Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease-specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders EXpanding (T-REX) was created to identify changes in both rare and common cells across human immune monitoring settings. T-REX identified cells with highly similar phenotypes that localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized MHCII tetramer reagents that mark rhinovirus-specific CD4+ cells were left out during analysis and then used to test whether T-REX identified biologically significant cells. T-REX identified rhinovirus-specific CD4+ T cells based on phenotypically homogeneous cells expanding by ≥95% following infection. T-REX successfully identified hotspots of virus-specific T cells by comparing infection (day 7) to either pre-infection (day 0) or post-infection (day 28) samples. Plotting the direction and degree of change for each individual donor provided a useful summary view and revealed patterns of immune system behavior across immune monitoring settings. For example, the magnitude and direction of change in some COVID-19 patients was comparable to blast crisis acute myeloid leukemia patients undergoing a complete response to chemotherapy. Other COVID-19 patients instead displayed an immune trajectory like that seen in rhinovirus infection or checkpoint inhibitor therapy for melanoma. The T-REX algorithm thus rapidly identifies and characterizes mechanistically significant cells and places emerging diseases into a systems immunology context for comparison to well-studied immune changes.

Immune checkpoint inhibitors increase T cell immunity during SARS-CoV-2 infection.

The COVID-19 pandemic has spread worldwide, yet the role of antiviral T cell immunity during infection and the contribution of immune checkpoints remain unclear. By prospectively following a cohort of 292 patients with melanoma, half of which treated with immune checkpoint inhibitors (ICIs), we identified 15 patients with acute or convalescent COVID-19 and investigated their transcriptomic, proteomic, and cellular profiles. We found that ICI treatment was not associated with severe COVID-19 and did not alter the induction of inflammatory and type I interferon responses. In-depth phenotyping demonstrated expansion of CD8 effector memory T cells, enhanced T cell activation, and impaired plasmablast induction in ICI-treated COVID-19 patients. The evaluation of specific adaptive immunity in convalescent patients showed higher spike (S), nucleoprotein (N), and membrane (M) antigen-specific T cell responses and similar induction of spike-specific antibody responses. Our findings provide evidence that ICI during COVID-19 enhanced T cell immunity without exacerbating inflammation.

A digital single-molecule nanopillar SERS platform for predicting and monitoring immune toxicities in immunotherapy.

The introduction of immune checkpoint inhibitors has demonstrated significant improvements in survival for subsets of cancer patients. However, they carry significant and sometimes life-threatening toxicities. Prompt prediction and monitoring of immune toxicities have the potential to maximise the benefits of immune checkpoint therapy. Herein, we develop a digital nanopillar SERS platform that achieves real-time single cytokine counting and enables dynamic tracking of immune toxicities in cancer patients receiving immune checkpoint inhibitor treatment - broader applications are anticipated in other disease indications. By analysing four prospective cytokine biomarkers that initiate inflammatory responses, the digital nanopillar SERS assay achieves both highly specific and highly sensitive cytokine detection down to attomolar level. Significantly, we report the capability of the assay to longitudinally monitor 10 melanoma patients during immune inhibitor blockade treatment. Here, we show that elevated cytokine concentrations predict for higher risk of developing severe immune toxicities in our pilot cohort of patients.

Clinical and immunologic implications of COVID-19 in patients with melanoma and renal cell carcinoma receiving immune checkpoint inhibitors.

The clinical and immunologic implications of the SARS-CoV-2 pandemic for patients with cancer receiving systemic anticancer therapy have introduced a multitude of clinical challenges and academic controversies. This review summarizes the current evidence, discussion points, and recommendations regarding the use of immune checkpoint inhibitors (ICIs) in patients with cancer during the SARS-CoV-2 pandemic, with a focus on patients with melanoma and renal cell carcinoma (RCC). More specifically, we summarize the theoretical concepts and available objective data regarding the relationships between ICIs and the antiviral immune response, along with recommended clinical approaches to the management of melanoma and RCC patient cohorts receiving ICIs throughout the course of the COVID-19 pandemic. Additional insights regarding the use of ICIs in the setting of current and upcoming COVID-19 vaccines and broader implications toward future pandemics are also discussed.

Detection of differentially abundant cell subpopulations in scRNA-seq data.

Comprehensive and accurate comparisons of transcriptomic distributions of cells from samples taken from two different biological states, such as healthy versus diseased individuals, are an emerging challenge in single-cell RNA sequencing (scRNA-seq) analysis. Current methods for detecting differentially abundant (DA) subpopulations between samples rely heavily on initial clustering of all cells in both samples. Often, this clustering step is inadequate since the DA subpopulations may not align with a clear cluster structure, and important differences between the two biological states can be missed. Here, we introduce DA-seq, a targeted approach for identifying DA subpopulations not restricted to clusters. DA-seq is a multiscale method that quantifies a local DA measure for each cell, which is computed from its k nearest neighboring cells across a range of k values. Based on this measure, DA-seq delineates contiguous significant DA subpopulations in the transcriptomic space. We apply DA-seq to several scRNA-seq datasets and highlight its improved ability to detect differences between distinct phenotypes in severe versus mildly ill COVID-19 patients, melanomas subjected to immune checkpoint therapy comparing responders to nonresponders, embryonic development at two time points, and young versus aging brain tissue. DA-seq enabled us to detect differences between these phenotypes. Importantly, we find that DA-seq not only recovers the DA cell types as discovered in the original studies but also reveals additional DA subpopulations that were not described before. Analysis of these subpopulations yields biological insights that would otherwise be undetected using conventional computational approaches.

18F-FDG-Avid Lymph Nodes After COVID-19 Vaccination on 18F-FDG PET/CT.

ABSTRACT: A 68-year-old man with right cheek melanoma after resection underwent 18F-FDG PET/CT, which was unremarkable except for multiple FDG-avid subcentimeter but rounded lymph nodes in the left axilla. The patient had undergone a COVID-19 vaccination in the left arm 3 weeks prior. As under vaccinations have been documented to cause reactive FDG-avid lymph nodes, the nodes in our patient were considered benign, reactive to the COVID-19 vaccination. Although FDG-avid benign, reactive nodes have been an uncommon finding in the past, the upcoming surge in COVID-19 vaccinations makes this an important finding for the interpreting physician to consider and recognize.

FDG PET Findings Post-COVID Vaccinations: Signs of the Times?

ABSTRACT: Vaccinations can cause hypermetabolic axillary lymphadenopathy on FDG PET. We present the case of a 71-year-old man who underwent FDG PET/CT for melanoma staging 6 days following a COVID (coronavirus disease) vaccination. Imaging showed a prominent intramuscular mass at the vaccination site, in addition to extensive axillary lymphadenopathy. The mass was compatible with a hematoma at the vaccination site, and the lymphadenopathy was most likely reactive. This case demonstrates unconventional findings in response to a routine vaccination event-findings that, in light of current world events, are likely to be routinely encountered on PET imaging and that should be recognized reactive rather malignant.

COVID-19 Vaccination Manifesting as Incidental Lymph Nodal Uptake on 18F-FDG PET/CT.

ABSTRACT: Benign uptake on 18F-FDG PET can be seen with inflammatory conditions. We report a case of an 86-year-old woman with successfully treated nasal melanoma who underwent routine follow-up 18F-FDG PET, day 6 after the second dose of Pfizer-BioNTech COVID-19 vaccine inoculated in the left deltoid muscle. 18F-FDG PET showed increase tracer uptake in the left deltoid muscle and in 2 normal-sized left subpectoral nodes. These findings were considered secondary to vaccination. With the current drive of global COVID-19 immunization, this case highlights the importance of documenting vaccination history at the time of scanning to avoid false-positive results.

Data of Italian Cancer Centers from two regions with high incidence of SARS CoV-2 infection provide evidence for the successful management of patients with locally advanced and metastatic melanoma treated with immunotherapy in the era of COVID-19.

BACKGROUND: Patients with cancer are presumed to have a higher risk to contract SARS-CoV-2 infection, because of their immunosuppressed status. The impact and course of COVID-19 infection in cancer patients receiving immunotherapy remains unknown. OBJECTIVES: To evaluate the safety of the management of patients with advanced melanoma treated with immunotherapy in 2 Cancer Centers located in areas of Italy with a high incidence of COVID-19 infections. METHODS: We retrospectively analyzed data from January 1 to April 30, 2020 on patients with locally advanced and metastatic melanoma receiving immunotherapy at either Istituto Europeo di Oncologia or Città della Salute e della Scienza University Hospital. RESULTS: One-hundred and sixty-nine patients with stage III and IV melanoma were treated with an immunotherapy regimen at either Istituto Europeo di Oncologia or Città della Salute e della Scienza University Hospital. One-hundred and four patients continued treatment without interruption or delay, while 49 patients had a treatment delay. The main reasons for treatment delay were older age (median age of the group of patients with or without treatment-delay, respectively 60 and 69 years, P value <0.001) and/or presence of comorbidities (percentage of patients with at least one comorbidity respectively 81% and 62%, in patients with or without treatment delay, P value = 0.001). One-hundred and twelve patients had at least 1 thoracic CT scan performed and radiological findings suspicious for COVID-19 were observed in only 7 cases (4%). Fifteen patients (9%) developed symptoms potentially related to COVID-19; nasopharyngeal swabs were collected in 9 patients and only 1 was positive for SARS-CoV-2. CONCLUSIONS: The incidence of symptomatic COVID-19 infection observed in our cohort of patients with advanced malignant melanoma treated with immunotherapy appears meaningfully lower as compared with that reported in the overall population in Italy as well as in patients affected by solid tumors. We conclude that in patients with locally advanced and metastatic melanoma, immunotherapy can be safely continued without delay in the majority of cases, reserving precautionary delay only for the most frail patients.