AMG 193, a Clinical Stage MTA-Cooperative PRMT5 Inhibitor, Drives Antitumor Activity Preclinically and in Patients With MTAP-Deleted Cancers.

One of the most robust synthetic lethal interactions observed in multiple functional genomic screens has been dependency on PRMT5 in cancer cells with MTAP deletion. We report the discovery of the clinical stage MTA-cooperative PRMT5 inhibitor AMG 193, which preferentially binds PRMT5 in the presence of MTA and has potent biochemical and cellular activity in MTAP-deleted cells across multiple cancer lineages. In vitro, PRMT5 inhibition induces DNA damage, cell cycle arrest, and aberrant alternative mRNA splicing in MTAP-deleted cells. In human cell line and patient-derived xenograft models, AMG 193 induces robust antitumor activity and is well tolerated with no impact on normal hematopoietic cell lineages. AMG 193 synergizes with chemotherapies or the KRAS G12C inhibitor sotorasib in vitro, and combination treatment in vivo significantly inhibits tumor growth. AMG 193 is demonstrating promising clinical activity, including confirmed partial responses in patients with MTAP-deleted solid tumors from an ongoing phase 1/2 study.

Corrigendum: Translatability of findings from cynomolgus monkey to human suggests a mechanistic role for IL-21 in promoting immunogenicity to an anti-PD-1/IL-21 mutein fusion protein.

[This corrects the article DOI: 10.3389/fimmu.2024.1345473.].

Translatability of findings from cynomolgus monkey to human suggests a mechanistic role for IL-21 in promoting immunogenicity to an anti-PD-1/IL-21 mutein fusion protein.

AMG 256 is a bi-specific, heteroimmunoglobulin molecule with an anti-PD-1 antibody domain and a single IL-21 mutein domain on the C-terminus. Nonclinical studies in cynomolgus monkeys revealed that AMG 256 administration led to the development of immunogenicity-mediated responses and indicated that the IL-21 mutein domain of AMG 256 could enhance the anti-drug antibody response directed toward the monoclonal antibody domain. Anti-AMG 256 IgE were also observed in cynomolgus monkeys. A first-in-human (FIH) study in patients with advanced solid tumors was designed with these risks in mind. AMG 256 elicited ADA in 28 of 33 subjects (84.8%). However, ADA responses were only robust and exposure-impacting at the 2 lowest doses. At mid to high doses, ADA responses remained low magnitude and all subjects maintained exposure, despite most subjects developing ADA. Limited drug-specific IgE were also observed during the FIH study. ADA responses were not associated with any type of adverse event. The AMG 256 program represents a unique case where nonclinical studies informed on the risk of immunogenicity in humans, due to the IL-21-driven nature of the response.

AMG 509 (Xaluritamig), an Anti-STEAP1 XmAb 2+1 T-cell Redirecting Immune Therapy with Avidity-Dependent Activity against Prostate Cancer.

The tumor-associated antigen STEAP1 is a potential therapeutic target that is expressed in most prostate tumors and at increased levels in metastatic castration-resistant prostate cancer (mCRPC). We developed a STEAP1-targeted XmAb 2+1 T-cell engager (TCE) molecule, AMG 509 (also designated xaluritamig), that is designed to redirect T cells to kill prostate cancer cells that express STEAP1. AMG 509 mediates potent T cell-dependent cytotoxicity of prostate cancer cell lines in vitro and promotes tumor regression in xenograft and syngeneic mouse models of prostate cancer in vivo. The avidity-driven activity of AMG 509 enables selectivity for tumor cells with high STEAP1 expression compared with normal cells. AMG 509 is the first STEAP1 TCE to advance to clinical testing, and we report a case study of a patient with mCRPC who achieved an objective response on AMG 509 treatment. SIGNIFICANCE: Immunotherapy in prostate cancer has met with limited success due to the immunosuppressive microenvironment and lack of tumor-specific targets. AMG 509 provides a targeted immunotherapy approach to engage a patient's T cells to kill STEAP1-expressing tumor cells and represents a new treatment option for mCRPC and potentially more broadly for prostate cancer. See related commentary by Hage Chehade et al., p. 20. See related article by Kelly et al., p. 76. This article is featured in Selected Articles from This Issue, p. 5.

Histopathology and levels of proteins in plasma associate with survival after colorectal cancer diagnosis.

BACKGROUND: The TNM system is used to assess prognosis after colorectal cancer (CRC) diagnosis. Other prognostic factors reported include histopathological assessments of the tumour, tumour mutations and proteins in the blood. As some of these factors are strongly correlated, it is important to evaluate the independent effects they may have on survival. METHODS: Tumour samples from 2162 CRC patients were visually assessed for amount of tumour stroma, severity of lymphocytic infiltrate at the tumour margins and the presence of lymphoid follicles. Somatic mutations in the tumour were assessed for 2134 individuals. Pre-surgical levels of 4963 plasma proteins were measured in 128 individuals. The associations between these features and prognosis were inspected by a Cox Proportional Hazards Model (CPH). RESULTS: Levels of stroma, lymphocytic infiltration and presence of lymphoid follicles all associate with prognosis, along with high tumour mutation burden, high microsatellite instability and TP53 and BRAF mutations. The somatic mutations are correlated with the histopathology and none of the somatic mutations associate with survival in a multivariate analysis. Amount of stroma and lymphocytic infiltration associate with local invasion of tumours. Elevated levels of two plasma proteins, CA-125 and PPP1R1A, associate with a worse prognosis. CONCLUSIONS: Tumour stroma and lymphocytic infiltration variables are strongly associated with prognosis of CRC and capture the prognostic effects of tumour mutation status. CA-125 and PPP1R1A may be useful prognostic biomarkers in CRC.

Scientific and Regulatory Policy Committee Points to Consider: Primary Digital Histopathology Evaluation and Peer Review for Good Laboratory Practice (GLP) Nonclinical Toxicology Studies.

The Society of Toxicologic Pathology's Scientific and Regulatory Policy Committee formed a working group to consider the present and future use of digital pathology in toxicologic pathology in general and specifically its use in primary evaluation and peer review in Good Laboratory Practice (GLP) environments. Digital histopathology systems can save costs by reducing travel, enhancing organizational flexibility, decreasing slide handling, improving collaboration, increasing access to historical images, and improving quality and efficiency through integration with laboratory information management systems. However, the resources to implement and operate a digital pathology system can be significant. Given the magnitude and risks involved in the decision to adopt digital histopathology, this working group used pertinent previously published survey results and its members' expertise to create a Points-to-Consider article to assist organizations with building and implementing digital pathology workflows. With the aim of providing a comprehensive perspective, the current publication summarizes aspects of digital whole-slide imaging relevant to nonclinical histopathology evaluations, and then presents points to consider applicable to both primary digital histopathology evaluation and digital peer review in GLP toxicology studies. The Supplemental Appendices provide additional tabulated resources.

Scientific and Regulatory Policy Committee Brief Communication: 2019 Survey on Use of Digital Histopathology Systems in Nonclinical Toxicology Studies.

Histopathologic evaluation and peer review using digital whole-slide images (WSIs) is a relatively new medium for assessing nonclinical toxicology studies in Good Laboratory Practice (GLP) environments. To better understand the present and future use of digital pathology in nonclinical toxicology studies, the Society of Toxicologic Pathology (STP) formed a working group to survey STP members with the goal of creating recommendations for implementation. The survey was administered in December 2019, immediately before the COVID-19 pandemic, and the results suggested that the use of digital histopathology for routine GLP histopathology assessment was not widespread. Subsequently, in follow-up correspondence during the pandemic, many responding institutions either began investigating or adopting digital WSI systems to reduce employee exposure to COVID-19. Therefore, the working group presents the survey results as a pre-pandemic baseline data set. Recommendations for use of WSI systems in GLP environments will be the subject of a separate publication.

Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review.

Since whole-slide imaging has been commercially available for over 2 decades, digital pathology has become a constantly expanding aspect of the pathology profession that will continue to significantly impact how pathologists conduct their craft. While some aspects, such as whole-slide imaging for archiving, consulting, and teaching, have gained broader acceptance, other facets such as quantitative tissue image analysis and artificial intelligence-based assessments are still met with some reservations. While most vendors in this space have focused on diagnostic applications, that is, viewing one or few slides at a time, some are developing solutions tailored more specifically to the various aspects of veterinary pathology including updated diagnostic, discovery, and research applications. This has especially advanced the use of digital pathology in toxicologic pathology and drug development, for primary reads as well as peer reviews. It is crucial that pathologists gain a deeper understanding of digital pathology and tissue image analysis technology and their applications in order to fully use these tools in a way that enhances and improves the pathologist's assessment as well as work environment. This review focuses on an updated introduction to the basics of digital pathology and image analysis and introduces emerging topics around artificial intelligence and machine learning.

Preclinical Assessment of a MUC12-Targeted BiTE (Bispecific T-cell Engager) Molecule.

MUC12 is a transmembrane mucin that is highly expressed in >50% of primary and metastatic colorectal tumors. MUC12 is also expressed by normal epithelial cells of the colon and small intestine. Although MUC12 localization in normal epithelial cells is restricted to the apical membrane, expression in tumors is depolarized and shows broad membrane localization. The differential localization of MUC12 in tumor cells as compared with normal cells makes it a potential therapeutic target. Here, we evaluated targeting of MUC12 with a BiTE (bispecific T-cell engager) molecule. We generated a panel of proof-of-concept half-life extended (HLE) BiTE molecules that bind MUC12 on tumor cells and CD3 on T cells. We prioritized one molecule based on in vitro activity for further characterization in vivo In vitro, the MUC12 HLE BiTE molecule mediated T-cell-redirected lysis of MUC12-expressing cells with half-maximal lysis of 4.4 ± 0.9 to 117 ± 78 pmol/L. In an exploratory cynomolgus monkey toxicology study, the MUC12 HLE BiTE molecule administered at 200 µg/kg with a step dose to 1,000 µg/kg was tolerated with minimal clinical observations. However, higher doses were not tolerated, and there was evidence of damage in the gastrointestinal tract, suggesting dose levels projected to be required for antitumor activity may be associated with on-target toxicity. Together, these data demonstrate that the apically restricted expression of MUC12 in normal tissues is accessible to BiTE molecule target engagement and highlight the difficult challenge of identifying tumor-selective antigens for solid tumor T-cell engagers.

Immunotherapy combinations overcome resistance to bispecific T cell engager treatment in T cell-cold solid tumors.

Therapeutic approaches are needed to promote T cell-mediated destruction of poorly immunogenic, "cold" tumors typically associated with minimal response to immune checkpoint blockade (ICB) therapy. Bispecific T cell engager (BiTE) molecules induce redirected lysis of cancer cells by polyclonal T cells and have demonstrated promising clinical activity against solid tumors in some patients. However, little is understood about the key factors that govern clinical responses to these therapies. Using an immunocompetent mouse model expressing a humanized CD3ε chain (huCD3e mice) and BiTE molecules directed against mouse CD19, mouse CLDN18.2, or human EPCAM antigens, we investigated the pharmacokinetic and pharmacodynamic parameters and immune correlates associated with BiTE efficacy across multiple syngeneic solid-tumor models. These studies demonstrated that pretreatment tumor-associated T cell density is a critical determinant of response to BiTE therapy, identified CD8+ T cells as important targets and mediators of BiTE activity, and revealed an antagonistic role for CD4+ T cells in BiTE efficacy. We also identified therapeutic combinations, including ICB and 4-1BB agonism, that synergized with BiTE treatment in poorly T cell-infiltrated, immunotherapy-refractory tumors. In these models, BiTE efficacy was dependent on local expansion of tumor-associated CD8+ T cells, rather than their recruitment from circulation. Our findings highlight the relative contributions of baseline T cell infiltration, local T cell proliferation, and peripheral T cell trafficking for BiTE molecule-mediated efficacy, identify combination strategies capable of overcoming resistance to BiTE therapy, and have clinical relevance for the development of BiTE and other T cell engager therapies.

Special Issue on Digital Pathology, Tissue Image Analysis, Artificial Intelligence, and Machine Learning: Approximation of the Effect of Novel Technologies on Toxicologic Pathology.

For decades, it has been postulated that digital pathology is the future. By now it is safe to say that we are living that future. Digital pathology has expanded into all aspects of pathology, including human diagnostic pathology, veterinary diagnostics, research, drug development, regulatory toxicologic pathology primary reads, and peer review. Digital tissue image analysis has enabled users to extract quantitative and complex data from digitized whole-slide images. The following editorial provides an overview of the content of this special issue of Toxicologic Pathology to highlight the range of key topics that are included in this compilation. In addition, the editors provide a commentary on important current aspects to consider in this space, such as accessibility of publication content to the machine learning-novice pathologist, the importance of adequate test set selection, and allowing for data reproducibility.

Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association.

Tissue biomarkers have been of increasing utility for scientific research, diagnosing disease, and treatment response prediction. There has been a steady shift away from qualitative assessment toward providing more quantitative scores for these biomarkers. The application of quantitative image analysis has thus become an indispensable tool for in-depth tissue biomarker interrogation in these contexts. This white paper reviews current technologies being employed for quantitative image analysis, their application and pitfalls, regulatory framework demands, and guidelines established for promoting their safe adoption in clinical practice.

The PSMA-targeting Half-life Extended BiTE Therapy AMG 160 has Potent Antitumor Activity in Preclinical Models of Metastatic Castration-resistant Prostate Cancer.

PURPOSE: Metastatic castration-resistant prostate cancer (mCRPC) remains a disease with high unmet medical need, as most patients do not achieve durable response with available treatments. Prostate-specific membrane antigen (PSMA) is a compelling target for mCRPC. It is highly expressed by primary and metastatic prostate cancer cells, with increased expression after progression on androgen deprivation therapy. EXPERIMENTAL DESIGN: We developed AMG 160, a half-life extended, bispecific T-cell engager immuno-oncology therapy that binds PSMA on prostate cancer cells and cluster of differentiation 3 on T cells for treatment of mCRPC. AMG 160 was evaluated in vitro and in mCRPC xenograft models. AMG 160 tolerability was assessed in nonhuman primates (NHP). AMG 160 activity as monotherapy and in combination with a PSMA-imaging agent, novel hormonal therapy, and immune checkpoint blockade was evaluated. RESULTS: AMG 160 induces potent, specific killing of PSMA-expressing prostate cancer cell lines in vitro, with half-maximal lysis of 6-42 pmol/L. In vivo, AMG 160 administered weekly at 0.2 mg/kg engages T cells administered systemically and promotes regression of established 22Rv-1 mCRPC xenograft tumors. AMG 160 is compatible with the imaging agent gallium 68-labeled PSMA-11, and shows enhanced cytotoxic activity when combined with enzalutamide or an anti-programmed death-1 antibody. AMG 160 exhibits an extended half-life and has an acceptable safety profile in NHPs. CONCLUSIONS: The preclinical characterization of AMG 160 highlights its potent antitumor activity in vitro and in vivo, and its potential for use with known diagnostic or therapeutic agents in mCRPC. These data support the ongoing clinical evaluation of AMG 160 in patients with mCRPC.See related commentary by Kamat et al., p. 2675.

The Application, Challenges, and Advancement Toward Regulatory Acceptance of Digital Toxicologic Pathology: Results of the 7th ESTP International Expert Workshop (September 20-21, 2019).

With advancements in whole slide imaging technology and improved understanding of the features of pathologist workstations required for digital slide evaluation, many institutions are investigating broad digital pathology adoption. The benefits of digital pathology evaluation include remote access to study or diagnostic case materials and integration of analysis and reporting tools. Diagnosis based on whole slide images is established in human medical pathology, and the use of digital pathology in toxicologic pathology is increasing. However, there has not been broad adoption in toxicologic pathology, particularly in the context of regulatory studies, due to lack of precedence. To address this topic, as well as practical aspects, the European Society of Toxicologic Pathology coordinated an expert international workshop to assess current applications and challenges and outline a set of minimal requirements needed to gain future regulatory acceptance for the use of digital toxicologic pathology workflows in research and development, so that toxicologic pathologists can benefit from digital slide technology.

Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology.

Digital tissue image analysis is a computational method for analyzing whole-slide images and extracting large, complex, and quantitative data sets. However, as with any analysis method, the quality of generated results is dependent on a well-designed quality control system for the entire digital pathology workflow. Such system requires clear procedural controls, appropriate user training, and involvement of specialists to oversee key steps of the workflow. The toxicologic pathologist is responsible for reporting data obtained by digital image analysis and therefore needs to ensure that it is correct. To accomplish that, they must understand the main parameters of the quality control system and should play an integral part in its conception and implementation. This manuscript describes the most common digital tissue image analysis end points and potential sources of analysis errors. In addition, it outlines recommended approaches for ensuring quality and correctness of results for both classical and machine-learning based image analysis solutions, as adapted from a recently proposed Food and Drug Administration regulatory framework for modifications to artificial intelligence/machine learning-based software as a medical device. These approaches are beneficial for any type of toxicopathologic study which uses the described end points and can be adjusted based on the intended use of the image analysis solution.

Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology.

Toxicologic pathology is transitioning from analog to digital methods. This transition seems inevitable due to a host of ongoing social and medical technological forces. Of these, artificial intelligence (AI) and in particular machine learning (ML) are globally disruptive, rapidly growing sectors of technology whose impact on the long-established field of histopathology is quickly being realized. The development of increasing numbers of algorithms, peering ever deeper into the histopathological space, has demonstrated to the scientific community that AI pathology platforms are now poised to truly impact the future of precision and personalized medicine. However, as with all great technological advances, there are implementation and adoption challenges. This review aims to define common and relevant AI and ML terminology, describe data generation and interpretation, outline current and potential future business cases, discuss validation and regulatory hurdles, and most importantly, propose how overcoming the challenges of this burgeoning technology may shape toxicologic pathology for years to come, enabling pathologists to contribute even more effectively to answering scientific questions and solving global health issues. [Box: see text].

Animal Models of Peripheral Pain: Biology Review and Application for Drug Discovery.

Pain is a complex constellation of cognitive, unpleasant sensory, and emotional experiences that primarily serves as a survival mechanism. Pain arises in the peripheral nervous system and pain signals synapse with nerve tracts extending into the central nervous system. Several different schemes are used to classify pain, including the underlying mechanism, tissues primarily affected, and time-course. Numerous animal models of pain, which should be employed with appropriate Institutional Animal Care and Use approvals, have been developed to elucidate pathophysiology mechanisms and aid in identification of novel therapeutic targets. The variety of available models underscores the observations that pain phenotypes are driven by several distinct mechanisms. Pain outcome measurement encompasses both reflexive (responses to heat, cold, mechanical and electrical stimuli) and nonreflexive (spontaneous pain responses to stimuli) behaviors. However, the question of translatability to human pain conditions and potential treatment outcomes remains a topic of continued scrutiny. In this review we discuss the different types of pain and their mechanisms and pathways, available rodent pain models with an emphasis on type of pain stimulations and pain outcome measures and discuss the role of pathologists in assessing and validating pain models.

Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.

In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber-security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association.

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.

Validation of a Muscle-Specific Tissue Image Analysis Tool for Quantitative Assessment of Dystrophin Staining in Frozen Muscle Biopsies.

CONTEXT.­: Duchenne muscular dystrophy is a rare, progressive, and fatal neuromuscular disease caused by dystrophin protein loss. Common investigational treatment approaches aim at increasing dystrophin expression in diseased muscle. Some clinical trials include assessments of novel dystrophin production as a surrogate biomarker of efficacy, which may predict a clinical benefit from treatment. OBJECTIVES.­: To establish an immunofluorescent scanning and digital image analysis workflow that provides an objective approach for staining intensity assessment of the immunofluorescence dystrophin labeling and determination of the percentage of biomarker-positive fibers in muscle cryosections. DESIGN.­: Optimal and repeatable digital image capture was achieved by a rigorously qualified fluorescent scanning process. After scanning qualification, the MuscleMap (Flagship Biosciences, Westminster, Colorado) algorithm was validated by comparing high-power microscopic field total and dystrophin-positive fiber counts obtained by trained pathologists to data derived by MuscleMap. Next, the algorithm was tested on whole-slide images of immunofluorescent-labeled muscle sections from Duchenne muscular dystrophy, Becker muscular dystrophy, and control patients. RESULTS.­: When used under the guidance of a trained pathologist, the digital image analysis tool met predefined validation criteria and demonstrated functional and statistical equivalence with manual assessment. This work is the first, to our knowledge, to qualify and validate immunofluorescent scanning and digital tissue image-analysis workflow, respectively, with the rigor required to support the clinical trial environments. CONCLUSIONS.­: MuscleMap enables analysis of all fibers within an entire muscle biopsy section and provides data on a fiber-by-fiber basis. This will allow future clinical trials to objectively investigate myofibers' dystrophin expression at a greater level of consistency and detail.