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In metastatic breast cancer a personalized multi-omic analysis may provide better information versus biomarker assessment alone
Not just DNA sequencing: compared with DNA sequencing alone, a multi-omics approach to personalized therapy that incorporates information about actionable oncogenic drivers with critical biological data was shown to be feasible in the metastatic breast cancer setting, according to the data of the SMMART Program (Serial Measurements of Molecular and Architectural Responses to Therapy) presented at the last congress of the American Association for Cancer Research. A broader view of tumor characteristics is possible, pairing DNA sequencing with an in-depth multi-omics analysis.
The SMMART Program, led by Gordon Mills and Joe Gray of Oregon Health & Science University Knight Cancer Institute, has multiple clinical and research objectives such as real-time adaptation of therapy to each patient’s needs, durable and tolerable tumor control over the life of the patient, and the ability to identify resistance mechanisms and biomarkers of cancer treatment. When the patient enters the program, data are generated to design a therapy plan. Patients are then monitored over the course of treatment to identify tumor response. If progression occurs, they are asked to undergo additional biopsies. The main focus is on solid tumors, especially triple negative breast cancer. Patients are followed longitudinally, which means that they are monitored before, during and after being given study treatments to assess how they are doing and how well the treatments are working. Treatments are tailored to each individual patient in the study, prescribed according to both cancer characteristics and patient genetics. The new data presented at the AACR refer to 38 female patients for whom an in-depth and multi-omic analysis was possible.
For each patient a detailed clinical history was collected (including, for example, tumor response to previous therapies, imaging data, tumor biomarkers) and biopsies were performed (63 from various tissues, plus serial biopsies for 15 patients). A comprehensive set of clinical assays was performed on newly obtained tumor biopsies, including immunohistochemistry, a targeted next-generation sequencing panel covering 225 genes, whole exome sequencing, whole transcriptomic sequencing, and a multiplex protein analysis of 22 key cancer proteins and phosphoproteins. After analytics were generated and disease progression occurred, patients were assigned to either a matched or an unmatched therapy. The multi-omics clinical tumor board was presented and the optimal therapy management was recommended accordingly, consisting of either additive care, off-label treatment, or inclusion in a clinical trial.
In the 12 patients who had received ‘matched’ therapy, the ratio between progression-free survival in second line and first line (PFS2 / PFS1) was calculated, highlighting for 8 of them a value equal to or greater than the predetermined threshold of 1.3. Ben Kong of Oregon Health & Science University, who presented the data at the congress, then focused on three cases: a patient in whom the HER2 and ER status has changed significantly over time, one in which a new association between a PARP inhibitor and the MEK inhibitor cobimetinib was used after the analysis of serial biopsies over time, a third in which an ERBB3 mutation was identified with a downstream activation of the HER2 axis that allowed to start trastuzumab with a dramatic decrease in tumor markers.
«We demonstrate the feasibility of implementing a deep real-time analytics platform for patients with metastatic breast cancer that can provide new insight into therapeutic opportunities», Kong said during a presentation of the data. «The observed clinical responses support the use and investigation of this approach. The best way to fulfill the essence of the tumor board is to make sure that the best treatment is recommended; we want to make sure that we identify the best treatment matching with the available evidence».