Clinical trial NCT06625203: A Non-interventional, International, Multicentre Clinical Research Study to Build the Largest Collection of Multimodal Data (Including Clinical Data, Imaging Data and Omics Data) in Oncology
Official title: Multi Omics and Spatial Atlas In Cancer
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Diffuse Large B Cell Lymphoma
Solid Tumor Cancer
Keywords: Oncology
Multimodal Data
Omics Data
Database
Spatial Omics Data
Spatial Transcriptomic
Lead sponsor: OWKIN
Sponsor class: UNKNOWN
First posted: 2024-10-03
Enrollment: 7000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients across different cancer indications, which include, but are not restricted to histologically confirmed solid tumors (all types and subtypes) and diffuse large B cell lymphoma (DLBCL) and for which there is a formalin fixed and paraffin embedded (FFPE) tumor sample from already performed biopsy and/or surgical resection available.

Brief summary: Cancer is amongst the leading causes of disease-related morbidity and mortality. A major challenge in cancer treatment is the development of biology-informed, personalised treatment strategies. Recent advances in artificial intelligence (AI) and next-generation sequencing (NGS) technologies have shed further insights into disease biology and treatment pathways, thus identifying new, precision medicine-based therapeutic opportunities.

The biological mechanisms leading to cancer development and progression arise from complex and plastic networks of dysregulated cellular programs involving many signalling pathways and effector molecules. Cancer cells alter their surrounding environment via cell-cell interactions with non-tumor cells or by secreting cytokines, chemokines and other factors. This reprogramming of the tumour microenvironment (TME) is critical for cancer progression, invasion, and metastasis. Moreover, there are increasing studies that show that both innate and adaptive immune cell types contribute to tumorigenesis and treatment resistance when present within the TME. Understanding the crosstalk between cancer cells and the surrounding TME will inform on mechanisms of sensitivity and resistance to treatment, including immunotherapy (IO) and targeted therapies.

Spatially resolved-Omics is an emerging field that characterises cell types by gene/protein expressions within their spatial context in the tissue organisation. Recent high profile spatial transcriptomics studies have uncovered specific cell identities that define the surrounding TME.

The MOSAIC study, a collaborative initiative across industry and top oncology hospitals, proposes to go way beyond current cancer molecular profiling projects by combining the generation and analysis of multiple data modalities (3 essential mandatory modalities: Clinical Data, Hematoxylin and Eosin (H\&E) microscopic image, Spatial transcriptomics; up to 3 high priority data modalities depending on technical feasibility and sample size: bulk Ribonucleic Acid Sequencing (RNAseq), bulk Whole Exome Sequencing (WES), Single-cell transcriptomics; and potentially other optional data modalities and follow-up experiments such as single-cell omics, immunohistochemistry and spatial proteomics or other molecular profiling of proteins and molecules) on a minimum of 2,000 tumour samples across a different cancer indications. This will generate broad molecular and cellular profiling data of the tumour and its microenvironment from cancer patients, integrated with clinical data, at an unprecedented scale and resolution.

This study will enroll patients diagnosed with one of the eligible cancer indications and for which a formalin fixed paraffin embedded (FFPE) tumor sample from already performed biopsy and/or surgical resection is available within their local pathology archive or their affiliate centers archives.

The MOSAIC study expects to have a strong impact for patients in terms of new targeted therapeutic drug discovery, identification of patient subgroups requiring either specific treatment or broader clinical care and identification of novel treatment response and resistance mechanisms.


Eligibility criteria: Inclusion Criteria:

1. That the patient was over the age of 18 when the sample was taken/consented to.
2. Availability of patient informed consent or non-opposition form to perform exploratory research matching at least one of the MOSAIC objectives (unless authorisation is granted by the local Institutional Review Board / Independent Ethics Committee (IRB/IEC) for the use of samples in the study according to local regulations and law).
3. Have a confirmed diagnosis based on international criteria for the relevant tumor type.
4. Confirmed formalin fixed and paraffin embedded (FFPE) tissue availability to generate at least the 3 core data modalities, and preferentially all MOSAIC data modalities.
5. Confirmed availability of associated clinical data.
6. Qualification of the paraffin tissue block meeting all of the following:

   * Being of the expected tumor type
   * For solid tumors (all cancer indications except diffuse large B cell lymphoma (DLBCL)): Tumor cell content ranging from 40% to 80% on an hematoxylin and eosin (H\&E) section within a specified area as dictated by the lab protocol specific to the technique utilized
   * For DLBCL, a minimum of 80% of high grade component on an H\&amp;E section within a specified area as dictated by the lab protocol specific to the technique utilized
   * Wherever possible, the remaining tissue thickness must be over 125 micrometers (indicative range)
   * Tumor sample must be \<10 years old

In addition, each sub-cohort within each cancer indication will have specific inclusion criteria (e.g., disease stage; sampling site; treatment received...).

Exclusion Criteria:

1. Samples without a preserved tissue architecture, such as cytologies and cytoblocks.

In addition, each sub-cohort within each cancer indication may have specific exclusion criteria (e.g. histological subtype; history of immunosuppression; etc...).

Primary outcomes:
measure: The primary endpoint will be genes or proteins that present features compatible with drug targeting and/or novel biomarkers, and that are specific to a given patient population within one or more cancer indications.
timeFrame: From date of cancer diagnosis until date of death, date of lost of follow-up, date of consent withdrawal, or date of end of study (Dec 2028), whichever occurs first, assessed up to 16 years
Secondary outcomes:
measure: Biological mechanisms and/or pathways associated to patient outcomes under treatment, independently from therapeutic potential
timeFrame: From date of cancer diagnosis until date of death, date of lost of follow-up, date of consent withdrawal, or date of end of study (Dec 2028), whichever occurs first, assessed up to 16 years
measure: Novel classification of patients subgroups within each cancer indications, based on one or more data modalities, representing homogeneous biology
timeFrame: From date of cancer diagnosis until date of death, date of lost of follow-up, date of consent withdrawal, or date of end of study (Dec 2028), whichever occurs first, assessed up to 16 years
measure: Novel biomarkers that may be: a single gene/protein or a signature; a histological or spatial feature; a combined biomarker, ideally transferable to routine clinical use, and which predicts prognosis or other clinically actionable information.
timeFrame: From date of cancer diagnosis until date of death, date of lost of follow-up, date of consent withdrawal, or date of end of study (Dec 2028), whichever occurs first, assessed up to 16 years
Other outcomes:
Locations:
city: Pittsburgh
country: United States
facility: University of Pittsburgh
state: Pennsylvania
city: Paris
country: France
facility: Gustave Roussy
city: Berlin
country: Germany
facility: Charité - Universitätsmedizin Berlin
city: Erlangen
country: Germany
facility: Universiy Hospital Erlangen & FAU Erlangen-Nürnberg
city: Lausanne
country: Switzerland
facility: Centre Hospitalier Universitaire Vaudois
Study officials:
affiliation: Owkin
name: Dr. Vassili Soumelis
role: STUDY_CHAIR
References:
citation: Wu Y, Yang S, Ma J, Chen Z, Song G, Rao D, Cheng Y, Huang S, Liu Y, Jiang S, Liu J, Huang X, Wang X, Qiu S, Xu J, Xi R, Bai F, Zhou J, Fan J, Zhang X, Gao Q. Spatiotemporal Immune Landscape of Colorectal Cancer Liver Metastasis at Single-Cell Level. Cancer Discov. 2022 Jan;12(1):134-153. doi: 10.1158/2159-8290.CD-21-0316. Epub 2021 Aug 20.
citation: Rao A, Barkley D, Franca GS, Yanai I. Exploring tissue architecture using spatial transcriptomics. Nature. 2021 Aug;596(7871):211-220. doi: 10.1038/s41586-021-03634-9. Epub 2021 Aug 11.
citation: Meylan M, Petitprez F, Becht E, Bougouin A, Pupier G, Calvez A, Giglioli I, Verkarre V, Lacroix G, Verneau J, Sun CM, Laurent-Puig P, Vano YA, Elaidi R, Mejean A, Sanchez-Salas R, Barret E, Cathelineau X, Oudard S, Reynaud CA, de Reynies A, Sautes-Fridman C, Fridman WH. Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer. Immunity. 2022 Mar 8;55(3):527-541.e5. doi: 10.1016/j.immuni.2022.02.001. Epub 2022 Feb 28.
citation: Gonzalez H, Hagerling C, Werb Z. Roles of the immune system in cancer: from tumor initiation to metastatic progression. Genes Dev. 2018 Oct 1;32(19-20):1267-1284. doi: 10.1101/gad.314617.118.
citation: Hegde PS, Chen DS. Top 10 Challenges in Cancer Immunotherapy. Immunity. 2020 Jan 14;52(1):17-35. doi: 10.1016/j.immuni.2019.12.011.
citation: Hanahan D, Coussens LM. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell. 2012 Mar 20;21(3):309-22. doi: 10.1016/j.ccr.2012.02.022.
citation: Cable DM, Murray E, Zou LS, Goeva A, Macosko EZ, Chen F, Irizarry RA. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol. 2022 Apr;40(4):517-526. doi: 10.1038/s41587-021-00830-w. Epub 2021 Feb 18.