Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies.

Understanding the pharmacokinetic (PK) properties of a drug, such as clearance, is a crucial step for evaluating efficacy. The PK of therapeutic antibodies can be complex and is influenced by interactions with the target, Fc-receptors, anti-drug antibodies, and antibody intrinsic factors. A growing body of literature has linked biophysical properties of antibodies, particularly nonspecific-binding propensity, hydrophobicity and charged regions to rapid clearance in preclinical species and selected human PK studies. A clear understanding of the connection between biophysical properties and their impact on PK would allow for early selection and optimization of antibodies and reduce costly attrition during clinical trials due to sub-optimal human clearance. Due to the difficulty in obtaining large and unbiased human PK data, previous studies have focused mostly on preclinical PK. For this study, we obtained and curated the most comprehensive clinical PK dataset to date and calculated accurate estimates of linear clearance for 64 monoclonal antibodies ranging from investigational candidates in Phase 2 trials to marketed products. This allows for the first time a deep analysis of the influence of biophysical and sequence-based in silico properties directly on human clearance. We use statistical analysis and a Random Forest classifier to identify properties that have the greatest influence in our dataset. Our findings indicate that in vitro poly-specificity assay and in silico estimated isoelectric point can discriminate fast and slow clearing antibodies, extending previous observations on preclinical clearance. This provides a simple yet powerful approach to select antibodies with desirable PK during early-stage screening.

Quantifying size and diversity of the human T cell alloresponse.

Alloreactive T lymphocytes are the primary mediators of immune responses in transplantation, both in the graft-versus-host and host-versus-graft directions. While essentially all clones comprising the human T cell repertoire have been selected on self-peptide presented by self-human leukocyte antigens (self-HLAs), much remains to be understood about the nature of clones capable of responding to allo-HLA molecules. Quantitative tools to study these cells are critical to understand fundamental features of this important response; however, the large size and diversity of the alloreactive T cell repertoire in humans presents a great technical challenge. We have developed a high-throughput T cell receptor (TCR) sequencing approach to characterize the human alloresponse. We present a statistical method to model T cell clonal frequency distribution and quantify repertoire diversity. Using these approaches, we measured the diversity and frequency of distinct alloreactive CD4+ and CD8+ T cell populations in HLA-mismatched responder-stimulator pairs. Our findings indicate that the alloimmune repertoire is highly specific for a given pair of individuals, that most alloreactive clones circulate at low frequencies, and that a high proportion of TCRs is likely able to recognize alloantigens.

Diversity and divergence of the glioma-infiltrating T-cell receptor repertoire.

Although immune signaling has emerged as a defining feature of the glioma microenvironment, how the underlying structure of the glioma-infiltrating T-cell population differs from that of the blood from which it originates has been difficult to measure directly in patients. High-throughput sequencing of T-cell receptor (TCR) repertoires (TCRseq) provides a population-wide statistical description of how T cells respond to disease. We have defined immunophenotypes of whole repertoires based on TCRseq of the α- and ß-chains from glioma tissue, nonneoplastic brain tissue, and peripheral blood from patients. Using information theory, we partitioned the diversity of these TCR repertoires into that from the distribution of VJ cassette combinations and diversity due to VJ-independent factors, such as selection due to antigen binding. Tumor-infiltrating lymphocytes (TILs) possessed higher VJ-independent diversity than nonneoplastic tissue, stratifying patients according to tumor grade. We found that the VJ-independent components of tumor-associated repertoires diverge more from their corresponding peripheral repertoires than T-cell populations in nonneoplastic brain tissue, particularly for low-grade gliomas. Finally, we identified a "signature" set of TCRs whose use in peripheral blood is associated with patients exhibiting low TIL divergence and is depleted in patients with highly divergent TIL repertoires. This signature is detectable in peripheral blood, and therefore accessible noninvasively. We anticipate that these immunophenotypes will be foundational to monitoring and predicting response to antiglioma vaccines and immunotherapy.

Longterm maintenance of human naive T cells through in situ homeostasis in lymphoid tissue sites.

Naïve T cells develop in the thymus and coordinate immune responses to new antigens; however, mechanisms for their long-term persistence over the human lifespan remain undefined. Here, we investigated human naïve T cell development and maintenance in primary and secondary lymphoid tissues obtained from individual organ donors aged 3 months-73 years. In the thymus, the frequency of double-positive thymocytes declined sharply in donors over age 40 coincident with reduced recent thymic emigrants (RTE) in lymphoid tissues, while naïve T cells were functionally maintained predominantly in lymph nodes (LN). Analysis of TCR clonal distribution by CDR3 sequencing of naïve CD4+ and CD8+ T cells in spleen and LNs reveal site-specific clonal expansions of naïve T cells from individuals >40 years of age with minimal clonal overlap between lymphoid tissues. We also identified biased naïve T cell clonal distribution within specific lymph nodes based on VJ usage. Together these results suggest prolonged maintenance of naïve T cells through in situ homeostasis and retention in lymphoid tissue.

Spatial map of human T cell compartmentalization and maintenance over decades of life.

Mechanisms for human memory T cell differentiation and maintenance have largely been inferred from studies of peripheral blood, though the majority of T cells are found in lymphoid and mucosal sites. We present here a multidimensional, quantitative analysis of human T cell compartmentalization and maintenance over six decades of life in blood, lymphoid, and mucosal tissues obtained from 56 individual organ donors. Our results reveal that the distribution and tissue residence of naive, central, and effector memory, and terminal effector subsets is contingent on both their differentiation state and tissue localization. Moreover, T cell homeostasis driven by cytokine or TCR-mediated signals is different in CD4+ or CD8+ T cell lineages, varies with their differentiation stage and tissue localization, and cannot be inferred from blood. Our data provide an unprecedented spatial and temporal map of human T cell compartmentalization and maintenance, supporting distinct pathways for human T cell fate determination and homeostasis.

Statistical method for revealing form-function relations in biological networks.

Over the past decade, a number of researchers in systems biology have sought to relate the function of biological systems to their network-level descriptions--lists of the most important players and the pairwise interactions between them. Both for large networks (in which statistical analysis is often framed in terms of the abundance of repeated small subgraphs) and for small networks which can be analyzed in greater detail (or even synthesized in vivo and subjected to experiment), revealing the relationship between the topology of small subgraphs and their biological function has been a central goal. We here seek to pose this revelation as a statistical task, illustrated using a particular setup which has been constructed experimentally and for which parameterized models of transcriptional regulation have been studied extensively. The question "how does function follow form" is here mathematized by identifying which topological attributes correlate with the diverse possible information-processing tasks which a transcriptional regulatory network can realize. The resulting method reveals one form-function relationship which had earlier been predicted based on analytic results, and reveals a second for which we can provide an analytic interpretation. Resulting source code is distributed via http://formfunction.sourceforge.net.