Publications

The following are trademarks or registered trademarks of Biodesix, Inc.: Nodify Lung, Nodify XL2, Nodify CDT.

Validation of a blood-based autoantibody test to assess lung cancer risk in 4–30 mm pulmonary nodules: a retrospective pooled analysis of four cohort studies

February 2026 | Trevor Pitcher PhD, et al. | Future Oncology | Clinical Validation

Purpose: To validate a blood-based autoantibody test (AAT) as a high specificity, rule-in biomarker for 4–30 mm indeterminate pulmonary nodules (IPN) across malignancy risk. Summary: Retrospective pooled analysis of four cohorts including adults with a 4–30 mm IPN, AAT result, and benign or malignant diagnosis. AAT results were classified as Moderate Level (all patients with elevated autoantibodies), High Level (stricter subset within Moderate Level), or No Significant Level of Autoantibodies Detected (NSLAD). Post-test probability of cancer (pCA) was calculated by applying AAT likelihood ratios to pretest pCA. Performance was assessed overall, by nodule size, and risk strata. Conclusion: AAT provides size- and risk-independent, high-specificity rule-in performance, identifying subsets of patients whose malignancy risk may justify expedited evaluation.

Using a Blood Biomarker to Distinguish Benign From Malignant Pulmonary Nodules: A Subgroup Analysis Comparing Screen Detection, Sex, Smoking History, and Nodule Size

December 2023 | Kathryn J. Long MD, et al. | CHEST | Clinical Validation

Background: An estimated 1.5 million pulmonary nodules are identified in the United States annually, with a prevalence of malignancy of 5%, which rises to 25% by the time patients are referred to a pulmonologist. Current guidelines recommend evaluating the pretest probability of cancer for indeterminant pulmonary nodules using clinical experience and validated clinical risk prediction models to determine next steps in management. Low-risk nodules (probability of cancer, < 5%) are managed with surveillance, whereas those at higher risk undergo functional imaging, biopsy, or surgery. Unfortunately, in current practice many patients undergo invasive testing for benign disease, including 22% of screen-detected nodules. A rule-out biomarker (Biodesix Nodify XL2 test) could aid in risk stratification by determining which nodules can be managed with surveillance, thereby preventing unnecessary invasive procedures. Purpose: To evaluate the performance of the IC among various subgroups of patients using two large prospective cohorts based on screen detection, sex, smoking status, and nodule size. Summary: A proteomic integrated classifier (IC) that combines two plasma proteins (LG3BP and C163A) with five clinical and imaging factors (age, smoking status, nodule size, edge, and location) previously was shown to assist in identifying benign nodules with a negative predictive value (NPV) of 98%. Conclusion: It is estimated that use of the Biodesix biomarker in clinical practice could decrease the rate of invasive testing by as much as 40%.

Impact of an integrated classifier using biomarkers, clinical and imaging factors on clinical decisions making for lung nodules

July 2023 | Fayez Kheir MD, et al. | Journal of Thoracic Disease | Clinical Utility

Goal: An integrated classifier that utilizes plasma proteomic biomarker along with five clinical and imaging factors was previously shown to be potentially useful in lung nodule evaluation. This study evaluated the impact of the integrated proteomic classifier on management decisions in patients with a pretest probability of cancer (pCA) ≤50% in "real-world" clinical setting. Conclusion: In patients with lung nodules with a pCA ≤50%, use of the integrated classifier was associated with fewer invasive procedures and clinic visits without misclassifying patients with likely benign lung nodules results at 1-year follow-up.

Assessing a biomarker's ability to reduce invasive procedures in patients with benign lung nodules: Results from the ORACLE study

July 2023 | Michael A. Pritchett DO, et al. | PLOS One | Clinical Utility

Background: A blood-based integrated classifier (IC) has been clinically validated to improve accuracy in assessing probability of cancer risk (pCA) for pulmonary nodules (PN). This study evaluated the clinical utility of this biomarker for its ability to reduce invasive procedures in patients with pre-test pCA ≤ 50%. Goal: The primary aim of this study was to evaluate invasive procedure use on benign PNs of registry patients as compared to control patients. Summary: This was a propensity score matching (PSM) cohort study comparing patients in the ORACLE prospective, multicenter, observational registry to control patients treated with usual care. This study enrolled patients meeting the intended use criteria for IC testing: pCA ≤ 50%, age ≥40 years, nodule diameter 8–30 mm, and no history of lung cancer and/or active cancer (except for non-melanomatous skin cancer) within 5 years. Conclusion: The IC for patients with a newly discovered PN has demonstrated valuable clinical utility in a real-world setting. Use of this biomarker can change physicians' practice and reduce invasive procedures in patients with benign pulmonary nodules.

Assessment of Integrated Classifier's Ability to Distinguish Benign From Malignant Lung Nodules Extended Analyses and 2-Year Follow-Up Results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial

March 2021 | Nichole T. Tanner MD, et al. | CHEST | Clinical Validation

Background: Pulmonary nodules pose a diagnostic dilemma for clinicians and patients. Guidelines for nodule management emphasize assessment of pretest probability for malignancy (pCA) in determining next steps. The goal for nodule management is to avoid diagnostic procedures in those with benign disease and expedite diagnosis and treatment in those with malignancy. A rule-out biomarker (Biodesix Nodify Lung XL2 test) can assist in improving risk stratification to shift benign nodules into surveillance, thereby minimizing invasive procedures. Summary: Here we report the 2-year follow-up results of the PulmonAry NOdule Plasma proTeomIc Classifier (PANOPTIC) trial and an extended analysis to those with multiple pulmonary nodules. An integrated proteomic biomarker combining two plasma proteins (LG3BP and C163A) with five clinical/imaging factors (age, smoking status, nodule size, edge, and location) was previously shown to be potentially useful in nodule patients with a pretest probability of malignancy of 50% or less. Conclusion: Using 1-year follow-up to determine benignity, the biomarkers accuracy reported a sensitivity of 97%, specificity of 44%, and negative predictive value of 98%. The biomarker was more accurate than physician and risk calculator assessments.

Assessment of Plasma Proteomics Biomarker's Ability to Distinguish Benign From Malignant Lung Nodules Results of the PANOPTIC (Pulmonary Nodule Plasma Proteomic Classifier) Trial

September 2018 | Gerard A. Silvestri MD, et al. | CHEST | Clinical Validation

Background: Lung nodules are a diagnostic challenge, with an estimated yearly incidence of 1.6 million in the United States. Goal: This study evaluated the accuracy of an integrated proteomic classifier (Biodesix Nodify XL2 test) in identifying benign nodules in patients with a pretest probability of cancer (pCA) ≤ 50%. Summary: When used in patients with lung nodules with a pCA ≤ 50%, the integrated classifier accurately identifies benign lung nodules. If used in clinical practice, invasive procedures could be reduced by diverting benign nodules to surveillance.

An integrated risk predictor for pulmonary nodules

May 2017 | Paul Kearney PhD, et al. | PLOS One | Discovery

Background: It is estimated that over 1.5 million lung nodules are detected annually in the United States. Most of these are benign but frequently undergo invasive and costly procedures to rule out malignancy. A risk predictor that can accurately differentiate benign and malignant lung nodules could be used to route benign lung nodules more efficiently to non-invasive observation by CT surveillance and route malignant lung nodules to invasive procedures. The majority of risk predictors developed to date are based exclusively on clinical risk factors, imaging technology, or molecular markers. Summary: Assessed here are the relative performances of previously reported clinical risk factors and proteomic molecular markers for assessing cancer risk in lung nodules. From this analysis an integrated model incorporating clinical risk factors and proteomic molecular markers is developed and its performance assessed on a subset of 222 lung nodules, between 8mm and 20mm in diameter, collected in a previously reported prospective study. Conclusion: In this analysis, it is found that the molecular marker is most predictive. However, the integration of clinical and molecular markers is superior to both clinical and molecular markers separately.

Autoantibody Signature Enhances the Positive Predictive Power of Computed Tomography and Nodule-Based Risk Models for Detection of Lung Cancer

March 2017 | Pierre P. Massion MD, et al. | Journal of Thoracic Oncology | Clinical Validation

Background: The incidence of pulmonary nodules is increasing with the movement toward screening for lung cancer by low-dose computed tomography. Given the large number of benign nodules detected by computed tomography, an adjunctive test capable of distinguishing malignant from benign nodules would benefit practitioners. Summary: The ability of the EarlyCDT-Lung blood test (Biodesix Nodify Lung Nodule Risk Assessment) to make this distinction by measuring autoantibodies to seven tumor-associated antigens was evaluated in a prospective registry. Conclusion: These data confirm that Nodify Lung testing may add value to the armamentarium of the practitioner in assessing the risk for malignancy in indeterminate pulmonary nodules.

An integrated quantification method to increase the precision, robustness, and resolution of protein measurement in human plasma samples

January 2015 | Xiao-jun Li PhD, et al. | Clinical Proteomics | Analytical Validation

Background: Current quantification methods for mass spectrometry (MS)-based proteomics either do not provide sufficient control of variability or are difficult to implement for routine clinical testing. MS is used with the Biodesix Nodify XL2 test. Summary: We present here an integrated quantification (InteQuan) method that better controls pre-analytical and analytical variability than the popular quantification method using stable isotope-labeled standard peptides (SISQuan). We quantified 16 lung cancer biomarker candidates in human plasma samples in three assessment studies, using immunoaffinity depletion coupled with multiple reaction monitoring (MRM) MS. InteQuan outperformed SISQuan in precision in all three studies and tolerated a two-fold difference in sample loading. Conclusion: We demonstrated that InteQuan is a simple yet robust quantification method for MS-based quantitative proteomics, especially for applications in biomarker research and in routine clinical testing.

A Blood-Based Proteomic Classifier for the Molecular Characterization of Pulmonary Nodules

October 2013 | Xiao-jun Li PhD, et al. | Science Translational Medicine | Discovery

Summary: We present a 13-protein blood-based classifier (Biodesix Nodify Lung testing) that differentiates malignant and benign nodules with high confidence, thereby providing a diagnostic tool to avoid invasive biopsy on benign nodules. Validation performance on samples from a non-discovery clinical site showed an NPV of 94%, indicating the general effectiveness of the classifier. A pathway analysis demonstrated that the classifier proteins are likely modulated by a few transcription regulators (NF2L2, AHR, MYC, and FOS) that are associated with lung cancer, lung inflammation, and oxidative stress networks. Conclusion: The (Biodesix Nodify Lung) classifier score was independent of patient nodule size, smoking history, and age, which are risk factors used for clinical management of pulmonary nodules. Thus, this molecular test provides a potential complementary tool to help physicians in lung cancer diagnosis.

EarlyCDT-Lung test: improved clinical utility through additional autoantibody assays

April 2012 | Caroline J. Chapman PhD, et al. | Tumor Biology | Discovery

Tumor-associated autoantibodies (AAbs) have been described in patients with lung cancer, and the EarlyCDT Lung test (today's product is called Biodesix Nodify Lung Nodule Risk Assessment) that measures such AAbs is available as an aid for the early detection of lung cancer in high-risk populations. Samples from 235 patients with newly diagnosed lung cancer and matched controls were measured for the presence of AAbs to a panel of six antigens. Data were assessed in relation to cancer type and stage. The sensitivity and specificity of these two panels were also compared in two prospective consecutive series of 776 and 836 individuals at an increased risk of developing lung cancer.

Technical validation of an autoantibody test for lung cancer

February 2010 | Andrea Murray, et al. | Annals of Oncology | Analytical Validation

Background: Three separate groups of patients with newly diagnosed lung cancer were identified, along with control individuals, and their samples used to validate an enzyme-linked immunosorbant assay. Precision, linearity, assay reproducibility, and antigen batch reproducibility were all assessed. Goal: The validation of a panel of six tumor-associated antigens (TAAs) to which autoantibodies have been described is reported. Summary: The sensitivity and specificity of individual batches of antigen varied slightly between groups of patients; however, the sensitivity and specificity of the panel of antigens as a whole remained constant. The validity of the calibration system was demonstrated. Conclusion: A calibrated six-panel assay of TAAs has been validated for identifying nearly 40% of primary lung cancers via a peripheral blood test. Levels of reproducibility, precision, and linearity would be acceptable for an assay used in a regulated clinical setting.

Autoantibodies in lung cancer: possibilities for early detection and subsequent cure

August 2007 | Caroline J. Chapmen PhD, et al. | Thorax | Discovery

Background: Autoantibodies have been shown to be present in the circulation of people with various forms of solid tumor before cancer-associated antigens can be detected, and these molecules can be measured up to 5 years before symptomatic disease. Goal: To assess the potential of a panel of tumor-associated autoantibody profiles as an aid to other lung cancer screening modalities. Results: Raised levels of autoantibodies were seen to at least 1/7 antigens in 76% of all the patients with lung cancer plasma tested, and 89% of node-negative patients, with a specificity of 92%. There was no significant difference between the detection rates in the lung cancer subgroups, although more patients with squamous cell carcinomas (92%) could be identified. Conclusion: measurement of an autoantibody response to one or more tumor-associated antigens in an optimized panel assay may provide a sensitive and specific blood test to aid the early detection of lung cancer.

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