advanced algorithms with better performance are still needed. Low-rank subspace clustering is a popular algorithm in recent years. In this paper, we propose a novel one-step robust low-rank subspace segmentation method (ORLRS) for clustering the tumor sample. For a gene expression data set, we seek its lowest rank representation matrix and the noise matrix. By imposing the discrete constraint on the low-rank matrix, without performing spectral clustering, ORLRS learns the cluster indicators of subspaces directly, i.e., performing the clustering task in one step. To improve the robustness of the method, capped norm is adopted to remove the extreme data outliers in the noise matrix. Furthermore, we conduct an efficient solution to solve the problem of ORLRS. Experiments on several tumor gene expression data demonstrate the effectiveness of ORLRS.

Cytological evaluation of a single cell culture of Luminal A subtype breast carcinoma cells

Introduction. Despite significant advances in the creation of stable cell lines, the focus of research has recently shifted toward the creation of primary cell cultures derived directly from patient tumor samples, which include both tumor cells and microenvironmental cells.The aim of the study was to compare the morphological characteristics of the cells of a breast carcinoma sample when cultured over three passages.Materials and methods. Material for the study was obtained during surgical intervention in a patient diagnosed with breast carcinoma. Slices were prepared from the tumor sample according to the standard histological protocol and stained with monoclonal antibodies to estrogen, progesterone, Ki-67, Her2/neu receptors. Cell nuclei were stained with hematoxylin. Immunohistochemical reaction was performed in DAKO autostainer (Denmark). Part of the material was placed in Hanks' solution with 5% antibiotic antimycotics and delivered to the Cell Culture Laboratory, where after performing the standard protocol for obtaining cell culture, tumor cells were diluted in Mammocult nutrient medium and placed in culture vials. For morphological evaluation, cells were stained by Pappenheim. For immunocytochemical analysis in determining the belonging of cells to epithelial cells using anti-Pan Keratin Primary Antibody antibody. The number of cells was counted in an automatic TC20 counter, and culture growth was monitored using an Eclipse TS100 microscope, Nikon (Japan).Results and Discussion. On the basis of immunohistochemical study, the tumor sample was classified as Luminal-A subtype. During the study several groups of cells were isolated and cytologically evaluated. The results of immunocytochemical analysis of the cultured cells confirm that the tumor cells retained their epithelial phenotype during culturing. In spite of the manifestation of cell polymorphism in BML cell culture, during three passages the cultured tumor cells retained their epithelial nature and showed a tendency to form a monolayer.Conclusion. A detailed study of cytomorphology and immunocytological characteristics of cultured cells of different immunohistochemical PBMC subtypes will help to evaluate the main regularities of tumor cell vital functions in vitro and allow a more differentiated approach to the creation of personalized cell cultures in order to develop a targeted chemotherapeutic effect on tumors of specific patients.

An Integrated Analysis of Tumor Purity of Common Central Nervous System Tumors in Children Based on Machine Learning Methods

Background: Tumor purity is defined as the proportion of cancer cells in the tumor tissue, and its effects on molecular genetics, the immune microenvironment, and the prognosis of children’s central nervous system (CNS) tumors are under-researched.Methods: We applied random forest machine learning, the InfiniumPurify algorithm, and the ESTIMATE algorithm to estimate the tumor purity of every child’s CNS tumor sample in several published pediatric CNS tumor sample datasets from Gene Expression Omnibus (GEO), aiming to perform an integrated analysis on the tumor purity of children’s CNS tumors.Results: Only the purity of CNS tumors in children based on the random forest (RF) machine learning method was normally distributed. In addition, the children’s CNS tumor purity was associated with primary clinical pathological and molecular indicators. Enrichment analysis of biological pathways related to the purity of medulloblastoma (MB) revealed some classical signaling pathways associated with MB biology and development-related pathways. According to the correlation analysis between MB purity and the immune microenvironment, three immune-related genes, namely, CD8A, CXCR2, and TNFRSF14, were negatively related to MB purity. In contrast, no significant correlation was detected between immunotherapy-associated markers, such as PD-1, PD-L1, and CTLA4; most infiltrating immune cells; and MB purity. In the tumor purity–related survival analysis of MB, ependymoma (EPN), and children’s high-grade glioma, we discovered a minor effect of tumor purity on the survival of the aforementioned pediatric patients with CNS tumors.Conclusion: Our purity pediatric pan-CNS tumor analysis provides a deeper understanding and helps with the clinical management of pediatric CNS tumors.

Why are oncologists not testing all NSCLC patients for biomarkers in the U.S. market?

e21003 Background: NSCLC has the greatest number of biomarkers of any tumor type. If a patient is not biomarker tested, he or she may not receive access to targeted therapies that often offer greater efficacy through personalized therapy. The purpose of this research study is to examine the reasons certain lung cancer patients are not tested in the United States for biomarkers. Methods: This study followed market research best practices. The study was based on a survey of the BrandImpact Oncology panel conducted on patient visits during Q4 2020 at the point-of-prescribing. Results: The baseline measure of NSCLC patients that were being treated but had a biomarker status that was unknown ranges from 9% to 12% of patient visits. The biomarkers examined included ALK, ROS-1, EGFR and PD-L1. The Oncologists who treat NSCLC patients received the following question: When treating patients in your practice with NSCLC cancer what are your top 3 reasons for not conducting biomarker testing? As outlined in the table below which reflects all survey responses, the top four reasons for not conducting biomarker testing are: not enough tumor sample for testing purposes, patient has early stage of disease, patient is not healthy enough and patient cost associated with testing. Conclusions: The overall results indicate the absence of biomarker testing for lung cancer patients is mainly due to two different patient types. The early-stage patient was a key reason for not testing despite advances in early stage indications and diagnostic technology which provide increasing evidence that testing should be done earlier in the treatment journey. The late-stage patient which is seen more often in Academic institutions and who often has more aggressive cancer. The speed of receiving the biomarker test results for these more severe patients likely needs to be addressed. In the Community, setting cost of branded therapies can be an issue and in some cases biomarker testing is not readily available in all practices. It should also be noted that “not enough tumor sample for testing” was the number one reason for not conducting biomarker testing, but this issue can now be addressed through liquid NGS technology.[Table: see text]

Prospective high-throughput genome profiling of advanced cancers: results of the PERMED-01 clinical trial

Background The benefit of precision medicine based on relatively limited gene sets and often-archived samples remains unproven. PERMED-01 (NCT02342158) was a prospective monocentric clinical trial assessing, in adults with advanced solid cancer, the feasibility and impact of extensive molecular profiling applied to newly biopsied tumor sample and based on targeted NGS (t-NGS) of the largest gene panel to date and whole-genome array-comparative genomic hybridization (aCGH) with assessment of single-gene alterations and clinically relevant genomic scores. Methods Eligible patients with refractory cancer had one tumor lesion accessible to biopsy. Extracted tumor DNA was profiled by t-NGS and aCGH. We assessed alterations of 802 “candidate cancer” genes and global genomic scores, such as homologous recombination deficiency (HRD) score and tumor mutational burden. The primary endpoint was the number of patients with actionable genetic alterations (AGAs). Secondary endpoints herein reported included a description of patients with AGA who received a “matched therapy” and their clinical outcome, and a comparison of AGA identification with t-NGS and aCGH versus whole-exome sequencing (WES). Results Between November 2014 and September 2019, we enrolled 550 patients heavily pretreated. An exploitable complete molecular profile was obtained in 441/550 patients (80%). At least one AGA, defined in real time by our molecular tumor board, was found in 393/550 patients (71%, two-sided 90%CI 68–75%). Only 94/550 patients (17%, 95%CI 14–21) received an “AGA-matched therapy” on progression. The most frequent AGAs leading to “matched therapy” included PIK3CA mutations, KRAS mutations/amplifications, PTEN deletions/mutations, ERBB2 amplifications/mutations, and BRCA1/2 mutations. Such “matched therapy” improved by at least 1.3-fold the progression-free survival on matched therapy (PFS2) compared to PFS on prior therapy (PFS1) in 36% of cases, representing 6% of the enrolled patients. Within patients with AGA treated on progression, the use of “matched therapy” was the sole variable associated with an improved PFS2/PFS1 ratio. Objective responses were observed in 19% of patients treated with “matched therapy,” and 6-month overall survival (OS) was 62% (95%CI 52–73). In a subset of 112 metastatic breast cancers, WES did not provide benefit in term of AGA identification when compared with t-NGS/aCGH. Conclusions Extensive molecular profiling of a newly biopsied tumor sample identified AGA in most of cases, leading to delivery of a “matched therapy” in 17% of screened patients, of which 36% derived clinical benefit. WES did not seem to improve these results. Trial registration ID-RCB identifier: 2014-A00966-41; ClinicalTrials.gov identifier: NCT02342158.

Identification of a Novel Papillomavirus Type (MfoiPV1) Associated with Acrochordon in a Stone Marten (Martes foina)

Papillomaviruses (PVs) are an extremely large group of viruses that cause skin and mucosal infections in humans and various domestic and wild animals. Nevertheless, there is limited knowledge about PVs in wildlife hosts, including mustelid species. This study describes a case in stone marten (Martes foina) with a clinical manifestation of skin tumor, which is rather atypical for infections with PVs. The result of the papillomavirus PCR performed on the skin tumor sample was positive, and the complete PV genome was determined in the studied sample using next-generation sequencing technology. The analysis of the PV genome revealed infection of the stone marten with a putative new PV type belonging to the Dyonupapillomavirus genus. The proposed new stone marten PV type was named MfoiPV1.

Joint Lp-Norm and L2,1-Norm Constrained Graph Laplacian PCA for Robust Tumor Sample Clustering and Gene Network Module Discovery

The dimensionality reduction method accompanied by different norm constraints plays an important role in mining useful information from large-scale gene expression data. In this article, a novel method named Lp-norm and L2,1-norm constrained graph Laplacian principal component analysis (PL21GPCA) based on traditional principal component analysis (PCA) is proposed for robust tumor sample clustering and gene network module discovery. Three aspects are highlighted in the PL21GPCA method. First, to degrade the high sensitivity to outliers and noise, the non-convex proximal Lp-norm (0 < p < 1)constraint is applied on the loss function. Second, to enhance the sparsity of gene expression in cancer samples, the L2,1-norm constraint is used on one of the regularization terms. Third, to retain the geometric structure of the data, we introduce the graph Laplacian regularization item to the PL21GPCA optimization model. Extensive experiments on five gene expression datasets, including one benchmark dataset, two single-cancer datasets from The Cancer Genome Atlas (TCGA), and two integrated datasets of multiple cancers from TCGA, are performed to validate the effectiveness of our method. The experimental results demonstrate that the PL21GPCA method performs better than many other methods in terms of tumor sample clustering. Additionally, this method is used to discover the gene network modules for the purpose of finding key genes that may be associated with some cancers.