More than 90% of all cancer-related deaths are caused by metastasis, the spread of cancer from its origin. By the time most cancer metastases become clinically visible, the disease has progressed too far to benefit from early-stage interventions such as surgery or radiation. Thus, new approaches accessing specific diagnostic biomarkers are highly desired to improve therapeutic outcomes. Microenvironmental signatures such as extracellular matrix (ECM) alterations, stromal composition, or immune components exhibit critical determinants of metastatic dissemination broadly across cancers. Herein, the main goal of this proposal is to converge the disease hall markers and rational design of biomolecular engineering to develop multidisciplinary approaches towards precision diagnostics of cancer metastasis. As metastases start to invade, they alter the extracellular matrix through aberrant proteolytic activities that could be leveraged as biomarkers. The applicant set out to systematically identify proteases expressed in metastatic colorectal cancer (CRC) by transcriptomic and proteomic analysis. To improve the detection sensitivity, it is proposed to integrate the proteolytic activity to formulate a library of enzyme activated sensors by reengineering the ECM targeting nanobody with extraordinarily tumor targeting efficacy for maximal on-target signal generation (Aim 1). To optimize the detection specificity, the multiplexity of these activity-based sensors will be extensively expanded for disease classification using CRISPR-Cas-based nucleic acid barcode readout. Preliminary investigation into the in vivo DNA barcodes revealed that they could be detected noninvasively as a urinary reporter, but could also enable portable detection on paper (Aim 2). Beyond initial diagnosis, disease stratification and treatment monitoring are critical to establishing a robust therapy. The novel sensors will thus be evaluated for noninvasive tumor monitoring and imaging in disease recapitulating metastatic CRC models (Aim 3). Successful completion of these three aims would offer a tumoral activation responsive, genetically encoded tracking (TARGET) platform can 1) unveil new biology at the metastasis-specific tumor microenvironment, 2) provide a completely noninvasive way to track tumor metastasis, and 3) offer a pipeline for validating novel therapies, which are currently unachievable by single modality agents. This project requires innovative integration across several fields. The candidate has assembled an exceptional team to help her achieve the goals of technology development and career transition, including her mentor Dr. Sangeeta Bhatia (MIT, medical engineering) and Drs. Tyler Jacks (MIT, tumor genetics), Dr. Richard Hynes (MIT, extracellular matrix), Dr. Frank Gertler (MIT, cell motility) and Dr. Shawn Chen (NIH, theranostics) on the mentoring committee. This training period will allow the candidate to gain experience in tumor microenvironment network, pre-clinical cancer models and analytical chemistry. In the future, the principles of this modular platform could apply to other disease areas. The research program here aligns well with the candidate?s long-term goal to develop multi-scale engineered tools in the context of cancer.
Efforts to improve oncology therapeuticoutcomes will be greatly aided by the integrationofearly detection and new precision therapies, reflected by the significantly improved survival rate of patients with metastatic colorectal cancer (CRC) that isdetected at anearly stage when resection isespeciallyeffective.Inlightof the lack of a generalizable approach accessing specific diagnostic biomarkers, this proposed study seeks to leverage the molecular signatures in the tumor microenvironment to develop multiplexed in vivo sensors for precisiondiagnosisofcancermetastasis. Successfulcompletionoftheproposedresearchprogramwillexhibit a preclinical framework of disease detection, stratifyingand monitoringand will provide rationale for extending thismodularplatformtothegeneralcontextofcancerthroughtailoredtargetsspecificities.
