COVID-19-neutralizing antibodies predict disease severity and survival.

Coronavirus disease 2019 (COVID-19) exhibits variable symptom severity ranging from asymptomatic to life-threatening, yet the relationship between severity and the humoral immune response is poorly understood. We examined antibody responses in 113 COVID-19 patients and found that severe cases resulting in intubation or death exhibited increased inflammatory markers, lymphopenia, pro-inflammatory cytokines, and high anti-receptor binding domain (RBD) antibody levels. Although anti-RBD immunoglobulin G (IgG) levels generally correlated with neutralization titer, quantitation of neutralization potency revealed that high potency was a predictor of survival. In addition to neutralization of wild-type SARS-CoV-2, patient sera were also able to neutralize the recently emerged SARS-CoV-2 mutant D614G, suggesting cross-protection from reinfection by either strain. However, SARS-CoV-2 sera generally lacked cross-neutralization to a highly homologous pre-emergent bat coronavirus, WIV1-CoV, which has not yet crossed the species barrier. These results highlight the importance of neutralizing humoral immunity on disease progression and the need to develop broadly protective interventions to prevent future coronavirus pandemics.

Rapid tumor DNA analysis of cerebrospinal fluid accelerates treatment of central nervous system lymphoma.

ABSTRACT: Delays and risks associated with neurosurgical biopsies preclude timely diagnosis and treatment of central nervous system (CNS) lymphoma and other CNS neoplasms. We prospectively integrated targeted rapid genotyping of cerebrospinal fluid (CSF) into the evaluation of 70 patients with CNS lesions of unknown cause. Participants underwent genotyping of CSF-derived DNA using a quantitative polymerase chain reaction-based approach for parallel detection of single-nucleotide variants in the MYD88, TERT promoter, IDH1, IDH2, BRAF, and H3F3A genes within 80 minutes of sample acquisition. Canonical mutations were detected in 42% of patients with neoplasms, including cases of primary and secondary CNS lymphoma, glioblastoma, IDH-mutant brainstem glioma, and H3K27M-mutant diffuse midline glioma. Genotyping results eliminated the need for surgical biopsies in 7 of 33 cases (21.2%) of newly diagnosed neoplasms, resulting in significantly accelerated initiation of disease-directed treatment (median, 3 vs 12 days; P = .027). This assay was then implemented in a Clinical Laboratory Improvement Amendments environment, with 2-day median turnaround for diagnosis of CNS lymphoma from 66 patients across 4 clinical sites. Our study prospectively demonstrates that targeted rapid CSF genotyping influences oncologic management for suspected CNS tumors.

Virus-Induced Acute Respiratory Distress Syndrome Causes Cardiomyopathy Through Eliciting Inflammatory Responses in the Heart.

BACKGROUND: Viral infections can cause acute respiratory distress syndrome (ARDS), systemic inflammation, and secondary cardiovascular complications. Lung macrophage subsets change during ARDS, but the role of heart macrophages in cardiac injury during viral ARDS remains unknown. Here we investigate how immune signals typical for viral ARDS affect cardiac macrophage subsets, cardiovascular health, and systemic inflammation. METHODS: We assessed cardiac macrophage subsets using immunofluorescence histology of autopsy specimens from 21 patients with COVID-19 with SARS-CoV-2-associated ARDS and 33 patients who died from other causes. In mice, we compared cardiac immune cell dynamics after SARS-CoV-2 infection with ARDS induced by intratracheal instillation of Toll-like receptor ligands and an ACE2 (angiotensin-converting enzyme 2) inhibitor. RESULTS: In humans, SARS-CoV-2 increased total cardiac macrophage counts and led to a higher proportion of CCR2+ (C-C chemokine receptor type 2 positive) macrophages. In mice, SARS-CoV-2 and virus-free lung injury triggered profound remodeling of cardiac resident macrophages, recapitulating the clinical expansion of CCR2+ macrophages. Treating mice exposed to virus-like ARDS with a tumor necrosis factor α-neutralizing antibody reduced cardiac monocytes and inflammatory MHCIIlo CCR2+ macrophages while also preserving cardiac function. Virus-like ARDS elevated mortality in mice with pre-existing heart failure. CONCLUSIONS: Our data suggest that viral ARDS promotes cardiac inflammation by expanding the CCR2+ macrophage subset, and the associated cardiac phenotypes in mice can be elicited by activating the host immune system even without viral presence in the heart.

Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models.

This work puts forth and demonstrates the utility of a reporting framework for collecting and evaluating annotations of medical images used for training and testing artificial intelligence (AI) models in assisting detection and diagnosis. AI has unique reporting requirements, as shown by the AI extensions to the Consolidated Standards of Reporting Trials (CONSORT) and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklists and the proposed AI extensions to the Standards for Reporting Diagnostic Accuracy (STARD) and Transparent Reporting of a Multivariable Prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklists. AI for detection and/or diagnostic image analysis requires complete, reproducible, and transparent reporting of the annotations and metadata used in training and testing data sets. In an earlier work by other researchers, an annotation workflow and quality checklist for computational pathology annotations were proposed. In this manuscript, we operationalize this workflow into an evaluable quality checklist that applies to any reader-interpreted medical images, and we demonstrate its use for an annotation effort in digital pathology. We refer to this quality framework as the Collection and Evaluation of Annotations for Reproducible Reporting of Artificial Intelligence (CLEARR-AI).

Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research.

A growing body of research supports stromal tumour-infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin-stained slides. Artificial intelligence/machine-learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment. Visual TIL assessment may suffer from significant interobserver variability. To improve interobserver agreement, regulatory science experts at the US Food and Drug Administration partnered with academic pathologists internationally to create a freely available online continuing medical education (CME) course to train pathologists in assessing breast cancer stromal TILs using an interactive format with expert commentary. Here we describe and provide a user guide to this CME course, whose content was designed to improve pathologist accuracy in scoring breast cancer TILs. We also suggest subsequent steps to translate knowledge into clinical practice with proficiency testing.

Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects.

Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging from the prospect of automation of routine diagnostic tasks to the discovery of new prognostic and predictive biomarkers from tissue morphology. Despite the promising potential of computational pathology, its integration in clinical settings has been limited by a range of obstacles including operational, technical, regulatory, ethical, financial, and cultural challenges. Here, we focus on the pathologists' perspective of computational pathology: we map its current translational research landscape, evaluate its clinical utility, and address the more common challenges slowing clinical adoption and implementation. We conclude by describing contemporary approaches to drive forward these techniques. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Initial interactions with the FDA on developing a validation dataset as a medical device development tool.

Quantifying tumor-infiltrating lymphocytes (TILs) in breast cancer tumors is a challenging task for pathologists. With the advent of whole slide imaging that digitizes glass slides, it is possible to apply computational models to quantify TILs for pathologists. Development of computational models requires significant time, expertise, consensus, and investment. To reduce this burden, we are preparing a dataset for developers to validate their models and a proposal to the Medical Device Development Tool (MDDT) program in the Center for Devices and Radiological Health of the U.S. Food and Drug Administration (FDA). If the FDA qualifies the dataset for its submitted context of use, model developers can use it in a regulatory submission within the qualified context of use without additional documentation. Our dataset aims at reducing the regulatory burden placed on developers of models that estimate the density of TILs and will allow head-to-head comparison of multiple computational models on the same data. In this paper, we discuss the MDDT preparation and submission process, including the feedback we received from our initial interactions with the FDA and propose how a qualified MDDT validation dataset could be a mechanism for open, fair, and consistent measures of computational model performance. Our experiences will help the community understand what the FDA considers relevant and appropriate (from the perspective of the submitter), at the early stages of the MDDT submission process, for validating stromal TIL density estimation models and other potential computational models. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

A vision to the future: value-based laboratory medicine.

The ultimate goal of value-based laboratory medicine is maximizing the effectiveness of laboratory tests in improving patient outcomes, optimizing resources and minimizing unnecessary costs. This approach abandons the oversimplified notion of test volume and cost, in favor of emphasizing the clinical utility and quality of diagnostic tests in the clinical decision-making. Several key elements characterize value-based laboratory medicine, which can be summarized in some basic concepts, such as organization of in vitro diagnostics (including appropriateness, integrated diagnostics, networking, remote patient monitoring, disruptive innovations), translation of laboratory data into clinical information and measurable outcomes, sustainability, reimbursement, ethics (e.g., patient empowerment and safety, data protection, analysis of big data, scientific publishing). Education and training are also crucial, along with considerations for the future of the profession, which will be largely influenced by advances in automation, information technology, artificial intelligence, and regulations concerning in vitro diagnostics. This collective opinion paper, composed of summaries from presentations given at the two-day European Federation of Laboratory Medicine (EFLM) Strategic Conference "A vision to the future: value-based laboratory medicine" (Padova, Italy; September 23-24, 2024), aims to provide a comprehensive overview of value-based laboratory medicine, projecting the profession into a more clinically effective and sustainable future.

Dimensions of health data Integrity.

Health data integrity, as an emergent concept, stands to reshape the lifecycle of data-driven healthcare and research, ensuring a shared commitment to ethical practices and improved patient care.

Real-world data for precision cancer medicine-A European perspective.

Leveraging real-world data (RWD) for drug access is necessary to overcome a key challenge of modern precision oncology: tackling numerous low-prevalence oncogenic mutations across cancers. Withholding a potentially active medication in patients with rare mutations for the sake of control chemotherapy or "best" supportive care is neither practicable nor ethically justifiable anymore, particularly as RWD could meanwhile be used instead, according to scientific principles outlined by the US Food and Drug Administration, European Medicines Agency and other stakeholders. However, practical implementation varies, with occasionally opposite recommendations based on the same evidence in different countries. In the face of growing need for precision drugs, more transparency of evaluation, a priori availability of guidance for the academia and industry, as well as a harmonized framework for health technology assessment across the European Union (EU) are imperative. These could in turn trigger infrastructural changes in national and pan-European registries, cancer management guidelines (e.g., frequency of routine radiologic restaging, inclusion of patient-reported outcomes), and the health data space, to ensure conformity with declared standards and facilitate extraction of RWD sets (including patient-level data) suitable for approval and pricing with minimal effort. For an EU-wide unification of precision cancer medicine, collective negotiation of drug supply contracts and funding solidarity would additionally be required to handle the financial burden. According to experience from pivotal European programs, off-label use could potentially also be harmonized across EU-states to accelerate availability of novel drugs, streamline collection of valuable RWD, and mitigate related costs through wider partnerships with pharmaceutical companies.

Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling.

The complexity of diagnostic (surgical) pathology has increased substantially over the last decades with respect to histomorphological and molecular profiling. Pathology has steadily expanded its role in tumor diagnostics and beyond from disease entity identification via prognosis estimation to precision therapy prediction. It is therefore not surprising that pathology is among the disciplines in medicine with high expectations in the application of artificial intelligence (AI) or machine learning approaches given their capabilities to analyze complex data in a quantitative and standardized manner to further enhance scope and precision of diagnostics. While an obvious application is the analysis of histological images, recent applications for the analysis of molecular profiling data from different sources and clinical data support the notion that AI will enhance both histopathology and molecular pathology in the future. At the same time, current literature should not be misunderstood in a way that pathologists will likely be replaced by AI applications in the foreseeable future. Although AI will transform pathology in the coming years, recent studies reporting AI algorithms to diagnose cancer or predict certain molecular properties deal with relatively simple diagnostic problems that fall short of the diagnostic complexity pathologists face in clinical routine. Here, we review the pertinent literature of AI methods and their applications to pathology, and put the current achievements and what can be expected in the future in the context of the requirements for research and routine diagnostics.

Slide-to-Slide Tissue Transfer and Array Assembly From Limited Samples for Comprehensive Molecular Profiling.

Tissue microarrays (TMA) have become an important tool in high-throughput molecular profiling of tissue samples in the translational research setting. Unfortunately, high-throughput profiling in small biopsy specimens or rare tumor samples (eg, orphan diseases or unusual tumors) is often precluded owing to limited amounts of tissue. To overcome these challenges, we devised a method that allows tissue transfer and construction of TMAs from individual 2- to 5-µm sections for subsequent molecular profiling. We named the technique slide-to-slide (STS) transfer, and it requires a series of chemical exposures (so-called xylene-methacrylate exchange) in combination with rehydrated lifting, microdissection of donor tissues into multiple small tissue fragments (methacrylate-tissue tiles), and subsequent remounting on separate recipient slides (STS array slide). We developed the STS technique by assessing the efficacy and analytical performance using the following key metrics: (a) dropout rate, (b) transfer efficacy, (c) success rates using different antigen-retrieval methods, (d) success rates of immunohistochemical stains, (e) fluorescent in situ hybridization success rates, and (f) DNA and (g) RNA extraction yields from single slides, which all functioned appropriately. The dropout rate ranged from 0.7% to 6.2%; however, we applied the same STS technique successfully to fill these dropouts ("rescue" transfer). Hematoxylin and eosin assessment of donor slides confirmed a transfer efficacy of >93%, depending on the size of the tissue (range, 76%-100%). Fluorescent in situ hybridization success rates and nucleic acid yields were comparable with those of traditional workflows. In this study, we present a quick, reliable, and cost-effective method that offers the key advantages of TMAs and other molecular techniques-even when tissue is sparse. The perspectives of this technology in biomedical sciences and clinical practice are promising, given that it allows laboratories to create more data with less tissue.

Clinical Implications and Treatment Strategies for ESR1 Fusions in Hormone Receptor-Positive Metastatic Breast Cancer: A Case Series.

In hormone receptor-positive metastatic breast cancer (HR+ MBC), endocrine resistance is commonly due to genetic alterations of ESR1, the gene encoding estrogen receptor alpha (ERα). While ESR1 point mutations (ESR1-MUT) cause acquired resistance to aromatase inhibition (AI) through constitutive activation, far less is known about the molecular functions and clinical consequences of ESR1 fusions (ESR1-FUS). This case series discusses 4 patients with HR+ MBC with ESR1-FUS in the context of the existing ESR1-FUS literature. We consider therapeutic strategies and raise the hypothesis that CDK4/6 inhibition (CDK4/6i) may be effective against ESR1-FUS with functional ligand-binding domain swaps. These cases highlight the importance of screening for ESR1-FUS in patients with HR+ MBC while continuing investigation of precision treatments for these genomic rearrangements.

A rapid genotyping panel for detection of primary central nervous system lymphoma.

Diagnosing primary central nervous system lymphoma (PCNSL) frequently requires neurosurgical biopsy due to nonspecific radiologic features and the low yield of cerebrospinal fluid (CSF) studies. We characterized the clinical evaluation of suspected PCNSL (N = 1007 patients) and designed a rapid multiplexed genotyping assay for MYD88, TERT promoter, IDH1/2, H3F3A, and BRAF mutations to facilitate the diagnosis of PCNSL from CSF and detect other neoplasms in the differential diagnosis. Among 159 patients with confirmed PCNSL, the median time to secure a diagnosis of PCNSL was 10 days, with a range of 0 to 617 days. Permanent histopathology confirmed PCNSL in 142 of 152 biopsies (93.4%), whereas CSF analyses were diagnostic in only 15/113 samplings (13.3%). Among 86 archived clinical specimens, our targeted genotyping assay accurately detected hematologic malignancies with 57.6% sensitivity and 100% specificity (95% confidence interval [CI]: 44.1% to 70.4% and 87.2% to 100%, respectively). MYD88 and TERT promoter mutations were prospectively identified in DNA extracts of CSF obtained from patients with PCNSL and glioblastoma, respectively, within 80 minutes. Across 132 specimens, hallmark mutations indicating the presence of malignancy were detected with 65.8% sensitivity and 100% specificity (95% CI: 56.2%-74.5% and 83.9%-100%, respectively). This targeted genotyping approach offers a rapid, scalable adjunct to reduce diagnostic and treatment delays in PCNSL.

Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories.

OBJECTIVES: The field of artificial intelligence (AI) has grown in the past 10 years. Despite the crucial role of laboratory diagnostics in clinical decision-making, we found that the majority of AI studies focus on surgery, radiology, and oncology, and there is little attention given to AI integration into laboratory medicine. METHODS: We dedicated a session at the 3rd annual European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) strategic conference in 2022 to the topic of AI in the laboratory of the future. The speakers collaborated on generating a concise summary of the content that is presented in this paper. RESULTS: The five key messages are (1) Laboratory specialists and technicians will continue to improve the analytical portfolio, diagnostic quality and laboratory turnaround times; (2) The modularized nature of laboratory processes is amenable to AI solutions; (3) Laboratory sub-specialization continues and from test selection to interpretation, tasks increase in complexity; (4) Expertise in AI implementation and partnerships with industry will emerge as a professional competency and require novel educational strategies for broad implementation; and (5) regulatory frameworks and guidances have to be adopted to new computational paradigms. CONCLUSIONS: In summary, the speakers opine that the ability to convert the value-proposition of AI in the laboratory will rely heavily on hands-on expertise and well designed quality improvement initiative from within laboratory for improved patient care.

Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML.

BACKGROUND: Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality - for the specific purpose of assessing AI/ML improvements - is currently missing. METHODS: A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on "AI in the Laboratory of the Future" prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. RESULTS: The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. CONCLUSIONS: A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.

Exploring the Skin Brain Link: Biomarkers in the Skin with Implications for Aging Research and Alzheimer's Disease Diagnostics.

Neurodegenerative diseases, including Alzheimer's disease (AD), are challenging to diagnose. Currently the field must rely on imperfect diagnostic modalities. A recent study identified differences in several key bio-mechano-physiological parameters of the skin between AD patients and healthy controls. Here, we visually align these differences with the relevant histological, aging, and embryological paradigms to raise awareness for these potential biomarkers. In a study conducted by Wu et al., a series of n = 41 patients (n = 29 with AD and n = 12 healthy controls) were evaluated, demonstrating that AD patients exhibit a less acidic skin pH, increased skin hydration, and reduced skin elasticity compared to healthy controls. We constructed a visual overview and explored the relevant paradigms. We present a visual comparison of these factors, highlighting four paradigms: (1) the findings emphasize a shared ectodermal origin of the brain and the skin; (2) functional systems such as micro-vascularization, innervation, eccrine excretory functions, and the extracellular matrix undergo distinct changes in patients with AD; (3) the human skin mirrors the alterations in brain stiffness observed in aging studies; (4) assessment of physiological features of the skin is cost-effective, accessible, and easily amenable for monitoring and integration with cognitive assessment studies. Understanding the relationship between aging skin and aging brain is an exciting frontier, holding great promise for improved diagnostics. Further prospective and larger-scale investigations are needed to solidify the brain-skin link and determine the extent to which this relationship can be leveraged for diagnostic applications.

Profile and Predictors of Blood Tumor Mutational Burden in Advanced Hepatocellular Carcinoma.

Advanced hepatocellular carcinoma (HCC) is responsive to immune checkpoint inhibitors, but there are currently no known biomarkers to predict treatment benefit. Blood TMB (bTMB) estimation via circulating tumor DNA (ctDNA) profiling can provide a convenient means to estimate HCC TMB. Here we provide the first landscape of bTMB in advanced HCC using a commercially available next-generation sequencing assay, show that it is approximately three times as high as matched tissue TMB, and show that bTMB correlates with NAFLD cirrhosis etiology and the presence of genomic alterations in HTERT and TP53. These results lay the foundation for subsequent studies evaluating bTMB as an immune therapy predictive biomarker in HCC.

Implementation and Clinical Adoption of Precision Oncology Workflows Across a Healthcare Network.

BACKGROUND: Precision oncology relies on molecular diagnostics, and the value-proposition of modern healthcare networks promises a higher standard of care across partner sites. We present the results of a clinical pilot to standardize precision oncology workflows. METHODS: Workflows are defined as the development, roll-out, and updating of disease-specific molecular order sets. We tracked the timeline, composition, and effort of consensus meetings to define the combination of molecular tests. To assess clinical impact, we examined order set adoption over a two-year period (before and after roll-out) across all gastrointestinal and hepatopancreatobiliary (GI) malignancies, and by provider location within the network. RESULTS: Development of 12 disease center-specific order sets took ~9 months, and the average number of tests per indication changed from 2.9 to 2.8 (P = .74). After roll-out, we identified significant increases in requests for GI patients (17%; P < .001), compliance with testing recommendations (9%; P < .001), and the fraction of "abnormal" results (6%; P < .001). Of 1088 GI patients, only 3 received targeted agents based on findings derived from non-recommended orders (1 before and 2 after roll-out); indicating that our practice did not negatively affect patient treatments. Preliminary analysis showed 99% compliance by providers in network sites, confirming the adoption of the order sets across the network. CONCLUSION: Our study details the effort of establishing precision oncology workflows, the adoption pattern, and the absence of harm from the reduction of non-recommended orders. Establishing a modifiable communication tool for molecular testing is an essential component to optimize patient care via precision oncology.

Immune cell profiles in the tumor microenvironment of early-onset, intermediate-onset, and later-onset colorectal cancer.

BACKGROUND: Despite heightened interest in early-onset colorectal cancer (CRC) diagnosed before age 50, little is known on immune cell profiles of early-onset CRC. It also remains to be studied whether CRCs diagnosed at or shortly after age 50 are similar to early-onset CRC. We therefore hypothesized that immune cell infiltrates in CRC tissue might show differential heterogeneity patterns between three age groups (< 50 "early onset," 50-54 "intermediate onset," ≥ 55 "later onset"). METHODS: We examined 1,518 incident CRC cases with available tissue data, including 35 early-onset and 73 intermediate-onset cases. To identify immune cells in tumor intraepithelial and stromal areas, we developed three multiplexed immunofluorescence assays combined with digital image analyses and machine learning algorithms, with the following markers: (1) CD3, CD4, CD8, CD45RO (PTPRC), and FOXP3 for T cells; (2) CD68, CD86, IRF5, MAF, and MRC1 (CD206) for macrophages; and (3) ARG1, CD14, CD15, CD33, and HLA-DR for myeloid cells. RESULTS: Although no comparisons between age groups showed statistically significant differences at the stringent two-sided α level of 0.005, compared to later-onset CRC, early-onset CRC tended to show lower levels of tumor-infiltrating lymphocytes (P = 0.013), intratumoral periglandular reaction (P = 0.025), and peritumoral lymphocytic reaction (P = 0.044). Compared to later-onset CRC, intermediate-onset CRC tended to show lower densities of overall macrophages (P = 0.050), M1-like macrophages (P = 0.062), CD14+HLA-DR+ cells (P = 0.015), and CD3+CD4+FOXP3+ cells (P = 0.039). CONCLUSIONS: This hypothesis-generating study suggests possible differences in histopathologic lymphocytic reaction patterns, macrophages, and regulatory T cells in the tumor microenvironment by age at diagnosis.