Low booster uptake in cancer patients despite health benefits

Patients with cancer are at increased risk of death from COVID-19 and have reduced immune responses to SARS-CoV2 vaccines, necessitating regular boosters. We performed comprehensive chart reviews, surveys of patients attitudes, serology for SARS-CoV-2 antibodies and T-cell receptor (TCR) β sequencing for cellular responses on a cohort of 982 cancer patients receiving active cancer therapy accrued between November-3-2020 and Mar-31-2023. We found that 92·3% of patients received the primer vaccine, 70·8% received one monovalent booster, but only 30·1% received a bivalent booster. Booster uptake was lower under age 50, and among African American or Hispanic patients. Nearly all patients seroconverted after 2+ booster vaccinations (>99%) and improved cellular responses, demonstrating that repeated boosters could overcome poor response to vaccination. Receipt of booster vaccinations was associated with a lower risk of all-cause mortality (HR=0·61, P=0·024). Booster uptake in high-risk cancer patients remains low and strategies to encourage booster uptake are needed.

Highlights

COVID-19 booster vaccinations increase antibody levels and maintain T-cell responses against SARS-CoV-2 in patients receiving various anti-cancer therapiesBooster vaccinations reduced all-cause mortality in patientsA significant proportion of patients remain unboosted and strategies are needed to encourage patients to be up-to-date with vaccinations.

Cryobanking of Human Distal Lung Epithelial Cells for Preservation of Their Phenotypic and Functional Characteristics

The epithelium lining airspaces of the human lung is maintained by regional stem cells including basal cells of pseudostratified airways and alveolar type 2 pneumocytes (AT2) of the gas-exchange region. Despite effective techniques for long-term preservation of airway basal cells, procedures for efficient preservation of functional epithelial cell types of the distal gas-exchange region are lacking. Here we detail a method for cryobanking of epithelial cells from either mouse or human lung tissue for preservation of their phenotypic and functional characteristics. Flow cytometric profiling, epithelial organoid-forming efficiency, and single cell transcriptomic analysis, were used to compare cells recovered from cryobanked tissue with those of freshly dissociated tissue. Alveolar type 2 cells within single cell suspensions of enzymatically digested cryobanked distal lung tissue retained expression of the pan-epithelial marker CD326 and the AT2 cell surface antigen recognized by monoclonal antibody HTII-280, allowing antibody-mediated enrichment and downstream analysis. Isolated AT2 cells from cryobanked tissue were comparable with those of freshly dissociated tissue both in their single cell transcriptome and their capacity for in vitro organoid formation in 3D cultures. We conclude that the cryobanking method described herein allows long-term preservation of distal human lung tissue for downstream analysis of lung cell function and molecular phenotype, and is ideally suited for creation of an easily accessible tissue resource for the research community.

The Molecular Twin platform: a novel machine learning tool for democratization of precision cancer medicine

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Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive cancers. Contemporary analyses focused on a handful of molecular and clinical variables combined with machine learning algorithms (MLA) are unable to accurately predict therapy outcomes. Here, we use the Molecular Twin multi-omic analytical platform that evaluates tumor and host features extracted from 10 multi-omic analytes and provides an array of MLA, including a Parsimonious Biomarker Model that can predict survival and recurrence with limited analytic burden, while maintaining a high degree of fidelity. Methods: Retrospectively collected serum and tissue samples from 74 patients with Stage I/II resectable PDAC were subjected to targeted NGS DNA sequencing, whole transcriptome RNA sequencing, paired tissue proteomics, unpaired serum proteomics, lipidomics and computational pathology. Analytes including plasma proteins, RNA fusions, tissue proteins, plasma lipids, RNA gene expressions, CNVs, INDELS, SNVs and tumor nuclei characteristics, were processed to obtain a panel of 6363 features. 1024 single-omic and multi-omics feature combinations generated from this panel served as input for 7 different types of MLA to predict binary survival (SR) and disease recurrence (DR) outcomes. The resultant 70 single and 7098 multi-omic biomarker models were evaluated for positive predictive value (PPV) and accuracy (ACC) in predicting DR and SR, and feature proportions learned by each ML model using leave-one-patient-out cross-validation strategy. By recursively eliminating features with low importance, we developed progressively parsimonious biomarker models for predicting SR and DR. Results: Our top model was multi-omic and predicted the SR with ACC = 0.85, PPV = 0.87 and the DR with ACC = 0.90, and PPV = 0.91. It outperformed all models based only on one single analyte type including plasma protein, RNA fusion, tissue protein, plasma lipid, clinical, RNA gene expression, tumor nuclei characteristics, CNV, INDEL and SNV, in predicting the SR. This model contained predominantly plasma protein features. Interestingly, less accurate models contained a greater proportion of other features in addition to plasma proteins. Parsimonious feature reduction of the top model stabilized at 589 features yielding an ACC = 0.85, and PPV = 0.85, comparable to the intact model. Conclusions: This proof-of-concept of the Molecular Twin precision medicine platform applied in PDAC reveals the potential of our unique MLA to provide a novel parsimonious biomarker panel with similar fidelity as much larger biomarker panels. If these results are reproduced on larger datasets, across tumor types, the Molecular Twin platform would have significant potential to democratize precision cancer medicine by discovering smaller biomarker panels with the predictive performance of much larger ones thus reducing cost and simplifying assays.

Longitudinal SARS-CoV-2 mRNA Vaccine-Induced Humoral Immune Responses in Patients with Cancer

Longitudinal studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine-induced immune responses in patients with cancer are needed to optimize clinical care. In a prospective cohort study of 366 (291 vaccinated) patients, we measured antibody levels [anti-spike (IgG-(S-RBD) and anti-nucleocapsid immunoglobulin] at three time points. Antibody level trajectories and frequency of breakthrough infections were evaluated by tumor type and timing of treatment relative to vaccination. IgG-(S-RBD) at peak response (median = 42 days after dose 2) was higher (P = 0.002) and remained higher after 4 to 6 months (P = 0.003) in patients receiving mRNA-1273 compared with BNT162b2. Patients with solid tumors attained higher peak levels (P = 0.001) and sustained levels after 4 to 6 months (P < 0.001) compared with those with hematologic malignancies. B-cell targeted treatment reduced peak (P = 0.001) and sustained antibody responses (P = 0.003). Solid tumor patients receiving immune checkpoint inhibitors before vaccination had lower sustained antibody levels than those who received treatment after vaccination (P = 0.043). Two (0.69%) vaccinated and one (1.9%) unvaccinated patient had severe COVID-19 illness during follow-up. Our study shows variation in sustained antibody responses across cancer populations receiving various therapeutic modalities, with important implications for vaccine booster timing and patient selection. SIGNIFICANCE: Long-term studies of immunogenicity of SARS-CoV-2 vaccines in patients with cancer are needed to inform evidence-based guidelines for booster vaccinations and to tailor sequence and timing of vaccinations to elicit improved humoral responses.