Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in the Gleason grade group (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or overtreatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B, and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomize prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

The Akt/mTOR and MNK/eIF4E pathways rewire the prostate cancer translatome to secrete HGF, SPP1 and BGN and recruit suppressive myeloid cells

Cancer is highly infiltrated by myeloid-derived suppressor cells (MDSCs). Currently available immunotherapies do not completely eradicate MDSCs. Through a genome-wide analysis of the translatome of prostate cancers driven by different genetic alterations, we demonstrate that prostate cancer rewires its secretome at the translational level to recruit MDSCs. Among different secreted proteins released by prostate tumor cells, we identified Hgf, Spp1 and Bgn as the key factors that regulate MDSC migration. Mechanistically, we found that the coordinated loss of Pdcd4 and activation of the MNK/eIF4E pathways regulate the mRNAs translation of Hgf, Spp1 and Bgn. MDSC infiltration and tumor growth were dampened in prostate cancer treated with the MNK1/2 inhibitor eFT508 and/or the AKT inhibitor ipatasertib, either alone or in combination with a clinically available MDSC-targeting immunotherapy. This work provides a therapeutic strategy that combines translation inhibition with available immunotherapies to restore immune surveillance in prostate cancer.

Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group

PURPOSE

Rhabdomyosarcoma (RMS) is an aggressive soft-tissue sarcoma, which primarily occurs in children and young adults. We previously reported specific genomic alterations in RMS, which strongly correlated with survival; however, predicting these mutations or high-risk disease at diagnosis remains a significant challenge. In this study, we utilized convolutional neural networks (CNN) to learn histologic features associated with driver mutations and outcome using hematoxylin and eosin (H&E) images of RMS.

EXPERIMENTAL DESIGN

Digital whole slide H&E images were collected from clinically annotated diagnostic tumor samples from 321 patients with RMS enrolled in Children's Oncology Group (COG) trials (1998-2017). Patches were extracted and fed into deep learning CNNs to learn features associated with mutations and relative event-free survival risk. The performance of the trained models was evaluated against independent test sample data (n = 136) or holdout test data.

RESULTS

The trained CNN could accurately classify alveolar RMS, a high-risk subtype associated with PAX3/7-FOXO1 fusion genes, with an ROC of 0.85 on an independent test dataset. CNN models trained on mutationally-annotated samples identified tumors with RAS pathway with a ROC of 0.67, and high-risk mutations in MYOD1 or TP53 with a ROC of 0.97 and 0.63, respectively. Remarkably, CNN models were superior in predicting event-free and overall survival compared with current molecular-clinical risk stratification.

CONCLUSIONS

This study demonstrates that high-risk features, including those associated with certain mutations, can be readily identified at diagnosis using deep learning. CNNs are a powerful tool for diagnostic and prognostic prediction of rhabdomyosarcoma, which will be tested in prospective COG clinical trials.

Breast Cancer Heterogeneity

Breast tumor heterogeneity is a major challenge in the clinical management of breast cancer patients. Both inter-tumor and intra-tumor heterogeneity imply that each breast cancer (BC) could have different prognosis and would benefit from specific therapy. Breast cancer is a dynamic entity, changing during tumor progression and metastatization and this poses fundamental issues to the feasibility of a personalized medicine approach. The most effective therapeutic strategy for patients with recurrent disease should be assessed evaluating biopsies obtained from metastatic sites. Furthermore, the tumor progression and the treatment response should be strictly followed and radiogenomics and liquid biopsy might be valuable tools to assess BC heterogeneity in a non-invasive way.

Astragaloside IV Suppresses Hepatic Proliferation in Regenerating Rat Liver after 70% Partial Hepatectomy via Down-Regulation of Cell Cycle Pathway and DNA Replication

Astragaloside IV (AS-IV) is one of the major bio-active ingredients of huang qi which is the dried root of Astragalus membranaceus (a traditional Chinese medicinal plant). The pharmacological effects of AS-IV, including anti-oxidative, anti-cancer, and anti-diabetic effects have been actively studied, however, the effects of AS-IV on liver regeneration have not yet been fully described. Thus, the aim of this study was to explore the effects of AS-IV on regenerating liver after 70% partial hepatectomy (PHx) in rats. Differentially expressed mRNAs, proliferative marker and growth factors were analyzed. AS-IV (10 mg/kg) was administrated orally 2 h before surgery. We found 20 core genes showed effects of AS-IV, many of which were involved with functions related to DNA replication during cell division. AS-IV down-regulates MAPK signaling, PI3/Akt signaling, and cell cycle pathway. Hepatocyte growth factor (HGF) and cyclin D1 expression were also decreased by AS-IV administration. Transforming growth factor β1 (TGFβ1, growth regulation signal) was slightly increased. In short, AS-IV down-regulated proliferative signals and genes related to DNA replication. In conclusion, AS-IV showed anti-proliferative activity in regenerating liver tissue after 70% PHx.

Senescence Reprogramming by TIMP1 Deficiency Promotes Prostate Cancer Metastasis

Metastases account for most cancer-related deaths, yet the mechanisms underlying metastatic spread remain poorly understood. Recent evidence demonstrates that senescent cells, while initially restricting tumorigenesis, can induce tumor progression. Here, we identify the metalloproteinase inhibitor TIMP1 as a molecular switch that determines the effects of senescence in prostate cancer. Senescence driven either by PTEN deficiency or chemotherapy limits the progression of prostate cancer in mice. TIMP1 deletion allows senescence to promote metastasis, and elimination of senescent cells with a senolytic BCL-2 inhibitor impairs metastasis. Mechanistically, TIMP1 loss reprograms the senescence-associated secretory phenotype (SASP) of senescent tumor cells through activation of matrix metalloproteinases (MMPs). Loss of PTEN and TIMP1 in prostate cancer is frequent and correlates with resistance to docetaxel and worst clinical outcomes in patients treated in an adjuvant setting. Altogether, these findings provide insights into the dual roles of tumor-associated senescence and can potentially impact the treatment of prostate cancer.

Cross-Platform Comparison of Computer-assisted Image Analysis Quantification of In Situ mRNA Hybridization in Investigative Pathology

Although availability of automated platforms has proliferated, there is no standard practice for computer-assisted generation of scores for mRNA in situ hybridization (ISH) visualized by brightfield microscopic imaging on tissue sections. To address this systematically, an ISH for peptidylprolyl isomerase B (PPIB) (cyclophilin B) mRNA was optimized and applied to a tissue microarray of archival non-small cell lung carcinoma cases, and then automated image analysis for PPIB was refined across 4 commercially available software platforms. Operator experience and scoring results from ImageScope, HALO, CellMap, and Developer XD were systematically compared with each other and to manual pathologist scoring. Markup images were compared and contrasted for accuracy, the ability of the platform to identify cells, and the ease of visual assessment to determine appropriate interpretation. Comparing weighted scoring approaches using H-scores (Developer XD, ImageScope, and manual scoring) a correlation was observed (R value=0.7955), and association between the remaining 2 approaches (HALO and CellMap) was of similar value. ImageScope showed the highest R value in comparison with manual scoring (0.7377). Mean-difference plots showed that HALO produced the highest relative normalized values, suggesting higher relative sensitivity. ImageScope overestimated PPIB ISH signal at the high end of the range scores; however, this tendency was not observed in other platforms. HALO emerged with the highest number of favorable observations, no apparent systematic bias in score generation compared with the other methods, and potentially higher sensitivity to detect ISH. HALO may serve as a tool to empower teams of investigative pathology laboratory scientists to assist pathologists readily with quantitative scoring of ISH.

Tumor heterogeneity: does it matter?

Introduction: It has long been recognized that tumors are composed of a mosaic of cells and numerous methods have been developed to detect tumor heterogeneity, including in situ hybridization, multi-regional sampling, cytological assays, and whole genome and single cell sequencing. Using these methods, heterogeneity has been observed at the genetic, epigenetic, and phenotypic level in numerous cancers. With the advent of deep sequencing technology, we now appreciate a greater complexity of distinct genotypes and phenotypes that drive the biological behavior of cancer. Despite decades of progress in detecting tumor heterogeneity, the question remains: to what extent does it matter? Areas covered: This review explores the evidence for and against the importance of tumor heterogeneity in three main areas: prognostication, development of targeted therapeutics and tumor resistance; summarizing current understanding before evaluating ongoing experimental and clinical developments. Expert opinion: Theoretical understanding and in vitro detection of intratumour heterogeneity promises much but is yet to translate into meaningful clinical benefit. However, the recent emergence of a host of technological innovations and upcoming clinical trials may soon change the landscape of this field.

High-content, cell-by-cell assessment of HER2 overexpression and amplification: a tool for intratumoral heterogeneity detection in breast cancer

Immunohistochemistry and fluorescence in situ hybridization are the two standard methods for human epidermal growth factor receptor 2 (HER2) assessment. However, they have severe limitations to assess quantitatively intratumoral heterogeneity (ITH) when multiple subclones of tumor cells co-exist. We develop here a high-content, quantitative analysis of breast cancer tissues based on microfluidic experimentation and image processing, to characterize both HER2 protein overexpression and HER2 gene amplification at the cellular level. The technique consists of performing sequential steps on the same tissue slide: an immunofluorescence (IF) assay using a microfluidic protocol, an elution step for removing the IF staining agents, a standard FISH staining protocol, followed by automated quantitative cell-by-cell image processing. Moreover, ITH is accurately detected in both cluster and mosaic form using an analysis of spatial association and a mathematical model that allows discriminating true heterogeneity from artifacts due to the use of thin tissue sections. This study paves the way to evaluate ITH with high accuracy and content while requiring standard staining methods.

19q12 amplified and non-amplified subsets of high grade serous ovarian cancer with overexpression of cyclin E1 differ in their molecular drivers and clinical outcomes

OBJECTIVES

Readily apparent cyclin E1 expression occurs in 50% of HGSOC, but only half are linked to 19q12 locus amplification. The amplified/cyclin E1^hi subset has intact BRCA1/2, unfavorable outcome, and is potentially therapeutically targetable. We studied whether non-amplified/cyclin E1^hi HGSOC has similar characteristics. We also assessed the expression of cyclin E1 degradation-associated proteins, FBXW7 and USP28, as potential drivers of high cyclin E1 expression in both subsets.

METHODS

262 HGSOC cases were analyzed by in situ hybridization for 19q12 locus amplification and immunohistochemistry for cyclin E1, URI1 (another protein encoded by the 19q12 locus), FBXW7 and USP28 expression. Tumors were classified by 19q12 amplification status and correlated to cyclin E1 and URI1 expression, BRCA1/2 germline mutation, FBXW7 and USP28 expression, and clinical outcomes. Additionally, we assessed the relative genomic instability of amplified/cyclin E1^hi and non-amplified/cyclin E1^hi groups of HGSOC datasets from The Cancer Genome Atlas.

RESULTS

Of the 82 cyclin E1^hi cases, 43 (52%) were amplified and 39 (48%) were non-amplified. Unlike amplified tumors, non-amplified/cyclin E1^hi tumor status was not mutually exclusive with gBRCA1/2 mutation. The non-amplified/cyclin E1^hi group had significantly increased USP28, while the amplified/cyclin E1^hi cancers had significantly lower FBXW7 expression consistent with a role for both in stabilizing cyclin E1. Notably, only the amplified/cyclin E1^hi subset was associated with genomic instability and had a worse outcome than non-amplified/cyclin E1^hi group.

CONCLUSIONS

Amplified/cyclin E1^hi and non-amplified/cyclin E1^hi tumors have different pathological and biological characteristics and clinical outcomes indicating that they are separate subsets of cyclin E1^hi HGSOC.

Automated Gleason grading of prostate cancer tissue microarrays via deep learning

The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations. In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohen’s quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa = 0.71). Furthermore, the model’s Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort. Overall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.

Computational Analysis of Cell Dynamics in Videos with Hierarchical-Pooled Deep-Convolutional Features

Computational analysis of cellular appearance and its dynamics is used to investigate physiological properties of cells in biomedical research. In consideration of the great success of deep learning in video analysis, we first introduce two-stream convolutional networks (ConvNets) to automatically learn the biologically meaningful dynamics from raw live-cell videos. However, the two-stream ConvNets lack the ability to capture long-range video evolution. Therefore, a novel hierarchical pooling strategy is proposed to model the cell dynamics in a whole video, which is composed of trajectory pooling for short-term dynamics and rank pooling for long-range ones. Experimental results demonstrate that the proposed pipeline effectively captures the spatiotemporal dynamics from the raw live-cell videos and outperforms existing methods on our cell video database.

Breast cancer intratumour heterogeneity: current status and clinical implications

Breast cancer (BC) is a heterogeneous disease that varies in presentation, morphological features, behaviour, and response to therapy. High-throughput molecular profiling studies have revolutionised our understanding of BC heterogeneity, and have demonstrated that molecular profiles of tumours are variable not only between tumours, but also within individual tumours. Current evidence indicates that spatial and temporal intratumour heterogeneity of BC exists at levels beyond what are commonly expected. Intratumour heterogeneity poses critical challenges in the diagnosis, prediction of behaviour and management of BC. For instance, heterogeneous expression of oestrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 can be seen not only in primary tumours between different regions, but also between primary tumours and their corresponding metastatic/recurrent lesions. The demonstration of molecularly distinct subclones within individual tumours may explain, at least in part, the mechanisms controlling the variable behaviour of BC, and may change our approach to BC sampling and treatment. In this review, BC intratumour heterogeneity is highlighted, with a special emphasis on the current knowledge pertaining to the relationship between intratumour heterogeneity and BC pathogenesis, evolution, and progression, with consideration of its impact on disease diagnosis, management, and the emergence of novel therapeutic targets. The key role of high-throughput molecular and imaging techniques is also addressed.

Differential Expression of PGC1α in Intratumor Redox Subpopulations of Breast Cancer

Our previous studies indicate that the mitochondrial redox state and its intratumor heterogeneity are associated with invasiveness and metastatic potential in human breast cancer cell models and mouse xenografts. To further study the molecular basis of redox heterogeneity, we obtained the fluorescence images of Fp (oxidized flavoproteins containing flavin adenine dinucleotide, i.e., FAD), NADH (reduced nicotinamide adenine dinucleotide), and the Fp redox ratio (FpR = Fp/(Fp + NADH)) of MDA-MB-231 xenografts by the Chance redox scanner, then isolated the intratumoral redox subpopulations by dissection according to the redox ratio image. A total of 12 subpopulations were isolated from 4 tumors (2-4 locations from each tumor). The 12 subpopulations were classified into 3 FpR groups: high FpR (HFpR, n = 4, FpR range 0.78-0.92, average 0.85), medium FpR (MFpR, n = 5, FpR range 0.39-0.68, average 0.52), and low FpR (LFpR, n = 3, FpR range 0.15-0.28, average 0.20). The RT-PCR (reverse transcription polymerase chain reaction) analysis on these redox subpopulations showed that PGC-1α is significantly upregulated in the HFpR redox group compared to the MFpR group (fold change 2.1, p = 0.008), but not significantly different between MFpR and LFpR groups, or between HFpR and LFpR groups. These results indicate that optical redox imaging (ORI)-based redox subpopulations exhibit differential expression of PGC1α gene and suggest that PGC1α might play a role in redox mediation of breast cancer progression.

Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures

Background

Tumor heterogeneity can manifest itself by sub-populations of cells having distinct phenotypic profiles expressed as diverse molecular, morphological and spatial distributions. This inherent heterogeneity poses challenges in terms of diagnosis, prognosis and efficient treatment. Consequently, tools and techniques are being developed to properly characterize and quantify tumor heterogeneity. Multiplexed immunofluorescence (MxIF) is one such technology that offers molecular insight into both inter-individual and intratumor heterogeneity. It enables the quantification of both the concentration and spatial distribution of 60+ proteins across a tissue section. Upon bioimage processing, protein expression data can be generated for each cell from a tissue field of view.

Results

The Multi-Omics Heterogeneity Analysis (MOHA) tool was developed to compute tissue heterogeneity metrics from MxIF spatially resolved tissue imaging data. This technique computes the molecular state of each cell in a sample based on a pathway or gene set. Spatial states are then computed based on the spatial arrangements of the cells as distinguished by their respective molecular states. MOHA computes tissue heterogeneity metrics from the distributions of these molecular and spatially defined states. A colorectal cancer cohort of approximately 700 subjects with MxIF data is presented to demonstrate the MOHA methodology. Within this dataset, statistically significant correlations were found between the intratumor AKT pathway state diversity and cancer stage and histological tumor grade. Furthermore, intratumor spatial diversity metrics were found to correlate with cancer recurrence.

Conclusions

MOHA provides a simple and robust approach to characterize molecular and spatial heterogeneity of tissues. Research projects that generate spatially resolved tissue imaging data can take full advantage of this useful technique. The MOHA algorithm is implemented as a freely available R script (see supplementary information).

Detection of CCNE1/URI (19q12) amplification by in situ hybridisation is common in high grade and type II endometrial cancer

One TCGA subgroup of endometrial cancer (EC) is characterised by extensive genomic DNA copy number alterations. CCNE1 located at 19q12 is frequently amplified in EC and a target for anti-cancer therapy. The relevance of URI, also located at 19q12, is unknown. To evaluate the prevalence of 19q12 (CCNE1/URI) in EC, we investigated different histologic types by in situ hybridisation (ISH) and copy number assay. We applied a previously established 19q12 ISH for the detection of CCNE1/URI copy numbers in EC (n = 270) using conventional bright field microscopy. In a subset (n = 21), 19q12 amplification status was validated by OncoScan assay. Manual ISH was controlled by a recently developed computational ISHProfiler algorithm. Associations of 19q12 status with Cyclin E1, URI and p53 expression, and clinico-pathological parameters were tested.

Amplification of 19q12 (CCNE1/URI) was found in 10.4% (28/270) and was significantly associated with type II EC (high grade and non-endometrioid; p < 0.0001), advanced FIGO stage (p = 0.001), high Cyclin E1 expression (p = 0.008) and aberrant p53 expression (p = 0.04). 19q12 ISH data were confirmed by OncoScan and computational ISHProfiler techniques. The 19q12 in situ hybridisation is a feasible and robust biomarker assay in molecular pathology. Amplification of CCNE1/URI predominantly occurred in type II endometrial cancer. Prospective clinical trials are warranted to assess the utility of combined 19q12 amplification and Cyclin E1/URI protein expression analysis for the prediction of therapeutic response to chemotherapy and/or cyclin-dependent kinase inhibitors in patients with endometrial cancer.

Prostate cancer-associated SPOP mutations confer resistance to BET inhibitors through stabilization of BRD4

The bromodomain and extra-terminal (BET) family of proteins, comprised of four members including BRD2, BRD3, BRD4 and the testis-specific isoform BRDT, largely function as transcriptional co-activators ^1–3 and play critical roles in various cellular processes, including cell cycle, apoptosis, migration and invasion ^4,5. As such, BET proteins enhance the oncogenic functions of major cancer drivers by either elevating their expression such as c-Myc in leukemia ^6,7 or by promoting transcriptional activities of oncogenic factors such as AR and ERG in the prostate cancer setting ⁸. Pathologically, BET proteins are frequently overexpressed and clinically linked to various types of human cancers ^5,9,10, therefore pursued as attractive therapeutic targets for selective inhibition in patients. To this end, a number of bromodomain inhibitors, including JQ1 and I-BET, have been developed ^11,12 and shown promising outcomes in early clinical trials. Despite resistance to BET inhibitor has been documented in pre-clinical models ^13–15 the molecular mechanisms underlying acquired resistance are largely unknown. Here, we report that Cullin 3^SPOP earmarks BET proteins including BRD2, BRD3 and BRD4 for ubiquitination-mediated degradation. Pathologically, prostate cancer-associated SPOP mutants fail to interact with and promote the destruction of BET proteins, leading to their elevated abundance in SPOP-deficient prostate cancer. As a result, prostate cancer cells and prostate cancer patient-derived organoids harboring SPOP mutations are more resistant to BET inhibitor-induced cell growth arrest and apoptosis. Therefore, our results elucidate the tumor suppressor role of SPOP in prostate cancer by negatively controlling BET protein stability, and also provide a molecular mechanism for BET inhibitor resistance in prostate cancer patients bearing SPOP mutations.

Prostate cancer, PI3K, PTEN and prognosis

Loss of function of the PTEN tumour suppressor, resulting in dysregulated activation of the phosphoinositide 3-kinase (PI3K) signalling network, is recognized as one of the most common driving events in prostate cancer development. The observed mechanisms of PTEN loss are diverse, but both homozygous and heterozygous genomic deletions including PTEN are frequent, and often accompanied by loss of detectable protein as assessed by immunohistochemistry (IHC). The occurrence of PTEN loss is highest in aggressive metastatic disease and this has driven the development of PTEN as a prognostic biomarker, either alone or in combination with other factors, to distinguish indolent tumours from those likely to progress. Here, we discuss these factors and the consequences of PTEN loss, in the context of its role as a lipid phosphatase, as well as current efforts to use available inhibitors of specific components of the PI3K/PTEN/TOR signalling network in prostate cancer treatment.

Winding back Wnt signalling: potential therapeutic targets for treating gastric cancers

Gastric cancer persists as a frequent and deadly disease that claims over 700 000 lives annually. Gastric cancer is a multifactorial disease that is genetically, cytologically and architecturally more heterogeneous than other gastrointestinal cancers, making it therapeutically challenging. As such, and largely attributed to late-stage diagnosis, gastric cancer patients show only partial response to standard chemo and targeted molecular therapies, highlighting an urgent need to develop new targeted therapies for this disease. Wnt signalling has a well-documented history in the genesis of many cancers and is, therefore, an attractive therapeutic target. As such, drug discovery has focused on developing inhibitors that target multiple nodes of the Wnt signalling cascade, some of which have progressed to clinical trials. The collective efforts of patient genomic profiling has uncovered genetic lesions to multiple components of the Wnt pathway in gastric cancer patients, which strongly suggest that Wnt-targeted therapies could offer therapeutic benefits for gastric cancer patients. These data have been supported by studies in mouse models of gastric cancer, which identify Wnt signalling as a driver of gastric tumourigenesis. Here, we review the current literature regarding Wnt signalling in gastric cancer and highlight the suitability of each class of Wnt inhibitor as a potential treatment for gastric cancer patients, in relation to the type of Wnt deregulation observed.

LINKED ARTICLES

This article is part of a themed section on WNT Signalling: Mechanisms and Therapeutic Opportunities. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.24/issuetoc.

Clonal cooperativity in heterogenous cancers

Tumor heterogeneity is a major obstacle to the development of effective therapies and is thus an important focus of cancer research. Genetic and epigenetic alterations, as well as altered tumor microenvironments, result in tumors made up of diverse subclones with different genetic and phenotypic characteristics. Intratumor heterogeneity enables competition, but also supports clonal cooperation via cell-cell contact or secretion of factors, resulting in enhanced tumor progression. Here, we summarize recent findings related to interclonal interactions within a tumor and the therapeutic implications of such interactions, with an emphasis on how different subclones collaborate with each other to promote proliferation, metastasis and therapy-resistance. Furthermore, we propose that disruption of clonal cooperation by targeting key factors (such as Wnt and Hedgehog, amongst others) can be an alternative approach to improving clinical outcomes.

A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients

Microscopy image data of human cancers provide detailed phenotypes of spatially and morphologically intact tissues at single-cell resolution, thus complementing large-scale molecular analyses, e.g., next generation sequencing or proteomic profiling. Here we describe a high-resolution tissue microarray (TMA) image dataset from a cohort of 71 prostate tissue samples, which was hybridized with bright-field dual colour chromogenic and silver in situ hybridization probes for the tumour suppressor gene PTEN. These tissue samples were digitized and supplemented with expert annotations, clinical information, statistical models of PTEN genetic status, and computer source codes. For validation, we constructed an additional TMA dataset for 424 prostate tissues, hybridized with FISH probes for PTEN, and performed survival analysis on a subset of 339 radical prostatectomy specimens with overall, disease-specific and recurrence-free survival (maximum 167 months). For application, we further produced 6,036 image patches derived from two whole slides. Our curated collection of prostate cancer data sets provides reuse potential for both biomedical and computational studies.

Too much to handle - how gaining chromosomes destabilizes the genome

Most eukaryotic organisms are diploid, with 2 chromosome sets in their nuclei. Whole chromosomal aneuploidy, a deviation from multiples of the haploid chromosome number, arises from chromosome segregation errors and often has detrimental consequences for cells. In humans, numerical aneuploidy severely impairs embryonic development and the rare survivors develop disorders characterized by multiple pathologies. Moreover, as many as 75 % of malignant tumors display aneuploidy. Although the exact contribution of aneuploidy to tumorigenesis remains unclear, previous studies have suggested that aneuploidy may affect the maintenance of genome integrity. We found that human cells with extra chromosomes showed phenotypes suggestive of replication defects, a phenomenon which we went on to characterize as being due to the aneuploidy-driven downregulation of replication factors, in particular of the replicative helicase MCM2-7. Thus, missegregation of even a single chromosome can further promote genomic instability and thereby contribute to tumor development. In this review we will examine the possible causes of downregulation of replicative factors and discuss the consequences of genomic instability in aneuploid cells.