The expanding role of BAP1 in clear cell renal cell carcinoma.

Mutations drive renal cell carcinoma biology and tumor growth. The BRCA1-associated protein-1 (BAP1) gene is frequently mutated in clear cell renal cell carcinoma (ccRCC) and has emerged as a prognostic and putative predictive biomarker. In this review, we discuss the role of BAP1 as a signature event of a subtype of ccRCC marked by aggressiveness, inflammation, and possibly a heightened response to immunotherapy.

Intratumoral Resolution of Driver Gene Mutation Heterogeneity in Renal Cancer Using Deep Learning.

Intratumoral heterogeneity arising from tumor evolution poses significant challenges biologically and clinically. Dissecting this complexity may benefit from deep learning (DL) algorithms, which can infer molecular features from ubiquitous hematoxylin and eosin (H&E)-stained tissue sections. Although DL algorithms have been developed to predict some driver mutations from H&E images, the ability of these DL algorithms to resolve intratumoral mutation heterogeneity at subclonal spatial resolution is unexplored. Here, we apply DL to a paradigm of intratumoral heterogeneity, clear cell renal cell carcinoma (ccRCC), the most common type of kidney cancer. Matched IHC and H&E images were leveraged to develop DL models for predicting intratumoral genetic heterogeneity of the three most frequently mutated ccRCC genes, BAP1, PBRM1, and SETD2. DL models were generated on a large cohort (N = 1,282) and tested on several independent cohorts, including a TCGA cohort (N = 363 patients) and two tissue microarray (TMA) cohorts (N = 118 and 365 patients). These models were also expanded to a patient-derived xenograft (PDX) TMA, affording analysis of homotopic and heterotopic interactions of tumor and stroma. The status of all three genes could be inferred by DL, with BAP1 showing the highest sensitivity and performance within and across tissue samples (AUC = 0.87-0.89 on holdout). BAP1 results were validated on independent human (AUC = 0.77-0.84) and PDX (AUC = 0.80) cohorts. Finally, BAP1 predictions correlated with clinical outputs such as disease-specific survival. Overall, these data show that DL models can resolve intratumoral heterogeneity in cancer with potential diagnostic, prognostic, and biological implications. SIGNIFICANCE: This work demonstrates the potential for deep learning analysis of histopathologic images to serve as a fast, low-cost method to assess genetic intratumoral heterogeneity. See related commentary by Song et al., p. 2672.

Deep learning reveals disease-specific signatures of white matter pathology in tauopathies.

Although pathology of tauopathies is characterized by abnormal tau protein aggregation in both gray and white matter regions of the brain, neuropathological investigations have generally focused on abnormalities in the cerebral cortex because the canonical aggregates that form the diagnostic criteria for these disorders predominate there. This corticocentric focus tends to deemphasize the relevance of the more complex white matter pathologies, which remain less well characterized and understood. We took a data-driven machine-learning approach to identify novel disease-specific morphologic signatures of white matter aggregates in three tauopathies: Alzheimer disease (AD), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD). We developed automated approaches using whole slide images of tau immunostained sections from 49 human autopsy brains (16 AD,13 CBD, 20 PSP) to identify cortex/white matter regions and individual tau aggregates, and compared tau-aggregate morphology across these diseases. Tau burden in the gray and white matter for individual subjects strongly correlated in a highly disease-specific fashion. We discovered previously unrecognized tau morphologies for AD, CBD and PSP that may be of importance in disease classification. Intriguingly, our models classified diseases equally well based on either white or gray matter tau staining. Our results suggest that tau pathology in white matter is informative, disease-specific, and linked to gray matter pathology. Machine learning has the potential to reveal latent information in histologic images that may represent previously unrecognized patterns of neuropathology, and additional studies of tau pathology in white matter could improve diagnostic accuracy.

The Evolution of Angiogenic and Inflamed Tumors: The Renal Cancer Paradigm.

Gene expression analyses have identified subtypes of conventional renal cell carcinoma broadly distributed into angiogenic and proliferative/ immunogenic clades. Integration with genomic and functional experiments in animal models yields an evolutionary model. Evolutionary trajectories illustrate remarkable plasticity, particularly for a tumor that typically begins with inactivation of a single gene.

What morphology can teach us about renal cell carcinoma clonal evolution.

While cancer is a clonal process, cumulative evidence suggest that tumors are rather heterogenous and are composed of multiple genetically-distinct subclones that arise at different times and either persist and co-exist, expand and evolve, or are eliminated. A paradigm of tumor heterogeneity is renal cell carcinoma (RCC). By exploiting morphological traits and building upon a framework around three axes (architecture, cytology and the microenvironment), we review recent advances in our understanding of RCC evolution leading to an integrated molecular genetic and morphologic evolutionary model with both prognostic and therapeutic implications. The ability to predict cancer evolution may have profound implications for clinical care and is central to oncology.

Ontological analyses reveal clinically-significant clear cell renal cell carcinoma subtypes with convergent evolutionary trajectories into an aggressive type.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a particularly challenging tumor type because of its extensive phenotypic variability as well as intra-tumoral heterogeneity (ITH). Clinically, this complexity has been reduced to a handful of pathological variables such as stage, grade and necrosis, but these variables fail to capture the breadth of the disease. How different phenotypes affect patient prognosis and influence therapeutic response is poorly understood. Extensive ITH illustrates remarkable plasticity, providing a framework to study tumor evolution. While multiregional genomic analyses have shown evolution from an ancient clone that acquires metastatic competency over time, these studies have been conducted agnostic to morphological cues and phenotypic plasticity. METHODS: We established a systematic ontology of ccRCC phenotypic variability by developing a multi-scale framework along three fundamental axes: tumor architecture, cytology and the microenvironment. We defined 33 parameters, which we comprehensively evaluated in 549 consecutive ccRCCs retrospectively. We systematically evaluated the impact of each parameter on patient outcomes, and assessed their contribution through multivariate analyses. We measured therapeutic impact in the context of anti-angiogenic therapies. We applied dimensionality reduction by t-distributed stochastic neighbor embedding (t-SNE) algorithms to tumor architectures for the study of tumor evolution superimposing tumor size and grade vectors. Evolutionary models were refined through empirical analyses of directed evolution of tumor intravascular extensions, and metastatic competency (as determined by tumor reconstitution in a heterologous host). FINDINGS: We discovered several novel ccRCC phenotypes, developed an integrated taxonomy, and identified features that improve current prognostic models. We identified a subset of ccRCCs refractory to anti-angiogenic therapies. We developed a model of tumor evolution, which revealed converging evolutionary trajectories into an aggressive type. INTERPRETATION: This work serves as a paradigm for deconvoluting tumor complexity and illustrates how morphological analyses can improve our understanding of ccRCC pleiotropy. We identified several subtypes associated with aggressive biology, and differential response to targeted therapies. By analyzing patterns of spatial and temporal co-occurrence, intravascular tumor extensions and metastatic competency, we were able to identify distinct trajectories of convergent phenotypic evolution.

A multi-modal data resource for investigating topographic heterogeneity in patient-derived xenograft tumors.

Patient-derived xenografts (PDXs) are an essential pre-clinical resource for investigating tumor biology. However, cellular heterogeneity within and across PDX tumors can strongly impact the interpretation of PDX studies. Here, we generated a multi-modal, large-scale dataset to investigate PDX heterogeneity in metastatic colorectal cancer (CRC) across tumor models, spatial scales and genomic, transcriptomic, proteomic and imaging assay modalities. To showcase this dataset, we present analysis to assess sources of PDX variation, including anatomical orientation within the implanted tumor, mouse contribution, and differences between replicate PDX tumors. A unique aspect of our dataset is deep characterization of intra-tumor heterogeneity via immunofluorescence imaging, which enables investigation of variation across multiple spatial scales, from subcellular to whole tumor levels. Our study provides a benchmark data resource to investigate PDX models of metastatic CRC and serves as a template for future, quantitative investigations of spatial heterogeneity within and across PDX tumor models.

Distinct Expression Patterns of Carbonic Anhydrase IX in Clear Cell, Microcystic, and Angiomatous Meningiomas.

Clear cell, microcytic, and angiomatous meningiomas are 3 vasculature-rich variants with overlapping morphological features but different prognostic and treatment implications. Distinction between them is not always straightforward. We compared the expression patterns of the hypoxia marker carbonic anhydrase IX (CA-IX) in meningiomas with predominant clear cell (n = 15), microcystic (n = 9), or angiomatous (n = 11) morphologies, as well as 117 cases of other World Health Organization recognized histological meningioma variants. Immunostaining for SMARCE1 protein, whose loss-of-function has been associated with clear cell meningiomas, was performed on all clear cell meningiomas, and selected variants of meningiomas as controls. All clear cell meningiomas showed absence of CA-IX expression and loss of nuclear SMARCE1 expression. All microcystic and angiomatous meningiomas showed diffuse CA-IX immunoreactivity and retained nuclear SMARCE1 expression. In other meningioma variants, CA-IX was expressed in a hypoxia-restricted pattern and was highly associated with atypical features such as necrosis, small cell change, and focal clear cell change. In conclusion, CA-IX may serve as a useful diagnostic marker in differentiating clear cell, microcystic, and angiomatous meningiomas.

Sampling strategies to capture single-cell heterogeneity.

Advances in single-cell technologies have highlighted the prevalence and biological significance of cellular heterogeneity. A critical question researchers face is how to design experiments that faithfully capture the true range of heterogeneity from samples of cellular populations. Here we develop a data-driven approach, illustrated in the context of image data, that estimates the sampling depth required for prospective investigations of single-cell heterogeneity from an existing collection of samples.

Combination Therapy Targeting BCL6 and Phospho-STAT3 Defeats Intratumor Heterogeneity in a Subset of Non-Small Cell Lung Cancers.

Oncogene-specific changes in cellular signaling have been widely observed in lung cancer. Here, we investigated how these alterations could affect signaling heterogeneity and suggest novel therapeutic strategies. We compared signaling changes across six human bronchial epithelial cell (HBEC) strains that were systematically transformed with various combinations of TP53, KRAS, and MYC-oncogenic alterations commonly found in non-small cell lung cancer (NSCLC). We interrogated at single-cell resolution how these alterations could affect classic readouts (ß-CATENIN, SMAD2/3, phospho-STAT3, P65, FOXO1, and phospho-ERK1/2) of key pathways commonly affected in NSCLC. All three oncogenic alterations were required concurrently to observe significant signaling changes, and significant heterogeneity arose in this condition. Unexpectedly, we found two mutually exclusive altered subpopulations: one with STAT3 upregulation and another with SMAD2/3 downregulation. Treatment with a STAT3 inhibitor eliminated the upregulated STAT3 subpopulation, but left a large surviving subpopulation with downregulated SMAD2/3. A bioinformatics search identified BCL6, a gene downstream of SMAD2/3, as a novel pharmacologically accessible target of our transformed HBECs. Combination treatment with STAT3 and BCL6 inhibitors across a panel of NSCLC cell lines and in xenografted tumors significantly reduced tumor cell growth. We conclude that BCL6 is a new therapeutic target in NSCLC and combination therapy that targets multiple vulnerabilities (STAT3 and BCL6) downstream of common oncogenes, and tumor suppressors may provide a potent way to defeat intratumor heterogeneity. Cancer Res; 77(11); 3070-81. ©2017 AACR.

Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells.

Cancer therapy has traditionally focused on eliminating fast-growing populations of cells. Yet, an increasing body of evidence suggests that small subpopulations of cancer cells can evade strong selective drug pressure by entering a 'persister' state of negligible growth. This drug-tolerant state has been hypothesized to be part of an initial strategy towards eventual acquisition of bona fide drug-resistance mechanisms. However, the diversity of drug-resistance mechanisms that can expand from a persister bottleneck is unknown. Here we compare persister-derived, erlotinib-resistant colonies that arose from a single, EGFR-addicted lung cancer cell. We find, using a combination of large-scale drug screening and whole-exome sequencing, that our erlotinib-resistant colonies acquired diverse resistance mechanisms, including the most commonly observed clinical resistance mechanisms. Thus, the drug-tolerant persister state does not limit--and may even provide a latent reservoir of cells for--the emergence of heterogeneous drug-resistance mechanisms.

On comparing heterogeneity across biomarkers.

Microscopy reveals complex patterns of cellular heterogeneity that can be biologically informative. However, a limitation of microscopy is that only a small number of biomarkers can typically be monitored simultaneously. Thus, a natural question is whether additional biomarkers provide a deeper characterization of the distribution of cellular states in a population. How much information about a cell's phenotypic state in one biomarker is gained by knowing its state in another biomarker? Here, we describe a framework for comparing phenotypic states across biomarkers. Our approach overcomes the current limitation of microscopy by not requiring costaining biomarkers on the same cells; instead, we require staining of biomarkers (possibly separately) on a common collection of phenotypically diverse cell lines. We evaluate our approach on two image datasets: 33 oncogenically diverse lung cancer cell lines stained with 7 biomarkers, and 49 less diverse subclones of one lung cancer cell line stained with 12 biomarkers. We first validate our method by comparing it to the "gold standard" of costaining. We then apply our approach to all pairs of biomarkers and use it to identify biomarkers that yield similar patterns of heterogeneity. The results presented in this work suggest that many biomarkers provide redundant information about heterogeneity. Thus, our approach provides a practical guide for selecting independently informative biomarkers and, more generally, will yield insights into both the connectivity of biological networks and the complexity of the state space of biological systems.

Rapid analysis and exploration of fluorescence microscopy images.

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard. Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.

NeatMap--non-clustering heat map alternatives in R.

BACKGROUND: The clustered heat map is the most popular means of visualizing genomic data. It compactly displays a large amount of data in an intuitive format that facilitates the detection of hidden structures and relations in the data. However, it is hampered by its use of cluster analysis which does not always respect the intrinsic relations in the data, often requiring non-standardized reordering of rows/columns to be performed post-clustering. This sometimes leads to uninformative and/or misleading conclusions. Often it is more informative to use dimension-reduction algorithms (such as Principal Component Analysis and Multi-Dimensional Scaling) which respect the topology inherent in the data. Yet, despite their proven utility in the analysis of biological data, they are not as widely used. This is at least partially due to the lack of user-friendly visualization methods with the visceral impact of the heat map. RESULTS: NeatMap is an R package designed to meet this need. NeatMap offers a variety of novel plots (in 2 and 3 dimensions) to be used in conjunction with these dimension-reduction techniques. Like the heat map, but unlike traditional displays of such results, it allows the entire dataset to be displayed while visualizing relations between elements. It also allows superimposition of cluster analysis results for mutual validation. NeatMap is shown to be more informative than the traditional heat map with the help of two well-known microarray datasets. CONCLUSIONS: NeatMap thus preserves many of the strengths of the clustered heat map while addressing some of its deficiencies. It is hoped that NeatMap will spur the adoption of non-clustering dimension-reduction algorithms.

A novel meta-analysis method exploiting consistency of high-throughput experiments.

MOTIVATION: Large-scale biological experiments provide snapshots into the huge number of processes running in parallel within the organism. These processes depend on a large number of (hidden) (epi)genetic, social, environmental and other factors that are out of experimentalists' control. This makes it extremely difficult to identify the dominant processes and the elements involved in them based on a single experiment. It is therefore desirable to use multiple sets of experiments targeting the same phenomena while differing in some experimental parameters (hidden or controllable). Although such datasets are becoming increasingly common, their analysis is complicated by the fact that the various biological elements could be influenced by different sets of factors. RESULTS: The central hypothesis of this article is that biologically related elements and processes are affected by changes in similar ways while unrelated ones are affected differently. Thus, the relations between related elements are more consistent across experiments. The method outlined here looks for groups of elements with robust intra-group relationships in the expectation that they are related. The major groups of elements may be identified in this way. The strengths of relationships per se are not valued, just their consistency. This represents a completely novel and unutilized source of information. In the analysis of time course microarray experiments, I found cell cycle- and ribosome-related genes to be the major groups. Despite not looking for these groups in particular, the identification of these genes rivals that of methods designed specifically for this purpose. AVAILABILITY: A C++ implementation is available at http://www.rinst.org/ICS/ICS_Programs.tar.gz.