SPARC - Screening for Lung Cancer With Platelets Via an AI-enabled RNA-based Classifier (SPARC)

The purpose of this study is to test whether combining a unique analytical approach with changes in platelet RNA expression accurately diagnoses lung cancer. Using retrospective platelet transcriptomic data from 522 patients with non-small cell lung cancer (NSCLC, the most common type of lung cancer), an approach that appears to accurately classify lung cancer has been developed.

The study will build upon these retrospective analyses to prospectively recruit patients with newly diagnosed lung cancer, obtain platelet RNA samples from whole blood, and perform validation analyses. This research will also test whether this approach accurately distinguishes benign from malignant lung nodules.

Study Overview

• Status: Not yet recruiting
• Conditions: Lung Cancer; Lung Nodule
• Intervention / Treatment: Diagnostic test: Platelet RNA-based assay

• Study Type: Observational
• Enrollment (Estimated): 240
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Matthew Rondina, MD, MS; Phone Number: 801-587-8308; Email: matthew.rondina@hsc.utah.edu

Study Locations

• United States
  • Utah
    • Salt Lake City, Utah, United States, 84112
      • University of Utah
      • Contact: Shiven Patel, MD/MBA; Phone Number: 801-585-0255; Email: u0820856@umail.utah.edu
      • Principal Investigator: Shiven Patel, MD/MBA
    • Salt Lake City, Utah, United States, 84148-0001
      • Veterans Affairs SLC Health Care System (VAMC)
      • Contact: Emily Beck, MD; Phone Number: 208-830-6514; Email: Emily.Beck@hsc.utah.edu
      • Principal Investigator: Emily Beck, MD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

The population from which the cohorts will be selected will be from the Huntsman Cancer Institute University of Utah (HCI/UU) and the George E. Wahlen Veterans Affairs Medical Center (VAMC) in Salt Lake City, Utah.

Description

Study Population 1: Treatment-Naïve Patients with a Lung Nodule or Lung Cancer

Inclusion Criteria:

• Aged 21 years or older
• Meeting at least one of the following two criteria:
• Diagnosed with any stage or type of lung cancer
• Having at least one lung nodule identified on imaging (e.g., low dose computed tomography [LDCT] or other diagnostic chest imaging) that was completed within 180 days prior to enrollment, and where there is planned or recommended biopsy, surgical resection, radiation therapy, systemic therapy, or other invasive diagnostic or therapeutic procedure to further evaluate the nodule(s) in the next six months based on the clinical judgment of the patient's provider(s)

Exclusion Criteria:

• Other diagnosis of, or treatment for, any cancer within the last 6 months except for non-melanoma skin cancer, carcinoma in situ of the cervix, or low-grade prostate cancer (defined as Gleason score ≤ 6) treated locally
• A history of lung cancer or metastatic cancer to the lungs from an extrathoracic primary site with definitive treatment (surgical, medical, or radiotherapy) within the past 2 years
• Currently receiving any systemic therapy (chemotherapy, immunotherapy, and/or targeted therapy) or radiation therapy for lung cancer
• Already undergone surgical resection of the lung cancer in part or in whole
• Have undergone invasive diagnostic or therapeutic procedures (e.g., biopsy, surgery) related to the lung nodule within the past 3 months
• Unable to provide blood sample

Study Population 2: Control Subjects

Inclusion Criteria:

• Aged 21 years or older

Exclusion Criteria:

• Any active malignancy or diagnosis of cancer within the last 6 months except for non-melanoma skin cancer, carcinoma in situ of the cervix, or low-grade prostate cancer (defined as Gleason score ≤ 6) treated locally
• Treatment for any cancer within the last 6 months except for non-melanoma skin cancer, carcinoma in situ of the cervix, or low-grade prostate cancer (defined as Gleason score ≤ 6) treated locally
• Hospitalization or surgery (other than minor surgery such as mole removal) within the last 8 weeks
• Renal failure (defined as eGFR < 60 mL/min/1.73m² or on dialysis)
• Liver failure (defined as having hepatic encephalopathy of any degree, OR moderately severe coagulopathy defined as INR ≥ 1.5, OR known cirrhosis, OR ALT of ≥ 10X ULN, OR total bilirubin of ≥ 3.0 mg/dL, OR diagnosis of liver failure)
• Decompensated or end-stage heart failure (defined as ACC/AHA Stage C or Stage D heart failure)
• Venous thrombosis, myocardial infarction, or stroke within the last 8 weeks
• Currently pregnant or have been pregnant within the last 12 weeks
• Any blood product transfusion within the last 8 weeks
• Personal history of lung cancer at any time
• Unable to provide blood sample

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Cohort 1: Lung Cancer; Cohort 1 will enroll 90 participants with Lung Cancer (30 patients with Stage I/II and 60 participants with Stage III/IV). Following informed consent and during the same visit (study visit 1), up to 20mL (the maximum blood volume collected) of whole blood will be collected by peripheral venipuncture by a site phlebotomist into sterile, 4mL EDTA-containing venipuncture tubes.	Diagnostic test: Platelet RNA-based assay; Up to 20mL (the maximum blood volume collected) of whole blood will be collected by peripheral venipuncture by a site phlebotomist into sterile, 4mL EDTA-containing venipuncture tubes. The minimum whole blood collected will be 4mL.
Cohort 2: Lung Nodule; Cohort 1 will enroll 120 participants with Lung Nodules (72 patients with Benign Nodules and 48 participants with Malignant Nodules). Following informed consent and during the same visit (study visit 1), up to 20mL (the maximum blood volume collected) of whole blood will be collected by peripheral venipuncture by a site phlebotomist into sterile, 4mL EDTA-containing venipuncture tubes.	Diagnostic test: Platelet RNA-based assay; Up to 20mL (the maximum blood volume collected) of whole blood will be collected by peripheral venipuncture by a site phlebotomist into sterile, 4mL EDTA-containing venipuncture tubes. The minimum whole blood collected will be 4mL.
Cohort 3: Control Participants without Cancer or Nodule; Cohort 3 will enroll 30 participants without Lunch Cancer or lunch Nodules. Following informed consent and during the same visit (study visit 1), up to 20mL (the maximum blood volume collected) of whole blood will be collected by peripheral venipuncture by a site phlebotomist into sterile, 4mL EDTA-containing venipuncture tubes.	Diagnostic test: Platelet RNA-based assay; Up to 20mL (the maximum blood volume collected) of whole blood will be collected by peripheral venipuncture by a site phlebotomist into sterile, 4mL EDTA-containing venipuncture tubes. The minimum whole blood collected will be 4mL.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity	Sensitivity of the assay in detecting lung cancer	up to one day after study enrollment
Specificity	Specificity of the assay in detecting lung cancer.	up to one day after study enrollment
Positive predictive value (PPV)	Positive predictive value (PPV) in distinguishing benign vs. malignant lung nodules.	up to one day after study enrollment
Negative Predictive Value (NPV)	Negative predictive value (NPV) in distinguishing benign vs. malignant lung nodules.	up to one day after study enrollment

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Performance Across Lung Cancer Stage	Performance of the assay across lung cancer stage (stage I-IV).	up to one day after study enrollment
Performance Across Lung Cancer Histologic Subtypes	Performance of assay across lung cancer histologic subtypes (e.g., adenocarcinoma, squamous, small cell).	up to one day after study enrollment
Validation Completion	Technical and analytical validation completion.	up to one day after study enrollment

Collaborators and Investigators

• Sponsor: University of Utah
• Collaborators: United States Department of Defense
• Investigators: Principal Investigator: Shiven Patel, MD/MBA, University of Utah

Publications and helpful links

General Publications

• Best MG, Sol N, Kooi I, Tannous J, Westerman BA, Rustenburg F, Schellen P, Verschueren H, Post E, Koster J, Ylstra B, Ameziane N, Dorsman J, Smit EF, Verheul HM, Noske DP, Reijneveld JC, Nilsson RJA, Tannous BA, Wesseling P, Wurdinger T. RNA-Seq of Tumor-Educated Platelets Enables Blood-Based Pan-Cancer, Multiclass, and Molecular Pathway Cancer Diagnostics. Cancer Cell. 2015 Nov 9;28(5):666-676. doi: 10.1016/j.ccell.2015.09.018. Epub 2015 Oct 29.
• National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011 Aug 4;365(5):395-409. doi: 10.1056/NEJMoa1102873. Epub 2011 Jun 29.
• Kurzrock R, Chaudhuri AA, Feller-Kopman D, Florez N, Gorden J, Wistuba II. Healthcare disparities, screening, and molecular testing in the changing landscape of non-small cell lung cancer in the United States: a review. Cancer Metastasis Rev. 2024 Dec;43(4):1217-1231. doi: 10.1007/s10555-024-10187-6. Epub 2024 May 16.
• Henderson LM, Benefield T, Bosemani T, Long JM, Rivera MP. Impact of the COVID-19 Pandemic on Volumes and Disparities in Lung Cancer Screening. Chest. 2021 Jul;160(1):379-382. doi: 10.1016/j.chest.2020.12.033. Epub 2021 Jan 5. No abstract available.
• Kalinke L, Thakrar R, Janes SM. The promises and challenges of early non-small cell lung cancer detection: patient perceptions, low-dose CT screening, bronchoscopy and biomarkers. Mol Oncol. 2021 Oct;15(10):2544-2564. doi: 10.1002/1878-0261.12864. Epub 2020 Dec 14.
• Bach PB, Jett JR, Pastorino U, Tockman MS, Swensen SJ, Begg CB. Computed tomography screening and lung cancer outcomes. JAMA. 2007 Mar 7;297(9):953-61. doi: 10.1001/jama.297.9.953.
• Croswell JM, Baker SG, Marcus PM, Clapp JD, Kramer BS. Cumulative incidence of false-positive test results in lung cancer screening: a randomized trial. Ann Intern Med. 2010 Apr 20;152(8):505-12, W176-80. doi: 10.7326/0003-4819-152-8-201004200-00007.
• Brenner DJ. Radiation risks potentially associated with low-dose CT screening of adult smokers for lung cancer. Radiology. 2004 May;231(2):440-5. doi: 10.1148/radiol.2312030880.
• Best MG, Vancura A, Wurdinger T. Platelet RNA as a circulating biomarker trove for cancer diagnostics. J Thromb Haemost. 2017 Jul;15(7):1295-1306. doi: 10.1111/jth.13720.
• Tjon-Kon-Fat LA, Sol N, Wurdinger T, Nilsson RJA. Platelet RNA in Cancer Diagnostics. Semin Thromb Hemost. 2018 Mar;44(2):135-141. doi: 10.1055/s-0037-1606182. Epub 2017 Sep 13.
• Sol N, Leurs CE, Veld SGI', Strijbis EM, Vancura A, Schweiger MW, Teunissen CE, Mateen FJ, Tannous BA, Best MG, Wurdinger T, Killestein J. Blood platelet RNA enables the detection of multiple sclerosis. Mult Scler J Exp Transl Clin. 2020 Jul 30;6(3):2055217320946784. doi: 10.1177/2055217320946784. eCollection 2020 Jul-Sep.
• Gao Y, Liu CJ, Li HY, Xiong XM, Li GL, In 't Veld SGJG, Cai GY, Xie GY, Zeng SQ, Wu Y, Chi JH, Liu JH, Zhang Q, Jiao XF, Shi LL, Lu WR, Lv WG, Yang XS, Piek JMJ, de Kroon CD, Lok CAR, Supernat A, Lapinska-Szumczyk S, Lojkowska A, Zaczek AJ, Jassem J, Tannous BA, Sol N, Post E, Best MG, Kong BH, Xie X, Ma D, Wurdinger T, Guo AY, Gao QL. Platelet RNA enables accurate detection of ovarian cancer: an intercontinental, biomarker identification study. Protein Cell. 2023 Jun 7;14(6):579-590. doi: 10.1093/procel/pwac056.
• Rondina MT, Voora D, Simon LM, Schwertz H, Harper JF, Lee O, Bhatlekar SC, Li Q, Eustes AS, Montenont E, Campbell RA, Tolley ND, Kosaka Y, Weyrich AS, Bray PF, Rowley JW. Longitudinal RNA-Seq Analysis of the Repeatability of Gene Expression and Splicing in Human Platelets Identifies a Platelet SELP Splice QTL. Circ Res. 2020 Feb 14;126(4):501-516. doi: 10.1161/CIRCRESAHA.119.315215. Epub 2019 Dec 19.
• In 't Veld SGJG, Arkani M, Post E, Antunes-Ferreira M, D'Ambrosi S, Vessies DCL, Vermunt L, Vancura A, Muller M, Niemeijer AN, Tannous J, Meijer LL, Le Large TYS, Mantini G, Wondergem NE, Heinhuis KM, van Wilpe S, Smits AJ, Drees EEE, Roos E, Leurs CE, Tjon Kon Fat LA, van der Lelij EJ, Dwarshuis G, Kamphuis MJ, Visser LE, Harting R, Gregory A, Schweiger MW, Wedekind LE, Ramaker J, Zwaan K, Verschueren H, Bahce I, de Langen AJ, Smit EF, van den Heuvel MM, Hartemink KJ, Kuijpers MJE, Oude Egbrink MGA, Griffioen AW, Rossel R, Hiltermann TJN, Lee-Lewandrowski E, Lewandrowski KB, De Witt Hamer PC, Kouwenhoven M, Reijneveld JC, Leenders WPJ, Hoeben A, Verdonck-de Leeuw IM, Leemans CR, Baatenburg de Jong RJ, Terhaard CHJ, Takes RP, Langendijk JA, de Jager SC, Kraaijeveld AO, Pasterkamp G, Smits M, Schalken JA, Lapinska-Szumczyk S, Lojkowska A, Zaczek AJ, Lokhorst H, van de Donk NWCJ, Nijhof I, Prins HJ, Zijlstra JM, Idema S, Baayen JC, Teunissen CE, Killestein J, Besselink MG, Brammen L, Bachleitner-Hofmann T, Mateen F, Plukker JTM, Heger M, de Mast Q, Lisman T, Pegtel DM, Bogaard HJ, Jassem J, Supernat A, Mehra N, Gerritsen W, de Kroon CD, Lok CAR, Piek JMJ, Steeghs N, van Houdt WJ, Brakenhoff RH, Sonke GS, Verheul HM, Giovannetti E, Kazemier G, Sabrkhany S, Schuuring E, Sistermans EA, Wolthuis R, Meijers-Heijboer H, Dorsman J, Oudejans C, Ylstra B, Westerman BA, van den Broek D, Koppers-Lalic D, Wesseling P, Nilsson RJA, Vandertop WP, Noske DP, Tannous BA, Sol N, Best MG, Wurdinger T. Detection and localization of early- and late-stage cancers using platelet RNA. Cancer Cell. 2022 Sep 12;40(9):999-1009.e6. doi: 10.1016/j.ccell.2022.08.006. Epub 2022 Sep 1.
• Supernat A, Popeda M, Pastuszak K, Best MG, Gresner P, Veld SI', Siek B, Bednarz-Knoll N, Rondina MT, Stokowy T, Wurdinger T, Jassem J, Zaczek AJ. Transcriptomic landscape of blood platelets in healthy donors. Sci Rep. 2021 Aug 3;11(1):15679. doi: 10.1038/s41598-021-94003-z.
• Jopek MA, Pastuszak K, Sieczczynski M, Cygert S, Zaczek AJ, Rondina MT, Supernat A. Improving platelet-RNA-based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification. Mol Oncol. 2024 Nov;18(11):2743-2754. doi: 10.1002/1878-0261.13689. Epub 2024 Jun 17.

Study record dates

Study Major Dates

• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): June 1, 2028
• Study Completion (Estimated): June 1, 2028

Study Registration Dates

• First Submitted: March 3, 2026
• First Submitted That Met QC Criteria: March 3, 2026
• First Posted (Actual): March 10, 2026

Study Record Updates

• Last Update Posted (Actual): May 15, 2026
• Last Update Submitted That Met QC Criteria: May 12, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: 193163; LC240423 (Other Grant/Funding Number: US DEPARTMENT OF DEFENSE)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No