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Biomarkers remain the highest value proposition in cancer medicine today-especially protein biomarkers. Despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system, and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last 2 decades have seen an explosion of multiomics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single-cell analysis, artificial intelligence (machine and deep learning) for data analysis, and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking toward viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to redefine biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients., Competing Interests: Conflict of interest A. D. B. declares the following financial relationships: Ellison Institute for Transformative Medicine, Caris Life Sciences, Pancreatic Cancer Action Network, and Sage Bionetworks. A. D. B. declares the following noncompensated relationships: NCI National Cancer Advisory Board, American Association for Cancer Research, Arizona State University, Milken Institute, Quantum Leap Healthcare Collaborative, and Friends of Cancer Research. P. M. is a paid faculty member of Stanford University, Paid Founder and Chief Scientist of Nautilus Biotechnology, a compensated member of the Scientific Advisory Boards of ChromaCode, OncImmune, and January AI, and an uncompensated President of The Dream Finder Foundation. J. S. H. L. is the paid Chief Science and Innovation Officer of EITM, paid faculty member of USC, advisory board member of AtlasXomics, Inc, and paid consultant of the Henry M. Jackson Foundation outside the submitted work. The other authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Competing Interests: DBA is the paid Founding Director and Chief Executive Officer of the Ellison Institute for Transformative Medicine, a public good for-profit (the Institute comprises both a for-profit entity, whose profits will be reinvested into future public health and disease research, as well as a not-for-profit research foundation), which draws collaborators from across conventional health fields, as well as from a broad range of other disciplines to study disease and potential ways to prevent, detect, and treat the disease, and is a paid faculty member of the University of Southern California. JB is a paid faculty member of Oxford University, a paid Non-Executive Director of Oxford Science Enterprises, an independent investment company that has active investments in life sciences (novel platforms, technologies, and approaches advancing the discovery of new therapeutics and vaccines), health tech (medical devices, tools, digital diagnostics, AI-powered clinical imaging, virtual reality therapy, and solutions to improve health-care delivery), and deep tech (quantum computing and enabling technologies, fusion energy, industrial heat, novel computer hardware, electrified transportation, and solutions to tackle depleting food resources), and is a paid Chair of the Bill & Melinda Gates Foundation's Global Health Scientific Advisory Board. DBA and JB are non-paid Co-Chairs of the Global Health Security Consortium, a partnership to support and guide leaders on the global health security agenda. AN is a paid Special Adviser to Gavi, the Vaccine Alliance. AAS is the paid General Administrator of Institut Pasteur de Dakar, which produces a yellow fever vaccine. We declare no other competing interests. We thank Tamsin Berry, Romina Mariano, Gabriel Seidman, and Emily Stanger Sfeile from the Global Health Security Consortium for their assistance with preparing this Comment.

Background: Colorectal cancer (CRC) mortality is principally due to metastatic disease, with the most frequent organ of metastasis being the liver. Biochemical and mechanical factors residing in the tumor microenvironment are considered to play a pivotal role in metastatic growth and response to therapy. However, it is difficult to study the tumor microenvironment systematically owing to a lack of fully controlled model systems that can be investigated in rigorous detail., Results: We present a quantitative imaging dataset of CRC cell growth dynamics influenced by in vivo-mimicking conditions. They consist of tumor cells grown in various biochemical and biomechanical microenvironmental contexts. These contexts include varying oxygen and drug concentrations, and growth on conventional stiff plastic, softer matrices, and bioengineered acellular liver extracellular matrix. Growth rate analyses under these conditions were performed via the cell phenotype digitizer (CellPD)., Conclusions: Our data indicate that the growth of highly aggressive HCT116 cells is affected by oxygen, substrate stiffness, and liver extracellular matrix. In addition, hypoxia has a protective effect against oxaliplatin-induced cytotoxicity on plastic and liver extracellular matrix. This expansive dataset of CRC cell growth measurements under in situ relevant environmental perturbations provides insights into critical tumor microenvironment features contributing to metastatic seeding and tumor growth. Such insights are essential to dynamical modeling and understanding the multicellular tumor-stroma dynamics that contribute to metastatic colonization. It also establishes a benchmark dataset for training and testing data-driven dynamical models of cancer cell lines and therapeutic response in a variety of microenvironmental conditions., (© The Author(s) 2021. Published by Oxford University Press GigaScience.)

Small molecules that target the androgen receptor (AR) are the mainstay of therapy for lethal castration-resistant prostate cancer (CRPC), yet existing drugs lose their efficacy during continued treatment. This evolution of resistance is due to heterogenous mechanisms which include AR mutations causing the identical drug to activate instead of inhibit the receptor. Understanding in molecular detail the paradoxical phenomenon wherein an AR antagonist is transformed into an agonist by structural mutations in the target receptor is thus of paramount importance. Herein, we describe a reciprocal paradox: opposing antagonist and agonist AR regulation determined uniquely by enantiomeric forms of the same drug structure. The antiandrogen BMS-641988, which has ( R )-chirality at C-5 encompasses a previously uncharacterized ( S )-stereoisomer that is, surprisingly, a potent agonist of AR, as demonstrated by transcriptional assays supported by cell imaging studies. This duality was reproduced in a series of novel compounds derived from the BMS-641988 scaffold. Coupled with in silico modeling studies, the results inform an AR model that explains the switch from potent antagonist to high-affinity agonist in terms of C-5 substituent steric interactions with helix 12 of the ligand binding site. They imply strategies to overcome AR drug resistance and demonstrate that insufficient enantiopurity in this class of AR antagonist can confound efforts to correlate structure with function., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Purpose: Monoamine oxidase A (MAOA) influences prostate cancer growth and metastasis in pre-clinical models. We examined effects of phenelzine (a monoamine oxidase inhibitor) in patients with biochemical recurrent castrate-sensitive prostate cancer., Materials and Methods: An open-label single arm clinical trial enrolled subjects with biochemical recurrent prostate cancer defined by PSA ≥ 0.4 ng/ml (post prostatectomy) or PSA ≥ 2 ng/ml above nadir (post-radiation therapy); no evidence of metastasis on imaging; and normal androgen levels. Subjects received phenelzine 30 mg orally twice daily. Mood symptoms were assessed with the hospital anxiety depression score (HADS) questionnaire. The primary endpoint was the proportion of patients who achieved a PSA decline of ≥50% from baseline., Results: Characteristics of the 20 eligible patients enrolled included: mean ± SD age 66.9 ± 4.8 years and PSA 4.7 ± 5.8 ng/dl. Maximal PSA declines ≥30% and ≥50% were observed in 25% (n = 5/20) and 10% (n = 2/20) of subjects, respectively. At 12 weeks, 17 subjects remained on treatment with PSA declines ≥30% and ≥50% of 24% (n = 4/17) and 6% (n = 1/17), respectively. Common toxicities observed included dizziness (grade 1 = 45%, grade 2 = 35%), hypertension (grade ≥ 2 = 30%), and edema (grade 1 = 25%, grade 2 = 10%). There was one episode of grade 4 hypertension (cycle 4) and two episodes of grade 3 syncope (cycle 12 and cycle 14) requiring treatment discontinuation. HADS questionnaires demonstrated a significant decrease in anxiety with no change in depressive symptoms on treatment., Conclusions: Phenelzine demonstrated efficacy in patients with biochemical recurrent castrate-sensitive prostate cancer. Most treatment-related toxicities were mild, but rare significant and reversible cardiovascular toxicities were observed. Therapies directed at MAOA may represent a new avenue for treatment in patients with recurrent prostate cancer.

For newly diagnosed breast cancer, estrogen receptor status (ERS) is a key molecular marker used for prognosis and treatment decisions. During clinical management, ERS is determined by pathologists from immunohistochemistry (IHC) staining of biopsied tissue for the targeted receptor, which highlights the presence of cellular surface antigens. This is an expensive, time-consuming process which introduces discordance in results due to variability in IHC preparation and pathologist subjectivity. In contrast, hematoxylin and eosin (H&E) staining-which highlights cellular morphology-is quick, less expensive, and less variable in preparation. Here we show that machine learning can determine molecular marker status, as assessed by hormone receptors, directly from cellular morphology. We develop a multiple instance learning-based deep neural network that determines ERS from H&E-stained whole slide images (WSI). Our algorithm-trained strictly with WSI-level annotations-is accurate on a varied, multi-country dataset of 3,474 patients, achieving an area under the curve (AUC) of 0.92 for sensitivity and specificity. Our approach has the potential to augment clinicians' capabilities in cancer prognosis and theragnosis by harnessing biological signals imperceptible to the human eye.

Because histologic types are subjective and difficult to reproduce between pathologists, tissue morphology often takes a back seat to molecular testing for the selection of breast cancer treatments. This work explores whether a deep-learning algorithm can learn objective histologic H&E features that predict the clinical subtypes of breast cancer, as assessed by immunostaining for estrogen, progesterone, and Her2 receptors (ER/PR/Her2). Translating deep learning to this and related problems in histopathology presents a challenge due to the lack of large, well-annotated data sets, which are typically required for the algorithms to learn statistically significant discriminatory patterns. To overcome this limitation, we introduce the concept of "tissue fingerprints," which leverages large, unannotated datasets in a label-free manner to learn H&E features that can distinguish one patient from another. The hypothesis is that training the algorithm to learn the morphological differences between patients will implicitly teach it about the biologic variation between them. Following this training internship, we used the features the network learned, which we call "fingerprints," to predict ER, PR, and Her2 status in two datasets. Despite the discovery dataset being relatively small by the standards of the machine learning community (n = 939), fingerprints enabled the determination of ER, PR, and Her2 status from whole slide H&E images with 0.89 AUC (ER), 0.81 AUC (PR), and 0.79 AUC (Her2) on a large, independent test set (n = 2531). Tissue fingerprints are concise but meaningful histopathologic image representations that capture biological information and may enable machine learning algorithms that go beyond the traditional ER/PR/Her2 clinical groupings by directly predicting theragnosis.

Objective: Cell-free DNA (cfDNA) is an attractive cancer biomarker, as it is thought to reflect a component of the underlying genetic makeup of the tumor and is readily accessible in serial fashion. Because chemotherapy regimens are expected to act rapidly on cancer and cfDNA is cleared from the blood within minutes, we hypothesized that cfDNA would reflect immediate effects of treatment. Here, we developed a method for monitoring long cfDNA fragments, and report dynamic changes in response to cytotoxic chemotherapy., Results: Peripheral blood was obtained from 15 patients with metastatic castration-resistant prostate cancer (CRPC) immediately before and after cytotoxic chemotherapy infusion. cfDNA was extracted and quantified for long interspersed nuclear elements (LINE1; 297 bp) using qPCR. Targeted deep sequencing was performed to quantify the frequency of mutations in exon 8 of the androgen receptor (AR), a mutational hotspot region in CRPC. Single nucleotide mutations in AR exon 8 were found in 6 subjects (6/15 = 40%). Analytical variability was minimized by pooling independent PCR reactions for each library. In 5 patients, tumor-derived long cfDNA levels were found to change immediately after infusion. Detailed analysis of one subject suggests that cytotoxic chemotherapy can produce rapidly observable effects on cfDNA.

In this pilot study, we introduce a machine learning framework to identify relationships between cancer tissue morphology and hormone receptor pathway activation in breast cancer pathology hematoxylin and eosin (H&E)-stained samples. As a proof-of-concept, we focus on predicting clinical estrogen receptor (ER) status-defined as greater than one percent of cells positive for estrogen receptor by immunohistochemistry staining-from spatial arrangement of nuclear features. Our learning pipeline segments nuclei from H&E images, extracts their position, shape and orientation descriptors, and then passes them to a deep neural network to predict ER status. After training on 57 tissue cores of invasive ductal carcinoma (IDC), our pipeline predicted ER status in an independent test set of patient samples (AUC ROC = 0.72, 95%CI = 0.55-0.89, n  = 56). This proof of concept shows that machine-derived descriptors of morphologic histology patterns can be correlated to signaling pathway status. Unlike other deep learning approaches to pathology, our system uses deep neural networks to learn spatial relationships between pre-defined biological features, which improves the interpretability of the system and sheds light on the features the neural network uses to predict ER status. Future studies will correlate morphometry to quantitative measures of estrogen receptor status and, ultimately response to hormonal therapy., Competing Interests: The authors declare no competing interests.

We are in the midst of a technological revolution that is providing new insights into human biology and cancer. In this era of big data, we are amassing large amounts of information that is transforming how we approach cancer treatment and prevention. Enactment of the Cancer Moonshot within the 21st Century Cures Act in the USA arrived at a propitious moment in the advancement of knowledge, providing nearly US$2 billion of funding for cancer research and precision medicine. In 2016, the Blue Ribbon Panel (BRP) set out a roadmap of recommendations designed to exploit new advances in cancer diagnosis, prevention, and treatment. Those recommendations provided a high-level view of how to accelerate the conversion of new scientific discoveries into effective treatments and prevention for cancer. The US National Cancer Institute is already implementing some of those recommendations. As experts in the priority areas identified by the BRP, we bolster those recommendations to implement this important scientific roadmap. In this Commission, we examine the BRP recommendations in greater detail and expand the discussion to include additional priority areas, including surgical oncology, radiation oncology, imaging, health systems and health disparities, regulation and financing, population science, and oncopolicy. We prioritise areas of research in the USA that we believe would accelerate efforts to benefit patients with cancer. Finally, we hope the recommendations in this report will facilitate new international collaborations to further enhance global efforts in cancer control., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Mutations or deletions in exons 18-21 in the EGFR ) are present in approximately 15% of tumors in patients with non-small cell lung cancer (NSCLC). They lead to activation of the EGFR kinase domain and sensitivity to molecularly targeted therapeutics aimed at this domain (gefitinib or erlotinib). These drugs have demonstrated objective clinical response in many of these patients; however, invariably, all patients acquire resistance. To examine the molecular origins of resistance, we derived a set of gefitinib-resistant cells by exposing lung adenocarcinoma cell line, HCC827, with an activating mutation in the EGFR tyrosine kinase domain, to increasing gefitinib concentrations. Gefitinib-resistant cells acquired an increased expression and activation of JUN, a known oncogene involved in cancer progression. Ectopic overexpression of JUN in HCC827 cells increased gefitinib IC 50 from 49 nmol/L to 8 μmol/L ( P < 0.001). Downregulation of JUN expression through shRNA resensitized HCC827 cells to gefitinib (IC 50 from 49 nmol/L to 2 nmol/L; P < 0.01). Inhibitors targeting JUN were 3-fold more effective in the gefitinib-resistant cells than in the parental cell line ( P < 0.01). Analysis of gene expression in patient tumors with EGFR-activating mutations and poor response to erlotinib revealed a similar pattern as the top 260 differentially expressed genes in the gefitinib-resistant cells (Spearman correlation coefficient of 0.78, P < 0.01). These findings suggest that increased JUN expression and activity may contribute to gefitinib resistance in NSCLC and that JUN pathway therapeutics merit investigation as an alternate treatment strategy. Mol Cancer Ther; 16(8); 1645-57. ©2017 AACR ., (©2017 American Association for Cancer Research.)

Background: Previous data suggests that co-targeting mammalian target of rapamycin and angiogenic pathways may potentiate effects of cytotoxic chemotherapy. We studied combining mammalian target of rapamycin and vascular endothelial growth factor inhibition with docetaxel in castrate-resistant prostate cancer (CRPC)., Methods: Eligible patients had progressive, metastatic, chemotherapy-naive CRPC. Docetaxel and bevacizumab were given intravenously day 1 with everolimus orally daily on a 21-day cycle across 3 dose levels (75:15:2.5, 75:15:5, and 65:15:5; docetaxel mg/m 2 , mg/kg bevacizumab, and mg everolimus, respectively). Maintenance therapy with bevacizumab/everolimus without docetaxel was allowed after ≥ 6 cycles., Results: Forty-three subjects were treated across all dose levels. Maximal tolerated doses for the combined therapies observed in the phase 1B portion of the trial were: docetaxel 75 mg/m 2 , bevacizumab 15 mg/kg, and everolimus 2.5 mg. Maximal prostate-specific antigen decline ≥ 30% and ≥ 50% was achieved in 33 (79%) and 31 (74%) of patients, respectively. Best response by modified Response Evaluation Criteria In Solid Tumors criteria in 25 subjects with measurable disease at baseline included complete or partial response in 20 (80%) patients. The median progression-free and overall survival were 8.9 months (95% confidence interval, 7.4-10.6 months) and 21.9 months (95% confidence interval, 18.4-30.3 months), respectively. Hematologic toxicities were the most common treatment-related grade ≥ 3 adverse events including: febrile neutropenia (12; 28%), lymphopenia (12; 28%), leukocytes (10; 23%), neutrophils (9; 21%), and hemoglobin (2; 5%). Nonhematologic grade ≥ 3 adverse events included: hypertension (8; 19%), fatigue (3; 7%), pneumonia (3; 7%), and mucositis (4; 5%). There was 1 treatment-related death owing to neutropenic fever and pneumonia in a patient treated at dose level 3 despite dose modifications and prophylactic growth factor support., Conclusions: Docetaxel, bevacizumab, and everolimus can be safely administered in CRPC and demonstrate a significant level of anticancer activity, meeting the predetermined response criteria. However, any potential benefit of combined therapy must be balanced against increased risk for toxicities. Our results do not support the hypothesis that this combination of agents improves upon the results obtained with docetaxel monotherapy in an unselected population of chemotherapy-naive patients with CRPC., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: Registry studies and case-control studies have demonstrated that the risk of acute myocardial infarction (AMI) is increased following influenza infection. Small randomized trials, underpowered for clinical end points, indicate that future cardiovascular events can be reduced following influenza vaccination in patients with established cardiovascular disease. Influenza vaccination is recommended by international guidelines for patients with cardiovascular disease, but uptake is varying and vaccination is rarely prioritized during hospitalization for AMI., Methods/design: The Influenza vaccination After Myocardial Infarction (IAMI) trial is a double-blind, multicenter, prospective, registry-based, randomized, placebo-controlled, clinical trial. A total of 4,400 patients with ST-segment elevation myocardial infarction (STEMI) or non-STEMI undergoing coronary angiography will randomly be assigned either to in-hospital influenza vaccination or to placebo. Baseline information is collected from national heart disease registries, and follow-up will be performed using both registries and a structured telephone interview. The primary end point is a composite of time to all-cause death, a new AMI, or stent thrombosis at 1 year., Implications: The IAMI trial is the largest randomized trial to date to evaluate the effect of in-hospital influenza vaccination on death and cardiovascular outcomes in patients with STEMI or non-STEMI. The trial is expected to provide highly relevant clinical data on the efficacy of influenza vaccine as secondary prevention after AMI., (Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: The usefulness of aspirin to defend against cardiovascular disease in both primary and secondary settings is well recognized by the medical profession. Multiple studies also have found that daily aspirin significantly reduces cancer incidence and mortality. Despite these proven health benefits, aspirin use remains low among populations targeted by cardiovascular prevention guidelines. This article seeks to determine the long-term economic and population-health impact of broader use of aspirin by older Americans at higher risk for cardiovascular disease., Methods and Findings: We employ the Future Elderly Model, a dynamic microsimulation that follows Americans aged 50 and older, to project their lifetime health and spending under the status quo and in various scenarios of expanded aspirin use. The model is based primarily on data from the Health and Retirement Study, a large, representative, national survey that has been ongoing for more than two decades. Outcomes are chosen to provide a broad perspective of the individual and societal impacts of the interventions and include: heart disease, stroke, cancer, life expectancy, quality-adjusted life expectancy, disability-free life expectancy, and medical costs. Eligibility for increased aspirin use in simulations is based on the 2011-2012 questionnaire on preventive aspirin use of the National Health and Nutrition Examination Survey. These data reveal a large unmet need for daily aspirin, with over 40% of men and 10% of women aged 50 to 79 presenting high cardiovascular risk but not taking aspirin. We estimate that increased use by high-risk older Americans would improve national life expectancy at age 50 by 0.28 years (95% CI 0.08-0.50) and would add 900,000 people (95% CI 300,000-1,400,000) to the American population by 2036. After valuing the quality-adjusted life-years appropriately, Americans could expect $692 billion (95% CI 345-975) in net health benefits over that period., Conclusions: Expanded use of aspirin by older Americans with elevated risk of cardiovascular disease could generate substantial population health benefits over the next twenty years and do so very cost-effectively., Competing Interests: One of the authors [AM] is employed by a commercial company: Analysis Group, Inc. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Live cell imaging has improved our ability to measure phenotypic heterogeneity. However, bottlenecks in imaging and image processing often make it difficult to differentiate interesting biological behavior from technical artifact. Thus there is a need for new methods that improve data quality without sacrificing throughput. Here we present a 3-step workflow to improve dynamic phenotype measurements of heterogeneous cell populations. We provide guidelines for image acquisition, phenotype tracking, and data filtering to remove erroneous cell tracks using the novel Tracking Aberration Measure (TrAM). Our workflow is broadly applicable across imaging platforms and analysis software. By applying this workflow to cancer cell assays, we reduced aberrant cell track prevalence from 17% to 2%. The cost of this improvement was removing 15% of the well-tracked cells. This enabled detection of significant motility differences between cell lines. Similarly, we avoided detecting a false change in translocation kinetics by eliminating the true cause: varied proportions of unresponsive cells. Finally, by systematically seeking heterogeneous behaviors, we detected subpopulations that otherwise could have been missed, including early apoptotic events and pre-mitotic cells. We provide optimized protocols for specific applications and step-by-step guidelines for adapting them to a variety of biological systems.

Background: The increased availability of high-throughput datasets has revealed a need for reproducible and accessible analyses which can quantitatively relate molecular changes to phenotypic behavior. Existing tools for quantitative analysis generally require expert knowledge., Results: CellPD (cell phenotype digitizer) facilitates quantitative phenotype analysis, allowing users to fit mathematical models of cell population dynamics without specialized training. CellPD requires one input (a spreadsheet) and generates multiple outputs including parameter estimation reports, high-quality plots, and minable XML files. We validated CellPD's estimates by comparing it with a previously published tool (cellGrowth) and with Microsoft Excel's built-in functions. CellPD correctly estimates the net growth rate of cell cultures and is more robust to data sparsity than cellGrowth. When we tested CellPD's usability, biologists (without training in computational modeling) ran CellPD correctly on sample data within 30 min. To demonstrate CellPD's ability to aid in the analysis of high throughput data, we created a synthetic high content screening (HCS) data set, where a simulated cell line is exposed to two hypothetical drug compounds at several doses. CellPD correctly estimates the drug-dependent birth, death, and net growth rates. Furthermore, CellPD's estimates quantify and distinguish between the cytostatic and cytotoxic effects of both drugs-analyses that cannot readily be performed with spreadsheet software such as Microsoft Excel or without specialized computational expertise and programming environments., Conclusions: CellPD is an open source tool that can be used by scientists (with or without a background in computational or mathematical modeling) to quantify key aspects of cell phenotypes (such as cell cycle and death parameters). Early applications of CellPD may include drug effect quantification, functional analysis of gene knockout experiments, data quality control, minable big data generation, and integration of biological data with computational models.

Tumor progression results from a complex interplay between cellular heterogeneity, treatment response, microenvironment and heterocellular interactions. Existing approaches to characterize this interplay suffer from an inability to distinguish between multiple cell types, often lack environmental context, and are unable to perform multiplex phenotypic profiling of cell populations. Here we present a high-throughput platform for characterizing, with single-cell resolution, the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations both during standard growth and in response to multiple, co-occurring selective pressures. The speed of this platform enables a thorough investigation of the impacts of diverse selective pressures including genetic alterations, therapeutic interventions, heterocellular components and microenvironmental factors. The platform has been applied to both 2D and 3D culture systems and readily distinguishes between (1) cytotoxic versus cytostatic cellular responses; and (2) changes in morphological features over time and in response to perturbation. These important features can directly influence tumor evolution and clinical outcome. Our image-based approach provides a deeper insight into the cellular dynamics and heterogeneity of tumors (or other complex systems), with reduced reagents and time, offering advantages over traditional biological assays.

Introduction: Colorectal cancer (CRC) testing programs reduce mortality; however, approximately 40% of the recommended population who should undergo CRC testing does not. Early colon cancer detection in patient populations ineligible for testing, such as the elderly or those with significant comorbidities, could have clinical benefit. Despite many attempts to identify individual protein markers of this disease, little progress has been made. Targeted mass spectrometry, using multiple reaction monitoring (MRM) technology, enables the simultaneous assessment of groups of candidates for improved detection performance., Materials and Methods: A multiplex assay was developed for 187 candidate marker proteins, using 337 peptides monitored through 674 simultaneously measured MRM transitions in a 30-minute liquid chromatography-mass spectrometry analysis of immunodepleted blood plasma. To evaluate the combined candidate marker performance, the present study used 274 individual patient blood plasma samples, 137 with biopsy-confirmed colorectal cancer and 137 age- and gender-matched controls. Using 2 well-matched platforms running 5 days each week, all 274 samples were analyzed in 52 days., Results: Using one half of the data as a discovery set (69 disease cases and 69 control cases), the elastic net feature selection and random forest classifier assembly were used in cross-validation to identify a 15-transition classifier. The mean training receiver operating characteristic area under the curve was 0.82. After final classifier assembly using the entire discovery set, the 136-sample (68 disease cases and 68 control cases) validation set was evaluated. The validation area under the curve was 0.91. At the point of maximum accuracy (84%), the sensitivity was 87% and the specificity was 81%., Conclusion: These results have demonstrated the ability of simultaneous assessment of candidate marker proteins using high-multiplex, targeted-mass spectrometry to identify a subset group of CRC markers with significant and meaningful performance., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

The androgen receptor (AR) pathway plays a central role in prostate cancer (PCa) growth and progression and is a validated therapeutic target. In response to ligand binding AR translocates to the nucleus, though the molecular mechanism is not well understood. We therefore developed multimodal Image Correlation Spectroscopy (mICS) to measure anisotropic molecular motion across a live cell. We applied mICS to AR translocation dynamics to reveal its multimodal motion. By integrating fluorescence imaging methods we observed evidence for diffusion, confined movement, and binding of AR within both the cytoplasm and nucleus of PCa cells. Our findings suggest that in presence of cytoplasmic diffusion, the probability of AR crossing the nuclear membrane is an important factor in determining the AR distribution between cytoplasm and the nucleus, independent of functional microtubule transport. These findings may have implications for the future design of novel therapeutics targeting the AR pathway in PCa.

Competing Interests: Conflicts of Interest None

Therapeutic resistance arises as a result of evolutionary processes driven by dynamic feedback between a heterogeneous cell population and environmental selective pressures. Previous studies have suggested that mutations conferring resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) in non-small-cell lung cancer (NSCLC) cells lower the fitness of resistant cells relative to drug-sensitive cells in a drug-free environment. Here, we hypothesize that the local tumor microenvironment could influence the magnitude and directionality of the selective effect, both in the presence and absence of a drug. Using a combined experimental and computational approach, we developed a mathematical model of preexisting drug resistance describing multiple cellular compartments, each representing a specific tumor environmental niche. This model was parameterized using a novel experimental dataset derived from the HCC827 erlotinib-sensitive and -resistant NSCLC cell lines. We found that, in contrast to in the drug-free environment, resistant cells may hold a fitness advantage compared to parental cells in microenvironments deficient in oxygen and nutrients. We then utilized the model to predict the impact of drug and nutrient gradients on tumor composition and recurrence times, demonstrating that these endpoints are strongly dependent on the microenvironment. Our interdisciplinary approach provides a model system to quantitatively investigate the impact of microenvironmental effects on the evolutionary dynamics of tumor cells.

Background: Docetaxel-prednisone (DP) is an approved therapy for metastatic castration-resistant prostate cancer (mCRPC). Orteronel (TAK-700) is an investigational, selective, non-steroidal inhibitor of 17,20-lyase, a key enzyme in androgenic hormone production. This phase 1/2 study evaluated orteronel plus DP in mCRPC patients., Methods: Adult men with chemotherapy-naïve mCRPC, serum prostate-specific antigen (PSA) ≥5 ng/mL, and serum testosterone <50 ng/dL received oral orteronel 200 or 400 mg twice-daily (BID) in phase 1 to determine the recommended dose for phase 2, plus intravenous docetaxel 75 mg/m(2) every 3 weeks, and oral prednisone 5 mg BID. Phase 2 objectives included safety, pharmacokinetics, and efficacy., Results: In phase 1 (n = 6, orteronel 200 mg; n = 8, orteronel 400 mg), there was one dose-limiting toxicity of grade 3 febrile neutropenia at 400 mg BID. This dose was evaluated further in phase 2 (n = 23). After 4 cycles, 68, 59, and 23% of patients achieved ≥30, ≥50, and ≥90% PSA reductions, respectively; median best PSA response was -77%. Seven of 10 (70%) RECIST-evaluable patients achieved objective partial responses. Median time to PSA progression and radiographic disease progression was 6.7 and 12.9 months, respectively. Dehydroepiandrosterone-sulfate (DHEA-S) and testosterone levels were rapidly and durably reduced. Common adverse events were fatigue (78%), alopecia (61%), diarrhea (48%), nausea (43%), dysgeusia (39%), and neutropenia (39%). Orteronel and docetaxel pharmacokinetics were similar alone and in combination., Conclusions: Orteronel plus DP was tolerable, with substantial reductions in PSA, DHEA-S, and testosterone levels, and evidence for measurable disease responses.

Objective: Prostate cancer survivors have reported cognitive complaints following treatment, and these difficulties may be associated with survivors' ongoing cancer-related distress. Intolerance of uncertainty may exacerbate this hypothesized relationship by predisposing individuals to approach uncertain situations such as cancer survivorship in an inflexible and negative manner. We investigated whether greater cognitive complaints and higher intolerance of uncertainty would interact in their relation to more cancer-related distress symptoms., Methods: This cross-sectional, questionnaire-based study included 67 prostate cancer survivors who were 3 to 5 years post treatment. Hierarchical multiple regression analyses tested the extent to which intolerance of uncertainty, cognitive complaints, and their interaction were associated with cancer-related distress (measured with the Impact of Event Scale-Revised; IES-R) after adjusting for age, education, physical symptoms, and fear of cancer recurrence., Results: Intolerance of uncertainty was positively associated with the IES-R avoidance and hyperarousal subscales. More cognitive complaints were associated with higher scores on the IES-R hyperarousal subscale. The interaction of intolerance of uncertainty and cognitive complaints was significantly associated with IES-R intrusion, such that greater cognitive complaints were associated with greater intrusive thoughts in survivors high in intolerance of uncertainty but not those low in it., Conclusions: Prostate cancer survivors who report cognitive difficulties or who find uncertainty uncomfortable and unacceptable may be at greater risk for cancer-related distress, even 3 to 5 years after completing treatment. It may be beneficial to address both cognitive complaints and intolerance of uncertainty in psychosocial interventions., (Copyright © 2014 John Wiley & Sons, Ltd.)

Purpose: The androgen receptor pathway remains active in men with prostate cancer whose disease has progressed following surgical or medical castration. Orteronel (TAK-700) is an investigational, oral, nonsteroidal, selective, reversible inhibitor of 17,20-lyase, a key enzyme in the production of androgenic hormones., Experimental Design: We conducted a phase I/II study in men with progressive, chemotherapy-naïve, metastatic castration-resistant prostate cancer, and serum testosterone <50 ng/dL. In the phase I part, patients received orteronel 100 to 600 mg twice daily or 400 mg twice a day plus prednisone 5 mg twice a day. In phase II, patients received orteronel 300 mg twice a day, 400 mg twice a day plus prednisone, 600 mg twice a day plus prednisone, or 600 mg once a day without prednisone., Results: In phase I (n = 26), no dose-limiting toxicities were observed and 13 of 20 evaluable patients (65%) achieved ≥50% prostate-specific antigen (PSA) decline from baseline at 12 weeks. In phase II (n = 97), 45 of 84 evaluable patients (54%) achieved a ≥50% decline in PSA and at 12 weeks, substantial mean reductions from baseline in testosterone (-7.5 ng/dL) and dehydroepiandrosterone-sulfate (-45.3 μg/dL) were observed. Unconfirmed partial responses were reported in 10 of 51 evaluable phase II patients (20%). Decreases in circulating tumor cells were documented. Fifty-three percent of phase II patients experienced grade ≥3 adverse events irrespective of causality; most common were fatigue, hypokalemia, hyperglycemia, and diarrhea., Conclusions: 17,20-Lyase inhibition by orteronel was tolerable and results in declines in PSA and testosterone, with evidence of radiographic responses., (©2014 AACR)

Objective: Most men diagnosed with prostate cancer in the USA will survive. Of the many aspects of survivorship affected by prostate cancer, body image receives limited attention despite some indication that it may be important to men with the disease. The present study investigated how body image changes over time and the relations between changes in body image and quality of life (QOL) in men with prostate cancer., Methods: In a longitudinal design, patients (N = 74) completed questionnaires before treatment (T1) and at 1 month (T2) and 2 years (T3) following treatment completion., Results: Growth curve modeling indicated that there was no significant change over time in group-level body image scores. However, hormone treatment was associated with a negative trajectory of change over 2 years. Also, analysis of individual difference scores indicated that ≥50% of patients demonstrated change of at least 0.5 standard deviation between time points. Hierarchical regression indicated that change in body image between T1 and T2 was significantly associated with change in QOL between T1 and T3, while controlling for demographic variables, treatment, treatment-related functioning, and general and treatment-specific positive expectations. In predicting change in body image between T1 and T2, treatment-specific positive expectation was the only significant predictor., Conclusions: The present study demonstrates that body image is an important component of the prostate cancer experience. Findings suggest that body image has a meaningful association with QOL among prostate cancer survivors., (Copyright © 2012 John Wiley & Sons, Ltd.)

Background: This trial evaluated the safety, tolerability and maximum tolerated dose (MTD) of afatinib, a novel ErbB Family Blocker., Methods: In this open-label, dose-escalation Phase I study, afatinib was administered continuously, orally, once-daily for 28 days to patients with advanced or metastatic solid tumors. Dose escalation was performed in a 3 + 3 design, with a starting dose of 10 mg/day (d); doses were doubled for each successive cohort until the MTD was defined. The MTD cohort was expanded to a total of 19 patients. Incidence and severity of adverse events (AEs), antitumor activity and pharmacokinetics were assessed., Results: Thirty patients received at least one dose of afatinib. Twenty-nine patients were evaluable for response. Dose-limiting toxicities (DLTs) consisting of Grade 3 diarrhea were observed in two out of three patients treated at 60 mg/d. The MTD was determined at 40 mg/d. The most frequent treatment-related AEs were diarrhea and mucosal inflammation reported in 76.7% and 43.3% of patients respectively. Five patients had stable disease with a median progression-free survival of 111 days. No objective responses occurred. Pharmacokinetic data showed no deviation from dose-proportionality and steady-state was reached on Day 8 at the latest., Conclusions: Afatinib was well tolerated with manageable side effects when administered once-daily, continuously at a dose of 40 mg.

Background: Anterior gradient 2 (AGR2) is associated with metastatic progression in prostate cancer cells as well as other normal and malignant tissues. We investigated AGR2 expression in patients with metastatic prostate cancer., Methods: Blood was collected from 44 patients with metastatic prostate cancer separated as: castration sensitive prostate cancer (CSPC, n = 5); castration resistant prostate cancer (CRPC, n = 36); and neuroendocrine-predominate CRPC defined by PSA ≤ 1 ng/ml in the presence of wide-spread metastatic disease (NE-CRPC, n = 3). AGR2 mRNA levels were measured with RT-PCR in circulating tumor cell (CTC)-enriched peripheral blood. Plasma AGR2 levels were determined via ELISA assay. AGR2 expression was modulated in prostate cancer cell lines using plasmid and viral vectors., Results: AGR2 mRNA levels are elevated in CTCs and strongly correlated with CTC enumeration. Plasma AGR2 levels are elevated in all sub-groups. AGR2 levels vary independently to PSA and change in some patients in response to androgen-directed and other therapies. Plasma AGR2 levels are highest in the NE-CRPC sub-group. A correlation between AGR2, chromagranin A (CGA), and neuron-specific enolase (NSE) expression is demonstrated in prostate cancer cell lines., Conclusions: We conclude that AGR2 expression is elevated at the mRNA and protein level in patients with metastatic prostate cancer. In particular, we find that AGR2 expression is associated features consistent with neuroendocrine, or anaplastic, prostate cancer, exemplified by an aggressive clinical phenotype without elevation in circulating PSA levels. Further studies are warranted to explore the mechanistic and prognostic implications of AGR2 expression in this patient population., (Copyright © 2012 Wiley Periodicals, Inc.)

To investigate the transition from non-cancerous to metastatic from a physical sciences perspective, the Physical Sciences-Oncology Centers (PS-OC) Network performed molecular and biophysical comparative studies of the non-tumorigenic MCF-10A and metastatic MDA-MB-231 breast epithelial cell lines, commonly used as models of cancer metastasis. Experiments were performed in 20 laboratories from 12 PS-OCs. Each laboratory was supplied with identical aliquots and common reagents and culture protocols. Analyses of these measurements revealed dramatic differences in their mechanics, migration, adhesion, oxygen response, and proteomic profiles. Model-based multi-omics approaches identified key differences between these cells' regulatory networks involved in morphology and survival. These results provide a multifaceted description of cellular parameters of two widely used cell lines and demonstrate the value of the PS-OC Network approach for integration of diverse experimental observations to elucidate the phenotypes associated with cancer metastasis.

Clinical oncology is hampered by lack of tools to accurately assess a patient's response to pathway-targeted therapies. Serum and tumor cell surface proteins whose abundance, or change in abundance in response to therapy, differentiates patients responding to a therapy from patients not responding to a therapy could be usefully incorporated into tools for monitoring response. Here, we posit and then verify that proteomic discovery in in vitro tissue culture models can identify proteins with concordant in vivo behavior and further, can be a valuable approach for identifying tumor-derived serum proteins. In this study, we use stable isotope labeling of amino acids in culture (SILAC) with proteomic technologies to quantitatively analyze the gefitinib-related protein changes in a model system for sensitivity to EGF receptor (EGFR)-targeted tyrosine kinase inhibitors. We identified 3,707 intracellular proteins, 1,276 cell surface proteins, and 879 shed proteins. More than 75% of the proteins identified had quantitative information, and a subset consisting of 400 proteins showed a statistically significant change in abundance following gefitinib treatment. We validated the change in expression profile in vitro and screened our panel of response markers in an in vivo isogenic resistant model and showed that these were markers of gefitinib response and not simply markers of phospho-EGFR downregulation. In doing so, we also were able to identify which proteins might be useful as markers for monitoring response and which proteins might be useful as markers for a priori prediction of response., (©2012 AACR)

Purpose: Previous studies established the safety of continuous gefitinib 250 or 500 mg daily. It was postulated that a higher dose may have increased efficacy by inhibiting signaling in both the mitogen-activated protein kinase and AKT pathways. This study investigated the tolerability, pharmacokinetics, and antitumor activity of high-dose gefitinib in patients with refractory solid malignancies., Methods: Sequential cohorts received oral gefitinib once or twice-weekly, with dose escalation from 1,500 to 3,500 mg., Results: Twenty-three patients received gefitinib at seven dose levels (1,500, 2,000, 2,500, 3,000, and 3,500 mg once-weekly; 1,500 and 2,000 mg twice-weekly). Gefitinib was well tolerated, with no dose-limiting toxicities. The maximum tolerated dose was not reached. The most common gefitinib-related adverse events were nausea and diarrhea, vomiting, and rash. Pharmacokinetic data demonstrated no consistent increase in exposure to gefitinib with increasing dose across cohorts. Consequently, the study was stopped early and gefitinib 2,000 mg twice-weekly was the highest dose administered. One of eight patients with non-small-cell lung cancer achieved a partial response., Conclusions: Exposure to gefitinib did not increase consistently with increasing dose beyond gefitinib 1,500 mg once-weekly or twice-weekly. These data do not support further evaluation of gefitinib at high-dose schedules.

Many initially successful anticancer therapies lose effectiveness over time, and eventually, cancer cells acquire resistance to the therapy. Acquired resistance remains a major obstacle to improving remission rates and achieving prolonged disease-free survival. Consequently, novel approaches to overcome or prevent resistance are of significant clinical importance. There has been considerable interest in treating non-small cell lung cancer (NSCLC) with combinations of EGFR-targeted therapeutics (e.g., erlotinib) and cytotoxic therapeutics (e.g., paclitaxel); however, acquired resistance to erlotinib, driven by a variety of mechanisms, remains an obstacle to treatment success. In about 50% of cases, resistance is due to a T790M point mutation in EGFR, and T790M-containing cells ultimately dominate the tumor composition and lead to tumor regrowth. We employed a combined experimental and mathematical modeling-based approach to identify treatment strategies that impede the outgrowth of primary T790M-mediated resistance in NSCLC populations. Our mathematical model predicts the population dynamics of mixtures of sensitive and resistant cells, thereby describing how the tumor composition, initial fraction of resistant cells, and degree of selective pressure influence the time until progression of disease. Model development relied upon quantitative experimental measurements of cell proliferation and death using a novel microscopy approach. Using this approach, we systematically explored the space of combination treatment strategies and demonstrated that optimally timed sequential strategies yielded large improvements in survival outcome relative to monotherapies at the same concentrations. Our investigations revealed regions of the treatment space in which low-dose sequential combination strategies, after preclinical validation, may lead to a tumor reduction and improved survival outcome for patients with T790M-mediated resistance.

Advanced prostate cancer can progress to systemic metastatic tumors, which are generally androgen insensitive and ultimately lethal. Here, we report a comprehensive genomic survey for somatic events in systemic metastatic prostate tumors using both high-resolution copy number analysis and targeted mutational survey of 3508 exons from 577 cancer-related genes using next generation sequencing. Focal homozygous deletions were detected at 8p22, 10q23.31, 13q13.1, 13q14.11, and 13q14.12. Key genes mapping within these deleted regions include PTEN, BRCA2, C13ORF15, and SIAH3. Focal high-level amplifications were detected at 5p13.2-p12, 14q21.1, 7q22.1, and Xq12. Key amplified genes mapping within these regions include SKP2, FOXA1, and AR. Furthermore, targeted mutational analysis of normal-tumor pairs has identified somatic mutations in genes known to be associated with prostate cancer including AR and TP53, but has also revealed novel somatic point mutations in genes including MTOR, BRCA2, ARHGEF12, and CHD5. Finally, in one patient where multiple independent metastatic tumors were available, we show common and divergent somatic alterations that occur at both the copy number and point mutation level, supporting a model for a common clonal progenitor with metastatic tumor-specific divergence. Our study represents a deep genomic analysis of advanced metastatic prostate tumors and has revealed candidate somatic alterations, possibly contributing to lethal prostate cancer.

Tumor-derived, circulating proteins are potentially useful as biomarkers for detection of cancer, for monitoring of disease progression, regression and recurrence, and for assessment of therapeutic response. Here we interrogated how a protein's stability, cellular localization, and abundance affect its observability in blood by mass-spectrometry-based proteomics techniques. We performed proteomic profiling on tumors and plasma from two different xenograft mouse models. A statistical analysis of this data revealed protein properties indicative of the detection level in plasma. Though 20% of the proteins identified in plasma were tumor-derived, only 5% of the proteins observed in the tumor tissue were found in plasma. Both intracellular and extracellular tumor proteins were observed in plasma; however, after normalizing for tumor abundance, extracellular proteins were seven times more likely to be detected. Although proteins that were more abundant in the tumor were also more likely to be observed in plasma, the relationship was nonlinear: Doubling the spectral count increased detection rate by only 50%. Many secreted proteins, even those with relatively low spectral count, were observed in plasma, but few low abundance intracellular proteins were observed. Proteins predicted to be stable by dipeptide composition were significantly more likely to be identified in plasma than less stable proteins. The number of tryptic peptides in a protein was not significantly related to the chance of a protein being observed in plasma. Quantitative comparison of large versus small tumors revealed that the abundance of proteins in plasma as measured by spectral count was associated with the tumor size, but the relationship was not one-to-one; a 3-fold decrease in tumor size resulted in a 16-fold decrease in protein abundance in plasma. This study provides quantitative support for a tumor-derived marker prioritization strategy that favors secreted and stable proteins over all but the most abundant intracellular proteins.

Background: Coding mutations in the AR (androgen receptor) gene have been identified in tissue samples from patients with advanced prostate cancer and represent a possible mechanism underlying the development of castration-resistant prostate cancer (CRPC). There is a paucity of tumor-derived tissue available for molecular studies of CRPC patients. Circulating tumor cells (CTCs) in the blood of CRPC patients represent a possible avenue for interrogating the disease of such patients., Methods: Circulating tumor cells were captured with the CellSearch Circulating Tumor Cell (CTC) Kit and with the CellSearch Profile Kit plus Qiagen's AllPrep DNA/RNA Micro Kit for the measurement of the CTC count per 7.5 mL of blood and for the isolation of nucleic acids, respectively. The AR gene was amplified by the PCR, and mutation status and relative abundance were analyzed by applying Transgenomic's WAVE denaturing HPLC technology followed by direct sequencing., Results: AR mutations were detected in 20 of 35 CRPC patients; 19 missense mutations, 2 silent mutations, 5 deletions, and 1 insertion were observed. The relative abundance of the mutants in the amplified products ranged from 5% to 50%. Many of the AR mutations were identified in surgical biopsies or at autopsy and were associated with resistance to androgen-directed therapies., Conclusions: AR mutations can be identified in CTC-enriched peripheral blood samples from CRPC patients. This approach has the potential to open new perspectives in understanding CTCs and the mechanisms for tumor progression and metastasis in CRPC.

Background: Previously, we identified Beta-2-microglobulin (beta2M) as an androgen-regulated secreted protein elevated in the serum of prostate cancer patients. In this study, we explore an interaction between beta2M expression, prostate cancer tissue, and the androgen signaling axis., Methods: beta2M expression in relation to clinical and pathologic variables was examined in a tissue microarray representing specimens obtained at the time of radical prostatectomy. Viral vectors were designed to down-regulate beta2M expression, and the effects on androgen-dependent growth, transcriptional regulation, and androgen receptor recruitment was investigated in human prostate cancer cell lines., Results: Variation in beta2M expression in human prostate cancer is associated with characteristics of clinically aggressive disease such as high tumor grade. Knockdown of beta2M expression in human prostate cancer cells resulted in selective defects in androgen-dependent events including growth, gene regulation, and chromatin assembly., Conclusions: beta2M expression may provide prognostic information in patients treated with surgery for prostate cancer. Targeting beta2M expression or activity may represent a new and important mechanism to manipulate the androgen signaling axis in patients with prostate cancer., (2010. (c) 2010 Wiley-Liss, Inc.)

Epidermal growth factor receptor (EGFR) plays a critical role in oncogenesis, which makes it an attractive target for pharmacologic inhibition. Yet, EGFR inhibition with tyrosine kinase inhibitors (TKI) does not result in a measurable and sustainable clinical benefit in a vast majority of tumors. This emphasizes the need for further investigations into resistance mechanisms against EGFR-TKIs. We previously reported the generation of an in vivo adenocarcinoma model of EGFR-TKI-acquired resistance that was devoid of the known mechanisms of resistance. Using this same xenograft model, we now show that the tumor stroma plays an important role in limiting responsiveness to EGFR-TKIs. EGFR-TKI-resistant tumors display increased surface expression of CD44(hi)/CD24(lo) and markers of epithelial to mesenchymal transition (EMT), SNAI1, and N-cadherin. An in vivo green fluorescent protein-tagging approach reveals that the tumor stroma of the EGFR-TKI-resistant tumors is distinct in that 24% of its cancer-associated fibroblast (CAF) population is composed of EMT-derived tumor cells that represent the in vivo escape from EGFR-TKIs. We further show that EMT subpopulation-harboring CAFs isolated from the EGFR-TKI-resistant tumors are tumorigenic and express the biomarker of gefitinib resistance, epithelial membrane protein-1. Finally, we provide evidence that paracrine factors secreted from the EGFR-TKI-resistant CAFs mitigate the EGFR-TKI-mediated blockade of pEGFR and pMAPK in cocultured tumor cells, regardless of their EGFR mutational status. This is the first demonstration that the tumor stroma is modified with acquisition of EGFR-TKI resistance and that it further contributes in promoting drug resistance.

The human epidermal receptor (HER) axis consists of a dynamic, interconnected family of receptors that make critical contributions to a number of malignancies. Therapeutics targeting epidermal growth factor receptor (EGFR) are unable to effectively inhibit tumor growth in a majority of cases. These tumors are assumed to possess primary resistance to anti-EGFR therapies, but the consequence of inhibiting EGFR in these tumors is unclear. We established isogenic cell lines by prolonged gefitinib treatment at concentrations that are in excess of that which is required for complete EGFR kinase inhibition but only minimally affected growth. Subsequently, we monitored the ligand-dependent HER profiles based on receptor expression, phosphorylation, and dimerization in conjunction with measurements of cellular susceptibility to gefitinib. Chronic EGFR kinase inhibition rapidly switched the HER network from dependence on EGFR to HER2. However, both receptors activated the critical signaling proteins AKT and mitogen-activated protein kinase, and in both cases, HER3 was the common association partner. Remarkably, the switch in receptor dimers caused diminished susceptibility to EGFR-targeted inhibitors gefitinib and cetuximab but acquired susceptibility to the HER2-targeted inhibitor pertuzumab. Overall, our study indicates that the EGFR pathway is responsive to EGFR inhibiting therapies that are not dependent on EGFR for their growth and survival, thus challenging the current definition of primary therapeutic resistance. Furthermore, EGFR kinase inhibition induces HER kinase receptors to engage in alternative dimerization that can ultimately influence therapeutic selection and responsiveness.

The serine/threonine family of Pim kinases function as oncogenes and have been implicated in prostate cancer progression, particularly in hormone-refractory prostate disease, as a result of their antiapoptotic function. In this study, we used a pharmacologic inhibitor targeting the Pim family members, SGI-1776, to determine whether modulation of Pim kinase activity could alter prostate cancer cell survival and modulate chemotherapy resistance. Extensive biochemical characterization of SGI-1776 confirmed its specificity for the three isoforms of the Pim family. Treatment of prostate cancer cells with SGI-1776 resulted in a dose-dependent reduction in phosphorylation of known Pim kinase substrates that are involved in cell cycle progression and apoptosis (p21(Cip1/WAF1) and Bad). Consequently, SGI-1776 compromised overall cell viability by inducing G(1) cell cycle arrest and triggering apoptosis. Overexpression of recombinant Pim-1 markedly increased sensitivity of SGI-1776-mediated prostate cancer cell apoptosis and p21(Cip1/WAF1) phosphorylation inhibition, reinforcing the specificity of SGI-1776. An additional cytotoxic effect was observed when SGI-1776 was combined with taxane-based chemotherapy agents. SGI-1776 was able to reduce cell viability in a multidrug resistance 1 protein-based taxane-refractory prostate cancer cell line. In addition, SGI-1776 treatment was able to resensitize chemoresistant cells to taxane-based therapies by inhibiting multidrug resistance 1 activity and inducing apoptosis. These findings support the idea that inhibiting Pim kinases, in combination with a chemotherapeutic agent, could play an important role in prostate cancer treatment by targeting the clinical problem of chemoresistance.

Clinical outcome prognostication in oncology is a guiding principle in therapeutic choice. A wealth of qualitative empirical evidence links disease progression with tumor morphology, histopathology, invasion, and associated molecular phenomena. However, the quantitative contribution of each of the known parameters in this progression remains elusive. Mathematical modeling can provide the capability to quantify the connection between variables governing growth, prognosis, and treatment outcome. By quantifying the link between the tumor boundary morphology and the invasive phenotype, this work provides a quantitative tool for the study of tumor progression and diagnostic/prognostic applications. This establishes a framework for monitoring system perturbation towards development of therapeutic strategies and correlation to clinical outcome for prognosis.

Objective: Research suggests that cancer patients who are more involved in treatment decision-making (TDM) report better quality of life following treatment. This study examines the association and possible mechanisms between prostate cancer patient's discussions about TDM and affect following treatment. We predicted that the length of time patients spent discussing treatment options with social networks and physicians prior to treatment would predict emotional adjustment after treatment. We further predicted that cognitive processing, coping, and patient understanding of treatment options would mediate this association., Methods: Fifty-seven patients completed questionnaires prior to treatment and at 1 and 6 months following treatment completion., Results: Findings from the present study suggest that discussing treatment options with others, prior to beginning treatment for prostate cancer, significantly contributed to improvements in affect 1 and 6 months following treatment. Residualized regression analyses indicated that discussing treatment options with patient's social networks predicted a decrease in negative affect 1 and 6 months following treatment, while discussions with physicians predicted an increase in positive affect 1 month following treatment. Patients who spent more time discussing treatment options with family and friends also reported greater pre-treatment social support and emotional expression. Mediation analyses indicated that these coping strategies facilitated cognitive processing (as measured by a decrease in intrusive thoughts) and that cognitive processing predicted improvement in affect., Conclusions: Greater time spent talking with family and friends about treatment options may provide opportunities for patients to cope with their cancer diagnosis and facilitate cognitive processing, which may improve patient distress over time., (Copyright (c) 2008 John Wiley & Sons Ltd.)

Mass spectrometry-based proteomics experiments have become an important tool for studying biological systems. Identifying the proteins in complex mixtures by assigning peptide fragmentation spectra to peptide sequences is an important step in the proteomics process. The 1-2 ppm mass-accuracy of hybrid instruments, like the LTQ-FT, has been cited as a key factor in their ability to identify a larger number of peptides with greater confidence than competing instruments. However, in replicate experiments of an 18-protein mixture, we note parent masses deviate 171 ppm, on average, for ion-trap data directed identifications and 8 ppm, on average, for preview Fourier transform (FT) data directed identifications. These deviations are neither caused by poor calibration nor by excessive ion-loading and are most likely due to errors in parent mass estimation. To improve these deviations, we introduce msPrefix, a program to re-estimate a peptide's parent mass from an associated high-accuracy full-scan survey spectrum. In 18-protein mixture experiments, msPrefix parent mass estimates deviate only 1 ppm, on average, from the identified peptides. In a cell lysate experiment searched with a tolerance of 50 ppm, 2295 peptides were confidently identified using native data and 4560 using msPrefixed data. Likewise, in a plasma experiment searched with a tolerance of 50 ppm, 326 peptides were identified using native data and 1216 using msPrefixed data. msPrefix is also able to determine which MS/MS spectra were possibly derived from multiple precursor ions. In complex mixture experiments, we demonstrate that more than 50% of triggered MS/MS may have had multiple precursor ions and note that spectra with multiple candidate ions are less likely to result in an identification using TANDEM. These results demonstrate integration of msPrefix into traditional shotgun proteomics workflows significantly improves identification results.

Prostate cancer continues to be a major cause of morbidity and mortality in men around the world. The field of prostate cancer research continues to be hindered by the lack of relevant preclinical models to study tumorigenesis and to further development of effective prevention and therapeutic strategies. The Prostate Cancer Foundation held a Prostate Cancer Models Working Group (PCMWG) Summit on August 6th and 7th, 2007 to address these issues. The PCMWG reviewed the state of prostate cancer preclinical models and identified the current limitations of cell line, xenograft and genetically engineered mouse models that have hampered the transition of scientific findings from these models to human clinical trials. In addition the PCMWG identified administrative issues that inhibit the exchange of models and impede greater interactions between academic centers and these centers with industry. The PCMWG identified potential solutions for discovery bottlenecks that include: (1) insufficient number of models with insufficient molecular and biologic diversity to reflect human cancer, (2) a lack of understanding of the molecular events that define tumorigenesis, (3) a lack of tools for studying tumor-host interactions, (4) difficulty in accessing model systems across institutions, and (5) addressing why preclinical studies appear not to be predictive of human clinical trials. It should be possible to apply the knowledge gained molecular and epigenetic studies to develop new cell lines and models that mimic progressive and fatal prostate cancer and ultimately improve interventions.

Background: Docetaxel is the standard first-line agent for the treatment of androgen-independent prostate cancer (AIPC). The combination of docetaxel with molecularly targeted therapies may offer the potential to increase the efficacy and decrease the toxicity of cytotoxic chemotherapy for prostate cancer. Previous studies demonstrate activation of the human epidermal growth factor receptor (EGFR) in prostate cancer. Erlotinib is a specific inhibitor of the tyrosine-kinase activity of EGFR. The goal of this study is to determine the anti-cancer activity docetaxel combined with erlotinib for the treatment of elderly subjects with AIPC., Methods: This is a multi-institutional Phase II study in patients with histologically confirmed adenocarcinoma of the prostate and age > or = 65 years. Patients were requred to have progressive disease despite androgen-deprivation therapy as determined by: (1) measurable lesions on cross-sectional imaging; (2) metastatic disease by radionucleotide bone imaging; or (3) elevated prostate specific antigen (PSA). Treatment cycles consisted of docetaxel 60 mg/m2 IV on day 1 and erlotinib 150 mg PO days 1-21. Patients with responding or stable disease after 9 cycles were eligible to continue on erlotinib alone as maintenance therapy., Results: Characteristics of 22 patients enrolled included: median age 73.5 years (range, 65-80); median Karnofsky Performance Status 90 (range 70-100); median hemoglobin 12.1 g/dl (range, 10.0-14.3); median PSA 218.3 ng/ml (range, 9-5754). A median of 6 treatment cycles were delivered per patient (range 1-17). No objective responses were observed in 8 patients with measurable lesions (0%, 95% CI 0-31%). Bone scan improvement and PSA decline was seen in 1 patient (5%, 95% CI 0.1-25%). Five of 22 patients experienced > or = 50 % decline in PSA (23%, 95% CI 8-45%). Hematologic toxicity included grade 3 neutropenia in 9 patients and neutropenic fever in 2 patients. Common non-hematologic toxicities (> or = grade 3) included fatigue, anorexia, and diarrhea., Conclusion: Docetaxel/erlotinib can be delivered safely in elderly patients with AIPC. Anti-cancer disease activity appears generally comparable to docetaxel when used as monotherapy. Hematologic and non-hematologic toxicity may be increased over docetaxel monotherapy. Prospective randomized studies would be required to determine if the toxicity of docetaxel and erlotinib justifies its use in this setting.