Development Of Workflow Research Articles

Framework for a High-Throughput Screening Method to Assess Polymer/Plasticizer Miscibility: The Case of Hydrocarbons in Polyolefins.

Polymer composite materials require softening to reduce their glass transition temperature and improve processability. To this end, plasticizers (PLs), which are small organic molecules, are added to the polymer matrix. The miscibility of these PLs has a large impact on their effectiveness and, therefore, their interactions with the polymer matrix must be carefully considered. Many PL characteristics, including their size, topology, and flexibility, can impact their miscibility and, because of the exponentially large number of PLs, the current trial-and-error approach is very ineffective. In this work, we show that using coarse-grained molecular simulations of a small dataset of 48 PLs, it is possible to identify topological and thermodynamic descriptors that are proxy for their miscibility. Using ad-hoc molecular dynamics simulation setups that are relatively computationally inexpensive, we establish correlations between the PLs' topology, internal flexibility, thermodynamics of aggregation, and degree of miscibility, and use these descriptors to classify the molecules as miscible or immiscible. With all available data, we also construct a decision tree model, which achieves a F1 score of 0.86 ± 0.01 with repeated, stratified 5-fold cross-validation, indicating that this machine learning method can be a promising route to fully automate the screening. By evaluating the individual performance of the descriptors, we show this procedure enables a 10-fold reduction of the test space and provides the basis for the development of workflows that can efficiently screen PLs with a variety of topological features. The approach is used here to screen for apolar PLs in polyisoprene melts, but similar proxies would be valid for other polyolefins, while, in cases where polar interactions drive the miscibility, other descriptors are likely to be needed.

A metagenomics-based workflow for the detection and genomic characterization of GBS in raw freshwater fish.

The need for a rapid and accurate food microbiological testing method is apparent for a timely and effective foodborne outbreak response. This is particularly relevant for emerging foodborne pathogens such as Group B Streptococcus (GBS) whose associated food safety risk might be undercharacterized. By using GBS in raw freshwater fish as a case example, this study describes the development of a metagenomics-based workflow for rapid food microbiological safety testing and surveillance. This study can inform as a working model for various foodborne pathogens in other complex food matrices, paving the way for future methodological development of metagenomics for food microbiological safety testing.

Development of Virtual Tours for Understanding the Built Environment of an Educational Building

Though we spend a significant amount of time in indoor and built environments as general occupants of residential or commercial spaces, we do not necessarily know how the heating, cooling, and ventilation services work in our occupied spaces. As the mechanical systems of buildings become more complex for energy saving and better indoor air quality, it is beneficial for occupants to learn more their built environment so that they can cooperate effectively for the building’s performance. In this context, the purpose of this research is to develop and evaluate how virtual reality (VR) technology can support occupants in understanding their built environment. An educational building on campus was selected for the development as it provides familiar spaces for potential participants in this research. This research was carried out in two stages. In Stage One, we, as researchers in mechanical engineering, explored the workflow for VR development and developed VR tours for four spaces: a classroom, an auditorium, a conference room, and a mechanical room. In Stage Two, we conducted a survey study to examine the VR experience from the perspective of users. In this survey study, we recruited 34 participants from engineering students/graduates, industry participants, and a sustainability group. The participants generally indicated a positive experience with the VR tours, although the quiz scores on the VR content were weak. From our reflection, we consider that positive and effective VR experiences for the education of the built environment require collaboration from three domains: (1) mechanical systems of buildings, (2) VR technology, and (3) pedagogy.

729: Development of an automatic deformable registration workflow for multi-modal prostate imaging

729: Development of an automatic deformable registration workflow for multi-modal prostate imaging

The aspects of implementation of digital technologies under rendering of services in health care: The publications review

The article presents review summarizing contemporary National and foreign experience of implementing digital technologies under provision of services in health care. The systematic analysis of data from Scopus, eLibrary, PubMed and others electronic databases permitted to select 30 sources in Russian and English for 2016-2023. Modern digitization trends affect collaborations of companies where vectors of modernization become development of unified digital framework based on common state information system of health care; common educational platform for medical knowledge enriching by AI capabilities, through development of organizational activities and workflows based on digital technologies and services utilizing digital transformation. The deficiency of tools evaluating efficiency of reading software products and scalability of health digitization processes become problematic issues. As of today, diagnostic technologies using AI systems, technologies of lung cancer screening, examination methods of patients with post-traumatic deformations of cheekbone-orbital complex, monitoring of readings of devices with digital registration, further office consulting of various levels and application of AI in neurosurgery were described already. The article also considers issues of telemedicine consultations and development of modernized care models. The authors expect that digital ecosystem in development addressing issues of legal concept of management, financing and system of patient legal protection will do everything necessary to mitigate cyber incidents.

Combined approach of selective and accelerated cloning for microfluidic chip-based system increases clone specific productivity.

Improving current cell line development workflows can either focus on increasing the specific productivity of the cell lines or shortening timelines to reach the clinic as fast as possible. In this work, using the Beacon platform, we have combined two distinct protocols - early cloning with low-viability pools, and IgG membrane staining-, to concomitantly reach both objectives, and generate highly productive CHO clones in shorter timelines. Fast-sorting approaches using low-viability pools in combination with the Beacon platform have recently been reported to shorten CLD timelines. However, the low recovery led to a drastic reduction in the clone number obtained postcloning. Here, we report a combined approach of fast-sorting and fluorescent membrane staining. With this new protocol, the cells reach a correct recovery, allowing to fully exploit the Beacon screening capacities. In addition, by using a fluorescent staining recognizing the secreted IgG, we were able to enrich the fraction of highly secreting cells prior to cloning and we obtained significant increases in the cell's specific productivity. The combination of these two protocols has a synergistic effect, and as they help discarding the dead and nonproducing populations prior to cloning, they increase the throughput power of the Beacon platform and the detection of super productive clones.

Physics-based modeling of the effective gas transport properties of single jersey knitted fabrics based on images

Physics-based modeling of the effective gas transport properties of single jersey knitted fabrics based on images

Supporting Coaches to Learn Through and From Their Everyday Experiences: A 1:1 Coach Development Workflow for Performance Sport

This paper overviews an intensive 1:1 coach development workflow developed and used in U.K. performance and high-performance sport. The workflow has been field tested with over 60 coaches in mainly Olympic and Paralympic settings in a variety of sports. The workflow proposes six main stages: “beginning new relationships,” “seeking first to understand,” “preparing for reflective conversations,” “engaging in reflective conversations,” “working with difference,” and “supporting change.” The stages are tailored pragmatically to context, and the workflow does not suggest a fixed sequence. The application of the workflow requires adaptive expertise based on considerable coach development experience and a breadth and depth of coaching and coach development knowledge. The workflow suggests the need for coach developers to build and support trusting, collaborative, and supportive relationships with the coach, as a foundation for the coach development task. Coach development practices and the workflow are continually being developed and refined in a U.K. context, and future work will provide case studies, evidence of outcomes, and refinements to the work.

BTR: a bioinformatics tool recommendation system.

The rapid expansion of Bioinformatics research has led to a proliferation of computational tools for scientific analysis pipelines. However, constructing these pipelines is a demanding task, requiring extensive domain knowledge and careful consideration. As the Bioinformatics landscape evolves, researchers, both novice and expert, may feel overwhelmed in unfamiliar fields, potentially leading to the selection of unsuitable tools during workflow development. In this article, we introduce the Bioinformatics Tool Recommendation system (BTR), a deep learning model designed to recommend suitable tools for a given workflow-in-progress. BTR leverages recent advances in graph neural network technology, representing the workflow as a graph to capture essential context. Natural language processing techniques enhance tool recommendations by analyzing associated tool descriptions. Experiments demonstrate that BTR outperforms the existing Galaxy tool recommendation system, showcasing its potential to streamline scientific workflow construction. The Python source code is available at https://github.com/ryangreenj/bioinformatics_tool_recommendation.

AI-Powered Real-time Accessibility Enhancement: A Solution for Web Content Accessibility Issues

The web accessibility landscape is a significant challenge, with 96.3% of home pages displaying issues with Web Content Accessibility Guidelines (WCAG). This paper addresses the primary accessibility issues, such as missing Accessible Rich Internet Applications (ARIA) landmarks, ill-formed headings, low contrast text, and inadequate form labeling. The dynamic nature of modern web and cloud applications presents challenges, such as developers' limited awareness of accessibility implications, potential code bugs, and API failures. To address these issues, an AI-enabled system is proposed to dynamically enhance web accessibility. The system uses machine learning algorithms to identify and rectify accessibility issues in real-time, integrating with existing development workflows. Empirical evaluation and case studies demonstrate the efficacy of this solution in improving web accessibility across diverse scenarios.

Synthetic Knockout Protein Standard for Evaluating Interference in Tandem Mass Tag-Based Proteomics.

Isobaric labeling is widely used for unbiased, proteome-wide studies, and it provides several advantages, such as fewer missing values among samples and higher quantitative precision. However, ion interference may lead to compressed or distorted observed ratios due to the coelution and coanalysis of peptides. Here, we introduced a synthetic KnockOut standard (sKO) for evaluating interference in tandem mass tags-based proteomics. sKO is made by mixing TMTpro-labeled tryptic peptides derived from four nonhuman proteins and a whole human proteome as background at different proportions. We showcased the utility of the sKO standard by exploring ion interference at different peptide concentrations (up to a 30-fold change in abundance) and using a variety of mass spectrometer data acquisition strategies. We also demonstrated that the sKO standard could provide valuable information for the rational design of acquisition strategies to achieve optimal data quality and discussed its potential applications for high-throughput proteomics workflows development.

Global O-glycoproteome enrichment and analysis enabled by a combinatorial enzymatic workflow

A comprehensive analysis of site-specific protein O-glycosylation is hindered by the absence of a consensus O-glycosylation motif, the diversity of O-glycan structures, and the lack of a universal enzyme that cleaves attached O-glycans. Here, we report the development of a robust O-glycoproteomic workflow for analyzing complex biological samples by combining four different strategies: removal of N-glycans, complementary digestion using O-glycoprotease (IMPa) with/without another protease, glycopeptide enrichment, and mass spectrometry with fragmentation of glycopeptides using stepped collision energy. Using this workflow, we cataloged 474 O-glycopeptides on 189 O-glycosites derived from 79 O-glycoproteins from human plasma. These data revealed O-glycosylation of several abundant proteins that have not been previously reported. Because many of the proteins that contained unannotated O-glycosylation sites have been extensively studied, we wished to confirm glycosylation at these sites in a targeted fashion. Thus, we analyzed selected purified proteins (kininogen-1, fetuin-A, fibrinogen, apolipoprotein E, and plasminogen) in independent experiments and validated the previously unknown O-glycosites.

Mixed Reality as a Digital Visualisation Solution for the Head and Neck Tumour Board: Application Creation and Implementation Study.

The preparation and implementation of interdisciplinary oncological case reviews are time-consuming and complex. The variety of clinical and radiological information must be presented in a clear and comprehensible manner. Only if all relevant patient-specific information is demonstrated in a short time frame can well-founded treatment decisions be made on this basis. Mixed reality (MR) technology as a multimodal interactive user interface could enhance understanding in multidisciplinary collaboration by visualising radiological or clinical data. The aim of the work was to develop an MR-based software prototype for a head and neck tumour board (HNTB) to support clinical decision-making. The article describes the development phases and workflows in the planning and creation of a MR-based software prototype that were required to meet the multidisciplinary characteristics of a HNTB.

Abstract 4933: A workflow for cloud-based AI development of multiplex IF image analysis using the OMERO Plus platform

Abstract Background: The advancement of AI in digital pathology is essential for analyzing image data containing billions of cells. This is true for digital pathology workflows using bright-field images, but this is even more critical for multiplex immunofluorescence (mIF) whole-slides due to the increased number of fluorescent channels and bit-depth per pixel. These complexities necessitate improved data management, and efficient visualization for both the mIF image and AI-based analysis results. We present a workflow using OMERO Plus, AWS SageMaker, and Ultivue software for AI model development in mIF. Method: Images were uploaded to AWS S3 and imported to the data management system OMERO Plus, where they were organized into training and validation groups. Annotations: PathViewer was used to place rectangular fields of view (FOV) in locations selected by the pathologist where all positive cells for each target marker were manually annotated. Data Preparation: Using the OMERO Plus API, annotation coordinates, image tiles, and image metadata were extracted and formatted for model training. All training data is under a version control system using Gitlab and Data Version Control tools using AWS S3. Additionally, strict quality checks were performed to ensure data integrity. Training: For both semantic segmentation and cell object detection, the training data was used in a customized training workflow using AWS SageMaker, including modern deep learning architectures, data augmentation, training logging, evaluation, and deployment. All AI models were trained using Pytorch and deployed with the open model format ONNX. Results Visualization: AI segmentation results of cells and regions are visualized using PathViewer as overlays on the original images by converting them to the OME-NGFF label image format that uses Zarr multidimensional arrays. Results: We utilized 58 and 24 mIF images for training and validation, respectively, on 15 markers using ISP Ultivue technology with 1681 and 296 FOVs, encompassing over 60K cells. Using this data and methodology we trained 3 semantic segmentation models and over five object detection models suitable for all twenty-four OmniVUE markers. For instance, the CD3 positive cell detection model, trained with 390 FOVs, achieved highly accurate results: f1-score of 0.915 evaluated on 34 FOVs. Conclusions: The combination of OMERO Plus, PathViewer, AWS SageMaker, and internal tools can successfully address mIF Big Data data management complexities in a cloud environment for digital pathology. The architecture is highly scalable, cost effective, and reliable, enabling a very efficient AI workflow. Citation Format: Ruben Cardenes, Erin Diel, Douglas Wood, Je Lee, Angela Vasaturo, Martin Schulze, Lorenz Rognoni. A workflow for cloud-based AI development of multiplex IF image analysis using the OMERO Plus platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4933.

Abstract 3670: Reducing the patient burden for ctDNA biomarkers: Advancing small volume home-based collection technologies

Abstract There is broad consensus from regulatory agencies, clinicians and patients for the need to move to decentralized trials while focusing on patient-centric approaches with the COVID-19 pandemic being the catalyst. This requires the development of new sample collection capabilities and workflows to ensure that the necessary biomarkers can be measured. Circulating tumor DNA (ctDNA) has become an increasingly important biomarker for cancer detection and genetics, and monitoring patients during treatment. Typical measurements of ctDNA involve venous draws of >10mls of whole blood that require patients to be in clinic. Here, we test the feasibility of using a new collection device, Tasso+, to collect capillary blood for ctDNA analysis using the Guardant360 panel. A pilot of whole blood microsamples (< 1 mL) with a cfDNA yield of 0.7-1.4 ng/ml spiked to 0.5% MAF with Seraseq ctDNA reference material. This resulted in detection of ~20% of the total expected variants across variant classes showing the feasibility of using the G360 panel to detect low volume/low MAF samples. Initial testing of Tasso+ collected samples revealed a 5-10x increase in high MW gDNA compared to traditional venipuncture. SPRI bead-based size selection was used in the extraction protocol to remove >95% of the high MW DNA, resulting in a 35% loss of target cfDNA fraction. Traditional venipuncture samples and Tasso+ samples were collected from 5 cancer patients and run using the G360 platform. Microsamples detected 75% of the variants across all variant classes found in venipuncture samples. Tasso+ lost sensitivity of variants when the MAF was <0.5%. To expand usage of Tasso+ to at-home collection of blood for ctDNA analysis, we sought to determine if lyophilization of the Streck DNA BCT RUO tube reagent could still provide stability to samples and a potential framework for at-home collections. Venipuncture and Tasso+ samples were collected from control patients, pooled and spiked with Seraseq cfDNA reference material. Samples were aliquoted into standard liquid Streck tubes or tubes with lyophilized reagent at the concentration per manufacturer’s directions and evaluated for stability over time. The Tasso+ samples with a target MAF between 0.5%-2.0% detected ~26% of all variants compared to the standard samples with a similar MAF. Lyophilization of the Streck reagent had minimal effect (<20% reduction in variants detected) on the reagent’s performance as there were approximately an equal number of total variants detected when comparing liquid and lyophilized samples for both the standard tubes and Tasso+ samples. Overall, these results provide support and a framework for using a home-based patient-centric sampling approach for monitoring ctDNA from cancer patients in clinical development. Citation Format: Brad R. Evans, Robert J. Foley, Steven M. Townson, Emily J. Welch, Kevin P. Bateman. Reducing the patient burden for ctDNA biomarkers: Advancing small volume home-based collection technologies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3670.

Abstract 7177: Development and validation of clinical pharmacodynamic biomarkers to assess treatment response to HPK1 inhibition and guide dosing decisions

Abstract Checkpoint inhibitors represent promising cancer therapeutics as they have the potential to reignite T cell activity and reverse T cell exhaustion, which is a critical mechanism of immune evasion by tumors. Hematopoietic progenitor kinase 1 (HPK1) is a negative regulator of T-cell signaling that has recently emerged as a novel target for cancer immunotherapy. Inhibition of HPK1 can restore T cell function and anti-tumor immune responses, and several HPK1 inhibitors are currently under pre-clinical development, or clinical evaluation. Identifying clinically useful pharmacodynamic (PD) biomarkers in response to HPK1 inhibition is critical to characterize exposure-response relationship in early-phase trials and inform dose selection for later-phase studies. However, development and implementation of clinical PD assays are challenging because high sensitivity and specificity are required. Here, we developed a multi-biomarker strategy that combines a proximal biomarker (SLP-76 (Ser376) phosphorylation, a specific substrate of HPK1) and distal biomarkers (cytokine production) to evaluate target engagement (TE) and establish proof of mechanism (POM) of HPK1 inhibitors to determine their comprehensive PD profile. These PD biomarker assays were developed using peripheral blood as a surrogate tissue to assess HPK1 inhibition-induced pSLP-76 level decrease and cytokine levels increase, thus reflecting drug inhibitory effects and T-cell activity recovery. However, the lack of basal levels of these biomarkers presented a challenge for clinical workflow, as ex-vivo T-cell receptor stimulation of the clinical specimen is required after collection. Phosphoflow cytometry was prioritized over several technology platforms evaluated for quantifying blood pSLP-76 levels because of superior assay performance and signal stability under the developed clinical workflow. Blood cytokines upregulated upon HPK1 inhibition were first identified using targeted proteomics. IFNγ, IL2, TNFα, and IL17A were next confirmed and validated using quantitative immunoassays. Both pSLP-76 and whole blood cytokine workflows were thoroughly optimized for every step of sample handling from specimen processing at clinical sites to sample analysis at testing lab to ensure standardized, precise, and robust assay performance for clinical application. In addition, consistent HPK1 inhibition potency values were achieved using blood from cancer patients, demonstrating that the established PD biomarker assays apply to the clinical target population. In conclusion, robust peripheral TE and POM biomarkers were successfully developed to support future phase I/II studies with HPK1 inhibitors. These are expected to enable characterization of the exposure-response relationships and help define the therapeutic window and optimal dose. Citation Format: Lin Jia, Yasmin Marikar-Coplin, Jeff Chen, Kevin Xu, Deanna A. Mele, Nadia Luheshi, Sonia Iyer, Anton I. Rosenbaum, Sonia Terrillon. Development and validation of clinical pharmacodynamic biomarkers to assess treatment response to HPK1 inhibition and guide dosing decisions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7177.

Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data

BackgroundOncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to the large number of metabolites in a cell and the presence of multiple genes associated with cancer development.ResultsHere we report the development of a computational workflow that predicts metabolite-gene-pathway sets. Metabolite-gene-pathway sets present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM is a computational model that predicts reaction fluxes at a genome scale and can be constructed in a cell-specific manner by using omics data. The computational workflow is first validated by comparing the resulting metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from acute myeloid leukemia and renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting metabolite-gene-pathway sets is also discussed.ConclusionsValidation of the metabolite-gene-pathway set-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting metabolite-gene-pathway sets will help identify novel oncometabolites and also suggest cancer treatment strategies.

The Shelf Life of Milk-A Novel Concept for the Identification of Marker Peptides Using Multivariate Analysis.

The quality of food is influenced by several factors during production and storage. When using marker compounds, different steps in the production chain, as well as during storage, can be monitored. This might enable an optimum prediction of food's shelf life and avoid food waste. Especially, proteoforms and peptides thereof can serve as indicators for exogenous influences. The development of a proteomics-based workflow for detecting and identifying differences in the proteome is complex and time-consuming. The aim of the study was to develop a fast and universal workflow with ultra-high temperature (UHT) milk as a proteinaceous model food with expectable changes in protein/peptide composition. To find an optimum shelf life without sticking to a theoretically fixed best-before date, new evaluation and analytical methods are needed. Consequently, a modeling approach was used to monitor the shelf life of the milk after it was treated thermally and stored. The different peptide profiles determined with high-resolution mass spectrometry (HRMS) showed a significant difference depending on the preparation method of the samples. Potential marker peptides were determined using orthogonal projections to latent structures discriminant analysis (OPLSDA) and principal component analysis (PCA) following a typical proteomics protocol with tryptic hydrolysis. An additional Python-based algorithm enabled the identification of eight potential tryptic marker peptides (with mass spectrometric structural indications m/z 885.4843, m/z 639.3500, m/z 635.8622, m/z 634.3570, m/z 412.7191, m/z 623.2967, m/z 880.4767, and m/z 692.4041), indicating the effect of the heat treatment. The developed workflow is flexible and can be easily adapted to different research questions in the field of peptide analysis. In particular, the process of feature identification can be carried out with significantly less effort than with conventional methods.

Machine-learning-guided Directed Evolution for AAV Capsid Engineering.

Target gene delivery is crucial to gene therapy. Adeno-associated virus (AAV) has emerged as a primary gene therapy vector due to its broad host range, long-term expression, and low pathogenicity. However, AAV vectors have some limitations, such as immunogenicity and insufficient targeting. Designing or modifying capsids is a potential method of improving the efficacy of gene delivery, but hindered by weak biological basis of AAV, complexity of the capsids, and limitations of current screening methods. Artificial intelligence (AI), especially machine learning (ML), has great potential to accelerate and improve the optimization of capsid properties as well as decrease their development time and manufacturing costs. This review introduces the traditional methods of designing AAV capsids and the general steps of building a sequence-function ML model, highlights the applications of ML in the development workflow, and summarizes its advantages and challenges.

Generation of murine tumor models refractory to αPD-1/-L1 therapies due to defects in antigen processing/presentation or IFNγ signaling using CRISPR/Cas9.

Immune checkpoint blockade (ICB) targeting the programmed cell death protein 1 (PD-1) and its ligand 1 (PD-L1) fails to provide clinical benefit for most cancer patients due to primary or acquired resistance. Drivers of ICB resistance include tumor antigen processing/presentation machinery (APM) and IFNγ signaling mutations. Thus, there is an unmet clinical need to develop alternative therapies for these patients. To this end, we have developed a CRISPR/Cas9 approach to generate murine tumor models refractory to PD-1/-L1 inhibition due to APM/IFNγ signaling mutations. Guide RNAs were employed to delete B2m, Jak1, or Psmb9 genes in ICB-responsive EMT6 murine tumor cells. B2m was deleted in ICB-responsive MC38 murine colon cancer cells. We report a detailed development and validation workflow including whole exome and Sanger sequencing, western blotting, and flow cytometry to assess target gene deletion. Tumor response to ICB and immune effects of gene deletion were assessed in syngeneic mice. This workflow can help accelerate the discovery and development of alternative therapies and a deeper understanding of the immune consequences of tumor mutations, with potential clinical implications.