BLUF (Bottom Line Up Front)

The new PanMAN data compression breakthrough from UC San Diego could revolutionize prostate cancer care by enabling researchers to analyze genetic data from millions of patients simultaneously, accelerating discovery of treatment-resistant mutations, improving genetic risk prediction, and making personalized genomic medicine more accessible and affordable. This technology addresses a critical bottleneck that has limited large-scale genomic studies in prostate cancer, where understanding tumor evolution and inherited risk factors is increasingly central to diagnosis and treatment decisions.

What Patients Need to Know

The Promise and Challenge of Genomic Medicine in Prostate Cancer

If you've been diagnosed with prostate cancer in recent years, there's a good chance genomics has already played a role in your care. Perhaps your doctor ordered Decipher testing on your tumor biopsy to predict how aggressive your cancer might be. Maybe you had germline genetic testing to see if you carry inherited mutations in genes like BRCA2 that affect both your treatment options and your family's cancer risk. Or possibly your medical team used genomic biomarkers to help decide whether you're a candidate for newer targeted therapies like PARP inhibitors or immunotherapy.

Genomics—the study of all your genes and how they interact—has become increasingly important in prostate cancer care over the past decade. The National Comprehensive Cancer Network (NCCN) now recommends germline genetic testing for men with metastatic prostate cancer, high-risk localized disease, or family histories suggestive of hereditary cancer.[1] Tumor genomic profiling has become standard for guiding treatment in advanced disease.[2]

Yet even as genomic testing becomes more routine, researchers face a hidden crisis: they're drowning in data. The cost of sequencing a complete human genome has dropped from $100 million in 2001 to under $1,000 today.[3] This dramatic price reduction means we can now sequence thousands or even millions of patient genomes to understand cancer better—but storing, sharing, and analyzing all that genetic information has become a massive challenge.

Now, engineers at UC San Diego have developed a solution that could fundamentally change how genomic research works in prostate cancer and other diseases. Their new data compression technique, called PanMAN (Pangenome Mutation-Annotated Network), can squeeze genetic data down to a tiny fraction of its original size—in some cases, reducing storage requirements by 3,000-fold—while actually preserving more biological information than current methods.[4]

What This Means for Prostate Cancer Patients

Why should prostate cancer patients care about data compression? Because the size of studies that researchers can conduct directly affects how quickly we discover new treatments and improve care. Here's how this breakthrough could impact your cancer journey:

Faster Discovery of Treatment-Resistant Mutations

One of the most frustrating aspects of advanced prostate cancer is treatment resistance. Drugs like enzalutamide (Xtandi) and abiraterone (Zytiga) work well initially for many men, but cancer cells eventually develop resistance through genetic mutations in the androgen receptor or other pathways.[5] Understanding exactly which mutations cause resistance—and how they evolve over time—requires analyzing tumor samples from thousands of patients.

Current data storage limitations mean researchers typically study hundreds or at most a few thousand patient samples at a time. PanMAN's compression could allow studies of tens or hundreds of thousands of patients simultaneously, dramatically accelerating the discovery of resistance mechanisms and potential ways to overcome them.

"The ability to analyze genetic data from millions of patients at once could completely change the timeline for understanding how prostate cancer evolves and develops resistance," explains Dr. Yatish Turakhia, the UC San Diego electrical and computer engineering professor who led the PanMAN development.[4]

Better Genetic Risk Prediction

About 10-15% of prostate cancers have a hereditary component, involving inherited mutations in DNA repair genes like BRCA1, BRCA2, ATM, CHEK2, and others.[6] Men with these mutations face higher risks of aggressive disease and may benefit from different treatment approaches, including PARP inhibitors like olaparib (Lynparza) and rucaparib (Rubraca).[7]

However, our current understanding of genetic risk is based on relatively small studies, primarily involving men of European ancestry. Large-scale databases like the UK Biobank are working to sequence genomes from 5 million people to better understand genetic disease risk across diverse populations.[8] The storage requirements are staggering—over a petabyte of data—making such projects enormously expensive and difficult to manage.[9]

PanMAN could make it practical to build much larger genetic databases that include men from all racial and ethnic backgrounds, leading to better risk prediction for everyone. This is particularly important for African American men, who have higher prostate cancer incidence and mortality rates but have been underrepresented in genetic research.[10]

More Accessible Genomic Testing

The cost of storing and analyzing genomic data affects what tests your doctor can order. When data storage is expensive, healthcare systems may limit which patients get comprehensive genomic testing. More efficient data compression could reduce these costs, potentially making advanced genomic testing available to more patients regardless of their insurance or location.

Tracking Cancer Evolution in Individual Patients

Your prostate cancer isn't a single disease but rather a collection of related cancer cells that evolve over time, much like a family tree. Different parts of your tumor—and different metastases in your body—may have different mutations that affect how they respond to treatment.[11]

Understanding this "tumor evolution" requires sequencing samples from multiple sites in your body and tracking how mutations accumulate. Currently, this level of detailed analysis is typically only done in research settings because of the data analysis burden. PanMAN's ability to efficiently store and analyze this evolutionary information could make such tracking more practical in clinical care, potentially helping doctors make better treatment decisions.

How Genomics Currently Guides Prostate Cancer Care

To appreciate the potential impact of this compression breakthrough, it helps to understand how genomics already influences prostate cancer diagnosis and treatment:

Germline Testing: Inherited Risk

Germline genetic testing looks at the DNA you were born with—the genes you inherited from your parents. The NCCN now recommends germline testing for:[1]

All men with metastatic prostate cancer (any stage)
Men with high-risk, very-high-risk, regional, or node-positive disease
Men with prostate cancer and a family history of certain cancers (ovarian, pancreatic, breast, colorectal, etc.)
Men with Ashkenazi Jewish ancestry and prostate cancer
Men with a concerning family history of cancer, even without a prostate cancer diagnosis

The genes typically tested include BRCA1, BRCA2, ATM, CHEK2, PALB2, MLH1, MSH2, MSH6, PMS2, and others involved in DNA repair and cancer risk.[12] Finding a mutation in these genes can affect your treatment options and has implications for your family members, who may want genetic counseling and testing.

Somatic Testing: Tumor Mutations

Somatic genetic testing looks at mutations that developed in your cancer cells, not mutations you were born with. For metastatic castration-resistant prostate cancer (mCRPC), tumor genomic profiling helps identify:[2]

Homologous recombination repair (HRR) gene mutations (BRCA1, BRCA2, ATM, and others): These predict response to PARP inhibitors like olaparib and rucaparib.[13]
Microsatellite instability (MSI-high) or mismatch repair deficiency (dMMR): These rare findings (about 3% of prostate cancers) predict response to immunotherapy with pembrolizumab (Keytruda).[14]
Tumor mutational burden (TMB): High TMB, though rare in prostate cancer, may also predict immunotherapy response.[15]
Androgen receptor (AR) mutations and amplifications: These may explain resistance to hormonal therapies and guide treatment selection.[16]

Genomic Classifiers: Predicting Aggressiveness

Several commercial tests analyze gene expression patterns in prostate tumor samples to predict how aggressive your cancer is likely to be:

Decipher (Veracyte): Analyzes 22 genes to predict metastasis risk after surgery or radiation.[17]
Oncotype DX Genomic Prostate Score (Exact Sciences): Measures expression of 12 cancer-related genes to estimate cancer aggressiveness.[18]
Prolaris (Myriad Genetics): Measures cell cycle progression genes to assess cancer growth rate.[19]
ProMark (Metamark Genetics): Analyzes protein markers to help distinguish aggressive from indolent cancers.[20]

These tests help men with localized prostate cancer and their doctors make more informed decisions about whether immediate treatment is necessary or whether active surveillance might be appropriate.

The Data Deluge Challenge

The success of genomic testing in prostate cancer has created an unexpected problem: too much data. A single human genome sequence generates about 200 gigabytes of data.[21] When researchers want to study thousands of patient genomes to discover new patterns, they need massive data storage facilities and powerful computers.

The Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium, for example, has assembled genetic data from over 140,000 prostate cancer patients to study inherited risk factors.[22] Projects like this are incredibly valuable but enormously expensive to maintain and analyze.

The problem gets worse when you want to study how cancer evolves over time. Researchers increasingly recognize that prostate cancer isn't a single clone of identical cells but rather an evolving population of related cells, each acquiring different mutations.[23] To understand this evolution—which affects treatment resistance—you need to sequence many samples from the same patient and track how mutations appear and spread.

Current data formats for storing multiple genomes are inefficient. Most formats either:

Store each genome separately (hugely redundant, since humans share 99.9% of their DNA sequence)[24]
Store only the genetic variations between genomes (efficient but loses evolutionary information about when mutations occurred and how they relate to each other)

SIDEBAR: The "JPEG Moment" for Genomic Medicine

Why a Data Compression Breakthrough Could Change Your Cancer Care

By Stephen L. Pendergast

If you're over 40, you probably remember when sharing photos meant mailing prints or passing around physical albums. Digital cameras existed in the 1990s, but a single uncompressed photo could be 10-30 megabytes—far too large to email over dial-up internet or store more than a handful on early hard drives. Digital photography seemed destined to remain a niche technology for professionals with expensive equipment.

Then came JPEG compression in 1992. By reducing image file sizes by 10-20 times while preserving quality, JPEG made digital photography practical. Within a decade, film cameras had virtually disappeared. Today, we carry thousands of photos in our pockets, share images instantly worldwide, and take for granted technologies—from Google Images to Instagram to facial recognition—that would have been impossible without efficient image compression.

The PanMAN genomic compression breakthrough from UC San Diego may represent a similar inflection point for medicine. Just as JPEG transformed what was possible with digital images, PanMAN could fundamentally change what's possible with genomic medicine—including your prostate cancer care.

The Parallel: From Impossible to Ubiquitous

Consider the trajectory of visual media compression:

Before JPEG (1992):

Single photo: 10-30 MB uncompressed
Sharing photos online: impractical
Storing photo collections: required expensive storage
Digital photography: limited to professionals
Image-heavy websites: impossible on dial-up internet

After JPEG:

Same photo: 1-2 MB compressed (10-20× smaller)
Email photos: practical even on dial-up
Store thousands of photos: feasible on consumer hard drives
Digital cameras: replaced film within 10 years
Enabled: Web 2.0, social media, smartphone photography, image search, AI vision

Video compression (MPEG, 1993) followed a similar path, with even more dramatic results—100-200× compression that enabled YouTube, Netflix, video conferencing, and the entire streaming media industry.

Genomics Today: Where Photography Was in 1990

Now consider where genomic medicine stands today:

Current State:

Single human genome: ~200 GB of raw sequence data
Analyzing millions of genomes: requires supercomputing centers
Sharing genomic datasets: expensive and slow
Large-scale genomic studies: limited to major institutions
Real-time clinical genomics: mostly impractical

After PanMAN:

8 million genomes: 366 MB compressed (3,000× smaller)
Analyzing population-scale data: feasible on standard servers
Sharing datasets internationally: practical
Large studies: accessible to smaller research centers
Real-time analysis: potentially routine in clinics

What This Means for Your Prostate Cancer Care

Just as JPEG didn't just make photos smaller—it enabled entirely new applications—PanMAN could enable new approaches to cancer care:

1. Treatment Decisions Based on Millions, Not Hundreds

Current genomic studies in prostate cancer typically analyze hundreds or at most a few thousand patients. With PanMAN compression, researchers could routinely analyze outcomes from hundreds of thousands of men with similar genetic profiles.

When your oncologist recommends a treatment, instead of basing that decision on data from 500 similar patients, it could be informed by outcomes from 50,000 similar patients. That's not incremental improvement—it's a different category of evidence.

2. Real-Time Resistance Tracking

Liquid biopsy blood tests can detect tumor DNA mutations, but tracking how your cancer's genetics change over months and years generates massive data. PanMAN's efficient storage of time-series genetic data could make it practical to create a "genomic movie" of your cancer's evolution—seeing resistance mutations emerge before your PSA rises, potentially enabling earlier intervention.

3. Clinical Decision Support in Minutes, Not Days

Imagine your oncologist uploading your tumor's genomic profile and instantly getting back treatment response data from 100,000 patients with similar profiles, along with which subsequent therapies worked when first-line treatment failed. The compressed database would be small enough to query in seconds rather than hours.

4. Genomic Medicine Anywhere

JPEG made it possible to carry 10,000 photos on your phone. PanMAN could theoretically enable carrying reference databases of millions of cancer genomes on a tablet—bringing sophisticated genomic analysis to rural clinics, developing countries, or anywhere without high-speed internet connections.

5. Discovering the Undiscoverable

Machine learning models that predict treatment response need massive training datasets. Currently limited to thousands of examples, PanMAN could enable training on millions, potentially discovering subtle patterns that predict which men will respond to PARP inhibitors, which will develop enzalutamide resistance quickly, or which carry unrecognized genetic risk factors.

The Critical Advantage: Better Compression WITH More Information

Here's where PanMAN actually exceeds the JPEG/MPEG analogy: those formats achieve compression by deliberately discarding information your eye won't notice. A JPEG photo isn't quite as sharp as the original. MPEG video loses fine detail.

PanMAN achieves 100-1000× better compression than existing genomic formats while preserving more biological information—complete evolutionary histories, mutation timelines, and ancestral sequences that other formats discard. It's like discovering a way to compress images 100× better than JPEG while making them sharper.

This seemingly impossible feat works because genomics has something photographs don't: biological structure. Related genomes share evolutionary history through common ancestry. PanMAN exploits that structure the same way JPEG exploits patterns in how the human eye perceives images—but without the loss of information.

Network Effects: Applications We Can't Yet Imagine

JPEG and MPEG didn't just make existing applications more efficient—they enabled cascading innovations that created entirely new industries:

JPEG → Digital cameras → Photo sharing → Social media → Smartphone cameras → Instagram → Visual search → AI image recognition

The most transformative JPEG applications—Instagram, Pinterest, Google Images—weren't envisioned when the format was created. They emerged once the enabling infrastructure existed.

Similarly, PanMAN could trigger its own cascade:

PanMAN → Population-scale genomics → Real-time clinical genomics → Federated genomic networks → AI treatment optimization → Preventive genomic medicine → ...?

The applications we can envision today—better treatment selection, faster resistance detection, larger trials—may be just the beginning. The most transformative uses might be things we can't imagine yet, just as no one in 1992 predicted Instagram.

Your Engineering Perspective Matters

If you have a technical background—engineering, computer science, physics, mathematics—you might recognize another parallel: compression enables real-time processing.

In radar systems, data from phased arrays must be compressed so returns can be processed in real-time rather than stored and analyzed later. In telecommunications, compression enables real-time video calls. In scientific computing, compression determines what problems can be solved interactively versus requiring overnight batch jobs.

Similarly, PanMAN could enable real-time genomic epidemiology—tracking how drug-resistant mutations spread through patient populations, identifying emerging resistance mechanisms as they appear, and updating treatment guidelines based on continuously analyzed outcomes rather than waiting for traditional studies to complete.

Timeline: When Will This Impact Your Care?

Based on the JPEG precedent:

1992: JPEG standard published
1995: Web browsers support JPEG natively
1997: Consumer digital cameras widely available
2000: JPEG ubiquitous, digital photography revolution complete

A similar timeline for PanMAN might be:

2026: PanMAN published in Nature Genetics (now)
2027-28: Major genomics software tools add PanMAN support
2029-30: Clinical genomics laboratories begin adoption
2031-32: Population-scale genomic medicine becomes routine

Research applications could accelerate faster—clinical trials might begin using PanMAN for data management and analysis within 2-3 years, potentially affecting prostate cancer studies you might consider joining.

Barriers and Challenges

Of course, universal adoption won't happen overnight. JPEG and MPEG faced significant barriers:

Standards battles: Competing formats (GIF vs. JPEG, MPEG vs. RealVideo)
Patent licensing: Legal fights that slowed adoption
Software ecosystem: Needed viewers, editors, converters for every platform
Hardware acceleration: Eventually required dedicated compression chips
Industry inertia: Existing systems resistant to change

PanMAN will likely face analogous challenges:

Standardization: Will it become the standard or compete with alternatives?
Tool integration: Existing genomics software (used in virtually every lab) must add support
Clinical validation: FDA and regulatory acceptance for diagnostic use
Workforce training: Bioinformaticians and clinicians need to learn new approaches
Legacy data: Converting existing genomic databases to PanMAN format
Institutional adoption: Hospital systems and labs are typically slow to change

The team at UC San Diego has demonstrated PanMAN works on 8 million SARS-CoV-2 genomes and is now extending it to human cancer genomes. The technology is real and proven. The question is adoption timeline, not feasibility.

For Prostate Cancer Patients: What You Should Know

Near-term (next 2-3 years):

PanMAN will primarily impact research studies
Clinical trials may begin using it for data management
Your direct clinical care likely won't change yet
Genetic testing you receive will use current formats

Medium-term (3-7 years):

Research discoveries from larger-scale studies may lead to new insights
Clinical genomics databases may adopt PanMAN
Treatment guidelines might incorporate findings from much larger datasets
Some advanced medical centers may offer PanMAN-enabled analysis

Long-term (7+ years):

Population-scale genomic medicine could become standard
Treatment selection routinely informed by millions of patient outcomes
Real-time tumor evolution tracking possibly routine
Genomic analysis accessible at most cancer centers

What you can do now:

If you haven't had genetic testing, ask your oncologist if germline or somatic testing is appropriate for your situation
Consider participating in research studies—your genomic data contributes to the databases that benefit future patients
Stay informed about genomic medicine advances through resources like NCCN Guidelines for Patients, Prostate Cancer Foundation updates, and organizations like ZERO Prostate Cancer
If you consent to research, understand that technologies like PanMAN make your data more valuable while potentially making it more secure (compressed encrypted data is harder to breach than uncompressed)

The Bigger Picture: Infrastructure Enables Innovation

Data compression seems like an obscure technical detail far removed from cancer care. But history shows that infrastructure advances—often invisible to end users—enable revolutionary applications.

The interstate highway system enabled suburbs and big-box retail. Fiber optic cables enabled the internet economy. Lithium-ion batteries enabled smartphones and electric vehicles. JPEG enabled social media and smartphone photography.

PanMAN may be the infrastructure advance that enables the full promise of precision medicine—where treatment decisions are based not on population averages but on comprehensive analysis of outcomes from vast numbers of patients genetically similar to you.

For those of us in the prostate cancer community, this represents tangible hope: hope that answers to our toughest questions about treatment resistance, cancer evolution, and optimal therapy selection will come faster because researchers can ask bigger questions of more comprehensive data.

From Your Perspective: Why I Care

As a prostate cancer patient with an 11-year history of the disease and an engineering background (radar systems, signal processing, aerospace defense), I've learned that the most important medical advances often come from unexpected places. The mRNA vaccines that may eventually treat cancer emerged from decades of obscure biochemistry research. Targeted therapies like PARP inhibitors came from understanding DNA repair mechanisms discovered through basic science.

Now, advances in data compression—a field usually associated with computer science and communications engineering—may fundamentally change cancer medicine. The engineers at UC San Diego who developed PanMAN weren't thinking about prostate cancer specifically. They were solving a data management problem. But their solution could have profound implications for our cancer care.

This reminds me why supporting broad scientific research matters, why cross-disciplinary collaboration is valuable, and why seemingly technical advances deserve our attention. The breakthrough that changes your treatment options might come from genomics, immunology, drug development—or from electrical engineering and computer science.

Conclusion: The Inflection Point

We may be living through genomic medicine's "JPEG moment"—the point where a fundamental infrastructure advance transforms what's possible. The applications we can envision today are probably just the beginning. The most transformative changes may be things we can't yet imagine.

For prostate cancer patients, this could mean the difference between treatments guided by studies of hundreds of similar patients versus treatments optimized based on outcomes from hundreds of thousands of similar patients. That's not incremental improvement—it's a qualitative change in the foundation of medical decision-making.

Just as we can barely remember the era before digital photography became ubiquitous, future prostate cancer patients may barely remember the era before population-scale genomic medicine became routine. We're fortunate to be here at the inflection point, able to watch—and hopefully benefit from—this transformation as it unfolds.

Technical Reference: Walia, S., Motwani, H., Tseng, Y.H., et al. "Compressive pangenomics using mutation-annotated networks." Nature Genetics (2026). https://doi.org/10.1038/s41588-025-02478-7

This sidebar accompanies the main article "Data Compression Breakthrough Could Transform Prostate Cancer Genomics: What Patients Need to Know" in the IPCSG Newsletter, January 2026.

How PanMAN Works: Compression with Intelligence

What makes PanMAN different from previous data compression approaches is that it's biologically intelligent. Instead of treating genomes as meaningless strings of letters to compress, it recognizes that related genomes share evolutionary history.

Think of it like a family tree. If you wanted to store information about a large family, you wouldn't write down each person's complete life story separately. Instead, you'd note what they inherited from their parents and only record what's unique about each individual. PanMAN applies this same logic to genomes.

The system stores a single ancestral genome sequence (like the common ancestor in a family tree) and then annotates only the mutations that distinguish descendants—changes in DNA letters, insertions, deletions, and more complex events like gene duplications or rearrangements.[4] This approach exploits the fact that closely related genomes share most of their sequence, differing only in mutations accumulated since their common ancestor.

The compression results are remarkable. When the UC San Diego team applied PanMAN to 8 million SARS-CoV-2 viral genomes (from COVID-19 pandemic surveillance), the entire dataset occupied just 366 megabytes—about 3,000 times less storage than traditional formats.[4] That's less space than a typical smartphone photo for the complete genetic record of 8 million viral samples.

But here's the crucial point: PanMAN doesn't just compress data—it preserves more biological information than current formats. The system stores evolutionary relationships, mutation histories, and ancestral sequences that other compression methods discard. This means researchers can ask richer questions about how cancer evolves and responds to treatment.

From Viral Genomes to Prostate Tumors

The UC San Diego team initially demonstrated PanMAN on microbial genomes, including the massive SARS-CoV-2 dataset. Now they're extending the approach to human genomes, supported by a Jacobs School Early Career Faculty Development Award.[4]

Human genomes present bigger challenges than viral genomes. Human DNA is about 3 billion letters long—roughly 10,000 times larger than the SARS-CoV-2 genome.[25] Human genomes also contain more complex structural variations: large chunks of DNA that are deleted, duplicated, inverted, or moved to different locations.[26]

However, the shared ancestry among human populations creates opportunities for compression. Any two humans share approximately 99.9% of their genetic sequence,[24] and cancer genomes from the same patient share even more sequence identity, differing mainly in the somatic mutations that drive cancer.

"Extending compressive pangenomics to human genomes can fundamentally transform how we store, analyze, and share large-scale human genetic data," said Dr. Turakhia. "It can depict detailed evolutionary and mutational histories which shape diverse human populations, something that current representations do not capture."[4]

Potential Applications in Prostate Cancer Research

Here are specific ways PanMAN could advance prostate cancer research and care:

Understanding Treatment Resistance Evolution

When prostate cancer becomes resistant to androgen deprivation therapy (ADT) and next-generation hormonal agents like enzalutamide and abiraterone, tumor cells have typically acquired mutations in the androgen receptor gene (AR) or activated alternative pathways.[27] The order in which these mutations occur, and how they interact, affects what treatments might still work.

Researchers from Memorial Sloan Kettering, Dana-Farber, Johns Hopkins, and other cancer centers have been sequencing tumor samples from men at different disease stages to map resistance evolution.[28] However, current studies typically include hundreds of patients at most. PanMAN could enable studies with tens of thousands of patients, providing much more detailed maps of how resistance develops.

Precision Medicine for African American Men

African American men have approximately 70% higher prostate cancer incidence and more than twice the mortality rate compared to white men.[29] Some of this disparity reflects differences in access to care, but there are also biological differences, including higher rates of certain genetic alterations.[30]

Most large genetic studies have primarily included men of European ancestry, limiting their applicability to other populations.[31] The computational efficiency of PanMAN could make it practical to build much larger, more diverse genetic databases that better represent all populations affected by prostate cancer.

Tracking Liquid Biopsy Results Over Time

Liquid biopsies—blood tests that detect circulating tumor DNA (ctDNA)—are increasingly used to monitor prostate cancer.[32] Companies like Guardant Health, Foundation Medicine, and others offer tests that can detect tumor mutations from a simple blood draw.

A single liquid biopsy gives you a snapshot of your cancer's genetics at one moment. But tracking how mutations change over months and years of treatment provides much richer information about how your cancer is evolving and potentially becoming resistant. PanMAN's efficient storage of time-series genetic data could make it practical to routinely track these changes for large numbers of patients, helping identify resistance earlier.

Improved Clinical Trial Matching

Many newer prostate cancer treatments target specific genetic alterations. PARP inhibitors work best in tumors with BRCA1/2 or other HRR mutations.[33] Pembrolizumab immunotherapy is FDA-approved for MSI-high or dMMR tumors.[34] AKT inhibitors target tumors with PTEN loss.[35] Clinical trials increasingly require specific genetic profiles for enrollment.

More efficient genomic data management could improve systems that match patients to appropriate clinical trials based on their tumor's genetic profile, potentially helping more men access promising experimental treatments.

Germline-Somatic Integration

Your cancer risk and treatment response depend on both germline genetics (what you inherited) and somatic mutations (what developed in your tumor). Currently, germline and somatic testing are often done separately and stored in different databases. PanMAN's ability to efficiently store both types of genetic information together could enable better integrated analysis.

The Broader Genomics Revolution in Prostate Cancer

PanMAN emerges at a time when genomics is rapidly transforming prostate cancer care:

Expanding Treatment Options

The FDA has approved several genetically targeted therapies for prostate cancer in recent years:

Olaparib (Lynparza): Approved in 2020 for mCRPC with HRR gene mutations (BRCA1/2, ATM, and others).[36]
Rucaparib (Rubraca): Approved in 2020 for mCRPC with BRCA1/2 mutations.[37]
Pembrolizumab (Keytruda): Approved in 2017 for any solid tumor with MSI-high or dMMR, including prostate cancer.[38]
Dostarlimab (Jemperli): Approved in 2021 for dMMR solid tumors, including prostate cancer.[39]

Several other targeted therapies are in late-stage clinical trials. The more efficiently we can analyze genetic data from large patient populations, the faster we'll identify which patients benefit from which treatments.

Polygenic Risk Scores

Beyond single high-risk genes like BRCA2, researchers have identified hundreds of common genetic variants that each contribute a small amount to prostate cancer risk.[40] Combining these into "polygenic risk scores" could eventually help identify men who should begin screening earlier or be screened more intensively.

The IMPACT study demonstrated that genetic risk assessment could identify men with BRCA1/2 mutations who might benefit from earlier PSA screening.[41] Similar approaches using polygenic risk scores are under investigation but require genetic data from hundreds of thousands of men to refine.[42]

Tumor Heterogeneity and Clonal Evolution

Advanced imaging techniques can now identify multiple metastatic sites in men with widespread disease. Research has shown that different metastases in the same patient often have different genetic profiles—they've evolved along separate paths from their common origin.[43]

This "tumor heterogeneity" helps explain why treatments sometimes shrink some metastases while others progress. Understanding the evolutionary relationships between different tumor sites could guide treatment selection, but it requires sequencing multiple samples per patient and analyzing their relationships. PanMAN's efficient storage of evolutionary information makes such analysis more practical.

Integrating Multi-Omic Data

Genomics (DNA sequencing) is just one piece of the puzzle. Researchers also study:

Transcriptomics: Which genes are turned on or off
Proteomics: Which proteins are present and in what amounts
Metabolomics: Which metabolic compounds are produced
Epigenomics: Chemical modifications to DNA that affect gene expression

Integrating these different types of molecular data provides a more complete picture of cancer biology, but it creates even more data to store and analyze. Compression techniques like PanMAN could eventually extend to these other molecular data types.

Current Limitations and Future Directions

While PanMAN shows great promise, it's important to understand its current limitations:

Not Yet in Clinical Use

PanMAN is a research tool at this stage. It will take time for the technology to be validated, integrated into clinical systems, and adopted by diagnostic laboratories. The immediate impact will be in research rather than direct patient care.

Focuses on Sequencing Data

PanMAN compresses sequencing data—the raw genetic information. Clinical genomic reports also include interpretation, annotations about which mutations are clinically relevant, and treatment recommendations. These components require different approaches.

Requires Specialized Expertise

Using PanMAN currently requires computational expertise. For the technology to have broad impact, it will need to be integrated into user-friendly software that clinicians and researchers without specialized bioinformatics training can use.

Human Genome Applications Still Developing

While PanMAN has been demonstrated on millions of viral genomes, the research team is still extending the approach to human genomes, which are larger and more complex. This work is ongoing but shows promise.[4]

What This Means for You

If you're living with prostate cancer, here's what you should know:

Genetic Testing Is Increasingly Important

If you haven't had genetic testing, ask your oncologist whether germline or somatic testing is appropriate for your situation. NCCN guidelines now recommend testing for many men with prostate cancer, and results can affect treatment decisions.[1,2]

Your Genetic Information May Contribute to Research

If you consent to research, your genetic data could help scientists understand prostate cancer better. Technologies like PanMAN make it possible for your data to be included in much larger studies without increased privacy risk, as the compressed format actually makes data more secure.

Better Treatments Are Coming

The ability to analyze genetic data from millions of patients simultaneously should accelerate the discovery of new treatment targets and biomarkers. While individual breakthroughs are hard to predict, the overall trajectory is toward increasingly personalized cancer care.

Consider Family Implications

If genetic testing reveals inherited mutations, your family members may also be at risk and could benefit from genetic counseling and testing. Inherited mutations in BRCA2, BRCA1, and other genes can affect risks for multiple cancer types.[44]

Stay Informed

Genomic medicine in prostate cancer is evolving rapidly. Resources for staying current include:

NCCN Guidelines for Patients: Free, patient-friendly versions of treatment guidelines (www.nccn.org/patients)
Prostate Cancer Foundation: Research updates and educational resources (www.pcf.org)
ZERO Prostate Cancer: Patient advocacy and education (www.zerocancer.org)
Your medical team, including genetic counselors if you've had positive genetic testing

The Road Ahead

Data compression might seem like a technical detail far removed from patient care, but it's actually fundamental infrastructure for the future of precision medicine. Just as improvements in internet bandwidth enabled the streaming video and telehealth we now take for granted, improvements in genomic data storage and analysis will enable advances in cancer care that currently seem out of reach.

The PanMAN breakthrough from UC San Diego exemplifies how advances in one field—in this case, electrical engineering and computer science—can have profound impacts on medicine. As Dr. Turakhia and his colleagues continue extending their compressive pangenomics approach to human cancer genomes, we move closer to a future where truly comprehensive, personalized genomic medicine is not just possible for research patients at major cancer centers, but practical for all men facing prostate cancer.

For those of us in the prostate cancer community, this represents hope: hope that the answers to our toughest questions about treatment resistance, cancer evolution, and optimal therapy selection will come faster because researchers can ask bigger questions of more comprehensive data. In the fight against prostate cancer, every advantage matters—even ones that come in the form of elegantly compressed data.

Verified Sources with Formal Citations

[1] National Comprehensive Cancer Network. "NCCN Clinical Practice Guidelines in Oncology: Prostate Cancer." Version 4.2024. https://www.nccn.org/professionals/physician_gls/pdf/prostate.pdf

[2] Gillessen, S., et al. "Management of patients with advanced prostate cancer—metastatic and/or castration-resistant prostate cancer: Report of the Advanced Prostate Cancer Consensus Conference (APCCC) 2022." European Journal of Cancer 185 (2023): 178-215. https://doi.org/10.1016/j.ejca.2023.02.018

[3] National Human Genome Research Institute. "The Cost of Sequencing a Human Genome." Genome.gov. Updated 2023. https://www.genome.gov/about-genomics/fact-sheets/Sequencing-Human-Genome-cost

[4] Walia, S., Motwani, H., Tseng, Y.H., et al. "Compressive pangenomics using mutation-annotated networks." Nature Genetics (2026). https://doi.org/10.1038/s41588-025-02478-7

[5] Watson, P. A., et al. "Emerging mechanisms of resistance to androgen receptor inhibitors in prostate cancer." Nature Reviews Cancer 15.12 (2015): 701-711. https://doi.org/10.1038/nrc4016

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Data Compression Breakthrough Could Transform Prostate Cancer Genomics: