The Metastatic Breast Cancer (MBC) project: Accelerating translational research through direct patient engagement.

Abstract

1076 Background: The Metastatic Breast Cancer Project is a nationwide research study, launched in Oct 2015 in collaboration with patients (pts) and advocacy groups, that directly engages pts through social media and seeks to empower them to share their experiences, clinical information, and samples to accelerate research. Methods: MBC pts enroll by providing their information at mbcproject.org. Pts are sent a saliva kit and asked to mail back a sample which is used to extract germline DNA. We contact pts medical providers and obtain medical records (MRs) and stored tumor samples. Pts may also submit a blood sample, used to extract cell free DNA (cfDNA). Whole exome sequencing (WES) is performed on tumor, germline, and cfDNA; transcriptome sequencing is performed on tumor. Clinical and genomic data are used to generate genomic landscapes in pt subgroups and to identify mechanisms of response and resistance to therapies. Data are shared widely through public databases. Pts receive regular study updates. Results: In 12 months, 2908 MBC pts from 50 states enrolled. 95% completed the 16-question survey about their cancer, treatments, and demographics. 1730 (60%) completed the online consent form. 100-200 pts continue to enroll monthly. To date, 1539 saliva kits were mailed and 1120 samples were received (73%). 992 unique treating institutions were reported by pts, including 733 institutions reported by only 1 pt each and 5 institutions reported by more than 40 pts each. We have obtained MRs from 253 patients (67% yield) and tumor samples from 85 pts (67% yield). WES was successfully completed for 79 tumors of 88 attempted (90%). WES has been performed on initial cfDNA samples. Conclusions: A direct-to-patient approach enabled rapid identification of thousands of MBC pts willing to share MRs, saliva, and tumor samples, including many with rare phenotypes. Remote acquisition of MRs, saliva, tumor, and blood for pts located throughout the US is feasible. We estimate that for ~33% of consenting patients, we can obtain medical records, saliva, and tumor tissue. Genomic analysis of tumor and cfDNA from subgroups including young pts, pts with extraordinary responses, and pts with de novo MBC will be presented.

A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer

“…The model can be straightforwardly adapted to HER2+ breast cancer; it already recapitulates multiple outcomes in this setting. The model can be expanded to incorporate multiple additional genetic alterations observed in breast cancer patient cohorts (Koboldt et al, 2012 ; Pereira et al, 2016 ; Wagle et al, 2017 ; Cohen et al, 2017 ) by appropriately introducing these alterations into the model (e.g., as a node activation or inactivation). The inclusion of the most probable intrinsic or acquired resistance mechanisms to a treatment, informed by pre-, on- and post-treatment genetic characterization of tumors (Cohen et al, 2017 ), will allow the identification and ranking of the combinatorial interventions that are effective even in the presence of tumor drug resistance.…”

A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer

“…The breast cancer model and its predictions will serve as a basis for guiding and interpreting drug resistance and drug combination studies in the context of ER+ and/or HER2+ breast cancer. The model can be expanded to incorporate multiple genetic alterations observed in breast cancer patient cohorts (38,40,76,77) by introducing these alterations into the model appropriately (e.g., as a node activation or inactivation). The inclusion of the most probable intrinsic or acquired resistance mechanisms to a treatment, informed by pre-, on-and post-treatment genetic characterization of tumors (77), will allow the identification and ranking of the combinatorial interventions that are effective even in the presence of tumor drug resistance.…”

EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

“…It has been illustrated that breast cancer stem cells, which are resistant to treatment and are responsible for metastasis, are the target of the histone deacetylase (HDAC) inhibitors (74). On the other, The results of enrichment in cBioPortal show that the above-mentioned 16 genes are altered in 243 (54%) of 450 MBCA samples in two studies performed in 2016 (51) and 2017 (52). Genomic alterations (Fig.…”