Andreas Heindl

Capture Hi-C identifies the chromatin interactome of colorectal cancer risk loci

Multiple regulatory elements distant from their targets on the linear genome can influence the expression of a single gene through chromatin looping. Chromosome conformation capture implemented in Hi-C allows for genome-wide agnostic characterization of chromatin contacts. However, detection of functional enhancer–promoter interactions is precluded by its effective resolution that is determined by both restriction fragmentation and sensitivity of the experiment. Here we develop a capture Hi-C (cHi-C) approach to allow an agnostic characterization of these physical interactions on a genome-wide scale. Single-nucleotide polymorphisms associated with complex diseases often reside within regulatory elements and exert effects through long-range regulation of gene expression. Applying this cHi-C approach to 14 colorectal cancer risk loci allows us to identify key long-range chromatin interactions in cis and trans involving these loci.

Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer.

The abundance of tumor-infiltrating lymphocytes has been associated with a favorable prognosis in estrogen receptor-negative breast cancer. However, a high degree of spatial heterogeneity in lymphocytic infiltration is often observed and its clinical implication remains unclear. Here we combine automated histological image processing with methods of spatial statistics used in ecological data analysis to quantify spatial heterogeneity in the distribution patterns of tumor-infiltrating lymphocytes. Hematoxylin and eosin-stained sections from two cohorts of estrogen receptor-negative breast cancer patients (discovery: n=120; validation: n=125) were processed with our automated cell classification algorithm to identify the location of lymphocytes and cancer cells. Subsequently, hotspot analysis (Getis–Ord Gi*) was applied to identify statistically significant hotspots of cancer and immune cells, defined as tumor regions with a significantly high number of cancer cells or immune cells, respectively. We found that the amount of co-localized cancer and immune hotspots weighted by tumor area, rather than number of cancer or immune hotspots, correlates with a better prognosis in estrogen receptor-negative breast cancer in univariate and multivariate analysis. Moreover, co-localization of cancer and immune hotspots further stratified patients with immune cell-rich tumors. Our study demonstrates the importance of quantifying not only the abundance of lymphocytes but also their spatial variation in the tumor specimen for which methods from other disciplines such as spatial statistics can be successfully applied.

Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology

The emergent field of digital pathology employing automated image analysis techniques is to revolutionize traditional pathology at the center of clinical diagnostics. Histological images provide important tumor features unavailable in molecular profiling or omics data— the spatial context of tumor and stromal cells at single-cell resolution. Methods to map the spatial and morphological patterns of cancer and normal cells can contribute to a more comprehensive understanding of the highly heterogeneous tumor microenvironment. This review focuses on methods that help expand our knowledge of intra-tumoral spatial heterogeneity of the tumor microenvironment and their potential synergies with molecular profiling technologies.

Quantitative histology analysis of the ovarian tumour microenvironment

Concerted efforts in genomic studies examining RNA transcription and DNA methylation patterns have revealed profound insights in prognostic ovarian cancer subtypes. On the other hand, abundant histology slides have been generated to date, yet their uses remain very limited and largely qualitative. Our goal is to develop automated histology analysis as an alternative subtyping technology for ovarian cancer that is cost-efficient and does not rely on DNA quality. We developed an automated system for scoring primary tumour sections of 91 late-stage ovarian cancer to identify single cells. We demonstrated high accuracy of our system based on expert pathologists’ scores (cancer = 97.1%, stromal = 89.1%) as well as compared to immunohistochemistry scoring (correlation = 0.87). The percentage of stromal cells in all cells is significantly associated with poor overall survival after controlling for clinical parameters including debulking status and age (multivariate analysis p = 0.0021, HR = 2.54, CI = 1.40–4.60) and progression-free survival (multivariate analysis p = 0.022, HR = 1.75, CI = 1.09–2.82). We demonstrate how automated image analysis enables objective quantification of microenvironmental composition of ovarian tumours. Our analysis reveals a strong effect of the tumour microenvironment on ovarian cancer progression and highlights the potential of therapeutic interventions that target the stromal compartment or cancer-stroma signalling in the stroma-high, late-stage ovarian cancer subset.

Similarity and diversity of the tumor microenvironment in multiple metastases: critical implications for overall and progression-free survival of high-grade serous ovarian cancer

The tumor microenvironment is pivotal in influencing cancer progression and metastasis. Different cells co-exist with high spatial diversity within a patient, yet their combinatorial effects are poorly understood. We investigate the similarity of the tumor microenvironment of 192 local metastatic lesions in 61 ovarian cancer patients. An ecologically inspired measure of microenvironmental diversity derived from multiple metastasis sites is correlated with clinicopathological characteristics and prognostic outcome. We demonstrate a high accuracy of our automated analysis across multiple sites. A low level of similarity in microenvironmental composition is observed between ovary tumor and corresponding local metastases (stromal ratio r = 0.30, lymphocyte ratio r = 0.37). We identify a new measure of microenvironmental diversity derived from Shannon entropy that is highly predictive of poor overall (p = 0.002, HR = 3.18, 95% CI = 1.51-6.68) and progression-free survival (p = 0.0036, HR = 2.83, 95% CI = 1.41-5.7), independent of and stronger than clinical variables, subtype stratifications based on single cell types alone and number of sites. Although stromal influence in ovary tumors is known to have significant clinical implications, our findings reveal an even stronger impact orchestrated by diverse cell types. Quantitative histology-based measures can further enable objective selection of patients who are in urgent need of new therapeutic strategies such as combinatorial treatments targeting heterogeneous tumor microenvironment.

Diffusion‐weighted MRI for early detection and characterization of prostate cancer in the transgenic adenocarcinoma of the mouse prostate model

To improve early diagnosis of prostate cancer to aid clinical decision‐making. Diffusion‐weighted magnetic resonance imaging (DW‐MRI) is sensitive to water diffusion throughout tissues, which correlates with Gleason score, a histological measure of prostate cancer aggressiveness. In this study the ability of DW‐MRI to detect prostate cancer onset and development was evaluated in transgenic adenocarcinoma of the mouse prostate (TRAMP) mice.

The interface of malignant and immunologic clonal dynamics in high-grade serous ovarian cancer

High-grade serous ovarian cancer exhibits extensive intratumoral heterogeneity coupled with widespread intraperitoneal disease. Despite this, metastatic spread of tumor clones is non-random, implying the existence of local microenvironmental factors that shape tumor progression. We interrogated the molecular interface between tumor-infiltrating lymphocytes (TIL) and cancer cells in 143 samples from 21 patients using whole-genome sequencing, immunohistochemistry, histologic image analysis, gene expression profiling, and T- and B-cell receptor sequencing. We identify 3 immunologic response categories, which frequently co-exist within individual patients. Furthermore, epithelial CD8+ TIL were inversely associated with malignant cell diversity, evidenced by subclonal neoepitope elimination and spatial tracking between tumor and T-cell clones. Intersecting mutational signatures and immune analysis showed that foldback inversion genomic aberrations lead to worse outcomes even in the presence of cytotoxic TIL (n=433). Thus, regional variation in immune contexture mirrors the pattern of intraperitoneal malignant spread, provoking new perspectives for treatment of this challenging disease.

Non-Invasive Prostate Cancer Characterization with Diffusion-Weighted MRI: Insight from In silico Studies of a Transgenic Mouse Model

Diffusion-weighted magnetic resonance imaging (DWI) enables non-invasive, quantitative staging of prostate cancer via measurement of the apparent diffusion coefficient (ADC) of water within tissues. In cancer, more advanced disease is often characterized by higher cellular density (cellularity), which is generally accepted to correspond to a lower measured ADC. A quantitative relationship between tissue structure and in vivo measurements of ADC has yet to be determined for prostate cancer. In this study, we establish a theoretical framework for relating ADC measurements with tissue cellularity and the proportion of space occupied by prostate lumina, both of which are estimated through automatic image processing of whole-slide digital histology samples taken from a cohort of six healthy mice and nine transgenic adenocarcinoma of the mouse prostate (TRAMP) mice. We demonstrate that a significant inverse relationship exists between ADC and tissue cellularity that is well characterized by our model, and that a decrease of the luminal space within the prostate is associated with a decrease in ADC and more aggressive tumor subtype. The parameters estimated from our model in this mouse cohort predict the diffusion coefficient of water within the prostate-tissue to be 2.18 × 10−3 mm2/s (95% CI: 1.90, 2.55). This value is significantly lower than the diffusion coefficient of free water at body temperature suggesting that the presence of organelles and macromolecules within tissues can drastically hinder the random motion of water molecules within prostate tissue. We validate the assumptions made by our model using novel in silico analysis of whole-slide histology to provide the simulated ADC (sADC); this is demonstrated to have a significant positive correlation with in vivo measured ADC (r2 = 0.55) in our mouse population. The estimation of the structural properties of prostate tissue is vital for predicting and staging cancer aggressiveness, but prostate tissue biopsies are painful, invasive, and are prone to complications such as sepsis. The developments made in this study provide the possibility of estimating the structural properties of prostate tissue via non-invasive virtual biopsies from MRI, minimizing the need for multiple tissue biopsies and allowing sequential measurements to be made for prostate cancer monitoring.

Microenvironmental niche divergence shapes BRCA1-dysregulated ovarian cancer morphological plasticity

How tumor microenvironmental forces shape plasticity of cancer cell morphology is poorly understood. Here, we conduct automated histology image and spatial statistical analyses in 514 high grade serous ovarian samples to define cancer morphological diversification within the spatial context of the microenvironment. Tumor spatial zones, where cancer cell nuclei diversify in shape, are mapped in each tumor. Integration of this spatially explicit analysis with omics and clinical data reveals a relationship between morphological diversification and the dysregulation of DNA repair, loss of nuclear integrity, and increased disease mortality. Within the Immunoreactive subtype, spatial analysis further reveals significantly lower lymphocytic infiltration within diversified zones compared with other tumor zones, suggesting that even immune-hot tumors contain cells capable of immune escape. Our findings support a model whereby a subpopulation of morphologically plastic cancer cells with dysregulated DNA repair promotes ovarian cancer progression through positive selection by immune evasion.Cancer cells can actively engage in overcoming microenvironmental constraints such as tissue stiffness through adapting their shapes; however it is unclear how microenvironmental cells shape cancer nuclear morphology in human tumors in situ. Here the authors merge machine learning, digital pathology and spatial statistics to study this issue; furthermore the authors identify decreased immune infiltration in the surrounding of diversified cancer cells in a subset of ovarian tumors.

Abstract B2-55: Critical role of immune spatial heterogeneity and the molecular scaffold in estrogen receptor-negative breast cancer

Purpose: The abundance of tumor-infiltrating lymphocytes has been associated with a favorable prognosis in estrogen receptor-negative breast cancer. However, a high degree of spatial heterogeneity in lymphocytic infiltration is often observed in histology samples and its clinical implications and underpinning molecular scaffold remain unclear. Materials and methods: To quantify spatial heterogeneity in the distribution of tumor infiltrating lymphocytes, we combined automated histological image processing with methods of spatial statistics used in ecological data analysis. Hematoxylin and eosin-stained sections from two cohorts of estrogen receptor-negative breast cancer patients (discovery: n=120; validation: n=125) were processed with our automated cell classification algorithm to identify the location of lymphocytes and cancer cells. Subsequently, hotspot analysis (Getis-Ord Gi*) was applied to identify statistically significant hotspots of cancer and immune cells, defined as tumor regions with a significantly high number of cancer or immune cells respectively. To identify molecular aberrations that explain tumor spatial heterogeneity, we integrated our image-based hotspots results with microarray gene expression and copy number data profiled for the same set of tumors. Molecular data were generated with tumor materials sandwiched between these sections, thereby maximizing the biological relevance of multiple data types being generated. Results: We found that the amount of colocalized cancer and immune hotspots weighted by tumor area, rather than number of cancer or immune hotspots, significantly correlates with a better prognosis in estrogen receptor-negative breast cancer in uni- and multivariate Cox analysis. Moreover, colocalization of cancer and immune hotspots further stratified patients with immune cell-rich tumors. Subsequently, we developed a bioinformatics tool, iMmune hOTspoT Omics (iMOTTO), to explain the hotspots as a clinically relevant phenotype using molecular profiling data. Our preliminary analysis revealed significant correlations between this phenotype and expression of immune-specific genes such as CD79, CCL19 and SLAMF1, as well as an immunotherapy target, CTLA4. By incorporating the expression of hotspots-associated genes and copy number alteration data into a multivariate regression model, we aim to define a minimal set of genes to explain the observed degree of cancer-immune hotspot colocalization. Conclusion: Taken together, our study demonstrates the importance of quantifying not only the abundance of lymphocytes but also their spatial variation in the tumor specimen for which methods from other disciplines such as spatial statistics can be successfully applied. Systematic integration of histology and omics data revealed key immune regulators as well as novel genes which warrant further investigation to help elucidate the biological processes underlying immune spatial heterogeneity with potentially important clinical implications. Furthermore, our computational approach can be adapted for studying other cancer types for which immunotherapy has been applied, such as melanoma and non-small cell lung cancer, where our hotspot measures can potentially serve as prognostic and predictive biomarkers. Citation Format: Sidra Nawaz, Andreas Heindl, Andrea Agostinelli, Yinyin Yuan. Critical role of immune spatial heterogeneity and the molecular scaffold in estrogen receptor-negative breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-55.