2022 White Paper on Recent Issues in Bioanalysis: Enzyme Assay Validation, BAV for Primary End Points, Vaccine Functional Assays, Cytometry in Tissue, LBA in Rare Matrices, Complex NAb Assays, Spectral Cytometry, Endogenous Analytes, Extracellular Vesicles Part 2 - Recommendations on Biomarkers/CDx, Flow Cytometry, Ligand-Binding Assays Development & Validation; Emerging Technologies; Critical Reagents Deep Characterization.

The 16th Workshop on Recent Issues in Bioanalysis (16th WRIB) took place in Atlanta, GA, USA on September 26-30, 2022. Over 1000 professionals representing pharma/biotech companies, CROs, and multiple regulatory agencies convened to actively discuss the most current topics of interest in bioanalysis. The 16th WRIB included 3 Main Workshops and 7 Specialized Workshops that together spanned 1 week in order to allow exhaustive and thorough coverage of all major issues in bioanalysis, biomarkers, immunogenicity, gene therapy, cell therapy and vaccines. Moreover, in-depth workshops on ICH M10 BMV final guideline (focused on this guideline training, interpretation, adoption and transition); mass spectrometry innovation (focused on novel technologies, novel modalities, and novel challenges); and flow cytometry bioanalysis (rising of the 3rd most common/important technology in bioanalytical labs) were the special features of the 16th edition. As in previous years, WRIB continued to gather a wide diversity of international, industry opinion leaders and regulatory authority experts working on both small and large molecules as well as gene, cell therapies and vaccines to facilitate sharing and discussions focused on improving quality, increasing regulatory compliance, and achieving scientific excellence on bioanalytical issues. This 2022 White Paper encompasses recommendations emerging from the extensive discussions held during the workshop and is aimed to provide the bioanalytical community with key information and practical solutions on topics and issues addressed, in an effort to enable advances in scientific excellence, improved quality and better regulatory compliance. Due to its length, the 2022 edition of this comprehensive White Paper has been divided into three parts for editorial reasons. This publication (Part 2) covers the recommendations on LBA, Biomarkers/CDx and Cytometry. Part 1 (Mass Spectrometry and ICH M10) and Part 3 (Gene Therapy, Cell therapy, Vaccines and Biotherapeutics Immunogenicity) are published in volume 15 of Bioanalysis, issues 16 and 14 (2023), respectively.

Mechanistic convergence of the TIGIT and PD-1 inhibitory pathways necessitates co-blockade to optimize anti-tumor CD8+ T cell responses.

Dual blockade of the PD-1 and TIGIT coinhibitory receptors on T cells shows promising early results in cancer patients. Here, we studied the mechanisms whereby PD-1 and/or TIGIT blockade modulate anti-tumor CD8+ T cells. Although PD-1 and TIGIT are thought to regulate different costimulatory receptors (CD28 and CD226), effectiveness of PD-1 or TIGIT inhibition in preclinical tumor models was reduced in the absence of CD226. CD226 expression associated with clinical benefit in patients with non-small cell lung carcinoma (NSCLC) treated with anti-PD-L1 antibody atezolizumab. CD226 and CD28 were co-expressed on NSCLC infiltrating CD8+ T cells poised for expansion. Mechanistically, PD-1 inhibited phosphorylation of both CD226 and CD28 via its ITIM-containing intracellular domain (ICD); TIGIT's ICD was dispensable, with TIGIT restricting CD226 co-stimulation by blocking interaction with their common ligand PVR (CD155). Thus, full restoration of CD226 signaling, and optimal anti-tumor CD8+ T cell responses, requires blockade of TIGIT and PD-1, providing a mechanistic rationale for combinatorial targeting in the clinic.

Intratumoral plasma cells predict outcomes to PD-L1 blockade in non-small cell lung cancer.

Inhibitors of the programmed cell death-1 (PD-1/PD-L1) signaling axis are approved to treat non-small cell lung cancer (NSCLC) patients, based on their significant overall survival (OS) benefit. Using transcriptomic analysis of 891 NSCLC tumors from patients treated with either the PD-L1 inhibitor atezolizumab or chemotherapy from two large randomized clinical trials, we find a significant B cell association with extended OS with PD-L1 blockade, independent of CD8+ T cell signals. We then derive gene signatures corresponding to the dominant B cell subsets present in NSCLC from single-cell RNA sequencing (RNA-seq) data. Importantly, we find increased plasma cell signatures to be predictive of OS in patients treated with atezolizumab, but not chemotherapy. B and plasma cells are also associated with the presence of tertiary lymphoid structures and organized lymphoid aggregates. Our results suggest an important contribution of B and plasma cells to the efficacy of PD-L1 blockade in NSCLC.

Transcriptomic profiling of adjuvant colorectal cancer identifies three key prognostic biological processes and a disease specific role for granzyme B.

BACKGROUND: Colorectal cancer (CRC) is a leading cause of cancer-related deaths, with a 5% 5-year survival rate for metastatic disease, yet with limited therapeutic advancements due to insufficient understanding of and inability to accurately capture high-risk CRC patients who are most likely to recur. We aimed to improve high-risk classification by identifying biological pathways associated with outcome in adjuvant stage II/III CRC. METHODS AND FINDINGS: We included 1062 patients with stage III or high-risk stage II colon carcinoma from the prospective three-arm randomized phase 3 AVANT trial, and performed expression profiling to identify a prognostic signature. Data from validation cohort GSE39582, The Cancer Genome Atlas, and cell lines were used to further validate the prognostic biology. Our retrospective analysis of the adjuvant AVANT trial uncovered a prognostic signature capturing three biological functions-stromal, proliferative and immune-that outperformed the Consensus Molecular Subtypes (CMS) and recurrence prediction signatures like Oncotype Dx in an independent cohort. Importantly, within the immune component, high granzyme B (GZMB) expression had a significant prognostic impact while other individual T-effector genes were less or not prognostic. In addition, we found GZMB to be endogenously expressed in CMS2 tumor cells and to be prognostic in a T cell independent fashion. A limitation of our study is that these results, although robust and derived from a large dataset, still need to be clinically validated in a prospective study. CONCLUSIONS: This work furthers our understanding of the underlying biology that propagates stage II/III CRC disease progression and provides scientific rationale for future high-risk stratification and targeted treatment evaluation in biomarker defined subpopulations of resectable high-risk CRC. Our results also shed light on an alternative GZMB source with context-specific implications on the disease's unique biology.

Intratumoral CD103+ CD8+ T cells predict response to PD-L1 blockade.

BACKGROUND: CD8+ tissue-resident memory T (TRM) cells, marked by CD103 (ITGAE) expression, are thought to actively suppress cancer progression, leading to the hypothesis that their presence in tumors may predict response to immunotherapy. METHODS: Here, we test this by combining high-dimensional single-cell modalities with bulk tumor transcriptomics from 1868 patients enrolled in lung and bladder cancer clinical trials of atezolizumab (anti-programmed cell death ligand 1 (PD-L1)). RESULTS: ITGAE was identified as the most significantly upregulated gene in inflamed tumors. Tumor CD103+ CD8+ TRM cells exhibited a complex phenotype defined by the expression of checkpoint regulators, cytotoxic proteins, and increased clonal expansion. CONCLUSIONS: Our analyses indeed demonstrate that the presence of CD103+ CD8+ TRM cells, quantified by tracking intratumoral CD103 expression, can predict treatment outcome, suggesting that patients who respond to PD-1/PD-L1 blockade are those who exhibit an ongoing antitumor T-cell response.

Peripheral T cell expansion predicts tumour infiltration and clinical response.

Despite the resounding clinical success in cancer treatment of antibodies that block the interaction of PD1 with its ligand PDL11, the mechanisms involved remain unknown. A major limitation to understanding the origin and fate of T cells in tumour immunity is the lack of quantitative information on the distribution of individual clonotypes of T cells in patients with cancer. Here, by performing deep single-cell sequencing of RNA and T cell receptors in patients with different types of cancer, we survey the profiles of various populations of T cells and T cell receptors in tumours, normal adjacent tissue, and peripheral blood. We find clear evidence of clonotypic expansion of effector-like T cells not only within the tumour but also in normal adjacent tissue. Patients with gene signatures of such clonotypic expansion respond best to anti-PDL1 therapy. Notably, expanded clonotypes found in the tumour and normal adjacent tissue can also typically be detected in peripheral blood, which suggests a convenient approach to patient identification. Analyses of our data together with several external datasets suggest that intratumoural T cells, especially in responsive patients, are replenished with fresh, non-exhausted replacement cells from sites outside the tumour, suggesting continued activity of the cancer immunity cycle in these patients, the acceleration of which may be associated with clinical response.

Quantitative Comparison of Conventional and t-SNE-guided Gating Analyses.

Dimensionality reduction using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm has emerged as a popular tool for visualizing high-parameter single-cell data. While this approach has obvious potential for data visualization it remains unclear how t-SNE analysis compares to conventional manual hand-gating in stratifying and quantitating the frequency of diverse immune cell populations. We applied a comprehensive 38-parameter mass cytometry panel to human blood and compared the frequencies of 28 immune cell subsets using both conventional bivariate and t-SNE-guided manual gating. t-SNE analysis was capable of stratifying every general cellular lineage and most sub-lineages with high correlation between conventional and t-SNE-guided cell frequency calculations. However, specific immune cell subsets delineated by the manual gating of continuous variables were not fully separated in t-SNE space thus causing discrepancies in subset identification and quantification between these analytical approaches. Overall, these studies highlight the consistency between t-SNE and conventional hand-gating in stratifying general immune cell lineages while demonstrating that particular cell subsets defined by conventional manual gating may be intermingled in t-SNE space.

Visualization of Mass Cytometry Signal Background to Enable Optimal Core Panel Customization and Signal Threshold Gating.

Signal interference or overlap in mass cytometry is minimal compared to flow cytometry but must still be considered for optimal panel design and assay sensitivity. Here we describe a procedure for evaluating signal interference dynamics in the context of a 25-parameter core immunophenotyping panel. Specifically, a mass-minus-many (MMM) approach was used to assess background signals in "empty" or "blank" channels intended for further customization. Through this approach cell type-specific variability in signal background is revealed. Further panel customization can thus be performed with an understanding of cell type and channel-specific background levels to enable rational panel design and the objective delineation of gating thresholds during analysis.

Mass cytometry panel optimization through the designed distribution of signal interference.

Mass cytometry is capable of measuring more than 40 distinct proteins on individual cells making it a promising technology for innovating biomarker discovery. However, in order for this potential to be fully realized, best practices in panel design need to be further defined in order to achieve consistency and reproducibility in data analysis. Of particular importance are controls that reveal, and panel design principles that mitigate the effects of signal interference or overlap. We observed a disparity between the staining profiles of two noncompeting anti- integrin ß7 mAbs and hypothesized that signal interference was responsible. A mass-minus-one (MMO) control was applied and demonstrated that signal overlap caused the perceived interclonal discrepancy in ß7 expression. Panel redesign in consideration of mass-cytometry specific interference dynamics dramatically improved concordance between both mAbs by redistributing background signals caused by overlap. These studies visualize how signal overlap can complicate mass cytometry data interpretation and demonstrate how the rational distribution of interference can greatly improve panel design and data quality. © 2016 International Society for Advancement of Cytometry.