Tumor classification: molecular analysis meets Aristotle

Chemical genomics reveals targetable programs of human cancers rooted in pluripotency

Overlapping principles of embryonic and tumor biology have been described, with recent multi-omics campaigns uncovering shared molecular profiles between human pluripotent stem cells (hPSCs) and adult tumors. Here, using a chemical genomic approach, we provide biological evidence that early germ layer fate decisions of hPSCs reveal targets of human cancers. Single-cell deconstruction of hPSCs-defined subsets that share transcriptional patterns with transformed adult tissues. Chemical screening using a unique germ layer specification assay for hPSCs identified drugs that enriched for compounds that selectively suppressed the growth of patient-derived tumors corresponding exclusively to their germ layer origin. Transcriptional response of hPSCs to germ layer inducing drugs could be used to identify targets capable of regulating hPSC specification as well as inhibiting adult tumors. Our study demonstrates properties of adult tumors converge with hPSCs drug induced differentiation in a germ layer specific manner, thereby expanding our understanding of cancer stemness and pluripotency.

Dissociation protocols used for sarcoma tissues bias the transcriptome observed in single-cell and single-nucleus RNA sequencing

BACKGROUND

Single-cell RNA-seq has emerged as an innovative technology used to study complex tissues and characterize cell types, states, and lineages at a single-cell level. Classification of bulk tumors by their individual cellular constituents has also created new opportunities to generate single-cell atlases for many organs, cancers, and developmental models. Despite the tremendous promise of this technology, recent evidence studying epithelial tissues and diverse carcinomas suggests the methods used for tissue processing, cell disaggregation, and preservation can significantly bias gene expression and alter the observed cell types. To determine whether sarcomas - tumors of mesenchymal origin - are subject to the same technical artifacts, we profiled patient-derived tumor explants (PDXs) propagated from three aggressive subtypes: osteosarcoma (OS), Ewing sarcoma (ES), desmoplastic small round cell tumor (DSRCT). Given the rarity of these sarcoma subtypes, we explored whether single-nuclei RNA-seq from more widely available archival frozen specimens could accurately be identified by gene expression signatures linked to tissue phenotype or pathognomonic fusion proteins.

RESULTS

We systematically assessed dissociation methods across different sarcoma subtypes. We compared gene expression from single-cell and single-nucleus RNA-sequencing of 125,831 whole-cells and nuclei from ES, DSRCT, and OS PDXs. We detected warm dissociation artifacts in single-cell samples and gene length bias in single-nucleus samples. Classic sarcoma gene signatures were observed regardless of the dissociation method. In addition, we showed that dissociation method biases could be computationally corrected.

CONCLUSIONS

We highlighted transcriptional biases, including warm dissociation and gene-length biases, introduced by the dissociation method for various sarcoma subtypes. This work is the first to characterize how the dissociation methods used for sc/snRNA-seq may affect the interpretation of the molecular features in sarcoma PDXs.

Mitochondrially targeted tamoxifen in patients with metastatic solid tumours: an open-label, phase I/Ib single-centre trial

Background

Mitochondria present an emerging target for cancer treatment. We have investigated the effect of mitochondrially targeted tamoxifen (MitoTam), a first-in-class anti-cancer agent, in patients with solid metastatic tumours.

Methods

MitoTam was tested in an open-label, single-centre (Department of Oncology, General Faculty Hospital, Charles University, Czech Republic), phase I/Ib trial in metastatic patients with various malignancies and terminated oncological therapies. In total, 75 patients were enrolled between May 23, 2018 and July 22, 2020. Phase I evaluated escalating doses of MitoTam in two therapeutic regimens using the 3 + 3 design to establish drug safety and maximum tolerated dose (MTD). In phase Ib, three dosing regimens were applied over 8 and 6 weeks to evaluate long-term toxicity of MitoTam as the primary objective and its anti-cancer effect as a secondary objective. This trial was registered with the European Medicines Agency under EudraCT 2017-004441-25.

Findings

In total, 37 patients were enrolled into phase I and 38 into phase Ib. In phase I, the initial application of MitoTam via peripheral vein indicated high risk of thrombophlebitis, which was avoided by central vein administration. The highest dose with acceptable side effects was 5.0 mg/kg. The prevailing adverse effects (AEs) in phase I were neutropenia (30%), anaemia (30%) and fever/hyperthermia (30%), and in phase Ib fever/hyperthermia (58%) together with anaemia (26%) and neutropenia (16%). Serious AEs were mostly related to thromboembolic (TE) complications that affected 5% and 13% of patients in phase I and Ib, respectively. The only statistically significant AE related to MitoTam treatment was anaemia in phase Ib (p = 0.004). Of the tested regimens weekly dosing with 3.0 mg/kg for 6 weeks afforded the best safety profile with almost all being grade 1 (G1) AEs. Altogether, five fatalities occurred during the study, two of them meeting criteria for Suspected Unexpected Serious Adverse Events Reporting (SUSAR) (G4 thrombocytopenia and G5 stroke). MitoTam showed benefit evaluated as clinical benefit rate (CBR) in 37% patients with the largest effect in renal cell carcinoma (RCC) where four out of six patients reached disease stabilisation (SD), one reached partial response (PR) so that in total, five out of six (83%) patients showed CBR.

Interpretation

In this study, the MTD was established as 5.0 mg/kg and the recommended dose of MitoTam as 3.0 mg/kg given once per week via central vein with recommended preventive anti-coagulation therapy. The prevailing toxicity included haematological AEs, hyperthermia/fever and TE complications. One fatal stroke and non-fatal G4 thrombocytopenia were recorded. MitoTam showed high efficacy against RCC.

Funding

Smart Brain Ltd.

Translation

For the Czech translation of the abstract see Supplementary Materials section.

Association between cancer genes and germ layer specificity

Cancer signaling pathways defining cell fates are related to differentiation. During the developmental process, three germ layers (endoderm, mesoderm, and ectoderm) are formed during embryonic development that differentiate into organs via the epigenetic regulation of specific genes. To examine the relationship, the specificities of cancer gene mutations that depend on the germ layers are studied. The major organs affected by cancer were determined based on statistics from the National Cancer Information Center of Korea, and were grouped according to their germ layer origins. Then, the gene mutation frequencies were evaluated to identify any bias based on the differentiation group using the Catalogue of Somatic Mutations in Cancer (COSMIC) database. The chi-square test showed that the p-value of 152 of 166 genes was less than 0.05, and 151 genes showed p-values of less than 0.05 even after adjusting for the false discovery rate (FDR). The germ layer-specific genes were evaluated using visualization based on basic statistics, and the results matched the top ranking genes depending on organs in the COSMIC database.The current study confirmed the germ layer specificity of major cancer genes. The germ layer specificity of mutated driver genes is possibly important in cancer treatments because each mutated gene may react differently depending on the germ layer of origin. By understanding the mechanism of gene mutation in the development and progression of cancer in the context of cell-fate pathways, a more effective therapeutic strategy for cancer can be established.

ZC3HC1 Is a Novel Inherent Component of the Nuclear Basket, Resident in a State of Reciprocal Dependence with TPR

The nuclear basket (NB) scaffold, a fibrillar structure anchored to the nuclear pore complex (NPC), is regarded as constructed of polypeptides of the coiled-coil dominated protein TPR to which other proteins can bind without contributing to the NB's structural integrity. Here we report vertebrate protein ZC3HC1 as a novel inherent constituent of the NB, common at the nuclear envelopes (NE) of proliferating and non-dividing, terminally differentiated cells of different morphogenetic origin. Formerly described as a protein of other functions, we instead present the NB component ZC3HC1 as a protein required for enabling distinct amounts of TPR to occur NB-appended, with such ZC3HC1-dependency applying to about half the total amount of TPR at the NEs of different somatic cell types. Furthermore, pointing to an NB structure more complex than previously anticipated, we discuss how ZC3HC1 and the ZC3HC1-dependent TPR polypeptides could enlarge the NB's functional repertoire.

Transitional dynamics of cancer stem cells in invasion and metastasis

At the onset, few cancer cells amidst the tumor bulk, identified as cancer stem cells (CSCs) or early disseminated cancer cells (eDCCs) are capable of survival post conventional therapy and persist as minimal residual disease (MRD). Metastatic subclones emerge both early and late in the life of primary tumor ensuing an ongoing regional clonal evolution of progenitor cells in metastatic and primary tumors. In the last decade, multiple studies proposed various identities of stem-like cells that undergo transitions to adapt to the changing microenvironment as the disease progresses. This review advocates with substantial evidence the dynamic model of tumor propagation by exploring the specific cell types, reversible phenotypic plasticity between the tumorigenic leader seeds and the supporting follower cancer cells both in circulation and in solid tissue to accurately decipher tumor promoting clones and its role in metastatic dissemination and tumor re-growth. (142 words).

Nimbolide inhibits tumor growth by restoring hepatic tight junction protein expression and reduced inflammation in an experimental hepatocarcinogenesis

BACKGROUND

Altered tight junction (TJ) proteins are correlated with carcinogenesis and tumor development. Nimbolide is a tetranotriterpenoid that has been shown to have antioxidant and anti-proliferative properties; however, its anticancer effects and molecular mechanism in hepatocellular carcinoma (HCC) remains obscure.

AIM

To investigate the effect of nimbolide on TJ proteins, cell cycle progression, and hepatic inflammation in a mouse model of HCC.

METHODS

HCC was induced in male Swiss albino mice (CD-1 strain) by a single intraperitoneal injection of 100 mg/kg diethylnitrosamine (DEN) followed by 80 ppm N-nitrosomorpholine (NMOR) in drinking water for 28 wk. After 28 wk, nimbolide (6 mg/kg) was given orally for four consecutive weeks in DEN/NMOR induced HCC mice. At the end of the 32^nd week, all the mice were sacrificed and blood and liver samples were collected for various analyses. Macroscopic examinations of hepatic nodules were assessed. Liver histology and HCC tumor markers such as alpha-fetoprotein (AFP) and glypican-3 were measured. Expression of TJ proteins, cell proliferation, and cell cycle markers, inflammatory markers, and oxidative stress markers were analyzed. In silico analysis was performed to confirm the binding and modulatory effect of nimbolide on zonula occludens 1 (ZO-1), nuclear factor of kappa light polypeptide gene enhancer in B-cells (NF-κB), and tumor necrosis factor alpha (TNF-α).

RESULTS

We found nimbolide treatment at a concentration of 6 mg/kg to HCC mice reduced hepatic tumor size by 52.08% and tumor volume (P < 0.01), and delayed tumor growth in HCC mice with a concomitant reduction in tumor markers such as AFP levels (P < 0.01) and glypican-3 expression (P < 0.05). Furthermore, nimbolide treatment increased tight junction proteins such as ZO-1 and occludin expression (P < 0.05, respectively) and reduced ZO-1 associated nucleic acid binding protein expression (P < 0.001) in HCC mice liver. Nimbolide treatment to HCC mice also inhibited cell proliferation and suppressed cell cycle progression by attenuating proliferating cell nuclear antigen (P < 0.01), cyclin dependent kinase (P < 0.05), and CyclinD1 (P < 0.05) expression. In addition, nimbolide treatment to HCC mice ameliorated hepatic inflammation by reducing NF-κB, interleukin 1 beta and TNF-α expression (P < 0.05, respectively) and abrogated oxidative stress by attenuating 4-hydroxynonenal expression (P < 0.01). Molecular docking studies further confirmed that nimbolide interacts with ZO-1, NF-κB, and TNF-α.

CONCLUSION

Our current study showed for the first time that nimbolide exhibits anticancer effect by reducing tumor size, tumor burden and by suppressing cell cycle progression in HCC mice. Furthermore, nimbolide treatment to HCC mice ameliorated inflammation and oxidative stress, and improved TJ proteins expression. Consequently, nimbolide could be potentially used as a natural therapeutic agent for HCC treatment, however further human studies are warranted.

Detecting Ordinal Subcascades

Ordinal classifier cascades are constrained by a hypothesised order of the semantic class labels of a dataset. This order determines the overall structure of the decision regions in feature space. Assuming the correct order on these class labels will allow a high generalisation performance, while an incorrect one will lead to diminished results. In this way ordinal classifier systems can facilitate explorative data analysis allowing to screen for potential candidate orders of the class labels. Previously, we have shown that screening is possible for total orders of all class labels. However, as datasets might comprise samples of ordinal as well as non-ordinal classes, the assumption of a total ordering might be not appropriate. An analysis of subsets of classes is required to detect such hidden ordinal substructures. In this work, we devise a novel screening procedure for exhaustive evaluations of all order permutations of all subsets of classes by bounding the number of enumerations we have to examine. Experiments with multi-class data from diverse applications revealed ordinal substructures that generate new and support known relations.

Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology

Data-independent acquisition mass spectrometry (DIA-MS) is a next generation proteomic methodology that generates permanent digital proteome maps offering highly reproducible retrospective analysis of cellular and tissue specimens. The adoption of this technology has ushered a new wave of oncology studies across a wide range of applications including its use in molecular classification, oncogenic pathway analysis, drug and biomarker discovery and unravelling mechanisms of therapy response and resistance. In this review, we provide an overview of the experimental workflows commonly used in DIA-MS, including its current strengths and limitations versus conventional data-dependent acquisition mass spectrometry (DDA-MS). We further summarise a number of key studies to illustrate the power of this technology when applied to different facets of oncology. Finally we offer a perspective of the latest innovations in DIA-MS technology and machine learning-based algorithms necessary for driving the development of high-throughput, in-depth and reproducible proteomic assays that are compatible with clinical diagnostic workflows, which will ultimately enable the delivery of precision cancer medicine to achieve optimal patient outcomes.

Embryo-derived teratoma in vitro biological system reveals antitumor and embryotoxic activity of valproate

Antiepileptic/teratogen valproate (VPA) is a histone deacetylase inhibitor/epigenetic drug proposed for the antitumor therapy where it is generally crucial to target poorly or undifferentiated cells to prevent a recurrence. Transplanted rodent gastrulating embryos‐proper (primitive streak and three germ layers) are the source of teratoma/teratocarcinoma tumors. Human primitive‐streak remnants develop sacrococcygeal teratomas that may recur even when benign (well differentiated). To screen for unknown VPA impact on teratoma‐type tumors, we used original 2‐week embryo‐derived teratoma in vitro biological system completed by a spent media metabolome analysis. Gastrulating 9.5‐day‐old rat embryos‐proper were cultivated in Eagle's minimal essential medium (MEM) with 50% rat serum (controls) or with the addition of 2 mmVPA. Spent media metabolomes were analyzed by FTIR. Compared to controls, VPA acetylated histones; significantly diminished overall teratoma growth, impaired survival, increased the apoptotic index, and decreased proliferation index and incidence of differentiated tissues (e.g., neural tissue). Control teratomas continued to grow and differentiate for 14 days in isotransplants in vivo, but in vitro VPA‐treated teratomas resorbed. Principal component analysis of FTIR results showed that spent media metabolomes formed well‐separated clusters reflecting the treatment and day of cultivation. In metabolomes of VPA‐treated teratomas, we found elevation of previously described histone acetylation biomarkers [amide I α‐helix and A(CH₃)/A(CH₂)]) with apoptotic biomarkers within the amide I region for β‐sheets, and unordered and CH₂ vibrations of lipids. VPA may be proposed for therapy of the undifferentiated component of teratoma tumors and this biological system completed by metabolome analysis, for a faster dual screening of antitumor/embryotoxic agents.

Common and Unique microRNAs in Multiple Carcinomas Regulate Similar Network of Pathways to Mediate Cancer Progression

Cancer is a complex disease with a fatal outcome. Early detection of cancer, by monitoring appropriate molecular markers is very important for its therapeutic management. In this regard, the short non-coding RNA molecules, microRNAs (miRNAs) have shown great promise due to their availability in circulating fluids facilitating non-invasive detection of cancer. In this study, an in silico comparative analysis was performed to identify specific signature miRNAs dysregulated across multiple carcinomas and simultaneously identify unique miRNAs for each cancer type as well. The miRNA-seq data of cancer patient was obtained from GDC portal and their differential expressions along with the pathways regulated by both common and unique miRNAs were analyzed. Our studies show twelve miRNAs commonly dysregulated across seven different cancer types. Interestingly, four of those miRNAs (hsa-mir-210, hsa-mir-19a, hsa-mir-7 and hsa-mir-3662) are already reported as circulatory miRNAs (circRNAs); while, the miR-183 cluster along with hsa-mir-93 have been found to be incorporated in exosomes signifying the importance of the identified miRNAs for their use as prospective, non-invasive biomarkers. Further, the target mRNAs and pathways regulated by both common and unique miRNAs were analyzed, which interestingly had significant commonality. This suggests that miRNAs that are commonly de-regulated and specifically altered in multiple cancers might regulate similar pathways to promote cancer. Our data is of significance because we not only identify a set of common and unique miRNAs for multiple cancers but also highlight the pathways regulated by them, which might facilitate the development of future non-invasive biomarkers conducive for early detection of cancers.

Some Considerations on the WHO Histological Classification of Laryngeal Neoplasms

A new edition of the World Health Organization (WHO) Histological classification of tumours of the hypopharynx, larynx, trachea and parapharyngeal space was published in 2017. We have considered this classification regarding laryngeal neoplasms and discuss the grounds for said revision. Many of the laryngeal neoplasms described in the literature and in the previous WHO edition from 2005 have been omitted from this current revision. Many are described elsewhere in the book but it may give the new generation of pathologists/surgeons/oncologists the false impression that these tumour entities do not exist in the larynx.

Changing how we think about infectious diseases

As we learn more and more about the classes of organisms that infect humans, we are discovering that many organisms, including pathogenic organisms, may have a complex relationship with humans in which infection seldom results in the production disease. In some cases, infection may be just one biological event that occurs during a multievent process that develops sequentially, over time, and involves genetic and environmental factors that may vary among individuals. Consequently, the role of infectious organisms in the development of human disease may not meet all of the criteria normally required to determine when an organism can be called the cause of a disease. This chapter reviews the expanding role of infections in the development of human disease. We discuss prion diseases of humans, a fascinating example of an infectious disease-causing agent that is not a living organism. We also discuss the diseases of unknown etiology for which infectious organisms may play a role. In addition, this chapter reviews some of the misconceptions and recurring errors associated with the classification of infectious diseases that have led to misdiagnoses and have impeded our understanding of the role of organisms in the development of human diseases.

The uptake exploration of 68Ga-labeled NGR in well-differentiated hepatocellular carcinoma xenografts: Indication for the new clinical translational of a tracer based on NGR

18F-FDG has low uptake and poor diagnostic efficiency in hepatocellular carcinoma (HCC), particularly in well-differentiated HCC. The NGR peptide selectively targets CD13, which is overexpressed in many types of tumor cells as well as neovasculature cells. In the present study, we aimed to evaluate the feasibility of utilizing 68Ga-NGR to image CD13-positive well-differentiated HCC xenografts. The in vitro cellular uptake, in vivo micro-PET/CT imaging and biodistribution studies of 68Ga-NGR and 18F-FDG were quantitatively compared in SMMC-7721-based well‑differentiated HCC xenografts. The human fibrosarcoma (HT-1080) and human colorectal adenocarcinoma (HT-29) xenografts were respectively used as positive and negative reference groups for CD13. The expression of CD13 was qualitatively verified by immunofluorescence staining and immunohistostaining studies. The expression levels of CD13 and glucose-6-phosphatase (G6Pase) were semi-quantitatively analyzed by western blotting. The in vitro SMMC-7721 cellular uptake of 68Ga‑NGR was significantly higher than that of 18F-FDG (1.23±0.11 vs. 0.515±0.14%; P<0.01). The in vivo micro-PET/CT imaging results revealed that the uptake of 68Ga-NGR in SMMC-7721-derived tumors was 2.17±0.21% ID/g (percentage of injected dose per gram of tissue), which was higher compared to that of 18F-FDG (0.73±0.26% ID/g; P<0.01); however, the tumor/liver ratio of 68Ga-NGR was 2-fold higher than that of 18F-FDG. We concluded that the uptake of 68Ga-NGR was significantly higher both in vitro and in vivo than 18F-FDG in the well‑differentiated HCC xenografts and therefore, it is promising for further clinical translation in well-differentiated HCC PET/CT diagnosis.

Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.

Omega-3 polyunsaturated fatty acids and cancer: lessons learned from clinical trials

Over the past decades, extensive studies have addressed the therapeutic effects of omega-3 polyunsaturated fatty acids (omega-3 FAs) against different human diseases such as cardiovascular and neurodegenerative diseases, cancer, etc. A growing body of scientific research shows the pharmacokinetic information and safety of these natural occurring substances. Moreover, during recent years, a plethora of studies has demonstrated that omega-3 FAs possess therapeutic role against certain types of cancer. It is also known that omega-3 FAs can improve efficacy and tolerability of chemotherapy. Previous reports showed that suppression of nuclear factor-κB, activation of AMPK/SIRT1, modulation of cyclooxygenase (COX) activity, and up-regulation of novel anti-inflammatory lipid mediators such as protectins, maresins, and resolvins, are the main mechanisms of antineoplastic effect of omega-3 FAs. In this review, we have collected the available clinical data on the therapeutic role of omega-3 FAs against breast cancer, colorectal cancer, leukemia, gastric cancer, pancreatic cancer, esophageal cancer, prostate cancer, lung cancer, head and neck cancer, as well as cancer cachexia. We also discussed the chemistry, dietary source, and bioavailability of omega-3 FAs, and the potential molecular mechanisms of anticancer and adverse effects.

Copy Number Alterations in Enzyme-Coding and Cancer-Causing Genes Reprogram Tumor Metabolism

Somatic copy number alterations frequently occur in the cancer genome affecting not only oncogenic or tumor suppressive genes, but also passenger and potential codriver genes. An intrinsic feature resulting from such genomic perturbations is the deregulation in the metabolism of tumor cells. In this study, we have shown that metabolic and cancer-causing genes are unexpectedly often proximally positioned in the chromosome and share loci with coaltered copy numbers across multiple cancers (19 cancer types from The Cancer Genome Atlas). We have developed an analysis pipeline, Identification of Metabolic Cancer Genes (iMetCG), to infer the functional impact on metabolic remodeling from such coamplifications and codeletions and delineate genes driving cancer metabolism from those that are neutral. Using our identified metabolic genes, we were able to classify tumors based on their tissue and developmental origins. These metabolic genes were similar to known cancer genes in terms of their network connectivity, isoform frequency, and evolutionary features. We further validated these identified metabolic genes by (i) using gene essentiality data from several tumor cell lines, (ii) showing that these identified metabolic genes are strong indicators for patient survival, and (iii) observing a significant overlap between our identified metabolic genes and known cancer-metabolic genes. Our analyses revealed a hitherto unknown generic mechanism for large-scale metabolic reprogramming in cancer cells based on linear gene proximities between cancer-causing and -metabolic genes. We have identified 119 new metabolic cancer genes likely to be involved in rewiring cancer cell metabolism. Cancer Res; 76(14); 4058-67. ©2016 AACR.

Topographic confinement of epithelial clusters induces epithelial-to-mesenchymal transition in compliant matrices

Epithelial cells disengage from their clusters and become motile by undergoing epithelial-to-mesenchymal transition (EMT), an essential process for both embryonic development and tumor metastasis. Growing evidence suggests that high extracellular matrix (ECM) stiffness induces EMT. In reality, epithelial clusters reside in a heterogeneous microenvironment whose mechanical properties vary not only in terms of stiffness, but also topography, dimensionality, and confinement. Yet, very little is known about how various geometrical parameters of the ECM might influence EMT. Here, we adapt a hydrogel-microchannels based matrix platform to culture mammary epithelial cell clusters in ECMs of tunable stiffness and confinement. We report a previously unidentified role of ECM confinement in EMT induction. Surprisingly, confinement induces EMT even in the cell clusters surrounded by a soft matrix, which otherwise protects against EMT in unconfined environments. Further, we demonstrate that stiffness-induced and confinement-induced EMT work through cell-matrix adhesions and cytoskeletal polarization, respectively. These findings highlight that both the structure and the stiffness of the ECM can independently regulate EMT, which brings a fresh perspective to the existing paradigm of matrix stiffness-dependent dissemination and invasion of tumor cells.

The variation and clinical significance of hormone receptors and Her-2 status from primary to metastatic lesions in breast cancer patients

The objective of this study is to investigate how the change of hormone receptor (HR) and human epidermal growth factor receptor-2 (Her-2) status is related to patients' clinical features. One hundred ninety-three cases of patients treated at general hospital of PLA from 2000 to 2015 with advanced breast cancer were included. All patients developed recurrence that were re-biopsied and had complete pathological profile both at initial diagnosis and at relapse. HR status before and after relapse were available for all patients, while only 143 cases had Her-2 status at the two stages. The changes of ER, PR, and Her-2 status and their association with clincopathological factors and DFS were analyzed. The discordant rates of ER, PR, and Her-2 status between primary breast cancer and recurrent tumor were 34.2, 38.3, and 16.8 %, respectively. At relapse, the rates of gain of ER and PR positivity were 10.9 and 13.5 %, respectively; the rates of loss of ER and PR positivity were 23.3 and 24.9 %. Loss of positivity was more frequent than gain of positivity (p ER < 0.000, p PR = 0.001). Among patients with Her-2 negative primary tumors, 15.4 % acquired Her-2 positivity at relapse; and among Her-2 positive patients at initial diagnosis, 1.4 % turned to Her-2 negative at relapse; gain of positivity was more frequent than loss of positivity (p < 0.000). Patients with tumor larger than 2 cm in diameter were more likely to experience change of Her-2 status (25.0 vs 5.8 %, p = 0.005). Yet, the change of ER/PR was not significantly associated with the size of primary tumor. Patients with ER positive recurrent disease and PR positive primary tumor had a DFS of more than 40 months. Compared to patients who maintained PR negative, patients who gained PR positivity at relapse had significantly longer DFS by 8.5 % (35.2 vs 26.7 months, p = 0.024). Patients losing ER positivity at relapse had shorter DFS by 7.8 months compared to those with stable ER positive tumors; patients gaining ER positivity experienced longer DFS by 8.3 months; but both differences were not statistically significant. Loss of Her-2 positivity was associated with longer DFS by 13.8 months as opposed to stable Her-2 status, without statistical significance. For patients with Her-2 negative primary tumor, the changes of Her-2 status were not associated with DFS. 34.2, 38.3, and 16.8 % of breast cancer patients had their ER, PR, and Her-2 status changed after recurrence, and these changes of receptor status were associated with DFS to some degree. Gain of PR positivity at relapse was significantly correlated with longer DFS.

Pathological bases for a robust application of cancer molecular classification

Any robust classification system depends on its purpose and must refer to accepted standards, its strength relying on predictive values and a careful consideration of known factors that can affect its reliability. In this context, a molecular classification of human cancer must refer to the current gold standard (histological classification) and try to improve it with key prognosticators for metastatic potential, staging and grading. Although organ-specific examples have been published based on proteomics, transcriptomics and genomics evaluations, the most popular approach uses gene expression analysis as a direct correlate of cellular differentiation, which represents the key feature of the histological classification. RNA is a labile molecule that varies significantly according with the preservation protocol, its transcription reflect the adaptation of the tumor cells to the microenvironment, it can be passed through mechanisms of intercellular transference of genetic information (exosomes), and it is exposed to epigenetic modifications. More robust classifications should be based on stable molecules, at the genetic level represented by DNA to improve reliability, and its analysis must deal with the concept of intratumoral heterogeneity, which is at the origin of tumor progression and is the byproduct of the selection process during the clonal expansion and progression of neoplasms. The simultaneous analysis of multiple DNA targets and next generation sequencing offer the best practical approach for an analytical genomic classification of tumors.

Analysis of mouse model pathology: a primer for studying the anatomic pathology of genetically engineered mice

This primer of pathology is intended to introduce investigators to the structure (morphology) of cancer with an emphasis on genetically engineered mouse (GEM) models (GEMMs). We emphasize the necessity of using the entire biological context for the interpretation of anatomic pathology. Because the primary investigator is responsible for almost all of the information and procedures leading up to microscopic examination, they should also be responsible for documentation of experiments so that the microscopic interpretation can be rendered in context of the biology. The steps involved in this process are outlined, discussed, and illustrated. Because GEMMs are unique experimental subjects, some of the more common pitfalls are discussed. Many of these errors can be avoided with attention to detail and continuous quality assurance.

Biomedical data integration: using XML to link clinical and research data sets

Data integration occurs when a query proceeds through multiple data sets, thereby relating diverse data extracted from different data sources. Data integration is particularly important to biomedical researchers since data obtained from experiments on human tissue specimens have little applied value unless they can be combined with medical data (i.e., pathologic and clinical information). In the past, research data were correlated with medical data by manually retrieving, reading, assembling and abstracting patient charts, pathology reports, radiology reports and the results of special tests and procedures. Manual annotation of research data is impractical when experiments involve hundreds or thousands of tissue specimens resulting in large, complex data collections. The purpose of this paper is to review how XML (eXtensible Markup Language) provides the fundamental tools that support biomedical data integration. The article also discusses some of the most important challenges that block the widespread availability of annotated biomedical data sets.

Vesicular stomatitis virus has extensive oncolytic activity against human sarcomas: rare resistance is overcome by blocking interferon pathways

Oncolytic viruses have been tested against many carcinomas of ectodermal and endodermal origin; however, sarcomas, arising from mesoderm, have received relatively little attention. Using 13 human sarcomas representing seven tumor types, we assessed the efficiency of infection, cytolysis, and replication of green fluorescent protein (GFP)-expressing vesicular stomatitis virus (VSV) and its oncolytically enhanced mutant VSV-rp30a. Both viruses efficiently infected and killed 12 of 13 sarcomas. VSV-rp30a showed a faster rate of infection and replication. In vitro and in vivo, VSV was selective for sarcomas compared with normal mesoderm. A single intravenous injection of VSV-rp30a selectively infected all subcutaneous human sarcomas tested in mice and arrested the growth of tumors that otherwise grew 11-fold. In contrast to other sarcomas, synovial sarcoma SW982 demonstrated remarkable resistance, even to high titers of virus (multiplicity of infection [MOI] of 100). We found no dysfunction in VSV binding or internalization. SW982 also resisted infection by human cytomegalovirus and Sindbis virus, suggesting a virus resistance mechanism based on an altered antiviral state. Quantitative reverse transcriptase (qRT)-PCR analysis revealed a heightened basal expression of interferon-stimulated genes (ISGs). Pretreatment, but not cotreatment, with interferon attenuators valproate, Jak1 inhibitor, or vaccinia virus B18R protein rendered SW982 highly susceptible, and this correlated with downregulation of ISG expression. Jak1 inhibitor pretreatment also enhanced susceptibility in moderately VSV-resistant liposarcoma and bladder carcinoma. Overall, we find that the potential efficacy of VSV as an oncolytic agent extends to nonhematologic mesodermal tumors and that unusually strong resistance to VSV oncolysis can be overcome with interferon attenuators.

Oncogenic regulators and substrates of the anaphase promoting complex/cyclosome are frequently overexpressed in malignant tumors

The fidelity of cell division is dependent on the accumulation and ordered destruction of critical protein regulators. By triggering the appropriately timed, ubiquitin-dependent proteolysis of the mitotic regulatory proteins securin, cyclin B, aurora A kinase, and polo-like kinase 1, the anaphase promoting complex/cyclosome (APC/C) ubiquitin ligase plays an essential role in maintaining genomic stability. Misexpression of these APC/C substrates, individually, has been implicated in genomic instability and cancer. However, no comprehensive survey of the extent of their misregulation in tumors has been performed. Here, we analyzed more than 1600 benign and malignant tumors by immunohistochemical staining of tissue microarrays and found frequent overexpression of securin, polo-like kinase 1, aurora A, and Skp2 in malignant tumors. Positive and negative APC/C regulators, Cdh1 and Emi1, respectively, were also more strongly expressed in malignant versus benign tumors. Clustering and statistical analysis supports the finding that malignant tumors generally show broad misregulation of mitotic APC/C substrates not seen in benign tumors, suggesting that a "mitotic profile" in tumors may result from misregulation of the APC/C destruction pathway. This profile of misregulated mitotic APC/C substrates and regulators in malignant tumors suggests that analysis of this pathway may be diagnostically useful and represent a potentially important therapeutic target.

The clinical significance of circulating tumor cells in the peripheral blood

Tumors launch malignant cells into the circulation continuously. In early stages, the immune surveillance system eliminates these cells from the circulation, but at later times they may persist longer and be detected. The first recorded evidence of the presence of circulating tumor cells in the peripheral blood of cancer patients was documented in 1869. Now, modern molecular biologic and cell sorting techniques make their detection and characterization more practicable. This review will consider the methods currently available for their detection and characterization, and the clinical implications of their presence in various malignant conditions.

Automatic extraction of candidate nomenclature terms using the doublet method

Background

New terminology continuously enters the biomedical literature. How can curators identify new terms that can be added to existing nomenclatures? The most direct method, and one that has served well, involves reading the current literature. The scholarly curator adds new terms as they are encountered. Present-day scholars are severely challenged by the enormous volume of biomedical literature. Curators of medical nomenclatures need computational assistance if they hope to keep their terminologies current. The purpose of this paper is to describe a method of rapidly extracting new, candidate terms from huge volumes of biomedical text. The resulting lists of terms can be quickly reviewed by curators and added to nomenclatures, if appropriate. The candidate term extractor uses a variation of the previously described doublet coding method. The algorithm, which operates on virtually any nomenclature, derives from the observation that most terms within a knowledge domain are composed entirely of word combinations found in other terms from the same knowledge domain. Terms can be expressed as sequences of overlapping word doublets that have more specific meaning than the individual words that compose the term. The algorithm parses through text, finding contiguous sequences of word doublets that are known to occur somewhere in the reference nomenclature. When a sequence of matching word doublets is encountered, it is compared with whole terms already included in the nomenclature. If the doublet sequence is not already in the nomenclature, it is extracted as a candidate new term. Candidate new terms can be reviewed by a curator to determine if they should be added to the nomenclature. An implementation of the algorithm is demonstrated, using a corpus of published abstracts obtained through the National Library of Medicine's PubMed query service and using "The developmental lineage classification and taxonomy of neoplasms" as a reference nomenclature.

Results

A 31+ Megabyte corpus of pathology journal abstracts was parsed using the doublet extraction method. This corpus consisted of 4,289 records, each containing an abstract title. The total number of words included in the abstract titles was 50,547. New candidate terms for the nomenclature were automatically extracted from the titles of abstracts in the corpus. Total execution time on a desktop computer with CPU speed of 2.79 GHz was 2 seconds. The resulting output consisted of 313 new candidate terms, each consisting of concatenated doublets found in the reference nomenclature. Human review of the 313 candidate terms yielded a list of 285 terms approved by a curator. A final automatic extraction of duplicate terms yielded a final list of 222 new terms (71% of the original 313 extracted candidate terms) that could be added to the reference nomenclature.

Conclusion

The doublet method for automatically extracting candidate nomenclature terms can be used to quickly find new terms from vast amounts of text. The method can be immediately adapted for virtually any text and any nomenclature. An implementation of the algorithm, in the Perl programming language, is provided with this article.

Modern classification of neoplasms: reconciling differences between morphologic and molecular approaches

BACKGROUND

For over 150 years, pathologists have relied on histomorphology to classify and diagnose neoplasms. Their success has been stunning, permitting the accurate diagnosis of thousands of different types of neoplasms using only a microscope and a trained eye. In the past two decades, cancer genomics has challenged the supremacy of histomorphology by identifying genetic alterations shared by morphologically diverse tumors and by finding genetic features that distinguish subgroups of morphologically homogeneous tumors.

DISCUSSION

The Developmental Lineage Classification and Taxonomy of Neoplasms groups neoplasms by their embryologic origin. The putative value of this classification is based on the expectation that tumors of a common developmental lineage will share common metabolic pathways and common responses to drugs that target these pathways. The purpose of this manuscript is to show that grouping tumors according to their developmental lineage can reconcile certain fundamental discrepancies resulting from morphologic and molecular approaches to neoplasm classification. In this study, six issues in tumor classification are described that exemplify the growing rift between morphologic and molecular approaches to tumor classification: 1) the morphologic separation between epithelial and non-epithelial tumors; 2) the grouping of tumors based on shared cellular functions; 3) the distinction between germ cell tumors and pluripotent tumors of non-germ cell origin; 4) the distinction between tumors that have lost their differentiation and tumors that arise from uncommitted stem cells; 5) the molecular properties shared by morphologically disparate tumors that have a common developmental lineage, and 6) the problem of re-classifying morphologically identical but clinically distinct subsets of tumors. The discussion of these issues in the context of describing different methods of tumor classification is intended to underscore the clinical value of a robust tumor classification.

SUMMARY

A classification of neoplasms should guide the rational design and selection of a new generation of cancer medications targeted to metabolic pathways. Without a scientifically sound neoplasm classification, biological measurements on individual tumor samples cannot be generalized to class-related tumors, and constitutive properties common to a class of tumors cannot be distinguished from uninformative data in complex and chaotic biological systems. This paper discusses the importance of biological classification and examines several different approaches to the specific problem of tumor classification.

Tumor taxonomy for the developmental lineage classification of neoplasms

BACKGROUND

The new "Developmental lineage classification of neoplasms" was described in a prior publication. The classification is simple (the entire hierarchy is described with just 39 classifiers), comprehensive (providing a place for every tumor of man), and consistent with recent attempts to characterize tumors by cytogenetic and molecular features. A taxonomy is a list of the instances that populate a classification. The taxonomy of neoplasia attempts to list every known term for every known tumor of man.

METHODS

The taxonomy provides each concept with a unique code and groups synonymous terms under the same concept. A Perl script validated successive drafts of the taxonomy ensuring that: 1) each term occurs only once in the taxonomy; 2) each term occurs in only one tumor class; 3) each concept code occurs in one and only one hierarchical position in the classification; and 4) the file containing the classification and taxonomy is a well-formed XML (eXtensible Markup Language) document.

RESULTS

The taxonomy currently contains 122,632 different terms encompassing 5,376 neoplasm concepts. Each concept has, on average, 23 synonyms. The taxonomy populates "The developmental lineage classification of neoplasms," and is available as an XML file, currently 9+ Megabytes in length. A representation of the classification/taxonomy listing each term followed by its code, followed by its full ancestry, is available as a flat-file, 19+ Megabytes in length. The taxonomy is the largest nomenclature of neoplasms, with more than twice the number of neoplasm names found in other medical nomenclatures, including the 2004 version of the Unified Medical Language System, the Systematized Nomenclature of Medicine Clinical Terminology, the National Cancer Institute's Thesaurus, and the International Classification of Diseases Oncolology version.

CONCLUSIONS

This manuscript describes a comprehensive taxonomy of neoplasia that collects synonymous terms under a unique code number and assigns each tumor to a single class within the tumor hierarchy. The entire classification and taxonomy are available as open access files (in XML and flat-file formats) with this article.

Doublet method for very fast autocoding

BACKGROUND

Autocoding (or automatic concept indexing) occurs when a software program extracts terms contained within text and maps them to a standard list of concepts contained in a nomenclature. The purpose of autocoding is to provide a way of organizing large documents by the concepts represented in the text. Because textual data accumulates rapidly in biomedical institutions, the computational methods used to autocode text must be very fast. The purpose of this paper is to describe the doublet method, a new algorithm for very fast autocoding.

METHODS

An autocoder was written that transforms plain-text into intercalated word doublets (e.g. "The ciliary body produces aqueous humor" becomes "The ciliary, ciliary body, body produces, produces aqueous, aqueous humor"). Each doublet is checked against an index of doublets extracted from a standard nomenclature. Matching doublets are assigned a numeric code specific for each doublet found in the nomenclature. Text doublets that do not match the index of doublets extracted from the nomenclature are not part of valid nomenclature terms. Runs of matching doublets from text are concatenated and matched against nomenclature terms (also represented as runs of doublets).

RESULTS

The doublet autocoder was compared for speed and performance against a previously published phrase autocoder. Both autocoders are Perl scripts, and both autocoders used an identical text (a 170+ Megabyte collection of abstracts collected through a PubMed search) and the same nomenclature (neocl.xml, containing over 102,271 unique names of neoplasms). In side-by-side comparison on the same computer, the doublet method autocoder was 8.4 times faster than the phrase autocoder (211 seconds versus 1,776 seconds). The doublet method codes 0.8 Megabytes of text per second on a desktop computer with a 1.6 GHz processor. In addition, the doublet autocoder successfully matched terms that were missed by the phrase autocoder, while the phrase autocoder found no terms that were missed by the doublet autocoder.

CONCLUSIONS

The doublet method of autocoding is a novel algorithm for rapid text autocoding. The method will work with any nomenclature and will parse any ascii plain-text. An implementation of the algorithm in Perl is provided with this article. The algorithm, the Perl implementation, the neoplasm nomenclature, and Perl itself, are all open source materials.

Resources for comparing the speed and performance of medical autocoders

BACKGROUND

Concept indexing is a popular method for characterizing medical text, and is one of the most important early steps in many data mining efforts. Concept indexing differs from simple word or phrase indexing because concepts are typically represented by a nomenclature code that binds a medical concept to all equivalent representations. A concept search on the term renal cell carcinoma would be expected to find occurrences of hypernephroma, and renal carcinoma (concept equivalents). The purpose of this study is to provide freely available resources to compare speed and performance among different autocoders. These tools consist of: 1) a public domain autocoder written in Perl (a free and open source programming language that installs on any operating system); 2) a nomenclature database derived from the unencumbered subset of the publicly available Unified Medical Language System; 3) a large corpus of autocoded output derived from a publicly available medical text.

METHODS

A simple lexical autocoder was written that parses plain-text into a listing of all 1,2,3, and 4-word strings contained in text, assigning a nomenclature code for text strings that match terms in the nomenclature. The nomenclature used is the unencumbered subset of the 2003 Unified Medical Language System (UMLS). The unencumbered subset of UMLS was reduced to exclude homonymous one-word terms and proper names, resulting in a term/code data dictionary containing about a half million medical terms. The Online Mendelian Inheritance in Man (OMIM), a 92+ Megabyte publicly available medical opus, was used as sample medical text for the autocoder.

RESULTS

The autocoding Perl script is remarkably short, consisting of just 38 command lines. The 92+ Megabyte OMIM file was completely autocoded in 869 seconds on a 2.4 GHz processor (less than 10 seconds per Megabyte of text). The autocoded output file (9,540,442 bytes) contains 367,963 coded terms from OMIM and is distributed with this manuscript.

CONCLUSIONS

A public domain Perl script is provided that can parse through plain-text files of any length, matching concepts against an external nomenclature. The script and associated files can be used freely to compare the speed and performance of autocoding software.