Neoplasms

Blood and Lymph Conditions

Immune System Diseases

All Conditions

Rare Diseases

Lymphoma

Lymphoma, Mantle-Cell

Neoplasms by Histologic Type

Lymphoproliferative Disorders

Lymphatic Diseases

Immunoproliferative Disorders

Lymphoma, Non-Hodgkin

Lymphosarcoma

Mantle Cell Lymphoma

Antineoplastic Agents

Antirheumatic Agents

All Drugs and Chemicals

Amino Acids

Rituximab

Tyrosine Kinase Inhibitors

Tyrosine

An historical cohort of patients will be identified and selected both on a clinical base and according to the availability of Formaline-fixed paraffin-embedded (FFPE) material, frozen material or viable cryopreserved cells at Mantle Cell Lymphoma (MCL) diagnosis. Samples will be analyzed in 4 subgroups, each with different clinical specificity:

1. refractory to Induction Chemoimmunotherapy (CIT);
2. refractory to Bruton Tyrosine kinase (BTK) inhibitors (BTKi);
3. sensitive to Induction Chemoimmunotherapy (CIT);
4. sensitive to Bruton Tyrosine kinase (BTK) inhibitors (BTKi).

Marina Cesaretti

Elena Borgo

Elsa Pennese, MD

Alberto Fabbri, MD

Guido Gini

Guido Gini, MD

Pietro Bulian, MD

Attilio Guarini, MD

Alessandro Re, MD

Luca Nassi

Luca Nassi, MD

Nicola Di Renzo, MD

Andres Ferreri, Dott.

Francesca G Rossi, Dott.ssa

Francesca G Rossi, MD

Riccardo Moia, Dott.

Riccardo Moia, MD

Luca Arcaini, Prof.

Annalisa Arcari, MD

Angelo Fama, MD

Alice Di Rocco, MD

Stefan Hohaus, Prof.

Carola Boccomini, MD

Simone Ferrero, MD

Chandrakala Toldo, Dott.ssa

Chandrakala Toldo, MD

Piero Maria Stefani, MD

Michele Merli, MD

Francesca Maria Quaglia, Dott.ssa

Francesca Maria Quaglia, MD

Maria Chiara Tisi, Dott.ssa

Maria Chiara Tisi, MD

Francesca Maria Quaglia

Retrospective observational study with a prospective biological evaluation of an historical cohort of first relapsed-refractory patients with mantle cell lymphoma who were relapsed or refractory to rituximab and chemotherapy containing induction regimens with curative intent.

Biological samples from 80 Mantle Cell Lymphoma (MCL) patients will be collected and analyzed in 30 Italian sites in 36 months.

Patients will be identified and selected both on a clinical base and according to the availability of Formaline-fixed paraffin-embedded (FFPE) material, frozen material or viable cryopreserved cells at Mantle Cell Lymphoma (MCL) diagnosis. They will be analyzed in 4 subgroups, each with different clinical specificity: 1) refractory to Induction Chemoimmunotherapy (CIT); 2) refractory to Bruton Tyrosine kinase (BTK) inhibitors (BTKi); 3) sensitive to induction Induction Chemoimmunotherapy (CIT); 4) sensitive to BTK inhibitors (BTKi).

Each group will undergo central pathology revision with other immunohistochemical studies (Verona, Dr. Parisi; Milano, Prof. Ponzoni; Vicenza, Dr. D'Amore; Brescia, Prof. Facchetti). Formaline-fixed paraffin-embedded (FFPE) diagnostic specimens of Mantle Cell Lymphoma (MCL) will be screened by immunohistochemistry for the expression of immunoglobulin (Ig) heavy chains to identify Mantle Cell Lymphoma (MCL) cases lacking Immunoglobulin (Ig) expression.

Cytofluorimetric and molecular studies will be performed in the laboratories of the Verona University and at IFOM (Istituto FIRC di Oncologia Molecolare (FIRC = Fondazione Italiana per la Ricerca sul Cancro)). Specific methods will be used: Flow cytometry for the status of surface Ig expression and multiplexed phospho-specific flow cytometry, NGS (Next Generation Sequencing), Immunoglobulin (IgH/IgL V(D)J) profiling by BIOMED2 protocol-based, NGS (Next Generation Sequencing) technology in the relapsed-refractory mantle cell lymphoma (R/R MCL) cases, Chromatin accessibility studies by Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq) (on viable cell suspensions or frozen pellets) and Gene Expression Profiling (GEP) by RNA-sequencing and RNA studies of the MALT1-MYC ((Mucosa-associated lymphoid tissue lymphoma translocation protein 1 - MYC) pathway.

For the primary objective of the study: Formaline-fixed paraffin-embedded (FFPE) material.

For the secondary objectives of the study: viable cell suspensions, DNA extracted from Formaline-fixed paraffin-embedded (FFPE) tissue sections or frozen pellets, frozen cell suspensions.

Biological Insights of First Relapsed-refractory Patients With Mantle Cell Lymphoma: the MANTLE-FIRST BIO Study.

Histopathological characterization of patients with mantle cell lymphoma (MCL) who were relapsed or refractory to rituximab and chemotherapy containing induction regimens with curative intent. 80 Mantel Cell Lymphoma (MCL) patients stratified according to Chemo-immunotherapy (CIT) or Bruton Tyrosine kinase inhibitors (BTKi) resistance (20 for each of the groups previously identified) will undergo central pathology revision with assessment of Immunoglobulin (Ig) expression and other histopathological studies.

Performed on formalin-fixed paraffin-embedded (FFPE) tissue sections as well as, when available, on frozen material or viable cryopreserved cells at Mantle Cell Lymphoma (MCL) diagnosis. The aim is to perform a mutational analysis for Mantle Cell Lymphoma (MCL) driver genes: Next Generation Sequencing (NGS) on approximately 80 cases of Mantle Cell Lymphoma (MCL), including 20 cases Relapsed/Refractory (R/R) to Bruton Tyrosine kinase inhibitors (BTKi)

Performed on formalin-fixed paraffin-embedded (FFPE) tissue sections as well as, when available, on frozen material or viable cryopreserved cells at Mantle Cell Lymphoma (MCL) diagnosis. The aim is to perform a functional study of the B-Cell Receptor (BCR) activity: multiplexed phospho-specific flow cytometry on 10-20 Relapsed/Refractory (R/R) Mantle Cell Lymphoma cases.

Performed on formalin-fixed paraffin-embedded (FFPE) tissue sections as well as, when available, on frozen material or viable cryopreserved cells at Mantle Cell Lymphoma (MCL) diagnosis. The aim is to understand the mechanisms of evasion from B-Cell Receptor (BCR) requirement as follow:

* Immunoglobulin (IgH/IgL V(D)J) profiling by Next Generation Sequencing (NGS) on 80 mantle cell lymphoma (MCL) cases;
* Chromatin accessibility studies (Assay for Transposase-Accessible Chromatin sequencing, ATAC-seq) on approximately 20 cases among the Immunoglobulin (Ig)-negative identified cases or Bruton Tyrosine kinase inhibitors (BTKi) refractory cases, irrespective of their B-Cell Receptor (BCR) status, and on other 20 cases, from the group of patients sensitive to Bruton Tyrosine kinase inhibitors (BTKi);
* Gene Expression Profiling (GEP) by RNA-seq, on about 20 cases selected on the basis of results of the Assay for Transposase-Accessible Chromatin sequencing (ATAC-Seq) analysis;

Performed on formalin-fixed paraffin-embedded (FFPE) tissue sections as well as, when available, on frozen material or viable cryopreserved cells at Mantle Cell Lymphoma (MCL) diagnosis. The aim is to perform RNA studies on 20 cases

About 80 cases of Relapsed/Refractory (R/R) Mantle Cell Lymphoma (MCL) with available tissue samples will be considered for correlation between biological studies with clinical characteristics of patients, response to treatment and conventional outcomes (Progression Free Survival, Progression Free Survival at 2 years, Overall Survival, Overall Survival at 2 years)

Fondazione Italiana Linfomi - ETS

Skin and Connective Tissue Diseases

Melanoma

Neuroendocrine Tumors

Neuroectodermal Tumors

Neuroectodermal Tumors, Primitive

Neoplasms, Germ Cell and Embryonal

Neoplasms, Nerve Tissue

Nevus, Pigmented

Nevus

Nevi and Melanomas

Skin Neoplasms

Skin Diseases

Neuroendocrine Tumor

Neuroepithelioma

Vaccines

Immunomodulating Agents

HLA-A2.1 patients are vaccinated with dendritic cells loaded with MHC Class I restricted epitopes of tumor antigens gp100 and tyrosinase

HLA-A2.1 and HLA-DR4 patients are vaccinated with dendritic cells loaded with MHC Class I and II restricted epitopes of tumor antigens gp100 and tyrosinase

HLA-A2.1 and/or HLA-DR4 patients are vaccinated with dendritic cells transfected with mRNA encoding tumor antigens gp100 and tyrosinase

autologous dendritic cell vaccine

Prof. C.J.A. Punt, MD, PhD

Prof. G.J. Adema, PhD

Dendritic cells (DCs)are the most potent antigen-presenting cells of the immune system, as such they are able to direct the immune system specifically against cancer cells. Currently DCs are used in clinical vaccination studies and immunological and clinical responses have been observed. For inducing anti-tumor immunity, the DCs have to be loaded with tumor antigen (i.e. molecular structures that are presented by the tumor, that are recognized by the immune system). Currently most studies use tumor peptides (small protein fragments) for this purpose. This approach has several disadvantages: only patients with a certain HLA-type can be treated and the immune response that is induced by the vaccine is limited to the used peptides. These disadvantages do not exist when the DCs present antigen which is endogenously processed, for example after RNA transfection. For this reason we investigate the immunogenicity of DCs that are pulsed with peptides or transfected with mRNA encoding melanoma associated antigens in stage III and IV melanoma patients.

Peptide-pulsed vs. RNA-transfected Dendritic Cell Vaccines in Melanoma Patients

In Vivo Responses of DC Vaccines Presenting HLA Class I and II Restricted Tumor Epitopes Either by Peptide-pulsing or mRNA Transfection in Melanoma Patients

Dutch Cancer Society

Radboud University Medical Center

Urinary Tract, Sexual Organs, and Pregnancy Conditions

Gland and Hormone Related Diseases

Symptoms and General Pathology

Ovarian Neoplasms

Carcinoma, Ovarian Epithelial

Cysts

Ovarian Cysts

Carcinoma

Endocrine Gland Neoplasms

Ovarian Diseases

Adnexal Diseases

Genital Diseases, Female

Genital Diseases

Urogenital Diseases

Female Urogenital Diseases

Pregnancy Complications

Female Urogenital Diseases and Pregnancy Complications

Genital Neoplasms, Female

Urogenital Neoplasms

Endocrine System Diseases

Gonadal Disorders

Neoplasms, Glandular and Epithelial

Ovarian Cancer

Ovarian Epithelial Cancer

For patients with a benign ovarian cyst, 1 saliva sample taken at 3 times (concomitant with a visit performed in routine care):

* During the inclusion visit,
* At the postoperative visit,
* At the 6-month follow-up visit

For patients with ovarian cancer, 1 saliva sample taken at different times (concomitant with a visit performed in routine care):

* At the inclusion visit,
* During the postoperative visit,
* At the follow-up visits (6 months post-surgery and then every 6 months until 24 months)

For patients with a borderline ovarian tumor, 1 saliva sample taken at different times (concomitant with a visit performed in routine care):

* At the inclusion visit,
* During the postoperative visit,
* During pre- and post-chemotherapy visits (before and after each cycle)
* During follow-up visits (every 4 months until 24 months)

Salivary samples

Raffaèle Fauvet, Pr

Philippe Descamps, Pr

Laetitia Elisabeth Rocca, Dr

François Golfier, Pr

Mathieu Poilblanc, Dr

Marie Gosset, MD

Sandrine Richard, Dr

Sofiane Bendifallah, Dr

Cyril Touboul, Pr

Emile Darai, Pr

Vincent Lavoué, Pr

Vinvent Lavoué, Pr

Claire Marie Roger, MD

Clotilde Hennetier, MD

Ludivine Genre, MD

Lobna Ouldamer, Pr

Lobna Ouldamer

OVAmiARN is a multicentre, prospective, longitudinal, non-interventional, observational study carried out in 8 obstetrics and gynecology departments in France; in order to describe the evolution of salivary miRNA expression between the pre-operative and post-therapy visits according to the type of mass. In time, the clinical application will be to significantly reduce the time to diagnosis and improve the care pathway for ovarian adnexal mass.

The study population consists of patients with an ovarian adnexal mass diagnosed by clinical examination and imaging (pelvic ultrasound and/or MRI) and requiring surgical management in routine care.

The patients concerned by the study will be managed without modification of the care pathway, nor modification of the therapeutic indications, nor modification of the diagnostic or follow-up examinations (imaging or biology) necessary according to the context, which are carried out according to the recommendations of the HAS, CNGOF.

In this study, the management and follow-up of patients :

* Are not imposed by the study: the doctor remains free to make medical prescriptions (treatments and examinations) and to determine the interval between consultation visits,
* Are not modified in comparison with the usual follow-up, except for the performance of the Collection of saliva

Evaluation of Salivary RNAs in the Presence of an Adnexal Mass of Ovarian Origin

Evaluation of Salivary RNAs in the Presence of an Adnexal Mass of Ovarian Origin - OVAmiARN Study

Identifying a signature of ovarian cancer diagnosis by analysis of coding and non-coding RNAs in the saliva of subjects

Monitoring Force Group

iGenSeq

ZIWIG

Respiratory Tract (Lung and Bronchial) Diseases

Lung Neoplasms

Respiratory Tract Neoplasms

Thoracic Neoplasms

Lung Diseases

Respiratory Tract Diseases

* Fresh frozen tumor tissue and normal tissue;
* Recording of clinical characteristics, imaging, surgery features.

After treatment: FU at 2-3 weeks post surgery, 3,6,12,24 and 36 months post-surgery

(including stereotactic radiotherapy)

* Before start RT (during staging):Optional: Biopsies, frozen or RNA later, of tumor and/or lymph nodes;
* Day 0 (before start RT): Recording of clinical characteristics, imaging, and radiotherapy features;
* Day 8-12 (during RT): Scoring of toxicity.

After treatment: FU at 2-3 weeks post RT, 3,6,12,24 and 36 months post-RT

* Day -30 (before start CT):

  * Optional: Biopsies, frozen or RNA later, of tumor and/or lymph nodes;
  * Recording of clinical characteristics, imaging, and chemotherapy features.
* Day 0 (before start RT):

  * Recording of clinical characteristics, imaging, and radiotherapy features.
  * Scoring of toxicity.
* Day 8-12 (during RT):

  * Scoring of toxicity.

After treatment: FU at 2-3 weeks post RT, 3,6,12,24 and 36 months post-RT

* Day -30 until-18 (before start CT):

  * Optional: Biopsies, frozen or RNA later, of tumor and/or lymph nodes;
  * Recording of clinical characteristics, imaging, and chemotherapy features.
* Day 0 (before start RT):

  * Recording of clinical characteristics, imaging, and radiotherapy features.
  * Scoring of toxicity.
* Day 8-12 (during RT):

  * Scoring of toxicity.

After treatment: FU at 2-3 weeks post RT, 3,6,12,24 and 36 months post-RT

* Day 0 (before start CRT):

  * Optional: Biopsies, frozen or RNA later, of tumor and/or lymph nodes;
  * Recording of clinical characteristics, imaging, chemotherapy features, and radiotherapy features.
* Day 8-12 (during CRT):

  * Scoring of toxicity.

After treatment: FU at 2-3 weeks post RT, 3,6,12,24 and 36 months post-RT

Day 0:

* Optional: Biopsies, frozen or RNA later, of tumor and/or lymph nodes;
* Recording of clinical characteristics, imaging, surgery or any systemic (MoAb) features.

After treatment: FU at 2-3 weeks post treatment, 3,6,12,24 and 36 months post-treatment

Aim of the study: The main aim is to collect data of patients with lung cancer, and to perform different analyses on this data. The data contains information on patient and tumor characteristics, imaging, and treatment characteristics. With this data it is possible to improve and validate the predictive model for survival and long term toxicity in lung cancer by multicentric prospective data collection. The long term aim, beyond this specific study, is to build a Decision Support System based on the predictive models validated in this study.

Hypothesis: The general hypothesis is that we get a better prediction in terms of AUC (area under the curve) of survival and long term toxicity when we combine multifactorial variables. These variables consist of information from clinical data, imaging data, data related to treatment type and treatment quality.

1. Treatment of lung cancer

   Lung cancer is the most common cause of cancer death in The Netherlands, with an annual incidence exceeding 8,000. Two main variants of lung cancer can be identified: small cell and non-small cell lung cancer (SCLC and NSCLC respectively), the latter comprising approximately 80% of the lung cancer cases. Despite treatment improvement, the prognosis of NSCLC remains poor, with a median survival of 8 months after diagnosis, and a 5 year survival of less than 13%.

   Radiotherapy plays a key role in the treatment of NSCLC. Over the years, radiotherapeutical treatment options have increased tremendously. These include dose escalation, more intensive schedules and concurrent chemo-radiotherapy. These schedules have improved both local control and survival in patients. However, they also induce more toxicity, and the radiation oncologist faces the challenging task of choosing the optimal therapy for each patient: taking into account tumor characteristics as well as the patient's condition. In other words: the physician must estimate the expected therapeutic ratio often on a background of insufficient outcomes information.

   The same problem arises in other therapies for lung cancer, chemotherapy and surgery. In early disease, surgery is the mainstay of the treatment of lung cancer patients. This can be combined with neoadjuvant or adjuvant chemotherapy and/or radiotherapy.
2. Prediction of response

   A major problem in lung cancer management is the lack of data dealing with predictive factors for prognosis and treatment outcome. The currently used staging system (TNM) does not accurately predict outcome within homogeneous treatment groups. As a result, an individualized therapeutic ratio cannot be calculated, leading to either over- or under-treatment of many patients and hampering further optimization of any therapy.

   Attempts were undertaken to refine and improve the risk stratification, leading to the development of several prediction models. The performance of the models is usually expressed as the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). The maximum value of the AUC is 1.0; indicating a perfect prediction model. A value of 0.5 indicates that patients are correctly classified in 50% of the cases, e.g. as good as chance.

   While high prediction accuracy (AUC=0.85) has been achieved for a population of NSCLC patients of all stages, treated with different modalities, it is a more challenging task to predict survival accurately when focusing on a subgroup. A pretreatment prediction model for patients treated with surgery yielded an AUC of 0.61 while a pretreatment model for patients treated with radiotherapy resulted in an AUC of 0.75. Further improvement of the radiotherapy model was obtained by adding information about blood biomarkers and this extended model yielded an AUC of 0.83. Investigating other blood biomarkers or possible combinations of biomarkers is a challenge and our results underline the importance of using these data in addition to clinical and imaging parameters.

   Survival remains certainly an outcome of major importance, but the last decades other treatment-related outcome measures, such as radiation induced lung injury or esophageal damage, became more important for the evaluation of treatment results.

   Pneumonitis or radiation induced lung injury has been subject of many studies. However, results are quite difficult to interpret, because many different variables, dosimetric parameters as well as other treatment or patient related characteristics, have been identified, studies showed inconsistent or even conflicting results, and sample sizes were often very limited.

   Recently, Chen et al. published a neural network model for prediction of grade 2 or higher pneumonitis, which yielded an AUC of 0.74 in the test dataset31. Compared to other models, these results are promising, although external validation of the model is warranted before it can be used in clinical practice. Our group developed a model predicting dyspnea ≥ grade 2 according to CTCv3.0. Patient as well as dosimetric parameters were incorporated in the model, which resulted in a cross-validated AUC of 0.62.

   In summary, existing models perform rather well, but there is a lot of room for improvement by adding new factors as well as applying advanced model building techniques. Prediction models still have to be developed for a number of clinically relevant outcomes. Finally, incorporating confidence intervals in the prediction as well as quantifying the gain in prediction precision if a certain diagnostic/ prognostic test is performed, would certainly be of great value for clinical use of the models (http://www.predictcancer.org/).
3. Strategies to improve prediction models for lung cancer

In order to improve the prediction models for survival as well as toxicity outcome one can include many variables as possible predictors including imaging, genomics and proteomics information.

3.1 Imaging

An important feature for prognosis on the FDG-PET-scan is the maximal Standardized Uptake Value (SUVmax). There is a statistically significant difference in 2-year survival between patients with a high pretreatment SUV and a low pretreatment SUV. Patients with a low SUVmax had a 2-year survival of 90.6%, while patients with a high SUVmax had a 2-year survival of only 58.6%. There is a significant correlation between high SUVmax and a high HIF1α staining in the biopsies, which is a marker for hypoxia. Non significant relations were shown for CA IX, Ki67 and Glut-1 and SUVmax.

Besides FDG, new PET-tracers are being developed. One of the new tracers is HX4, which is a hypoxia tracer. Regulation of tissue oxygen homeostasis is critical for cell function, proliferation and survival. Evidence for this continues to accumulate along with our understanding of the complex oxygen-sensing pathways present within cells. The microenvironment of tumors in particular is very oxygen heterogeneous, with hypoxic areas, which may explain much of our difficulty in treating cancer effectively. This is true when comparing levels of hypoxia among different patient tumors, but also within individual tumors. Accumulating evidence implicates the biological responses to hypoxia and the alterations in these pathways in cancer as important contributors to overall malignancy and treatment efficacy. This has recently prompted several investigations into the possibility of imaging and targeting treatment at the biological responses to hypoxia.

3.2 Gene signatures

Analysis of gene signatures can help to improve the predictive value of the model. An example of this, is the proliferation signature investigated by Starmans et al. Two different signatures of 110 genes were compared in prognostic value. Both showed a very good prognostic value on breast cancer data sets. The AUC (area under the curve) improved when the proliferation signature were added to the models of clinical factors. Another gene profile was tested on early stage NSCLC. This profile consists of 72 genes and is validated on stage I and II NSCLC patients of five centers. It was possible to identify early-stage NSCLC patients with high and low risk for disease recurrence and death within 3 years after primary surgical treatment.

3.3 Tumor biopsies

Hypoxia is (besides in serum) also measurable in the tissue itself. Several markers of hypoxia are predictive for survival. An example is HIF1α, which is upregulated is case of hypoxia. A higher staining of HIF1α is correlated with a worse prognosis in NSCLC. CA IX correlated with severe and chronic hypoxia, and has a strong association with a poor outcome in NSCLC.

Another marker is Ki67, which is expressed in proliferating cells. A higher Ki67 indicates more proliferation, and in a systemic review of Martin et. al. a worse prognosis was shown when Ki67 expression is increased.

3.4 Application of machine learning techniques

The availability of genomic data, together with improved imaging modalities, leads to unprecedented amounts of biological and medical data, which can only be dealt with using computational methods, not only for storing the data, but also for integrating, analyzing, displaying and eventually understanding it.

Machine learning offers a number of techniques for these purposes. These techniques can overcome problems encountered with conventional statistical methods especially if data is highly correlated, many variables are available but a limited number of patients (high-dimensional data), or many different models have to be tested for their predictive value. In the field of radiotherapy and especially for the prediction of treatment responses, machine learning is an upcoming modality. Successes over traditional statistics have already been published 43and first promising results for building predictive models concerning survival of non-small-cell lung-cancer are already found in the literature.

Radiomics: a Study of Outcome in Lung Cancer

Radiomics: a Prospective Study of Outcome in Lung Cancer

H. Lee Moffitt Cancer Center and Research Institute

Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Maastricht Radiation Oncology

Molecular profiled group receiving treatment based on genetics

Patients and their treating physician will obtain the patient's genetic risk assessment

Molecular profile

Brian Leyland-Jones

The primary aim of this study is to identify drivers of cancer by performing comprehensive genetic, proteomic, and metabolomic characterization of patient samples as a basis for understanding the underlying cause of disease.

This study involves looking at the DNA, RNA, proteins, and metabolites in blood and tissue samples collected from subjects and linking them to their health information. By doing so, researchers hope to discover the underlying molecular causes of human diseases with the hope that this information can be used for the development of diagnostic tests and more effective treatments.

Participants will have up to 3 blood tubes (about 30 ml) drawn under the condition that protocol criteria for blood sampling is met. Blood samples may be collected each time a participant undergoes a provider-ordered blood draw. Urine samples may also be collected approximately every 4-6 weeks.

Participants may be asked to provide approximately 60-120 ml of first-morning-void urine. Tissue from a standard care surgical procedure or biopsy may be obtained. Additional core biopsies (for research) or excess surgical waste will be collected during each biopsy procedure when appropriate and will be processed and stored.

Identifying Molecular Drivers of Cancer

To determine genetic changes associated with the development and growth of human disease. This should lead to better ways to detect, prevent, and treat a wide variety of human health conditions, including cancer.

Avera McKennan Hospital & University Health Center

Digestive System Diseases

Carcinoma, Hepatocellular

Liver Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Liver Diseases

Hematinics

Liver Extracts

Sorafenib

Protein Kinase Inhibitors

Enzyme Inhibitors

MTL-CEBPA administered weekly, twice weekly or thrice weekly over 3 weeks followed by 1 week of rest defining a 28-day cycle.

MTL-CEBPA is administered weekly or twice weekly in combination with sorafenib over 3 weeks followed by 1 week of rest defining a 28-day cycle.

MTL-CEBPA is administered weekly or twice weekly for 2 cycles (28-day cycle) followed by 2 cycles of sorafenib

MTL-CEBPA

Sorafenib 200mg

Dr Debashis Sarker, MBChB, MRCP

Professor Nagy Habib, FRCS

MNA-3521-011 study is a multi-centre, open-label, first-in-human, phase 1a/b clinical study dose/dose frequency escalation followed by a cohort expansion part. MTL-CEBPA is administered as monotherapy or in combination with sorafenib to patients with advanced hepatocellular carcinoma and cirrhosis of the liver. All participants will be considered unsuitable for liver tumour resection and/or is refractory to radiotherapy and other loco-regional therapies.

MTL-CEBPA consists of a double stranded RNA formulated into a SMARTICLES® liposomal nanoparticle and is designed to activate the CEBPA gene.

First-in-Human Safety, Tolerability and Antitumour Activity Study of MTL-CEBPA in Patients With Advanced Liver Cancer

A First-in-Human, Multi-centre, Open-label, Phase 1a/b Clinical Study With RNA Oligonucleotide Drug MTL-CEBPA to Investigate Its Safety, Tolerability, and Antitumour Activity in Patients With Advanced Liver Cancer

Frequency of adverse events graded according to NCI CTCAE v5.0

Increase or decrease in tumour measurement using Response Evaluation Criteria in Solid Tumours (RECIST) reports

Frequency of treatment-related adverse events graded according to NCI CTCAE v5.0

Mina Alpha Limited

Carcinoma, Squamous Cell

Carcinoma, Basal Cell

Neoplasms, Basal Cell

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Biological Insights of First Relapsed-refractory Patients With Mantle Cell Lymphoma: the MANTLE-FIRST BIO Study.	NCT04882475	Mantle Cell Lym...		18 Years - 80 Years	Fondazione Italiana Linfomi - ETS
Peptide-pulsed vs. RNA-transfected Dendritic Cell Vaccines in Melanoma Patients	NCT00243529	Melanoma Stage ...	autologous dend...	18 Years - 75 Years	Radboud University Medical Center
Evaluation of Salivary RNAs in the Presence of an Adnexal Mass of Ovarian Origin	NCT05514028	Ovarian Cancer Ovarian Cyst Be... Borderline Ovar...	Salivary sample...	18 Years -	ZIWIG
Radiomics: a Study of Outcome in Lung Cancer	NCT01302626	Lung Cancer		18 Years -	Maastricht Radiation Oncology
Biological Insights of First Relapsed-refractory Patients With Mantle Cell Lymphoma: the MANTLE-FIRST BIO Study.	NCT04882475	Mantle Cell Lym...		18 Years - 80 Years	Fondazione Italiana Linfomi - ETS
Identifying Molecular Drivers of Cancer	NCT02470715	Cancer Tumor Advanced Cancer	Molecular profi...	18 Years -	Avera McKennan Hospital & University Health Center
First-in-Human Safety, Tolerability and Antitumour Activity Study of MTL-CEBPA in Patients With Advanced Liver Cancer	NCT02716012	Hepatocellular ... Liver Cancer	MTL-CEBPA Sorafenib 200mg	16 Years -	Mina Alpha Limited
Evaluation of Salivary RNAs in the Presence of an Adnexal Mass of Ovarian Origin	NCT05514028	Ovarian Cancer Ovarian Cyst Be... Borderline Ovar...	Salivary sample...	18 Years -	ZIWIG
Identifying Molecular Drivers of Cancer	NCT02470715	Cancer Tumor Advanced Cancer	Molecular profi...	18 Years -	Avera McKennan Hospital & University Health Center
Molecular Characterization of Cutaneous Tumors	NCT03092830	Squamous Cell C... Basal Cell Carc...	Molecular testi...	18 Years -	Rabin Medical Center
Peptide-pulsed vs. RNA-transfected Dendritic Cell Vaccines in Melanoma Patients	NCT00243529	Melanoma Stage ...	autologous dend...	18 Years - 75 Years	Radboud University Medical Center
Peptide-pulsed vs. RNA-transfected Dendritic Cell Vaccines in Melanoma Patients	NCT00243529	Melanoma Stage ...	autologous dend...	18 Years - 75 Years	Radboud University Medical Center
Genome Sequencing of Human Cancer Tissues	NCT02105545	Cancer	Cancer Treatmen...	-	New Mexico Cancer Care Alliance
PRO-MERIT (Prostate Cancer Messenger RNA Immunotherapy)	NCT04382898	Prostate Cancer	BNT112 Cemiplimab	18 Years -	BioNTech SE

Spots Global Cancer Trial Database for rna

Every month we try and update this database with for rna cancer trials from around the world to help patients and their families find trials that might be right for them.
We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.

Spots Global Cancer Trial Database for rna

Every month we try and update this database with for rna cancer trials from around the world to help patients and their families find trials that might be right for them. We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.

Every month we try and update this database with for rna cancer trials from around the world to help patients and their families find trials that might be right for them.
We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.