Chromosomal Instability as a Surrogate Biomarker of Drug Resistance in Immunotherapy for Lung Cancer Patients

NCT ID: NCT04203095

Last Updated: 2019-12-24

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

UNKNOWN

Total Enrollment

40 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-11-10

Study Completion Date

2021-11-11

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

PD1, as an immune checkpoint inhibitor, has provided a new therapeutic approach for patients with cancer, including patients. Although immunotherapy has proven effective, most patients do not benefit from it because of a large proportion which developing primary and acquired resistance. However, there is still a lack of accurate and effective molecular biomarkers to accurately evaluate the drug resistance of patients treated with immune checkpoint inhibitors (ICI), so as to maximize the therapeutic effect in patients. Chromosomal instability (CIN) is one of the most prominent and common characteristics of solid tumors, accelerating the development of anti-cancer drug resistance, often leading to treatment failure and disease recurrence, which limits the effectiveness of most current treatments. Hence the aim of this study is to evaluate dynamic CIN continuously monitored in the blood of patients with lung cancer treated with ICIs with Ultrasensitive Chromosomal Aneuploidy Detection (UCAD) to establish a new molecular immune resistance evaluation index. Further, the correlation between the evolution of tumor cloning and ICI resistance in patients during treatment was analyzed based on the results of dynamic CIN detection. This not only evaluate the efficacy of the ICI treatment in real-time, but also enables better understanding and overcoming the resistance mechanism of immunotherapy in the future.

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Immune checkpoint inhibitors (ICI) targeted to PD-1/PD-L1 axis has a higher response rate and lower incidence of side effects compared with anti-CTLA4, and has been proved to have survival advantages in many different malignant tumors, which has been approved as a second-line or first-line treatment for a growing number of malignancies, including lung cancer. As results of retrospective analysis led by Roberto Ferrara, although the efficacy of ICI treatment is obvious in non-small cell lung cancer (NSCLC), there are significant differences in efficacy and responsiveness in different patients. Therefore, establishing predictive biomarkers for immunotherapy is the key to maximizing the therapeutic effect and studying drug resistance. According to clinical trial data after immunotherapy, there are three main groups: (1) those who respond initially and continue to respond (responders); (2) those who have never responded (primary resistance); (3) Those who initially respond but eventually develop into disease progression (secondary resistance).Currently, PD-L1 expression is one of the most common biomarkers for immunotherapy, PD-L1 expression itself does not accurately predict immunotherapy response, due to that the many patients with higher PD-L1 have no response to clinical treatment, and many patients with lower PD-L1 respond better. Although tumor mutation burden (TMB ) used as a biomarker for the treatment of NSCLC by Opdivo could better differentiate the people who benefit compared with PD-L1, however, TMB as a biomarker to determine the criteria for the application of ICI treatment resistance is also limited because of its specific mechanism involved in tumor immune regulation needs to be further clarified and high cost of TMB detection using NGS for whole exome sequencing analysis.

As one of the most prominent and common features of solid tumors, chromosomal instability (CIN) accelerates the development of anticancer drug resistance, often leading to treatment failure and disease recurrence, which limits the effectiveness of most current treatments. Previous studies have shown that CIN promotes the emergence of multidrug resistance by providing higher levels of genetic diversity, leading to multidrug resistance. In NSCLC, the researchers found that genomic doubling and sustained dynamic CIN were associated with intratumoral heterogeneity and led to parallel evolution of CDNAs, including CDK4, FOXA1, and BCL11A. It is worth noting that the study found consistency in the variation of mutation levels, indicating that CIN in lung cancer is more likely to select driving events than other mutation processes. CIN enables cells to enter several different evolutionary trajectories and adapt to the selective pressure generated by treatment, which is the basis of drug resistance. Based on the above, CIN may become a more accurate and effective biomarker for the study of drug resistance mechanism of ICI in lung cancer. NGS technology can obtain more comprehensive genomic information while detecting cost reduction, making CIN detection more accurate and practical than FISH used for evaluating CIN in patient commonly.As a new Detection method based on NGS technology, Ultrasensitive Chromosomal Aneuploidy Detection (UCAD) has been developed in our previous study. In which, low-coverage whole-genome sequencing technology based on NGS was adopted to detect CIN of ctDNA in patients' peripheral blood, and bioinformatics analysis was performed to determine the risk of malignancy (or recurrence) and the extent of tumor burden and CIN. It has important clinical value in auxiliary diagnosis, therapeutic effect monitoring, recurrence and metastasis monitoring and prognosis evaluation of tumor patients.

This study proposes that continuous dynamic CIN is related to intratumor heterogeneity, which drives parallel evolution of somatic copy-number alterations (SCNAs) and promotes the emergence of drug-resistant clones by providing a higher level of genetic diversity of tumor cells, thus leading to drug resistance in patients treated with ICI. Investigators aimed to continuously monitor dynamic CIN in the blood of patients with lung cancer after second-line treatment with UCAD to establish a new molecular immune resistance evaluation index. Further, the correlation between the evolution of tumor cloning and ICI resistance in patients during treatment was analyzed based on the detection results of dynamic CIN.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Lung Cancer

Keywords

Explore important study keywords that can help with search, categorization, and topic discovery.

Chromosomal aneuploidy detection lung cancer PD-1/PD-L1 Drug resistance in Immunotherapy

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

patient with PD1 antibody treatment

Investigators will detect cfDNA CIN of lung cancer patients 1day (Day 0) before treatment with PD1 antibody, then Day 22 and Day 64 after treatment with PD1 antibody, as well as at the time of disease progression confirmed.

The correlation of CIN and drug resistance to PD1 antibody was analyzed.

the level of plasma cfDNA CINs

Intervention Type DIAGNOSTIC_TEST

The extracted cfDNA from PB will be analyzed by UCAD to determine the level of CINs.

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

the level of plasma cfDNA CINs

The extracted cfDNA from PB will be analyzed by UCAD to determine the level of CINs.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* Stage IIIa-IVb Non-small-cell lung cancer patients without EGFR,ALK,ROS1,c-Met driven gene mutation. Male or female patients aged 20-70 years.
* Patients planed to receive PD1 antibody treatment with or without chemotherapy, including as the neo-adjuvant therapy.
* The subjects' age, sex, marital and reproductive history, collection time, pathology, cytology and imaging diagnosis were complete.
* Participants signed informed consent form.

Exclusion Criteria

* Eligible to target therapy with driven gene mutation.
* Without measurable target lesion according to the RECIST criteria.
* Age under 20 years or more than 70.
* Individuals unwilling to sign the consent form or unwilling to provide PB for test or unwilling to provide the medical record.
Minimum Eligible Age

20 Years

Maximum Eligible Age

70 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

Shanghai Pulmonary Hospital, Shanghai, China

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Di Zheng

Director of medical oncology

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

Learn about the lead researchers overseeing the trial and their institutional affiliations.

Di Zheng, PhD

Role: STUDY_DIRECTOR

Shanghai Pulmonary Hospital, Shanghai, China

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Di Zheng

Shanghai, Shanghai Municipality, China

Site Status RECRUITING

Countries

Review the countries where the study has at least one active or historical site.

China

Central Contacts

Reach out to these primary contacts for questions about participation or study logistics.

Di Zheng, PhD

Role: CONTACT

Phone: 8613801683953

Email: [email protected]

ziliang qian, PhD

Role: CONTACT

Phone: 8615000902318

Email: [email protected]

Facility Contacts

Find local site contact details for specific facilities participating in the trial.

Di Du, PhD

Role: primary

References

Explore related publications, articles, or registry entries linked to this study.

Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012 Mar 22;12(4):252-64. doi: 10.1038/nrc3239.

Reference Type BACKGROUND
PMID: 22437870 (View on PubMed)

Brahmer JR, Tykodi SS, Chow LQ, Hwu WJ, Topalian SL, Hwu P, Drake CG, Camacho LH, Kauh J, Odunsi K, Pitot HC, Hamid O, Bhatia S, Martins R, Eaton K, Chen S, Salay TM, Alaparthy S, Grosso JF, Korman AJ, Parker SM, Agrawal S, Goldberg SM, Pardoll DM, Gupta A, Wigginton JM. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N Engl J Med. 2012 Jun 28;366(26):2455-65. doi: 10.1056/NEJMoa1200694. Epub 2012 Jun 2.

Reference Type BACKGROUND
PMID: 22658128 (View on PubMed)

Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, Powderly JD, Carvajal RD, Sosman JA, Atkins MB, Leming PD, Spigel DR, Antonia SJ, Horn L, Drake CG, Pardoll DM, Chen L, Sharfman WH, Anders RA, Taube JM, McMiller TL, Xu H, Korman AJ, Jure-Kunkel M, Agrawal S, McDonald D, Kollia GD, Gupta A, Wigginton JM, Sznol M. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med. 2012 Jun 28;366(26):2443-54. doi: 10.1056/NEJMoa1200690. Epub 2012 Jun 2.

Reference Type BACKGROUND
PMID: 22658127 (View on PubMed)

Wan MT, Ming ME. Nivolumab versus ipilimumab in the treatment of advanced melanoma: a critical appraisal: ORIGINAL ARTICLE: Wolchok JD, Chiarion-Sileni V, Gonzalez R et al. Overall survival with combined nivolumab and ipilimumab in advanced melanoma. N Engl J Med 2017; 377:1345-56. Br J Dermatol. 2018 Aug;179(2):296-300. doi: 10.1111/bjd.16785. Epub 2018 Jun 5.

Reference Type BACKGROUND
PMID: 29766492 (View on PubMed)

Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy. Cell. 2017 Feb 9;168(4):707-723. doi: 10.1016/j.cell.2017.01.017.

Reference Type BACKGROUND
PMID: 28187290 (View on PubMed)

Gong J, Chehrazi-Raffle A, Reddi S, Salgia R. Development of PD-1 and PD-L1 inhibitors as a form of cancer immunotherapy: a comprehensive review of registration trials and future considerations. J Immunother Cancer. 2018 Jan 23;6(1):8. doi: 10.1186/s40425-018-0316-z.

Reference Type BACKGROUND
PMID: 29357948 (View on PubMed)

Estin CD, Stevenson U, Kahn M, Hellstrom I, Hellstrom KE. Transfected mouse melanoma lines that express various levels of human melanoma-associated antigen p97. J Natl Cancer Inst. 1989 Mar 15;81(6):445-8. doi: 10.1093/jnci/81.6.445.

Reference Type BACKGROUND
PMID: 2918553 (View on PubMed)

Restifo NP, Smyth MJ, Snyder A. Acquired resistance to immunotherapy and future challenges. Nat Rev Cancer. 2016 Feb;16(2):121-6. doi: 10.1038/nrc.2016.2.

Reference Type BACKGROUND
PMID: 26822578 (View on PubMed)

O'Donnell JS, Long GV, Scolyer RA, Teng MW, Smyth MJ. Resistance to PD1/PDL1 checkpoint inhibition. Cancer Treat Rev. 2017 Jan;52:71-81. doi: 10.1016/j.ctrv.2016.11.007. Epub 2016 Nov 27.

Reference Type BACKGROUND
PMID: 27951441 (View on PubMed)

Haratani K, Hayashi H, Tanaka T, Kaneda H, Togashi Y, Sakai K, Hayashi K, Tomida S, Chiba Y, Yonesaka K, Nonagase Y, Takahama T, Tanizaki J, Tanaka K, Yoshida T, Tanimura K, Takeda M, Yoshioka H, Ishida T, Mitsudomi T, Nishio K, Nakagawa K. Tumor immune microenvironment and nivolumab efficacy in EGFR mutation-positive non-small-cell lung cancer based on T790M status after disease progression during EGFR-TKI treatment. Ann Oncol. 2017 Jul 1;28(7):1532-1539. doi: 10.1093/annonc/mdx183.

Reference Type BACKGROUND
PMID: 28407039 (View on PubMed)

Sansregret L, Vanhaesebroeck B, Swanton C. Determinants and clinical implications of chromosomal instability in cancer. Nat Rev Clin Oncol. 2018 Mar;15(3):139-150. doi: 10.1038/nrclinonc.2017.198. Epub 2018 Jan 3.

Reference Type BACKGROUND
PMID: 29297505 (View on PubMed)

Lee AJ, Endesfelder D, Rowan AJ, Walther A, Birkbak NJ, Futreal PA, Downward J, Szallasi Z, Tomlinson IP, Howell M, Kschischo M, Swanton C. Chromosomal instability confers intrinsic multidrug resistance. Cancer Res. 2011 Mar 1;71(5):1858-70. doi: 10.1158/0008-5472.CAN-10-3604.

Reference Type BACKGROUND
PMID: 21363922 (View on PubMed)

Kuznetsova AY, Seget K, Moeller GK, de Pagter MS, de Roos JA, Durrbaum M, Kuffer C, Muller S, Zaman GJ, Kloosterman WP, Storchova Z. Chromosomal instability, tolerance of mitotic errors and multidrug resistance are promoted by tetraploidization in human cells. Cell Cycle. 2015;14(17):2810-20. doi: 10.1080/15384101.2015.1068482.

Reference Type BACKGROUND
PMID: 26151317 (View on PubMed)

Jamal-Hanjani M, Wilson GA, McGranahan N, Birkbak NJ, Watkins TBK, Veeriah S, Shafi S, Johnson DH, Mitter R, Rosenthal R, Salm M, Horswell S, Escudero M, Matthews N, Rowan A, Chambers T, Moore DA, Turajlic S, Xu H, Lee SM, Forster MD, Ahmad T, Hiley CT, Abbosh C, Falzon M, Borg E, Marafioti T, Lawrence D, Hayward M, Kolvekar S, Panagiotopoulos N, Janes SM, Thakrar R, Ahmed A, Blackhall F, Summers Y, Shah R, Joseph L, Quinn AM, Crosbie PA, Naidu B, Middleton G, Langman G, Trotter S, Nicolson M, Remmen H, Kerr K, Chetty M, Gomersall L, Fennell DA, Nakas A, Rathinam S, Anand G, Khan S, Russell P, Ezhil V, Ismail B, Irvin-Sellers M, Prakash V, Lester JF, Kornaszewska M, Attanoos R, Adams H, Davies H, Dentro S, Taniere P, O'Sullivan B, Lowe HL, Hartley JA, Iles N, Bell H, Ngai Y, Shaw JA, Herrero J, Szallasi Z, Schwarz RF, Stewart A, Quezada SA, Le Quesne J, Van Loo P, Dive C, Hackshaw A, Swanton C; TRACERx Consortium. Tracking the Evolution of Non-Small-Cell Lung Cancer. N Engl J Med. 2017 Jun 1;376(22):2109-2121. doi: 10.1056/NEJMoa1616288. Epub 2017 Apr 26.

Reference Type BACKGROUND
PMID: 28445112 (View on PubMed)

Xue Y, Martelotto L, Baslan T, Vides A, Solomon M, Mai TT, Chaudhary N, Riely GJ, Li BT, Scott K, Cechhi F, Stierner U, Chadalavada K, de Stanchina E, Schwartz S, Hembrough T, Nanjangud G, Berger MF, Nilsson J, Lowe SW, Reis-Filho JS, Rosen N, Lito P. An approach to suppress the evolution of resistance in BRAFV600E-mutant cancer. Nat Med. 2017 Aug;23(8):929-937. doi: 10.1038/nm.4369. Epub 2017 Jul 17.

Reference Type BACKGROUND
PMID: 28714990 (View on PubMed)

Juric D, Castel P, Griffith M, Griffith OL, Won HH, Ellis H, Ebbesen SH, Ainscough BJ, Ramu A, Iyer G, Shah RH, Huynh T, Mino-Kenudson M, Sgroi D, Isakoff S, Thabet A, Elamine L, Solit DB, Lowe SW, Quadt C, Peters M, Derti A, Schegel R, Huang A, Mardis ER, Berger MF, Baselga J, Scaltriti M. Convergent loss of PTEN leads to clinical resistance to a PI(3)Kalpha inhibitor. Nature. 2015 Feb 12;518(7538):240-4. doi: 10.1038/nature13948. Epub 2014 Nov 17.

Reference Type BACKGROUND
PMID: 25409150 (View on PubMed)

Heng HH, Spyropoulos B, Moens PB. FISH technology in chromosome and genome research. Bioessays. 1997 Jan;19(1):75-84. doi: 10.1002/bies.950190112.

Reference Type BACKGROUND
PMID: 9008419 (View on PubMed)

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

ph-pg001

Identifier Type: -

Identifier Source: org_study_id