Linoleoyl-lysophosphatidylcholine suppresses immune-related adverse events due to immune checkpoint blockade.

Immune related adverse events (irAEs) after immune checkpoint blockade (ICB) therapy occur in a significant proportion of cancer patients. To date, the circulating mediators of ICB-irAEs remain poorly understood. Using non-targeted mass spectrometry, here we identify the circulating bio-active lipid linoleoyl-lysophosphatidylcholine (LPC 18:2) as a modulator of ICB-irAEs. In three independent human studies of ICB treatment for solid tumor, loss of circulating LPC 18:2 preceded the development of severe irAEs across multiple organ systems. In both healthy humans and severe ICB-irAE patients, low LPC 18:2 was found to correlate with high blood neutrophilia. Reduced LPC 18:2 biosynthesis was confirmed in preclinical ICB-irAE models, and LPC 18:2 supplementation in vivo suppressed neutrophilia and tissue inflammation without impacting ICB anti-tumor response. Results indicate that circulating LPC 18:2 suppresses human ICB-irAEs, and LPC 18:2 supplementation may improve ICB outcomes by preventing severe inflammation while maintaining anti-tumor immunity.

Validation of an Updated Algorithm to Identify Patients With Incident Non-Small Cell Lung Cancer in Administrative Claims Databases.

PURPOSE: Real-world lung cancer data in administrative claims databases often lack staging information and specific diagnostic codes for lung cancer histology subtypes. This study updates and validates Turner's 2017 treatment-based algorithm using more recent claims and electronic health record (EHR) data. METHODS: This study used Optum's deidentified Market Clarity Data of linked medical and pharmacy claims with EHR data. Eligible patients had an incident lung cancer diagnosis (January 2014-December 2020) and ≥one valid histology code for lung cancer 30 days before to 60 days after diagnosis. Histology and stage information from the EHR were used to evaluate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We evaluated the Turner algorithm using cohort 1 patients diagnosed between June 2014 and October 2015 (step 1) and between November 2015 and December 2020 after approval of immunotherapies (step 2). Next, we evaluated cohort 2 patients diagnosed between November 2015 and December 2020 using an updated algorithm incorporating the latest US treatment guidelines (step 3), and compared the results for cohort 2 (Turner algorithm, step 2 patients). Furthermore, an algorithm to determine early NSCLC (eNSCLC; stage I-III) versus metastatic or advanced/metastatic non-small cell lung cancer (stage IV) was evaluated among patients with available histology and stage information. RESULTS: A total of 5,012 patients were included (cohort 1, step 1: n = 406; cohort 1, step 2: n = 2,573; cohort 2, step 3: n = 2,744). The updated algorithm showed improved performance relative to the previous Turner algorithm for sensitivity (0.920-0.932), specificity (0.865-0.923), PPV (0.976-0.988), and NPV (0.640-0.673). The eNSCLC algorithm showed high specificity (0.874) and relatively low sensitivity (0.539). CONCLUSION: An updated treatment-based algorithm identifying patients with incident NSCLC was validated using EHR data and distinguished lung cancer subtypes in claims databases when EHR data were not available.

Robustness of cancer microbiome signals over a broad range of methodological variation.

In 2020, we identified cancer-specific microbial signals in The Cancer Genome Atlas (TCGA) [1]. Multiple peer-reviewed papers independently verified or extended our findings [2-12]. Given this impact, we carefully considered concerns by Gihawi et al. [13] that batch correction and database contamination with host sequences artificially created the appearance of cancer type-specific microbiomes. (1) We tested batch correction by comparing raw and Voom-SNM-corrected data per-batch, finding predictive equivalence and significantly similar features. We found consistent results with a modern microbiome-specific method (ConQuR [14]), and when restricting to taxa found in an independent, highly-decontaminated cohort. (2) Using Conterminator [15], we found low levels of human contamination in our original databases (~1% of genomes). We demonstrated that the increased detection of human reads in Gihawi et al. [13] was due to using a newer human genome reference. (3) We developed Exhaustive, a method twice as sensitive as Conterminator, to clean RefSeq. We comprehensively host-deplete TCGA with many human (pan)genome references. We repeated all analyses with this and the Gihawi et al. [13] pipeline, and found cancer type-specific microbiomes. These extensive re-analyses and updated methods validate our original conclusion that cancer type-specific microbial signatures exist in TCGA, and show they are robust to methodology.

Immuno-Oncology: New Insights into Targets and Therapies.

The role of immunotherapy in the care of surgical oncology patients promises to expand as investigators and clinicians evaluate new targets and approaches. Currently active clinical trials evaluate new immune checkpoints, including lymphocyte activation gene 3, T cell immunoreceptor with Ig and ITIM domains, and killer Ig-like receptor 2DL1/2L3. Vaccines delivered through mRNA have demonstrated exciting results in early clinical trials and hold promise for expanded application. Investigational approaches include dendritic cell vaccines, peptide vaccines, cytokines therapies, and cellular therapies. These studies have the potential to revolutionize the management of surgical oncology patients and promote durable cures following surgical resection.