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.
Study Type
OBSERVATIONAL
Enrollment
7,000
University of Pittsburgh
Pittsburgh, Pennsylvania, United States
RECRUITINGGustave Roussy
Paris, France
RECRUITINGCharité - Universitätsmedizin Berlin
Berlin, Germany
RECRUITINGUniversiy Hospital Erlangen & FAU Erlangen-Nürnberg
Erlangen, Germany
RECRUITINGCentre Hospitalier Universitaire Vaudois
Lausanne, Switzerland
RECRUITINGThe 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.
Unsupervised data analysis and outcome measures will be used to achieve this, such as prognosis under treatment, response to specific therapies. The response to therapy will be assessed based on the data related to the treatment and its efficacy collected via an electronic Case Report Form. For each patient at baseline and throughout the MOSAIC follow up period, the following data will help to assess this: all the different cancer treatments (surgery,…) and their outcomes ; the concurrent treatment (drug name, …); the state of the cancer, i.e complete or partial response, or progression, measured at various time-points specific to each tumor type. Because the study will utilize tumor samples collected largely \<10 years ago, survival information may not be present for all patients by the end of the study, especially in slow evolving tumor types. For this reason, we will use tumor type-specific prognostic markers and scores as surrogates of prognosis whenever available and necessary.
Time frame: 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
Biological mechanisms and/or pathways associated to patient outcomes under treatment, independently from therapeutic potential
Time frame: 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
Novel classification of patients subgroups within each cancer indications, based on one or more data modalities, representing homogeneous biology
Time frame: 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
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.
Time frame: 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
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