Predicting and controlling the reactivity of immune cell populations against cancer

Heterogeneous cell populations form an interconnected network that determine their collective output. One example of such a heterogeneous immune population is tumor-infiltrating lymphocytes (TILs), whose output can be measured in terms of its reactivity against tumors. While the degree of reactivity varies considerably between different TILs, ranging from null to a potent response, the underlying network that governs the reactivity is poorly understood. Here, we asked whether one can predict and even control this reactivity. To address this we measured the subpopulation compositions of 91 TILs surgically removed from 27 metastatic melanoma patients. Despite the large number of subpopulations compositions, we were able to computationally extract a simple set of subpopulation-based rules that accurately predict the degree of reactivity. This raised the conjecture of whether one could control reactivity of TILs by manipulating their subpopulation composition. Remarkably, by rationally enriching and depleting selected subsets of subpopulations, we were able to restore anti-tumor reactivity to nonreactive TILs. Altogether, this work describes a general framework for predicting and controlling the output of a cell mixture

Mixed Alkanethiol Monolayers on Submicrometric Gold Patterns: A Controlled Platform for Studying Cell–Ligand Interactions

Nanoscale organization of surface ligands often has a critical effect on cell–surface interactions. We have developed an experimental system that allows a high degree of control over the 2-D spatial distribution of ligands. As a proof of concept, we used the developed system to study how T-cell activation is independently affected by antigen density and antigen amount per cell. Arrays of submicrometer gold islands at varying surface coverage were defined on silicon by electron beam lithography (EBL). The gold islands were functionalized with alkanethiol self-assembled monolayers (SAMs) containing a small antigen, 2,4,6-trinotrophenyl (TNP), at various densities. Genetically engineered T-cell hybridomas expressing TNP-specific chimeric T-cell antigen receptor (CAR) were cultured on the SAMs, and their activation was assessed by IL-2 secretion and CD69 expression. It was found that, at constant antigen density, activation increased monotonically with the amount of antigen, while at constant antigen amount activation was maximal at an intermediate antigen density, whose value was independent of the amount of antigen

A Novel Host-Proteome Signature for Distinguishing between Acute Bacterial and Viral Infections

<div><p>Bacterial and viral infections are often clinically indistinguishable, leading to inappropriate patient management and antibiotic misuse. Bacterial-induced host proteins such as procalcitonin, C-reactive protein (CRP), and Interleukin-6, are routinely used to support diagnosis of infection. However, their performance is negatively affected by inter-patient variability, including time from symptom onset, clinical syndrome, and pathogens. Our aim was to identify novel viral-induced host proteins that can complement bacterial-induced proteins to increase diagnostic accuracy. Initially, we conducted a bioinformatic screen to identify putative circulating host immune response proteins. The resulting 600 candidates were then quantitatively screened for diagnostic potential using blood samples from 1002 prospectively recruited patients with suspected acute infectious disease and controls with no apparent infection. For each patient, three independent physicians assigned a diagnosis based on comprehensive clinical and laboratory investigation including PCR for 21 pathogens yielding 319 bacterial, 334 viral, 112 control and 98 indeterminate diagnoses; 139 patients were excluded based on predetermined criteria. The best performing host-protein was TNF-related apoptosis-inducing ligand (TRAIL) (area under the curve [AUC] of 0.89; 95% confidence interval [CI], 0.86 to 0.91), which was consistently up-regulated in viral infected patients. We further developed a multi-protein signature using logistic-regression on half of the patients and validated it on the remaining half. The signature with the highest precision included both viral- and bacterial-induced proteins: TRAIL, Interferon gamma-induced protein-10, and CRP (AUC of 0.94; 95% CI, 0.92 to 0.96). The signature was superior to any of the individual proteins (P<0.001), as well as routinely used clinical parameters and their combinations (P<0.001). It remained robust across different physiological systems, times from symptom onset, and pathogens (AUCs 0.87-1.0). The accurate differential diagnosis provided by this novel combination of viral- and bacterial-induced proteins has the potential to improve management of patients with acute infections and reduce antibiotic misuse.</p></div

Signature measures of accuracy for diagnosing bacterial vs viral infections.

<p>Left: Performance estimates and their 95% CIs were obtained using a leave-10%-out cross-validation on all patients in the study cohort (n_Bacterial = 319, n_Viral = 334), Unanimous sub-cohort (n_Bacterial = 256, n_Viral = 271), and Microbiologically confirmed sub-cohort (n_Bacterial = 68, n_Viral = 173). Right: The analysis was repeated after filtering out patients with an equivocal immune response (study cohort [n_Bacterial = 290, n_Viral = 277, n_equivocal = 86], Unanimous [n_Bacterial = 233, n_Viral = 232, n_equivocal = 62] and Microbiologically confirmed [n_Bacterial = 64, n_Viral = 160, n_equivocal = 17]), which resembles the way clinicians are likely to use the signature. Additional measures of accuracy, including positive predictive value and negative predictive value, and their dependency on bacterial prevalence are described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120012#pone.0120012.s005" target="_blank">S5 Data</a>.</p><p>Signature measures of accuracy for diagnosing bacterial vs viral infections.</p

Signature performance is robust across different patient subgroups and outperforms lab parameters and protein biomarkers.

<p>(A) Signature AUCs in subgroups of the study cohort (bacterial and viral) are depicted. Square size is proportional to number of patients and error bars represent 95% CI. In the Pathogens analysis, each virus was compared to bacteria affecting the same physiological system, indicated in brackets. R-respiratory, C-central nervous system, G-gastrointestinal, U-urinary, K-skin, S-systemic (i.e. non-localized). Only pathogens detected in more than 5 patients are presented. PED—pediatric emergency departments, ED—emergency departments. For subgroup definitions see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120012#pone.0120012.t001" target="_blank">Table 1</a> legend. (B) Performance of clinical and lab parameters as well as the best performing pair (ANC and Lym %), triplet (ANC, Lym % and Pulse), and quadruplets (ANC, Lym %, Pulse, Mono %) of parameters, the values of which were combined using a logistic regression. Comparison was done on the entire study cohort (n = 653), apart from pulse (recorded in 292 bacterial and 326 viral patients), and respiratory rate (recorded in 292 bacterial and 326 viral patients). The signature performed significantly better (<i>P</i><10^–15) than the optimal quadruplet. (C) The signature performed significantly better (<i>P</i><10^–8) than biomarkers with a well-established role in the host response to infections. For each of the select biomarkers, analysis was performed in a subgroup of the study cohort (43≤n≤154 for each analysis, a convenience sample, n depended on the strength of the signal). Error bars represent 95% CI.</p

Baseline characteristics of the study cohort patients.

<p>Values are presented as total numbers, followed by the corresponding percentages in brackets. Only microorganisms that were detected in more than five patients are presented. CNS- central nervous system, GI—gastroenteritis, LRTI—lower respiratory tract infection, UTRI—upper respiratory tract infection, UTI—urinary tract infection, N/A—healthy controls or patients in which data was not obtained. Influenza A subgroup included H1N1 strains. The atypical bacteria subgroup included <i>Chlamydophila pneumoniae</i>, <i>Mycoplasma pneumonia</i> and <i>Legionella pneumophila</i>. The Enteric viruses subgroup included Rota virus, Astrovirus, Enteric Adenovirus and Norovirus G I/II. In the clinical syndrome analysis the LRTI group included pneumonia, bronchiolitis, acute bronchitis, and laryngitis; the URTI group included pharyngitis, acute otitis media, acute sinusitis and acute tonsillitis.</p><p>Baseline characteristics of the study cohort patients.</p

The proteins TRAIL, IP-10 and CRP are differentially expressed in bacterial, viral and non-infectious patients.

<p>Box plots for TRAIL (A), IP-10 (B), and CRP (C), measured over the entire study cohort (n = 765) are presented. Red line and circle correspond to group median and average respectively; t-test p-values between bacterial and viral groups and between infectious (bacterial and viral) vs non-infectious (including healthy subjects) are depicted.</p