Giant Magnetoresistive Nanosensor Analysis of Circulating Tumor DNA Epidermal Growth Factor Receptor Mutations for Diagnosis and Therapy Response Monitoring.

BACKGROUND: Liquid biopsy circulating tumor DNA (ctDNA) mutational analysis holds great promises for precision medicine targeted therapy and more effective cancer management. However, its wide adoption is hampered by high cost and long turnaround time of sequencing assays, or by inadequate analytical sensitivity of existing portable nucleic acid tests to mutant allelic fraction in ctDNA. METHODS: We developed a ctDNA Epidermal Growth Factor Receptor (EGFR) mutational assay using giant magnetoresistive (GMR) nanosensors. This assay was validated in 36 plasma samples of non-small cell lung cancer patients with known EGFR mutations. We assessed therapy response through follow-up blood draws, determined concordance between the GMR assay and radiographic response, and ascertained progression-free survival of patients. RESULTS: The GMR assay achieved analytical sensitivities of 0.01% mutant allelic fraction. In clinical samples, the assay had 87.5% sensitivity (95% CI = 64.0-97.8%) for Exon19 deletion and 90% sensitivity (95% CI = 69.9-98.2%) for L858R mutation with 100% specificity; our assay detected T790M resistance with 96.3% specificity (95% CI = 81.7-99.8%) with 100% sensitivity. After 2 weeks of therapy, 10 patients showed disappearance of ctDNA by GMR (predicted responders), whereas 3 patients did not (predicted nonresponders). These predictions were 100% concordant with radiographic response. Kaplan-Meier analysis showed responders had significantly (P < 0.0001) longer PFS compared to nonresponders (N/A vs. 12 weeks, respectively). CONCLUSIONS: The GMR assay has high diagnostic sensitivity and specificity and is well suited for detecting EGFR mutations at diagnosis and noninvasively monitoring treatment response at the point-of-care.

Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.

PURPOSE: Computational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student assignments. METHODS: We developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated data. RESULTS: Deploying Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research RDs. CONCLUSION: Phenotate enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase.

Correction: Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

T cell characteristics associated with toxicity to immune checkpoint blockade in patients with melanoma.

Severe immune-related adverse events (irAEs) occur in up to 60% of patients with melanoma treated with immune checkpoint inhibitors (ICIs). However, it is unknown whether a common baseline immunological state precedes irAE development. Here we applied mass cytometry by time of flight, single-cell RNA sequencing, single-cell V(D)J sequencing, bulk RNA sequencing and bulk T cell receptor (TCR) sequencing to study peripheral blood samples from patients with melanoma treated with anti-PD-1 monotherapy or anti-PD-1 and anti-CTLA-4 combination ICIs. By analyzing 93 pre- and early on-ICI blood samples and 3 patient cohorts (n = 27, 26 and 18), we found that 2 pretreatment factors in circulation-activated CD4 memory T cell abundance and TCR diversity-are associated with severe irAE development regardless of organ system involvement. We also explored on-treatment changes in TCR clonality among patients receiving combination therapy and linked our findings to the severity and timing of irAE onset. These results demonstrate circulating T cell characteristics associated with ICI-induced toxicity, with implications for improved diagnostics and clinical management.

Clinical longitudinal evaluation of COVID-19 patients and prediction of organ-specific recovery using artificial intelligence.

Within COVID-19 there is an urgent unmet need to predict at the time of hospital admission which COVID-19 patients will recover from the disease, and how fast they recover to deliver personalized treatments and to properly allocate hospital resources so that healthcare systems do not become overwhelmed. To this end, we have combined clinically salient CT imaging data synergistically with laboratory testing data in an integrative machine learning model to predict organ-specific recovery of patients from COVID-19. We trained and validated our model in 285 patients on each separate major organ system impacted by COVID-19 including the renal, pulmonary, immune, cardiac, and hepatic systems. To greatly enhance the speed and utility of our model, we applied an artificial intelligence method to segment and classify regions on CT imaging, from which interpretable data could be directly fed into the predictive machine learning model for overall recovery. Across all organ systems we achieved validation set area under the receiver operator characteristic curve (AUC) values for organ-specific recovery ranging from 0.80 to 0.89, and significant overall recovery prediction in Kaplan-Meier analyses. This demonstrates that the synergistic use of an artificial intelligence (AI) framework applied to CT lung imaging and a machine learning model that integrates laboratory test data with imaging data can accurately predict the overall recovery of COVID-19 patients from baseline characteristics.

Viral Delivery of CAR Targets to Solid Tumors Enables Effective Cell Therapy.

Chimeric antigen receptor (CAR) T cell therapy has had limited efficacy for solid tumors, largely due to a lack of selectively and highly expressed surface antigens. To avoid reliance on a tumor's endogenous antigens, here we describe a method of tumor-selective delivery of surface antigens using an oncolytic virus to enable a generalizable CAR T cell therapy. Using CD19 as our proof of concept, we engineered a thymidine kinase-disrupted vaccinia virus to selectively deliver CD19 to malignant cells, and thus demonstrated potentiation of CD19 CAR T cell activity against two tumor types in vitro. In an immunocompetent model of B16 melanoma, this combination markedly delayed tumor growth and improved median survival compared with antigen-mismatched combinations. We also found that CD19 delivery could improve CAR T cell activity against tumor cells that express low levels of cognate antigen, suggesting a potential application in counteracting antigen-low escape. This approach highlights the potential of engineering tumors for effective adoptive cell therapy.

Publisher Correction: A mountable toilet system for personalized health monitoring via the analysis of excreta.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A mountable toilet system for personalized health monitoring via the analysis of excreta.

Technologies for the longitudinal monitoring of a person's health are poorly integrated with clinical workflows, and have rarely produced actionable biometric data for healthcare providers. Here, we describe easily deployable hardware and software for the long-term analysis of a user's excreta through data collection and models of human health. The 'smart' toilet, which is self-contained and operates autonomously by leveraging pressure and motion sensors, analyses the user's urine using a standard-of-care colorimetric assay that traces red-green-blue values from images of urinalysis strips, calculates the flow rate and volume of urine using computer vision as a uroflowmeter, and classifies stool according to the Bristol stool form scale using deep learning, with performance that is comparable to the performance of trained medical personnel. Each user of the toilet is identified through their fingerprint and the distinctive features of their anoderm, and the data are securely stored and analysed in an encrypted cloud server. The toilet may find uses in the screening, diagnosis and longitudinal monitoring of specific patient populations.

A high quantum yield molecule-protein complex fluorophore for near-infrared II imaging.

Fluorescence imaging in the second near-infrared window (NIR-II) allows visualization of deep anatomical features with an unprecedented degree of clarity. NIR-II fluorophores draw from a broad spectrum of materials spanning semiconducting nanomaterials to organic molecular dyes, yet unfortunately all water-soluble organic molecules with >1,000 nm emission suffer from low quantum yields that have limited temporal resolution and penetration depth. Here, we report tailoring the supramolecular assemblies of protein complexes with a sulfonated NIR-II organic dye (CH-4T) to produce a brilliant 110-fold increase in fluorescence, resulting in the highest quantum yield molecular fluorophore thus far. The bright molecular complex allowed for the fastest video-rate imaging in the second NIR window with ∼50-fold reduced exposure times at a fast 50 frames-per-second (FPS) capable of resolving mouse cardiac cycles. In addition, we demonstrate that the NIR-II molecular complexes are superior to clinically approved ICG for lymph node imaging deep within the mouse body.