Decoding Cancer Risk: How New Tech Resolves Genetic Uncertainty

📊 Key Data
  • 6,718 variants assayed in the PALB2 study, representing 84% of all possible missense mutations in key parts of the gene.
  • 6% of variants were functionally abnormal, indicating a likely role in increasing cancer risk.
  • 53% lifetime risk of breast cancer for individuals with a pathogenic PALB2 variant.
🎯 Expert Consensus

Experts agree that MAVE technology is transforming hereditary cancer diagnostics by reclassifying variants of uncertain significance (VUS) into actionable pathogenic or benign categories, significantly improving patient care and clinical decision-making.

2 months ago

Decoding Cancer Risk: How New Tech Resolves Genetic Uncertainty

ALISO VIEJO, CA – February 04, 2026 – For thousands of individuals, a genetic test for hereditary cancer risk ends not with a clear answer, but with a question mark. The result, a “variant of uncertain significance” (VUS), leaves patients and their doctors in a state of diagnostic limbo, unsure of how to proceed. Now, a powerful technology is systematically erasing that uncertainty, transforming the landscape of hereditary cancer diagnostics.

Ambry Genetics, a genomic testing leader and subsidiary of Tempus AI, recently announced a significant contribution to research that is accelerating this change. A new study published in Nature Communications, led by researchers at Leiden University Medical Center in the Netherlands, leveraged Ambry’s extensive clinical data to clarify the function of thousands of variants in the PALB2 gene, a critical gene linked to hereditary breast and pancreatic cancer. By providing clear functional evidence, the research helps reclassify previously ambiguous variants, offering definitive answers to patients who have been waiting.

A New Blueprint for Understanding Genetic Variants

The technology driving this shift is known as Multiplexed Assays of Variant Effect, or MAVEs. These ultra-high-throughput functional screens represent a monumental leap from older, slower methods of studying genetic mutations one by one. Instead of analyzing variants in a piecemeal fashion, MAVEs allow scientists to test the functional impact of thousands of genetic variants simultaneously in a single, large-scale experiment.

The process is elegant in its scale. First, scientists create a vast “library” containing thousands of different versions of a gene, each with a specific mutation. This library is then introduced into cells, which are subjected to a biological test—for example, measuring their ability to repair DNA. Using advanced next-generation sequencing, researchers can then count which variants survive the test and which do not. This generates a “variant effect map,” a comprehensive functional score for nearly every possible mutation in a given gene region.

This approach provides the very functional evidence that the American College of Medical Genetics and Genomics (ACMG) recommends for classifying variants. Evidence from well-validated MAVEs can be categorized as strong evidence for or against a variant causing disease, providing the statistical power needed to move a VUS into a “pathogenic” or “benign” category.

In the PALB2 study, researchers assayed a staggering 6,718 variants, representing 84% of all possible missense mutations across key parts of the gene. The results provided a clear functional breakdown: 58% were normal, 36% had an intermediate effect, and a crucial 6% were functionally abnormal, revealing their likely role in increasing cancer risk. This work builds on Ambry’s previous contributions to similar MAVE studies for other major cancer genes, including BRCA2 and MUTYH, which have collectively helped clarify the status of tens of thousands of variants.

The Patient Impact of a Clearer Diagnosis

The clinical implications of this work are profound, moving the science from the lab directly into patient care. A pathogenic variant in the PALB2 gene can increase a woman’s lifetime risk of breast cancer to as high as 53% and also elevates the risk for pancreatic and ovarian cancers. However, clinical guidelines from leading bodies like the ACMG are clear: a PALB2 VUS should not be used to guide medical decisions. Patients are told to wait as science catches up.

MAVE-driven reclassification ends that wait. When a VUS is reclassified as pathogenic, it unlocks a clear pathway for personalized medical management. An individual carrying a pathogenic PALB2 variant is often recommended for BRCA1/2-equivalent surveillance, which may include annual breast MRIs and mammograms starting as early as age 30. It also opens the door to discussions about risk-reducing medications or surgeries and can inform therapeutic decisions if cancer develops.

Conversely, reclassifying a VUS as benign provides immense relief, freeing patients and their families from unnecessary anxiety and potentially invasive screening protocols. Ambry has already integrated eight of these cancer-focused MAVE studies into its reporting workflows, contributing to thousands of patient variant reclassifications and demonstrating the immediate, real-world impact of this technology.

“At Ambry, we’re committed to advancing the clinical utility of genomic data,” said Tom Schoenherr, CEO of Ambry Genetics, in the company's press release. “Our work with researchers conducting MAVEs validates high-throughput functional analysis of genetic variants, transforming large-scale sequencing data into clinically actionable insights that support more precise risk assessment and patient care.”

A Strategic Play in AI-Powered Medicine

This commitment to resolving VUS is not just a scientific endeavor; it is a core component of a larger business strategy. For Ambry’s parent company, Tempus AI, high-quality, well-annotated data is the essential fuel for its artificial intelligence platforms. Tempus aims to build one of the world’s largest libraries of clinical and molecular data to power AI-driven solutions that personalize patient care and accelerate drug discovery.

Ambiguous data, like VUS, is less valuable for training these sophisticated AI models. By investing in MAVE technology to systematically clean up and clarify its vast genomic dataset, Ambry is significantly enhancing the quality of the data flowing into the Tempus ecosystem. This creates a powerful feedback loop: better data leads to more accurate AI models, which in turn generate more precise clinical insights. This strategic focus on generating definitive functional data serves as a key market differentiator in the competitive landscape of genetic testing, setting the company apart from competitors who may have higher VUS rates.

From Lab Bench to Clinical Standard

The momentum behind MAVE technology suggests it is on a path from a specialized research tool to a clinical standard. The endorsement from bodies like the ACMG and its integration into clinical frameworks like ClinGen provide a clear pathway for regulatory and clinical acceptance. As these functional assays become more widespread, they promise to dramatically lower the rate of uncertain findings across all of genetic medicine.

This shift also has significant implications for reimbursement. Payers are often hesitant to cover the cost of genetic tests that yield inconclusive results. By substantially increasing the clinical utility and actionability of its tests, Ambry and its collaborators are building a strong case for their value and cost-effectiveness, potentially easing access for more patients in the future.

“This study represents a major step forward in our ability to interpret PALB2 variants at scale,” noted Steven M. Lipkin, Chief Medical Officer at Ambry Genetics. “MAVEs are generating the type of functional evidence that will help bridge the gap between genomic data and clinical decision-making. This research is a crucial part of our broader mission to advance precision medicine, reduce the rate of variants of uncertain significance, and improve outcomes for individuals at risk for hereditary cancer.”

Product: AI & Software Platforms Oncology Drugs
Sector: Biotechnology AI & Machine Learning Data & Analytics Genomics Health IT
Theme: Precision Medicine Large Language Models Artificial Intelligence Data-Driven Decision Making
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