Neoplasms

Heart and Blood Diseases

Blood and Lymph Conditions

Immune System Diseases

All Conditions

Rare Diseases

Multiple Myeloma

Neoplasms, Plasma Cell

Lymphoma

Leukemia

Hematologic Neoplasms

Myeloproliferative Disorders

Preleukemia

Myelodysplastic Syndromes

Hematologic Diseases

Lymphosarcoma

Chronic Myeloproliferative Disorders

Artturi Mäkinen, MD

Olli Lohi, MD, PhD

Merja Heinäniemi, PhD

Matti Vänskä, MD, PhD

Tiina Lyly-Yrjänäinen, MD, PhD

The aim of the study is to create new tools for improving management of patients with hematological malignancies by combining extensive clinical data from patients newly diagnosed with hematological malignancies and innovative laboratory analyses made on available tissue samples in regional biobanks from these patients.

Firstly, clinical information is collected on all hematological malignancies diagnosed in our hospital district area retrospectively between the years 2000 and 2019. Clinical outcomes, laboratory results, clinically relevant diagnoses, characteristics defining clinical stage and established prognostic parameters are gathered.

Simultaneously a tissue microarray (TMA) of diagnostic samples is compiled using representative annotated tissue areas. This TMA is used in combination with additional control material to identify prognostic and predictive biomarkers.

A combined microarray dataset of hematological malignancies (Hemap) is utilized to point out genes of possible drug targets, disease specific markers, prognostic markers, or predictive markers.

The clinical datasets and Hemap dataset is ultimately utilized to gain knowledge, new tools for prognostication and diagnostics, and targets for treatment. Artificial intelligence -assisted differential diagnostics will be tested.

Diagnostic samples containing cells and tissues.

Tissue Microarray of Hematological Malignancies

Tissue Microarray of Hematological Malignancies: Search for Novel Regulators of Disease Pathology Across Disease Entities

Time from the first line treatment for hematological malignancy until the date of first documented relapse or transformation or death of any cause, whichever came first, assessed up to the end of the study period (April 2019).

Survival time from the diagnosis of hematological malignancy until the date of death of any cause, assessed up to the end of the study period (April 2019).

Best response to the first line treatment for the hematological malignancy, according to malignancy in question, e.g. complete response (CR), stringent complete response (sCR), partial response (PR), very good partial response (VGPR), stable disease (SD), progressive disease (PD), treatment failure, clinical response, hematological response etc.

Survival time from the first line treatment for hematological malignancy until any primary event (death, relapse, disease progression/transformation, secondary malignancy, resistant disease etc.), whichever came first, assessed up to the end of the study period (April 2019).

Altered organ function in acutely ill patient

Hematological malignancy transforms into another malignancy

Secondary malignancy after the diagnosis of hematological malignancy

Best response e.g. hematological remission, molecular remission, radiological remission

Time to best response e.g. hematological remission, molecular remission, radiological remission

Tampere University

University of Eastern Finland

Fimlab Oy

Helsinki University Central Hospital

Tampere University Hospital

Principal Investigator, MD PhD

Digestive System Diseases

Gland and Hormone Related Diseases

Adenocarcinoma

Pancreatic Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Endocrine Gland Neoplasms

Gastrointestinal Diseases

Pancreatic Diseases

Endocrine System Diseases

Pancreatic Cancer

REGIS SOUCHE, MD

Jean-Michel Fabre, MD, PhD

Regis SOUCHE, MD

REGIS SOUCHE

The investigators recently engineered a novel biomarker discovery approach that is non-destructive and fully compatible with OMICS profiling as well as routine clinical procedures (5). The latter approach - termed EXPEL - mines the interstitial tissue fluid that is the richest source of soluble, undiluted and uncontaminated biomarkers. The method notably gives access to proteins, metabolites, RNA, DNA and exosomes, thus enabling holistic biomarker discovery. Owing to its non-destructive nature, EXPEL provides for the first time both clinicians and researchers with the opportunity to analyze identical material.

The investigators hypothesize that the conservation liquid used to collect cells and biopsy after endoscopic ultrasound for pancreatic biopsy could contain the tissue secretome and permit a comprehensive OMICS analysis of PDAC (all stages confounded) by using a "modified EXPEL" procedure. These are ideal conditions for EXPEL approach that will additionally to finding novel biomarkers also shed light on complex network of cancer cell-stroma interactions.

Biomarkers that are readily detectable in fluid biopsies are of outmost importance for diagnosis and prediction of therapeutic response in all cancer patients. Pancreatic ductal adenocarcinoma (PDAC) is an example of a deadly malignancy that is diagnosed very late due to lack of sensitive and reliable markers \[1\]. The incidence and mortality of PDAC will double in the next 15 years, which is unprecedented among all cancer types \[2\]. Secondary cancer prevention through early diagnosis of PDAC will therefore be an essential preparedness asset for facing the immense healthcare burden.

Discovery of diagnostic biomarkers depends essentially on the availability of patient material ideally collected at different time points during the disease progression (e.g. early lesion, before treatment and after recurrence). Unfortunately, it is very difficult to access fresh biopsies from pre-malignant lesions for research purposes. These are needed in their entirety for histo-pathological evaluation and diagnosis. The ability to use liquid biopsies (e.g. serum, urine, saliva) brought the promise to circumvent this problem, opening access to many different biomolecules such as circulating DNA and proteins. However, upon their release from the tumor, these molecules are diluted up to several billion-fold in blood with molecular species originating from healthy tissues \[3-4\]. This hampers de novo discovery of biomarkers for diagnosing and understanding the disease.

To circumvent these limitations, the investigators recently engineered a novel biomarker discovery approach that is non-destructive and fully compatible with OMICS profiling as well as routine clinical procedures (5). The latter approach - termed EXPEL - mines the interstitial tissue fluid that is the richest source of soluble, undiluted and uncontaminated biomarkers. The method notably gives access to proteins, metabolites, RNA, DNA and exosomes, thus enabling holistic biomarker discovery. Owing to its non-destructive nature, EXPEL provides for the first time both clinicians and researchers with the opportunity to analyze identical material, while having virtually no disadvantage in their respective tasks. The investigators have recently shown the ability of our methodology to find new markers in the context of precious human colorectal cancer samples. Thus, the investigators are convinced that EXPEL can be successfully applied to pancreatic cancer. In addition, knowing the important proportion of stroma in PDAC (usually 2/3 of tumor mass), the secretome of pancreatic cancer is expected to be much richer in soluble "communication factors" exchanged between cancer cells and host tissue.

In clinical practice, most of patients undergo endoscopic ultrasound with fine needle aspiration/biopsy of the pancreatic mass to confirm the diagnosis of PDAC. The needle is rinsed in a conservation liquid Cytolyt® (Methanol 30% et water 50%) and sent to the pathologists. Microbiopsy are fixed in paraffin then analyzed and liquid is cytofiltrated to keep potential tumor cells. The remnant Cytolyt® liquid is systematically trashed.

The present study is based on the hypothesis that this liquid, previously in contact with tumoral tissue, could contain the tissue secretome and permit a comprehensive OMICS analysis of PDAC (all stages confounded) by using a "modified EXPEL" procedure. These are ideal conditions for EXPEL approach that will additionally to finding novel biomarkers also shed light on complex network of cancer cell - stroma interactions.

For this purpose, the investigators aim to perform proteomic analysis on the conservation liquid (Cytolyt) in patients who underwent endoscopic ultrasound with fine needle aspiration/biopsy for suspicious pancreatic mass at the CHU de Montpellier between Jan 2018 and Jan 2019.

Cytolyt Samples:

Liquid conservation samples from patients who underwent endoscopic ultrasound with fine needle aspiration/biopsy for suspicious pancreatic mass at the CHU de Montpellier were conserved at +4°c.

Cytolyt samples do not contain any cell or tissue from patient, this liquid is systematically trashed in current practice. No genetics analysis will be performed from these particular samples in the present study.

New Biomarkers in Pancreatic Cancer Using EXPEL Concept

Discovery of New Biomarkers in Pancreatic Cancer Using Novel Nondestructive OMICS Methodology

Correlation with positive diagnosis of pancreatic ductal adenocarcinoma and proteins quantity in Cytolyt® samples using ROC curves (sensibility/specificity: Area Under the Curve \> 0.85) and binary logistic regression method with controlling for the effects of the co-variates (such as age, disease cTNM stage, and treatment details).

Disease-free survival (percentage of patients without recurrence) at 1 year

Overall survival at 1 year (percentage of patients alive)

Disease-free survival (DFS) (time from diagnosis to time of first radiological evidence of local, regional, or distant relapse, or death due to any cause)

Overall survival (OS) (time from diagnosis until death, regardless of cause)

Kaplan Meier analysis, logrank test and Cox's proportional hazards regression model to identify biomarkers that are prognostically significant (Hazard ratio (HR) and its 95% confidence interval (CI)).

IRCM U1194 INSERM, Tumor Microenvironment and Resistance to Treatment Lab

IRCM U1194 INSERM, Cancer Bioinformatics and Systems Biology's team

University Hospital, Montpellier

Symptoms and General Pathology

Neoplasm Metastasis

Blood samples and, for some patients, buccal (mouth cell) samples will be collected at diagnosis and/or relapse. Solid tumor samples will be collected in the normal course of treatment, from biopsy, blood or other specimens. For blood-based cancers such as leukemia, blood and bone marrow specimens will be collected before treatment begins, on day 29 and possibly at a later time point if relapse occurs.

Patients may meet, depending on test results, with their physician and a genetic counselor to discuss possible changes in their treatment options and/or lifestyle changes.

Cancer Treatment Options

Stuart S Winter, MD

The goal of this study is to develop a new, local system that will use special tests based on patients' genetic makeup to better tailor cancer care at the University of New Mexico Cancer Center.

The Food and Drug Administration has already approved over forty (40) drugs to treat cancer patients based on specific genetic makeup, and more agents are in development that will support this new approach to treatment, often referred to as "personalized medicine."

The goal of performing specific tests on patients' genetic material is to discover tumor-specific, single nucleotide variations (SNVs) and other forms of genetic changes (called epigenetic changes) that can be detected when comparing normal tissue and tumor tissue. This can help guide cancer care decisions that may be more effective for patients. These will be called clinically actionable findings, or CAF.

Additional health related findings may be made, not related to cancer but to other conditions, diseases or syndromes. These are called secondary findings (SF). In this study the investigators will also measure how often they find SF and will discuss their possible impact on other aspects of patients' health. If patients want to know about these findings, they will be discussed with a panel of experts including genetic counselors.

Finally, the investigators will compare how often CAF and SF differ from those identified in nationwide samples.

For brevity, the investigators include the approaches of whole genome sequencing, whole exome sequencing of just the protein-coding exons, enrichment and sequencing of methylated DNA and RNA or transcriptome-sequencing under the broad category term, Whole Genome Sequencing (WGS).

The development of rapid and low cost next-generation genome sequencing technologies brings the promise of a new era of precision therapeutics to clinical practice, but it is associated with significant challenges, including reproducible generation of high-quality sequence data and the need for comprehensive data analysis and interpretation that is translatable to clinical action. Equally critical are the ethical, legal, and social issues surrounding the introduction of WGS testing and its clinical application in multiethnic, multicultural populations, particularly those who have historically experienced discrimination or even unethical research practices. Of particular concern are issues surrounding the privacy, ownership, storage, and use of WGS data.

Genome Sequencing of Human Cancer Tissues

The investigators will utilize new genomics technologies in to identify potentially Clinically Actionable Findings (CAF) in patient tumor and other samples. Based on individual patient data, alternative treatment options will be offered to patients.

The investigators will measure the frequency with which clinically actionable findings (CAF) that could impact current patient care decisions occur in tumor and other samples.

Determine whether CAF identified in the unique patient populations treated at the University of New Mexico Cancer Center differ from those identified in nationwide data sets such as The 1000 Genomes Project or The Cancer Genome Atlas (TCGA) data collections.

Identify the numbers and types technological, social, ethical and/or legal barriers that might limit the use of genomics information in patient care at the University of New Mexico Cancer Center and the University of New Mexico Health Sciences Center. The investigators will assess the frequency of use of CAF by local treating physicians. They will assess how often cancer treatment choice is altered as a result of providing CAF to physicians and patients. The investigators will assess how often identified CAF are entered into patient medical records. They will assess the frequency with which patients choose to receive information about potentially detrimental SF unrelated to their cancer care.

Identify other genomics-based findings in patient samples unrelated to cancer care, but which might impact other aspects of patients' health. These will be referred to as Secondary Findings, or SF. For example, if a SF is identified that suggests an increased risk of diabetes, this would be considered a secondary finding. This will be discussed by a panel of medical experts, and if patients wish to have this information returned to them, their doctor and genetic counselor will discuss the SF. Patients can choose not to have SF returned to them.

Determine whether SF identified in the unique patient populations treated at the University of New Mexico Cancer Center differ from those identified in nationwide samples such as The 1000 Genomes Project or The Cancer Genome Atlas (TCGA).

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Tissue Microarray of Hematological Malignancies	NCT04142372	Hematological M...		-	Tampere University Hospital
New Biomarkers in Pancreatic Cancer Using EXPEL Concept	NCT03791073	Oncology		18 Years -	University Hospital, Montpellier
Genome Sequencing of Human Cancer Tissues	NCT02105545	Cancer	Cancer Treatmen...	-	New Mexico Cancer Care Alliance
Tissue Microarray of Hematological Malignancies	NCT04142372	Hematological M...		-	Tampere University Hospital

Spots Global Cancer Trial Database for bioinformatics