PHASE GENOMICS, INC. — Department of Health and Human Services SBIR Phase II: 394
PHASE GENOMICS, INC. — SBIR Phase II award from Department of Health and Human Services.
Phase II SBIR prototype / development signal
- Phase II is where Department of Health and Human Services funds deeper R&D after feasibility. Incumbents with Phase II history are serious competitors on adjacent topics.
- Use this award as past-performance context and to map customer organizations for STRATFI/TACFI-style transition planning.
- Obligated amount $1,000,000 is consistent with substantial Phase II-scale effort; compare to related awards from the same agency.
- Topic code 394 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $1,000,000
- Agency
- Department of Health and Human Services · National Institutes of Health
- Program / Phase
- SBIR · Phase II
- Topic
- 394
- Solicitation
- PA22-176
- NAICS
- —
- Place of performance
- WA
- Period
- 2023-07-01 → 2025-06-30
Description
ABSTRACTChromosome aberrations are a hallmark of acute myeloid leukemia and offer mechanistic and prognostic insights into disease. As such, a combination of cytogenetic assays are routinely applied as a part of the AML diagnostic workflow. While offering invaluable information on disease severity, most chromosome aberrations fall into the “cytogenetic abnormalities not classified” or “complex karyotype” categories. A range of studies have shown that, while ambiguous, these variants have prognostic value, suggesting the existence of cryptic variants of significance or complex epistases that drive the AML phenotype. However, there is currently no system for translating genome-wide chromosomal aberration information into patient risk.To improve the predictive potential of chromosome aberration profiles, we propose the development of a risk-prediction metric that will add new prognostic value to AML studies. Specifically, we will produce a method which will establish a patient risk metric that can help guide treatment decisions for patients traditionally judged as of intermediate risk. This development will employ our scalable cytogenomic tools and novel machine learning analytics to generate a large collection of cytogenomic datasets and analyze them to identify patterns linked to AML phenotypes. Once completed, we will have a combined kit and software solution that will not only improve upon existing cytogenetic applications in AML, but will offer new prognostic insights beyond what is possible with current tools. This product will deliver high-resolution view of the chromosome aberration landscape in AML and an offer a data-driven interpretation of how variants will impact disease severity.