An Inflection Point in Cancer Protein Biomarkers: What was and What's Next.

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.

High-throughput microscopy reveals the impact of multifactorial environmental perturbations on colorectal cancer cell growth.

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.

Paradoxical androgen receptor regulation by small molecule enantiomers.

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.

Phase 2 trial of monoamine oxidase inhibitor phenelzine in biochemical recurrent prostate cancer.

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.

Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains.

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.

Deep learned tissue "fingerprints" classify breast cancers by ER/PR/Her2 status from H&E images.

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.

Monitoring dynamic cytotoxic chemotherapy response in castration-resistant prostate cancer using plasma cell-free DNA (cfDNA).

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.

Correlating nuclear morphometric patterns with estrogen receptor status in breast cancer pathologic specimens.

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.

Future cancer research priorities in the USA: a Lancet Oncology Commission.

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.

Safety and Efficacy of Docetaxel, Bevacizumab, and Everolimus for Castration-resistant Prostate Cancer (CRPC).

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/m2, 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/m2, 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.

JUN-Mediated Downregulation of EGFR Signaling Is Associated with Resistance to Gefitinib in EGFR-mutant NSCLC Cell Lines.

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 IC50 from 49 nmol/L to 8 µmol/L (P < 0.001). Downregulation of JUN expression through shRNA resensitized HCC827 cells to gefitinib (IC50 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.

Design and rationale for the Influenza vaccination After Myocardial Infarction (IAMI) trial. A registry-based randomized clinical trial.

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.

The Long-Term Benefits of Increased Aspirin Use by At-Risk Americans Aged 50 and Older.

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.

Single cell dynamic phenotyping.

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.

Quantifying differences in cell line population dynamics using CellPD.

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.

A high-content image-based method for quantitatively studying context-dependent cell population dynamics.

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.

A Plasma-Based Protein Marker Panel for Colorectal Cancer Detection Identified by Multiplex Targeted Mass Spectrometry.

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.