Abstract 5886: Pre-treatment adiposity measured by computed tomography and survival of women with high-grade serous ovarian cancer

The association of body mass index (BMI) with survival of women with ovarian cancer remains unclear due to mixed epidemiological evidence. This may be due, in part, to the fact that BMI is an imperfect measure of body fat as BMI does not distinguish weight from lean muscle versus adipose tissue. Here, we investigated the association of adiposity measured by computed tomography (CT) with survival among the most common histotype of ovarian cancer, high-grade serous ovarian cancer (HGSOC). The present study included 383 women diagnosed with HGSOC from 2008 to 2019 who were evaluated at H. Lee Moffitt Cancer Center and Research Institute and had pre-treatment computed tomography scans available for analysis. The sliceOmatic v5.0 rev13 (Tomovision, Magog, Canada) medical image analysis software and accompanying ABACS module for segmentation was used to quantify subcutaneous (SAT), visceral (VAT), and intermuscular adipose tissue (IMAT) from the third lumbar (L3) axial slice including the transverse processes. We used Cox proportional hazard regression to estimate hazard ratios (HR) and 95% confidence intervals (CIs) for the association of each measure of adiposity with overall survival (OS) and recurrence-free survival (RFS) while adjusting for age at diagnosis, stage, race and ethnicity, and first-line treatment. The degree of ascites was included in the VAT models as ascites fluid density can mask VAT. We also assessed these associations within first-line treatment groups (upfront chemotherapy [n=147], upfront surgery [n=236]). In the overall study population, we observed a positive but not statistically significant association with OS and RFS for the highest vs. lowest tertile of IMAT (HR= 1.18, 95% CI=0.83, 1.67 and HR=1.16, 95% CI=0.85, 1.58, respectively). Among women who received upfront surgery, the highest tertile of IMAT was associated with a 57% increased risk of recurrence compared to the lowest tertile (HR=1.57, 95% CI=1.04, 2.37), while the association between IMAT and OS was similar to the findings in the overall population (HR=1.14, 95% CI=0.73, 1.78). No association was observed between IMAT and OS or RFS among women who received upfront chemotherapy. No associations with OS or RFS were observed for SAT or VAT overall or within first-line treatment groups. In summary, we observed inferior RFS among HGSOC patients with higher IMAT. These findings suggest that IMAT measured from standard-of-care imaging may represent a biomarker of recurrence among HGSOC patients, and incorporating lifestyle and behavioral changes (e.g., diet, exercise) to decrease IMAT may be warranted for this patient population.

Citation Format: Christelle Colin-Leitzinger, Daniel Jeong, Mahmoud Abdalah, Rikki Cannioto, Jing-Yi Chern, Evan Davis, Robert Gillies, Melissa McGettigan, Jaileene Perez-Morales, Natarajan Raghunand, Sweta Sinha, Olya Stringfield, Rajwantee Tirbene, Matthew Schabath, Lauren C. Peres. Pre-treatment adiposity measured by computed tomography and survival of women with high-grade serous ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5886.

A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features

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Main factors impacting feature stability: Image acquisition, reconstruction, tumor segmentation, and interpolation.

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Textural features are less robust than morphological or statistical features.

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A checklist is provided including items that should be reported in a radiomic study.

Purpose

Although quantitative image biomarkers (radiomics) show promising value for cancer diagnosis, prognosis, and treatment assessment, these biomarkers still lack reproducibility. In this systematic review, we aimed to assess the progress in radiomics reproducibility and repeatability in the recent years.

Methods and materials

Four hundred fifty-one abstracts were retrieved according to the original PubMed search pattern with the publication dates ranging from 2017/05/01 to 2020/12/01. Each abstract including the keywords was independently screened by four observers. Forty-two full-text articles were selected for further analysis. Patient population data, radiomic feature classes, feature extraction software, image preprocessing, and reproducibility results were extracted from each article. To support the community with a standardized reporting strategy, we propose a specific reporting checklist to evaluate the feasibility to reproduce each study.

Results

Many studies continue to under-report essential reproducibility information: all but one clinical and all but two phantom studies missed to report at least one important item reporting image acquisition. The studies included in this review indicate that all radiomic features are sensitive to image acquisition, reconstruction, tumor segmentation, and interpolation. However, the amount of sensitivity is feature dependent, for instance, textural features were, in general, less robust than statistical features.

Conclusions

Radiomics repeatability, reproducibility, and reporting quality can substantially be improved regarding feature extraction software and settings, image preprocessing and acquisition, cutoff values for stable feature selection. Our proposed radiomics reporting checklist can serve to simplify and improve the reporting and, eventually, guarantee the possibility to fully replicate and validate radiomic studies.

Acid-Induced Inflammatory Cytokines in Osteoblasts: A Guided Path to Osteolysis in Bone Metastasis

Bone metastasis (BM) is a dismal complication of cancer that frequently occurs in patients with advanced carcinomas and that often manifests as an osteolytic lesion. In bone, tumor cells promote an imbalance in bone remodeling via the release of growth factors that, directly or indirectly, stimulate osteoclast resorption activity. However, carcinoma cells are also characterized by an altered metabolism responsible for a decrease of extracellular pH, which, in turn, directly intensifies osteoclast bone erosion. Here, we speculated that tumor-derived acidosis causes the osteoblast–osteoclast uncoupling in BM by modulating the pro-osteoclastogenic phenotype of osteoblasts. According to our results, a low pH recruits osteoclast precursors and promotes their differentiation through the secretome of acid-stressed osteoblasts that includes pro-osteoclastogenic factors and inflammatory mediators, such as RANKL, M-CSF, TNF, IL-6, and, above the others, IL-8. The treatment with the anti-IL-6R antibody tocilizumab or with an anti-IL-8 antibody reverted this effect. Finally, in a series of BM patients, circulating levels of the osteolytic marker TRACP5b significantly correlated with IL-8. Our findings brought out that tumor-derived acidosis promotes excessive osteolysis at least in part by inducing an inflammatory phenotype in osteoblasts, and these results strengthen the use of anti-IL-6 or anti-IL-8 strategies to treat osteolysis in BM.

Predicting the results of competition between two breast cancer lines grown in 3-D spheroid culture

This study develops a novel model of a consumer-resource system with mobility included, in order to explain a novel experiment of competition between two breast cancer cell lines grown in 3D in vitro spheroid culture. The model reproduces observed differences in monoculture, such as overshoot phenomena and final size. It also explains both theoretically and through simulation the inevitable triumph of the same cell line in co-culture, independent of initial conditions. The mobility of one cell line (MDA-MB-231) is required to explain both the success and the rapidity with which that species dominates the population and drives the other species (MCF-7) to extinction. It is shown that mobility directly interferes with the other species and that the cost of that mobility is in resource usage rate.

Abstract 2562: Causes and consequences of adiposomogenesis in breast cancer cells

Malignant tumors exhibit altered metabolism resulting in a highly acidic extracellular microenvironment. Adaptation to acidic conditions is a pre-requisite for tumor cells to survive and thrive and to out-compete the stroma into which they invade. Acid adaptation has been associated with chronic activation of autophagy and redistribution of the lysosomal proteins to the plasma membrane. In addition to these survival mechanisms, tumor cells under acidic conditions accumulate cytoplasmic lipid droplets (adiposomes); dynamic organelles that store neutral lipids surrounded by a shell of perilipin (PLIN) proteins and a phospholipid monolayer. Adiposomes are dynamic organelles that store neutral lipids surrounded by a shell of proteins (PLIN2) and a phospholipid monolayer. High expression of PLIN2 was observed to be strongly associated with poor overall survival in breast cancer patients. In vitro, breast cancer cells rapidly and robustly accumulated adiposomes when grown in acidic media revealed by nile red and PLIN-2 staining. The acid-induced lipogenic phenotype persists even when the cells are grown in de-lipidated serum, indicating that the source of lipids is de-novo and endogenous. Adiposome formation at low pH was attenuated when cells were treated with inhibitors of fatty acid synthesis, FAS; such as TOFA or C75. Further, these inhibitors were selectively cytotoxic under acidic conditions indicating that adiposomogenesis is a survival mechanism. Consistent with increased FAS we also observed using 13C isotopomer analysis a major shift in glucose metabolism from Embden Meyerhof to the Pentose Phosphate Pathway, which results in increased production of NADPH, necessary for de novo lipid synthesis. In addition, we observed that cells at low pH had higher rates of oxygen consumption (OCR) compared to controls using Seahorse profiling. The major source of the lipid precursors was identified to be autophagy-derived ketogenic amino acids using LC-LS/MS following 13C tracer pre-incubation. Further, we tested the hypothesis that adiposomogenesis is induced by acid signal and this signaling is mediated by one (or more) acid sensing G-protein coupled receptors. Both OGR1 and TDAG8 were strongly expressed in our systems. CRISPR/Cas9 mediated depletion of these receptors showed that TDAG8 KO had no effect, but that KO of OGR1 abrogated adiposome accumulation under acidic conditions in MCF7 and T47D cells. Further, OGR1 knockout cells were defective in acid induced autophagy. In xenograft models, OGR1 knockout MCF7 cells formed significantly smaller tumors compared to control cells. Hence, accumulation of adiposomes is a highly regulated process related to storing autophagic products, and appears to be important in cell survival in acid stress. This increased dependence on lipid metabolism revealed novel therapeutic vulnerabilities

Citation Format: Smitha R. Pillai, Iqbal Mahmud, Michael Langsen, Jonathan Wojtkowiak, Jonathan Nguyen, Marylin Bui, Robert Gatenby, Timothy Garrett, Robert Gillies. Causes and consequences of adiposomogenesis in breast cancer cells [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 2562.

Convolutional Neural Network ensembles for accurate lung nodule malignancy prediction 2 years in the future

Convolutional Neural Networks (CNNs) have been utilized for to distinguish between benign lung nodules and those that will become malignant. The objective of this study was to use an ensemble of CNNs to predict which baseline nodules would be diagnosed as lung cancer in a second follow up screening after more than one year. Low-dose helical computed tomography images and data were utilized from the National Lung Screening Trial (NLST). The malignant nodules and nodule positive controls were divided into training and test cohorts. T0 nodules were used to predict lung cancer incidence at T1 or T2. To increase the sample size, image augmentation was performed using rotations, flipping, and elastic deformation. Three CNN architectures were designed for malignancy prediction, and each architecture was trained using seven different seeds to create the initial weights. This enabled variability in the CNN models which were combined to generate a robust, more accurate ensemble model. Augmenting images using only rotation and flipping and training with images from T0 yielded the best accuracy to predict lung cancer incidence at T2 from a separate test cohort (Accuracy = 90.29%; AUC = 0.96) based on an ensemble 21 models. Images augmented by rotation and flipping enabled effective learning by increasing the relatively small sample size. Ensemble learning with deep neural networks is a compelling approach that accurately predicted lung cancer incidence at the second screening after the baseline screen mostly 2 years later.

Abstract C091: Racial disparities in pancreatic cancer pancreatic cancer patients in Florida and an investigation into a possible role of cancer cachexia

Background: Five-year survival for pancreatic cancer remains low at 8%. While pancreatic cancer health disparities exist among different racial groups, these disparities have not been well investigated in the State of Florida. We aimed to investigate these disparities and hypothesized that cancer cachexia may play a role.

Methods: A retrospective review of data from the Florida Cancer Data System and Florida Agency for Healthcare administration was performed to assess for PC disparities between racial groups in the State of Florida. A cohort of patients at a single center was analyzed for differences in cachexia indicators such as psoas muscle index (PSI) and albumin at presentation.

Results: African Americans (AA) had significantly higher mean age-adjusted PC incidence (12.5/100,000) and mortality rates (10.97/100,000) than NHW (incidence=11.2/100,000; mortality=10.3/100,000) and Hispanics (incidence=9.6/100,000; mortality=8.7/100,000). Of the 67 counties in the State of Florida, 43 (64.2%) observed higher PC incidence rates in AA than NHW and Hispanics. AA are often diagnosed with PC at a younger age than NHW. AA and Hispanics are more likely to be insured by Medicaid compared to NHW (16% and 14% vs 7%, respectively) and less likely to undergo surgical treatment for their condition (31% vs. 37%). AA present with significantly lower serum albumin levels (3.2 vs. 3.7 g/dL). Serum albumin levels < 3.5 correlated with significantly lower survival. When compared to healthy controls presenting for cholecystectomy, AA patients present with a more significant reduction in psoas muscle index compared to Caucasians.

Conclusion: African-Americans with PC have higher incidence rates and mortality than their NHW and Hispanics counterparts. AA are also younger at age of diagnosis, more likely to be insured by Medicaid, and less likely to undergo potential curative surgical treatment for PC. We demonstrated that AA had significantly lower albumin levels and that this correlated with worse survival. AA also present with a significantly greater reduction in psoas muscle index when compared to healthy controls. Further investigation into potential reasons for this disparity in cancer cachexia is warranted.

Citation Format: Patrick Underwood, Miles Cameron, Ashley Clark Daly, Tracey Barnett, Clement Gwede, Anthony Magliocco, Barbara Centeno, Dung-Tsa Chen, Jung Choi, Daniel Jeong, Robert Gillies, Mokenge Malafa, Andrew Judge, Nipun Merchant, Jennifer Permuth, Jose Trevino. Racial disparities in pancreatic cancer pancreatic cancer patients in Florida and an investigation into a possible role of cancer cachexia [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr C091.

Repeatability of Quantitative Imaging Features in Prostate Magnetic Resonance Imaging

Background: Multiparametric magnetic resonance imaging (mpMRI) has emerged as a non-invasive modality to diagnose and monitor prostate cancer. Quantitative metrics on the regions of abnormality have shown to be useful descriptors to discriminate clinically significant cancers. In this study, we evaluate the reproducibility of quantitative imaging features using repeated mpMRI on the same patients. Methods: We retrospectively obtained the deidentified records of patients, who underwent two mpMRI scans within 2 weeks of the first baseline scan. The patient records were obtained as deidentified data (including imaging), obtained through the TCIA (The Cancer Imaging Archive) repository and analyzed in our institution with an institutional review board-approved Health Insurance Portability and Accountability Act-compliant retrospective study protocol. Indicated biopsied regions were used as a marker for our study radiologists to delineate the regions of interest. We extracted 307 quantitative features in each mpMRI modality [T2-weighted MR sequence image (T2w) and apparent diffusion coefficient (ADC) with b values of 0 and 1,400 mm/s2] across the two sequential scans. Concordance correlation coefficients (CCCs) were computed on the features extracted from sequential scans. Redundant features were removed by computing the coefficient of determination (R 2) among them and replaced with a feature that had the highest dynamic range within intercorrelated groups. Results: We have assessed the reproducibility of quantitative imaging features among sequential scans and found that habitat region characterization improves repeatability in ADC maps. There were 19 T2w features and two ADC features in radiologist drawn regions (native raw image), compared to 18 T2w and 15 ADC features in habitat regions (sphere), which were reproducible (CCC ≥0.65) and non-redundant (R 2 ≥ 0.99). We also found that z-transformation of the images prior to feature extraction reduced the number of reproducible features with no detrimental effect. Conclusion: We have shown that there are quantitative imaging features that are reproducible across sequential prostate mpMRI acquisition at a preset level of filters. We also found that a habitat approach improves feature repeatability in ADC. A validated set of reproducible image features in mpMRI will allow us to develop clinically useful disease risk stratification, enabling the possibility of using imaging as a surrogate to invasive biopsies.

A Shallow Convolutional Neural Network Predicts Prognosis of Lung Cancer Patients in Multi-Institutional CT-Image Data

Lung cancer is the most common fatal malignancy in adults worldwide, and non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer diagnoses. Computed tomography (CT) is routinely used in clinical practice to determine lung cancer treatment and assess prognosis. Here, we developed LungNet, a shallow convolutional neural network for predicting outcomes of NSCLC patients. We trained and evaluated LungNet on four independent cohorts of NSCLC patients from four medical centers: Stanford Hospital (n = 129), H. Lee Moffitt Cancer Center and Research Institute (n = 185), MAASTRO Clinic (n = 311) and Charité - Universitätsmedizin (n=84). We show that outcomes from LungNet are predictive of overall survival in all four independent survival cohorts as measured by concordance indices of 0.62, 0.62, 0.62 and 0.58 on cohorts 1, 2, 3, and 4, respectively. Further, the survival model can be used, via transfer learning, for classifying benign vs malignant nodules on the Lung Image Database Consortium (n = 1010), with improved performance (AUC=0.85) versus training from scratch (AUC=0.82). LungNet can be used as a noninvasive predictor for prognosis in NSCLC patients and can facilitate interpretation of CT images for lung cancer stratification and prognostication.

A pharmacodynamic model of clinical synergy in multiple myeloma

BACKGROUND

Multiagent therapies, due to their ability to delay or overcome resistance, are a hallmark of treatment in multiple myeloma (MM). The growing number of therapeutic options in MM requires high-throughput combination screening tools to better allocate treatment, and facilitate personalized therapy.

METHODS

A second-order drug response model was employed to fit patient-specific ex vivo responses of 203 MM patients to single-agent models. A novel pharmacodynamic model, developed to account for two-way combination effects, was tested with 130 two-drug combinations. We have demonstrated that this model is sufficiently parameterized by single-agent and fixed-ratio combination responses, by validating model estimates with ex vivo combination responses for different concentration ratios, using a checkerboard assay. This new model reconciles ex vivo observations from both Loewe and BLISS synergy models, by accounting for the dimension of time, as opposed to focusing on arbitrary time-points or drug effect. Clinical outcomes of patients were simulated by coupling patient-specific drug combination models with pharmacokinetic data.

FINDINGS

Combination screening showed 1 in 5 combinations (21.43% by LD50, 18.42% by AUC) were synergistic ex vivo with statistical significance (P < 0.05), but clinical synergy was predicted for only 1 in 10 combinations (8.69%), which was attributed to the role of pharmacokinetics and dosing schedules.

INTERPRETATION

The proposed framework can inform clinical decisions from ex vivo observations, thus providing a path toward personalized therapy using combination regimens.

FUNDING

This research was funded by the H. Lee Moffitt Cancer Center Physical Sciences in Oncology (PSOC) Grant (1U54CA193489-01A1) and by H. Lee Moffitt Cancer Center's Team Science Grant. This work has been supported in part by the PSOC Pilot Project Award (5U54CA193489-04), the Translational Research Core Facility at the H. Lee Moffitt Cancer Center & Research Institute, an NCI-designated Comprehensive Cancer Center (P30-CA076292), the Pentecost Family Foundation, and Miles for Moffitt Foundation.

Hybrid models for lung nodule malignancy prediction utilizing convolutional neural network ensembles and clinical data

Purpose: Due to the high incidence and mortality rates of lung cancer worldwide, early detection of a precancerous lesion is essential. Low-dose computed tomography is a commonly used technique for screening, diagnosis, and prognosis of non-small-cell lung cancer. Recently, convolutional neural networks (CNN) had shown great potential in lung nodule classification. Clinical information (family history, gender, and smoking history) together with nodule size provide information about lung cancer risk. Large nodules have greater risk than small nodules. Approach: A subset of cases from the National Lung Screening Trial was chosen as a dataset in our study. We divided the nodules into large and small nodules based on different clinical guideline thresholds and then analyzed the groups individually. Similarly, we also analyzed clinical features by dividing them into groups. CNNs were designed and trained over each of these groups individually. To our knowledge, this is the first study to incorporate nodule size and clinical features for classification using CNN. We further made a hybrid model using an ensemble with the CNN models of clinical and size information to enhance malignancy prediction. Results: From our study, we obtained 0.9 AUC and 83.12% accuracy, which was a significant improvement over our previous best results. Conclusions: In conclusion, we found that dividing the nodules by size and clinical information for building predictive models resulted in improved malignancy predictions. Our analysis also showed that appropriately integrating clinical information and size groups could further improve risk prediction.

Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features

Quantitative features are generated from a tumor phenotype by various data characterization, feature-extraction approaches and have been used successfully as a biomarker. These features give us information about a nodule, for example, nodule size, pixel intensity, histogram-based information, and texture information from wavelets or a convolution kernel. Semantic features, on the other hand, can be generated by an experienced radiologist and consist of the common characteristics of a tumor, for example, location of a tumor, fissure, or pleural wall attachment, presence of fibrosis or emphysema, concave cut on nodule surface. These features have been derived for lung nodules by our group. Semantic features have also shown promise in predicting malignancy. Deep features from images are generally extracted from the last layers before the classification layer of a convolutional neural network (CNN). By training with the use of different types of images, the CNN learns to recognize various patterns and textures. But when we extract deep features, there is no specific naming approach for them, other than denoting them by the feature column number (position of a neuron in a hidden layer). In this study, we tried to relate and explain deep features with respect to traditional quantitative features and semantic features. We discovered that 26 deep features from the Vgg-S neural network and 12 deep features from our trained CNN could be explained by semantic or traditional quantitative features. From this, we concluded that those deep features can have a recognizable definition via semantic or quantitative features.

Mix and Match: Phenotypic Coexistence as a Key Facilitator of Cancer Invasion

Invasion of healthy tissue is a defining feature of malignant tumours. Traditionally, invasion is thought to be driven by cells that have acquired all the necessary traits to overcome the range of biological and physical defences employed by the body. However, in light of the ever-increasing evidence for geno- and phenotypic intra-tumour heterogeneity, an alternative hypothesis presents itself: could invasion be driven by a collection of cells with distinct traits that together facilitate the invasion process? In this paper, we use a mathematical model to assess the feasibility of this hypothesis in the context of acid-mediated invasion. We assume tumour expansion is obstructed by stroma which inhibits growth and extra-cellular matrix (ECM) which blocks cancer cell movement. Further, we assume that there are two types of cancer cells: (i) a glycolytic phenotype which produces acid that kills stromal cells and (ii) a matrix-degrading phenotype that locally remodels the ECM. We extend the Gatenby-Gawlinski reaction-diffusion model to derive a system of five coupled reaction-diffusion equations to describe the resulting invasion process. We characterise the spatially homogeneous steady states and carry out a simulation study in one spatial dimension to determine how the tumour develops as we vary the strength of competition between the two phenotypes. We find that overall tumour growth is most extensive when both cell types can stably coexist, since this allows the cells to locally mix and benefit most from the combination of traits. In contrast, when inter-species competition exceeds intra-species competition the populations spatially separate and invasion arrests either: (i) rapidly (matrix-degraders dominate) or (ii) slowly (acid-producers dominate). Overall, our work demonstrates that the spatial and ecological relationship between a heterogeneous population of tumour cells is a key factor in determining their ability to cooperate. Specifically, we predict that tumours in which different phenotypes coexist stably are more invasive than tumours in which phenotypes are spatially separated.

Radiological semantics discriminate clinically significant grade prostate cancer

Background

Identification of imaging traits to discriminate clinically significant prostate cancer is challenging due to the multi focal nature of the disease. The difficulty in obtaining a consensus by the Prostate Imaging and Data Systems (PI-RADS) scores coupled with disagreements in interpreting multi-parametric Magnetic Resonance Imaging (mpMRI) has resulted in increased variability in reporting findings and evaluating the utility of this imaging modality in detecting clinically significant prostate cancer. This study assess the ability of radiological traits (semantics) observed on multi-parametric Magnetic Resonance images (mpMRI) to discriminate clinically significant prostate cancer.

Methods

We obtained multi-parametric MRI studies from 103 prostate cancer patients with 167 targeted biopsies from a single institution. The study was approved by our Institutional Review Board (IRB) for retrospective analysis. The biopsy location had been identified and marked by a clinical radiologist for targeted biopsy based on initial study interpretation. Using the target locations, two study radiologists independently re-evaluated the scans and scored 16 semantic traits on a point scale (up to 5 levels) based on mpMRI images. The semantic traits describe size, shape, and border characteristics of the prostate lesion, as well as presence of disease around lymph nodes (lymphadenopathy). We built a linear classifier model on these semantic traits and related to pathological outcome to identify clinically significant tumors (Gleason Score ≥ 7). The discriminatory ability of the predictors was tested using cross validation method randomly repeated and ensemble values were reported. We then compared the performance of semantic predictors with the PI-RADS predictors.

Results

We found several semantic features individually discriminated high grade Gleason score (ADC-intensity, Homogeneity, early-enhancement, T2-intensity and extraprostatic extention), these univariate predictors had an average area under the receiver operator characteristics (AUROC) ranging from 0.54 to 0.68. Multivariable semantic predictors with three features (ADC-intensity; T2-intensity, enhancement homogenicity) had an average AUROC of 0.7 [0.43, 0.94]. The PI-RADS based predictor had average AUROC of 0.6 [0.47, 0.75].

Conclusion

We find semantics traits are related to pathological findings with relatively higher reproducibility between radiologists. Multivariable predictors formed on these traits shows higher discriminatory ability compared to PI-RADS scores.

Translating preclinical MRI methods to clinical oncology

The complexity of modern in vivo magnetic resonance imaging (MRI) methods in oncology has dramatically changed in the last 10 years. The field has long since moved passed its (unparalleled) ability to form images with exquisite soft-tissue contrast and morphology, allowing for the enhanced identification of primary tumors and metastatic disease. Currently, it is not uncommon to acquire images related to blood flow, cellularity, and macromolecular content in the clinical setting. The acquisition of images related to metabolism, hypoxia, pH, and tissue stiffness are also becoming common. All of these techniques have had some component of their invention, development, refinement, validation, and initial applications in the preclinical setting using in vivo animal models of cancer. In this review, we discuss the genesis of quantitative MRI methods that have been successfully translated from preclinical research and developed into clinical applications. These include methods that interrogate perfusion, diffusion, pH, hypoxia, macromolecular content, and tissue mechanical properties for improving detection, staging, and response monitoring of cancer. For each of these techniques, we summarize the 1) underlying biological mechanism(s); 2) preclinical applications; 3) available repeatability and reproducibility data; 4) clinical applications; and 5) limitations of the technique. We conclude with a discussion of lessons learned from translating MRI methods from the preclinical to clinical setting, and a presentation of four fundamental problems in cancer imaging that, if solved, would result in a profound improvement in the lives of oncology patients. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1377-1392.

Acidity promotes tumour progression by altering macrophage phenotype in prostate cancer

BACKGROUND

Tumours rapidly ferment glucose to lactic acid even in the presence of oxygen, and coupling high glycolysis with poor perfusion leads to extracellular acidification. We hypothesise that acidity, independent from lactate, can augment the pro-tumour phenotype of macrophages.

METHODS

We analysed publicly available data of human prostate cancer for linear correlation between macrophage markers and glycolysis genes. We used zwitterionic buffers to adjust the pH in series of in vitro experiments. We then utilised subcutaneous and transgenic tumour models developed in C57BL/6 mice as well as computer simulations to correlate tumour progression with macrophage infiltration and to delineate role of acidity.

RESULTS

Activating macrophages at pH 6.8 in vitro enhanced an IL-4-driven phenotype as measured by gene expression, cytokine profiling, and functional assays. These results were recapitulated in vivo wherein neutralising intratumoural acidity reduced the pro-tumour phenotype of macrophages, while also decreasing tumour incidence and invasion in the TRAMP model of prostate cancer. These results were recapitulated using an in silico mathematical model that simulate macrophage responses to environmental signals. By turning off acid-induced cellular responses, our in silico mathematical modelling shows that acid-resistant macrophages can limit tumour progression.

CONCLUSIONS

This study suggests that tumour acidity contributes to prostate carcinogenesis by altering the state of macrophage activation.

Abstract 3773: Investigating the tumor-host evolutionary arms race as a possible strategy for cancer therapy

As first proposed by Cairns and Nowell more than 60 years ago, cancer is a Darwinian process so that cancer progression and resistance to therapy are governed by natural selection and eco-evolutionary dynamics. This creates an “evolutionary arms race” between the cancer cells and the tumor suppressor mechanisms of the host. To mechanistically investigate these dynamics we have conducted an experiment in which we selected for resistance to growth of implanted Lewis lung (mouse) cancer tumor (LL/2-Luc-M38) cells in immuno-deficient (SCID) mice and immuno-competent (C57BL/6). At each round a fixed numbers of LL/2-Luc-M38 cells were implanted in 10 male SCID and 10 male C57BL/6 mice. Tumor growth was measured and the two animals that exhibited the slowest tumor growth were bred and their male progeny were used as host in the subsequent round. After 12 generations, the tumors grew at approximately 1/10th the rate compared their initial generation. We observed that the hosts evolved cancer suppression through two general strategies. The evolved SCID mice suppressed tumor growth via mechanical restriction with increased in collagen deposition in and around the tumor. In contrast BL/6 mice enhance their immunologic response. We investigated the molecular properties of tumor cells growing in the different cohorts suing microarray. We identified genes significantly different in their expression of cancer cells harvested at different days following injection in Wild Type and Evolved SCID and BL/6 mice using Principle component analysis (PCA) method. The genes were functionally classified based on Gene Ontology and the majority was found to be related to integrin binding, cell adhesion molecular binding, and extra cellular matrix (ECM) binding in SCID mice with collagen, type XII, alpha (Col12a1) being the significantly decreased (results validated with RT-PCR (**p=0.009)). However, in BL/6 mice Molecular studies, contrary to expectation, did not show significant changes in expression of immune-related genes. Rather we found a decrease in expression of genes associated with the angiogenesis and ATP production as well as increased expression of genes regulating EMT. Thus, our results highly suggest that the counter adaptive strategies deployed by the cancer cells in response to the host suppressive mechanisms is host dependent and can be a possible target for therapy.

Citation Format: Arig A. Ibrahim Hashim, Dominique Abrahams, Kim Luddy, Robert Gillies, Christina L. Richards, Joel Brown, Robert Gatenby. Investigating the tumor-host evolutionary arms race as a possible strategy for cancer therapy [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 3773.

Abstract 1846: Acid sensing G protein-coupled receptor OGR1 is required for acid induced adiposomogenesis in breast cancer cells

Malignant tumors exhibit altered metabolism and consume higher levels of glucose compared to surrounding normal tissue, resulting in acidic extracellular microenvironment. Acidity in the microenvironment is a critical stress factor and selection force for the evolution of aggressive tumor types. There are several acid sensing cell surface receptors and ion channels (ASICs) that can sense acidity in the microenvironment; among them proton sensing G protein-coupled receptors (GPCRs) such as OGR1 (GPR68), TDAG8 (GPR65), GPR4 and GPR132 form a major class of acid receptors. Our previous studies demonstrated that acid adaptation is associated with survival mechanisms like chronic activation of autophagy and redistribution of the lysosomal proteins to the plasma membrane. When grown under acidic pH, breast cancer cells accumulate lipids as revealed by staining with Nile Red and perilipin 2, a protein that coats adiposomes or lipid droplets. These are dynamic organelles that store neutral lipids surrounded by a shell of proteins and a phospholipid monolayer. The lipids stored in adiposomes are produced de novo, as acid-induced lipogenic phenotype is maintained, even if cells are grown with de-lipidated serum. Inhibition of fatty acid synthesis was selectively toxic under acidic conditions and attenuated adiposome accumulation. Among the acid sensing receptors, TDAG8 and OGR1 are highly expressed in a panel of breast cancer cell lines compared to non-malignant breast epithelial cells. Highly invasive and acid tolerant MDA-MB-231 cells express significantly higher levels of TDAG8 while MCF7 and T47D cells have high levels of OGR1. We investigated the role of these receptors in transducing the acid signal that results in the accumulation of lipid droplets. CRISPR/Cas9 mediated depletion of the major acid sensors in breast cancer cell lines MCF7 and T47D showed that depletion of OGR1, not TDAG8, specifically inhibited acid induced adiposome accumulation. Additionally, inducing the cells with ogerin, an allosteric activator of OGR1 resulted in adiposomogenesis. Further, OGR1 knock out cells showed inhibition in cell growth under acidic growth conditions compared to OGR1 expressing cells. OGR1 knockout cells were defective in acid induced autophagy. OGR1 is coupled with Gq/11 and, upon ligand (H+) binding, triggers activation of phospholipase C stimulating the formation of the second messenger IP3. Adiposome formation was inhibited in presence of PLC inhibitors, Edelfosine or U73122. These results indicate that OGR1 is the major acid sensing GPCR in these cells that mediate adiposomogenesis. Accumulation of adiposomes is a highly regulated process related to storing autophagic products, and appears to be important in cell survival in acid stress. Taken together, increased dependence on lipid metabolism by cancer cells under acidic conditions reveals novel therapeutic vulnerabilities.

Citation Format: Smitha Pillai, Michael Langsen, Jonathan Nguyen, Jonathan Wojtkowiak, Marilyn Bui, Robert Gatenby, Robert Gillies. Acid sensing G protein-coupled receptor OGR1 is required for acid induced adiposomogenesis in breast cancer cells [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 1846.

Abstract 3317: Development of a lung nodule cohort with integrated clinical, molecular and imaging biomarkers

Background: We are facing an epidemic of indeterminate pulmonary nodules (IPN). Current diagnostic strategies lack accuracy such that the management of IPNs 6-30 mm leads to an unacceptable rate of invasive biopsies and of missed opportunities for cure. Based on the critical need of evaluating candidate biomarkers across heterogeneous populations, we intended to assemble a large cohort of fully annotated IPNs from five collaborative institutions.

Methods: Standard operating procedures (SOPs) were developed to capture subject demographics and assign consistent identifiers across clinical, bio-specimens, and imaging data. REDCap database was created for clinical, imaging and biospecimen data capture. Sample collection, processing, storage and shipping SOPs were created and shared among institutions to assure for consistency and quality accuracy. DICOM files, demographic and clinical data, blood and tissue samples were collected prospectively through IRB approved studies at each institution (VUMC, Nashville VAMC, Moffitt Cancer Center, and UPMC). Imaging studies and specimens were de-identified locally using custom JavaScript program in a secure web browser and assigned as specific identifier. De-identified thin slice, non-contrast chest CT studies were tested for quality control and transmitted to an imaging repository (eXtensible Neuroimaging Repository-XNAT) that can be mined by all collaborators.

Results: To date, a cohort of 845 subjects, 507 (60%) males and 338 (40%) females, with lung nodules was assembled. 36 % are current smokers, 56 % former smokers and 8% never smokers, with an average of 46 pack year smoking history. Clinical data including risk prediction models such as the Mayo and PLCO m2012 are reported. Pathological confirmation of nodules is available for 322 benign and 444 malignant nodules. The cohort 283 lung adenocarcinomas, 71 squamous cell carcinomas, 53 small cell carcinoma, 17 non-small cell lung cancer, 9 carcinoid, and 79 subjects considered benign based on CT follow without growth. Serum, plasma and peripheral blood monocyte related DNA is available on all. All diagnostic chest CT are available in our thoracic imaging repository (XNAT) in a de-identified format.

Conclusions: We assembled a unique cohort of incidental and screening detected lung nodules prospectively enrolled at four institutions for which full clinical data capture, chest CT DICOM files and blood specimens were collected. This repository allows the derivation and independent validation of candidate molecular and imaging biomarkers for the management of IPNs. This work is supported by UO1 152662, UO1CA186145 and UO1CA196405

Citation Format: Sanja Antic, Travis Osterman, Aneri Balar, Dhairya Lakhani, Rina Nguyen, Sara Block, Kimberly Fileds, Brandon Winston, Anel Muterspaugh, Yuankai Huo, Riqiang Gao, Joseph Leader, David Wilson, Viswam Nair, Robert Gillies, Matthew Schabath, Chirayu Shah, Bennett Landman, Pierre Massion. Development of a lung nodule cohort with integrated clinical, molecular and imaging biomarkers [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 3317.

Semiautomated Measure of Abdominal Adiposity Using Computed Tomography Scan Analysis

BACKGROUND

The obesity epidemic has prompted the need to better understand the impact of adipose tissue on human pathophysiology. However, accurate, efficient, and replicable models of quantifying adiposity have yet to be developed and clinically implemented. We propose a novel semiautomated radiologic method of measuring the visceral fat area (VFA) using computed tomography scan analysis.

MATERIALS AND METHODS

We obtained a cohort of 100 patients with rectal adenocarcinoma, with a median age of 60.9 y (age range: 35-87 y) and an average body mass index of 28.8 kg/m2 ± 6.56 kg/m2. The semiautomated quantification method of adiposity was developed using a commercial imaging suite. The method was compared to two manual delineations performed using two different picture archiving communication systems. We quantified VFA, subcutaneous fat area (SFA), total fat area (TFA), and visceral-to-subcutaneous fat ratio (V/S ratio) on computed tomography axial slices that were at the L4-L5 intervertebral level.

RESULTS

The semiautomated method was comparable to manual measurements for TFA, VFA, and SFA with intraclass correlation (ICC) of 0.99, 0.97, and 0.96, respectively. However, the ICC for the V/S ratio was only 0.44, which led to the identification of technical outliers that were identified using robust regression. After removal of these outliers, the ICC improved to 0.99 for TFA, VFA, and SFA and 0.97 for the V/S ratio. Measurements from the manual methodology highly correlated between the two picture archiving communication system platforms, with ICC of 0.98 for TFA, 0.98 for VFA, 0.96 for SFA, and 0.95 for the V/S ratio.

CONCLUSIONS

This semiautomated method is able to generate precise and reproducible results. In the future, this method may be applied on a larger scale to facilitate risk stratification of patients using measures of abdominal adiposity.

The Future of Radiation Oncology in Soft Tissue Sarcoma

Radiotherapy (RT) is an important component of the treatment of soft tissue sarcomas (STS) and has been traditionally incorporated with a homogenous approach despite the reality that STS displays a known heterogeneity in clinicopathologic features and treatment outcomes. In this article, we explore the principle components of personalized medicine, including genomics, radiomics, and treatment response, along with their impact on the future of radiation therapy for STS. We propose a shift in the treatment paradigm for STS from a one-size-fits-all technique to one that implements the tenets of personalized medicine and includes the framework for a potential clinical trial technique in this heterogeneous disease.

Representation of Deep Features using Radiologist defined Semantic Features

Semantic features are common radiological traits used to characterize a lesion by a trained radiologist. These features have been recently formulated, quantified on a point scale in the context of lung nodules by our group. Certain radiological semantic traits have been shown to extremely predictive of malignancy [26]. Semantic traits observed by a radiologist at examination describe the nodules and the morphology of the lung nodule shape, size, border, attachment to vessel or pleural wall, location and texture etc. Deep features are numeric descriptors often obtained from a convolutional neural network (CNN) which are widely used for classification and recognition. Deep features may contain information about texture and shape, primarily. Lately, with the advancement of deep learning, convolutional neural networks (CNN) are also being used to analyze lung nodules. In this study, we relate deep features to semantic features by looking for similarity in ability to classify. Deep features were obtained using a transfer learning approach from both an ImageNet pre-trained CNN and our trained CNN architecture. We found that some of the semantic features can be represented by one or more deep features. In this process, we can infer that some deep feature(s) have similar discriminatory ability as semantic features.

Predicting Nodule Malignancy using a CNN Ensemble Approach

Lung cancer is the leading cause of cancer-related deaths globally, which makes early detection and diagnosis a high priority. Computed tomography (CT) is the method of choice for early detection and diagnosis of lung cancer. Radiomics features extracted from CT-detected lung nodules provide a good platform for early detection, diagnosis, and prognosis. In particular when using low dose CT for lung cancer screening, effective use of radiomics can yield a precise non-invasive approach to nodule tracking. Lately, with the advancement of deep learning, convolutional neural networks (CNN) are also being used to analyze lung nodules. In this study, our own trained CNNs, a pre-trained CNN and radiomics features were used for predictive analysis. Using subsets of participants from the National Lung Screening Trial, we investigated if the prediction of nodule malignancy could be further enhanced by an ensemble of classifiers using different feature sets and learning approaches. We extracted probability predictions from our different models on an unseen test set and combined them to generate better predictions. Ensembles were able to yield increased accuracy and area under the receiver operating characteristic curve (AUC). The best-known AUC of 0.96 and accuracy of 89.45% were obtained, which are significant improvements over the previous best AUC of 0.87 and accuracy of 76.79%.

Acid Suspends the Circadian Clock in Hypoxia through Inhibition of mTOR

Recent reports indicate that hypoxia influences the circadian clock through the transcriptional activities of hypoxia-inducible factors (HIFs) at clock genes. Unexpectedly, we uncover a profound disruption of the circadian clock and diurnal transcriptome when hypoxic cells are permitted to acidify to recapitulate the tumor microenvironment. Buffering against acidification or inhibiting lactic acid production fully rescues circadian oscillation. Acidification of several human and murine cell lines, as well as primary murine T cells, suppresses mechanistic target of rapamycin complex 1 (mTORC1) signaling, a key regulator of translation in response to metabolic status. We find that acid drives peripheral redistribution of normally perinuclear lysosomes away from perinuclear RHEB, thereby inhibiting the activity of lysosome-bound mTOR. Restoring mTORC1 signaling and the translation it governs rescues clock oscillation. Our findings thus reveal a model in which acid produced during the cellular metabolic response to hypoxia suppresses the circadian clock through diminished translation of clock constituents.

Repeatability and Reproducibility of Radiomic Features: A Systematic Review

Purpose:

An ever-growing number of predictive models used to inform clinical decision making have included quantitative, computer-extracted imaging biomarkers, or “radiomic features.” Broadly generalizable validity of radiomics-assisted models may be impeded by concerns about reproducibility. We offer a qualitative synthesis of 41 studies that specifically investigated the repeatability and reproducibility of radiomic features, derived from a systematic review of published peer-reviewed literature.

Methods and Materials:

The PubMed electronic database was searched using combinations of the broad Haynes and Ingui filters along with a set of text words specific to cancer, radiomics (including texture analyses), reproducibility, and repeatability. This review has been reported in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From each full-text article, information was extracted regarding cancer type, class of radiomic feature examined, reporting quality of key processing steps, and statistical metric used to segregate stable features.

Results:

Among 624 unique records, 41 full-text articles were subjected to review. The studies primarily addressed non-small cell lung cancer and oropharyngeal cancer. Only 7 studies addressed in detail every methodologic aspect related to image acquisition, preprocessing, and feature extraction. The repeatability and reproducibility of radiomic features are sensitive at various degrees to processing details such as image acquisition settings, image reconstruction algorithm, digital image preprocessing, and software used to extract radiomic features. First-order features were overall more reproducible than shape metrics and textural features. Entropy was consistently reported as one of the most stable first-order features. There was no emergent consensus regarding either shape metrics or textural features; however, coarseness and contrast appeared among the least reproducible.

Conclusions:

Investigations of feature repeatability and reproducibility are currently limited to a small number of cancer types. Reporting quality could be improved regarding details of feature extraction software, digital image manipulation (preprocessing), and the cutoff value used to distinguish stable features.

We offer a qualitative synthesis of 41 studies that specifically investigated the repeatability and reproducibility of radiomic features. The repeatability and reproducibility of radiomic features are sensitive at various degrees to image quality and to software used to extract radiomic features. Investigations of feature repeatability and reproducibility are currently limited to a small number of cancer types. No consensus was found regarding the most repeatable and reproducible features with respect to different settings.

Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms

Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic cancer precursors incidentally discovered by cross-sectional imaging. Consensus guidelines for IPMN management rely on standard radiologic features to predict pathology, but they lack accuracy. Using a retrospective cohort of 38 surgically-resected, pathologically-confirmed IPMNs (20 benign; 18 malignant) with preoperative computed tomography (CT) images and matched plasma-based ‘miRNA genomic classifier (MGC)’ data, we determined whether quantitative ‘radiomic’ CT features (+/- the MGC) can more accurately predict IPMN pathology than standard radiologic features ‘high-risk’ or ‘worrisome’ for malignancy. Logistic regression, principal component analyses, and cross-validation were used to examine associations. Sensitivity, specificity, positive and negative predictive value (PPV, NPV) were estimated. The MGC, ‘high-risk,’ and ‘worrisome’ radiologic features had area under the receiver operating characteristic curve (AUC) values of 0.83, 0.84, and 0.54, respectively. Fourteen radiomic features differentiated malignant from benign IPMNs (p<0.05) and collectively had an AUC=0.77. Combining radiomic features with the MGC revealed an AUC=0.92 and superior sensitivity (83%), specificity (89%), PPV (88%), and NPV (85%) than other models. Evaluation of uncertainty by 10-fold cross-validation retained an AUC>0.80 (0.87 (95% CI:0.84-0.89)). This proof-of-concept study suggests a noninvasive radiogenomic approach may more accurately predict IPMN pathology than ‘worrisome’ radiologic features considered in consensus guidelines.

Semi-automated pulmonary nodule interval segmentation using the NLST data

PURPOSE

To study the variability in volume change estimates of pulmonary nodules due to segmentation approaches used across several algorithms and to evaluate these effects on the ability to predict nodule malignancy.

METHODS

We obtained 100 patient image datasets from the National Lung Screening Trial (NLST) that had a nodule detected on each of two consecutive low dose computed tomography (LDCT) scans, with an equal proportion of malignant and benign cases (50 malignant, 50 benign). Information about the nodule location for the cases was provided by a screen capture with a bounding box and its axial location was indicated. Five participating quantitative imaging network (QIN) institutions performed nodule segmentation using their preferred semi-automated algorithms with no manual correction; teams were allowed to provide additional manually corrected segmentations (analyzed separately). The teams were asked to provide segmentation masks for each nodule at both time points. From these masks, the volume was estimated for the nodule at each time point; the change in volume (absolute and percent change) across time points was estimated as well. We used the concordance correlation coefficient (CCC) to compare the similarity of computed nodule volumes (absolute and percent change) across algorithms. We used Logistic regression model on the change in volume (absolute change and percent change) of the nodules to predict the malignancy status, the area under the receiver operating characteristic curve (AUROC) and confidence intervals were reported. Because the size of nodules was expected to have a substantial effect on segmentation variability, analysis of change in volumes was stratified by lesion size, where lesions were grouped into those with a longest diameter of <8 mm and those with longest diameter ≥ 8 mm.

RESULTS

We find that segmentation of the nodules shows substantial variability across algorithms, with the CCC ranging from 0.56 to 0.95 for change in volume (percent change in volume range was [0.15 to 0.86]) across the nodules. When examining nodules based on their longest diameter, we find the CCC had higher values for large nodules with a range of [0.54 to 0.93] among the algorithms, while percent change in volume was [0.3 to 0.95]. Compared to that of smaller nodules which had a range of [-0.0038 to 0.69] and percent change in volume was [-0.039 to 0.92]. The malignancy prediction results showed fairly consistent results across the institutions, the AUC using change in volume ranged from 0.65 to 0.89 (Percent change in volume was 0.64 to 0.86) for entire nodule range. Prediction improves for large nodule range (≥ 8 mm) with AUC range 0.75 to 0.90 (percent change in volume was 0.74 to 0.92). Compared to smaller nodule range (<8 mm) with AUC range 0.57 to 0.78 (percent change in volume was 0.59 to 0.77).

CONCLUSIONS

We find there is a fairly high concordance in the size measurements for larger nodules (≥8 mm) than the lower sizes (<8 mm) across algorithms. We find the change in nodule volume (absolute and percent change) were consistent predictors of malignancy across institutions, despite using different segmentation algorithms. Using volume change estimates without corrections shows slightly lower predictability (for two teams).

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biologic characteristics and qualitative imaging properties familiar to radiologists. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiologic studies (eg, imaging interpretation) through computational models (eg, computer vision and machine learning) that provide novel clinical insights. We outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making. We further discuss machine-learning challenges and data opportunities to advance radiomic studies.

Combined PET/MRI: Global Warming-Summary Report of the 6th International Workshop on PET/MRI, March 27-29, 2017, Tübingen, Germany

The 6th annual meeting to address key issues in positron emission tomography (PET)/magnetic resonance imaging (MRI) was held again in Tübingen, Germany, from March 27 to 29, 2017. Over three days of invited plenary lectures, round table discussions and dialogue board deliberations, participants critically assessed the current state of PET/MRI, both clinically and as a research tool, and attempted to chart future directions. The meeting addressed the use of PET/MRI and workflows in oncology, neurosciences, infection, inflammation and chronic pain syndromes, as well as deeper discussions about how best to characterise the tumour microenvironment, optimise the complementary information available from PET and MRI, and how advanced data mining and bioinformatics, as well as information from liquid biomarkers (circulating tumour cells and nucleic acids) and pathology, can be integrated to give a more complete characterisation of disease phenotype. Some issues that have dominated previous meetings, such as the accuracy of MR-based attenuation correction (AC) of the PET scan, were finally put to rest as having been adequately addressed for the majority of clinical situations. Likewise, the ability to standardise PET systems for use in multicentre trials was confirmed, thus removing a perceived barrier to larger clinical imaging trials. The meeting openly questioned whether PET/MRI should, in all cases, be used as a whole-body imaging modality or whether in many circumstances it would best be employed to give an in-depth study of previously identified disease in a single organ or region. The meeting concluded that there is still much work to be done in the integration of data from different fields and in developing a common language for all stakeholders involved. In addition, the participants advocated joint training and education for individuals who engage in routine PET/MRI. It was agreed that PET/MRI can enhance our understanding of normal and disrupted biology, and we are in a position to describe the in vivo nature of disease processes, metabolism, evolution of cancer and the monitoring of response to pharmacological interventions and therapies. As such, PET/MRI is a key to advancing medicine and patient care.

Linc-ing Circulating Long Non-coding RNAs to the Diagnosis and Malignant Prediction of Intraductal Papillary Mucinous Neoplasms of the Pancreas

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease that lacks effective biomarkers for early detection. We hypothesized that circulating long non-coding RNAs (lncRNAs) may act as diagnostic markers of incidentally-detected cystic PDAC precursors known as intraductal papillary mucinous neoplasms (IPMNs) and predictors of their pathology/histological classification. Using NanoString nCounter® technology, we measured the abundance of 28 candidate lncRNAs in pre-operative plasma from a cohort of pathologically-confirmed IPMN cases of various grades of severity and non-diseased controls. Results showed that two lncRNAs (GAS5 and SRA) aided in differentiating IPMNs from controls. An 8-lncRNA signature (including ADARB2-AS1, ANRIL, GLIS3-AS1, LINC00472, MEG3, PANDA, PVT1, and UCA1) had greater accuracy than standard clinical and radiologic features in distinguishing 'aggressive/malignant' IPMNs that warrant surgical removal from 'indolent/benign' IPMNs that can be observed. When the 8-lncRNA signature was combined with plasma miRNA data and quantitative 'radiomic' imaging features, the accuracy of predicting IPMN pathological classification improved. Our findings provide novel information on the ability to detect lncRNAs in plasma from patients with IPMNs and suggest that an lncRNA-based blood test may have utility as a diagnostic adjunct for identifying IPMNs and their pathology, especially when incorporated with biomarkers such as miRNAs, quantitative imaging features, and clinical data.

Abstract 2695: Role of tumor generated acidity in immune stromal interactions during prostate carcinogenesis

Insufficiency in tumor perfusion and high rate glycolysis combine to reduce the pH of tumor microenvironment. In a TRAMP model of prostate cancer, we had shown that carcinogenesis is associated with increasing acidification of the microenvironment and that neutralization of this acidity can prevent cancer emergence or metastases. Carcinogenesis in the TRAMP model is also associated with increased fibrosis and immune cells infiltration. We thus sought to determine if fibrosis drives immune infiltration in early tumorigenesis or vice-versa; and whether this dynamics is affected by tumor acidity. To investigate this, we harvested prostates from TRAMP mice or their matching non-transgenic controls at different time points and stained serial prostate tissue sections with F4/80 (macrophages), SMA (cancer-associated fibroblasts, CAFs), and Masson’s Trichome (collagen). Quantitative image analysis reveals that increase in fibrosis occur prior to macrophage infiltration and that both events preceded tumor development. However, the relative amount of collagen fibers was unchanged across all time points. Notably, neither fibrosis nor macrophage infiltration occurred in mice treated with buffer, suggesting an involvement of acidity in this immune stromal interactions. Interestingly, macrophages isolated from latter time points in the untreated group as well as macrophages co-cultured with prostate tumor cells at acidic pH, possessed an M2-like phenotype by expressing immunosuppressive genes (e.g. Arginase 1, Arg1) and a range of scavenging receptors (e.g. mannose receptor, Cd206), as well as releasing more angiogenic factors (e.g. VEGF and MMPs). Similar results were recapitulated when M2 macrophages were stimulated at acidic pH by showing enhanced Cd206 and Arg1 expression. On the functional level, macrophages activated at acidic pH had a higher ability to uptake fluorescently labelled ovalbumin and collagen, as examples of mannosylated ligands that prevail the fibrotic microenvironment. In summary, these results suggest that tumor acidity may promote fibrosis, with subsequent macrophage infiltration and phenotypic switching, leading to increased collagen turnover. It is suspected that this extracellular matrix remodeling may be permissive for tumor progression.

Citation Format: Asmaa El-Kenawi, Jasreman Dhillon, Arig Ibrahim-Hashim, Dominique Abrahams, Shari Pilon-Thomas, Brian Ruffell, Robert Gatenby, Robert Gillies. Role of tumor generated acidity in immune stromal interactions during prostate carcinogenesis [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 2695. doi:10.1158/1538-7445.AM2017-2695

Abstract 2929: Exploratory evolved strategies that limit cancer growth for possible new therapeutic strategies

Strong selection can promote mammalian evolution of a remarkably diverse phenotypic range over a short period of time. With appropriate selection forces, we tested the hypothesis that laboratory animals can evolve phenotypes that are resistant to the growth of implanted tumors. We propose that strong selection can produce evolution of different strategies to generate resistance to the growth of inoculated tumors,1. Changes in the supportive cell layer that decrease limited growth of cancer cell populations. 2. Increase of immunologic response to tumor antigens.

Hence, we examined the evolution of resistance in immuno-competent and immuno-deficient mice. Fixed number of cells from luciferase tagged LL/2 (Lewis lung carcinoma) cancer cells were implanted in groups of 10 male C57BL/6 and SCID mice. All tumor cells were obtained from frozen samples of a single large tumor population to eliminate any contribution from tumor cell evolution. Tumor growth in each animal was measured by calipers and luciferase imaging. The two animals that exhibited the slowest tumor growth in each cohort were bred with females from the same litter. Over 12 generations, the selection pressures resulted in emergence of SCID mice in which tumors grew at approximately 1/10th the rate compared their initial generation. The immunocompetent C57BL/6 mice evolved significant changes in immune-mediated parental tumor cell killing but this has yet resulted in significantly delayed tumor growth because the LL/2 cells rapidly evolve resistance strategies. Injection of the same tumor cells into unevolved wild type strains of both SCID and C57BL/6 mice produced rapid tumor growth identical to that seen in the first generation.

Using immunohistochemistry we observed decrease in blood supply among generations, proliferation, and apoptosis while no differences were observed in Glut-1 and CA-9 expression. To investigate this observation, first we examined changes in the molecular characteristics of the tumor cells during in-vivo growth by microarray on tumor cells isolated from “final” adapted cell population(s) in the animals that have been selected to decrease tumor growth compared to the wild type as well as normal cells in parallel. Our results showed that there are 158 genes different between tumors growing in the evolved and selected mice, among them are genes involved in extracellular matrix organization, hence we used second-harmonic generation (SHG) microscopy to image and quantify collagen,

our results showed significant increase in collagen at the edge as well as the core of the tumor of the evolved mice compared to wild type mice.

In conclusion our evolutionary study has reduced tumor growth in SCID mice but has had limited success in the C57BL/6. The SCID mice adaptation to tumor was likely through alteration in function of the supportive cell layer (Collagen), thus generating biomechanical forces and protective cellular events during tumor progression at early stages.

Citation Format: Dominique Abrahams, Arig Ibrahim Hashim, Kim Luddy, Robert Gillies, Robert Gatenby, Joel Brown. Exploratory evolved strategies that limit cancer growth for possible new therapeutic strategies [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 2929. doi:10.1158/1538-7445.AM2017-2929

Abstract 3538: Enhanced dependence on lipid metabolism is a cellular adaptation to acidic microenvironment

Malignant tumors exhibit altered metabolism and consume higher levels of glucose compared to surrounding normal tissue, resulting in highly acidic microenvironment. Adaptation to acidic conditions is a pre-requisite for tumor cells to survive and thrive and to out-compete the stroma into which they invade. Acid adaptation is associated with chronic activation of autophagy as well as redistribution of the lysosomal proteins to the plasma membrane. These processes are major survival mechanisms adopted by tumor cells under acidic conditions. We have also observed that under acidic conditions, there is a rapid, reversible and robust increase in the accumulation of cytoplasmic lipid droplets (adiposomes) in a panel of breast cancer cells. Adiposomes are dynamic organelles that store neutral lipids surrounded by a shell of proteins and a phospholipid monolayer. Breast cancer cells when grown in acidic media accumulated adiposomes as revealed by nile red and perilipin-2 staining followed by confocal microscopy. The acid-induced lipogenic phenotype persists even when the cells are grown in de-lipidated serum, indicating that the source of lipids is de-novo and endogenous. When cells were treated with inhibitors of fatty acid synthesis such as TOFA, an inhibitor of Acetyl CoA Carboxylase or FAS inhibitor C75, adiposome formation at low pH was attenuated. Inhibition of either lipid anabolic or catabolic pathways was specifically cytotoxic in acid-adapted cells, but not in control cells where the FAS inhibitor, C75, is selectively toxic under acidic conditions. Additionally, when treated with etomoxir, an inhibitor of carnitine palmitoyltransferase 1, the rate limiting step in βeta-oxidation, acid adapted cells showed increased sensitivity. To investigate the role of de novo fatty acid synthesis further, we employed high resolution NMR spectroscopy to measure 13C enriched lactate isotopomers following metabolism of D-[1,2-13C] glucose. These analyses showed that glucose flux through the pentose phosphate pathway (PPP) was significantly (&gt;2.5 fold) higher in low pH exposed cells, compared to controls, representing a major shift in glucose metabolism from Embden Meyerhof to PPP, which results in increased production of NADPH, necessary for de novo lipid synthesis. Additional metabolic profiling using the Seahorse XF revealed that cells at low pH had higher rates of oxygen consumption (OCR) and that this was reversible. Further, we investigated the role of various acid sensing G-protein coupled receptors such as OGR1, TDAG8 and GPR4 in transducing the acid signal that results in the accumulation of lipid droplets. CRISPR/Cas9 mediated depletion of these receptors indicates that they might play a major role in inducing adiposome accumulation under acidic conditions. Taken together, increased dependence on lipid metabolism by cancer cells under acidic conditions reveals novel therapeutic vulnerabilities.

Citation Format: Smitha Pillai, Jonathan W. Wojtkowiak, Mehdi Damaghi, Robert Gatenby, Robert Gillies. Enhanced dependence on lipid metabolism is a cellular adaptation to acidic microenvironment [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 3538. doi:10.1158/1538-7445.AM2017-3538

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics. Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered CT image analytics. We developed radiomic signatures capable of distinguishing between tumor genotypes in a discovery cohort (n = 353) and verified them in an independent validation cohort (n = 352). All radiomic signatures significantly outperformed conventional radiographic predictors (tumor volume and maximum diameter). We found a radiomic signature related to radiographic heterogeneity that successfully discriminated between EGFR⁺ and EGFR^- cases (AUC = 0.69). Combining this signature with a clinical model of EGFR status (AUC = 0.70) significantly improved prediction accuracy (AUC = 0.75). The highest performing signature was capable of distinguishing between EGFR⁺ and KRAS⁺ tumors (AUC = 0.80) and, when combined with a clinical model (AUC = 0.81), substantially improved its performance (AUC = 0.86). A KRAS⁺/KRAS^- radiomic signature also showed significant albeit lower performance (AUC = 0.63) and did not improve the accuracy of a clinical predictor of KRAS status. Our results argue that somatic mutations drive distinct radiographic phenotypes that can be predicted by radiomics. This work has implications for the use of imaging-based biomarkers in the clinic, as applied noninvasively, repeatedly, and at low cost. Cancer Res; 77(14); 3922-30. ©2017 AACR.

Intratumoral acidosis fosters cancer-induced bone pain through the activation of the mesenchymal tumor-associated stroma in bone metastasis from breast carcinoma

Cancer-induced bone pain (CIBP) is common in patients with bone metastases (BM), significantly impairing quality of life. The current treatments for CIBP are limited since they are often ineffective. Local acidosis derived from glycolytic carcinoma and tumor-induced osteolysis is only barely explored cause of pain. We found that breast carcinoma cells that prefer bone as a metastatic site have very high extracellular proton efflux and expression of pumps/ion transporters associated with acid-base balance (MCT4, CA9, and V-ATPase). Further, the impairment of intratumoral acidification via V-ATPase targeting in xenografts with BM significantly reduced CIBP, as measured by incapacitance test. We hypothesize that in addition to the direct acid-induced stimulation of nociceptors in the bone, a novel mechanism mediated by the acid-induced and tumor-associated mesenchymal stroma might ultimately lead to nociceptor sensitization and hyperalgesia. Consistent with this, short-term exposure of cancer-associated fibroblasts, mesenchymal stem cells, and osteoblasts to pH 6.8 promotes the expression of inflammatory and nociceptive mediators (NGF, BDNF, IL6, IL8, IL1b and CCL5). This is also consistent with a significant correlation between breakthrough pain, measured by pain questionnaire, and combined high serum levels of BDNF and IL6 in patients with BM, and also by immunofluorescence staining showing IL8 expression that was more in mesenchymal stromal cells rather than in tumors cells, and close to LAMP-2 positive acidifying carcinoma cells in BM tissue sections.

In summary, intratumoral acidification in BM might promote CIBP also by activating the tumor-associated stroma, offering a new target for palliative treatments in advanced cancer.

An Ex Vivo Platform for the Prediction of Clinical Response in Multiple Myeloma

Multiple myeloma remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system, Ex vivo Mathematical Myeloma Advisor (EMMA), consisting of patient-specific mathematical models parameterized by an ex vivo assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from multiple myeloma patients, allowing us to predict clinical response to up to 31 drugs within 5 days after bone marrow biopsy. From a cohort of 52 multiple myeloma patients, EMMA correctly classified 96% as responders/nonresponders and correctly classified 79% according to International Myeloma Working Group stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson r = 0.5658, P < 0.0001). Preliminary estimates indicate that, among the patients enrolled in this study, 60% were treated with at least one ineffective agent from their therapy combination regimen, whereas 30% would have responded better if treated with another available drug or combination. Two in silico clinical trials with experimental agents ricolinostat and venetoclax, in a cohort of 19 multiple myeloma patient samples, yielded consistent results with recent phase I/II trials, suggesting that EMMA is a feasible platform for estimating clinical efficacy of drugs and inclusion criteria screening. This unique platform, specifically designed to predict therapeutic response in multiple myeloma patients within a clinically actionable time frame, has shown high predictive accuracy in patients treated with combinations of different classes of drugs. The accuracy, reproducibility, short turnaround time, and high-throughput potential of this platform demonstrate EMMA's promise as a decision support system for therapeutic management of multiple myeloma. Cancer Res; 77(12); 3336-51. ©2017 AACR.

Abstract B51: Tumor cell evolutionary strategies to overcome immune response

Introduction: The human immune system is complex, dynamic, and highly effective with system tools ranging from simplistic barrier formation to innate and adaptive cellular responses. In turn, the organisms subject to the immune response can evolve a number of different adaptive strategies. Here we examine these evolutionary dynamics in host immune response to cancer focusing on available strategies that permit cancer cells to evade the immune response.

Materials and Methods: Two rapidly proliferating human cell lines, SW620 colon cancer and MDA-MB-231 triple negative breast cancers were subjected to repeated exposure to either immune conditioned media created by lipopolysaccharide stimulated immune cells or intermittent direct culturing with human peripheral blood leucocytes. Resulting phenotypes were evaluated for alterations in growth dynamics, immune resistance, and gene expression.

Results: Conditioned media had only slight effects on tumor cell death. However, after 4 months selected cells have an increased resistance to T cell mediated killing. Co-culturing with immune cells at high effector to target ratios resulted in strong selection with greater than 50% tumor cell death. After 15 rounds we evolved cancer cells that were resistant to this killing. Interestingly, In-vitro selection of SW620, a more rapidly proliferating cell line, resulted in an anti-apoptotic strategy when confronted with immune cells while MDA-MB-231 cells increased fecundity.

Conclusion: Utilizing different arms of the immune system resulted in different styles and strengths of selection force. Additionally, we demonstrate that the two different cell lines employed distinctly different strategies to overcome host immune response. The MDA-MB-231 population adapts to immune attack by accelerating proliferation so that it exceeds the death rate imposed by the immune system. Interestingly, this has been observed clinically as some tumors show explosive growth during immunotherapy. In contrast, the SW620 cells upregulate anti-apoptotic cellular machinery which appears to be phenotypically costly so that proliferation of resistant cells is significantly diminished. As clinical applications of immunotherapy continue to grow it is imperative that we do not ignore the evolutionary consequences of immune selection on the tumor phenotype. While investigations of immune evasive strategies in tumor cells has led to a growing list of specific mechanisms, here we look to not only expand this list but to exploit it. Detailed understanding of the specific adaptive strategy for each tumor population may reveal phenotypic vulnerabilities to second line treatments.

Citation Format: Kimberly A. Luddy, Jan Poleszczuk, Arig Ibrahim Hashim, Mehdi Damaghi, Robert Gillies, Joel Brown, Robert Gatenby. Tumor cell evolutionary strategies to overcome immune response. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr B51.

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features

Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic “feature” sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy.

Combined PET/MRI: from Status Quo to Status Go. Summary Report of the Fifth International Workshop on PET/MR Imaging; February 15-19, 2016; Tübingen, Germany

This article provides a collaborative perspective of the discussions and conclusions from the fifth international workshop of combined positron emission tomorgraphy (PET)/magnetic resonance imaging (MRI) that was held in Tübingen, Germany, from February 15 to 19, 2016. Specifically, we summarise the second part of the workshop made up of invited presentations from active researchers in the field of PET/MRI and associated fields augmented by round table discussions and dialogue boards with specific topics. This year, this included practical advice as to possible approaches to moving PET/MRI into clinical routine, the use of PET/MRI in brain receptor imaging, in assessing cardiovascular diseases, cancer, infection, and inflammatory diseases. To address perceived challenges still remaining to innovatively integrate PET and MRI system technologies, a dedicated round table session brought together key representatives from industry and academia who were engaged with either the conceptualisation or early adoption of hybrid PET/MRI systems. Discussions during the workshop highlighted that emerging unique applications of PET/MRI such as the ability to provide multi-parametric quantitative and visual information which will enable not only overall disease detection but also disease characterisation would eventually be regarded as compelling arguments for the adoption of PET/MR. However, as indicated by previous workshops, evidence in favour of this observation is only growing slowly, mainly due to the ongoing inability to pool data cohorts from independent trials as well as different systems and sites. The participants emphasised that moving from status quo to status go entails the need to adopt standardised imaging procedures and the readiness to act together prospectively across multiple PET/MRI sites and vendors.

Phenotypic changes of acid-adapted cancer cells push them toward aggressiveness in their evolution in the tumor microenvironment

The inter- and intra-tumoral metabolic phenotypes of tumors are heterogeneous, and this is related to microenvironments that select for increased glycolysis. Increased glycolysis leads to decreased pH, and these local microenvironment effects lead to further selection. Hence, heterogeneity of phenotypes is an indirect consequence of altering microenvironments during carcinogenesis. In early stages of growth, tumors are stratified, with the most aggressive cells developing within the acidic interior of the tumor. However, these cells eventually find themselves at the tumor edge, where they invade into the normal tissue via acid-mediated invasion. We believe acid adaptation during the evolution of cancer cells in their niche is a Rubicon that, once crossed, allows cells to invade into and outcompete normal stromal tissue. In this study, we illustrate some acid-induced phenotypic changes due to acidosis resulting in more aggressiveness and invasiveness of cancer cells.