Search

Presents the results of a study on the accuracy of magnetic resonance imaging (MRI) and ductal lavage in the diagnosis of breast cancer in women at high genetic risk of the disease. Background on screening methods used for breast cancer, including mammography; Percentage of women in the study who had prior ovarian cancer; Number of patients with abnormal MRI.

Multiple cancers, including colorectal cancer (CRC), are more frequent and often more aggressive in individuals with obesity. Here, we showed that macrophages accumulated within tumors of patients with obesity and CRC and in obese CRC mice and that they promoted accelerated tumor growth. These changes were initiated by oleic acid accumulation and subsequent tumor cell-derived acid production and were driven by macrophage signaling through the acid-sensing receptor GPR65. We found a similar role for GPR65 in hepatocellular carcinoma (HCC) in obese mice. Tumors in patients with obesity and CRC or HCC also exhibited increased GPR65 expression, suggesting that the mechanism revealed here may contribute to tumor growth in a range of obesity-associated cancers and represent a potential therapeutic target.

The immune system plays a crucial role in the regulation of metastasis. Tumor cells systemically change immune functions to facilitate metastatic progression. Through this study, we deciphered how tumoral galectin-1 (Gal1) expression shapes the systemic immune environment to promote metastasis in head and neck cancer (HNC). In multiple preclinical models of HNC and lung cancer in immunogenic mice, Gal1 fostered the establishment of a premetastatic niche through polymorphonuclear myeloid-derived suppressor cells (PMN-MDSC), which altered the local microenvironment to support metastatic spread. RNA sequencing of MDSCs from premetastatic lungs in these models demonstrated the role of PMN-MDSCs in collagen and extracellular matrix remodeling in the premetastatic compartment. Gal1 promoted MDSC accumulation in the premetastatic niche through the NF-κB signaling axis, triggering enhanced CXCL2-mediated MDSC migration. Mechanistically, Gal1 sustained NF-κB activation in tumor cells by enhancing stimulator of interferon gene (STING) protein stability, leading to prolonged inflammation-driven MDSC expansion. These findings suggest an unexpected protumoral role of STING activation in metastatic progression and establish Gal1 as an endogenous-positive regulator of STING in advanced-stage cancers., Significance: Galectin-1 increases STING stability in cancer cells that activates NF-κB signaling and CXCL2 expression to promote MDSC trafficking, which stimulates the generation of a premetastatic niche and facilitates metastatic progression., (©2023 American Association for Cancer Research.)

TNFα-related apoptosis-inducing ligand (TRAIL), specifically initiates programmed cell death, but often fails to eradicate all cells, making it an ineffective therapy for cancer. This fractional killing is linked to cellular variation that bulk assays cannot capture. Here, we quantify the diversity in cellular signaling responses to TRAIL, linking it to apoptotic frequency across numerous cell systems with single-cell mass cytometry (CyTOF). Although all cells respond to TRAIL, a variable fraction persists without apoptotic progression. This cell-specific behavior is nonheritable where both the TRAIL-induced signaling responses and frequency of apoptotic resistance remain unaffected by prior exposure. The diversity of signaling states upon exposure is correlated to TRAIL resistance. Concomitantly, constricting the variation in signaling response with kinase inhibitors proportionally decreases TRAIL resistance. Simultaneously, TRAIL-induced de novo translation in resistant cells, when blocked by cycloheximide, abrogated all TRAIL resistance. This work highlights how cell signaling diversity, and subsequent translation response, relates to nonheritable fractional escape from TRAIL-induced apoptosis. This refined view of TRAIL resistance provides new avenues to study death ligands in general., (© 2019 Baskar et al.)

Drug screening studies typically involve assaying the sensitivity of a range of cancer cell lines across an array of anti-cancer therapeutics. Alongside these sensitivity measurements high dimensional molecular characterizations of the cell lines are typically available, including gene expression, copy number variation and genomic mutations. We propose a sparse multitask regression model which learns discriminative latent characteristics that predict drug sensitivity and are associated with specific molecular features. We use ideas from Bayesian nonparametrics to automatically infer the appropriate number of these latent characteristics. The resulting analysis couples high predictive performance with interpretability since each latent characteristic involves a typically small set of drugs, cell lines and genomic features. Our model uncovers a number of drug-gene sensitivity associations missed by single gene analyses. We functionally validate one such novel association: that increased expression of the cell-cycle regulator C/EBPδ decreases sensitivity to the histone deacetylase (HDAC) inhibitor panobinostat., Competing Interests: The authors have declared that no competing interests exist.

Background: Controversy persists about optimal mammography screening strategies., Objective: To evaluate screening outcomes, taking into account advances in mammography and treatment of breast cancer., Design: Collaboration of 6 simulation models using national data on incidence, digital mammography performance, treatment effects, and other-cause mortality., Setting: United States., Patients: Average-risk U.S. female population and subgroups with varying risk, breast density, or comorbidity., Intervention: Eight strategies differing by age at which screening starts (40, 45, or 50 years) and screening interval (annual, biennial, and hybrid [annual for women in their 40s and biennial thereafter]). All strategies assumed 100% adherence and stopped at age 74 years., Measurements: Benefits (breast cancer-specific mortality reduction, breast cancer deaths averted, life-years, and quality-adjusted life-years); number of mammograms used; harms (false-positive results, benign biopsies, and overdiagnosis); and ratios of harms (or use) and benefits (efficiency) per 1000 screens., Results: Biennial strategies were consistently the most efficient for average-risk women. Biennial screening from age 50 to 74 years avoided a median of 7 breast cancer deaths versus no screening; annual screening from age 40 to 74 years avoided an additional 3 deaths, but yielded 1988 more false-positive results and 11 more overdiagnoses per 1000 women screened. Annual screening from age 50 to 74 years was inefficient (similar benefits, but more harms than other strategies). For groups with a 2- to 4-fold increased risk, annual screening from age 40 years had similar harms and benefits as screening average-risk women biennially from 50 to 74 years. For groups with moderate or severe comorbidity, screening could stop at age 66 to 68 years., Limitation: Other imaging technologies, polygenic risk, and nonadherence were not considered., Conclusion: Biennial screening for breast cancer is efficient for average-risk populations. Decisions about starting ages and intervals will depend on population characteristics and the decision makers' weight given to the harms and benefits of screening., Primary Funding Source: National Institutes of Health., Competing Interests: Potential Conflicts of Interest: None disclosed

We have found evidence suggesting that ARF and p53 are essential for tumor regression upon MYC inactivation through distinct mechanisms ARF through p53-independent affect, is required to for MYC to regulate the expression of genes that are required for both the induction of cellular senescence as well as recruitment of innate immune activation. Our observations have possible implications for mechanisms of therapeutic resistance to targeted oncogene inactivation.

MYC-induced T-ALL exhibit oncogene addiction. Addiction to MYC is a consequence of both cell-autonomous mechanisms, such as proliferative arrest, cellular senescence, and apoptosis, as well as non-cell autonomous mechanisms, such as shutdown of angiogenesis, and recruitment of immune effectors. Here, we show, using transgenic mouse models of MYC-induced T-ALL, that the loss of either p19ARF or p53 abrogates the ability of MYC inactivation to induce sustained tumor regression. Loss of p53 or p19ARF, influenced the ability of MYC inactivation to elicit the shutdown of angiogenesis; however the loss of p19ARF, but not p53, impeded cellular senescence, as measured by SA-beta-galactosidase staining, increased expression of p16INK4A, and specific histone modifications. Moreover, comparative gene expression analysis suggested that a multitude of genes involved in the innate immune response were expressed in p19ARF wild-type, but not null, tumors upon MYC inactivation. Indeed, the loss of p19ARF, but not p53, impeded the in situ recruitment of macrophages to the tumor microenvironment. Finally, p19ARF null-associated gene signature prognosticated relapse-free survival in human patients with ALL. Therefore, p19ARF appears to be important to regulating cellular senescence and innate immune response that may contribute to the therapeutic response of ALL.

Introduction: Breast cancer is commonly classified into intrinsic molecular subtypes. Standard gene centering is routinely done prior to molecular subtyping, but it can produce inaccurate classifications when the distribution of clinicopathological characteristics in the study cohort differs from that of the training cohort used to derive the classifier., Methods: We propose a subgroup-specific gene-centering method to perform molecular subtyping on a study cohort that has a skewed distribution of clinicopathological characteristics relative to the training cohort. On such a study cohort, we center each gene on a specified percentile, where the percentile is determined from a subgroup of the training cohort with clinicopathological characteristics similar to the study cohort. We demonstrate our method using the PAM50 classifier and its associated University of North Carolina (UNC) training cohort. We considered study cohorts with skewed clinicopathological characteristics, including subgroups composed of a single prototypic subtype of the UNC-PAM50 training cohort (n = 139), an external estrogen receptor (ER)-positive cohort (n = 48) and an external triple-negative cohort (n = 77)., Results: Subgroup-specific gene centering improved prediction performance with the accuracies between 77% and 100%, compared to accuracies between 17% and 33% from standard gene centering, when applied to the prototypic tumor subsets of the PAM50 training cohort. It reduced classification error rates on the ER-positive (11% versus 28%; P = 0.0389), the ER-negative (5% versus 41%; P < 0.0001) and the triple-negative (11% versus 56%; P = 0.1336) subgroups of the PAM50 training cohort. In addition, it produced higher accuracy for subtyping study cohorts composed of varying proportions of ER-positive versus ER-negative cases. Finally, it increased the percentage of assigned luminal subtypes on the external ER-positive cohort and basal-like subtype on the external triple-negative cohort., Conclusions: Gene centering is often necessary to accurately apply a molecular subtype classifier. Compared with standard gene centering, our proposed subgroup-specific gene centering produced more accurate molecular subtype assignments in a study cohort with skewed clinicopathological characteristics relative to the training cohort.

To provide a better understanding of the relationship between primary tumor growth rates and metastatic burden, we present a method that bridges tumor growth dynamics at the population level, extracted from the SEER database, to those at the tissue level. Specifically, with this method, we are able to relate estimates of tumor growth rates and metastatic burden derived from a population-level model to estimates of the primary tumor vascular response and the circulating tumor cell (CTC) fraction derived from a tissue-level model. Variation in the population-level model parameters produces differences in cancer-specific survival and cure fraction. Variation in the tissue-level model parameters produces different primary tumor dynamics that subsequently lead to different growth dynamics of the CTCs. Our method to bridge the population and tissue scales was applied to lung and breast cancer separately, and the results were compared. The population model suggests that lung tumors grow faster and shed a significant number of lethal metastatic cells at small sizes, whereas breast tumors grow slower and do not significantly shed lethal metastatic cells until becoming larger. Although the tissue-level model does not explicitly model the metastatic population, we are able to disengage the direct dependency of the metastatic burden on primary tumor growth by introducing the CTC population as an intermediary and assuming dependency. We calibrate the tissue-level model to produce results consistent with the population model while also revealing a more dynamic relationship between the primary tumor and the CTCs. This leads to exponential tumor growth in lung and power law tumor growth in breast. We conclude that the vascular response of the primary tumor is a major player in the dynamics of both the primary tumor and the CTCs, and is significantly different in breast and lung cancer.

Vast amounts of molecular data characterizing the genome, epigenome and transcriptome are becoming available for a variety of cancers. The current challenge is to integrate these diverse layers of molecular biology information to create a more comprehensive view of key biological processes underlying cancer. We developed a biocomputational algorithm that integrates copy number, DNA methylation, and gene expression data to study master regulators of cancer and identify their targets. Our algorithm starts by generating a list of candidate driver genes based on the rationale that genes that are driven by multiple genomic events in a subset of samples are unlikely to be randomly deregulated. We then select the master regulators from the candidate driver and identify their targets by inferring the underlying regulatory network of gene expression. We applied our biocomputational algorithm to identify master regulators and their targets in glioblastoma multiforme (GBM) and serous ovarian cancer. Our results suggest that the expression of candidate drivers is more likely to be influenced by copy number variations than DNA methylation. Next, we selected the master regulators and identified their downstream targets using module networks analysis. As a proof-of-concept, we show that the GBM and ovarian cancer module networks recapitulate known processes in these cancers. In addition, we identify master regulators that have not been previously reported and suggest their likely role. In summary, focusing on genes whose expression can be explained by their genomic and epigenomic aberrations is a promising strategy to identify master regulators of cancer.

Cancer-associated fibroblasts (CAF) have been reported to support tumor progression by a variety of mechanisms. However, their role in the progression of non-small cell lung cancer (NSCLC) remains poorly defined. In addition, the extent to which specific proteins secreted by CAFs contribute directly to tumor growth is unclear. To study the role of CAFs in NSCLCs, a cross-species functional characterization of mouse and human lung CAFs was conducted. CAFs supported the growth of lung cancer cells in vivo by secretion of soluble factors that directly stimulate the growth of tumor cells. Gene expression analysis comparing normal mouse lung fibroblasts and mouse lung CAFs identified multiple genes that correlate with the CAF phenotype. A gene signature of secreted genes upregulated in CAFs was an independent marker of poor survival in patients with NSCLC. This secreted gene signature was upregulated in normal lung fibroblasts after long-term exposure to tumor cells, showing that lung fibroblasts are "educated" by tumor cells to acquire a CAF-like phenotype. Functional studies identified important roles for CLCF1-CNTFR and interleukin (IL)-6-IL-6R signaling in promoting growth of NSCLCs. This study identifies novel soluble factors contributing to the CAF protumorigenic phenotype in NSCLCs and suggests new avenues for the development of therapeutic strategies., (©2012 AACR.)

Purpose: To identify morphologic and dynamic enhancement magnetic resonance imaging (MRI) features of pure ductal carcinoma in situ (DCIS) by using a new intensity-modulated parametric mapping technique, and to correlate the MRI features with histopathologic findings., Materials and Methods: Fourteen patients with pure DCIS on pathology underwent conventional mammography and contrast-enhanced (CE) MRI using the intensity-modulated parametric mapping technique. The MR images were reviewed and the lesions were categorized according to morphologic and kinetic criteria from the ACR BI-RADS-MRI Lexicon, with BI-RADS 4 and 5 lesions classified as suspicious., Results: With the use of a kinetic curve shape analysis, MRI classified seven of 14 lesions (50%) as suspicious, including four with initial-rapid/late-washout and three with initial-rapid/late-plateau. Using morphologic criteria, MRI classified 10/14 (71%) as suspicious, with the most prominent morphologic feature being a regional enhancement pattern. Using the intensity modulated parametric mapping technique, MRI classified 12/14 cases (86%) as suspicious. Parametric mapping identified all intermediate- and high-grade DCIS lesions., Conclusion: The intensity-modulated parametric mapping technique for breast MRI resulted in the highest detection rate for the DCIS cases. Furthermore, the parametric mapping technique identified all intermediate- and high-grade DCIS lesions, suggesting that a negative MRI using the parametric mapping technique may exclude intermediate- and high-grade DCIS. This finding has potential clinical implications., ((c) 2005 Wiley-Liss, Inc.)

Objective: The objective of this study was to identify magnetic resonance imaging (MRI) characteristics of fibrocystic change (FCC) of the breast., Materials and Methods: Fourteen patients with a histopathologic diagnosis of solitary FCC of the breast underwent x-ray mammography and MRI of the breast. Three experienced breast imaging radiologists retrospectively reviewed the MRI findings and categorized the lesions on morphologic and kinetic criteria according to the ACR BI-RADS-MRI Lexicon., Results: The most striking morphologic feature of fibrocystic change was nonmass-like regional enhancement found in 6 of 14 (43%) FCC lesions. Based on morphologic criteria alone, 12 of 14 (86%) lesions were correctly classified as benign. According to analysis of the time-intensity curves, 10 of 14 (71%) FCC lesions were correctly classified as benign., Conclusion: Although FCC has a wide spectrum of morphologic and kinetic features on MRI, it most often presents as a mass or a nonmass-like regional enhancing lesion with benign enhancement kinetics.

Purpose: To examine the effect of patient and tumor characteristics on breast cancer survival as recorded in the U.S. National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database from 1973 to 1998., Methods: A sample of 72,367 female cases from 1973 to 1998 aged 21-90 years with invasive ductal breast cancer were examined with Cox proportional hazards regression to determine the effect of age at diagnosis, race, tumor size, tumor grade, disease stage, and year of diagnosis on disease-specific survival., Results: Larger tumor size and higher tumor grade were found to have large negative effects on survival. Blacks had a 47 % greater risk of death than whites. Year of diagnosis had a positive effect, with a 15 % reduction in risk for each decade in the time period under study. The effects of patient age and disease stage violated the proportional hazards assumption, with distant disease having much poorer short-term survival than one would expect from a proportional hazards model, and younger age groups matching or even falling below the survival rate of the oldest group over time., Conclusion: Tumor size, grade, race, and year of diagnosis all have significant constant effects on disease-specific survival in breast cancer, while the effects of age at diagnosis and disease stage have significant effects that vary over time.

The National Cancer Institute-sponsored Cancer Intervention and Surveillance Network program on breast cancer is composed of seven research groups working largely independently to model the impact of screening and adjuvant therapy on breast cancer mortality trends in the US from 1975 to 2000. Each of the groups has chosen a different modeling methodology without purposeful attempt to be in contrast with each other. The seven groups have met biannually since November 2000 to discuss their methodology and results. This article investigates the differences in methodology. To facilitate this comparison, each of the groups submitted a description of their model into a uniformly structured web based 'model profiler'. Six of the seven models simulate a preclinical natural history that cannot be observed directly with parameters estimated from published evidence concerning screening and therapy effects. The remaining model regards published evidence on intervention effects as prior information and updates that with information from the US population in a Bayesian type analysis. In general, the differences between the models appear to be small, particularly among the models driven by natural history assumptions. However, we demonstrate that such apparently small differences can have a large impact on surveillance of population trends. We describe a systematic approach to evaluating differences in model assumptions and results, as well as differences in modeling culture underlying the differences in model structure and parameters.

Purpose: To compare observer performance in the detection of abnormalities on 1,760 x 2,140 matrix (2K) and 3,520 x 4,280 matrix (4K) digital storage phosphor chest radiographs., Materials and Methods: One hundred sixty patients who underwent dedicated computed tomography (CT) of the thorax were prospectively recruited into the study. Posteroanterior and lateral computed radiographs of the chest were acquired in each patient and printed in 2K and 4K formats. Six radiologists independently analyzed the hard-copy images and scored the presence of parenchymal (opacities 2 cm, and subtle interstitial), mediastinal, and pleural abnormalities on a five-point confidence scale. With CT as the reference standard, observer performance tests were carried out by using receiver operating characteristic (ROC) analysis., Results: Analysis of averaged observer performance showed 2K and 4K images were equally effective in detection of all three groups of abnormalities. In the detection of the three subtypes of parenchymal abnormalities, there were no significant differences in averaged performance between the 2K and 4K formats (area below ROC curve [A(z)] values: opacities 2 cm, 0.86 +/-.025 and 0.85 +/- 0.030; subtle interstitial abnormalities, 0.73 +/- 0.041 and 0.72 +/- 0.041). Averaged performance in detection of mediastinal and pleural abnormalities was equivalent (A(z) values: mediastinal, 0.70 +/- 0.046 and 0.73 +/- 0.033; pleural, 0.85 +/- 0.032 and 0.86 +/- 0.033)., Conclusion: Observer performance in detection of parenchymal, mediastinal, and pleural abnormalities was not significantly different on 2K and 4K storage phosphor chest radiographs.

A comprehensive strategy for the acquisition, reconstruction, and postprocessing of MR spectroscopic images is presented. The reconstruction algorithm is the most critical component of this strategy. It is assumes that the desired image is spatially bounded, meaning that the desired image contains an object that is surrounded by a background of zeros. The reconstruction algorithm relies on prior knowledge of the background zeros for k-space extrapolation. This algorithm is a good candidate for proton MR spectroscopic image reconstruction because these images are often spatially bounded and prior knowledge of the zeros is easily obtained from a rapidly acquired high resolution conventional MRI. Although the reconstruction algorithm can be used with the standard 3DFT k-space distribution, a distribution that relies on anatomical features that are likely to occur in the spectroscopic image can produce better results. Prior knowledge of these anatomical features is also obtained from a conventional MRI. Finally, the postprocessing component of this strategy is valuable for reducing subcutaneous lipid contamination. Overall, the comprehensive approach presented here produces images that are better resolved than standard approaches without increasing acquisition time or reducing SNR. Examples using NAA data are provided.

A spectral extrapolation algorithm for spatially bounded images is presented. An image is said to be spatially bounded when it is confined to a closed region and is surrounded by a background of zeros. With prior knowledge of the spatial domain zeros, the extrapolation algorithm extends the image's spectrum beyond a known interval of low-frequency components. The result, which is referred to as the finite support solution, has space variant resolution; features near the edge of the support region are better resolved than those in the center. The resolution of the finite support solution is discussed as a function of the number of known spatial zeros and known spectral components. A regularized version of the finite support solution is included for handling the case where the known spectral components are noisy. For both the noiseless and noisy cases, the resolution of the finite support solution is measured in terms of its impulse response characteristics, and compared to the resolution of the zerofilled and Nyquist solutions. The finite support solution is superior to the zerofilled solution for both the noisy and noiseless data cases. When compared to the Nyquist solution, the finite support solution may be preferred in the noisy data case. Examples using medical image data are provided.