Nature가 주최하는 웨비나!

Rarecyte 회사의 Orion™ 을 이용한 향상된 prognostic signatures 의 식별 기술

Multimodal tissue imaging and machine learning to advance precision medicine

Spatial profiling of human tissue for cancer research and diagnosis

일정 : 2023년 5월 24일 오전 12시 (한국시간)

The effective treatment of cancer and many other diseases is increasingly dependent on a precision approach in which the quantification of molecular features at the level of individual patients is used to guide treatment plans.

However, cancer diagnosis and staging are currently performed primarily via direct examination of biopsy and resection specimens by histopathologists. These classical methods provide insufficient molecular insight to guide the use of targeted and immunotherapies even when supplemented by knowledge of tumor genotypes.

Fortunately, the last few years have witnessed the introduction of innovative new methods for performing high-plex imaging of histological specimens at subcellular resolution. When processed to extract single cell data, high-plex tissue images promise to combine the historical strength of histopathology with deep insight into cancer biology.

In a research setting, such data are a natural complement to dissociative single cell sequencing. In the context of clinical trials, they can provide unprecedented insight into mechanism of action and pharmacodynamics. And in a diagnostic setting, they promise to improve outcomes and reduce the burden of therapy.

Realizing these benefits requires the development of new instruments, reagents, and machine-learning algorithms. This webcast will describe progress in these areas with reference to the characterization of immune landscapes in primary melanoma and predicting tumor progression in colorectal cancer.

You will learn:

• How spatial biomarkers of high prognostic value can be identified using biomarker quantitation and traditional histology of the same tissue section.
• The features required of a multi-modal digital pathology approach for validation of spatial biomarkers across patient cohorts.
• The importance of unbiased, whole-slide analysis and reproducible, trustworthy quantitation and cellular classification for patient stratification.