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BELIEVE is a translational research study that aims to collect samples of breast cancer tissue and blood from individuals undergoing breast cancer treatment (such as chemotherapy, targeted therapy and immunotherapy) before surgery. In certain cases, MRI scans and stool samples will also be obtained before and during treatment. The samples collected from this study will be used for molecular and genetic research to understand why some cancers respond very well to anticancer treatments, and some do not, develop novel ways of accurately measuring response during treatment, as well as identify which patients are at a higher risk of the cancer coming back after surgery.
Breast cancer is the most common female cancer in the UK and remains one of the leading causes of cancer death. Survival rates have greatly improved over the past forty years due to improvements in early diagnosis, surgical techniques, effective chemotherapy treatments, hormonal therapy, and the introduction of targeted biological treatments. However, despite this, breast cancer continues to be the 4th most common cause of cancer death in the UK and 20% of women diagnosed with this disease do not survive more than ten years after diagnosis.
Systemic anticancer treatments (that is, oral or intravenous treatments such as chemotherapy, targeted therapy and immunotherapy) are routinely given before surgery (called neoadjuvant therapy) in patients with aggressive early and locally advanced breast cancer in order to shrink the cancer and make it more operable. As a result, women receiving neoadjuvant therapy often require less radical surgery to remove their cancer. Clinical trials have shown that the timing of systemic therapy (that is, whether it is given before or after surgery), does not make a difference to survival, and because of this systemic therapy before surgery is now a mainstay of breast cancer treatment.
Clinical trials have also shown that the level of response in both primary breast tumour and nearby lymph nodes to systemic anticancer therapy are strongly associated with cancer relapse and survival. The presence of extensive cancer at the time of surgery is associated with a higher risk of metastatic relapse and a poor prognosis.
This study will deeply analyse samples from a patient's tumour, blood, and stool, as well as data from MRI images, to identify specific features of either the patient or their cancer which could be used to predict whether (a) a treatment is likely to work and (b) once started, whether the treatment is still working. All this data will be combined together to build predictors that can identify: (a) patients who are not responding to therapy and could be directed towards different treatments and (b) patients who are responding very well to treatment and might benefit from therapy de-escalation.
Additionally, by profiling these breast cancers in great detail, this project will allow us to identify new cancer vulnerabilities and drug targets that could then be used for treatment in the future.
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| Measure | Description | Time Frame |
|---|---|---|
| Proportion of patients recruited undertaking sequential tumour biopsies, blood samples, and MRI. | Total 10 year recruitment period | |
| Proportion of patients undergoing study procedures within time frames specified by protocol. | Total 10 year recruitment period |
| Measure | Description | Time Frame |
|---|---|---|
| Characterisation of serial molecular and imaging tumour and host profiles and correlation with clinical characteristics, treatment responses and patient outcomes. | Total 10 year recruitment period | |
| Integration of multiplatform profiling data to develop predictors of response to therapy. |
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Inclusion Criteria:
Exclusion Criteria:
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Early breast cancer patients suitable for neoadjuvant chemotherapy, targeted therapy, or immunotherapy.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| BELIEVE Trial Manager | Contact | 02078082887 | 6667 | BELIEVE@rmh.nhs.uk |
| Sophie Cooke | Contact | 02034373610 | sophie.cooke@rmh.nhs.uk |
| Name | Affiliation | Role |
|---|---|---|
| Stephen-John Sammut | Royal Marsden NHS Foundation Trust | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Royal Marsden NHS Foundation Trust | Recruiting | London | SW3 6JJ | United Kingdom |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 34875674 | Background | Sammut SJ, Crispin-Ortuzar M, Chin SF, Provenzano E, Bardwell HA, Ma W, Cope W, Dariush A, Dawson SJ, Abraham JE, Dunn J, Hiller L, Thomas J, Cameron DA, Bartlett JMS, Hayward L, Pharoah PD, Markowetz F, Rueda OM, Earl HM, Caldas C. Multi-omic machine learning predictor of breast cancer therapy response. Nature. 2022 Jan;601(7894):623-629. doi: 10.1038/s41586-021-04278-5. Epub 2021 Dec 7. |
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| ID | Term |
|---|---|
| D001943 | Breast Neoplasms |
| D064726 | Triple Negative Breast Neoplasms |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
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Tissue samples Blood samples Stool samples
| Total 10 year recruitment period |
| D017437 |
| Skin and Connective Tissue Diseases |