Advancing Cancer Care Through Large-Scale Proteomics
Large-scale proteomics could offer deeper insights into the proteome to improve personalized treatments.
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Proteomics has emerged as a powerful tool for advancing personalized cancer treatments. However, translating proteomic insights into clinical applications remains challenging due to the sheer complexity of the proteome. Achieving a high-resolution, unbiased view of the full proteome is critical for identifying novel cancer biomarkers and developing more effective, tailored treatments.
Technology Networks recently spoke with Asim Siddiqui, senior vice president and scientific fellow at Seer, to explore the evolving role of unbiased proteomics in cancer research. In this interview, Siddiqui discusses how Seer’s Proteograph™ Product Suite is expanding the limits of proteomic profiling, how large-scale proteomics is reshaping biomarker discovery and how these innovations could transform cancer patient care.
What challenges exist in translating proteomic insights into personalized cancer treatments?
Asim Siddiqui, PhD (AS):
Personalized cancer treatment requires a comprehensive analysis of the proteins that drive cancer progression and influence individual treatment response. Millions of protein variants (proteoforms) interact in complex, combinatorial ways to impact disease progression and therapeutic response. Understanding this at an individual level will require approaches that allow researchers to interrogate the full complexity of the proteome. Approaches that group proteoforms together or target only specific proteoforms may be limited in the resolution necessary for a complete picture.
KR:
How can large-scale proteomics improve cancer biomarker discovery and targeted therapy development?
AS:
Large-scale proteomic analysis of plasma samples can provide valuable insights into early cancer detection, as well as monitoring remission and recurrence. One key challenge is the vast dynamic range of protein concentrations in plasma, which can obscure the detection of low abundance yet clinically relevant proteins. However, advanced proteomic sample preparation techniques– combined with mass spectrometry – enhance our ability to identify proteoforms driving cancer, ultimately aiding in the discovery of potential biomarkers.
In our own work, we demonstrated the potential of unbiased plasma proteomics to identify markers for detection of early non-small cell lung cancer. More recently, PrognomiQ used unbiased proteomics to identify blood-based biomarkers capable of detecting lung cancer at Stage 1 with 80% sensitivity at 89% specificity. This identification of novel makers could spur discovery of new therapeutic targets.
KR:
How can proteomic profiling help understand and overcome cancer drug resistance?
AS:
Cancer drug resistance is a complex biological process that can arise through many different mechanisms, including, but not limited to, genetic variations among individuals as well as protein mutations or alterations that can affect downstream signaling. Comprehensive profiling of the full proteome may aid in untangling these complex interactions.
KR:
How do you see the field of proteomics improving cancer patient care?
AS:
Unbiased proteomics with Seer’s Proteograph Product Suite can provide deeper insights into the cellular proteomic responses following cancer treatment. With our new cellular assay, we have seen a 10% boost in protein counts overall with a focused 40% boost in low-abundance proteins. When taken together with the 5–8x boost in proteins detected in plasma over conventional neat plasma unbiased proteomics, this deeper view of the proteome may offer critical biological insights into how treatments influence protein-level and proteoform alterations.
KR:
How is the Proteograph Product Suite being used beyond cancer research?
AS:
Both academic researchers and biopharma companies use Seer’s Proteograph Product Suite for deep, unbiased proteomic discovery. For example, researchers are using the platform for protein quantitative trait locus (pQTL) identification to understand how genetic variants affect protein levels. A study published last year in Nature Communications by Dr. Karsten Suhre’s group at Weill Cornell Medicine-Qatar used Proteograph for pQTL mapping to identify potential protein altering variants responsible for Type 2 diabetes and cardiovascular disease.
Other work using the Proteograph includes studies from Salk Institute’s Professor Satchidananda Panda, who is using the technology to study preclinical mouse models to uncover proteins linked to relative energy deficiency (RED). Additional studies using Seer’s Proteograph have revealed proteins important in Alzheimer’s disease.
