Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction.

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.

Association of the multi-biomarker disease activity score with joint destruction in patients with rheumatoid arthritis receiving tumor necrosis factor-alpha inhibitor treatment in clinical practice.

OBJECTIVE: Evaluate the association between the multi-biomarker disease activity (MBDA) score and radiographic progression in patients with rheumatoid arthritis (RA) treated with tumor necrosis factor (TNF)-α inhibitors. METHODS: Change (Δ) in modified total Sharp score (mTSS) over 52 weeks and disease activity scores were examined retrospectively by Spearman's rank correlation coefficient in patients (N = 83) with RA initiating TNF-inhibitor treatment. Relative risk (RR) of ΔmTSS >0.5 for low MBDA score and 28-joint count disease activity score (DAS28) categories and associations between ΔmTSS and MBDA score categories conditional on DAS28 categories were assessed. RESULTS: At 52 weeks, 34% of patients had ΔmTSS >0.5 and 12% had ΔmTSS >3. Strongest correlations were observed between ΔmTSS and MBDA score (r = 0.47) or DAS28 (r = 0.42) at Week 24 and for area under the curve at Week 52 (MBDA score: r = 0.44, DAS28: r = 0.41), all p < 0.001. At Week 24, RR of ΔmTSS >0.5 for moderate/high MBDA score (≥30) or DAS28 (>3.2) were 6.6 (p < 0.001) and 2.7 (p = 0.005), respectively. Low DAS28 had greater risk of ΔmTSS >0.5 at 52 weeks when MBDA score was ≥30 (p < 0.05). CONCLUSION: Higher MBDA score or DAS28 at Week 24 was associated with greater radiographic progression over 52 weeks of TNF-inhibitor treatment. MBDA score improved risk discrimination for radiographic progression within DAS28 categories.

AAV-GRN partially corrects motor deficits and ALS/FTLD-related pathology in Tmem106b-/-Grn-/- mice.

Loss of function of progranulin (PGRN), encoded by the granulin (GRN) gene, is implicated in several neurodegenerative diseases. Several therapeutics to boost PGRN levels are currently in clinical trials. However, it is difficult to test the efficacy of PGRN-enhancing drugs in mouse models due to the mild phenotypes of Grn-/- mice. Recently, mice deficient in both PGRN and TMEM106B were shown to develop severe motor deficits and pathology. Here, we show that intracerebral ventricle injection of PGRN-expressing AAV1/9 viruses partially rescues motor deficits, neuronal loss, glial activation, and lysosomal abnormalities in Tmem106b-/-Grn-/- mice. Widespread expression of PGRN is detected in both the brain and spinal cord for both AAV subtypes. However, AAV9 but not AAV1-mediated expression of PGRN results in high levels of PGRN in the serum. Together, these data support using the Tmem106b-/-Grn-/- mouse strain as a robust mouse model to determine the efficacy of PGRN-elevating therapeutics.

TCR signal strength defines distinct mechanisms of T cell dysfunction and cancer evasion.

T cell receptor (TCR) signal strength is a key determinant of T cell responses. We developed a cancer mouse model in which tumor-specific CD8 T cells (TST cells) encounter tumor antigens with varying TCR signal strength. High-signal-strength interactions caused TST cells to up-regulate inhibitory receptors (IRs), lose effector function, and establish a dysfunction-associated molecular program. TST cells undergoing low-signal-strength interactions also up-regulated IRs, including PD1, but retained a cell-intrinsic functional state. Surprisingly, neither high- nor low-signal-strength interactions led to tumor control in vivo, revealing two distinct mechanisms by which PD1hi TST cells permit tumor escape; high signal strength drives dysfunction, while low signal strength results in functional inertness, where the signal strength is too low to mediate effective cancer cell killing by functional TST cells. CRISPR-Cas9-mediated fine-tuning of signal strength to an intermediate range improved anti-tumor activity in vivo. Our study defines the role of TCR signal strength in TST cell function, with important implications for T cell-based cancer immunotherapies.

In silico modeling and simulation of bone biology: a proposal.

Contemporary, computer-based mathematical modeling techniques make it possible to represent complex biological mechanisms in a manner that permits hypothesis testing in silico. This perspective shows how such approaches might be applied to bone remodeling and therapeutic research. Currently, the dominant conceptual model applied in bone research involves the dynamic balance between the continual build-up and breakdown of bone matrix by two cell types, the osteoblasts and osteoclasts, acting together as a coordinated, remodeling unit. This conceptualization has served extraordinarily well as a focal point for understanding how mutations, chemical mediators, and mechanical force, as well as external influences (e.g., drugs, diet) affect bone structure and function. However, the need remains to better understand and predict the consequences of manipulating any single factor, or combination of factors, within the context of this complex system's multiple interacting pathways. Mathematical models are a natural extension of conceptual models, providing dynamic, quantitative descriptions of the relationships among interacting components. This formalization creates the ability to simulate the natural behavior of a system, as well as its modulation by therapeutic or dietetic interventions. A number of mathematical models have been developed to study complex bone functions, but most include only a limited set of biological components needed to address a few specific questions. However, it is possible to develop larger, multiscale models that capture the dynamic interactions of many biological components and relate them to important physiological or pathological outcomes that allow broader study. Examples of such models include entelos' physiolab platforms. These models simulate the dynamic, quantitative interactions among a biological system's biochemicals, cells, tissues, and organs and how they give rise to key physiologic and pathophysiologic outcomes. We propose that a similar predictive, dynamical, multiscale mathematical model of bone remodeling and metabolism would provide a better understanding of the mechanisms governing these phenomena as well as serve as an in silico platform for testing pharmaceutical and clinical interventions on metabolic bone disease.