Colorectal (CRC) detection has become a red hot field, as concerns about rising numbers of young adult cases and competition among multi-cancer panels heat up. Guardant Health took two giant steps forward recently with first the approval of its liquid biopsy test, Guardant360 Liquid CDx, and then the listing of its blood-based Shield test by the American Cancer Society (ACS) as a choice for CRC screening of adults age 45 and older who are at average risk of the disease.
In an updated guideline released today, the ACS added blood-based screening tests, and specifically Shield, to its list of recommended choices for the patients in this subset who have not completed or have declined visual exams and stool tests. The group specifically names Shield, which was approved by the U.S. Food and Drug Administration in 2024.
Shield is an in vitro diagnostic test that detects CRC-derived alterations in cell-free DNA from blood collected in the Guardant Blood Collection Kit. The test is performed at Guardant.
These two advances put Guardant in an excellent position in the cancer liquid biopsy market, which is currently valued at between $7B and $12B and expected to double over the next ten years.
A spokesperson for Guardant told Inside Precision Medicine, “Our current focus is on ensuring the approval and successful launch of the Shield test, with an initial focus on eligible adults age 65 and older across the U.S. who are enrolled in Medicare. In parallel, we are continuing to optimize and improve the performance of the Shield test, with the goal of upgrading the test post-approval.”
This week also, Guardant announced that the Molecular and Clinical Genetics Panel of the U.S. The Food and Drug Administration (FDA)’s Medical Devices Advisory Committee strongly recommended FDA approval of the Shield blood test for these types of patients.
“The advisory committee’s strong support for the approval of Shield reinforces the crucial role that a blood test option can have in improving CRC screening rates for those at average risk,” said AmirAli Talasaz, Guardant Health co-CEO. “Despite the importance of detecting colorectal cancer early, there are notable barriers that can deter average-risk Americans from completing existing screening methods. Shield effectively detects cancer at an early stage when it is most treatable. Providing people with this blood test alongside other non-invasive stool tests can increase the rate of colorectal screeningand potentially reduce preventable CRC deaths.”
Colorectal cancer is the second-leading cause of cancer-related deaths in the U.S. The disease has a 91% five-year survival rate when caught at stage I (localized), but one out of three eligible Americans—50 million people—are not being screened for CRC.
Current primary non-invasive screening options include stool-based tests which have proven efficacy in detecting CRC; however, studies have consistently found that barriers such as handling stool and challenges performing the test impact adherence.
“Sadly, 76% of deaths caused by colorectal cancer occur in individuals who are not up to date with their screening,” said Daniel Chung, MD, gastroenterologist at Massachusetts General Hospital and professor of medicine at Harvard Medical School. “Clinical evidence and CRC screening guidelines acknowledge the value of offering choice to individuals at average risk for CRC and highlight the role of patient preference in test selection and CRC screening completion.”
The FDA panel’s recommendation is based on Guardant’s premarket approval (PMA) application for Shield, including the results of the pivotal ECLIPSE study evaluating the performance of the test for detecting CRC in average-risk adults. Results from the study appeared in the March 2024 issue of The New England Journal of Medicine. (Chung was an author on this study.) Shield demonstrated 83% sensitivity for the detection of CRC, with 90% specificity for advanced neoplasia. Guardant notes that this performance is within range of existing stool-based tests used as primary CRC screening options, in which overall sensitivity ranges from 67% to 92%.
A maturing, data-rich and more commercially-focused cell and gene therapy (CGT) industry is finally embracing innovative manufacturing technologies, according to the head of U.K.-based industrial software firm, Autolomous.
Early CGT manufacturing relied heavily on technologies borrowed from biologics manufacturing, systems originally built for producing therapeutic proteins rather than patient-specific therapies. As a result, production processes were often highly manual, fragmented, and operator-dependent. Indeed, in many cases, manufacturing resembled a specialized laboratory process, rather than a scalable industrial operation.
But the situation is changing, says Autolomous CEO, Alexander Seyf, who argues CGT manufacturers are embracing innovation with product quality, cost control, and scalability in mind.
“As the industry moves beyond early clinical programs and toward commercialization, manufacturers are recognizing that traditional approaches simply won’t support the scale, consistency, or economics needed long term.
“There’s now much greater focus on automation, closed processing, and digital integration to improve reproducibility, reduce contamination risk, and lower the cost of goods,” Seyf tells GEN.
Increasingly, CGT firms are opting for purpose-built technologies, he says, citing automated cell handling, closed-system processing, robotic fill-finish, and integrated single-use platforms as examples.
“The broader shift is toward what many describe as ’CGT 4.0:’ applying the principles of Industry 4.0 to cell therapy manufacturing. The aim is to move away from highly manual, artisanal production toward scalable, repeatable manufacturing that can support broader patient access,” he adds.
Data
Data is also driving the adoption of new technologies. In cell and gene therapy production, large volumes of data are collected by different instruments, software platforms, QC systems, environmental monitoring tools, and manual inputs—many of which were never designed to communicate with one another.
In such circumstances, digital technologies provide manufacturers with an infrastructure that can gather process information and ensure it is usable, traceable, and reliable, Seyf says.
“Maintaining data integrity and full chain-of-custody visibility across fragmented systems can become incredibly difficult, particularly as operations scale.
“To solve this, companies are investing heavily in integrated digital architectures that bring manufacturing and quality data together into unified environments. Standardized data models, interoperable software platforms, and automated data capture are becoming increasingly important. Cloud infrastructure also plays a key role because it allows manufacturers to aggregate and analyze data across multiple facilities in real time,” Seyf says.
A typical, modern digital cell and gene therapy manufacturing setup includes systems that manage manufacturing execution, laboratory information, quality, electronic batch records, and cloud-based data platforms.
“Together, these systems help manage scheduling, batch tracking, compliance, traceability, and quality oversight in real time,” Seyf says, adding, “Modern facilities are also increasingly using process analytical technologies and integrated sensors to monitor critical process parameters continuously, rather than relying only on end-point testing.”
“A digital CGT manufacturing system is really about connectivity and visibility across the entire process. It’s not just about replacing paper records with electronic systems—it’s about creating an environment where manufacturing equipment, quality systems, analytics, and logistics are all connected and continuously sharing data.”
Material traceability
The need to keep track of cell and gene therapy raw materials is also fueling the adoption of innovative technologies, with patient-specific therapies being a case in point.
Seyf tells GEN, “Maintaining chain-of-identity and chain-of-custody is particularly important in CGT manufacturing, especially for autologous therapies where every batch is tied to an individual patient. That requires seamless integration between manufacturing systems, analytics platforms, and logistics operations.”
Researchers at University of California, San Diego have developed a new artificial intelligence (AI) model that can translate a tumor’s complex genetic profile into predictions about how that cancer may respond to treatment. The foundation model, called MutationProjector, was trained on genomic data from more than 30,000 tumors across 10 solid cancer types, and validated through testing across multiple independent patient cohorts. Led by Trey Ideker, PhD, professor of medicine at UC San Diego School of Medicine and director of the Big Data Institute at the University of Oxford, the researchers say the model offers a new framework for connecting cancer mutations to the biological pathways that drive treatment response.
“Genetic sequencing is already routine in cancer care, but we still struggle to fully interpret the many mutations found in a patient’s tumor,” said Ideker, who also holds a second appointment at UC San Diego Jacobs School of Engineering and is a member of UC San Diego Moores Cancer Center. “Our goal with MutationProjector was to build a general-purpose model that can learn from tens of thousands of tumor genomes and turn those mutation patterns into more precise predictions about treatment response.”
Ideker is co-senior and co-corresponding author of the team’s published paper in Cancer Discovery, titled “A foundation model of cancer genotype enables precise predictions of therapeutic response,” in which the authors stated, “These results establish a unifying framework for connecting tumor genotypes to biological mechanisms and therapeutic outcomes.”
Following a cancer diagnosis, one of the next steps is often genetic testing, which helps doctors classify the tumor and decide which treatments to pursue. “DNA sequencing panels—and in particular those that broadly identify alterations in cancer-associated genes—have been widely adopted in the clinic due to their relatively low cost, rapid turnaround, and established relevance to treatment outcomes,” the authors explained.
Trey Ideker is a professor of medicine at UC San Diego School of Medicine and director of the Big Data Institute at the University of Oxford. [Erik Jepsen / UC San Diego]
However, while genetic testing is relatively low cost, fast, and has a strong track record in cases where validated genetic biomarkers are available, those cases remain limited, because this type of treatment stratification is currently based on only a small number of known biomarkers. Today, only about 8% of cases are successfully matched to an FDA-approved therapy on the basis of genetics and usually on the basis of a single gene, the team continued. “While this situation may reflect the incomplete scope of genes covered by current sequencing panels, it clearly also reflects a fundamental lack of knowledge about how gene mutations should be interpreted.”
They suggest that an “average” tumor has approximately 11 distinct genetic alterations identified by clinical sequencing, representing a potentially rich source of molecular information, if this information could be used to help select treatment. One of the challenges to matching cancer mutations with treatment outcomes is that most mutations are rare, the investigators pointed out. Another is that individual biomarkers do not function in isolation, but act together to influence drug response.
Unlike existing approaches that rely on a small number of biomarkers, MutationProjector analyzes the broader combination of genetic alterations present in a tumor. The model then uses this information to generate a compact representation of the tumor’s biological state, helping researchers interpret which molecular pathways may be disrupted and, by extension, which treatments may be most effective. “Foundation models, which are pre-trained on large datasets and then applied to solve diverse new challenges with relatively few samples, are especially well positioned to advance precision oncology,” Ideker and team noted.
The investigators trained their foundational model, MutationProjector, using genetic profile data from more than 30,000 tumors samples across different cancer types. They then showed that across several independent cohorts of cancer patients, including those with bladder cancer, lung cancer and melanoma, MutationProjector matched or exceeded existing methods for predicting response to common immunotherapy and chemotherapy treatments. The model also identified both known and unexpected biomarkers associated with treatment outcomes, which could help improve current approaches to genetic testing and patient stratification.
“When applied to predict immunotherapy or chemotherapy resistance across multiple cancer types and cohorts, MutationProjector achieves or exceeds state-of-the-art performance in all contexts,” the team wrote. “It identifies unexpected biomarkers, including KMT2D mutation in immunotherapy sensitivity and joint alteration of SMARCA4 and STK11 in immunotherapy resistance.”
JungHo Kong, PhD, first author of the study and a postdoctoral researcher in the department of medicine at UC San Diego School of Medicine, said, “Many cancer mutations are individually rare, which makes them difficult to study one at a time. By pretraining on a large collection of tumors and integrating molecular network knowledge, MutationProjector can detect patterns that would be easy to miss with conventional biomarker approaches. That gives us a way to move from long lists of mutations toward a more functional understanding of the tumor.”
First study author JungHo Kong, shown here, is a postdoctoral researcher at UC San Diego School of Medicine. [UC San Diego Health Sciences]
The researchers emphasize that the model was designed not only to make predictions, but also to provide insight into why those predictions are made, which could help when refining biomarkers and treatment strategies. This interpretability is especially important in precision oncology, where clinicians need to understand how tumor genotypes relate to treatment decisions.
The team also hopes to expand the model to additional cancer types and data sources, including international cancer genome datasets and other forms of clinical information, such as imaging, transcriptomics, and electronic health records. “While 30,000+ genomes representing 10 solid tumor types were considered in our study, numerous additional tumor samples are available for expansion of MutationProjector to tumor types such as pancreatic cancer, prostate cancer or sarcomas,” the authors said. “Other future studies should explore the extent to which the MutationProject concept can be applied to further clinical tasks of interest, including application to liquid biopsies of circulating tumor DNAs for early cancer detection.”
Ideker added, “Our results suggest that tumor genome foundation models may help extend the clinical value of sequencing beyond a handful of well-known genes. This could support a more comprehensive and biologically grounded approach to precision oncology.”
In Uganda’s fishing villages along lakes and rivers, wearable GPS devices are offering a new, detailed picture of how schistosomiasis spreads, helping refine control strategies for a disease affecting about 250 million people globally, mostly in rural sub-Saharan Africa.
A Nature Health study by researchers at the University of Oxford’s Big Data Institute shows that simple models using GPS-tracked movement can accurately predict which open-water sites people use, how often they visit them, and which sites are most likely to drive transmission of Schistosoma mansoni, the parasite that causes schistosomiasis.
“Snail fever”
Schistosomiasis, or “snail fever,” is caused by Schistosoma mansoni flatworms that parasitize freshwater snails. Infection occurs when people contact contaminated water where larval forms of the flatworm penetrate the skin. Repeated exposure leads to reinfection and chronic disease, including liver damage, portal hypertension, bladder fibrosis, kidney damage, and increased cancer risk.
While praziquantel can cure infection, mass drug administration (MDA) with the antiparasitic medication has failed to interrupt transmission of the parasite. The World Health Organization (WHO) notes that MDA alone is insufficient, since transmission persists in localized hotspots. Focal interventions are needed, but researchers have limited them due to poor data on where water contact actually occurs.
Conventional assumptions have limited modeling of human contact with open-water sites. Though open-water contact is heterogeneous within villages and households, it is often assumed that people use only the site closest to their household or village. However, how mobility affects site usage patterns and whether assignment rules beyond nearest distance can be more realistic are unclear.
Along with praziquantel MDA, the 2022 WHO schistosomiasis control guidelines recommend safe water, sanitation, and hygiene (WASH) as the main intervention, but evidence is mixed. Lack of reliable data has made it difficult to determine why the intervention did not improve biannual MDA. Most water contact and WASH studies use self-reported data or household distance to sites and taps, which lacks objective, spatially granular data to characterize fine-scale water usage patterns and quantify WASH’s impact on water contact and (re)infection.
Focal exposure
Lead author Fabian Reitzug, PhD, and colleagues from Goylette F. Chami’s lab tracked 452 people using GPS loggers in three Ugandan districts for 10 days. A total of 8,200 water contact events occurred at 69 open-water sites and 32 improved sources like taps and boreholes—deep drilling to groundwater. Of the participants, 63.9% used open water and 33.2% improved sources.
Reitzug and colleagues found, unsurprisingly, that distance strongly predicted behavior: usage dropped sharply with distance. Open-water contact was ~70% at 20 meters from home and 11% at 500 meters. Nearly all tap and borehole use occurred within 1 km of home, and over 99.5% of open-water contact occurred within 3 km, showing highly localized exposure.
Adding mobility metrics (such as “radius of gyration”) did not improve predictions. This simple finding challenges the assumption that mobile phone tracking can reliably estimate infection risk. Schistosomiasis exposure appears to be caused by local, routine movements, not long-distance travel.
The study found little evidence that safe water infrastructure reduces risky water contact. Taps and boreholes rarely replaced open-water use; fewer than 2% of people fully substituted safe water for natural sources. Daily activities like bathing, fishing, and washing still require lake or river contact. Behavior varied by location. In the Western Ugandan district of Buliisa, nearly 90% visited open water, compared with 44% in the Eastern Ugandan district of Mayuge.
When incorporated into transmission models, GPS-informed movement patterns closely reproduced observed reinfection rates. Simple “nearest-water-source” assumptions overestimated risk. The improved model also identified high-risk water sites by combining human use with ecological suitability for snail habitats.
Targeted interventions at key sites
These findings suggest control programs could shift toward targeted interventions at key transmission sites, such as focal snail control, environmental modification, or localized treatment. The study also indicates that a 1 km intervention radius may be more realistic than the current 500 m guideline. Importantly, reliable spatial patterns emerged from as few as 15 participants over 10 days, suggesting the approach is scalable. Key limitations include using proximity as a proxy for water contact and limited seasonal coverage.
Overall, the study reframes schistosomiasis transmission as a highly local, measurable process, enabling more precise, data-driven control strategies. Future research should examine whether similar models apply to other waterborne diseases. Identifying pathogen-specific exposure pathways and collecting GPS logger data from various locations could test this approach’s generalizability.
Amid rapidly growing adoption of enterprise-level AI agents, there’s a disconnect emerging between ambition and execution.
Although 85% of organizations say they want to be agentic within the next three years, 76% say their current operations and infrastructure can’t support that change. They cite a lack of readiness across people, processes, and workflows.
The sticky tape problem
The challenge is that many organisations are often layering AI agents onto existing operations, rather than reimagine the operating model and how work will need to be rewired, explains Prasun Shah, global CTO for workforce consulting and chief AI officer at PwC UK Consulting. “They’re embedding AI employees into what is a human operating model,” layering on AI agents to existing workplace structures when “this is like adding sticky tapes to parts of an operating model that is breaking.”
Doing so may be preventing organizations from unlocking the full value agentic AI offers, creating circumstances where disillusionment can quickly creep in. That full value lies in agents’ capacity to execute entire workflows with limited human input. They can coordinate complex tasks, make independent decisions, adjust to changing conditions, and iterate performance.
In early proving grounds that span customer service, HR, and sales, it’s already estimated that AI agents could accelerate business processes by as much as 30% to 50% and low-value work time by 25% to 40% when deployed at scale. But with this capability comes greater complexity and the need for an enterprise-wide change.
Growing the AI vocabulary
Enterprise agentic AI platform Ema describes this change as agentic business transformation (ABT), a term it coined last year in partnership with HFS Research, in an attempt to plug what it sees as a gap in the existing lexicon about AI agents, and to provide enterprises with a new framework with which to think about their own adoption of the technology.
“None of the existing vocabulary captures the full scope of the change,” explains Ema CEO and founder Surojit Chatterjee. “Digital transformation was about moving from paper to software. AI transformation was about adding artificial intelligence to existing processes. Co-pilot is about AI assisting in various human tasks. But ABT is something categorically different: It’s the integration of AI agents into the fabric of the organization.”
For Shah, the dedicated term (ABT) “helps drive the need to redesign an organization in its entirety: its operating model, its workflows, decision rights, and performance management systems.” He emphasizes that “everything that’s needed to ensure those agents are actually active participants in value creation, rather than just point tools or productivity aids.”
According to Ema, ABT encompasses three core pillars: an organization’s technology stack, its workforce, and the metrics used for success.
AI agents as connective tissue
The first pillar of ABT is the technology stack. “Your existing tech stack was designed for human-operated, application-centric workflows,” says Chatterjee. “It needs to be reconsidered when the actor is an AI agent operating at machine speed across multiple systems simultaneously.”
As AI agents are integrated into an organization, enterprises will need to pivot from a set of linear processes and steps, to rewiring work in a very different way, explains Shah. That’s because the value in AI agents isn’t as another layer in an existing technology stack but as a connective tissue, he explains, moving between or across layers to coordinate a high-level task or retrieve and interpret data from multiple discrete applications. AI agents can create “a true competitive differentiation for an enterprise” by making decisions based on this capacity to contextualize, he says. “That is where the next battleground will be.”
To build this connective tissue, leaders need to adapt their technology stack to surface higher quality decisions from AI agents, prioritizing access to multiple datasets and applications simultaneously to develop tacit knowledge. “Organizations that make this architectural shift become genuinely more adaptive,” says Chatterjee. “When a new business requirement emerges, you don’t wait six months for a software vendor to build a feature. You configure an AI employee using natural language and connect it to the systems it needs. The time from business to production workflow drops from months to days.”
The workforce, redesigned
As AI agents are deployed for more use cases, enterprise leaders must consider what this means for dynamics across their workforce, the second pillar of ABT.
Workforce structures today deviate little from the hierarchical model of the early days of industrialization. To maximize efficiency and scale, processes are standardized, tasks are clearly delineated between strategic business units (SBUs), and employees progress up through an organization based on their capacity to optimize output from teams below them. But with AI agents that can execute, coordinate, and optimize tasks—often without managerial coordination—the lines of that established hierarchy become blurred.
In a workforce that blends AI agents and human employees, managers will be freed up from many execution-based tasks but take on new responsibilities associated with managing hybrid teams. Managers “will need to be able to manage issues around trust, explainability, psychological safety, and even status dynamics” to navigate new tensions that could arise in a hybrid workforce, says Shah.
The impact of agentic AI on existing workforce structures goes far beyond the management layer, too. McKinsey predicts that by 2030, three-quarters of current jobs will require redesign, upskilling, or redeployment, and organizations will need to act swiftly to amend recruitment, retention, and remuneration.
From output to outcome
Success metrics are the third and final pillar of ABT.
As AI agents assume greater ownership of core enterprise processes, taking on collaborative roles alongside human employees, traditional workforce metrics that focus on activity or output—such as calls handled or reports filed—no longer make sense.
“When you add AI employees into the workforce, activity metrics become meaningless or actively misleading,” says Chatterjee. “An AI employee can handle a thousand customer interactions in the time it takes a human to handle ten. If you measure success by interactions handled, you’ll conclude the AI is working brilliantly while missing whether any of those interactions actually drove customer satisfaction, retention, or revenue.” To correct this, enterprises must develop a new set of metrics that focus on outcome rather than output. That is, metrics on the broader benefits or changes achieved, rather than individual deliverables.
For example, when one of Ema’s large enterprise customers overhauled its own metrics, switching from tool metrics like cost per query and AI accuracy, to outcomes like the percentage of contracts reviewed without human escalation, the measured ROI from agentic AI tripled within two quarters. The changes meant “this customer stopped building point solutions in high-volume, low-complexity workflows and started deploying AI employees where the outcome value was highest,” says Chatterjee.
Integrating new metrics may also require a complete reconfiguration of reward and talent management processes, as well as accountability and ownership within organizations, points out Shah. In human-AI teams, for example, although ethical and fiduciary responsibilities will likely remain with human employees, operational accountability will become significantly more diffused to reflect the systemic role of AI agents.
This change will raise new questions that senior leadership teams will need to wrestle with, Shah adds. They’ll need to consider: Who is accountable when an AI employee makes a mistake? What happens when AI and humans disagree? What guardrails should be erected to safeguard customers?
Laying the groundwork for systems-level change
Systems-level change is gradual. These are complex lines of inquiry that experts continue to grapple with. But in kickstarting internal dialogue about the core pillars of ABT—the workforce, the technology stack, and the metrics by which success can be gauged—leaders can lay the groundwork for an enterprise better poised to embrace AI agents at a systems level and start to close the gap between their ambition and execution.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
![Trey Ideker is a professor of medicine at UC San Diego School of Medicine and director of the Big Data Institute at the University of Oxford. [Erik Jepsen / UC San Diego]](https://www.genengnews.com/wp-content/uploads/2026/05/Low-Res_TreyIdekerLab-ErikJepsen-800px-300x218.jpg)
![First study author JungHo Kong, shown here, is a postdoctoral researcher at UC San Diego School of Medicine. [UC San Diego Health Sciences]](https://www.genengnews.com/wp-content/uploads/2026/05/Low-Res_IMG_8731-1024x768-1-300x225.jpg)
