Clinical trial · Interventional

Quantifying Systemic Immunosuppression to Personalize Cancer Therapy

NCT04941365CI-TRIAL-00062832SERPENTINEwithdrawnN/AClinicalTrials.gov clinicaltrialsPublishedExplore in graph →

Why stopped (as posted): please refer to NCT05621837

Summary

Brief summary (as posted)

It is nowadays well established that the immune system can profoundly influence disease outcome in cancer patients. Increasing evidence is indeed showing that patients displaying spontaneous T cell-mediated immune response against their tumor (defined as immune surveillance) have higher chance to respond to therapies and display globally better prognosis. Conversely, patients whose tumor is characterized by immunosuppression, usually involving myeloid cells and chronic inflammation pathways, often undergo rapid progression and rarely benefit from therapy. Hence, capturing the immune features of individual tumors can help to predict disease course and tailor the therapeutic workup in clinical setting.

Conditions

Conditions (6)

Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.

Condition (as posted)	Mapped entity	Match	Confidence
Advanced Renal Cell Carcinoma	Renal Cell Carcinoma	CURATED_BROADER	0.80
Healthy	—	UNRESOLVED	—
Metastatic Breast Cancer	Malignant Breast Neoplasm	CURATED_BROADER	0.78
Metastatic Melanoma	Melanoma	CURATED_BROADER	0.78
Non-small Cell Lung Cancer Stage III	Lung Non-Small Cell Carcinoma	CURATED_BROADER	0.78
Squamous Cell Carcinoma of Head and Neck	Head and Neck Squamous Cell Carcinoma	ALIAS	0.90

Interventions

Interventions (1)

Intervention	Type	Mapped drug	Match
single arm	Diagnostic Test	—	UNRESOLVED

Design

Arms and outcomes

Arms (1)

type
EXPERIMENTAL
label
single arm
description
Blood samples will be collected at baseline(Visit 1), and during therapy at visit 2 (around one month after the treatment starting) and at Visit 3 (around three months after the treatment starting. And, optionally, in case of a disease progression (PD).
interventionNames
1. Diagnostic Test: single arm

Primary outcomes (1)

measure
Investigation of whether a flow cytometry blood-based MDSC quantification assay, does predict disease course in different cancer patients undergoing standard therapies including immunotherapy, chemotherapy, target therapies and surgery.
timeFrame
during 3 months after the start of the treatment
description
Correlation of myeloid-related blood biomarkers (including quantification of myeloid cell subsets in peripheral blood mononuclear cells and whole blood) with disease outcome including objective response to therapy, progression-free survival and overall survival, to identify tool for predicting resistance to treatment and poor prognosis.

Secondary outcomes (4)

Eligibility

Eligibility (as posted)

Sex: All
Minimum age: 18 Years
Maximum age: 90 Years

Show eligibility criteria text

Inclusion Criteria:

* Histologically documented diagnosis of metastatic/locally advanced melanoma, hormone-refractory breast cancer, RCC and UC, SCCHN, SCC or NSCLC, stage III resectable NSCLC will also be included
* Will and ability to comply with the protocol
* Willingness and ability to provide an adequate archival Formalin- Fixed Paraffin-Embedded (FFPE) tumor sample available for exploratory biomarker analysis
* Age from 18 to 90 years at the time of recruitment
* ECOG Performance Status \< 2
* Understanding and signature of the informed consent

Exclusion Criteria:

* Known history of HIV infection
* Serious neurological or psychiatric disorders
* Pregnancy or lactation
* Inability or unwillingness of participant to give written informed consent
* Inability or unwillingness to be regularly followed up at the enrolling center

References

Publications (23)

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BACKGROUNDGalluzzi L, Buque A, Kepp O, Zitvogel L, Kroemer G. Immunological Effects of Conventional Chemotherapy and Targeted Anticancer Agents. Cancer Cell. 2015 Dec 14;28(6):690-714. doi: 10.1016/j.ccell.2015.10.012. PMID 26678337
BACKGROUNDApetoh L, Tesniere A, Ghiringhelli F, Kroemer G, Zitvogel L. Molecular interactions between dying tumor cells and the innate immune system determine the efficacy of conventional anticancer therapies. Cancer Res. 2008 Jun 1;68(11):4026-30. doi: 10.1158/0008-5472.CAN-08-0427. PMID 18519658
BACKGROUNDPeguillet I, Milder M, Louis D, Vincent-Salomon A, Dorval T, Piperno-Neumann S, Scholl SM, Lantz O. High numbers of differentiated effector CD4 T cells are found in patients with cancer and correlate with clinical response after neoadjuvant therapy of breast cancer. Cancer Res. 2014 Apr 15;74(8):2204-16. doi: 10.1158/0008-5472.CAN-13-2269. Epub 2014 Feb 17. PMID 24535711
BACKGROUNDWilmott JS, Long GV, Howle JR, Haydu LE, Sharma RN, Thompson JF, Kefford RF, Hersey P, Scolyer RA. Selective BRAF inhibitors induce marked T-cell infiltration into human metastatic melanoma. Clin Cancer Res. 2012 Mar 1;18(5):1386-94. doi: 10.1158/1078-0432.CCR-11-2479. Epub 2011 Dec 12. PMID 22156613
BACKGROUNDThorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, Porta-Pardo E, Gao GF, Plaisier CL, Eddy JA, Ziv E, Culhane AC, Paull EO, Sivakumar IKA, Gentles AJ, Malhotra R, Farshidfar F, Colaprico A, Parker JS, Mose LE, Vo NS, Liu J, Liu Y, Rader J, Dhankani V, Reynolds SM, Bowlby R, Califano A, Cherniack AD, Anastassiou D, Bedognetti D, Mokrab Y, Newman AM, Rao A, Chen K, Krasnitz A, Hu H, Malta TM, Noushmehr H, Pedamallu CS, Bullman S, Ojesina AI, Lamb A, Zhou W, Shen H, Choueiri TK, Weinstein JN, Guinney J, Saltz J, Holt RA, Rabkin CS; Cancer Genome Atlas Research Network; Lazar AJ, Serody JS, Demicco EG, Disis ML, Vincent BG, Shmulevich I. The Immune Landscape of Cancer. Immunity. 2018 Apr 17;48(4):812-830.e14. doi: 10.1016/j.immuni.2018.03.023. Epub 2018 Apr 5.