AZGP1 deficiency promotes angiogenesis in prostate cancer

BACKGROUND

Loss of AZGP1 expression is a biomarker associated with progression to castration resistance, development of metastasis, and poor disease-specific survival in prostate cancer. However, high expression of AZGP1 cells in prostate cancer has been reported to increase proliferation and invasion. The exact role of AZGP1 in prostate cancer progression remains elusive.

METHOD

AZGP1 knockout and overexpressing prostate cancer cells were generated using a lentiviral system. The effects of AZGP1 under- or over-expression in prostate cancer cells were evaluated by in vitro cell proliferation, migration, and invasion assays. Heterozygous AZGP1± mice were obtained from European Mouse Mutant Archive (EMMA), and prostate tissues from homozygous knockout male mice were collected at 2, 6 and 10 months for histological analysis. In vivo xenografts generated from AZGP1 under- or over-expressing prostate cancer cells were used to determine the role of AZGP1 in prostate cancer tumor growth, and subsequent proteomics analysis was conducted to elucidate the mechanisms of AZGP1 action in prostate cancer progression. AZGP1 expression and microvessel density were measured in human prostate cancer samples on a tissue microarray of 215 independent patient samples.

RESULT

Neither the knockout nor overexpression of AZGP1 exhibited significant effects on prostate cancer cell proliferation, clonal growth, migration, or invasion in vitro. The prostates of AZGP1-/- mice initially appeared to have grossly normal morphology; however, we observed fibrosis in the periglandular stroma and higher blood vessel density in the mouse prostate by 6 months. In PC3 and DU145 mouse xenografts, over-expression of AZGP1 did not affect tumor growth. Instead, these tumors displayed decreased microvessel density compared to xenografts derived from PC3 and DU145 control cells, suggesting that AZGP1 functions to inhibit angiogenesis in prostate cancer. Proteomics profiling further indicated that, compared to control xenografts, AZGP1 overexpressing PC3 xenografts are enriched with angiogenesis pathway proteins, including YWHAZ, EPHA2, SERPINE1, and PDCD6, MMP9, GPX1, HSPB1, COL18A1, RNH1, and ANXA1. In vitro functional studies show that AZGP1 inhibits human umbilical vein endothelial cell proliferation, migration, tubular formation and branching. Additionally, tumor microarray analysis shows that AZGP1 expression is negatively correlated with blood vessel density in human prostate cancer tissues.

CONCLUSION

AZGP1 is a negative regulator of angiogenesis, such that loss of AZGP1 promotes angiogenesis in prostate cancer. AZGP1 likely exerts heterotypical effects on cells in the tumor microenvironment, such as stromal and endothelial cells. This study sheds light on the anti-angiogenic characteristics of AZGP1 in the prostate and provides a rationale to target AZGP1 to inhibit prostate cancer progression.

Spatial Effects of Infiltrating T cells on Neighbouring Cancer Cells and Prognosis in Stage III CRC patients

Colorectal cancer (CRC) is one of the most frequently occurring cancers, but prognostic biomarkers identifying patients at risk of recurrence are still lacking. In this study, we aimed to investigate in more detail the spatial relationship between intratumoural T cells, cancer cells, and cancer cell hallmarks, as prognostic biomarkers in stage III colorectal cancer patients. We conducted multiplexed imaging of 56 protein markers at single cell resolution on resected fixed tissue from stage III CRC patients who received adjuvant 5-fluorouracil-based chemotherapy. Images underwent segmentation for tumour, stroma and immune cells, and cancer cell 'state' protein marker expression was quantified at a cellular level. We developed a Python package for estimation of spatial proximity, nearest neighbour analysis focusing on cancer cell - T cell interactions at single-cell level. In our discovery cohort (MSK), we processed 462 core samples (total number of cells: 1,669,228) from 221 adjuvant 5FU-treated stage III patients. The validation cohort (HV) consisted of 272 samples (total number of cells: 853,398) from 98 stage III CRC patients. While there were trends for an association between percentage of cytotoxic T cells (across the whole cancer core), it did not reach significance (Discovery cohort: p = 0.07, Validation cohort: p = 0.19). We next utilized our region-based nearest neighbourhood approach to determine the spatial relationships between cytotoxic T cells, helper T cells and cancer cell clusters. In the both cohorts, we found that lower distance between cytotoxic T cells, T helper cells and cancer cells was significantly associated with increased disease-free survival. An unsupervised trained model that clustered patients based on the median distance between immune cells and cancer cells, as well as protein expression profiles, successfully classified patients into low-risk and high-risk groups (Discovery cohort: p = 0.01, Validation cohort: p = 0.003).

Identification of unique rectal cancer-specific subtypes

BACKGROUND

Existing colorectal cancer subtyping methods were generated without much consideration of potential differences in expression profiles between colon and rectal tissues. Moreover, locally advanced rectal cancers at resection often have received neoadjuvant chemoradiotherapy which likely has a significant impact on gene expression.

METHODS

We collected mRNA expression profiles for rectal and colon cancer samples (n = 2121). We observed that (i) Consensus Molecular Subtyping (CMS) had a different prognosis in treatment-naïve rectal vs. colon cancers, and (ii) that neoadjuvant chemoradiotherapy exposure produced a strong shift in CMS subtypes in rectal cancers. We therefore clustered 182 untreated rectal cancers to find rectal cancer-specific subtypes (RSSs).

RESULTS

We identified three robust subtypes. We observed that RSS1 had better, and RSS2 had worse disease-free survival. RSS1 showed high expression of MYC target genes and low activity of angiogenesis genes. RSS2 exhibited low regulatory T cell abundance, strong EMT and angiogenesis signalling, and high activation of TGF-β, NF-κB, and TNF-α signalling. RSS3 was characterised by the deactivation of EGFR, MAPK and WNT pathways.

CONCLUSIONS

We conclude that RSS subtyping allows for more accurate prognosis predictions in rectal cancers than CMS subtyping and provides new insight into targetable disease pathways within these subtypes.

Deep learning-based automated pipeline for blood vessel detection and distribution analysis in multiplexed prostate cancer images

Introduction: Prostate cancer is a highly heterogeneous disease, presenting varying levels of aggressiveness and response to treatment. Angiogenesis is one of the hallmarks of cancer, providing oxygen and nutrient supply to tumors. Micro vessel density has previously been correlated with higher Gleason score and poor prognosis. Manual segmentation of blood vessels (BVs) In microscopy images is challenging, time consuming and may be prone to inter-rater variabilities. In this study, an automated pipeline is presented for BV detection and distribution analysis in multiplexed prostate cancer images. Methods: A deep learning model was trained to segment BVs by combining CD31, CD34 and collagen IV images. In addition, the trained model was used to analyze the size and distribution patterns of BVs in relation to disease progression in a cohort of prostate cancer patients (N = 215). Results: The model was capable of accurately detecting and segmenting BVs, as compared to ground truth annotations provided by two reviewers. The precision (P), recall (R) and dice similarity coefficient (DSC) were equal to 0.93 (SD 0.04), 0.97 (SD 0.02) and 0.71 (SD 0.07) with respect to reviewer 1, and 0.95 (SD 0.05), 0.94 (SD 0.07) and 0.70 (SD 0.08) with respect to reviewer 2, respectively. BV count was significantly associated with 5-year recurrence (adjusted p = 0.0042), while both count and area of blood vessel were significantly associated with Gleason grade (adjusted p = 0.032 and 0.003 respectively). Discussion: The proposed methodology is anticipated to streamline and standardize BV analysis, offering additional insights into the biology of prostate cancer, with broad applicability to other cancers.

Pathobiology of Candida auris infection analyzed by multiplexed imaging and single cell analysis

Fungal organisms contribute to significant human morbidity and mortality and Candida auris (C. auris) infections are of utmost concern due to multi-drug resistant strains and persistence in critical care and hospital settings. Pathogenesis and pathology of C. auris is still poorly understood and in this study, we demonstrate how the use of multiplex immunofluorescent imaging (MxIF) and single-cell analysis can contribute to a deeper understanding of fungal infections within organs. We used two different neutrophil depletion murine models (treated with either 1A8-an anti-Ly6G antibody, or RB6-8C5-an anti-Ly6G/Ly6C antibody; both 1A8 and RB6-8C5 antibodies have been shown to deplete neutrophils) and compared to wildtype, non-neutropenic mice. Following pathologist assessment, fixed samples underwent MxIF imaging using a C. albicans antibody (shown to be cross-reactive to C. auris) and immune cell biomarkers-CD3 (T cells), CD68 (macrophages), B220 (B cells), CD45 (monocytes), and Ly6G (neutrophils) to quantify organ specific immune niches. MxIF analysis highlighted the heterogenous distribution of C. auris infection within heart, kidney, and brain 7 days post-infection. Size and number of fungal abscesses was greatest in the heart and lowest in brain. Infected mice had an increased count of CD3+, CD68+, B220+, and CD45+ immune cells, concentrated around C. auris abscesses. CD68+ cells were predominant in wildtype (non-neutropenic mice) and CD3+/CD45+ cells were predominant in neutropenic mice, with B cells being the least abundant. These findings suggest a Th2 driven immune response in neutropenic C. auris infection mice models. This study demonstrates the value of MxIF to broaden understanding of C. auris pathobiology, and mechanistic understanding of fungal infections.

Decoding mTOR signalling heterogeneity in the tumour microenvironment using multiplexed imaging and graph convolutional networks

Evaluating the contribution of the tumour microenvironment (TME) in tumour progression has proven a complex challenge due to the intricate interactions within the TME. Multiplexed imaging is an emerging technology that allows concurrent assessment of multiple of these components simultaneously. Here we utilise a highly multiplexed dataset of 61 markers across 746 colorectal tumours to investigate how complex mTOR signalling in different tissue compartments influences patient prognosis. We found that the signalling of mTOR pathway can have heterogeneous activation patterns in tumour and immune compartments which correlate with patient prognosis. Using graph neural networks, we determined the most predictive features of mTOR activity in immune cells and identified relevant cellular subpopulations. We validated our observations using spatial transcriptomics data analysis in an independent patient cohort. Our work provides a framework for studying complex cell signalling and reveals important insights for developing mTOR-based therapies.

Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP)

The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.

Organ Mapping Antibody Panels: a community resource for standardized multiplexed tissue imaging

Multiplexed antibody-based imaging enables the detailed characterization of molecular and cellular organization in tissues. Advances in the field now allow high-parameter data collection (>60 targets); however, considerable expertise and capital are needed to construct the antibody panels employed by these methods. Organ mapping antibody panels are community-validated resources that save time and money, increase reproducibility, accelerate discovery and support the construction of a Human Reference Atlas.

3D reconstruction of skin and spatial mapping of immune cell density, vascular distance and effects of sun exposure and aging

Mapping the human body at single cell resolution in three dimensions (3D) is important for understanding cellular interactions in context of tissue and organ organization. 2D spatial cell analysis in a single tissue section may be limited by cell numbers and histology. Here we show a workflow for 3D reconstruction of multiplexed sequential tissue sections: MATRICS-A (Multiplexed Image Three-D Reconstruction and Integrated Cell Spatial - Analysis). We demonstrate MATRICS-A in 26 serial sections of fixed skin (stained with 18 biomarkers) from 12 donors aged between 32-72 years. Comparing the 3D reconstructed cellular data with the 2D data, we show significantly shorter distances between immune cells and vascular endothelial cells (56 µm in 3D vs 108 µm in 2D). We also show 10-70% more T cells (total) within 30 µm of a neighboring T helper cell in 3D vs 2D. Distances of p53, DDB2 and Ki67 positive cells to the skin surface were consistent across all ages/sun exposure and largely localized to the lower stratum basale layer of the epidermis. MATRICS-A provides a framework for analysis of 3D spatial cell relationships in healthy and aging organs and could be further extended to diseased organs.

Multiplexed Immunofluorescence Imaging Reveals an Immune-Rich Tumor Microenvironment in Mucinous Rectal Cancer Characterized by Increased Lymphocyte Infiltration and Enhanced Programmed Cell Death Protein 1 Expression

BACKGROUND

Mucinous rectal cancer is associated with a higher incidence of microsatellite instability and a poorer response to neoadjuvant chemoradiotherapy compared to other subtypes of rectal adenocarcinoma. Immune checkpoint inhibitors are an emerging family of anticancer therapeutics associated with highly variable outcomes in colorectal cancer. Although the immune landscape of mucinous rectal cancer has not been fully explored, the presence of mucin is thought to act as a barrier preventing immune-cell infiltration.

OBJECTIVE

The aim of this study was to determine the immune properties of mucinous rectal cancer and investigate the degree of lymphocyte infiltration in this cohort.

DESIGN

This is a retrospective cohort study that involved multiplexed immunofluorescence staining of tumor microarrays.

SETTINGS

Samples originated from a single university teaching hospital.

PATIENTS

Our cohort included 15 cases of mucinous and 43 cases of nonmucinous rectal cancer.

MAIN OUTCOME MEASURES

Immune cells were classified and quantified. Immune-cell counts were compared between mucinous and nonmucinous cohorts. Immune marker expression within tumor epithelial tissue was evaluated to determine the degree of lymphocyte infiltration.

RESULTS

Cytotoxic ( p = 0.022) and regulatory T cells ( p = 0.010) were found to be overrepresented in the mucinous cohort compared to the nonmucinous group. Programmed cell death protein 1 expression was also found to be significantly greater in the mucinous group ( p = 0.001). CD3 ( p = 0.001) and CD8 ( p = 0.054) expressions within the tumor epithelium were also higher in the mucinous group, suggesting adequate immune infiltration despite the presence of mucin. In our analysis, microsatellite instability status was not a predictor of immune marker expression.

LIMITATIONS

The relatively small size of the cohort.

CONCLUSIONS

Mucinous rectal cancer is associated with an immune-rich tumor microenvironment, which was not associated with microsatellite instability status. See Video Abstract at http://links.lww.com/DCR/C65 .

IMGENES DE INMUNOFLUORESCENCIA MULTIPLEXADAS REVELAN UN MICROAMBIENTE TUMORAL RICO EN INMUNIDAD EN EL CNCER RECTAL MUCINOSO CARACTERIZADO POR UNA MAYOR INFILTRACIN DE LINFOCITOS Y UNA EXPRESIN MEJORADA DE PD

ANTECEDENTES:El cáncer rectal mucinoso se asocia con una mayor incidencia de inestabilidad de microsatélites y una peor respuesta a la quimiorradioterapia neoadyuvante en comparación con otros subtipos de adenocarcinoma rectal. Los inhibidores de puntos de control inmunitarios son una familia emergente de tratamientos contra el cáncer asociados con resultados muy variables en el cáncer colorrectal. Aunque el panorama inmunitario del cáncer rectal mucinoso no se ha explorado completamente, se cree que la presencia de mucina actúa como una barrera que previene la infiltración de células inmunitarias.OBJETIVO:El objetivo de este estudio fue determinar las propiedades inmunes del cáncer de recto mucinoso e investigar el grado de infiltración de linfocitos en esta cohorte.DISEÑO:Este es un estudio de cohorte retrospectivo que involucró la tinción de inmunofluorescencia multiplexada de micromatrices tumorales.AJUSTES:Las muestras se originaron en un solo hospital docente universitario.PACIENTES:Nuestra cohorte incluyó 15 casos de cáncer de recto mucinoso y 43 casos de cáncer de recto no mucinosoPRINCIPALES MEDIDAS DE RESULTADO:Las células inmunitarias se clasificaron y cuantificaron. Se compararon los recuentos de células inmunitarias entre cohortes mucinosas y no mucinosas. Se evaluó la expresión del marcador inmunitario dentro del tejido epitelial tumoral para determinar el grado de infiltración de linfocitos.RESULTADOS:Se encontró que las células T citotóxicas ( p = 0,022) y reguladoras ( p = 0,010) estaban sobrerrepresentadas en la cohorte mucinosa en comparación con el grupo no mucinoso. También se encontró que la expresión de PD-1 era significativamente mayor en el grupo mucinoso ( p = 0,001). La expresión de CD3 ( p = 0,001) y CD8 ( p = 0,054) dentro del epitelio tumoral también fue mayor en el grupo mucinoso, lo que sugiere una infiltración inmunitaria adecuada a pesar de la presencia de mucina. En nuestro análisis, no se encontró que el estado de inestabilidad de los microsatélites sea un predictor de la expresión del marcador inmunitario.LIMITACIONES:El tamaño relativamente pequeño de la cohorte.CONCLUSIONES:El cáncer rectal mucinoso se asocia con un microambiente tumoral rico en inmunidad, que no se asoció con el estado de inestabilidad de microsatélites. Consulte el Video del Resumen en http://links.lww.com/DCR/C65 . (Traducción- Dr. Yesenia Rojas-Khalil ).

Increased Fusobacterium tumoural abundance affects immunogenicity in mucinous colorectal cancer and may be associated with improved clinical outcome

There is currently an urgent need to identify factors predictive of immunogenicity in colorectal cancer (CRC). Mucinous CRC is a distinct histological subtype of CRC, associated with a poor response to chemotherapy. Recent evidence suggests the commensal facultative anaerobe Fusobacterium may be especially prevalent in mucinous CRC. The objectives of this study were to assess the association of Fusobacterium abundance with immune cell composition and prognosis in mucinous CRC. Our study included two independent colorectal cancer patient cohorts, The Cancer Genome Atlas (TCGA) cohort, and a cohort of rectal cancers from the Beaumont RCSI Cancer Centre (BRCC). Multiplexed immunofluorescence staining of a tumour microarray (TMA) from the BRCC cohort was undertaken using Cell DIVE technology. Our cohorts included 87 cases (13.3%) of mucinous and 565 cases (86.7%) of non-mucinous CRC. Mucinous CRC in the TCGA dataset was associated with an increased proportion of CD8 + lymphocytes (p = 0.018), regulatory T-cells (p = 0.001) and M2 macrophages (p = 0.001). In the BRCC cohort, mucinous RC was associated with enhanced CD8 + lymphocyte (p = 0.022), regulatory T-cell (p = 0.047), and B-cell (p = 0.025) counts. High Fusobacterium abundance was associated with an increased proportion of CD4 + lymphocytes (p = 0.031) and M1 macrophages (p = 0.006), whilst M2 macrophages (p = 0.043) were under-represented in this cohort. Patients with increased Fusobacterium relative abundance in our mucinous CRC TCGA cohort tended to have better clinical outcomes (DSS: likelihood ratio p = 0.04, logrank p = 0.052). Fusobacterium abundance may be associated with improved outcomes in mucinous CRC, possibly due to a modulatory effect on the host immune response. KEY MESSAGES: • Increased Fusobacterium relative abundance was not found to be associated with microsatellite instability in mucinous CRC. • Increased Fusobacterium relative abundance was associated with an M2/M1 macrophage switch, which is especially significant in mucinous CRC, where M2 macrophages are overexpressed. • Increased Fusobacterium relative abundance was associated with a significant improvement in disease specific survival in mucinous CRC. • Our findings were validated at a protein level within our own in house mucinous and non-mucinous rectal cancer cohorts.

Predicting Breast Cancer Events in Ductal Carcinoma In Situ (DCIS) Using Generative Adversarial Network Augmented Deep Learning Model

Standard clinicopathological parameters (age, growth pattern, tumor size, margin status, and grade) have been shown to have limited value in predicting recurrence in ductal carcinoma in situ (DCIS) patients. Early and accurate recurrence prediction would facilitate a more aggressive treatment policy for high-risk patients (mastectomy or adjuvant radiation therapy), and simultaneously reduce over-treatment of low-risk patients. Generative adversarial networks (GAN) are a class of DL models in which two adversarial neural networks, generator and discriminator, compete with each other to generate high quality images. In this work, we have developed a deep learning (DL) classification network that predicts breast cancer events (BCEs) in DCIS patients using hematoxylin and eosin (H & E) images. The DL classification model was trained on 67 patients using image patches from the actual DCIS cores and GAN generated image patches to predict breast cancer events (BCEs). The hold-out validation dataset (n = 66) had an AUC of 0.82. Bayesian analysis further confirmed the independence of the model from classical clinicopathological parameters. DL models of H & E images may be used as a risk stratification strategy for DCIS patients to personalize therapy.

Abstract P6-04-10: Recurrence Prediction in Ductal Carcinoma In Situ (DCIS) Patients from Tissue Microarrays (TMAs)

Recurrence Prediction in Ductal Carcinoma In Situ (DCIS) Patients Using Generative Adversarial Network (GAN) Augmented Deep Learning Model Background: DCIS patients have an excellent overall survival rate and over-treatment is always a cause for concern due to potential side-effects. Standard clinicopathological factors (age, growth pattern, tumor size, margin status and grade) have been shown to have limited value in predicting recurrence and segregation of high and low risk patients. Early and accurate recurrence prediction would facilitate a more aggressive treatment policy for high-risk patients (mastectomy or adjuvant radiation therapy), and simultaneously reduce over-treatment of low-risk patients. In this work, we have developed a deep learning (DL) classification framework that predicts recurrence in DCIS patients from Tissue microarrays (TMAs) hematoxylin and eosin (H&E) images using a generative adversarial network (GAN) augmented deep learning (DL) classification model. A GAN is a class of DL models, in which two adversarial neural networks, generator and discriminator contest among each other to generate high quality images. During the adversarial training process, the generator learns to synthesize realistic images similar to those in the training set while the discriminator learns to distinguish between real and generated images. In recent years, high quality medical images have been generated by GAN models. To the best of our knowledge, this is the first time a GAN model has been used to generate H&E images to train a DL classification model to predict recurrence in DCIS patients. Materials and methods: The cohort was comprised of 68 DCIS patients, aged between 35-89 years, lesion size of 5-90 mm, with a mix of low (15%), intermediate (35%) and 50% high grade cases. Patients were treated with mastectomy and/or a combination of lumpectomy, radiation and hormone therapy. TMAs were constructed from 2mm cores (1-3 cores per patient) in consultation with a breast pathologist to create hematoxylin and eosin (H&E) images for further analysis. The cohort was split into independent training (n=50 patients, 10 with recurrences at 5years) and validation groups (n=18 patients, 6 with recurrences at 5years). TMA (H&E) images were divided into smaller image patches of size 256x256 to train a GAN to generate image patches. A DL classification network (Resnet-Inception v2) was trained using TMA image patches and aggressive image patches generated by GAN to predict recurrence. The ability to generate synthetic image patches of aggressive lesions permitted training of a large DL classification network and predict recurrence in DCIS patients. Importantly, manual annotation was not necessary for the process. Results: The DL classification model trained with both TMA and GAN generated image patches predicted recurrence with an AUC of 0.87, sensitivity of 0.83 and specificity of 0.91 in the validation dataset. The DL classification model trained with image patches from TMAs only predicted recurrence with an AUC of 0.81. Conclusions: The use of a GAN model to generate H&E images circumvents the needs for a large cohort and accurate labor-intensive manual annotation of histopathological images, which is often required for training a large DL classification model. The use of GAN generated aggressive image patches during training significantly improves recurrence prediction accuracy of the DL classification model. Validation in independent larger cohorts is ongoing, and if successful, could provide a novel assay for risk prediction that does not waste precious tissue samples.

Citation Format: Ghose Soumya, Yesim Gokmen-Polar, Sanghee Cho, Elizabeth McDonough, Cynthia Davis, Jhimli Mitra, Zhanpan Zhang, Fiona Ginty, Sunil Badve. Recurrence Prediction in Ductal Carcinoma In Situ (DCIS) Patients from Tissue Microarrays (TMAs) [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-04-10.

Epithelial TGFβ engages growth-factor signalling to circumvent apoptosis and drive intestinal tumourigenesis with aggressive features

The pro-tumourigenic role of epithelial TGFβ signalling in colorectal cancer (CRC) is controversial. Here, we identify a cohort of born to be bad early-stage (T1) colorectal tumours, with aggressive features and a propensity to disseminate early, that are characterised by high epithelial cell-intrinsic TGFβ signalling. In the presence of concurrent Apc and Kras mutations, activation of epithelial TGFβ signalling rampantly accelerates tumourigenesis and share transcriptional signatures with those of the born to be bad T1 human tumours and predicts recurrence in stage II CRC. Mechanistically, epithelial TGFβ signalling induces a growth-promoting EGFR-signalling module that synergises with mutant APC and KRAS to drive MAPK signalling that re-sensitise tumour cells to MEK and/or EGFR inhibitors. Together, we identify epithelial TGFβ signalling both as a determinant of early dissemination and a potential therapeutic vulnerability of CRC's with born to be bad traits.

Accelerating cutaneous healing in a rodent model of type II diabetes utilizing non-invasive focused ultrasound targeted at the spleen

Healing of wounds is delayed in Type 2 Diabetes Mellitus (T2DM), and new treatment approaches are urgently needed. Our earlier work showed that splenic pulsed focused ultrasound (pFUS) alters inflammatory cytokines in models of acute endotoxemia and pneumonia via modulation of the cholinergic anti-inflammatory pathway (CAP) (ref below). Based on these earlier results, we hypothesized that daily splenic exposure to pFUS during wound healing would accelerate closure rate via altered systemic cytokine titers. In this study, we applied non-invasive ultrasound directed to the spleen of a rodent model [Zucker Diabetic Sprague Dawley (ZDSD) rats] of T2DM with full thickness cutaneous excisional wounds in an attempt to accelerate wound healing via normalization of T2DM-driven aberrant cytokine expression. Daily (1x/day, Monday-Friday) pFUS pulses were targeted externally to the spleen area for 3 min over the course of 15 days. Wound diameter was measured daily, and levels of cytokines were evaluated in spleen and wound bed lysates. Non-invasive splenic pFUS accelerated wound closure by up to 4.5 days vs. sham controls. The time to heal in all treated groups was comparable to that of healthy rats from previously published studies (ref below), suggesting that the pFUS treatment restored a normal wound healing phenotype to the ZDSD rats. IL-6 was lower in stimulated spleen (-2.24 ± 0.81 Log2FC, p = 0.02) while L-selectin was higher in the wound bed of stimulated rodents (2.53 ± 0.72 Log2FC, p = 0.003). In summary, splenic pFUS accelerates healing in a T2DM rat model, demonstrating the potential of the method to provide a novel, non-invasive approach for wound care in diabetes.

Tumor Infiltrating Lymphocytes in Multi-National Cohorts of Ductal Carcinoma In Situ (DCIS) of Breast

Tumor-infiltrating lymphocytes (TILs) are prognostic in invasive breast cancer. However, their prognostic significance in ductal carcinoma in situ (DCIS) has been controversial. To investigate the prognostic role of TILs in DCIS outcome, we used different scoring methods for TILs in multi-national cohorts from Asian and European women. Self-described race was genetically confirmed using QC Infinium array combined with radmixture software. Stromal TILs, touching TILs, circumferential TILs, and hotspots were quantified on H&E-stained slides and correlated with the development of second breast cancer events (BCE) and other clinico-pathological variables. In univariate survival analysis, age older than 50 years, hormone receptor positivity and the presence of circumferential TILs were weakly associated with the absence of BCE at the 5-year follow-up in all cohorts (p < 0.03; p < 0.02; and p < 0.02, respectively, adjusted p = 0.11). In the multivariable analysis, circumferential TILs were an independent predictor of a better outcome (Wald test p = 0.01), whereas younger age was associated with BCE. Asian patients were younger with larger, higher grade, HR negative DCIS lesions, and higher TIL variables. The spatial arrangement of TILs may serve as a better prognostic indicator in DCIS cases than stromal TILs alone and may be added in guidelines for TILs evaluation in DCIS.

High resolution DLP stereolithography to fabricate biocompatible hydroxyapatite structures that support osteogenesis

Lithography based additive manufacturing techniques, specifically digital light processing (DLP), are considered innovative manufacturing techniques for orthopaedic implants because of their potential for construction of complex geometries using polymers, metals, and ceramics. Hydroxyapatite (HA) coupons, printed using DLP, were evaluated for biological performance in supporting viability, proliferation, and osteogenic differentiation of the human cell line U2OS and human mesenchymal stem cells (MSCs) up to 35 days in culture to determine feasibility for future use in development of complex scaffold geometries. Contact angle, profilometry, and scanning electron microscopy (SEM) measurements showed the HA coupons to be hydrophilic, porous, and having micro size surface roughness, all within favourable cell culture ranges. The study found no impact of leachable and extractables form the DLP printing process. Cells seeded on coupons exhibited morphologies comparable to conventional tissue culture polystyrene plates. Cell proliferation rates, as determined by direct cell count and the RealTime-Glo^TM MT Cell Viability Assay, were similar on HA coupons and standard tissue culture polystyrene plates). Osteogenic differentiation of human MSCs on HA coupons was confirmed using alkaline phosphatase, Alizarin Red S and von Kossa staining. The morphology of MSCs cultured in osteogenic medium for 14 to 35 days was similar on HA coupons and tissue culture polystyrene plates, with osteogenic (geometric, cuboidal morphology with dark nodules) and adipogenic (lipid vesicles and deposits) features. We conclude that the DLP process and LithaBone HA400 slurry are biocompatible and are suitable for osteogenic applications. Coupons served as an effective evaluation design in the characterization and visualization of cell responses on DLP printed HA material. Results support the feasibility of future technical development for 3D printing of sophisticated scaffold designs, which can be constructed to meet the mechanical, chemical, and porosity requirements of an artificial bone scaffold.

Stratification of chemotherapy-treated stage III colorectal cancer patients using multiplexed imaging and single-cell analysis of T-cell populations

Colorectal cancer (CRC) has one of the highest cancer incidences and mortality rates. In stage III, postoperative chemotherapy benefits <20% of patients, while more than 50% will develop distant metastases. Biomarkers for identification of patients at increased risk of disease recurrence following adjuvant chemotherapy are currently lacking. In this study, we assessed immune signatures in the tumor and tumor microenvironment (TME) using an in situ multiplexed immunofluorescence imaging and single-cell analysis technology (Cell DIVETM) and evaluated their correlations with patient outcomes. Tissue microarrays (TMAs) with up to three 1 mm diameter cores per patient were prepared from 117 stage III CRC patients treated with adjuvant fluoropyrimidine/oxaliplatin (FOLFOX) chemotherapy. Single sections underwent multiplexed immunofluorescence staining for immune cell markers (CD45, CD3, CD4, CD8, FOXP3, PD1) and tumor/cell segmentation markers (DAPI, pan-cytokeratin, AE1, NaKATPase, and S6). We used annotations and a probabilistic classification algorithm to build statistical models of immune cell types. Images were also qualitatively assessed independently by a Pathologist as 'high', 'moderate' or 'low', for stromal and total immune cell content. Excellent agreement was found between manual assessment and total automated scores (p < 0.0001). Moreover, compared to single markers, a multi-marker classification of regulatory T cells (Tregs: CD3+/CD4+FOXP3+/PD1-) was significantly associated with disease-free survival (DFS) and overall survival (OS) (p = 0.049 and 0.032) of FOLFOX-treated patients. Our results also showed that PD1- Tregs rather than PD1+ Tregs were associated with improved survival. These findings were supported by results from an independent FOLFOX-treated cohort of 191 stage III CRC patients, where higher PD1- Tregs were associated with an increase overall survival (p = 0.015) for CD3+/CD4+/FOXP3+/PD1-. Overall, compared to single markers, multi-marker classification provided more accurate quantitation of immune cell types with stronger correlations with outcomes.

Tumor MHC Class I Expression Associates with Intralesional Interleukin-2 Response in Melanoma

Cancer immunotherapy can result in lasting tumor regression, but predictive biomarkers of treatment response remain ill-defined. Here, we performed single-cell proteomics, transcriptomics, and genomics on matched untreated and interleukin-2 (IL-2) injected metastases from patients with melanoma. Lesions that completely regressed following intralesional IL-2 harbored increased fractions and densities of non-proliferating CD8+ T cells lacking expression of PD-1, LAG-3 and TIM-3 (PD-1-LAG-3-TIM-3-). Untreated lesions from patients who subsequently responded with complete eradication of all tumor cells in all injected lesions (individuals referred to herein as "extreme responders") were characterized by proliferating CD8+ T cells with an exhausted phenotype (PD-1+LAG-3+TIM-3+), stromal B-cell aggregates, and expression of IFNgamma and IL-2 response genes. Loss of membranous MHC class I expression in tumor cells of untreated lesions was associated with resistance to IL-2 therapy. We validated this finding in an independent cohort of metastatic melanoma patients treated with intralesional or systemic IL-2. Our study suggests that intact tumor cell antigen presentation is required for melanoma response to IL-2 and describes a multi-dimensional and spatial approach to develop immuno-oncology biomarker hypotheses using routinely collected clinical biospecimens.

FLINO: a new method for immunofluorescence bioimage normalization

MOTIVATION

Multiplexed immunofluorescence bioimaging of single-cells and their spatial organization in tissue holds great promise to the development of future precision diagnostics and therapeutics. Current multiplexing pipelines typically involve multiple rounds of immunofluorescence staining across multiple tissue slides. This introduces experimental batch effects that can hide underlying biological signal. It is important to have robust algorithms that can correct for the batch effects while not introducing biases into the data. Performance of data normalization methods can vary among different assay pipelines. To evaluate differences, it is critical to have a ground truth dataset that is representative of the assay.

RESULTS

A new immunoFLuorescence Image NOrmalization method is presented and evaluated against alternative methods and workflows. Multiround immunofluorescence staining of the same tissue with the nuclear dye DAPI was used to represent virtual slides and a ground truth. DAPI was restained on a given tissue slide producing multiple images of the same underlying structure but undergoing multiple representative tissue handling steps. This ground truth dataset was used to evaluate and compare multiple normalization methods including median, quantile, smooth quantile, median ratio normalization and trimmed mean of the M-values. These methods were applied in both an unbiased grid object and segmented cell object workflow to 24 multiplexed biomarkers. An upper quartile normalization of grid objects in log space was found to obtain almost equivalent performance to directly normalizing segmented cell objects by the middle quantile. The developed grid-based technique was then applied with on-slide controls for evaluation. Using five or fewer controls per slide can introduce biases into the data. Ten or more on-slide controls were able to robustly correct for batch effects.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article along with the FLINO R-scripts used to perform the evaluation of image normalizations methods and workflows can be downloaded from https://github.com/GE-Bio/FLINO.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Apoptotic and Necroptotic Mediators are Differentially Expressed in Mucinous and Non-Mucinous Colorectal Cancer

Background

Mucinous colorectal cancer (CRC) represents 10% of all CRC and is associated with chemotherapy resistance. This study aimed to determine expression of apoptosis and necroptosis mediators in mucinous CRC.

Methods

RNA gene expression data were extracted from TCGA. Protein levels in 14 mucinous and 39 non-mucinous tumors were measured by multiplexed immunofluorescence. Levels of apoptosis and necroptosis signalling proteins were analysed in SW1463 (mucinous rectal), SW837 (non-mucinous rectal), LS174T (mucinous colon) and HCT116 (non-mucinous colon) cell lines by western blot. Cell death was investigated by flow cytometry measurement of propidium iodide stained cells.

Results

High cleaved-Caspase 3 expression was noted in resected mucinous tumors. Western blot identified alterations in apoptosis proteins in mucinous CRC, most prominently downregulation of Bcl-xL protein levels (p=0.029) which was also observed at the mRNA level in patients by analysis of TCGA gene expression data (p<0.001). Treatment with 5-FU did not significantly elevate cell death in mucinous cells, while non-mucinous cells showed robust cell death responses. However, 5-FU-induced phosphorylation of MLKL in mucinous cancer cells, suggestive of a switch to necroptotic cell death signaling.

Conclusion

Apoptotic and necroptotic mediators are differentially expressed in mucinous and non-mucinous colorectal cancers and represent targets for investigation of cell death mechanisms in the mucinous subtype.

Quantitative single-cell analysis of immunofluorescence protein multiplex images illustrates biomarker spatial heterogeneity within breast cancer subtypes

BACKGROUND

The extent of cellular heterogeneity in breast cancer could have potential impact on diagnosis and long-term outcome. However, pathology evaluation is limited to biomarker immunohistochemical staining and morphology of the bulk cancer. Inter-cellular heterogeneity of biomarkers is not usually assessed. As an initial evaluation of the extent of breast cancer cellular heterogeneity, we conducted quantitative and spatial imaging of Estrogen Receptor (ER), Progesterone Receptor (PR), Epidermal Growth Factor Receptor-2 (HER2), Ki67, TP53, CDKN1A (P21/WAF1), CDKN2A (P16INK4A), CD8 and CD20 of a tissue microarray (TMA) representing subtypes defined by St. Gallen surrogate classification.

METHODS

Quantitative, single cell-based imaging was conducted using an Immunofluorescence protein multiplexing platform (MxIF) to study protein co-expression signatures and their spatial localization patterns. The range of MxIF intensity values of each protein marker was compared to the respective IHC score for the TMA core. Extent of heterogeneity in spatial neighborhoods was analyzed using co-occurrence matrix and Diversity Index measures.

RESULTS

On the 101 cores from 59 cases studied, diverse expression levels and distributions were observed in MxIF measures of ER and PR among the hormonal receptor-positive tumor cores. As expected, Luminal A-like cancers exhibit higher proportions of cell groups that co-express ER and PR, while Luminal B-like (HER2-negative) cancers were composed of ER+, PR- groups. Proliferating cells defined by Ki67 positivity were mainly found in groups with PR-negative cells. Triple-Negative Breast Cancer (TNBC) exhibited the highest proliferative fraction and incidence of abnormal P53 and P16 expression. Among the tumors exhibiting P53 overexpression by immunohistochemistry, a group of TNBC was found with much higher MxIF-measured P53 signal intensity compared to HER2+, Luminal B-like and other TNBC cases. Densities of CD8 and CD20 cells were highest in HER2+ cancers. Spatial analysis demonstrated variability in heterogeneity in cellular neighborhoods in the cancer and the tumor microenvironment.

CONCLUSIONS

Protein marker multiplexing and quantitative image analysis demonstrated marked heterogeneity in protein co-expression signatures and cellular arrangement within each breast cancer subtype. These refined descriptors of biomarker expressions and spatial patterns could be valuable in the development of more informative tools to guide diagnosis and treatment.

An atlas of inter- and intra-tumor heterogeneity of apoptosis competency in colorectal cancer tissue at single-cell resolution

Cancer cells’ ability to inhibit apoptosis is key to malignant transformation and limits response to therapy. Here, we performed multiplexed immunofluorescence analysis on tissue microarrays with 373 cores from 168 patients, segmentation of 2.4 million individual cells, and quantification of 18 cell lineage and apoptosis proteins. We identified an enrichment for BCL2 in immune, and BAK, SMAC, and XIAP in cancer cells. Ordinary differential equation-based modeling of apoptosis sensitivity at single-cell resolution was conducted and an atlas of inter- and intra-tumor heterogeneity in apoptosis susceptibility generated. Systems modeling at single-cell resolution identified an enhanced sensitivity of cancer cells to mitochondrial permeabilization and executioner caspase activation compared to immune and stromal cells, but showed significant inter- and intra-tumor heterogeneity.

Anatomical structures, cell types and biomarkers of the Human Reference Atlas

The Human Reference Atlas (HRA) aims to map all of the cells of the human body to advance biomedical research and clinical practice. This Perspective presents collaborative work by members of 16 international consortia on two essential and interlinked parts of the HRA: (1) three-dimensional representations of anatomy that are linked to (2) tables that name and interlink major anatomical structures, cell types, plus biomarkers (ASCT+B). We discuss four examples that demonstrate the practical utility of the HRA.

Multi-protein spatial signatures in ductal carcinoma in situ (DCIS) of breast

BACKGROUND

There is limited knowledge about DCIS cellular composition and relationship with breast cancer events (BCE).

METHODS

Immunofluorescence multiplexing (MxIF) was used to image and quantify 32 cellular biomarkers in FFPE DCIS tissue microarrays. Over 75,000 DCIS cells from 51 patients (median 9 years follow-up for non-BCE cases) were analysed for profiles predictive of BCE. K-means clustering was used to evaluate cellular co-expression of epithelial markers with ER and HER2.

RESULTS

Only ER, PR and HER2 significantly correlated with BCE. Cluster analysis identified 6 distinct cell groups with different levels of ER, Her2, cMET and SLC7A5. Clusters 1 and 3 were not significant. Clusters 2 and 4 (high ER/low HER2 and SLC7A5/mixed cMET) significantly correlated with low BCE risk (P = 0.001 and P = 0.034), while cluster 6 (high HER2/low ER, cMET and SLC7A5) correlated with increased risk (P = 0.018). Cluster 5 (similar to cluster 6, except high SLC7A5) trended towards significance (P = 0.072). A continuous expression score (Escore) based on these 4 clusters predicted likelihood of BCE (AUC = 0.79, log-rank test P = 5E-05; LOOCV AUC = 0.74, log-rank test P = 0.006).

CONCLUSION

Multiplexed spatial analysis of limited tissue is a novel method for biomarker analysis and predicting BCEs. Further validation of Escore is needed in a larger cohort.

Abstract PO-084: Protein marker heterogeneity in breast cancer subtypes measured using immunofluorescence protein multiplexing and quantitative, single cell image analysis

The extent of intra-tumoral heterogeneity - the variation in the composition of cells in a given tumor and those in the local tumor microenvironment - could have potential impact on diagnosis, treatment planning and subsequent response to treatment. To evaluate the extent of cancer cellular heterogeneity, we conducted quantitative protein marker multiplex imaging to study the variations in protein marker expression patterns on individual cells and spatial localizations. A multiplexed immunofluorescence imaging platform (MxIF, Cell DIVE) was used to measure the cellular expression of Estrogen Receptor (ER), Progesterone Receptor (PR), Epidermal Growth Factor Receptor 2 (HER2), Ki67, p53, p21WAF1 and p16INK4A in cancer epithelium. Analysis was conducted on a tissue microarray (TMA) representing subtypes classified as Luminal A-like, Luminal B-like (HER2-negative), Luminal B-like (HER2-positive), HER2-positive (non-luminal) or Triple-negative based on tumor grade and immune activity according to the St. Gallen surrogate classification. Of the 101 cores from 59 cases studied, high levels of heterogeneity were observed in ER and PR expression among the hormonal receptor-positive tumors. As expected Luminal A-like cancers exhibited higher proportions of individual cells co-expressing ER and PR, while cells in Luminal B-like, HER2-negative cancers showed ER expression only. Luminal B-like, HER2-positive cores were composed of cells with strong HER2 staining, and some cells co-expressing PR and HER2. Single cells with strong ER and HER2 labelling were rarely observed. Spatial visualizations illustrated that cells with similar expression signatures tend to be clustered together. Among cases which showed p53 overexpression with immunohistochemistry, the overall MxIF-measured p53 level was highest in TNBC compared to HER2+ and Luminal B-like cases. TNBC exhibited the highest proliferative fraction and most incidence of abnormal p53 and p16. We did not observe an association of p21 expression to P53 or P16 patterns, yet a slightly higher proportion of Luminal B-like cancers showed increased P21 levels compared to the other subtypes. Our study demonstrated the application of protein marker multiplexing and quantitative image analysis in measuring heterogeneity of protein co-expression signatures within breast cancer subtypes. Our next step is to apply the methods developed here to study a cohort where molecular profiling and radiomics were conducted (Bayani et al.) to reveal the extent of heterogeneity of breast cancer with a multi-omics approach.

Citation Format: Alison M. Cheung, Dan Wang, Kela Liu, Fiona Ginty, Sharon Nofech-Mozes, Jane Bayani, John M.S. Bartlett, Anne Martel, Martin J. Yaffe. Protein marker heterogeneity in breast cancer subtypes measured using immunofluorescence protein multiplexing and quantitative, single cell image analysis [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-084.

Abstract 2676: Hyperplexed immunofluorescence analysis (Cell DIVETM) of immune-related tumor heterogeneity in stage III colorectal cancer

Colorectal cancer (CRC) has one of the highest Worldwide incidences and mortality rates. Genotoxic chemotherapy following surgery in stage III patients confers treatment benefit to less than 20% of the patients, with more than 50% of stage III patients going on to develop distant metastases. Currently, there are no predictive biomarkers that can identify which stage III patients will recur, which patients will benefit from chemotherapy and which should be redirected towards alternative therapeutic interventions. A major challenge in identifying such a universal biomarker is that CRC is a heterogeneous disease with multiple subtypes. In the current study, we assessed clinically-relevant immune cell populations in the tumour microenvironment (TME) of stage III tumours using a novel hyperplex in situ immunofluorescence imaging technology (Cell DIVETM, GE Healthcare, Issaquah, WA).

Tissue microarrays (TMAs) with up to three 1mm diameter cores per patient were prepared from 139 stage III CRC patients treated with adjuvant FOLFOX chemotherapy. Single sections (5 µm) were iteratively stained with Cy3- and Cy5-conjugated antibodies for immune cell markers as well as markers of cell death and metabolism. The images underwent illumination correction, DAPI-based registration and autofluorescence removal. After image quality control corrections, single cell segmentation was performed using a combination of DAPI [nuclear], pan-cytokeratin [epithelial], NaKATPase [membrane] and S6 [cytoplasmic] segmentation markers and an average of ~3,000 stromal cells and ~ 4,000 epithelial were segmented per tumour core.

A machine learning-based algorithm for immune cell classification and quantification was used to analyse the immune markers CD45, CD3, CD4, CD8, FOXP3 and PD1 to identify: cytotoxic T cells, T helper cells, regulatory T cells and potential relevance of immune checkpoint therapy. In the tumour tissues, the median proportion of CD3+ segmented cells was ~8%. Classified immune cells were counted within epithelial and stromal regions, with patients categorised as Low, Intermediate and High (based on &lt;25th, 25th - 75th and &gt;75th percentile, respectively) for each cell type. Preliminary survival analyses show that patients with ‘CD8 High' intratumoural cytotoxic T cells have better Disease-Free Survival compared to ‘CD8 Low' patients in this FOLFOX-treated cohort. By combining single-cell data with clinicopathological patient data, we aim to identify immune-, cell death- and metabolism-related signatures that can predict benefit from adjuvant FOLFOX chemotherapy for Stage III CRC patients.

Citation Format: Xanthi Stachtea, Andreas Lindner, Manuela Salvucci, Sanghee Cho, Anup Sood, Elizabeth McDonough, Alberto Santamaria-Pang, John Graf, Philip Dunne, Mark Lawler, Jochen Prehn, Fiona Ginty, Daniel Longley. Hyperplexed immunofluorescence analysis (Cell DIVETM) of immune-related tumor heterogeneity in stage III colorectal cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2676.

Comparison of Multiplexed Immunofluorescence Imaging to Chromogenic Immunohistochemistry of Skin Biomarkers in Response to Monkeypox Virus Infection

Over the last 15 years, advances in immunofluorescence-imaging based cycling methods, antibody conjugation methods, and automated image processing have facilitated the development of a high-resolution, multiplexed tissue immunofluorescence (MxIF) method with single cell-level quantitation termed Cell DIVETM. Originally developed for fixed oncology samples, here it was evaluated in highly fixed (up to 30 days), archived monkeypox virus-induced inflammatory skin lesions from a retrospective study in 11 rhesus monkeys to determine whether MxIF was comparable to manual H-scoring of chromogenic stains. Six protein markers related to immune and cellular response (CD68, CD3, Hsp70, Hsp90, ERK1/2, ERK1/2 pT202_pY204) were manually quantified (H-scores) by a pathologist from chromogenic IHC double stains on serial sections and compared to MxIF automated single cell quantification of the same markers that were multiplexed on a single tissue section. Overall, there was directional consistency between the H-score and the MxIF results for all markers except phosphorylated ERK1/2 (ERK1/2 pT202_pY204), which showed a decrease in the lesion compared to the adjacent non-lesioned skin by MxIF vs an increase via H-score. Improvements to automated segmentation using machine learning and adding additional cell markers for cell viability are future options for improvement. This method could be useful in infectious disease research as it conserves tissue, provides marker colocalization data on thousands of cells, allowing further cell level data mining as well as a reduction in user bias.

National Cancer Institute Think-Tank Meeting Report on Proteomic Cartography and Biomarkers at the Single-Cell Level: Interrogation of Premalignant Lesions

A Think-Tank Meeting was convened by the National Cancer Institute (NCI) to solicit experts' opinion on the development and application of multiomic single-cell analyses, and especially single-cell proteomics, to improve the development of a new generation of biomarkers for cancer risk, early detection, diagnosis, and prognosis as well as to discuss the discovery of new targets for prevention and therapy. It is anticipated that such markers and targets will be based on cellular, subcellular, molecular, and functional aberrations within the lesion and within individual cells. Single-cell proteomic data will be essential for the establishment of new tools with searchable and scalable features that include spatial and temporal cartographies of premalignant and malignant lesions. Challenges and potential solutions that were discussed included (i) The best way/s to analyze single-cells from fresh and preserved tissue; (ii) Detection and analysis of secreted molecules and from single cells, especially from a tissue slice; (iii) Detection of new, previously undocumented cell type/s in the premalignant and early stage cancer tissue microenvironment; (iv) Multiomic integration of data to support and inform proteomic measurements; (v) Subcellular organelles-identifying abnormal structure, function, distribution, and location within individual premalignant and malignant cells; (vi) How to improve the dynamic range of single-cell proteomic measurements for discovery of differentially expressed proteins and their post-translational modifications (PTM); (vii) The depth of coverage measured concurrently using single-cell techniques; (viii) Quantitation - absolute or semiquantitative? (ix) Single methodology or multiplexed combinations? (x) Application of analytical methods for identification of biologically significant subsets; (xi) Data visualization of N-dimensional data sets; (xii) How to construct intercellular signaling networks in individual cells within premalignant tumor microenvironments (TME); (xiii) Associations between intrinsic cellular processes and extrinsic stimuli; (xiv) How to predict cellular responses to stress-inducing stimuli; (xv) Identification of new markers for prediction of progression from precursor, benign, and localized lesions to invasive cancer, based on spatial and temporal changes within individual cells; (xvi) Identification of new targets for immunoprevention or immunotherapy-identification of neoantigens and surfactome of individual cells within a lesion.

Prolyl 4-hydroxylase alpha 1 protein expression risk-stratifies early stage colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent and lethal malignancies. Especially for early stage CRC, prognostic molecular markers are needed to guide therapy. In this study, we first extracted total proteomes from matched pairs of fresh cancer and benign mucosal tissues from 22 CRC patients. Global proteomic profiling with Fourier transform liquid chromatography-mass spectrometry sequencing and label free quantitation uncovered that P4HA1 (prolyl 4-hydroxylase alpha 1) was overexpressed in CRC relative to benign colonic mucosa. We then investigated expression by immunohistochemical staining with P4HA1-specific antibodies using CRC tissue microarrays. Independent validation cohorts of 599 cases of early stage CRC and 91 cases of late stage CRC were examined. Multivariate and univariate survival analyses revealed that high expression of P4HA1 protein was an independent poor prognostic marker for patients with early stage CRC, especially of the microsatellite stable subtype. Our study provides strong support for P4HA1 as a predictive protein marker for precision diagnostics, therapeutic decision-making, and drug development for early stage colorectal cancer and demonstrates the utility of proteomic profiling to identify novel protein biomarkers.

The Cancer Microbiome: Distinguishing Direct and Indirect Effects Requires a Systemic View

The collection of microbes that live in and on the human body - the human microbiome - can impact on cancer initiation, progression, and response to therapy, including cancer immunotherapy. The mechanisms by which microbiomes impact on cancers can yield new diagnostics and treatments, but much remains unknown. The interactions between microbes, diet, host factors, drugs, and cell-cell interactions within the cancer itself likely involve intricate feedbacks, and no single component can explain all the behavior of the system. Understanding the role of host-associated microbial communities in cancer systems will require a multidisciplinary approach combining microbial ecology, immunology, cancer cell biology, and computational biology - a systems biology approach.

Multiscale, multimodal analysis of tumor heterogeneity in IDH1 mutant vs wild-type diffuse gliomas

Glioma is recognized to be a highly heterogeneous CNS malignancy, whose diverse cellular composition and cellular interactions have not been well characterized. To gain new clinical- and biological-insights into the genetically-bifurcated IDH1 mutant (mt) vs wildtype (wt) forms of glioma, we integrated data from protein, genomic and MR imaging from 20 treatment-naïve glioma cases and 16 recurrent GBM cases. Multiplexed immunofluorescence (MxIF) was used to generate single cell data for 43 protein markers representing all cancer hallmarks, Genomic sequencing (exome and RNA (normal and tumor) and magnetic resonance imaging (MRI) quantitative features (protocols were T1-post, FLAIR and ADC) from whole tumor, peritumoral edema and enhancing core vs equivalent normal region were also collected from patients. Based on MxIF analysis, 85,767 cells (glioma cases) and 56,304 cells (GBM cases) were used to generate cell-level data for 24 biomarkers. K-means clustering was used to generate 7 distinct groups of cells with divergent biomarker profiles and deconvolution was used to assign RNA data into three classes. Spatial and molecular heterogeneity metrics were generated for the cell data. All features were compared between IDH mt and IDHwt patients and were finally combined to provide a holistic/integrated comparison. Protein expression by hallmark was generally lower in the IDHmt vs wt patients. Molecular and spatial heterogeneity scores for angiogenesis and cell invasion also differed between IDHmt and wt gliomas irrespective of prior treatment and tumor grade; these differences also persisted in the MR imaging features of peritumoral edema and contrast enhancement volumes. A coherent picture of enhanced angiogenesis in IDHwt tumors was derived from multiple platforms (genomic, proteomic and imaging) and scales from individual proteins to cell clusters and heterogeneity, as well as bulk tumor RNA and imaging features. Longer overall survival for IDH1mt glioma patients may reflect mutation-driven alterations in cellular, molecular, and spatial heterogeneity which manifest in discernable radiological manifestations.

Abstract A039: Lower cellular activation of cMET signaling network is associated with reduced recurrence risk in stage II colorectal cancer

Colorectal cancer (CRC) has one of the highest worldwide incidences and mortality rates. About 25% of stage II patients develop recurrence within 5 years. However, use of adjuvant chemotherapy yields minimal therapeutic benefit. Clinical decision-making in this population would be informed by prognostic and predictive biomarkers that help indicate more targeted interventions and/or intensified disease monitoring. Higher cMET expression has been shown to be a prognostic marker in CRC and has been evaluated as a drug target in several cancers, including CRC. cMET acts as the receptor for hepatocyte growth factor (HGF), and is associated with increased proliferation, migration and morphogenesis of epithelial cells via activation of multiple downstream pathways including PI3K/AKT, MAPK and the NF-kB pathways. Here, we investigated whether increased cellular expression of multiple markers in the cMET/adjacent pathways was correlated with clinical outcome. The study population consisted of 283 patients with stage II CRC, without neoadjuvant treatment. Using a multiplexed immunofluorescence method (MxIF) with cell-level quantification (Cell DIVETM, GE Healthcare), 41 biomarkers in the cMET, mTOR, MAPK and associated pathways, and lymphocyte markers were analyzed in an iterative sequence of staining, imaging and dye inactivation, followed by image registration, cell segmentation and biomarker intensity-quantitation. Poor quality images and cells were filtered based on poor segmentation and tissue quality following manual review. Images that did not perfectly register were also excluded. For epithelial cell analysis, all stromal cells were filtered, resulting in a total of 559,952 epithelial cells. K-means clustering was conducted on cMET and related pathways (cMET, pGSK3β, 4EBP1, p4EBP1, S6, pS6, pStat3, pp38MAPK, pNFkBp65, pNFkBp105, EGFR, pERK1/2, HER2, IGF1R, CA-IX, Glut1, SLC7A5 and Ki67). Cluster analysis identified 6 cell clusters with varying expression levels of cMET and other pathway markers. A cluster corresponding to low cMET network activation (cMETLow) was correlated with reduced risk of recurrence (cox pH model Likelihood ratio test p-value = 0.056). Furthermore, MMR proficient patients (who had a higher recurrence rate) had a significantly greater proportion of cells with elevated cMET and related pathway marker (cMETHigh) s(p= 0.007). They also had a significantly lower fraction of infiltrating helper T cells (CD3+CD4+, pvalue 0.076) and CD68+ cells in the epithelial region, compared to MMR deficient patients (p=0.005). Further research will include continuing in-depth analysis of MMR status, cMET pathway and lymphocyte response as well as the role of cellular heterogeneity. In summary, comprehensive cMET pathway analysis using a multiplexed single cell approach indicates for the first time an association between low cMET network activation and superior clinical outcomes.

Citation Format: Elizabeth McDonough, Anup Sood, Fiona Ginty, Sanghee Cho, John Graf, Jochen Prehn, Philip Dunne, Andreas Lindner, Manuela Salvucci, Daniel Longley, Mark Lawler, Travis Hollmann, Jinru Shia. Lower cellular activation of cMET signaling network is associated with reduced recurrence risk in stage II colorectal cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr A039. doi:10.1158/1535-7163.TARG-19-A039

Efficacy and tolerability of anti-programmed death-ligand 1 (PD-L1) antibody (Avelumab) treatment in advanced thymoma

Background

Thymic epithelial tumors are PD-L1–expressing tumors of thymic epithelial origin characterized by varying degrees of lymphocytic infiltration and a predisposition towards development of paraneoplastic autoimmunity. PD-1–targeting antibodies have been evaluated, largely in patients with thymic carcinoma. We sought to evaluate the efficacy and safety of the anti-PD-L1 antibody, avelumab (MSB0010718C), in patients with relapsed, advanced thymic epithelial tumors and conduct correlative immunological studies.

Methods

Seven patients with thymoma and one patient with thymic carcinoma were enrolled in a phase I, dose-escalation trial of avelumab (MSB0010718C), and treated with avelumab at doses of 10 mg/kg to 20 mg/kg every 2 weeks until disease progression or development of intolerable side effects. Tissue and blood immunological analyses were conducted.

Results

Two of seven (29%) patients with thymoma had a confirmed Response Evaluation Criteria in Solid Tumors–defined partial response, two (29%) had an unconfirmed partial response and three patients (two thymoma; one thymic carcinoma) had stable disease (43%). Three of four responses were observed after a single dose of avelumab. All responders developed immune-related adverse events that resolved with immunosuppressive therapy. Only one of four patients without a clinical response developed immune-related adverse events. Responders had a higher absolute lymphocyte count, lower frequencies of B cells, regulatory T cells, conventional dendritic cells, and natural killer cells prior to therapy.

Conclusion

These results demonstrate anti-tumor activity of PD-L1 inhibition in patients with relapsed thymoma accompanied by a high frequency of immune-related adverse events. Pre-treatment immune cell subset populations differ between responders and non-responders.

Trial registration

ClinicalTrials.gov - NCT01772004. Date of registration – January 21, 2013.

Electronic supplementary material

The online version of this article (10.1186/s40425-019-0723-9) contains supplementary material, which is available to authorized users.

Abstract 1642: Spatial proteomics with hyperplexed fluorescence imaging predicts risk of colorectal cancer recurrence and infers recurrence-specific protein-protein networks

Introduction: Colorectal cancer (CRC) is the third leading cause of cancer related deaths in United States with recurrence after resection-with-a-curative-intent being frequently implicated in these deaths. The basis for CRC recurrence is not completely understood, is multifactorial, and involves dysregulation of heterocellular signaling among tumor cells and their microenvironment. Based on hyperplexed immunofluorescence imaging and novel computational analyses, we have developed a recurrence-risk prediction method that samples these signaling networks within the epithelial and stromal domains of the tumor microenvironment and provides improved performance over current state-of-the-art recurrence-risk prediction assays.

Data: In the retrospective study presented here, we used 52 hyperplexed immunofluorescence biomarkers associated with either canonical oncogenic pathways, immune response, or colon cancer per se to spatially profile tissue microarrays obtained from resected tissue samples from 432 chemo-naïve CRC patients.

Results: Using epithelial- and stromal-domain expression and co-expression diversity of the biomarkers, our preliminary results predicted the risk of CRC recurrence with a concordance index of 0.91. We also generated training and validation sets from the CRC patient cohort and demonstrated that the area under the curve (AUC) of the prediction receiver operating characteristic (ROC) was 0.90. We utilized stratified bootstrapping to show that the AUC was stable with a standard deviation of 0.02. Significantly, the penalized model selection used within our method allowed us to infer epithelial and stromal-domain protein networks specific to the risk-of-recurrence from the underlying signaling networks. Despite the limited sampling intrinsic to tissue microarrays we were able to capture immune cell infiltration and the differential modulation of these outcome specific protein-protein networks.

Conclusions: Our CRC recurrence-risk prediction method exploits our spatial proteomics computational pathology platform involving hyperplexed immunofluorescence imaging. This study demonstrates the potential of this paradigm to not only accurately predict risk of CRC recurrence but also to reveal the underlying systems pathophysiology. Inferring outcome- and domain-specific CRC protein networks will enable biomarkers mechanistically linked to disease progression to be determined and their causality corroborated. In turn, this knowledge can be used to inform optimal therapeutic strategies for individual patients.

Citation Format: Shikhar Uttam, Andrew M. Stern, Samantha A. Furman, Filippo Pullara, Fiona Ginty, D. Lansing Taylor, S. Chakra Chennubhotla. Spatial proteomics with hyperplexed fluorescence imaging predicts risk of colorectal cancer recurrence and infers recurrence-specific protein-protein networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1642.

Understanding heterogeneous tumor microenvironment in metastatic melanoma

A systemic analysis of the tumor-immune interactions within the heterogeneous tumor microenvironment is of particular importance for understanding the antitumor immune response. We used multiplexed immunofluorescence to elucidate cellular spatial interactions and T-cell infiltrations in metastatic melanoma tumor microenvironment. We developed two novel computational approaches that enable infiltration clustering and single cell analysis-cell aggregate algorithm and cell neighborhood analysis algorithm-to reveal and to compare the spatial distribution of various immune cells relative to tumor cell in sub-anatomic tumor microenvironment areas. We showed that the heterogeneous tumor human leukocyte antigen-1 expressions differently affect the magnitude of cytotoxic T-cell infiltration and the distributions of CD20+ B cells and CD4+FOXP3+ regulatory T cells within and outside of T-cell infiltrated tumor areas. In a cohort of 166 stage III melanoma samples, high tumor human leukocyte antigen-1 expression is required but not sufficient for high T-cell infiltration, with significantly improved overall survival. Our results demonstrate that tumor cells with heterogeneous properties are associated with differential but predictable distributions of immune cells within heterogeneous tumor microenvironment with various biological features and impacts on clinical outcomes. It establishes tools necessary for systematic analysis of the tumor microenvironment, allowing the elucidation of the "homogeneous patterns" within the heterogeneous tumor microenvironment.

Abstract P4-08-17: Expression score (Escore) for the prediction of likelihood of recurrence of DCIS

Background: Ductal carcinoma in situ (DCIS) accounts for at least 20% of breast cancers. Factors associated with recurrence of DCIS or progression to invasive carcinoma are not well delineated. The goals of the current study were to profile the epithelial cells using the GE Cell DIVE™ immuno-fluorescent based analyses. This was coupled with semi-automated algorithms to characterize the inter-relationships between cell populations and likelihood of recurrence.

Patients and Methods: A TMA-based (total 8 TMAs) cohort of cases of DCIS with and without recurrence was obtained from Oxford University. Recurrence in this cohort was defined as ipsilateral DCIS, ipsilateral invasive, contralateral invasive and metastatic. Analysis for 31 epithelial markers (HER4, CK56, ABCG2, PTEN, S6, CKAE1, PR, ER, NaKATPase, CK19, ALDH1, CK PCK26, cMET, CD44v6, HER2, CDCP1, p53, CK15, COX2, VEGFR2, ABCb1, HTF9C, CD10, MRP4, CEACAM5, EGFR, p21, MRP5, SLC7A5, Ki67, DAPI) was performed on a single FFPE TMA section containing cases of DCIS. Briefly, FFPE sections from TMAs containing DCIS were sequentially (cyclically) stained for the markers. Each cycle entailed staining with 2-3 markers followed by imaging, dye inactivation, and re-staining. DAPI was used for nuclear demarcation and for registration of the images, while S6, pan-cadherin, Na+K+ATPase and pan-cytokeratin were used for epithelial segmentation. K-means clustering followed regression analysis was performed to identify inter-relationships between markers and association with likelihood of recurrence. Log-rank analysis was performed and the relapse-free survival data depicted using Kaplan Meier plots. Escore was developed by logistic regression model, classification model on recurrence

Results: Filtering of the expression analysis by the quality, specificity, compartment localization and fields entirely composed of DCIS, in addition to availability of clinical data resulted final analysis of 31 markers in 67 cases. Correlation analyses were performed on each of the markers to identify markers that were significantly correlated in univariate analysis. K-means cluster analysis was performed using a set of 4 markers (ER, HER2, SLC7A5 and cMET) to identify 6 clusters. High cMET (cluster 1; low HER2 and SLC7A5) and High ER (low cMET, HER2, SLC7A5; Cluster 5) were associated with low risk of recurrence (p values 0.014 and &lt;0.0001). In contrast, Cluster 2 (High HER2, high SLC7A5, low ER) and Cluster 3 (High HER2, low ER, SLC7A5and cMET) were associated with increased risk of recurrence (P values 0.038 and 0.076). A regression analysis based algorithm was developed using these markers to calculate a numerical score which could predict likelihood of recurrence. As depicted in the KM plots, the HR for recurrence increases significantly (P-value 2.4E-05; p=0.02 with LOOCV) with increase in expression score (Escore).

Conclusions: We describe the development of an Escore using expression 4 markers to predict likelihood of recurrence. Additional ongoing studies will seek to validate the utility of the Escore in predicting likelihood of recurrence of DCIS and development of invasive carcinomas and comparison with other scoring systems.

Citation Format: Badve SS, Cho S, Gokmen-Polar Y, Zavodszky M, Sui Y, Chadwick C, Tan PH, Gerdes M, Harris AL, Ginty F. Expression score (Escore) for the prediction of likelihood of recurrence of DCIS [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-08-17.

Abstract 3039: Role of IDH mutation status on molecular and spatial heterogeneity in glial tumors across progression and recurrence

We developed and deployed a workflow for generating multi-scale, multiparametric imaging data, feature extraction and/or converting to higher scales which equips multiple analysis approaches to differentiate clinically variable phenotypes of glial tumors. The workflow quantifies spatial heterogeneity (concordance of adjoining cells) and molecular heterogeneity (varied cell states determined by protein abundance) of glial tumors at the genomic, tissue, and medical imaging scales including IDH mutation status and progression/recurrence status. A panel of 24 multiplexed immunofluorescence (MxIF) markers (addressing 9 hallmarks of cancer) was used to profile single cells (in the thousands) in tissue sections from each of 31 glial tumors (ranging from primary grade II to IV, and recurrent grade IV). Pre-resection multi-parameter MR images were feature extracted from discreet habitats (necrosis, enhancing, and edema); whole exome and transcriptome sequencing from bulk viable tumor were analyzed. By MxIF, the various states of individual cells from treatment-naive patient specimens resolved unsupervised into 7 clusters, for which Cluster 2 (including cells from 9 patients) and Cluster 6 (including cells from 8 patients) contained the two larger bundles of patient cases. When separated into IDHmt and IDHwt cases, cells from IDHmt cases frequently contained cell populations dominated by a single cluster (low molecular heterogeneity); cells from cases with IDHwt represented multiple different clusters (high molecular heterogeneity). In grade III astrocytomas, and grade IV recurrent glioblastomas, spatial heterogeneity of the hallmark “inducing angiogenesis” was elevated in the IDHmt tumors compared to IDHwt, while between the same groups, molecular heterogeneity was lower in the IDHmt cases than wild type. Edema from T1w post contrast MR imaging was found to be elevated in IDHwt gliomas relative to IDHmt, while enhancement was reduced in IDHwt compared to IDHmt tumors. The findings demonstrate that IDHmt gliomas, irrespective of grade, show less edema, greater enhancement, and greater spatial heterogeneity of the “inducing angiogenesis” hallmark but lower molecular heterogeneity than IDHwt tumors. Molecular heterogeneity of “cancer invasion” also differed between IDHmt and IDHwt cases. Longer survival duration following diagnosis for patients with IDHmt gliomas may reflect generalized altered molecular and spatial heterogeneity, which is a phenotype evident on medical imaging. [Clinically-annotated specimens originated from the Ohio Brain Tumor Study and the Ivy GBM Clinical Trials Consortium]

Citation Format: Michael E. Berens, Jill S. Barnholtz-Sloan, Miribella Rusu, John Graf, Anup Sood, Sanghee Cho, Maria Zavodszky, Sara Byron, Rebecca Halperin, Yi Fritz, Seungchan Kim, Fiona Ginty. Role of IDH mutation status on molecular and spatial heterogeneity in glial tumors across progression and recurrence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3039.

Single-cell heterogeneity in ductal carcinoma in situ of breast

Heterogeneous patterns of mutations and RNA expression have been well documented in invasive cancers. However, technological challenges have limited the ability to study heterogeneity of protein expression. This is particularly true for pre-invasive lesions such as ductal carcinoma in situ of the breast. Cell-level heterogeneity in ductal carcinoma in situ was analyzed in a single 5 μm tissue section using a multiplexed immunofluorescence analysis of 11 disease-related markers (EGFR, HER2, HER4, S6, pmTOR, CD44v6, SLC7A5 and CD10, CD4, CD8 and CD20, plus pan-cytokeratin, pan-cadherin, DAPI, and Na+K+ATPase for cell segmentation). Expression was quantified at cell level using a single-cell segmentation algorithm. K-means clustering was used to determine co-expression patterns of epithelial cell markers and immune markers. We document for the first time the presence of epithelial cell heterogeneity within ducts, between ducts and between patients with ductal carcinoma in situ. There was moderate heterogeneity in a distribution of eight clusters within each duct (average Shannon index 0.76; range 0-1.61). Furthermore, within each patient, the average Shannon index across all ducts ranged from 0.33 to 1.02 (s.d. 0.09-0.38). As the distribution of clusters within ducts was uneven, the analysis of eight ducts might be sufficient to represent all the clusters ie within- and between-duct heterogeneity. The pattern of epithelial cell clustering was associated with the presence and type of immune infiltrates, indicating a complex interaction between the epithelial tumor and immune system for each patient. This analysis also provides the first evidence that simultaneous analysis of both the epithelial and immune/stromal components might be necessary to understand the complex milieu in ductal carcinoma in situ lesions.

Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images

We introduce THRIVE (Tumor Heterogeneity Research Interactive Visualization Environment), an open-source tool developed to assist cancer researchers in interactive hypothesis testing. The focus of this tool is to quantify spatial intratumoral heterogeneity (ITH), and the interactions between different cell phenotypes and noncellular constituents. Specifically, we foresee applications in phenotyping cells within tumor microenvironments, recognizing tumor boundaries, identifying degrees of immune infiltration and epithelial/stromal separation, and identification of heterotypic signaling networks underlying microdomains. The THRIVE platform provides an integrated workflow for analyzing whole-slide immunofluorescence images and tissue microarrays, including algorithms for segmentation, quantification, and heterogeneity analysis. THRIVE promotes flexible deployment, a maintainable code base using open-source libraries, and an extensible framework for customizing algorithms with ease. THRIVE was designed with highly multiplexed immunofluorescence images in mind, and, by providing a platform to efficiently analyze high-dimensional immunofluorescence signals, we hope to advance these data toward mainstream adoption in cancer research. Cancer Res; 77(21); e71-74. ©2017 AACR.

Stromal-Based Signatures for the Classification of Gastric Cancer

Treatment of metastatic gastric cancer typically involves chemotherapy and monoclonal antibodies targeting HER2 (ERBB2) and VEGFR2 (KDR). However, reliable methods to identify patients who would benefit most from a combination of treatment modalities targeting the tumor stroma, including new immunotherapy approaches, are still lacking. Therefore, we integrated a mouse model of stromal activation and gastric cancer genomic information to identify gene expression signatures that may inform treatment strategies. We generated a mouse model in which VEGF-A is expressed via adenovirus, enabling a stromal response marked by immune infiltration and angiogenesis at the injection site, and identified distinct stromal gene expression signatures. With these data, we designed multiplexed IHC assays that were applied to human primary gastric tumors and classified each tumor to a dominant stromal phenotype representative of the vascular and immune diversity found in gastric cancer. We also refined the stromal gene signatures and explored their relation to the dominant patient phenotypes identified by recent large-scale studies of gastric cancer genomics (The Cancer Genome Atlas and Asian Cancer Research Group), revealing four distinct stromal phenotypes. Collectively, these findings suggest that a genomics-based systems approach focused on the tumor stroma can be used to discover putative predictive biomarkers of treatment response, especially to antiangiogenesis agents and immunotherapy, thus offering an opportunity to improve patient stratification. Cancer Res; 76(9); 2573-86. ©2016 AACR.

Abstract 883: Elucidating cancer hallmark context from glioma MR imaging and RNA expression data

Radiogenomics or radiomics is an emerging field where tumor genomic data is correlated with radiology image features, thereby potentially providing more biological information about the tumor phenotype. A central challenge is the potential for model over-fitting due to analysis of many thousands of genomic data-points with hundreds of corresponding patient image features. Biological interpretation of the imaging feature correlations is also challenged by overlapping pathways and common gene effects. Our goals were: i) to explore correlations between gene expression and corresponding Magnetic Resonance (MR) Apparent Diffusion Coefficient (ADC) derived imaging features in low grade glioma (LGG); ii) to classify significant gene and imaging correlates by cancer hallmark1. RNA expression data from 32 LGG patients were extracted from The Cancer Genome Atlas (TCGA) and matched with corresponding MR image data from The Cancer Imaging Archive (TCIA). Among 32 patients, 18 were males (56%), and ages ranged from 21 to 74 years (mean age 44). Tumor and normal regions in the MR images were annotated by an expert radiologist using ITK-Snap. The normal reference region was used normalize image intensities in corresponding tumor regions. Tumor texture features were computed on ADC Maps at each voxel location within the disease region (including first and second order statistics, Run Length and co-occurrence matrix derived measures features. The voxel features were finally aggregated within the tumor region using statistical measures of mean, variance, median, kurtosis, and skewness. ADC imaging features (n=310) were correlated with each single gene expression value (11614 genes after MAD&gt;0.4 filtering). Only image features and genes with pairwise correlations higher than 0.68 (0.68 is the 3-standard deviation above average correlation) and FDR (False Discovery Rate) &lt;0.1 were used for follow-up analyses. Significant genes and MR image features were aggregated into 3 groups based on gene expression and correlated with cancer hallmarks. Seven Haralick image features (reflecting the average level of image intensity heterogeneity) were independently, significantly correlated with the Angiogenesis Hallmark (FDR all &lt; 0.001). Three Haralick image features (reflecting asymmetric distribution of intensity) were significantly correlated with the Activating Invasion and Metastasis Hallmark (FDR all &lt; 0.001). Validation of these findings in additional LGG cases with additional imaging protocols and features is ongoing. Radiogenomics informed by genomic profiling may usher in processes to infer cancer hallmarks to aid treatment planning and prognosis of glioma patients.1 Hanahan D and Weinberg RA (2011). Hallmarks of cancer: the next generation. Cell 144(5):646-74.

Citation Format: Yunxia Sui, Mirabela Rusu, Dattesh Shanbhag, Uday Patil, Jeffrey Kiefer, Jill Barnholtz-Sloan, Michael Berens, Fiona Ginty, Graf John, Sandeep Gupta, Chinnappa Kodira, Lee Newberg, Sushravya Raghunath, Anup Sood, Sushravya Raghunath. Elucidating cancer hallmark context from glioma MR imaging and RNA expression data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 883. doi:10.1158/1538-7445.AM2017-883

Abstract 882: Interpreting glioma MR imaging and somatic mutations in a cancer hallmark context

Extracting biologically relevant data from radiology images can enable better monitoring of disease progression and therapy response. The field of radiogenomics is providing new approaches for such genomic/radiology correlations. However, there are several challenges in validation and clinical translation in that few DNA mutations are shared between tumors from different individuals and the differences in scale between imaging and genomic features can limit interpretation of underlying mechanisms. The goals of this work were to i) analyze correlations between low grade glioma (LGG) DNA somatic mutations, using a novel DNA impact scoring approach, and MRI derived imaging features; and ii) to interpret results in context of cancer hallmarks1. Multi-parametric MRI and corresponding DNA data from 32 LGG patients were extracted from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). The cohort included 18 males (56%), with mean age of 44 years (range: 21-74 years). An expert radiologist outlined the normal and tumor regions of interest using ITK-Snap tool. The normal region was used as a reference to normalize image intensities in the tumor region. Tumor mean intensity and mean variance were computed from Apparent Diffusion Coefficient (ADC), T1 enhancement ratio (derived from T1 pre- and post- contract MRI), and Fluid-Attenuated Inversion Recovery (FLAIR) images. A novel algorithm was used to compute DNA impact scores for each somatic mutation. The score represents the probability of a DNA variant being pathogenic vs. nonpathogenic. First, the scoring algorithm computes a score for nucleotide base insertions, deletions, or single base changes and then computes the consequence of such changes on amino acid coding, binding sites, splice sites and protein phosphorylation sites. An impact score was then computed based on the individual DNA impact scores of mutations within the gene. Finally, an average DNA impact score was computed at the Cancer Hallmark level using a gene-cancer hallmark map. At gene level, significant positive correlations were found between the ATRX (p=0.0002), TP53 (p=0.02) and ADC mean intensity. At pathway level, regulation of TP53 expression and degradation, and DNA damage response, signal transduction by p53 class mediator, and DNA translocase activity were found to be enriched with genes that correlated with ADC and FLAIR. These pathways also contained genes that were enriched in the following cancer hallmarks: replicative immortality, evading growth suppression and genome instability. The ATRX gene is a member of all three hallmarks and TP53 a member of two. Since ADC is a measure of water diffusion and hence an indirect measure of cellularity, these findings demonstrate that mutations in replication and repair pathways are contributing to imaging features at the tumor level.1 Hanahan, D. and Weinberg, R.A. (2011). Hallmarks of cancer: the next generation. Cell 144(5):646-74.

Citation Format: John Graf, Mirabela Rusu, Yunxia Sui, Dattesh Shanbhag, Uday Patil, Jeffrey Kiefer, Jill Barnholtz-Sloan, Michael Berens, Fiona Ginty, Sandeep Gupta, Chinnappa Kodira, Lee Newberg, Sushravya Raghunath, Anup Sood. Interpreting glioma MR imaging and somatic mutations in a cancer hallmark context [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 882. doi:10.1158/1538-7445.AM2017-882

Abstract A71: Tumor HLA Class I expression influences immune cells infiltration in metastatic melanoma tumor microenvironment

The variable clinical success with immune checkpoint inhibitors in the treatment of solid tumors challenges us to further understand the regulatory mechanisms of immune cells infiltration – a critical step of tumor elimination – in a highly heterogenous tumor microenvironment (TME). Systemic analyses of the tumor-immune cell interface and interactions are of particular significance with direct clinical impact. We employed a multiplexed immunofluorescence (MxIF) method, a novel technology that allows in situ quantitative single cell characterization with high order protein multiplexing on whole tumor sections, to elucidate the key components in TME governing the immune cells' composition and function in metastatic melanoma lymph node metastases. Here, we show that the expression of tumor cell Human Leukocyte Antigen 1 (HLA-1) is highly heterogeneous both within the TME and between patients. The level of tumor HLA-1 expression directly correlates with the magnitude of cytotoxic T lymphocytes (CTLs) infiltration in the TME. In addition, decreased tumor HLA-1 expression is associated with accumulation of CD20+ cells at the edge of the tumor as well as an increased population of CD4+FOXP3+ cells. The association between tumor HLA-1 expression and immune cell distribution is highly consistent yet heterogenous across the entire tumor mass. Moreover, using quantitative single cell data, we developed algorithms for computational and spatial modeling of tumor-immune cell interfaces, allowing statistical comparison of cellular interactions in the context of tumor heterogeneity. Spatial analysis demonstrates that tumor HLA-1 expression favors CTL invasion, while lack of tumor HLA-1 expression results in CTL evasion. Furthermore, in a cohort of 166 Stage III melanoma patients, we confirm that CTLs infiltration is only present in regions with high tumor HLA-1 expressions, and that patients with tumors high in both tumor HLA-1 and CTLs have significantly improved progression free survival (PFS) compared with those with tumors low in HLA-1 and/or CTLs. Our study demonstrates that the heterogeneous tumor HLA-1 expression results in various immune cell distribution patterns in the TME, directly contributing to the varied antitumor immunities and ultimately influencing tumor outcomes. As part of the antigen presentation machinery, HLA-1 expression on tumors is necessary for CTLs infiltration and is indispensable for T cell mediated tumor elimination with direct clinical benefits. We also establish a novel platform for visualization and spatial representation of the cellular heterogeneity within the TME, providing data processing and modeling algorithms necessary for understanding the cellular interplay mediating tumor surveillance, which will ultimately improve therapeutic efficacy and clinical outcomes.

Citation Format: Yiyi Yan, Alexey Leontovich, Thomas Flotte, Michael Gerdes, Fiona Ginty, Alberto Santamaria-Pang, Anup Sood, Keyur Desai, Chrystal Chadwick, Rong Zhang, Svetomir N. Markovic. Tumor HLA Class I expression influences immune cells infiltration in metastatic melanoma tumor microenvironment. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2016 Oct 20-23; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2017;5(3 Suppl):Abstract nr A71.

Abstract P1-06-02: Impact of heterogeneity of DCIS on immune cell infiltrations

Background: Ductal carcinoma in situ (DCIS) accounts for at least 20% of breast cancers. Factors associated with recurrence of DCIS or progression to invasive carcinoma are not well delineated. The goals of the current study were to profile the epithelial and immune cells using the MultiOmyx hyperplexed immuno-fluorescent based analyses. This was coupled with semi-automated algorithms to characterize the inter-relationships between cell populations within individual DCIS lesions.

Patients and Methods: Analysis for 15 antibody markers (EGFR, Her2, Her4, S6, pMTOR, PCAD, CD44v6, NaKATPase, SLC7A5, CD4, CD8, CD20, CD68, and CD10) was performed on a single FFPE section containing 10-20 distinct ducts from 13 cases of DCIS. Briefly, approximately 40 fields of view (FOV) from digitized sections containing DCIS or normal tissue were sequentially (cyclically) stained for the 15 markers. Each cycle entailed staining with 2-3 markers followed by imaging, dye inactivation, and re-staining. DAPI was used for nuclear demarcation and for registration of the images, while S6, pan-cadherin, Na+K+ATPase and pan-cytokeratin were used for epithelial segmentation. K-means clustering was used to determine patterns of co-expression of markers at the single cell, duct, and patient levels. These clusters were then correlated with immune marker expression by tumor infiltrating lymphocytes (TILs) by marker type (CD4, CD8, and CD20) and tumor compartment (stromal versus intraepithelial).

Results: Analysis of the epithelial component in each of 13 cases of DCIS (n= 415 ducts) revealed 8 distinct expression patterns (clusters) using a panel of 7 markers (EGFR, Her2, Her4, pmTOR, CD44v6, SLC7A5, and CD10). The frequency and distribution of clusters, annotated at the single cell level, showed that 4 DCIS's were dominated (&gt;80%) by a single cell phenotype represented by cluster groups 3 and 7 (high Her2), cluster 6 (High Her4 and SLC7A5 and low Her2), or cluster 4 (non-descript). In 5 pts, the pattern was more heterogeneous consisting of mixture of cell populations with 50-70% of the cells belonging to cluster 1 (moderate to high levels for all markers except EGFR and CD10). The remaining pts had a strong representation of cluster 4 and 5 (CD44v6 and phospho-mTOR) cells. The distribution of both intra-epithelial and stromal TILs in DCIS cases were either consisted of a mixed B-cell (CD20+) and T-cell response (n=4), or one dominated by T-cells. Cluster 2 (High EGFR and CD10) was associated with a largely T-cell response (rs = 0.83, P value = 0.0004), while Cluster 7 (strong HER2) was associated with a B-cell response (rs = 0.68, P value = is 0.009).

Conclusions: Analysis 15 markers and use of K-means clustering algorithm, shows prominent inter-tumoral (but not intra-tumoral) heterogeneity in DCIS. Furthermore, epithelial cell specific clusters (high HER2 or EGFR) were associated with distinct B or T cell infiltration by TILs. Additional ongoing studies will determine the clinical significance of the clusters with respect to recurrence of DCIS and development of invasive carcinomas.

Citation Format: Badve S, Gökmen-Polar Y, Harris AL, Sui Y, Sevinsky C, Santamaria-Pang A, Ginty F, Tan PH, Gerdes MJ. Impact of heterogeneity of DCIS on immune cell infiltrations [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-06-02.

Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers

BACKGROUND

Measures of spatial intratumor heterogeneity are potentially important diagnostic biomarkers for cancer progression, proliferation, and response to therapy. Spatial relationships among cells including cancer and stromal cells in the tumor microenvironment (TME) are key contributors to heterogeneity.

METHODS

We demonstrate how to quantify spatial heterogeneity from immunofluorescence pathology samples, using a set of 3 basic breast cancer biomarkers as a test case. We learn a set of dominant biomarker intensity patterns and map the spatial distribution of the biomarker patterns with a network. We then describe the pairwise association statistics for each pattern within the network using pointwise mutual information (PMI) and visually represent heterogeneity with a two-dimensional map.

RESULTS

We found a salient set of 8 biomarker patterns to describe cellular phenotypes from a tissue microarray cohort containing 4 different breast cancer subtypes. After computing PMI for each pair of biomarker patterns in each patient and tumor replicate, we visualize the interactions that contribute to the resulting association statistics. Then, we demonstrate the potential for using PMI as a diagnostic biomarker, by comparing PMI maps and heterogeneity scores from patients across the 4 different cancer subtypes. Estrogen receptor positive invasive lobular carcinoma patient, AL13-6, exhibited the highest heterogeneity score among those tested, while estrogen receptor negative invasive ductal carcinoma patient, AL13-14, exhibited the lowest heterogeneity score.

CONCLUSIONS

This paper presents an approach for describing intratumor heterogeneity, in a quantitative fashion (via PMI), which departs from the purely qualitative approaches currently used in the clinic. PMI is generalizable to highly multiplexed/hyperplexed immunofluorescence images, as well as spatial data from complementary in situ methods including FISSEQ and CyTOF, sampling many different components within the TME. We hypothesize that PMI will uncover key spatial interactions in the TME that contribute to disease proliferation and progression.

ARID1A expression in early stage colorectal adenocarcinoma: an exploration of its prognostic significance

ARID1A is a chromatin remodeling gene that is mutated in a number of cancers including colorectal carcinoma (CRC). Loss of ARID1A has been associated with an adverse outcome in some types of cancer. However, literature data have not been consistent. Major limitations of some outcome studies include small sample size and heterogeneous patient population. In this study, we evaluated the prognostic value of ARID1A in a homogeneous group of early stage CRC patients, a population where prognostic markers are particularly relevant. We collected a retrospective series of 578 stage I or II CRCs. All patients underwent surgery with curative intent and without neoadjuvant or adjuvant therapy. ARID1A expression was analyzed by immunohistochemistry using tissue microarray. We found ARID1A loss in 49 of 552 analyzable tumors (8.9%). Compared with the ARID1A-retained group, cases with ARID1A loss were associated with female sex (P<.001), mismatch-repair protein deficiency (P<.001), poor differentiation (P<.001), lymphovascular invasion (P=.001), and higher pT stage (P=.047). However, at a median follow-up of 49months, ARID1A loss did not correlate with overall, disease-specific, or recurrence-free survival. This is the first systematic analysis to evaluate the prognostic significance of ARID1A in stage I/II CRCs, and our data indicate that ARID1A loss lacks prognostic significance in this population despite its association with other adverse features. Such data are clinically relevant, as efforts are ongoing in identifying markers that can detect the small but significant subset of early stage CRCs that will have a poor outcome.