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Patient-level clinical outcome data (overall survival) and bTMB scores

Mutations Analyzed

GuardantOMNI assay success rates

Purpose:The role of plasma-based tumor mutation burden (pTMB) in predicting response to pembrolizumab-based first-line standard-of-care therapy for metastatic non–small cell lung cancer (mNSCLC) has not been explored.Experimental Design:A 500-gene next-generation sequencing panel was used to assess pTMB. Sixty-six patients with newly diagnosed mNSCLC starting first-line pembrolizumab-based therapy, either alone or in combination with chemotherapy, were enrolled (Clinicaltrial.gov identifier: NCT03047616). Response was assessed using RECIST 1.1. Associations were made for patient characteristics, 6-month durable clinical benefit (DCB), progression-free survival (PFS), and overall survival (OS).Results:Of 66 patients, 52 (78.8%) were pTMB-evaluable. Median pTMB was 16.8 mutations per megabase (mut/Mb; range, 1.9–52.5) and was significantly higher for patients achieving DCB compared with no durable benefit (21.3 mut/Mb vs. 12.4 mut/Mb, P = 0.003). For patients with pTMB ≥ 16 mut/Mb, median PFS was 14.1 versus 4.7 months for patients with pTMB < 16 mut/Mb [HR, 0.30 (0.16–0.60); P < 0.001]. Median OS for patients with pTMB ≥ 16 was not reached versus 8.8 months for patients with pTMB < 16 mut/Mb [HR, 0.48 (0.22–1.03); P = 0.061]. Mutations in ERBB2 exon 20, STK11, KEAP1, or PTEN were more common in patients with no DCB. A combination of pTMB ≥ 16 and absence of negative predictor mutations was associated with PFS [HR, 0.24 (0.11–0.49); P < 0.001] and OS [HR, 0.31 (0.13–0.74); P = 0.009].Conclusions:pTMB ≥ 16 mut/Mb is associated with improved PFS after first-line standard-of-care pembrolizumab-based therapy in mNSCLC. STK11/KEAP1/PTEN and ERBB2 mutations may help identify pTMB-high patients unlikely to respond. These results should be validated in larger prospective studies.

pTMBand PD-L1 expression levels.

Purpose:Tumor mutational burden (TMB) has been shown to be predictive of survival benefit in patients with non–small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors. Measuring TMB in the blood (bTMB) using circulating cell-free tumor DNA (ctDNA) offers practical advantages compared with TMB measurement in tissue (tTMB); however, there is a need for validated assays and identification of optimal cutoffs. We describe the analytic validation of a new bTMB algorithm and its clinical utility using data from the phase III MYSTIC trial.Patients and Methods:The dataset used for the clinical validation was from MYSTIC, which evaluated first-line durvalumab (anti–PD-L1 antibody) ± tremelimumab (anticytotoxic T-lymphocyte-associated antigen-4 antibody) or chemotherapy for metastatic NSCLC. bTMB and tTMB were evaluated using the GuardantOMNI and FoundationOne CDx assays, respectively. A Cox proportional hazards model and minimal P value cross-validation approach were used to identify the optimal bTMB cutoff.Results:In MYSTIC, somatic mutations could be detected in ctDNA extracted from plasma samples in a majority of patients, allowing subsequent calculation of bTMB. The success rate for obtaining valid TMB scores was higher for bTMB (809/1,001; 81%) than for tTMB (460/735; 63%). Minimal P value cross-validation analysis confirmed the selection of bTMB ≥20 mutations per megabase (mut/Mb) as the optimal cutoff for clinical benefit with durvalumab + tremelimumab.Conclusions:Our study demonstrates the feasibility, accuracy, and reproducibility of the GuardantOMNI ctDNA platform for quantifying bTMB from plasma samples. Using the new bTMB algorithm and an optimal bTMB cutoff of ≥20 mut/Mb, high bTMB was predictive of clinical benefit with durvalumab + tremelimumab versus chemotherapy.

Development and validation of a plasma-based panel for mutation detection and assessment of TMB.

PURPOSE As immune checkpoint inhibitors (ICI) become increasingly used in frontline settings, identifying early indicators of response is needed. Recent studies suggest a role for circulating tumor DNA (ctDNA) in monitoring response to ICI, but uncertainty exists in the generalizability of these studies. Here, the role of ctDNA for monitoring response to ICI is assessed through a standardized approach by assessing clinical trial data from five independent studies. PATIENTS AND METHODS Patient-level clinical and ctDNA data were pooled and harmonized from 200 patients across five independent clinical trials investigating the treatment of patients with non–small-cell lung cancer with programmed cell death-1 (PD-1)/programmed death ligand-1 (PD-L1)–directed monotherapy or in combination with chemotherapy. CtDNA levels were measured using different ctDNA assays across the studies. Maximum variant allele frequencies were calculated using all somatic tumor-derived variants in each unique patient sample to correlate ctDNA changes with overall survival (OS) and progression-free survival (PFS). RESULTS We observed strong associations between reductions in ctDNA levels from on-treatment liquid biopsies with improved OS (OS; hazard ratio, 2.28; 95% CI, 1.62 to 3.20; P < .001) and PFS (PFS; hazard ratio 1.76; 95% CI, 1.31 to 2.36; P < .001). Changes in the maximum variant allele frequencies ctDNA values showed strong association across different outcomes. CONCLUSION In this pooled analysis of five independent clinical trials, consistent and robust associations between reductions in ctDNA and outcomes were found across multiple end points assessed in patients with non–small-cell lung cancer treated with an ICI. Additional tumor types, stages, and drug classes should be included in future analyses to further validate this. CtDNA may serve as an important tool in clinical development and an early indicator of treatment benefit.

Background: The combination of a CDK4/6 inhibitor and an aromatase inhibitor (AI) is the gold standard for AI-sensitive first line treatment of ER+ HER2- advanced breast cancer. Nevertheless, some patients progress rapidly and may benefit from alternative strategies. Early ctDNA dynamics have been shown to predict disease course in several clinical situations. Here, we use samples from the PADA-1 trial to assess this strategy for patients receiving AI and palbociclib as first line treatment. PADA-1 was designed to assess the clinical utility of sequential analysis of ctDNA for emerging ESR1 mutations to trigger an early switch from AI plus palbociclib to fulvestrant plus palbociclib treatment. The study included 1,017 patients and was positive on its primary end-point. The objective of this translational study was to analyze the predictive value of 4-week molecular response (MR) for patient progression. Material & Method: First, a CLIA-validated targeted next-generation sequencing-based test (Guardant360 Response) was used to characterize changes in ctDNA level via detection of somatic single-nucleotide variants (SNVs), insertion/deletion mutations (indels), and gene fusions in 74 genes frequently mutated in cancer. A second analysis was restricted to cancer-associated mutations in 11 genes commonly mutated in breast cancer (PIK3CA, GATA3, TP53, AKT1, ERBB2, BRCA1, BRCA2, ATM, ESR1, PALB2 and RB1). The threshold for molecular response was defined as ≥ 50% decrease in ctDNA (MR score < 0.5). Subjects with ctDNA levels below the test’s limit of quantitation (ctDNA-low) were considered molecular responders. Results: 372 subjects with matched baseline and 4-weeks samples were available for analysis. Of these, 134 subjects (36%) were ctDNA-low, and 238 subjects (64%) quantifiable. Among the quantifiable subjects, 183 (77%) were molecular responders (MR+, MR < 0.5), and 55 (23%) were not (MR–, MR ≥ 0.5). PFS was moderately improved for both MR+ and ctDNA-low relative to MR– (HR=0.61 (95%CI 0.44-0.85), p< 0.01) over the full 29 months of follow up. Differential PFS event rate was observed only in the first 8 months following ctDNA assessment; during this time MR+ and ctDNA-low were associated with more significantly decreased risk of progression (HR 0.24, 95% CI 0.13 – 0.43, p=0.0001). Limiting ctDNA assessment to genes commonly mutated in breast cancer enhanced the predictive power of MR (HR=0.08, 95% CI 0.04 0.17, p< 0.001, for MR+ and ctDNA-low vs. MR– across 8 months post-assessment); however, fewer samples were quantifiable by this method (169 [45%] quantifiable; 203 [55%] ctDNA-low). Combining MR status with additional molecular features (e.g.tumor mutational burden and maximum mutation allele fraction) did not improve prediction of non-response. Conclusion: Changes in ctDNA fraction during the first weeks of treatment are predictive of long term clinical benefit on an individual patient basis, particularly during the first year of therapy. Adjusting the MR threshold and/or limiting to genes known to be relevant in the specific tumor can tailor the assessment of ctDNA change to specific clinical scenarios where greater sensitivity or specificity may be required. The identification of patients at high risk for early clinical failure at the onset of treatment may allow for therapy escalation and/or change to improve outcome in this population. Funding: Pfizer and Guardant Health Citation Format: Caroline Bailleux, Thomas Bachelot, Francois-Clement Bidard, Anne-Claire Hardy-Bessard, Ivan Bièche, Anne Pradines, Florian Clatot, thibault DE LA MOTTE ROUGE, Jean-Luc Canon, Barbara Pistilli, Kyle Chang, Katie J. Quinn, Heather L. Gustafson, Florence Dalenc, Cyril Foa, Hanifa Ammarguellat, Chantal Bernard-Marty, Brigitte Lucas, Sophie Barthier, Fabrice Lorchel, Olivier Gisserot, Laurent Arnould, Marjorie Mauduit, Jérôme Lemonnier, Frédérique Berger, Suzette Delaloge, Fabrice Andre. ctDNA Molecular Response based on breast cancer driver mutations predicts progression in aromatase inhibitor-sensitive first line treatment of oestrogen receptor-positive (ER+) HER2-negative (HER2-) advanced breast cancer. [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD17-02.

Summary Background There is an urgent need for biomarkers to better stratify patients with idiopathic pulmonary fibrosis by risk for lung transplantation allocation who have the same clinical presentation. We aimed to investigate whether a specific immune cell type from patients with idiopathic pulmonary fibrosis could identify those at higher risk of poor outcomes. We then sought to validate our findings using cytometry and electronic health records. Methods We first did a discovery analysis with transcriptome data from the Gene Expression Omnibus at the National Center for Biotechnology Information for 120 peripheral blood mononuclear cell (PBMC) samples of patients with idiopathic pulmonary fibrosis. We estimated percentages of 13 immune cell types using statistical deconvolution, and investigated the association of these cell types with transplant-free survival. We validated these results using PBMC samples from patients with idiopathic pulmonary fibrosis in two independent cohorts (COMET and Yale). COMET profiled monocyte counts in 45 patients with idiopathic pulmonary fibrosis from March 12, 2010, to March 10, 2011, using flow cytometry; we tested if increased monocyte count was associated with the primary outcome of disease progression. In the Yale cohort, 15 patients with idiopathic pulmonary fibrosis (with five healthy controls) were classed as high risk or low risk from April 28, 2014, to Aug 20, 2015, using a 52-gene signature, and we assessed whether monocyte percentage (measured by cytometry by time of flight) was higher in high-risk patients. We then examined complete blood count values in the electronic health records (EHR) of 45 068 patients with idiopathic pulmonary fibrosis, systemic sclerosis, hypertrophic cardiomyopathy, or myelofibrosis from Stanford (Jan 01, 2008, to Dec 31, 2015), Northwestern (Feb 15, 2001 to July 31, 2017), Vanderbilt (Jan 01, 2008, to Dec 31, 2016), and Optum Clinformatics DataMart (Jan 01, 2004, to Dec 31, 2016) cohorts, and examined whether absolute monocyte counts of 0·95 K/μL or greater were associated with all-cause mortality in these patients. Findings In the discovery analysis, estimated CD14+ classical monocyte percentages above the mean were associated with shorter transplant-free survival times (hazard ratio [HR] 1·82, 95% CI 1·05–3·14), whereas higher percentages of T cells and B cells were not (0·97, 0·59–1·66; and 0·78, 0·45–1·34 respectively). In two validation cohorts (COMET trial and the Yale cohort), patients with higher monocyte counts were at higher risk for poor outcomes (COMET Wilcoxon p=0·025; Yale Wilcoxon p=0·049). Monocyte counts of 0·95 K/μL or greater were associated with mortality after adjusting for forced vital capacity (HR 2·47, 95% CI 1·48–4·15; p=0·0063), and the gender, age, and physiology index (HR 2·06, 95% CI 1·22–3·47; p=0·0068) across the COMET, Stanford, and Northwestern datasets). Analysis of medical records of 7459 patients with idiopathic pulmonary fibrosis showed that patients with monocyte counts of 0·95 K/μL or greater were at increased risk of mortality with lung transplantation as a censoring event, after adjusting for age at diagnosis and sex (Stanford HR=2·30, 95% CI 0·94–5·63; Vanderbilt 1·52, 1·21–1·89; Optum 1·74, 1·33–2·27). Likewise, higher absolute monocyte count was associated with shortened survival in patients with hypertrophic cardiomyopathy across all three cohorts, and in patients with systemic sclerosis or myelofibrosis in two of the three cohorts. Interpretation Monocyte count could be incorporated into the clinical assessment of patients with idiopathic pulmonary fibrosis and other fibrotic disorders. Further investigation into the mechanistic role of monocytes in fibrosis might lead to insights that assist the development of new therapies. Funding Bill & Melinda Gates Foundation, US National Institute of Allergy and Infectious Diseases, and US National Library of Medicine.

Polypharmacy is increasingly common in the United States, and contributes to the substantial burden of drug-related morbidity. Yet real-world polypharmacy patterns remain poorly characterized. We have counted the incidence of multi-drug combinations observed in four billion patient-months of outpatient prescription drug claims from 2007–2014 in the Truven Health MarketScan® Databases. Prescriptions are grouped into discrete windows of concomitant drug exposure, which are used to count exposure incidences for combinations of up to five drug ingredients or ATC drug classes. Among patients taking any prescription drug, half are exposed to two or more drugs, and 5% are exposed to 8 or more. The most common multi-drug combinations treat manifestations of metabolic syndrome. Patients are exposed to unique drug combinations in 10% of all exposure windows. Our analysis of multi-drug exposure incidences provides a detailed summary of polypharmacy in a large US cohort, which can prioritize common drug combinations for future safety and efficacy studies.