Deletion of the mRNA stability factor ELAVL1 (HuR) in pancreatic cancer cells disrupts the tumor microenvironment integrity.

Stromal cells promote extensive fibrosis in pancreatic ductal adenocarcinoma (PDAC), which is associated with poor prognosis and therapeutic resistance. We report here for the first time that loss of the RNA-binding protein human antigen R (HuR, ELAVL1) in PDAC cells leads to reprogramming of the tumor microenvironment. In multiple in vivo models, CRISPR deletion of ELAVL1 in PDAC cells resulted in a decrease of collagen deposition, accompanied by a decrease of stromal markers (i.e. podoplanin, α-smooth muscle actin, desmin). RNA-sequencing data showed that HuR plays a role in cell-cell communication. Accordingly, cytokine arrays identified that HuR regulates the secretion of signaling molecules involved in stromal activation and extracellular matrix organization [i.e. platelet-derived growth factor AA (PDGFAA) and pentraxin 3]. Ribonucleoprotein immunoprecipitation analysis and transcription inhibition studies validated PDGFA mRNA as a novel HuR target. These data suggest that tumor-intrinsic HuR supports extrinsic activation of the stroma to produce collagen and desmoplasia through regulating signaling molecules (e.g. PDGFAA). HuR-deficient PDAC in vivo tumors with an altered tumor microenvironment are more sensitive to the standard of care gemcitabine, as compared to HuR-proficient tumors. Taken together, we identified a novel role of tumor-intrinsic HuR in its ability to modify the surrounding tumor microenvironment and regulate PDGFAA.

Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics.

This paper is the fifth in a five-part series on statistical methodology for performance assessment of multi-parametric quantitative imaging biomarkers (mpQIBs) for radiomic analysis. Radiomics is the process of extracting visually imperceptible features from radiographic medical images using data-driven algorithms. We refer to the radiomic features as data-driven imaging markers (DIMs), which are quantitative measures discovered under a data-driven framework from images beyond visual recognition but evident as patterns of disease processes irrespective of whether or not ground truth exists for the true value of the DIM. This paper aims to set guidelines on how to build machine learning models using DIMs in radiomics and to apply and report them appropriately. We provide a list of recommendations, named RANDAM (an abbreviation of "Radiomic ANalysis and DAta Modeling"), for analysis, modeling, and reporting in a radiomic study to make machine learning analyses in radiomics more reproducible. RANDAM contains five main components to use in reporting radiomics studies: design, data preparation, data analysis and modeling, reporting, and material availability. Real case studies in lung cancer research are presented along with simulation studies to compare different feature selection methods and several validation strategies.

Postoperative surveillance of pancreatic ductal adenocarcinoma (PDAC) recurrence: practice pattern on standardized imaging and reporting from the society of abdominal radiology disease focus panel on PDAC.

PURPOSE: Surgical resection is the only potential curative treatment for patients with pancreatic ductal adenocarcinoma (PDAC), but unfortunately most patients recur within 5 years of surgery. This article aims to assess the practice patterns across major academic institutions and develop consensus recommendations for postoperative imaging and interpretation in patients with PDAC. METHODS: The consensus recommendations for postoperative imaging surveillance following PDAC resection were developed using the Delphi method. Members of the Society of Abdominal Radiology (SAR) PDAC Disease Focused Panel (DFP) underwent three rounds of surveys followed by live webinar group discussions to develop consensus recommendations. RESULTS: Significant variations currently exist in the postoperative surveillance of PDAC, even among academic institutions. Differentiating common postoperative inflammatory and fibrotic changes from tumor recurrence remains a diagnostic challenge, and there is no reliable size threshold or growth rate of imaging findings that can provide differentiation. A new liver lesion or peritoneal nodule should be considered suspicious for tumor recurrence, and the imaging features should be interpreted in the appropriate clinical context (e.g., CA 19-9, clinical presentation, pathologic staging). CONCLUSION: Postoperative imaging following PDAC resection is challenging to interpret due to the presence of confounding postoperative inflammatory changes. A standardized reporting template for locoregional findings and report impression may improve communication of relaying risk of recurrence with referring providers, which merits validation in future studies.

Multiparametric Quantitative Imaging in Risk Prediction: Recommendations for Data Acquisition, Technical Performance Assessment, and Model Development and Validation.

Combinations of multiple quantitative imaging biomarkers (QIBs) are often able to predict the likelihood of an event of interest such as death or disease recurrence more effectively than single imaging measurements can alone. The development of such multiparametric quantitative imaging and evaluation of its fitness of use differs from the analogous processes for individual QIBs in several key aspects. A computational procedure to combine the QIB values into a model output must be specified. The output must also be reproducible and be shown to have reasonably strong ability to predict the risk of an event of interest. Attention must be paid to statistical issues not often encountered in the single QIB scenario, including overfitting and bias in the estimates of model performance. This is the fourth in a five-part series on statistical methodology for assessing the technical performance of multiparametric quantitative imaging. Considerations for data acquisition are discussed and recommendations from the literature on methodology to construct and evaluate QIB-based models for risk prediction are summarized. The findings in the literature upon which these recommendations are based are demonstrated through simulation studies. The concepts in this manuscript are applied to a real-life example involving prediction of major adverse cardiac events using automated plaque analysis.

Radiotherapy reimagined: Integrating nanomedicines into radiotherapy clinical trials.

Radioenhancing nanoparticles (NPs) are being evaluated in ongoing clinical trials for various cancers including head and neck, lung, esophagus, pancreas, prostate, and soft tissue sarcoma. Supported by decades of preclinical investigation and recent randomized trial data establishing clinical activity, these agents are poised to influence future multimodality treatment paradigms involving radiotherapy. Although the physical interactions between NPs and ionizing radiation are well characterized, less is known about how these agents modify the tumor microenvironment, particularly regarding tumor immunogenicity. In this review, we describe the key multidisciplinary considerations related to radiation, surgery, immunology, and pathology for designing radioenhancing NP clinical trials. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.

Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described.

Multiparametric Quantitative Imaging Biomarkers for Phenotype Classification: A Framework for Development and Validation.

This manuscript is the third in a five-part series related to statistical assessment methodology for technical performance of multi-parametric quantitative imaging biomarkers (mp-QIBs). We outline approaches and statistical methodologies for developing and evaluating a phenotype classification model from a set of multiparametric QIBs. We then describe validation studies of the classifier for precision, diagnostic accuracy, and interchangeability with a comparator classifier. We follow with an end-to-end real-world example of development and validation of a classifier for atherosclerotic plaque phenotypes. We consider diagnostic accuracy and interchangeability to be clinically meaningful claims for a phenotype classification model informed by mp-QIB inputs, aiming to provide tools to demonstrate agreement between imaging-derived characteristics and clinically established phenotypes. Understanding that we are working in an evolving field, we close our manuscript with an acknowledgement of existing challenges and a discussion of where additional work is needed. In particular, we discuss the challenges involved with technical performance and analytical validation of mp-QIBs. We intend for this manuscript to further advance the robust and promising science of multiparametric biomarker development.

3D MR fingerprinting using Seiffert spirals.

PURPOSE: Seiffert spirals were recently explored as an efficient way to traverse 3D k-space compared to traditional 3D techniques. Several studies have shown the ability of 3D MR fingerprinting (MRF) techniques to acquire T1 and T2 relaxation maps in a short period of time. However, these sequences do not sample across a large region of 3D k-space every TR, especially in the way that Seiffert trajectories can. METHODS: A 3D MRF sequence was designed using 8 Seiffert spirals rotated in 3D k-space, with flip angle modulation for T1 and T2 sensitivity. The sequence was compared to an MRF sequence using a 2D spiral rotated in 3D k-space using the tiny golden angle acquisition with similar resolution/readout duration. Both sequences were evaluated using simulations, phantom validation, and in vivo imaging. RESULTS: In all experiments, the Seiffert spiral MRF sequence performed similar to if not better than the multi-axis 2D spiral MRF sequence. Strong intraclass correlation coefficients (> 0.9) were found between conventional and MRF sequences in phantoms, whereas the in vivo results showed slightly less aliasing artifact with the Seiffert trajectory. CONCLUSION: In this study, Seiffert spirals were used within the MRF framework to acquire high-resolution T1 and T2 relaxation time maps in less than 2.5 min. The reduced aliasing artifacts seen with the Seiffert sequence suggests that sampling over 3D k-space evenly each TR can improve quantification or shorten scan times.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer.

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

Pregnancy and weaning regulate human maternal liver size and function.

During pregnancy, the rodent liver undergoes hepatocyte proliferation and increases in size, followed by weaning-induced involution via hepatocyte cell death and stromal remodeling, creating a prometastatic niche. These data suggest a mechanism for increased liver metastasis in breast cancer patients with recent childbirth. It is unknown whether the human liver changes in size and function during pregnancy and weaning. In this study, abdominal imaging was obtained in healthy women at early and late pregnancy and postwean. During pregnancy time points, glucose production and utilization and circulating bile acids were measured. Independently of weight gain, most women's livers increased in size with pregnancy, then returned to baseline postwean. Putative roles for bile acids in liver growth and regression were observed. Together, the data support the hypothesis that the human liver is regulated by reproductive state with growth during pregnancy and volume loss postwean. These findings have implications for sex-specific liver diseases and for breast cancer outcomes.

Multiomics analysis of serial PARP inhibitor treated metastatic TNBC inform on rational combination therapies.

In a pilot study, we evaluated the feasibility of real-time deep analysis of serial tumor samples from triple negative breast cancer patients to identify mechanisms of resistance and treatment opportunities as they emerge under therapeutic stress engendered by poly-ADP-ribose polymerase (PARP) inhibitors (PARPi). In a BRCA-mutant basal breast cancer exceptional long-term survivor, a striking tumor destruction was accompanied by a marked infiltration of immune cells containing CD8 effector cells, consistent with pre-clinical evidence for association between STING mediated immune activation and benefit from PARPi and immunotherapy. Tumor cells in the exceptional responder underwent extensive protein network rewiring in response to PARP inhibition. In contrast, there were minimal changes in the ecosystem of a luminal androgen receptor rapid progressor, likely due to indifference to the effects of PARP inhibition. Together, identification of PARPi-induced emergent changes could be used to select patient specific combination therapies, based on tumor and immune state changes.

Characterizing advanced breast cancer heterogeneity and treatment resistance through serial biopsies and comprehensive analytics.

Molecular heterogeneity in metastatic breast cancer presents multiple clinical challenges in accurately characterizing and treating the disease. Current diagnostic approaches offer limited ability to assess heterogeneity that exists among multiple metastatic lesions throughout the treatment course. We developed a precision oncology platform that combines serial biopsies, multi-omic analysis, longitudinal patient monitoring, and molecular tumor boards, with the goal of improving cancer management through enhanced understanding of the entire cancer ecosystem within each patient. We describe this integrative approach using comprehensive analytics generated from serial-biopsied lesions in a metastatic breast cancer patient. The serial biopsies identified remarkable heterogeneity among metastatic lesions that presented clinically as discordance in receptor status and genomic alterations with mixed treatment response. Based on our study, we highlight clinical scenarios, such as rapid progression or mixed response, that indicate consideration for repeat biopsies to evaluate intermetastatic heterogeneity (IMH), with the objective of refining targeted therapy. We present a framework for understanding the clinical significance of heterogeneity in breast cancer between metastatic lesions utilizing multi-omic analyses of serial biopsies and its implication for effective personalized treatment.

T1ρ magnetic resonance fingerprinting.

T1ρ relaxation imaging is a quantitative imaging technique that has been used to assess cartilage integrity, liver fibrosis, tumors, cardiac infarction, and Alzheimer's disease. T1 , T2 , and T1ρ relaxation time constants have each demonstrated different degrees of sensitivity to several markers of fibrosis and inflammation, allowing for a potential multi-parametric approach to tissue quantification. Traditional magnetic resonance fingerprinting (MRF) has been shown to provide quick, quantitative mapping of T1 and T2 relaxation time constants. In this study, T1ρ relaxation is added to the MRF framework using spin lock preparations. An MRF sequence involving an RF-spoiled sequence with TR , flip angle, T1ρ , and T2 preparation variation is described. The sequence is then calibrated against conventional T1 , T2 , and T1ρ relaxation mapping techniques in agar phantoms and the abdomens of four healthy volunteers. Strong intraclass correlation coefficients (ICC > 0.9) were found between conventional and MRF sequences in phantoms and also in healthy volunteers (ICC > 0.8). The highest ICC correlation values were seen in T1 , followed by T1ρ and then T2 . In this study, T1ρ relaxation has been incorporated into the MRF framework by using spin lock preparations, while still fitting for T1 and T2 relaxation time constants. The acquisition of these parameters within a single breath hold in the abdomen alleviates the issues of movement between breath holds in conventional techniques.

Inter-patient variations of radiation-induced normal-tissue changes in Gd-EOB-DTPA-enhanced hepatic MRI scans during fractionated proton therapy.

BACKGROUND AND PURPOSE: Previous MRI studies have shown a substantial decrease in normal-tissue uptake of a hepatobiliary-directed contrast agent 6-9 weeks after liver irradiation. In this prospective clinical study, we investigated whether this effect is detectable during the course of proton therapy. MATERIAL AND METHODS: Gd-EOB-DTPA enhanced MRI was performed twice during hypo-fractionated proton therapy of liver lesions in 9 patients (plus two patients with only one scan available). Dose-correlated signal changes were qualitatively scored based on difference images from the two scans. We evaluated the correlation between the MRI signal change with the planned dose map. The GTV was excluded from all analyses. In addition, were examined timing, irradiated liver volume, changes in liver function parameters as well as circulating biomarkers of inflammation. RESULTS: Strong, moderate or no dose-correlated signal changes were detected for 2, 3 and 5 patients, respectively. Qualitative scoring was consistent with the quantitative dose to signal change correlation. In an exploratory analysis, the strongest correlation was found between the qualitative scoring and pretreatment IL-6 concentration. For all patients, a clear dose-correlated signal decrease was seen in late follow-up scans. CONCLUSION: Radiation-induced effects can be detected with Gd-EOB-DTPA enhanced MRI in a subgroup of patients within a few days after proton irradiation. The reason for the large inter-patient variations is not yet understood and will require validation in larger studies.

Implementing a comprehensive translational oncology platform: from molecular testing to actionability.

BACKGROUND: In order to establish the workflows required to implement a real-time process involving multi-omic analysis of patient samples to support precision-guided therapeutic intervention, a tissue acquisition and analysis trial was implemented. This report describes our findings to date, including the frequency with which mutational testing led to precision-guided therapy and outcome for those patients. METHODS: Eligible patients presenting to Oregon Health and Science University Knight Cancer Institute were enrolled on the study. Patients with biopsy proven metastatic or locally advanced unresectable prostate cancer, breast cancer, pancreatic adenocarcinoma, or refractory acute myelogenous leukemia receiving standard of care therapy were eligible. Metastatic site biopsies were collected and analyzed using the Knight Diagnostic Lab GeneTrails comprehensive solid tumor panel (124 genes). CLIA certified genomic information was made available to the treating physician. RESULTS: Between 1/26/2017 and 5/30/2018, 38 patients were enrolled, with 28 successfully undergoing biopsy. Of these, 25 samples yielded sufficient tumor for analysis. The median biopsy cellularity and number of cores collected were 70% (15-90%) and 5 (2-20), respectively. No procedure-related complications occurred. GeneTrails analysis revealed that 22 of 25 (88%) tumor samples harbored at least one potentially actionable mutation, and 18 (72%) samples harbored 2 or more potentially actionable mutations. The most common genetic alterations identified involved: DNA damage repair genes, cell cycle regulating genes, PIK3CA/Akt/mTOR pathway, and FGF gene family. To date, CLIA certified genomic results were used by treating physicians for precision-guided therapy in 5 (23%) patients. CONCLUSION: We report the feasibility of real-time tissue acquisition and analysis to support a successful translational oncology platform. The workflow will provide the foundation to improve access and accrual to biomarker driven precision oncology trials.

PET/MR Imaging of the Pancreas.

PET/MR imaging has the potential to markedly alter pancreatic care in both the malignant, and premalignant states with the ability to perform robust, high-resolution, quantitative molecular imaging. The ability of PET/MR imaging to monitor disease processes, potentially correct for motion in the upper abdomen, and provide novel biomarkers that may be a combination of MR imaging and PET biomarkers, offers a unique, precise interrogation of the pancreatic milieu going forward.

Multi-parametric T2 * magnetic resonance fingerprinting using variable echo times.

The use of quantitative imaging biomarkers in the imaging of various disease states, including cancer and neurodegenerative disease, has increased in recent years. T1 , T2 , and T2 * relaxation time constants have been shown to be affected by tissue structure or contrast infusion. Acquiring these biomarkers simultaneously in a multi-parametric acquisition could provide more robust detection of tissue changes in various disease states including neurodegeneration and cancer. Traditional magnetic resonance fingerprinting (MRF) has been shown to provide quick, quantitative mapping of T1 and T2 relaxation time constants. In this study, T2 * relaxation is added to the MRF framework using variable echo times (TE). To demonstrate the feasibility of the method and compare incremental and golden angle spiral rotations, simulated phantom data was fit using the proposed method. Additionally, T1 /T2 /T2 */δf MRF as well as conventional T1 , T2 , and T2 * acquisitions were acquired in agar phantoms and the brains of three healthy volunteers. Golden angle spiral rotation was found to reduce inaccuracy resulting from off resonance effects. Strong correlations were found between conventional and MRF values in the T1 , T2 , and T2 * relaxation time constants of the agar phantoms and healthy volunteers. In this study, T2 * relaxation has been incorporated into the MRF framework by using variable echo times, while still fitting for T1 and T2 relaxation time constants. In addition to fitting these relaxation time constants, a novel method for fitting and correcting off resonance effects has been developed.

Sarcopenia is not associated with morbidity, mortality, or recurrence after esophagectomy for cancer.

BACKGROUND: Sarcopenia is associated with increased morbidity and mortality in hepatic, pancreatic and colorectal cancer. We examined the effect of sarcopenia on morbidity, mortality, and recurrence after resection for esophageal cancer. METHODS: Retrospective review of consecutive esophagectomies from 2010 to 2015. Computed tomography studies were analyzed for sarcopenia. Morbidity was analyzed using Fischer's test and survival data with Kaplan Meier curves. RESULTS: The sarcopenic group (n = 127) had lower BMI, later stage disease, and higher incidence of neoadjuvant radiation than those without sarcopenia (n = 46). There were no differences in morbidity or mortality between the groups (p = .75 and p = .31, respectively). Mean length of stay was similar (p = .70). Disease free and overall survival were similar (p = .20 and p = .39, respectively). CONCLUSION: There is no association between sarcopenia and increased morbidity, mortality and disease-free survival in patients undergoing esophagectomy for cancer. Sarcopenia in esophageal cancer may not portend worse outcomes that have been reported in other solid tumors.